From: Andrey Kamaev Date: Fri, 15 Jun 2012 13:04:17 +0000 (+0000) Subject: Merged the trunk r8589:8653 - all changes related to build warnings X-Git-Tag: accepted/2.0/20130307.220821~470 X-Git-Url: http://review.tizen.org/git/?a=commitdiff_plain;h=bd0e0b5800f940f09aef83fa268d8707f1f01cee;p=profile%2Fivi%2Fopencv.git Merged the trunk r8589:8653 - all changes related to build warnings --- diff --git a/3rdparty/libjasper/CMakeLists.txt b/3rdparty/libjasper/CMakeLists.txt index 61bb6a8..2c98a23 100644 --- a/3rdparty/libjasper/CMakeLists.txt +++ b/3rdparty/libjasper/CMakeLists.txt @@ -7,31 +7,24 @@ project(${JASPER_LIBRARY}) add_definitions(-DEXCLUDE_MIF_SUPPORT -DEXCLUDE_PNM_SUPPORT -DEXCLUDE_BMP_SUPPORT -DEXCLUDE_RAS_SUPPORT -DEXCLUDE_JPG_SUPPORT -DEXCLUDE_PGX_SUPPORT) -# List of C++ files: ocv_include_directories(${CMAKE_CURRENT_SOURCE_DIR}) -# The .cpp files: file(GLOB lib_srcs *.c) file(GLOB lib_hdrs *.h) file(GLOB lib_ext_hdrs jasper/*.h) # ---------------------------------------------------------------------------------- -# Define the library target: +# Define the library target: # ---------------------------------------------------------------------------------- add_library(${JASPER_LIBRARY} STATIC ${lib_srcs} ${lib_hdrs} ${lib_ext_hdrs}) if(MSVC) - if(NOT ENABLE_NOISY_WARNINGS) - string(REPLACE "/W3" "/W0" CMAKE_C_FLAGS "${CMAKE_C_FLAGS}") - string(REPLACE "/W4" "/W0" CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}") - endif() add_definitions(-DJAS_WIN_MSVC_BUILD) endif() -if(CMAKE_COMPILER_IS_GNUCXX) - set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -Wno-uninitialized") -endif() +ocv_warnings_disable(CMAKE_C_FLAGS -Wno-implicit-function-declaration -Wno-uninitialized -Wmissing-prototypes -Wmissing-declarations -Wunused -Wshadow + /wd4013 /wd4018 /wd4715 /wd4244 /wd4101 /wd4267) if(UNIX) if(CMAKE_COMPILER_IS_GNUCXX OR CV_ICC) @@ -39,21 +32,17 @@ if(UNIX) endif() endif() -if(CMAKE_COMPILER_IS_GNUCXX AND NOT ENABLE_NOISY_WARNINGS) - set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -Wno-implicit-function-declaration -Wno-unused") -endif() - set_target_properties(${JASPER_LIBRARY} - PROPERTIES - OUTPUT_NAME ${JASPER_LIBRARY} - DEBUG_POSTFIX "${OPENCV_DEBUG_POSTFIX}" - ARCHIVE_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/3rdparty/${OPENCV_LIB_INSTALL_PATH}" - ) - + PROPERTIES + OUTPUT_NAME ${JASPER_LIBRARY} + DEBUG_POSTFIX "${OPENCV_DEBUG_POSTFIX}" + ARCHIVE_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/3rdparty/${OPENCV_LIB_INSTALL_PATH}" + ) + if(ENABLE_SOLUTION_FOLDERS) set_target_properties(${JASPER_LIBRARY} PROPERTIES FOLDER "3rdparty") -endif() - +endif() + if(NOT BUILD_SHARED_LIBS) install(TARGETS ${JASPER_LIBRARY} ARCHIVE DESTINATION share/OpenCV/3rdparty/${OPENCV_LIB_INSTALL_PATH} COMPONENT main) endif() diff --git a/3rdparty/libjpeg/CMakeLists.txt b/3rdparty/libjpeg/CMakeLists.txt index a406d1c..be2b417 100644 --- a/3rdparty/libjpeg/CMakeLists.txt +++ b/3rdparty/libjpeg/CMakeLists.txt @@ -4,24 +4,17 @@ # ---------------------------------------------------------------------------- project(${JPEG_LIBRARY}) -# List of C++ files: - ocv_include_directories(${CMAKE_CURRENT_SOURCE_DIR}) -# The .cpp files: file(GLOB lib_srcs *.c) file(GLOB lib_hdrs *.h) # ---------------------------------------------------------------------------------- -# Define the library target: +# Define the library target: # ---------------------------------------------------------------------------------- add_library(${JPEG_LIBRARY} STATIC ${lib_srcs} ${lib_hdrs}) -if(MSVC) - set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} /W3") -endif() - if(UNIX) if(CMAKE_COMPILER_IS_GNUCXX OR CV_ICC) set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -fPIC") @@ -32,16 +25,18 @@ if(CMAKE_COMPILER_IS_GNUCXX) set_source_files_properties(jcdctmgr.c PROPERTIES COMPILE_FLAGS "-O1") endif() +ocv_warnings_disable(CMAKE_C_FLAGS -Wcast-align -Wshadow) + set_target_properties(${JPEG_LIBRARY} - PROPERTIES OUTPUT_NAME ${JPEG_LIBRARY} - DEBUG_POSTFIX "${OPENCV_DEBUG_POSTFIX}" - ARCHIVE_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/3rdparty/${OPENCV_LIB_INSTALL_PATH} - ) - + PROPERTIES OUTPUT_NAME ${JPEG_LIBRARY} + DEBUG_POSTFIX "${OPENCV_DEBUG_POSTFIX}" + ARCHIVE_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/3rdparty/${OPENCV_LIB_INSTALL_PATH} + ) + if(ENABLE_SOLUTION_FOLDERS) set_target_properties(${JPEG_LIBRARY} PROPERTIES FOLDER "3rdparty") -endif() - +endif() + if(NOT BUILD_SHARED_LIBS) install(TARGETS ${JPEG_LIBRARY} ARCHIVE DESTINATION share/OpenCV/3rdparty/${OPENCV_LIB_INSTALL_PATH} COMPONENT main) endif() diff --git a/3rdparty/libpng/CMakeLists.txt b/3rdparty/libpng/CMakeLists.txt index cb76610..bfbf501 100644 --- a/3rdparty/libpng/CMakeLists.txt +++ b/3rdparty/libpng/CMakeLists.txt @@ -4,39 +4,35 @@ # ---------------------------------------------------------------------------- project(${PNG_LIBRARY}) -# List of C++ files: - ocv_include_directories("${CMAKE_CURRENT_SOURCE_DIR}" ${ZLIB_INCLUDE_DIR}) file(GLOB lib_srcs *.c) file(GLOB lib_hdrs *.h) # ---------------------------------------------------------------------------------- -# Define the library target: +# Define the library target: # ---------------------------------------------------------------------------------- add_library(${PNG_LIBRARY} STATIC ${lib_srcs} ${lib_hdrs}) -if(MSVC) - set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} /W3") -endif() - if(UNIX) if(CMAKE_COMPILER_IS_GNUCXX OR CV_ICC) set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -fPIC") endif() endif() +ocv_warnings_disable(CMAKE_C_FLAGS -Wcast-align) + set_target_properties(${PNG_LIBRARY} - PROPERTIES OUTPUT_NAME ${PNG_LIBRARY} - DEBUG_POSTFIX "${OPENCV_DEBUG_POSTFIX}" - ARCHIVE_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/3rdparty/${OPENCV_LIB_INSTALL_PATH}" - ) - + PROPERTIES OUTPUT_NAME ${PNG_LIBRARY} + DEBUG_POSTFIX "${OPENCV_DEBUG_POSTFIX}" + ARCHIVE_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/3rdparty/${OPENCV_LIB_INSTALL_PATH}" + ) + if(ENABLE_SOLUTION_FOLDERS) set_target_properties(${PNG_LIBRARY} PROPERTIES FOLDER "3rdparty") endif() - + if(NOT BUILD_SHARED_LIBS) install(TARGETS ${PNG_LIBRARY} ARCHIVE DESTINATION share/OpenCV/3rdparty/${OPENCV_LIB_INSTALL_PATH} COMPONENT main) endif() diff --git a/3rdparty/libtiff/CMakeLists.txt b/3rdparty/libtiff/CMakeLists.txt index 881eebd..69dd5ff 100644 --- a/3rdparty/libtiff/CMakeLists.txt +++ b/3rdparty/libtiff/CMakeLists.txt @@ -26,7 +26,6 @@ configure_file("${CMAKE_CURRENT_SOURCE_DIR}/tif_config.h.cmakein" ocv_include_directories("${CMAKE_CURRENT_SOURCE_DIR}" "${CMAKE_CURRENT_BINARY_DIR}" ${ZLIB_INCLUDE_DIR}) -# List of C++ files: set(lib_srcs tif_aux.c tif_close.c @@ -91,10 +90,9 @@ if(WIN32) list(APPEND lib_srcs tif_win32.c) endif(WIN32) -if(MSVC AND NOT ENABLE_NOISY_WARNINGS) - string(REPLACE "/W4" "/W0" CMAKE_C_FLAGS "${CMAKE_C_FLAGS}") - string(REPLACE "/W4" "/W0" CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}") -endif() +ocv_warnings_disable(CMAKE_C_FLAGS -Wno-unused-but-set-variable -Wmissing-prototypes -Wmissing-declarations -Wundef + -Wcast-align -Wshadow -Wno-maybe-uninitialized -Wno-pointer-to-int-cast -Wno-int-to-pointer-cast) +ocv_warnings_disable(CMAKE_CXX_FLAGS -Wmissing-declarations /wd4100 /wd4244 /wd4706 /wd4127 /wd4701 /wd4018 /wd4267 /wd4306 /wd4305 /wd4312 /wd4311) if(UNIX AND (CMAKE_COMPILER_IS_GNUCXX OR CV_ICC)) set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -fPIC") @@ -104,15 +102,15 @@ add_library(${TIFF_LIBRARY} STATIC ${lib_srcs}) target_link_libraries(${TIFF_LIBRARY} ${ZLIB_LIBRARIES}) set_target_properties(${TIFF_LIBRARY} - PROPERTIES - OUTPUT_NAME "${TIFF_LIBRARY}" - DEBUG_POSTFIX "${OPENCV_DEBUG_POSTFIX}" - ARCHIVE_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/3rdparty/${OPENCV_LIB_INSTALL_PATH}" - ) - + PROPERTIES + OUTPUT_NAME "${TIFF_LIBRARY}" + DEBUG_POSTFIX "${OPENCV_DEBUG_POSTFIX}" + ARCHIVE_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/3rdparty/${OPENCV_LIB_INSTALL_PATH}" + ) + if(ENABLE_SOLUTION_FOLDERS) set_target_properties(${TIFF_LIBRARY} PROPERTIES FOLDER "3rdparty") -endif() +endif() if(NOT BUILD_SHARED_LIBS) install(TARGETS ${TIFF_LIBRARY} ARCHIVE DESTINATION share/OpenCV/3rdparty/${OPENCV_LIB_INSTALL_PATH} COMPONENT main) diff --git a/3rdparty/libtiff/tif_config.h.cmakein b/3rdparty/libtiff/tif_config.h.cmakein index abb583e..1e6bc04 100644 --- a/3rdparty/libtiff/tif_config.h.cmakein +++ b/3rdparty/libtiff/tif_config.h.cmakein @@ -143,7 +143,7 @@ /* Signed 64-bit type formatter */ /* Unsigned 64-bit type formatter */ -#ifdef _MSC_VER +#if defined _MSC_VER || defined __MINGW__ || defined __MINGW32__ # define TIFF_UINT64_FORMAT "%I64u" # define TIFF_SSIZE_FORMAT "%Iu" #else diff --git a/3rdparty/tbb/CMakeLists.txt b/3rdparty/tbb/CMakeLists.txt index a127473..6ccd5b0 100644 --- a/3rdparty/tbb/CMakeLists.txt +++ b/3rdparty/tbb/CMakeLists.txt @@ -72,7 +72,7 @@ if(NOT EXISTS "${tbb_tarball}") file(REMOVE "${tbb_tarball}") message(FATAL_ERROR "Downloaded TBB source tarball has invalid MD5 hash: ${tbb_local_md5} (expected: ${tbb_md5})") endif() - + if(EXISTS "${tbb_src_dir}") file(REMOVE_RECURSE "${tbb_src_dir}") endif() @@ -119,18 +119,21 @@ endif() add_library(tbb STATIC ${lib_srcs} ${lib_hdrs} "${CMAKE_CURRENT_SOURCE_DIR}/android_additional.h" "${CMAKE_CURRENT_SOURCE_DIR}/${tbb_version_file}") target_link_libraries(tbb c m dl) -set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -w -include \"${CMAKE_CURRENT_SOURCE_DIR}/android_additional.h\"") +ocv_warnings_disable(CMAKE_CXX_FLAGS -Wundef -Wmissing-declarations) +string(REPLACE "-Werror=non-virtual-dtor" "" CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}") + +set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -include \"${CMAKE_CURRENT_SOURCE_DIR}/android_additional.h\"") set_target_properties(tbb - PROPERTIES OUTPUT_NAME tbb - DEBUG_POSTFIX "${OPENCV_DEBUG_POSTFIX}" - ARCHIVE_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/3rdparty/${OPENCV_LIB_INSTALL_PATH}" - ) - + PROPERTIES OUTPUT_NAME tbb + DEBUG_POSTFIX "${OPENCV_DEBUG_POSTFIX}" + ARCHIVE_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/3rdparty/${OPENCV_LIB_INSTALL_PATH}" + ) + if(ENABLE_SOLUTION_FOLDERS) set_target_properties(tbb PROPERTIES FOLDER "3rdparty") endif() - + if(NOT BUILD_SHARED_LIBS) install(TARGETS tbb ARCHIVE DESTINATION share/OpenCV/3rdparty/${OPENCV_LIB_INSTALL_PATH} COMPONENT main) endif() diff --git a/3rdparty/tbb/version_string.tmp b/3rdparty/tbb/version_string.tmp index 257bfa1..81e5e22 100644 --- a/3rdparty/tbb/version_string.tmp +++ b/3rdparty/tbb/version_string.tmp @@ -6,4 +6,4 @@ "TBB: BUILD_GLIBC Unknown" ENDL \ "TBB: BUILD_LD Unknown" ENDL \ "TBB: BUILD_TARGET Unknown" ENDL \ -"TBB: BUILD_COMMAND use cv::getBuildInformation() for details" ENDL \ +"TBB: BUILD_COMMAND use cv::getBuildInformation() for details" ENDL diff --git a/3rdparty/tbb/version_string.ver b/3rdparty/tbb/version_string.ver index 90e1686..8704849 100644 --- a/3rdparty/tbb/version_string.ver +++ b/3rdparty/tbb/version_string.ver @@ -6,4 +6,4 @@ #N": BUILD_GLIBC Unknown" ENDL \ #N": BUILD_LD Unknown" ENDL \ #N": BUILD_TARGET Unknown" ENDL \ -#N": BUILD_COMMAND use cv::getBuildInformation() for details" ENDL \ +#N": BUILD_COMMAND use cv::getBuildInformation() for details" ENDL diff --git a/3rdparty/zlib/CMakeLists.txt b/3rdparty/zlib/CMakeLists.txt index c298f7d..25df533 100644 --- a/3rdparty/zlib/CMakeLists.txt +++ b/3rdparty/zlib/CMakeLists.txt @@ -5,20 +5,11 @@ project(${ZLIB_LIBRARY} C) -include(CheckTypeSize) include(CheckFunctionExists) include(CheckIncludeFile) include(CheckCSourceCompiles) # -# Check to see if we have large file support -# -check_type_size(off64_t OFF64_T) -if(HAVE_OFF64_T) - add_definitions(-D_LARGEFILE64_SOURCE=1) -endif() - -# # Check for fseeko # check_function_exists(fseeko HAVE_FSEEKO) @@ -82,9 +73,7 @@ if(UNIX) endif() endif() -if(MSVC AND NOT ENABLE_NOISY_WARNINGS) - set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} /wd4013") -endif() +ocv_warnings_disable(CMAKE_C_FLAGS /wd4013 -Wattributes -Wstrict-prototypes -Wmissing-prototypes -Wmissing-declarations) set_target_properties(${ZLIB_LIBRARY} PROPERTIES OUTPUT_NAME ${ZLIB_LIBRARY} @@ -95,7 +84,7 @@ set_target_properties(${ZLIB_LIBRARY} PROPERTIES if(ENABLE_SOLUTION_FOLDERS) set_target_properties(${ZLIB_LIBRARY} PROPERTIES FOLDER "3rdparty") endif() - + if(NOT BUILD_SHARED_LIBS) install(TARGETS ${ZLIB_LIBRARY} ARCHIVE DESTINATION share/OpenCV/3rdparty/${OPENCV_LIB_INSTALL_PATH} COMPONENT main) endif() diff --git a/3rdparty/zlib/zconf.h.cmakein b/3rdparty/zlib/zconf.h.cmakein index 3ea5531..6d3ea59 100644 --- a/3rdparty/zlib/zconf.h.cmakein +++ b/3rdparty/zlib/zconf.h.cmakein @@ -410,10 +410,18 @@ typedef uLong FAR uLongf; * both "#undef _LARGEFILE64_SOURCE" and "#define _LARGEFILE64_SOURCE 0" as * equivalently requesting no 64-bit operations */ -#if -_LARGEFILE64_SOURCE - -1 == 1 +#if defined _LARGEFILE64_SOURCE && -_LARGEFILE64_SOURCE - -1 == 1 # undef _LARGEFILE64_SOURCE #endif +#ifndef _LFS64_LARGEFILE +# define _LFS64_LARGEFILE 0 +#endif + +#ifndef _FILE_OFFSET_BITS +# define _FILE_OFFSET_BITS 0 +#endif + #if defined(_LARGEFILE64_SOURCE) && _LFS64_LARGEFILE-0 # define Z_LARGE #endif diff --git a/CMakeLists.txt b/CMakeLists.txt index bb6ba19..095ca9c 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -188,6 +188,8 @@ OCV_OPTION(ENABLE_SSE42 "Enable SSE4.2 instructions" OCV_OPTION(ENABLE_NOISY_WARNINGS "Show all warnings even if they are too noisy" OFF ) OCV_OPTION(OPENCV_WARNINGS_ARE_ERRORS "Treat warnings as errors" OFF ) +OCV_OPTION(OPENCV_CAN_BREAK_BINARY_COMPATIBILITY "Allow changes breaking binary compatibility with OpenCV 2.4.0" OFF ) + # uncategorized options # =================================================== OCV_OPTION(CMAKE_VERBOSE "Verbose mode" OFF ) @@ -286,13 +288,13 @@ endif() # ---------------------------------------------------------------------------- # OpenCV compiler and linker options # ---------------------------------------------------------------------------- -include(cmake/OpenCVCompilerOptions.cmake REQUIRED) - # In case of Makefiles if the user does not setup CMAKE_BUILD_TYPE, assume it's Release: if(CMAKE_GENERATOR MATCHES "Makefiles|Ninja" AND "${CMAKE_BUILD_TYPE}" STREQUAL "") set(CMAKE_BUILD_TYPE Release) endif() +include(cmake/OpenCVCompilerOptions.cmake REQUIRED) + # ---------------------------------------------------------------------------- # Use statically or dynamically linked CRT? @@ -328,6 +330,15 @@ if(UNIX) endif() endif() +# +# Check to see if we have large file support (needed by zlib) +# +include(CheckTypeSize) +check_type_size(off64_t OFF64_T) +if(HAVE_OFF64_T) + add_definitions(-D_LARGEFILE64_SOURCE=1) +endif() + include(cmake/OpenCVPCHSupport.cmake REQUIRED) include(cmake/OpenCVModule.cmake REQUIRED) @@ -471,6 +482,7 @@ else() status(" Linker flags (Release):" ${CMAKE_SHARED_LINKER_FLAGS} ${CMAKE_SHARED_LINKER_FLAGS_RELEASE}) status(" Linker flags (Debug):" ${CMAKE_SHARED_LINKER_FLAGS} ${CMAKE_SHARED_LINKER_FLAGS_DEBUG}) endif() +status(" Precompiled headers:" PCHSupport_FOUND AND ENABLE_PRECOMPILED_HEADERS THEN YES ELSE NO) # ========================== OpenCV modules ========================== status("") @@ -560,7 +572,7 @@ if(WITH_TIFF) if(TIFF_VERSION_STRING AND TIFF_FOUND) status(" TIFF:" "${TIFF_LIBRARY} (ver ${TIFF_VERSION} - ${TIFF_VERSION_STRING})") else() - status(" TIFF:" TIFF_FOUND THEN "${TIFF_LIBRARY} (ver ${TIFF_VERSION})" ELSE "build (ver ${TIFF_VERSION})") + status(" TIFF:" TIFF_FOUND THEN "${TIFF_LIBRARY} (ver ${TIFF_VERSION})" ELSE "build (ver ${TIFF_VERSION} - ${TIFF_VERSION_STRING})") endif() else() status(" TIFF:" "NO") diff --git a/apps/haartraining/_cvcommon.h b/apps/haartraining/_cvcommon.h index 688e3b2..e4f1081 100644 --- a/apps/haartraining/_cvcommon.h +++ b/apps/haartraining/_cvcommon.h @@ -42,6 +42,9 @@ #ifndef __CVCOMMON_H_ #define __CVCOMMON_H_ +#include "opencv2/core/core.hpp" +#include "opencv2/core/internal.hpp" + #include "cxcore.h" #include "cv.h" #include "cxmisc.h" diff --git a/apps/haartraining/cvboost.cpp b/apps/haartraining/cvboost.cpp index 2ee3637..8dfd3dd 100644 --- a/apps/haartraining/cvboost.cpp +++ b/apps/haartraining/cvboost.cpp @@ -80,11 +80,11 @@ typedef struct CvValArray ( *( (float*) (aux->data + ((int) (idx1)) * aux->step ) ) < \ *( (float*) (aux->data + ((int) (idx2)) * aux->step ) ) ) -CV_IMPLEMENT_QSORT_EX( icvSortIndexedValArray_16s, short, CMP_VALUES, CvValArray* ) +static CV_IMPLEMENT_QSORT_EX( icvSortIndexedValArray_16s, short, CMP_VALUES, CvValArray* ) -CV_IMPLEMENT_QSORT_EX( icvSortIndexedValArray_32s, int, CMP_VALUES, CvValArray* ) +static CV_IMPLEMENT_QSORT_EX( icvSortIndexedValArray_32s, int, CMP_VALUES, CvValArray* ) -CV_IMPLEMENT_QSORT_EX( icvSortIndexedValArray_32f, float, CMP_VALUES, CvValArray* ) +static CV_IMPLEMENT_QSORT_EX( icvSortIndexedValArray_32f, float, CMP_VALUES, CvValArray* ) CV_BOOST_IMPL void cvGetSortedIndices( CvMat* val, CvMat* idx, int sortcols ) @@ -181,16 +181,16 @@ float cvEvalStumpClassifier( CvClassifier* classifier, CvMat* sample ) assert( classifier != NULL ); assert( sample != NULL ); assert( CV_MAT_TYPE( sample->type ) == CV_32FC1 ); - + if( (CV_MAT_ELEM( (*sample), float, 0, ((CvStumpClassifier*) classifier)->compidx )) < - ((CvStumpClassifier*) classifier)->threshold ) + ((CvStumpClassifier*) classifier)->threshold ) return ((CvStumpClassifier*) classifier)->left; return ((CvStumpClassifier*) classifier)->right; } #define ICV_DEF_FIND_STUMP_THRESHOLD( suffix, type, error ) \ -CV_BOOST_IMPL int icvFindStumpThreshold_##suffix( \ +static int icvFindStumpThreshold_##suffix( \ uchar* data, size_t datastep, \ uchar* wdata, size_t wstep, \ uchar* ydata, size_t ystep, \ @@ -213,13 +213,10 @@ CV_BOOST_IMPL int icvFindStumpThreshold_##suffix( float* curval = NULL; \ float curlerror = 0.0F; \ float currerror = 0.0F; \ - float wposl; \ - float wposr; \ \ int i = 0; \ int idx = 0; \ \ - wposl = wposr = 0.0F; \ if( *sumw == FLT_MAX ) \ { \ /* calculate sums */ \ @@ -298,8 +295,8 @@ CV_BOOST_IMPL int icvFindStumpThreshold_##suffix( */ #define ICV_DEF_FIND_STUMP_THRESHOLD_MISC( suffix, type ) \ ICV_DEF_FIND_STUMP_THRESHOLD( misc_##suffix, type, \ - wposl = 0.5F * ( wl + wyl ); \ - wposr = 0.5F * ( wr + wyr ); \ + float wposl = 0.5F * ( wl + wyl ); \ + float wposr = 0.5F * ( wr + wyr ); \ curleft = 0.5F * ( 1.0F + curleft ); \ curright = 0.5F * ( 1.0F + curright ); \ curlerror = MIN( wposl, wl - wposl ); \ @@ -311,8 +308,8 @@ CV_BOOST_IMPL int icvFindStumpThreshold_##suffix( */ #define ICV_DEF_FIND_STUMP_THRESHOLD_GINI( suffix, type ) \ ICV_DEF_FIND_STUMP_THRESHOLD( gini_##suffix, type, \ - wposl = 0.5F * ( wl + wyl ); \ - wposr = 0.5F * ( wr + wyr ); \ + float wposl = 0.5F * ( wl + wyl ); \ + float wposr = 0.5F * ( wr + wyr ); \ curleft = 0.5F * ( 1.0F + curleft ); \ curright = 0.5F * ( 1.0F + curright ); \ curlerror = 2.0F * wposl * ( 1.0F - curleft ); \ @@ -326,8 +323,8 @@ CV_BOOST_IMPL int icvFindStumpThreshold_##suffix( */ #define ICV_DEF_FIND_STUMP_THRESHOLD_ENTROPY( suffix, type ) \ ICV_DEF_FIND_STUMP_THRESHOLD( entropy_##suffix, type, \ - wposl = 0.5F * ( wl + wyl ); \ - wposr = 0.5F * ( wr + wyr ); \ + float wposl = 0.5F * ( wl + wyl ); \ + float wposr = 0.5F * ( wr + wyr ); \ curleft = 0.5F * ( 1.0F + curleft ); \ curright = 0.5F * ( 1.0F + curright ); \ curlerror = currerror = 0.0F; \ @@ -430,13 +427,13 @@ CvClassifier* cvCreateStumpClassifier( CvMat* trainData, int ystep = 0; uchar* idxdata = NULL; int idxstep = 0; - int l = 0; /* number of indices */ + int l = 0; /* number of indices */ uchar* wdata = NULL; int wstep = 0; int* idx = NULL; int i = 0; - + float sumw = FLT_MAX; float sumwy = FLT_MAX; float sumwyy = FLT_MAX; @@ -553,7 +550,7 @@ CvClassifier* cvCreateStumpClassifier( CvMat* trainData, ( data + i * ((size_t) cstep), sstep, wdata, wstep, ydata, ystep, (uchar*) idx, sizeof( int ), l, &(stump->lerror), &(stump->rerror), - &(stump->threshold), &(stump->left), &(stump->right), + &(stump->threshold), &(stump->left), &(stump->right), &sumw, &sumwy, &sumwyy ) ) { stump->compidx = i; @@ -601,7 +598,7 @@ CvClassifier* cvCreateMTStumpClassifier( CvMat* trainData, size_t ystep = 0; uchar* idxdata = NULL; size_t idxstep = 0; - int l = 0; /* number of indices */ + int l = 0; /* number of indices */ uchar* wdata = NULL; size_t wstep = 0; @@ -614,7 +611,7 @@ CvClassifier* cvCreateMTStumpClassifier( CvMat* trainData, char* filter = NULL; int i = 0; - + int compidx = 0; int stumperror; int portion; @@ -635,7 +632,7 @@ CvClassifier* cvCreateMTStumpClassifier( CvMat* trainData, int t_compidx; int t_n; - + int ti; int tj; int tk; @@ -722,7 +719,7 @@ CvClassifier* cvCreateMTStumpClassifier( CvMat* trainData, if( ((CvMTStumpTrainParams*) trainParams)->getTrainData != NULL ) { n = ((CvMTStumpTrainParams*) trainParams)->numcomp; - } + } } assert( datan <= n ); @@ -755,14 +752,14 @@ CvClassifier* cvCreateMTStumpClassifier( CvMat* trainData, memset( (void*) stump, 0, sizeof( CvStumpClassifier ) ); portion = ((CvMTStumpTrainParams*)trainParams)->portion; - + if( portion < 1 ) { /* auto portion */ portion = n; #ifdef _OPENMP - portion /= omp_get_max_threads(); - #endif /* _OPENMP */ + portion /= omp_get_max_threads(); + #endif /* _OPENMP */ } stump->eval = cvEvalStumpClassifier; @@ -796,7 +793,7 @@ CvClassifier* cvCreateMTStumpClassifier( CvMat* trainData, t_compidx = 0; t_n = 0; - + ti = 0; tj = 0; tk = 0; @@ -811,7 +808,7 @@ CvClassifier* cvCreateMTStumpClassifier( CvMat* trainData, t_idx = NULL; mat.data.ptr = NULL; - + if( datan < n ) { /* prepare matrix for callback */ @@ -848,7 +845,7 @@ CvClassifier* cvCreateMTStumpClassifier( CvMat* trainData, { t_idx[ti] = ti; } - } + } } } @@ -902,12 +899,12 @@ CvClassifier* cvCreateMTStumpClassifier( CvMat* trainData, t_idx[tk++] = curidx; } } - if( findStumpThreshold_32s[stumperror]( + if( findStumpThreshold_32s[stumperror]( t_data + ti * t_cstep, t_sstep, wdata, wstep, ydata, ystep, (uchar*) t_idx, sizeof( int ), tk, &lerror, &rerror, - &threshold, &left, &right, + &threshold, &left, &right, &sumw, &sumwy, &sumwyy ) ) { optcompidx = ti; @@ -927,12 +924,12 @@ CvClassifier* cvCreateMTStumpClassifier( CvMat* trainData, t_idx[tk++] = curidx; } } - if( findStumpThreshold_32s[stumperror]( + if( findStumpThreshold_32s[stumperror]( t_data + ti * t_cstep, t_sstep, wdata, wstep, ydata, ystep, (uchar*) t_idx, sizeof( int ), tk, &lerror, &rerror, - &threshold, &left, &right, + &threshold, &left, &right, &sumw, &sumwy, &sumwyy ) ) { optcompidx = ti; @@ -952,12 +949,12 @@ CvClassifier* cvCreateMTStumpClassifier( CvMat* trainData, t_idx[tk++] = curidx; } } - if( findStumpThreshold_32s[stumperror]( + if( findStumpThreshold_32s[stumperror]( t_data + ti * t_cstep, t_sstep, wdata, wstep, ydata, ystep, (uchar*) t_idx, sizeof( int ), tk, &lerror, &rerror, - &threshold, &left, &right, + &threshold, &left, &right, &sumw, &sumwy, &sumwyy ) ) { optcompidx = ti; @@ -977,12 +974,12 @@ CvClassifier* cvCreateMTStumpClassifier( CvMat* trainData, case CV_16SC1: for( ti = t_compidx; ti < MIN( sortedn, t_compidx + t_n ); ti++ ) { - if( findStumpThreshold_16s[stumperror]( + if( findStumpThreshold_16s[stumperror]( t_data + ti * t_cstep, t_sstep, wdata, wstep, ydata, ystep, sorteddata + ti * sortedcstep, sortedsstep, sortedm, &lerror, &rerror, - &threshold, &left, &right, + &threshold, &left, &right, &sumw, &sumwy, &sumwyy ) ) { optcompidx = ti; @@ -992,12 +989,12 @@ CvClassifier* cvCreateMTStumpClassifier( CvMat* trainData, case CV_32SC1: for( ti = t_compidx; ti < MIN( sortedn, t_compidx + t_n ); ti++ ) { - if( findStumpThreshold_32s[stumperror]( + if( findStumpThreshold_32s[stumperror]( t_data + ti * t_cstep, t_sstep, wdata, wstep, ydata, ystep, sorteddata + ti * sortedcstep, sortedsstep, sortedm, &lerror, &rerror, - &threshold, &left, &right, + &threshold, &left, &right, &sumw, &sumwy, &sumwyy ) ) { optcompidx = ti; @@ -1007,12 +1004,12 @@ CvClassifier* cvCreateMTStumpClassifier( CvMat* trainData, case CV_32FC1: for( ti = t_compidx; ti < MIN( sortedn, t_compidx + t_n ); ti++ ) { - if( findStumpThreshold_32f[stumperror]( + if( findStumpThreshold_32f[stumperror]( t_data + ti * t_cstep, t_sstep, wdata, wstep, ydata, ystep, sorteddata + ti * sortedcstep, sortedsstep, sortedm, &lerror, &rerror, - &threshold, &left, &right, + &threshold, &left, &right, &sumw, &sumwy, &sumwyy ) ) { optcompidx = ti; @@ -1032,12 +1029,12 @@ CvClassifier* cvCreateMTStumpClassifier( CvMat* trainData, va.data = t_data + ti * t_cstep; va.step = t_sstep; icvSortIndexedValArray_32s( t_idx, l, &va ); - if( findStumpThreshold_32s[stumperror]( + if( findStumpThreshold_32s[stumperror]( t_data + ti * t_cstep, t_sstep, wdata, wstep, ydata, ystep, (uchar*)t_idx, sizeof( int ), l, &lerror, &rerror, - &threshold, &left, &right, + &threshold, &left, &right, &sumw, &sumwy, &sumwyy ) ) { optcompidx = ti; @@ -1117,7 +1114,7 @@ float cvEvalCARTClassifier( CvClassifier* classifier, CvMat* sample ) { if( (CV_MAT_ELEM( (*sample), float, 0, ((CvCARTClassifier*) classifier)->compidx[idx] )) < - ((CvCARTClassifier*) classifier)->threshold[idx] ) + ((CvCARTClassifier*) classifier)->threshold[idx] ) { idx = ((CvCARTClassifier*) classifier)->left[idx]; } @@ -1133,7 +1130,7 @@ float cvEvalCARTClassifier( CvClassifier* classifier, CvMat* sample ) { if( (CV_MAT_ELEM( (*sample), float, ((CvCARTClassifier*) classifier)->compidx[idx], 0 )) < - ((CvCARTClassifier*) classifier)->threshold[idx] ) + ((CvCARTClassifier*) classifier)->threshold[idx] ) { idx = ((CvCARTClassifier*) classifier)->left[idx]; } @@ -1142,14 +1139,14 @@ float cvEvalCARTClassifier( CvClassifier* classifier, CvMat* sample ) idx = ((CvCARTClassifier*) classifier)->right[idx]; } } while( idx > 0 ); - } + } __END__; return ((CvCARTClassifier*) classifier)->val[-idx]; } -CV_BOOST_IMPL +static float cvEvalCARTClassifierIdx( CvClassifier* classifier, CvMat* sample ) { CV_FUNCNAME( "cvEvalCARTClassifierIdx" ); @@ -1170,7 +1167,7 @@ float cvEvalCARTClassifierIdx( CvClassifier* classifier, CvMat* sample ) { if( (CV_MAT_ELEM( (*sample), float, 0, ((CvCARTClassifier*) classifier)->compidx[idx] )) < - ((CvCARTClassifier*) classifier)->threshold[idx] ) + ((CvCARTClassifier*) classifier)->threshold[idx] ) { idx = ((CvCARTClassifier*) classifier)->left[idx]; } @@ -1186,7 +1183,7 @@ float cvEvalCARTClassifierIdx( CvClassifier* classifier, CvMat* sample ) { if( (CV_MAT_ELEM( (*sample), float, ((CvCARTClassifier*) classifier)->compidx[idx], 0 )) < - ((CvCARTClassifier*) classifier)->threshold[idx] ) + ((CvCARTClassifier*) classifier)->threshold[idx] ) { idx = ((CvCARTClassifier*) classifier)->left[idx]; } @@ -1195,7 +1192,7 @@ float cvEvalCARTClassifierIdx( CvClassifier* classifier, CvMat* sample ) idx = ((CvCARTClassifier*) classifier)->right[idx]; } } while( idx > 0 ); - } + } __END__; @@ -1209,7 +1206,7 @@ void cvReleaseCARTClassifier( CvClassifier** classifier ) *classifier = NULL; } -void CV_CDECL icvDefaultSplitIdx_R( int compidx, float threshold, +static void CV_CDECL icvDefaultSplitIdx_R( int compidx, float threshold, CvMat* idx, CvMat** left, CvMat** right, void* userdata ) { @@ -1258,7 +1255,7 @@ void CV_CDECL icvDefaultSplitIdx_R( int compidx, float threshold, } } -void CV_CDECL icvDefaultSplitIdx_C( int compidx, float threshold, +static void CV_CDECL icvDefaultSplitIdx_C( int compidx, float threshold, CvMat* idx, CvMat** left, CvMat** right, void* userdata ) { @@ -1333,13 +1330,13 @@ CvClassifier* cvCreateCARTClassifier( CvMat* trainData, int count = 0; int i = 0; int j = 0; - + CvCARTNode* intnode = NULL; CvCARTNode* list = NULL; int listcount = 0; CvMat* lidx = NULL; CvMat* ridx = NULL; - + float maxerrdrop = 0.0F; int idx = 0; @@ -1349,17 +1346,17 @@ CvClassifier* cvCreateCARTClassifier( CvMat* trainData, void* userdata; count = ((CvCARTTrainParams*) trainParams)->count; - + assert( count > 0 ); - datasize = sizeof( *cart ) + (sizeof( float ) + 3 * sizeof( int )) * count + + datasize = sizeof( *cart ) + (sizeof( float ) + 3 * sizeof( int )) * count + sizeof( float ) * (count + 1); - + cart = (CvCARTClassifier*) cvAlloc( datasize ); memset( cart, 0, datasize ); - + cart->count = count; - + cart->eval = cvEvalCARTClassifier; cart->save = NULL; cart->release = cvReleaseCARTClassifier; @@ -1399,7 +1396,7 @@ CvClassifier* cvCreateCARTClassifier( CvMat* trainData, /* split last added node */ splitIdxCallback( intnode[i-1].stump->compidx, intnode[i-1].stump->threshold, intnode[i-1].sampleIdx, &lidx, &ridx, userdata ); - + if( intnode[i-1].stump->lerror != 0.0F ) { list[listcount].sampleIdx = lidx; @@ -1436,7 +1433,7 @@ CvClassifier* cvCreateCARTClassifier( CvMat* trainData, { cvReleaseMat( &ridx ); } - + if( listcount == 0 ) break; /* find the best node to be added to the tree */ @@ -1474,7 +1471,7 @@ CvClassifier* cvCreateCARTClassifier( CvMat* trainData, cart->count++; cart->compidx[i] = intnode[i].stump->compidx; cart->threshold[i] = intnode[i].stump->threshold; - + /* leaves */ if( cart->left[i] <= 0 ) { @@ -1489,7 +1486,7 @@ CvClassifier* cvCreateCARTClassifier( CvMat* trainData, j++; } } - + /* CLEAN UP */ for( i = 0; i < count && (intnode[i].stump != NULL); i++ ) { @@ -1504,7 +1501,7 @@ CvClassifier* cvCreateCARTClassifier( CvMat* trainData, list[i].stump->release( (CvClassifier**) &(list[i].stump) ); cvReleaseMat( &(list[i].sampleIdx) ); } - + cvFree( &intnode ); return (CvClassifier*) cart; @@ -1529,7 +1526,7 @@ typedef struct CvBoostTrainer * using ANY appropriate weak classifier */ -CV_BOOST_IMPL +static CvBoostTrainer* icvBoostStartTraining( CvMat* trainClasses, CvMat* weakTrainVals, CvMat* /*weights*/, @@ -1560,7 +1557,7 @@ CvBoostTrainer* icvBoostStartTraining( CvMat* trainClasses, CV_MAT2VEC( *trainClasses, ydata, ystep, m ); CV_MAT2VEC( *weakTrainVals, traindata, trainstep, trainnum ); - assert( m == trainnum ); + CV_Assert( m == trainnum ); idxnum = 0; idxstep = 0; @@ -1569,7 +1566,7 @@ CvBoostTrainer* icvBoostStartTraining( CvMat* trainClasses, { CV_MAT2VEC( *sampleIdx, idxdata, idxstep, idxnum ); } - + datasize = sizeof( *ptr ) + sizeof( *ptr->idx ) * idxnum; ptr = (CvBoostTrainer*) cvAlloc( datasize ); memset( ptr, 0, datasize ); @@ -1578,7 +1575,7 @@ CvBoostTrainer* icvBoostStartTraining( CvMat* trainClasses, ptr->count = m; ptr->type = type; - + if( idxnum > 0 ) { CvScalar s; @@ -1595,7 +1592,7 @@ CvBoostTrainer* icvBoostStartTraining( CvMat* trainClasses, { idx = (ptr->idx) ? ptr->idx[i] : i; - *((float*) (traindata + idx * trainstep)) = + *((float*) (traindata + idx * trainstep)) = 2.0F * (*((float*) (ydata + idx * ystep))) - 1.0F; } @@ -1607,7 +1604,7 @@ CvBoostTrainer* icvBoostStartTraining( CvMat* trainClasses, * Discrete AdaBoost functions * */ -CV_BOOST_IMPL +static float icvBoostNextWeakClassifierDAB( CvMat* weakEvalVals, CvMat* trainClasses, CvMat* /*weakTrainVals*/, @@ -1640,8 +1637,8 @@ float icvBoostNextWeakClassifierDAB( CvMat* weakEvalVals, CV_MAT2VEC( *trainClasses, ydata, ystep, ynum ); CV_MAT2VEC( *weights, wdata, wstep, wnum ); - assert( m == ynum ); - assert( m == wnum ); + CV_Assert( m == ynum ); + CV_Assert( m == wnum ); sumw = 0.0F; err = 0.0F; @@ -1651,18 +1648,18 @@ float icvBoostNextWeakClassifierDAB( CvMat* weakEvalVals, sumw += *((float*) (wdata + idx*wstep)); err += (*((float*) (wdata + idx*wstep))) * - ( (*((float*) (evaldata + idx*evalstep))) != + ( (*((float*) (evaldata + idx*evalstep))) != 2.0F * (*((float*) (ydata + idx*ystep))) - 1.0F ); } err /= sumw; err = -cvLogRatio( err ); - + for( i = 0; i < trainer->count; i++ ) { idx = (trainer->idx) ? trainer->idx[i] : i; - *((float*) (wdata + idx*wstep)) *= expf( err * - ((*((float*) (evaldata + idx*evalstep))) != + *((float*) (wdata + idx*wstep)) *= expf( err * + ((*((float*) (evaldata + idx*evalstep))) != 2.0F * (*((float*) (ydata + idx*ystep))) - 1.0F) ); sumw += *((float*) (wdata + idx*wstep)); } @@ -1672,7 +1669,7 @@ float icvBoostNextWeakClassifierDAB( CvMat* weakEvalVals, *((float*) (wdata + idx * wstep)) /= sumw; } - + return err; } @@ -1681,7 +1678,7 @@ float icvBoostNextWeakClassifierDAB( CvMat* weakEvalVals, * Real AdaBoost functions * */ -CV_BOOST_IMPL +static float icvBoostNextWeakClassifierRAB( CvMat* weakEvalVals, CvMat* trainClasses, CvMat* /*weakTrainVals*/, @@ -1731,7 +1728,7 @@ float icvBoostNextWeakClassifierRAB( CvMat* weakEvalVals, *((float*) (wdata + idx*wstep)) /= sumw; } - + return 1.0F; } @@ -1743,7 +1740,7 @@ float icvBoostNextWeakClassifierRAB( CvMat* weakEvalVals, #define CV_LB_PROB_THRESH 0.01F #define CV_LB_WEIGHT_THRESHOLD 0.0001F -CV_BOOST_IMPL +static void icvResponsesAndWeightsLB( int num, uchar* wdata, int wstep, uchar* ydata, int ystep, uchar* fdata, int fstep, @@ -1761,18 +1758,18 @@ void icvResponsesAndWeightsLB( int num, uchar* wdata, int wstep, *((float*) (wdata + idx*wstep)) = MAX( p * (1.0F - p), CV_LB_WEIGHT_THRESHOLD ); if( *((float*) (ydata + idx*ystep)) == 1.0F ) { - *((float*) (traindata + idx*trainstep)) = + *((float*) (traindata + idx*trainstep)) = 1.0F / (MAX( p, CV_LB_PROB_THRESH )); } else { - *((float*) (traindata + idx*trainstep)) = + *((float*) (traindata + idx*trainstep)) = -1.0F / (MAX( 1.0F - p, CV_LB_PROB_THRESH )); } } } -CV_BOOST_IMPL +static CvBoostTrainer* icvBoostStartTrainingLB( CvMat* trainClasses, CvMat* weakTrainVals, CvMat* weights, @@ -1808,8 +1805,8 @@ CvBoostTrainer* icvBoostStartTrainingLB( CvMat* trainClasses, CV_MAT2VEC( *weakTrainVals, traindata, trainstep, trainnum ); CV_MAT2VEC( *weights, wdata, wstep, wnum ); - assert( m == trainnum ); - assert( m == wnum ); + CV_Assert( m == trainnum ); + CV_Assert( m == wnum ); idxnum = 0; @@ -1819,7 +1816,7 @@ CvBoostTrainer* icvBoostStartTrainingLB( CvMat* trainClasses, { CV_MAT2VEC( *sampleIdx, idxdata, idxstep, idxnum ); } - + datasize = sizeof( *ptr ) + sizeof( *ptr->F ) * m + sizeof( *ptr->idx ) * idxnum; ptr = (CvBoostTrainer*) cvAlloc( datasize ); memset( ptr, 0, datasize ); @@ -1828,7 +1825,7 @@ CvBoostTrainer* icvBoostStartTrainingLB( CvMat* trainClasses, ptr->count = m; ptr->type = type; - + if( idxnum > 0 ) { CvScalar s; @@ -1854,7 +1851,7 @@ CvBoostTrainer* icvBoostStartTrainingLB( CvMat* trainClasses, return ptr; } -CV_BOOST_IMPL +static float icvBoostNextWeakClassifierLB( CvMat* weakEvalVals, CvMat* trainClasses, CvMat* weakTrainVals, @@ -1889,9 +1886,9 @@ float icvBoostNextWeakClassifierLB( CvMat* weakEvalVals, CV_MAT2VEC( *weakTrainVals, traindata, trainstep, trainnum ); CV_MAT2VEC( *weights, wdata, wstep, wnum ); - assert( m == ynum ); - assert( m == wnum ); - assert( m == trainnum ); + CV_Assert( m == ynum ); + CV_Assert( m == wnum ); + CV_Assert( m == trainnum ); //assert( m == trainer->count ); for( i = 0; i < trainer->count; i++ ) @@ -1900,7 +1897,7 @@ float icvBoostNextWeakClassifierLB( CvMat* weakEvalVals, trainer->F[idx] += *((float*) (evaldata + idx * evalstep)); } - + icvResponsesAndWeightsLB( trainer->count, wdata, wstep, ydata, ystep, (uchar*) trainer->F, sizeof( *trainer->F ), traindata, trainstep, trainer->idx ); @@ -1913,7 +1910,7 @@ float icvBoostNextWeakClassifierLB( CvMat* weakEvalVals, * Gentle AdaBoost * */ -CV_BOOST_IMPL +static float icvBoostNextWeakClassifierGAB( CvMat* weakEvalVals, CvMat* trainClasses, CvMat* /*weakTrainVals*/, @@ -1944,20 +1941,20 @@ float icvBoostNextWeakClassifierGAB( CvMat* weakEvalVals, CV_MAT2VEC( *trainClasses, ydata, ystep, ynum ); CV_MAT2VEC( *weights, wdata, wstep, wnum ); - assert( m == ynum ); - assert( m == wnum ); + CV_Assert( m == ynum ); + CV_Assert( m == wnum ); sumw = 0.0F; for( i = 0; i < trainer->count; i++ ) { idx = (trainer->idx) ? trainer->idx[i] : i; - *((float*) (wdata + idx*wstep)) *= + *((float*) (wdata + idx*wstep)) *= expf( -(*((float*) (evaldata + idx*evalstep))) * ( 2.0F * (*((float*) (ydata + idx*ystep))) - 1.0F ) ); sumw += *((float*) (wdata + idx*wstep)); } - + for( i = 0; i < trainer->count; i++ ) { idx = (trainer->idx) ? trainer->idx[i] : i; @@ -2033,10 +2030,10 @@ float cvBoostNextWeakClassifier( CvMat* weakEvalVals, typedef struct CvBtTrainer { - /* {{ external */ + /* {{ external */ CvMat* trainData; int flags; - + CvMat* trainClasses; int m; uchar* ydata; @@ -2044,7 +2041,7 @@ typedef struct CvBtTrainer CvMat* sampleIdx; int numsamples; - + float param[2]; CvBoostType type; int numclasses; @@ -2071,7 +2068,7 @@ typedef struct CvBtTrainer typedef void (*CvZeroApproxFunc)( float* approx, CvBtTrainer* trainer ); /* Mean zero approximation */ -void icvZeroApproxMean( float* approx, CvBtTrainer* trainer ) +static void icvZeroApproxMean( float* approx, CvBtTrainer* trainer ) { int i; int idx; @@ -2088,7 +2085,7 @@ void icvZeroApproxMean( float* approx, CvBtTrainer* trainer ) /* * Median zero approximation */ -void icvZeroApproxMed( float* approx, CvBtTrainer* trainer ) +static void icvZeroApproxMed( float* approx, CvBtTrainer* trainer ) { int i; int idx; @@ -2098,7 +2095,7 @@ void icvZeroApproxMed( float* approx, CvBtTrainer* trainer ) idx = icvGetIdxAt( trainer->sampleIdx, i ); trainer->f[i] = *((float*) (trainer->ydata + idx * trainer->ystep)); } - + icvSort_32f( trainer->f, trainer->numsamples, 0 ); approx[0] = trainer->f[trainer->numsamples / 2]; } @@ -2106,7 +2103,7 @@ void icvZeroApproxMed( float* approx, CvBtTrainer* trainer ) /* * 0.5 * log( mean(y) / (1 - mean(y)) ) where y in {0, 1} */ -void icvZeroApproxLog( float* approx, CvBtTrainer* trainer ) +static void icvZeroApproxLog( float* approx, CvBtTrainer* trainer ) { float y_mean; @@ -2117,7 +2114,7 @@ void icvZeroApproxLog( float* approx, CvBtTrainer* trainer ) /* * 0 zero approximation */ -void icvZeroApprox0( float* approx, CvBtTrainer* trainer ) +static void icvZeroApprox0( float* approx, CvBtTrainer* trainer ) { int i; @@ -2143,7 +2140,7 @@ static CvZeroApproxFunc icvZeroApproxFunc[] = CV_BOOST_IMPL void cvBtNext( CvCARTClassifier** trees, CvBtTrainer* trainer ); -CV_BOOST_IMPL +static CvBtTrainer* cvBtStart( CvCARTClassifier** trees, CvMat* trainData, int flags, @@ -2164,13 +2161,13 @@ CvBtTrainer* cvBtStart( CvCARTClassifier** trees, float* zero_approx; int m; int i, j; - + if( trees == NULL ) { CV_ERROR( CV_StsNullPtr, "Invalid trees parameter" ); } - - if( type < CV_DABCLASS || type > CV_MREG ) + + if( type < CV_DABCLASS || type > CV_MREG ) { CV_ERROR( CV_StsUnsupportedFormat, "Unsupported type parameter" ); } @@ -2198,7 +2195,7 @@ CvBtTrainer* cvBtStart( CvCARTClassifier** trees, ptr->flags = flags; ptr->trainClasses = trainClasses; CV_MAT2VEC( *trainClasses, ptr->ydata, ptr->ystep, ptr->m ); - + memset( &(ptr->cartParams), 0, sizeof( ptr->cartParams ) ); memset( &(ptr->stumpParams), 0, sizeof( ptr->stumpParams ) ); @@ -2229,10 +2226,10 @@ CvBtTrainer* cvBtStart( CvCARTClassifier** trees, ptr->sampleIdx = sampleIdx; ptr->numsamples = ( sampleIdx == NULL ) ? ptr->m : MAX( sampleIdx->rows, sampleIdx->cols ); - + ptr->weights = cvCreateMat( 1, m, CV_32FC1 ); - cvSet( ptr->weights, cvScalar( 1.0 ) ); - + cvSet( ptr->weights, cvScalar( 1.0 ) ); + if( type <= CV_GABCLASS ) { ptr->boosttrainer = cvBoostStartTraining( ptr->trainClasses, ptr->y, @@ -2261,7 +2258,7 @@ CvBtTrainer* cvBtStart( CvCARTClassifier** trees, { trees[i]->val[j] += zero_approx[i]; } - } + } CV_CALL( cvFree( &zero_approx ) ); } @@ -2270,14 +2267,14 @@ CvBtTrainer* cvBtStart( CvCARTClassifier** trees, return ptr; } -void icvBtNext_LSREG( CvCARTClassifier** trees, CvBtTrainer* trainer ) +static void icvBtNext_LSREG( CvCARTClassifier** trees, CvBtTrainer* trainer ) { int i; /* yhat_i = y_i - F_(m-1)(x_i) */ for( i = 0; i < trainer->m; i++ ) { - trainer->y->data.fl[i] = + trainer->y->data.fl[i] = *((float*) (trainer->ydata + i * trainer->ystep)) - trainer->f[i]; } @@ -2288,7 +2285,7 @@ void icvBtNext_LSREG( CvCARTClassifier** trees, CvBtTrainer* trainer ) } -void icvBtNext_LADREG( CvCARTClassifier** trees, CvBtTrainer* trainer ) +static void icvBtNext_LADREG( CvCARTClassifier** trees, CvBtTrainer* trainer ) { CvCARTClassifier* ptr; int i, j; @@ -2296,7 +2293,7 @@ void icvBtNext_LADREG( CvCARTClassifier** trees, CvBtTrainer* trainer ) int sample_step; uchar* sample_data; int index; - + int data_size; int* idx; float* resp; @@ -2356,19 +2353,19 @@ void icvBtNext_LADREG( CvCARTClassifier** trees, CvBtTrainer* trainer ) cvFree( &idx ); cvFree( &resp ); - + trees[0] = ptr; } -void icvBtNext_MREG( CvCARTClassifier** trees, CvBtTrainer* trainer ) +static void icvBtNext_MREG( CvCARTClassifier** trees, CvBtTrainer* trainer ) { CvCARTClassifier* ptr; int i, j; CvMat sample; int sample_step; uchar* sample_data; - + int data_size; int* idx; float* resid; @@ -2395,7 +2392,7 @@ void icvBtNext_MREG( CvCARTClassifier** trees, CvBtTrainer* trainer ) /* for delta */ resp[i] = (float) fabs( resid[index] ); } - + /* delta = quantile_alpha{abs(resid_i)} */ icvSort_32f( resp, trainer->numsamples, 0 ); delta = resp[(int)(trainer->param[1] * (trainer->numsamples - 1))]; @@ -2407,7 +2404,7 @@ void icvBtNext_MREG( CvCARTClassifier** trees, CvBtTrainer* trainer ) trainer->y->data.fl[index] = MIN( delta, ((float) fabs( resid[index] )) ) * CV_SIGN( resid[index] ); } - + ptr = (CvCARTClassifier*) cvCreateCARTClassifier( trainer->trainData, trainer->flags, trainer->y, NULL, NULL, NULL, trainer->sampleIdx, trainer->weights, (CvClassifierTrainParams*) &trainer->cartParams ); @@ -2439,7 +2436,7 @@ void icvBtNext_MREG( CvCARTClassifier** trees, CvBtTrainer* trainer ) /* rhat = median(y_i - F_(m-1)(x_i)) */ icvSort_32f( resp, respnum, 0 ); rhat = resp[respnum / 2]; - + /* val = sum{sign(r_i - rhat_i) * min(delta, abs(r_i - rhat_i)} * r_i = y_i - F_(m-1)(x_i) */ @@ -2464,7 +2461,7 @@ void icvBtNext_MREG( CvCARTClassifier** trees, CvBtTrainer* trainer ) cvFree( &resid ); cvFree( &resp ); cvFree( &idx ); - + trees[0] = ptr; } @@ -2476,14 +2473,14 @@ void icvBtNext_MREG( CvCARTClassifier** trees, CvBtTrainer* trainer ) #define CV_LOG_VAL_MAX 18.0 -void icvBtNext_L2CLASS( CvCARTClassifier** trees, CvBtTrainer* trainer ) +static void icvBtNext_L2CLASS( CvCARTClassifier** trees, CvBtTrainer* trainer ) { CvCARTClassifier* ptr; int i, j; CvMat sample; int sample_step; uchar* sample_data; - + int data_size; int* idx; int respnum; @@ -2505,7 +2502,7 @@ void icvBtNext_L2CLASS( CvCARTClassifier** trees, CvBtTrainer* trainer ) weights = (float*) cvAlloc( data_size ); data_size = trainer->m * sizeof( *sorted_weights ); sorted_weights = (float*) cvAlloc( data_size ); - + /* yhat_i = (4 * y_i - 2) / ( 1 + exp( (4 * y_i - 2) * F_(m-1)(x_i) ) ). * y_i in {0, 1} */ @@ -2523,32 +2520,32 @@ void icvBtNext_L2CLASS( CvCARTClassifier** trees, CvBtTrainer* trainer ) sorted_weights[i] = weights[index]; sum_weights += sorted_weights[i]; } - + trimmed_idx = NULL; sample_idx = trainer->sampleIdx; trimmed_num = trainer->numsamples; if( trainer->param[1] < 1.0F ) { /* perform weight trimming */ - + float threshold; int count; - + icvSort_32f( sorted_weights, trainer->numsamples, 0 ); sum_weights *= (1.0F - trainer->param[1]); - + i = -1; do { sum_weights -= sorted_weights[++i]; } while( sum_weights > 0.0F && i < (trainer->numsamples - 1) ); - + threshold = sorted_weights[i]; while( i > 0 && sorted_weights[i-1] == threshold ) i--; if( i > 0 ) { - trimmed_num = trainer->numsamples - i; + trimmed_num = trainer->numsamples - i; trimmed_idx = cvCreateMat( 1, trimmed_num, CV_32FC1 ); count = 0; for( i = 0; i < trainer->numsamples; i++ ) @@ -2560,12 +2557,12 @@ void icvBtNext_L2CLASS( CvCARTClassifier** trees, CvBtTrainer* trainer ) count++; } } - + assert( count == trimmed_num ); sample_idx = trimmed_idx; - printf( "Used samples %%: %g\n", + printf( "Used samples %%: %g\n", (float) trimmed_num / (float) trainer->numsamples * 100.0F ); } } @@ -2608,22 +2605,22 @@ void icvBtNext_L2CLASS( CvCARTClassifier** trees, CvBtTrainer* trainer ) } ptr->val[j] = val; } - + if( trimmed_idx != NULL ) cvReleaseMat( &trimmed_idx ); cvFree( &sorted_weights ); cvFree( &weights ); cvFree( &idx ); - + trees[0] = ptr; } -void icvBtNext_LKCLASS( CvCARTClassifier** trees, CvBtTrainer* trainer ) +static void icvBtNext_LKCLASS( CvCARTClassifier** trees, CvBtTrainer* trainer ) { int i, j, k, kk, num; CvMat sample; int sample_step; uchar* sample_data; - + int data_size; int* idx; int respnum; @@ -2673,7 +2670,7 @@ void icvBtNext_LKCLASS( CvCARTClassifier** trees, CvBtTrainer* trainer ) sum_exp_f += exp_f; } - val = (float) ( (*((float*) (trainer->ydata + index * trainer->ystep))) + val = (float) ( (*((float*) (trainer->ydata + index * trainer->ystep))) == (float) k ); val -= (float) ( (sum_exp_f == CV_VAL_MAX) ? 0.0 : ( 1.0 / sum_exp_f ) ); @@ -2692,25 +2689,25 @@ void icvBtNext_LKCLASS( CvCARTClassifier** trees, CvBtTrainer* trainer ) if( trainer->param[1] < 1.0F ) { /* perform weight trimming */ - + float threshold; int count; - + icvSort_32f( sorted_weights, trainer->numsamples, 0 ); sum_weights *= (1.0F - trainer->param[1]); - + i = -1; do { sum_weights -= sorted_weights[++i]; } while( sum_weights > 0.0F && i < (trainer->numsamples - 1) ); - + threshold = sorted_weights[i]; while( i > 0 && sorted_weights[i-1] == threshold ) i--; if( i > 0 ) { - trimmed_num = trainer->numsamples - i; + trimmed_num = trainer->numsamples - i; trimmed_idx->cols = trimmed_num; count = 0; for( i = 0; i < trainer->numsamples; i++ ) @@ -2722,12 +2719,12 @@ void icvBtNext_LKCLASS( CvCARTClassifier** trees, CvBtTrainer* trainer ) count++; } } - + assert( count == trimmed_num ); sample_idx = trimmed_idx; - printf( "k: %d Used samples %%: %g\n", k, + printf( "k: %d Used samples %%: %g\n", k, (float) trimmed_num / (float) trainer->numsamples * 100.0F ); } } /* weight trimming */ @@ -2773,7 +2770,7 @@ void icvBtNext_LKCLASS( CvCARTClassifier** trees, CvBtTrainer* trainer ) trees[k]->val[j] = val; } } /* for each class */ - + cvReleaseMat( &trimmed_idx ); cvFree( &sorted_weights ); cvFree( &weights ); @@ -2781,7 +2778,7 @@ void icvBtNext_LKCLASS( CvCARTClassifier** trees, CvBtTrainer* trainer ) } -void icvBtNext_XXBCLASS( CvCARTClassifier** trees, CvBtTrainer* trainer ) +static void icvBtNext_XXBCLASS( CvCARTClassifier** trees, CvBtTrainer* trainer ) { float alpha; int i; @@ -2799,19 +2796,19 @@ void icvBtNext_XXBCLASS( CvCARTClassifier** trees, CvBtTrainer* trainer ) num_samples = ( sample_idx == NULL ) ? trainer->m : MAX( sample_idx->rows, sample_idx->cols ); - printf( "Used samples %%: %g\n", + printf( "Used samples %%: %g\n", (float) num_samples / (float) trainer->numsamples * 100.0F ); trees[0] = (CvCARTClassifier*) cvCreateCARTClassifier( trainer->trainData, trainer->flags, trainer->y, NULL, NULL, NULL, sample_idx, trainer->weights, (CvClassifierTrainParams*) &trainer->cartParams ); - + /* evaluate samples */ CV_GET_SAMPLE( *trainer->trainData, trainer->flags, 0, sample ); CV_GET_SAMPLE_STEP( *trainer->trainData, trainer->flags, sample_step ); sample_data = sample.data.ptr; - + for( i = 0; i < trainer->m; i++ ) { sample.data.ptr = sample_data + i * sample_step; @@ -2820,7 +2817,7 @@ void icvBtNext_XXBCLASS( CvCARTClassifier** trees, CvBtTrainer* trainer ) alpha = cvBoostNextWeakClassifier( weak_eval_vals, trainer->trainClasses, trainer->y, trainer->weights, trainer->boosttrainer ); - + /* multiply tree by alpha */ for( i = 0; i <= trees[0]->count; i++ ) { @@ -2833,7 +2830,7 @@ void icvBtNext_XXBCLASS( CvCARTClassifier** trees, CvBtTrainer* trainer ) trees[0]->val[i] = cvLogRatio( trees[0]->val[i] ); } } - + if( sample_idx != NULL && sample_idx != trainer->sampleIdx ) { cvReleaseMat( &sample_idx ); @@ -2865,7 +2862,7 @@ void cvBtNext( CvCARTClassifier** trees, CvBtTrainer* trainer ) int sample_step; uchar* sample_data; - icvBtNextFunc[trainer->type]( trees, trainer ); + icvBtNextFunc[trainer->type]( trees, trainer ); /* shrinkage */ if( trainer->param[0] != 1.0F ) @@ -2890,26 +2887,26 @@ void cvBtNext( CvCARTClassifier** trees, CvBtTrainer* trainer ) index = icvGetIdxAt( trainer->sampleIdx, i ); sample.data.ptr = sample_data + index * sample_step; for( j = 0; j < trainer->numclasses; j++ ) - { - trainer->f[index * trainer->numclasses + j] += + { + trainer->f[index * trainer->numclasses + j] += trees[j]->eval( (CvClassifier*) (trees[j]), &sample ); } } } } -CV_BOOST_IMPL +static void cvBtEnd( CvBtTrainer** trainer ) { CV_FUNCNAME( "cvBtEnd" ); - + __BEGIN__; - + if( trainer == NULL || (*trainer) == NULL ) { CV_ERROR( CV_StsNullPtr, "Invalid trainer parameter" ); } - + if( (*trainer)->y != NULL ) { CV_CALL( cvReleaseMat( &((*trainer)->y) ) ); @@ -2931,7 +2928,7 @@ void cvBtEnd( CvBtTrainer** trainer ) * Boosted tree model as a classifier * \****************************************************************************************/ -CV_BOOST_IMPL +static float cvEvalBtClassifier( CvClassifier* classifier, CvMat* sample ) { float val; @@ -2939,7 +2936,7 @@ float cvEvalBtClassifier( CvClassifier* classifier, CvMat* sample ) CV_FUNCNAME( "cvEvalBtClassifier" ); __BEGIN__; - + int i; val = 0.0F; @@ -2972,7 +2969,7 @@ float cvEvalBtClassifier( CvClassifier* classifier, CvMat* sample ) return val; } -CV_BOOST_IMPL +static float cvEvalBtClassifier2( CvClassifier* classifier, CvMat* sample ) { float val; @@ -2980,7 +2977,7 @@ float cvEvalBtClassifier2( CvClassifier* classifier, CvMat* sample ) CV_FUNCNAME( "cvEvalBtClassifier2" ); __BEGIN__; - + CV_CALL( val = cvEvalBtClassifier( classifier, sample ) ); __END__; @@ -2988,7 +2985,7 @@ float cvEvalBtClassifier2( CvClassifier* classifier, CvMat* sample ) return (float) (val >= 0.0F); } -CV_BOOST_IMPL +static float cvEvalBtClassifierK( CvClassifier* classifier, CvMat* sample ) { int cls = 0; @@ -2996,7 +2993,7 @@ float cvEvalBtClassifierK( CvClassifier* classifier, CvMat* sample ) CV_FUNCNAME( "cvEvalBtClassifierK" ); __BEGIN__; - + int i, k; float max_val; int numclasses; @@ -3072,7 +3069,7 @@ static CvEvalBtClassifier icvEvalBtClassifier[] = cvEvalBtClassifier }; -CV_BOOST_IMPL +static int cvSaveBtClassifier( CvClassifier* classifier, const char* filename ) { CV_FUNCNAME( "cvSaveBtClassifier" ); @@ -3087,7 +3084,7 @@ int cvSaveBtClassifier( CvClassifier* classifier, const char* filename ) CV_ASSERT( classifier ); CV_ASSERT( filename ); - + if( !icvMkDir( filename ) || (file = fopen( filename, "w" )) == 0 ) { CV_ERROR( CV_StsError, "Unable to create file" ); @@ -3101,7 +3098,7 @@ int cvSaveBtClassifier( CvClassifier* classifier, const char* filename ) ((CvBtClassifier*) classifier)->numclasses, ((CvBtClassifier*) classifier)->numfeatures, ((CvBtClassifier*) classifier)->numiter ); - + for( i = 0; i < ((CvBtClassifier*) classifier)->numclasses * ((CvBtClassifier*) classifier)->numiter; i++ ) { @@ -3137,7 +3134,7 @@ int cvSaveBtClassifier( CvClassifier* classifier, const char* filename ) } -CV_BOOST_IMPL +static void cvReleaseBtClassifier( CvClassifier** ptr ) { CV_FUNCNAME( "cvReleaseBtClassifier" ); @@ -3183,7 +3180,7 @@ void cvReleaseBtClassifier( CvClassifier** ptr ) __END__; } -void cvTuneBtClassifier( CvClassifier* classifier, CvMat*, int flags, +static void cvTuneBtClassifier( CvClassifier* classifier, CvMat*, int flags, CvMat*, CvMat* , CvMat*, CvMat*, CvMat* ) { CV_FUNCNAME( "cvTuneBtClassifier" ); @@ -3231,7 +3228,7 @@ void cvTuneBtClassifier( CvClassifier* classifier, CvMat*, int flags, ((CvBtClassifier*) classifier)->seq->total; CV_CALL( ptr = cvAlloc( data_size ) ); CV_CALL( cvCvtSeqToArray( ((CvBtClassifier*) classifier)->seq, ptr ) ); - CV_CALL( cvReleaseMemStorage( + CV_CALL( cvReleaseMemStorage( &(((CvBtClassifier*) classifier)->seq->storage) ) ); ((CvBtClassifier*) classifier)->trees = (CvCARTClassifier**) ptr; classifier->flags &= ~CV_TUNABLE; @@ -3244,7 +3241,7 @@ void cvTuneBtClassifier( CvClassifier* classifier, CvMat*, int flags, __END__; } -CvBtClassifier* icvAllocBtClassifier( CvBoostType type, int flags, int numclasses, +static CvBtClassifier* icvAllocBtClassifier( CvBoostType type, int flags, int numclasses, int numiter ) { CvBtClassifier* ptr; @@ -3317,7 +3314,7 @@ CvClassifier* cvCreateBtClassifier( CvMat* trainData, CV_ASSERT( trainParams != NULL ); type = ((CvBtClassifierTrainParams*) trainParams)->type; - + if( type >= CV_DABCLASS && type <= CV_GABCLASS && sampleIdx ) { CV_ERROR( CV_StsBadArg, "Sample indices are not supported for this type" ); @@ -3330,7 +3327,7 @@ CvClassifier* cvCreateBtClassifier( CvMat* trainData, cvMinMaxLoc( trainClasses, &min_val, &max_val ); num_classes = (int) (max_val + 1.0); - + CV_ASSERT( num_classes >= 2 ); } else @@ -3338,12 +3335,12 @@ CvClassifier* cvCreateBtClassifier( CvMat* trainData, num_classes = 1; } num_iter = ((CvBtClassifierTrainParams*) trainParams)->numiter; - + CV_ASSERT( num_iter > 0 ); ptr = icvAllocBtClassifier( type, CV_TUNABLE | flags, num_classes, num_iter ); ptr->numfeatures = (CV_IS_ROW_SAMPLE( flags )) ? trainData->cols : trainData->rows; - + i = 0; printf( "Iteration %d\n", 1 ); @@ -3358,7 +3355,7 @@ CvClassifier* cvCreateBtClassifier( CvMat* trainData, CV_CALL( cvSeqPushMulti( ptr->seq, trees, ptr->numclasses ) ); CV_CALL( cvFree( &trees ) ); ptr->numiter++; - + for( i = 1; i < num_iter; i++ ) { ptr->tune( (CvClassifier*) ptr, NULL, CV_TUNABLE, NULL, NULL, NULL, NULL, NULL ); @@ -3380,7 +3377,7 @@ CvClassifier* cvCreateBtClassifierFromFile( const char* filename ) CvBtClassifier* ptr = 0; CV_FUNCNAME( "cvCreateBtClassifierFromFile" ); - + __BEGIN__; FILE* file; @@ -3400,7 +3397,7 @@ CvClassifier* cvCreateBtClassifierFromFile( const char* filename ) { CV_ERROR( CV_StsError, "Unable to open file" ); } - + values_read = fscanf( file, "%d %d %d %d", &type, &num_classes, &num_features, &num_classifiers ); CV_Assert(values_read == 4); @@ -3414,7 +3411,7 @@ CvClassifier* cvCreateBtClassifierFromFile( const char* filename ) } ptr = icvAllocBtClassifier( (CvBoostType) type, 0, num_classes, num_classifiers ); ptr->numfeatures = num_features; - + for( i = 0; i < num_classes * num_classifiers; i++ ) { int count; @@ -3532,7 +3529,7 @@ CvMat* cvTrimWeights( CvMat* weights, CvMat* idx, float factor ) count++; } } - + assert( count == ptr->cols ); } cvFree( &sorted_weights ); @@ -3572,7 +3569,7 @@ void cvReadTrainData( const char* filename, int flags, { CV_ERROR( CV_StsNullPtr, "trainClasses must be not NULL" ); } - + *trainData = NULL; *trainClasses = NULL; file = fopen( filename, "r" ); @@ -3592,7 +3589,7 @@ void cvReadTrainData( const char* filename, int flags, { CV_CALL( *trainData = cvCreateMat( n, m, CV_32FC1 ) ); } - + CV_CALL( *trainClasses = cvCreateMat( 1, m, CV_32FC1 ) ); for( i = 0; i < m; i++ ) @@ -3618,7 +3615,7 @@ void cvReadTrainData( const char* filename, int flags, fclose( file ); __END__; - + } CV_BOOST_IMPL @@ -3665,7 +3662,7 @@ void cvWriteTrainData( const char* filename, int flags, { CV_ERROR( CV_StsUnmatchedSizes, "Incorrect trainData and trainClasses sizes" ); } - + if( sampleIdx != NULL ) { count = (sampleIdx->rows == 1) ? sampleIdx->cols : sampleIdx->rows; @@ -3674,7 +3671,7 @@ void cvWriteTrainData( const char* filename, int flags, { count = m; } - + file = fopen( filename, "w" ); if( !file ) @@ -3705,7 +3702,7 @@ void cvWriteTrainData( const char* filename, int flags, for( j = 0; j < n; j++ ) { fprintf( file, "%g ", ( (CV_IS_ROW_SAMPLE( flags )) - ? CV_MAT_ELEM( *trainData, float, idx, j ) + ? CV_MAT_ELEM( *trainData, float, idx, j ) : CV_MAT_ELEM( *trainData, float, j, idx ) ) ); } fprintf( file, "%g\n", ( (clsrow) @@ -3714,13 +3711,13 @@ void cvWriteTrainData( const char* filename, int flags, } fclose( file ); - + __END__; } #define ICV_RAND_SHUFFLE( suffix, type ) \ -void icvRandShuffle_##suffix( uchar* data, size_t step, int num ) \ +static void icvRandShuffle_##suffix( uchar* data, size_t step, int num ) \ { \ time_t seed; \ type tmp; \ diff --git a/apps/haartraining/cvhaarclassifier.cpp b/apps/haartraining/cvhaarclassifier.cpp index 458712b..f217976 100644 --- a/apps/haartraining/cvhaarclassifier.cpp +++ b/apps/haartraining/cvhaarclassifier.cpp @@ -394,7 +394,7 @@ void icvSaveStageHaarClassifier( CvIntHaarClassifier* classifier, FILE* file ) -CvIntHaarClassifier* icvLoadCARTStageHaarClassifierF( FILE* file, int step ) +static CvIntHaarClassifier* icvLoadCARTStageHaarClassifierF( FILE* file, int step ) { CvStageHaarClassifier* ptr = NULL; @@ -525,9 +525,9 @@ float icvEvalTreeCascadeClassifierFilter( CvIntHaarClassifier* classifier, sum_t sum_type* tilted, float normfactor ) { CvTreeCascadeNode* ptr; - CvTreeCascadeClassifier* tree; + //CvTreeCascadeClassifier* tree; - tree = (CvTreeCascadeClassifier*) classifier; + //tree = (CvTreeCascadeClassifier*) classifier; diff --git a/apps/haartraining/cvhaartraining.cpp b/apps/haartraining/cvhaartraining.cpp index dc9f3ac..661bc95 100644 --- a/apps/haartraining/cvhaartraining.cpp +++ b/apps/haartraining/cvhaartraining.cpp @@ -108,7 +108,7 @@ CvBackgroundData* cvbgdata = NULL; /* - * get sum image offsets for corner points + * get sum image offsets for corner points * step - row step (measured in image pixels!) of sum image */ #define CV_SUM_OFFSETS( p0, p1, p2, p3, rect, step ) \ @@ -122,7 +122,7 @@ CvBackgroundData* cvbgdata = NULL; (p3) = (rect).x + (rect).width + (step) * ((rect).y + (rect).height); /* - * get tilted image offsets for corner points + * get tilted image offsets for corner points * step - row step (measured in image pixels!) of tilted image */ #define CV_TILTED_OFFSETS( p0, p1, p2, p3, rect, step ) \ @@ -154,7 +154,7 @@ CvIntHaarFeatures* icvCreateIntHaarFeatures( CvSize winsize, { CvIntHaarFeatures* features = NULL; CvTHaarFeature haarFeature; - + CvMemStorage* storage = NULL; CvSeq* seq = NULL; CvSeqWriter writer; @@ -169,10 +169,11 @@ CvIntHaarFeatures* icvCreateIntHaarFeatures( CvSize winsize, int dx = 0; int dy = 0; +#if 0 float factor = 1.0F; factor = ((float) winsize.width) * winsize.height / (24 * 24); -#if 0 + s0 = (int) (s0 * factor); s1 = (int) (s1 * factor); s2 = (int) (s2 * factor); @@ -252,7 +253,7 @@ CvIntHaarFeatures* icvCreateIntHaarFeatures( CvSize winsize, CV_WRITE_SEQ_ELEM( haarFeature, writer ); } } - + // haar_y4 if ( (x+dx <= winsize.width ) && (y+dy*4 <= winsize.height) ) { if (dx*4*dy < s0) continue; @@ -277,7 +278,7 @@ CvIntHaarFeatures* icvCreateIntHaarFeatures( CvSize winsize, } } - if (mode != 0 /*BASIC*/) { + if (mode != 0 /*BASIC*/) { // point if ( (x+dx*3 <= winsize.width) && (y+dy*3 <= winsize.height) ) { if (dx*9*dy < s0) continue; @@ -289,12 +290,12 @@ CvIntHaarFeatures* icvCreateIntHaarFeatures( CvSize winsize, } } } - - if (mode == 2 /*ALL*/) { + + if (mode == 2 /*ALL*/) { // tilted haar_x2 (x, y, w, h, b, weight) if ( (x+2*dx <= winsize.width) && (y+2*dx+dy <= winsize.height) && (x-dy>= 0) ) { if (dx*2*dy < s1) continue; - + if (!symmetric || (x <= (winsize.width / 2) )) { haarFeature = cvHaarFeature( "tilted_haar_x2", x, y, dx*2, dy, -1, @@ -302,11 +303,11 @@ CvIntHaarFeatures* icvCreateIntHaarFeatures( CvSize winsize, CV_WRITE_SEQ_ELEM( haarFeature, writer ); } } - + // tilted haar_y2 (x, y, w, h, b, weight) if ( (x+dx <= winsize.width) && (y+dx+2*dy <= winsize.height) && (x-2*dy>= 0) ) { if (dx*2*dy < s1) continue; - + if (!symmetric || (x <= (winsize.width / 2) )) { haarFeature = cvHaarFeature( "tilted_haar_y2", x, y, dx, 2*dy, -1, @@ -314,11 +315,11 @@ CvIntHaarFeatures* icvCreateIntHaarFeatures( CvSize winsize, CV_WRITE_SEQ_ELEM( haarFeature, writer ); } } - + // tilted haar_x3 (x, y, w, h, b, weight) if ( (x+3*dx <= winsize.width) && (y+3*dx+dy <= winsize.height) && (x-dy>= 0) ) { if (dx*3*dy < s2) continue; - + if (!symmetric || (x <= (winsize.width / 2) )) { haarFeature = cvHaarFeature( "tilted_haar_x3", x, y, dx*3, dy, -1, @@ -326,11 +327,11 @@ CvIntHaarFeatures* icvCreateIntHaarFeatures( CvSize winsize, CV_WRITE_SEQ_ELEM( haarFeature, writer ); } } - + // tilted haar_y3 (x, y, w, h, b, weight) if ( (x+dx <= winsize.width) && (y+dx+3*dy <= winsize.height) && (x-3*dy>= 0) ) { if (dx*3*dy < s2) continue; - + if (!symmetric || (x <= (winsize.width / 2) )) { haarFeature = cvHaarFeature( "tilted_haar_y3", x, y, dx, 3*dy, -1, @@ -338,12 +339,12 @@ CvIntHaarFeatures* icvCreateIntHaarFeatures( CvSize winsize, CV_WRITE_SEQ_ELEM( haarFeature, writer ); } } - - + + // tilted haar_x4 (x, y, w, h, b, weight) if ( (x+4*dx <= winsize.width) && (y+4*dx+dy <= winsize.height) && (x-dy>= 0) ) { if (dx*4*dy < s3) continue; - + if (!symmetric || (x <= (winsize.width / 2) )) { haarFeature = cvHaarFeature( "tilted_haar_x4", @@ -353,11 +354,11 @@ CvIntHaarFeatures* icvCreateIntHaarFeatures( CvSize winsize, CV_WRITE_SEQ_ELEM( haarFeature, writer ); } } - + // tilted haar_y4 (x, y, w, h, b, weight) if ( (x+dx <= winsize.width) && (y+dx+4*dy <= winsize.height) && (x-4*dy>= 0) ) { if (dx*4*dy < s3) continue; - + if (!symmetric || (x <= (winsize.width / 2) )) { haarFeature = cvHaarFeature( "tilted_haar_y4", x, y, dx, 4*dy, -1, @@ -365,10 +366,10 @@ CvIntHaarFeatures* icvCreateIntHaarFeatures( CvSize winsize, CV_WRITE_SEQ_ELEM( haarFeature, writer ); } } - + /* - + // tilted point if ( (x+dx*3 <= winsize.width - 1) && (y+dy*3 <= winsize.height - 1) && (x-3*dy>= 0)) { if (dx*9*dy < 36) continue; @@ -395,10 +396,10 @@ CvIntHaarFeatures* icvCreateIntHaarFeatures( CvSize winsize, features->winsize = winsize; cvCvtSeqToArray( seq, (CvArr*) features->feature ); cvReleaseMemStorage( &storage ); - + icvConvertToFastHaarFeature( features->feature, features->fastfeature, features->count, (winsize.width + 1) ); - + return features; } @@ -438,7 +439,7 @@ void icvConvertToFastHaarFeature( CvTHaarFeature* haarFeature, fastHaarFeature[i].rect[j].p3, haarFeature[i].rect[j].r, step ) } - + } else { @@ -469,15 +470,15 @@ static CvHaarTrainigData* icvCreateHaarTrainingData( CvSize winsize, int maxnumsamples ) { CvHaarTrainigData* data; - + CV_FUNCNAME( "icvCreateHaarTrainingData" ); - + __BEGIN__; data = NULL; uchar* ptr = NULL; size_t datasize = 0; - + datasize = sizeof( CvHaarTrainigData ) + /* sum and tilted */ ( 2 * (winsize.width + 1) * (winsize.height + 1) * sizeof( sum_type ) + @@ -548,7 +549,7 @@ void icvGetTrainingDataCallback( CvMat* mat, CvMat* sampleIdx, CvMat*, int j = 0; float val = 0.0F; float normfactor = 0.0F; - + CvHaarTrainingData* training_data; CvIntHaarFeatures* haar_features; @@ -639,7 +640,7 @@ void icvGetTrainingDataCallback( CvMat* mat, CvMat* sampleIdx, CvMat*, #if 0 /*def CV_VERBOSE*/ if( first % 5000 == 0 ) { - fprintf( stderr, "%3d%%\r", (int) (100.0 * first / + fprintf( stderr, "%3d%%\r", (int) (100.0 * first / haar_features->count) ); fflush( stderr ); } @@ -692,7 +693,7 @@ void icvPrecalculate( CvHaarTrainingData* data, CvIntHaarFeatures* haarFeatures, t_data = *data->valcache; t_idx = *data->idxcache; t_portion = MIN( portion, (numprecalculated - first) ); - + /* indices */ t_idx.rows = t_portion; t_idx.data.ptr = data->idxcache->data.ptr + first * ((size_t)t_idx.step); @@ -766,7 +767,7 @@ void icvSplitIndicesCallback( int compidx, float threshold, { if( cvEvalFastHaarFeature( fastfeature, (sum_type*) (data->sum.data.ptr + i * data->sum.step), - (sum_type*) (data->tilted.data.ptr + i * data->tilted.step) ) + (sum_type*) (data->tilted.data.ptr + i * data->tilted.step) ) < threshold * data->normfactor.data.fl[i] ) { (*left)->data.fl[(*left)->cols++] = (float) i; @@ -792,7 +793,7 @@ void icvSplitIndicesCallback( int compidx, float threshold, index = (int) *((float*) (idxdata + i * idxstep)); if( cvEvalFastHaarFeature( fastfeature, (sum_type*) (data->sum.data.ptr + index * data->sum.step), - (sum_type*) (data->tilted.data.ptr + index * data->tilted.step) ) + (sum_type*) (data->tilted.data.ptr + index * data->tilted.step) ) < threshold * data->normfactor.data.fl[index] ) { (*left)->data.fl[(*left)->cols++] = (float) index; @@ -858,7 +859,7 @@ CvIntHaarClassifier* icvCreateCARTStageClassifier( CvHaarTrainingData* data, float sum_stage = 0.0F; float threshold = 0.0F; float falsealarm = 0.0F; - + //CvMat* sampleIdx = NULL; CvMat* trimmedIdx; //float* idxdata = NULL; @@ -871,7 +872,7 @@ CvIntHaarClassifier* icvCreateCARTStageClassifier( CvHaarTrainingData* data, int idx; int numsamples; int numtrimmed; - + CvCARTHaarClassifier* classifier; CvSeq* seq = NULL; CvMemStorage* storage = NULL; @@ -885,7 +886,7 @@ CvIntHaarClassifier* icvCreateCARTStageClassifier( CvHaarTrainingData* data, printf( "| N |%%SMP|F| ST.THR | HR | FA | EXP. ERR|\n" ); printf( "+----+----+-+---------+---------+---------+---------+\n" ); #endif /* CV_VERBOSE */ - + n = haarFeatures->count; m = data->sum.rows; numsamples = (sampleIdx) ? MAX( sampleIdx->rows, sampleIdx->cols ) : m; @@ -909,7 +910,7 @@ CvIntHaarClassifier* icvCreateCARTStageClassifier( CvHaarTrainingData* data, trainParams.userdata = &userdata; eval = cvMat( 1, m, CV_32FC1, cvAlloc( sizeof( float ) * m ) ); - + storage = cvCreateMemStorage(); seq = cvCreateSeq( 0, sizeof( *seq ), sizeof( classifier ), storage ); @@ -919,7 +920,7 @@ CvIntHaarClassifier* icvCreateCARTStageClassifier( CvHaarTrainingData* data, num_splits = 0; sumalpha = 0.0F; do - { + { #ifdef CV_VERBOSE int v_wt = 0; @@ -947,12 +948,12 @@ CvIntHaarClassifier* icvCreateCARTStageClassifier( CvHaarTrainingData* data, num_splits += classifier->count; cart->release( (CvClassifier**) &cart ); - + if( symmetric && (seq->total % 2) ) { float normfactor = 0.0F; CvStumpClassifier* stump; - + /* flip haar features */ for( i = 0; i < classifier->count; i++ ) { @@ -961,9 +962,9 @@ CvIntHaarClassifier* icvCreateCARTStageClassifier( CvHaarTrainingData* data, for( j = 0; j < CV_HAAR_FEATURE_MAX && classifier->feature[i].rect[j].weight != 0.0F; j++ ) { - classifier->feature[i].rect[j].r.x = data->winsize.width - + classifier->feature[i].rect[j].r.x = data->winsize.width - classifier->feature[i].rect[j].r.x - - classifier->feature[i].rect[j].r.width; + classifier->feature[i].rect[j].r.width; } } else @@ -975,7 +976,7 @@ CvIntHaarClassifier* icvCreateCARTStageClassifier( CvHaarTrainingData* data, for( j = 0; j < CV_HAAR_FEATURE_MAX && classifier->feature[i].rect[j].weight != 0.0F; j++ ) { - classifier->feature[i].rect[j].r.x = data->winsize.width - + classifier->feature[i].rect[j].r.x = data->winsize.width - classifier->feature[i].rect[j].r.x; CV_SWAP( classifier->feature[i].rect[j].r.width, classifier->feature[i].rect[j].r.height, tmp ); @@ -1010,7 +1011,7 @@ CvIntHaarClassifier* icvCreateCARTStageClassifier( CvHaarTrainingData* data, weakTrainVals, 0, 0, 0, trimmedIdx, &(data->weights), trainParams.stumpTrainParams ); - + classifier->threshold[i] = stump->threshold; if( classifier->left[i] <= 0 ) { @@ -1021,8 +1022,8 @@ CvIntHaarClassifier* icvCreateCARTStageClassifier( CvHaarTrainingData* data, classifier->val[-classifier->right[i]] = stump->right; } - stump->release( (CvClassifier**) &stump ); - + stump->release( (CvClassifier**) &stump ); + } stumpTrainParams.getTrainData = icvGetTrainingDataCallback; @@ -1040,7 +1041,7 @@ CvIntHaarClassifier* icvCreateCARTStageClassifier( CvHaarTrainingData* data, cvReleaseMat( &trimmedIdx ); trimmedIdx = NULL; } - + for( i = 0; i < numsamples; i++ ) { idx = icvGetIdxAt( sampleIdx, i ); @@ -1054,10 +1055,10 @@ CvIntHaarClassifier* icvCreateCARTStageClassifier( CvHaarTrainingData* data, alpha = cvBoostNextWeakClassifier( &eval, &data->cls, weakTrainVals, &data->weights, trainer ); sumalpha += alpha; - + for( i = 0; i <= classifier->count; i++ ) { - if( boosttype == CV_RABCLASS ) + if( boosttype == CV_RABCLASS ) { classifier->val[i] = cvLogRatio( classifier->val[i] ); } @@ -1077,7 +1078,7 @@ CvIntHaarClassifier* icvCreateCARTStageClassifier( CvHaarTrainingData* data, for( j = 0; j < seq->total; j++ ) { classifier = *((CvCARTHaarClassifier**) cvGetSeqElem( seq, j )); - eval.data.fl[numpos] += classifier->eval( + eval.data.fl[numpos] += classifier->eval( (CvIntHaarClassifier*) classifier, (sum_type*) (data->sum.data.ptr + idx * data->sum.step), (sum_type*) (data->tilted.data.ptr + idx * data->tilted.step), @@ -1163,7 +1164,7 @@ CvIntHaarClassifier* icvCreateCARTStageClassifier( CvHaarTrainingData* data, fflush( stdout ); } #endif /* CV_VERBOSE */ - + } while( falsealarm > maxfalsealarm && (!maxsplits || (num_splits < maxsplits) ) ); cvBoostEndTraining( &trainer ); @@ -1177,12 +1178,12 @@ CvIntHaarClassifier* icvCreateCARTStageClassifier( CvHaarTrainingData* data, threshold ); cvCvtSeqToArray( seq, (CvArr*) stage->classifier ); } - + /* CLEANUP */ cvReleaseMemStorage( &storage ); cvReleaseMat( &weakTrainVals ); cvFree( &(eval.data.ptr) ); - + return (CvIntHaarClassifier*) stage; } @@ -1192,7 +1193,7 @@ CvBackgroundData* icvCreateBackgroundData( const char* filename, CvSize winsize { CvBackgroundData* data = NULL; - const char* dir = NULL; + const char* dir = NULL; char full[PATH_MAX]; char* imgfilename = NULL; size_t datasize = 0; @@ -1202,7 +1203,7 @@ CvBackgroundData* icvCreateBackgroundData( const char* filename, CvSize winsize int len = 0; assert( filename != NULL ); - + dir = strrchr( filename, '\\' ); if( dir == NULL ) { @@ -1223,7 +1224,7 @@ CvBackgroundData* icvCreateBackgroundData( const char* filename, CvSize winsize { count = 0; datasize = 0; - + /* count */ while( !feof( input ) ) { @@ -1257,11 +1258,11 @@ CvBackgroundData* icvCreateBackgroundData( const char* filename, CvSize winsize while( !feof( input ) ) { *imgfilename = '\0'; - if( !fgets( imgfilename, PATH_MAX - (int)(imgfilename - full) - 1, input )) + if( !fgets( imgfilename, PATH_MAX - (int)(imgfilename - full) - 1, input )) break; len = (int)strlen( imgfilename ); - if( len > 0 && imgfilename[len-1] == '\n' ) - imgfilename[len-1] = 0, len--; + if( len > 0 && imgfilename[len-1] == '\n' ) + imgfilename[len-1] = 0, len--; if( len > 0 ) { if( (*imgfilename) == '#' ) continue; /* comment */ @@ -1351,14 +1352,14 @@ void icvGetNextFromBackgroundData( CvBackgroundData* data, { round = data->round; -//#ifdef CV_VERBOSE +//#ifdef CV_VERBOSE // printf( "Open background image: %s\n", data->filename[data->last] ); //#endif /* CV_VERBOSE */ - + data->last = rand() % data->count; data->last %= data->count; img = cvLoadImage( data->filename[data->last], 0 ); - if( !img ) + if( !img ) continue; data->round += data->last / data->count; data->round = data->round % (data->winsize.width * data->winsize.height); @@ -1368,7 +1369,7 @@ void icvGetNextFromBackgroundData( CvBackgroundData* data, offset.x = MIN( offset.x, img->width - data->winsize.width ); offset.y = MIN( offset.y, img->height - data->winsize.height ); - + if( img != NULL && img->depth == IPL_DEPTH_8U && img->nChannels == 1 && offset.x >= 0 && offset.y >= 0 ) { @@ -1403,7 +1404,7 @@ void icvGetNextFromBackgroundData( CvBackgroundData* data, reader->scale = MAX( ((float) data->winsize.width + reader->point.x) / ((float) reader->src.cols), ((float) data->winsize.height + reader->point.y) / ((float) reader->src.rows) ); - + reader->img = cvMat( (int) (reader->scale * reader->src.rows + 0.5F), (int) (reader->scale * reader->src.cols + 0.5F), CV_8UC1, (void*) cvAlloc( datasize ) ); @@ -1576,11 +1577,11 @@ void icvGetAuxImages( CvMat* img, CvMat* sum, CvMat* tilted, sum_type valsum = 0; sqsum_type valsqsum = 0; double area = 0.0; - + cvIntegral( img, sum, sqsum, tilted ); normrect = cvRect( 1, 1, img->cols - 2, img->rows - 2 ); CV_SUM_OFFSETS( p0, p1, p2, p3, normrect, img->cols + 1 ) - + area = normrect.width * normrect.height; valsum = ((sum_type*) (sum->data.ptr))[p0] - ((sum_type*) (sum->data.ptr))[p1] - ((sum_type*) (sum->data.ptr))[p2] + ((sum_type*) (sum->data.ptr))[p3]; @@ -1621,28 +1622,28 @@ int icvGetHaarTrainingData( CvHaarTrainingData* data, int first, int count, int i = 0; ccounter_t getcount = 0; ccounter_t thread_getcount = 0; - ccounter_t consumed_count; + ccounter_t consumed_count; ccounter_t thread_consumed_count; - + /* private variables */ CvMat img; CvMat sum; CvMat tilted; CvMat sqsum; - + sum_type* sumdata; sum_type* tilteddata; float* normfactor; - + /* end private variables */ - + assert( data != NULL ); assert( first + count <= data->maxnum ); assert( cascade != NULL ); assert( callback != NULL ); - + // if( !cvbgdata ) return 0; this check needs to be done in the callback for BG - + CCOUNTER_SET_ZERO(getcount); CCOUNTER_SET_ZERO(thread_getcount); CCOUNTER_SET_ZERO(consumed_count); @@ -1691,14 +1692,14 @@ int icvGetHaarTrainingData( CvHaarTrainingData* data, int first, int count, normfactor = data->normfactor.data.fl + i; sum.data.ptr = (uchar*) sumdata; tilted.data.ptr = (uchar*) tilteddata; - icvGetAuxImages( &img, &sum, &tilted, &sqsum, normfactor ); + icvGetAuxImages( &img, &sum, &tilted, &sqsum, normfactor ); if( cascade->eval( cascade, sumdata, tilteddata, *normfactor ) != 0.0F ) { CCOUNTER_INC(thread_getcount); break; } } - + #ifdef CV_VERBOSE if( (i - first) % 500 == 0 ) { @@ -1720,7 +1721,7 @@ int icvGetHaarTrainingData( CvHaarTrainingData* data, int first, int count, CCOUNTER_ADD(consumed_count, thread_consumed_count); } } /* omp parallel */ - + if( consumed != NULL ) { *consumed = (int)consumed_count; @@ -1731,7 +1732,7 @@ int icvGetHaarTrainingData( CvHaarTrainingData* data, int first, int count, /* *acceptance_ratio = ((double) count) / consumed_count; */ *acceptance_ratio = CCOUNTER_DIV(count, consumed_count); } - + return static_cast(getcount); } @@ -1791,7 +1792,7 @@ int icvGetHaarTrainingData( CvHaarTrainingData* data, int first, int count, // CV_SQSUM_MAT_TYPE, // cvAlloc( sizeof( sqsum_type ) * (data->winsize.height + 1) // * (data->winsize.width + 1) ) ); -// +// // #ifdef CV_OPENMP // #pragma omp for schedule(static, 1) // #endif /* CV_OPENMP */ @@ -1800,7 +1801,7 @@ int icvGetHaarTrainingData( CvHaarTrainingData* data, int first, int count, // for( ; ; ) // { // icvGetBackgroundImage( cvbgdata, cvbgreader, &img ); -// +// // CCOUNTER_INC(thread_consumed_count); // // sumdata = (sum_type*) (data->sum.data.ptr + i * data->sum.step); @@ -1808,7 +1809,7 @@ int icvGetHaarTrainingData( CvHaarTrainingData* data, int first, int count, // normfactor = data->normfactor.data.fl + i; // sum.data.ptr = (uchar*) sumdata; // tilted.data.ptr = (uchar*) tilteddata; -// icvGetAuxImages( &img, &sum, &tilted, &sqsum, normfactor ); +// icvGetAuxImages( &img, &sum, &tilted, &sqsum, normfactor ); // if( cascade->eval( cascade, sumdata, tilteddata, *normfactor ) != 0.0F ) // { // break; @@ -1822,7 +1823,7 @@ int icvGetHaarTrainingData( CvHaarTrainingData* data, int first, int count, // fflush( stderr ); // } //#endif /* CV_VERBOSE */ -// +// // } // // cvFree( &(img.data.ptr) ); @@ -1842,7 +1843,7 @@ int icvGetHaarTrainingData( CvHaarTrainingData* data, int first, int count, // /* *acceptance_ratio = ((double) count) / consumed_count; */ // *acceptance_ratio = CCOUNTER_DIV(count, consumed_count); // } -// +// // return count; //} @@ -1853,24 +1854,24 @@ int icvGetHaarTraininDataFromVecCallback( CvMat* img, void* userdata ) int c = 0; assert( img->rows * img->cols == ((CvVecFile*) userdata)->vecsize ); - + size_t elements_read = fread( &tmp, sizeof( tmp ), 1, ((CvVecFile*) userdata)->input ); CV_Assert(elements_read == 1); elements_read = fread( ((CvVecFile*) userdata)->vector, sizeof( short ), ((CvVecFile*) userdata)->vecsize, ((CvVecFile*) userdata)->input ); CV_Assert(elements_read == (size_t)((CvVecFile*) userdata)->vecsize); - - if( feof( ((CvVecFile*) userdata)->input ) || + + if( feof( ((CvVecFile*) userdata)->input ) || (((CvVecFile*) userdata)->last)++ >= ((CvVecFile*) userdata)->count ) { return 0; } - + for( r = 0; r < img->rows; r++ ) { for( c = 0; c < img->cols; c++ ) { - CV_MAT_ELEM( *img, uchar, r, c ) = + CV_MAT_ELEM( *img, uchar, r, c ) = (uchar) ( ((CvVecFile*) userdata)->vector[r * img->cols + c] ); } } @@ -1878,14 +1879,14 @@ int icvGetHaarTraininDataFromVecCallback( CvMat* img, void* userdata ) return 1; } -int icvGetHaarTrainingDataFromBGCallback ( CvMat* img, void* /*userdata*/ ) +static int icvGetHaarTrainingDataFromBGCallback ( CvMat* img, void* /*userdata*/ ) { if (! cvbgdata) return 0; - + if (! cvbgreader) return 0; - + // just in case icvGetBackgroundImage is not thread-safe ... #ifdef CV_OPENMP #pragma omp critical (get_background_image_callback) @@ -1893,7 +1894,7 @@ int icvGetHaarTrainingDataFromBGCallback ( CvMat* img, void* /*userdata*/ ) { icvGetBackgroundImage( cvbgdata, cvbgreader, img ); } - + return 1; } @@ -1902,7 +1903,7 @@ int icvGetHaarTrainingDataFromBGCallback ( CvMat* img, void* /*userdata*/ ) * Get training data from .vec file */ static -int icvGetHaarTrainingDataFromVec( CvHaarTrainingData* data, int first, int count, +int icvGetHaarTrainingDataFromVec( CvHaarTrainingData* data, int first, int count, CvIntHaarClassifier* cascade, const char* filename, int* consumed ) @@ -1914,8 +1915,8 @@ int icvGetHaarTrainingDataFromVec( CvHaarTrainingData* data, int first, int coun __BEGIN__; CvVecFile file; - short tmp = 0; - + short tmp = 0; + file.input = NULL; if( filename ) file.input = fopen( filename, "rb" ); @@ -1967,8 +1968,8 @@ int icvGetHaarTrainingDataFromBG( CvHaarTrainingData* data, int first, int count if (filename) { CvVecFile file; - short tmp = 0; - + short tmp = 0; + file.input = NULL; if( filename ) file.input = fopen( filename, "rb" ); @@ -2009,7 +2010,7 @@ int icvGetHaarTrainingDataFromBG( CvHaarTrainingData* data, int first, int count void cvCreateCascadeClassifier( const char* dirname, const char* vecfilename, - const char* bgfilename, + const char* bgfilename, int npos, int nneg, int nstages, int numprecalculated, int numsplits, @@ -2048,7 +2049,7 @@ void cvCreateCascadeClassifier( const char* dirname, cascade = (CvCascadeHaarClassifier*) icvCreateCascadeHaarClassifier( nstages ); cascade->count = 0; - + if( icvInitBackgroundReaders( bgfilename, winsize ) ) { data = icvCreateHaarTrainingData( winsize, npos + nneg ); @@ -2061,7 +2062,7 @@ void cvCreateCascadeClassifier( const char* dirname, for( i = 0; i < nstages; i++, cascade->count++ ) { sprintf( stagename, "%s%d/%s", dirname, i, CV_STAGE_CART_FILE_NAME ); - cascade->classifier[i] = + cascade->classifier[i] = icvLoadCARTStageHaarClassifier( stagename, winsize.width + 1 ); if( !icvMkDir( stagename ) ) @@ -2129,7 +2130,7 @@ void cvCreateCascadeClassifier( const char* dirname, data->sum.rows = data->tilted.rows = poscount + negcount; data->normfactor.cols = data->weights.cols = data->cls.cols = poscount + negcount; - + posweight = (equalweights) ? 1.0F / (poscount + negcount) : (0.5F / poscount); negweight = (equalweights) ? 1.0F / (poscount + negcount) : (0.5F / negcount); for( j = 0; j < poscount; j++ ) @@ -2169,7 +2170,7 @@ void cvCreateCascadeClassifier( const char* dirname, file = fopen( stagename, "w" ); if( file != NULL ) { - cascade->classifier[i]->save( + cascade->classifier[i]->save( (CvIntHaarClassifier*) cascade->classifier[i], file ); fclose( file ); } @@ -2190,15 +2191,15 @@ void cvCreateCascadeClassifier( const char* dirname, { char xml_path[1024]; int len = (int)strlen(dirname); - CvHaarClassifierCascade* cascade = 0; + CvHaarClassifierCascade* cascade1 = 0; strcpy( xml_path, dirname ); if( xml_path[len-1] == '\\' || xml_path[len-1] == '/' ) len--; strcpy( xml_path + len, ".xml" ); - cascade = cvLoadHaarClassifierCascade( dirname, cvSize(winwidth,winheight) ); - if( cascade ) - cvSave( xml_path, cascade ); - cvReleaseHaarClassifierCascade( &cascade ); + cascade1 = cvLoadHaarClassifierCascade( dirname, cvSize(winwidth,winheight) ); + if( cascade1 ) + cvSave( xml_path, cascade1 ); + cvReleaseHaarClassifierCascade( &cascade1 ); } } else @@ -2207,7 +2208,7 @@ void cvCreateCascadeClassifier( const char* dirname, printf( "FAILED TO INITIALIZE BACKGROUND READERS\n" ); #endif /* CV_VERBOSE */ } - + /* CLEAN UP */ icvDestroyBackgroundReaders(); cascade->release( (CvIntHaarClassifier**) &cascade ); @@ -2215,7 +2216,7 @@ void cvCreateCascadeClassifier( const char* dirname, /* tree cascade classifier */ -int icvNumSplits( CvStageHaarClassifier* stage ) +static int icvNumSplits( CvStageHaarClassifier* stage ) { int i; int num; @@ -2229,7 +2230,7 @@ int icvNumSplits( CvStageHaarClassifier* stage ) return num; } -void icvSetNumSamples( CvHaarTrainingData* training_data, int num ) +static void icvSetNumSamples( CvHaarTrainingData* training_data, int num ) { assert( num <= training_data->maxnum ); @@ -2238,7 +2239,7 @@ void icvSetNumSamples( CvHaarTrainingData* training_data, int num ) training_data->cls.cols = training_data->weights.cols = num; } -void icvSetWeightsAndClasses( CvHaarTrainingData* training_data, +static void icvSetWeightsAndClasses( CvHaarTrainingData* training_data, int num1, float weight1, float cls1, int num2, float weight2, float cls2 ) { @@ -2258,7 +2259,7 @@ void icvSetWeightsAndClasses( CvHaarTrainingData* training_data, } } -CvMat* icvGetUsedValues( CvHaarTrainingData* training_data, +static CvMat* icvGetUsedValues( CvHaarTrainingData* training_data, int start, int num, CvIntHaarFeatures* haar_features, CvStageHaarClassifier* stage ) @@ -2302,7 +2303,7 @@ CvMat* icvGetUsedValues( CvHaarTrainingData* training_data, } total = last + 1; CV_CALL( ptr = cvCreateMat( num, total, CV_32FC1 ) ); - + #ifdef CV_OPENMP #pragma omp parallel for @@ -2351,7 +2352,7 @@ typedef struct CvSplit void cvCreateTreeCascadeClassifier( const char* dirname, const char* vecfilename, - const char* bgfilename, + const char* bgfilename, int npos, int nneg, int nstages, int numprecalculated, int numsplits, @@ -2425,11 +2426,11 @@ void cvCreateTreeCascadeClassifier( const char* dirname, sprintf( stage_name, "%s/", dirname ); suffix = stage_name + strlen( stage_name ); - + if (! bg_vecfile) if( !icvInitBackgroundReaders( bgfilename, winsize ) && nstages > 0 ) CV_ERROR( CV_StsError, "Unable to read negative images" ); - + if( nstages > 0 ) { /* width-first search in the tree */ @@ -2438,7 +2439,7 @@ void cvCreateTreeCascadeClassifier( const char* dirname, CvSplit* first_split; CvSplit* last_split; CvSplit* cur_split; - + CvTreeCascadeNode* parent; CvTreeCascadeNode* cur_node; CvTreeCascadeNode* last_node; @@ -2447,7 +2448,7 @@ void cvCreateTreeCascadeClassifier( const char* dirname, parent = leaves; leaves = NULL; do - { + { int best_clusters; /* best selected number of clusters */ float posweight, negweight; double leaf_fa_rate; @@ -2501,7 +2502,6 @@ void cvCreateTreeCascadeClassifier( const char* dirname, { CvTreeCascadeNode* single_cluster; CvTreeCascadeNode* multiple_clusters; - CvSplit* cur_split; int single_num; icvSetNumSamples( training_data, poscount + negcount ); @@ -2536,7 +2536,7 @@ void cvCreateTreeCascadeClassifier( const char* dirname, multiple_clusters = NULL; printf( "Number of used features: %d\n", single_num ); - + if( maxtreesplits >= 0 ) { max_clusters = MIN( max_clusters, maxtreesplits - total_splits + 1 ); @@ -2594,7 +2594,7 @@ void cvCreateTreeCascadeClassifier( const char* dirname, printf( "Clusters are too small. Clustering aborted.\n" ); break; } - + cur_num = 0; cur_node = last_node = NULL; for( cluster = 0; (cluster < k) && (cur_num < best_num); cluster++ ) @@ -2674,18 +2674,19 @@ void cvCreateTreeCascadeClassifier( const char* dirname, } /* try different number of clusters */ cvReleaseMat( &vals ); - CV_CALL( cur_split = (CvSplit*) cvAlloc( sizeof( *cur_split ) ) ); - CV_ZERO_OBJ( cur_split ); - - if( last_split ) last_split->next = cur_split; - else first_split = cur_split; - last_split = cur_split; - - cur_split->single_cluster = single_cluster; - cur_split->multiple_clusters = multiple_clusters; - cur_split->num_clusters = best_clusters; - cur_split->parent = parent; - cur_split->single_multiple_ratio = (float) single_num / best_num; + CvSplit* curSplit; + CV_CALL( curSplit = (CvSplit*) cvAlloc( sizeof( *curSplit ) ) ); + CV_ZERO_OBJ( curSplit ); + + if( last_split ) last_split->next = curSplit; + else first_split = curSplit; + last_split = curSplit; + + curSplit->single_cluster = single_cluster; + curSplit->multiple_clusters = multiple_clusters; + curSplit->num_clusters = best_clusters; + curSplit->parent = parent; + curSplit->single_multiple_ratio = (float) single_num / best_num; } if( parent ) parent = parent->next_same_level; @@ -2734,7 +2735,7 @@ void cvCreateTreeCascadeClassifier( const char* dirname, ? last_split->multiple_clusters : last_split->single_cluster; parent = last_split->parent; if( parent ) parent->child = cur_node; - + /* connect leaves via next_same_level and save them */ for( ; cur_node; cur_node = cur_node->next ) { @@ -2768,14 +2769,14 @@ void cvCreateTreeCascadeClassifier( const char* dirname, printf( "\nParent node: %s\n", buf ); printf( "Chosen number of splits: %d\n\n", (last_split->multiple_clusters) ? (last_split->num_clusters - 1) : 0 ); - + cur_split = last_split; last_split = last_split->next; cvFree( &cur_split ); } /* for each split point */ printf( "Total number of splits: %d\n", total_splits ); - + if( !(tcc->root) ) tcc->root = leaves; CV_CALL( icvPrintTreeCascade( tcc->root ) ); @@ -2903,7 +2904,7 @@ void cvCreateTrainingSamples( const char* filename, inverse = (rand() > (RAND_MAX/2)); } icvPlaceDistortedSample( &sample, inverse, maxintensitydev, - maxxangle, maxyangle, maxzangle, + maxxangle, maxyangle, maxzangle, 0 /* nonzero means placing image without cut offs */, 0.0 /* nozero adds random shifting */, 0.0 /* nozero adds random scaling */, @@ -2931,13 +2932,13 @@ void cvCreateTrainingSamples( const char* filename, cvFree( &(sample.data.ptr) ); fclose( output ); } /* if( output != NULL ) */ - + icvEndSampleDistortion( &data ); } - + #ifdef CV_VERBOSE printf( "\r \r" ); -#endif /* CV_VERBOSE */ +#endif /* CV_VERBOSE */ } @@ -2986,7 +2987,7 @@ void cvCreateTestSamples( const char* infoname, { cvNamedWindow( "Image", CV_WINDOW_AUTOSIZE ); } - + info = fopen( infoname, "w" ); strcpy( fullname, infoname ); filename = strrchr( fullname, '\\' ); @@ -3008,7 +3009,7 @@ void cvCreateTestSamples( const char* infoname, for( i = 0; i < count; i++ ) { icvGetNextFromBackgroundData( cvbgdata, cvbgreader ); - + maxscale = MIN( 0.7F * cvbgreader->src.cols / winwidth, 0.7F * cvbgreader->src.rows / winheight ); if( maxscale < 1.0F ) continue; @@ -3025,14 +3026,14 @@ void cvCreateTestSamples( const char* infoname, inverse = (rand() > (RAND_MAX/2)); } icvPlaceDistortedSample( &win, inverse, maxintensitydev, - maxxangle, maxyangle, maxzangle, + maxxangle, maxyangle, maxzangle, 1, 0.0, 0.0, &data ); - - + + sprintf( filename, "%04d_%04d_%04d_%04d_%04d.jpg", (i + 1), x, y, width, height ); - - if( info ) + + if( info ) { fprintf( info, "%s %d %d %d %d %d\n", filename, 1, x, y, width, height ); diff --git a/apps/haartraining/cvsamples.cpp b/apps/haartraining/cvsamples.cpp index 2702384..b477b92 100644 --- a/apps/haartraining/cvsamples.cpp +++ b/apps/haartraining/cvsamples.cpp @@ -83,7 +83,7 @@ * cij - coeffs[i][j], coeffs[2][2] = 1 * (ui, vi) - rectangle vertices */ -void cvGetPerspectiveTransform( CvSize src_size, double quad[4][2], +static void cvGetPerspectiveTransform( CvSize src_size, double quad[4][2], double coeffs[3][3] ) { //CV_FUNCNAME( "cvWarpPerspective" ); @@ -130,7 +130,7 @@ void cvGetPerspectiveTransform( CvSize src_size, double quad[4][2], } /* Warps source into destination by a perspective transform */ -void cvWarpPerspective( CvArr* src, CvArr* dst, double quad[4][2] ) +static void cvWarpPerspective( CvArr* src, CvArr* dst, double quad[4][2] ) { CV_FUNCNAME( "cvWarpPerspective" ); @@ -323,8 +323,6 @@ void cvWarpPerspective( CvArr* src, CvArr* dst, double quad[4][2] ) int i00, i10, i01, i11; i00 = i10 = i01 = i11 = (int) fill_value; - double i = fill_value; - /* linear interpolation using 2x2 neighborhood */ if( isrc_x >= 0 && isrc_x <= src_size.width && isrc_y >= 0 && isrc_y <= src_size.height ) @@ -349,9 +347,8 @@ void cvWarpPerspective( CvArr* src, CvArr* dst, double quad[4][2] ) double i0 = i00 + (i10 - i00)*delta_x; double i1 = i01 + (i11 - i01)*delta_x; - i = i0 + (i1 - i0)*delta_y; - ((uchar*)(dst_data + y * dst_step))[x] = (uchar) i; + ((uchar*)(dst_data + y * dst_step))[x] = (uchar) (i0 + (i1 - i0)*delta_y); } x_min += k_left; x_max += k_right; diff --git a/apps/haartraining/performance.cpp b/apps/haartraining/performance.cpp index c14e41c..2fe98f8 100644 --- a/apps/haartraining/performance.cpp +++ b/apps/haartraining/performance.cpp @@ -44,6 +44,9 @@ * * Measure performance of classifier */ +#include "opencv2/core/core.hpp" +#include "opencv2/core/internal.hpp" + #include "cv.h" #include "highgui.h" @@ -211,7 +214,7 @@ int main( int argc, char* argv[] ) totaltime = 0.0; if( info != NULL ) { - int x, y, width, height; + int x, y; IplImage* img; int hits, missed, falseAlarms; int totalHits, totalMissed, totalFalseAlarms; @@ -246,11 +249,12 @@ int main( int argc, char* argv[] ) ref = (ObjectPos*) cvAlloc( refcount * sizeof( *ref ) ); for( i = 0; i < refcount; i++ ) { - error = (fscanf( info, "%d %d %d %d", &x, &y, &width, &height ) != 4); + int w, h; + error = (fscanf( info, "%d %d %d %d", &x, &y, &w, &h ) != 4); if( error ) break; - ref[i].x = 0.5F * width + x; - ref[i].y = 0.5F * height + y; - ref[i].width = sqrtf( 0.5F * (width * width + height * height) ); + ref[i].x = 0.5F * w + x; + ref[i].y = 0.5F * h + y; + ref[i].width = sqrtf( 0.5F * (w * w + h * h) ); ref[i].found = 0; ref[i].neghbors = 0; } diff --git a/apps/traincascade/HOGfeatures.cpp b/apps/traincascade/HOGfeatures.cpp index 68c1943..69f5b4a 100644 --- a/apps/traincascade/HOGfeatures.cpp +++ b/apps/traincascade/HOGfeatures.cpp @@ -1,3 +1,6 @@ +#include "opencv2/core/core.hpp" +#include "opencv2/core/internal.hpp" + #include "HOGfeatures.h" #include "cascadeclassifier.h" @@ -54,7 +57,7 @@ void CvHOGEvaluator::writeFeatures( FileStorage &fs, const Mat& featureMap ) con features[featIdx].write( fs, componentIdx ); fs << "}"; } - fs << "]"; + fs << "]"; } void CvHOGEvaluator::generateFeatures() @@ -85,11 +88,11 @@ void CvHOGEvaluator::generateFeatures() } } w = 4*t; - h = 2*t; + h = 2*t; for (x = 0; x <= winSize.width - w; x += blockStep.width) { for (y = 0; y <= winSize.height - h; y += blockStep.height) - { + { features.push_back(Feature(offset, x, y, 2*t, t)); } } @@ -136,7 +139,7 @@ void CvHOGEvaluator::Feature::write(FileStorage &fs) const // int cellIdx = featComponent / N_BINS; // int binIdx = featComponent % N_BINS; // -// fs << CC_RECTS << "[:" << rect[cellIdx].x << rect[cellIdx].y << +// fs << CC_RECTS << "[:" << rect[cellIdx].x << rect[cellIdx].y << // rect[cellIdx].width << rect[cellIdx].height << binIdx << "]"; //} @@ -144,7 +147,7 @@ void CvHOGEvaluator::Feature::write(FileStorage &fs) const //All block is nessesary for block normalization void CvHOGEvaluator::Feature::write(FileStorage &fs, int featComponentIdx) const { - fs << CC_RECT << "[:" << rect[0].x << rect[0].y << + fs << CC_RECT << "[:" << rect[0].x << rect[0].y << rect[0].width << rect[0].height << featComponentIdx << "]"; } @@ -228,7 +231,7 @@ void CvHOGEvaluator::integralHistogram(const Mat &img, vector &histogram, M memset( histBuf, 0, histSize.width * sizeof(histBuf[0]) ); histBuf += histStep + 1; for( y = 0; y < qangle.rows; y++ ) - { + { histBuf[-1] = 0.f; float strSum = 0.f; for( x = 0; x < qangle.cols; x++ ) diff --git a/apps/traincascade/boost.cpp b/apps/traincascade/boost.cpp index f05f458..18165fd 100644 --- a/apps/traincascade/boost.cpp +++ b/apps/traincascade/boost.cpp @@ -1,3 +1,6 @@ +#include "opencv2/core/core.hpp" +#include "opencv2/core/internal.hpp" + #include "boost.h" #include "cascadeclassifier.h" #include @@ -139,7 +142,7 @@ static CvMat* cvPreprocessIndexArray( const CvMat* idx_arr, int data_arr_size, b //----------------------------- CascadeBoostParams ------------------------------------------------- CvCascadeBoostParams::CvCascadeBoostParams() : minHitRate( 0.995F), maxFalseAlarm( 0.5F ) -{ +{ boost_type = CvBoost::GENTLE; use_surrogates = use_1se_rule = truncate_pruned_tree = false; } @@ -157,7 +160,7 @@ CvCascadeBoostParams::CvCascadeBoostParams( int _boostType, void CvCascadeBoostParams::write( FileStorage &fs ) const { - String boostTypeStr = boost_type == CvBoost::DISCRETE ? CC_DISCRETE_BOOST : + String boostTypeStr = boost_type == CvBoost::DISCRETE ? CC_DISCRETE_BOOST : boost_type == CvBoost::REAL ? CC_REAL_BOOST : boost_type == CvBoost::LOGIT ? CC_LOGIT_BOOST : boost_type == CvBoost::GENTLE ? CC_GENTLE_BOOST : String(); @@ -197,7 +200,7 @@ bool CvCascadeBoostParams::read( const FileNode &node ) void CvCascadeBoostParams::printDefaults() const { cout << "--boostParams--" << endl; - cout << " [-bt <{" << CC_DISCRETE_BOOST << ", " + cout << " [-bt <{" << CC_DISCRETE_BOOST << ", " << CC_REAL_BOOST << ", " << CC_LOGIT_BOOST ", " << CC_GENTLE_BOOST << "(default)}>]" << endl; @@ -210,7 +213,7 @@ void CvCascadeBoostParams::printDefaults() const void CvCascadeBoostParams::printAttrs() const { - String boostTypeStr = boost_type == CvBoost::DISCRETE ? CC_DISCRETE_BOOST : + String boostTypeStr = boost_type == CvBoost::DISCRETE ? CC_DISCRETE_BOOST : boost_type == CvBoost::REAL ? CC_REAL_BOOST : boost_type == CvBoost::LOGIT ? CC_LOGIT_BOOST : boost_type == CvBoost::GENTLE ? CC_GENTLE_BOOST : String(); @@ -259,7 +262,7 @@ bool CvCascadeBoostParams::scanAttr( const String prmName, const String val) else res = false; - return res; + return res; } CvDTreeNode* CvCascadeBoostTrainData::subsample_data( const CvMat* _subsample_idx ) @@ -440,7 +443,7 @@ CvCascadeBoostTrainData::CvCascadeBoostTrainData( const CvFeatureEvaluator* _fea set_params( _params ); max_c_count = MAX( 2, featureEvaluator->getMaxCatCount() ); var_type = cvCreateMat( 1, var_count + 2, CV_32SC1 ); - if ( featureEvaluator->getMaxCatCount() > 0 ) + if ( featureEvaluator->getMaxCatCount() > 0 ) { numPrecalcIdx = 0; cat_var_count = var_count; @@ -448,7 +451,7 @@ CvCascadeBoostTrainData::CvCascadeBoostTrainData( const CvFeatureEvaluator* _fea for( int vi = 0; vi < var_count; vi++ ) { var_type->data.i[vi] = vi; - } + } } else { @@ -457,8 +460,8 @@ CvCascadeBoostTrainData::CvCascadeBoostTrainData( const CvFeatureEvaluator* _fea for( int vi = 1; vi <= var_count; vi++ ) { var_type->data.i[vi-1] = -vi; - } - } + } + } var_type->data.i[var_count] = cat_var_count; var_type->data.i[var_count+1] = cat_var_count+1; @@ -467,7 +470,7 @@ CvCascadeBoostTrainData::CvCascadeBoostTrainData( const CvFeatureEvaluator* _fea treeBlockSize = MAX(treeBlockSize + BlockSizeDelta, MinBlockSize); tree_storage = cvCreateMemStorage( treeBlockSize ); node_heap = cvCreateSet( 0, sizeof(node_heap[0]), sizeof(CvDTreeNode), tree_storage ); - split_heap = cvCreateSet( 0, sizeof(split_heap[0]), maxSplitSize, tree_storage ); + split_heap = cvCreateSet( 0, sizeof(split_heap[0]), maxSplitSize, tree_storage ); } CvCascadeBoostTrainData::CvCascadeBoostTrainData( const CvFeatureEvaluator* _featureEvaluator, @@ -477,15 +480,15 @@ CvCascadeBoostTrainData::CvCascadeBoostTrainData( const CvFeatureEvaluator* _fea { setData( _featureEvaluator, _numSamples, _precalcValBufSize, _precalcIdxBufSize, _params ); } - + void CvCascadeBoostTrainData::setData( const CvFeatureEvaluator* _featureEvaluator, int _numSamples, int _precalcValBufSize, int _precalcIdxBufSize, - const CvDTreeParams& _params ) -{ + const CvDTreeParams& _params ) +{ int* idst = 0; unsigned short* udst = 0; - + clear(); shared = true; have_labels = true; @@ -503,16 +506,16 @@ void CvCascadeBoostTrainData::setData( const CvFeatureEvaluator* _featureEvaluat _resp = featureEvaluator->getCls(); responses = &_resp; // TODO: check responses: elements must be 0 or 1 - - if( _precalcValBufSize < 0 || _precalcIdxBufSize < 0) + + if( _precalcValBufSize < 0 || _precalcIdxBufSize < 0) CV_Error( CV_StsOutOfRange, "_numPrecalcVal and _numPrecalcIdx must be positive or 0" ); - var_count = var_all = featureEvaluator->getNumFeatures() * featureEvaluator->getFeatureSize(); + var_count = var_all = featureEvaluator->getNumFeatures() * featureEvaluator->getFeatureSize(); sample_count = _numSamples; - - is_buf_16u = false; - if (sample_count < 65536) - is_buf_16u = true; + + is_buf_16u = false; + if (sample_count < 65536) + is_buf_16u = true; numPrecalcVal = min( cvRound((double)_precalcValBufSize*1048576. / (sizeof(float)*sample_count)), var_count ); numPrecalcIdx = min( cvRound((double)_precalcIdxBufSize*1048576. / @@ -522,8 +525,8 @@ void CvCascadeBoostTrainData::setData( const CvFeatureEvaluator* _featureEvaluat valCache.create( numPrecalcVal, sample_count, CV_32FC1 ); var_type = cvCreateMat( 1, var_count + 2, CV_32SC1 ); - - if ( featureEvaluator->getMaxCatCount() > 0 ) + + if ( featureEvaluator->getMaxCatCount() > 0 ) { numPrecalcIdx = 0; cat_var_count = var_count; @@ -531,7 +534,7 @@ void CvCascadeBoostTrainData::setData( const CvFeatureEvaluator* _featureEvaluat for( int vi = 0; vi < var_count; vi++ ) { var_type->data.i[vi] = vi; - } + } } else { @@ -540,14 +543,14 @@ void CvCascadeBoostTrainData::setData( const CvFeatureEvaluator* _featureEvaluat for( int vi = 1; vi <= var_count; vi++ ) { var_type->data.i[vi-1] = -vi; - } + } } var_type->data.i[var_count] = cat_var_count; var_type->data.i[var_count+1] = cat_var_count+1; work_var_count = ( cat_var_count ? 0 : numPrecalcIdx ) + 1/*cv_lables*/; buf_size = (work_var_count + 1) * sample_count/*sample_indices*/; buf_count = 2; - + if ( is_buf_16u ) buf = cvCreateMat( buf_count, buf_size, CV_16UC1 ); else @@ -556,7 +559,7 @@ void CvCascadeBoostTrainData::setData( const CvFeatureEvaluator* _featureEvaluat cat_count = cvCreateMat( 1, cat_var_count + 1, CV_32SC1 ); // precalculate valCache and set indices in buf - precalculate(); + precalculate(); // now calculate the maximum size of split, // create memory storage that will keep nodes and splits of the decision tree @@ -574,7 +577,7 @@ void CvCascadeBoostTrainData::setData( const CvFeatureEvaluator* _featureEvaluat tempBlockSize = MAX( tempBlockSize + BlockSizeDelta, MinBlockSize ); temp_storage = cvCreateMemStorage( tempBlockSize ); nv_heap = cvCreateSet( 0, sizeof(*nv_heap), nvSize, temp_storage ); - + data_root = new_node( 0, sample_count, 0, 0 ); // set sample labels @@ -617,7 +620,7 @@ void CvCascadeBoostTrainData::free_train_data() const int* CvCascadeBoostTrainData::get_class_labels( CvDTreeNode* n, int* labelsBuf) { - int nodeSampleCount = n->sample_count; + int nodeSampleCount = n->sample_count; int rStep = CV_IS_MAT_CONT( responses->type ) ? 1 : responses->step / CV_ELEM_SIZE( responses->type ); int* sampleIndicesBuf = labelsBuf; // @@ -626,7 +629,7 @@ const int* CvCascadeBoostTrainData::get_class_labels( CvDTreeNode* n, int* label { int sidx = sampleIndices[si]; labelsBuf[si] = (int)responses->data.fl[sidx*rStep]; - } + } return labelsBuf; } @@ -643,9 +646,9 @@ const int* CvCascadeBoostTrainData::get_cv_labels( CvDTreeNode* n, int* labels_b void CvCascadeBoostTrainData::get_ord_var_data( CvDTreeNode* n, int vi, float* ordValuesBuf, int* sortedIndicesBuf, const float** ordValues, const int** sortedIndices, int* sampleIndicesBuf ) { - int nodeSampleCount = n->sample_count; + int nodeSampleCount = n->sample_count; const int* sampleIndices = get_sample_indices(n, sampleIndicesBuf); - + if ( vi < numPrecalcIdx ) { if( !is_buf_16u ) @@ -659,7 +662,7 @@ void CvCascadeBoostTrainData::get_ord_var_data( CvDTreeNode* n, int vi, float* o *sortedIndices = sortedIndicesBuf; } - + if( vi < numPrecalcVal ) { for( int i = 0; i < nodeSampleCount; i++ ) @@ -705,10 +708,10 @@ void CvCascadeBoostTrainData::get_ord_var_data( CvDTreeNode* n, int vi, float* o ordValuesBuf[i] = (&sampleValues[0])[sortedIndicesBuf[i]]; *sortedIndices = sortedIndicesBuf; } - + *ordValues = ordValuesBuf; } - + const int* CvCascadeBoostTrainData::get_cat_var_data( CvDTreeNode* n, int vi, int* catValuesBuf ) { int nodeSampleCount = n->sample_count; @@ -739,8 +742,8 @@ const int* CvCascadeBoostTrainData::get_cat_var_data( CvDTreeNode* n, int vi, in float CvCascadeBoostTrainData::getVarValue( int vi, int si ) { if ( vi < numPrecalcVal && !valCache.empty() ) - return valCache.at( vi, si ); - return (*featureEvaluator)( vi, si ); + return valCache.at( vi, si ); + return (*featureEvaluator)( vi, si ); } @@ -858,7 +861,7 @@ CvDTreeNode* CvCascadeBoostTree::predict( int sampleIdx ) const CvDTreeNode* node = root; if( !node ) CV_Error( CV_StsError, "The tree has not been trained yet" ); - + if ( ((CvCascadeBoostTrainData*)data)->featureEvaluator->getMaxCatCount() == 0 ) // ordered { while( node->left ) @@ -946,7 +949,7 @@ void CvCascadeBoostTree::read( const FileNode &node, CvBoost* _ensemble, int maxCatCount = ((CvCascadeBoostTrainData*)_data)->featureEvaluator->getMaxCatCount(); int subsetN = (maxCatCount + 31)/32; int step = 3 + ( maxCatCount>0 ? subsetN : 1 ); - + queue internalNodesQueue; FileNodeIterator internalNodesIt, leafValsuesIt; CvDTreeNode* prntNode, *cldNode; @@ -986,11 +989,11 @@ void CvCascadeBoostTree::read( const FileNode &node, CvBoost* _ensemble, { prntNode->right = cldNode = data->new_node( 0, 0, 0, 0 ); *leafValsuesIt >> cldNode->value; leafValsuesIt--; - cldNode->parent = prntNode; + cldNode->parent = prntNode; } else { - prntNode->right = internalNodesQueue.front(); + prntNode->right = internalNodesQueue.front(); prntNode->right->parent = prntNode; internalNodesQueue.pop(); } @@ -999,7 +1002,7 @@ void CvCascadeBoostTree::read( const FileNode &node, CvBoost* _ensemble, { prntNode->left = cldNode = data->new_node( 0, 0, 0, 0 ); *leafValsuesIt >> cldNode->value; leafValsuesIt--; - cldNode->parent = prntNode; + cldNode->parent = prntNode; } else { @@ -1089,7 +1092,7 @@ void CvCascadeBoostTree::split_node_data( CvDTreeNode* node ) } } CV_Assert( n1 == n ); - } + } else { int *ldst, *rdst; @@ -1116,7 +1119,7 @@ void CvCascadeBoostTree::split_node_data( CvDTreeNode* node ) } } CV_Assert( n1 == n ); - } + } } // split cv_labels using newIdx relocation table @@ -1171,7 +1174,7 @@ void CvCascadeBoostTree::split_node_data( CvDTreeNode* node ) } } } - + // split sample indices int *sampleIdx_src_buf = tempBuf + n; const int* sampleIdx_src = data->get_sample_indices(node, sampleIdx_src_buf); @@ -1181,9 +1184,9 @@ void CvCascadeBoostTree::split_node_data( CvDTreeNode* node ) if (data->is_buf_16u) { - unsigned short* ldst = (unsigned short*)(buf->data.s + left->buf_idx*buf->cols + + unsigned short* ldst = (unsigned short*)(buf->data.s + left->buf_idx*buf->cols + workVarCount*scount + left->offset); - unsigned short* rdst = (unsigned short*)(buf->data.s + right->buf_idx*buf->cols + + unsigned short* rdst = (unsigned short*)(buf->data.s + right->buf_idx*buf->cols + workVarCount*scount + right->offset); for (int i = 0; i < n; i++) { @@ -1202,9 +1205,9 @@ void CvCascadeBoostTree::split_node_data( CvDTreeNode* node ) } else { - int* ldst = buf->data.i + left->buf_idx*buf->cols + + int* ldst = buf->data.i + left->buf_idx*buf->cols + workVarCount*scount + left->offset; - int* rdst = buf->data.i + right->buf_idx*buf->cols + + int* rdst = buf->data.i + right->buf_idx*buf->cols + workVarCount*scount + right->offset; for (int i = 0; i < n; i++) { @@ -1229,10 +1232,10 @@ void CvCascadeBoostTree::split_node_data( CvDTreeNode* node ) } // deallocate the parent node data that is not needed anymore - data->free_node_data(node); + data->free_node_data(node); } -void auxMarkFeaturesInMap( const CvDTreeNode* node, Mat& featureMap) +static void auxMarkFeaturesInMap( const CvDTreeNode* node, Mat& featureMap) { if ( node && node->split ) { @@ -1265,7 +1268,7 @@ bool CvCascadeBoost::train( const CvFeatureEvaluator* _featureEvaluator, set_params( _params ); if ( (_params.boost_type == LOGIT) || (_params.boost_type == GENTLE) ) data->do_responses_copy(); - + update_weights( 0 ); cout << "+----+---------+---------+" << endl; @@ -1316,7 +1319,7 @@ bool CvCascadeBoost::set_params( const CvBoostParams& _params ) minHitRate = ((CvCascadeBoostParams&)_params).minHitRate; maxFalseAlarm = ((CvCascadeBoostParams&)_params).maxFalseAlarm; return ( ( minHitRate > 0 ) && ( minHitRate < 1) && - ( maxFalseAlarm > 0 ) && ( maxFalseAlarm < 1) && + ( maxFalseAlarm > 0 ) && ( maxFalseAlarm < 1) && CvBoost::set_params( _params )); } @@ -1364,7 +1367,7 @@ void CvCascadeBoost::update_weights( CvBoostTree* tree ) if (data->is_buf_16u) { - unsigned short* labels = (unsigned short*)(buf->data.s + data->data_root->buf_idx*buf->cols + + unsigned short* labels = (unsigned short*)(buf->data.s + data->data_root->buf_idx*buf->cols + data->data_root->offset + (data->work_var_count-1)*data->sample_count); for( int i = 0; i < n; i++ ) { @@ -1382,7 +1385,7 @@ void CvCascadeBoost::update_weights( CvBoostTree* tree ) } else { - int* labels = buf->data.i + data->data_root->buf_idx*buf->cols + + int* labels = buf->data.i + data->data_root->buf_idx*buf->cols + data->data_root->offset + (data->work_var_count-1)*data->sample_count; for( int i = 0; i < n; i++ ) @@ -1425,7 +1428,7 @@ void CvCascadeBoost::update_weights( CvBoostTree* tree ) { // invert the subsample mask cvXorS( subsample_mask, cvScalar(1.), subsample_mask ); - + // run tree through all the non-processed samples for( int i = 0; i < n; i++ ) if( subsample_mask->data.ptr[i] ) @@ -1565,7 +1568,7 @@ bool CvCascadeBoost::isErrDesired() int sCount = data->sample_count, numPos = 0, numNeg = 0, numFalse = 0, numPosTrue = 0; vector eval(sCount); - + for( int i = 0; i < sCount; i++ ) if( ((CvCascadeBoostTrainData*)data)->featureEvaluator->getCls( i ) == 1.0F ) eval[numPos++] = predict( i, true ); @@ -1625,7 +1628,7 @@ bool CvCascadeBoost::read( const FileNode &node, set_params( _params ); node[CC_STAGE_THRESHOLD] >> threshold; - FileNode rnode = node[CC_WEAK_CLASSIFIERS]; + FileNode rnode = node[CC_WEAK_CLASSIFIERS]; storage = cvCreateMemStorage(); weak = cvCreateSeq( 0, sizeof(CvSeq), sizeof(CvBoostTree*), storage ); diff --git a/apps/traincascade/cascadeclassifier.cpp b/apps/traincascade/cascadeclassifier.cpp index 3433d74..ef6d181 100644 --- a/apps/traincascade/cascadeclassifier.cpp +++ b/apps/traincascade/cascadeclassifier.cpp @@ -1,3 +1,6 @@ +#include "opencv2/core/core.hpp" +#include "opencv2/core/internal.hpp" + #include "cascadeclassifier.h" #include @@ -6,14 +9,14 @@ using namespace std; static const char* stageTypes[] = { CC_BOOST }; static const char* featureTypes[] = { CC_HAAR, CC_LBP, CC_HOG }; -CvCascadeParams::CvCascadeParams() : stageType( defaultStageType ), +CvCascadeParams::CvCascadeParams() : stageType( defaultStageType ), featureType( defaultFeatureType ), winSize( cvSize(24, 24) ) -{ - name = CC_CASCADE_PARAMS; +{ + name = CC_CASCADE_PARAMS; } CvCascadeParams::CvCascadeParams( int _stageType, int _featureType ) : stageType( _stageType ), featureType( _featureType ), winSize( cvSize(24, 24) ) -{ +{ name = CC_CASCADE_PARAMS; } @@ -25,7 +28,7 @@ void CvCascadeParams::write( FileStorage &fs ) const CV_Assert( !stageTypeStr.empty() ); fs << CC_STAGE_TYPE << stageTypeStr; String featureTypeStr = featureType == CvFeatureParams::HAAR ? CC_HAAR : - featureType == CvFeatureParams::LBP ? CC_LBP : + featureType == CvFeatureParams::LBP ? CC_LBP : featureType == CvFeatureParams::HOG ? CC_HOG : 0; CV_Assert( !stageTypeStr.empty() ); @@ -51,7 +54,7 @@ bool CvCascadeParams::read( const FileNode &node ) return false; rnode >> featureTypeStr; featureType = !featureTypeStr.compare( CC_HAAR ) ? CvFeatureParams::HAAR : - !featureTypeStr.compare( CC_LBP ) ? CvFeatureParams::LBP : + !featureTypeStr.compare( CC_LBP ) ? CvFeatureParams::LBP : !featureTypeStr.compare( CC_HOG ) ? CvFeatureParams::HOG : -1; if (featureType == -1) @@ -125,15 +128,15 @@ bool CvCascadeParams::scanAttr( const String prmName, const String val ) bool CvCascadeClassifier::train( const String _cascadeDirName, const String _posFilename, - const String _negFilename, - int _numPos, int _numNeg, + const String _negFilename, + int _numPos, int _numNeg, int _precalcValBufSize, int _precalcIdxBufSize, int _numStages, const CvCascadeParams& _cascadeParams, const CvFeatureParams& _featureParams, const CvCascadeBoostParams& _stageParams, bool baseFormatSave ) -{ +{ if( _cascadeDirName.empty() || _posFilename.empty() || _negFilename.empty() ) CV_Error( CV_StsBadArg, "_cascadeDirName or _bgfileName or _vecFileName is NULL" ); @@ -181,17 +184,17 @@ bool CvCascadeClassifier::train( const String _cascadeDirName, cout << endl << "Stages 0-" << startNumStages-1 << " are loaded" << endl; else if ( startNumStages == 1) cout << endl << "Stage 0 is loaded" << endl; - + double requiredLeafFARate = pow( (double) stageParams->maxFalseAlarm, (double) numStages ) / (double)stageParams->max_depth; double tempLeafFARate; - + for( int i = startNumStages; i < numStages; i++ ) { cout << endl << "===== TRAINING " << i << "-stage =====" << endl; cout << "" << endl; - + // save params String filename; - if ( i == 0) + if ( i == 0) { filename = dirName + CC_PARAMS_FILENAME; FileStorage fs( filename, FileStorage::WRITE); @@ -289,7 +292,7 @@ int CvCascadeClassifier::fillPassedSamples( int first, int count, bool isPositiv { bool isGetImg = isPositive ? imgReader.getPos( img ) : imgReader.getNeg( img ); - if( !isGetImg ) + if( !isGetImg ) return getcount; consumed++; @@ -313,14 +316,14 @@ void CvCascadeClassifier::writeParams( FileStorage &fs ) const void CvCascadeClassifier::writeFeatures( FileStorage &fs, const Mat& featureMap ) const { - ((CvFeatureEvaluator*)((Ptr)featureEvaluator))->writeFeatures( fs, featureMap ); + ((CvFeatureEvaluator*)((Ptr)featureEvaluator))->writeFeatures( fs, featureMap ); } void CvCascadeClassifier::writeStages( FileStorage &fs, const Mat& featureMap ) const { char cmnt[30]; int i = 0; - fs << CC_STAGES << "["; + fs << CC_STAGES << "["; for( vector< Ptr >::const_iterator it = stageClassifiers.begin(); it != stageClassifiers.end(); it++, i++ ) { @@ -337,17 +340,17 @@ bool CvCascadeClassifier::readParams( const FileNode &node ) { if ( !node.isMap() || !cascadeParams.read( node ) ) return false; - + stageParams = new CvCascadeBoostParams; FileNode rnode = node[CC_STAGE_PARAMS]; if ( !stageParams->read( rnode ) ) return false; - + featureParams = CvFeatureParams::create(cascadeParams.featureType); rnode = node[CC_FEATURE_PARAMS]; if ( !featureParams->read( rnode ) ) return false; - return true; + return true; } bool CvCascadeClassifier::readStages( const FileNode &node) @@ -396,7 +399,7 @@ void CvCascadeClassifier::save( const String filename, bool baseFormat ) fs << FileStorage::getDefaultObjectName(filename) << "{"; if ( !baseFormat ) { - Mat featureMap; + Mat featureMap; getUsedFeaturesIdxMap( featureMap ); writeParams( fs ); fs << CC_STAGE_NUM << (int)stageClassifiers.size(); @@ -409,7 +412,7 @@ void CvCascadeClassifier::save( const String filename, bool baseFormat ) CvSeq* weak; if ( cascadeParams.featureType != CvFeatureParams::HAAR ) CV_Error( CV_StsBadFunc, "old file format is used for Haar-like features only"); - fs << ICV_HAAR_SIZE_NAME << "[:" << cascadeParams.winSize.width << + fs << ICV_HAAR_SIZE_NAME << "[:" << cascadeParams.winSize.width << cascadeParams.winSize.height << "]"; fs << ICV_HAAR_STAGES_NAME << "["; for( size_t si = 0; si < stageClassifiers.size(); si++ ) @@ -424,16 +427,16 @@ void CvCascadeClassifier::save( const String filename, bool baseFormat ) int inner_node_idx = -1, total_inner_node_idx = -1; queue inner_nodes_queue; CvCascadeBoostTree* tree = *((CvCascadeBoostTree**) cvGetSeqElem( weak, wi )); - + fs << "["; /*sprintf( buf, "tree %d", wi ); CV_CALL( cvWriteComment( fs, buf, 1 ) );*/ const CvDTreeNode* tempNode; - + inner_nodes_queue.push( tree->get_root() ); total_inner_node_idx++; - + while (!inner_nodes_queue.empty()) { tempNode = inner_nodes_queue.front(); @@ -498,7 +501,7 @@ bool CvCascadeClassifier::load( const String cascadeDirName ) node = fs.getFirstTopLevelNode(); if ( !fs.isOpened() ) break; - CvCascadeBoost *tempStage = new CvCascadeBoost; + CvCascadeBoost *tempStage = new CvCascadeBoost; if ( !tempStage->read( node, (CvFeatureEvaluator*)featureEvaluator, *((CvCascadeBoostParams*)stageParams )) ) { @@ -516,11 +519,11 @@ void CvCascadeClassifier::getUsedFeaturesIdxMap( Mat& featureMap ) int varCount = featureEvaluator->getNumFeatures() * featureEvaluator->getFeatureSize(); featureMap.create( 1, varCount, CV_32SC1 ); featureMap.setTo(Scalar(-1)); - + for( vector< Ptr >::const_iterator it = stageClassifiers.begin(); it != stageClassifiers.end(); it++ ) ((CvCascadeBoost*)((Ptr)(*it)))->markUsedFeaturesInMap( featureMap ); - + for( int fi = 0, idx = 0; fi < varCount; fi++ ) if ( featureMap.at(0, fi) >= 0 ) featureMap.ptr(0)[fi] = idx++; diff --git a/apps/traincascade/features.cpp b/apps/traincascade/features.cpp index c117a99..8ecdfcc 100644 --- a/apps/traincascade/features.cpp +++ b/apps/traincascade/features.cpp @@ -1,3 +1,6 @@ +#include "opencv2/core/core.hpp" +#include "opencv2/core/internal.hpp" + #include "traincascade_features.h" #include "cascadeclassifier.h" @@ -28,7 +31,7 @@ bool CvParams::scanAttr( const String prmName, const String val ) { return false CvFeatureParams::CvFeatureParams() : maxCatCount( 0 ), featSize( 1 ) { - name = CC_FEATURE_PARAMS; + name = CC_FEATURE_PARAMS; } void CvFeatureParams::init( const CvFeatureParams& fp ) @@ -55,7 +58,7 @@ bool CvFeatureParams::read( const FileNode &node ) Ptr CvFeatureParams::create( int featureType ) { return featureType == HAAR ? Ptr(new CvHaarFeatureParams) : - featureType == LBP ? Ptr(new CvLBPFeatureParams) : + featureType == LBP ? Ptr(new CvLBPFeatureParams) : featureType == HOG ? Ptr(new CvHOGFeatureParams) : Ptr(); } @@ -84,7 +87,7 @@ void CvFeatureEvaluator::setImage(const Mat &img, uchar clsLabel, int idx) Ptr CvFeatureEvaluator::create(int type) { return type == CvFeatureParams::HAAR ? Ptr(new CvHaarEvaluator) : - type == CvFeatureParams::LBP ? Ptr(new CvLBPEvaluator) : + type == CvFeatureParams::LBP ? Ptr(new CvLBPEvaluator) : type == CvFeatureParams::HOG ? Ptr(new CvHOGEvaluator) : Ptr(); } diff --git a/apps/traincascade/haarfeatures.cpp b/apps/traincascade/haarfeatures.cpp index 6344af5..0298b42 100644 --- a/apps/traincascade/haarfeatures.cpp +++ b/apps/traincascade/haarfeatures.cpp @@ -1,16 +1,19 @@ +#include "opencv2/core/core.hpp" +#include "opencv2/core/internal.hpp" + #include "haarfeatures.h" #include "cascadeclassifier.h" using namespace std; CvHaarFeatureParams::CvHaarFeatureParams() : mode(BASIC) -{ +{ name = HFP_NAME; } CvHaarFeatureParams::CvHaarFeatureParams( int _mode ) : mode( _mode ) { - name = HFP_NAME; + name = HFP_NAME; } void CvHaarFeatureParams::init( const CvFeatureParams& fp ) @@ -22,7 +25,7 @@ void CvHaarFeatureParams::init( const CvFeatureParams& fp ) void CvHaarFeatureParams::write( FileStorage &fs ) const { CvFeatureParams::write( fs ); - String modeStr = mode == BASIC ? CC_MODE_BASIC : + String modeStr = mode == BASIC ? CC_MODE_BASIC : mode == CORE ? CC_MODE_CORE : mode == ALL ? CC_MODE_ALL : String(); CV_Assert( !modeStr.empty() ); @@ -55,7 +58,7 @@ void CvHaarFeatureParams::printDefaults() const void CvHaarFeatureParams::printAttrs() const { CvFeatureParams::printAttrs(); - String mode_str = mode == BASIC ? CC_MODE_BASIC : + String mode_str = mode == BASIC ? CC_MODE_BASIC : mode == CORE ? CC_MODE_CORE : mode == ALL ? CC_MODE_ALL : 0; cout << "mode: " << mode_str << endl; @@ -156,7 +159,7 @@ void CvHaarEvaluator::generateFeatures() if( mode != CvHaarFeatureParams::BASIC ) { // haar_x4 - if ( (x+dx*4 <= winSize.width) && (y+dy <= winSize.height) ) + if ( (x+dx*4 <= winSize.width) && (y+dy <= winSize.height) ) { features.push_back( Feature( offset, false, x, y, dx*4, dy, -1, @@ -171,61 +174,61 @@ void CvHaarEvaluator::generateFeatures() } } // x2_y2 - if ( (x+dx*2 <= winSize.width) && (y+dy*2 <= winSize.height) ) + if ( (x+dx*2 <= winSize.width) && (y+dy*2 <= winSize.height) ) { features.push_back( Feature( offset, false, x, y, dx*2, dy*2, -1, x, y, dx, dy, +2, x+dx, y+dy, dx, dy, +2 ) ); } - if (mode != CvHaarFeatureParams::BASIC) - { - if ( (x+dx*3 <= winSize.width) && (y+dy*3 <= winSize.height) ) + if (mode != CvHaarFeatureParams::BASIC) + { + if ( (x+dx*3 <= winSize.width) && (y+dy*3 <= winSize.height) ) { features.push_back( Feature( offset, false, x , y , dx*3, dy*3, -1, x+dx, y+dy, dx , dy , +9) ); } } - if (mode == CvHaarFeatureParams::ALL) - { + if (mode == CvHaarFeatureParams::ALL) + { // tilted haar_x2 - if ( (x+2*dx <= winSize.width) && (y+2*dx+dy <= winSize.height) && (x-dy>= 0) ) + if ( (x+2*dx <= winSize.width) && (y+2*dx+dy <= winSize.height) && (x-dy>= 0) ) { features.push_back( Feature( offset, true, x, y, dx*2, dy, -1, x, y, dx, dy, +2 ) ); } // tilted haar_y2 - if ( (x+dx <= winSize.width) && (y+dx+2*dy <= winSize.height) && (x-2*dy>= 0) ) + if ( (x+dx <= winSize.width) && (y+dx+2*dy <= winSize.height) && (x-2*dy>= 0) ) { features.push_back( Feature( offset, true, x, y, dx, 2*dy, -1, x, y, dx, dy, +2 ) ); } // tilted haar_x3 - if ( (x+3*dx <= winSize.width) && (y+3*dx+dy <= winSize.height) && (x-dy>= 0) ) + if ( (x+3*dx <= winSize.width) && (y+3*dx+dy <= winSize.height) && (x-dy>= 0) ) { features.push_back( Feature( offset, true, x, y, dx*3, dy, -1, x+dx, y+dx, dx, dy, +3 ) ); } // tilted haar_y3 - if ( (x+dx <= winSize.width) && (y+dx+3*dy <= winSize.height) && (x-3*dy>= 0) ) + if ( (x+dx <= winSize.width) && (y+dx+3*dy <= winSize.height) && (x-3*dy>= 0) ) { features.push_back( Feature( offset, true, x, y, dx, 3*dy, -1, x-dy, y+dy, dx, dy, +3 ) ); } // tilted haar_x4 - if ( (x+4*dx <= winSize.width) && (y+4*dx+dy <= winSize.height) && (x-dy>= 0) ) + if ( (x+4*dx <= winSize.width) && (y+4*dx+dy <= winSize.height) && (x-dy>= 0) ) { features.push_back( Feature( offset, true, x, y, dx*4, dy, -1, x+dx, y+dx, dx*2, dy, +2 ) ); } // tilted haar_y4 - if ( (x+dx <= winSize.width) && (y+dx+4*dy <= winSize.height) && (x-4*dy>= 0) ) + if ( (x+dx <= winSize.width) && (y+dx+4*dy <= winSize.height) && (x-4*dy>= 0) ) { features.push_back( Feature( offset, true, x, y, dx, 4*dy, -1, @@ -296,7 +299,7 @@ void CvHaarEvaluator::Feature::write( FileStorage &fs ) const fs << CC_RECTS << "["; for( int ri = 0; ri < CV_HAAR_FEATURE_MAX && rect[ri].r.width != 0; ++ri ) { - fs << "[:" << rect[ri].r.x << rect[ri].r.y << + fs << "[:" << rect[ri].r.x << rect[ri].r.y << rect[ri].r.width << rect[ri].r.height << rect[ri].weight << "]"; } fs << "]" << CC_TILTED << tilted; diff --git a/apps/traincascade/imagestorage.cpp b/apps/traincascade/imagestorage.cpp index 64089c6..3830a4b 100644 --- a/apps/traincascade/imagestorage.cpp +++ b/apps/traincascade/imagestorage.cpp @@ -1,3 +1,6 @@ +#include "opencv2/core/core.hpp" +#include "opencv2/core/internal.hpp" + #include "cv.h" #include "imagestorage.h" #include @@ -55,7 +58,7 @@ bool CvCascadeImageReader::NegReader::nextImg() for( size_t i = 0; i < count; i++ ) { src = imread( imgFilenames[last++], 0 ); - if( src.empty() ) + if( src.empty() ) continue; round += last / count; round = round % (winSize.width * winSize.height); @@ -63,7 +66,7 @@ bool CvCascadeImageReader::NegReader::nextImg() _offset.x = min( (int)round % winSize.width, src.cols - winSize.width ); _offset.y = min( (int)round / winSize.width, src.rows - winSize.height ); - if( !src.empty() && src.type() == CV_8UC1 + if( !src.empty() && src.type() == CV_8UC1 && offset.x >= 0 && offset.y >= 0 ) break; } @@ -73,7 +76,7 @@ bool CvCascadeImageReader::NegReader::nextImg() point = offset = _offset; scale = max( ((float)winSize.width + point.x) / ((float)src.cols), ((float)winSize.height + point.y) / ((float)src.rows) ); - + Size sz( (int)(scale*src.cols + 0.5F), (int)(scale*src.rows + 0.5F) ); resize( src, img, sz ); return true; @@ -87,7 +90,7 @@ bool CvCascadeImageReader::NegReader::get( Mat& _img ) CV_Assert( _img.rows == winSize.height ); if( img.empty() ) - if ( !nextImg() ) + if ( !nextImg() ) return false; Mat mat( winSize.height, winSize.width, CV_8UC1, @@ -109,7 +112,7 @@ bool CvCascadeImageReader::NegReader::get( Mat& _img ) resize( src, img, Size( (int)(scale*src.cols), (int)(scale*src.rows) ) ); else { - if ( !nextImg() ) + if ( !nextImg() ) return false; } } @@ -131,7 +134,7 @@ bool CvCascadeImageReader::PosReader::create( const String _filename ) if( !file ) return false; - short tmp = 0; + short tmp = 0; if( fread( &count, sizeof( count ), 1, file ) != 1 || fread( &vecSize, sizeof( vecSize ), 1, file ) != 1 || fread( &tmp, sizeof( tmp ), 1, file ) != 1 || diff --git a/apps/traincascade/lbpfeatures.cpp b/apps/traincascade/lbpfeatures.cpp index ac1d23c..cf9bb7b 100644 --- a/apps/traincascade/lbpfeatures.cpp +++ b/apps/traincascade/lbpfeatures.cpp @@ -1,3 +1,6 @@ +#include "opencv2/core/core.hpp" +#include "opencv2/core/internal.hpp" + #include "lbpfeatures.h" #include "cascadeclassifier.h" diff --git a/apps/traincascade/traincascade.cpp b/apps/traincascade/traincascade.cpp index 07dbe3e..5a969f4 100644 --- a/apps/traincascade/traincascade.cpp +++ b/apps/traincascade/traincascade.cpp @@ -1,3 +1,6 @@ +#include "opencv2/core/core.hpp" +#include "opencv2/core/internal.hpp" + #include "cv.h" #include "cascadeclassifier.h" @@ -13,11 +16,11 @@ int main( int argc, char* argv[] ) int precalcValBufSize = 256, precalcIdxBufSize = 256; bool baseFormatSave = false; - + CvCascadeParams cascadeParams; CvCascadeBoostParams stageParams; Ptr featureParams[] = { Ptr(new CvHaarFeatureParams), - Ptr(new CvLBPFeatureParams), + Ptr(new CvLBPFeatureParams), Ptr(new CvHOGFeatureParams) }; int fc = sizeof(featureParams)/sizeof(featureParams[0]); @@ -85,7 +88,7 @@ int main( int argc, char* argv[] ) { for( int fi = 0; fi < fc; fi++ ) { - set = featureParams[fi]->scanAttr(argv[i], argv[i+1]); + set = featureParams[fi]->scanAttr(argv[i], argv[i+1]); if ( !set ) { i++; @@ -94,11 +97,11 @@ int main( int argc, char* argv[] ) } } } - + classifier.train( cascadeDirName, vecName, - bgName, - numPos, numNeg, + bgName, + numPos, numNeg, precalcValBufSize, precalcIdxBufSize, numStages, cascadeParams, diff --git a/cmake/OpenCVCompilerOptions.cmake b/cmake/OpenCVCompilerOptions.cmake index e00128a..dc74aa7 100644 --- a/cmake/OpenCVCompilerOptions.cmake +++ b/cmake/OpenCVCompilerOptions.cmake @@ -14,6 +14,8 @@ if(MINGW) endif() if(MSVC) + string(REGEX REPLACE "^ *| * $" "" CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}") + string(REGEX REPLACE "^ *| * $" "" CMAKE_CXX_FLAGS_INIT "${CMAKE_CXX_FLAGS_INIT}") if(CMAKE_CXX_FLAGS STREQUAL CMAKE_CXX_FLAGS_INIT) # override cmake default exception handling option string(REPLACE "/EHsc" "/EHa" CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}") @@ -21,73 +23,112 @@ if(MSVC) endif() endif() +set(OPENCV_EXTRA_FLAGS "") set(OPENCV_EXTRA_C_FLAGS "") -set(OPENCV_EXTRA_C_FLAGS_RELEASE "") -set(OPENCV_EXTRA_C_FLAGS_DEBUG "") +set(OPENCV_EXTRA_CXX_FLAGS "") +set(OPENCV_EXTRA_FLAGS_RELEASE "") +set(OPENCV_EXTRA_FLAGS_DEBUG "") set(OPENCV_EXTRA_EXE_LINKER_FLAGS "") set(OPENCV_EXTRA_EXE_LINKER_FLAGS_RELEASE "") set(OPENCV_EXTRA_EXE_LINKER_FLAGS_DEBUG "") +macro(add_extra_compiler_option option) + if(CMAKE_BUILD_TYPE) + set(CMAKE_TRY_COMPILE_CONFIGURATION ${CMAKE_BUILD_TYPE}) + endif() + ocv_check_flag_support(CXX "${option}" _varname "${OPENCV_EXTRA_CXX_FLAGS} ${ARGN}") + if(${_varname}) + set(OPENCV_EXTRA_CXX_FLAGS "${OPENCV_EXTRA_CXX_FLAGS} ${option}") + endif() + + ocv_check_flag_support(C "${option}" _varname "${OPENCV_EXTRA_C_FLAGS} ${ARGN}") + if(${_varname}) + set(OPENCV_EXTRA_C_FLAGS "${OPENCV_EXTRA_C_FLAGS} ${option}") + endif() +endmacro() + if(MINGW) # http://gcc.gnu.org/bugzilla/show_bug.cgi?id=40838 # here we are trying to workaround the problem - include(CheckCXXCompilerFlag) - CHECK_CXX_COMPILER_FLAG(-mstackrealign HAVE_STACKREALIGN_FLAG) - if(HAVE_STACKREALIGN_FLAG) - set(OPENCV_EXTRA_C_FLAGS "${OPENCV_EXTRA_C_FLAGS} -mstackrealign") - else() - CHECK_CXX_COMPILER_FLAG(-mpreferred-stack-boundary=2 HAVE_PREFERRED_STACKBOUNDARY_FLAG) - if(HAVE_PREFERRED_STACKBOUNDARY_FLAG) - set(OPENCV_EXTRA_C_FLAGS "${OPENCV_EXTRA_C_FLAGS} -mstackrealign") - endif() + add_extra_compiler_option(-mstackrealign) + if(NOT HAVE_CXX_MSTACKREALIGN) + add_extra_compiler_option(-mpreferred-stack-boundary=2) endif() endif() +if(OPENCV_CAN_BREAK_BINARY_COMPATIBILITY) + add_definitions(-DOPENCV_CAN_BREAK_BINARY_COMPATIBILITY) +endif() + if(CMAKE_COMPILER_IS_GNUCXX) # High level of warnings. - set(OPENCV_EXTRA_C_FLAGS "${OPENCV_EXTRA_C_FLAGS} -Wall") + add_extra_compiler_option(-Wall) + add_extra_compiler_option(-Werror=return-type) + if(OPENCV_CAN_BREAK_BINARY_COMPATIBILITY) + add_extra_compiler_option(-Werror=non-virtual-dtor) + endif() + add_extra_compiler_option(-Werror=address) + add_extra_compiler_option(-Werror=sequence-point) + add_extra_compiler_option(-Wformat) + add_extra_compiler_option(-Werror=format-security -Wformat) + add_extra_compiler_option(-Wmissing-declarations) + add_extra_compiler_option(-Wmissing-prototypes) + add_extra_compiler_option(-Wstrict-prototypes) + add_extra_compiler_option(-Wundef) + add_extra_compiler_option(-Winit-self) + add_extra_compiler_option(-Wpointer-arith) + add_extra_compiler_option(-Wshadow) + + if(ENABLE_NOISY_WARNINGS) + add_extra_compiler_option(-Wcast-align) + add_extra_compiler_option(-Wstrict-aliasing=2) + else() + add_extra_compiler_option(-Wno-narrowing) + add_extra_compiler_option(-Wno-delete-non-virtual-dtor) + add_extra_compiler_option(-Wno-unnamed-type-template-args) + endif() # The -Wno-long-long is required in 64bit systems when including sytem headers. if(X86_64) - set(OPENCV_EXTRA_C_FLAGS "${OPENCV_EXTRA_C_FLAGS} -Wno-long-long") + add_extra_compiler_option(-Wno-long-long) endif() # We need pthread's if(UNIX AND NOT ANDROID) - set(OPENCV_EXTRA_C_FLAGS "${OPENCV_EXTRA_C_FLAGS} -pthread") + add_extra_compiler_option(-pthread) endif() if(OPENCV_WARNINGS_ARE_ERRORS) - set(OPENCV_EXTRA_C_FLAGS "${OPENCV_EXTRA_C_FLAGS} -Werror") + add_extra_compiler_option(-Werror) endif() if(X86 AND NOT MINGW64 AND NOT X86_64 AND NOT APPLE) - set(OPENCV_EXTRA_C_FLAGS "${OPENCV_EXTRA_C_FLAGS} -march=i686") + add_extra_compiler_option(-march=i686) endif() # Other optimizations if(ENABLE_OMIT_FRAME_POINTER) - set(OPENCV_EXTRA_C_FLAGS "${OPENCV_EXTRA_C_FLAGS} -fomit-frame-pointer") + add_extra_compiler_option(-fomit-frame-pointer) else() - set(OPENCV_EXTRA_C_FLAGS "${OPENCV_EXTRA_C_FLAGS} -fno-omit-frame-pointer") + add_extra_compiler_option(-fno-omit-frame-pointer) endif() if(ENABLE_FAST_MATH) - set(OPENCV_EXTRA_C_FLAGS "${OPENCV_EXTRA_C_FLAGS} -ffast-math") + add_extra_compiler_option(-ffast-math) endif() if(ENABLE_POWERPC) - set(OPENCV_EXTRA_C_FLAGS "${OPENCV_EXTRA_C_FLAGS} -mcpu=G3 -mtune=G5") + add_extra_compiler_option("-mcpu=G3 -mtune=G5") endif() if(ENABLE_SSE) - set(OPENCV_EXTRA_C_FLAGS "${OPENCV_EXTRA_C_FLAGS} -msse") + add_extra_compiler_option(-msse) endif() if(ENABLE_SSE2) - set(OPENCV_EXTRA_C_FLAGS "${OPENCV_EXTRA_C_FLAGS} -msse2") + add_extra_compiler_option(-msse2) endif() # SSE3 and further should be disabled under MingW because it generates compiler errors if(NOT MINGW) if(ENABLE_SSE3) - set(OPENCV_EXTRA_C_FLAGS "${OPENCV_EXTRA_C_FLAGS} -msse3") + add_extra_compiler_option(-msse3) endif() if(${CMAKE_OPENCV_GCC_VERSION_NUM} GREATER 402) @@ -99,14 +140,14 @@ if(CMAKE_COMPILER_IS_GNUCXX) if(HAVE_GCC42_OR_NEWER OR APPLE) if(ENABLE_SSSE3) - set(OPENCV_EXTRA_C_FLAGS "${OPENCV_EXTRA_C_FLAGS} -mssse3") + add_extra_compiler_option(-mssse3) endif() if(HAVE_GCC43_OR_NEWER) if(ENABLE_SSE41) - set(OPENCV_EXTRA_C_FLAGS "${OPENCV_EXTRA_C_FLAGS} -msse4.1") + add_extra_compiler_option(-msse4.1) endif() if(ENABLE_SSE42) - set(OPENCV_EXTRA_C_FLAGS "${OPENCV_EXTRA_C_FLAGS} -msse4.2") + add_extra_compiler_option(-msse4.2) endif() endif() endif() @@ -114,39 +155,40 @@ if(CMAKE_COMPILER_IS_GNUCXX) if(X86 OR X86_64) if(NOT APPLE AND CMAKE_SIZEOF_VOID_P EQUAL 4) - if(ENABLE_SSE2) - set(OPENCV_EXTRA_C_FLAGS "${OPENCV_EXTRA_C_FLAGS} -mfpmath=sse")# !! important - be on the same wave with x64 compilers - else() - set(OPENCV_EXTRA_C_FLAGS "${OPENCV_EXTRA_C_FLAGS} -mfpmath=387") - endif() + if(ENABLE_SSE2) + add_extra_compiler_option(-mfpmath=sse)# !! important - be on the same wave with x64 compilers + else() + add_extra_compiler_option(-mfpmath=387) + endif() endif() endif() # Profiling? if(ENABLE_PROFILING) - set(OPENCV_EXTRA_C_FLAGS "${OPENCV_EXTRA_C_FLAGS} -pg -g") + add_extra_compiler_option("-pg -g") # turn off incompatible options - foreach(flags CMAKE_CXX_FLAGS CMAKE_C_FLAGS CMAKE_CXX_FLAGS_RELEASE CMAKE_C_FLAGS_RELEASE CMAKE_CXX_FLAGS_DEBUG CMAKE_C_FLAGS_DEBUG OPENCV_EXTRA_C_FLAGS_RELEASE) + foreach(flags CMAKE_CXX_FLAGS CMAKE_C_FLAGS CMAKE_CXX_FLAGS_RELEASE CMAKE_C_FLAGS_RELEASE CMAKE_CXX_FLAGS_DEBUG CMAKE_C_FLAGS_DEBUG + OPENCV_EXTRA_FLAGS_RELEASE OPENCV_EXTRA_FLAGS_DEBUG OPENCV_EXTRA_C_FLAGS OPENCV_EXTRA_CXX_FLAGS) string(REPLACE "-fomit-frame-pointer" "" ${flags} "${${flags}}") string(REPLACE "-ffunction-sections" "" ${flags} "${${flags}}") endforeach() elseif(NOT APPLE AND NOT ANDROID) # Remove unreferenced functions: function level linking - set(OPENCV_EXTRA_C_FLAGS "${OPENCV_EXTRA_C_FLAGS} -ffunction-sections") + add_extra_compiler_option(-ffunction-sections) endif() - set(OPENCV_EXTRA_C_FLAGS_RELEASE "${OPENCV_EXTRA_C_FLAGS_RELEASE} -DNDEBUG") - set(OPENCV_EXTRA_C_FLAGS_DEBUG "${OPENCV_EXTRA_C_FLAGS_DEBUG} -O0 -DDEBUG -D_DEBUG") + set(OPENCV_EXTRA_FLAGS_RELEASE "${OPENCV_EXTRA_FLAGS_RELEASE} -DNDEBUG") + set(OPENCV_EXTRA_FLAGS_DEBUG "${OPENCV_EXTRA_FLAGS_DEBUG} -O0 -DDEBUG -D_DEBUG") if(BUILD_WITH_DEBUG_INFO) - set(OPENCV_EXTRA_C_FLAGS_DEBUG "${OPENCV_EXTRA_C_FLAGS_DEBUG} -ggdb3") + set(OPENCV_EXTRA_FLAGS_DEBUG "${OPENCV_EXTRA_FLAGS_DEBUG} -ggdb3") endif() endif() if(MSVC) - set(OPENCV_EXTRA_C_FLAGS "${OPENCV_EXTRA_C_FLAGS} /D _CRT_SECURE_NO_DEPRECATE /D _CRT_NONSTDC_NO_DEPRECATE /D _SCL_SECURE_NO_WARNINGS") + set(OPENCV_EXTRA_FLAGS "${OPENCV_EXTRA_FLAGS} /D _CRT_SECURE_NO_DEPRECATE /D _CRT_NONSTDC_NO_DEPRECATE /D _SCL_SECURE_NO_WARNINGS") # 64-bit portability warnings, in MSVC80 if(MSVC80) - set(OPENCV_EXTRA_C_FLAGS "${OPENCV_EXTRA_C_FLAGS} /Wp64") + set(OPENCV_EXTRA_FLAGS "${OPENCV_EXTRA_FLAGS} /Wp64") endif() if(BUILD_WITH_DEBUG_INFO) @@ -154,38 +196,38 @@ if(MSVC) endif() # Remove unreferenced functions: function level linking - set(OPENCV_EXTRA_C_FLAGS "${OPENCV_EXTRA_C_FLAGS} /Gy") + set(OPENCV_EXTRA_FLAGS "${OPENCV_EXTRA_FLAGS} /Gy") if(NOT MSVC_VERSION LESS 1400) - set(OPENCV_EXTRA_C_FLAGS "${OPENCV_EXTRA_C_FLAGS} /bigobj") + set(OPENCV_EXTRA_FLAGS "${OPENCV_EXTRA_FLAGS} /bigobj") endif() if(BUILD_WITH_DEBUG_INFO) - set(OPENCV_EXTRA_C_FLAGS_RELEASE "${OPENCV_EXTRA_C_FLAGS_RELEASE} /Zi") + set(OPENCV_EXTRA_FLAGS_RELEASE "${OPENCV_EXTRA_FLAGS_RELEASE} /Zi") endif() if(NOT MSVC64) # 64-bit MSVC compiler uses SSE/SSE2 by default if(ENABLE_SSE) - set(OPENCV_EXTRA_C_FLAGS "${OPENCV_EXTRA_C_FLAGS} /arch:SSE") + set(OPENCV_EXTRA_FLAGS "${OPENCV_EXTRA_FLAGS} /arch:SSE") endif() if(ENABLE_SSE2) - set(OPENCV_EXTRA_C_FLAGS "${OPENCV_EXTRA_C_FLAGS} /arch:SSE2") + set(OPENCV_EXTRA_FLAGS "${OPENCV_EXTRA_FLAGS} /arch:SSE2") endif() endif() - + if(ENABLE_SSE3) - set(OPENCV_EXTRA_C_FLAGS "${OPENCV_EXTRA_C_FLAGS} /arch:SSE3") + set(OPENCV_EXTRA_FLAGS "${OPENCV_EXTRA_FLAGS} /arch:SSE3") endif() if(ENABLE_SSE4_1) - set(OPENCV_EXTRA_C_FLAGS "${OPENCV_EXTRA_C_FLAGS} /arch:SSE4.1") + set(OPENCV_EXTRA_FLAGS "${OPENCV_EXTRA_FLAGS} /arch:SSE4.1") endif() - + if(ENABLE_SSE OR ENABLE_SSE2 OR ENABLE_SSE3 OR ENABLE_SSE4_1) - set(OPENCV_EXTRA_C_FLAGS "${OPENCV_EXTRA_C_FLAGS} /Oi") + set(OPENCV_EXTRA_FLAGS "${OPENCV_EXTRA_FLAGS} /Oi") endif() - + if(X86 OR X86_64) if(CMAKE_SIZEOF_VOID_P EQUAL 4 AND ENABLE_SSE2) - set(OPENCV_EXTRA_C_FLAGS "${OPENCV_EXTRA_C_FLAGS} /fp:fast")# !! important - be on the same wave with x64 compilers + set(OPENCV_EXTRA_FLAGS "${OPENCV_EXTRA_FLAGS} /fp:fast")# !! important - be on the same wave with x64 compilers endif() endif() endif() @@ -194,25 +236,27 @@ endif() if(NOT BUILD_SHARED_LIBS AND CMAKE_COMPILER_IS_GNUCXX AND NOT ANDROID) # Android does not need these settings because they are already set by toolchain file set(OPENCV_LINKER_LIBS ${OPENCV_LINKER_LIBS} stdc++) - set(OPENCV_EXTRA_C_FLAGS "-fPIC ${OPENCV_EXTRA_C_FLAGS}") + set(OPENCV_EXTRA_FLAGS "-fPIC ${OPENCV_EXTRA_FLAGS}") endif() # Add user supplied extra options (optimization, etc...) # ========================================================== -set(OPENCV_EXTRA_C_FLAGS "${OPENCV_EXTRA_C_FLAGS}" CACHE INTERNAL "Extra compiler options") -set(OPENCV_EXTRA_C_FLAGS_RELEASE "${OPENCV_EXTRA_C_FLAGS_RELEASE}" CACHE INTERNAL "Extra compiler options for Release build") -set(OPENCV_EXTRA_C_FLAGS_DEBUG "${OPENCV_EXTRA_C_FLAGS_DEBUG}" CACHE INTERNAL "Extra compiler options for Debug build") +set(OPENCV_EXTRA_FLAGS "${OPENCV_EXTRA_FLAGS}" CACHE INTERNAL "Extra compiler options") +set(OPENCV_EXTRA_C_FLAGS "${OPENCV_EXTRA_C_FLAGS}" CACHE INTERNAL "Extra compiler options for C sources") +set(OPENCV_EXTRA_CXX_FLAGS "${OPENCV_EXTRA_CXX_FLAGS}" CACHE INTERNAL "Extra compiler options for C++ sources") +set(OPENCV_EXTRA_FLAGS_RELEASE "${OPENCV_EXTRA_FLAGS_RELEASE}" CACHE INTERNAL "Extra compiler options for Release build") +set(OPENCV_EXTRA_FLAGS_DEBUG "${OPENCV_EXTRA_FLAGS_DEBUG}" CACHE INTERNAL "Extra compiler options for Debug build") set(OPENCV_EXTRA_EXE_LINKER_FLAGS "${OPENCV_EXTRA_EXE_LINKER_FLAGS}" CACHE INTERNAL "Extra linker flags") set(OPENCV_EXTRA_EXE_LINKER_FLAGS_RELEASE "${OPENCV_EXTRA_EXE_LINKER_FLAGS_RELEASE}" CACHE INTERNAL "Extra linker flags for Release build") set(OPENCV_EXTRA_EXE_LINKER_FLAGS_DEBUG "${OPENCV_EXTRA_EXE_LINKER_FLAGS_DEBUG}" CACHE INTERNAL "Extra linker flags for Debug build") #combine all "extra" options -set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${OPENCV_EXTRA_C_FLAGS}") -set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OPENCV_EXTRA_C_FLAGS}") -set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} ${OPENCV_EXTRA_C_FLAGS_RELEASE}") -set(CMAKE_C_FLAGS_RELEASE "${CMAKE_C_FLAGS_RELEASE} ${OPENCV_EXTRA_C_FLAGS_RELEASE}") -set(CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG} ${OPENCV_EXTRA_C_FLAGS_DEBUG}") -set(CMAKE_C_FLAGS_DEBUG "${CMAKE_C_FLAGS_DEBUG} ${OPENCV_EXTRA_C_FLAGS_DEBUG}") +set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${OPENCV_EXTRA_FLAGS} ${OPENCV_EXTRA_C_FLAGS}") +set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OPENCV_EXTRA_FLAGS} ${OPENCV_EXTRA_CXX_FLAGS}") +set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} ${OPENCV_EXTRA_FLAGS_RELEASE}") +set(CMAKE_C_FLAGS_RELEASE "${CMAKE_C_FLAGS_RELEASE} ${OPENCV_EXTRA_FLAGS_RELEASE}") +set(CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG} ${OPENCV_EXTRA_FLAGS_DEBUG}") +set(CMAKE_C_FLAGS_DEBUG "${CMAKE_C_FLAGS_DEBUG} ${OPENCV_EXTRA_FLAGS_DEBUG}") set(CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} ${OPENCV_EXTRA_EXE_LINKER_FLAGS}") set(CMAKE_EXE_LINKER_FLAGS_RELEASE "${CMAKE_EXE_LINKER_FLAGS_RELEASE} ${OPENCV_EXTRA_EXE_LINKER_FLAGS_RELEASE}") set(CMAKE_EXE_LINKER_FLAGS_DEBUG "${CMAKE_EXE_LINKER_FLAGS_DEBUG} ${OPENCV_EXTRA_EXE_LINKER_FLAGS_DEBUG}") @@ -225,12 +269,16 @@ if(MSVC) string(REPLACE "/W3" "/W4" CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}") string(REPLACE "/W3" "/W4" CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE}") string(REPLACE "/W3" "/W4" CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG}") - + + if(NOT ENABLE_NOISY_WARNINGS AND MSVC_VERSION EQUAL 1400) + ocv_warnings_disable(CMAKE_CXX_FLAGS /wd4510 /wd4610 /wd4312 /wd4201 /wd4244 /wd4328 /wd4267) + endif() + # allow extern "C" functions throw exceptions foreach(flags CMAKE_C_FLAGS CMAKE_C_FLAGS_RELEASE CMAKE_C_FLAGS_RELEASE CMAKE_CXX_FLAGS CMAKE_CXX_FLAGS_RELEASE CMAKE_CXX_FLAGS_DEBUG) string(REPLACE "/EHsc-" "/EHs" ${flags} "${${flags}}") string(REPLACE "/EHsc" "/EHs" ${flags} "${${flags}}") - + string(REPLACE "/Zm1000" "" ${flags} "${${flags}}") endforeach() diff --git a/cmake/OpenCVDetectCXXCompiler.cmake b/cmake/OpenCVDetectCXXCompiler.cmake index 2c4acd5..1c4746c 100644 --- a/cmake/OpenCVDetectCXXCompiler.cmake +++ b/cmake/OpenCVDetectCXXCompiler.cmake @@ -44,6 +44,12 @@ if(MSVC AND CMAKE_C_COMPILER MATCHES "icc") set(CV_ICC __INTEL_COMPILER_FOR_WINDOWS) endif() +if(CMAKE_COMPILER_IS_GNUCXX OR CMAKE_CXX_COMPILER_ID STREQUAL "Clang" OR (UNIX AND CV_ICC)) + set(CV_COMPILER_IS_GNU TRUE) +else() + set(CV_COMPILER_IS_GNU FALSE) +endif() + # ---------------------------------------------------------------------------- # Detect GNU version: # ---------------------------------------------------------------------------- diff --git a/cmake/OpenCVDetectTBB.cmake b/cmake/OpenCVDetectTBB.cmake index 51ec34e..b15f9f7 100644 --- a/cmake/OpenCVDetectTBB.cmake +++ b/cmake/OpenCVDetectTBB.cmake @@ -1,6 +1,6 @@ if(ANDROID) add_subdirectory("${OpenCV_SOURCE_DIR}/3rdparty/tbb") - ocv_include_directories(${TBB_INCLUDE_DIRS}) + include_directories(SYSTEM ${TBB_INCLUDE_DIRS}) set(OPENCV_LINKER_LIBS ${OPENCV_LINKER_LIBS} tbb) add_definitions(-DTBB_USE_GCC_BUILTINS=1 -D__TBB_GCC_BUILTIN_ATOMICS_PRESENT=1 -D__TBB_USE_GENERIC_DWORD_LOAD_STORE=1) set(HAVE_TBB 1) diff --git a/cmake/OpenCVFindLibsGrfmt.cmake b/cmake/OpenCVFindLibsGrfmt.cmake index 47632cd..d5bfa1f 100644 --- a/cmake/OpenCVFindLibsGrfmt.cmake +++ b/cmake/OpenCVFindLibsGrfmt.cmake @@ -61,6 +61,16 @@ if(TIFF_BIGTIFF_VERSION AND NOT TIFF_VERSION_BIG) set(TIFF_VERSION_BIG ${TIFF_BIGTIFF_VERSION}) endif() +if(NOT TIFF_VERSION_STRING AND TIFF_INCLUDE_DIR) + list(GET TIFF_INCLUDE_DIR 0 _TIFF_INCLUDE_DIR) + if(EXISTS "${_TIFF_INCLUDE_DIR}/tiffvers.h") + file(STRINGS "${_TIFF_INCLUDE_DIR}/tiffvers.h" tiff_version_str REGEX "^#define[\t ]+TIFFLIB_VERSION_STR[\t ]+\"LIBTIFF, Version .*") + string(REGEX REPLACE "^#define[\t ]+TIFFLIB_VERSION_STR[\t ]+\"LIBTIFF, Version +([^ \\n]*).*" "\\1" TIFF_VERSION_STRING "${tiff_version_str}") + unset(tiff_version_str) + endif() + unset(_TIFF_INCLUDE_DIR) +endif() + # --- libjpeg (optional) --- if(WITH_JPEG) if(BUILD_JPEG) diff --git a/cmake/OpenCVFindXimea.cmake b/cmake/OpenCVFindXimea.cmake index 23284e6..7528956 100755 --- a/cmake/OpenCVFindXimea.cmake +++ b/cmake/OpenCVFindXimea.cmake @@ -18,7 +18,7 @@ if(WIN32) # Try to find the XIMEA API path in registry. GET_FILENAME_COMPONENT(XIMEA_PATH "[HKEY_CURRENT_USER\\Software\\XIMEA\\CamSupport\\API;Path]" ABSOLUTE) - if(XIMEA_PATH) + if(EXISTS XIMEA_PATH) set(XIMEA_FOUND 1) # set LIB folders diff --git a/cmake/OpenCVGenHeaders.cmake b/cmake/OpenCVGenHeaders.cmake index f342c63..e37f436 100644 --- a/cmake/OpenCVGenHeaders.cmake +++ b/cmake/OpenCVGenHeaders.cmake @@ -20,17 +20,17 @@ set(OPENCV_MOD_LIST ${OPENCV_MODULES_PUBLIC}) ocv_list_sort(OPENCV_MOD_LIST) foreach(m ${OPENCV_MOD_LIST}) string(TOUPPER "${m}" m) - set(OPENCV_MODULE_DEFINITIONS_CONFIGMAKE "${OPENCV_MODULE_DEFINITIONS_CONFIGMAKE}#define HAVE_${m} 1\n") + set(OPENCV_MODULE_DEFINITIONS_CONFIGMAKE "${OPENCV_MODULE_DEFINITIONS_CONFIGMAKE}#define HAVE_${m}\n") endforeach() set(OPENCV_MODULE_DEFINITIONS_CONFIGMAKE "${OPENCV_MODULE_DEFINITIONS_CONFIGMAKE}\n") -set(OPENCV_MOD_LIST ${OPENCV_MODULES_DISABLED_USER} ${OPENCV_MODULES_DISABLED_AUTO}) -ocv_list_sort(OPENCV_MOD_LIST) -foreach(m ${OPENCV_MOD_LIST}) - string(TOUPPER "${m}" m) - set(OPENCV_MODULE_DEFINITIONS_CONFIGMAKE "${OPENCV_MODULE_DEFINITIONS_CONFIGMAKE}#undef HAVE_${m}\n") -endforeach() +#set(OPENCV_MOD_LIST ${OPENCV_MODULES_DISABLED_USER} ${OPENCV_MODULES_DISABLED_AUTO} ${OPENCV_MODULES_DISABLED_FORCE}) +#ocv_list_sort(OPENCV_MOD_LIST) +#foreach(m ${OPENCV_MOD_LIST}) +# string(TOUPPER "${m}" m) +# set(OPENCV_MODULE_DEFINITIONS_CONFIGMAKE "${OPENCV_MODULE_DEFINITIONS_CONFIGMAKE}#undef HAVE_${m}\n") +#endforeach() configure_file("${OpenCV_SOURCE_DIR}/cmake/templates/opencv_modules.hpp.in" "${OPENCV_CONFIG_FILE_INCLUDE_DIR}/opencv2/opencv_modules.hpp") install(FILES "${OPENCV_CONFIG_FILE_INCLUDE_DIR}/opencv2/opencv_modules.hpp" DESTINATION ${OPENCV_INCLUDE_PREFIX}/opencv2 COMPONENT main) diff --git a/cmake/OpenCVModule.cmake b/cmake/OpenCVModule.cmake index 8d37ffe..317ea4c 100644 --- a/cmake/OpenCVModule.cmake +++ b/cmake/OpenCVModule.cmake @@ -131,7 +131,7 @@ macro(ocv_add_module _name) set(OPENCV_MODULES_PUBLIC ${OPENCV_MODULES_PUBLIC} "${the_module}" CACHE INTERNAL "List of OpenCV modules marked for export") endif() endif() - + # add self to the world dependencies if(NOT DEFINED OPENCV_MODULE_IS_PART_OF_WORLD AND NOT OPENCV_MODULE_${the_module}_CLASS STREQUAL "BINDINGS" OR OPENCV_MODULE_IS_PART_OF_WORLD) ocv_add_dependencies(opencv_world OPTIONAL ${the_module}) @@ -512,6 +512,8 @@ endmacro() macro(ocv_add_precompiled_headers the_target) if("${the_target}" MATCHES "^opencv_test_.*$") SET(pch_path "test/test_") + elseif("${the_target}" MATCHES "opencv_perf_gpu_cpu") + SET(pch_path "perf_cpu/perf_cpu_") elseif("${the_target}" MATCHES "^opencv_perf_.*$") SET(pch_path "perf/perf_") else() diff --git a/cmake/OpenCVPCHSupport.cmake b/cmake/OpenCVPCHSupport.cmake index 7e7ef15..55b712c 100644 --- a/cmake/OpenCVPCHSupport.cmake +++ b/cmake/OpenCVPCHSupport.cmake @@ -24,10 +24,12 @@ IF(CMAKE_COMPILER_IS_GNUCXX) ENDIF() SET(_PCH_include_prefix "-I") + SET(_PCH_isystem_prefix "-isystem") ELSEIF(WIN32) SET(PCHSupport_FOUND TRUE) # for experimental msvc support SET(_PCH_include_prefix "/I") + SET(_PCH_isystem_prefix "/I") ELSE() SET(PCHSupport_FOUND FALSE) ENDIF() @@ -50,7 +52,11 @@ MACRO(_PCH_GET_COMPILE_FLAGS _out_compile_flags) GET_DIRECTORY_PROPERTY(DIRINC INCLUDE_DIRECTORIES ) FOREACH(item ${DIRINC}) - LIST(APPEND ${_out_compile_flags} "${_PCH_include_prefix}\"${item}\"") + if(item MATCHES "^${OpenCV_SOURCE_DIR}/modules/") + LIST(APPEND ${_out_compile_flags} "${_PCH_include_prefix}\"${item}\"") + else() + LIST(APPEND ${_out_compile_flags} "${_PCH_isystem_prefix}\"${item}\"") + endif() ENDFOREACH(item) GET_DIRECTORY_PROPERTY(_directory_flags DEFINITIONS) @@ -72,6 +78,7 @@ MACRO(_PCH_WRITE_PCHDEP_CXX _targetName _include_file _dephelp) ADD_CUSTOM_COMMAND( OUTPUT "${${_dephelp}}" COMMAND ${CMAKE_COMMAND} -E echo "#include \\\"${_include_file}\\\"" > "${${_dephelp}}" + COMMAND ${CMAKE_COMMAND} -E echo "int testfunction();" >> "${${_dephelp}}" COMMAND ${CMAKE_COMMAND} -E echo "int testfunction()" >> "${${_dephelp}}" COMMAND ${CMAKE_COMMAND} -E echo "{" >> "${${_dephelp}}" COMMAND ${CMAKE_COMMAND} -E echo " return 0;" >> "${${_dephelp}}" @@ -82,6 +89,7 @@ MACRO(_PCH_WRITE_PCHDEP_CXX _targetName _include_file _dephelp) ADD_CUSTOM_COMMAND( OUTPUT "${${_dephelp}}" COMMAND ${CMAKE_COMMAND} -E echo "\\#include \\\"${_include_file}\\\"" > "${${_dephelp}}" + COMMAND ${CMAKE_COMMAND} -E echo "int testfunction\\(\\)\\;" >> "${${_dephelp}}" COMMAND ${CMAKE_COMMAND} -E echo "int testfunction\\(\\)" >> "${${_dephelp}}" COMMAND ${CMAKE_COMMAND} -E echo "{" >> "${${_dephelp}}" COMMAND ${CMAKE_COMMAND} -E echo " \\return 0\\;" >> "${${_dephelp}}" diff --git a/cmake/OpenCVUtils.cmake b/cmake/OpenCVUtils.cmake index caeaa68..aef7525 100644 --- a/cmake/OpenCVUtils.cmake +++ b/cmake/OpenCVUtils.cmake @@ -19,7 +19,7 @@ function(ocv_include_directories) if("${__abs_dir}" MATCHES "^${OpenCV_SOURCE_DIR}" OR "${__abs_dir}" MATCHES "^${OpenCV_BINARY_DIR}") list(APPEND __add_before "${dir}") else() - include_directories(AFTER "${dir}") + include_directories(AFTER SYSTEM "${dir}") endif() endforeach() include_directories(BEFORE ${__add_before}) @@ -32,6 +32,125 @@ macro(ocv_clear_vars) endforeach() endmacro() +set(OCV_COMPILER_FAIL_REGEX + "command line option .* is valid for .* but not for C\\+\\+" # GNU + "unrecognized .*option" # GNU + "unknown .*option" # Clang + "ignoring unknown option" # MSVC + "warning D9002" # MSVC, any lang + "option .*not supported" # Intel + "[Uu]nknown option" # HP + "[Ww]arning: [Oo]ption" # SunPro + "command option .* is not recognized" # XL + "not supported in this configuration; ignored" # AIX + "File with unknown suffix passed to linker" # PGI + "WARNING: unknown flag:" # Open64 + ) + +MACRO(ocv_check_compiler_flag LANG FLAG RESULT) + if(NOT DEFINED ${RESULT}) + if("_${LANG}_" MATCHES "_CXX_") + set(_fname "${CMAKE_BINARY_DIR}${CMAKE_FILES_DIRECTORY}/CMakeTmp/src.cxx") + if("${CMAKE_CXX_FLAGS} ${FLAG} " MATCHES "-Werror " OR "${CMAKE_CXX_FLAGS} ${FLAG} " MATCHES "-Werror=unknown-pragmas ") + FILE(WRITE "${_fname}" "int main() { return 0; }\n") + else() + FILE(WRITE "${_fname}" "#pragma\nint main() { return 0; }\n") + endif() + elseif("_${LANG}_" MATCHES "_C_") + set(_fname "${CMAKE_BINARY_DIR}${CMAKE_FILES_DIRECTORY}/CMakeTmp/src.c") + if("${CMAKE_C_FLAGS} ${FLAG} " MATCHES "-Werror " OR "${CMAKE_C_FLAGS} ${FLAG} " MATCHES "-Werror=unknown-pragmas ") + FILE(WRITE "${_fname}" "int main(void) { return 0; }\n") + else() + FILE(WRITE "${_fname}" "#pragma\nint main(void) { return 0; }\n") + endif() + else() + unset(_fname) + endif() + if(_fname) + MESSAGE(STATUS "Performing Test ${RESULT}") + TRY_COMPILE(${RESULT} + ${CMAKE_BINARY_DIR} + "${_fname}" + COMPILE_DEFINITIONS "${FLAG}" + OUTPUT_VARIABLE OUTPUT) + + FOREACH(_regex ${OCV_COMPILER_FAIL_REGEX}) + IF("${OUTPUT}" MATCHES "${_regex}") + SET(${RESULT} 0) + break() + ENDIF() + ENDFOREACH() + + IF(${RESULT}) + SET(${RESULT} 1 CACHE INTERNAL "Test ${RESULT}") + MESSAGE(STATUS "Performing Test ${RESULT} - Success") + ELSE(${RESULT}) + MESSAGE(STATUS "Performing Test ${RESULT} - Failed") + SET(${RESULT} "" CACHE INTERNAL "Test ${RESULT}") + ENDIF(${RESULT}) + else() + SET(${RESULT} 0) + endif() + endif() +ENDMACRO() + +macro(ocv_check_flag_support lang flag varname) + if("_${lang}_" MATCHES "_CXX_") + set(_lang CXX) + elseif("_${lang}_" MATCHES "_C_") + set(_lang C) + else() + set(_lang ${lang}) + endif() + + string(TOUPPER "${flag}" ${varname}) + string(REGEX REPLACE "^(/|-)" "HAVE_${_lang}_" ${varname} "${${varname}}") + string(REGEX REPLACE " -|-|=| |\\." "_" ${varname} "${${varname}}") + + ocv_check_compiler_flag("${_lang}" "${ARGN} ${flag}" ${${varname}}) +endmacro() + +# turns off warnings +macro(ocv_warnings_disable) + if(NOT ENABLE_NOISY_WARNINGS) + set(_flag_vars "") + set(_msvc_warnings "") + set(_gxx_warnings "") + foreach(arg ${ARGN}) + if(arg MATCHES "^CMAKE_") + list(APPEND _flag_vars ${arg}) + elseif(arg MATCHES "^/wd") + list(APPEND _msvc_warnings ${arg}) + elseif(arg MATCHES "^-W") + list(APPEND _gxx_warnings ${arg}) + endif() + endforeach() + if(MSVC AND _msvc_warnings AND _flag_vars) + foreach(var ${_flag_vars}) + foreach(warning ${_msvc_warnings}) + set(${var} "${${var}} ${warning}") + endforeach() + endforeach() + elseif(CV_COMPILER_IS_GNU AND _gxx_warnings AND _flag_vars) + foreach(var ${_flag_vars}) + foreach(warning ${_gxx_warnings}) + if(NOT warning MATCHES "^-Wno-") + string(REPLACE "${warning}" "" ${var} "${${var}}") + string(REPLACE "-W" "-Wno-" warning "${warning}") + endif() + ocv_check_flag_support(${var} "${warning}" _varname) + if(${_varname}) + set(${var} "${${var}} ${warning}") + endif() + endforeach() + endforeach() + endif() + unset(_flag_vars) + unset(_msvc_warnings) + unset(_gxx_warnings) + endif(NOT ENABLE_NOISY_WARNINGS) +endmacro() + # Provides an option that the user can optionally select. # Can accept condition to control when option is available for user. # Usage: diff --git a/modules/calib3d/perf/perf_precomp.hpp b/modules/calib3d/perf/perf_precomp.hpp index ccadd24..ce79542 100644 --- a/modules/calib3d/perf/perf_precomp.hpp +++ b/modules/calib3d/perf/perf_precomp.hpp @@ -1,3 +1,7 @@ +#ifdef __GNUC__ +# pragma GCC diagnostic ignored "-Wmissing-declarations" +#endif + #ifndef __OPENCV_PERF_PRECOMP_HPP__ #define __OPENCV_PERF_PRECOMP_HPP__ @@ -6,7 +10,7 @@ #include "opencv2/highgui/highgui.hpp" #include "opencv2/imgproc/imgproc.hpp" -#if GTEST_CREATE_SHARED_LIBRARY +#ifdef GTEST_CREATE_SHARED_LIBRARY #error no modules except ts should have GTEST_CREATE_SHARED_LIBRARY defined #endif diff --git a/modules/calib3d/src/calibinit.cpp b/modules/calib3d/src/calibinit.cpp index 4088602..5e6ea1d 100644 --- a/modules/calib3d/src/calibinit.cpp +++ b/modules/calib3d/src/calibinit.cpp @@ -230,7 +230,7 @@ int cvFindChessboardCorners( const void* arr, CvSize pattern_size, int found = 0; CvCBQuad *quads = 0, **quad_group = 0; CvCBCorner *corners = 0, **corner_group = 0; - + try { int k = 0; @@ -252,11 +252,11 @@ int cvFindChessboardCorners( const void* arr, CvSize pattern_size, if( out_corner_count ) *out_corner_count = 0; - + IplImage _img; int check_chessboard_result; - int quad_count = 0, group_idx = 0, i = 0, dilations = 0; - + int quad_count = 0, group_idx = 0, dilations = 0; + img = cvGetMat( img, &stub ); //debug_img = img; @@ -316,8 +316,8 @@ int cvFindChessboardCorners( const void* arr, CvSize pattern_size, for( dilations = min_dilations; dilations <= max_dilations; dilations++ ) { if (found) - break; // already found it - + break; // already found it + cvFree(&quads); cvFree(&corners); @@ -378,7 +378,7 @@ int cvFindChessboardCorners( const void* arr, CvSize pattern_size, cvCopy(dbg_img, dbg1_img); cvNamedWindow("all_quads", 1); // copy corners to temp array - for( i = 0; i < quad_count; i++ ) + for(int i = 0; i < quad_count; i++ ) { for (int k=0; k<4; k++) { @@ -432,7 +432,7 @@ int cvFindChessboardCorners( const void* arr, CvSize pattern_size, cvCopy(dbg_img,dbg2_img); cvNamedWindow("connected_group", 1); // copy corners to temp array - for( i = 0; i < quad_count; i++ ) + for(int i = 0; i < quad_count; i++ ) { if (quads[i].group_idx == group_idx) for (int k=0; k<4; k++) @@ -455,7 +455,7 @@ int cvFindChessboardCorners( const void* arr, CvSize pattern_size, #endif if (count == 0) - continue; // haven't found inner quads + continue; // haven't found inner quads // If count is more than it should be, this will remove those quads @@ -472,7 +472,7 @@ int cvFindChessboardCorners( const void* arr, CvSize pattern_size, float sum_dist = 0; int total = 0; - for( i = 0; i < n; i++ ) + for(int i = 0; i < n; i++ ) { int ni = 0; float avgi = corner_group[i]->meanDist(&ni); @@ -484,7 +484,7 @@ int cvFindChessboardCorners( const void* arr, CvSize pattern_size, if( count > 0 || (out_corner_count && -count > *out_corner_count) ) { // copy corners to output array - for( i = 0; i < n; i++ ) + for(int i = 0; i < n; i++ ) out_corners[i] = corner_group[i]->pt; if( out_corner_count ) @@ -505,19 +505,19 @@ int cvFindChessboardCorners( const void* arr, CvSize pattern_size, if( found ) found = icvCheckBoardMonotony( out_corners, pattern_size ); - // check that none of the found corners is too close to the image boundary + // check that none of the found corners is too close to the image boundary if( found ) - { - const int BORDER = 8; - for( k = 0; k < pattern_size.width*pattern_size.height; k++ ) - { - if( out_corners[k].x <= BORDER || out_corners[k].x > img->cols - BORDER || - out_corners[k].y <= BORDER || out_corners[k].y > img->rows - BORDER ) - break; - } - - found = k == pattern_size.width*pattern_size.height; - } + { + const int BORDER = 8; + for( k = 0; k < pattern_size.width*pattern_size.height; k++ ) + { + if( out_corners[k].x <= BORDER || out_corners[k].x > img->cols - BORDER || + out_corners[k].y <= BORDER || out_corners[k].y > img->rows - BORDER ) + break; + } + + found = k == pattern_size.width*pattern_size.height; + } if( found && pattern_size.height % 2 == 0 && pattern_size.width % 2 == 0 ) { @@ -525,8 +525,8 @@ int cvFindChessboardCorners( const void* arr, CvSize pattern_size, double dy0 = out_corners[last_row].y - out_corners[0].y; if( dy0 < 0 ) { - int i, n = pattern_size.width*pattern_size.height; - for( i = 0; i < n/2; i++ ) + int n = pattern_size.width*pattern_size.height; + for(int i = 0; i < n/2; i++ ) { CvPoint2D32f temp; CV_SWAP(out_corners[i], out_corners[n-i-1], temp); @@ -559,7 +559,7 @@ int cvFindChessboardCorners( const void* arr, CvSize pattern_size, cvFree(&corner_group); throw; } - + cvFree(&quads); cvFree(&corners); cvFree(&quad_group); @@ -582,7 +582,7 @@ static int icvCheckBoardMonotony( CvPoint2D32f* corners, CvSize pattern_size ) { int i, j, k; - + for( k = 0; k < 2; k++ ) { for( i = 0; i < (k == 0 ? pattern_size.height : pattern_size.width); i++ ) @@ -627,11 +627,10 @@ icvOrderFoundConnectedQuads( int quad_count, CvCBQuad **quads, { cv::Ptr temp_storage = cvCreateChildMemStorage( storage ); CvSeq* stack = cvCreateSeq( 0, sizeof(*stack), sizeof(void*), temp_storage ); - int i; // first find an interior quad CvCBQuad *start = NULL; - for (i=0; icount == 4) { @@ -682,7 +681,7 @@ icvOrderFoundConnectedQuads( int quad_count, CvCBQuad **quads, case 1: col += 2; break; case 2: - row += 2; break; + row += 2; break; case 3: col -= 2; break; } @@ -700,7 +699,7 @@ icvOrderFoundConnectedQuads( int quad_count, CvCBQuad **quads, } } - for (i=col_min; i<=col_max; i++) + for (int i=col_min; i<=col_max; i++) PRINTF("HIST[%d] = %d\n", i, col_hist[i]); // analyze inner quad structure @@ -763,7 +762,7 @@ icvOrderFoundConnectedQuads( int quad_count, CvCBQuad **quads, // if there is an outer quad missing, fill it in // first order all inner quads int found = 0; - for (i=0; icount == 4) { // ok, look at neighbors @@ -778,7 +777,7 @@ icvOrderFoundConnectedQuads( int quad_count, CvCBQuad **quads, case 1: col += 2; break; case 2: - row += 2; break; + row += 2; break; case 3: col -= 2; break; } @@ -817,7 +816,7 @@ icvOrderFoundConnectedQuads( int quad_count, CvCBQuad **quads, // final trimming of outer quads - if (dcol == w && drow == h) // found correct inner quads + if (dcol == w && drow == h) // found correct inner quads { PRINTF("Inner bounds ok, check outer quads\n"); int rcount = quad_count; @@ -832,7 +831,7 @@ icvOrderFoundConnectedQuads( int quad_count, CvCBQuad **quads, if (quads[i]->neighbors[j] && quads[i]->neighbors[j]->ordered) outer = true; } - if (!outer) // not an outer quad, eliminate + if (!outer) // not an outer quad, eliminate { PRINTF("Removing quad %d\n", i); icvRemoveQuadFromGroup(quads,rcount,quads[i]); @@ -876,7 +875,7 @@ icvAddOuterQuad( CvCBQuad *quad, CvCBQuad **quads, int quad_count, quad->count += 1; q->neighbors[j] = quad; q->group_idx = quad->group_idx; - q->count = 1; // number of neighbors + q->count = 1; // number of neighbors q->ordered = false; q->edge_len = quad->edge_len; @@ -1262,7 +1261,7 @@ icvCheckQuadGroup( CvCBQuad **quad_group, int quad_count, int width = 0, height = 0; int hist[5] = {0,0,0,0,0}; CvCBCorner* first = 0, *first2 = 0, *right, *cur, *below, *c; - + // build dual graph, which vertices are internal quad corners // and two vertices are connected iff they lie on the same quad edge for( i = 0; i < quad_count; i++ ) @@ -1485,7 +1484,7 @@ icvCheckQuadGroup( CvCBQuad **quad_group, int quad_count, result = corner_count; finalize: - + if( result <= 0 ) { corner_count = MIN( corner_count, pattern_size.width*pattern_size.height ); @@ -1697,7 +1696,7 @@ icvGenerateQuads( CvCBQuad **out_quads, CvCBCorner **out_corners, CV_POLY_APPROX_DP, (float)approx_level ); if( dst_contour->total == 4 ) break; - + // we call this again on its own output, because sometimes // cvApproxPoly() does not simplify as much as it should. dst_contour = cvApproxPoly( dst_contour, sizeof(CvContour), temp_storage, @@ -2006,17 +2005,17 @@ bool cv::findCirclesGrid( InputArray _image, Size patternSize, #endif if (isFound) { - switch(parameters.gridType) - { + switch(parameters.gridType) + { case CirclesGridFinderParameters::SYMMETRIC_GRID: boxFinder.getHoles(centers); break; case CirclesGridFinderParameters::ASYMMETRIC_GRID: - boxFinder.getAsymmetricHoles(centers); - break; + boxFinder.getAsymmetricHoles(centers); + break; default: CV_Error(CV_StsBadArg, "Unkown pattern type"); - } + } if (i != 0) { @@ -2027,7 +2026,7 @@ bool cv::findCirclesGrid( InputArray _image, Size patternSize, Mat(centers).copyTo(_centers); return true; } - + boxFinder.getHoles(centers); if (i != attempts - 1) { diff --git a/modules/calib3d/src/calibration.cpp b/modules/calib3d/src/calibration.cpp index 684110a..f211c5d 100644 --- a/modules/calib3d/src/calibration.cpp +++ b/modules/calib3d/src/calibration.cpp @@ -1153,7 +1153,7 @@ CV_IMPL void cvFindExtrinsicCameraParams2( const CvMat* objectPoints, int useExtrinsicGuess ) { const int max_iter = 20; - Ptr matM, _Mxy, _m, _mn, matL, matJ; + Ptr matM, _Mxy, _m, _mn, matL; int i, count; double a[9], ar[9]={1,0,0,0,1,0,0,0,1}, R[9]; diff --git a/modules/calib3d/src/checkchessboard.cpp b/modules/calib3d/src/checkchessboard.cpp index 61e44a7..60e275d 100644 --- a/modules/calib3d/src/checkchessboard.cpp +++ b/modules/calib3d/src/checkchessboard.cpp @@ -55,12 +55,12 @@ # endif #endif -void icvGetQuadrangleHypotheses(CvSeq* contours, std::vector >& quads, int class_id) +static void icvGetQuadrangleHypotheses(CvSeq* contours, std::vector >& quads, int class_id) { const float min_aspect_ratio = 0.3f; const float max_aspect_ratio = 3.0f; const float min_box_size = 10.0f; - + for(CvSeq* seq = contours; seq != NULL; seq = seq->h_next) { CvBox2D box = cvMinAreaRect2(seq); @@ -75,12 +75,12 @@ void icvGetQuadrangleHypotheses(CvSeq* contours, std::vector(box_size, class_id)); } } -void countClasses(const std::vector >& pairs, size_t idx1, size_t idx2, std::vector& counts) +static void countClasses(const std::vector >& pairs, size_t idx1, size_t idx2, std::vector& counts) { counts.assign(2, 0); for(size_t i = idx1; i != idx2; i++) @@ -89,36 +89,36 @@ void countClasses(const std::vector >& pairs, size_t idx1, } } -bool less_pred(const std::pair& p1, const std::pair& p2) +inline bool less_pred(const std::pair& p1, const std::pair& p2) { return p1.first < p2.first; } -// does a fast check if a chessboard is in the input image. This is a workaround to +// does a fast check if a chessboard is in the input image. This is a workaround to // a problem of cvFindChessboardCorners being slow on images with no chessboard // - src: input image // - size: chessboard size -// Returns 1 if a chessboard can be in this image and findChessboardCorners should be called, +// Returns 1 if a chessboard can be in this image and findChessboardCorners should be called, // 0 if there is no chessboard, -1 in case of error int cvCheckChessboard(IplImage* src, CvSize size) { if(src->nChannels > 1) { - cvError(CV_BadNumChannels, "cvCheckChessboard", "supports single-channel images only", + cvError(CV_BadNumChannels, "cvCheckChessboard", "supports single-channel images only", __FILE__, __LINE__); } - + if(src->depth != 8) { - cvError(CV_BadDepth, "cvCheckChessboard", "supports depth=8 images only", + cvError(CV_BadDepth, "cvCheckChessboard", "supports depth=8 images only", __FILE__, __LINE__); } - + const int erosion_count = 1; const float black_level = 20.f; const float white_level = 130.f; const float black_white_gap = 70.f; - + #if defined(DEBUG_WINDOWS) cvNamedWindow("1", 1); cvShowImage("1", src); @@ -126,46 +126,46 @@ int cvCheckChessboard(IplImage* src, CvSize size) #endif //DEBUG_WINDOWS CvMemStorage* storage = cvCreateMemStorage(); - + IplImage* white = cvCloneImage(src); IplImage* black = cvCloneImage(src); - + cvErode(white, white, NULL, erosion_count); cvDilate(black, black, NULL, erosion_count); IplImage* thresh = cvCreateImage(cvGetSize(src), IPL_DEPTH_8U, 1); - + int result = 0; for(float thresh_level = black_level; thresh_level < white_level && !result; thresh_level += 20.0f) { cvThreshold(white, thresh, thresh_level + black_white_gap, 255, CV_THRESH_BINARY); - + #if defined(DEBUG_WINDOWS) cvShowImage("1", thresh); cvWaitKey(0); #endif //DEBUG_WINDOWS - + CvSeq* first = 0; std::vector > quads; - cvFindContours(thresh, storage, &first, sizeof(CvContour), CV_RETR_CCOMP); + cvFindContours(thresh, storage, &first, sizeof(CvContour), CV_RETR_CCOMP); icvGetQuadrangleHypotheses(first, quads, 1); - + cvThreshold(black, thresh, thresh_level, 255, CV_THRESH_BINARY_INV); - + #if defined(DEBUG_WINDOWS) cvShowImage("1", thresh); cvWaitKey(0); #endif //DEBUG_WINDOWS - + cvFindContours(thresh, storage, &first, sizeof(CvContour), CV_RETR_CCOMP); icvGetQuadrangleHypotheses(first, quads, 0); - + const size_t min_quads_count = size.width*size.height/2; std::sort(quads.begin(), quads.end(), less_pred); - + // now check if there are many hypotheses with similar sizes // do this by floodfill-style algorithm const float size_rel_dev = 0.4f; - + for(size_t i = 0; i < quads.size(); i++) { size_t j = i + 1; @@ -176,7 +176,7 @@ int cvCheckChessboard(IplImage* src, CvSize size) break; } } - + if(j + 1 > min_quads_count + i) { // check the number of black and white squares @@ -194,12 +194,12 @@ int cvCheckChessboard(IplImage* src, CvSize size) } } } - - + + cvReleaseImage(&thresh); cvReleaseImage(&white); cvReleaseImage(&black); cvReleaseMemStorage(&storage); - + return result; } diff --git a/modules/calib3d/src/circlesgrid.cpp b/modules/calib3d/src/circlesgrid.cpp index 870a657..853e3ad 100644 --- a/modules/calib3d/src/circlesgrid.cpp +++ b/modules/calib3d/src/circlesgrid.cpp @@ -65,14 +65,14 @@ void drawPoints(const vector &points, Mat &outImage, int radius = 2, S } #endif -void CirclesGridClusterFinder::hierarchicalClustering(const vector points, const Size &patternSize, vector &patternPoints) +void CirclesGridClusterFinder::hierarchicalClustering(const vector points, const Size &patternSz, vector &patternPoints) { #ifdef HAVE_TEGRA_OPTIMIZATION - if(tegra::hierarchicalClustering(points, patternSize, patternPoints)) + if(tegra::hierarchicalClustering(points, patternSz, patternPoints)) return; #endif - int i, j, n = (int)points.size(); - size_t pn = static_cast(patternSize.area()); + int j, n = (int)points.size(); + size_t pn = static_cast(patternSz.area()); patternPoints.clear(); if (pn >= points.size()) @@ -84,7 +84,7 @@ void CirclesGridClusterFinder::hierarchicalClustering(const vector poin Mat dists(n, n, CV_32FC1, Scalar(0)); Mat distsMask(dists.size(), CV_8UC1, Scalar(0)); - for(i = 0; i < n; i++) + for(int i = 0; i < n; i++) { for(j = i+1; j < n; j++) { @@ -122,7 +122,7 @@ void CirclesGridClusterFinder::hierarchicalClustering(const vector poin } //the largest cluster can have more than pn points -- we need to filter out such situations - if(clusters[patternClusterIdx].size() != static_cast(patternSize.area())) + if(clusters[patternClusterIdx].size() != static_cast(patternSz.area())) { return; } @@ -505,11 +505,11 @@ void Graph::floydWarshall(cv::Mat &distanceMatrix, int infinity) const { for (Vertices::const_iterator it3 = vertices.begin(); it3 != vertices.end(); it3++) { - int i1 = (int)it1->first, i2 = (int)it2->first, i3 = (int)it3->first; + int i1 = (int)it1->first, i2 = (int)it2->first, i3 = (int)it3->first; int val1 = distanceMatrix.at (i2, i3); int val2; if (distanceMatrix.at (i2, i1) == infinity || - distanceMatrix.at (i1, i3) == infinity) + distanceMatrix.at (i1, i3) == infinity) val2 = val1; else { @@ -1223,7 +1223,7 @@ void computePredecessorMatrix(const Mat &dm, int verticesCount, Mat &predecessor } } -void computeShortestPath(Mat &predecessorMatrix, size_t v1, size_t v2, vector &path) +static void computeShortestPath(Mat &predecessorMatrix, size_t v1, size_t v2, vector &path) { if (predecessorMatrix.at ((int)v1, (int)v2) < 0) { @@ -1403,7 +1403,7 @@ void CirclesGridFinder::getHoles(vector &outHoles) const } } -bool areIndicesCorrect(Point pos, vector > *points) +static bool areIndicesCorrect(Point pos, vector > *points) { if (pos.y < 0 || pos.x < 0) return false; diff --git a/modules/calib3d/src/epnp.cpp b/modules/calib3d/src/epnp.cpp index 678ccfa..087b898 100644 --- a/modules/calib3d/src/epnp.cpp +++ b/modules/calib3d/src/epnp.cpp @@ -8,26 +8,26 @@ epnp::epnp(const cv::Mat& cameraMatrix, const cv::Mat& opoints, const cv::Mat& i if (cameraMatrix.depth() == CV_32F) init_camera_parameters(cameraMatrix); else - init_camera_parameters(cameraMatrix); + init_camera_parameters(cameraMatrix); number_of_correspondences = std::max(opoints.checkVector(3, CV_32F), opoints.checkVector(3, CV_64F)); pws.resize(3 * number_of_correspondences); - us.resize(2 * number_of_correspondences); - + us.resize(2 * number_of_correspondences); + if (opoints.depth() == ipoints.depth()) { - if (opoints.depth() == CV_32F) - init_points(opoints, ipoints); - else - init_points(opoints, ipoints); + if (opoints.depth() == CV_32F) + init_points(opoints, ipoints); + else + init_points(opoints, ipoints); } else if (opoints.depth() == CV_32F) - init_points(opoints, ipoints); + init_points(opoints, ipoints); else - init_points(opoints, ipoints); + init_points(opoints, ipoints); - alphas.resize(4 * number_of_correspondences); + alphas.resize(4 * number_of_correspondences); pcs.resize(3 * number_of_correspondences); max_nr = 0; @@ -97,15 +97,15 @@ void epnp::compute_barycentric_coordinates(void) for(int j = 0; j < 3; j++) a[1 + j] = - ci[3 * j ] * (pi[0] - cws[0][0]) + - ci[3 * j + 1] * (pi[1] - cws[0][1]) + - ci[3 * j + 2] * (pi[2] - cws[0][2]); + ci[3 * j ] * (pi[0] - cws[0][0]) + + ci[3 * j + 1] * (pi[1] - cws[0][1]) + + ci[3 * j + 2] * (pi[2] - cws[0][2]); a[0] = 1.0f - a[1] - a[2] - a[3]; } } void epnp::fill_M(CvMat * M, - const int row, const double * as, const double u, const double v) + const int row, const double * as, const double u, const double v) { double * M1 = M->data.db + row * 12; double * M2 = M1 + 12; @@ -130,7 +130,7 @@ void epnp::compute_ccs(const double * betas, const double * ut) const double * v = ut + 12 * (11 - i); for(int j = 0; j < 4; j++) for(int k = 0; k < 3; k++) - ccs[j][k] += betas[i] * v[3 * j + k]; + ccs[j][k] += betas[i] * v[3 * j + k]; } } @@ -195,7 +195,7 @@ void epnp::compute_pose(cv::Mat& R, cv::Mat& t) } void epnp::copy_R_and_t(const double R_src[3][3], const double t_src[3], - double R_dst[3][3], double t_dst[3]) + double R_dst[3][3], double t_dst[3]) { for(int i = 0; i < 3; i++) { for(int j = 0; j < 3; j++) @@ -282,7 +282,7 @@ void epnp::solve_for_sign(void) if (pcs[2] < 0.0) { for(int i = 0; i < 4; i++) for(int j = 0; j < 3; j++) - ccs[i][j] = -ccs[i][j]; + ccs[i][j] = -ccs[i][j]; for(int i = 0; i < number_of_correspondences; i++) { pcs[3 * i ] = -pcs[3 * i]; @@ -293,7 +293,7 @@ void epnp::solve_for_sign(void) } double epnp::compute_R_and_t(const double * ut, const double * betas, - double R[3][3], double t[3]) + double R[3][3], double t[3]) { compute_ccs(betas, ut); compute_pcs(); @@ -322,13 +322,13 @@ double epnp::reprojection_error(const double R[3][3], const double t[3]) } return sum2 / number_of_correspondences; -} +} // betas10 = [B11 B12 B22 B13 B23 B33 B14 B24 B34 B44] // betas_approx_1 = [B11 B12 B13 B14] void epnp::find_betas_approx_1(const CvMat * L_6x10, const CvMat * Rho, - double * betas) + double * betas) { double l_6x4[6 * 4], b4[4]; CvMat L_6x4 = cvMat(6, 4, CV_64F, l_6x4); @@ -360,7 +360,7 @@ void epnp::find_betas_approx_1(const CvMat * L_6x10, const CvMat * Rho, // betas_approx_2 = [B11 B12 B22 ] void epnp::find_betas_approx_2(const CvMat * L_6x10, const CvMat * Rho, - double * betas) + double * betas) { double l_6x3[6 * 3], b3[3]; CvMat L_6x3 = cvMat(6, 3, CV_64F, l_6x3); @@ -392,7 +392,7 @@ void epnp::find_betas_approx_2(const CvMat * L_6x10, const CvMat * Rho, // betas_approx_3 = [B11 B12 B22 B13 B23 ] void epnp::find_betas_approx_3(const CvMat * L_6x10, const CvMat * Rho, - double * betas) + double * betas) { double l_6x5[6 * 5], b5[5]; CvMat L_6x5 = cvMat(6, 5, CV_64F, l_6x5); @@ -440,8 +440,8 @@ void epnp::compute_L_6x10(const double * ut, double * l_6x10) b++; if (b > 3) { - a++; - b = a + 1; + a++; + b = a + 1; } } } @@ -473,7 +473,7 @@ void epnp::compute_rho(double * rho) } void epnp::compute_A_and_b_gauss_newton(const double * l_6x10, const double * rho, - const double betas[4], CvMat * A, CvMat * b) + const double betas[4], CvMat * A, CvMat * b) { for(int i = 0; i < 6; i++) { const double * rowL = l_6x10 + i * 10; @@ -485,23 +485,22 @@ void epnp::compute_A_and_b_gauss_newton(const double * l_6x10, const double * rh rowA[3] = rowL[6] * betas[0] + rowL[7] * betas[1] + rowL[8] * betas[2] + 2 * rowL[9] * betas[3]; cvmSet(b, i, 0, rho[i] - - ( - rowL[0] * betas[0] * betas[0] + - rowL[1] * betas[0] * betas[1] + - rowL[2] * betas[1] * betas[1] + - rowL[3] * betas[0] * betas[2] + - rowL[4] * betas[1] * betas[2] + - rowL[5] * betas[2] * betas[2] + - rowL[6] * betas[0] * betas[3] + - rowL[7] * betas[1] * betas[3] + - rowL[8] * betas[2] * betas[3] + - rowL[9] * betas[3] * betas[3] - )); + ( + rowL[0] * betas[0] * betas[0] + + rowL[1] * betas[0] * betas[1] + + rowL[2] * betas[1] * betas[1] + + rowL[3] * betas[0] * betas[2] + + rowL[4] * betas[1] * betas[2] + + rowL[5] * betas[2] * betas[2] + + rowL[6] * betas[0] * betas[3] + + rowL[7] * betas[1] * betas[3] + + rowL[8] * betas[2] * betas[3] + + rowL[9] * betas[3] * betas[3] + )); } } -void epnp::gauss_newton(const CvMat * L_6x10, const CvMat * Rho, - double betas[4]) +void epnp::gauss_newton(const CvMat * L_6x10, const CvMat * Rho, double betas[4]) { const int iterations_number = 5; @@ -510,12 +509,13 @@ void epnp::gauss_newton(const CvMat * L_6x10, const CvMat * Rho, CvMat B = cvMat(6, 1, CV_64F, b); CvMat X = cvMat(4, 1, CV_64F, x); - for(int k = 0; k < iterations_number; k++) { + for(int k = 0; k < iterations_number; k++) + { compute_A_and_b_gauss_newton(L_6x10->data.db, Rho->data.db, - betas, &A, &B); + betas, &A, &B); qr_solve(&A, &B, &X); for(int i = 0; i < 4; i++) - betas[i] += x[i]; + betas[i] += x[i]; } } @@ -524,53 +524,64 @@ void epnp::qr_solve(CvMat * A, CvMat * b, CvMat * X) const int nr = A->rows; const int nc = A->cols; - if (max_nr != 0 && max_nr < nr) { + if (max_nr != 0 && max_nr < nr) + { delete [] A1; delete [] A2; } - if (max_nr < nr) { + if (max_nr < nr) + { max_nr = nr; A1 = new double[nr]; A2 = new double[nr]; } double * pA = A->data.db, * ppAkk = pA; - for(int k = 0; k < nc; k++) { - double * ppAik = ppAkk, eta = fabs(*ppAik); - for(int i = k + 1; i < nr; i++) { - double elt = fabs(*ppAik); + for(int k = 0; k < nc; k++) + { + double * ppAik1 = ppAkk, eta = fabs(*ppAik1); + for(int i = k + 1; i < nr; i++) + { + double elt = fabs(*ppAik1); if (eta < elt) eta = elt; - ppAik += nc; + ppAik1 += nc; } - if (eta == 0) { + if (eta == 0) + { A1[k] = A2[k] = 0.0; //cerr << "God damnit, A is singular, this shouldn't happen." << endl; return; - } else { - double * ppAik = ppAkk, sum = 0.0, inv_eta = 1. / eta; - for(int i = k; i < nr; i++) { - *ppAik *= inv_eta; - sum += *ppAik * *ppAik; - ppAik += nc; + } + else + { + double * ppAik2 = ppAkk, sum2 = 0.0, inv_eta = 1. / eta; + for(int i = k; i < nr; i++) + { + *ppAik2 *= inv_eta; + sum2 += *ppAik2 * *ppAik2; + ppAik2 += nc; } - double sigma = sqrt(sum); + double sigma = sqrt(sum2); if (*ppAkk < 0) - sigma = -sigma; + sigma = -sigma; *ppAkk += sigma; A1[k] = sigma * *ppAkk; A2[k] = -eta * sigma; - for(int j = k + 1; j < nc; j++) { - double * ppAik = ppAkk, sum = 0; - for(int i = k; i < nr; i++) { - sum += *ppAik * ppAik[j - k]; - ppAik += nc; - } - double tau = sum / A1[k]; - ppAik = ppAkk; - for(int i = k; i < nr; i++) { - ppAik[j - k] -= tau * *ppAik; - ppAik += nc; - } + for(int j = k + 1; j < nc; j++) + { + double * ppAik = ppAkk, sum = 0; + for(int i = k; i < nr; i++) + { + sum += *ppAik * ppAik[j - k]; + ppAik += nc; + } + double tau = sum / A1[k]; + ppAik = ppAkk; + for(int i = k; i < nr; i++) + { + ppAik[j - k] -= tau * *ppAik; + ppAik += nc; + } } } ppAkk += nc + 1; @@ -578,15 +589,18 @@ void epnp::qr_solve(CvMat * A, CvMat * b, CvMat * X) // b <- Qt b double * ppAjj = pA, * pb = b->data.db; - for(int j = 0; j < nc; j++) { + for(int j = 0; j < nc; j++) + { double * ppAij = ppAjj, tau = 0; - for(int i = j; i < nr; i++) { + for(int i = j; i < nr; i++) + { tau += *ppAij * pb[i]; ppAij += nc; } tau /= A1[j]; ppAij = ppAjj; - for(int i = j; i < nr; i++) { + for(int i = j; i < nr; i++) + { pb[i] -= tau * *ppAij; ppAij += nc; } @@ -596,10 +610,12 @@ void epnp::qr_solve(CvMat * A, CvMat * b, CvMat * X) // X = R-1 b double * pX = X->data.db; pX[nc - 1] = pb[nc - 1] / A2[nc - 1]; - for(int i = nc - 2; i >= 0; i--) { + for(int i = nc - 2; i >= 0; i--) + { double * ppAij = pA + i * nc + (i + 1), sum = 0; - for(int j = i + 1; j < nc; j++) { + for(int j = i + 1; j < nc; j++) + { sum += *ppAij * pX[j]; ppAij++; } diff --git a/modules/calib3d/src/p3p.cpp b/modules/calib3d/src/p3p.cpp index 300f230..a02da3e 100644 --- a/modules/calib3d/src/p3p.cpp +++ b/modules/calib3d/src/p3p.cpp @@ -9,151 +9,151 @@ using namespace std; void p3p::init_inverse_parameters() { - inv_fx = 1. / fx; - inv_fy = 1. / fy; - cx_fx = cx / fx; - cy_fy = cy / fy; + inv_fx = 1. / fx; + inv_fy = 1. / fy; + cx_fx = cx / fx; + cy_fy = cy / fy; } p3p::p3p(cv::Mat cameraMatrix) { - if (cameraMatrix.depth() == CV_32F) - init_camera_parameters(cameraMatrix); - else - init_camera_parameters(cameraMatrix); - init_inverse_parameters(); + if (cameraMatrix.depth() == CV_32F) + init_camera_parameters(cameraMatrix); + else + init_camera_parameters(cameraMatrix); + init_inverse_parameters(); } p3p::p3p(double _fx, double _fy, double _cx, double _cy) { - fx = _fx; - fy = _fy; - cx = _cx; - cy = _cy; - init_inverse_parameters(); + fx = _fx; + fy = _fy; + cx = _cx; + cy = _cy; + init_inverse_parameters(); } bool p3p::solve(cv::Mat& R, cv::Mat& tvec, const cv::Mat& opoints, const cv::Mat& ipoints) { - double rotation_matrix[3][3], translation[3]; - std::vector points; - if (opoints.depth() == ipoints.depth()) - { - if (opoints.depth() == CV_32F) - extract_points(opoints, ipoints, points); - else - extract_points(opoints, ipoints, points); - } - else if (opoints.depth() == CV_32F) - extract_points(opoints, ipoints, points); - else - extract_points(opoints, ipoints, points); - - bool result = solve(rotation_matrix, translation, points[0], points[1], points[2], points[3], points[4], points[5], - points[6], points[7], points[8], points[9], points[10], points[11], points[12], points[13], points[14], - points[15], points[16], points[17], points[18], points[19]); - cv::Mat(3, 1, CV_64F, translation).copyTo(tvec); + double rotation_matrix[3][3], translation[3]; + std::vector points; + if (opoints.depth() == ipoints.depth()) + { + if (opoints.depth() == CV_32F) + extract_points(opoints, ipoints, points); + else + extract_points(opoints, ipoints, points); + } + else if (opoints.depth() == CV_32F) + extract_points(opoints, ipoints, points); + else + extract_points(opoints, ipoints, points); + + bool result = solve(rotation_matrix, translation, points[0], points[1], points[2], points[3], points[4], points[5], + points[6], points[7], points[8], points[9], points[10], points[11], points[12], points[13], points[14], + points[15], points[16], points[17], points[18], points[19]); + cv::Mat(3, 1, CV_64F, translation).copyTo(tvec); cv::Mat(3, 3, CV_64F, rotation_matrix).copyTo(R); - return result; + return result; } bool p3p::solve(double R[3][3], double t[3], - double mu0, double mv0, double X0, double Y0, double Z0, - double mu1, double mv1, double X1, double Y1, double Z1, - double mu2, double mv2, double X2, double Y2, double Z2, - double mu3, double mv3, double X3, double Y3, double Z3) + double mu0, double mv0, double X0, double Y0, double Z0, + double mu1, double mv1, double X1, double Y1, double Z1, + double mu2, double mv2, double X2, double Y2, double Z2, + double mu3, double mv3, double X3, double Y3, double Z3) { - double Rs[4][3][3], ts[4][3]; - - int n = solve(Rs, ts, mu0, mv0, X0, Y0, Z0, mu1, mv1, X1, Y1, Z1, mu2, mv2, X2, Y2, Z2); - - if (n == 0) - return false; - - int ns = 0; - double min_reproj = 0; - for(int i = 0; i < n; i++) { - double X3p = Rs[i][0][0] * X3 + Rs[i][0][1] * Y3 + Rs[i][0][2] * Z3 + ts[i][0]; - double Y3p = Rs[i][1][0] * X3 + Rs[i][1][1] * Y3 + Rs[i][1][2] * Z3 + ts[i][1]; - double Z3p = Rs[i][2][0] * X3 + Rs[i][2][1] * Y3 + Rs[i][2][2] * Z3 + ts[i][2]; - double mu3p = cx + fx * X3p / Z3p; - double mv3p = cy + fy * Y3p / Z3p; - double reproj = (mu3p - mu3) * (mu3p - mu3) + (mv3p - mv3) * (mv3p - mv3); - if (i == 0 || min_reproj > reproj) { - ns = i; - min_reproj = reproj; - } - } - - for(int i = 0; i < 3; i++) { - for(int j = 0; j < 3; j++) - R[i][j] = Rs[ns][i][j]; - t[i] = ts[ns][i]; - } - - return true; + double Rs[4][3][3], ts[4][3]; + + int n = solve(Rs, ts, mu0, mv0, X0, Y0, Z0, mu1, mv1, X1, Y1, Z1, mu2, mv2, X2, Y2, Z2); + + if (n == 0) + return false; + + int ns = 0; + double min_reproj = 0; + for(int i = 0; i < n; i++) { + double X3p = Rs[i][0][0] * X3 + Rs[i][0][1] * Y3 + Rs[i][0][2] * Z3 + ts[i][0]; + double Y3p = Rs[i][1][0] * X3 + Rs[i][1][1] * Y3 + Rs[i][1][2] * Z3 + ts[i][1]; + double Z3p = Rs[i][2][0] * X3 + Rs[i][2][1] * Y3 + Rs[i][2][2] * Z3 + ts[i][2]; + double mu3p = cx + fx * X3p / Z3p; + double mv3p = cy + fy * Y3p / Z3p; + double reproj = (mu3p - mu3) * (mu3p - mu3) + (mv3p - mv3) * (mv3p - mv3); + if (i == 0 || min_reproj > reproj) { + ns = i; + min_reproj = reproj; + } + } + + for(int i = 0; i < 3; i++) { + for(int j = 0; j < 3; j++) + R[i][j] = Rs[ns][i][j]; + t[i] = ts[ns][i]; + } + + return true; } int p3p::solve(double R[4][3][3], double t[4][3], - double mu0, double mv0, double X0, double Y0, double Z0, - double mu1, double mv1, double X1, double Y1, double Z1, - double mu2, double mv2, double X2, double Y2, double Z2) + double mu0, double mv0, double X0, double Y0, double Z0, + double mu1, double mv1, double X1, double Y1, double Z1, + double mu2, double mv2, double X2, double Y2, double Z2) { - double mk0, mk1, mk2; - double norm; + double mk0, mk1, mk2; + double norm; - mu0 = inv_fx * mu0 - cx_fx; - mv0 = inv_fy * mv0 - cy_fy; - norm = sqrt(mu0 * mu0 + mv0 * mv0 + 1); - mk0 = 1. / norm; mu0 *= mk0; mv0 *= mk0; + mu0 = inv_fx * mu0 - cx_fx; + mv0 = inv_fy * mv0 - cy_fy; + norm = sqrt(mu0 * mu0 + mv0 * mv0 + 1); + mk0 = 1. / norm; mu0 *= mk0; mv0 *= mk0; - mu1 = inv_fx * mu1 - cx_fx; - mv1 = inv_fy * mv1 - cy_fy; - norm = sqrt(mu1 * mu1 + mv1 * mv1 + 1); - mk1 = 1. / norm; mu1 *= mk1; mv1 *= mk1; + mu1 = inv_fx * mu1 - cx_fx; + mv1 = inv_fy * mv1 - cy_fy; + norm = sqrt(mu1 * mu1 + mv1 * mv1 + 1); + mk1 = 1. / norm; mu1 *= mk1; mv1 *= mk1; - mu2 = inv_fx * mu2 - cx_fx; - mv2 = inv_fy * mv2 - cy_fy; - norm = sqrt(mu2 * mu2 + mv2 * mv2 + 1); - mk2 = 1. / norm; mu2 *= mk2; mv2 *= mk2; + mu2 = inv_fx * mu2 - cx_fx; + mv2 = inv_fy * mv2 - cy_fy; + norm = sqrt(mu2 * mu2 + mv2 * mv2 + 1); + mk2 = 1. / norm; mu2 *= mk2; mv2 *= mk2; - double distances[3]; - distances[0] = sqrt( (X1 - X2) * (X1 - X2) + (Y1 - Y2) * (Y1 - Y2) + (Z1 - Z2) * (Z1 - Z2) ); - distances[1] = sqrt( (X0 - X2) * (X0 - X2) + (Y0 - Y2) * (Y0 - Y2) + (Z0 - Z2) * (Z0 - Z2) ); - distances[2] = sqrt( (X0 - X1) * (X0 - X1) + (Y0 - Y1) * (Y0 - Y1) + (Z0 - Z1) * (Z0 - Z1) ); + double distances[3]; + distances[0] = sqrt( (X1 - X2) * (X1 - X2) + (Y1 - Y2) * (Y1 - Y2) + (Z1 - Z2) * (Z1 - Z2) ); + distances[1] = sqrt( (X0 - X2) * (X0 - X2) + (Y0 - Y2) * (Y0 - Y2) + (Z0 - Z2) * (Z0 - Z2) ); + distances[2] = sqrt( (X0 - X1) * (X0 - X1) + (Y0 - Y1) * (Y0 - Y1) + (Z0 - Z1) * (Z0 - Z1) ); - // Calculate angles - double cosines[3]; - cosines[0] = mu1 * mu2 + mv1 * mv2 + mk1 * mk2; - cosines[1] = mu0 * mu2 + mv0 * mv2 + mk0 * mk2; - cosines[2] = mu0 * mu1 + mv0 * mv1 + mk0 * mk1; + // Calculate angles + double cosines[3]; + cosines[0] = mu1 * mu2 + mv1 * mv2 + mk1 * mk2; + cosines[1] = mu0 * mu2 + mv0 * mv2 + mk0 * mk2; + cosines[2] = mu0 * mu1 + mv0 * mv1 + mk0 * mk1; - double lengths[4][3]; - int n = solve_for_lengths(lengths, distances, cosines); + double lengths[4][3]; + int n = solve_for_lengths(lengths, distances, cosines); - int nb_solutions = 0; - for(int i = 0; i < n; i++) { - double M_orig[3][3]; + int nb_solutions = 0; + for(int i = 0; i < n; i++) { + double M_orig[3][3]; - M_orig[0][0] = lengths[i][0] * mu0; - M_orig[0][1] = lengths[i][0] * mv0; - M_orig[0][2] = lengths[i][0] * mk0; + M_orig[0][0] = lengths[i][0] * mu0; + M_orig[0][1] = lengths[i][0] * mv0; + M_orig[0][2] = lengths[i][0] * mk0; - M_orig[1][0] = lengths[i][1] * mu1; - M_orig[1][1] = lengths[i][1] * mv1; - M_orig[1][2] = lengths[i][1] * mk1; + M_orig[1][0] = lengths[i][1] * mu1; + M_orig[1][1] = lengths[i][1] * mv1; + M_orig[1][2] = lengths[i][1] * mk1; - M_orig[2][0] = lengths[i][2] * mu2; - M_orig[2][1] = lengths[i][2] * mv2; - M_orig[2][2] = lengths[i][2] * mk2; + M_orig[2][0] = lengths[i][2] * mu2; + M_orig[2][1] = lengths[i][2] * mv2; + M_orig[2][2] = lengths[i][2] * mk2; - if (!align(M_orig, X0, Y0, Z0, X1, Y1, Z1, X2, Y2, Z2, R[nb_solutions], t[nb_solutions])) - continue; + if (!align(M_orig, X0, Y0, Z0, X1, Y1, Z1, X2, Y2, Z2, R[nb_solutions], t[nb_solutions])) + continue; - nb_solutions++; - } + nb_solutions++; + } - return nb_solutions; + return nb_solutions; } /// Given 3D distances between three points and cosines of 3 angles at the apex, calculates @@ -170,247 +170,247 @@ int p3p::solve(double R[4][3][3], double t[4][3], int p3p::solve_for_lengths(double lengths[4][3], double distances[3], double cosines[3]) { - double p = cosines[0] * 2; - double q = cosines[1] * 2; - double r = cosines[2] * 2; + double p = cosines[0] * 2; + double q = cosines[1] * 2; + double r = cosines[2] * 2; - double inv_d22 = 1. / (distances[2] * distances[2]); - double a = inv_d22 * (distances[0] * distances[0]); - double b = inv_d22 * (distances[1] * distances[1]); + double inv_d22 = 1. / (distances[2] * distances[2]); + double a = inv_d22 * (distances[0] * distances[0]); + double b = inv_d22 * (distances[1] * distances[1]); - double a2 = a * a, b2 = b * b, p2 = p * p, q2 = q * q, r2 = r * r; - double pr = p * r, pqr = q * pr; + double a2 = a * a, b2 = b * b, p2 = p * p, q2 = q * q, r2 = r * r; + double pr = p * r, pqr = q * pr; - // Check reality condition (the four points should not be coplanar) - if (p2 + q2 + r2 - pqr - 1 == 0) - return 0; + // Check reality condition (the four points should not be coplanar) + if (p2 + q2 + r2 - pqr - 1 == 0) + return 0; - double ab = a * b, a_2 = 2*a; + double ab = a * b, a_2 = 2*a; - double A = -2 * b + b2 + a2 + 1 + ab*(2 - r2) - a_2; + double A = -2 * b + b2 + a2 + 1 + ab*(2 - r2) - a_2; - // Check reality condition - if (A == 0) return 0; + // Check reality condition + if (A == 0) return 0; - double a_4 = 4*a; + double a_4 = 4*a; - double B = q*(-2*(ab + a2 + 1 - b) + r2*ab + a_4) + pr*(b - b2 + ab); - double C = q2 + b2*(r2 + p2 - 2) - b*(p2 + pqr) - ab*(r2 + pqr) + (a2 - a_2)*(2 + q2) + 2; - double D = pr*(ab-b2+b) + q*((p2-2)*b + 2 * (ab - a2) + a_4 - 2); - double E = 1 + 2*(b - a - ab) + b2 - b*p2 + a2; + double B = q*(-2*(ab + a2 + 1 - b) + r2*ab + a_4) + pr*(b - b2 + ab); + double C = q2 + b2*(r2 + p2 - 2) - b*(p2 + pqr) - ab*(r2 + pqr) + (a2 - a_2)*(2 + q2) + 2; + double D = pr*(ab-b2+b) + q*((p2-2)*b + 2 * (ab - a2) + a_4 - 2); + double E = 1 + 2*(b - a - ab) + b2 - b*p2 + a2; - double temp = (p2*(a-1+b) + r2*(a-1-b) + pqr - a*pqr); - double b0 = b * temp * temp; - // Check reality condition - if (b0 == 0) - return 0; + double temp = (p2*(a-1+b) + r2*(a-1-b) + pqr - a*pqr); + double b0 = b * temp * temp; + // Check reality condition + if (b0 == 0) + return 0; - double real_roots[4]; - int n = solve_deg4(A, B, C, D, E, real_roots[0], real_roots[1], real_roots[2], real_roots[3]); + double real_roots[4]; + int n = solve_deg4(A, B, C, D, E, real_roots[0], real_roots[1], real_roots[2], real_roots[3]); - if (n == 0) - return 0; + if (n == 0) + return 0; - int nb_solutions = 0; - double r3 = r2*r, pr2 = p*r2, r3q = r3 * q; - double inv_b0 = 1. / b0; + int nb_solutions = 0; + double r3 = r2*r, pr2 = p*r2, r3q = r3 * q; + double inv_b0 = 1. / b0; - // For each solution of x - for(int i = 0; i < n; i++) { - double x = real_roots[i]; + // For each solution of x + for(int i = 0; i < n; i++) { + double x = real_roots[i]; - // Check reality condition - if (x <= 0) - continue; + // Check reality condition + if (x <= 0) + continue; - double x2 = x*x; + double x2 = x*x; - double b1 = - ((1-a-b)*x2 + (q*a-q)*x + 1 - a + b) * - (((r3*(a2 + ab*(2 - r2) - a_2 + b2 - 2*b + 1)) * x + + double b1 = + ((1-a-b)*x2 + (q*a-q)*x + 1 - a + b) * + (((r3*(a2 + ab*(2 - r2) - a_2 + b2 - 2*b + 1)) * x + - (r3q*(2*(b-a2) + a_4 + ab*(r2 - 2) - 2) + pr2*(1 + a2 + 2*(ab-a-b) + r2*(b - b2) + b2))) * x2 + + (r3q*(2*(b-a2) + a_4 + ab*(r2 - 2) - 2) + pr2*(1 + a2 + 2*(ab-a-b) + r2*(b - b2) + b2))) * x2 + - (r3*(q2*(1-2*a+a2) + r2*(b2-ab) - a_4 + 2*(a2 - b2) + 2) + r*p2*(b2 + 2*(ab - b - a) + 1 + a2) + pr2*q*(a_4 + 2*(b - ab - a2) - 2 - r2*b)) * x + + (r3*(q2*(1-2*a+a2) + r2*(b2-ab) - a_4 + 2*(a2 - b2) + 2) + r*p2*(b2 + 2*(ab - b - a) + 1 + a2) + pr2*q*(a_4 + 2*(b - ab - a2) - 2 - r2*b)) * x + - 2*r3q*(a_2 - b - a2 + ab - 1) + pr2*(q2 - a_4 + 2*(a2 - b2) + r2*b + q2*(a2 - a_2) + 2) + - p2*(p*(2*(ab - a - b) + a2 + b2 + 1) + 2*q*r*(b + a_2 - a2 - ab - 1))); + 2*r3q*(a_2 - b - a2 + ab - 1) + pr2*(q2 - a_4 + 2*(a2 - b2) + r2*b + q2*(a2 - a_2) + 2) + + p2*(p*(2*(ab - a - b) + a2 + b2 + 1) + 2*q*r*(b + a_2 - a2 - ab - 1))); - // Check reality condition - if (b1 <= 0) - continue; + // Check reality condition + if (b1 <= 0) + continue; - double y = inv_b0 * b1; - double v = x2 + y*y - x*y*r; + double y = inv_b0 * b1; + double v = x2 + y*y - x*y*r; - if (v <= 0) - continue; + if (v <= 0) + continue; - double Z = distances[2] / sqrt(v); - double X = x * Z; - double Y = y * Z; + double Z = distances[2] / sqrt(v); + double X = x * Z; + double Y = y * Z; - lengths[nb_solutions][0] = X; - lengths[nb_solutions][1] = Y; - lengths[nb_solutions][2] = Z; + lengths[nb_solutions][0] = X; + lengths[nb_solutions][1] = Y; + lengths[nb_solutions][2] = Z; - nb_solutions++; - } + nb_solutions++; + } - return nb_solutions; + return nb_solutions; } bool p3p::align(double M_end[3][3], - double X0, double Y0, double Z0, - double X1, double Y1, double Z1, - double X2, double Y2, double Z2, - double R[3][3], double T[3]) + double X0, double Y0, double Z0, + double X1, double Y1, double Z1, + double X2, double Y2, double Z2, + double R[3][3], double T[3]) { - // Centroids: - double C_start[3], C_end[3]; - for(int i = 0; i < 3; i++) C_end[i] = (M_end[0][i] + M_end[1][i] + M_end[2][i]) / 3; - C_start[0] = (X0 + X1 + X2) / 3; - C_start[1] = (Y0 + Y1 + Y2) / 3; - C_start[2] = (Z0 + Z1 + Z2) / 3; - - // Covariance matrix s: - double s[3 * 3]; - for(int j = 0; j < 3; j++) { - s[0 * 3 + j] = (X0 * M_end[0][j] + X1 * M_end[1][j] + X2 * M_end[2][j]) / 3 - C_end[j] * C_start[0]; - s[1 * 3 + j] = (Y0 * M_end[0][j] + Y1 * M_end[1][j] + Y2 * M_end[2][j]) / 3 - C_end[j] * C_start[1]; - s[2 * 3 + j] = (Z0 * M_end[0][j] + Z1 * M_end[1][j] + Z2 * M_end[2][j]) / 3 - C_end[j] * C_start[2]; - } - - double Qs[16], evs[4], U[16]; - - Qs[0 * 4 + 0] = s[0 * 3 + 0] + s[1 * 3 + 1] + s[2 * 3 + 2]; - Qs[1 * 4 + 1] = s[0 * 3 + 0] - s[1 * 3 + 1] - s[2 * 3 + 2]; - Qs[2 * 4 + 2] = s[1 * 3 + 1] - s[2 * 3 + 2] - s[0 * 3 + 0]; - Qs[3 * 4 + 3] = s[2 * 3 + 2] - s[0 * 3 + 0] - s[1 * 3 + 1]; - - Qs[1 * 4 + 0] = Qs[0 * 4 + 1] = s[1 * 3 + 2] - s[2 * 3 + 1]; - Qs[2 * 4 + 0] = Qs[0 * 4 + 2] = s[2 * 3 + 0] - s[0 * 3 + 2]; - Qs[3 * 4 + 0] = Qs[0 * 4 + 3] = s[0 * 3 + 1] - s[1 * 3 + 0]; - Qs[2 * 4 + 1] = Qs[1 * 4 + 2] = s[1 * 3 + 0] + s[0 * 3 + 1]; - Qs[3 * 4 + 1] = Qs[1 * 4 + 3] = s[2 * 3 + 0] + s[0 * 3 + 2]; - Qs[3 * 4 + 2] = Qs[2 * 4 + 3] = s[2 * 3 + 1] + s[1 * 3 + 2]; - - jacobi_4x4(Qs, evs, U); - - // Looking for the largest eigen value: - int i_ev = 0; - double ev_max = evs[i_ev]; - for(int i = 1; i < 4; i++) - if (evs[i] > ev_max) - ev_max = evs[i_ev = i]; - - // Quaternion: - double q[4]; - for(int i = 0; i < 4; i++) - q[i] = U[i * 4 + i_ev]; - - double q02 = q[0] * q[0], q12 = q[1] * q[1], q22 = q[2] * q[2], q32 = q[3] * q[3]; - double q0_1 = q[0] * q[1], q0_2 = q[0] * q[2], q0_3 = q[0] * q[3]; - double q1_2 = q[1] * q[2], q1_3 = q[1] * q[3]; - double q2_3 = q[2] * q[3]; - - R[0][0] = q02 + q12 - q22 - q32; - R[0][1] = 2. * (q1_2 - q0_3); - R[0][2] = 2. * (q1_3 + q0_2); - - R[1][0] = 2. * (q1_2 + q0_3); - R[1][1] = q02 + q22 - q12 - q32; - R[1][2] = 2. * (q2_3 - q0_1); - - R[2][0] = 2. * (q1_3 - q0_2); - R[2][1] = 2. * (q2_3 + q0_1); - R[2][2] = q02 + q32 - q12 - q22; - - for(int i = 0; i < 3; i++) - T[i] = C_end[i] - (R[i][0] * C_start[0] + R[i][1] * C_start[1] + R[i][2] * C_start[2]); - - return true; + // Centroids: + double C_start[3], C_end[3]; + for(int i = 0; i < 3; i++) C_end[i] = (M_end[0][i] + M_end[1][i] + M_end[2][i]) / 3; + C_start[0] = (X0 + X1 + X2) / 3; + C_start[1] = (Y0 + Y1 + Y2) / 3; + C_start[2] = (Z0 + Z1 + Z2) / 3; + + // Covariance matrix s: + double s[3 * 3]; + for(int j = 0; j < 3; j++) { + s[0 * 3 + j] = (X0 * M_end[0][j] + X1 * M_end[1][j] + X2 * M_end[2][j]) / 3 - C_end[j] * C_start[0]; + s[1 * 3 + j] = (Y0 * M_end[0][j] + Y1 * M_end[1][j] + Y2 * M_end[2][j]) / 3 - C_end[j] * C_start[1]; + s[2 * 3 + j] = (Z0 * M_end[0][j] + Z1 * M_end[1][j] + Z2 * M_end[2][j]) / 3 - C_end[j] * C_start[2]; + } + + double Qs[16], evs[4], U[16]; + + Qs[0 * 4 + 0] = s[0 * 3 + 0] + s[1 * 3 + 1] + s[2 * 3 + 2]; + Qs[1 * 4 + 1] = s[0 * 3 + 0] - s[1 * 3 + 1] - s[2 * 3 + 2]; + Qs[2 * 4 + 2] = s[1 * 3 + 1] - s[2 * 3 + 2] - s[0 * 3 + 0]; + Qs[3 * 4 + 3] = s[2 * 3 + 2] - s[0 * 3 + 0] - s[1 * 3 + 1]; + + Qs[1 * 4 + 0] = Qs[0 * 4 + 1] = s[1 * 3 + 2] - s[2 * 3 + 1]; + Qs[2 * 4 + 0] = Qs[0 * 4 + 2] = s[2 * 3 + 0] - s[0 * 3 + 2]; + Qs[3 * 4 + 0] = Qs[0 * 4 + 3] = s[0 * 3 + 1] - s[1 * 3 + 0]; + Qs[2 * 4 + 1] = Qs[1 * 4 + 2] = s[1 * 3 + 0] + s[0 * 3 + 1]; + Qs[3 * 4 + 1] = Qs[1 * 4 + 3] = s[2 * 3 + 0] + s[0 * 3 + 2]; + Qs[3 * 4 + 2] = Qs[2 * 4 + 3] = s[2 * 3 + 1] + s[1 * 3 + 2]; + + jacobi_4x4(Qs, evs, U); + + // Looking for the largest eigen value: + int i_ev = 0; + double ev_max = evs[i_ev]; + for(int i = 1; i < 4; i++) + if (evs[i] > ev_max) + ev_max = evs[i_ev = i]; + + // Quaternion: + double q[4]; + for(int i = 0; i < 4; i++) + q[i] = U[i * 4 + i_ev]; + + double q02 = q[0] * q[0], q12 = q[1] * q[1], q22 = q[2] * q[2], q32 = q[3] * q[3]; + double q0_1 = q[0] * q[1], q0_2 = q[0] * q[2], q0_3 = q[0] * q[3]; + double q1_2 = q[1] * q[2], q1_3 = q[1] * q[3]; + double q2_3 = q[2] * q[3]; + + R[0][0] = q02 + q12 - q22 - q32; + R[0][1] = 2. * (q1_2 - q0_3); + R[0][2] = 2. * (q1_3 + q0_2); + + R[1][0] = 2. * (q1_2 + q0_3); + R[1][1] = q02 + q22 - q12 - q32; + R[1][2] = 2. * (q2_3 - q0_1); + + R[2][0] = 2. * (q1_3 - q0_2); + R[2][1] = 2. * (q2_3 + q0_1); + R[2][2] = q02 + q32 - q12 - q22; + + for(int i = 0; i < 3; i++) + T[i] = C_end[i] - (R[i][0] * C_start[0] + R[i][1] * C_start[1] + R[i][2] * C_start[2]); + + return true; } bool p3p::jacobi_4x4(double * A, double * D, double * U) { - double B[4], Z[4]; - double Id[16] = {1., 0., 0., 0., - 0., 1., 0., 0., - 0., 0., 1., 0., - 0., 0., 0., 1.}; - - memcpy(U, Id, 16 * sizeof(double)); - - B[0] = A[0]; B[1] = A[5]; B[2] = A[10]; B[3] = A[15]; - memcpy(D, B, 4 * sizeof(double)); - memset(Z, 0, 4 * sizeof(double)); - - for(int iter = 0; iter < 50; iter++) { - double sum = fabs(A[1]) + fabs(A[2]) + fabs(A[3]) + fabs(A[6]) + fabs(A[7]) + fabs(A[11]); - - if (sum == 0.0) - return true; - - double tresh = (iter < 3) ? 0.2 * sum / 16. : 0.0; - for(int i = 0; i < 3; i++) { - double * pAij = A + 5 * i + 1; - for(int j = i + 1 ; j < 4; j++) { - double Aij = *pAij; - double eps_machine = 100.0 * fabs(Aij); - - if ( iter > 3 && fabs(D[i]) + eps_machine == fabs(D[i]) && fabs(D[j]) + eps_machine == fabs(D[j]) ) - *pAij = 0.0; - else if (fabs(Aij) > tresh) { - double h = D[j] - D[i], t; - if (fabs(h) + eps_machine == fabs(h)) - t = Aij / h; - else { - double theta = 0.5 * h / Aij; - t = 1.0 / (fabs(theta) + sqrt(1.0 + theta * theta)); - if (theta < 0.0) t = -t; - } - - h = t * Aij; - Z[i] -= h; - Z[j] += h; - D[i] -= h; - D[j] += h; - *pAij = 0.0; - - double c = 1.0 / sqrt(1 + t * t); - double s = t * c; - double tau = s / (1.0 + c); - for(int k = 0; k <= i - 1; k++) { - double g = A[k * 4 + i], h = A[k * 4 + j]; - A[k * 4 + i] = g - s * (h + g * tau); - A[k * 4 + j] = h + s * (g - h * tau); - } - for(int k = i + 1; k <= j - 1; k++) { - double g = A[i * 4 + k], h = A[k * 4 + j]; - A[i * 4 + k] = g - s * (h + g * tau); - A[k * 4 + j] = h + s * (g - h * tau); - } - for(int k = j + 1; k < 4; k++) { - double g = A[i * 4 + k], h = A[j * 4 + k]; - A[i * 4 + k] = g - s * (h + g * tau); - A[j * 4 + k] = h + s * (g - h * tau); - } - for(int k = 0; k < 4; k++) { - double g = U[k * 4 + i], h = U[k * 4 + j]; - U[k * 4 + i] = g - s * (h + g * tau); - U[k * 4 + j] = h + s * (g - h * tau); - } - } - pAij++; - } - } - - for(int i = 0; i < 4; i++) B[i] += Z[i]; - memcpy(D, B, 4 * sizeof(double)); - memset(Z, 0, 4 * sizeof(double)); - } - - return false; + double B[4], Z[4]; + double Id[16] = {1., 0., 0., 0., + 0., 1., 0., 0., + 0., 0., 1., 0., + 0., 0., 0., 1.}; + + memcpy(U, Id, 16 * sizeof(double)); + + B[0] = A[0]; B[1] = A[5]; B[2] = A[10]; B[3] = A[15]; + memcpy(D, B, 4 * sizeof(double)); + memset(Z, 0, 4 * sizeof(double)); + + for(int iter = 0; iter < 50; iter++) { + double sum = fabs(A[1]) + fabs(A[2]) + fabs(A[3]) + fabs(A[6]) + fabs(A[7]) + fabs(A[11]); + + if (sum == 0.0) + return true; + + double tresh = (iter < 3) ? 0.2 * sum / 16. : 0.0; + for(int i = 0; i < 3; i++) { + double * pAij = A + 5 * i + 1; + for(int j = i + 1 ; j < 4; j++) { + double Aij = *pAij; + double eps_machine = 100.0 * fabs(Aij); + + if ( iter > 3 && fabs(D[i]) + eps_machine == fabs(D[i]) && fabs(D[j]) + eps_machine == fabs(D[j]) ) + *pAij = 0.0; + else if (fabs(Aij) > tresh) { + double hh = D[j] - D[i], t; + if (fabs(hh) + eps_machine == fabs(hh)) + t = Aij / hh; + else { + double theta = 0.5 * hh / Aij; + t = 1.0 / (fabs(theta) + sqrt(1.0 + theta * theta)); + if (theta < 0.0) t = -t; + } + + hh = t * Aij; + Z[i] -= hh; + Z[j] += hh; + D[i] -= hh; + D[j] += hh; + *pAij = 0.0; + + double c = 1.0 / sqrt(1 + t * t); + double s = t * c; + double tau = s / (1.0 + c); + for(int k = 0; k <= i - 1; k++) { + double g = A[k * 4 + i], h = A[k * 4 + j]; + A[k * 4 + i] = g - s * (h + g * tau); + A[k * 4 + j] = h + s * (g - h * tau); + } + for(int k = i + 1; k <= j - 1; k++) { + double g = A[i * 4 + k], h = A[k * 4 + j]; + A[i * 4 + k] = g - s * (h + g * tau); + A[k * 4 + j] = h + s * (g - h * tau); + } + for(int k = j + 1; k < 4; k++) { + double g = A[i * 4 + k], h = A[j * 4 + k]; + A[i * 4 + k] = g - s * (h + g * tau); + A[j * 4 + k] = h + s * (g - h * tau); + } + for(int k = 0; k < 4; k++) { + double g = U[k * 4 + i], h = U[k * 4 + j]; + U[k * 4 + i] = g - s * (h + g * tau); + U[k * 4 + j] = h + s * (g - h * tau); + } + } + pAij++; + } + } + + for(int i = 0; i < 4; i++) B[i] += Z[i]; + memcpy(D, B, 4 * sizeof(double)); + memset(Z, 0, 4 * sizeof(double)); + } + + return false; } diff --git a/modules/calib3d/src/precomp.hpp b/modules/calib3d/src/precomp.hpp index 50b4d40..9b1f433 100644 --- a/modules/calib3d/src/precomp.hpp +++ b/modules/calib3d/src/precomp.hpp @@ -42,11 +42,7 @@ #ifndef __OPENCV_PRECOMP_H__ #define __OPENCV_PRECOMP_H__ -#if _MSC_VER >= 1200 -#pragma warning( disable: 4251 4710 4711 4514 4996 ) -#endif - -#ifdef HAVE_CVCONFIG_H +#ifdef HAVE_CVCONFIG_H #include "cvconfig.h" #endif diff --git a/modules/calib3d/src/quadsubpix.cpp b/modules/calib3d/src/quadsubpix.cpp index 35257fb..2f2dae3 100644 --- a/modules/calib3d/src/quadsubpix.cpp +++ b/modules/calib3d/src/quadsubpix.cpp @@ -52,41 +52,41 @@ #undef max namespace cv { - - -void drawCircles(Mat& img, const vector& corners, const vector& radius) -{ - for(size_t i = 0; i < corners.size(); i++) - { - circle(img, corners[i], cvRound(radius[i]), CV_RGB(255, 0, 0)); - } -} - -int histQuantile(const Mat& hist, float quantile) -{ - if(hist.dims > 1) return -1; // works for 1D histograms only - - float cur_sum = 0; - float total_sum = (float)sum(hist).val[0]; - float quantile_sum = total_sum*quantile; - for(int j = 0; j < hist.size[0]; j++) - { - cur_sum += (float)hist.at(j); - if(cur_sum > quantile_sum) - { - return j; - } - } - - return hist.size[0] - 1; -} - -bool is_smaller(const std::pair& p1, const std::pair& p2) + + +// static void drawCircles(Mat& img, const vector& corners, const vector& radius) +// { +// for(size_t i = 0; i < corners.size(); i++) +// { +// circle(img, corners[i], cvRound(radius[i]), CV_RGB(255, 0, 0)); +// } +// } + +// static int histQuantile(const Mat& hist, float quantile) +// { +// if(hist.dims > 1) return -1; // works for 1D histograms only + +// float cur_sum = 0; +// float total_sum = (float)sum(hist).val[0]; +// float quantile_sum = total_sum*quantile; +// for(int j = 0; j < hist.size[0]; j++) +// { +// cur_sum += (float)hist.at(j); +// if(cur_sum > quantile_sum) +// { +// return j; +// } +// } + +// return hist.size[0] - 1; +// } + +inline bool is_smaller(const std::pair& p1, const std::pair& p2) { return p1.second < p2.second; } -void orderContours(const vector >& contours, Point2f point, vector >& order) +static void orderContours(const vector >& contours, Point2f point, vector >& order) { order.clear(); size_t i, j, n = contours.size(); @@ -101,58 +101,58 @@ void orderContours(const vector >& contours, Point2f point, vector } order.push_back(std::pair((int)i, (float)min_dist)); } - + std::sort(order.begin(), order.end(), is_smaller); } // fit second order curve to a set of 2D points -void fitCurve2Order(const vector& /*points*/, vector& /*curve*/) +inline void fitCurve2Order(const vector& /*points*/, vector& /*curve*/) { // TBD } - -void findCurvesCross(const vector& /*curve1*/, const vector& /*curve2*/, Point2f& /*cross_point*/) + +inline void findCurvesCross(const vector& /*curve1*/, const vector& /*curve2*/, Point2f& /*cross_point*/) { } - -void findLinesCrossPoint(Point2f origin1, Point2f dir1, Point2f origin2, Point2f dir2, Point2f& cross_point) + +static void findLinesCrossPoint(Point2f origin1, Point2f dir1, Point2f origin2, Point2f dir2, Point2f& cross_point) { float det = dir2.x*dir1.y - dir2.y*dir1.x; Point2f offset = origin2 - origin1; - + float alpha = (dir2.x*offset.y - dir2.y*offset.x)/det; cross_point = origin1 + dir1*alpha; } - -void findCorner(const vector& contour, Point2f point, Point2f& corner) -{ - // find the nearest point - double min_dist = std::numeric_limits::max(); - int min_idx = -1; - - // find corner idx - for(size_t i = 0; i < contour.size(); i++) - { - double dist = norm(Point2f((float)contour[i].x, (float)contour[i].y) - point); - if(dist < min_dist) - { - min_dist = dist; - min_idx = (int)i; - } - } - assert(min_idx >= 0); - - // temporary solution, have to make something more precise - corner = contour[min_idx]; - return; -} -void findCorner(const vector& contour, Point2f point, Point2f& corner) +// static void findCorner(const vector& contour, Point2f point, Point2f& corner) +// { +// // find the nearest point +// double min_dist = std::numeric_limits::max(); +// int min_idx = -1; + +// // find corner idx +// for(size_t i = 0; i < contour.size(); i++) +// { +// double dist = norm(Point2f((float)contour[i].x, (float)contour[i].y) - point); +// if(dist < min_dist) +// { +// min_dist = dist; +// min_idx = (int)i; +// } +// } +// assert(min_idx >= 0); + +// // temporary solution, have to make something more precise +// corner = contour[min_idx]; +// return; +// } + +static void findCorner(const vector& contour, Point2f point, Point2f& corner) { // find the nearest point double min_dist = std::numeric_limits::max(); int min_idx = -1; - + // find corner idx for(size_t i = 0; i < contour.size(); i++) { @@ -164,23 +164,23 @@ void findCorner(const vector& contour, Point2f point, Point2f& corner) } } assert(min_idx >= 0); - + // temporary solution, have to make something more precise corner = contour[min_idx]; return; } - -int segment_hist_max(const Mat& hist, int& low_thresh, int& high_thresh) + +static int segment_hist_max(const Mat& hist, int& low_thresh, int& high_thresh) { Mat bw; //const double max_bell_width = 20; // we expect two bells with width bounded above //const double min_bell_width = 5; // and below - + double total_sum = sum(hist).val[0]; //double thresh = total_sum/(2*max_bell_width)*0.25f; // quarter of a bar inside a bell - + // threshold(hist, bw, thresh, 255.0, CV_THRESH_BINARY); - + double quantile_sum = 0.0; //double min_quantile = 0.2; double low_sum = 0; @@ -193,7 +193,7 @@ int segment_hist_max(const Mat& hist, int& low_thresh, int& high_thresh) { quantile_sum += hist.at(x); if(quantile_sum < 0.2*total_sum) continue; - + if(quantile_sum - low_sum > out_of_bells_fraction*total_sum) { if(max_segment_length < x - start_x) @@ -207,7 +207,7 @@ int segment_hist_max(const Mat& hist, int& low_thresh, int& high_thresh) start_x = x; } } - + if(start_x == -1) { return 0; @@ -219,9 +219,9 @@ int segment_hist_max(const Mat& hist, int& low_thresh, int& high_thresh) return 1; } } - + } - + bool cv::find4QuadCornerSubpix(InputArray _img, InputOutputArray _corners, Size region_size) { Mat img = _img.getMat(), cornersM = _corners.getMat(); @@ -232,33 +232,33 @@ bool cv::find4QuadCornerSubpix(InputArray _img, InputOutputArray _corners, Size float ranges[] = {0, 256}; const float* _ranges = ranges; Mat hist; - + #if defined(_SUBPIX_VERBOSE) vector radius; radius.assign(corners.size(), 0.0f); #endif //_SUBPIX_VERBOSE - - + + Mat black_comp, white_comp; for(int i = 0; i < ncorners; i++) - { + { int channels = 0; Rect roi(cvRound(corners[i].x - region_size.width), cvRound(corners[i].y - region_size.height), region_size.width*2 + 1, region_size.height*2 + 1); Mat img_roi = img(roi); calcHist(&img_roi, 1, &channels, Mat(), hist, 1, &nbins, &_ranges); - + #if 0 int black_thresh = histQuantile(hist, 0.45f); int white_thresh = histQuantile(hist, 0.55f); #else - int black_thresh, white_thresh; + int black_thresh = 0, white_thresh = 0; segment_hist_max(hist, black_thresh, white_thresh); #endif - + threshold(img, black_comp, black_thresh, 255.0, CV_THRESH_BINARY_INV); threshold(img, white_comp, white_thresh, 255.0, CV_THRESH_BINARY); - + const int erode_count = 1; erode(black_comp, black_comp, Mat(), Point(-1, -1), erode_count); erode(white_comp, white_comp, Mat(), Point(-1, -1), erode_count); @@ -275,28 +275,28 @@ bool cv::find4QuadCornerSubpix(InputArray _img, InputOutputArray _corners, Size imwrite("black.jpg", black_comp); imwrite("white.jpg", white_comp); #endif - - + + vector > white_contours, black_contours; vector white_hierarchy, black_hierarchy; findContours(black_comp, black_contours, black_hierarchy, CV_RETR_LIST, CV_CHAIN_APPROX_SIMPLE); findContours(white_comp, white_contours, white_hierarchy, CV_RETR_LIST, CV_CHAIN_APPROX_SIMPLE); - + if(black_contours.size() < 5 || white_contours.size() < 5) continue; - + // find two white and black blobs that are close to the input point vector > white_order, black_order; orderContours(black_contours, corners[i], black_order); orderContours(white_contours, corners[i], white_order); const float max_dist = 10.0f; - if(black_order[0].second > max_dist || black_order[1].second > max_dist || + if(black_order[0].second > max_dist || black_order[1].second > max_dist || white_order[0].second > max_dist || white_order[1].second > max_dist) { continue; // there will be no improvement in this corner position } - - const vector* quads[4] = {&black_contours[black_order[0].first], &black_contours[black_order[1].first], + + const vector* quads[4] = {&black_contours[black_order[0].first], &black_contours[black_order[1].first], &white_contours[white_order[0].first], &white_contours[white_order[1].first]}; vector quads_approx[4]; Point2f quad_corners[4]; @@ -306,14 +306,14 @@ bool cv::find4QuadCornerSubpix(InputArray _img, InputOutputArray _corners, Size vector temp; for(size_t j = 0; j < quads[k]->size(); j++) temp.push_back((*quads[k])[j]); approxPolyDP(Mat(temp), quads_approx[k], 0.5, true); - + findCorner(quads_approx[k], corners[i], quad_corners[k]); #else findCorner(*quads[k], corners[i], quad_corners[k]); #endif quad_corners[k] += Point2f(0.5f, 0.5f); } - + // cross two lines Point2f origin1 = quad_corners[0]; Point2f dir1 = quad_corners[1] - quad_corners[0]; @@ -321,12 +321,12 @@ bool cv::find4QuadCornerSubpix(InputArray _img, InputOutputArray _corners, Size Point2f dir2 = quad_corners[3] - quad_corners[2]; double angle = acos(dir1.dot(dir2)/(norm(dir1)*norm(dir2))); if(cvIsNaN(angle) || cvIsInf(angle) || angle < 0.5 || angle > CV_PI - 0.5) continue; - + findLinesCrossPoint(origin1, dir1, origin2, dir2, corners[i]); - + #if defined(_SUBPIX_VERBOSE) radius[i] = norm(corners[i] - ground_truth_corners[ground_truth_idx])*6; - + #if 1 Mat test(img.size(), CV_32FC3); cvtColor(img, test, CV_GRAY2RGB); @@ -349,9 +349,9 @@ bool cv::find4QuadCornerSubpix(InputArray _img, InputOutputArray _corners, Size waitKey(0); #endif #endif //_SUBPIX_VERBOSE - + } - + #if defined(_SUBPIX_VERBOSE) Mat test(img.size(), CV_32FC3); cvtColor(img, test, CV_GRAY2RGB); @@ -361,6 +361,6 @@ bool cv::find4QuadCornerSubpix(InputArray _img, InputOutputArray _corners, Size imshow("corners", test); waitKey(); #endif //_SUBPIX_VERBOSE - + return true; } diff --git a/modules/calib3d/src/solvepnp.cpp b/modules/calib3d/src/solvepnp.cpp index 9d0f621..125d7a9 100644 --- a/modules/calib3d/src/solvepnp.cpp +++ b/modules/calib3d/src/solvepnp.cpp @@ -52,48 +52,48 @@ bool cv::solvePnP( InputArray _opoints, InputArray _ipoints, { Mat opoints = _opoints.getMat(), ipoints = _ipoints.getMat(); int npoints = std::max(opoints.checkVector(3, CV_32F), opoints.checkVector(3, CV_64F)); - CV_Assert( npoints >= 0 && npoints == std::max(ipoints.checkVector(2, CV_32F), ipoints.checkVector(2, CV_64F)) ); + CV_Assert( npoints >= 0 && npoints == std::max(ipoints.checkVector(2, CV_32F), ipoints.checkVector(2, CV_64F)) ); _rvec.create(3, 1, CV_64F); _tvec.create(3, 1, CV_64F); Mat cameraMatrix = _cameraMatrix.getMat(), distCoeffs = _distCoeffs.getMat(); if (flags == CV_EPNP) { - cv::Mat undistortedPoints; - cv::undistortPoints(ipoints, undistortedPoints, cameraMatrix, distCoeffs); - epnp PnP(cameraMatrix, opoints, undistortedPoints); - + cv::Mat undistortedPoints; + cv::undistortPoints(ipoints, undistortedPoints, cameraMatrix, distCoeffs); + epnp PnP(cameraMatrix, opoints, undistortedPoints); + cv::Mat R, rvec = _rvec.getMat(), tvec = _tvec.getMat(); PnP.compute_pose(R, tvec); cv::Rodrigues(R, rvec); - return true; - } - else if (flags == CV_P3P) - { - CV_Assert( npoints == 4); - cv::Mat undistortedPoints; - cv::undistortPoints(ipoints, undistortedPoints, cameraMatrix, distCoeffs); - p3p P3Psolver(cameraMatrix); + return true; + } + else if (flags == CV_P3P) + { + CV_Assert( npoints == 4); + cv::Mat undistortedPoints; + cv::undistortPoints(ipoints, undistortedPoints, cameraMatrix, distCoeffs); + p3p P3Psolver(cameraMatrix); cv::Mat R, rvec = _rvec.getMat(), tvec = _tvec.getMat(); bool result = P3Psolver.solve(R, tvec, opoints, undistortedPoints); if (result) - cv::Rodrigues(R, rvec); - return result; - } - else if (flags == CV_ITERATIVE) - { - CvMat c_objectPoints = opoints, c_imagePoints = ipoints; - CvMat c_cameraMatrix = cameraMatrix, c_distCoeffs = distCoeffs; - CvMat c_rvec = _rvec.getMat(), c_tvec = _tvec.getMat(); - cvFindExtrinsicCameraParams2(&c_objectPoints, &c_imagePoints, &c_cameraMatrix, - c_distCoeffs.rows*c_distCoeffs.cols ? &c_distCoeffs : 0, - &c_rvec, &c_tvec, useExtrinsicGuess ); - return true; - } - else + cv::Rodrigues(R, rvec); + return result; + } + else if (flags == CV_ITERATIVE) + { + CvMat c_objectPoints = opoints, c_imagePoints = ipoints; + CvMat c_cameraMatrix = cameraMatrix, c_distCoeffs = distCoeffs; + CvMat c_rvec = _rvec.getMat(), c_tvec = _tvec.getMat(); + cvFindExtrinsicCameraParams2(&c_objectPoints, &c_imagePoints, &c_cameraMatrix, + c_distCoeffs.rows*c_distCoeffs.cols ? &c_distCoeffs : 0, + &c_rvec, &c_tvec, useExtrinsicGuess ); + return true; + } + else CV_Error(CV_StsBadArg, "The flags argument must be one of CV_ITERATIVE or CV_EPNP"); - return false; + return false; } namespace cv @@ -101,8 +101,8 @@ namespace cv namespace pnpransac { const int MIN_POINTS_COUNT = 4; - - void project3dPoints(const Mat& points, const Mat& rvec, const Mat& tvec, Mat& modif_points) + + static void project3dPoints(const Mat& points, const Mat& rvec, const Mat& tvec, Mat& modif_points) { modif_points.create(1, points.cols, CV_32FC3); Mat R(3, 3, CV_64FC1); @@ -114,32 +114,32 @@ namespace cv tvec.copyTo(t); transform(points, modif_points, transformation); } - + class Mutex { public: Mutex() { - } + } void lock() { #ifdef HAVE_TBB - resultsMutex.lock(); + resultsMutex.lock(); #endif } - + void unlock() { #ifdef HAVE_TBB resultsMutex.unlock(); #endif } - + private: #ifdef HAVE_TBB tbb::mutex resultsMutex; #endif }; - + struct CameraParameters { void init(Mat _intrinsics, Mat _distCoeffs) @@ -147,22 +147,22 @@ namespace cv _intrinsics.copyTo(intrinsics); _distCoeffs.copyTo(distortion); } - + Mat intrinsics; Mat distortion; }; - + struct Parameters { int iterationsCount; float reprojectionError; int minInliersCount; bool useExtrinsicGuess; - int flags; + int flags; CameraParameters camera; }; - - void pnpTask(const vector& pointsMask, const Mat& objectPoints, const Mat& imagePoints, + + static void pnpTask(const vector& pointsMask, const Mat& objectPoints, const Mat& imagePoints, const Parameters& params, vector& inliers, Mat& rvec, Mat& tvec, const Mat& rvecInit, const Mat& tvecInit, Mutex& resultsMutex) { @@ -178,7 +178,7 @@ namespace cv colIndex = colIndex+1; } } - + //filter same 3d points, hang in solvePnP double eps = 1e-10; int num_same_points = 0; @@ -190,22 +190,22 @@ namespace cv } if (num_same_points > 0) return; - + Mat localRvec, localTvec; rvecInit.copyTo(localRvec); tvecInit.copyTo(localTvec); - - solvePnP(modelObjectPoints, modelImagePoints, params.camera.intrinsics, params.camera.distortion, localRvec, localTvec, - params.useExtrinsicGuess, params.flags); - - + + solvePnP(modelObjectPoints, modelImagePoints, params.camera.intrinsics, params.camera.distortion, localRvec, localTvec, + params.useExtrinsicGuess, params.flags); + + vector projected_points; projected_points.resize(objectPoints.cols); projectPoints(objectPoints, localRvec, localTvec, params.camera.intrinsics, params.camera.distortion, projected_points); - + Mat rotatedPoints; project3dPoints(objectPoints, localRvec, localTvec, rotatedPoints); - + vector localInliers; for (int i = 0; i < objectPoints.cols; i++) { @@ -216,21 +216,21 @@ namespace cv localInliers.push_back(i); } } - + if (localInliers.size() > inliers.size()) { resultsMutex.lock(); - + inliers.clear(); inliers.resize(localInliers.size()); memcpy(&inliers[0], &localInliers[0], sizeof(int) * localInliers.size()); localRvec.copyTo(rvec); localTvec.copyTo(tvec); - + resultsMutex.unlock(); } } - + class PnPSolver { public: @@ -253,27 +253,27 @@ namespace cv } } } - PnPSolver(const Mat& objectPoints, const Mat& imagePoints, const Parameters& parameters, - Mat& rvec, Mat& tvec, vector& inliers): - objectPoints(objectPoints), imagePoints(imagePoints), parameters(parameters), - rvec(rvec), tvec(tvec), inliers(inliers) + PnPSolver(const Mat& _objectPoints, const Mat& _imagePoints, const Parameters& _parameters, + Mat& _rvec, Mat& _tvec, vector& _inliers): + objectPoints(_objectPoints), imagePoints(_imagePoints), parameters(_parameters), + rvec(_rvec), tvec(_tvec), inliers(_inliers) { rvec.copyTo(initRvec); tvec.copyTo(initTvec); } private: - PnPSolver& operator=(const PnPSolver&); - + PnPSolver& operator=(const PnPSolver&); + const Mat& objectPoints; const Mat& imagePoints; const Parameters& parameters; Mat &rvec, &tvec; vector& inliers; Mat initRvec, initTvec; - + static RNG generator; static Mutex syncMutex; - + void generateVar(vector& mask) const { int size = (int)mask.size(); @@ -287,10 +287,10 @@ namespace cv } } }; - + Mutex PnPSolver::syncMutex; RNG PnPSolver::generator; - + } } @@ -302,21 +302,21 @@ void cv::solvePnPRansac(InputArray _opoints, InputArray _ipoints, { Mat opoints = _opoints.getMat(), ipoints = _ipoints.getMat(); Mat cameraMatrix = _cameraMatrix.getMat(), distCoeffs = _distCoeffs.getMat(); - + CV_Assert(opoints.isContinuous()); CV_Assert(opoints.depth() == CV_32F); CV_Assert((opoints.rows == 1 && opoints.channels() == 3) || opoints.cols*opoints.channels() == 3); CV_Assert(ipoints.isContinuous()); CV_Assert(ipoints.depth() == CV_32F); CV_Assert((ipoints.rows == 1 && ipoints.channels() == 2) || ipoints.cols*ipoints.channels() == 2); - + _rvec.create(3, 1, CV_64FC1); _tvec.create(3, 1, CV_64FC1); Mat rvec = _rvec.getMat(); Mat tvec = _tvec.getMat(); - + Mat objectPoints = opoints.reshape(3, 1), imagePoints = ipoints.reshape(2, 1); - + if (minInliersCount <= 0) minInliersCount = objectPoints.cols; cv::pnpransac::Parameters params; @@ -325,36 +325,36 @@ void cv::solvePnPRansac(InputArray _opoints, InputArray _ipoints, params.reprojectionError = reprojectionError; params.useExtrinsicGuess = useExtrinsicGuess; params.camera.init(cameraMatrix, distCoeffs); - params.flags = flags; - + params.flags = flags; + vector localInliers; Mat localRvec, localTvec; rvec.copyTo(localRvec); tvec.copyTo(localTvec); - + if (objectPoints.cols >= pnpransac::MIN_POINTS_COUNT) { parallel_for(BlockedRange(0,iterationsCount), cv::pnpransac::PnPSolver(objectPoints, imagePoints, params, localRvec, localTvec, localInliers)); } - + if (localInliers.size() >= (size_t)pnpransac::MIN_POINTS_COUNT) { - if (flags != CV_P3P) - { - int i, pointsCount = (int)localInliers.size(); - Mat inlierObjectPoints(1, pointsCount, CV_32FC3), inlierImagePoints(1, pointsCount, CV_32FC2); - for (i = 0; i < pointsCount; i++) - { - int index = localInliers[i]; - Mat colInlierImagePoints = inlierImagePoints(Rect(i, 0, 1, 1)); - imagePoints.col(index).copyTo(colInlierImagePoints); - Mat colInlierObjectPoints = inlierObjectPoints(Rect(i, 0, 1, 1)); - objectPoints.col(index).copyTo(colInlierObjectPoints); - } - solvePnP(inlierObjectPoints, inlierImagePoints, params.camera.intrinsics, params.camera.distortion, localRvec, localTvec, true, flags); - } - localRvec.copyTo(rvec); + if (flags != CV_P3P) + { + int i, pointsCount = (int)localInliers.size(); + Mat inlierObjectPoints(1, pointsCount, CV_32FC3), inlierImagePoints(1, pointsCount, CV_32FC2); + for (i = 0; i < pointsCount; i++) + { + int index = localInliers[i]; + Mat colInlierImagePoints = inlierImagePoints(Rect(i, 0, 1, 1)); + imagePoints.col(index).copyTo(colInlierImagePoints); + Mat colInlierObjectPoints = inlierObjectPoints(Rect(i, 0, 1, 1)); + objectPoints.col(index).copyTo(colInlierObjectPoints); + } + solvePnP(inlierObjectPoints, inlierImagePoints, params.camera.intrinsics, params.camera.distortion, localRvec, localTvec, true, flags); + } + localRvec.copyTo(rvec); localTvec.copyTo(tvec); if (_inliers.needed()) Mat(localInliers).copyTo(_inliers); diff --git a/modules/calib3d/src/stereobm.cpp b/modules/calib3d/src/stereobm.cpp index 5a0b6b7..b69fa23 100644 --- a/modules/calib3d/src/stereobm.cpp +++ b/modules/calib3d/src/stereobm.cpp @@ -155,7 +155,7 @@ static void prefilterNorm( const Mat& src, Mat& dst, int winsize, int ftzero, uc val = ((curr[x]*4 + curr[x-1] + curr[x+1] + prev[x] + next[x])*scale_g - sum*scale_s) >> 10; dptr[x] = tab[val + OFS]; } - + sum += vsum[x+wsz2] - vsum[x-wsz2-1]; val = ((curr[x]*5 + curr[x-1] + prev[x] + next[x])*scale_g - sum*scale_s) >> 10; dptr[x] = tab[val + OFS]; @@ -170,15 +170,15 @@ prefilterXSobel( const Mat& src, Mat& dst, int ftzero ) const int OFS = 256*4, TABSZ = OFS*2 + 256; uchar tab[TABSZ]; Size size = src.size(); - + for( x = 0; x < TABSZ; x++ ) tab[x] = (uchar)(x - OFS < -ftzero ? 0 : x - OFS > ftzero ? ftzero*2 : x - OFS + ftzero); uchar val0 = tab[0 + OFS]; - + #if CV_SSE2 volatile bool useSIMD = checkHardwareSupport(CV_CPU_SSE2); #endif - + for( y = 0; y < size.height-1; y += 2 ) { const uchar* srow1 = src.ptr(y); @@ -187,10 +187,10 @@ prefilterXSobel( const Mat& src, Mat& dst, int ftzero ) const uchar* srow3 = y < size.height-2 ? srow1 + src.step*2 : srow1; uchar* dptr0 = dst.ptr(y); uchar* dptr1 = dptr0 + dst.step; - + dptr0[0] = dptr0[size.width-1] = dptr1[0] = dptr1[size.width-1] = val0; x = 1; - + #if CV_SSE2 if( useSIMD ) { @@ -205,26 +205,26 @@ prefilterXSobel( const Mat& src, Mat& dst, int ftzero ) d0 = _mm_sub_epi16(d0, c0); d1 = _mm_sub_epi16(d1, c1); - + __m128i c2 = _mm_unpacklo_epi8(_mm_loadl_epi64((__m128i*)(srow2 + x - 1)), z); __m128i c3 = _mm_unpacklo_epi8(_mm_loadl_epi64((__m128i*)(srow2 + x - 1)), z); __m128i d2 = _mm_unpacklo_epi8(_mm_loadl_epi64((__m128i*)(srow2 + x + 1)), z); __m128i d3 = _mm_unpacklo_epi8(_mm_loadl_epi64((__m128i*)(srow2 + x + 1)), z); - + d2 = _mm_sub_epi16(d2, c2); d3 = _mm_sub_epi16(d3, c3); - + __m128i v0 = _mm_add_epi16(d0, _mm_add_epi16(d2, _mm_add_epi16(d1, d1))); __m128i v1 = _mm_add_epi16(d1, _mm_add_epi16(d3, _mm_add_epi16(d2, d2))); v0 = _mm_packus_epi16(_mm_add_epi16(v0, ftz), _mm_add_epi16(v1, ftz)); v0 = _mm_min_epu8(v0, ftz2); - + _mm_storel_epi64((__m128i*)(dptr0 + x), v0); _mm_storel_epi64((__m128i*)(dptr1 + x), _mm_unpackhi_epi64(v0, v0)); } } #endif - + for( ; x < size.width-1; x++ ) { int d0 = srow0[x+1] - srow0[x-1], d1 = srow1[x+1] - srow1[x-1], @@ -235,7 +235,7 @@ prefilterXSobel( const Mat& src, Mat& dst, int ftzero ) dptr1[x] = (uchar)v1; } } - + for( ; y < size.height; y++ ) { uchar* dptr = dst.ptr(y); @@ -336,7 +336,7 @@ static void findStereoCorrespondenceBM_SSE2( const Mat& left, const Mat& right, short* costptr = cost.data ? (short*)cost.data + lofs + x : &costbuf; int x0 = x - wsz2 - 1, x1 = x + wsz2; const uchar* cbuf_sub = cbuf0 + ((x0 + wsz2 + 1) % (wsz + 1))*cstep - dy0*ndisp; - uchar* cbuf = cbuf0 + ((x1 + wsz2 + 1) % (wsz + 1))*cstep - dy0*ndisp; + cbuf = cbuf0 + ((x1 + wsz2 + 1) % (wsz + 1))*cstep - dy0*ndisp; hsad = hsad0 - dy0*ndisp; lptr_sub = lptr0 + MIN(MAX(x0, -lofs), width-1-lofs) - dy0*sstep; lptr = lptr0 + MIN(MAX(x1, -lofs), width-1-lofs) - dy0*sstep; @@ -374,7 +374,7 @@ static void findStereoCorrespondenceBM_SSE2( const Mat& left, const Mat& right, // initialize sums for( d = 0; d < ndisp; d++ ) sad[d] = (ushort)(hsad0[d-ndisp*dy0]*(wsz2 + 2 - dy0)); - + hsad = hsad0 + (1 - dy0)*ndisp; for( y = 1 - dy0; y < wsz2; y++, hsad += ndisp ) for( d = 0; d < ndisp; d += 16 ) @@ -405,28 +405,28 @@ static void findStereoCorrespondenceBM_SSE2( const Mat& left, const Mat& right, { __m128i u0 = _mm_load_si128((__m128i*)(hsad_sub + d)); __m128i u1 = _mm_load_si128((__m128i*)(hsad + d)); - + __m128i v0 = _mm_load_si128((__m128i*)(hsad_sub + d + 8)); __m128i v1 = _mm_load_si128((__m128i*)(hsad + d + 8)); - + __m128i usad8 = _mm_load_si128((__m128i*)(sad + d)); __m128i vsad8 = _mm_load_si128((__m128i*)(sad + d + 8)); - + u1 = _mm_sub_epi16(u1, u0); v1 = _mm_sub_epi16(v1, v0); usad8 = _mm_add_epi16(usad8, u1); vsad8 = _mm_add_epi16(vsad8, v1); - + mask = _mm_cmpgt_epi16(minsad8, usad8); minsad8 = _mm_min_epi16(minsad8, usad8); mind8 = _mm_max_epi16(mind8, _mm_and_si128(mask, d8)); - + _mm_store_si128((__m128i*)(sad + d), usad8); _mm_store_si128((__m128i*)(sad + d + 8), vsad8); - + mask = _mm_cmpgt_epi16(minsad8, vsad8); minsad8 = _mm_min_epi16(minsad8, vsad8); - + d8 = _mm_add_epi16(d8, dd_8); mind8 = _mm_max_epi16(mind8, _mm_and_si128(mask, d8)); d8 = _mm_add_epi16(d8, dd_8); @@ -438,32 +438,33 @@ static void findStereoCorrespondenceBM_SSE2( const Mat& left, const Mat& right, dptr[y*dstep] = FILTERED; continue; } - + __m128i minsad82 = _mm_unpackhi_epi64(minsad8, minsad8); __m128i mind82 = _mm_unpackhi_epi64(mind8, mind8); mask = _mm_cmpgt_epi16(minsad8, minsad82); mind8 = _mm_xor_si128(mind8,_mm_and_si128(_mm_xor_si128(mind82,mind8),mask)); minsad8 = _mm_min_epi16(minsad8, minsad82); - + minsad82 = _mm_shufflelo_epi16(minsad8, _MM_SHUFFLE(3,2,3,2)); mind82 = _mm_shufflelo_epi16(mind8, _MM_SHUFFLE(3,2,3,2)); mask = _mm_cmpgt_epi16(minsad8, minsad82); mind8 = _mm_xor_si128(mind8,_mm_and_si128(_mm_xor_si128(mind82,mind8),mask)); minsad8 = _mm_min_epi16(minsad8, minsad82); - + minsad82 = _mm_shufflelo_epi16(minsad8, 1); mind82 = _mm_shufflelo_epi16(mind8, 1); mask = _mm_cmpgt_epi16(minsad8, minsad82); mind8 = _mm_xor_si128(mind8,_mm_and_si128(_mm_xor_si128(mind82,mind8),mask)); mind = (short)_mm_cvtsi128_si32(mind8); minsad = sad[mind]; - + if( uniquenessRatio > 0 ) { int thresh = minsad + ((minsad * uniquenessRatio) >> 8); __m128i thresh8 = _mm_set1_epi16((short)(thresh + 1)); __m128i d1 = _mm_set1_epi16((short)(mind-1)), d2 = _mm_set1_epi16((short)(mind+1)); - __m128i dd_16 = _mm_add_epi16(dd_8, dd_8), d8 = _mm_sub_epi16(d0_8, dd_16); + __m128i dd_16 = _mm_add_epi16(dd_8, dd_8); + d8 = _mm_sub_epi16(d0_8, dd_16); for( d = 0; d < ndisp; d += 16 ) { @@ -492,7 +493,8 @@ static void findStereoCorrespondenceBM_SSE2( const Mat& left, const Mat& right, if( 0 < mind && mind < ndisp - 1 ) { - int p = sad[mind+1], n = sad[mind-1], d = p + n - 2*sad[mind] + std::abs(p - n); + int p = sad[mind+1], n = sad[mind-1]; + d = p + n - 2*sad[mind] + std::abs(p - n); dptr[y*dstep] = (short)(((ndisp - mind - 1 + mindisp)*256 + (d != 0 ? (p-n)*256/d : 0) + 15) >> 4); } else @@ -567,7 +569,7 @@ findStereoCorrespondenceBM( const Mat& left, const Mat& right, htext[y] += tab[lval]; } } - + // initialize the left and right borders of the disparity map for( y = 0; y < height; y++ ) { @@ -583,7 +585,7 @@ findStereoCorrespondenceBM( const Mat& left, const Mat& right, int* costptr = cost.data ? (int*)cost.data + lofs + x : &costbuf; int x0 = x - wsz2 - 1, x1 = x + wsz2; const uchar* cbuf_sub = cbuf0 + ((x0 + wsz2 + 1) % (wsz + 1))*cstep - dy0*ndisp; - uchar* cbuf = cbuf0 + ((x1 + wsz2 + 1) % (wsz + 1))*cstep - dy0*ndisp; + cbuf = cbuf0 + ((x1 + wsz2 + 1) % (wsz + 1))*cstep - dy0*ndisp; hsad = hsad0 - dy0*ndisp; lptr_sub = lptr0 + MIN(MAX(x0, -lofs), width-1-lofs) - dy0*sstep; lptr = lptr0 + MIN(MAX(x1, -lofs), width-1-lofs) - dy0*sstep; @@ -611,7 +613,7 @@ findStereoCorrespondenceBM( const Mat& left, const Mat& right, // initialize sums for( d = 0; d < ndisp; d++ ) sad[d] = (int)(hsad0[d-ndisp*dy0]*(wsz2 + 2 - dy0)); - + hsad = hsad0 + (1 - dy0)*ndisp; for( y = 1 - dy0; y < wsz2; y++, hsad += ndisp ) for( d = 0; d < ndisp; d++ ) @@ -662,7 +664,8 @@ findStereoCorrespondenceBM( const Mat& left, const Mat& right, { sad[-1] = sad[1]; sad[ndisp] = sad[ndisp-2]; - int p = sad[mind+1], n = sad[mind-1], d = p + n - 2*sad[mind] + std::abs(p - n); + int p = sad[mind+1], n = sad[mind-1]; + d = p + n - 2*sad[mind] + std::abs(p - n); dptr[y*dstep] = (short)(((ndisp - mind - 1 + mindisp)*256 + (d != 0 ? (p-n)*256/d : 0) + 15) >> 4); costptr[y*coststep] = sad[mind]; } @@ -681,16 +684,16 @@ struct PrefilterInvoker state = _state; } - void operator()( int ind ) const + void operator()( int ind ) const { if( state->preFilterType == CV_STEREO_BM_NORMALIZED_RESPONSE ) prefilterNorm( *imgs0[ind], *imgs[ind], state->preFilterSize, state->preFilterCap, buf[ind] ); else - prefilterXSobel( *imgs0[ind], *imgs[ind], state->preFilterCap ); + prefilterXSobel( *imgs0[ind], *imgs[ind], state->preFilterCap ); } - + const Mat* imgs0[2]; - Mat* imgs[2]; + Mat* imgs[2]; uchar* buf[2]; CvStereoBMState *state; }; @@ -709,21 +712,21 @@ struct FindStereoCorrespInvoker useShorts = _useShorts; validDisparityRect = _validDisparityRect; } - - void operator()( const BlockedRange& range ) const + + void operator()( const BlockedRange& range ) const { int cols = left->cols, rows = left->rows; int _row0 = min(cvRound(range.begin() * rows / nstripes), rows); int _row1 = min(cvRound(range.end() * rows / nstripes), rows); uchar *ptr = state->slidingSumBuf->data.ptr + range.begin() * stripeBufSize; int FILTERED = (state->minDisparity - 1)*16; - + Rect roi = validDisparityRect & Rect(0, _row0, cols, _row1 - _row0); if( roi.height == 0 ) return; int row0 = roi.y; int row1 = roi.y + roi.height; - + Mat part; if( row0 > _row0 ) { @@ -735,22 +738,22 @@ struct FindStereoCorrespInvoker part = disp->rowRange(row1, _row1); part = Scalar::all(FILTERED); } - + Mat left_i = left->rowRange(row0, row1); Mat right_i = right->rowRange(row0, row1); Mat disp_i = disp->rowRange(row0, row1); Mat cost_i = state->disp12MaxDiff >= 0 ? Mat(state->cost).rowRange(row0, row1) : Mat(); - -#if CV_SSE2 + +#if CV_SSE2 if( useShorts ) findStereoCorrespondenceBM_SSE2( left_i, right_i, disp_i, cost_i, *state, ptr, row0, rows - row1 ); else -#endif +#endif findStereoCorrespondenceBM( left_i, right_i, disp_i, cost_i, *state, ptr, row0, rows - row1 ); - + if( state->disp12MaxDiff >= 0 ) validateDisparity( disp_i, cost_i, state->minDisparity, state->numberOfDisparities, state->disp12MaxDiff ); - + if( roi.x > 0 ) { part = disp_i.colRange(0, roi.x); @@ -767,7 +770,7 @@ protected: const Mat *left, *right; Mat* disp; CvStereoBMState *state; - + int nstripes; int stripeBufSize; bool useShorts; @@ -775,7 +778,7 @@ protected: }; static void findStereoCorrespondenceBM( const Mat& left0, const Mat& right0, Mat& disp0, CvStereoBMState* state) -{ +{ if (left0.size() != right0.size() || disp0.size() != left0.size()) CV_Error( CV_StsUnmatchedSizes, "All the images must have the same size" ); @@ -783,7 +786,7 @@ static void findStereoCorrespondenceBM( const Mat& left0, const Mat& right0, Mat CV_Error( CV_StsUnsupportedFormat, "Both input images must have CV_8UC1" ); if (disp0.type() != CV_16SC1 && disp0.type() != CV_32FC1) - CV_Error( CV_StsUnsupportedFormat, "Disparity image must have CV_16SC1 or CV_32FC1 format" ); + CV_Error( CV_StsUnsupportedFormat, "Disparity image must have CV_16SC1 or CV_32FC1 format" ); if( !state ) CV_Error( CV_StsNullPtr, "Stereo BM state is NULL." ); @@ -809,7 +812,7 @@ static void findStereoCorrespondenceBM( const Mat& left0, const Mat& right0, Mat if( state->uniquenessRatio < 0 ) CV_Error( CV_StsOutOfRange, "uniqueness ratio must be non-negative" ); - + if( !state->preFilteredImg0 || state->preFilteredImg0->cols * state->preFilteredImg0->rows < left0.cols * left0.rows ) { cvReleaseMat( &state->preFilteredImg0 ); @@ -822,7 +825,7 @@ static void findStereoCorrespondenceBM( const Mat& left0, const Mat& right0, Mat } Mat left(left0.size(), CV_8U, state->preFilteredImg0->data.ptr); Mat right(right0.size(), CV_8U, state->preFilteredImg1->data.ptr); - + int mindisp = state->minDisparity; int ndisp = state->numberOfDisparities; @@ -832,15 +835,15 @@ static void findStereoCorrespondenceBM( const Mat& left0, const Mat& right0, Mat int rofs = -min(ndisp - 1 + mindisp, 0); int width1 = width - rofs - ndisp + 1; int FILTERED = (state->minDisparity - 1) << DISPARITY_SHIFT; - + if( lofs >= width || rofs >= width || width1 < 1 ) { - disp0 = Scalar::all( FILTERED * ( disp0.type() < CV_32F ? 1 : 1./(1 << DISPARITY_SHIFT) ) ); + disp0 = Scalar::all( FILTERED * ( disp0.type() < CV_32F ? 1 : 1./(1 << DISPARITY_SHIFT) ) ); return; } Mat disp = disp0; - + if( disp0.type() == CV_32F) { if( !state->disp || state->disp->rows != disp0.rows || state->disp->cols != disp0.cols ) @@ -850,8 +853,8 @@ static void findStereoCorrespondenceBM( const Mat& left0, const Mat& right0, Mat } disp = cv::cvarrToMat(state->disp); } - - int wsz = state->SADWindowSize; + + int wsz = state->SADWindowSize; int bufSize0 = (int)((ndisp + 2)*sizeof(int)); bufSize0 += (int)((height+wsz+2)*ndisp*sizeof(int)); bufSize0 += (int)((height + wsz + 2)*sizeof(int)); @@ -861,16 +864,16 @@ static void findStereoCorrespondenceBM( const Mat& left0, const Mat& right0, Mat int bufSize2 = 0; if( state->speckleRange >= 0 && state->speckleWindowSize > 0 ) bufSize2 = width*height*(sizeof(cv::Point_) + sizeof(int) + sizeof(uchar)); - + #if CV_SSE2 bool useShorts = state->preFilterCap <= 31 && state->SADWindowSize <= 21 && checkHardwareSupport(CV_CPU_SSE2); #else const bool useShorts = false; #endif - -#ifdef HAVE_TBB + +#ifdef HAVE_TBB const double SAD_overhead_coeff = 10.0; - double N0 = 8000000 / (useShorts ? 1 : 4); // approx tbb's min number instructions reasonable for one thread + double N0 = 8000000 / (useShorts ? 1 : 4); // approx tbb's min number instructions reasonable for one thread double maxStripeSize = min(max(N0 / (width * ndisp), (wsz-1) * SAD_overhead_coeff), (double)height); int nstripes = cvCeil(height / maxStripeSize); #else @@ -878,27 +881,27 @@ static void findStereoCorrespondenceBM( const Mat& left0, const Mat& right0, Mat #endif int bufSize = max(bufSize0 * nstripes, max(bufSize1 * 2, bufSize2)); - + if( !state->slidingSumBuf || state->slidingSumBuf->cols < bufSize ) { cvReleaseMat( &state->slidingSumBuf ); state->slidingSumBuf = cvCreateMat( 1, bufSize, CV_8U ); } - + uchar *_buf = state->slidingSumBuf->data.ptr; int idx[] = {0,1}; parallel_do(idx, idx+2, PrefilterInvoker(left0, right0, left, right, _buf, _buf + bufSize1, state)); - + Rect validDisparityRect(0, 0, width, height), R1 = state->roi1, R2 = state->roi2; validDisparityRect = getValidDisparityROI(R1.area() > 0 ? Rect(0, 0, width, height) : validDisparityRect, R2.area() > 0 ? Rect(0, 0, width, height) : validDisparityRect, state->minDisparity, state->numberOfDisparities, - state->SADWindowSize); - + state->SADWindowSize); + parallel_for(BlockedRange(0, nstripes), FindStereoCorrespInvoker(left, right, disp, state, nstripes, bufSize0, useShorts, validDisparityRect)); - + if( state->speckleRange >= 0 && state->speckleWindowSize > 0 ) { Mat buf(state->slidingSumBuf); @@ -906,7 +909,7 @@ static void findStereoCorrespondenceBM( const Mat& left0, const Mat& right0, Mat } if (disp0.data != disp.data) - disp.convertTo(disp0, disp0.type(), 1./(1 << DISPARITY_SHIFT), 0); + disp.convertTo(disp0, disp0.type(), 1./(1 << DISPARITY_SHIFT), 0); } StereoBM::StereoBM() @@ -928,13 +931,13 @@ void StereoBM::operator()( InputArray _left, InputArray _right, CV_Assert( disptype == CV_16S || disptype == CV_32F ); _disparity.create(left.size(), disptype); Mat disparity = _disparity.getMat(); - + findStereoCorrespondenceBM(left, right, disparity, state); } template<> void Ptr::delete_obj() { cvReleaseStereoBMState(&obj); } - + } CV_IMPL void cvFindStereoCorrespondenceBM( const CvArr* leftarr, const CvArr* rightarr, @@ -942,7 +945,7 @@ CV_IMPL void cvFindStereoCorrespondenceBM( const CvArr* leftarr, const CvArr* ri { cv::Mat left = cv::cvarrToMat(leftarr), right = cv::cvarrToMat(rightarr), - disp = cv::cvarrToMat(disparr); + disp = cv::cvarrToMat(disparr); cv::findStereoCorrespondenceBM(left, right, disp, state); } diff --git a/modules/calib3d/src/stereosgbm.cpp b/modules/calib3d/src/stereosgbm.cpp index c794da0..53fedef 100644 --- a/modules/calib3d/src/stereosgbm.cpp +++ b/modules/calib3d/src/stereosgbm.cpp @@ -44,17 +44,17 @@ This is a variation of "Stereo Processing by Semiglobal Matching and Mutual Information" by Heiko Hirschmuller. - + We match blocks rather than individual pixels, thus the algorithm is called SGBM (Semi-global block matching) - */ + */ #include "precomp.hpp" #include namespace cv { - + typedef uchar PixType; typedef short CostType; typedef short DispType; @@ -105,7 +105,7 @@ StereoSGBM::~StereoSGBM() row1[x] and row2[x-d]. The subpixel algorithm from "Depth Discontinuities by Pixel-to-Pixel Stereo" by Stan Birchfield and C. Tomasi is used, hence the suffix BT. - + the temporary buffer should contain width2*2 elements */ static void calcPixelCostBT( const Mat& img1, const Mat& img2, int y, @@ -119,25 +119,25 @@ static void calcPixelCostBT( const Mat& img1, const Mat& img2, int y, int D = maxD - minD, width1 = maxX1 - minX1, width2 = maxX2 - minX2; const PixType *row1 = img1.ptr(y), *row2 = img2.ptr(y); PixType *prow1 = buffer + width2*2, *prow2 = prow1 + width*cn*2; - + tab += tabOfs; - + for( c = 0; c < cn*2; c++ ) { - prow1[width*c] = prow1[width*c + width-1] = + prow1[width*c] = prow1[width*c + width-1] = prow2[width*c] = prow2[width*c + width-1] = tab[0]; } - + int n1 = y > 0 ? -(int)img1.step : 0, s1 = y < img1.rows-1 ? (int)img1.step : 0; int n2 = y > 0 ? -(int)img2.step : 0, s2 = y < img2.rows-1 ? (int)img2.step : 0; - + if( cn == 1 ) { for( x = 1; x < width-1; x++ ) { prow1[x] = tab[(row1[x+1] - row1[x-1])*2 + row1[x+n1+1] - row1[x+n1-1] + row1[x+s1+1] - row1[x+s1-1]]; prow2[width-1-x] = tab[(row2[x+1] - row2[x-1])*2 + row2[x+n2+1] - row2[x+n2-1] + row2[x+s2+1] - row2[x+s2-1]]; - + prow1[x+width] = row1[x]; prow2[width-1-x+width] = row2[x]; } @@ -149,35 +149,35 @@ static void calcPixelCostBT( const Mat& img1, const Mat& img2, int y, prow1[x] = tab[(row1[x*3+3] - row1[x*3-3])*2 + row1[x*3+n1+3] - row1[x*3+n1-3] + row1[x*3+s1+3] - row1[x*3+s1-3]]; prow1[x+width] = tab[(row1[x*3+4] - row1[x*3-2])*2 + row1[x*3+n1+4] - row1[x*3+n1-2] + row1[x*3+s1+4] - row1[x*3+s1-2]]; prow1[x+width*2] = tab[(row1[x*3+5] - row1[x*3-1])*2 + row1[x*3+n1+5] - row1[x*3+n1-1] + row1[x*3+s1+5] - row1[x*3+s1-1]]; - + prow2[width-1-x] = tab[(row2[x*3+3] - row2[x*3-3])*2 + row2[x*3+n2+3] - row2[x*3+n2-3] + row2[x*3+s2+3] - row2[x*3+s2-3]]; prow2[width-1-x+width] = tab[(row2[x*3+4] - row2[x*3-2])*2 + row2[x*3+n2+4] - row2[x*3+n2-2] + row2[x*3+s2+4] - row2[x*3+s2-2]]; prow2[width-1-x+width*2] = tab[(row2[x*3+5] - row2[x*3-1])*2 + row2[x*3+n2+5] - row2[x*3+n2-1] + row2[x*3+s2+5] - row2[x*3+s2-1]]; - + prow1[x+width*3] = row1[x*3]; prow1[x+width*4] = row1[x*3+1]; prow1[x+width*5] = row1[x*3+2]; - + prow2[width-1-x+width*3] = row2[x*3]; prow2[width-1-x+width*4] = row2[x*3+1]; prow2[width-1-x+width*5] = row2[x*3+2]; } } - + memset( cost, 0, width1*D*sizeof(cost[0]) ); - + buffer -= minX2; cost -= minX1*D + minD; // simplify the cost indices inside the loop - -#if CV_SSE2 + +#if CV_SSE2 volatile bool useSIMD = checkHardwareSupport(CV_CPU_SSE2); #endif - -#if 1 + +#if 1 for( c = 0; c < cn*2; c++, prow1 += width, prow2 += width ) { int diff_scale = c < cn ? 0 : 2; - + // precompute // v0 = min(row2[x-1/2], row2[x], row2[x+1/2]) and // v1 = max(row2[x-1/2], row2[x], row2[x+1/2]) and @@ -191,7 +191,7 @@ static void calcPixelCostBT( const Mat& img1, const Mat& img2, int y, buffer[x] = (PixType)v0; buffer[x + width2] = (PixType)v1; } - + for( x = minX1; x < maxX1; x++ ) { int u = prow1[x]; @@ -199,14 +199,14 @@ static void calcPixelCostBT( const Mat& img1, const Mat& img2, int y, int ur = x < width-1 ? (u + prow1[x+1])/2 : u; int u0 = min(ul, ur); u0 = min(u0, u); int u1 = max(ul, ur); u1 = max(u1, u); - + #if CV_SSE2 if( useSIMD ) { __m128i _u = _mm_set1_epi8((char)u), _u0 = _mm_set1_epi8((char)u0); __m128i _u1 = _mm_set1_epi8((char)u1), z = _mm_setzero_si128(); __m128i ds = _mm_cvtsi32_si128(diff_scale); - + for( int d = minD; d < maxD; d += 16 ) { __m128i _v = _mm_loadu_si128((const __m128i*)(prow2 + width-x-1 + d)); @@ -215,10 +215,10 @@ static void calcPixelCostBT( const Mat& img1, const Mat& img2, int y, __m128i c0 = _mm_max_epu8(_mm_subs_epu8(_u, _v1), _mm_subs_epu8(_v0, _u)); __m128i c1 = _mm_max_epu8(_mm_subs_epu8(_v, _u1), _mm_subs_epu8(_u0, _v)); __m128i diff = _mm_min_epu8(c0, c1); - + c0 = _mm_load_si128((__m128i*)(cost + x*D + d)); c1 = _mm_load_si128((__m128i*)(cost + x*D + d + 8)); - + _mm_store_si128((__m128i*)(cost + x*D + d), _mm_adds_epi16(c0, _mm_srl_epi16(_mm_unpacklo_epi8(diff,z), ds))); _mm_store_si128((__m128i*)(cost + x*D + d + 8), _mm_adds_epi16(c1, _mm_srl_epi16(_mm_unpackhi_epi8(diff,z), ds))); } @@ -233,7 +233,7 @@ static void calcPixelCostBT( const Mat& img1, const Mat& img2, int y, int v1 = buffer[width-x-1 + d + width2]; int c0 = max(0, u - v1); c0 = max(c0, v0 - u); int c1 = max(0, v - u1); c1 = max(c1, u0 - v); - + cost[x*D + d] = (CostType)(cost[x*D+d] + (min(c0, c1) >> diff_scale)); } } @@ -249,14 +249,14 @@ static void calcPixelCostBT( const Mat& img1, const Mat& img2, int y, if( useSIMD ) { __m128i _u = _mm_set1_epi8(u), z = _mm_setzero_si128(); - + for( int d = minD; d < maxD; d += 16 ) { __m128i _v = _mm_loadu_si128((const __m128i*)(prow2 + width-1-x + d)); __m128i diff = _mm_adds_epu8(_mm_subs_epu8(_u,_v), _mm_subs_epu8(_v,_u)); __m128i c0 = _mm_load_si128((__m128i*)(cost + x*D + d)); __m128i c1 = _mm_load_si128((__m128i*)(cost + x*D + d + 8)); - + _mm_store_si128((__m128i*)(cost + x*D + d), _mm_adds_epi16(c0, _mm_unpacklo_epi8(diff,z))); _mm_store_si128((__m128i*)(cost + x*D + d + 8), _mm_adds_epi16(c1, _mm_unpackhi_epi8(diff,z))); } @@ -282,22 +282,22 @@ static void calcPixelCostBT( const Mat& img1, const Mat& img2, int y, minD <= d < maxD. disp2full is the reverse disparity map, that is: disp2full(x+roi.x,y+roi.y)=d means that img2(x+roi.x, y+roi.y) ~ img1(x+roi.x+d, y+roi.y) - + note that disp1buf will have the same size as the roi and disp2full will have the same size as img1 (or img2). On exit disp2buf is not the final disparity, it is an intermediate result that becomes final after all the tiles are processed. - + the disparity in disp1buf is written with sub-pixel accuracy (4 fractional bits, see CvStereoSGBM::DISP_SCALE), using quadratic interpolation, while the disparity in disp2buf is written as is, without interpolation. - + disp2cost also has the same size as img1 (or img2). It contains the minimum current cost, used to find the best disparity, corresponding to the minimal cost. - */ + */ static void computeDisparitySGBM( const Mat& img1, const Mat& img2, - Mat& disp1, const StereoSGBM& params, + Mat& disp1, const StereoSGBM& params, Mat& buffer ) { #if CV_SSE2 @@ -312,15 +312,15 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2, 6, 0, 1, 0, 2, 0, 1, 0, 3, 0, 1, 0, 2, 0, 1, 0, 4, 0, 1, 0, 2, 0, 1, 0, 3, 0, 1, 0, 2, 0, 1, 0, 5, 0, 1, 0, 2, 0, 1, 0, 3, 0, 1, 0, 2, 0, 1, 0, 4, 0, 1, 0, 2, 0, 1, 0, 3, 0, 1, 0, 2, 0, 1, 0 }; - + volatile bool useSIMD = checkHardwareSupport(CV_CPU_SSE2); -#endif - +#endif + const int ALIGN = 16; const int DISP_SHIFT = StereoSGBM::DISP_SHIFT; const int DISP_SCALE = StereoSGBM::DISP_SCALE; const CostType MAX_COST = SHRT_MAX; - + int minD = params.minDisparity, maxD = minD + params.numberOfDisparities; Size SADWindowSize; SADWindowSize.width = SADWindowSize.height = params.SADWindowSize > 0 ? params.SADWindowSize : 5; @@ -336,28 +336,28 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2, int npasses = params.fullDP ? 2 : 1; const int TAB_OFS = 256*4, TAB_SIZE = 256 + TAB_OFS*2; PixType clipTab[TAB_SIZE]; - + for( k = 0; k < TAB_SIZE; k++ ) clipTab[k] = (PixType)(min(max(k - TAB_OFS, -ftzero), ftzero) + ftzero); - + if( minX1 >= maxX1 ) { disp1 = Scalar::all(INVALID_DISP_SCALED); return; } - + CV_Assert( D % 16 == 0 ); - + // NR - the number of directions. the loop on x below that computes Lr assumes that NR == 8. // if you change NR, please, modify the loop as well. int D2 = D+16, NRD2 = NR2*D2; - + // the number of L_r(.,.) and min_k L_r(.,.) lines in the buffer: // for 8-way dynamic programming we need the current row and // the previous row, i.e. 2 rows in total const int NLR = 2; const int LrBorder = NLR - 1; - + // for each possible stereo match (img1(x,y) <=> img2(x-d,y)) // we keep pixel difference cost (C) and the summary cost over NR directions (S). // we also keep all the partial costs for the previous line L_r(x,d) and also min_k L_r(x, k) @@ -370,29 +370,29 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2, CSBufSize*2*sizeof(CostType) + // C, S width*16*img1.channels()*sizeof(PixType) + // temp buffer for computing per-pixel cost width*(sizeof(CostType) + sizeof(DispType)) + 1024; // disp2cost + disp2 - + if( !buffer.data || !buffer.isContinuous() || buffer.cols*buffer.rows*buffer.elemSize() < totalBufSize ) buffer.create(1, (int)totalBufSize, CV_8U); - + // summary cost over different (nDirs) directions CostType* Cbuf = (CostType*)alignPtr(buffer.data, ALIGN); CostType* Sbuf = Cbuf + CSBufSize; CostType* hsumBuf = Sbuf + CSBufSize; CostType* pixDiff = hsumBuf + costBufSize*hsumBufNRows; - + CostType* disp2cost = pixDiff + costBufSize + (LrSize + minLrSize)*NLR; DispType* disp2ptr = (DispType*)(disp2cost + width); PixType* tempBuf = (PixType*)(disp2ptr + width); - + // add P2 to every C(x,y). it saves a few operations in the inner loops for( k = 0; k < width1*D; k++ ) Cbuf[k] = (CostType)P2; - + for( int pass = 1; pass <= npasses; pass++ ) { int x1, y1, x2, y2, dx, dy; - + if( pass == 1 ) { y1 = 0; y2 = height; dy = 1; @@ -403,9 +403,9 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2, y1 = height-1; y2 = -1; dy = -1; x1 = width1-1; x2 = -1; dx = -1; } - + CostType *Lr[NLR]={0}, *minLr[NLR]={0}; - + for( k = 0; k < NLR; k++ ) { // shift Lr[k] and minLr[k] pointers, because we allocated them with the borders, @@ -418,26 +418,26 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2, minLr[k] = pixDiff + costBufSize + LrSize*NLR + minLrSize*k + NR2*2; memset( minLr[k] - LrBorder*NR2, 0, minLrSize*sizeof(CostType) ); } - + for( int y = y1; y != y2; y += dy ) { int x, d; DispType* disp1ptr = disp1.ptr(y); CostType* C = Cbuf + (!params.fullDP ? 0 : y*costBufSize); CostType* S = Sbuf + (!params.fullDP ? 0 : y*costBufSize); - + if( pass == 1 ) // compute C on the first pass, and reuse it on the second pass, if any. { int dy1 = y == 0 ? 0 : y + SH2, dy2 = y == 0 ? SH2 : dy1; - + for( k = dy1; k <= dy2; k++ ) { CostType* hsumAdd = hsumBuf + (min(k, height-1) % hsumBufNRows)*costBufSize; - + if( k < height ) { calcPixelCostBT( img1, img2, k, minD, maxD, pixDiff, tempBuf, clipTab, TAB_OFS, ftzero ); - + memset(hsumAdd, 0, D*sizeof(CostType)); for( x = 0; x <= SW2*D; x += D ) { @@ -445,17 +445,17 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2, for( d = 0; d < D; d++ ) hsumAdd[d] = (CostType)(hsumAdd[d] + pixDiff[x + d]*scale); } - + if( y > 0 ) { const CostType* hsumSub = hsumBuf + (max(y - SH2 - 1, 0) % hsumBufNRows)*costBufSize; const CostType* Cprev = !params.fullDP || y == 0 ? C : C - costBufSize; - + for( x = D; x < width1*D; x += D ) { const CostType* pixAdd = pixDiff + min(x + SW2*D, (width1-1)*D); const CostType* pixSub = pixDiff + max(x - (SW2+1)*D, 0); - + #if CV_SSE2 if( useSIMD ) { @@ -490,13 +490,13 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2, { const CostType* pixAdd = pixDiff + min(x + SW2*D, (width1-1)*D); const CostType* pixSub = pixDiff + max(x - (SW2+1)*D, 0); - + for( d = 0; d < D; d++ ) hsumAdd[x + d] = (CostType)(hsumAdd[x - D + d] + pixAdd[d] - pixSub[d]); } } } - + if( y == 0 ) { int scale = k == 0 ? SH2 + 1 : 1; @@ -504,18 +504,18 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2, C[x] = (CostType)(C[x] + hsumAdd[x]*scale); } } - + // also, clear the S buffer for( k = 0; k < width1*D; k++ ) S[k] = 0; } - + // clear the left and the right borders memset( Lr[0] - NRD2*LrBorder - 8, 0, NRD2*LrBorder*sizeof(CostType) ); memset( Lr[0] + width1*NRD2 - 8, 0, NRD2*LrBorder*sizeof(CostType) ); memset( minLr[0] - NR2*LrBorder, 0, NR2*LrBorder*sizeof(CostType) ); memset( minLr[0] + width1*NR2, 0, NR2*LrBorder*sizeof(CostType) ); - + /* [formula 13 in the paper] compute L_r(p, d) = C(p, d) + @@ -537,87 +537,87 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2, for( x = x1; x != x2; x += dx ) { int xm = x*NR2, xd = xm*D2; - + int delta0 = minLr[0][xm - dx*NR2] + P2, delta1 = minLr[1][xm - NR2 + 1] + P2; int delta2 = minLr[1][xm + 2] + P2, delta3 = minLr[1][xm + NR2 + 3] + P2; - + CostType* Lr_p0 = Lr[0] + xd - dx*NRD2; CostType* Lr_p1 = Lr[1] + xd - NRD2 + D2; CostType* Lr_p2 = Lr[1] + xd + D2*2; CostType* Lr_p3 = Lr[1] + xd + NRD2 + D2*3; - + Lr_p0[-1] = Lr_p0[D] = Lr_p1[-1] = Lr_p1[D] = Lr_p2[-1] = Lr_p2[D] = Lr_p3[-1] = Lr_p3[D] = MAX_COST; - + CostType* Lr_p = Lr[0] + xd; const CostType* Cp = C + x*D; CostType* Sp = S + x*D; - + #if CV_SSE2 if( useSIMD ) { __m128i _P1 = _mm_set1_epi16((short)P1); - + __m128i _delta0 = _mm_set1_epi16((short)delta0); __m128i _delta1 = _mm_set1_epi16((short)delta1); __m128i _delta2 = _mm_set1_epi16((short)delta2); __m128i _delta3 = _mm_set1_epi16((short)delta3); __m128i _minL0 = _mm_set1_epi16((short)MAX_COST); - + for( d = 0; d < D; d += 8 ) { __m128i Cpd = _mm_load_si128((const __m128i*)(Cp + d)); __m128i L0, L1, L2, L3; - + L0 = _mm_load_si128((const __m128i*)(Lr_p0 + d)); L1 = _mm_load_si128((const __m128i*)(Lr_p1 + d)); L2 = _mm_load_si128((const __m128i*)(Lr_p2 + d)); L3 = _mm_load_si128((const __m128i*)(Lr_p3 + d)); - + L0 = _mm_min_epi16(L0, _mm_adds_epi16(_mm_loadu_si128((const __m128i*)(Lr_p0 + d - 1)), _P1)); L0 = _mm_min_epi16(L0, _mm_adds_epi16(_mm_loadu_si128((const __m128i*)(Lr_p0 + d + 1)), _P1)); - + L1 = _mm_min_epi16(L1, _mm_adds_epi16(_mm_loadu_si128((const __m128i*)(Lr_p1 + d - 1)), _P1)); L1 = _mm_min_epi16(L1, _mm_adds_epi16(_mm_loadu_si128((const __m128i*)(Lr_p1 + d + 1)), _P1)); - + L2 = _mm_min_epi16(L2, _mm_adds_epi16(_mm_loadu_si128((const __m128i*)(Lr_p2 + d - 1)), _P1)); L2 = _mm_min_epi16(L2, _mm_adds_epi16(_mm_loadu_si128((const __m128i*)(Lr_p2 + d + 1)), _P1)); - + L3 = _mm_min_epi16(L3, _mm_adds_epi16(_mm_loadu_si128((const __m128i*)(Lr_p3 + d - 1)), _P1)); L3 = _mm_min_epi16(L3, _mm_adds_epi16(_mm_loadu_si128((const __m128i*)(Lr_p3 + d + 1)), _P1)); - + L0 = _mm_min_epi16(L0, _delta0); L0 = _mm_adds_epi16(_mm_subs_epi16(L0, _delta0), Cpd); - + L1 = _mm_min_epi16(L1, _delta1); L1 = _mm_adds_epi16(_mm_subs_epi16(L1, _delta1), Cpd); - + L2 = _mm_min_epi16(L2, _delta2); L2 = _mm_adds_epi16(_mm_subs_epi16(L2, _delta2), Cpd); - + L3 = _mm_min_epi16(L3, _delta3); L3 = _mm_adds_epi16(_mm_subs_epi16(L3, _delta3), Cpd); - + _mm_store_si128( (__m128i*)(Lr_p + d), L0); _mm_store_si128( (__m128i*)(Lr_p + d + D2), L1); _mm_store_si128( (__m128i*)(Lr_p + d + D2*2), L2); _mm_store_si128( (__m128i*)(Lr_p + d + D2*3), L3); - + __m128i t0 = _mm_min_epi16(_mm_unpacklo_epi16(L0, L2), _mm_unpackhi_epi16(L0, L2)); __m128i t1 = _mm_min_epi16(_mm_unpacklo_epi16(L1, L3), _mm_unpackhi_epi16(L1, L3)); t0 = _mm_min_epi16(_mm_unpacklo_epi16(t0, t1), _mm_unpackhi_epi16(t0, t1)); _minL0 = _mm_min_epi16(_minL0, t0); - + __m128i Sval = _mm_load_si128((const __m128i*)(Sp + d)); - + L0 = _mm_adds_epi16(L0, L1); L2 = _mm_adds_epi16(L2, L3); Sval = _mm_adds_epi16(Sval, L0); Sval = _mm_adds_epi16(Sval, L2); - + _mm_store_si128((__m128i*)(Sp + d), Sval); } - + _minL0 = _mm_min_epi16(_minL0, _mm_srli_si128(_minL0, 8)); _mm_storel_epi64((__m128i*)&minLr[0][xm], _minL0); } @@ -625,28 +625,28 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2, #endif { int minL0 = MAX_COST, minL1 = MAX_COST, minL2 = MAX_COST, minL3 = MAX_COST; - + for( d = 0; d < D; d++ ) { int Cpd = Cp[d], L0, L1, L2, L3; - + L0 = Cpd + min((int)Lr_p0[d], min(Lr_p0[d-1] + P1, min(Lr_p0[d+1] + P1, delta0))) - delta0; - L1 = Cpd + min((int)Lr_p1[d], min(Lr_p1[d-1] + P1, min(Lr_p1[d+1] + P1, delta1))) - delta1; + L1 = Cpd + min((int)Lr_p1[d], min(Lr_p1[d-1] + P1, min(Lr_p1[d+1] + P1, delta1))) - delta1; L2 = Cpd + min((int)Lr_p2[d], min(Lr_p2[d-1] + P1, min(Lr_p2[d+1] + P1, delta2))) - delta2; L3 = Cpd + min((int)Lr_p3[d], min(Lr_p3[d-1] + P1, min(Lr_p3[d+1] + P1, delta3))) - delta3; - + Lr_p[d] = (CostType)L0; minL0 = min(minL0, L0); - + Lr_p[d + D2] = (CostType)L1; minL1 = min(minL1, L1); - + Lr_p[d + D2*2] = (CostType)L2; minL2 = min(minL2, L2); - + Lr_p[d + D2*3] = (CostType)L3; minL3 = min(minL3, L3); - + Sp[d] = saturate_cast(Sp[d] + L0 + L1 + L2 + L3); } minLr[0][xm] = (CostType)minL0; @@ -655,7 +655,7 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2, minLr[0][xm+3] = (CostType)minL3; } } - + if( pass == npasses ) { for( x = 0; x < width; x++ ) @@ -663,73 +663,73 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2, disp1ptr[x] = disp2ptr[x] = (DispType)INVALID_DISP_SCALED; disp2cost[x] = MAX_COST; } - + for( x = width1 - 1; x >= 0; x-- ) { CostType* Sp = S + x*D; int minS = MAX_COST, bestDisp = -1; - + if( npasses == 1 ) { int xm = x*NR2, xd = xm*D2; - + int minL0 = MAX_COST; int delta0 = minLr[0][xm + NR2] + P2; CostType* Lr_p0 = Lr[0] + xd + NRD2; Lr_p0[-1] = Lr_p0[D] = MAX_COST; CostType* Lr_p = Lr[0] + xd; - + const CostType* Cp = C + x*D; - + #if CV_SSE2 if( useSIMD ) { __m128i _P1 = _mm_set1_epi16((short)P1); __m128i _delta0 = _mm_set1_epi16((short)delta0); - + __m128i _minL0 = _mm_set1_epi16((short)minL0); __m128i _minS = _mm_set1_epi16(MAX_COST), _bestDisp = _mm_set1_epi16(-1); __m128i _d8 = _mm_setr_epi16(0, 1, 2, 3, 4, 5, 6, 7), _8 = _mm_set1_epi16(8); - + for( d = 0; d < D; d += 8 ) { __m128i Cpd = _mm_load_si128((const __m128i*)(Cp + d)), L0; - + L0 = _mm_load_si128((const __m128i*)(Lr_p0 + d)); L0 = _mm_min_epi16(L0, _mm_adds_epi16(_mm_loadu_si128((const __m128i*)(Lr_p0 + d - 1)), _P1)); L0 = _mm_min_epi16(L0, _mm_adds_epi16(_mm_loadu_si128((const __m128i*)(Lr_p0 + d + 1)), _P1)); L0 = _mm_min_epi16(L0, _delta0); L0 = _mm_adds_epi16(_mm_subs_epi16(L0, _delta0), Cpd); - + _mm_store_si128((__m128i*)(Lr_p + d), L0); _minL0 = _mm_min_epi16(_minL0, L0); L0 = _mm_adds_epi16(L0, *(__m128i*)(Sp + d)); _mm_store_si128((__m128i*)(Sp + d), L0); - + __m128i mask = _mm_cmpgt_epi16(_minS, L0); _minS = _mm_min_epi16(_minS, L0); _bestDisp = _mm_xor_si128(_bestDisp, _mm_and_si128(_mm_xor_si128(_bestDisp,_d8), mask)); _d8 = _mm_adds_epi16(_d8, _8); } - + short CV_DECL_ALIGNED(16) bestDispBuf[8]; _mm_store_si128((__m128i*)bestDispBuf, _bestDisp); - + _minL0 = _mm_min_epi16(_minL0, _mm_srli_si128(_minL0, 8)); _minL0 = _mm_min_epi16(_minL0, _mm_srli_si128(_minL0, 4)); _minL0 = _mm_min_epi16(_minL0, _mm_srli_si128(_minL0, 2)); - + __m128i qS = _mm_min_epi16(_minS, _mm_srli_si128(_minS, 8)); qS = _mm_min_epi16(qS, _mm_srli_si128(qS, 4)); qS = _mm_min_epi16(qS, _mm_srli_si128(qS, 2)); - + minLr[0][xm] = (CostType)_mm_cvtsi128_si32(_minL0); minS = (CostType)_mm_cvtsi128_si32(qS); - + qS = _mm_shuffle_epi32(_mm_unpacklo_epi16(qS, qS), 0); qS = _mm_cmpeq_epi16(_minS, qS); int idx = _mm_movemask_epi8(_mm_packs_epi16(qS, qS)) & 255; - + bestDisp = bestDispBuf[LSBTab[idx]]; } else @@ -738,10 +738,10 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2, for( d = 0; d < D; d++ ) { int L0 = Cp[d] + min((int)Lr_p0[d], min(Lr_p0[d-1] + P1, min(Lr_p0[d+1] + P1, delta0))) - delta0; - + Lr_p[d] = (CostType)L0; minL0 = min(minL0, L0); - + int Sval = Sp[d] = saturate_cast(Sp[d] + L0); if( Sval < minS ) { @@ -764,7 +764,7 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2, } } } - + for( d = 0; d < D; d++ ) { if( Sp[d]*(100 - uniquenessRatio) < minS*100 && std::abs(bestDisp - d) > 1 ) @@ -773,13 +773,13 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2, if( d < D ) continue; d = bestDisp; - int x2 = x + minX1 - d - minD; - if( disp2cost[x2] > minS ) + int _x2 = x + minX1 - d - minD; + if( disp2cost[_x2] > minS ) { - disp2cost[x2] = (CostType)minS; - disp2ptr[x2] = (DispType)(d + minD); + disp2cost[_x2] = (CostType)minS; + disp2ptr[_x2] = (DispType)(d + minD); } - + if( 0 < d && d < D-1 ) { // do subpixel quadratic interpolation: @@ -792,24 +792,24 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2, d *= DISP_SCALE; disp1ptr[x + minX1] = (DispType)(d + minD*DISP_SCALE); } - + for( x = minX1; x < maxX1; x++ ) { // we round the computed disparity both towards -inf and +inf and check // if either of the corresponding disparities in disp2 is consistent. // This is to give the computed disparity a chance to look valid if it is. - int d = disp1ptr[x]; - if( d == INVALID_DISP_SCALED ) + int d1 = disp1ptr[x]; + if( d1 == INVALID_DISP_SCALED ) continue; - int _d = d >> DISP_SHIFT; - int d_ = (d + DISP_SCALE-1) >> DISP_SHIFT; + int _d = d1 >> DISP_SHIFT; + int d_ = (d1 + DISP_SCALE-1) >> DISP_SHIFT; int _x = x - _d, x_ = x - d_; if( 0 <= _x && _x < width && disp2ptr[_x] >= minD && std::abs(disp2ptr[_x] - _d) > disp12MaxDiff && 0 <= x_ && x_ < width && disp2ptr[x_] >= minD && std::abs(disp2ptr[x_] - d_) > disp12MaxDiff ) disp1ptr[x] = (DispType)INVALID_DISP_SCALED; } } - + // now shift the cyclic buffers std::swap( Lr[0], Lr[1] ); std::swap( minLr[0], minLr[1] ); @@ -825,13 +825,13 @@ void StereoSGBM::operator ()( InputArray _left, InputArray _right, Mat left = _left.getMat(), right = _right.getMat(); CV_Assert( left.size() == right.size() && left.type() == right.type() && left.depth() == DataType::depth ); - + _disp.create( left.size(), CV_16S ); Mat disp = _disp.getMat(); - + computeDisparitySGBM( left, right, disp, *this, buffer ); medianBlur(disp, disp, 3); - + if( speckleWindowSize > 0 ) filterSpeckles(disp, (minDisparity - 1)*DISP_SCALE, speckleWindowSize, DISP_SCALE*speckleRange, buffer); } @@ -844,33 +844,33 @@ Rect getValidDisparityROI( Rect roi1, Rect roi2, { int SW2 = SADWindowSize/2; int minD = minDisparity, maxD = minDisparity + numberOfDisparities - 1; - + int xmin = max(roi1.x, roi2.x + maxD) + SW2; int xmax = min(roi1.x + roi1.width, roi2.x + roi2.width - minD) - SW2; int ymin = max(roi1.y, roi2.y) + SW2; int ymax = min(roi1.y + roi1.height, roi2.y + roi2.height) - SW2; - + Rect r(xmin, ymin, xmax - xmin, ymax - ymin); - + return r.width > 0 && r.height > 0 ? r : Rect(); -} - } - + +} + void cv::filterSpeckles( InputOutputArray _img, double _newval, int maxSpeckleSize, double _maxDiff, InputOutputArray __buf ) { Mat img = _img.getMat(); Mat temp, &_buf = __buf.needed() ? __buf.getMatRef() : temp; CV_Assert( img.type() == CV_16SC1 ); - + int newVal = cvRound(_newval); int maxDiff = cvRound(_maxDiff); int width = img.cols, height = img.rows, npixels = width*height; size_t bufSize = npixels*(int)(sizeof(Point2s) + sizeof(int) + sizeof(uchar)); if( !_buf.isContinuous() || !_buf.data || _buf.cols*_buf.rows*_buf.elemSize() < bufSize ) _buf.create(1, (int)bufSize, CV_8U); - + uchar* buf = _buf.data; int i, j, dstep = (int)(img.step/sizeof(short)); int* labels = (int*)buf; @@ -879,33 +879,33 @@ void cv::filterSpeckles( InputOutputArray _img, double _newval, int maxSpeckleSi buf += npixels*sizeof(wbuf[0]); uchar* rtype = (uchar*)buf; int curlabel = 0; - + // clear out label assignments memset(labels, 0, npixels*sizeof(labels[0])); - + for( i = 0; i < height; i++ ) { short* ds = img.ptr(i); int* ls = labels + width*i; - + for( j = 0; j < width; j++ ) { - if( ds[j] != newVal ) // not a bad disparity + if( ds[j] != newVal ) // not a bad disparity { - if( ls[j] ) // has a label, check for bad label - { + if( ls[j] ) // has a label, check for bad label + { if( rtype[ls[j]] ) // small region, zero out disparity ds[j] = (short)newVal; } // no label, assign and propagate else { - Point2s* ws = wbuf; // initialize wavefront - Point2s p((short)j, (short)i); // current pixel - curlabel++; // next label - int count = 0; // current region size + Point2s* ws = wbuf; // initialize wavefront + Point2s p((short)j, (short)i); // current pixel + curlabel++; // next label + int count = 0; // current region size ls[j] = curlabel; - + // wavefront propagation while( ws >= wbuf ) // wavefront not empty { @@ -914,50 +914,50 @@ void cv::filterSpeckles( InputOutputArray _img, double _newval, int maxSpeckleSi short* dpp = &img.at(p.y, p.x); short dp = *dpp; int* lpp = labels + width*p.y + p.x; - + if( p.x < width-1 && !lpp[+1] && dpp[+1] != newVal && std::abs(dp - dpp[+1]) <= maxDiff ) { lpp[+1] = curlabel; *ws++ = Point2s(p.x+1, p.y); } - + if( p.x > 0 && !lpp[-1] && dpp[-1] != newVal && std::abs(dp - dpp[-1]) <= maxDiff ) { lpp[-1] = curlabel; *ws++ = Point2s(p.x-1, p.y); } - + if( p.y < height-1 && !lpp[+width] && dpp[+dstep] != newVal && std::abs(dp - dpp[+dstep]) <= maxDiff ) { lpp[+width] = curlabel; *ws++ = Point2s(p.x, p.y+1); } - + if( p.y > 0 && !lpp[-width] && dpp[-dstep] != newVal && std::abs(dp - dpp[-dstep]) <= maxDiff ) { lpp[-width] = curlabel; *ws++ = Point2s(p.x, p.y-1); } - + // pop most recent and propagate // NB: could try least recent, maybe better convergence p = *--ws; } - + // assign label type - if( count <= maxSpeckleSize ) // speckle region + if( count <= maxSpeckleSize ) // speckle region { - rtype[ls[j]] = 1; // small region label + rtype[ls[j]] = 1; // small region label ds[j] = (short)newVal; } else - rtype[ls[j]] = 0; // large region label + rtype[ls[j]] = 0; // large region label } } } } -} - +} + void cv::validateDisparity( InputOutputArray _disp, InputArray _cost, int minDisparity, int numberOfDisparities, int disp12MaxDiff ) { @@ -971,32 +971,32 @@ void cv::validateDisparity( InputOutputArray _disp, InputArray _cost, int minDis const int DISP_SHIFT = 4, DISP_SCALE = 1 << DISP_SHIFT; int INVALID_DISP = minD - 1, INVALID_DISP_SCALED = INVALID_DISP*DISP_SCALE; int costType = cost.type(); - + disp12MaxDiff *= DISP_SCALE; - + CV_Assert( numberOfDisparities > 0 && disp.type() == CV_16S && (costType == CV_16S || costType == CV_32S) && disp.size() == cost.size() ); - + for( int y = 0; y < rows; y++ ) { short* dptr = disp.ptr(y); - + for( x = 0; x < cols; x++ ) { disp2buf[x] = INVALID_DISP_SCALED; disp2cost[x] = INT_MAX; } - + if( costType == CV_16S ) { const short* cptr = cost.ptr(y); - + for( x = minX1; x < maxX1; x++ ) { int d = dptr[x], c = cptr[x]; int x2 = x - ((d + DISP_SCALE/2) >> DISP_SHIFT); - + if( disp2cost[x2] > c ) { disp2cost[x2] = c; @@ -1007,12 +1007,12 @@ void cv::validateDisparity( InputOutputArray _disp, InputArray _cost, int minDis else { const int* cptr = cost.ptr(y); - + for( x = minX1; x < maxX1; x++ ) { int d = dptr[x], c = cptr[x]; int x2 = x - ((d + DISP_SCALE/2) >> DISP_SHIFT); - + if( disp2cost[x2] < c ) { disp2cost[x2] = c; @@ -1020,7 +1020,7 @@ void cv::validateDisparity( InputOutputArray _disp, InputArray _cost, int minDis } } } - + for( x = minX1; x < maxX1; x++ ) { // we round the computed disparity both towards -inf and +inf and check diff --git a/modules/calib3d/test/test_cameracalibration.cpp b/modules/calib3d/test/test_cameracalibration.cpp index 23bf5f0..0b9d794 100644 --- a/modules/calib3d/test/test_cameracalibration.cpp +++ b/modules/calib3d/test/test_cameracalibration.cpp @@ -254,13 +254,13 @@ public: protected: int compare(double* val, double* refVal, int len, double eps, const char* paramName); - virtual void calibrate( int imageCount, int* pointCounts, - CvSize imageSize, CvPoint2D64f* imagePoints, CvPoint3D64f* objectPoints, - double* distortionCoeffs, double* cameraMatrix, double* translationVectors, - double* rotationMatrices, int flags ) = 0; - virtual void project( int pointCount, CvPoint3D64f* objectPoints, - double* rotationMatrix, double* translationVector, - double* cameraMatrix, double* distortion, CvPoint2D64f* imagePoints ) = 0; + virtual void calibrate( int imageCount, int* pointCounts, + CvSize imageSize, CvPoint2D64f* imagePoints, CvPoint3D64f* objectPoints, + double* distortionCoeffs, double* cameraMatrix, double* translationVectors, + double* rotationMatrices, int flags ) = 0; + virtual void project( int pointCount, CvPoint3D64f* objectPoints, + double* rotationMatrix, double* translationVector, + double* cameraMatrix, double* distortion, CvPoint2D64f* imagePoints ) = 0; void run(int); }; @@ -276,7 +276,7 @@ CV_CameraCalibrationTest::~CV_CameraCalibrationTest() void CV_CameraCalibrationTest::clear() { - cvtest::BaseTest::clear(); + cvtest::BaseTest::clear(); } int CV_CameraCalibrationTest::compare(double* val, double* ref_val, int len, @@ -529,14 +529,14 @@ void CV_CameraCalibrationTest::run( int start_from ) /* ---- Reproject points to the image ---- */ for( currImage = 0; currImage < numImages; currImage++ ) { - int numPoints = etalonSize.width * etalonSize.height; - project( numPoints, - objectPoints + currImage * numPoints, + int nPoints = etalonSize.width * etalonSize.height; + project( nPoints, + objectPoints + currImage * nPoints, rotMatrs + currImage * 9, transVects + currImage * 3, cameraMatrix, distortion, - reprojectPoints + currImage * numPoints); + reprojectPoints + currImage * nPoints); } /* ----- Compute reprojection error ----- */ @@ -669,26 +669,26 @@ _exit_: class CV_CameraCalibrationTest_C : public CV_CameraCalibrationTest { public: - CV_CameraCalibrationTest_C(){} + CV_CameraCalibrationTest_C(){} protected: - virtual void calibrate( int imageCount, int* pointCounts, - CvSize imageSize, CvPoint2D64f* imagePoints, CvPoint3D64f* objectPoints, - double* distortionCoeffs, double* cameraMatrix, double* translationVectors, - double* rotationMatrices, int flags ); - virtual void project( int pointCount, CvPoint3D64f* objectPoints, - double* rotationMatrix, double* translationVector, - double* cameraMatrix, double* distortion, CvPoint2D64f* imagePoints ); + virtual void calibrate( int imageCount, int* pointCounts, + CvSize imageSize, CvPoint2D64f* imagePoints, CvPoint3D64f* objectPoints, + double* distortionCoeffs, double* cameraMatrix, double* translationVectors, + double* rotationMatrices, int flags ); + virtual void project( int pointCount, CvPoint3D64f* objectPoints, + double* rotationMatrix, double* translationVector, + double* cameraMatrix, double* distortion, CvPoint2D64f* imagePoints ); }; void CV_CameraCalibrationTest_C::calibrate( int imageCount, int* pointCounts, - CvSize imageSize, CvPoint2D64f* imagePoints, CvPoint3D64f* objectPoints, - double* distortionCoeffs, double* cameraMatrix, double* translationVectors, - double* rotationMatrices, int flags ) + CvSize imageSize, CvPoint2D64f* imagePoints, CvPoint3D64f* objectPoints, + double* distortionCoeffs, double* cameraMatrix, double* translationVectors, + double* rotationMatrices, int flags ) { int i, total = 0; for( i = 0; i < imageCount; i++ ) total += pointCounts[i]; - + CvMat _objectPoints = cvMat(1, total, CV_64FC3, objectPoints); CvMat _imagePoints = cvMat(1, total, CV_64FC2, imagePoints); CvMat _pointCounts = cvMat(1, imageCount, CV_32S, pointCounts); @@ -696,23 +696,23 @@ void CV_CameraCalibrationTest_C::calibrate( int imageCount, int* pointCounts, CvMat _distCoeffs = cvMat(4, 1, CV_64F, distortionCoeffs); CvMat _rotationMatrices = cvMat(imageCount, 9, CV_64F, rotationMatrices); CvMat _translationVectors = cvMat(imageCount, 3, CV_64F, translationVectors); - + cvCalibrateCamera2(&_objectPoints, &_imagePoints, &_pointCounts, imageSize, &_cameraMatrix, &_distCoeffs, &_rotationMatrices, &_translationVectors, flags); } void CV_CameraCalibrationTest_C::project( int pointCount, CvPoint3D64f* objectPoints, - double* rotationMatrix, double* translationVector, - double* cameraMatrix, double* distortion, CvPoint2D64f* imagePoints ) + double* rotationMatrix, double* translationVector, + double* cameraMatrix, double* distortion, CvPoint2D64f* imagePoints ) { - CvMat _objectPoints = cvMat(1, pointCount, CV_64FC3, objectPoints); + CvMat _objectPoints = cvMat(1, pointCount, CV_64FC3, objectPoints); CvMat _imagePoints = cvMat(1, pointCount, CV_64FC2, imagePoints); CvMat _cameraMatrix = cvMat(3, 3, CV_64F, cameraMatrix); CvMat _distCoeffs = cvMat(4, 1, CV_64F, distortion); CvMat _rotationMatrix = cvMat(3, 3, CV_64F, rotationMatrix); CvMat _translationVector = cvMat(1, 3, CV_64F, translationVector); - + cvProjectPoints2(&_objectPoints, &_rotationMatrix, &_translationVector, &_cameraMatrix, &_distCoeffs, &_imagePoints); } @@ -721,97 +721,97 @@ void CV_CameraCalibrationTest_C::project( int pointCount, CvPoint3D64f* objectPo class CV_CameraCalibrationTest_CPP : public CV_CameraCalibrationTest { public: - CV_CameraCalibrationTest_CPP(){} + CV_CameraCalibrationTest_CPP(){} protected: - virtual void calibrate( int imageCount, int* pointCounts, - CvSize imageSize, CvPoint2D64f* imagePoints, CvPoint3D64f* objectPoints, - double* distortionCoeffs, double* cameraMatrix, double* translationVectors, - double* rotationMatrices, int flags ); - virtual void project( int pointCount, CvPoint3D64f* objectPoints, - double* rotationMatrix, double* translationVector, - double* cameraMatrix, double* distortion, CvPoint2D64f* imagePoints ); + virtual void calibrate( int imageCount, int* pointCounts, + CvSize imageSize, CvPoint2D64f* imagePoints, CvPoint3D64f* objectPoints, + double* distortionCoeffs, double* cameraMatrix, double* translationVectors, + double* rotationMatrices, int flags ); + virtual void project( int pointCount, CvPoint3D64f* objectPoints, + double* rotationMatrix, double* translationVector, + double* cameraMatrix, double* distortion, CvPoint2D64f* imagePoints ); }; void CV_CameraCalibrationTest_CPP::calibrate( int imageCount, int* pointCounts, - CvSize _imageSize, CvPoint2D64f* _imagePoints, CvPoint3D64f* _objectPoints, - double* _distortionCoeffs, double* _cameraMatrix, double* translationVectors, - double* rotationMatrices, int flags ) + CvSize _imageSize, CvPoint2D64f* _imagePoints, CvPoint3D64f* _objectPoints, + double* _distortionCoeffs, double* _cameraMatrix, double* translationVectors, + double* rotationMatrices, int flags ) { - vector > objectPoints( imageCount ); - vector > imagePoints( imageCount ); - Size imageSize = _imageSize; - Mat cameraMatrix, distCoeffs(1,4,CV_64F,Scalar::all(0)); - vector rvecs, tvecs; - - CvPoint3D64f* op = _objectPoints; - CvPoint2D64f* ip = _imagePoints; - vector >::iterator objectPointsIt = objectPoints.begin(); - vector >::iterator imagePointsIt = imagePoints.begin(); - for( int i = 0; i < imageCount; ++objectPointsIt, ++imagePointsIt, i++ ) - { - int num = pointCounts[i]; - objectPointsIt->resize( num ); - imagePointsIt->resize( num ); - vector::iterator oIt = objectPointsIt->begin(); - vector::iterator iIt = imagePointsIt->begin(); - for( int j = 0; j < num; ++oIt, ++iIt, j++, op++, ip++) - { - oIt->x = (float)op->x, oIt->y = (float)op->y, oIt->z = (float)op->z; - iIt->x = (float)ip->x, iIt->y = (float)ip->y; - } - } - - calibrateCamera( objectPoints, - imagePoints, - imageSize, - cameraMatrix, - distCoeffs, - rvecs, - tvecs, - flags ); - - assert( cameraMatrix.type() == CV_64FC1 ); - memcpy( _cameraMatrix, cameraMatrix.data, 9*sizeof(double) ); - - assert( cameraMatrix.type() == CV_64FC1 ); - memcpy( _distortionCoeffs, distCoeffs.data, 4*sizeof(double) ); - - vector::iterator rvecsIt = rvecs.begin(); - vector::iterator tvecsIt = tvecs.begin(); - double *rm = rotationMatrices, - *tm = translationVectors; - assert( rvecsIt->type() == CV_64FC1 ); - assert( tvecsIt->type() == CV_64FC1 ); - for( int i = 0; i < imageCount; ++rvecsIt, ++tvecsIt, i++, rm+=9, tm+=3 ) - { - Mat r9( 3, 3, CV_64FC1 ); - Rodrigues( *rvecsIt, r9 ); - memcpy( rm, r9.data, 9*sizeof(double) ); - memcpy( tm, tvecsIt->data, 3*sizeof(double) ); - } + vector > objectPoints( imageCount ); + vector > imagePoints( imageCount ); + Size imageSize = _imageSize; + Mat cameraMatrix, distCoeffs(1,4,CV_64F,Scalar::all(0)); + vector rvecs, tvecs; + + CvPoint3D64f* op = _objectPoints; + CvPoint2D64f* ip = _imagePoints; + vector >::iterator objectPointsIt = objectPoints.begin(); + vector >::iterator imagePointsIt = imagePoints.begin(); + for( int i = 0; i < imageCount; ++objectPointsIt, ++imagePointsIt, i++ ) + { + int num = pointCounts[i]; + objectPointsIt->resize( num ); + imagePointsIt->resize( num ); + vector::iterator oIt = objectPointsIt->begin(); + vector::iterator iIt = imagePointsIt->begin(); + for( int j = 0; j < num; ++oIt, ++iIt, j++, op++, ip++) + { + oIt->x = (float)op->x, oIt->y = (float)op->y, oIt->z = (float)op->z; + iIt->x = (float)ip->x, iIt->y = (float)ip->y; + } + } + + calibrateCamera( objectPoints, + imagePoints, + imageSize, + cameraMatrix, + distCoeffs, + rvecs, + tvecs, + flags ); + + assert( cameraMatrix.type() == CV_64FC1 ); + memcpy( _cameraMatrix, cameraMatrix.data, 9*sizeof(double) ); + + assert( cameraMatrix.type() == CV_64FC1 ); + memcpy( _distortionCoeffs, distCoeffs.data, 4*sizeof(double) ); + + vector::iterator rvecsIt = rvecs.begin(); + vector::iterator tvecsIt = tvecs.begin(); + double *rm = rotationMatrices, + *tm = translationVectors; + assert( rvecsIt->type() == CV_64FC1 ); + assert( tvecsIt->type() == CV_64FC1 ); + for( int i = 0; i < imageCount; ++rvecsIt, ++tvecsIt, i++, rm+=9, tm+=3 ) + { + Mat r9( 3, 3, CV_64FC1 ); + Rodrigues( *rvecsIt, r9 ); + memcpy( rm, r9.data, 9*sizeof(double) ); + memcpy( tm, tvecsIt->data, 3*sizeof(double) ); + } } void CV_CameraCalibrationTest_CPP::project( int pointCount, CvPoint3D64f* _objectPoints, - double* rotationMatrix, double* translationVector, - double* _cameraMatrix, double* distortion, CvPoint2D64f* _imagePoints ) + double* rotationMatrix, double* translationVector, + double* _cameraMatrix, double* distortion, CvPoint2D64f* _imagePoints ) { - Mat objectPoints( pointCount, 3, CV_64FC1, _objectPoints ); - Mat rmat( 3, 3, CV_64FC1, rotationMatrix ), - rvec( 1, 3, CV_64FC1 ), - tvec( 1, 3, CV_64FC1, translationVector ); - Mat cameraMatrix( 3, 3, CV_64FC1, _cameraMatrix ); - Mat distCoeffs( 1, 4, CV_64FC1, distortion ); - vector imagePoints; - Rodrigues( rmat, rvec ); - - objectPoints.convertTo( objectPoints, CV_32FC1 ); - projectPoints( objectPoints, rvec, tvec, - cameraMatrix, distCoeffs, imagePoints ); - vector::const_iterator it = imagePoints.begin(); - for( int i = 0; it != imagePoints.end(); ++it, i++ ) - { - _imagePoints[i] = cvPoint2D64f( it->x, it->y ); - } + Mat objectPoints( pointCount, 3, CV_64FC1, _objectPoints ); + Mat rmat( 3, 3, CV_64FC1, rotationMatrix ), + rvec( 1, 3, CV_64FC1 ), + tvec( 1, 3, CV_64FC1, translationVector ); + Mat cameraMatrix( 3, 3, CV_64FC1, _cameraMatrix ); + Mat distCoeffs( 1, 4, CV_64FC1, distortion ); + vector imagePoints; + Rodrigues( rmat, rvec ); + + objectPoints.convertTo( objectPoints, CV_32FC1 ); + projectPoints( objectPoints, rvec, tvec, + cameraMatrix, distCoeffs, imagePoints ); + vector::const_iterator it = imagePoints.begin(); + for( int i = 0; it != imagePoints.end(); ++it, i++ ) + { + _imagePoints[i] = cvPoint2D64f( it->x, it->y ); + } } @@ -820,103 +820,103 @@ void CV_CameraCalibrationTest_CPP::project( int pointCount, CvPoint3D64f* _objec class CV_CalibrationMatrixValuesTest : public cvtest::BaseTest { public: - CV_CalibrationMatrixValuesTest() {} + CV_CalibrationMatrixValuesTest() {} protected: - void run(int); - virtual void calibMatrixValues( const Mat& cameraMatrix, Size imageSize, - double apertureWidth, double apertureHeight, double& fovx, double& fovy, double& focalLength, - Point2d& principalPoint, double& aspectRatio ) = 0; + void run(int); + virtual void calibMatrixValues( const Mat& cameraMatrix, Size imageSize, + double apertureWidth, double apertureHeight, double& fovx, double& fovy, double& focalLength, + Point2d& principalPoint, double& aspectRatio ) = 0; }; void CV_CalibrationMatrixValuesTest::run(int) { - int code = cvtest::TS::OK; - const double fcMinVal = 1e-5; - const double fcMaxVal = 1000; - const double apertureMaxVal = 0.01; - - RNG rng = ts->get_rng(); - - double fx, fy, cx, cy, nx, ny; - Mat cameraMatrix( 3, 3, CV_64FC1 ); - cameraMatrix.setTo( Scalar(0) ); - fx = cameraMatrix.at(0,0) = rng.uniform( fcMinVal, fcMaxVal ); - fy = cameraMatrix.at(1,1) = rng.uniform( fcMinVal, fcMaxVal ); - cx = cameraMatrix.at(0,2) = rng.uniform( fcMinVal, fcMaxVal ); - cy = cameraMatrix.at(1,2) = rng.uniform( fcMinVal, fcMaxVal ); - cameraMatrix.at(2,2) = 1; - - Size imageSize( 600, 400 ); - - double apertureWidth = (double)rng * apertureMaxVal, - apertureHeight = (double)rng * apertureMaxVal; - - double fovx, fovy, focalLength, aspectRatio, - goodFovx, goodFovy, goodFocalLength, goodAspectRatio; - Point2d principalPoint, goodPrincipalPoint; - - - calibMatrixValues( cameraMatrix, imageSize, apertureWidth, apertureHeight, - fovx, fovy, focalLength, principalPoint, aspectRatio ); - - // calculate calibration matrix values - goodAspectRatio = fy / fx; - - if( apertureWidth != 0.0 && apertureHeight != 0.0 ) - { - nx = imageSize.width / apertureWidth; - ny = imageSize.height / apertureHeight; - } - else - { - nx = 1.0; - ny = goodAspectRatio; - } - - goodFovx = 2 * atan( imageSize.width / (2 * fx)) * 180.0 / CV_PI; - goodFovy = 2 * atan( imageSize.height / (2 * fy)) * 180.0 / CV_PI; - - goodFocalLength = fx / nx; - - goodPrincipalPoint.x = cx / nx; - goodPrincipalPoint.y = cy / ny; - - // check results - if( fabs(fovx - goodFovx) > FLT_EPSILON ) - { - ts->printf( cvtest::TS::LOG, "bad fovx (real=%f, good = %f\n", fovx, goodFovx ); - code = cvtest::TS::FAIL_BAD_ACCURACY; - goto _exit_; - } - if( fabs(fovy - goodFovy) > FLT_EPSILON ) - { - ts->printf( cvtest::TS::LOG, "bad fovy (real=%f, good = %f\n", fovy, goodFovy ); - code = cvtest::TS::FAIL_BAD_ACCURACY; - goto _exit_; - } - if( fabs(focalLength - goodFocalLength) > FLT_EPSILON ) - { - ts->printf( cvtest::TS::LOG, "bad focalLength (real=%f, good = %f\n", focalLength, goodFocalLength ); - code = cvtest::TS::FAIL_BAD_ACCURACY; - goto _exit_; - } - if( fabs(aspectRatio - goodAspectRatio) > FLT_EPSILON ) - { - ts->printf( cvtest::TS::LOG, "bad aspectRatio (real=%f, good = %f\n", aspectRatio, goodAspectRatio ); - code = cvtest::TS::FAIL_BAD_ACCURACY; - goto _exit_; - } - if( norm( principalPoint - goodPrincipalPoint ) > FLT_EPSILON ) - { - ts->printf( cvtest::TS::LOG, "bad principalPoint\n" ); - code = cvtest::TS::FAIL_BAD_ACCURACY; - goto _exit_; - } + int code = cvtest::TS::OK; + const double fcMinVal = 1e-5; + const double fcMaxVal = 1000; + const double apertureMaxVal = 0.01; + + RNG rng = ts->get_rng(); + + double fx, fy, cx, cy, nx, ny; + Mat cameraMatrix( 3, 3, CV_64FC1 ); + cameraMatrix.setTo( Scalar(0) ); + fx = cameraMatrix.at(0,0) = rng.uniform( fcMinVal, fcMaxVal ); + fy = cameraMatrix.at(1,1) = rng.uniform( fcMinVal, fcMaxVal ); + cx = cameraMatrix.at(0,2) = rng.uniform( fcMinVal, fcMaxVal ); + cy = cameraMatrix.at(1,2) = rng.uniform( fcMinVal, fcMaxVal ); + cameraMatrix.at(2,2) = 1; + + Size imageSize( 600, 400 ); + + double apertureWidth = (double)rng * apertureMaxVal, + apertureHeight = (double)rng * apertureMaxVal; + + double fovx, fovy, focalLength, aspectRatio, + goodFovx, goodFovy, goodFocalLength, goodAspectRatio; + Point2d principalPoint, goodPrincipalPoint; + + + calibMatrixValues( cameraMatrix, imageSize, apertureWidth, apertureHeight, + fovx, fovy, focalLength, principalPoint, aspectRatio ); + + // calculate calibration matrix values + goodAspectRatio = fy / fx; + + if( apertureWidth != 0.0 && apertureHeight != 0.0 ) + { + nx = imageSize.width / apertureWidth; + ny = imageSize.height / apertureHeight; + } + else + { + nx = 1.0; + ny = goodAspectRatio; + } + + goodFovx = 2 * atan( imageSize.width / (2 * fx)) * 180.0 / CV_PI; + goodFovy = 2 * atan( imageSize.height / (2 * fy)) * 180.0 / CV_PI; + + goodFocalLength = fx / nx; + + goodPrincipalPoint.x = cx / nx; + goodPrincipalPoint.y = cy / ny; + + // check results + if( fabs(fovx - goodFovx) > FLT_EPSILON ) + { + ts->printf( cvtest::TS::LOG, "bad fovx (real=%f, good = %f\n", fovx, goodFovx ); + code = cvtest::TS::FAIL_BAD_ACCURACY; + goto _exit_; + } + if( fabs(fovy - goodFovy) > FLT_EPSILON ) + { + ts->printf( cvtest::TS::LOG, "bad fovy (real=%f, good = %f\n", fovy, goodFovy ); + code = cvtest::TS::FAIL_BAD_ACCURACY; + goto _exit_; + } + if( fabs(focalLength - goodFocalLength) > FLT_EPSILON ) + { + ts->printf( cvtest::TS::LOG, "bad focalLength (real=%f, good = %f\n", focalLength, goodFocalLength ); + code = cvtest::TS::FAIL_BAD_ACCURACY; + goto _exit_; + } + if( fabs(aspectRatio - goodAspectRatio) > FLT_EPSILON ) + { + ts->printf( cvtest::TS::LOG, "bad aspectRatio (real=%f, good = %f\n", aspectRatio, goodAspectRatio ); + code = cvtest::TS::FAIL_BAD_ACCURACY; + goto _exit_; + } + if( norm( principalPoint - goodPrincipalPoint ) > FLT_EPSILON ) + { + ts->printf( cvtest::TS::LOG, "bad principalPoint\n" ); + code = cvtest::TS::FAIL_BAD_ACCURACY; + goto _exit_; + } _exit_: - RNG& _rng = ts->get_rng(); - _rng = rng; - ts->set_failed_test_info( code ); + RNG& _rng = ts->get_rng(); + _rng = rng; + ts->set_failed_test_info( code ); } //----------------------------------------- CV_CalibrationMatrixValuesTest_C -------------------------------- @@ -924,24 +924,24 @@ _exit_: class CV_CalibrationMatrixValuesTest_C : public CV_CalibrationMatrixValuesTest { public: - CV_CalibrationMatrixValuesTest_C(){} + CV_CalibrationMatrixValuesTest_C(){} protected: - virtual void calibMatrixValues( const Mat& cameraMatrix, Size imageSize, - double apertureWidth, double apertureHeight, double& fovx, double& fovy, double& focalLength, - Point2d& principalPoint, double& aspectRatio ); + virtual void calibMatrixValues( const Mat& cameraMatrix, Size imageSize, + double apertureWidth, double apertureHeight, double& fovx, double& fovy, double& focalLength, + Point2d& principalPoint, double& aspectRatio ); }; void CV_CalibrationMatrixValuesTest_C::calibMatrixValues( const Mat& _cameraMatrix, Size imageSize, - double apertureWidth, double apertureHeight, - double& fovx, double& fovy, double& focalLength, - Point2d& principalPoint, double& aspectRatio ) + double apertureWidth, double apertureHeight, + double& fovx, double& fovy, double& focalLength, + Point2d& principalPoint, double& aspectRatio ) { - CvMat cameraMatrix = _cameraMatrix; - CvPoint2D64f pp; - cvCalibrationMatrixValues( &cameraMatrix, imageSize, apertureWidth, apertureHeight, - &fovx, &fovy, &focalLength, &pp, &aspectRatio ); - principalPoint.x = pp.x; - principalPoint.y = pp.y; + CvMat cameraMatrix = _cameraMatrix; + CvPoint2D64f pp; + cvCalibrationMatrixValues( &cameraMatrix, imageSize, apertureWidth, apertureHeight, + &fovx, &fovy, &focalLength, &pp, &aspectRatio ); + principalPoint.x = pp.x; + principalPoint.y = pp.y; } @@ -950,20 +950,20 @@ void CV_CalibrationMatrixValuesTest_C::calibMatrixValues( const Mat& _cameraMatr class CV_CalibrationMatrixValuesTest_CPP : public CV_CalibrationMatrixValuesTest { public: - CV_CalibrationMatrixValuesTest_CPP() {} + CV_CalibrationMatrixValuesTest_CPP() {} protected: - virtual void calibMatrixValues( const Mat& cameraMatrix, Size imageSize, - double apertureWidth, double apertureHeight, double& fovx, double& fovy, double& focalLength, - Point2d& principalPoint, double& aspectRatio ); + virtual void calibMatrixValues( const Mat& cameraMatrix, Size imageSize, + double apertureWidth, double apertureHeight, double& fovx, double& fovy, double& focalLength, + Point2d& principalPoint, double& aspectRatio ); }; void CV_CalibrationMatrixValuesTest_CPP::calibMatrixValues( const Mat& cameraMatrix, Size imageSize, - double apertureWidth, double apertureHeight, - double& fovx, double& fovy, double& focalLength, - Point2d& principalPoint, double& aspectRatio ) + double apertureWidth, double apertureHeight, + double& fovx, double& fovy, double& focalLength, + Point2d& principalPoint, double& aspectRatio ) { - calibrationMatrixValues( cameraMatrix, imageSize, apertureWidth, apertureHeight, - fovx, fovy, focalLength, principalPoint, aspectRatio ); + calibrationMatrixValues( cameraMatrix, imageSize, apertureWidth, apertureHeight, + fovx, fovy, focalLength, principalPoint, aspectRatio ); } @@ -971,17 +971,17 @@ void CV_CalibrationMatrixValuesTest_CPP::calibMatrixValues( const Mat& cameraMat void calcdfdx( const vector >& leftF, const vector >& rightF, double eps, Mat& dfdx ) { const int fdim = 2; - CV_Assert( !leftF.empty() && !rightF.empty() && !leftF[0].empty() && !rightF[0].empty() ); - CV_Assert( leftF[0].size() == rightF[0].size() ); - CV_Assert( fabs(eps) > std::numeric_limits::epsilon() ); - int fcount = (int)leftF[0].size(), xdim = (int)leftF.size(); + CV_Assert( !leftF.empty() && !rightF.empty() && !leftF[0].empty() && !rightF[0].empty() ); + CV_Assert( leftF[0].size() == rightF[0].size() ); + CV_Assert( fabs(eps) > std::numeric_limits::epsilon() ); + int fcount = (int)leftF[0].size(), xdim = (int)leftF.size(); - dfdx.create( fcount*fdim, xdim, CV_64FC1 ); + dfdx.create( fcount*fdim, xdim, CV_64FC1 ); - vector >::const_iterator arrLeftIt = leftF.begin(); - vector >::const_iterator arrRightIt = rightF.begin(); - for( int xi = 0; xi < xdim; xi++, ++arrLeftIt, ++arrRightIt ) - { + vector >::const_iterator arrLeftIt = leftF.begin(); + vector >::const_iterator arrRightIt = rightF.begin(); + for( int xi = 0; xi < xdim; xi++, ++arrLeftIt, ++arrRightIt ) + { CV_Assert( (int)arrLeftIt->size() == fcount ); CV_Assert( (int)arrRightIt->size() == fcount ); vector::const_iterator lIt = arrLeftIt->begin(); @@ -989,150 +989,150 @@ void calcdfdx( const vector >& leftF, const vector(fi, xi ) = 0.5 * ((double)(rIt->x - lIt->x)) / eps; - dfdx.at(fi+1, xi ) = 0.5 * ((double)(rIt->y - lIt->y)) / eps; - } - } + dfdx.at(fi+1, xi ) = 0.5 * ((double)(rIt->y - lIt->y)) / eps; + } + } } class CV_ProjectPointsTest : public cvtest::BaseTest { public: - CV_ProjectPointsTest() {} + CV_ProjectPointsTest() {} protected: - void run(int); - virtual void project( const Mat& objectPoints, - const Mat& rvec, const Mat& tvec, - const Mat& cameraMatrix, - const Mat& distCoeffs, - vector& imagePoints, - Mat& dpdrot, Mat& dpdt, Mat& dpdf, - Mat& dpdc, Mat& dpddist, - double aspectRatio=0 ) = 0; + void run(int); + virtual void project( const Mat& objectPoints, + const Mat& rvec, const Mat& tvec, + const Mat& cameraMatrix, + const Mat& distCoeffs, + vector& imagePoints, + Mat& dpdrot, Mat& dpdt, Mat& dpdf, + Mat& dpdc, Mat& dpddist, + double aspectRatio=0 ) = 0; }; void CV_ProjectPointsTest::run(int) { //typedef float matType; - int code = cvtest::TS::OK; - const int pointCount = 100; + int code = cvtest::TS::OK; + const int pointCount = 100; - const float zMinVal = 10.0f, zMaxVal = 100.0f, + const float zMinVal = 10.0f, zMaxVal = 100.0f, rMinVal = -0.3f, rMaxVal = 0.3f, - tMinVal = -2.0f, tMaxVal = 2.0f; + tMinVal = -2.0f, tMaxVal = 2.0f; const float imgPointErr = 1e-3f, dEps = 1e-3f; - + double err; Size imgSize( 600, 800 ); Mat_ objPoints( pointCount, 3), rvec( 1, 3), rmat, tvec( 1, 3 ), cameraMatrix( 3, 3 ), distCoeffs( 1, 4 ), leftRvec, rightRvec, leftTvec, rightTvec, leftCameraMatrix, rightCameraMatrix, leftDistCoeffs, rightDistCoeffs; - RNG rng = ts->get_rng(); + RNG rng = ts->get_rng(); - // generate data - cameraMatrix << 300.f, 0.f, imgSize.width/2.f, + // generate data + cameraMatrix << 300.f, 0.f, imgSize.width/2.f, 0.f, 300.f, imgSize.height/2.f, 0.f, 0.f, 1.f; - distCoeffs << 0.1, 0.01, 0.001, 0.001; + distCoeffs << 0.1, 0.01, 0.001, 0.001; - rvec(0,0) = rng.uniform( rMinVal, rMaxVal ); - rvec(0,1) = rng.uniform( rMinVal, rMaxVal ); - rvec(0,2) = rng.uniform( rMinVal, rMaxVal ); - Rodrigues( rvec, rmat ); + rvec(0,0) = rng.uniform( rMinVal, rMaxVal ); + rvec(0,1) = rng.uniform( rMinVal, rMaxVal ); + rvec(0,2) = rng.uniform( rMinVal, rMaxVal ); + Rodrigues( rvec, rmat ); - tvec(0,0) = rng.uniform( tMinVal, tMaxVal ); - tvec(0,1) = rng.uniform( tMinVal, tMaxVal ); - tvec(0,2) = rng.uniform( tMinVal, tMaxVal ); + tvec(0,0) = rng.uniform( tMinVal, tMaxVal ); + tvec(0,1) = rng.uniform( tMinVal, tMaxVal ); + tvec(0,2) = rng.uniform( tMinVal, tMaxVal ); for( int y = 0; y < objPoints.rows; y++ ) - { - Mat point(1, 3, CV_32FC1, objPoints.ptr(y) ); - float z = rng.uniform( zMinVal, zMaxVal ); - point.at(0,2) = z; + { + Mat point(1, 3, CV_32FC1, objPoints.ptr(y) ); + float z = rng.uniform( zMinVal, zMaxVal ); + point.at(0,2) = z; point.at(0,0) = (rng.uniform(2.f,(float)(imgSize.width-2)) - cameraMatrix(0,2)) / cameraMatrix(0,0) * z; point.at(0,1) = (rng.uniform(2.f,(float)(imgSize.height-2)) - cameraMatrix(1,2)) / cameraMatrix(1,1) * z; point = (point - tvec) * rmat; - } + } - vector imgPoints; - vector > leftImgPoints; - vector > rightImgPoints; - Mat dpdrot, dpdt, dpdf, dpdc, dpddist, - valDpdrot, valDpdt, valDpdf, valDpdc, valDpddist; + vector imgPoints; + vector > leftImgPoints; + vector > rightImgPoints; + Mat dpdrot, dpdt, dpdf, dpdc, dpddist, + valDpdrot, valDpdt, valDpdf, valDpdc, valDpddist; - project( objPoints, rvec, tvec, cameraMatrix, distCoeffs, - imgPoints, dpdrot, dpdt, dpdf, dpdc, dpddist, 0 ); + project( objPoints, rvec, tvec, cameraMatrix, distCoeffs, + imgPoints, dpdrot, dpdt, dpdf, dpdc, dpddist, 0 ); // calculate and check image points assert( (int)imgPoints.size() == pointCount ); - vector::const_iterator it = imgPoints.begin(); - for( int i = 0; i < pointCount; i++, ++it ) - { - Point3d p( objPoints(i,0), objPoints(i,1), objPoints(i,2) ); - double z = p.x*rmat(2,0) + p.y*rmat(2,1) + p.z*rmat(2,2) + tvec(0,2), + vector::const_iterator it = imgPoints.begin(); + for( int i = 0; i < pointCount; i++, ++it ) + { + Point3d p( objPoints(i,0), objPoints(i,1), objPoints(i,2) ); + double z = p.x*rmat(2,0) + p.y*rmat(2,1) + p.z*rmat(2,2) + tvec(0,2), x = (p.x*rmat(0,0) + p.y*rmat(0,1) + p.z*rmat(0,2) + tvec(0,0)) / z, y = (p.x*rmat(1,0) + p.y*rmat(1,1) + p.z*rmat(1,2) + tvec(0,1)) / z, r2 = x*x + y*y, - r4 = r2*r2; - Point2f validImgPoint; - double a1 = 2*x*y, + r4 = r2*r2; + Point2f validImgPoint; + double a1 = 2*x*y, a2 = r2 + 2*x*x, a3 = r2 + 2*y*y, cdist = 1+distCoeffs(0,0)*r2+distCoeffs(0,1)*r4; - validImgPoint.x = static_cast((double)cameraMatrix(0,0)*(x*cdist + (double)distCoeffs(0,2)*a1 + (double)distCoeffs(0,3)*a2) + validImgPoint.x = static_cast((double)cameraMatrix(0,0)*(x*cdist + (double)distCoeffs(0,2)*a1 + (double)distCoeffs(0,3)*a2) + (double)cameraMatrix(0,2)); - validImgPoint.y = static_cast((double)cameraMatrix(1,1)*(y*cdist + (double)distCoeffs(0,2)*a3 + distCoeffs(0,3)*a1) + validImgPoint.y = static_cast((double)cameraMatrix(1,1)*(y*cdist + (double)distCoeffs(0,2)*a3 + distCoeffs(0,3)*a1) + (double)cameraMatrix(1,2)); if( fabs(it->x - validImgPoint.x) > imgPointErr || fabs(it->y - validImgPoint.y) > imgPointErr ) - { - ts->printf( cvtest::TS::LOG, "bad image point\n" ); - code = cvtest::TS::FAIL_BAD_ACCURACY; - goto _exit_; - } - } - - // check derivatives - // 1. rotation - leftImgPoints.resize(3); + { + ts->printf( cvtest::TS::LOG, "bad image point\n" ); + code = cvtest::TS::FAIL_BAD_ACCURACY; + goto _exit_; + } + } + + // check derivatives + // 1. rotation + leftImgPoints.resize(3); rightImgPoints.resize(3); - for( int i = 0; i < 3; i++ ) - { + for( int i = 0; i < 3; i++ ) + { rvec.copyTo( leftRvec ); leftRvec(0,i) -= dEps; project( objPoints, leftRvec, tvec, cameraMatrix, distCoeffs, leftImgPoints[i], valDpdrot, valDpdt, valDpdf, valDpdc, valDpddist, 0 ); rvec.copyTo( rightRvec ); rightRvec(0,i) += dEps; project( objPoints, rightRvec, tvec, cameraMatrix, distCoeffs, rightImgPoints[i], valDpdrot, valDpdt, valDpdf, valDpdc, valDpddist, 0 ); - } + } calcdfdx( leftImgPoints, rightImgPoints, dEps, valDpdrot ); err = norm( dpdrot, valDpdrot, NORM_INF ); if( err > 3 ) - { - ts->printf( cvtest::TS::LOG, "bad dpdrot: too big difference = %g\n", err ); - code = cvtest::TS::FAIL_BAD_ACCURACY; - } + { + ts->printf( cvtest::TS::LOG, "bad dpdrot: too big difference = %g\n", err ); + code = cvtest::TS::FAIL_BAD_ACCURACY; + } // 2. translation for( int i = 0; i < 3; i++ ) - { + { tvec.copyTo( leftTvec ); leftTvec(0,i) -= dEps; project( objPoints, rvec, leftTvec, cameraMatrix, distCoeffs, leftImgPoints[i], valDpdrot, valDpdt, valDpdf, valDpdc, valDpddist, 0 ); tvec.copyTo( rightTvec ); rightTvec(0,i) += dEps; project( objPoints, rvec, rightTvec, cameraMatrix, distCoeffs, rightImgPoints[i], valDpdrot, valDpdt, valDpdf, valDpdc, valDpddist, 0 ); - } + } calcdfdx( leftImgPoints, rightImgPoints, dEps, valDpdt ); if( norm( dpdt, valDpdt, NORM_INF ) > 0.2 ) - { - ts->printf( cvtest::TS::LOG, "bad dpdtvec\n" ); - code = cvtest::TS::FAIL_BAD_ACCURACY; - } + { + ts->printf( cvtest::TS::LOG, "bad dpdtvec\n" ); + code = cvtest::TS::FAIL_BAD_ACCURACY; + } // 3. camera matrix // 3.1. focus @@ -1181,47 +1181,47 @@ void CV_ProjectPointsTest::run(int) // 4. distortion leftImgPoints.resize(distCoeffs.cols); rightImgPoints.resize(distCoeffs.cols); - for( int i = 0; i < distCoeffs.cols; i++ ) - { + for( int i = 0; i < distCoeffs.cols; i++ ) + { distCoeffs.copyTo( leftDistCoeffs ); leftDistCoeffs(0,i) -= dEps; project( objPoints, rvec, tvec, cameraMatrix, leftDistCoeffs, leftImgPoints[i], valDpdrot, valDpdt, valDpdf, valDpdc, valDpddist, 0 ); distCoeffs.copyTo( rightDistCoeffs ); rightDistCoeffs(0,i) += dEps; project( objPoints, rvec, tvec, cameraMatrix, rightDistCoeffs, rightImgPoints[i], valDpdrot, valDpdt, valDpdf, valDpdc, valDpddist, 0 ); - } + } calcdfdx( leftImgPoints, rightImgPoints, dEps, valDpddist ); if( norm( dpddist, valDpddist ) > 0.3 ) - { - ts->printf( cvtest::TS::LOG, "bad dpddist\n" ); - code = cvtest::TS::FAIL_BAD_ACCURACY; - } + { + ts->printf( cvtest::TS::LOG, "bad dpddist\n" ); + code = cvtest::TS::FAIL_BAD_ACCURACY; + } _exit_: - RNG& _rng = ts->get_rng(); - _rng = rng; - ts->set_failed_test_info( code ); + RNG& _rng = ts->get_rng(); + _rng = rng; + ts->set_failed_test_info( code ); } //----------------------------------------- CV_ProjectPointsTest_C -------------------------------- class CV_ProjectPointsTest_C : public CV_ProjectPointsTest { public: - CV_ProjectPointsTest_C() {} + CV_ProjectPointsTest_C() {} protected: - virtual void project( const Mat& objectPoints, - const Mat& rvec, const Mat& tvec, - const Mat& cameraMatrix, - const Mat& distCoeffs, - vector& imagePoints, - Mat& dpdrot, Mat& dpdt, Mat& dpdf, - Mat& dpdc, Mat& dpddist, - double aspectRatio=0 ); + virtual void project( const Mat& objectPoints, + const Mat& rvec, const Mat& tvec, + const Mat& cameraMatrix, + const Mat& distCoeffs, + vector& imagePoints, + Mat& dpdrot, Mat& dpdt, Mat& dpdf, + Mat& dpdc, Mat& dpddist, + double aspectRatio=0 ); }; void CV_ProjectPointsTest_C::project( const Mat& opoints, const Mat& rvec, const Mat& tvec, - const Mat& cameraMatrix, const Mat& distCoeffs, vector& ipoints, - Mat& dpdrot, Mat& dpdt, Mat& dpdf, Mat& dpdc, Mat& dpddist, double aspectRatio) + const Mat& cameraMatrix, const Mat& distCoeffs, vector& ipoints, + Mat& dpdrot, Mat& dpdt, Mat& dpdf, Mat& dpdc, Mat& dpddist, double aspectRatio) { int npoints = opoints.cols*opoints.rows*opoints.channels()/3; ipoints.resize(npoints); @@ -1234,7 +1234,7 @@ void CV_ProjectPointsTest_C::project( const Mat& opoints, const Mat& rvec, const CvMat _rvec = rvec, _tvec = tvec, _cameraMatrix = cameraMatrix, _distCoeffs = distCoeffs; CvMat _dpdrot = dpdrot, _dpdt = dpdt, _dpdf = dpdf, _dpdc = dpdc, _dpddist = dpddist; - cvProjectPoints2( &_objectPoints, &_rvec, &_tvec, &_cameraMatrix, &_distCoeffs, + cvProjectPoints2( &_objectPoints, &_rvec, &_tvec, &_cameraMatrix, &_distCoeffs, &_imagePoints, &_dpdrot, &_dpdt, &_dpdf, &_dpdc, &_dpddist, aspectRatio ); } @@ -1243,24 +1243,24 @@ void CV_ProjectPointsTest_C::project( const Mat& opoints, const Mat& rvec, const class CV_ProjectPointsTest_CPP : public CV_ProjectPointsTest { public: - CV_ProjectPointsTest_CPP() {} + CV_ProjectPointsTest_CPP() {} protected: - virtual void project( const Mat& objectPoints, - const Mat& rvec, const Mat& tvec, - const Mat& cameraMatrix, - const Mat& distCoeffs, - vector& imagePoints, - Mat& dpdrot, Mat& dpdt, Mat& dpdf, - Mat& dpdc, Mat& dpddist, - double aspectRatio=0 ); + virtual void project( const Mat& objectPoints, + const Mat& rvec, const Mat& tvec, + const Mat& cameraMatrix, + const Mat& distCoeffs, + vector& imagePoints, + Mat& dpdrot, Mat& dpdt, Mat& dpdf, + Mat& dpdc, Mat& dpddist, + double aspectRatio=0 ); }; void CV_ProjectPointsTest_CPP::project( const Mat& objectPoints, const Mat& rvec, const Mat& tvec, - const Mat& cameraMatrix, const Mat& distCoeffs, vector& imagePoints, - Mat& dpdrot, Mat& dpdt, Mat& dpdf, Mat& dpdc, Mat& dpddist, double aspectRatio) + const Mat& cameraMatrix, const Mat& distCoeffs, vector& imagePoints, + Mat& dpdrot, Mat& dpdt, Mat& dpdf, Mat& dpdc, Mat& dpddist, double aspectRatio) { Mat J; - projectPoints( objectPoints, rvec, tvec, cameraMatrix, distCoeffs, imagePoints, J, aspectRatio); + projectPoints( objectPoints, rvec, tvec, cameraMatrix, distCoeffs, imagePoints, J, aspectRatio); J.colRange(0, 3).copyTo(dpdrot); J.colRange(3, 6).copyTo(dpdt); J.colRange(6, 8).copyTo(dpdf); @@ -1273,39 +1273,39 @@ void CV_ProjectPointsTest_CPP::project( const Mat& objectPoints, const Mat& rvec class CV_StereoCalibrationTest : public cvtest::BaseTest { public: - CV_StereoCalibrationTest(); - ~CV_StereoCalibrationTest(); - void clear(); + CV_StereoCalibrationTest(); + ~CV_StereoCalibrationTest(); + void clear(); protected: - bool checkPandROI( int test_case_idx, - const Mat& M, const Mat& D, const Mat& R, - const Mat& P, Size imgsize, Rect roi ); - - // covers of tested functions - virtual double calibrateStereoCamera( const vector >& objectPoints, - const vector >& imagePoints1, - const vector >& imagePoints2, - Mat& cameraMatrix1, Mat& distCoeffs1, - Mat& cameraMatrix2, Mat& distCoeffs2, - Size imageSize, Mat& R, Mat& T, - Mat& E, Mat& F, TermCriteria criteria, int flags ) = 0; - virtual void rectify( const Mat& cameraMatrix1, const Mat& distCoeffs1, - const Mat& cameraMatrix2, const Mat& distCoeffs2, - Size imageSize, const Mat& R, const Mat& T, - Mat& R1, Mat& R2, Mat& P1, Mat& P2, Mat& Q, - double alpha, Size newImageSize, - Rect* validPixROI1, Rect* validPixROI2, int flags ) = 0; - virtual bool rectifyUncalibrated( const Mat& points1, - const Mat& points2, const Mat& F, Size imgSize, - Mat& H1, Mat& H2, double threshold=5 ) = 0; - virtual void triangulate( const Mat& P1, const Mat& P2, + bool checkPandROI( int test_case_idx, + const Mat& M, const Mat& D, const Mat& R, + const Mat& P, Size imgsize, Rect roi ); + + // covers of tested functions + virtual double calibrateStereoCamera( const vector >& objectPoints, + const vector >& imagePoints1, + const vector >& imagePoints2, + Mat& cameraMatrix1, Mat& distCoeffs1, + Mat& cameraMatrix2, Mat& distCoeffs2, + Size imageSize, Mat& R, Mat& T, + Mat& E, Mat& F, TermCriteria criteria, int flags ) = 0; + virtual void rectify( const Mat& cameraMatrix1, const Mat& distCoeffs1, + const Mat& cameraMatrix2, const Mat& distCoeffs2, + Size imageSize, const Mat& R, const Mat& T, + Mat& R1, Mat& R2, Mat& P1, Mat& P2, Mat& Q, + double alpha, Size newImageSize, + Rect* validPixROI1, Rect* validPixROI2, int flags ) = 0; + virtual bool rectifyUncalibrated( const Mat& points1, + const Mat& points2, const Mat& F, Size imgSize, + Mat& H1, Mat& H2, double threshold=5 ) = 0; + virtual void triangulate( const Mat& P1, const Mat& P2, const Mat &points1, const Mat &points2, Mat &points4D ) = 0; - virtual void correct( const Mat& F, + virtual void correct( const Mat& F, const Mat &points1, const Mat &points2, Mat &newPoints1, Mat &newPoints2 ) = 0; - void run(int); + void run(int); }; @@ -1316,196 +1316,196 @@ CV_StereoCalibrationTest::CV_StereoCalibrationTest() CV_StereoCalibrationTest::~CV_StereoCalibrationTest() { - clear(); + clear(); } void CV_StereoCalibrationTest::clear() { - cvtest::BaseTest::clear(); + cvtest::BaseTest::clear(); } bool CV_StereoCalibrationTest::checkPandROI( int test_case_idx, const Mat& M, const Mat& D, const Mat& R, - const Mat& P, Size imgsize, Rect roi ) + const Mat& P, Size imgsize, Rect roi ) { - const double eps = 0.05; - const int N = 21; - int x, y, k; - vector pts, upts; - - // step 1. check that all the original points belong to the destination image - for( y = 0; y < N; y++ ) - for( x = 0; x < N; x++ ) - pts.push_back(Point2f((float)x*imgsize.width/(N-1), (float)y*imgsize.height/(N-1))); - - undistortPoints(Mat(pts), upts, M, D, R, P ); - for( k = 0; k < N*N; k++ ) - if( upts[k].x < -imgsize.width*eps || upts[k].x > imgsize.width*(1+eps) || - upts[k].y < -imgsize.height*eps || upts[k].y > imgsize.height*(1+eps) ) - { - ts->printf(cvtest::TS::LOG, "Test #%d. The point (%g, %g) was mapped to (%g, %g) which is out of image\n", - test_case_idx, pts[k].x, pts[k].y, upts[k].x, upts[k].y); - return false; - } - - // step 2. check that all the points inside ROI belong to the original source image - Mat temp(imgsize, CV_8U), utemp, map1, map2; - temp = Scalar::all(1); - initUndistortRectifyMap(M, D, R, P, imgsize, CV_16SC2, map1, map2); - remap(temp, utemp, map1, map2, INTER_LINEAR); - - if(roi.x < 0 || roi.y < 0 || roi.x + roi.width > imgsize.width || roi.y + roi.height > imgsize.height) - { - ts->printf(cvtest::TS::LOG, "Test #%d. The ROI=(%d, %d, %d, %d) is outside of the imge rectangle\n", - test_case_idx, roi.x, roi.y, roi.width, roi.height); - return false; - } - double s = sum(utemp(roi))[0]; - if( s > roi.area() || roi.area() - s > roi.area()*(1-eps) ) - { - ts->printf(cvtest::TS::LOG, "Test #%d. The ratio of black pixels inside the valid ROI (~%g%%) is too large\n", - test_case_idx, s*100./roi.area()); - return false; - } - - return true; + const double eps = 0.05; + const int N = 21; + int x, y, k; + vector pts, upts; + + // step 1. check that all the original points belong to the destination image + for( y = 0; y < N; y++ ) + for( x = 0; x < N; x++ ) + pts.push_back(Point2f((float)x*imgsize.width/(N-1), (float)y*imgsize.height/(N-1))); + + undistortPoints(Mat(pts), upts, M, D, R, P ); + for( k = 0; k < N*N; k++ ) + if( upts[k].x < -imgsize.width*eps || upts[k].x > imgsize.width*(1+eps) || + upts[k].y < -imgsize.height*eps || upts[k].y > imgsize.height*(1+eps) ) + { + ts->printf(cvtest::TS::LOG, "Test #%d. The point (%g, %g) was mapped to (%g, %g) which is out of image\n", + test_case_idx, pts[k].x, pts[k].y, upts[k].x, upts[k].y); + return false; + } + + // step 2. check that all the points inside ROI belong to the original source image + Mat temp(imgsize, CV_8U), utemp, map1, map2; + temp = Scalar::all(1); + initUndistortRectifyMap(M, D, R, P, imgsize, CV_16SC2, map1, map2); + remap(temp, utemp, map1, map2, INTER_LINEAR); + + if(roi.x < 0 || roi.y < 0 || roi.x + roi.width > imgsize.width || roi.y + roi.height > imgsize.height) + { + ts->printf(cvtest::TS::LOG, "Test #%d. The ROI=(%d, %d, %d, %d) is outside of the imge rectangle\n", + test_case_idx, roi.x, roi.y, roi.width, roi.height); + return false; + } + double s = sum(utemp(roi))[0]; + if( s > roi.area() || roi.area() - s > roi.area()*(1-eps) ) + { + ts->printf(cvtest::TS::LOG, "Test #%d. The ratio of black pixels inside the valid ROI (~%g%%) is too large\n", + test_case_idx, s*100./roi.area()); + return false; + } + + return true; } void CV_StereoCalibrationTest::run( int ) { - const int ntests = 1; - const double maxReprojErr = 2; - const double maxScanlineDistErr_c = 3; - const double maxScanlineDistErr_uc = 4; - FILE* f = 0; - - for(int testcase = 1; testcase <= ntests; testcase++) - { - char filepath[1000]; - char buf[1000]; - sprintf( filepath, "%sstereo/case%d/stereo_calib.txt", ts->get_data_path().c_str(), testcase ); - f = fopen(filepath, "rt"); - Size patternSize; - vector imglist; - - if( !f || !fgets(buf, sizeof(buf)-3, f) || sscanf(buf, "%d%d", &patternSize.width, &patternSize.height) != 2 ) - { - ts->printf( cvtest::TS::LOG, "The file %s can not be opened or has invalid content\n", filepath ); - ts->set_failed_test_info( f ? cvtest::TS::FAIL_INVALID_TEST_DATA : cvtest::TS::FAIL_MISSING_TEST_DATA ); - return; - } - - for(;;) - { - if( !fgets( buf, sizeof(buf)-3, f )) - break; - size_t len = strlen(buf); - while( len > 0 && isspace(buf[len-1])) - buf[--len] = '\0'; - if( buf[0] == '#') - continue; - sprintf(filepath, "%sstereo/case%d/%s", ts->get_data_path().c_str(), testcase, buf ); - imglist.push_back(string(filepath)); - } - fclose(f); - - if( imglist.size() == 0 || imglist.size() % 2 != 0 ) - { - ts->printf( cvtest::TS::LOG, "The number of images is 0 or an odd number in the case #%d\n", testcase ); - ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA ); - return; - } - - int nframes = (int)(imglist.size()/2); - int npoints = patternSize.width*patternSize.height; - vector > objpt(nframes); - vector > imgpt1(nframes); - vector > imgpt2(nframes); - Size imgsize; - int total = 0; - - for( int i = 0; i < nframes; i++ ) - { - Mat left = imread(imglist[i*2]); - Mat right = imread(imglist[i*2+1]); - if(!left.data || !right.data) - { - ts->printf( cvtest::TS::LOG, "Can not load images %s and %s, testcase %d\n", - imglist[i*2].c_str(), imglist[i*2+1].c_str(), testcase ); - ts->set_failed_test_info( cvtest::TS::FAIL_MISSING_TEST_DATA ); - return; - } - imgsize = left.size(); - bool found1 = findChessboardCorners(left, patternSize, imgpt1[i]); - bool found2 = findChessboardCorners(right, patternSize, imgpt2[i]); - if(!found1 || !found2) - { - ts->printf( cvtest::TS::LOG, "The function could not detect boards on the images %s and %s, testcase %d\n", - imglist[i*2].c_str(), imglist[i*2+1].c_str(), testcase ); - ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT ); - return; - } - total += (int)imgpt1[i].size(); - for( int j = 0; j < npoints; j++ ) - objpt[i].push_back(Point3f((float)(j%patternSize.width), (float)(j/patternSize.width), 0.f)); - } - - // rectify (calibrated) - Mat M1 = Mat::eye(3,3,CV_64F), M2 = Mat::eye(3,3,CV_64F), D1(5,1,CV_64F), D2(5,1,CV_64F), R, T, E, F; - M1.at(0,2) = M2.at(0,2)=(imgsize.width-1)*0.5; - M1.at(1,2) = M2.at(1,2)=(imgsize.height-1)*0.5; - D1 = Scalar::all(0); - D2 = Scalar::all(0); - double err = calibrateStereoCamera(objpt, imgpt1, imgpt2, M1, D1, M2, D2, imgsize, R, T, E, F, - TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS, 30, 1e-6), - CV_CALIB_SAME_FOCAL_LENGTH - //+ CV_CALIB_FIX_ASPECT_RATIO - + CV_CALIB_FIX_PRINCIPAL_POINT - + CV_CALIB_ZERO_TANGENT_DIST + const int ntests = 1; + const double maxReprojErr = 2; + const double maxScanlineDistErr_c = 3; + const double maxScanlineDistErr_uc = 4; + FILE* f = 0; + + for(int testcase = 1; testcase <= ntests; testcase++) + { + char filepath[1000]; + char buf[1000]; + sprintf( filepath, "%sstereo/case%d/stereo_calib.txt", ts->get_data_path().c_str(), testcase ); + f = fopen(filepath, "rt"); + Size patternSize; + vector imglist; + + if( !f || !fgets(buf, sizeof(buf)-3, f) || sscanf(buf, "%d%d", &patternSize.width, &patternSize.height) != 2 ) + { + ts->printf( cvtest::TS::LOG, "The file %s can not be opened or has invalid content\n", filepath ); + ts->set_failed_test_info( f ? cvtest::TS::FAIL_INVALID_TEST_DATA : cvtest::TS::FAIL_MISSING_TEST_DATA ); + return; + } + + for(;;) + { + if( !fgets( buf, sizeof(buf)-3, f )) + break; + size_t len = strlen(buf); + while( len > 0 && isspace(buf[len-1])) + buf[--len] = '\0'; + if( buf[0] == '#') + continue; + sprintf(filepath, "%sstereo/case%d/%s", ts->get_data_path().c_str(), testcase, buf ); + imglist.push_back(string(filepath)); + } + fclose(f); + + if( imglist.size() == 0 || imglist.size() % 2 != 0 ) + { + ts->printf( cvtest::TS::LOG, "The number of images is 0 or an odd number in the case #%d\n", testcase ); + ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA ); + return; + } + + int nframes = (int)(imglist.size()/2); + int npoints = patternSize.width*patternSize.height; + vector > objpt(nframes); + vector > imgpt1(nframes); + vector > imgpt2(nframes); + Size imgsize; + int total = 0; + + for( int i = 0; i < nframes; i++ ) + { + Mat left = imread(imglist[i*2]); + Mat right = imread(imglist[i*2+1]); + if(!left.data || !right.data) + { + ts->printf( cvtest::TS::LOG, "Can not load images %s and %s, testcase %d\n", + imglist[i*2].c_str(), imglist[i*2+1].c_str(), testcase ); + ts->set_failed_test_info( cvtest::TS::FAIL_MISSING_TEST_DATA ); + return; + } + imgsize = left.size(); + bool found1 = findChessboardCorners(left, patternSize, imgpt1[i]); + bool found2 = findChessboardCorners(right, patternSize, imgpt2[i]); + if(!found1 || !found2) + { + ts->printf( cvtest::TS::LOG, "The function could not detect boards on the images %s and %s, testcase %d\n", + imglist[i*2].c_str(), imglist[i*2+1].c_str(), testcase ); + ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT ); + return; + } + total += (int)imgpt1[i].size(); + for( int j = 0; j < npoints; j++ ) + objpt[i].push_back(Point3f((float)(j%patternSize.width), (float)(j/patternSize.width), 0.f)); + } + + // rectify (calibrated) + Mat M1 = Mat::eye(3,3,CV_64F), M2 = Mat::eye(3,3,CV_64F), D1(5,1,CV_64F), D2(5,1,CV_64F), R, T, E, F; + M1.at(0,2) = M2.at(0,2)=(imgsize.width-1)*0.5; + M1.at(1,2) = M2.at(1,2)=(imgsize.height-1)*0.5; + D1 = Scalar::all(0); + D2 = Scalar::all(0); + double err = calibrateStereoCamera(objpt, imgpt1, imgpt2, M1, D1, M2, D2, imgsize, R, T, E, F, + TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS, 30, 1e-6), + CV_CALIB_SAME_FOCAL_LENGTH + //+ CV_CALIB_FIX_ASPECT_RATIO + + CV_CALIB_FIX_PRINCIPAL_POINT + + CV_CALIB_ZERO_TANGENT_DIST + CV_CALIB_FIX_K3 + CV_CALIB_FIX_K4 + CV_CALIB_FIX_K5 //+ CV_CALIB_FIX_K6 - ); - err /= nframes*npoints; - if( err > maxReprojErr ) - { - ts->printf( cvtest::TS::LOG, "The average reprojection error is too big (=%g), testcase %d\n", err, testcase); - ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT ); - return; - } - - Mat R1, R2, P1, P2, Q; - Rect roi1, roi2; - rectify(M1, D1, M2, D2, imgsize, R, T, R1, R2, P1, P2, Q, 1, imgsize, &roi1, &roi2, 0); - Mat eye33 = Mat::eye(3,3,CV_64F); - Mat R1t = R1.t(), R2t = R2.t(); - - if( norm(R1t*R1 - eye33) > 0.01 || - norm(R2t*R2 - eye33) > 0.01 || - abs(determinant(F)) > 0.01) - { - ts->printf( cvtest::TS::LOG, "The computed (by rectify) R1 and R2 are not orthogonal," - "or the computed (by calibrate) F is not singular, testcase %d\n", testcase); - ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT ); - return; - } - - if(!checkPandROI(testcase, M1, D1, R1, P1, imgsize, roi1)) - { - ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY ); - return; - } - - if(!checkPandROI(testcase, M2, D2, R2, P2, imgsize, roi2)) - { - ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY ); - return; - } + ); + err /= nframes*npoints; + if( err > maxReprojErr ) + { + ts->printf( cvtest::TS::LOG, "The average reprojection error is too big (=%g), testcase %d\n", err, testcase); + ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT ); + return; + } + + Mat R1, R2, P1, P2, Q; + Rect roi1, roi2; + rectify(M1, D1, M2, D2, imgsize, R, T, R1, R2, P1, P2, Q, 1, imgsize, &roi1, &roi2, 0); + Mat eye33 = Mat::eye(3,3,CV_64F); + Mat R1t = R1.t(), R2t = R2.t(); + + if( norm(R1t*R1 - eye33) > 0.01 || + norm(R2t*R2 - eye33) > 0.01 || + abs(determinant(F)) > 0.01) + { + ts->printf( cvtest::TS::LOG, "The computed (by rectify) R1 and R2 are not orthogonal," + "or the computed (by calibrate) F is not singular, testcase %d\n", testcase); + ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT ); + return; + } + + if(!checkPandROI(testcase, M1, D1, R1, P1, imgsize, roi1)) + { + ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY ); + return; + } + + if(!checkPandROI(testcase, M2, D2, R2, P2, imgsize, roi2)) + { + ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY ); + return; + } //check that Tx after rectification is equal to distance between cameras double tx = fabs(P2.at(0, 3) / P2.at(0, 0)); if (fabs(tx - norm(T)) > 1e-5) { - ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY ); - return; + ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY ); + return; } //check that Q reprojects points before the camera @@ -1514,8 +1514,8 @@ void CV_StereoCalibrationTest::run( int ) CV_Assert(reprojectedTestPoint.type() == CV_64FC1); if( reprojectedTestPoint.at(2) / reprojectedTestPoint.at(3) < 0 ) { - ts->printf( cvtest::TS::LOG, "A point after rectification is reprojected behind the camera, testcase %d\n", testcase); - ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT ); + ts->printf( cvtest::TS::LOG, "A point after rectification is reprojected behind the camera, testcase %d\n", testcase); + ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT ); } //check that Q reprojects the same points as reconstructed by triangulation @@ -1555,8 +1555,8 @@ void CV_StereoCalibrationTest::run( int ) if (norm(triangulatedPoints - reprojectedPoints) / sqrt((double)pointsCount) > requiredAccuracy) { - ts->printf( cvtest::TS::LOG, "Points reprojected with a matrix Q and points reconstructed by triangulation are different, testcase %d\n", testcase); - ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT ); + ts->printf( cvtest::TS::LOG, "Points reprojected with a matrix Q and points reconstructed by triangulation are different, testcase %d\n", testcase); + ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT ); } //check correctMatches @@ -1585,73 +1585,73 @@ void CV_StereoCalibrationTest::run( int ) } } - // rectifyUncalibrated - CV_Assert( imgpt1.size() == imgpt2.size() ); - Mat _imgpt1( total, 1, CV_32FC2 ), _imgpt2( total, 1, CV_32FC2 ); - vector >::const_iterator iit1 = imgpt1.begin(); - vector >::const_iterator iit2 = imgpt2.begin(); - for( int pi = 0; iit1 != imgpt1.end(); ++iit1, ++iit2 ) - { - vector::const_iterator pit1 = iit1->begin(); - vector::const_iterator pit2 = iit2->begin(); - CV_Assert( iit1->size() == iit2->size() ); - for( ; pit1 != iit1->end(); ++pit1, ++pit2, pi++ ) - { - _imgpt1.at(pi,0) = Point2f( pit1->x, pit1->y ); - _imgpt2.at(pi,0) = Point2f( pit2->x, pit2->y ); - } - } - - Mat _M1, _M2, _D1, _D2; - vector _R1, _R2, _T1, _T2; - calibrateCamera( objpt, imgpt1, imgsize, _M1, _D1, _R1, _T1, 0 ); - calibrateCamera( objpt, imgpt2, imgsize, _M2, _D2, _R2, _T1, 0 ); - undistortPoints( _imgpt1, _imgpt1, _M1, _D1, Mat(), _M1 ); - undistortPoints( _imgpt2, _imgpt2, _M2, _D2, Mat(), _M2 ); - - Mat matF, _H1, _H2; - matF = findFundamentalMat( _imgpt1, _imgpt2 ); - rectifyUncalibrated( _imgpt1, _imgpt2, matF, imgsize, _H1, _H2 ); - - Mat rectifPoints1, rectifPoints2; - perspectiveTransform( _imgpt1, rectifPoints1, _H1 ); - perspectiveTransform( _imgpt2, rectifPoints2, _H2 ); - - bool verticalStereo = abs(P2.at(0,3)) < abs(P2.at(1,3)); - double maxDiff_c = 0, maxDiff_uc = 0; - for( int i = 0, k = 0; i < nframes; i++ ) - { - vector temp[2]; - undistortPoints(Mat(imgpt1[i]), temp[0], M1, D1, R1, P1); - undistortPoints(Mat(imgpt2[i]), temp[1], M2, D2, R2, P2); - - for( int j = 0; j < npoints; j++, k++ ) - { - double diff_c = verticalStereo ? abs(temp[0][j].x - temp[1][j].x) : abs(temp[0][j].y - temp[1][j].y); - Point2f d = rectifPoints1.at(k,0) - rectifPoints2.at(k,0); - double diff_uc = verticalStereo ? abs(d.x) : abs(d.y); - maxDiff_c = max(maxDiff_c, diff_c); - maxDiff_uc = max(maxDiff_uc, diff_uc); - if( maxDiff_c > maxScanlineDistErr_c ) - { - ts->printf( cvtest::TS::LOG, "The distance between %s coordinates is too big(=%g) (used calibrated stereo), testcase %d\n", - verticalStereo ? "x" : "y", diff_c, testcase); - ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY ); - return; - } - if( maxDiff_uc > maxScanlineDistErr_uc ) - { - ts->printf( cvtest::TS::LOG, "The distance between %s coordinates is too big(=%g) (used uncalibrated stereo), testcase %d\n", - verticalStereo ? "x" : "y", diff_uc, testcase); - ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY ); - return; - } - } - } - - ts->printf( cvtest::TS::LOG, "Testcase %d. Max distance (calibrated) =%g\n" - "Max distance (uncalibrated) =%g\n", testcase, maxDiff_c, maxDiff_uc ); - } + // rectifyUncalibrated + CV_Assert( imgpt1.size() == imgpt2.size() ); + Mat _imgpt1( total, 1, CV_32FC2 ), _imgpt2( total, 1, CV_32FC2 ); + vector >::const_iterator iit1 = imgpt1.begin(); + vector >::const_iterator iit2 = imgpt2.begin(); + for( int pi = 0; iit1 != imgpt1.end(); ++iit1, ++iit2 ) + { + vector::const_iterator pit1 = iit1->begin(); + vector::const_iterator pit2 = iit2->begin(); + CV_Assert( iit1->size() == iit2->size() ); + for( ; pit1 != iit1->end(); ++pit1, ++pit2, pi++ ) + { + _imgpt1.at(pi,0) = Point2f( pit1->x, pit1->y ); + _imgpt2.at(pi,0) = Point2f( pit2->x, pit2->y ); + } + } + + Mat _M1, _M2, _D1, _D2; + vector _R1, _R2, _T1, _T2; + calibrateCamera( objpt, imgpt1, imgsize, _M1, _D1, _R1, _T1, 0 ); + calibrateCamera( objpt, imgpt2, imgsize, _M2, _D2, _R2, _T1, 0 ); + undistortPoints( _imgpt1, _imgpt1, _M1, _D1, Mat(), _M1 ); + undistortPoints( _imgpt2, _imgpt2, _M2, _D2, Mat(), _M2 ); + + Mat matF, _H1, _H2; + matF = findFundamentalMat( _imgpt1, _imgpt2 ); + rectifyUncalibrated( _imgpt1, _imgpt2, matF, imgsize, _H1, _H2 ); + + Mat rectifPoints1, rectifPoints2; + perspectiveTransform( _imgpt1, rectifPoints1, _H1 ); + perspectiveTransform( _imgpt2, rectifPoints2, _H2 ); + + bool verticalStereo = abs(P2.at(0,3)) < abs(P2.at(1,3)); + double maxDiff_c = 0, maxDiff_uc = 0; + for( int i = 0, k = 0; i < nframes; i++ ) + { + vector temp[2]; + undistortPoints(Mat(imgpt1[i]), temp[0], M1, D1, R1, P1); + undistortPoints(Mat(imgpt2[i]), temp[1], M2, D2, R2, P2); + + for( int j = 0; j < npoints; j++, k++ ) + { + double diff_c = verticalStereo ? abs(temp[0][j].x - temp[1][j].x) : abs(temp[0][j].y - temp[1][j].y); + Point2f d = rectifPoints1.at(k,0) - rectifPoints2.at(k,0); + double diff_uc = verticalStereo ? abs(d.x) : abs(d.y); + maxDiff_c = max(maxDiff_c, diff_c); + maxDiff_uc = max(maxDiff_uc, diff_uc); + if( maxDiff_c > maxScanlineDistErr_c ) + { + ts->printf( cvtest::TS::LOG, "The distance between %s coordinates is too big(=%g) (used calibrated stereo), testcase %d\n", + verticalStereo ? "x" : "y", diff_c, testcase); + ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY ); + return; + } + if( maxDiff_uc > maxScanlineDistErr_uc ) + { + ts->printf( cvtest::TS::LOG, "The distance between %s coordinates is too big(=%g) (used uncalibrated stereo), testcase %d\n", + verticalStereo ? "x" : "y", diff_uc, testcase); + ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY ); + return; + } + } + } + + ts->printf( cvtest::TS::LOG, "Testcase %d. Max distance (calibrated) =%g\n" + "Max distance (uncalibrated) =%g\n", testcase, maxDiff_c, maxDiff_uc ); + } } //-------------------------------- CV_StereoCalibrationTest_C ------------------------------ @@ -1659,111 +1659,111 @@ void CV_StereoCalibrationTest::run( int ) class CV_StereoCalibrationTest_C : public CV_StereoCalibrationTest { public: - CV_StereoCalibrationTest_C() {} + CV_StereoCalibrationTest_C() {} protected: - virtual double calibrateStereoCamera( const vector >& objectPoints, - const vector >& imagePoints1, - const vector >& imagePoints2, - Mat& cameraMatrix1, Mat& distCoeffs1, - Mat& cameraMatrix2, Mat& distCoeffs2, - Size imageSize, Mat& R, Mat& T, - Mat& E, Mat& F, TermCriteria criteria, int flags ); - virtual void rectify( const Mat& cameraMatrix1, const Mat& distCoeffs1, - const Mat& cameraMatrix2, const Mat& distCoeffs2, - Size imageSize, const Mat& R, const Mat& T, - Mat& R1, Mat& R2, Mat& P1, Mat& P2, Mat& Q, - double alpha, Size newImageSize, - Rect* validPixROI1, Rect* validPixROI2, int flags ); - virtual bool rectifyUncalibrated( const Mat& points1, - const Mat& points2, const Mat& F, Size imgSize, - Mat& H1, Mat& H2, double threshold=5 ); - virtual void triangulate( const Mat& P1, const Mat& P2, + virtual double calibrateStereoCamera( const vector >& objectPoints, + const vector >& imagePoints1, + const vector >& imagePoints2, + Mat& cameraMatrix1, Mat& distCoeffs1, + Mat& cameraMatrix2, Mat& distCoeffs2, + Size imageSize, Mat& R, Mat& T, + Mat& E, Mat& F, TermCriteria criteria, int flags ); + virtual void rectify( const Mat& cameraMatrix1, const Mat& distCoeffs1, + const Mat& cameraMatrix2, const Mat& distCoeffs2, + Size imageSize, const Mat& R, const Mat& T, + Mat& R1, Mat& R2, Mat& P1, Mat& P2, Mat& Q, + double alpha, Size newImageSize, + Rect* validPixROI1, Rect* validPixROI2, int flags ); + virtual bool rectifyUncalibrated( const Mat& points1, + const Mat& points2, const Mat& F, Size imgSize, + Mat& H1, Mat& H2, double threshold=5 ); + virtual void triangulate( const Mat& P1, const Mat& P2, const Mat &points1, const Mat &points2, Mat &points4D ); - virtual void correct( const Mat& F, + virtual void correct( const Mat& F, const Mat &points1, const Mat &points2, Mat &newPoints1, Mat &newPoints2 ); }; double CV_StereoCalibrationTest_C::calibrateStereoCamera( const vector >& objectPoints, - const vector >& imagePoints1, - const vector >& imagePoints2, - Mat& cameraMatrix1, Mat& distCoeffs1, - Mat& cameraMatrix2, Mat& distCoeffs2, - Size imageSize, Mat& R, Mat& T, - Mat& E, Mat& F, TermCriteria criteria, int flags ) + const vector >& imagePoints1, + const vector >& imagePoints2, + Mat& cameraMatrix1, Mat& distCoeffs1, + Mat& cameraMatrix2, Mat& distCoeffs2, + Size imageSize, Mat& R, Mat& T, + Mat& E, Mat& F, TermCriteria criteria, int flags ) { - cameraMatrix1.create( 3, 3, CV_64F ); - cameraMatrix2.create( 3, 3, CV_64F); - distCoeffs1.create( 1, 5, CV_64F); - distCoeffs2.create( 1, 5, CV_64F); - R.create(3, 3, CV_64F); - T.create(3, 1, CV_64F); - E.create(3, 3, CV_64F); - F.create(3, 3, CV_64F); - - int nimages = (int)objectPoints.size(), total = 0; - for( int i = 0; i < nimages; i++ ) - { - total += (int)objectPoints[i].size(); - } - - Mat npoints( 1, nimages, CV_32S ), - objPt( 1, total, DataType::type ), - imgPt( 1, total, DataType::type ), - imgPt2( 1, total, DataType::type ); - - Point2f* imgPtData2 = imgPt2.ptr(); - Point3f* objPtData = objPt.ptr(); - Point2f* imgPtData = imgPt.ptr(); - for( int i = 0, ni = 0, j = 0; i < nimages; i++, j += ni ) - { - ni = (int)objectPoints[i].size(); - ((int*)npoints.data)[i] = ni; - std::copy(objectPoints[i].begin(), objectPoints[i].end(), objPtData + j); - std::copy(imagePoints1[i].begin(), imagePoints1[i].end(), imgPtData + j); - std::copy(imagePoints2[i].begin(), imagePoints2[i].end(), imgPtData2 + j); - } - CvMat _objPt = objPt, _imgPt = imgPt, _imgPt2 = imgPt2, _npoints = npoints; - CvMat _cameraMatrix1 = cameraMatrix1, _distCoeffs1 = distCoeffs1; - CvMat _cameraMatrix2 = cameraMatrix2, _distCoeffs2 = distCoeffs2; - CvMat matR = R, matT = T, matE = E, matF = F; - - return cvStereoCalibrate(&_objPt, &_imgPt, &_imgPt2, &_npoints, &_cameraMatrix1, - &_distCoeffs1, &_cameraMatrix2, &_distCoeffs2, imageSize, - &matR, &matT, &matE, &matF, criteria, flags ); + cameraMatrix1.create( 3, 3, CV_64F ); + cameraMatrix2.create( 3, 3, CV_64F); + distCoeffs1.create( 1, 5, CV_64F); + distCoeffs2.create( 1, 5, CV_64F); + R.create(3, 3, CV_64F); + T.create(3, 1, CV_64F); + E.create(3, 3, CV_64F); + F.create(3, 3, CV_64F); + + int nimages = (int)objectPoints.size(), total = 0; + for( int i = 0; i < nimages; i++ ) + { + total += (int)objectPoints[i].size(); + } + + Mat npoints( 1, nimages, CV_32S ), + objPt( 1, total, DataType::type ), + imgPt( 1, total, DataType::type ), + imgPt2( 1, total, DataType::type ); + + Point2f* imgPtData2 = imgPt2.ptr(); + Point3f* objPtData = objPt.ptr(); + Point2f* imgPtData = imgPt.ptr(); + for( int i = 0, ni = 0, j = 0; i < nimages; i++, j += ni ) + { + ni = (int)objectPoints[i].size(); + ((int*)npoints.data)[i] = ni; + std::copy(objectPoints[i].begin(), objectPoints[i].end(), objPtData + j); + std::copy(imagePoints1[i].begin(), imagePoints1[i].end(), imgPtData + j); + std::copy(imagePoints2[i].begin(), imagePoints2[i].end(), imgPtData2 + j); + } + CvMat _objPt = objPt, _imgPt = imgPt, _imgPt2 = imgPt2, _npoints = npoints; + CvMat _cameraMatrix1 = cameraMatrix1, _distCoeffs1 = distCoeffs1; + CvMat _cameraMatrix2 = cameraMatrix2, _distCoeffs2 = distCoeffs2; + CvMat matR = R, matT = T, matE = E, matF = F; + + return cvStereoCalibrate(&_objPt, &_imgPt, &_imgPt2, &_npoints, &_cameraMatrix1, + &_distCoeffs1, &_cameraMatrix2, &_distCoeffs2, imageSize, + &matR, &matT, &matE, &matF, criteria, flags ); } void CV_StereoCalibrationTest_C::rectify( const Mat& cameraMatrix1, const Mat& distCoeffs1, - const Mat& cameraMatrix2, const Mat& distCoeffs2, - Size imageSize, const Mat& R, const Mat& T, - Mat& R1, Mat& R2, Mat& P1, Mat& P2, Mat& Q, - double alpha, Size newImageSize, - Rect* validPixROI1, Rect* validPixROI2, int flags ) + const Mat& cameraMatrix2, const Mat& distCoeffs2, + Size imageSize, const Mat& R, const Mat& T, + Mat& R1, Mat& R2, Mat& P1, Mat& P2, Mat& Q, + double alpha, Size newImageSize, + Rect* validPixROI1, Rect* validPixROI2, int flags ) { - int rtype = CV_64F; - R1.create(3, 3, rtype); - R2.create(3, 3, rtype); - P1.create(3, 4, rtype); - P2.create(3, 4, rtype); - Q.create(4, 4, rtype); - CvMat _cameraMatrix1 = cameraMatrix1, _distCoeffs1 = distCoeffs1; - CvMat _cameraMatrix2 = cameraMatrix2, _distCoeffs2 = distCoeffs2; - CvMat matR = R, matT = T, _R1 = R1, _R2 = R2, _P1 = P1, _P2 = P2, matQ = Q; - cvStereoRectify( &_cameraMatrix1, &_cameraMatrix2, &_distCoeffs1, &_distCoeffs2, - imageSize, &matR, &matT, &_R1, &_R2, &_P1, &_P2, &matQ, flags, - alpha, newImageSize, (CvRect*)validPixROI1, (CvRect*)validPixROI2); + int rtype = CV_64F; + R1.create(3, 3, rtype); + R2.create(3, 3, rtype); + P1.create(3, 4, rtype); + P2.create(3, 4, rtype); + Q.create(4, 4, rtype); + CvMat _cameraMatrix1 = cameraMatrix1, _distCoeffs1 = distCoeffs1; + CvMat _cameraMatrix2 = cameraMatrix2, _distCoeffs2 = distCoeffs2; + CvMat matR = R, matT = T, _R1 = R1, _R2 = R2, _P1 = P1, _P2 = P2, matQ = Q; + cvStereoRectify( &_cameraMatrix1, &_cameraMatrix2, &_distCoeffs1, &_distCoeffs2, + imageSize, &matR, &matT, &_R1, &_R2, &_P1, &_P2, &matQ, flags, + alpha, newImageSize, (CvRect*)validPixROI1, (CvRect*)validPixROI2); } bool CV_StereoCalibrationTest_C::rectifyUncalibrated( const Mat& points1, - const Mat& points2, const Mat& F, Size imgSize, Mat& H1, Mat& H2, double threshold ) + const Mat& points2, const Mat& F, Size imgSize, Mat& H1, Mat& H2, double threshold ) { - H1.create(3, 3, CV_64F); - H2.create(3, 3, CV_64F); - CvMat _pt1 = points1, _pt2 = points2, matF, *pF=0, _H1 = H1, _H2 = H2; - if( F.size() == Size(3, 3) ) - pF = &(matF = F); - return cvStereoRectifyUncalibrated(&_pt1, &_pt2, pF, imgSize, &_H1, &_H2, threshold) > 0; + H1.create(3, 3, CV_64F); + H2.create(3, 3, CV_64F); + CvMat _pt1 = points1, _pt2 = points2, matF, *pF=0, _H1 = H1, _H2 = H2; + if( F.size() == Size(3, 3) ) + pF = &(matF = F); + return cvStereoRectifyUncalibrated(&_pt1, &_pt2, pF, imgSize, &_H1, &_H2, threshold) > 0; } void CV_StereoCalibrationTest_C::triangulate( const Mat& P1, const Mat& P2, @@ -1792,25 +1792,25 @@ void CV_StereoCalibrationTest_C::correct( const Mat& F, class CV_StereoCalibrationTest_CPP : public CV_StereoCalibrationTest { public: - CV_StereoCalibrationTest_CPP() {} + CV_StereoCalibrationTest_CPP() {} protected: - virtual double calibrateStereoCamera( const vector >& objectPoints, - const vector >& imagePoints1, - const vector >& imagePoints2, - Mat& cameraMatrix1, Mat& distCoeffs1, - Mat& cameraMatrix2, Mat& distCoeffs2, - Size imageSize, Mat& R, Mat& T, - Mat& E, Mat& F, TermCriteria criteria, int flags ); - virtual void rectify( const Mat& cameraMatrix1, const Mat& distCoeffs1, - const Mat& cameraMatrix2, const Mat& distCoeffs2, - Size imageSize, const Mat& R, const Mat& T, - Mat& R1, Mat& R2, Mat& P1, Mat& P2, Mat& Q, - double alpha, Size newImageSize, - Rect* validPixROI1, Rect* validPixROI2, int flags ); - virtual bool rectifyUncalibrated( const Mat& points1, - const Mat& points2, const Mat& F, Size imgSize, - Mat& H1, Mat& H2, double threshold=5 ); - virtual void triangulate( const Mat& P1, const Mat& P2, + virtual double calibrateStereoCamera( const vector >& objectPoints, + const vector >& imagePoints1, + const vector >& imagePoints2, + Mat& cameraMatrix1, Mat& distCoeffs1, + Mat& cameraMatrix2, Mat& distCoeffs2, + Size imageSize, Mat& R, Mat& T, + Mat& E, Mat& F, TermCriteria criteria, int flags ); + virtual void rectify( const Mat& cameraMatrix1, const Mat& distCoeffs1, + const Mat& cameraMatrix2, const Mat& distCoeffs2, + Size imageSize, const Mat& R, const Mat& T, + Mat& R1, Mat& R2, Mat& P1, Mat& P2, Mat& Q, + double alpha, Size newImageSize, + Rect* validPixROI1, Rect* validPixROI2, int flags ); + virtual bool rectifyUncalibrated( const Mat& points1, + const Mat& points2, const Mat& F, Size imgSize, + Mat& H1, Mat& H2, double threshold=5 ); + virtual void triangulate( const Mat& P1, const Mat& P2, const Mat &points1, const Mat &points2, Mat &points4D ); virtual void correct( const Mat& F, @@ -1819,33 +1819,33 @@ protected: }; double CV_StereoCalibrationTest_CPP::calibrateStereoCamera( const vector >& objectPoints, - const vector >& imagePoints1, - const vector >& imagePoints2, - Mat& cameraMatrix1, Mat& distCoeffs1, - Mat& cameraMatrix2, Mat& distCoeffs2, - Size imageSize, Mat& R, Mat& T, - Mat& E, Mat& F, TermCriteria criteria, int flags ) + const vector >& imagePoints1, + const vector >& imagePoints2, + Mat& cameraMatrix1, Mat& distCoeffs1, + Mat& cameraMatrix2, Mat& distCoeffs2, + Size imageSize, Mat& R, Mat& T, + Mat& E, Mat& F, TermCriteria criteria, int flags ) { - return stereoCalibrate( objectPoints, imagePoints1, imagePoints2, - cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2, - imageSize, R, T, E, F, criteria, flags ); + return stereoCalibrate( objectPoints, imagePoints1, imagePoints2, + cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2, + imageSize, R, T, E, F, criteria, flags ); } void CV_StereoCalibrationTest_CPP::rectify( const Mat& cameraMatrix1, const Mat& distCoeffs1, - const Mat& cameraMatrix2, const Mat& distCoeffs2, - Size imageSize, const Mat& R, const Mat& T, - Mat& R1, Mat& R2, Mat& P1, Mat& P2, Mat& Q, - double alpha, Size newImageSize, - Rect* validPixROI1, Rect* validPixROI2, int flags ) + const Mat& cameraMatrix2, const Mat& distCoeffs2, + Size imageSize, const Mat& R, const Mat& T, + Mat& R1, Mat& R2, Mat& P1, Mat& P2, Mat& Q, + double alpha, Size newImageSize, + Rect* validPixROI1, Rect* validPixROI2, int flags ) { - stereoRectify( cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2, - imageSize, R, T, R1, R2, P1, P2, Q, flags, alpha, newImageSize,validPixROI1, validPixROI2 ); + stereoRectify( cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2, + imageSize, R, T, R1, R2, P1, P2, Q, flags, alpha, newImageSize,validPixROI1, validPixROI2 ); } bool CV_StereoCalibrationTest_CPP::rectifyUncalibrated( const Mat& points1, - const Mat& points2, const Mat& F, Size imgSize, Mat& H1, Mat& H2, double threshold ) + const Mat& points2, const Mat& F, Size imgSize, Mat& H1, Mat& H2, double threshold ) { - return stereoRectifyUncalibrated( points1, points2, F, imgSize, H1, H2, threshold ); + return stereoRectifyUncalibrated( points1, points2, F, imgSize, H1, H2, threshold ); } void CV_StereoCalibrationTest_CPP::triangulate( const Mat& P1, const Mat& P2, diff --git a/modules/calib3d/test/test_cameracalibration_artificial.cpp b/modules/calib3d/test/test_cameracalibration_artificial.cpp index 4b80640..e5585e9 100644 --- a/modules/calib3d/test/test_cameracalibration_artificial.cpp +++ b/modules/calib3d/test/test_cameracalibration_artificial.cpp @@ -55,22 +55,22 @@ using namespace cv; using namespace std; //template ostream& operator<<(ostream& out, const Mat_& mat) -//{ +//{ // for(Mat_::const_iterator pos = mat.begin(), end = mat.end(); pos != end; ++pos) // out << *pos << " "; // return out; //} -//ostream& operator<<(ostream& out, const Mat& mat) { return out << Mat_(mat); } +//ostream& operator<<(ostream& out, const Mat& mat) { return out << Mat_(mat); } Mat calcRvec(const vector& points, const Size& cornerSize) -{ +{ Point3f p00 = points[0]; Point3f p10 = points[1]; - Point3f p01 = points[cornerSize.width]; + Point3f p01 = points[cornerSize.width]; Vec3d ex(p10.x - p00.x, p10.y - p00.y, p10.z - p00.z); - Vec3d ey(p01.x - p00.x, p01.y - p00.y, p01.z - p00.z); - Vec3d ez = ex.cross(ey); + Vec3d ey(p01.x - p00.x, p01.y - p00.y, p01.z - p00.z); + Vec3d ez = ex.cross(ey); Mat rot(3, 3, CV_64F); *rot.ptr(0) = ex; @@ -89,7 +89,7 @@ public: { } ~CV_CalibrateCameraArtificialTest() {} -protected: +protected: int r; const static int JUST_FIND_CORNERS = 0; @@ -111,7 +111,7 @@ protected: { ts->printf( cvtest::TS::LOG, "Bad shape of camera matrix returned \n"); ts->set_failed_test_info(cvtest::TS::FAIL_MISMATCH); - } + } double fx_e = camMat_est.at(0, 0), fy_e = camMat_est.at(1, 1); double cx_e = camMat_est.at(0, 2), cy_e = camMat_est.at(1, 2); @@ -121,19 +121,19 @@ protected: const double eps = 1e-2; const double dlt = 1e-5; - bool fail = checkErr(fx_e, fx, eps, dlt) || checkErr(fy_e, fy, eps, dlt) || - checkErr(cx_e, cx, eps, dlt) || checkErr(cy_e, cy, eps, dlt); + bool fail = checkErr(fx_e, fx, eps, dlt) || checkErr(fy_e, fy, eps, dlt) || + checkErr(cx_e, cx, eps, dlt) || checkErr(cy_e, cy, eps, dlt); if (fail) { - ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY); - } + ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY); + } ts->printf( cvtest::TS::LOG, "%d) Expected [Fx Fy Cx Cy] = [%.3f %.3f %.3f %.3f]\n", r, fx, fy, cx, cy); - ts->printf( cvtest::TS::LOG, "%d) Estimated [Fx Fy Cx Cy] = [%.3f %.3f %.3f %.3f]\n", r, fx_e, fy_e, cx_e, cy_e); + ts->printf( cvtest::TS::LOG, "%d) Estimated [Fx Fy Cx Cy] = [%.3f %.3f %.3f %.3f]\n", r, fx_e, fy_e, cx_e, cy_e); } void compareDistCoeffs(const Mat_& distCoeffs, const Mat& distCoeffs_est) - { + { const double *dt_e = distCoeffs_est.ptr(); double k1_e = dt_e[0], k2_e = dt_e[1], k3_e = dt_e[4]; @@ -143,21 +143,21 @@ protected: double p1 = distCoeffs(0, 2), p2 = distCoeffs(0, 3); const double eps = 5e-2; - const double dlt = 1e-3; + const double dlt = 1e-3; const double eps_k3 = 5; - const double dlt_k3 = 1e-3; + const double dlt_k3 = 1e-3; - bool fail = checkErr(k1_e, k1, eps, dlt) || checkErr(k2_e, k2, eps, dlt) || checkErr(k3_e, k3, eps_k3, dlt_k3) || - checkErr(p1_e, p1, eps, dlt) || checkErr(p2_e, p2, eps, dlt); + bool fail = checkErr(k1_e, k1, eps, dlt) || checkErr(k2_e, k2, eps, dlt) || checkErr(k3_e, k3, eps_k3, dlt_k3) || + checkErr(p1_e, p1, eps, dlt) || checkErr(p2_e, p2, eps, dlt); if (fail) { // commented according to vp123's recomendation. TODO - improve accuaracy - //ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY); ss - } + //ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY); ss + } ts->printf( cvtest::TS::LOG, "%d) DistCoeff exp=(%.2f, %.2f, %.4f, %.4f %.2f)\n", r, k1, k2, p1, p2, k3); - ts->printf( cvtest::TS::LOG, "%d) DistCoeff est=(%.2f, %.2f, %.4f, %.4f %.2f)\n", r, k1_e, k2_e, p1_e, p2_e, k3_e); + ts->printf( cvtest::TS::LOG, "%d) DistCoeff est=(%.2f, %.2f, %.4f, %.4f %.2f)\n", r, k1_e, k2_e, p1_e, p2_e, k3_e); ts->printf( cvtest::TS::LOG, "%d) AbsError = [%.5f %.5f %.5f %.5f %.5f]\n", r, fabs(k1-k1_e), fabs(k2-k2_e), fabs(p1-p1_e), fabs(p2-p2_e), fabs(k3-k3_e)); } @@ -173,20 +173,20 @@ protected: const Point3d& tvec = *tvecs[i].ptr(); const Point3d& tvec_est = *tvecs_est[i].ptr(); - if (norm(tvec_est - tvec) > eps* (norm(tvec) + dlt)) + if (norm(tvec_est - tvec) > eps* (norm(tvec) + dlt)) { if (err_count++ < errMsgNum) { - if (err_count == errMsgNum) - ts->printf( cvtest::TS::LOG, "%d) ...\n", r); - else + if (err_count == errMsgNum) + ts->printf( cvtest::TS::LOG, "%d) ...\n", r); + else { - ts->printf( cvtest::TS::LOG, "%d) Bad accuracy in returned tvecs. Index = %d\n", r, i); + ts->printf( cvtest::TS::LOG, "%d) Bad accuracy in returned tvecs. Index = %d\n", r, i); ts->printf( cvtest::TS::LOG, "%d) norm(tvec_est - tvec) = %f, norm(tvec_exp) = %f \n", r, norm(tvec_est - tvec), norm(tvec)); } } ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY); - } + } } } @@ -199,20 +199,20 @@ protected: int err_count = 0; const int errMsgNum = 4; for(size_t i = 0; i < rvecs.size(); ++i) - { + { Rodrigues(rvecs[i], rmat); - Rodrigues(rvecs_est[i], rmat_est); + Rodrigues(rvecs_est[i], rmat_est); if (norm(rmat_est, rmat) > eps* (norm(rmat) + dlt)) { if (err_count++ < errMsgNum) { if (err_count == errMsgNum) - ts->printf( cvtest::TS::LOG, "%d) ...\n", r); + ts->printf( cvtest::TS::LOG, "%d) ...\n", r); else { - ts->printf( cvtest::TS::LOG, "%d) Bad accuracy in returned rvecs (rotation matrs). Index = %d\n", r, i); - ts->printf( cvtest::TS::LOG, "%d) norm(rot_mat_est - rot_mat_exp) = %f, norm(rot_mat_exp) = %f \n", r, norm(rmat_est, rmat), norm(rmat)); + ts->printf( cvtest::TS::LOG, "%d) Bad accuracy in returned rvecs (rotation matrs). Index = %d\n", r, i); + ts->printf( cvtest::TS::LOG, "%d) norm(rot_mat_est - rot_mat_exp) = %f, norm(rot_mat_exp) = %f \n", r, norm(rmat_est, rmat), norm(rmat)); } } @@ -221,19 +221,19 @@ protected: } } - double reprojectErrorWithoutIntrinsics(const vector& cb3d, const vector& rvecs_exp, const vector& tvecs_exp, + double reprojectErrorWithoutIntrinsics(const vector& cb3d, const vector& _rvecs_exp, const vector& _tvecs_exp, const vector& rvecs_est, const vector& tvecs_est) - { + { const static Mat eye33 = Mat::eye(3, 3, CV_64F); const static Mat zero15 = Mat::zeros(1, 5, CV_64F); - Mat chessboard3D(cb3d); + Mat _chessboard3D(cb3d); vector uv_exp, uv_est; - double res = 0; + double res = 0; - for(size_t i = 0; i < rvecs_exp.size(); ++i) - { - projectPoints(chessboard3D, rvecs_exp[i], tvecs_exp[i], eye33, zero15, uv_exp); - projectPoints(chessboard3D, rvecs_est[i], tvecs_est[i], eye33, zero15, uv_est); + for(size_t i = 0; i < rvecs_exp.size(); ++i) + { + projectPoints(_chessboard3D, _rvecs_exp[i], _tvecs_exp[i], eye33, zero15, uv_exp); + projectPoints(_chessboard3D, rvecs_est[i], tvecs_est[i], eye33, zero15, uv_est); for(size_t j = 0; j < cb3d.size(); ++j) res += norm(uv_exp[i] - uv_est[i]); } @@ -243,7 +243,7 @@ protected: Size2f sqSile; vector chessboard3D; - vector boards, rvecs_exp, tvecs_exp, rvecs_spnp, tvecs_spnp; + vector boards, rvecs_exp, tvecs_exp, rvecs_spnp, tvecs_spnp; vector< vector > objectPoints; vector< vector > imagePoints_art; vector< vector > imagePoints_findCb; @@ -268,29 +268,29 @@ protected: imagePoints_findCb.clear(); vector corners_art, corners_fcb; - for(size_t i = 0; i < brdsNum; ++i) - { + for(size_t i = 0; i < brdsNum; ++i) + { for(;;) { boards[i] = cbg(bg, camMat, distCoeffs, sqSile, corners_art); - if(findChessboardCorners(boards[i], cornersSize, corners_fcb)) - break; - } + if(findChessboardCorners(boards[i], cornersSize, corners_fcb)) + break; + } //cv::namedWindow("CB"); imshow("CB", boards[i]); cv::waitKey(); - imagePoints_art.push_back(corners_art); + imagePoints_art.push_back(corners_art); imagePoints_findCb.push_back(corners_fcb); tvecs_exp[i].create(1, 3, CV_64F); *tvecs_exp[i].ptr() = cbg.corners3d[0]; - rvecs_exp[i] = calcRvec(cbg.corners3d, cbg.cornersSize()); + rvecs_exp[i] = calcRvec(cbg.corners3d, cbg.cornersSize()); } } void runTest(const Size& imgSize, const Mat_& camMat, const Mat_& distCoeffs, size_t brdsNum, const Size& cornersSize, int flag = 0) - { + { const TermCriteria tc(TermCriteria::EPS|TermCriteria::MAX_ITER, 30, 0.1); vector< vector > imagePoints; @@ -300,9 +300,9 @@ protected: case JUST_FIND_CORNERS: imagePoints = imagePoints_findCb; break; case ARTIFICIAL_CORNERS: imagePoints = imagePoints_art; break; - case USE_CORNERS_SUBPIX: + case USE_CORNERS_SUBPIX: for(size_t i = 0; i < brdsNum; ++i) - { + { Mat gray; cvtColor(boards[i], gray, CV_BGR2GRAY); vector tmp = imagePoints_findCb[i]; @@ -312,9 +312,9 @@ protected: break; case USE_4QUAD_CORNERS: for(size_t i = 0; i < brdsNum; ++i) - { + { Mat gray; - cvtColor(boards[i], gray, CV_BGR2GRAY); + cvtColor(boards[i], gray, CV_BGR2GRAY); vector tmp = imagePoints_findCb[i]; find4QuadCornerSubpix(gray, tmp, Size(5, 5)); imagePoints.push_back(tmp); @@ -323,7 +323,7 @@ protected: default: throw std::exception(); } - + Mat camMat_est = Mat::eye(3, 3, CV_64F), distCoeffs_est = Mat::zeros(1, 5, CV_64F); vector rvecs_est, tvecs_est; @@ -342,9 +342,9 @@ protected: compareCameraMatrs(camMat, camMat_est); compareDistCoeffs(distCoeffs, distCoeffs_est); compareShiftVecs(tvecs_exp, tvecs_est); - compareRotationVecs(rvecs_exp, rvecs_est); + compareRotationVecs(rvecs_exp, rvecs_est); - double rep_errorWOI = reprojectErrorWithoutIntrinsics(chessboard3D, rvecs_exp, tvecs_exp, rvecs_est, tvecs_est); + double rep_errorWOI = reprojectErrorWithoutIntrinsics(chessboard3D, rvecs_exp, tvecs_exp, rvecs_est, tvecs_est); rep_errorWOI /= brdsNum * cornersSize.area(); const double thres2 = 0.01; @@ -352,8 +352,8 @@ protected: { ts->printf( cvtest::TS::LOG, "%d) Too big reproject error without intrinsics = %f\n", r, rep_errorWOI); ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY); - } - + } + ts->printf( cvtest::TS::LOG, "%d) Testing solvePnP...\n", r); rvecs_spnp.resize(brdsNum); tvecs_spnp.resize(brdsNum); @@ -361,11 +361,11 @@ protected: solvePnP(Mat(objectPoints[i]), Mat(imagePoints[i]), camMat, distCoeffs, rvecs_spnp[i], tvecs_spnp[i]); compareShiftVecs(tvecs_exp, tvecs_spnp); - compareRotationVecs(rvecs_exp, rvecs_spnp); + compareRotationVecs(rvecs_exp, rvecs_spnp); } void run(int) - { + { ts->set_failed_test_info(cvtest::TS::OK); RNG& rng = theRNG(); @@ -373,11 +373,11 @@ protected: int progress = 0; int repeat_num = 3; for(r = 0; r < repeat_num; ++r) - { - const int brds_num = 20; + { + const int brds_num = 20; - Mat bg(Size(640, 480), CV_8UC3); - randu(bg, Scalar::all(32), Scalar::all(255)); + Mat bg(Size(640, 480), CV_8UC3); + randu(bg, Scalar::all(32), Scalar::all(255)); GaussianBlur(bg, bg, Size(5, 5), 2); double fx = 300 + (20 * (double)rng - 10); @@ -399,20 +399,20 @@ protected: Mat_ distCoeffs(1, 5, 0.0); distCoeffs << k1, k2, p1, p2, k3; - ChessBoardGenerator cbg(Size(9, 8)); + ChessBoardGenerator cbg(Size(9, 8)); cbg.min_cos = 0.9; cbg.cov = 0.8; progress = update_progress(progress, r, repeat_num, 0); - ts->printf( cvtest::TS::LOG, "\n"); + ts->printf( cvtest::TS::LOG, "\n"); prepareForTest(bg, camMat, distCoeffs, brds_num, cbg); - ts->printf( cvtest::TS::LOG, "artificial corners\n"); - runTest(bg.size(), camMat, distCoeffs, brds_num, cbg.cornersSize(), ARTIFICIAL_CORNERS); + ts->printf( cvtest::TS::LOG, "artificial corners\n"); + runTest(bg.size(), camMat, distCoeffs, brds_num, cbg.cornersSize(), ARTIFICIAL_CORNERS); progress = update_progress(progress, r, repeat_num, 0); ts->printf( cvtest::TS::LOG, "findChessboard corners\n"); - runTest(bg.size(), camMat, distCoeffs, brds_num, cbg.cornersSize(), JUST_FIND_CORNERS); + runTest(bg.size(), camMat, distCoeffs, brds_num, cbg.cornersSize(), JUST_FIND_CORNERS); progress = update_progress(progress, r, repeat_num, 0); ts->printf( cvtest::TS::LOG, "cornersSubPix corners\n"); @@ -424,6 +424,6 @@ protected: progress = update_progress(progress, r, repeat_num, 0); } } -}; +}; TEST(Calib3d_CalibrateCamera_CPP, accuracy_on_artificial_data) { CV_CalibrateCameraArtificialTest test; test.safe_run(); } diff --git a/modules/calib3d/test/test_chesscorners.cpp b/modules/calib3d/test/test_chesscorners.cpp index f0e58e3..f6d513d 100644 --- a/modules/calib3d/test/test_chesscorners.cpp +++ b/modules/calib3d/test/test_chesscorners.cpp @@ -54,9 +54,9 @@ void show_points( const Mat& gray, const Mat& u, const vector& v, Size { Mat rgb( gray.size(), CV_8U); merge(vector(3, gray), rgb); - + for(size_t i = 0; i < v.size(); i++ ) - circle( rgb, v[i], 3, CV_RGB(255, 0, 0), CV_FILLED); + circle( rgb, v[i], 3, CV_RGB(255, 0, 0), CV_FILLED); if( !u.empty() ) { @@ -67,7 +67,7 @@ void show_points( const Mat& gray, const Mat& u, const vector& v, Size } if (!v.empty()) { - Mat corners((int)v.size(), 1, CV_32FC2, (void*)&v[0]); + Mat corners((int)v.size(), 1, CV_32FC2, (void*)&v[0]); drawChessboardCorners( rgb, pattern_size, corners, was_found ); } //namedWindow( "test", 0 ); imshow( "test", rgb ); waitKey(0); @@ -122,11 +122,11 @@ double calcError(const vector& v, const Mat& u) //printf("\n"); err = min(err, err1); } - + #if defined(_L2_ERR) err = sqrt(err/count_exp); #endif //_L2_ERR - + return err; } @@ -137,8 +137,7 @@ const double precise_success_error_level = 2; /* ///////////////////// chess_corner_test ///////////////////////// */ void CV_ChessboardDetectorTest::run( int /*start_from */) { - cvtest::TS& ts = *this->ts; - ts.set_failed_test_info( cvtest::TS::OK ); + ts->set_failed_test_info( cvtest::TS::OK ); /*if (!checkByGenerator()) return;*/ @@ -146,23 +145,23 @@ void CV_ChessboardDetectorTest::run( int /*start_from */) { case CHESSBOARD: checkByGenerator(); - if (ts.get_err_code() != cvtest::TS::OK) + if (ts->get_err_code() != cvtest::TS::OK) { break; } run_batch("negative_list.dat"); - if (ts.get_err_code() != cvtest::TS::OK) + if (ts->get_err_code() != cvtest::TS::OK) { break; } run_batch("chessboard_list.dat"); - if (ts.get_err_code() != cvtest::TS::OK) + if (ts->get_err_code() != cvtest::TS::OK) { break; } - + run_batch("chessboard_list_subpixel.dat"); break; case CIRCLES_GRID: @@ -176,36 +175,34 @@ void CV_ChessboardDetectorTest::run( int /*start_from */) void CV_ChessboardDetectorTest::run_batch( const string& filename ) { - cvtest::TS& ts = *this->ts; - - ts.printf(cvtest::TS::LOG, "\nRunning batch %s\n", filename.c_str()); + ts->printf(cvtest::TS::LOG, "\nRunning batch %s\n", filename.c_str()); //#define WRITE_POINTS 1 -#ifndef WRITE_POINTS +#ifndef WRITE_POINTS double max_rough_error = 0, max_precise_error = 0; #endif string folder; switch( pattern ) { case CHESSBOARD: - folder = string(ts.get_data_path()) + "cameracalibration/"; + folder = string(ts->get_data_path()) + "cameracalibration/"; break; case CIRCLES_GRID: - folder = string(ts.get_data_path()) + "cameracalibration/circles/"; + folder = string(ts->get_data_path()) + "cameracalibration/circles/"; break; case ASYMMETRIC_CIRCLES_GRID: - folder = string(ts.get_data_path()) + "cameracalibration/asymmetric_circles/"; + folder = string(ts->get_data_path()) + "cameracalibration/asymmetric_circles/"; break; } FileStorage fs( folder + filename, FileStorage::READ ); FileNode board_list = fs["boards"]; - + if( !fs.isOpened() || board_list.empty() || !board_list.isSeq() || board_list.size() % 2 != 0 ) { - ts.printf( cvtest::TS::LOG, "%s can not be readed or is not valid\n", (folder + filename).c_str() ); - ts.printf( cvtest::TS::LOG, "fs.isOpened=%d, board_list.empty=%d, board_list.isSeq=%d,board_list.size()%2=%d\n", + ts->printf( cvtest::TS::LOG, "%s can not be readed or is not valid\n", (folder + filename).c_str() ); + ts->printf( cvtest::TS::LOG, "fs.isOpened=%d, board_list.empty=%d, board_list.isSeq=%d,board_list.size()%2=%d\n", fs.isOpened(), (int)board_list.empty(), board_list.isSeq(), board_list.size()%2); - ts.set_failed_test_info( cvtest::TS::FAIL_MISSING_TEST_DATA ); + ts->set_failed_test_info( cvtest::TS::FAIL_MISSING_TEST_DATA ); return; } @@ -216,29 +213,29 @@ void CV_ChessboardDetectorTest::run_batch( const string& filename ) for(int idx = 0; idx < max_idx; ++idx ) { - ts.update_context( this, idx, true ); - + ts->update_context( this, idx, true ); + /* read the image */ - string img_file = board_list[idx * 2]; + string img_file = board_list[idx * 2]; Mat gray = imread( folder + img_file, 0); - + if( gray.empty() ) { - ts.printf( cvtest::TS::LOG, "one of chessboard images can't be read: %s\n", img_file.c_str() ); - ts.set_failed_test_info( cvtest::TS::FAIL_MISSING_TEST_DATA ); + ts->printf( cvtest::TS::LOG, "one of chessboard images can't be read: %s\n", img_file.c_str() ); + ts->set_failed_test_info( cvtest::TS::FAIL_MISSING_TEST_DATA ); return; } - string filename = folder + (string)board_list[idx * 2 + 1]; + string _filename = folder + (string)board_list[idx * 2 + 1]; bool doesContatinChessboard; Mat expected; { - FileStorage fs(filename, FileStorage::READ); - fs["corners"] >> expected; - fs["isFound"] >> doesContatinChessboard; - fs.release(); - } - size_t count_exp = static_cast(expected.cols * expected.rows); + FileStorage fs1(_filename, FileStorage::READ); + fs1["corners"] >> expected; + fs1["isFound"] >> doesContatinChessboard; + fs1.release(); + } + size_t count_exp = static_cast(expected.cols * expected.rows); Size pattern_size = expected.size(); vector v; @@ -256,11 +253,11 @@ void CV_ChessboardDetectorTest::run_batch( const string& filename ) break; } show_points( gray, Mat(), v, pattern_size, result ); - + if( result ^ doesContatinChessboard || v.size() != count_exp ) { - ts.printf( cvtest::TS::LOG, "chessboard is detected incorrectly in %s\n", img_file.c_str() ); - ts.set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT ); + ts->printf( cvtest::TS::LOG, "chessboard is detected incorrectly in %s\n", img_file.c_str() ); + ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT ); return; } @@ -291,45 +288,45 @@ void CV_ChessboardDetectorTest::run_batch( const string& filename ) #if 1 if( err > precise_success_error_level ) { - ts.printf( cvtest::TS::LOG, "Image %s: bad accuracy of adjusted corners %f\n", img_file.c_str(), err ); - ts.set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY ); + ts->printf( cvtest::TS::LOG, "Image %s: bad accuracy of adjusted corners %f\n", img_file.c_str(), err ); + ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY ); return; } #endif - ts.printf(cvtest::TS::LOG, "Error on %s is %f\n", img_file.c_str(), err); + ts->printf(cvtest::TS::LOG, "Error on %s is %f\n", img_file.c_str(), err); max_precise_error = MAX( max_precise_error, err ); -#endif +#endif } #ifdef WRITE_POINTS Mat mat_v(pattern_size, CV_32FC2, (void*)&v[0]); - FileStorage fs(filename, FileStorage::WRITE); + FileStorage fs(_filename, FileStorage::WRITE); fs << "isFound" << result; fs << "corners" << mat_v; fs.release(); #endif progress = update_progress( progress, idx, max_idx, 0 ); - } - + } + sum_error /= count; - ts.printf(cvtest::TS::LOG, "Average error is %f\n", sum_error); + ts->printf(cvtest::TS::LOG, "Average error is %f\n", sum_error); } double calcErrorMinError(const Size& cornSz, const vector& corners_found, const vector& corners_generated) { - Mat m1(cornSz, CV_32FC2, (Point2f*)&corners_generated[0]); + Mat m1(cornSz, CV_32FC2, (Point2f*)&corners_generated[0]); Mat m2; flip(m1, m2, 0); Mat m3; flip(m1, m3, 1); m3 = m3.t(); flip(m3, m3, 1); - + Mat m4 = m1.t(); flip(m4, m4, 1); - double min1 = min(calcError(corners_found, m1), calcError(corners_found, m2)); - double min2 = min(calcError(corners_found, m3), calcError(corners_found, m4)); + double min1 = min(calcError(corners_found, m1), calcError(corners_found, m2)); + double min2 = min(calcError(corners_found, m3), calcError(corners_found, m4)); return min(min1, min2); } -bool validateData(const ChessBoardGenerator& cbg, const Size& imgSz, +bool validateData(const ChessBoardGenerator& cbg, const Size& imgSz, const vector& corners_generated) { Size cornersSize = cbg.cornersSize(); @@ -341,7 +338,7 @@ bool validateData(const ChessBoardGenerator& cbg, const Size& imgSz, for(int j = 1; j < mat.cols - 2; ++j) { const Point2f& cur = mat(i, j); - + tmp = norm( cur - mat(i + 1, j + 1) ); if (tmp < minNeibDist) tmp = minNeibDist; @@ -361,33 +358,33 @@ bool validateData(const ChessBoardGenerator& cbg, const Size& imgSz, const double threshold = 0.25; double cbsize = (max(cornersSize.width, cornersSize.height) + 1) * minNeibDist; - int imgsize = min(imgSz.height, imgSz.width); + int imgsize = min(imgSz.height, imgSz.width); return imgsize * threshold < cbsize; } bool CV_ChessboardDetectorTest::checkByGenerator() -{ +{ bool res = true; //theRNG() = 0x58e6e895b9913160; //cv::DefaultRngAuto dra; //theRNG() = *ts->get_rng(); - Mat bg(Size(800, 600), CV_8UC3, Scalar::all(255)); - randu(bg, Scalar::all(0), Scalar::all(255)); - GaussianBlur(bg, bg, Size(7,7), 3.0); - + Mat bg(Size(800, 600), CV_8UC3, Scalar::all(255)); + randu(bg, Scalar::all(0), Scalar::all(255)); + GaussianBlur(bg, bg, Size(7,7), 3.0); + Mat_ camMat(3, 3); camMat << 300.f, 0.f, bg.cols/2.f, 0, 300.f, bg.rows/2.f, 0.f, 0.f, 1.f; - + Mat_ distCoeffs(1, 5); distCoeffs << 1.2f, 0.2f, 0.f, 0.f, 0.f; const Size sizes[] = { Size(6, 6), Size(8, 6), Size(11, 12), Size(5, 4) }; - const size_t sizes_num = sizeof(sizes)/sizeof(sizes[0]); - const int test_num = 16; + const size_t sizes_num = sizeof(sizes)/sizeof(sizes[0]); + const int test_num = 16; int progress = 0; for(int i = 0; i < test_num; ++i) - { + { progress = update_progress( progress, i, test_num, 0 ); ChessBoardGenerator cbg(sizes[i % sizes_num]); @@ -398,37 +395,37 @@ bool CV_ChessboardDetectorTest::checkByGenerator() if(!validateData(cbg, cb.size(), corners_generated)) { ts->printf( cvtest::TS::LOG, "Chess board skipped - too small" ); - continue; + continue; } - /*cb = cb * 0.8 + Scalar::all(30); + /*cb = cb * 0.8 + Scalar::all(30); GaussianBlur(cb, cb, Size(3, 3), 0.8); */ - //cv::addWeighted(cb, 0.8, bg, 0.2, 20, cb); + //cv::addWeighted(cb, 0.8, bg, 0.2, 20, cb); //cv::namedWindow("CB"); cv::imshow("CB", cb); cv::waitKey(); - + vector corners_found; int flags = i % 8; // need to check branches for all flags bool found = findChessboardCorners(cb, cbg.cornersSize(), corners_found, flags); - if (!found) - { + if (!found) + { ts->printf( cvtest::TS::LOG, "Chess board corners not found\n" ); ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY ); res = false; - return res; + return res; } - double err = calcErrorMinError(cbg.cornersSize(), corners_found, corners_generated); + double err = calcErrorMinError(cbg.cornersSize(), corners_found, corners_generated); if( err > rough_success_error_level ) { ts->printf( cvtest::TS::LOG, "bad accuracy of corner guesses" ); ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY ); res = false; return res; - } - } + } + } /* ***** negative ***** */ - { + { vector corners_found; bool found = findChessboardCorners(bg, Size(8, 7), corners_found); if (found) @@ -437,27 +434,27 @@ bool CV_ChessboardDetectorTest::checkByGenerator() ChessBoardGenerator cbg(Size(8, 7)); vector cg; - Mat cb = cbg(bg, camMat, distCoeffs, cg); + Mat cb = cbg(bg, camMat, distCoeffs, cg); found = findChessboardCorners(cb, Size(3, 4), corners_found); if (found) - res = false; + res = false; Point2f c = std::accumulate(cg.begin(), cg.end(), Point2f(), plus()) * (1.f/cg.size()); Mat_ aff(2, 3); aff << 1.0, 0.0, -(double)c.x, 0.0, 1.0, 0.0; Mat sh; - warpAffine(cb, sh, aff, cb.size()); + warpAffine(cb, sh, aff, cb.size()); found = findChessboardCorners(sh, cbg.cornersSize(), corners_found); if (found) - res = false; - + res = false; + vector< vector > cnts(1); vector& cnt = cnts[0]; - cnt.push_back(cg[ 0]); cnt.push_back(cg[0+2]); - cnt.push_back(cg[7+0]); cnt.push_back(cg[7+2]); + cnt.push_back(cg[ 0]); cnt.push_back(cg[0+2]); + cnt.push_back(cg[7+0]); cnt.push_back(cg[7+2]); cv::drawContours(cb, cnts, -1, Scalar::all(128), CV_FILLED); found = findChessboardCorners(cb, cbg.cornersSize(), corners_found); @@ -466,7 +463,7 @@ bool CV_ChessboardDetectorTest::checkByGenerator() cv::drawChessboardCorners(cb, cbg.cornersSize(), Mat(corners_found), found); } - + return res; } diff --git a/modules/calib3d/test/test_compose_rt.cpp b/modules/calib3d/test/test_compose_rt.cpp index b55318c..b71288e 100644 --- a/modules/calib3d/test/test_compose_rt.cpp +++ b/modules/calib3d/test/test_compose_rt.cpp @@ -47,87 +47,87 @@ using namespace std; class Differential { -public: - typedef Mat_ mat_t; +public: + typedef Mat_ mat_t; - Differential(double eps_, const mat_t& rv1_, const mat_t& tv1_, const mat_t& rv2_, const mat_t& tv2_) + Differential(double eps_, const mat_t& rv1_, const mat_t& tv1_, const mat_t& rv2_, const mat_t& tv2_) : rv1(rv1_), tv1(tv1_), rv2(rv2_), tv2(tv2_), eps(eps_), ev(3, 1) {} void dRv1(mat_t& dr3_dr1, mat_t& dt3_dr1) - { + { dr3_dr1.create(3, 3); dt3_dr1.create(3, 3); - - for(int i = 0; i < 3; ++i) + + for(int i = 0; i < 3; ++i) { - ev.setTo(Scalar(0)); ev(i, 0) = eps; - - composeRT( rv1 + ev, tv1, rv2, tv2, rv3_p, tv3_p); + ev.setTo(Scalar(0)); ev(i, 0) = eps; + + composeRT( rv1 + ev, tv1, rv2, tv2, rv3_p, tv3_p); composeRT( rv1 - ev, tv1, rv2, tv2, rv3_m, tv3_m); - dr3_dr1.col(i) = rv3_p - rv3_m; - dt3_dr1.col(i) = tv3_p - tv3_m; + dr3_dr1.col(i) = rv3_p - rv3_m; + dt3_dr1.col(i) = tv3_p - tv3_m; } dr3_dr1 /= 2 * eps; dt3_dr1 /= 2 * eps; } void dRv2(mat_t& dr3_dr2, mat_t& dt3_dr2) - { + { dr3_dr2.create(3, 3); dt3_dr2.create(3, 3); - - for(int i = 0; i < 3; ++i) + + for(int i = 0; i < 3; ++i) { - ev.setTo(Scalar(0)); ev(i, 0) = eps; - - composeRT( rv1, tv1, rv2 + ev, tv2, rv3_p, tv3_p); + ev.setTo(Scalar(0)); ev(i, 0) = eps; + + composeRT( rv1, tv1, rv2 + ev, tv2, rv3_p, tv3_p); composeRT( rv1, tv1, rv2 - ev, tv2, rv3_m, tv3_m); - dr3_dr2.col(i) = rv3_p - rv3_m; - dt3_dr2.col(i) = tv3_p - tv3_m; + dr3_dr2.col(i) = rv3_p - rv3_m; + dt3_dr2.col(i) = tv3_p - tv3_m; } dr3_dr2 /= 2 * eps; dt3_dr2 /= 2 * eps; } void dTv1(mat_t& drt3_dt1, mat_t& dt3_dt1) - { + { drt3_dt1.create(3, 3); dt3_dt1.create(3, 3); - - for(int i = 0; i < 3; ++i) + + for(int i = 0; i < 3; ++i) { - ev.setTo(Scalar(0)); ev(i, 0) = eps; - - composeRT( rv1, tv1 + ev, rv2, tv2, rv3_p, tv3_p); + ev.setTo(Scalar(0)); ev(i, 0) = eps; + + composeRT( rv1, tv1 + ev, rv2, tv2, rv3_p, tv3_p); composeRT( rv1, tv1 - ev, rv2, tv2, rv3_m, tv3_m); - drt3_dt1.col(i) = rv3_p - rv3_m; - dt3_dt1.col(i) = tv3_p - tv3_m; + drt3_dt1.col(i) = rv3_p - rv3_m; + dt3_dt1.col(i) = tv3_p - tv3_m; } drt3_dt1 /= 2 * eps; dt3_dt1 /= 2 * eps; } void dTv2(mat_t& dr3_dt2, mat_t& dt3_dt2) - { + { dr3_dt2.create(3, 3); dt3_dt2.create(3, 3); - - for(int i = 0; i < 3; ++i) + + for(int i = 0; i < 3; ++i) { - ev.setTo(Scalar(0)); ev(i, 0) = eps; - - composeRT( rv1, tv1, rv2, tv2 + ev, rv3_p, tv3_p); + ev.setTo(Scalar(0)); ev(i, 0) = eps; + + composeRT( rv1, tv1, rv2, tv2 + ev, rv3_p, tv3_p); composeRT( rv1, tv1, rv2, tv2 - ev, rv3_m, tv3_m); - dr3_dt2.col(i) = rv3_p - rv3_m; - dt3_dt2.col(i) = tv3_p - tv3_m; + dr3_dt2.col(i) = rv3_p - rv3_m; + dt3_dt2.col(i) = tv3_p - tv3_m; } dr3_dt2 /= 2 * eps; dt3_dt2 /= 2 * eps; } - + private: const mat_t& rv1, tv1, rv2, tv2; double eps; Mat_ ev; - + Differential& operator=(const Differential&); - Mat rv3_m, tv3_m, rv3_p, tv3_p; + Mat rv3_m, tv3_m, rv3_p, tv3_p; }; class CV_composeRT_Test : public cvtest::BaseTest @@ -135,24 +135,23 @@ class CV_composeRT_Test : public cvtest::BaseTest public: CV_composeRT_Test() {} ~CV_composeRT_Test() {} -protected: - +protected: + void run(int) { - cvtest::TS& ts = *this->ts; - ts.set_failed_test_info(cvtest::TS::OK); - - Mat_ rvec1(3, 1), tvec1(3, 1), rvec2(3, 1), tvec2(3, 1); + ts->set_failed_test_info(cvtest::TS::OK); + + Mat_ rvec1(3, 1), tvec1(3, 1), rvec2(3, 1), tvec2(3, 1); randu(rvec1, Scalar(0), Scalar(6.29)); randu(rvec2, Scalar(0), Scalar(6.29)); randu(tvec1, Scalar(-2), Scalar(2)); randu(tvec2, Scalar(-2), Scalar(2)); - + Mat rvec3, tvec3; composeRT(rvec1, tvec1, rvec2, tvec2, rvec3, tvec3); - + Mat rvec3_exp, tvec3_exp; Mat rmat1, rmat2; @@ -164,53 +163,53 @@ protected: const double thres = 1e-5; if (norm(rvec3_exp, rvec3) > thres || norm(tvec3_exp, tvec3) > thres) - ts.set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY); + ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY); const double eps = 1e-3; Differential diff(eps, rvec1, tvec1, rvec2, tvec2); - + Mat dr3dr1, dr3dt1, dr3dr2, dr3dt2, dt3dr1, dt3dt1, dt3dr2, dt3dt2; - composeRT(rvec1, tvec1, rvec2, tvec2, rvec3, tvec3, + composeRT(rvec1, tvec1, rvec2, tvec2, rvec3, tvec3, dr3dr1, dr3dt1, dr3dr2, dr3dt2, dt3dr1, dt3dt1, dt3dr2, dt3dt2); - + Mat_ dr3_dr1, dt3_dr1; diff.dRv1(dr3_dr1, dt3_dr1); if (norm(dr3_dr1, dr3dr1) > thres || norm(dt3_dr1, dt3dr1) > thres) - { - ts.printf( cvtest::TS::LOG, "Invalid derivates by r1\n" ); - ts.set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY); + { + ts->printf( cvtest::TS::LOG, "Invalid derivates by r1\n" ); + ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY); } Mat_ dr3_dr2, dt3_dr2; diff.dRv2(dr3_dr2, dt3_dr2); if (norm(dr3_dr2, dr3dr2) > thres || norm(dt3_dr2, dt3dr2) > thres) - { - ts.printf( cvtest::TS::LOG, "Invalid derivates by r2\n" ); - ts.set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY); + { + ts->printf( cvtest::TS::LOG, "Invalid derivates by r2\n" ); + ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY); } Mat_ dr3_dt1, dt3_dt1; diff.dTv1(dr3_dt1, dt3_dt1); if (norm(dr3_dt1, dr3dt1) > thres || norm(dt3_dt1, dt3dt1) > thres) - { - ts.printf( cvtest::TS::LOG, "Invalid derivates by t1\n" ); - ts.set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY); + { + ts->printf( cvtest::TS::LOG, "Invalid derivates by t1\n" ); + ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY); } - + Mat_ dr3_dt2, dt3_dt2; diff.dTv2(dr3_dt2, dt3_dt2); if (norm(dr3_dt2, dr3dt2) > thres || norm(dt3_dt2, dt3dt2) > thres) - { - ts.printf( cvtest::TS::LOG, "Invalid derivates by t2\n" ); - ts.set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY); + { + ts->printf( cvtest::TS::LOG, "Invalid derivates by t2\n" ); + ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY); } - } -}; - + } +}; + TEST(Calib3d_ComposeRT, accuracy) { CV_composeRT_Test test; test.safe_run(); } diff --git a/modules/calib3d/test/test_homography.cpp b/modules/calib3d/test/test_homography.cpp index a424782..49c20e7 100644 --- a/modules/calib3d/test/test_homography.cpp +++ b/modules/calib3d/test/test_homography.cpp @@ -86,20 +86,20 @@ protected: double sigma; private: - float max_diff, max_2diff; - bool check_matrix_size(const cv::Mat& H); - bool check_matrix_diff(const cv::Mat& original, const cv::Mat& found, const int norm_type, double &diff); + float max_diff, max_2diff; + bool check_matrix_size(const cv::Mat& H); + bool check_matrix_diff(const cv::Mat& original, const cv::Mat& found, const int norm_type, double &diff); int check_ransac_mask_1(const Mat& src, const Mat& mask); - int check_ransac_mask_2(const Mat& original_mask, const Mat& found_mask); - - void print_information_1(int j, int N, int method, const Mat& H); - void print_information_2(int j, int N, int method, const Mat& H, const Mat& H_res, int k, double diff); - void print_information_3(int j, int N, const Mat& mask); - void print_information_4(int method, int j, int N, int k, int l, double diff); - void print_information_5(int method, int j, int N, int l, double diff); - void print_information_6(int j, int N, int k, double diff, bool value); - void print_information_7(int j, int N, int k, double diff, bool original_value, bool found_value); - void print_information_8(int j, int N, int k, int l, double diff); + int check_ransac_mask_2(const Mat& original_mask, const Mat& found_mask); + + void print_information_1(int j, int N, int method, const Mat& H); + void print_information_2(int j, int N, int method, const Mat& H, const Mat& H_res, int k, double diff); + void print_information_3(int j, int N, const Mat& mask); + void print_information_4(int method, int j, int N, int k, int l, double diff); + void print_information_5(int method, int j, int N, int l, double diff); + void print_information_6(int j, int N, int k, double diff, bool value); + void print_information_7(int j, int N, int k, double diff, bool original_value, bool found_value); + void print_information_8(int j, int N, int k, int l, double diff); }; CV_HomographyTest::CV_HomographyTest() : max_diff(1e-2f), max_2diff(2e-2f) @@ -112,7 +112,7 @@ CV_HomographyTest::CV_HomographyTest() : max_diff(1e-2f), max_2diff(2e-2f) CV_HomographyTest::~CV_HomographyTest() {} -bool CV_HomographyTest::check_matrix_size(const cv::Mat& H) +bool CV_HomographyTest::check_matrix_size(const cv::Mat& H) { return (H.rows == 3) && (H.cols == 3); } @@ -138,25 +138,25 @@ int CV_HomographyTest::check_ransac_mask_2(const Mat& original_mask, const Mat& return 0; } -void CV_HomographyTest::print_information_1(int j, int N, int method, const Mat& H) +void CV_HomographyTest::print_information_1(int j, int N, int _method, const Mat& H) { cout << endl; cout << "Checking for homography matrix sizes..." << endl; cout << endl; cout << "Type of srcPoints: "; if ((j>-1) && (j<2)) cout << "Mat of CV_32FC2"; else cout << "vector "; cout << " Type of dstPoints: "; if (j % 2 == 0) cout << "Mat of CV_32FC2"; else cout << "vector "; cout << endl; cout << "Count of points: " << N << endl; cout << endl; - cout << "Method: "; if (method == 0) cout << 0; else if (method == 8) cout << "RANSAC"; else cout << "LMEDS"; cout << endl; + cout << "Method: "; if (_method == 0) cout << 0; else if (_method == 8) cout << "RANSAC"; else cout << "LMEDS"; cout << endl; cout << "Homography matrix:" << endl; cout << endl; cout << H << endl; cout << endl; cout << "Number of rows: " << H.rows << " Number of cols: " << H.cols << endl; cout << endl; } -void CV_HomographyTest::print_information_2(int j, int N, int method, const Mat& H, const Mat& H_res, int k, double diff) +void CV_HomographyTest::print_information_2(int j, int N, int _method, const Mat& H, const Mat& H_res, int k, double diff) { cout << endl; cout << "Checking for accuracy of homography matrix computing..." << endl; cout << endl; cout << "Type of srcPoints: "; if ((j>-1) && (j<2)) cout << "Mat of CV_32FC2"; else cout << "vector "; cout << " Type of dstPoints: "; if (j % 2 == 0) cout << "Mat of CV_32FC2"; else cout << "vector "; cout << endl; cout << "Count of points: " << N << endl; cout << endl; - cout << "Method: "; if (method == 0) cout << 0; else if (method == 8) cout << "RANSAC"; else cout << "LMEDS"; cout << endl; + cout << "Method: "; if (_method == 0) cout << 0; else if (_method == 8) cout << "RANSAC"; else cout << "LMEDS"; cout << endl; cout << "Original matrix:" << endl; cout << endl; cout << H << endl; cout << endl; cout << "Found matrix:" << endl; cout << endl; @@ -178,10 +178,10 @@ void CV_HomographyTest::print_information_3(int j, int N, const Mat& mask) cout << "Number of rows: " << mask.rows << " Number of cols: " << mask.cols << endl; cout << endl; } -void CV_HomographyTest::print_information_4(int method, int j, int N, int k, int l, double diff) +void CV_HomographyTest::print_information_4(int _method, int j, int N, int k, int l, double diff) { cout << endl; cout << "Checking for accuracy of reprojection error computing..." << endl; cout << endl; - cout << "Method: "; if (method == 0) cout << 0 << endl; else cout << "CV_LMEDS" << endl; + cout << "Method: "; if (_method == 0) cout << 0 << endl; else cout << "CV_LMEDS" << endl; cout << "Type of srcPoints: "; if ((j>-1) && (j<2)) cout << "Mat of CV_32FC2"; else cout << "vector "; cout << " Type of dstPoints: "; if (j % 2 == 0) cout << "Mat of CV_32FC2"; else cout << "vector "; cout << endl; cout << "Sigma of normal noise: " << sigma << endl; @@ -192,10 +192,10 @@ void CV_HomographyTest::print_information_4(int method, int j, int N, int k, int cout << "Maxumum allowed difference: " << max_2diff << endl; cout << endl; } -void CV_HomographyTest::print_information_5(int method, int j, int N, int l, double diff) -{ +void CV_HomographyTest::print_information_5(int _method, int j, int N, int l, double diff) +{ cout << endl; cout << "Checking for accuracy of reprojection error computing..." << endl; cout << endl; - cout << "Method: "; if (method == 0) cout << 0 << endl; else cout << "CV_LMEDS" << endl; + cout << "Method: "; if (_method == 0) cout << 0 << endl; else cout << "CV_LMEDS" << endl; cout << "Type of srcPoints: "; if ((j>-1) && (j<2)) cout << "Mat of CV_32FC2"; else cout << "vector "; cout << " Type of dstPoints: "; if (j % 2 == 0) cout << "Mat of CV_32FC2"; else cout << "vector "; cout << endl; cout << "Sigma of normal noise: " << sigma << endl; @@ -371,7 +371,7 @@ void CV_HomographyTest::run(int) if (code) { print_information_3(j, N, mask[j]); - + switch (code) { case 1: { CV_Error(CALIB3D_HOMOGRAPHY_ERROR_RANSAC_MASK, MESSAGE_RANSAC_MASK_1); break; } @@ -380,7 +380,7 @@ void CV_HomographyTest::run(int) default: break; } - + return; } @@ -412,7 +412,7 @@ void CV_HomographyTest::run(int) { case 0: case CV_LMEDS: - { + { Mat H_res_64 [4] = { cv::findHomography(src_mat_2f, dst_mat_2f), cv::findHomography(src_mat_2f, dst_vec), cv::findHomography(src_vec, dst_mat_2f), @@ -465,7 +465,7 @@ void CV_HomographyTest::run(int) } continue; - } + } case CV_RANSAC: { cv::Mat mask_res [4]; @@ -555,7 +555,7 @@ void CV_HomographyTest::run(int) } } } - + continue; } diff --git a/modules/calib3d/test/test_precomp.hpp b/modules/calib3d/test/test_precomp.hpp index 43792d6..62d876f 100644 --- a/modules/calib3d/test/test_precomp.hpp +++ b/modules/calib3d/test/test_precomp.hpp @@ -1,3 +1,7 @@ +#ifdef __GNUC__ +# pragma GCC diagnostic ignored "-Wmissing-declarations" +#endif + #ifndef __OPENCV_TEST_PRECOMP_HPP__ #define __OPENCV_TEST_PRECOMP_HPP__ diff --git a/modules/calib3d/test/test_solvepnp_ransac.cpp b/modules/calib3d/test/test_solvepnp_ransac.cpp index 6683fbc..8322214 100644 --- a/modules/calib3d/test/test_solvepnp_ransac.cpp +++ b/modules/calib3d/test/test_solvepnp_ransac.cpp @@ -106,7 +106,7 @@ protected: } } - virtual bool runTest(RNG& rng, int mode, int method, const vector& points, const double* eps, double& maxError) + virtual bool runTest(RNG& rng, int mode, int method, const vector& points, const double* epsilon, double& maxError) { Mat rvec, tvec; vector inliers; @@ -136,7 +136,7 @@ protected: bool isTestSuccess = inliers.size() >= points.size()*0.95; double rvecDiff = norm(rvec-trueRvec), tvecDiff = norm(tvec-trueTvec); - isTestSuccess = isTestSuccess && rvecDiff < eps[method] && tvecDiff < eps[method]; + isTestSuccess = isTestSuccess && rvecDiff < epsilon[method] && tvecDiff < epsilon[method]; double error = rvecDiff > tvecDiff ? rvecDiff : tvecDiff; //cout << error << " " << inliers.size() << " " << eps[method] << endl; if (error > maxError) @@ -147,8 +147,7 @@ protected: void run(int) { - cvtest::TS& ts = *this->ts; - ts.set_failed_test_info(cvtest::TS::OK); + ts->set_failed_test_info(cvtest::TS::OK); vector points; const int pointsCount = 500; @@ -157,7 +156,7 @@ protected: const int methodsCount = 3; - RNG rng = ts.get_rng(); + RNG rng = ts->get_rng(); for (int mode = 0; mode < 2; mode++) @@ -174,9 +173,9 @@ protected: //cout << maxError << " " << successfulTestsCount << endl; if (successfulTestsCount < 0.7*totalTestsCount) { - ts.printf( cvtest::TS::LOG, "Invalid accuracy for method %d, failed %d tests from %d, maximum error equals %f, distortion mode equals %d\n", + ts->printf( cvtest::TS::LOG, "Invalid accuracy for method %d, failed %d tests from %d, maximum error equals %f, distortion mode equals %d\n", method, totalTestsCount - successfulTestsCount, totalTestsCount, maxError, mode); - ts.set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY); + ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY); } } } @@ -198,7 +197,7 @@ public: ~CV_solvePnP_Test() {} protected: - virtual bool runTest(RNG& rng, int mode, int method, const vector& points, const double* eps, double& maxError) + virtual bool runTest(RNG& rng, int mode, int method, const vector& points, const double* epsilon, double& maxError) { Mat rvec, tvec; Mat trueRvec, trueTvec; @@ -226,7 +225,7 @@ protected: false, method); double rvecDiff = norm(rvec-trueRvec), tvecDiff = norm(tvec-trueTvec); - bool isTestSuccess = rvecDiff < eps[method] && tvecDiff < eps[method]; + bool isTestSuccess = rvecDiff < epsilon[method] && tvecDiff < epsilon[method]; double error = rvecDiff > tvecDiff ? rvecDiff : tvecDiff; if (error > maxError) diff --git a/modules/calib3d/test/test_stereomatching.cpp b/modules/calib3d/test/test_stereomatching.cpp index a2ba91f..4b35dad 100755 --- a/modules/calib3d/test/test_stereomatching.cpp +++ b/modules/calib3d/test/test_stereomatching.cpp @@ -421,7 +421,7 @@ void CV_StereoMatchingTest::run(int) ts->set_failed_test_info( code ); return; } - + string fullResultFilename = dataPath + ALGORITHMS_DIR + algorithmName + RESULT_FILE; FileStorage resFS( fullResultFilename, FileStorage::READ ); bool isWrite = true; // write or compare results @@ -593,11 +593,11 @@ int CV_StereoMatchingTest::readDatasetsParams( FileStorage& fs ) assert(fn.isSeq()); for( int i = 0; i < (int)fn.size(); i+=3 ) { - string name = fn[i]; + string _name = fn[i]; DatasetParams params; string sf = fn[i+1]; params.dispScaleFactor = atoi(sf.c_str()); string uv = fn[i+2]; params.dispUnknVal = atoi(uv.c_str()); - datasetsParams[name] = params; + datasetsParams[_name] = params; } return cvtest::TS::OK; } @@ -713,7 +713,7 @@ class CV_StereoSGBMTest : public CV_StereoMatchingTest public: CV_StereoSGBMTest() { - name = "stereosgbm"; + name = "stereosgbm"; fill(rmsEps.begin(), rmsEps.end(), 0.25f); fill(fracEps.begin(), fracEps.end(), 0.01f); } diff --git a/modules/contrib/include/opencv2/contrib/contrib.hpp b/modules/contrib/include/opencv2/contrib/contrib.hpp index 6732d19..83b3248 100644 --- a/modules/contrib/include/opencv2/contrib/contrib.hpp +++ b/modules/contrib/include/opencv2/contrib/contrib.hpp @@ -63,48 +63,48 @@ private: GSD_INTENSITY_LT = 15, GSD_INTENSITY_UT = 250 }; - + class CV_EXPORTS Histogram { private: enum { HistogramSize = (GSD_HUE_UT - GSD_HUE_LT + 1) }; - + protected: int findCoverageIndex(double surfaceToCover, int defaultValue = 0); - + public: CvHistogram *fHistogram; Histogram(); virtual ~Histogram(); - + void findCurveThresholds(int &x1, int &x2, double percent = 0.05); void mergeWith(Histogram *source, double weight); }; - + int nStartCounter, nFrameCount, nSkinHueLowerBound, nSkinHueUpperBound, nMorphingMethod, nSamplingDivider; double fHistogramMergeFactor, fHuePercentCovered; Histogram histogramHueMotion, skinHueHistogram; IplImage *imgHueFrame, *imgSaturationFrame, *imgLastGrayFrame, *imgMotionFrame, *imgFilteredFrame; IplImage *imgShrinked, *imgTemp, *imgGrayFrame, *imgHSVFrame; - + protected: void initData(IplImage *src, int widthDivider, int heightDivider); void adaptiveFilter(); - + public: - + enum { MORPHING_METHOD_NONE = 0, MORPHING_METHOD_ERODE = 1, MORPHING_METHOD_ERODE_ERODE = 2, MORPHING_METHOD_ERODE_DILATE = 3 }; - + CvAdaptiveSkinDetector(int samplingDivider = 1, int morphingMethod = MORPHING_METHOD_NONE); virtual ~CvAdaptiveSkinDetector(); - + virtual void process(IplImage *inputBGRImage, IplImage *outputHueMask); }; @@ -116,7 +116,7 @@ public: class CV_EXPORTS CvFuzzyPoint { public: double x, y, value; - + CvFuzzyPoint(double _x, double _y); }; @@ -124,13 +124,13 @@ class CV_EXPORTS CvFuzzyCurve { private: std::vector points; double value, centre; - + bool between(double x, double x1, double x2); - + public: CvFuzzyCurve(); ~CvFuzzyCurve(); - + void setCentre(double _centre); double getCentre(); void clear(); @@ -143,7 +143,7 @@ public: class CV_EXPORTS CvFuzzyFunction { public: std::vector curves; - + CvFuzzyFunction(); ~CvFuzzyFunction(); void addCurve(CvFuzzyCurve *curve, double value = 0); @@ -186,7 +186,7 @@ private: FuzzyResizer(); int calcOutput(double edgeDensity, double density); }; - + class SearchWindow { public: @@ -200,7 +200,7 @@ private: double density; unsigned int depthLow, depthHigh; int verticalEdgeLeft, verticalEdgeRight, horizontalEdgeTop, horizontalEdgeBottom; - + SearchWindow(); ~SearchWindow(); void setSize(int _x, int _y, int _width, int _height); @@ -212,7 +212,7 @@ private: void getResizeAttribsEdgeDensityFuzzy(int &resizeDx, int &resizeDy, int &resizeDw, int &resizeDh); bool meanShift(IplImage *maskImage, IplImage *depthMap, int maxIteration, bool initDepth); }; - + public: enum TrackingState { @@ -222,40 +222,40 @@ public: tsSetWindow = 3, tsDisabled = 10 }; - + enum ResizeMethod { rmEdgeDensityLinear = 0, rmEdgeDensityFuzzy = 1, rmInnerDensity = 2 }; - + enum { MinKernelMass = 1000 }; - + SearchWindow kernel; int searchMode; - + private: enum { MaxMeanShiftIteration = 5, MaxSetSizeIteration = 5 }; - + void findOptimumSearchWindow(SearchWindow &searchWindow, IplImage *maskImage, IplImage *depthMap, int maxIteration, int resizeMethod, bool initDepth); - + public: CvFuzzyMeanShiftTracker(); ~CvFuzzyMeanShiftTracker(); - + void track(IplImage *maskImage, IplImage *depthMap, int resizeMethod, bool resetSearch, int minKernelMass = MinKernelMass); }; namespace cv { - + class CV_EXPORTS Octree { public: @@ -268,11 +268,11 @@ namespace cv bool isLeaf; int children[8]; }; - + Octree(); Octree( const vector& points, int maxLevels = 10, int minPoints = 20 ); virtual ~Octree(); - + virtual void buildTree( const vector& points, int maxLevels = 10, int minPoints = 20 ); virtual void getPointsWithinSphere( const Point3f& center, float radius, vector& points ) const; @@ -281,85 +281,85 @@ namespace cv int minPoints; vector points; vector nodes; - + virtual void buildNext(size_t node_ind); }; - - + + class CV_EXPORTS Mesh3D { public: struct EmptyMeshException {}; - + Mesh3D(); Mesh3D(const vector& vtx); ~Mesh3D(); - + void buildOctree(); void clearOctree(); float estimateResolution(float tryRatio = 0.1f); void computeNormals(float normalRadius, int minNeighbors = 20); void computeNormals(const vector& subset, float normalRadius, int minNeighbors = 20); - + void writeAsVrml(const String& file, const vector& colors = vector()) const; - + vector vtx; vector normals; float resolution; Octree octree; - + const static Point3f allzero; }; - + class CV_EXPORTS SpinImageModel { public: - + /* model parameters, leave unset for default or auto estimate */ float normalRadius; int minNeighbors; - + float binSize; int imageWidth; - + float lambda; float gamma; - + float T_GeometriccConsistency; float T_GroupingCorespondances; - + /* public interface */ SpinImageModel(); explicit SpinImageModel(const Mesh3D& mesh); ~SpinImageModel(); - + void setLogger(std::ostream* log); void selectRandomSubset(float ratio); void setSubset(const vector& subset); void compute(); - + void match(const SpinImageModel& scene, vector< vector >& result); - + Mat packRandomScaledSpins(bool separateScale = false, size_t xCount = 10, size_t yCount = 10) const; - + size_t getSpinCount() const { return spinImages.rows; } Mat getSpinImage(size_t index) const { return spinImages.row((int)index); } const Point3f& getSpinVertex(size_t index) const { return mesh.vtx[subset[index]]; } const Point3f& getSpinNormal(size_t index) const { return mesh.normals[subset[index]]; } - + const Mesh3D& getMesh() const { return mesh; } Mesh3D& getMesh() { return mesh; } - + /* static utility functions */ static bool spinCorrelation(const Mat& spin1, const Mat& spin2, float lambda, float& result); - + static Point2f calcSpinMapCoo(const Point3f& point, const Point3f& vertex, const Point3f& normal); - + static float geometricConsistency(const Point3f& pointScene1, const Point3f& normalScene1, const Point3f& pointModel1, const Point3f& normalModel1, const Point3f& pointScene2, const Point3f& normalScene2, const Point3f& pointModel2, const Point3f& normalModel2); - + static float groupingCreteria(const Point3f& pointScene1, const Point3f& normalScene1, const Point3f& pointModel1, const Point3f& normalModel1, const Point3f& pointScene2, const Point3f& normalScene2, @@ -367,40 +367,40 @@ namespace cv float gamma); protected: void defaultParams(); - + void matchSpinToModel(const Mat& spin, vector& indeces, vector& corrCoeffs, bool useExtremeOutliers = true) const; - + void repackSpinImages(const vector& mask, Mat& spinImages, bool reAlloc = true) const; - + vector subset; Mesh3D mesh; Mat spinImages; std::ostream* out; }; - + class CV_EXPORTS TickMeter { public: TickMeter(); void start(); void stop(); - + int64 getTimeTicks() const; double getTimeMicro() const; double getTimeMilli() const; double getTimeSec() const; int64 getCounter() const; - + void reset(); private: int64 counter; int64 sumTime; int64 startTime; }; - + CV_EXPORTS std::ostream& operator<<(std::ostream& out, const TickMeter& tm); - + class CV_EXPORTS SelfSimDescriptor { public: @@ -412,29 +412,29 @@ namespace cv SelfSimDescriptor(const SelfSimDescriptor& ss); virtual ~SelfSimDescriptor(); SelfSimDescriptor& operator = (const SelfSimDescriptor& ss); - + size_t getDescriptorSize() const; Size getGridSize( Size imgsize, Size winStride ) const; - + virtual void compute(const Mat& img, vector& descriptors, Size winStride=Size(), const vector& locations=vector()) const; virtual void computeLogPolarMapping(Mat& mappingMask) const; virtual void SSD(const Mat& img, Point pt, Mat& ssd) const; - + int smallSize; int largeSize; int startDistanceBucket; int numberOfDistanceBuckets; int numberOfAngles; - + enum { DEFAULT_SMALL_SIZE = 5, DEFAULT_LARGE_SIZE = 41, DEFAULT_NUM_ANGLES = 20, DEFAULT_START_DISTANCE_BUCKET = 3, DEFAULT_NUM_DISTANCE_BUCKETS = 7 }; }; - - + + typedef bool (*BundleAdjustCallback)(int iteration, double norm_error, void* user_data); - + class LevMarqSparse { public: LevMarqSparse(); @@ -447,9 +447,9 @@ namespace cv Mat& visibility, // visibility matrix. rows correspond to points, columns correspond to cameras // 1 - point is visible for the camera, 0 - invisible Mat& P0, // starting vector of parameters, first cameras then points - Mat& X, // measurements, in order of visibility. non visible cases are skipped + Mat& X, // measurements, in order of visibility. non visible cases are skipped TermCriteria criteria, // termination criteria - + // callback for estimation of Jacobian matrices void (CV_CDECL * fjac)(int i, int j, Mat& point_params, Mat& cam_params, Mat& A, Mat& B, void* data), @@ -459,9 +459,9 @@ namespace cv void* data, // user-specific data passed to the callbacks BundleAdjustCallback cb, void* user_data ); - + virtual ~LevMarqSparse(); - + virtual void run( int npoints, // number of points int ncameras, // number of cameras int nPointParams, // number of params per one point (3 in case of 3D points) @@ -471,9 +471,9 @@ namespace cv Mat& visibility, // visibility matrix. rows correspond to points, columns correspond to cameras // 1 - point is visible for the camera, 0 - invisible Mat& P0, // starting vector of parameters, first cameras then points - Mat& X, // measurements, in order of visibility. non visible cases are skipped + Mat& X, // measurements, in order of visibility. non visible cases are skipped TermCriteria criteria, // termination criteria - + // callback for estimation of Jacobian matrices void (CV_CDECL * fjac)(int i, int j, Mat& point_params, Mat& cam_params, Mat& A, Mat& B, void* data), @@ -482,13 +482,13 @@ namespace cv Mat& cam_params, Mat& estim, void* data), void* data // user-specific data passed to the callbacks ); - + virtual void clear(); - + // useful function to do simple bundle adjustment tasks static void bundleAdjust(vector& points, // positions of points in global coordinate system (input and output) const vector >& imagePoints, // projections of 3d points for every camera - const vector >& visibility, // visibility of 3d points for every camera + const vector >& visibility, // visibility of 3d points for every camera vector& cameraMatrix, // intrinsic matrices of all cameras (input and output) vector& R, // rotation matrices of all cameras (input and output) vector& T, // translation vector of all cameras (input and output) @@ -496,123 +496,123 @@ namespace cv const TermCriteria& criteria= TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, DBL_EPSILON), BundleAdjustCallback cb = 0, void* user_data = 0); - + public: virtual void optimize(CvMat &_vis); //main function that runs minimization - + //iteratively asks for measurement for visible camera-point pairs void ask_for_proj(CvMat &_vis,bool once=false); //iteratively asks for Jacobians for every camera_point pair void ask_for_projac(CvMat &_vis); - + CvMat* err; //error X-hX double prevErrNorm, errNorm; double lambda; CvTermCriteria criteria; int iters; - + CvMat** U; //size of array is equal to number of cameras CvMat** V; //size of array is equal to number of points CvMat** inv_V_star; //inverse of V* - + CvMat** A; CvMat** B; CvMat** W; - - CvMat* X; //measurement - CvMat* hX; //current measurement extimation given new parameter vector - - CvMat* prevP; //current already accepted parameter. + + CvMat* X; //measurement + CvMat* hX; //current measurement extimation given new parameter vector + + CvMat* prevP; //current already accepted parameter. CvMat* P; // parameters used to evaluate function with new params - // this parameters may be rejected - + // this parameters may be rejected + CvMat* deltaP; //computed increase of parameters (result of normal system solution ) - + CvMat** ea; // sum_i AijT * e_ij , used as right part of normal equation - // length of array is j = number of cameras + // length of array is j = number of cameras CvMat** eb; // sum_j BijT * e_ij , used as right part of normal equation // length of array is i = number of points - + CvMat** Yj; //length of array is i = num_points - - CvMat* S; //big matrix of block Sjk , each block has size num_cam_params x num_cam_params - + + CvMat* S; //big matrix of block Sjk , each block has size num_cam_params x num_cam_params + CvMat* JtJ_diag; //diagonal of JtJ, used to backup diagonal elements before augmentation - + CvMat* Vis_index; // matrix which element is index of measurement for point i and camera j - + int num_cams; int num_points; int num_err_param; int num_cam_param; int num_point_param; - - //target function and jacobian pointers, which needs to be initialized + + //target function and jacobian pointers, which needs to be initialized void (*fjac)(int i, int j, Mat& point_params, Mat& cam_params, Mat& A, Mat& B, void* data); void (*func)(int i, int j, Mat& point_params, Mat& cam_params, Mat& estim, void* data); - + void* data; - + BundleAdjustCallback cb; void* user_data; - }; - + }; + CV_EXPORTS int chamerMatching( Mat& img, Mat& templ, vector >& results, vector& cost, double templScale=1, int maxMatches = 20, double minMatchDistance = 1.0, int padX = 3, int padY = 3, int scales = 5, double minScale = 0.6, double maxScale = 1.6, double orientationWeight = 0.5, double truncate = 20); - - + + class CV_EXPORTS StereoVar { public: - // Flags + // Flags enum {USE_INITIAL_DISPARITY = 1, USE_EQUALIZE_HIST = 2, USE_SMART_ID = 4, USE_AUTO_PARAMS = 8, USE_MEDIAN_FILTERING = 16}; enum {CYCLE_O, CYCLE_V}; enum {PENALIZATION_TICHONOV, PENALIZATION_CHARBONNIER, PENALIZATION_PERONA_MALIK}; - + //! the default constructor CV_WRAP StereoVar(); - + //! the full constructor taking all the necessary algorithm parameters CV_WRAP StereoVar(int levels, double pyrScale, int nIt, int minDisp, int maxDisp, int poly_n, double poly_sigma, float fi, float lambda, int penalization, int cycle, int flags); - + //! the destructor virtual ~StereoVar(); - + //! the stereo correspondence operator that computes disparity map for the specified rectified stereo pair CV_WRAP_AS(compute) virtual void operator()(const Mat& left, const Mat& right, Mat& disp); - - CV_PROP_RW int levels; - CV_PROP_RW double pyrScale; - CV_PROP_RW int nIt; - CV_PROP_RW int minDisp; - CV_PROP_RW int maxDisp; - CV_PROP_RW int poly_n; - CV_PROP_RW double poly_sigma; - CV_PROP_RW float fi; - CV_PROP_RW float lambda; - CV_PROP_RW int penalization; - CV_PROP_RW int cycle; - CV_PROP_RW int flags; - + + CV_PROP_RW int levels; + CV_PROP_RW double pyrScale; + CV_PROP_RW int nIt; + CV_PROP_RW int minDisp; + CV_PROP_RW int maxDisp; + CV_PROP_RW int poly_n; + CV_PROP_RW double poly_sigma; + CV_PROP_RW float fi; + CV_PROP_RW float lambda; + CV_PROP_RW int penalization; + CV_PROP_RW int cycle; + CV_PROP_RW int flags; + private: void autoParams(); - void FMG(Mat &I1, Mat &I2, Mat &I2x, Mat &u, int level); + void FMG(Mat &I1, Mat &I2, Mat &I2x, Mat &u, int level); void VCycle_MyFAS(Mat &I1_h, Mat &I2_h, Mat &I2x_h, Mat &u_h, int level); void VariationalSolver(Mat &I1_h, Mat &I2_h, Mat &I2x_h, Mat &u_h, int level); }; - + CV_EXPORTS void polyfit(const Mat& srcx, const Mat& srcy, Mat& dst, int order); - class CV_EXPORTS Directory + class CV_EXPORTS Directory { - public: - static std::vector GetListFiles ( const std::string& path, const std::string & exten = "*", bool addPath = true ); - static std::vector GetListFilesR ( const std::string& path, const std::string & exten = "*", bool addPath = true ); - static std::vector GetListFolders( const std::string& path, const std::string & exten = "*", bool addPath = true ); + public: + static std::vector GetListFiles ( const std::string& path, const std::string & exten = "*", bool addPath = true ); + static std::vector GetListFilesR ( const std::string& path, const std::string & exten = "*", bool addPath = true ); + static std::vector GetListFolders( const std::string& path, const std::string & exten = "*", bool addPath = true ); }; /* @@ -654,7 +654,7 @@ namespace cv class CV_EXPORTS LogPolar_Interp { public: - + LogPolar_Interp() {} /** @@ -664,11 +664,11 @@ namespace cv *\param center the transformation center: where the output precision is maximal *\param R the number of rings of the cortical image (default value 70 pixel) *\param ro0 the radius of the blind spot (default value 3 pixel) - *\param full \a 1 (default value) means that the retinal image (the inverse transform) is computed within the circumscribing circle. + *\param full \a 1 (default value) means that the retinal image (the inverse transform) is computed within the circumscribing circle. * \a 0 means that the retinal image is computed within the inscribed circle. *\param S the number of sectors of the cortical image (default value 70 pixel). * Its value is usually internally computed to obtain a pixel aspect ratio equals to 1. - *\param sp \a 1 (default value) means that the parameter \a S is internally computed. + *\param sp \a 1 (default value) means that the parameter \a S is internally computed. * \a 0 means that the parameter \a S is provided by the user. */ LogPolar_Interp(int w, int h, Point2i center, int R=70, double ro0=3.0, @@ -689,9 +689,9 @@ namespace cv *Destructor */ ~LogPolar_Interp(); - + protected: - + Mat Rsri; Mat Csri; @@ -716,10 +716,10 @@ namespace cv *More details can be found in http://dx.doi.org/10.1007/978-3-642-23968-7_5 */ class CV_EXPORTS LogPolar_Overlapping - { + { public: LogPolar_Overlapping() {} - + /** *Constructor *\param w the width of the input image @@ -727,11 +727,11 @@ namespace cv *\param center the transformation center: where the output precision is maximal *\param R the number of rings of the cortical image (default value 70 pixel) *\param ro0 the radius of the blind spot (default value 3 pixel) - *\param full \a 1 (default value) means that the retinal image (the inverse transform) is computed within the circumscribing circle. + *\param full \a 1 (default value) means that the retinal image (the inverse transform) is computed within the circumscribing circle. * \a 0 means that the retinal image is computed within the inscribed circle. *\param S the number of sectors of the cortical image (default value 70 pixel). * Its value is usually internally computed to obtain a pixel aspect ratio equals to 1. - *\param sp \a 1 (default value) means that the parameter \a S is internally computed. + *\param sp \a 1 (default value) means that the parameter \a S is internally computed. * \a 0 means that the parameter \a S is provided by the user. */ LogPolar_Overlapping(int w, int h, Point2i center, int R=70, @@ -752,9 +752,9 @@ namespace cv *Destructor */ ~LogPolar_Overlapping(); - + protected: - + Mat Rsri; Mat Csri; vector Rsr; @@ -793,7 +793,7 @@ namespace cv { public: LogPolar_Adjacent() {} - + /** *Constructor *\param w the width of the input image @@ -802,13 +802,13 @@ namespace cv *\param R the number of rings of the cortical image (default value 70 pixel) *\param ro0 the radius of the blind spot (default value 3 pixel) *\param smin the size of the subpixel (default value 0.25 pixel) - *\param full \a 1 (default value) means that the retinal image (the inverse transform) is computed within the circumscribing circle. + *\param full \a 1 (default value) means that the retinal image (the inverse transform) is computed within the circumscribing circle. * \a 0 means that the retinal image is computed within the inscribed circle. *\param S the number of sectors of the cortical image (default value 70 pixel). * Its value is usually internally computed to obtain a pixel aspect ratio equals to 1. - *\param sp \a 1 (default value) means that the parameter \a S is internally computed. + *\param sp \a 1 (default value) means that the parameter \a S is internally computed. * \a 0 means that the parameter \a S is provided by the user. - */ + */ LogPolar_Adjacent(int w, int h, Point2i center, int R=70, double ro0=3.0, double smin=0.25, int full=1, int S=117, int sp=1); /** *Transformation from Cartesian image to cortical (log-polar) image. @@ -845,10 +845,10 @@ namespace cv bool get_uv(double x, double y, int&u, int&v); void create_map(int M, int N, int R, int S, double ro0, double smin); }; - + CV_EXPORTS Mat subspaceProject(InputArray W, InputArray mean, InputArray src); CV_EXPORTS Mat subspaceReconstruct(InputArray W, InputArray mean, InputArray src); - + class CV_EXPORTS LDA { public: @@ -908,7 +908,7 @@ namespace cv // Returns the eigenvalues of this LDA. Mat eigenvalues() const { return _eigenvalues; } - + protected: bool _dataAsRow; int _num_components; @@ -917,7 +917,7 @@ namespace cv void lda(InputArray src, InputArray labels); }; - + class CV_EXPORTS FaceRecognizer { public: @@ -941,16 +941,16 @@ namespace cv // Deserializes this object from a given cv::FileStorage. virtual void load(const FileStorage& fs) = 0; - + // Returns eigenvectors (if any) virtual Mat eigenvectors() const { return Mat(); } }; - + CV_EXPORTS Ptr createEigenFaceRecognizer(int num_components = 0); CV_EXPORTS Ptr createFisherFaceRecognizer(int num_components = 0); CV_EXPORTS Ptr createLBPHFaceRecognizer(int radius=1, int neighbors=8, int grid_x=8, int grid_y=8); - + enum { COLORMAP_AUTUMN = 0, @@ -968,9 +968,9 @@ namespace cv COLORMAP_MKPJ1 = 12, COLORMAP_MKPJ2 = 13 }; - + CV_EXPORTS void applyColorMap(InputArray src, OutputArray dst, int colormap); - + CV_EXPORTS bool initModule_contrib(); } diff --git a/modules/contrib/include/opencv2/contrib/hybridtracker.hpp b/modules/contrib/include/opencv2/contrib/hybridtracker.hpp index 3173b23..418a7b8 100644 --- a/modules/contrib/include/opencv2/contrib/hybridtracker.hpp +++ b/modules/contrib/include/opencv2/contrib/hybridtracker.hpp @@ -86,10 +86,10 @@ struct CV_EXPORTS CvMeanShiftTrackerParams struct CV_EXPORTS CvFeatureTrackerParams { enum { SIFT = 0, SURF = 1, OPTICAL_FLOW = 2 }; - CvFeatureTrackerParams(int feature_type = 0, int window_size = 0) + CvFeatureTrackerParams(int featureType = 0, int windowSize = 0) { - feature_type = 0; - window_size = 0; + featureType = 0; + windowSize = 0; } int feature_type; // Feature type to use diff --git a/modules/contrib/src/ba.cpp b/modules/contrib/src/ba.cpp index 636ba59..a0f9046 100644 --- a/modules/contrib/src/ba.cpp +++ b/modules/contrib/src/ba.cpp @@ -55,38 +55,38 @@ LevMarqSparse::LevMarqSparse() { LevMarqSparse::~LevMarqSparse() { clear(); -} +} LevMarqSparse::LevMarqSparse(int npoints, // number of points - int ncameras, // number of cameras - int nPointParams, // number of params per one point (3 in case of 3D points) - int nCameraParams, // number of parameters per one camera - int nErrParams, // number of parameters in measurement vector - // for 1 point at one camera (2 in case of 2D projections) - Mat& visibility, // visibility matrix. rows correspond to points, columns correspond to cameras - // 1 - point is visible for the camera, 0 - invisible - Mat& P0, // starting vector of parameters, first cameras then points - Mat& X_, // measurements, in order of visibility. non visible cases are skipped - TermCriteria criteria, // termination criteria - - // callback for estimation of Jacobian matrices - void (CV_CDECL * fjac)(int i, int j, Mat& point_params, - Mat& cam_params, Mat& A, Mat& B, void* data), - // callback for estimation of backprojection errors - void (CV_CDECL * func)(int i, int j, Mat& point_params, - Mat& cam_params, Mat& estim, void* data), - void* data, // user-specific data passed to the callbacks - BundleAdjustCallback _cb, void* _user_data - ) { + int ncameras, // number of cameras + int nPointParams, // number of params per one point (3 in case of 3D points) + int nCameraParams, // number of parameters per one camera + int nErrParams, // number of parameters in measurement vector + // for 1 point at one camera (2 in case of 2D projections) + Mat& visibility, // visibility matrix. rows correspond to points, columns correspond to cameras + // 1 - point is visible for the camera, 0 - invisible + Mat& P0, // starting vector of parameters, first cameras then points + Mat& X_, // measurements, in order of visibility. non visible cases are skipped + TermCriteria _criteria, // termination criteria + + // callback for estimation of Jacobian matrices + void (CV_CDECL * _fjac)(int i, int j, Mat& point_params, + Mat& cam_params, Mat& A, Mat& B, void* data), + // callback for estimation of backprojection errors + void (CV_CDECL * _func)(int i, int j, Mat& point_params, + Mat& cam_params, Mat& estim, void* data), + void* _data, // user-specific data passed to the callbacks + BundleAdjustCallback _cb, void* _user_data + ) { Vis_index = X = prevP = P = deltaP = err = JtJ_diag = S = hX = NULL; U = ea = V = inv_V_star = eb = Yj = NULL; A = B = W = NULL; cb = _cb; user_data = _user_data; - + run(npoints, ncameras, nPointParams, nCameraParams, nErrParams, visibility, - P0, X_, criteria, fjac, func, data); + P0, X_, _criteria, _fjac, _func, _data); } void LevMarqSparse::clear() { @@ -95,19 +95,19 @@ void LevMarqSparse::clear() { //CvMat* tmp = ((CvMat**)(A->data.ptr + i * A->step))[j]; CvMat* tmp = A[j+i*num_cams]; if (tmp) - cvReleaseMat( &tmp ); + cvReleaseMat( &tmp ); //tmp = ((CvMat**)(B->data.ptr + i * B->step))[j]; tmp = B[j+i*num_cams]; if (tmp) - cvReleaseMat( &tmp ); - + cvReleaseMat( &tmp ); + //tmp = ((CvMat**)(W->data.ptr + j * W->step))[i]; tmp = W[j+i*num_cams]; if (tmp) - cvReleaseMat( &tmp ); + cvReleaseMat( &tmp ); } - } + } delete A; //cvReleaseMat(&A); delete B;//cvReleaseMat(&B); delete W;//cvReleaseMat(&W); @@ -122,7 +122,7 @@ void LevMarqSparse::clear() { cvReleaseMat( &ea[j] ); } delete ea; - + //allocate V and inv_V_star for( int i = 0; i < num_points; i++ ) { cvReleaseMat(&V[i]); @@ -138,16 +138,16 @@ void LevMarqSparse::clear() { for( int i = 0; i < num_points; i++ ) { cvReleaseMat(&Yj[i]); - } + } delete Yj; - + cvReleaseMat(&X); cvReleaseMat(&prevP); cvReleaseMat(&P); cvReleaseMat(&deltaP); - cvReleaseMat(&err); - + cvReleaseMat(&err); + cvReleaseMat(&JtJ_diag); cvReleaseMat(&S); cvReleaseMat(&hX); @@ -165,28 +165,28 @@ void LevMarqSparse::clear() { //num_errors - number of measurements. void LevMarqSparse::run( int num_points_, //number of points - int num_cams_, //number of cameras - int num_point_param_, //number of params per one point (3 in case of 3D points) - int num_cam_param_, //number of parameters per one camera - int num_err_param_, //number of parameters in measurement vector for 1 point at one camera (2 in case of 2D projections) - Mat& visibility, //visibility matrix . rows correspond to points, columns correspond to cameras - // 0 - point is visible for the camera, 0 - invisible - Mat& P0, //starting vector of parameters, first cameras then points - Mat& X_init, //measurements, in order of visibility. non visible cases are skipped - TermCriteria criteria_init, - void (*fjac_)(int i, int j, Mat& point_params, Mat& cam_params, Mat& A, Mat& B, void* data), - void (*func_)(int i, int j, Mat& point_params, Mat& cam_params, Mat& estim, void* data), - void* data_ - ) { //termination criteria + int num_cams_, //number of cameras + int num_point_param_, //number of params per one point (3 in case of 3D points) + int num_cam_param_, //number of parameters per one camera + int num_err_param_, //number of parameters in measurement vector for 1 point at one camera (2 in case of 2D projections) + Mat& visibility, //visibility matrix . rows correspond to points, columns correspond to cameras + // 0 - point is visible for the camera, 0 - invisible + Mat& P0, //starting vector of parameters, first cameras then points + Mat& X_init, //measurements, in order of visibility. non visible cases are skipped + TermCriteria criteria_init, + void (*fjac_)(int i, int j, Mat& point_params, Mat& cam_params, Mat& A, Mat& B, void* data), + void (*func_)(int i, int j, Mat& point_params, Mat& cam_params, Mat& estim, void* data), + void* data_ + ) { //termination criteria //clear(); - + func = func_; //assign evaluation function fjac = fjac_; //assign jacobian data = data_; num_cams = num_cams_; num_points = num_points_; - num_err_param = num_err_param_; + num_err_param = num_err_param_; num_cam_param = num_cam_param_; num_point_param = num_point_param_; @@ -204,9 +204,9 @@ void LevMarqSparse::run( int num_points_, //number of points int Wij_width = Bij_width; //allocate memory for all Aij, Bij, U, V, W - + //allocate num_points*num_cams matrices A - + //Allocate matrix A whose elements are nointers to Aij //if Aij is zero (point i is not visible in camera j) then A(i,j) contains NULL //A = cvCreateMat( num_points, num_cams, CV_32S /*pointer is stored here*/ ); @@ -221,39 +221,39 @@ void LevMarqSparse::run( int num_points_, //number of points //cvSetZero( B ); //cvSetZero( W ); cvSet( Vis_index, cvScalar(-1) ); - + //fill matrices A and B based on visibility CvMat _vis = visibility; int index = 0; for (int i = 0; i < num_points; i++ ) { for (int j = 0; j < num_cams; j++ ) { if (((int*)(_vis.data.ptr+ i * _vis.step))[j] ) { - ((int*)(Vis_index->data.ptr + i * Vis_index->step))[j] = index; - index += num_err_param; - - //create matrices Aij, Bij - CvMat* tmp = cvCreateMat(Aij_height, Aij_width, CV_64F ); - //((CvMat**)(A->data.ptr + i * A->step))[j] = tmp; - cvSet(tmp,cvScalar(1.0,1.0,1.0,1.0)); - A[j+i*num_cams] = tmp; - - tmp = cvCreateMat( Bij_height, Bij_width, CV_64F ); - //((CvMat**)(B->data.ptr + i * B->step))[j] = tmp; - cvSet(tmp,cvScalar(1.0,1.0,1.0,1.0)); - B[j+i*num_cams] = tmp; - - tmp = cvCreateMat( Wij_height, Wij_width, CV_64F ); - //((CvMat**)(W->data.ptr + j * W->step))[i] = tmp; //note indices i and j swapped - cvSet(tmp,cvScalar(1.0,1.0,1.0,1.0)); - W[j+i*num_cams] = tmp; + ((int*)(Vis_index->data.ptr + i * Vis_index->step))[j] = index; + index += num_err_param; + + //create matrices Aij, Bij + CvMat* tmp = cvCreateMat(Aij_height, Aij_width, CV_64F ); + //((CvMat**)(A->data.ptr + i * A->step))[j] = tmp; + cvSet(tmp,cvScalar(1.0,1.0,1.0,1.0)); + A[j+i*num_cams] = tmp; + + tmp = cvCreateMat( Bij_height, Bij_width, CV_64F ); + //((CvMat**)(B->data.ptr + i * B->step))[j] = tmp; + cvSet(tmp,cvScalar(1.0,1.0,1.0,1.0)); + B[j+i*num_cams] = tmp; + + tmp = cvCreateMat( Wij_height, Wij_width, CV_64F ); + //((CvMat**)(W->data.ptr + j * W->step))[i] = tmp; //note indices i and j swapped + cvSet(tmp,cvScalar(1.0,1.0,1.0,1.0)); + W[j+i*num_cams] = tmp; } else{ - A[j+i*num_cams] = NULL; - B[j+i*num_cams] = NULL; - W[j+i*num_cams] = NULL; + A[j+i*num_cams] = NULL; + B[j+i*num_cams] = NULL; + W[j+i*num_cams] = NULL; } - } + } } - + //allocate U U = new CvMat* [num_cams]; for (int j = 0; j < num_cams; j++ ) { @@ -267,7 +267,7 @@ void LevMarqSparse::run( int num_points_, //number of points ea[j] = cvCreateMat( U_size, 1, CV_64F ); cvSetZero(ea[j]); } - + //allocate V and inv_V_star V = new CvMat* [num_points]; inv_V_star = new CvMat* [num_points]; @@ -277,36 +277,36 @@ void LevMarqSparse::run( int num_points_, //number of points cvSetZero(V[i]); cvSetZero(inv_V_star[i]); } - + //allocate eb eb = new CvMat* [num_points]; for (int i = 0; i < num_points; i++ ) { eb[i] = cvCreateMat( V_size, 1, CV_64F ); cvSetZero(eb[i]); - } - + } + //allocate Yj Yj = new CvMat* [num_points]; for (int i = 0; i < num_points; i++ ) { Yj[i] = cvCreateMat( Wij_height, Wij_width, CV_64F ); //Yij has the same size as Wij cvSetZero(Yj[i]); - } - + } + //allocate matrix S S = cvCreateMat( num_cams * num_cam_param, num_cams * num_cam_param, CV_64F); cvSetZero(S); JtJ_diag = cvCreateMat( num_cams * num_cam_param + num_points * num_point_param, 1, CV_64F ); cvSetZero(JtJ_diag); - + //set starting parameters - CvMat _tmp_ = CvMat(P0); - prevP = cvCloneMat( &_tmp_ ); + CvMat _tmp_ = CvMat(P0); + prevP = cvCloneMat( &_tmp_ ); P = cvCloneMat( &_tmp_ ); deltaP = cvCloneMat( &_tmp_ ); - + //set measurements _tmp_ = CvMat(X_init); - X = cvCloneMat( &_tmp_ ); + X = cvCloneMat( &_tmp_ ); //create vector for estimated measurements hX = cvCreateMat( X->rows, X->cols, CV_64F ); cvSetZero(hX); @@ -334,9 +334,9 @@ void LevMarqSparse::run( int num_points_, //number of points prevErrNorm = cvNorm( err, 0, CV_L2 ); // std::cerr<<"prevErrNorm = "<data.ptr + i * Vis_index->step))[j]); ind+=1; - } - } + } + } } } @@ -372,20 +372,20 @@ void LevMarqSparse::ask_for_proj(CvMat &/*_vis*/,bool once) { void LevMarqSparse::ask_for_projac(CvMat &/*_vis*/) //should be evaluated at point prevP { // compute jacobians Aij and Bij - for (int i = 0; i < num_points; i++ ) + for (int i = 0; i < num_points; i++ ) { CvMat point_mat; cvGetSubRect( prevP, &point_mat, cvRect( 0, num_cams * num_cam_param + num_point_param * i, 1, num_point_param )); //CvMat** A_line = (CvMat**)(A->data.ptr + A->step * i); //CvMat** B_line = (CvMat**)(B->data.ptr + B->step * i); - for( int j = 0; j < num_cams; j++ ) + for( int j = 0; j < num_cams; j++ ) { //CvMat* Aij = A_line[j]; //if( Aij ) //Aij is not zero CvMat* Aij = A[j+i*num_cams]; CvMat* Bij = B[j+i*num_cams]; - if(Aij) + if(Aij) { //CvMat** A_line = (CvMat**)(A->data.ptr + A->step * i); //CvMat** B_line = (CvMat**)(B->data.ptr + B->step * i); @@ -403,13 +403,13 @@ void LevMarqSparse::ask_for_projac(CvMat &/*_vis*/) //should be evaluated at p } } } -} +} void LevMarqSparse::optimize(CvMat &_vis) { //main function that runs minimization bool done = false; - - CvMat* YWt = cvCreateMat( num_cam_param, num_cam_param, CV_64F ); //this matrix used to store Yij*Wik' - CvMat* E = cvCreateMat( S->height, 1 , CV_64F ); //this is right part of system with S + + CvMat* YWt = cvCreateMat( num_cam_param, num_cam_param, CV_64F ); //this matrix used to store Yij*Wik' + CvMat* E = cvCreateMat( S->height, 1 , CV_64F ); //this is right part of system with S cvSetZero(YWt); cvSetZero(E); @@ -419,303 +419,307 @@ void LevMarqSparse::optimize(CvMat &_vis) { //main function that runs minimizati int invisible_count=0; //compute U_j and ea_j for (int j = 0; j < num_cams; j++ ) { - cvSetZero(U[j]); + cvSetZero(U[j]); cvSetZero(ea[j]); //summ by i (number of points) for (int i = 0; i < num_points; i++ ) { - //get Aij - //CvMat* Aij = ((CvMat**)(A->data.ptr + A->step * i))[j]; - CvMat* Aij = A[j+i*num_cams]; - if (Aij ) { - //Uj+= AijT*Aij - cvGEMM( Aij, Aij, 1, U[j], 1, U[j], CV_GEMM_A_T ); - //ea_j += AijT * e_ij - CvMat eij; - - int index = ((int*)(Vis_index->data.ptr + i * Vis_index->step))[j]; - - cvGetSubRect( err, &eij, cvRect( 0, index, 1, Aij->height ) ); //width of transposed Aij - cvGEMM( Aij, &eij, 1, ea[j], 1, ea[j], CV_GEMM_A_T ); - } - else - invisible_count++; + //get Aij + //CvMat* Aij = ((CvMat**)(A->data.ptr + A->step * i))[j]; + CvMat* Aij = A[j+i*num_cams]; + if (Aij ) { + //Uj+= AijT*Aij + cvGEMM( Aij, Aij, 1, U[j], 1, U[j], CV_GEMM_A_T ); + //ea_j += AijT * e_ij + CvMat eij; + + int index = ((int*)(Vis_index->data.ptr + i * Vis_index->step))[j]; + + cvGetSubRect( err, &eij, cvRect( 0, index, 1, Aij->height ) ); //width of transposed Aij + cvGEMM( Aij, &eij, 1, ea[j], 1, ea[j], CV_GEMM_A_T ); + } + else + invisible_count++; } } //U_j and ea_j computed for all j // if (!(iters%100)) - int nviz = X->rows / num_err_param; - double e2 = prevErrNorm*prevErrNorm, e2n = e2 / nviz; - std::cerr<<"Iteration: "<rows / num_err_param; + double e2 = prevErrNorm*prevErrNorm, e2n = e2 / nviz; + std::cerr<<"Iteration: "<data.ptr + B->step * i))[j]; - CvMat* Bij = B[j+i*num_cams]; - if (Bij ) { - //Vi+= BijT*Bij - cvGEMM( Bij, Bij, 1, V[i], 1, V[i], CV_GEMM_A_T ); - - //eb_i += BijT * e_ij - int index = ((int*)(Vis_index->data.ptr + i * Vis_index->step))[j]; - - CvMat eij; - cvGetSubRect( err, &eij, cvRect( 0, index, 1, Bij->height ) ); //width of transposed Bij - cvGEMM( Bij, &eij, 1, eb[i], 1, eb[i], CV_GEMM_A_T ); - } + //get Bij + //CvMat* Bij = ((CvMat**)(B->data.ptr + B->step * i))[j]; + CvMat* Bij = B[j+i*num_cams]; + if (Bij ) { + //Vi+= BijT*Bij + cvGEMM( Bij, Bij, 1, V[i], 1, V[i], CV_GEMM_A_T ); + + //eb_i += BijT * e_ij + int index = ((int*)(Vis_index->data.ptr + i * Vis_index->step))[j]; + + CvMat eij; + cvGetSubRect( err, &eij, cvRect( 0, index, 1, Bij->height ) ); //width of transposed Bij + cvGEMM( Bij, &eij, 1, eb[i], 1, eb[i], CV_GEMM_A_T ); + } } } //V_i and eb_i computed for all i //compute W_ij for( int i = 0; i < num_points; i++ ) { for( int j = 0; j < num_cams; j++ ) { - //CvMat* Aij = ((CvMat**)(A->data.ptr + A->step * i))[j]; - CvMat* Aij = A[j+i*num_cams]; - if( Aij ) { //visible - //CvMat* Bij = ((CvMat**)(B->data.ptr + B->step * i))[j]; - CvMat* Bij = B[j+i*num_cams]; - //CvMat* Wij = ((CvMat**)(W->data.ptr + W->step * j))[i]; - CvMat* Wij = W[j+i*num_cams]; - - //multiply - cvGEMM( Aij, Bij, 1, NULL, 0, Wij, CV_GEMM_A_T ); - } + //CvMat* Aij = ((CvMat**)(A->data.ptr + A->step * i))[j]; + CvMat* Aij = A[j+i*num_cams]; + if( Aij ) { //visible + //CvMat* Bij = ((CvMat**)(B->data.ptr + B->step * i))[j]; + CvMat* Bij = B[j+i*num_cams]; + //CvMat* Wij = ((CvMat**)(W->data.ptr + W->step * j))[i]; + CvMat* Wij = W[j+i*num_cams]; + + //multiply + cvGEMM( Aij, Bij, 1, NULL, 0, Wij, CV_GEMM_A_T ); + } } } //Wij computed //backup diagonal of JtJ before we start augmenting it - { + { CvMat dia; CvMat subr; for( int j = 0; j < num_cams; j++ ) { - cvGetDiag(U[j], &dia); - cvGetSubRect(JtJ_diag, &subr, - cvRect(0, j*num_cam_param, 1, num_cam_param )); - cvCopy( &dia, &subr ); - } + cvGetDiag(U[j], &dia); + cvGetSubRect(JtJ_diag, &subr, + cvRect(0, j*num_cam_param, 1, num_cam_param )); + cvCopy( &dia, &subr ); + } for( int i = 0; i < num_points; i++ ) { - cvGetDiag(V[i], &dia); - cvGetSubRect(JtJ_diag, &subr, - cvRect(0, num_cams*num_cam_param + i * num_point_param, 1, num_point_param )); - cvCopy( &dia, &subr ); - } - } + cvGetDiag(V[i], &dia); + cvGetSubRect(JtJ_diag, &subr, + cvRect(0, num_cams*num_cam_param + i * num_point_param, 1, num_point_param )); + cvCopy( &dia, &subr ); + } + } if( iters == 0 ) { //initialize lambda. It is set to 1e-3 * average diagonal element in JtJ double average_diag = 0; for( int j = 0; j < num_cams; j++ ) { - average_diag += cvTrace( U[j] ).val[0]; + average_diag += cvTrace( U[j] ).val[0]; } for( int i = 0; i < num_points; i++ ) { - average_diag += cvTrace( V[i] ).val[0]; + average_diag += cvTrace( V[i] ).val[0]; } average_diag /= (num_cams*num_cam_param + num_points * num_point_param ); - - // lambda = 1e-3 * average_diag; - lambda = 1e-3 * average_diag; + + // lambda = 1e-3 * average_diag; + lambda = 1e-3 * average_diag; lambda = 0.245560; } - + //now we are going to find good step and make it for(;;) { //augmentation of diagonal for(int j = 0; j < num_cams; j++ ) { - CvMat diag; - cvGetDiag( U[j], &diag ); + CvMat diag; + cvGetDiag( U[j], &diag ); #if 1 - cvAddS( &diag, cvScalar( lambda ), &diag ); + cvAddS( &diag, cvScalar( lambda ), &diag ); #else - cvScale( &diag, &diag, 1 + lambda ); + cvScale( &diag, &diag, 1 + lambda ); #endif } for(int i = 0; i < num_points; i++ ) { - CvMat diag; - cvGetDiag( V[i], &diag ); + CvMat diag; + cvGetDiag( V[i], &diag ); #if 1 - cvAddS( &diag, cvScalar( lambda ), &diag ); + cvAddS( &diag, cvScalar( lambda ), &diag ); #else - cvScale( &diag, &diag, 1 + lambda ); + cvScale( &diag, &diag, 1 + lambda ); #endif - } + } bool error = false; //compute inv(V*) bool inverted_ok = true; for(int i = 0; i < num_points; i++ ) { - double det = cvInvert( V[i], inv_V_star[i] ); + double det = cvInvert( V[i], inv_V_star[i] ); - if( fabs(det) <= FLT_EPSILON ) { - inverted_ok = false; - std::cerr<<"V["<data.ptr + W->step * j))[i]; - CvMat* Wij = W[j+i*num_cams]; - if( Wij ) { - cvMatMul( Wij, inv_V_star[i], Yj[i] ); - } - } - - //compute Sjk for k>=j (because Sjk = Skj) - for( int k = j; k < num_cams; k++ ) { - cvSetZero( YWt ); - for( int i = 0; i < num_points; i++ ) { - //check that both Wij and Wik exist - // CvMat* Wij = ((CvMat**)(W->data.ptr + W->step * j))[i]; - CvMat* Wij = W[j+i*num_cams]; - //CvMat* Wik = ((CvMat**)(W->data.ptr + W->step * k))[i]; - CvMat* Wik = W[k+i*num_cams]; - - if( Wij && Wik ) { - //multiply YWt += Yj[i]*Wik' - cvGEMM( Yj[i], Wik, 1, YWt, 1, YWt, CV_GEMM_B_T ); ///*transpose Wik - } - } - - //copy result to matrix S - - CvMat Sjk; - //extract submat - cvGetSubRect( S, &Sjk, cvRect( k * num_cam_param, j * num_cam_param, num_cam_param, num_cam_param )); - - - //if j==k, add diagonal - if( j != k ) { - //just copy with minus - cvScale( YWt, &Sjk, -1 ); //if we set initial S to zero then we can use cvSub( Sjk, YWt, Sjk); - } else { - //add diagonal value - - //subtract YWt from augmented Uj - cvSub( U[j], YWt, &Sjk ); - } - } - - //compute right part of equation involving matrix S - // e_j=ea_j - \sum_i Y_ij eb_i - { - CvMat e_j; - - //select submat - cvGetSubRect( E, &e_j, cvRect( 0, j * num_cam_param, 1, num_cam_param ) ); - - for( int i = 0; i < num_points; i++ ) { - //CvMat* Wij = ((CvMat**)(W->data.ptr + W->step * j))[i]; - CvMat* Wij = W[j+i*num_cams]; - if( Wij ) - cvMatMulAdd( Yj[i], eb[i], &e_j, &e_j ); - } - - cvSub( ea[j], &e_j, &e_j ); - } - - } - //fill below diagonal elements of matrix S - cvCompleteSymm( S, 0 ); ///*from upper to low //operation may be done by nonzero blocks or during upper diagonal computation - - //Solve linear system S * deltaP_a = E - CvMat dpa; - cvGetSubRect( deltaP, &dpa, cvRect(0, 0, 1, S->width ) ); - int res = cvSolve( S, E, &dpa, CV_CHOLESKY ); - - if( res ) { //system solved ok - //compute db_i - for( int i = 0; i < num_points; i++ ) { - CvMat dbi; - cvGetSubRect( deltaP, &dbi, cvRect( 0, dpa.height + i * num_point_param, 1, num_point_param ) ); - - // compute \sum_j W_ij^T da_j - for( int j = 0; j < num_cams; j++ ) { - //get Wij - //CvMat* Wij = ((CvMat**)(W->data.ptr + W->step * j))[i]; - CvMat* Wij = W[j+i*num_cams]; - if( Wij ) { - //get da_j - CvMat daj; - cvGetSubRect( &dpa, &daj, cvRect( 0, j * num_cam_param, 1, num_cam_param )); - cvGEMM( Wij, &daj, 1, &dbi, 1, &dbi, CV_GEMM_A_T ); ///* transpose Wij - } - } - //finalize dbi - cvSub( eb[i], &dbi, &dbi ); - cvMatMul(inv_V_star[i], &dbi, &dbi ); //here we get final dbi - } //now we computed whole deltaP - - //add deltaP to delta - cvAdd( prevP, deltaP, P ); - - //evaluate function with new parameters - ask_for_proj(_vis); // func( P, hX ); - - //compute error - errNorm = cvNorm( X, hX, CV_L2 ); - - } else { - error = true; - } + cvSetZero( E ); + //loop through cameras, compute upper diagonal blocks of matrix S + for( int j = 0; j < num_cams; j++ ) { + //compute Yij = Wij (V*_i)^-1 for all i (if Wij exists/nonzero) + for( int i = 0; i < num_points; i++ ) { + // + //CvMat* Wij = ((CvMat**)(W->data.ptr + W->step * j))[i]; + CvMat* Wij = W[j+i*num_cams]; + if( Wij ) { + cvMatMul( Wij, inv_V_star[i], Yj[i] ); + } + } + + //compute Sjk for k>=j (because Sjk = Skj) + for( int k = j; k < num_cams; k++ ) { + cvSetZero( YWt ); + for( int i = 0; i < num_points; i++ ) { + //check that both Wij and Wik exist + // CvMat* Wij = ((CvMat**)(W->data.ptr + W->step * j))[i]; + CvMat* Wij = W[j+i*num_cams]; + //CvMat* Wik = ((CvMat**)(W->data.ptr + W->step * k))[i]; + CvMat* Wik = W[k+i*num_cams]; + + if( Wij && Wik ) { + //multiply YWt += Yj[i]*Wik' + cvGEMM( Yj[i], Wik, 1, YWt, 1, YWt, CV_GEMM_B_T ); ///*transpose Wik + } + } + + //copy result to matrix S + + CvMat Sjk; + //extract submat + cvGetSubRect( S, &Sjk, cvRect( k * num_cam_param, j * num_cam_param, num_cam_param, num_cam_param )); + + + //if j==k, add diagonal + if( j != k ) { + //just copy with minus + cvScale( YWt, &Sjk, -1 ); //if we set initial S to zero then we can use cvSub( Sjk, YWt, Sjk); + } else { + //add diagonal value + + //subtract YWt from augmented Uj + cvSub( U[j], YWt, &Sjk ); + } + } + + //compute right part of equation involving matrix S + // e_j=ea_j - \sum_i Y_ij eb_i + { + CvMat e_j; + + //select submat + cvGetSubRect( E, &e_j, cvRect( 0, j * num_cam_param, 1, num_cam_param ) ); + + for( int i = 0; i < num_points; i++ ) { + //CvMat* Wij = ((CvMat**)(W->data.ptr + W->step * j))[i]; + CvMat* Wij = W[j+i*num_cams]; + if( Wij ) + cvMatMulAdd( Yj[i], eb[i], &e_j, &e_j ); + } + + cvSub( ea[j], &e_j, &e_j ); + } + + } + //fill below diagonal elements of matrix S + cvCompleteSymm( S, 0 ); ///*from upper to low //operation may be done by nonzero blocks or during upper diagonal computation + + //Solve linear system S * deltaP_a = E + CvMat dpa; + cvGetSubRect( deltaP, &dpa, cvRect(0, 0, 1, S->width ) ); + int res = cvSolve( S, E, &dpa, CV_CHOLESKY ); + + if( res ) { //system solved ok + //compute db_i + for( int i = 0; i < num_points; i++ ) { + CvMat dbi; + cvGetSubRect( deltaP, &dbi, cvRect( 0, dpa.height + i * num_point_param, 1, num_point_param ) ); + + // compute \sum_j W_ij^T da_j + for( int j = 0; j < num_cams; j++ ) { + //get Wij + //CvMat* Wij = ((CvMat**)(W->data.ptr + W->step * j))[i]; + CvMat* Wij = W[j+i*num_cams]; + if( Wij ) { + //get da_j + CvMat daj; + cvGetSubRect( &dpa, &daj, cvRect( 0, j * num_cam_param, 1, num_cam_param )); + cvGEMM( Wij, &daj, 1, &dbi, 1, &dbi, CV_GEMM_A_T ); ///* transpose Wij + } + } + //finalize dbi + cvSub( eb[i], &dbi, &dbi ); + cvMatMul(inv_V_star[i], &dbi, &dbi ); //here we get final dbi + } //now we computed whole deltaP + + //add deltaP to delta + cvAdd( prevP, deltaP, P ); + + //evaluate function with new parameters + ask_for_proj(_vis); // func( P, hX ); + + //compute error + errNorm = cvNorm( X, hX, CV_L2 ); + + } else { + error = true; + } } else { - error = true; + error = true; } //check solution if( error || ///* singularities somewhere - errNorm > prevErrNorm ) { //step was not accepted - //increase lambda and reject change - lambda *= 10; - int nviz = X->rows / num_err_param; - double e2 = errNorm*errNorm, e2_prev = prevErrNorm*prevErrNorm; - double e2n = e2/nviz, e2n_prev = e2_prev/nviz; - std::cerr<<"move failed: lambda = "< "< prevErrNorm ) { //step was not accepted + //increase lambda and reject change + lambda *= 10; + { + int nviz = X->rows / num_err_param; + double e2 = errNorm*errNorm, e2_prev = prevErrNorm*prevErrNorm; + double e2n = e2/nviz, e2n_prev = e2_prev/nviz; + std::cerr<<"move failed: lambda = "< "< criteria.max_iter ) || - (criteria.type&CV_TERMCRIT_EPS && param_change_norm < criteria.epsilon) ) { + if( (criteria.type&CV_TERMCRIT_ITER && iters > criteria.max_iter ) || + (criteria.type&CV_TERMCRIT_EPS && param_change_norm < criteria.epsilon) ) { // std::cerr<<"relative norm change "<data.db[8]; intr_data[5] = cam_params->data.db[9]; - CvMat _A = cvMat(3,3, CV_64F, intr_data ); + CvMat _A = cvMat(3,3, CV_64F, intr_data ); CvMat _dpdr, _dpdt, _dpdf, _dpdc, _dpdk; - + bool have_dk = cam_params->height - 10 ? true : false; cvGetCols( A, &_dpdr, 0, 3 ); cvGetCols( A, &_dpdt, 3, 6 ); cvGetCols( A, &_dpdf, 6, 8 ); cvGetCols( A, &_dpdc, 8, 10 ); - + if( have_dk ) { cvGetRows( cam_params, &_k, 10, cam_params->height ); cvGetCols( A, &_dpdk, 10, A->width ); } cvProjectPoints2(&_Mi, &_ri, &_ti, &_A, have_dk ? &_k : NULL, _mp, &_dpdr, &_dpdt, - &_dpdf, &_dpdc, have_dk ? &_dpdk : NULL, 0); + &_dpdf, &_dpdc, have_dk ? &_dpdk : NULL, 0); - cvReleaseMat( &_mp ); + cvReleaseMat( &_mp ); //compute jacobian for point params //compute dMeasure/dPoint3D @@ -781,30 +785,30 @@ void fjac(int /*i*/, int /*j*/, CvMat *point_params, CvMat* cam_params, CvMat* A // y' = y/z //d(x') = ( dx*z - x*dz)/(z*z) - //d(y') = ( dy*z - y*dz)/(z*z) + //d(y') = ( dy*z - y*dz)/(z*z) //g = 1 + k1*r_2 + k2*r_4 + k3*r_6 //r_2 = x'*x' + y'*y' //d(r_2) = 2*x'*dx' + 2*y'*dy' - //dg = k1* d(r_2) + k2*2*r_2*d(r_2) + k3*3*r_2*r_2*d(r_2) + //dg = k1* d(r_2) + k2*2*r_2*d(r_2) + k3*3*r_2*r_2*d(r_2) //x" = x'*g + 2*p1*x'*y' + p2(r_2+2*x'_2) //y" = y'*g + p1(r_2+2*y'_2) + 2*p2*x'*y' - + //d(x") = d(x') * g + x' * d(g) + 2*p1*( d(x')*y' + x'*dy) + p2*(d(r_2) + 2*2*x'* dx') - //d(y") = d(y') * g + y' * d(g) + 2*p2*( d(x')*y' + x'*dy) + p1*(d(r_2) + 2*2*y'* dy') + //d(y") = d(y') * g + y' * d(g) + 2*p2*( d(x')*y' + x'*dy) + p1*(d(r_2) + 2*2*y'* dy') // u = fx*( x") + cx // v = fy*( y") + cy - + // du = fx * d(x") = fx * ( dx*z - x*dz)/ (z*z) // dv = fy * d(y") = fy * ( dy*z - y*dz)/ (z*z) - // dx/dX = r11, dx/dY = r12, dx/dZ = r13 + // dx/dX = r11, dx/dY = r12, dx/dZ = r13 // dy/dX = r21, dy/dY = r22, dy/dZ = r23 - // dz/dX = r31, dz/dY = r32, dz/dZ = r33 + // dz/dX = r31, dz/dY = r32, dz/dZ = r33 // du/dX = fx*(r11*z-x*r31)/(z*z) // du/dY = fx*(r12*z-x*r32)/(z*z) @@ -833,27 +837,27 @@ void fjac(int /*i*/, int /*j*/, CvMat *point_params, CvMat* cam_params, CvMat* A double y = R[3] * X + R[4] * Y + R[5] * Z + t[1]; double z = R[6] * X + R[7] * Y + R[8] * Z + t[2]; -#if 1 +#if 1 //compute x',y' double x_strike = x/z; - double y_strike = y/z; + double y_strike = y/z; //compute dx',dy' matrix // - // dx'/dX dx'/dY dx'/dZ = + // dx'/dX dx'/dY dx'/dZ = // dy'/dX dy'/dY dy'/dZ double coeff[6] = { z, 0, -x, - 0, z, -y }; + 0, z, -y }; CvMat coeffmat = cvMat( 2, 3, CV_64F, coeff ); CvMat* dstrike_dbig = cvCreateMat(2,3,CV_64F); cvMatMul(&coeffmat, &_R, dstrike_dbig); - cvScale(dstrike_dbig, dstrike_dbig, 1/(z*z) ); - + cvScale(dstrike_dbig, dstrike_dbig, 1/(z*z) ); + if( have_dk ) { double strike_[2] = {x_strike, y_strike}; - CvMat strike = cvMat(1, 2, CV_64F, strike_); - + CvMat strike = cvMat(1, 2, CV_64F, strike_); + //compute r_2 double r_2 = x_strike*x_strike + y_strike*y_strike; double r_4 = r_2*r_2; @@ -867,28 +871,28 @@ void fjac(int /*i*/, int /*j*/, CvMat *point_params, CvMat* cam_params, CvMat* A double& k1 = _k.data.db[0]; double& k2 = _k.data.db[1]; double& p1 = _k.data.db[2]; - double& p2 = _k.data.db[3]; + double& p2 = _k.data.db[3]; double k3 = 0; if( _k.cols*_k.rows == 5 ) { k3 = _k.data.db[4]; - } + } //compute dg/dbig double dg_dr2 = k1 + k2*2*r_2 + k3*3*r_4; double g = 1+k1*r_2+k2*r_4+k3*r_6; CvMat* dg_dbig = cvCreateMat(1,3,CV_64F); - cvScale( dr2_dbig, dg_dbig, dg_dr2 ); + cvScale( dr2_dbig, dg_dbig, dg_dr2 ); CvMat* tmp = cvCreateMat( 2, 3, CV_64F ); CvMat* dstrike2_dbig = cvCreateMat( 2, 3, CV_64F ); - + double c[4] = { g+2*p1*y_strike+4*p2*x_strike, 2*p1*x_strike, - 2*p2*y_strike, g+2*p2*x_strike + 4*p1*y_strike }; + 2*p2*y_strike, g+2*p2*x_strike + 4*p1*y_strike }; - CvMat coeffmat = cvMat(2,2,CV_64F, c ); + CvMat coeffmat2 = cvMat(2,2,CV_64F, c ); - cvMatMul(&coeffmat, dstrike_dbig, dstrike2_dbig ); + cvMatMul(&coeffmat2, dstrike_dbig, dstrike2_dbig ); cvGEMM( &strike, dg_dbig, 1, NULL, 0, tmp, CV_GEMM_A_T ); cvAdd( dstrike2_dbig, tmp, dstrike2_dbig ); @@ -897,7 +901,7 @@ void fjac(int /*i*/, int /*j*/, CvMat *point_params, CvMat* cam_params, CvMat* A CvMat pmat = cvMat(2, 1, CV_64F, p ); cvMatMul( &pmat, dr2_dbig ,tmp); - cvAdd( dstrike2_dbig, tmp, dstrike2_dbig ); + cvAdd( dstrike2_dbig, tmp, dstrike2_dbig ); cvCopy( dstrike2_dbig, B ); @@ -906,15 +910,15 @@ void fjac(int /*i*/, int /*j*/, CvMat *point_params, CvMat* cam_params, CvMat* A cvReleaseMat(&tmp); cvReleaseMat(&dstrike2_dbig); - cvReleaseMat(&tmp); + cvReleaseMat(&tmp); } else { cvCopy(dstrike_dbig, B); } //multiply by fx, fy CvMat row; cvGetRows( B, &row, 0, 1 ); - cvScale( &row, &row, fx ); - + cvScale( &row, &row, fx ); + cvGetRows( B, &row, 1, 2 ); cvScale( &row, &row, fy ); @@ -925,17 +929,17 @@ void fjac(int /*i*/, int /*j*/, CvMat *point_params, CvMat* cam_params, CvMat* A cvmSet( B, 0, 0, k*(R[0]*z-x*R[6])); cvmSet( B, 0, 1, k*(R[1]*z-x*R[7])); cvmSet( B, 0, 2, k*(R[2]*z-x*R[8])); - - k = fy/(z*z); - + + k = fy/(z*z); + cvmSet( B, 1, 0, k*(R[3]*z-y*R[6])); cvmSet( B, 1, 1, k*(R[4]*z-y*R[7])); cvmSet( B, 1, 2, k*(R[5]*z-y*R[8])); - + #endif - + }; -void func(int /*i*/, int /*j*/, CvMat *point_params, CvMat* cam_params, CvMat* estim, void* /*data*/) { +static void func(int /*i*/, int /*j*/, CvMat *point_params, CvMat* cam_params, CvMat* estim, void* /*data*/) { //just do projections CvMat _Mi; cvReshape( point_params, &_Mi, 3, 1 ); @@ -955,19 +959,19 @@ void func(int /*i*/, int /*j*/, CvMat *point_params, CvMat* cam_params, CvMat* e intr_data[2] = cam_params->data.db[8]; intr_data[5] = cam_params->data.db[9]; - CvMat _A = cvMat(3,3, CV_64F, intr_data ); + CvMat _A = cvMat(3,3, CV_64F, intr_data ); //int cn = CV_MAT_CN(_Mi.type); bool have_dk = cam_params->height - 10 ? true : false; - + if( have_dk ) { - cvGetRows( cam_params, &_k, 10, cam_params->height ); - } + cvGetRows( cam_params, &_k, 10, cam_params->height ); + } cvProjectPoints2( &_Mi, &_ri, &_ti, &_A, have_dk ? &_k : NULL, _mp, NULL, NULL, - NULL, NULL, NULL, 0); + NULL, NULL, NULL, 0); // std::cerr<<"_mp = "<<_mp->data.db[0]<<","<<_mp->data.db[1]<data.db[0] = _mp->data.db[0]; _mp2->data.db[1] = _mp->data.db[1]; cvTranspose( _mp2, estim ); @@ -975,41 +979,41 @@ void func(int /*i*/, int /*j*/, CvMat *point_params, CvMat* cam_params, CvMat* e cvReleaseMat( &_mp2 ); }; -void fjac_new(int i, int j, Mat& point_params, Mat& cam_params, Mat& A, Mat& B, void* data) { +static void fjac_new(int i, int j, Mat& point_params, Mat& cam_params, Mat& A, Mat& B, void* data) { CvMat _point_params = point_params, _cam_params = cam_params, _Al = A, _Bl = B; fjac(i,j, &_point_params, &_cam_params, &_Al, &_Bl, data); }; -void func_new(int i, int j, Mat& point_params, Mat& cam_params, Mat& estim, void* data) { +static void func_new(int i, int j, Mat& point_params, Mat& cam_params, Mat& estim, void* data) { CvMat _point_params = point_params, _cam_params = cam_params, _estim = estim; func(i,j,&_point_params,&_cam_params,&_estim,data); -}; +}; void LevMarqSparse::bundleAdjust( vector& points, //positions of points in global coordinate system (input and output) - const vector >& imagePoints, //projections of 3d points for every camera - const vector >& visibility, //visibility of 3d points for every camera - vector& cameraMatrix, //intrinsic matrices of all cameras (input and output) - vector& R, //rotation matrices of all cameras (input and output) - vector& T, //translation vector of all cameras (input and output) - vector& distCoeffs, //distortion coefficients of all cameras (input and output) - const TermCriteria& criteria, - BundleAdjustCallback cb, void* user_data) { + const vector >& imagePoints, //projections of 3d points for every camera + const vector >& visibility, //visibility of 3d points for every camera + vector& cameraMatrix, //intrinsic matrices of all cameras (input and output) + vector& R, //rotation matrices of all cameras (input and output) + vector& T, //translation vector of all cameras (input and output) + vector& distCoeffs, //distortion coefficients of all cameras (input and output) + const TermCriteria& criteria, + BundleAdjustCallback cb, void* user_data) { //,enum{MOTION_AND_STRUCTURE,MOTION,STRUCTURE}) int num_points = (int)points.size(); int num_cameras = (int)cameraMatrix.size(); - CV_Assert( imagePoints.size() == (size_t)num_cameras && - visibility.size() == (size_t)num_cameras && - R.size() == (size_t)num_cameras && - T.size() == (size_t)num_cameras && - (distCoeffs.size() == (size_t)num_cameras || distCoeffs.size() == 0) ); + CV_Assert( imagePoints.size() == (size_t)num_cameras && + visibility.size() == (size_t)num_cameras && + R.size() == (size_t)num_cameras && + T.size() == (size_t)num_cameras && + (distCoeffs.size() == (size_t)num_cameras || distCoeffs.size() == 0) ); int numdist = distCoeffs.size() ? (distCoeffs[0].rows * distCoeffs[0].cols) : 0; int num_cam_param = 3 /* rotation vector */ + 3 /* translation vector */ - + 2 /* fx, fy */ + 2 /* cx, cy */ + numdist; + + 2 /* fx, fy */ + 2 /* cx, cy */ + numdist; - int num_point_param = 3; + int num_point_param = 3; //collect camera parameters into vector Mat params( num_cameras * num_cam_param + num_points * num_point_param, 1, CV_64F ); @@ -1023,8 +1027,8 @@ void LevMarqSparse::bundleAdjust( vector& points, //positions of points //translation dst = params.rowRange(i*num_cam_param + 3, i*num_cam_param+6); - T[i].copyTo(dst); - + T[i].copyTo(dst); + //intrinsic camera matrix double* intr_data = (double*)cameraMatrix[i].data; double* intr = (double*)(params.data + params.step * (i*num_cam_param+6)); @@ -1033,14 +1037,14 @@ void LevMarqSparse::bundleAdjust( vector& points, //positions of points intr[1] = intr_data[4]; //fy //center of projection intr[2] = intr_data[2]; //cx - intr[3] = intr_data[5]; //cy + intr[3] = intr_data[5]; //cy //add distortion if exists if( distCoeffs.size() ) { dst = params.rowRange(i*num_cam_param + 10, i*num_cam_param+10+numdist); - distCoeffs[i].copyTo(dst); + distCoeffs[i].copyTo(dst); } - } + } //fill point params Mat ptparams(num_points, 1, CV_64FC3, params.data + num_cameras*num_cam_param*params.step); @@ -1059,26 +1063,26 @@ void LevMarqSparse::bundleAdjust( vector& points, //positions of points int num_proj = countNonZero(vismat); //total number of points projections //collect measurements - Mat X(num_proj*2,1,CV_64F); //measurement vector - + Mat X(num_proj*2,1,CV_64F); //measurement vector + int counter = 0; for(int i = 0; i < num_points; i++ ) { for(int j = 0; j < num_cameras; j++ ) { //check visibility if( visibility[j][i] ) { - //extract point and put tu vector - Point2d p = imagePoints[j][i]; - ((double*)(X.data))[counter] = p.x; - ((double*)(X.data))[counter+1] = p.y; - assert(p.x != -1 || p.y != -1); - counter+=2; - } - } + //extract point and put tu vector + Point2d p = imagePoints[j][i]; + ((double*)(X.data))[counter] = p.x; + ((double*)(X.data))[counter+1] = p.y; + assert(p.x != -1 || p.y != -1); + counter+=2; + } + } } LevMarqSparse levmar( num_points, num_cameras, num_point_param, num_cam_param, 2, vismat, params, X, - TermCriteria(criteria), fjac_new, func_new, NULL, - cb, user_data); + TermCriteria(criteria), fjac_new, func_new, NULL, + cb, user_data); //extract results //fill point params /*Mat final_points(num_points, 1, CV_64FC3, @@ -1101,7 +1105,7 @@ void LevMarqSparse::bundleAdjust( vector& points, //positions of points Mat rot_vec = Mat(levmar.P).rowRange(i*num_cam_param, i*num_cam_param+3); Rodrigues( rot_vec, R[i] ); //translation - T[i] = Mat(levmar.P).rowRange(i*num_cam_param + 3, i*num_cam_param+6); + T[i] = Mat(levmar.P).rowRange(i*num_cam_param + 3, i*num_cam_param+6); //intrinsic camera matrix double* intr_data = (double*)cameraMatrix[i].data; @@ -1111,11 +1115,11 @@ void LevMarqSparse::bundleAdjust( vector& points, //positions of points intr_data[4] = intr[1]; //fy //center of projection intr_data[2] = intr[2]; //cx - intr_data[5] = intr[3]; //cy + intr_data[5] = intr[3]; //cy //add distortion if exists if( distCoeffs.size() ) { Mat(levmar.P).rowRange(i*num_cam_param + 10, i*num_cam_param+10+numdist).copyTo(distCoeffs[i]); } - } -} + } +} diff --git a/modules/contrib/src/basicretinafilter.cpp b/modules/contrib/src/basicretinafilter.cpp index 52338af..e49e9d8 100644 --- a/modules/contrib/src/basicretinafilter.cpp +++ b/modules/contrib/src/basicretinafilter.cpp @@ -180,13 +180,13 @@ void BasicRetinaFilter::setLPfilterParameters(const float beta, const float tau, } float _temp = (1.0f+_beta)/(2.0f*_mu*_alpha); - float _a = _filteringCoeficientsTable[tableOffset] = 1.0f + _temp - (float)sqrt( (1.0f+_temp)*(1.0f+_temp) - 1.0f); - _filteringCoeficientsTable[1+tableOffset]=(1.0f-_a)*(1.0f-_a)*(1.0f-_a)*(1.0f-_a)/(1.0f+_beta); + float a = _filteringCoeficientsTable[tableOffset] = 1.0f + _temp - (float)sqrt( (1.0f+_temp)*(1.0f+_temp) - 1.0f); + _filteringCoeficientsTable[1+tableOffset]=(1.0f-a)*(1.0f-a)*(1.0f-a)*(1.0f-a)/(1.0f+_beta); _filteringCoeficientsTable[2+tableOffset] =tau; - //std::cout<<"BasicRetinaFilter::normal:"<<(1.0-_a)*(1.0-_a)*(1.0-_a)*(1.0-_a)/(1.0+_beta)<<" -> old:"<<(1-_a)*(1-_a)*(1-_a)*(1-_a)/(1+_beta)< old:"<<(1-a)*(1-a)*(1-a)*(1-a)/(1+_beta)< -#if defined _MSC_VER && _MSC_VER >= 1400 +#ifdef _MSC_VER #pragma warning( disable: 4305 ) #endif @@ -59,8 +59,8 @@ static Mat sortMatrixRowsByIndices(InputArray src, InputArray indices) return dst; } - -Mat argsort(InputArray _src, bool ascending=true) + +static Mat argsort(InputArray _src, bool ascending=true) { Mat src = _src.getMat(); if (src.rows != 1 && src.cols != 1) @@ -70,14 +70,14 @@ Mat argsort(InputArray _src, bool ascending=true) sortIdx(src.reshape(1,1),sorted_indices,flags); return sorted_indices; } - + template static Mat interp1_(const Mat& X_, const Mat& Y_, const Mat& XI) { int n = XI.rows; // sort input table vector sort_indices = argsort(X_); - + Mat X = sortMatrixRowsByIndices(X_,sort_indices); Mat Y = sortMatrixRowsByIndices(Y_,sort_indices); // interpolated values @@ -131,7 +131,7 @@ static Mat interp1(InputArray _x, InputArray _Y, InputArray _xi) } return Mat(); } - + namespace colormap { @@ -531,7 +531,7 @@ namespace colormap n); // number of sample points } }; - + void ColorMap::operator()(InputArray _src, OutputArray _dst) const { if(_lut.total() != 256) @@ -550,7 +550,7 @@ namespace colormap // Apply the ColorMap. LUT(src, _lut, _dst); } - + Mat ColorMap::linear_colormap(InputArray X, InputArray r, InputArray g, InputArray b, InputArray xi) { @@ -581,12 +581,12 @@ namespace colormap colormap == COLORMAP_HOT ? (colormap::ColorMap*)(new colormap::Hot) : colormap == COLORMAP_MKPJ1 ? (colormap::ColorMap*)(new colormap::MKPJ1) : colormap == COLORMAP_MKPJ2 ? (colormap::ColorMap*)(new colormap::MKPJ2) : 0; - + if( !cm ) CV_Error( CV_StsBadArg, "Unknown colormap id; use one of COLORMAP_*"); - + (*cm)(src, dst); - + delete cm; } } diff --git a/modules/contrib/src/colortracker.cpp b/modules/contrib/src/colortracker.cpp index 08664c8..a5d2391 100644 --- a/modules/contrib/src/colortracker.cpp +++ b/modules/contrib/src/colortracker.cpp @@ -68,10 +68,10 @@ void CvMeanShiftTracker::newTrackingWindow(Mat image, Rect selection) mixChannels(&hsv, 1, &hue, 1, channels, 2); Mat roi(hue, selection); - Mat maskroi(mask, selection); + Mat mskroi(mask, selection); int ch[] = {0, 1}; int chsize[] = {32, 32}; - calcHist(&roi, 1, ch, maskroi, hist, 1, chsize, ranges); + calcHist(&roi, 1, ch, mskroi, hist, 1, chsize, ranges); normalize(hist, hist, 0, 255, CV_MINMAX); prev_trackwindow = selection; diff --git a/modules/contrib/src/detection_based_tracker.cpp b/modules/contrib/src/detection_based_tracker.cpp index bbf27b8..d65e9d9 100644 --- a/modules/contrib/src/detection_based_tracker.cpp +++ b/modules/contrib/src/detection_based_tracker.cpp @@ -3,7 +3,7 @@ #define DEBUGLOGS 1 -#if ANDROID +#ifdef ANDROID #include #define LOG_TAG "OBJECT_DETECTOR" #define LOGD0(...) ((void)__android_log_print(ANDROID_LOG_DEBUG, LOG_TAG, __VA_ARGS__)) @@ -25,7 +25,7 @@ #define LOGI(_str, ...) LOGI0(_str , ## __VA_ARGS__) #define LOGW(_str, ...) LOGW0(_str , ## __VA_ARGS__) #define LOGE(_str, ...) LOGE0(_str , ## __VA_ARGS__) -#else +#else #define LOGD(...) do{} while(0) #define LOGI(...) do{} while(0) #define LOGW(...) do{} while(0) @@ -193,7 +193,7 @@ do { } catch(...) { \ LOGE0("\n ERROR: UNKNOWN Exception caught\n\n"); \ } \ -} while(0) +} while(0) #endif void* workcycleObjectDetectorFunction(void* p) @@ -214,7 +214,7 @@ void DetectionBasedTracker::SeparateDetectionWork::workcycleObjectDetector() vector objects; CV_Assert(stateThread==STATE_THREAD_WORKING_SLEEPING); - pthread_mutex_lock(&mutex); + pthread_mutex_lock(&mutex); { pthread_cond_signal(&objectDetectorThreadStartStop); @@ -268,7 +268,7 @@ void DetectionBasedTracker::SeparateDetectionWork::workcycleObjectDetector() LOGD("DetectionBasedTracker::SeparateDetectionWork::workcycleObjectDetector() --- imageSeparateDetecting is empty, continue"); continue; } - LOGD("DetectionBasedTracker::SeparateDetectionWork::workcycleObjectDetector() --- start handling imageSeparateDetecting, img.size=%dx%d, img.data=0x%p", + LOGD("DetectionBasedTracker::SeparateDetectionWork::workcycleObjectDetector() --- start handling imageSeparateDetecting, img.size=%dx%d, img.data=0x%p", imageSeparateDetecting.size().width, imageSeparateDetecting.size().height, (void*)imageSeparateDetecting.data); @@ -368,7 +368,7 @@ void DetectionBasedTracker::SeparateDetectionWork::resetTracking() pthread_mutex_unlock(&mutex); - + } bool DetectionBasedTracker::SeparateDetectionWork::communicateWithDetectingThread(const Mat& imageGray, vector& rectsWhereRegions) @@ -398,7 +398,7 @@ bool DetectionBasedTracker::SeparateDetectionWork::communicateWithDetectingThrea if (timeWhenDetectingThreadStartedWork > 0) { double time_from_previous_launch_in_ms=1000.0 * (((double)(getTickCount() - timeWhenDetectingThreadStartedWork )) / freq); //the same formula as for lastBigDetectionDuration shouldSendNewDataToWorkThread = (time_from_previous_launch_in_ms >= detectionBasedTracker.parameters.minDetectionPeriod); - LOGD("DetectionBasedTracker::SeparateDetectionWork::communicateWithDetectingThread: shouldSendNewDataToWorkThread was 1, now it is %d, since time_from_previous_launch_in_ms=%.2f, minDetectionPeriod=%d", + LOGD("DetectionBasedTracker::SeparateDetectionWork::communicateWithDetectingThread: shouldSendNewDataToWorkThread was 1, now it is %d, since time_from_previous_launch_in_ms=%.2f, minDetectionPeriod=%d", (shouldSendNewDataToWorkThread?1:0), time_from_previous_launch_in_ms, detectionBasedTracker.parameters.minDetectionPeriod); } @@ -454,7 +454,7 @@ DetectionBasedTracker::DetectionBasedTracker(const std::string& cascadeFilename, && (params.scaleFactor > 1.0) && (params.maxTrackLifetime >= 0) ); - if (!cascadeForTracking.load(cascadeFilename)) { + if (!cascadeForTracking.load(cascadeFilename)) { CV_Error(CV_StsBadArg, "DetectionBasedTracker::DetectionBasedTracker: Cannot load a cascade from the file '"+cascadeFilename+"'"); } @@ -495,7 +495,7 @@ void DetectionBasedTracker::process(const Mat& imageGray) Mat imageDetect=imageGray; int D=parameters.minObjectSize; - if (D < 1) + if (D < 1) D=1; vector rectsWhereRegions; @@ -633,7 +633,7 @@ void DetectionBasedTracker::updateTrackedObjects(const vector& detectedObj LOGD("DetectionBasedTracker::updateTrackedObjects: j=%d is rejected, because it is intersected with another rectangle", j); continue; } - LOGD("DetectionBasedTracker::updateTrackedObjects: detectedObjects[%d]={%d, %d, %d x %d}", + LOGD("DetectionBasedTracker::updateTrackedObjects: detectedObjects[%d]={%d, %d, %d x %d}", j, detectedObjects[j].x, detectedObjects[j].y, detectedObjects[j].width, detectedObjects[j].height); Rect r=prevRect & detectedObjects[j]; @@ -691,9 +691,9 @@ void DetectionBasedTracker::updateTrackedObjects(const vector& detectedObj std::vector::iterator it=trackedObjects.begin(); while( it != trackedObjects.end() ) { - if ( (it->numFramesNotDetected > parameters.maxTrackLifetime) + if ( (it->numFramesNotDetected > parameters.maxTrackLifetime) || - ( + ( (it->numDetectedFrames <= innerParameters.numStepsToWaitBeforeFirstShow) && (it->numFramesNotDetected > innerParameters.numStepsToTrackWithoutDetectingIfObjectHasNotBeenShown) @@ -718,7 +718,7 @@ Rect DetectionBasedTracker::calcTrackedObjectPositionToShow(int i) const return Rect(); } if (trackedObjects[i].numDetectedFrames <= innerParameters.numStepsToWaitBeforeFirstShow){ - LOGI("DetectionBasedTracker::calcTrackedObjectPositionToShow: trackedObjects[%d].numDetectedFrames=%d <= numStepsToWaitBeforeFirstShow=%d --- return empty Rect()", + LOGI("DetectionBasedTracker::calcTrackedObjectPositionToShow: trackedObjects[%d].numDetectedFrames=%d <= numStepsToWaitBeforeFirstShow=%d --- return empty Rect()", i, trackedObjects[i].numDetectedFrames, innerParameters.numStepsToWaitBeforeFirstShow); return Rect(); } diff --git a/modules/contrib/src/facerec.cpp b/modules/contrib/src/facerec.cpp index bfecd37..0f9e0ef 100644 --- a/modules/contrib/src/facerec.cpp +++ b/modules/contrib/src/facerec.cpp @@ -22,7 +22,7 @@ namespace cv { using std::set; - + // Reads a sequence from a FileNode::SEQ with type _Tp into a result vector. template inline void readFileNodeList(const FileNode& fn, vector<_Tp>& result) { @@ -48,7 +48,7 @@ inline void writeFileNodeList(FileStorage& fs, const string& name, } fs << "]"; } - + static Mat asRowMatrix(InputArrayOfArrays src, int rtype, double alpha=1, double beta=0) { // number of samples @@ -67,7 +67,7 @@ static Mat asRowMatrix(InputArrayOfArrays src, int rtype, double alpha=1, double } return data; } - + // Removes duplicate elements in a given vector. template inline vector<_Tp> remove_dups(const vector<_Tp>& src) { @@ -82,7 +82,7 @@ inline vector<_Tp> remove_dups(const vector<_Tp>& src) { return elems; } - + // Turk, M., and Pentland, A. "Eigenfaces for recognition.". Journal of // Cognitive Neuroscience 3 (1991), 71–86. class Eigenfaces : public FaceRecognizer @@ -100,15 +100,15 @@ public: using FaceRecognizer::load; // Initializes an empty Eigenfaces model. - Eigenfaces(int num_components = 0) : - _num_components(num_components) { } + Eigenfaces(int numComponents = 0) : + _num_components(numComponents) { } // Initializes and computes an Eigenfaces model with images in src and // corresponding labels in labels. num_components will be kept for // classification. Eigenfaces(InputArray src, InputArray labels, - int num_components = 0) : - _num_components(num_components) { + int numComponents = 0) : + _num_components(numComponents) { train(src, labels); } @@ -157,16 +157,16 @@ public: using FaceRecognizer::load; // Initializes an empty Fisherfaces model. - Fisherfaces(int num_components = 0) : - _num_components(num_components) {} + Fisherfaces(int numComponents = 0) : + _num_components(numComponents) {} // Initializes and computes a Fisherfaces model with images in src and // corresponding labels in labels. num_components will be kept for // classification. Fisherfaces(InputArray src, InputArray labels, - int num_components = 0) : - _num_components(num_components) { + int numComponents = 0) : + _num_components(numComponents) { train(src, labels); } @@ -228,11 +228,11 @@ public: // // radius, neighbors are used in the local binary patterns creation. // grid_x, grid_y control the grid size of the spatial histograms. - LBPH(int radius=1, int neighbors=8, int grid_x=8, int grid_y=8) : - _grid_x(grid_x), - _grid_y(grid_y), - _radius(radius), - _neighbors(neighbors) {} + LBPH(int radius_=1, int neighbors_=8, int grid_x_=8, int grid_y_=8) : + _grid_x(grid_x_), + _grid_y(grid_y_), + _radius(radius_), + _neighbors(neighbors_) {} // Initializes and computes this LBPH Model. The current implementation is // rather fixed as it uses the Extended Local Binary Patterns per default. @@ -241,12 +241,12 @@ public: // (grid_x=8), (grid_y=8) controls the grid size of the spatial histograms. LBPH(InputArray src, InputArray labels, - int radius=1, int neighbors=8, - int grid_x=8, int grid_y=8) : - _grid_x(grid_x), - _grid_y(grid_y), - _radius(radius), - _neighbors(neighbors) { + int radius_=1, int neighbors_=8, + int grid_x_=8, int grid_y_=8) : + _grid_x(grid_x_), + _grid_y(grid_y_), + _radius(radius_), + _neighbors(neighbors_) { train(src, labels); } @@ -582,7 +582,7 @@ static Mat histc(InputArray _src, int minVal, int maxVal, bool normed) return Mat(); } - + static Mat spatial_histogram(InputArray _src, int numPatterns, int grid_x, int grid_y, bool normed) { @@ -622,7 +622,7 @@ static Mat elbp(InputArray src, int radius, int neighbors) { elbp(src, dst, radius, neighbors); return dst; } - + void LBPH::load(const FileStorage& fs) { fs["radius"] >> _radius; fs["neighbors"] >> _neighbors; @@ -695,18 +695,18 @@ int LBPH::predict(InputArray _src) const { } return minClass; } - - + + Ptr createEigenFaceRecognizer(int num_components) { return new Eigenfaces(num_components); } - + Ptr createFisherFaceRecognizer(int num_components) { return new Fisherfaces(num_components); } - + Ptr createLBPHFaceRecognizer(int radius, int neighbors, int grid_x, int grid_y) { diff --git a/modules/contrib/src/gencolors.cpp b/modules/contrib/src/gencolors.cpp index 12ef9d9..42fc411 100644 --- a/modules/contrib/src/gencolors.cpp +++ b/modules/contrib/src/gencolors.cpp @@ -46,7 +46,7 @@ using namespace cv; -void downsamplePoints( const Mat& src, Mat& dst, size_t count ) +static void downsamplePoints( const Mat& src, Mat& dst, size_t count ) { CV_Assert( count >= 2 ); CV_Assert( src.cols == 1 || src.rows == 1 ); diff --git a/modules/contrib/src/lda.cpp b/modules/contrib/src/lda.cpp index 5983530..22ca97c 100644 --- a/modules/contrib/src/lda.cpp +++ b/modules/contrib/src/lda.cpp @@ -28,7 +28,7 @@ using std::map; using std::set; using std::cout; using std::endl; - + // Removes duplicate elements in a given vector. template inline vector<_Tp> remove_dups(const vector<_Tp>& src) { @@ -42,40 +42,58 @@ inline vector<_Tp> remove_dups(const vector<_Tp>& src) { elems.push_back(*it); return elems; } - + static Mat argsort(InputArray _src, bool ascending=true) { Mat src = _src.getMat(); - if (src.rows != 1 && src.cols != 1) - CV_Error(CV_StsBadArg, "cv::argsort only sorts 1D matrices."); + if (src.rows != 1 && src.cols != 1) { + string error_message = "Wrong shape of input matrix! Expected a matrix with one row or column."; + CV_Error(CV_StsBadArg, error_message); + } int flags = CV_SORT_EVERY_ROW+(ascending ? CV_SORT_ASCENDING : CV_SORT_DESCENDING); Mat sorted_indices; sortIdx(src.reshape(1,1),sorted_indices,flags); return sorted_indices; } -static Mat asRowMatrix(InputArrayOfArrays src, int rtype, double alpha=1, double beta=0) -{ +static Mat asRowMatrix(InputArrayOfArrays src, int rtype, double alpha=1, double beta=0) { + // make sure the input data is a vector of matrices or vector of vector + if(src.kind() != _InputArray::STD_VECTOR_MAT && src.kind() != _InputArray::STD_VECTOR_VECTOR) { + string error_message = "The data is expected as InputArray::STD_VECTOR_MAT (a std::vector) or _InputArray::STD_VECTOR_VECTOR (a std::vector< vector<...> >)."; + CV_Error(CV_StsBadArg, error_message); + } // number of samples - int n = (int) src.total(); - // return empty matrix if no data given + size_t n = src.total(); + // return empty matrix if no matrices given if(n == 0) return Mat(); - // dimensionality of samples - int d = (int)src.getMat(0).total(); + // dimensionality of (reshaped) samples + size_t d = src.getMat(0).total(); // create data matrix - Mat data(n, d, rtype); - // copy data - for(int i = 0; i < n; i++) { + Mat data((int)n, (int)d, rtype); + // now copy data + for(int i = 0; i < (int)n; i++) { + // make sure data can be reshaped, throw exception if not! + if(src.getMat(i).total() != d) { + string error_message = format("Wrong number of elements in matrix #%d! Expected %d was %d.", i, (int)d, (int)src.getMat(i).total()); + CV_Error(CV_StsBadArg, error_message); + } + // get a hold of the current row Mat xi = data.row(i); - src.getMat(i).reshape(1, 1).convertTo(xi, rtype, alpha, beta); + // make reshape happy by cloning for non-continuous matrices + if(src.getMat(i).isContinuous()) { + src.getMat(i).reshape(1, 1).convertTo(xi, rtype, alpha, beta); + } else { + src.getMat(i).clone().reshape(1, 1).convertTo(xi, rtype, alpha, beta); + } } return data; } - -void sortMatrixColumnsByIndices(InputArray _src, InputArray _indices, OutputArray _dst) { - if(_indices.getMat().type() != CV_32SC1) + +static void sortMatrixColumnsByIndices(InputArray _src, InputArray _indices, OutputArray _dst) { + if(_indices.getMat().type() != CV_32SC1) { CV_Error(CV_StsUnsupportedFormat, "cv::sortColumnsByIndices only works on integer indices!"); + } Mat src = _src.getMat(); vector indices = _indices.getMat(); _dst.create(src.rows, src.cols, src.type()); @@ -87,13 +105,13 @@ void sortMatrixColumnsByIndices(InputArray _src, InputArray _indices, OutputArra } } -Mat sortMatrixColumnsByIndices(InputArray src, InputArray indices) { +static Mat sortMatrixColumnsByIndices(InputArray src, InputArray indices) { Mat dst; sortMatrixColumnsByIndices(src, indices, dst); return dst; } - - + + template static bool isSymmetric_(InputArray src) { Mat _src = src.getMat(); @@ -151,33 +169,46 @@ static bool isSymmetric(InputArray src, double eps=1e-16) return false; } - + //------------------------------------------------------------------------------ -// subspace::project +// cv::subspaceProject //------------------------------------------------------------------------------ -Mat subspaceProject(InputArray _W, InputArray _mean, InputArray _src) -{ +Mat subspaceProject(InputArray _W, InputArray _mean, InputArray _src) { // get data matrices Mat W = _W.getMat(); Mat mean = _mean.getMat(); Mat src = _src.getMat(); + // get number of samples and dimension + int n = src.rows; + int d = src.cols; + // make sure the data has the correct shape + if(W.rows != d) { + string error_message = format("Wrong shapes for given matrices. Was size(src) = (%d,%d), size(W) = (%d,%d).", src.rows, src.cols, W.rows, W.cols); + CV_Error(CV_StsBadArg, error_message); + } + // make sure mean is correct if not empty + if(!mean.empty() && (mean.total() != (size_t) d)) { + string error_message = format("Wrong mean shape for the given data matrix. Expected %d, but was %d.", d, mean.total()); + CV_Error(CV_StsBadArg, error_message); + } // create temporary matrices Mat X, Y; - // copy data & make sure we are using the correct type + // make sure you operate on correct type src.convertTo(X, W.type()); - // get number of samples and dimension - int n = X.rows; - int d = X.cols; - // center the data if correct aligned sample mean is given - if(mean.total() == (size_t)d) - subtract(X, repeat(mean.reshape(1,1), n, 1), X); + // safe to do, because of above assertion + if(!mean.empty()) { + for(int i=0; i _Tp *alloc_1d(int m) { return new _Tp[m]; } - + // Allocates memory. template _Tp *alloc_1d(int m, _Tp val) { @@ -232,63 +279,63 @@ private: arr[i] = val; return arr; } - + // Allocates memory. template - _Tp **alloc_2d(int m, int n) { + _Tp **alloc_2d(int m, int _n) { _Tp **arr = new _Tp*[m]; for (int i = 0; i < m; i++) - arr[i] = new _Tp[n]; + arr[i] = new _Tp[_n]; return arr; } - + // Allocates memory. template - _Tp **alloc_2d(int m, int n, _Tp val) { - _Tp **arr = alloc_2d<_Tp> (m, n); + _Tp **alloc_2d(int m, int _n, _Tp val) { + _Tp **arr = alloc_2d<_Tp> (m, _n); for (int i = 0; i < m; i++) { - for (int j = 0; j < n; j++) { + for (int j = 0; j < _n; j++) { arr[i][j] = val; } } return arr; } - + void cdiv(double xr, double xi, double yr, double yi) { - double r, d; + double r, dv; if (std::abs(yr) > std::abs(yi)) { r = yi / yr; - d = yr + r * yi; - cdivr = (xr + r * xi) / d; - cdivi = (xi - r * xr) / d; + dv = yr + r * yi; + cdivr = (xr + r * xi) / dv; + cdivi = (xi - r * xr) / dv; } else { r = yr / yi; - d = yi + r * yr; - cdivr = (r * xr + xi) / d; - cdivi = (r * xi - xr) / d; + dv = yi + r * yr; + cdivr = (r * xr + xi) / dv; + cdivi = (r * xi - xr) / dv; } } - + // Nonsymmetric reduction from Hessenberg to real Schur form. - + void hqr2() { - + // This is derived from the Algol procedure hqr2, // by Martin and Wilkinson, Handbook for Auto. Comp., // Vol.ii-Linear Algebra, and the corresponding // Fortran subroutine in EISPACK. - + // Initialize int nn = this->n; - int n = nn - 1; + int n1 = nn - 1; int low = 0; int high = nn - 1; double eps = pow(2.0, -52.0); double exshift = 0.0; double p = 0, q = 0, r = 0, s = 0, z = 0, t, w, x, y; - + // Store roots isolated by balanc and compute matrix norm - + double norm = 0.0; for (int i = 0; i < nn; i++) { if (i < low || i > high) { @@ -299,13 +346,13 @@ private: norm = norm + std::abs(H[i][j]); } } - + // Outer loop over eigenvalue index int iter = 0; - while (n >= low) { - + while (n1 >= low) { + // Look for single small sub-diagonal element - int l = n; + int l = n1; while (l > low) { s = std::abs(H[l - 1][l - 1]) + std::abs(H[l][l]); if (s == 0.0) { @@ -316,114 +363,114 @@ private: } l--; } - + // Check for convergence // One root found - - if (l == n) { - H[n][n] = H[n][n] + exshift; - d[n] = H[n][n]; - e[n] = 0.0; - n--; + + if (l == n1) { + H[n1][n1] = H[n1][n1] + exshift; + d[n1] = H[n1][n1]; + e[n1] = 0.0; + n1--; iter = 0; - + // Two roots found - - } else if (l == n - 1) { - w = H[n][n - 1] * H[n - 1][n]; - p = (H[n - 1][n - 1] - H[n][n]) / 2.0; + + } else if (l == n1 - 1) { + w = H[n1][n1 - 1] * H[n1 - 1][n1]; + p = (H[n1 - 1][n1 - 1] - H[n1][n1]) / 2.0; q = p * p + w; z = sqrt(std::abs(q)); - H[n][n] = H[n][n] + exshift; - H[n - 1][n - 1] = H[n - 1][n - 1] + exshift; - x = H[n][n]; - + H[n1][n1] = H[n1][n1] + exshift; + H[n1 - 1][n1 - 1] = H[n1 - 1][n1 - 1] + exshift; + x = H[n1][n1]; + // Real pair - + if (q >= 0) { if (p >= 0) { z = p + z; } else { z = p - z; } - d[n - 1] = x + z; - d[n] = d[n - 1]; + d[n1 - 1] = x + z; + d[n1] = d[n1 - 1]; if (z != 0.0) { - d[n] = x - w / z; + d[n1] = x - w / z; } - e[n - 1] = 0.0; - e[n] = 0.0; - x = H[n][n - 1]; + e[n1 - 1] = 0.0; + e[n1] = 0.0; + x = H[n1][n1 - 1]; s = std::abs(x) + std::abs(z); p = x / s; q = z / s; r = sqrt(p * p + q * q); p = p / r; q = q / r; - + // Row modification - - for (int j = n - 1; j < nn; j++) { - z = H[n - 1][j]; - H[n - 1][j] = q * z + p * H[n][j]; - H[n][j] = q * H[n][j] - p * z; + + for (int j = n1 - 1; j < nn; j++) { + z = H[n1 - 1][j]; + H[n1 - 1][j] = q * z + p * H[n1][j]; + H[n1][j] = q * H[n1][j] - p * z; } - + // Column modification - - for (int i = 0; i <= n; i++) { - z = H[i][n - 1]; - H[i][n - 1] = q * z + p * H[i][n]; - H[i][n] = q * H[i][n] - p * z; + + for (int i = 0; i <= n1; i++) { + z = H[i][n1 - 1]; + H[i][n1 - 1] = q * z + p * H[i][n1]; + H[i][n1] = q * H[i][n1] - p * z; } - + // Accumulate transformations - + for (int i = low; i <= high; i++) { - z = V[i][n - 1]; - V[i][n - 1] = q * z + p * V[i][n]; - V[i][n] = q * V[i][n] - p * z; + z = V[i][n1 - 1]; + V[i][n1 - 1] = q * z + p * V[i][n1]; + V[i][n1] = q * V[i][n1] - p * z; } - + // Complex pair - + } else { - d[n - 1] = x + p; - d[n] = x + p; - e[n - 1] = z; - e[n] = -z; + d[n1 - 1] = x + p; + d[n1] = x + p; + e[n1 - 1] = z; + e[n1] = -z; } - n = n - 2; + n1 = n1 - 2; iter = 0; - + // No convergence yet - + } else { - + // Form shift - - x = H[n][n]; + + x = H[n1][n1]; y = 0.0; w = 0.0; - if (l < n) { - y = H[n - 1][n - 1]; - w = H[n][n - 1] * H[n - 1][n]; + if (l < n1) { + y = H[n1 - 1][n1 - 1]; + w = H[n1][n1 - 1] * H[n1 - 1][n1]; } - + // Wilkinson's original ad hoc shift - + if (iter == 10) { exshift += x; - for (int i = low; i <= n; i++) { + for (int i = low; i <= n1; i++) { H[i][i] -= x; } - s = std::abs(H[n][n - 1]) + std::abs(H[n - 1][n - 2]); + s = std::abs(H[n1][n1 - 1]) + std::abs(H[n1 - 1][n1 - 2]); x = y = 0.75 * s; w = -0.4375 * s * s; } - + // MATLAB's new ad hoc shift - + if (iter == 30) { s = (y - x) / 2.0; s = s * s + w; @@ -433,18 +480,18 @@ private: s = -s; } s = x - w / ((y - x) / 2.0 + s); - for (int i = low; i <= n; i++) { + for (int i = low; i <= n1; i++) { H[i][i] -= s; } exshift += s; x = y = w = 0.964; } } - + iter = iter + 1; // (Could check iteration count here.) - + // Look for two consecutive small sub-diagonal elements - int m = n - 2; + int m = n1 - 2; while (m >= l) { z = H[m][m]; r = x - z; @@ -466,18 +513,18 @@ private: } m--; } - - for (int i = m + 2; i <= n; i++) { + + for (int i = m + 2; i <= n1; i++) { H[i][i - 2] = 0.0; if (i > m + 2) { H[i][i - 3] = 0.0; } } - + // Double QR step involving rows l:n and columns m:n - - for (int k = m; k <= n - 1; k++) { - bool notlast = (k != n - 1); + + for (int k = m; k <= n1 - 1; k++) { + bool notlast = (k != n1 - 1); if (k != m) { p = H[k][k - 1]; q = H[k + 1][k - 1]; @@ -508,9 +555,9 @@ private: z = r / s; q = q / p; r = r / p; - + // Row modification - + for (int j = k; j < nn; j++) { p = H[k][j] + q * H[k + 1][j]; if (notlast) { @@ -520,10 +567,10 @@ private: H[k][j] = H[k][j] - p * x; H[k + 1][j] = H[k + 1][j] - p * y; } - + // Column modification - - for (int i = 0; i <= min(n, k + 3); i++) { + + for (int i = 0; i <= min(n1, k + 3); i++) { p = x * H[i][k] + y * H[i][k + 1]; if (notlast) { p = p + z * H[i][k + 2]; @@ -532,9 +579,9 @@ private: H[i][k] = H[i][k] - p; H[i][k + 1] = H[i][k + 1] - p * q; } - + // Accumulate transformations - + for (int i = low; i <= high; i++) { p = x * V[i][k] + y * V[i][k + 1]; if (notlast) { @@ -547,28 +594,28 @@ private: } // (s != 0) } // k loop } // check convergence - } // while (n >= low) - + } // while (n1 >= low) + // Backsubstitute to find vectors of upper triangular form - + if (norm == 0.0) { return; } - - for (n = nn - 1; n >= 0; n--) { - p = d[n]; - q = e[n]; - + + for (n1 = nn - 1; n1 >= 0; n1--) { + p = d[n1]; + q = e[n1]; + // Real vector - + if (q == 0) { - int l = n; - H[n][n] = 1.0; - for (int i = n - 1; i >= 0; i--) { + int l = n1; + H[n1][n1] = 1.0; + for (int i = n1 - 1; i >= 0; i--) { w = H[i][i] - p; r = 0.0; - for (int j = l; j <= n; j++) { - r = r + H[i][j] * H[j][n]; + for (int j = l; j <= n1; j++) { + r = r + H[i][j] * H[j][n1]; } if (e[i] < 0.0) { z = w; @@ -577,64 +624,62 @@ private: l = i; if (e[i] == 0.0) { if (w != 0.0) { - H[i][n] = -r / w; + H[i][n1] = -r / w; } else { - H[i][n] = -r / (eps * norm); + H[i][n1] = -r / (eps * norm); } - + // Solve real equations - + } else { x = H[i][i + 1]; y = H[i + 1][i]; q = (d[i] - p) * (d[i] - p) + e[i] * e[i]; t = (x * s - z * r) / q; - H[i][n] = t; + H[i][n1] = t; if (std::abs(x) > std::abs(z)) { - H[i + 1][n] = (-r - w * t) / x; + H[i + 1][n1] = (-r - w * t) / x; } else { - H[i + 1][n] = (-s - y * t) / z; + H[i + 1][n1] = (-s - y * t) / z; } } - + // Overflow control - - t = std::abs(H[i][n]); + + t = std::abs(H[i][n1]); if ((eps * t) * t > 1) { - for (int j = i; j <= n; j++) { - H[j][n] = H[j][n] / t; + for (int j = i; j <= n1; j++) { + H[j][n1] = H[j][n1] / t; } } } } - // Complex vector - } else if (q < 0) { - int l = n - 1; - + int l = n1 - 1; + // Last vector component imaginary so matrix is triangular - - if (std::abs(H[n][n - 1]) > std::abs(H[n - 1][n])) { - H[n - 1][n - 1] = q / H[n][n - 1]; - H[n - 1][n] = -(H[n][n] - p) / H[n][n - 1]; + + if (std::abs(H[n1][n1 - 1]) > std::abs(H[n1 - 1][n1])) { + H[n1 - 1][n1 - 1] = q / H[n1][n1 - 1]; + H[n1 - 1][n1] = -(H[n1][n1] - p) / H[n1][n1 - 1]; } else { - cdiv(0.0, -H[n - 1][n], H[n - 1][n - 1] - p, q); - H[n - 1][n - 1] = cdivr; - H[n - 1][n] = cdivi; + cdiv(0.0, -H[n1 - 1][n1], H[n1 - 1][n1 - 1] - p, q); + H[n1 - 1][n1 - 1] = cdivr; + H[n1 - 1][n1] = cdivi; } - H[n][n - 1] = 0.0; - H[n][n] = 1.0; - for (int i = n - 2; i >= 0; i--) { + H[n1][n1 - 1] = 0.0; + H[n1][n1] = 1.0; + for (int i = n1 - 2; i >= 0; i--) { double ra, sa, vr, vi; ra = 0.0; sa = 0.0; - for (int j = l; j <= n; j++) { - ra = ra + H[i][j] * H[j][n - 1]; - sa = sa + H[i][j] * H[j][n]; + for (int j = l; j <= n1; j++) { + ra = ra + H[i][j] * H[j][n1 - 1]; + sa = sa + H[i][j] * H[j][n1]; } w = H[i][i] - p; - + if (e[i] < 0.0) { z = w; r = ra; @@ -643,12 +688,12 @@ private: l = i; if (e[i] == 0) { cdiv(-ra, -sa, w, q); - H[i][n - 1] = cdivr; - H[i][n] = cdivi; + H[i][n1 - 1] = cdivr; + H[i][n1] = cdivi; } else { - + // Solve complex equations - + x = H[i][i + 1]; y = H[i + 1][i]; vr = (d[i] - p) * (d[i] - p) + e[i] * e[i] - q * q; @@ -659,37 +704,37 @@ private: } cdiv(x * r - z * ra + q * sa, x * s - z * sa - q * ra, vr, vi); - H[i][n - 1] = cdivr; - H[i][n] = cdivi; + H[i][n1 - 1] = cdivr; + H[i][n1] = cdivi; if (std::abs(x) > (std::abs(z) + std::abs(q))) { - H[i + 1][n - 1] = (-ra - w * H[i][n - 1] + q - * H[i][n]) / x; - H[i + 1][n] = (-sa - w * H[i][n] - q * H[i][n + H[i + 1][n1 - 1] = (-ra - w * H[i][n1 - 1] + q + * H[i][n1]) / x; + H[i + 1][n1] = (-sa - w * H[i][n1] - q * H[i][n1 - 1]) / x; } else { - cdiv(-r - y * H[i][n - 1], -s - y * H[i][n], z, + cdiv(-r - y * H[i][n1 - 1], -s - y * H[i][n1], z, q); - H[i + 1][n - 1] = cdivr; - H[i + 1][n] = cdivi; + H[i + 1][n1 - 1] = cdivr; + H[i + 1][n1] = cdivi; } } - + // Overflow control - - t = max(std::abs(H[i][n - 1]), std::abs(H[i][n])); + + t = max(std::abs(H[i][n1 - 1]), std::abs(H[i][n1])); if ((eps * t) * t > 1) { - for (int j = i; j <= n; j++) { - H[j][n - 1] = H[j][n - 1] / t; - H[j][n] = H[j][n] / t; + for (int j = i; j <= n1; j++) { + H[j][n1 - 1] = H[j][n1 - 1] / t; + H[j][n1] = H[j][n1] / t; } } } } } } - + // Vectors of isolated roots - + for (int i = 0; i < nn; i++) { if (i < low || i > high) { for (int j = i; j < nn; j++) { @@ -697,9 +742,9 @@ private: } } } - + // Back transformation to get eigenvectors of original matrix - + for (int j = nn - 1; j >= low; j--) { for (int i = low; i <= high; i++) { z = 0.0; @@ -710,7 +755,7 @@ private: } } } - + // Nonsymmetric reduction to Hessenberg form. void orthes() { // This is derived from the Algol procedures orthes and ortran, @@ -719,19 +764,19 @@ private: // Fortran subroutines in EISPACK. int low = 0; int high = n - 1; - + for (int m = low + 1; m <= high - 1; m++) { - + // Scale column. - + double scale = 0.0; for (int i = m; i <= high; i++) { scale = scale + std::abs(H[i][m - 1]); } if (scale != 0.0) { - + // Compute Householder transformation. - + double h = 0.0; for (int i = high; i >= m; i--) { ort[i] = H[i][m - 1] / scale; @@ -743,10 +788,10 @@ private: } h = h - ort[m] * g; ort[m] = ort[m] - g; - + // Apply Householder similarity transformation // H = (I-u*u'/h)*H*(I-u*u')/h) - + for (int j = m; j < n; j++) { double f = 0.0; for (int i = high; i >= m; i--) { @@ -757,7 +802,7 @@ private: H[i][j] -= f * ort[i]; } } - + for (int i = 0; i <= high; i++) { double f = 0.0; for (int j = high; j >= m; j--) { @@ -772,15 +817,15 @@ private: H[m][m - 1] = scale * g; } } - + // Accumulate transformations (Algol's ortran). - + for (int i = 0; i < n; i++) { for (int j = 0; j < n; j++) { V[i][j] = (i == j ? 1.0 : 0.0); } } - + for (int m = high - 1; m >= low + 1; m--) { if (H[m][m - 1] != 0.0) { for (int i = m + 1; i <= high; i++) { @@ -800,7 +845,7 @@ private: } } } - + // Releases all internal working memory. void release() { // releases the working data @@ -814,7 +859,7 @@ private: delete[] H; delete[] V; } - + // Computes the Eigenvalue Decomposition for a matrix given in H. void compute() { // Allocate memory for the working data. @@ -839,11 +884,11 @@ private: // Deallocate the memory by releasing all internal working data. release(); } - + public: EigenvalueDecomposition() : n(0) { } - + // Initializes & computes the Eigenvalue Decomposition for a general matrix // given in src. This function is a port of the EigenvalueSolver in JAMA, // which has been released to public domain by The MathWorks and the @@ -851,7 +896,7 @@ public: EigenvalueDecomposition(InputArray src) { compute(src); } - + // This function computes the Eigenvalue Decomposition for a general matrix // given in src. This function is a port of the EigenvalueSolver in JAMA, // which has been released to public domain by The MathWorks and the @@ -883,9 +928,9 @@ public: compute(); } } - + ~EigenvalueDecomposition() {} - + // Returns the eigenvalues of the Eigenvalue Decomposition. Mat eigenvalues() { return _eigenvalues; } // Returns the eigenvectors of the Eigenvalue Decomposition. @@ -898,8 +943,9 @@ public: //------------------------------------------------------------------------------ void LDA::save(const string& filename) const { FileStorage fs(filename, FileStorage::WRITE); - if (!fs.isOpened()) + if (!fs.isOpened()) { CV_Error(CV_StsError, "File can't be opened for writing!"); + } this->save(fs); fs.release(); } @@ -941,8 +987,8 @@ void LDA::lda(InputArray _src, InputArray _lbls) { vector mapped_labels(labels.size()); vector num2label = remove_dups(labels); map label2num; - for (size_t i = 0; i < num2label.size(); i++) - label2num[num2label[i]] = (int)i; + for (int i = 0; i < (int)num2label.size(); i++) + label2num[num2label[i]] = i; for (size_t i = 0; i < labels.size(); i++) mapped_labels[i] = label2num[labels[i]]; // get sample size, dimension @@ -950,17 +996,27 @@ void LDA::lda(InputArray _src, InputArray _lbls) { int D = data.cols; // number of unique labels int C = (int)num2label.size(); + // we can't do a LDA on one class, what do you + // want to separate from each other then? + if(C == 1) { + string error_message = "At least two classes are needed to perform a LDA. Reason: Only one class was given!"; + CV_Error(CV_StsBadArg, error_message); + } // throw error if less labels, than samples - if (labels.size() != (size_t)N) - CV_Error(CV_StsBadArg, "Error: The number of samples must equal the number of labels."); + if (labels.size() != static_cast(N)) { + string error_message = format("The number of samples must equal the number of labels. Given %d labels, %d samples. ", labels.size(), N); + CV_Error(CV_StsBadArg, error_message); + } // warn if within-classes scatter matrix becomes singular - if (N < D) + if (N < D) { cout << "Warning: Less observations than feature dimension given!" - << "Computation will probably fail." - << endl; + << "Computation will probably fail." + << endl; + } // clip number of components to be a valid number - if ((_num_components <= 0) || (_num_components > (C - 1))) + if ((_num_components <= 0) || (_num_components > (C - 1))) { _num_components = (C - 1); + } // holds the mean over all classes Mat meanTotal = Mat::zeros(1, D, data.type()); // holds the mean for each class @@ -979,12 +1035,12 @@ void LDA::lda(InputArray _src, InputArray _lbls) { add(meanClass[classIdx], instance, meanClass[classIdx]); numClass[classIdx]++; } - // calculate means - meanTotal.convertTo(meanTotal, meanTotal.type(), - 1.0 / static_cast (N)); - for (int i = 0; i < C; i++) - meanClass[i].convertTo(meanClass[i], meanClass[i].type(), - 1.0 / static_cast (numClass[i])); + // calculate total mean + meanTotal.convertTo(meanTotal, meanTotal.type(), 1.0 / static_cast (N)); + // calculate class means + for (int i = 0; i < C; i++) { + meanClass[i].convertTo(meanClass[i], meanClass[i].type(), 1.0 / static_cast (numClass[i])); + } // subtract class means for (int i = 0; i < N; i++) { int classIdx = mapped_labels[i]; @@ -1031,7 +1087,8 @@ void LDA::compute(InputArray _src, InputArray _lbls) { lda(_src.getMat(), _lbls); break; default: - CV_Error(CV_StsNotImplemented, "This data type is not supported by subspace::LDA::compute."); + string error_message= format("InputArray Datatype %d is not supported.", _src.kind()); + CV_Error(CV_StsBadArg, error_message); break; } } @@ -1045,6 +1102,6 @@ Mat LDA::project(InputArray src) { Mat LDA::reconstruct(InputArray src) { return subspaceReconstruct(_eigenvectors, Mat(), _dataAsRow ? src : src.getMat().t()); } - + } diff --git a/modules/contrib/src/logpolar_bsm.cpp b/modules/contrib/src/logpolar_bsm.cpp index 52917d7..3de6a61 100644 --- a/modules/contrib/src/logpolar_bsm.cpp +++ b/modules/contrib/src/logpolar_bsm.cpp @@ -60,9 +60,9 @@ ICVS 2011, Sophia Antipolis, France, September 20-22, 2011 namespace cv { - + //------------------------------------interp------------------------------------------- -LogPolar_Interp::LogPolar_Interp(int w, int h, Point2i center, int R, double ro0, int interp, int full, int S, int sp) +LogPolar_Interp::LogPolar_Interp(int w, int h, Point2i center, int _R, double _ro0, int _interp, int full, int _s, int sp) { if ( (center.x!=w/2 || center.y!=h/2) && full==0) full=1; @@ -97,23 +97,23 @@ LogPolar_Interp::LogPolar_Interp(int w, int h, Point2i center, int R, double ro0 if (sp){ int jc=M/2-1, ic=N/2-1; - int romax=min(ic, jc); - double a=exp(log((double)(romax/2-1)/(double)ro0)/(double)R); - S=(int) floor(2*CV_PI/(a-1)+0.5); + int _romax=min(ic, jc); + double _a=exp(log((double)(_romax/2-1)/(double)ro0)/(double)R); + S=(int) floor(2*CV_PI/(_a-1)+0.5); } - this->interp=interp; + interp=_interp; - create_map(M, N, R, S, ro0); + create_map(M, N, _R, _s, _ro0); } -void LogPolar_Interp::create_map(int M, int N, int R, int S, double ro0) +void LogPolar_Interp::create_map(int _M, int _n, int _R, int _s, double _ro0) { - this->M=M; - this->N=N; - this->R=R; - this->S=S; - this->ro0=ro0; + M=_M; + N=_n; + R=_R; + S=_s; + ro0=_ro0; int jc=N/2-1, ic=M/2-1; romax=min(ic, jc); @@ -130,7 +130,7 @@ void LogPolar_Interp::create_map(int M, int N, int R, int S, double ro0) for(int u=0; u(v,u)=(float)(ro0*pow(a,u)*sin(v/q)+jc); - Csri.at(v,u)=(float)(ro0*pow(a,u)*cos(v/q)+ic); + Csri.at(v,u)=(float)(ro0*pow(a,u)*cos(v/q)+ic); } } @@ -158,7 +158,7 @@ void LogPolar_Interp::create_map(int M, int N, int R, int S, double ro0) const Mat LogPolar_Interp::to_cortical(const Mat &source) { Mat out(S,R,CV_8UC1,Scalar(0)); - + Mat source_border; copyMakeBorder(source,source_border,top,bottom,left,right,BORDER_CONSTANT,Scalar(0)); @@ -173,7 +173,7 @@ const Mat LogPolar_Interp::to_cartesian(const Mat &source) Mat out(N,M,CV_8UC1,Scalar(0)); Mat source_border; - + if (interp==INTER_NEAREST || interp==INTER_LINEAR){ copyMakeBorder(source,source_border,0,1,0,0,BORDER_CONSTANT,Scalar(0)); Mat rowS0 = source_border.row(S); @@ -208,7 +208,7 @@ LogPolar_Interp::~LogPolar_Interp() //------------------------------------overlapping---------------------------------- -LogPolar_Overlapping::LogPolar_Overlapping(int w, int h, Point2i center, int R, double ro0, int full, int S, int sp) +LogPolar_Overlapping::LogPolar_Overlapping(int w, int h, Point2i center, int _R, double _ro0, int full, int _s, int sp) { if ( (center.x!=w/2 || center.y!=h/2) && full==0) full=1; @@ -244,21 +244,21 @@ LogPolar_Overlapping::LogPolar_Overlapping(int w, int h, Point2i center, int R, if (sp){ int jc=M/2-1, ic=N/2-1; - int romax=min(ic, jc); - double a=exp(log((double)(romax/2-1)/(double)ro0)/(double)R); - S=(int) floor(2*CV_PI/(a-1)+0.5); + int _romax=min(ic, jc); + double _a=exp(log((double)(_romax/2-1)/(double)ro0)/(double)R); + S=(int) floor(2*CV_PI/(_a-1)+0.5); } - create_map(M, N, R, S, ro0); + create_map(M, N, _R, _s, _ro0); } -void LogPolar_Overlapping::create_map(int M, int N, int R, int S, double ro0) +void LogPolar_Overlapping::create_map(int _M, int _n, int _R, int _s, double _ro0) { - this->M=M; - this->N=N; - this->R=R; - this->S=S; - this->ro0=ro0; + M=_M; + N=_n; + R=_R; + S=_s; + ro0=_ro0; int jc=N/2-1, ic=M/2-1; romax=min(ic, jc); @@ -280,14 +280,14 @@ void LogPolar_Overlapping::create_map(int M, int N, int R, int S, double ro0) for(int u=0; u(v,u)=(float)(ro0*pow(a,u)*sin(v/q)+jc); - Csri.at(v,u)=(float)(ro0*pow(a,u)*cos(v/q)+ic); + Csri.at(v,u)=(float)(ro0*pow(a,u)*cos(v/q)+ic); Rsr[v*R+u]=(int)floor(Rsri.at(v,u)); - Csr[v*R+u]=(int)floor(Csri.at(v,u)); + Csr[v*R+u]=(int)floor(Csri.at(v,u)); } } bool done=false; - + for(int i=0; i(j,i)=(float)(q*theta); - + double ro2=(j-jc)*(j-jc)+(i-ic)*(i-ic); CSIyx.at(j,i)=(float)(0.5*log(ro2/(ro0*ro0))/log(a)); } @@ -387,7 +387,7 @@ const Mat LogPolar_Overlapping::to_cartesian(const Mat &source) remap(source_border,out,CSIyx,ETAyx,INTER_LINEAR); int wm=w_ker_2D[R-1].w; - + vector IMG((N+2*wm+1)*(M+2*wm+1), 0.); vector NOR((N+2*wm+1)*(M+2*wm+1), 0.); @@ -426,14 +426,14 @@ const Mat LogPolar_Overlapping::to_cartesian(const Mat &source) Mat out_cropped=out(Range(top,N-1-bottom),Range(left,M-1-right)); return out_cropped; } - + LogPolar_Overlapping::~LogPolar_Overlapping() { } //----------------------------------------adjacent--------------------------------------- -LogPolar_Adjacent::LogPolar_Adjacent(int w, int h, Point2i center, int R, double ro0, double smin, int full, int S, int sp) +LogPolar_Adjacent::LogPolar_Adjacent(int w, int h, Point2i center, int _R, double _ro0, double smin, int full, int _s, int sp) { if ( (center.x!=w/2 || center.y!=h/2) && full==0) full=1; @@ -468,22 +468,22 @@ LogPolar_Adjacent::LogPolar_Adjacent(int w, int h, Point2i center, int R, double if (sp){ int jc=M/2-1, ic=N/2-1; - int romax=min(ic, jc); - double a=exp(log((double)(romax/2-1)/(double)ro0)/(double)R); - S=(int) floor(2*CV_PI/(a-1)+0.5); + int _romax=min(ic, jc); + double _a=exp(log((double)(_romax/2-1)/(double)ro0)/(double)R); + S=(int) floor(2*CV_PI/(_a-1)+0.5); } - create_map(M, N, R, S, ro0, smin); + create_map(M, N, _R, _s, _ro0, smin); } -void LogPolar_Adjacent::create_map(int M, int N, int R, int S, double ro0, double smin) +void LogPolar_Adjacent::create_map(int _M, int _n, int _R, int _s, double _ro0, double smin) { - LogPolar_Adjacent::M=M; - LogPolar_Adjacent::N=N; - LogPolar_Adjacent::R=R; - LogPolar_Adjacent::S=S; - LogPolar_Adjacent::ro0=ro0; + M=_M; + N=_n; + R=_R; + S=_s; + ro0=_ro0; romax=min(M/2.0, N/2.0); a=exp(log(romax/ro0)/(double)R); @@ -507,7 +507,7 @@ void LogPolar_Adjacent::create_map(int M, int N, int R, int S, double ro0, doubl void LogPolar_Adjacent::subdivide_recursively(double x, double y, int i, int j, double length, double smin) -{ +{ if(length<=smin) { int u, v; @@ -576,7 +576,7 @@ const Mat LogPolar_Adjacent::to_cortical(const Mat &source) for(int j=0; j(j,i)); @@ -641,7 +641,7 @@ bool LogPolar_Adjacent::get_uv(double x, double y, int&u, int&v) else v= (int) floor(q*(theta+2*CV_PI)); return true; - } + } } LogPolar_Adjacent::~LogPolar_Adjacent() diff --git a/modules/contrib/src/octree.cpp b/modules/contrib/src/octree.cpp index e62cfb8..0808b12 100644 --- a/modules/contrib/src/octree.cpp +++ b/modules/contrib/src/octree.cpp @@ -43,98 +43,99 @@ #include "precomp.hpp" #include -namespace cv +namespace { + using namespace cv; const size_t MAX_STACK_SIZE = 255; const size_t MAX_LEAFS = 8; - + bool checkIfNodeOutsideSphere(const Octree::Node& node, const Point3f& c, float r) { if (node.x_max < (c.x - r) || node.y_max < (c.y - r) || node.z_max < (c.z - r)) return true; - + if ((c.x + r) < node.x_min || (c.y + r) < node.y_min || (c.z + r) < node.z_min) return true; - + return false; } - + bool checkIfNodeInsideSphere(const Octree::Node& node, const Point3f& c, float r) { r *= r; - + float d2_xmin = (node.x_min - c.x) * (node.x_min - c.x); float d2_ymin = (node.y_min - c.y) * (node.y_min - c.y); float d2_zmin = (node.z_min - c.z) * (node.z_min - c.z); - + if (d2_xmin + d2_ymin + d2_zmin > r) return false; - + float d2_zmax = (node.z_max - c.z) * (node.z_max - c.z); - + if (d2_xmin + d2_ymin + d2_zmax > r) return false; - + float d2_ymax = (node.y_max - c.y) * (node.y_max - c.y); - + if (d2_xmin + d2_ymax + d2_zmin > r) return false; - + if (d2_xmin + d2_ymax + d2_zmax > r) return false; - + float d2_xmax = (node.x_max - c.x) * (node.x_max - c.x); - + if (d2_xmax + d2_ymin + d2_zmin > r) return false; - + if (d2_xmax + d2_ymin + d2_zmax > r) return false; - + if (d2_xmax + d2_ymax + d2_zmin > r) return false; - + if (d2_xmax + d2_ymax + d2_zmax > r) return false; - + return true; } - + void fillMinMax(const vector& points, Octree::Node& node) { node.x_max = node.y_max = node.z_max = std::numeric_limits::min(); node.x_min = node.y_min = node.z_min = std::numeric_limits::max(); - + for (size_t i = 0; i < points.size(); ++i) { const Point3f& point = points[i]; - + if (node.x_max < point.x) node.x_max = point.x; - + if (node.y_max < point.y) node.y_max = point.y; - + if (node.z_max < point.z) node.z_max = point.z; - + if (node.x_min > point.x) node.x_min = point.x; - + if (node.y_min > point.y) node.y_min = point.y; - + if (node.z_min > point.z) node.z_min = point.z; } } - + size_t findSubboxForPoint(const Point3f& point, const Octree::Node& node) { size_t ind_x = point.x < (node.x_max + node.x_min) / 2 ? 0 : 1; size_t ind_y = point.y < (node.y_max + node.y_min) / 2 ? 0 : 1; size_t ind_z = point.z < (node.z_max + node.z_min) / 2 ? 0 : 1; - + return (ind_x << 2) + (ind_y << 1) + (ind_z << 0); } void initChildBox(const Octree::Node& parent, size_t boxIndex, Octree::Node& child) @@ -142,58 +143,61 @@ namespace cv child.x_min = child.x_max = (parent.x_max + parent.x_min) / 2; child.y_min = child.y_max = (parent.y_max + parent.y_min) / 2; child.z_min = child.z_max = (parent.z_max + parent.z_min) / 2; - + if ((boxIndex >> 0) & 1) child.z_max = parent.z_max; else child.z_min = parent.z_min; - + if ((boxIndex >> 1) & 1) child.y_max = parent.y_max; else child.y_min = parent.y_min; - + if ((boxIndex >> 2) & 1) child.x_max = parent.x_max; else child.x_min = parent.x_min; } - + +}//namespace + //////////////////////////////////////////////////////////////////////////////////////// /////////////////////////// Octree ////////////////////////////////////// //////////////////////////////////////////////////////////////////////////////////////// - +namespace cv +{ Octree::Octree() { } - - Octree::Octree(const vector& points3d, int maxLevels, int minPoints) + + Octree::Octree(const vector& points3d, int maxLevels, int _minPoints) { - buildTree(points3d, maxLevels, minPoints); + buildTree(points3d, maxLevels, _minPoints); } - + Octree::~Octree() { } - + void Octree::getPointsWithinSphere(const Point3f& center, float radius, vector& out) const { out.clear(); - + if (nodes.empty()) return; - + int stack[MAX_STACK_SIZE]; int pos = 0; stack[pos] = 0; - + while (pos >= 0) { const Node& cur = nodes[stack[pos--]]; - + if (checkIfNodeOutsideSphere(cur, center, radius)) continue; - + if (checkIfNodeInsideSphere(cur, center, radius)) { size_t sz = out.size(); @@ -202,133 +206,133 @@ namespace cv out[sz++] = points[i]; continue; } - + if (cur.isLeaf) { double r2 = radius * radius; size_t sz = out.size(); out.resize(sz + (cur.end - cur.begin)); - + for (int i = cur.begin; i < cur.end; ++i) { const Point3f& point = points[i]; - + double dx = (point.x - center.x); double dy = (point.y - center.y); double dz = (point.z - center.z); - + double dist2 = dx * dx + dy * dy + dz * dz; - + if (dist2 < r2) out[sz++] = point; }; out.resize(sz); continue; } - + if (cur.children[0]) stack[++pos] = cur.children[0]; - + if (cur.children[1]) stack[++pos] = cur.children[1]; - + if (cur.children[2]) stack[++pos] = cur.children[2]; - + if (cur.children[3]) stack[++pos] = cur.children[3]; - + if (cur.children[4]) stack[++pos] = cur.children[4]; - + if (cur.children[5]) stack[++pos] = cur.children[5]; - + if (cur.children[6]) stack[++pos] = cur.children[6]; - + if (cur.children[7]) stack[++pos] = cur.children[7]; } } - - void Octree::buildTree(const vector& points3d, int maxLevels, int minPoints) + + void Octree::buildTree(const vector& points3d, int maxLevels, int _minPoints) { assert((size_t)maxLevels * 8 < MAX_STACK_SIZE); points.resize(points3d.size()); std::copy(points3d.begin(), points3d.end(), points.begin()); - this->minPoints = minPoints; - + minPoints = _minPoints; + nodes.clear(); nodes.push_back(Node()); Node& root = nodes[0]; fillMinMax(points, root); - + root.isLeaf = true; root.maxLevels = maxLevels; root.begin = 0; root.end = (int)points.size(); for (size_t i = 0; i < MAX_LEAFS; i++) root.children[i] = 0; - - if (maxLevels != 1 && (root.end - root.begin) > minPoints) + + if (maxLevels != 1 && (root.end - root.begin) > _minPoints) { root.isLeaf = false; buildNext(0); } } - + void Octree::buildNext(size_t nodeInd) { size_t size = nodes[nodeInd].end - nodes[nodeInd].begin; - + vector boxBorders(MAX_LEAFS+1, 0); vector boxIndices(size); vector tempPoints(size); - + for (int i = nodes[nodeInd].begin, j = 0; i < nodes[nodeInd].end; ++i, ++j) { const Point3f& p = points[i]; - + size_t subboxInd = findSubboxForPoint(p, nodes[nodeInd]); - + boxBorders[subboxInd+1]++; boxIndices[j] = subboxInd; tempPoints[j] = p; } - + for (size_t i = 1; i < boxBorders.size(); ++i) boxBorders[i] += boxBorders[i-1]; - + vector writeInds(boxBorders.begin(), boxBorders.end()); - + for (size_t i = 0; i < size; ++i) { size_t boxIndex = boxIndices[i]; Point3f& curPoint = tempPoints[i]; - + size_t copyTo = nodes[nodeInd].begin + writeInds[boxIndex]++; points[copyTo] = curPoint; } - + for (size_t i = 0; i < MAX_LEAFS; ++i) { if (boxBorders[i] == boxBorders[i+1]) continue; - + nodes.push_back(Node()); Node& child = nodes.back(); initChildBox(nodes[nodeInd], i, child); - + child.isLeaf = true; child.maxLevels = nodes[nodeInd].maxLevels - 1; child.begin = nodes[nodeInd].begin + (int)boxBorders[i+0]; child.end = nodes[nodeInd].begin + (int)boxBorders[i+1]; for (size_t k = 0; k < MAX_LEAFS; k++) child.children[k] = 0; - + nodes[nodeInd].children[i] = (int)(nodes.size() - 1); - + if (child.maxLevels != 1 && (child.end - child.begin) > minPoints) { child.isLeaf = false; @@ -336,5 +340,5 @@ namespace cv } } } - + } diff --git a/modules/contrib/src/precomp.hpp b/modules/contrib/src/precomp.hpp index 1f0ef9b..7c8e6bd 100644 --- a/modules/contrib/src/precomp.hpp +++ b/modules/contrib/src/precomp.hpp @@ -43,11 +43,7 @@ #ifndef __OPENCV_PRECOMP_H__ #define __OPENCV_PRECOMP_H__ -#if _MSC_VER >= 1200 -#pragma warning( disable: 4251 4710 4711 4514 4996 ) -#endif - -#ifdef HAVE_CVCONFIG_H +#ifdef HAVE_CVCONFIG_H #include "cvconfig.h" #endif diff --git a/modules/contrib/src/retina.cpp b/modules/contrib/src/retina.cpp index c86f2d8..1464896 100644 --- a/modules/contrib/src/retina.cpp +++ b/modules/contrib/src/retina.cpp @@ -74,17 +74,17 @@ namespace cv { - -Retina::Retina(const cv::Size inputSize) + +Retina::Retina(const cv::Size inputSz) { _retinaFilter = 0; - _init(inputSize, true, RETINA_COLOR_BAYER, false); + _init(inputSz, true, RETINA_COLOR_BAYER, false); } -Retina::Retina(const cv::Size inputSize, const bool colorMode, RETINA_COLORSAMPLINGMETHOD colorSamplingMethod, const bool useRetinaLogSampling, const double reductionFactor, const double samplingStrenght) +Retina::Retina(const cv::Size inputSz, const bool colorMode, RETINA_COLORSAMPLINGMETHOD colorSamplingMethod, const bool useRetinaLogSampling, const double reductionFactor, const double samplingStrenght) { _retinaFilter = 0; - _init(inputSize, colorMode, colorSamplingMethod, useRetinaLogSampling, reductionFactor, samplingStrenght); + _init(inputSz, colorMode, colorSamplingMethod, useRetinaLogSampling, reductionFactor, samplingStrenght); }; Retina::~Retina() @@ -94,19 +94,19 @@ Retina::~Retina() } /** -* retreive retina input buffer size +* retreive retina input buffer size */ Size Retina::inputSize(){return cv::Size(_retinaFilter->getInputNBcolumns(), _retinaFilter->getInputNBrows());} /** -* retreive retina output buffer size +* retreive retina output buffer size */ Size Retina::outputSize(){return cv::Size(_retinaFilter->getOutputNBcolumns(), _retinaFilter->getOutputNBrows());} void Retina::setColorSaturation(const bool saturateColors, const float colorSaturationValue) { - _retinaFilter->setColorSaturation(saturateColors, colorSaturationValue); + _retinaFilter->setColorSaturation(saturateColors, colorSaturationValue); } struct Retina::RetinaParameters Retina::getParameters(){return _retinaParameters;} @@ -121,71 +121,71 @@ void Retina::setup(std::string retinaParameterFile, const bool applyDefaultSetup setup(fs, applyDefaultSetupOnFailure); }catch(Exception &e) { - std::cout<<"Retina::setup: wrong/unappropriate xml parameter file : error report :`n=>"<"< keeping current parameters"< keeping current parameters"<>_retinaParameters.OPLandIplParvo.colorMode; - currFn["normaliseOutput"]>>_retinaParameters.OPLandIplParvo.normaliseOutput; - currFn["photoreceptorsLocalAdaptationSensitivity"]>>_retinaParameters.OPLandIplParvo.photoreceptorsLocalAdaptationSensitivity; - currFn["photoreceptorsTemporalConstant"]>>_retinaParameters.OPLandIplParvo.photoreceptorsTemporalConstant; - currFn["photoreceptorsSpatialConstant"]>>_retinaParameters.OPLandIplParvo.photoreceptorsSpatialConstant; - currFn["horizontalCellsGain"]>>_retinaParameters.OPLandIplParvo.horizontalCellsGain; - currFn["hcellsTemporalConstant"]>>_retinaParameters.OPLandIplParvo.hcellsTemporalConstant; - currFn["hcellsSpatialConstant"]>>_retinaParameters.OPLandIplParvo.hcellsSpatialConstant; - currFn["ganglionCellsSensitivity"]>>_retinaParameters.OPLandIplParvo.ganglionCellsSensitivity; - setupOPLandIPLParvoChannel(_retinaParameters.OPLandIplParvo.colorMode, _retinaParameters.OPLandIplParvo.normaliseOutput, _retinaParameters.OPLandIplParvo.photoreceptorsLocalAdaptationSensitivity, _retinaParameters.OPLandIplParvo.photoreceptorsTemporalConstant, _retinaParameters.OPLandIplParvo.photoreceptorsSpatialConstant, _retinaParameters.OPLandIplParvo.horizontalCellsGain, _retinaParameters.OPLandIplParvo.hcellsTemporalConstant, _retinaParameters.OPLandIplParvo.hcellsSpatialConstant, _retinaParameters.OPLandIplParvo.ganglionCellsSensitivity); - - // init retina IPL magno setup... update at the same time the parameters structure and the retina core - currFn=rootFn["IPLmagno"]; - currFn["normaliseOutput"]>>_retinaParameters.IplMagno.normaliseOutput; - currFn["parasolCells_beta"]>>_retinaParameters.IplMagno.parasolCells_beta; - currFn["parasolCells_tau"]>>_retinaParameters.IplMagno.parasolCells_tau; - currFn["parasolCells_k"]>>_retinaParameters.IplMagno.parasolCells_k; - currFn["amacrinCellsTemporalCutFrequency"]>>_retinaParameters.IplMagno.amacrinCellsTemporalCutFrequency; - currFn["V0CompressionParameter"]>>_retinaParameters.IplMagno.V0CompressionParameter; - currFn["localAdaptintegration_tau"]>>_retinaParameters.IplMagno.localAdaptintegration_tau; - currFn["localAdaptintegration_k"]>>_retinaParameters.IplMagno.localAdaptintegration_k; - - setupIPLMagnoChannel(_retinaParameters.IplMagno.normaliseOutput, _retinaParameters.IplMagno.parasolCells_beta, _retinaParameters.IplMagno.parasolCells_tau, _retinaParameters.IplMagno.parasolCells_k, _retinaParameters.IplMagno.amacrinCellsTemporalCutFrequency,_retinaParameters.IplMagno.V0CompressionParameter, _retinaParameters.IplMagno.localAdaptintegration_tau, _retinaParameters.IplMagno.localAdaptintegration_k); - - }catch(Exception &e) - { - std::cout<<"Retina::setup: resetting retina with default parameters"<"< keeping current parameters"<>_retinaParameters.OPLandIplParvo.colorMode; + currFn["normaliseOutput"]>>_retinaParameters.OPLandIplParvo.normaliseOutput; + currFn["photoreceptorsLocalAdaptationSensitivity"]>>_retinaParameters.OPLandIplParvo.photoreceptorsLocalAdaptationSensitivity; + currFn["photoreceptorsTemporalConstant"]>>_retinaParameters.OPLandIplParvo.photoreceptorsTemporalConstant; + currFn["photoreceptorsSpatialConstant"]>>_retinaParameters.OPLandIplParvo.photoreceptorsSpatialConstant; + currFn["horizontalCellsGain"]>>_retinaParameters.OPLandIplParvo.horizontalCellsGain; + currFn["hcellsTemporalConstant"]>>_retinaParameters.OPLandIplParvo.hcellsTemporalConstant; + currFn["hcellsSpatialConstant"]>>_retinaParameters.OPLandIplParvo.hcellsSpatialConstant; + currFn["ganglionCellsSensitivity"]>>_retinaParameters.OPLandIplParvo.ganglionCellsSensitivity; + setupOPLandIPLParvoChannel(_retinaParameters.OPLandIplParvo.colorMode, _retinaParameters.OPLandIplParvo.normaliseOutput, _retinaParameters.OPLandIplParvo.photoreceptorsLocalAdaptationSensitivity, _retinaParameters.OPLandIplParvo.photoreceptorsTemporalConstant, _retinaParameters.OPLandIplParvo.photoreceptorsSpatialConstant, _retinaParameters.OPLandIplParvo.horizontalCellsGain, _retinaParameters.OPLandIplParvo.hcellsTemporalConstant, _retinaParameters.OPLandIplParvo.hcellsSpatialConstant, _retinaParameters.OPLandIplParvo.ganglionCellsSensitivity); + + // init retina IPL magno setup... update at the same time the parameters structure and the retina core + currFn=rootFn["IPLmagno"]; + currFn["normaliseOutput"]>>_retinaParameters.IplMagno.normaliseOutput; + currFn["parasolCells_beta"]>>_retinaParameters.IplMagno.parasolCells_beta; + currFn["parasolCells_tau"]>>_retinaParameters.IplMagno.parasolCells_tau; + currFn["parasolCells_k"]>>_retinaParameters.IplMagno.parasolCells_k; + currFn["amacrinCellsTemporalCutFrequency"]>>_retinaParameters.IplMagno.amacrinCellsTemporalCutFrequency; + currFn["V0CompressionParameter"]>>_retinaParameters.IplMagno.V0CompressionParameter; + currFn["localAdaptintegration_tau"]>>_retinaParameters.IplMagno.localAdaptintegration_tau; + currFn["localAdaptintegration_k"]>>_retinaParameters.IplMagno.localAdaptintegration_k; + + setupIPLMagnoChannel(_retinaParameters.IplMagno.normaliseOutput, _retinaParameters.IplMagno.parasolCells_beta, _retinaParameters.IplMagno.parasolCells_tau, _retinaParameters.IplMagno.parasolCells_k, _retinaParameters.IplMagno.amacrinCellsTemporalCutFrequency,_retinaParameters.IplMagno.V0CompressionParameter, _retinaParameters.IplMagno.localAdaptintegration_tau, _retinaParameters.IplMagno.localAdaptintegration_k); + + }catch(Exception &e) + { + std::cout<<"Retina::setup: resetting retina with default parameters"<"< keeping current parameters"< colorMode : " << _retinaParameters.OPLandIplParvo.colorMode - << "\n==> normalizeParvoOutput :" << _retinaParameters.OPLandIplParvo.normaliseOutput - << "\n==> photoreceptorsLocalAdaptationSensitivity : " << _retinaParameters.OPLandIplParvo.photoreceptorsLocalAdaptationSensitivity - << "\n==> photoreceptorsTemporalConstant : " << _retinaParameters.OPLandIplParvo.photoreceptorsTemporalConstant - << "\n==> photoreceptorsSpatialConstant : " << _retinaParameters.OPLandIplParvo.photoreceptorsSpatialConstant - << "\n==> horizontalCellsGain : " << _retinaParameters.OPLandIplParvo.horizontalCellsGain - << "\n==> hcellsTemporalConstant : " << _retinaParameters.OPLandIplParvo.hcellsTemporalConstant - << "\n==> hcellsSpatialConstant : " << _retinaParameters.OPLandIplParvo.hcellsSpatialConstant - << "\n==> parvoGanglionCellsSensitivity : " << _retinaParameters.OPLandIplParvo.ganglionCellsSensitivity - <<"}\n"; - - // displaying IPL magno setup - outmessage<<"Current Retina instance setup :" - <<"\nIPLmagno"<<"{" - << "\n==> normaliseOutput : " << _retinaParameters.IplMagno.normaliseOutput - << "\n==> parasolCells_beta : " << _retinaParameters.IplMagno.parasolCells_beta - << "\n==> parasolCells_tau : " << _retinaParameters.IplMagno.parasolCells_tau - << "\n==> parasolCells_k : " << _retinaParameters.IplMagno.parasolCells_k - << "\n==> amacrinCellsTemporalCutFrequency : " << _retinaParameters.IplMagno.amacrinCellsTemporalCutFrequency - << "\n==> V0CompressionParameter : " << _retinaParameters.IplMagno.V0CompressionParameter - << "\n==> localAdaptintegration_tau : " << _retinaParameters.IplMagno.localAdaptintegration_tau - << "\n==> localAdaptintegration_k : " << _retinaParameters.IplMagno.localAdaptintegration_k - <<"}"; - return outmessage.str(); + std::stringstream outmessage; + + // displaying OPL and IPL parvo setup + outmessage<<"Current Retina instance setup :" + <<"\nOPLandIPLparvo"<<"{" + << "\n==> colorMode : " << _retinaParameters.OPLandIplParvo.colorMode + << "\n==> normalizeParvoOutput :" << _retinaParameters.OPLandIplParvo.normaliseOutput + << "\n==> photoreceptorsLocalAdaptationSensitivity : " << _retinaParameters.OPLandIplParvo.photoreceptorsLocalAdaptationSensitivity + << "\n==> photoreceptorsTemporalConstant : " << _retinaParameters.OPLandIplParvo.photoreceptorsTemporalConstant + << "\n==> photoreceptorsSpatialConstant : " << _retinaParameters.OPLandIplParvo.photoreceptorsSpatialConstant + << "\n==> horizontalCellsGain : " << _retinaParameters.OPLandIplParvo.horizontalCellsGain + << "\n==> hcellsTemporalConstant : " << _retinaParameters.OPLandIplParvo.hcellsTemporalConstant + << "\n==> hcellsSpatialConstant : " << _retinaParameters.OPLandIplParvo.hcellsSpatialConstant + << "\n==> parvoGanglionCellsSensitivity : " << _retinaParameters.OPLandIplParvo.ganglionCellsSensitivity + <<"}\n"; + + // displaying IPL magno setup + outmessage<<"Current Retina instance setup :" + <<"\nIPLmagno"<<"{" + << "\n==> normaliseOutput : " << _retinaParameters.IplMagno.normaliseOutput + << "\n==> parasolCells_beta : " << _retinaParameters.IplMagno.parasolCells_beta + << "\n==> parasolCells_tau : " << _retinaParameters.IplMagno.parasolCells_tau + << "\n==> parasolCells_k : " << _retinaParameters.IplMagno.parasolCells_k + << "\n==> amacrinCellsTemporalCutFrequency : " << _retinaParameters.IplMagno.amacrinCellsTemporalCutFrequency + << "\n==> V0CompressionParameter : " << _retinaParameters.IplMagno.V0CompressionParameter + << "\n==> localAdaptintegration_tau : " << _retinaParameters.IplMagno.localAdaptintegration_tau + << "\n==> localAdaptintegration_k : " << _retinaParameters.IplMagno.localAdaptintegration_k + <<"}"; + return outmessage.str(); } void Retina::write( std::string fs ) const @@ -240,98 +240,98 @@ void Retina::write( std::string fs ) const void Retina::write( FileStorage& fs ) const { - if (!fs.isOpened()) - return; // basic error case - fs<<"OPLandIPLparvo"<<"{"; - fs << "colorMode" << _retinaParameters.OPLandIplParvo.colorMode; - fs << "normaliseOutput" << _retinaParameters.OPLandIplParvo.normaliseOutput; - fs << "photoreceptorsLocalAdaptationSensitivity" << _retinaParameters.OPLandIplParvo.photoreceptorsLocalAdaptationSensitivity; - fs << "photoreceptorsTemporalConstant" << _retinaParameters.OPLandIplParvo.photoreceptorsTemporalConstant; - fs << "photoreceptorsSpatialConstant" << _retinaParameters.OPLandIplParvo.photoreceptorsSpatialConstant; - fs << "horizontalCellsGain" << _retinaParameters.OPLandIplParvo.horizontalCellsGain; - fs << "hcellsTemporalConstant" << _retinaParameters.OPLandIplParvo.hcellsTemporalConstant; - fs << "hcellsSpatialConstant" << _retinaParameters.OPLandIplParvo.hcellsSpatialConstant; - fs << "ganglionCellsSensitivity" << _retinaParameters.OPLandIplParvo.ganglionCellsSensitivity; - fs << "}"; - fs<<"IPLmagno"<<"{"; - fs << "normaliseOutput" << _retinaParameters.IplMagno.normaliseOutput; - fs << "parasolCells_beta" << _retinaParameters.IplMagno.parasolCells_beta; - fs << "parasolCells_tau" << _retinaParameters.IplMagno.parasolCells_tau; - fs << "parasolCells_k" << _retinaParameters.IplMagno.parasolCells_k; - fs << "amacrinCellsTemporalCutFrequency" << _retinaParameters.IplMagno.amacrinCellsTemporalCutFrequency; - fs << "V0CompressionParameter" << _retinaParameters.IplMagno.V0CompressionParameter; - fs << "localAdaptintegration_tau" << _retinaParameters.IplMagno.localAdaptintegration_tau; - fs << "localAdaptintegration_k" << _retinaParameters.IplMagno.localAdaptintegration_k; - fs<<"}"; + if (!fs.isOpened()) + return; // basic error case + fs<<"OPLandIPLparvo"<<"{"; + fs << "colorMode" << _retinaParameters.OPLandIplParvo.colorMode; + fs << "normaliseOutput" << _retinaParameters.OPLandIplParvo.normaliseOutput; + fs << "photoreceptorsLocalAdaptationSensitivity" << _retinaParameters.OPLandIplParvo.photoreceptorsLocalAdaptationSensitivity; + fs << "photoreceptorsTemporalConstant" << _retinaParameters.OPLandIplParvo.photoreceptorsTemporalConstant; + fs << "photoreceptorsSpatialConstant" << _retinaParameters.OPLandIplParvo.photoreceptorsSpatialConstant; + fs << "horizontalCellsGain" << _retinaParameters.OPLandIplParvo.horizontalCellsGain; + fs << "hcellsTemporalConstant" << _retinaParameters.OPLandIplParvo.hcellsTemporalConstant; + fs << "hcellsSpatialConstant" << _retinaParameters.OPLandIplParvo.hcellsSpatialConstant; + fs << "ganglionCellsSensitivity" << _retinaParameters.OPLandIplParvo.ganglionCellsSensitivity; + fs << "}"; + fs<<"IPLmagno"<<"{"; + fs << "normaliseOutput" << _retinaParameters.IplMagno.normaliseOutput; + fs << "parasolCells_beta" << _retinaParameters.IplMagno.parasolCells_beta; + fs << "parasolCells_tau" << _retinaParameters.IplMagno.parasolCells_tau; + fs << "parasolCells_k" << _retinaParameters.IplMagno.parasolCells_k; + fs << "amacrinCellsTemporalCutFrequency" << _retinaParameters.IplMagno.amacrinCellsTemporalCutFrequency; + fs << "V0CompressionParameter" << _retinaParameters.IplMagno.V0CompressionParameter; + fs << "localAdaptintegration_tau" << _retinaParameters.IplMagno.localAdaptintegration_tau; + fs << "localAdaptintegration_k" << _retinaParameters.IplMagno.localAdaptintegration_k; + fs<<"}"; } void Retina::setupOPLandIPLParvoChannel(const bool colorMode, const bool normaliseOutput, const float photoreceptorsLocalAdaptationSensitivity, const float photoreceptorsTemporalConstant, const float photoreceptorsSpatialConstant, const float horizontalCellsGain, const float HcellsTemporalConstant, const float HcellsSpatialConstant, const float ganglionCellsSensitivity) { - // retina core parameters setup - _retinaFilter->setColorMode(colorMode); - _retinaFilter->setPhotoreceptorsLocalAdaptationSensitivity(photoreceptorsLocalAdaptationSensitivity); - _retinaFilter->setOPLandParvoParameters(0, photoreceptorsTemporalConstant, photoreceptorsSpatialConstant, horizontalCellsGain, HcellsTemporalConstant, HcellsSpatialConstant, ganglionCellsSensitivity); - _retinaFilter->setParvoGanglionCellsLocalAdaptationSensitivity(ganglionCellsSensitivity); - _retinaFilter->activateNormalizeParvoOutput_0_maxOutputValue(normaliseOutput); - + // retina core parameters setup + _retinaFilter->setColorMode(colorMode); + _retinaFilter->setPhotoreceptorsLocalAdaptationSensitivity(photoreceptorsLocalAdaptationSensitivity); + _retinaFilter->setOPLandParvoParameters(0, photoreceptorsTemporalConstant, photoreceptorsSpatialConstant, horizontalCellsGain, HcellsTemporalConstant, HcellsSpatialConstant, ganglionCellsSensitivity); + _retinaFilter->setParvoGanglionCellsLocalAdaptationSensitivity(ganglionCellsSensitivity); + _retinaFilter->activateNormalizeParvoOutput_0_maxOutputValue(normaliseOutput); + // update parameters struture - _retinaParameters.OPLandIplParvo.colorMode = colorMode; - _retinaParameters.OPLandIplParvo.normaliseOutput = normaliseOutput; - _retinaParameters.OPLandIplParvo.photoreceptorsLocalAdaptationSensitivity = photoreceptorsLocalAdaptationSensitivity; - _retinaParameters.OPLandIplParvo.photoreceptorsTemporalConstant = photoreceptorsTemporalConstant; - _retinaParameters.OPLandIplParvo.photoreceptorsSpatialConstant = photoreceptorsSpatialConstant; - _retinaParameters.OPLandIplParvo.horizontalCellsGain = horizontalCellsGain; - _retinaParameters.OPLandIplParvo.hcellsTemporalConstant = HcellsTemporalConstant; - _retinaParameters.OPLandIplParvo.hcellsSpatialConstant = HcellsSpatialConstant; - _retinaParameters.OPLandIplParvo.ganglionCellsSensitivity = ganglionCellsSensitivity; + _retinaParameters.OPLandIplParvo.colorMode = colorMode; + _retinaParameters.OPLandIplParvo.normaliseOutput = normaliseOutput; + _retinaParameters.OPLandIplParvo.photoreceptorsLocalAdaptationSensitivity = photoreceptorsLocalAdaptationSensitivity; + _retinaParameters.OPLandIplParvo.photoreceptorsTemporalConstant = photoreceptorsTemporalConstant; + _retinaParameters.OPLandIplParvo.photoreceptorsSpatialConstant = photoreceptorsSpatialConstant; + _retinaParameters.OPLandIplParvo.horizontalCellsGain = horizontalCellsGain; + _retinaParameters.OPLandIplParvo.hcellsTemporalConstant = HcellsTemporalConstant; + _retinaParameters.OPLandIplParvo.hcellsSpatialConstant = HcellsSpatialConstant; + _retinaParameters.OPLandIplParvo.ganglionCellsSensitivity = ganglionCellsSensitivity; } void Retina::setupIPLMagnoChannel(const bool normaliseOutput, const float parasolCells_beta, const float parasolCells_tau, const float parasolCells_k, const float amacrinCellsTemporalCutFrequency, const float V0CompressionParameter, const float localAdaptintegration_tau, const float localAdaptintegration_k) { - _retinaFilter->setMagnoCoefficientsTable(parasolCells_beta, parasolCells_tau, parasolCells_k, amacrinCellsTemporalCutFrequency, V0CompressionParameter, localAdaptintegration_tau, localAdaptintegration_k); - _retinaFilter->activateNormalizeMagnoOutput_0_maxOutputValue(normaliseOutput); + _retinaFilter->setMagnoCoefficientsTable(parasolCells_beta, parasolCells_tau, parasolCells_k, amacrinCellsTemporalCutFrequency, V0CompressionParameter, localAdaptintegration_tau, localAdaptintegration_k); + _retinaFilter->activateNormalizeMagnoOutput_0_maxOutputValue(normaliseOutput); // update parameters struture - _retinaParameters.IplMagno.normaliseOutput = normaliseOutput; - _retinaParameters.IplMagno.parasolCells_beta = parasolCells_beta; - _retinaParameters.IplMagno.parasolCells_tau = parasolCells_tau; - _retinaParameters.IplMagno.parasolCells_k = parasolCells_k; - _retinaParameters.IplMagno.amacrinCellsTemporalCutFrequency = amacrinCellsTemporalCutFrequency; - _retinaParameters.IplMagno.V0CompressionParameter = V0CompressionParameter; - _retinaParameters.IplMagno.localAdaptintegration_tau = localAdaptintegration_tau; - _retinaParameters.IplMagno.localAdaptintegration_k = localAdaptintegration_k; + _retinaParameters.IplMagno.normaliseOutput = normaliseOutput; + _retinaParameters.IplMagno.parasolCells_beta = parasolCells_beta; + _retinaParameters.IplMagno.parasolCells_tau = parasolCells_tau; + _retinaParameters.IplMagno.parasolCells_k = parasolCells_k; + _retinaParameters.IplMagno.amacrinCellsTemporalCutFrequency = amacrinCellsTemporalCutFrequency; + _retinaParameters.IplMagno.V0CompressionParameter = V0CompressionParameter; + _retinaParameters.IplMagno.localAdaptintegration_tau = localAdaptintegration_tau; + _retinaParameters.IplMagno.localAdaptintegration_k = localAdaptintegration_k; } void Retina::run(const cv::Mat &inputMatToConvert) { - // first convert input image to the compatible format : std::valarray - const bool colorMode = _convertCvMat2ValarrayBuffer(inputMatToConvert, _inputBuffer); - // process the retina - if (!_retinaFilter->runFilter(_inputBuffer, colorMode, false, _retinaParameters.OPLandIplParvo.colorMode && colorMode, false)) - throw cv::Exception(-1, "Retina cannot be applied, wrong input buffer size", "Retina::run", "Retina.h", 0); + // first convert input image to the compatible format : std::valarray + const bool colorMode = _convertCvMat2ValarrayBuffer(inputMatToConvert, _inputBuffer); + // process the retina + if (!_retinaFilter->runFilter(_inputBuffer, colorMode, false, _retinaParameters.OPLandIplParvo.colorMode && colorMode, false)) + throw cv::Exception(-1, "Retina cannot be applied, wrong input buffer size", "Retina::run", "Retina.h", 0); } void Retina::getParvo(cv::Mat &retinaOutput_parvo) { - if (_retinaFilter->getColorMode()) - { - // reallocate output buffer (if necessary) - _convertValarrayBuffer2cvMat(_retinaFilter->getColorOutput(), _retinaFilter->getOutputNBrows(), _retinaFilter->getOutputNBcolumns(), true, retinaOutput_parvo); - }else - { - // reallocate output buffer (if necessary) - _convertValarrayBuffer2cvMat(_retinaFilter->getContours(), _retinaFilter->getOutputNBrows(), _retinaFilter->getOutputNBcolumns(), false, retinaOutput_parvo); - } - //retinaOutput_parvo/=255.0; + if (_retinaFilter->getColorMode()) + { + // reallocate output buffer (if necessary) + _convertValarrayBuffer2cvMat(_retinaFilter->getColorOutput(), _retinaFilter->getOutputNBrows(), _retinaFilter->getOutputNBcolumns(), true, retinaOutput_parvo); + }else + { + // reallocate output buffer (if necessary) + _convertValarrayBuffer2cvMat(_retinaFilter->getContours(), _retinaFilter->getOutputNBrows(), _retinaFilter->getOutputNBcolumns(), false, retinaOutput_parvo); + } + //retinaOutput_parvo/=255.0; } void Retina::getMagno(cv::Mat &retinaOutput_magno) { - // reallocate output buffer (if necessary) - _convertValarrayBuffer2cvMat(_retinaFilter->getMovingContours(), _retinaFilter->getOutputNBrows(), _retinaFilter->getOutputNBcolumns(), false, retinaOutput_magno); - //retinaOutput_magno/=255.0; + // reallocate output buffer (if necessary) + _convertValarrayBuffer2cvMat(_retinaFilter->getMovingContours(), _retinaFilter->getOutputNBrows(), _retinaFilter->getOutputNBcolumns(), false, retinaOutput_magno); + //retinaOutput_magno/=255.0; } // original API level data accessors : copy buffers if size matches @@ -342,112 +342,114 @@ const std::valarray & Retina::getMagno() const {return _retinaFilter->get const std::valarray & Retina::getParvo() const {if (_retinaFilter->getColorMode())return _retinaFilter->getColorOutput(); /* implicite else */return _retinaFilter->getContours();} // private method called by constructirs -void Retina::_init(const cv::Size inputSize, const bool colorMode, RETINA_COLORSAMPLINGMETHOD colorSamplingMethod, const bool useRetinaLogSampling, const double reductionFactor, const double samplingStrenght) +void Retina::_init(const cv::Size inputSz, const bool colorMode, RETINA_COLORSAMPLINGMETHOD colorSamplingMethod, const bool useRetinaLogSampling, const double reductionFactor, const double samplingStrenght) { - // basic error check - if (inputSize.height*inputSize.width <= 0) - throw cv::Exception(-1, "Bad retina size setup : size height and with must be superior to zero", "Retina::setup", "Retina.h", 0); + // basic error check + if (inputSz.height*inputSz.width <= 0) + throw cv::Exception(-1, "Bad retina size setup : size height and with must be superior to zero", "Retina::setup", "Retina.h", 0); - unsigned int nbPixels=inputSize.height*inputSize.width; - // resize buffers if size does not match - _inputBuffer.resize(nbPixels*3); // buffer supports gray images but also 3 channels color buffers... (larger is better...) + unsigned int nbPixels=inputSz.height*inputSz.width; + // resize buffers if size does not match + _inputBuffer.resize(nbPixels*3); // buffer supports gray images but also 3 channels color buffers... (larger is better...) - // allocate the retina model + // allocate the retina model if (_retinaFilter) delete _retinaFilter; - _retinaFilter = new RetinaFilter(inputSize.height, inputSize.width, colorMode, colorSamplingMethod, useRetinaLogSampling, reductionFactor, samplingStrenght); + _retinaFilter = new RetinaFilter(inputSz.height, inputSz.width, colorMode, colorSamplingMethod, useRetinaLogSampling, reductionFactor, samplingStrenght); - // prepare the default parameter XML file with default setup + // prepare the default parameter XML file with default setup setup(_retinaParameters); - // init retina - _retinaFilter->clearAllBuffers(); + // init retina + _retinaFilter->clearAllBuffers(); - // report current configuration - std::cout< &grayMatrixToConvert, const unsigned int nbRows, const unsigned int nbColumns, const bool colorMode, cv::Mat &outBuffer) { - // fill output buffer with the valarray buffer - const float *valarrayPTR=get_data(grayMatrixToConvert); - if (!colorMode) - { - outBuffer.create(cv::Size(nbColumns, nbRows), CV_8U); - for (unsigned int i=0;i(pixel)=(unsigned char)*(valarrayPTR++); - } - } - }else - { - const unsigned int doubleNBpixels=_retinaFilter->getOutputNBpixels()*2; - outBuffer.create(cv::Size(nbColumns, nbRows), CV_8UC3); - for (unsigned int i=0;igetOutputNBpixels()); - pixelValues[0]=(unsigned char)*(valarrayPTR+doubleNBpixels); - - outBuffer.at(pixel)=pixelValues; - } - } - } + // fill output buffer with the valarray buffer + const float *valarrayPTR=get_data(grayMatrixToConvert); + if (!colorMode) + { + outBuffer.create(cv::Size(nbColumns, nbRows), CV_8U); + for (unsigned int i=0;i(pixel)=(unsigned char)*(valarrayPTR++); + } + } + }else + { + const unsigned int doubleNBpixels=_retinaFilter->getOutputNBpixels()*2; + outBuffer.create(cv::Size(nbColumns, nbRows), CV_8UC3); + for (unsigned int i=0;igetOutputNBpixels()); + pixelValues[0]=(unsigned char)*(valarrayPTR+doubleNBpixels); + + outBuffer.at(pixel)=pixelValues; + } + } + } } bool Retina::_convertCvMat2ValarrayBuffer(const cv::Mat inputMatToConvert, std::valarray &outputValarrayMatrix) { - // first check input consistency - if (inputMatToConvert.empty()) - throw cv::Exception(-1, "Retina cannot be applied, input buffer is empty", "Retina::run", "Retina.h", 0); + // first check input consistency + if (inputMatToConvert.empty()) + throw cv::Exception(-1, "Retina cannot be applied, input buffer is empty", "Retina::run", "Retina.h", 0); + + // retreive color mode from image input + int imageNumberOfChannels = inputMatToConvert.channels(); - // retreive color mode from image input - int imageNumberOfChannels = inputMatToConvert.channels(); - // convert to float AND fill the valarray buffer - typedef float T; // define here the target pixel format, here, float + typedef float T; // define here the target pixel format, here, float const int dsttype = DataType::depth; // output buffer is float format - if(imageNumberOfChannels==4) + if(imageNumberOfChannels==4) + { + // create a cv::Mat table (for RGBA planes) + cv::Mat planes[4] = { - // create a cv::Mat table (for RGBA planes) - cv::Mat planes[] = - { - cv::Mat(inputMatToConvert.size(), dsttype, &outputValarrayMatrix[_retinaFilter->getInputNBpixels()*2]), - cv::Mat(inputMatToConvert.size(), dsttype, &outputValarrayMatrix[_retinaFilter->getInputNBpixels()]), - cv::Mat(inputMatToConvert.size(), dsttype, &outputValarrayMatrix[0]), - cv::Mat(inputMatToConvert.size(), dsttype) // last channel (alpha) does not point on the valarray (not usefull in our case) - }; - // split color cv::Mat in 4 planes... it fills valarray directely - cv::split(cv::Mat_ >(inputMatToConvert), planes); - }else if (imageNumberOfChannels==3) - { - // create a cv::Mat table (for RGB planes) - cv::Mat planes[] = - { - cv::Mat(inputMatToConvert.size(), dsttype, &outputValarrayMatrix[_retinaFilter->getInputNBpixels()*2]), - cv::Mat(inputMatToConvert.size(), dsttype, &outputValarrayMatrix[_retinaFilter->getInputNBpixels()]), - cv::Mat(inputMatToConvert.size(), dsttype, &outputValarrayMatrix[0]) - }; - // split color cv::Mat in 3 planes... it fills valarray directely - cv::split(cv::Mat_ >(inputMatToConvert), planes); - }else if(imageNumberOfChannels==1) - { - // create a cv::Mat header for the valarray - cv::Mat dst(inputMatToConvert.size(), dsttype, &outputValarrayMatrix[0]); - inputMatToConvert.convertTo(dst, dsttype); - } + cv::Mat(inputMatToConvert.size(), dsttype, &outputValarrayMatrix[_retinaFilter->getInputNBpixels()*2]), + cv::Mat(inputMatToConvert.size(), dsttype, &outputValarrayMatrix[_retinaFilter->getInputNBpixels()]), + cv::Mat(inputMatToConvert.size(), dsttype, &outputValarrayMatrix[0]) + }; + planes[3] = cv::Mat(inputMatToConvert.size(), dsttype); // last channel (alpha) does not point on the valarray (not usefull in our case) + // split color cv::Mat in 4 planes... it fills valarray directely + cv::split(cv::Mat_ >(inputMatToConvert), planes); + } + else if (imageNumberOfChannels==3) + { + // create a cv::Mat table (for RGB planes) + cv::Mat planes[] = + { + cv::Mat(inputMatToConvert.size(), dsttype, &outputValarrayMatrix[_retinaFilter->getInputNBpixels()*2]), + cv::Mat(inputMatToConvert.size(), dsttype, &outputValarrayMatrix[_retinaFilter->getInputNBpixels()]), + cv::Mat(inputMatToConvert.size(), dsttype, &outputValarrayMatrix[0]) + }; + // split color cv::Mat in 3 planes... it fills valarray directely + cv::split(cv::Mat_ >(inputMatToConvert), planes); + } + else if(imageNumberOfChannels==1) + { + // create a cv::Mat header for the valarray + cv::Mat dst(inputMatToConvert.size(), dsttype, &outputValarrayMatrix[0]); + inputMatToConvert.convertTo(dst, dsttype); + } else CV_Error(CV_StsUnsupportedFormat, "input image must be single channel (gray levels), bgr format (color) or bgra (color with transparency which won't be considered"); - + return imageNumberOfChannels>1; // return bool : false for gray level image processing, true for color mode } diff --git a/modules/contrib/src/retinacolor.cpp b/modules/contrib/src/retinacolor.cpp index 730e9b8..6418620 100644 --- a/modules/contrib/src/retinacolor.cpp +++ b/modules/contrib/src/retinacolor.cpp @@ -325,15 +325,15 @@ void RetinaColor::runColorDemultiplexing(const std::valarray &multiplexed }else { - register const float *multiplexedColorFramePTR= get_data(multiplexedColorFrame); - for (unsigned int indexc=0; indexc<_filterOutput.getNBpixels() ; ++indexc, ++chrominancePTR, ++colorLocalDensityPTR, ++luminance, ++multiplexedColorFramePTR) + register const float *multiplexedColorFramePTR1= get_data(multiplexedColorFrame); + for (unsigned int indexc=0; indexc<_filterOutput.getNBpixels() ; ++indexc, ++chrominancePTR, ++colorLocalDensityPTR, ++luminance, ++multiplexedColorFramePTR1) { // normalize by photoreceptors density float Cr=*(chrominancePTR)*_colorLocalDensity[indexc]; float Cg=*(chrominancePTR+_filterOutput.getNBpixels())*_colorLocalDensity[indexc+_filterOutput.getNBpixels()]; float Cb=*(chrominancePTR+_filterOutput.getDoubleNBpixels())*_colorLocalDensity[indexc+_filterOutput.getDoubleNBpixels()]; *luminance=(Cr+Cg+Cb)*_pG; - _demultiplexedTempBuffer[_colorSampling[indexc]] = *multiplexedColorFramePTR - *luminance; + _demultiplexedTempBuffer[_colorSampling[indexc]] = *multiplexedColorFramePTR1 - *luminance; } diff --git a/modules/contrib/src/spinimages.cpp b/modules/contrib/src/spinimages.cpp index e2818ad..f544fe4 100644 --- a/modules/contrib/src/spinimages.cpp +++ b/modules/contrib/src/spinimages.cpp @@ -60,9 +60,9 @@ namespace cv using std::min; using std::sqrt; } -namespace +namespace { - const static Scalar colors[] = + const static Scalar colors[] = { CV_RGB(255, 0, 0), CV_RGB( 0, 255, 0), @@ -87,21 +87,21 @@ namespace template void iota(FwIt first, FwIt last, T value) { while(first != last) *first++ = value++; } -void computeNormals( const Octree& Octree, const vector& centers, vector& normals, +void computeNormals( const Octree& Octree, const vector& centers, vector& normals, vector& mask, float normalRadius, int minNeighbors = 20) -{ +{ size_t normals_size = centers.size(); normals.resize(normals_size); - + if (mask.size() != normals_size) { - size_t m = mask.size(); + size_t m = mask.size(); mask.resize(normals_size); if (normals_size > m) for(; m < normals_size; ++m) mask[m] = 1; } - + vector buffer; buffer.reserve(128); SVD svd; @@ -132,7 +132,7 @@ void computeNormals( const Octree& Octree, const vector& centers, vecto mean.x /= buf_size; mean.y /= buf_size; mean.z /= buf_size; - + double pxpx = 0; double pypy = 0; double pzpz = 0; @@ -162,9 +162,9 @@ void computeNormals( const Octree& Octree, const vector& centers, vecto /*normals[n] = Point3f( (float)((double*)svd.vt.data)[6], (float)((double*)svd.vt.data)[7], - (float)((double*)svd.vt.data)[8] );*/ - normals[n] = reinterpret_cast(svd.vt.data)[2]; - mask[n] = 1; + (float)((double*)svd.vt.data)[8] );*/ + normals[n] = reinterpret_cast(svd.vt.data)[2]; + mask[n] = 1; } } @@ -213,22 +213,22 @@ inline __m128 transformSSE(const __m128* matrix, const __m128& in) } inline __m128i _mm_mullo_epi32_emul(const __m128i& a, __m128i& b) -{ +{ __m128i pack = _mm_packs_epi32(a, a); - return _mm_unpacklo_epi16(_mm_mullo_epi16(pack, b), _mm_mulhi_epi16(pack, b)); + return _mm_unpacklo_epi16(_mm_mullo_epi16(pack, b), _mm_mulhi_epi16(pack, b)); } #endif -void computeSpinImages( const Octree& Octree, const vector& points, const vector& normals, +void computeSpinImages( const Octree& Octree, const vector& points, const vector& normals, vector& mask, Mat& spinImages, int imageWidth, float binSize) -{ +{ float pixelsPerMeter = 1.f / binSize; - float support = imageWidth * binSize; - + float support = imageWidth * binSize; + assert(normals.size() == points.size()); assert(mask.size() == points.size()); - + size_t points_size = points.size(); mask.resize(points_size); @@ -257,7 +257,7 @@ void computeSpinImages( const Octree& Octree, const vector& points, con int t = cvGetThreadNum(); vector& pointsInSphere = pointsInSpherePool[t]; - + const Point3f& center = points[i]; Octree.getPointsWithinSphere(center, searchRad, pointsInSphere); @@ -269,7 +269,7 @@ void computeSpinImages( const Octree& Octree, const vector& points, con } const Point3f& normal = normals[i]; - + float rotmat[9]; initRotationMat(normal, rotmat); Point3f new_center; @@ -287,7 +287,7 @@ void computeSpinImages( const Octree& Octree, const vector& points, con { __m128 rotmatSSE[3]; convertTransformMatrix(rotmat, (float*)rotmatSSE); - + __m128 center_x4 = _mm_set1_ps(new_center.x); __m128 center_y4 = _mm_set1_ps(new_center.y); __m128 center_z4 = _mm_set1_ps(new_center.z + halfSuppport); @@ -313,7 +313,7 @@ void computeSpinImages( const Octree& Octree, const vector& points, con __m128 z0 = _mm_unpackhi_ps(pt0, pt1); // z0 z1 . . __m128 z1 = _mm_unpackhi_ps(pt2, pt3); // z2 z3 . . __m128 beta4 = _mm_sub_ps(center_z4, _mm_movelh_ps(z0, z1)); // b0 b1 b2 b3 - + __m128 xy0 = _mm_unpacklo_ps(pt0, pt1); // x0 x1 y0 y1 __m128 xy1 = _mm_unpacklo_ps(pt2, pt3); // x2 x3 y2 y3 __m128 x4 = _mm_movelh_ps(xy0, xy1); // x0 x1 x2 x3 @@ -322,7 +322,7 @@ void computeSpinImages( const Octree& Octree, const vector& points, con x4 = _mm_sub_ps(x4, center_x4); y4 = _mm_sub_ps(y4, center_y4); __m128 alpha4 = _mm_sqrt_ps(_mm_add_ps(_mm_mul_ps(x4,x4),_mm_mul_ps(y4,y4))); - + __m128 n1f4 = _mm_mul_ps( beta4, ppm4); /* beta4 float */ __m128 n2f4 = _mm_mul_ps(alpha4, ppm4); /* alpha4 float */ @@ -333,21 +333,21 @@ void computeSpinImages( const Octree& Octree, const vector& points, con __m128 f1 = _mm_sub_ps( n1f4, _mm_cvtepi32_ps(n1) ); /* { beta4 } */ __m128 f2 = _mm_sub_ps( n2f4, _mm_cvtepi32_ps(n2) ); /* { alpha4 } */ - __m128 f1f2 = _mm_mul_ps(f1, f2); // f1 * f2 + __m128 f1f2 = _mm_mul_ps(f1, f2); // f1 * f2 __m128 omf1omf2 = _mm_add_ps(_mm_sub_ps(_mm_sub_ps(one4f, f2), f1), f1f2); // (1-f1) * (1-f2) - - __m128i mask = _mm_and_si128( + + __m128i _mask = _mm_and_si128( _mm_andnot_si128(_mm_cmpgt_epi32(zero4, n1), _mm_cmpgt_epi32(height4m1, n1)), _mm_andnot_si128(_mm_cmpgt_epi32(zero4, n2), _mm_cmpgt_epi32(width4m1, n2))); - __m128 maskf = _mm_cmpneq_ps(_mm_cvtepi32_ps(mask), zero4f); - + __m128 maskf = _mm_cmpneq_ps(_mm_cvtepi32_ps(_mask), zero4f); + __m128 v00 = _mm_and_ps( omf1omf2 , maskf); // a00 b00 c00 d00 __m128 v01 = _mm_and_ps( _mm_sub_ps( f2, f1f2 ), maskf); // a01 b01 c01 d01 __m128 v10 = _mm_and_ps( _mm_sub_ps( f1, f1f2 ), maskf); // a10 b10 c10 d10 __m128 v11 = _mm_and_ps( f1f2 , maskf); // a11 b11 c11 d11 - __m128i ofs4 = _mm_and_si128(_mm_add_epi32(_mm_mullo_epi32_emul(n1, step4), n2), mask); + __m128i ofs4 = _mm_and_si128(_mm_add_epi32(_mm_mullo_epi32_emul(n1, step4), n2), _mask); _mm_store_si128((__m128i*)o, ofs4); __m128 t0 = _mm_unpacklo_ps(v00, v01); // a00 a01 b00 b01 @@ -395,9 +395,9 @@ void computeSpinImages( const Octree& Octree, const vector& points, con if (beta >= support || beta < 0) continue; - alpha = sqrt( (new_center.x - pt.x) * (new_center.x - pt.x) + - (new_center.y - pt.y) * (new_center.y - pt.y) ); - + alpha = sqrt( (new_center.x - pt.x) * (new_center.x - pt.x) + + (new_center.y - pt.y) * (new_center.y - pt.y) ); + float n1f = beta * pixelsPerMeter; float n2f = alpha * pixelsPerMeter; @@ -407,7 +407,7 @@ void computeSpinImages( const Octree& Octree, const vector& points, con float f1 = n1f - n1; float f2 = n2f - n2; - if ((unsigned)n1 >= (unsigned)(spinImage.rows-1) || + if ((unsigned)n1 >= (unsigned)(spinImage.rows-1) || (unsigned)n2 >= (unsigned)(spinImage.cols-1)) continue; @@ -454,27 +454,27 @@ float cv::Mesh3D::estimateResolution(float /*tryRatio*/) vector dist(tryNum * neighbors); vector inds(tryNum * neighbors); - vector query; + vector query; - RNG& rng = theRNG(); + RNG& rng = theRNG(); for(int i = 0; i < tryNum; ++i) query.push_back(vtx[rng.next() % vtx.size()]); - + CvMat cvinds = cvMat( (int)tryNum, neighbors, CV_32S, &inds[0] ); - CvMat cvdist = cvMat( (int)tryNum, neighbors, CV_64F, &dist[0] ); + CvMat cvdist = cvMat( (int)tryNum, neighbors, CV_64F, &dist[0] ); CvMat cvquery = cvMat( (int)tryNum, 3, CV_32F, &query[0] ); - cvFindFeatures(tr, &cvquery, &cvinds, &cvdist, neighbors, 50); + cvFindFeatures(tr, &cvquery, &cvinds, &cvdist, neighbors, 50); cvReleaseFeatureTree(tr); - const int invalid_dist = -2; + const int invalid_dist = -2; for(int i = 0; i < tryNum; ++i) if (inds[i] == -1) dist[i] = invalid_dist; dist.resize(remove(dist.begin(), dist.end(), invalid_dist) - dist.begin()); - + sort(dist, less()); - + return resolution = (float)dist[ dist.size() / 2 ]; #else CV_Error(CV_StsNotImplemented, ""); @@ -494,7 +494,7 @@ void cv::Mesh3D::computeNormals(const vector& subset, float normalRadius, i { buildOctree(); vector mask(vtx.size(), 0); - for(size_t i = 0; i < subset.size(); ++i) + for(size_t i = 0; i < subset.size(); ++i) mask[subset[i]] = 1; ::computeNormals(octree, vtx, normals, mask, normalRadius, minNeighbors); } @@ -504,31 +504,31 @@ void cv::Mesh3D::writeAsVrml(const String& file, const vector& _colors) ofstream ofs(file.c_str()); ofs << "#VRML V2.0 utf8" << endl; - ofs << "Shape" << std::endl << "{" << endl; - ofs << "geometry PointSet" << endl << "{" << endl; - ofs << "coord Coordinate" << endl << "{" << endl; - ofs << "point[" << endl; + ofs << "Shape" << std::endl << "{" << endl; + ofs << "geometry PointSet" << endl << "{" << endl; + ofs << "coord Coordinate" << endl << "{" << endl; + ofs << "point[" << endl; for(size_t i = 0; i < vtx.size(); ++i) ofs << vtx[i].x << " " << vtx[i].y << " " << vtx[i].z << endl; - - ofs << "]" << endl; //point[ - ofs << "}" << endl; //Coordinate{ + + ofs << "]" << endl; //point[ + ofs << "}" << endl; //Coordinate{ if (vtx.size() == _colors.size()) { ofs << "color Color" << endl << "{" << endl; ofs << "color[" << endl; - + for(size_t i = 0; i < _colors.size(); ++i) ofs << (float)_colors[i][2] << " " << (float)_colors[i][1] << " " << (float)_colors[i][0] << endl; - + ofs << "]" << endl; //color[ - ofs << "}" << endl; //color Color{ + ofs << "}" << endl; //color Color{ } - ofs << "}" << endl; //PointSet{ - ofs << "}" << endl; //Shape{ + ofs << "}" << endl; //PointSet{ + ofs << "}" << endl; //Shape{ } @@ -538,45 +538,45 @@ void cv::Mesh3D::writeAsVrml(const String& file, const vector& _colors) bool cv::SpinImageModel::spinCorrelation(const Mat& spin1, const Mat& spin2, float lambda, float& result) { struct Math { static double atanh(double x) { return 0.5 * std::log( (1 + x) / (1 - x) ); } }; - + const float* s1 = spin1.ptr(); const float* s2 = spin2.ptr(); - int spin_sz = spin1.cols * spin1.rows; + int spin_sz = spin1.cols * spin1.rows; double sum1 = 0.0, sum2 = 0.0, sum12 = 0.0, sum11 = 0.0, sum22 = 0.0; int N = 0; int i = 0; #if CV_SSE2//____________TEMPORARY_DISABLED_____________ - float CV_DECL_ALIGNED(16) su1[4], su2[4], su11[4], su22[4], su12[4], n[4]; - + float CV_DECL_ALIGNED(16) su1[4], su2[4], su11[4], su22[4], su12[4], n[4]; + __m128 zerof4 = _mm_setzero_ps(); __m128 onef4 = _mm_set1_ps(1.f); - __m128 Nf4 = zerof4; + __m128 Nf4 = zerof4; __m128 sum1f4 = zerof4; __m128 sum2f4 = zerof4; __m128 sum11f4 = zerof4; __m128 sum22f4 = zerof4; - __m128 sum12f4 = zerof4; + __m128 sum12f4 = zerof4; for(; i < spin_sz - 5; i += 4) { - __m128 v1f4 = _mm_loadu_ps(s1 + i); - __m128 v2f4 = _mm_loadu_ps(s2 + i); + __m128 v1f4 = _mm_loadu_ps(s1 + i); + __m128 v2f4 = _mm_loadu_ps(s2 + i); __m128 mskf4 = _mm_and_ps(_mm_cmpneq_ps(v1f4, zerof4), _mm_cmpneq_ps(v2f4, zerof4)); - if( !_mm_movemask_ps(mskf4) ) + if( !_mm_movemask_ps(mskf4) ) continue; - + Nf4 = _mm_add_ps(Nf4, _mm_and_ps(onef4, mskf4)); v1f4 = _mm_and_ps(v1f4, mskf4); v2f4 = _mm_and_ps(v2f4, mskf4); - + sum1f4 = _mm_add_ps(sum1f4, v1f4); sum2f4 = _mm_add_ps(sum2f4, v2f4); sum11f4 = _mm_add_ps(sum11f4, _mm_mul_ps(v1f4, v1f4)); sum22f4 = _mm_add_ps(sum22f4, _mm_mul_ps(v2f4, v2f4)); - sum12f4 = _mm_add_ps(sum12f4, _mm_mul_ps(v1f4, v2f4)); + sum12f4 = _mm_add_ps(sum12f4, _mm_mul_ps(v1f4, v2f4)); } _mm_store_ps( su1, sum1f4 ); _mm_store_ps( su2, sum2f4 ); @@ -601,11 +601,11 @@ bool cv::SpinImageModel::spinCorrelation(const Mat& spin1, const Mat& spin2, flo if( !v1 || !v2 ) continue; N++; - - sum1 += v1; - sum2 += v2; - sum11 += v1 * v1; - sum22 += v2 * v2; + + sum1 += v1; + sum2 += v2; + sum11 += v1 * v1; + sum22 += v2 * v2; sum12 += v1 * v2; } if( N < 4 ) @@ -624,13 +624,13 @@ bool cv::SpinImageModel::spinCorrelation(const Mat& spin1, const Mat& spin2, flo double corr = (Nsum12 - sum1 * sum2) / sqrt( (Nsum11 - sum1sum1) * (Nsum22 - sum2sum2) ); double atanh = Math::atanh(corr); result = (float)( atanh * atanh - lambda * ( 1.0 / (N - 3) ) ); - return true; + return true; } inline Point2f cv::SpinImageModel::calcSpinMapCoo(const Point3f& p, const Point3f& v, const Point3f& n) -{ - /*Point3f PmV(p.x - v.x, p.y - v.y, p.z - v.z); - float normalNorm = (float)norm(n); +{ + /*Point3f PmV(p.x - v.x, p.y - v.y, p.z - v.z); + float normalNorm = (float)norm(n); float beta = PmV.dot(n) / normalNorm; float pmcNorm = (float)norm(PmV); float alpha = sqrt( pmcNorm * pmcNorm - beta * beta); @@ -639,23 +639,23 @@ inline Point2f cv::SpinImageModel::calcSpinMapCoo(const Point3f& p, const Point3 float pmv_x = p.x - v.x, pmv_y = p.y - v.y, pmv_z = p.z - v.z; float beta = (pmv_x * n.x + pmv_y + n.y + pmv_z * n.z) / sqrt(n.x * n.x + n.y * n.y + n.z * n.z); - float alpha = sqrt( pmv_x * pmv_x + pmv_y * pmv_y + pmv_z * pmv_z - beta * beta); + float alpha = sqrt( pmv_x * pmv_x + pmv_y * pmv_y + pmv_z * pmv_z - beta * beta); return Point2f(alpha, beta); } inline float cv::SpinImageModel::geometricConsistency(const Point3f& pointScene1, const Point3f& normalScene1, const Point3f& pointModel1, const Point3f& normalModel1, - const Point3f& pointScene2, const Point3f& normalScene2, + const Point3f& pointScene2, const Point3f& normalScene2, const Point3f& pointModel2, const Point3f& normalModel2) -{ +{ Point2f Sm2_to_m1, Ss2_to_s1; Point2f Sm1_to_m2, Ss1_to_s2; double n_Sm2_to_m1 = norm(Sm2_to_m1 = calcSpinMapCoo(pointModel2, pointModel1, normalModel1)); - double n_Ss2_to_s1 = norm(Ss2_to_s1 = calcSpinMapCoo(pointScene2, pointScene1, normalScene1)); + double n_Ss2_to_s1 = norm(Ss2_to_s1 = calcSpinMapCoo(pointScene2, pointScene1, normalScene1)); double gc21 = 2 * norm(Sm2_to_m1 - Ss2_to_s1) / (n_Sm2_to_m1 + n_Ss2_to_s1 ) ; - + double n_Sm1_to_m2 = norm(Sm1_to_m2 = calcSpinMapCoo(pointModel1, pointModel2, normalModel2)); double n_Ss1_to_s2 = norm(Ss1_to_s2 = calcSpinMapCoo(pointScene1, pointScene2, normalScene2)); @@ -666,10 +666,10 @@ inline float cv::SpinImageModel::geometricConsistency(const Point3f& pointScene1 inline float cv::SpinImageModel::groupingCreteria(const Point3f& pointScene1, const Point3f& normalScene1, const Point3f& pointModel1, const Point3f& normalModel1, - const Point3f& pointScene2, const Point3f& normalScene2, - const Point3f& pointModel2, const Point3f& normalModel2, + const Point3f& pointScene2, const Point3f& normalScene2, + const Point3f& pointModel2, const Point3f& normalModel2, float gamma) -{ +{ Point2f Sm2_to_m1, Ss2_to_s1; Point2f Sm1_to_m2, Ss1_to_s2; @@ -680,7 +680,7 @@ inline float cv::SpinImageModel::groupingCreteria(const Point3f& pointScene1, co double gc21 = 2 * norm(Sm2_to_m1 - Ss2_to_s1) / (n_Sm2_to_m1 + n_Ss2_to_s1 ); double wgc21 = gc21 / (1 - exp( -(n_Sm2_to_m1 + n_Ss2_to_s1) * gamma05_inv ) ); - + double n_Sm1_to_m2 = norm(Sm1_to_m2 = calcSpinMapCoo(pointModel1, pointModel2, normalModel2)); double n_Ss1_to_s2 = norm(Ss1_to_s2 = calcSpinMapCoo(pointScene1, pointScene2, normalScene2)); @@ -692,10 +692,10 @@ inline float cv::SpinImageModel::groupingCreteria(const Point3f& pointScene1, co cv::SpinImageModel::SpinImageModel(const Mesh3D& _mesh) : mesh(_mesh) , out(0) -{ +{ if (mesh.vtx.empty()) throw Mesh3D::EmptyMeshException(); - defaultParams(); + defaultParams(); } cv::SpinImageModel::SpinImageModel() : out(0) { defaultParams(); } cv::SpinImageModel::~SpinImageModel() {} @@ -708,8 +708,8 @@ void cv::SpinImageModel::defaultParams() minNeighbors = 20; binSize = 0.f; /* autodetect according to mesh resolution */ - imageWidth = 32; - + imageWidth = 32; + lambda = 0.f; /* autodetect according to medan non zero images bin */ gamma = 0.f; /* autodetect according to mesh resolution */ @@ -725,28 +725,28 @@ Mat cv::SpinImageModel::packRandomScaledSpins(bool separateScale, size_t xCount, if (num == 0) return Mat(); - RNG& rng = theRNG(); + RNG& rng = theRNG(); vector spins; for(int i = 0; i < num; ++i) - spins.push_back(getSpinImage( rng.next() % spinNum ).reshape(1, imageWidth)); - + spins.push_back(getSpinImage( rng.next() % spinNum ).reshape(1, imageWidth)); + if (separateScale) for(int i = 0; i < num; ++i) { double max; Mat spin8u; - minMaxLoc(spins[i], 0, &max); + minMaxLoc(spins[i], 0, &max); spins[i].convertTo(spin8u, CV_8U, -255.0/max, 255.0); spins[i] = spin8u; } else - { + { double totalMax = 0; for(int i = 0; i < num; ++i) { double m; - minMaxLoc(spins[i], 0, &m); + minMaxLoc(spins[i], 0, &m); totalMax = max(m, totalMax); } @@ -760,12 +760,12 @@ Mat cv::SpinImageModel::packRandomScaledSpins(bool separateScale, size_t xCount, int sz = spins.front().cols; - Mat result((int)(yCount * sz + (yCount - 1)), (int)(xCount * sz + (xCount - 1)), CV_8UC3); + Mat result((int)(yCount * sz + (yCount - 1)), (int)(xCount * sz + (xCount - 1)), CV_8UC3); result = colors[(static_cast(cvGetTickCount()/cvGetTickFrequency())/1000) % colors_mum]; int pos = 0; for(int y = 0; y < (int)yCount; ++y) - for(int x = 0; x < (int)xCount; ++x) + for(int x = 0; x < (int)xCount; ++x) if (pos < num) { int starty = (y + 0) * sz + y; @@ -778,7 +778,7 @@ Mat cv::SpinImageModel::packRandomScaledSpins(bool separateScale, size_t xCount, cvtColor(spins[pos++], color, CV_GRAY2BGR); Mat roi = result(Range(starty, endy), Range(startx, endx)); color.copyTo(roi); - } + } return result; } @@ -808,11 +808,11 @@ void cv::SpinImageModel::selectRandomSubset(float ratio) subset.resize(setSize); for(size_t i = 0; i < setSize; ++i) { - int pos = rnd.next() % left.size(); + int pos = rnd.next() % (int)left.size(); subset[i] = (int)left[pos]; - left[pos] = left.back(); - left.resize(left.size() - 1); + left[pos] = left.back(); + left.resize(left.size() - 1); } sort(subset, less()); } @@ -823,21 +823,21 @@ void cv::SpinImageModel::setSubset(const vector& ss) subset = ss; } -void cv::SpinImageModel::repackSpinImages(const vector& mask, Mat& spinImages, bool reAlloc) const -{ +void cv::SpinImageModel::repackSpinImages(const vector& mask, Mat& _spinImages, bool reAlloc) const +{ if (reAlloc) { size_t spinCount = mask.size() - count(mask.begin(), mask.end(), (uchar)0); - Mat newImgs((int)spinCount, spinImages.cols, spinImages.type()); + Mat newImgs((int)spinCount, _spinImages.cols, _spinImages.type()); int pos = 0; for(size_t t = 0; t < mask.size(); ++t) if (mask[t]) { Mat row = newImgs.row(pos++); - spinImages.row((int)t).copyTo(row); + _spinImages.row((int)t).copyTo(row); } - spinImages = newImgs; + _spinImages = newImgs; } else { @@ -849,13 +849,13 @@ void cv::SpinImageModel::repackSpinImages(const vector& mask, Mat& spinIm int first = dest + 1; for (; first != last; ++first) - if (mask[first] != 0) + if (mask[first] != 0) { - Mat row = spinImages.row(dest); - spinImages.row(first).copyTo(row); + Mat row = _spinImages.row(dest); + _spinImages.row(first).copyTo(row); ++dest; } - spinImages = spinImages.rowRange(0, dest); + _spinImages = _spinImages.rowRange(0, dest); } } @@ -865,13 +865,13 @@ void cv::SpinImageModel::compute() if (binSize == 0.f) { if (mesh.resolution == -1.f) - mesh.estimateResolution(); + mesh.estimateResolution(); binSize = mesh.resolution; } - /* estimate normalRadius */ - normalRadius = normalRadius != 0.f ? normalRadius : binSize * imageWidth / 2; + /* estimate normalRadius */ + normalRadius = normalRadius != 0.f ? normalRadius : binSize * imageWidth / 2; - mesh.buildOctree(); + mesh.buildOctree(); if (subset.empty()) { mesh.computeNormals(normalRadius, minNeighbors); @@ -881,16 +881,16 @@ void cv::SpinImageModel::compute() else mesh.computeNormals(subset, normalRadius, minNeighbors); - vector mask(mesh.vtx.size(), 0); + vector mask(mesh.vtx.size(), 0); for(size_t i = 0; i < subset.size(); ++i) - if (mesh.normals[subset[i]] == Mesh3D::allzero) - subset[i] = -1; + if (mesh.normals[subset[i]] == Mesh3D::allzero) + subset[i] = -1; else mask[subset[i]] = 1; subset.resize( remove(subset.begin(), subset.end(), -1) - subset.begin() ); - + vector vtx; - vector normals; + vector normals; for(size_t i = 0; i < mask.size(); ++i) if(mask[i]) { @@ -906,7 +906,7 @@ void cv::SpinImageModel::compute() for(size_t i = 0; i < mask.size(); ++i) if(mask[i]) if (spinMask[mask_pos++] == 0) - subset.resize( remove(subset.begin(), subset.end(), (int)i) - subset.begin() ); + subset.resize( remove(subset.begin(), subset.end(), (int)i) - subset.begin() ); } void cv::SpinImageModel::matchSpinToModel(const Mat& spin, vector& indeces, vector& corrCoeffs, bool useExtremeOutliers) const @@ -920,46 +920,46 @@ void cv::SpinImageModel::matchSpinToModel(const Mat& spin, vector& indeces, vector masks(model.spinImages.rows); vector cleanCorrs; cleanCorrs.reserve(model.spinImages.rows); - + for(int i = 0; i < model.spinImages.rows; ++i) { - masks[i] = spinCorrelation(spin, model.spinImages.row(i), model.lambda, corrs[i]); + masks[i] = spinCorrelation(spin, model.spinImages.row(i), model.lambda, corrs[i]); if (masks[i]) cleanCorrs.push_back(corrs[i]); } - + /* Filtering by measure histogram */ size_t total = cleanCorrs.size(); if(total < 5) return; sort(cleanCorrs, less()); - + float lower_fourth = cleanCorrs[(1 * total) / 4 - 1]; float upper_fourth = cleanCorrs[(3 * total) / 4 - 0]; float fourth_spread = upper_fourth - lower_fourth; //extreme or moderate? - float coef = useExtremeOutliers ? 3.0f : 1.5f; + float coef = useExtremeOutliers ? 3.0f : 1.5f; + + float histThresHi = upper_fourth + coef * fourth_spread; + //float histThresLo = lower_fourth - coef * fourth_spread; - float histThresHi = upper_fourth + coef * fourth_spread; - //float histThresLo = lower_fourth - coef * fourth_spread; - for(size_t i = 0; i < corrs.size(); ++i) if (masks[i]) if (/* corrs[i] < histThresLo || */ corrs[i] > histThresHi) { indeces.push_back((int)i); - corrCoeffs.push_back(corrs[i]); + corrCoeffs.push_back(corrs[i]); } -} +} -namespace +namespace { struct Match { - int sceneInd; + int sceneInd; int modelInd; float measure; @@ -984,7 +984,7 @@ struct WgcHelper { const float* wgcLine = mat.ptr((int)corespInd); float maximum = numeric_limits::min(); - + for(citer pos = group.begin(); pos != group.end(); ++pos) maximum = max(wgcLine[*pos], maximum); @@ -997,7 +997,7 @@ private: } void cv::SpinImageModel::match(const SpinImageModel& scene, vector< vector >& result) -{ +{ if (mesh.vtx.empty()) throw Mesh3D::EmptyMeshException(); @@ -1006,25 +1006,25 @@ private: SpinImageModel& model = *this; const float infinity = numeric_limits::infinity(); const float float_max = numeric_limits::max(); - + /* estimate gamma */ if (model.gamma == 0.f) { if (model.mesh.resolution == -1.f) - model.mesh.estimateResolution(); + model.mesh.estimateResolution(); model.gamma = 4 * model.mesh.resolution; } /* estimate lambda */ if (model.lambda == 0.f) { - vector nonzero(model.spinImages.rows); + vector nonzero(model.spinImages.rows); for(int i = 0; i < model.spinImages.rows; ++i) nonzero[i] = countNonZero(model.spinImages.row(i)); sort(nonzero, less()); model.lambda = static_cast( nonzero[ nonzero.size()/2 ] ) / 2; - } - + } + TickMeter corr_timer; corr_timer.start(); vector allMatches; @@ -1032,37 +1032,37 @@ private: { vector indeces; vector coeffs; - matchSpinToModel(scene.spinImages.row(i), indeces, coeffs); + matchSpinToModel(scene.spinImages.row(i), indeces, coeffs); for(size_t t = 0; t < indeces.size(); ++t) - allMatches.push_back(Match(i, indeces[t], coeffs[t])); + allMatches.push_back(Match(i, indeces[t], coeffs[t])); - if (out) if (i % 100 == 0) *out << "Comparing scene spinimage " << i << " of " << scene.spinImages.rows << endl; + if (out) if (i % 100 == 0) *out << "Comparing scene spinimage " << i << " of " << scene.spinImages.rows << endl; } corr_timer.stop(); if (out) *out << "Spin correlation time = " << corr_timer << endl; if (out) *out << "Matches number = " << allMatches.size() << endl; - if(allMatches.empty()) + if(allMatches.empty()) return; - + /* filtering by similarity measure */ const float fraction = 0.5f; - float maxMeasure = max_element(allMatches.begin(), allMatches.end(), less())->measure; + float maxMeasure = max_element(allMatches.begin(), allMatches.end(), less())->measure; allMatches.erase( - remove_if(allMatches.begin(), allMatches.end(), bind2nd(less(), maxMeasure * fraction)), + remove_if(allMatches.begin(), allMatches.end(), bind2nd(less(), maxMeasure * fraction)), allMatches.end()); if (out) *out << "Matches number [filtered by similarity measure] = " << allMatches.size() << endl; int matchesSize = (int)allMatches.size(); if(matchesSize == 0) return; - - /* filtering by geometric consistency */ + + /* filtering by geometric consistency */ for(int i = 0; i < matchesSize; ++i) { int consistNum = 1; float gc = float_max; - + for(int j = 0; j < matchesSize; ++j) if (i != j) { @@ -1075,31 +1075,31 @@ private: { const Point3f& pointSceneI = scene.getSpinVertex(mi.sceneInd); const Point3f& normalSceneI = scene.getSpinNormal(mi.sceneInd); - + const Point3f& pointModelI = model.getSpinVertex(mi.modelInd); const Point3f& normalModelI = model.getSpinNormal(mi.modelInd); - + const Point3f& pointSceneJ = scene.getSpinVertex(mj.sceneInd); const Point3f& normalSceneJ = scene.getSpinNormal(mj.sceneInd); - + const Point3f& pointModelJ = model.getSpinVertex(mj.modelInd); const Point3f& normalModelJ = model.getSpinNormal(mj.modelInd); - + gc = geometricConsistency(pointSceneI, normalSceneI, pointModelI, normalModelI, - pointSceneJ, normalSceneJ, pointModelJ, normalModelJ); + pointSceneJ, normalSceneJ, pointModelJ, normalModelJ); } if (gc < model.T_GeometriccConsistency) ++consistNum; } - - + + if (consistNum < matchesSize / 4) /* failed consistensy test */ - allMatches[i].measure = infinity; + allMatches[i].measure = infinity; } allMatches.erase( - remove_if(allMatches.begin(), allMatches.end(), bind2nd(equal_to(), infinity)), - allMatches.end()); + remove_if(allMatches.begin(), allMatches.end(), bind2nd(equal_to(), infinity)), + allMatches.end()); if (out) *out << "Matches number [filtered by geometric consistency] = " << allMatches.size() << endl; @@ -1110,11 +1110,11 @@ private: if (out) *out << "grouping ..." << endl; Mat groupingMat((int)matchesSize, (int)matchesSize, CV_32F); - groupingMat = Scalar(0); - + groupingMat = Scalar(0); + /* grouping */ for(int j = 0; j < matchesSize; ++j) - for(int i = j + 1; i < matchesSize; ++i) + for(int i = j + 1; i < matchesSize; ++i) { const Match& mi = allMatches[i]; const Match& mj = allMatches[j]; @@ -1128,20 +1128,20 @@ private: const Point3f& pointSceneI = scene.getSpinVertex(mi.sceneInd); const Point3f& normalSceneI = scene.getSpinNormal(mi.sceneInd); - + const Point3f& pointModelI = model.getSpinVertex(mi.modelInd); const Point3f& normalModelI = model.getSpinNormal(mi.modelInd); - + const Point3f& pointSceneJ = scene.getSpinVertex(mj.sceneInd); const Point3f& normalSceneJ = scene.getSpinNormal(mj.sceneInd); - + const Point3f& pointModelJ = model.getSpinVertex(mj.modelInd); const Point3f& normalModelJ = model.getSpinNormal(mj.modelInd); float wgc = groupingCreteria(pointSceneI, normalSceneI, pointModelI, normalModelI, pointSceneJ, normalSceneJ, pointModelJ, normalModelJ, - model.gamma); - + model.gamma); + groupingMat.ptr(i)[j] = wgc; groupingMat.ptr(j)[i] = wgc; } @@ -1149,35 +1149,35 @@ private: group_t allMatchesInds; for(int i = 0; i < matchesSize; ++i) allMatchesInds.insert(i); - + vector buf(matchesSize); float *buf_beg = &buf[0]; vector groups; - + for(int g = 0; g < matchesSize; ++g) - { + { if (out) if (g % 100 == 0) *out << "G = " << g << endl; group_t left = allMatchesInds; group_t group; - + left.erase(g); group.insert(g); - + for(;;) { size_t left_size = left.size(); if (left_size == 0) break; - + std::transform(left.begin(), left.end(), buf_beg, WgcHelper(group, groupingMat)); size_t minInd = min_element(buf_beg, buf_beg + left_size) - buf_beg; - + if (buf[minInd] < model.T_GroupingCorespondances) /* can add corespondance to group */ { iter pos = left.begin(); advance(pos, minInd); - + group.insert(*pos); left.erase(pos); } @@ -1199,16 +1199,16 @@ private: { const Match& m = allMatches[*pos]; outgrp.push_back(Vec2i(subset[m.modelInd], scene.subset[m.sceneInd])); - } + } result.push_back(outgrp); - } + } } cv::TickMeter::TickMeter() { reset(); } int64 cv::TickMeter::getTimeTicks() const { return sumTime; } double cv::TickMeter::getTimeMicro() const { return (double)getTimeTicks()/cvGetTickFrequency(); } double cv::TickMeter::getTimeMilli() const { return getTimeMicro()*1e-3; } -double cv::TickMeter::getTimeSec() const { return getTimeMilli()*1e-3; } +double cv::TickMeter::getTimeSec() const { return getTimeMilli()*1e-3; } int64 cv::TickMeter::getCounter() const { return counter; } void cv::TickMeter::reset() {startTime = 0; sumTime = 0; counter = 0; } diff --git a/modules/contrib/src/stereovar.cpp b/modules/contrib/src/stereovar.cpp index 1197acc..88640d8 100755 --- a/modules/contrib/src/stereovar.cpp +++ b/modules/contrib/src/stereovar.cpp @@ -46,14 +46,14 @@ Proceedings of the 5th International Symposium on Visual Computing, Vegas, USA This code is written by Sergey G. Kosov for "Visir PX" application as part of Project X (www.project-10.de) - */ + */ #include "precomp.hpp" #include -namespace cv +namespace cv { -StereoVar::StereoVar() : levels(3), pyrScale(0.5), nIt(5), minDisp(0), maxDisp(16), poly_n(3), poly_sigma(0), fi(25.0f), lambda(0.03f), penalization(PENALIZATION_TICHONOV), cycle(CYCLE_V), flags(USE_SMART_ID | USE_AUTO_PARAMS) +StereoVar::StereoVar() : levels(3), pyrScale(0.5), nIt(5), minDisp(0), maxDisp(16), poly_n(3), poly_sigma(0), fi(25.0f), lambda(0.03f), penalization(PENALIZATION_TICHONOV), cycle(CYCLE_V), flags(USE_SMART_ID | USE_AUTO_PARAMS) { } @@ -67,9 +67,9 @@ StereoVar::~StereoVar() static Mat diffX(Mat &src) { - register int x, y, cols = src.cols - 1; - Mat dst(src.size(), src.type()); - for(y = 0; y < src.rows; y++){ + register int x, y, cols = src.cols - 1; + Mat dst(src.size(), src.type()); + for(y = 0; y < src.rows; y++){ const float* pSrc = src.ptr(y); float* pDst = dst.ptr(y); #if CV_SSE2 @@ -92,319 +92,319 @@ static Mat diffX(Mat &src) static Mat getGradient(Mat &src) { - register int x, y; - Mat dst(src.size(), src.type()); - dst.setTo(0); - for (y = 0; y < src.rows - 1; y++) { - float *pSrc = src.ptr(y); - float *pSrcF = src.ptr(y + 1); - float *pDst = dst.ptr(y); - for (x = 0; x < src.cols - 1; x++) - pDst[x] = fabs(pSrc[x + 1] - pSrc[x]) + fabs(pSrcF[x] - pSrc[x]); - } - return dst; + register int x, y; + Mat dst(src.size(), src.type()); + dst.setTo(0); + for (y = 0; y < src.rows - 1; y++) { + float *pSrc = src.ptr(y); + float *pSrcF = src.ptr(y + 1); + float *pDst = dst.ptr(y); + for (x = 0; x < src.cols - 1; x++) + pDst[x] = fabs(pSrc[x + 1] - pSrc[x]) + fabs(pSrcF[x] - pSrc[x]); + } + return dst; } static Mat getG_c(Mat &src, float l) { - Mat dst(src.size(), src.type()); - for (register int y = 0; y < src.rows; y++) { - float *pSrc = src.ptr(y); - float *pDst = dst.ptr(y); - for (register int x = 0; x < src.cols; x++) - pDst[x] = 0.5f*l / sqrtf(l*l + pSrc[x]*pSrc[x]); - } - return dst; + Mat dst(src.size(), src.type()); + for (register int y = 0; y < src.rows; y++) { + float *pSrc = src.ptr(y); + float *pDst = dst.ptr(y); + for (register int x = 0; x < src.cols; x++) + pDst[x] = 0.5f*l / sqrtf(l*l + pSrc[x]*pSrc[x]); + } + return dst; } static Mat getG_p(Mat &src, float l) { - Mat dst(src.size(), src.type()); - for (register int y = 0; y < src.rows; y++) { - float *pSrc = src.ptr(y); - float *pDst = dst.ptr(y); - for (register int x = 0; x < src.cols; x++) - pDst[x] = 0.5f*l*l / (l*l + pSrc[x]*pSrc[x]); - } - return dst; + Mat dst(src.size(), src.type()); + for (register int y = 0; y < src.rows; y++) { + float *pSrc = src.ptr(y); + float *pDst = dst.ptr(y); + for (register int x = 0; x < src.cols; x++) + pDst[x] = 0.5f*l*l / (l*l + pSrc[x]*pSrc[x]); + } + return dst; } void StereoVar::VariationalSolver(Mat &I1, Mat &I2, Mat &I2x, Mat &u, int level) { - register int n, x, y; - float gl = 1, gr = 1, gu = 1, gd = 1, gc = 4; - Mat g_c, g_p; - Mat U; - u.copyTo(U); - - int N = nIt; - float l = lambda; - float Fi = fi; - - - if (flags & USE_SMART_ID) { - double scale = pow(pyrScale, (double) level) * (1 + pyrScale); - N = (int) (N / scale); - } - - double scale = pow(pyrScale, (double) level); - Fi /= (float) scale; - l *= (float) scale; - - int width = u.cols - 1; - int height = u.rows - 1; - for (n = 0; n < N; n++) { - if (penalization != PENALIZATION_TICHONOV) { - Mat gradient = getGradient(U); - switch (penalization) { - case PENALIZATION_CHARBONNIER: g_c = getG_c(gradient, l); break; - case PENALIZATION_PERONA_MALIK: g_p = getG_p(gradient, l); break; - } - gradient.release(); - } - for (y = 1 ; y < height; y++) { - float *pU = U.ptr(y); - float *pUu = U.ptr(y + 1); - float *pUd = U.ptr(y - 1); - float *pu = u.ptr(y); - float *pI1 = I1.ptr(y); - float *pI2 = I2.ptr(y); - float *pI2x = I2x.ptr(y); - float *pG_c = NULL, *pG_cu = NULL, *pG_cd = NULL; - float *pG_p = NULL, *pG_pu = NULL, *pG_pd = NULL; - switch (penalization) { - case PENALIZATION_CHARBONNIER: - pG_c = g_c.ptr(y); - pG_cu = g_c.ptr(y + 1); - pG_cd = g_c.ptr(y - 1); - break; - case PENALIZATION_PERONA_MALIK: - pG_p = g_p.ptr(y); - pG_pu = g_p.ptr(y + 1); - pG_pd = g_p.ptr(y - 1); - break; - } - for (x = 1; x < width; x++) { - switch (penalization) { - case PENALIZATION_CHARBONNIER: - gc = pG_c[x]; - gl = gc + pG_c[x - 1]; - gr = gc + pG_c[x + 1]; - gu = gc + pG_cu[x]; - gd = gc + pG_cd[x]; - gc = gl + gr + gu + gd; - break; - case PENALIZATION_PERONA_MALIK: - gc = pG_p[x]; - gl = gc + pG_p[x - 1]; - gr = gc + pG_p[x + 1]; - gu = gc + pG_pu[x]; - gd = gc + pG_pd[x]; - gc = gl + gr + gu + gd; - break; - } - - float fi = Fi; - if (maxDisp > minDisp) { - if (pU[x] > maxDisp * scale) {fi *= 1000; pU[x] = static_cast(maxDisp * scale);} - if (pU[x] < minDisp * scale) {fi *= 1000; pU[x] = static_cast(minDisp * scale);} - } - - int A = static_cast(pU[x]); - int neg = 0; if (pU[x] <= 0) neg = -1; - - if (x + A > width) - pu[x] = pU[width - A]; - else if (x + A + neg < 0) - pu[x] = pU[- A + 2]; - else { - pu[x] = A + (pI2x[x + A + neg] * (pI1[x] - pI2[x + A]) - + fi * (gr * pU[x + 1] + gl * pU[x - 1] + gu * pUu[x] + gd * pUd[x] - gc * A)) - / (pI2x[x + A + neg] * pI2x[x + A + neg] + gc * fi) ; - } - }// x - pu[0] = pu[1]; - pu[width] = pu[width - 1]; - }// y - for (x = 0; x <= width; x++) { - u.at(0, x) = u.at(1, x); - u.at(height, x) = u.at(height - 1, x); - } - u.copyTo(U); - if (!g_c.empty()) g_c.release(); - if (!g_p.empty()) g_p.release(); - }//n + register int n, x, y; + float gl = 1, gr = 1, gu = 1, gd = 1, gc = 4; + Mat g_c, g_p; + Mat U; + u.copyTo(U); + + int N = nIt; + float l = lambda; + float Fi = fi; + + + if (flags & USE_SMART_ID) { + double scale = pow(pyrScale, (double) level) * (1 + pyrScale); + N = (int) (N / scale); + } + + double scale = pow(pyrScale, (double) level); + Fi /= (float) scale; + l *= (float) scale; + + int width = u.cols - 1; + int height = u.rows - 1; + for (n = 0; n < N; n++) { + if (penalization != PENALIZATION_TICHONOV) { + Mat gradient = getGradient(U); + switch (penalization) { + case PENALIZATION_CHARBONNIER: g_c = getG_c(gradient, l); break; + case PENALIZATION_PERONA_MALIK: g_p = getG_p(gradient, l); break; + } + gradient.release(); + } + for (y = 1 ; y < height; y++) { + float *pU = U.ptr(y); + float *pUu = U.ptr(y + 1); + float *pUd = U.ptr(y - 1); + float *pu = u.ptr(y); + float *pI1 = I1.ptr(y); + float *pI2 = I2.ptr(y); + float *pI2x = I2x.ptr(y); + float *pG_c = NULL, *pG_cu = NULL, *pG_cd = NULL; + float *pG_p = NULL, *pG_pu = NULL, *pG_pd = NULL; + switch (penalization) { + case PENALIZATION_CHARBONNIER: + pG_c = g_c.ptr(y); + pG_cu = g_c.ptr(y + 1); + pG_cd = g_c.ptr(y - 1); + break; + case PENALIZATION_PERONA_MALIK: + pG_p = g_p.ptr(y); + pG_pu = g_p.ptr(y + 1); + pG_pd = g_p.ptr(y - 1); + break; + } + for (x = 1; x < width; x++) { + switch (penalization) { + case PENALIZATION_CHARBONNIER: + gc = pG_c[x]; + gl = gc + pG_c[x - 1]; + gr = gc + pG_c[x + 1]; + gu = gc + pG_cu[x]; + gd = gc + pG_cd[x]; + gc = gl + gr + gu + gd; + break; + case PENALIZATION_PERONA_MALIK: + gc = pG_p[x]; + gl = gc + pG_p[x - 1]; + gr = gc + pG_p[x + 1]; + gu = gc + pG_pu[x]; + gd = gc + pG_pd[x]; + gc = gl + gr + gu + gd; + break; + } + + float _fi = Fi; + if (maxDisp > minDisp) { + if (pU[x] > maxDisp * scale) {_fi *= 1000; pU[x] = static_cast(maxDisp * scale);} + if (pU[x] < minDisp * scale) {_fi *= 1000; pU[x] = static_cast(minDisp * scale);} + } + + int A = static_cast(pU[x]); + int neg = 0; if (pU[x] <= 0) neg = -1; + + if (x + A > width) + pu[x] = pU[width - A]; + else if (x + A + neg < 0) + pu[x] = pU[- A + 2]; + else { + pu[x] = A + (pI2x[x + A + neg] * (pI1[x] - pI2[x + A]) + + _fi * (gr * pU[x + 1] + gl * pU[x - 1] + gu * pUu[x] + gd * pUd[x] - gc * A)) + / (pI2x[x + A + neg] * pI2x[x + A + neg] + gc * _fi) ; + } + }// x + pu[0] = pu[1]; + pu[width] = pu[width - 1]; + }// y + for (x = 0; x <= width; x++) { + u.at(0, x) = u.at(1, x); + u.at(height, x) = u.at(height - 1, x); + } + u.copyTo(U); + if (!g_c.empty()) g_c.release(); + if (!g_p.empty()) g_p.release(); + }//n } void StereoVar::VCycle_MyFAS(Mat &I1, Mat &I2, Mat &I2x, Mat &_u, int level) { - CvSize imgSize = _u.size(); - CvSize frmSize = cvSize((int) (imgSize.width * pyrScale + 0.5), (int) (imgSize.height * pyrScale + 0.5)); - Mat I1_h, I2_h, I2x_h, u_h, U, U_h; + CvSize imgSize = _u.size(); + CvSize frmSize = cvSize((int) (imgSize.width * pyrScale + 0.5), (int) (imgSize.height * pyrScale + 0.5)); + Mat I1_h, I2_h, I2x_h, u_h, U, U_h; - //PRE relaxation - VariationalSolver(I1, I2, I2x, _u, level); + //PRE relaxation + VariationalSolver(I1, I2, I2x, _u, level); - if (level >= levels - 1) return; - level ++; + if (level >= levels - 1) return; + level ++; - //scaling DOWN - resize(I1, I1_h, frmSize, 0, 0, INTER_AREA); - resize(I2, I2_h, frmSize, 0, 0, INTER_AREA); - resize(_u, u_h, frmSize, 0, 0, INTER_AREA); - u_h.convertTo(u_h, u_h.type(), pyrScale); - I2x_h = diffX(I2_h); + //scaling DOWN + resize(I1, I1_h, frmSize, 0, 0, INTER_AREA); + resize(I2, I2_h, frmSize, 0, 0, INTER_AREA); + resize(_u, u_h, frmSize, 0, 0, INTER_AREA); + u_h.convertTo(u_h, u_h.type(), pyrScale); + I2x_h = diffX(I2_h); - //Next level - U_h = u_h.clone(); - VCycle_MyFAS(I1_h, I2_h, I2x_h, U_h, level); + //Next level + U_h = u_h.clone(); + VCycle_MyFAS(I1_h, I2_h, I2x_h, U_h, level); - subtract(U_h, u_h, U_h); - U_h.convertTo(U_h, U_h.type(), 1.0 / pyrScale); + subtract(U_h, u_h, U_h); + U_h.convertTo(U_h, U_h.type(), 1.0 / pyrScale); - //scaling UP - resize(U_h, U, imgSize); + //scaling UP + resize(U_h, U, imgSize); - //correcting the solution - add(_u, U, _u); + //correcting the solution + add(_u, U, _u); - //POST relaxation - VariationalSolver(I1, I2, I2x, _u, level - 1); + //POST relaxation + VariationalSolver(I1, I2, I2x, _u, level - 1); - if (flags & USE_MEDIAN_FILTERING) medianBlur(_u, _u, 3); + if (flags & USE_MEDIAN_FILTERING) medianBlur(_u, _u, 3); - I1_h.release(); - I2_h.release(); - I2x_h.release(); - u_h.release(); - U.release(); - U_h.release(); + I1_h.release(); + I2_h.release(); + I2x_h.release(); + u_h.release(); + U.release(); + U_h.release(); } void StereoVar::FMG(Mat &I1, Mat &I2, Mat &I2x, Mat &u, int level) { - double scale = pow(pyrScale, (double) level); - CvSize frmSize = cvSize((int) (u.cols * scale + 0.5), (int) (u.rows * scale + 0.5)); - Mat I1_h, I2_h, I2x_h, u_h; - - //scaling DOWN - resize(I1, I1_h, frmSize, 0, 0, INTER_AREA); - resize(I2, I2_h, frmSize, 0, 0, INTER_AREA); - resize(u, u_h, frmSize, 0, 0, INTER_AREA); - u_h.convertTo(u_h, u_h.type(), scale); - I2x_h = diffX(I2_h); - - switch (cycle) { - case CYCLE_O: - VariationalSolver(I1_h, I2_h, I2x_h, u_h, level); - break; - case CYCLE_V: - VCycle_MyFAS(I1_h, I2_h, I2x_h, u_h, level); - break; - } - - u_h.convertTo(u_h, u_h.type(), 1.0 / scale); - - //scaling UP - resize(u_h, u, u.size(), 0, 0, INTER_CUBIC); - - I1_h.release(); - I2_h.release(); - I2x_h.release(); - u_h.release(); - - level--; - if ((flags & USE_AUTO_PARAMS) && (level < levels / 3)) { - penalization = PENALIZATION_PERONA_MALIK; - fi *= 100; - flags -= USE_AUTO_PARAMS; - autoParams(); - } - if (flags & USE_MEDIAN_FILTERING) medianBlur(u, u, 3); - if (level >= 0) FMG(I1, I2, I2x, u, level); + double scale = pow(pyrScale, (double) level); + CvSize frmSize = cvSize((int) (u.cols * scale + 0.5), (int) (u.rows * scale + 0.5)); + Mat I1_h, I2_h, I2x_h, u_h; + + //scaling DOWN + resize(I1, I1_h, frmSize, 0, 0, INTER_AREA); + resize(I2, I2_h, frmSize, 0, 0, INTER_AREA); + resize(u, u_h, frmSize, 0, 0, INTER_AREA); + u_h.convertTo(u_h, u_h.type(), scale); + I2x_h = diffX(I2_h); + + switch (cycle) { + case CYCLE_O: + VariationalSolver(I1_h, I2_h, I2x_h, u_h, level); + break; + case CYCLE_V: + VCycle_MyFAS(I1_h, I2_h, I2x_h, u_h, level); + break; + } + + u_h.convertTo(u_h, u_h.type(), 1.0 / scale); + + //scaling UP + resize(u_h, u, u.size(), 0, 0, INTER_CUBIC); + + I1_h.release(); + I2_h.release(); + I2x_h.release(); + u_h.release(); + + level--; + if ((flags & USE_AUTO_PARAMS) && (level < levels / 3)) { + penalization = PENALIZATION_PERONA_MALIK; + fi *= 100; + flags -= USE_AUTO_PARAMS; + autoParams(); + } + if (flags & USE_MEDIAN_FILTERING) medianBlur(u, u, 3); + if (level >= 0) FMG(I1, I2, I2x, u, level); } void StereoVar::autoParams() -{ - int maxD = MAX(labs(maxDisp), labs(minDisp)); - - if (!maxD) pyrScale = 0.85; - else if (maxD < 8) pyrScale = 0.5; - else if (maxD < 64) pyrScale = 0.5 + static_cast(maxD - 8) * 0.00625; - else pyrScale = 0.85; - - if (maxD) { - levels = 0; - while ( pow(pyrScale, levels) * maxD > 1.5) levels ++; - levels++; - } - - switch(penalization) { - case PENALIZATION_TICHONOV: cycle = CYCLE_V; break; - case PENALIZATION_CHARBONNIER: cycle = CYCLE_O; break; - case PENALIZATION_PERONA_MALIK: cycle = CYCLE_O; break; - } +{ + int maxD = MAX(labs(maxDisp), labs(minDisp)); + + if (!maxD) pyrScale = 0.85; + else if (maxD < 8) pyrScale = 0.5; + else if (maxD < 64) pyrScale = 0.5 + static_cast(maxD - 8) * 0.00625; + else pyrScale = 0.85; + + if (maxD) { + levels = 0; + while ( pow(pyrScale, levels) * maxD > 1.5) levels ++; + levels++; + } + + switch(penalization) { + case PENALIZATION_TICHONOV: cycle = CYCLE_V; break; + case PENALIZATION_CHARBONNIER: cycle = CYCLE_O; break; + case PENALIZATION_PERONA_MALIK: cycle = CYCLE_O; break; + } } void StereoVar::operator ()( const Mat& left, const Mat& right, Mat& disp ) { - CV_Assert(left.size() == right.size() && left.type() == right.type()); - CvSize imgSize = left.size(); - int MaxD = MAX(labs(minDisp), labs(maxDisp)); - int SignD = 1; if (MIN(minDisp, maxDisp) < 0) SignD = -1; - if (minDisp >= maxDisp) {MaxD = 256; SignD = 1;} - - Mat u; - if ((flags & USE_INITIAL_DISPARITY) && (!disp.empty())) { - CV_Assert(disp.size() == left.size() && disp.type() == CV_8UC1); - disp.convertTo(u, CV_32FC1, static_cast(SignD * MaxD) / 256); - } else { - u.create(imgSize, CV_32FC1); - u.setTo(0); - } - - // Preprocessing - Mat leftgray, rightgray; - if (left.type() != CV_8UC1) { - cvtColor(left, leftgray, CV_BGR2GRAY); - cvtColor(right, rightgray, CV_BGR2GRAY); - } else { - left.copyTo(leftgray); - right.copyTo(rightgray); - } - if (flags & USE_EQUALIZE_HIST) { - equalizeHist(leftgray, leftgray); - equalizeHist(rightgray, rightgray); - } - if (poly_sigma > 0.0001) { - GaussianBlur(leftgray, leftgray, cvSize(poly_n, poly_n), poly_sigma); - GaussianBlur(rightgray, rightgray, cvSize(poly_n, poly_n), poly_sigma); - } - - if (flags & USE_AUTO_PARAMS) { - penalization = PENALIZATION_TICHONOV; - autoParams(); - } - - Mat I1, I2; - leftgray.convertTo(I1, CV_32FC1); - rightgray.convertTo(I2, CV_32FC1); - leftgray.release(); - rightgray.release(); - - Mat I2x = diffX(I2); - - FMG(I1, I2, I2x, u, levels - 1); - - I1.release(); - I2.release(); - I2x.release(); - - - disp.create( left.size(), CV_8UC1 ); - u = abs(u); - u.convertTo(disp, disp.type(), 256 / MaxD, 0); - - u.release(); + CV_Assert(left.size() == right.size() && left.type() == right.type()); + CvSize imgSize = left.size(); + int MaxD = MAX(labs(minDisp), labs(maxDisp)); + int SignD = 1; if (MIN(minDisp, maxDisp) < 0) SignD = -1; + if (minDisp >= maxDisp) {MaxD = 256; SignD = 1;} + + Mat u; + if ((flags & USE_INITIAL_DISPARITY) && (!disp.empty())) { + CV_Assert(disp.size() == left.size() && disp.type() == CV_8UC1); + disp.convertTo(u, CV_32FC1, static_cast(SignD * MaxD) / 256); + } else { + u.create(imgSize, CV_32FC1); + u.setTo(0); + } + + // Preprocessing + Mat leftgray, rightgray; + if (left.type() != CV_8UC1) { + cvtColor(left, leftgray, CV_BGR2GRAY); + cvtColor(right, rightgray, CV_BGR2GRAY); + } else { + left.copyTo(leftgray); + right.copyTo(rightgray); + } + if (flags & USE_EQUALIZE_HIST) { + equalizeHist(leftgray, leftgray); + equalizeHist(rightgray, rightgray); + } + if (poly_sigma > 0.0001) { + GaussianBlur(leftgray, leftgray, cvSize(poly_n, poly_n), poly_sigma); + GaussianBlur(rightgray, rightgray, cvSize(poly_n, poly_n), poly_sigma); + } + + if (flags & USE_AUTO_PARAMS) { + penalization = PENALIZATION_TICHONOV; + autoParams(); + } + + Mat I1, I2; + leftgray.convertTo(I1, CV_32FC1); + rightgray.convertTo(I2, CV_32FC1); + leftgray.release(); + rightgray.release(); + + Mat I2x = diffX(I2); + + FMG(I1, I2, I2x, u, levels - 1); + + I1.release(); + I2.release(); + I2x.release(); + + + disp.create( left.size(), CV_8UC1 ); + u = abs(u); + u.convertTo(disp, disp.type(), 256 / MaxD, 0); + + u.release(); } } // namespace \ No newline at end of file diff --git a/modules/contrib/test/test_precomp.hpp b/modules/contrib/test/test_precomp.hpp index 2536ead..f9cbc91 100644 --- a/modules/contrib/test/test_precomp.hpp +++ b/modules/contrib/test/test_precomp.hpp @@ -1,3 +1,7 @@ +#ifdef __GNUC__ +# pragma GCC diagnostic ignored "-Wmissing-declarations" +#endif + #ifndef __OPENCV_TEST_PRECOMP_HPP__ #define __OPENCV_TEST_PRECOMP_HPP__ diff --git a/modules/core/CMakeLists.txt b/modules/core/CMakeLists.txt index de9c846..644e10e 100644 --- a/modules/core/CMakeLists.txt +++ b/modules/core/CMakeLists.txt @@ -5,10 +5,11 @@ ocv_module_include_directories(${ZLIB_INCLUDE_DIR}) if(HAVE_CUDA) file(GLOB lib_cuda "src/cuda/*.cu") source_group("Cuda" FILES "${lib_cuda}") - - ocv_include_directories(${CUDA_INCLUDE_DIRS} "${OpenCV_SOURCE_DIR}/modules/gpu/src" "${OpenCV_SOURCE_DIR}/modules/gpu/src/cuda") - OCV_CUDA_COMPILE(cuda_objs ${lib_cuda}) - + + ocv_include_directories("${OpenCV_SOURCE_DIR}/modules/gpu/src" "${OpenCV_SOURCE_DIR}/modules/gpu/src/cuda" ${CUDA_INCLUDE_DIRS}) + ocv_warnings_disable(CMAKE_CXX_FLAGS -Wundef) + ocv_cuda_compile(cuda_objs ${lib_cuda}) + set(cuda_link_libs ${CUDA_LIBRARIES} ${CUDA_npp_LIBRARY}) else() set(lib_cuda "") diff --git a/modules/core/include/opencv2/core/core.hpp b/modules/core/include/opencv2/core/core.hpp index 821e4d1..469186a 100644 --- a/modules/core/include/opencv2/core/core.hpp +++ b/modules/core/include/opencv2/core/core.hpp @@ -1299,6 +1299,7 @@ public: GPU_MAT = 9 << KIND_SHIFT }; _InputArray(); + _InputArray(const Mat& m); _InputArray(const MatExpr& expr); template _InputArray(const _Tp* vec, int n); @@ -1328,6 +1329,10 @@ public: virtual int channels(int i=-1) const; virtual bool empty() const; +#ifdef OPENCV_CAN_BREAK_BINARY_COMPATIBILITY + virtual ~_InputArray(); +#endif + int flags; void* obj; Size sz; @@ -1384,6 +1389,10 @@ public: virtual void create(int dims, const int* size, int type, int i=-1, bool allowTransposed=false, int fixedDepthMask=0) const; virtual void release() const; virtual void clear() const; + +#ifdef OPENCV_CAN_BREAK_BINARY_COMPATIBILITY + virtual ~_OutputArray(); +#endif }; typedef const _InputArray& InputArray; @@ -3970,7 +3979,7 @@ public: CV_WRAP virtual bool isOpened() const; //! closes the file and releases all the memory buffers CV_WRAP virtual void release(); - //! closes the file, releases all the memory buffers and returns the text string + //! closes the file, releases all the memory buffers and returns the text string CV_WRAP string releaseAndGetString(); //! returns the first element of the top-level mapping diff --git a/modules/core/include/opencv2/core/core_c.h b/modules/core/include/opencv2/core/core_c.h index 6806821..df763ab 100644 --- a/modules/core/include/opencv2/core/core_c.h +++ b/modules/core/include/opencv2/core/core_c.h @@ -666,7 +666,7 @@ CVAPI(int) cvSolveCubic( const CvMat* coeffs, CvMat* roots ); /* Finds all real and complex roots of a polynomial equation */ CVAPI(void) cvSolvePoly(const CvMat* coeffs, CvMat *roots2, - int maxiter CV_DEFAULT(20), int fig CV_DEFAULT(100)); + int maxiter CV_DEFAULT(20), int fig CV_DEFAULT(100)); /****************************************************************************************\ * Matrix operations * @@ -1127,9 +1127,9 @@ CVAPI(void) cvSetRemove( CvSet* set_header, int index ); /* Returns a set element by index. If the element doesn't belong to the set, NULL is returned */ -CV_INLINE CvSetElem* cvGetSetElem( const CvSet* set_header, int index ) +CV_INLINE CvSetElem* cvGetSetElem( const CvSet* set_header, int idx ) { - CvSetElem* elem = (CvSetElem*)cvGetSeqElem( (CvSeq*)set_header, index ); + CvSetElem* elem = (CvSetElem*)cvGetSeqElem( (CvSeq*)set_header, idx ); return elem && CV_IS_SET_ELEM( elem ) ? elem : 0; } @@ -1283,8 +1283,8 @@ CVAPI(void) cvRectangleR( CvArr* img, CvRect r, CvScalar color, int thickness CV_DEFAULT(1), int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0)); - - + + /* Draws a circle with specified center and radius. Thickness works in the same way as with cvRectangle */ CVAPI(void) cvCircle( CvArr* img, CvPoint center, int radius, @@ -1374,17 +1374,17 @@ CVAPI(int) cvInitLineIterator( const CvArr* image, CvPoint pt1, CvPoint pt2, /* Font structure */ typedef struct CvFont { - const char* nameFont; //Qt:nameFont - CvScalar color; //Qt:ColorFont -> cvScalar(blue_component, green_component, red\_component[, alpha_component]) - int font_face; //Qt: bool italic /* =CV_FONT_* */ - const int* ascii; /* font data and metrics */ + const char* nameFont; //Qt:nameFont + CvScalar color; //Qt:ColorFont -> cvScalar(blue_component, green_component, red\_component[, alpha_component]) + int font_face; //Qt: bool italic /* =CV_FONT_* */ + const int* ascii; /* font data and metrics */ const int* greek; const int* cyrillic; float hscale, vscale; - float shear; /* slope coefficient: 0 - normal, >0 - italic */ - int thickness; //Qt: weight /* letters thickness */ - float dx; /* horizontal interval between letters */ - int line_type; //Qt: PointSize + float shear; /* slope coefficient: 0 - normal, >0 - italic */ + int thickness; //Qt: weight /* letters thickness */ + float dx; /* horizontal interval between letters */ + int line_type; //Qt: PointSize } CvFont; @@ -1696,7 +1696,7 @@ CVAPI(double) cvGetTickFrequency( void ); /*********************************** CPU capabilities ***********************************/ -#define CV_CPU_NONE 0 +#define CV_CPU_NONE 0 #define CV_CPU_MMX 1 #define CV_CPU_SSE 2 #define CV_CPU_SSE2 3 @@ -1718,9 +1718,9 @@ CVAPI(void) cvSetNumThreads( int threads CV_DEFAULT(0) ); /* get index of the thread being executed */ CVAPI(int) cvGetThreadNum( void ); - + /********************************** Error Handling **************************************/ - + /* Get current OpenCV error status */ CVAPI(int) cvGetErrStatus( void ); @@ -1774,37 +1774,37 @@ CVAPI(int) cvStdErrReport( int status, const char* func_name, const char* err_ms const char* file_name, int line, void* userdata ); CVAPI(int) cvGuiBoxReport( int status, const char* func_name, const char* err_msg, - const char* file_name, int line, void* userdata ); - + const char* file_name, int line, void* userdata ); + #define OPENCV_ERROR(status,func,context) \ cvError((status),(func),(context),__FILE__,__LINE__) - + #define OPENCV_ERRCHK(func,context) \ {if (cvGetErrStatus() >= 0) \ {OPENCV_ERROR(CV_StsBackTrace,(func),(context));}} - + #define OPENCV_ASSERT(expr,func,context) \ {if (! (expr)) \ {OPENCV_ERROR(CV_StsInternal,(func),(context));}} - + #define OPENCV_RSTERR() (cvSetErrStatus(CV_StsOk)) - + #define OPENCV_CALL( Func ) \ { \ Func; \ -} - - +} + + /* CV_FUNCNAME macro defines icvFuncName constant which is used by CV_ERROR macro */ #ifdef CV_NO_FUNC_NAMES #define CV_FUNCNAME( Name ) #define cvFuncName "" -#else +#else #define CV_FUNCNAME( Name ) \ static char cvFuncName[] = Name #endif - - + + /* CV_ERROR macro unconditionally raises error with passed code and message. After raising error, control will be transferred to the exit label. @@ -1814,11 +1814,11 @@ static char cvFuncName[] = Name cvError( (Code), cvFuncName, Msg, __FILE__, __LINE__ ); \ __CV_EXIT__; \ } - + /* Simplified form of CV_ERROR */ #define CV_ERROR_FROM_CODE( code ) \ CV_ERROR( code, "" ) - + /* CV_CHECK macro checks error status after CV (or IPL) function call. If error detected, control will be transferred to the exit @@ -1829,8 +1829,8 @@ static char cvFuncName[] = Name if( cvGetErrStatus() < 0 ) \ CV_ERROR( CV_StsBackTrace, "Inner function failed." ); \ } - - + + /* CV_CALL macro calls CV (or IPL) function, checks error status and signals a error if the function failed. Useful in "parent node" @@ -1841,19 +1841,19 @@ static char cvFuncName[] = Name Func; \ CV_CHECK(); \ } - - + + /* Runtime assertion macro */ #define CV_ASSERT( Condition ) \ { \ if( !(Condition) ) \ CV_ERROR( CV_StsInternal, "Assertion: " #Condition " failed" ); \ } - + #define __CV_BEGIN__ { #define __CV_END__ goto exit; exit: ; } -#define __CV_EXIT__ goto exit - +#define __CV_EXIT__ goto exit + #ifdef __cplusplus } diff --git a/modules/core/include/opencv2/core/eigen.hpp b/modules/core/include/opencv2/core/eigen.hpp index dbaa9fc..5a7efe2 100644 --- a/modules/core/include/opencv2/core/eigen.hpp +++ b/modules/core/include/opencv2/core/eigen.hpp @@ -48,6 +48,11 @@ #include "opencv2/core/core_c.h" #include "opencv2/core/core.hpp" +#if defined _MSC_VER && _MSC_VER >= 1200 +#pragma warning( disable: 4714 ) //__forceinline is not inlined +#pragma warning( disable: 4127 ) //conditional expression is constant +#endif + namespace cv { diff --git a/modules/core/include/opencv2/core/gpumat.hpp b/modules/core/include/opencv2/core/gpumat.hpp index fda6990..ef86c5a 100644 --- a/modules/core/include/opencv2/core/gpumat.hpp +++ b/modules/core/include/opencv2/core/gpumat.hpp @@ -366,12 +366,12 @@ namespace cv { namespace gpu return m; } - inline void GpuMat::assignTo(GpuMat& m, int type) const + inline void GpuMat::assignTo(GpuMat& m, int _type) const { - if (type < 0) + if (_type < 0) m = *this; else - convertTo(m, type); + convertTo(m, _type); } inline size_t GpuMat::step1() const @@ -434,9 +434,9 @@ namespace cv { namespace gpu create(size_.height, size_.width, type_); } - inline GpuMat GpuMat::operator()(Range rowRange, Range colRange) const + inline GpuMat GpuMat::operator()(Range _rowRange, Range _colRange) const { - return GpuMat(*this, rowRange, colRange); + return GpuMat(*this, _rowRange, _colRange); } inline GpuMat GpuMat::operator()(Rect roi) const diff --git a/modules/core/include/opencv2/core/internal.hpp b/modules/core/include/opencv2/core/internal.hpp index d0b3cd4..39f8292 100644 --- a/modules/core/include/opencv2/core/internal.hpp +++ b/modules/core/include/opencv2/core/internal.hpp @@ -60,34 +60,34 @@ #endif #if defined WIN32 || defined WINCE -#ifndef _WIN32_WINNT // This is needed for the declaration of TryEnterCriticalSection in winbase.h with Visual Studio 2005 (and older?) -#define _WIN32_WINNT 0x0400 // http://msdn.microsoft.com/en-us/library/ms686857(VS.85).aspx -#endif -#include -#undef small -#undef min -#undef max +# ifndef _WIN32_WINNT // This is needed for the declaration of TryEnterCriticalSection in winbase.h with Visual Studio 2005 (and older?) +# define _WIN32_WINNT 0x0400 // http://msdn.microsoft.com/en-us/library/ms686857(VS.85).aspx +# endif +# include +# undef small +# undef min +# undef max #else -#include +# include #endif #ifdef __BORLANDC__ -#ifndef WIN32 - #define WIN32 -#endif -#ifndef _WIN32 - #define _WIN32 -#endif - #define CV_DLL - #undef _CV_ALWAYS_PROFILE_ - #define _CV_ALWAYS_NO_PROFILE_ +# ifndef WIN32 +# define WIN32 +# endif +# ifndef _WIN32 +# define _WIN32 +# endif +# define CV_DLL +# undef _CV_ALWAYS_PROFILE_ +# define _CV_ALWAYS_NO_PROFILE_ #endif #ifndef FALSE -#define FALSE 0 +# define FALSE 0 #endif #ifndef TRUE -#define TRUE 1 +# define TRUE 1 #endif #define __BEGIN__ __CV_BEGIN__ @@ -95,7 +95,7 @@ #define EXIT __CV_EXIT__ #ifdef HAVE_IPP -#include "ipp.h" +# include "ipp.h" CV_INLINE IppiSize ippiSize(int width, int height) { @@ -104,137 +104,132 @@ CV_INLINE IppiSize ippiSize(int width, int height) } #endif -#if defined __SSE2__ || _MSC_VER >= 1300 -#include "emmintrin.h" -#define CV_SSE 1 -#define CV_SSE2 1 -#if defined __SSE3__ || _MSC_VER >= 1500 -#include "pmmintrin.h" -#define CV_SSE3 1 -#endif -#if defined __SSSE3__ -#include "tmmintrin.h" -#define CV_SSSE3 1 -#endif +#if defined __SSE2__ || (defined _MSC_VER && _MSC_VER >= 1300) +# include "emmintrin.h" +# define CV_SSE 1 +# define CV_SSE2 1 +# if defined __SSE3__ || (defined _MSC_VER && _MSC_VER >= 1500) +# include "pmmintrin.h" +# define CV_SSE3 1 +# else +# define CV_SSE3 0 +# endif +# if defined __SSSE3__ +# include "tmmintrin.h" +# define CV_SSSE3 1 +# else +# define CV_SSSE3 0 +# endif #else -#define CV_SSE 0 -#define CV_SSE2 0 -#define CV_SSE3 0 -#define CV_SSSE3 0 +# define CV_SSE 0 +# define CV_SSE2 0 +# define CV_SSE3 0 +# define CV_SSSE3 0 #endif -#if defined ANDROID && defined __ARM_NEON__ && defined __GNUC__ -#include "arm_neon.h" -#define CV_NEON 1 +#if defined ANDROID && defined __ARM_NEON__ +# include "arm_neon.h" +# define CV_NEON 1 -#define CPU_HAS_NEON_FEATURE (true) +# define CPU_HAS_NEON_FEATURE (true) //TODO: make real check using stuff from "cpu-features.h" //((bool)android_getCpuFeatures() & ANDROID_CPU_ARM_FEATURE_NEON) #else -#define CV_NEON 0 -#define CPU_HAS_NEON_FEATURE (false) -#endif - -#ifdef CV_ICC -#define CV_ENABLE_UNROLLED 0 -#else -#define CV_ENABLE_UNROLLED 1 +# define CV_NEON 0 +# define CPU_HAS_NEON_FEATURE (false) #endif #ifndef IPPI_CALL -#define IPPI_CALL(func) CV_Assert((func) >= 0) +# define IPPI_CALL(func) CV_Assert((func) >= 0) #endif #ifdef HAVE_TBB - #include "tbb/tbb_stddef.h" - #if TBB_VERSION_MAJOR*100 + TBB_VERSION_MINOR >= 202 - #include "tbb/tbb.h" - #include "tbb/task.h" - #undef min - #undef max - #else - #undef HAVE_TBB - #endif +# include "tbb/tbb_stddef.h" +# if TBB_VERSION_MAJOR*100 + TBB_VERSION_MINOR >= 202 +# include "tbb/tbb.h" +# include "tbb/task.h" +# undef min +# undef max +# else +# undef HAVE_TBB +# endif #endif #ifdef HAVE_EIGEN - #include - #include "opencv2/core/eigen.hpp" +# include +# include "opencv2/core/eigen.hpp" #endif #ifdef __cplusplus +namespace cv +{ #ifdef HAVE_TBB - namespace cv + + typedef tbb::blocked_range BlockedRange; + + template static inline + void parallel_for( const BlockedRange& range, const Body& body ) { - typedef tbb::blocked_range BlockedRange; - - template static inline - void parallel_for( const BlockedRange& range, const Body& body ) - { - tbb::parallel_for(range, body); - } - - template static inline - void parallel_do( Iterator first, Iterator last, const Body& body ) - { - tbb::parallel_do(first, last, body); - } - - typedef tbb::split Split; - - template static inline - void parallel_reduce( const BlockedRange& range, Body& body ) - { - tbb::parallel_reduce(range, body); - } - - typedef tbb::concurrent_vector ConcurrentRectVector; - typedef tbb::concurrent_vector ConcurrentDoubleVector; + tbb::parallel_for(range, body); } + + template static inline + void parallel_do( Iterator first, Iterator last, const Body& body ) + { + tbb::parallel_do(first, last, body); + } + + typedef tbb::split Split; + + template static inline + void parallel_reduce( const BlockedRange& range, Body& body ) + { + tbb::parallel_reduce(range, body); + } + + typedef tbb::concurrent_vector ConcurrentRectVector; + typedef tbb::concurrent_vector ConcurrentDoubleVector; #else - namespace cv + class BlockedRange + { + public: + BlockedRange() : _begin(0), _end(0), _grainsize(0) {} + BlockedRange(int b, int e, int g=1) : _begin(b), _end(e), _grainsize(g) {} + int begin() const { return _begin; } + int end() const { return _end; } + int grainsize() const { return _grainsize; } + + protected: + int _begin, _end, _grainsize; + }; + + template static inline + void parallel_for( const BlockedRange& range, const Body& body ) { - class BlockedRange - { - public: - BlockedRange() : _begin(0), _end(0), _grainsize(0) {} - BlockedRange(int b, int e, int g=1) : _begin(b), _end(e), _grainsize(g) {} - int begin() const { return _begin; } - int end() const { return _end; } - int grainsize() const { return _grainsize; } - - protected: - int _begin, _end, _grainsize; - }; - - template static inline - void parallel_for( const BlockedRange& range, const Body& body ) - { - body(range); - } - typedef std::vector ConcurrentRectVector; - typedef std::vector ConcurrentDoubleVector; - - template static inline - void parallel_do( Iterator first, Iterator last, const Body& body ) - { - for( ; first != last; ++first ) - body(*first); - } - - class Split {}; - - template static inline - void parallel_reduce( const BlockedRange& range, Body& body ) - { - body(range); - } - + body(range); + } + typedef std::vector ConcurrentRectVector; + typedef std::vector ConcurrentDoubleVector; + + template static inline + void parallel_do( Iterator first, Iterator last, const Body& body ) + { + for( ; first != last; ++first ) + body(*first); + } + + class Split {}; + + template static inline + void parallel_reduce( const BlockedRange& range, Body& body ) + { + body(range); } #endif +} //namespace cv - #define CV_INIT_ALGORITHM(classname, algname, memberinit) \ +#define CV_INIT_ALGORITHM(classname, algname, memberinit) \ static Algorithm* create##classname() \ { \ return new classname; \ @@ -261,7 +256,7 @@ CV_INLINE IppiSize ippiSize(int width, int height) return &classname##_info(); \ } -#endif +#endif //__cplusplus /* maximal size of vector to run matrix operations on it inline (i.e. w/o ipp calls) */ #define CV_MAX_INLINE_MAT_OP_SIZE 10 @@ -305,9 +300,9 @@ CV_INLINE IppiSize ippiSize(int width, int height) #define CV_MAX_STRLEN 1024 #if 0 /*def CV_CHECK_FOR_NANS*/ - #define CV_CHECK_NANS( arr ) cvCheckArray((arr)) +# define CV_CHECK_NANS( arr ) cvCheckArray((arr)) #else - #define CV_CHECK_NANS( arr ) +# define CV_CHECK_NANS( arr ) #endif /****************************************************************************************\ @@ -316,38 +311,38 @@ CV_INLINE IppiSize ippiSize(int width, int height) /* get alloca declaration */ #ifdef __GNUC__ - #undef alloca - #define alloca __builtin_alloca - #define CV_HAVE_ALLOCA 1 +# undef alloca +# define alloca __builtin_alloca +# define CV_HAVE_ALLOCA 1 #elif defined WIN32 || defined _WIN32 || \ defined WINCE || defined _MSC_VER || defined __BORLANDC__ - #include - #define CV_HAVE_ALLOCA 1 +# include +# define CV_HAVE_ALLOCA 1 #elif defined HAVE_ALLOCA_H - #include - #define CV_HAVE_ALLOCA 1 +# include +# define CV_HAVE_ALLOCA 1 #elif defined HAVE_ALLOCA - #include - #define CV_HAVE_ALLOCA 1 +# include +# define CV_HAVE_ALLOCA 1 #else - #undef CV_HAVE_ALLOCA +# undef CV_HAVE_ALLOCA #endif #ifdef __GNUC__ -#define CV_DECL_ALIGNED(x) __attribute__ ((aligned (x))) +# define CV_DECL_ALIGNED(x) __attribute__ ((aligned (x))) #elif defined _MSC_VER -#define CV_DECL_ALIGNED(x) __declspec(align(x)) +# define CV_DECL_ALIGNED(x) __declspec(align(x)) #else -#define CV_DECL_ALIGNED(x) +# define CV_DECL_ALIGNED(x) #endif #if CV_HAVE_ALLOCA /* ! DO NOT make it an inline function */ -#define cvStackAlloc(size) cvAlignPtr( alloca((size) + CV_MALLOC_ALIGN), CV_MALLOC_ALIGN ) +# define cvStackAlloc(size) cvAlignPtr( alloca((size) + CV_MALLOC_ALIGN), CV_MALLOC_ALIGN ) #endif #ifndef CV_IMPL -#define CV_IMPL CV_EXTERN_C +# define CV_IMPL CV_EXTERN_C #endif #define CV_DBG_BREAK() { volatile int* crashMe = 0; *crashMe = 0; } @@ -687,25 +682,25 @@ typedef enum CvStatus CV_UNSUPPORTED_DEPTH_ERR = -101, CV_UNSUPPORTED_FORMAT_ERR = -100, - CV_BADARG_ERR = -49, //ipp comp - CV_NOTDEFINED_ERR = -48, //ipp comp - - CV_BADCHANNELS_ERR = -47, //ipp comp - CV_BADRANGE_ERR = -44, //ipp comp - CV_BADSTEP_ERR = -29, //ipp comp - - CV_BADFLAG_ERR = -12, - CV_DIV_BY_ZERO_ERR = -11, //ipp comp - CV_BADCOEF_ERR = -10, - - CV_BADFACTOR_ERR = -7, - CV_BADPOINT_ERR = -6, - CV_BADSCALE_ERR = -4, - CV_OUTOFMEM_ERR = -3, - CV_NULLPTR_ERR = -2, - CV_BADSIZE_ERR = -1, - CV_NO_ERR = 0, - CV_OK = CV_NO_ERR + CV_BADARG_ERR = -49, //ipp comp + CV_NOTDEFINED_ERR = -48, //ipp comp + + CV_BADCHANNELS_ERR = -47, //ipp comp + CV_BADRANGE_ERR = -44, //ipp comp + CV_BADSTEP_ERR = -29, //ipp comp + + CV_BADFLAG_ERR = -12, + CV_DIV_BY_ZERO_ERR = -11, //ipp comp + CV_BADCOEF_ERR = -10, + + CV_BADFACTOR_ERR = -7, + CV_BADPOINT_ERR = -6, + CV_BADSCALE_ERR = -4, + CV_OUTOFMEM_ERR = -3, + CV_NULLPTR_ERR = -2, + CV_BADSIZE_ERR = -1, + CV_NO_ERR = 0, + CV_OK = CV_NO_ERR } CvStatus; @@ -720,8 +715,7 @@ CvFuncTable; typedef struct CvBigFuncTable { void* fn_2d[CV_DEPTH_MAX*4]; -} -CvBigFuncTable; +} CvBigFuncTable; #define CV_INIT_FUNC_TAB( tab, FUNCNAME, FLAG ) \ (tab).fn_2d[CV_8U] = (void*)FUNCNAME##_8u##FLAG; \ @@ -732,13 +726,14 @@ CvBigFuncTable; (tab).fn_2d[CV_32F] = (void*)FUNCNAME##_32f##FLAG; \ (tab).fn_2d[CV_64F] = (void*)FUNCNAME##_64f##FLAG +#ifdef __cplusplus //! OpenGL extension table class CV_EXPORTS CvOpenGlFuncTab { public: virtual ~CvOpenGlFuncTab(); - virtual void genBuffers(int n, unsigned int* buffers) const = 0; + virtual void genBuffers(int n, unsigned int* buffers) const = 0; virtual void deleteBuffers(int n, const unsigned int* buffers) const = 0; virtual void bufferData(unsigned int target, ptrdiff_t size, const void* data, unsigned int usage) const = 0; @@ -764,4 +759,6 @@ CV_EXPORTS bool icvCheckGlError(const char* file, const int line, const char* fu #define CV_CheckGlError() CV_DbgAssert( (::icvCheckGlError(__FILE__, __LINE__)) ) #endif -#endif +#endif //__cplusplus + +#endif // __OPENCV_CORE_INTERNAL_HPP__ diff --git a/modules/core/include/opencv2/core/mat.hpp b/modules/core/include/opencv2/core/mat.hpp index 6b86cd7..1c79f50 100644 --- a/modules/core/include/opencv2/core/mat.hpp +++ b/modules/core/include/opencv2/core/mat.hpp @@ -63,7 +63,7 @@ inline void Mat::initEmpty() refcount = 0; allocator = 0; } - + inline Mat::Mat() : size(&rows) { initEmpty(); @@ -87,14 +87,14 @@ inline Mat::Mat(Size _sz, int _type) : size(&rows) initEmpty(); create( _sz.height, _sz.width, _type ); } - + inline Mat::Mat(Size _sz, int _type, const Scalar& _s) : size(&rows) { initEmpty(); create(_sz.height, _sz.width, _type); *this = _s; } - + inline Mat::Mat(int _dims, const int* _sz, int _type) : size(&rows) { initEmpty(); @@ -106,7 +106,7 @@ inline Mat::Mat(int _dims, const int* _sz, int _type, const Scalar& _s) : size(& initEmpty(); create(_dims, _sz, _type); *this = _s; -} +} inline Mat::Mat(const Mat& m) : flags(m.flags), dims(m.dims), rows(m.rows), cols(m.cols), data(m.data), @@ -187,8 +187,8 @@ template inline Mat::Mat(const vector<_Tp>& vec, bool copyData) else Mat((int)vec.size(), 1, DataType<_Tp>::type, (uchar*)&vec[0]).copyTo(*this); } - - + + template inline Mat::Mat(const Vec<_Tp, n>& vec, bool copyData) : flags(MAGIC_VAL | DataType<_Tp>::type | CV_MAT_CONT_FLAG), dims(2), rows(n), cols(1), data(0), refcount(0), @@ -218,10 +218,10 @@ template inline Mat::Mat(const Matx<_Tp,m,n>& M, boo datalimit = dataend = datastart + rows*step[0]; } else - Mat(m, n, DataType<_Tp>::type, (uchar*)M.val).copyTo(*this); + Mat(m, n, DataType<_Tp>::type, (uchar*)M.val).copyTo(*this); } - + template inline Mat::Mat(const Point_<_Tp>& pt, bool copyData) : flags(MAGIC_VAL | DataType<_Tp>::type | CV_MAT_CONT_FLAG), dims(2), rows(2), cols(1), data(0), refcount(0), @@ -240,7 +240,7 @@ template inline Mat::Mat(const Point_<_Tp>& pt, bool copyData) ((_Tp*)data)[1] = pt.y; } } - + template inline Mat::Mat(const Point3_<_Tp>& pt, bool copyData) : flags(MAGIC_VAL | DataType<_Tp>::type | CV_MAT_CONT_FLAG), @@ -262,7 +262,7 @@ template inline Mat::Mat(const Point3_<_Tp>& pt, bool copyData) } } - + template inline Mat::Mat(const MatCommaInitializer_<_Tp>& commaInitializer) : flags(MAGIC_VAL | DataType<_Tp>::type | CV_MAT_CONT_FLAG), dims(0), rows(0), cols(0), data(0), refcount(0), @@ -270,7 +270,7 @@ template inline Mat::Mat(const MatCommaInitializer_<_Tp>& commaIni { *this = *commaInitializer; } - + inline Mat::~Mat() { release(); @@ -305,7 +305,7 @@ inline Mat& Mat::operator = (const Mat& m) } return *this; } - + inline Mat Mat::row(int y) const { return Mat(*this, Range(y, y+1), Range::all()); } inline Mat Mat::col(int x) const { return Mat(*this, Range::all(), Range(x, x+1)); } inline Mat Mat::rowRange(int startrow, int endrow) const @@ -336,12 +336,12 @@ inline Mat Mat::clone() const return m; } -inline void Mat::assignTo( Mat& m, int type ) const +inline void Mat::assignTo( Mat& m, int _type ) const { - if( type < 0 ) + if( _type < 0 ) m = *this; else - convertTo(m, type); + convertTo(m, _type); } inline void Mat::create(int _rows, int _cols, int _type) @@ -370,19 +370,19 @@ inline void Mat::release() refcount = 0; } -inline Mat Mat::operator()( Range rowRange, Range colRange ) const +inline Mat Mat::operator()( Range _rowRange, Range _colRange ) const { - return Mat(*this, rowRange, colRange); + return Mat(*this, _rowRange, _colRange); } - + inline Mat Mat::operator()( const Rect& roi ) const { return Mat(*this, roi); } inline Mat Mat::operator()(const Range* ranges) const { return Mat(*this, ranges); -} - +} + inline Mat::operator CvMat() const { CV_DbgAssert(dims <= 2); @@ -435,7 +435,7 @@ template inline const _Tp* Mat::ptr(int y) const return (const _Tp*)(data + step.p[0]*y); } - + inline uchar* Mat::ptr(int i0, int i1) { CV_DbgAssert( dims >= 2 && data && @@ -505,7 +505,7 @@ template inline const _Tp* Mat::ptr(int i0, int i1, int i2) const } inline uchar* Mat::ptr(const int* idx) -{ +{ int i, d = dims; uchar* p = data; CV_DbgAssert( d >= 1 && p ); @@ -528,8 +528,8 @@ inline const uchar* Mat::ptr(const int* idx) const p += idx[i]*step.p[i]; } return p; -} - +} + template inline _Tp& Mat::at(int i0, int i1) { CV_DbgAssert( dims <= 2 && data && (unsigned)i0 < (unsigned)size.p[0] && @@ -545,7 +545,7 @@ template inline const _Tp& Mat::at(int i0, int i1) const CV_ELEM_SIZE1(DataType<_Tp>::depth) == elemSize1()); return ((const _Tp*)(data + step.p[0]*i0))[i1]; } - + template inline _Tp& Mat::at(Point pt) { CV_DbgAssert( dims <= 2 && data && (unsigned)pt.y < (unsigned)size.p[0] && @@ -574,7 +574,7 @@ template inline _Tp& Mat::at(int i0) int i = i0/cols, j = i0 - i*cols; return ((_Tp*)(data + step.p[0]*i))[j]; } - + template inline const _Tp& Mat::at(int i0) const { CV_DbgAssert( dims <= 2 && data && @@ -587,7 +587,7 @@ template inline const _Tp& Mat::at(int i0) const int i = i0/cols, j = i0 - i*cols; return ((const _Tp*)(data + step.p[0]*i))[j]; } - + template inline _Tp& Mat::at(int i0, int i1, int i2) { CV_DbgAssert( elemSize() == CV_ELEM_SIZE(DataType<_Tp>::type) ); @@ -618,8 +618,8 @@ template inline const _Tp& Mat::at(const Vec& idx) CV_DbgAssert( elemSize() == CV_ELEM_SIZE(DataType<_Tp>::type) ); return *(const _Tp*)ptr(idx.val); } - - + + template inline MatConstIterator_<_Tp> Mat::begin() const { CV_DbgAssert( elemSize() == sizeof(_Tp) ); @@ -659,18 +659,18 @@ template inline Mat::operator Vec<_Tp, n>() const { CV_Assert( data && dims <= 2 && (rows == 1 || cols == 1) && rows + cols - 1 == n && channels() == 1 ); - + if( isContinuous() && type() == DataType<_Tp>::type ) return Vec<_Tp, n>((_Tp*)data); Vec<_Tp, n> v; Mat tmp(rows, cols, DataType<_Tp>::type, v.val); convertTo(tmp, tmp.type()); return v; } - + template inline Mat::operator Matx<_Tp, m, n>() const { CV_Assert( data && dims <= 2 && rows == m && cols == n && channels() == 1 ); - + if( isContinuous() && type() == DataType<_Tp>::type ) return Matx<_Tp, m, n>((_Tp*)data); Matx<_Tp, m, n> mtx; Mat tmp(rows, cols, DataType<_Tp>::type, mtx.val); @@ -682,11 +682,11 @@ template inline Mat::operator Matx<_Tp, m, n>() cons template inline void Mat::push_back(const _Tp& elem) { if( !data ) - { - *this = Mat(1, 1, DataType<_Tp>::type, (void*)&elem).clone(); - return; - } - CV_Assert(DataType<_Tp>::type == type() && cols == 1 + { + *this = Mat(1, 1, DataType<_Tp>::type, (void*)&elem).clone(); + return; + } + CV_Assert(DataType<_Tp>::type == type() && cols == 1 /* && dims == 2 (cols == 1 implies dims == 2) */); uchar* tmp = dataend + step[0]; if( !isSubmatrix() && isContinuous() && tmp <= datalimit ) @@ -697,16 +697,16 @@ template inline void Mat::push_back(const _Tp& elem) else push_back_(&elem); } - + template inline void Mat::push_back(const Mat_<_Tp>& m) { push_back((const Mat&)m); -} - +} + inline Mat::MSize::MSize(int* _p) : p(_p) {} inline Size Mat::MSize::operator()() const { - CV_DbgAssert(p[-1] <= 2); + CV_DbgAssert(p[-1] <= 2); return Size(p[1], p[0]); } inline const int& Mat::MSize::operator[](int i) const { return p[i]; } @@ -720,18 +720,18 @@ inline bool Mat::MSize::operator == (const MSize& sz) const return false; if( d == 2 ) return p[0] == sz.p[0] && p[1] == sz.p[1]; - + for( int i = 0; i < d; i++ ) if( p[i] != sz.p[i] ) return false; return true; -} +} inline bool Mat::MSize::operator != (const MSize& sz) const { return !(*this == sz); } - + inline Mat::MStep::MStep() { p = buf; p[0] = p[1] = 0; } inline Mat::MStep::MStep(size_t s) { p = buf; p[0] = s; p[1] = 0; } inline const size_t& Mat::MStep::operator[](int i) const { return p[i]; } @@ -747,7 +747,7 @@ inline Mat::MStep& Mat::MStep::operator = (size_t s) buf[0] = s; return *this; } - + static inline Mat cvarrToMatND(const CvArr* arr, bool copyData=false, int coiMode=0) { return cvarrToMat(arr, copyData, true, coiMode); @@ -773,7 +773,7 @@ template inline void SVD::compute(_a, _w, _u, _vt); CV_Assert(_w.data == (uchar*)&w.val[0] && _u.data == (uchar*)&u.val[0] && _vt.data == (uchar*)&vt.val[0]); } - + template inline void SVD::compute( const Matx<_Tp, m, n>& a, Matx<_Tp, nm, 1>& w ) { @@ -782,7 +782,7 @@ SVD::compute( const Matx<_Tp, m, n>& a, Matx<_Tp, nm, 1>& w ) SVD::compute(_a, _w); CV_Assert(_w.data == (uchar*)&w.val[0]); } - + template inline void SVD::backSubst( const Matx<_Tp, nm, 1>& w, const Matx<_Tp, m, nm>& u, const Matx<_Tp, n, nm>& vt, const Matx<_Tp, m, nb>& rhs, @@ -793,12 +793,12 @@ SVD::backSubst( const Matx<_Tp, nm, 1>& w, const Matx<_Tp, m, nm>& u, SVD::backSubst(_w, _u, _vt, _rhs, _dst); CV_Assert(_dst.data == (uchar*)&dst.val[0]); } - + ///////////////////////////////// Mat_<_Tp> //////////////////////////////////// template inline Mat_<_Tp>::Mat_() : Mat() { flags = (flags & ~CV_MAT_TYPE_MASK) | DataType<_Tp>::type; } - + template inline Mat_<_Tp>::Mat_(int _rows, int _cols) : Mat(_rows, _cols, DataType<_Tp>::type) {} @@ -807,19 +807,19 @@ template inline Mat_<_Tp>::Mat_(int _rows, int _cols, const _Tp& v template inline Mat_<_Tp>::Mat_(Size _sz) : Mat(_sz.height, _sz.width, DataType<_Tp>::type) {} - + template inline Mat_<_Tp>::Mat_(Size _sz, const _Tp& value) : Mat(_sz.height, _sz.width, DataType<_Tp>::type) { *this = value; } - + template inline Mat_<_Tp>::Mat_(int _dims, const int* _sz) : Mat(_dims, _sz, DataType<_Tp>::type) {} - + template inline Mat_<_Tp>::Mat_(int _dims, const int* _sz, const _Tp& _s) : Mat(_dims, _sz, DataType<_Tp>::type, Scalar(_s)) {} - + template inline Mat_<_Tp>::Mat_(const Mat_<_Tp>& m, const Range* ranges) : Mat(m, ranges) {} - + template inline Mat_<_Tp>::Mat_(const Mat& m) : Mat() { flags = (flags & ~CV_MAT_TYPE_MASK) | DataType<_Tp>::type; *this = m; } @@ -829,8 +829,8 @@ template inline Mat_<_Tp>::Mat_(const Mat_& m) template inline Mat_<_Tp>::Mat_(int _rows, int _cols, _Tp* _data, size_t steps) : Mat(_rows, _cols, DataType<_Tp>::type, _data, steps) {} -template inline Mat_<_Tp>::Mat_(const Mat_& m, const Range& rowRange, const Range& colRange) - : Mat(m, rowRange, colRange) {} +template inline Mat_<_Tp>::Mat_(const Mat_& m, const Range& _rowRange, const Range& _colRange) + : Mat(m, _rowRange, _colRange) {} template inline Mat_<_Tp>::Mat_(const Mat_& m, const Rect& roi) : Mat(m, roi) {} @@ -852,7 +852,7 @@ template template inline if( copyData ) *this = clone(); } - + template inline Mat_<_Tp>::Mat_(const Point_::channel_type>& pt, bool copyData) : Mat(2/DataType<_Tp>::channels, 1, DataType<_Tp>::type, (void*)&pt) { @@ -871,7 +871,7 @@ template inline Mat_<_Tp>::Mat_(const Point3_ inline Mat_<_Tp>::Mat_(const MatCommaInitializer_<_Tp>& commaInitializer) : Mat(commaInitializer) {} - + template inline Mat_<_Tp>::Mat_(const vector<_Tp>& vec, bool copyData) : Mat(vec, copyData) {} @@ -917,9 +917,9 @@ template inline void Mat_<_Tp>::create(Size _sz) template inline void Mat_<_Tp>::create(int _dims, const int* _sz) { Mat::create(_dims, _sz, DataType<_Tp>::type); -} - - +} + + template inline Mat_<_Tp> Mat_<_Tp>::cross(const Mat_& m) const { return Mat_<_Tp>(Mat::cross(m)); } @@ -967,15 +967,15 @@ template inline size_t Mat_<_Tp>::step1(int i) const { return step template inline Mat_<_Tp>& Mat_<_Tp>::adjustROI( int dtop, int dbottom, int dleft, int dright ) { return (Mat_<_Tp>&)(Mat::adjustROI(dtop, dbottom, dleft, dright)); } -template inline Mat_<_Tp> Mat_<_Tp>::operator()( const Range& rowRange, const Range& colRange ) const -{ return Mat_<_Tp>(*this, rowRange, colRange); } +template inline Mat_<_Tp> Mat_<_Tp>::operator()( const Range& _rowRange, const Range& _colRange ) const +{ return Mat_<_Tp>(*this, _rowRange, _colRange); } template inline Mat_<_Tp> Mat_<_Tp>::operator()( const Rect& roi ) const { return Mat_<_Tp>(*this, roi); } template inline Mat_<_Tp> Mat_<_Tp>::operator()( const Range* ranges ) const -{ return Mat_<_Tp>(*this, ranges); } - +{ return Mat_<_Tp>(*this, ranges); } + template inline _Tp* Mat_<_Tp>::operator [](int y) { return (_Tp*)ptr(y); } template inline const _Tp* Mat_<_Tp>::operator [](int y) const @@ -1035,8 +1035,8 @@ template template inline _Tp& Mat_<_Tp>::operator ()(const template template inline const _Tp& Mat_<_Tp>::operator ()(const Vec& idx) const { return Mat::at<_Tp>(idx); -} - +} + template inline _Tp& Mat_<_Tp>::operator ()(int i0) { return this->at<_Tp>(i0); @@ -1045,7 +1045,7 @@ template inline _Tp& Mat_<_Tp>::operator ()(int i0) template inline const _Tp& Mat_<_Tp>::operator ()(int i0) const { return this->at<_Tp>(i0); -} +} template inline _Tp& Mat_<_Tp>::operator ()(int i0, int i1, int i2) { @@ -1055,9 +1055,9 @@ template inline _Tp& Mat_<_Tp>::operator ()(int i0, int i1, int i2 template inline const _Tp& Mat_<_Tp>::operator ()(int i0, int i1, int i2) const { return this->at<_Tp>(i0, i1, i2); -} - - +} + + template inline Mat_<_Tp>::operator vector<_Tp>() const { vector<_Tp> v; @@ -1075,7 +1075,7 @@ template template inline Mat_<_Tp>::operator Matx::channels == 0); return this->Mat::operator Matx::channel_type, m, n>(); -} +} template inline void process( const Mat_& m1, Mat_& m2, Op op ) @@ -1112,9 +1112,9 @@ process( const Mat_& m1, const Mat_& m2, Mat_& m3, Op op ) } } - + /////////////////////////////// Input/Output Arrays ///////////////////////////////// - + template inline _InputArray::_InputArray(const vector<_Tp>& vec) : flags(FIXED_TYPE + STD_VECTOR + DataType<_Tp>::type), obj((void*)&vec) {} @@ -1122,11 +1122,11 @@ template inline _InputArray::_InputArray(const vector : flags(FIXED_TYPE + STD_VECTOR_VECTOR + DataType<_Tp>::type), obj((void*)&vec) {} template inline _InputArray::_InputArray(const vector >& vec) - : flags(FIXED_TYPE + STD_VECTOR_MAT + DataType<_Tp>::type), obj((void*)&vec) {} - + : flags(FIXED_TYPE + STD_VECTOR_MAT + DataType<_Tp>::type), obj((void*)&vec) {} + template inline _InputArray::_InputArray(const Matx<_Tp, m, n>& mtx) : flags(FIXED_TYPE + FIXED_SIZE + MATX + DataType<_Tp>::type), obj((void*)&mtx), sz(n, m) {} - + template inline _InputArray::_InputArray(const _Tp* vec, int n) : flags(FIXED_TYPE + FIXED_SIZE + MATX + DataType<_Tp>::type), obj((void*)vec), sz(n, 1) {} @@ -1135,7 +1135,7 @@ inline _InputArray::_InputArray(const Scalar& s) template inline _InputArray::_InputArray(const Mat_<_Tp>& m) : flags(FIXED_TYPE + MAT + DataType<_Tp>::type), obj((void*)&m) {} - + template inline _OutputArray::_OutputArray(vector<_Tp>& vec) : _InputArray(vec) {} template inline _OutputArray::_OutputArray(vector >& vec) @@ -1155,22 +1155,22 @@ template inline _OutputArray::_OutputArray(const vector inline _OutputArray::_OutputArray(const vector >& vec) : _InputArray(vec) {flags |= FIXED_SIZE;} - + template inline _OutputArray::_OutputArray(const Mat_<_Tp>& m) : _InputArray(m) {flags |= FIXED_SIZE;} template inline _OutputArray::_OutputArray(const Matx<_Tp, m, n>& mtx) : _InputArray(mtx) {} template inline _OutputArray::_OutputArray(const _Tp* vec, int n) : _InputArray(vec, n) {} - + //////////////////////////////////// Matrix Expressions ///////////////////////////////////////// class CV_EXPORTS MatOp -{ +{ public: MatOp() {}; virtual ~MatOp() {}; - + virtual bool elementWise(const MatExpr& expr) const; virtual void assign(const MatExpr& expr, Mat& m, int type=-1) const = 0; virtual void roi(const MatExpr& expr, const Range& rowRange, @@ -1183,30 +1183,30 @@ public: virtual void augAssignAnd(const MatExpr& expr, Mat& m) const; virtual void augAssignOr(const MatExpr& expr, Mat& m) const; virtual void augAssignXor(const MatExpr& expr, Mat& m) const; - + virtual void add(const MatExpr& expr1, const MatExpr& expr2, MatExpr& res) const; virtual void add(const MatExpr& expr1, const Scalar& s, MatExpr& res) const; - + virtual void subtract(const MatExpr& expr1, const MatExpr& expr2, MatExpr& res) const; virtual void subtract(const Scalar& s, const MatExpr& expr, MatExpr& res) const; - + virtual void multiply(const MatExpr& expr1, const MatExpr& expr2, MatExpr& res, double scale=1) const; virtual void multiply(const MatExpr& expr1, double s, MatExpr& res) const; - + virtual void divide(const MatExpr& expr1, const MatExpr& expr2, MatExpr& res, double scale=1) const; virtual void divide(double s, const MatExpr& expr, MatExpr& res) const; - + virtual void abs(const MatExpr& expr, MatExpr& res) const; - + virtual void transpose(const MatExpr& expr, MatExpr& res) const; virtual void matmul(const MatExpr& expr1, const MatExpr& expr2, MatExpr& res) const; virtual void invert(const MatExpr& expr, int method, MatExpr& res) const; - + virtual Size size(const MatExpr& expr) const; virtual int type(const MatExpr& expr) const; }; - + class CV_EXPORTS MatExpr { public: @@ -1221,39 +1221,39 @@ public: op->assign(*this, m); return m; } - + template operator Mat_<_Tp>() const { Mat_<_Tp> m; op->assign(*this, m, DataType<_Tp>::type); return m; } - + MatExpr row(int y) const; MatExpr col(int x) const; MatExpr diag(int d=0) const; MatExpr operator()( const Range& rowRange, const Range& colRange ) const; MatExpr operator()( const Rect& roi ) const; - + Mat cross(const Mat& m) const; double dot(const Mat& m) const; - + MatExpr t() const; MatExpr inv(int method = DECOMP_LU) const; MatExpr mul(const MatExpr& e, double scale=1) const; MatExpr mul(const Mat& m, double scale=1) const; - + Size size() const; int type() const; - + const MatOp* op; int flags; - + Mat a, b, c; double alpha, beta; Scalar s; }; - + CV_EXPORTS MatExpr operator + (const Mat& a, const Mat& b); CV_EXPORTS MatExpr operator + (const Mat& a, const Scalar& s); @@ -1284,7 +1284,7 @@ CV_EXPORTS MatExpr operator * (const Mat& m, const MatExpr& e); CV_EXPORTS MatExpr operator * (const MatExpr& e, double s); CV_EXPORTS MatExpr operator * (double s, const MatExpr& e); CV_EXPORTS MatExpr operator * (const MatExpr& e1, const MatExpr& e2); - + CV_EXPORTS MatExpr operator / (const Mat& a, const Mat& b); CV_EXPORTS MatExpr operator / (const Mat& a, double s); CV_EXPORTS MatExpr operator / (double s, const Mat& a); @@ -1292,7 +1292,7 @@ CV_EXPORTS MatExpr operator / (const MatExpr& e, const Mat& m); CV_EXPORTS MatExpr operator / (const Mat& m, const MatExpr& e); CV_EXPORTS MatExpr operator / (const MatExpr& e, double s); CV_EXPORTS MatExpr operator / (double s, const MatExpr& e); -CV_EXPORTS MatExpr operator / (const MatExpr& e1, const MatExpr& e2); +CV_EXPORTS MatExpr operator / (const MatExpr& e1, const MatExpr& e2); CV_EXPORTS MatExpr operator < (const Mat& a, const Mat& b); CV_EXPORTS MatExpr operator < (const Mat& a, double s); @@ -1316,8 +1316,8 @@ CV_EXPORTS MatExpr operator >= (double s, const Mat& a); CV_EXPORTS MatExpr operator > (const Mat& a, const Mat& b); CV_EXPORTS MatExpr operator > (const Mat& a, double s); -CV_EXPORTS MatExpr operator > (double s, const Mat& a); - +CV_EXPORTS MatExpr operator > (double s, const Mat& a); + CV_EXPORTS MatExpr min(const Mat& a, const Mat& b); CV_EXPORTS MatExpr min(const Mat& a, double s); CV_EXPORTS MatExpr min(double s, const Mat& a); @@ -1339,7 +1339,7 @@ template static inline MatExpr min(const Mat_<_Tp>& a, double s) template static inline MatExpr min(double s, const Mat_<_Tp>& a) { return cv::min((const Mat&)a, s); -} +} template static inline MatExpr max(const Mat_<_Tp>& a, const Mat_<_Tp>& b) { @@ -1354,7 +1354,7 @@ template static inline MatExpr max(const Mat_<_Tp>& a, double s) template static inline MatExpr max(double s, const Mat_<_Tp>& a) { return cv::max((const Mat&)a, s); -} +} template static inline void min(const Mat_<_Tp>& a, const Mat_<_Tp>& b, Mat_<_Tp>& c) { @@ -1386,7 +1386,7 @@ template static inline void max(double s, const Mat_<_Tp>& a, Mat_ cv::max((const Mat&)a, s, (Mat&)c); } - + CV_EXPORTS MatExpr operator & (const Mat& a, const Mat& b); CV_EXPORTS MatExpr operator & (const Mat& a, const Scalar& s); CV_EXPORTS MatExpr operator & (const Scalar& s, const Mat& a); @@ -1400,22 +1400,22 @@ CV_EXPORTS MatExpr operator ^ (const Mat& a, const Scalar& s); CV_EXPORTS MatExpr operator ^ (const Scalar& s, const Mat& a); CV_EXPORTS MatExpr operator ~(const Mat& m); - + CV_EXPORTS MatExpr abs(const Mat& m); CV_EXPORTS MatExpr abs(const MatExpr& e); - + template static inline MatExpr abs(const Mat_<_Tp>& m) { return cv::abs((const Mat&)m); } ////////////////////////////// Augmenting algebraic operations ////////////////////////////////// - + inline Mat& Mat::operator = (const MatExpr& e) { e.op->assign(e, *this); return *this; -} +} template inline Mat_<_Tp>::Mat_(const MatExpr& e) { @@ -1438,7 +1438,7 @@ static inline Mat& operator += (const Mat& a, const Scalar& s) { add(a, s, (Mat&)a); return (Mat&)a; -} +} template static inline Mat_<_Tp>& operator += (const Mat_<_Tp>& a, const Mat_<_Tp>& b) @@ -1452,11 +1452,11 @@ Mat_<_Tp>& operator += (const Mat_<_Tp>& a, const Scalar& s) { add(a, s, (Mat&)a); return (Mat_<_Tp>&)a; -} +} static inline Mat& operator += (const Mat& a, const MatExpr& b) { - b.op->augAssignAdd(b, (Mat&)a); + b.op->augAssignAdd(b, (Mat&)a); return (Mat&)a; } @@ -1466,7 +1466,7 @@ Mat_<_Tp>& operator += (const Mat_<_Tp>& a, const MatExpr& b) b.op->augAssignAdd(b, (Mat&)a); return (Mat_<_Tp>&)a; } - + static inline Mat& operator -= (const Mat& a, const Mat& b) { subtract(a, b, (Mat&)a); @@ -1477,7 +1477,7 @@ static inline Mat& operator -= (const Mat& a, const Scalar& s) { subtract(a, s, (Mat&)a); return (Mat&)a; -} +} template static inline Mat_<_Tp>& operator -= (const Mat_<_Tp>& a, const Mat_<_Tp>& b) @@ -1491,11 +1491,11 @@ Mat_<_Tp>& operator -= (const Mat_<_Tp>& a, const Scalar& s) { subtract(a, s, (Mat&)a); return (Mat_<_Tp>&)a; -} +} static inline Mat& operator -= (const Mat& a, const MatExpr& b) { - b.op->augAssignSubtract(b, (Mat&)a); + b.op->augAssignSubtract(b, (Mat&)a); return (Mat&)a; } @@ -1504,7 +1504,7 @@ Mat_<_Tp>& operator -= (const Mat_<_Tp>& a, const MatExpr& b) { b.op->augAssignSubtract(b, (Mat&)a); return (Mat_<_Tp>&)a; -} +} static inline Mat& operator *= (const Mat& a, const Mat& b) { @@ -1516,7 +1516,7 @@ static inline Mat& operator *= (const Mat& a, double s) { a.convertTo((Mat&)a, -1, s); return (Mat&)a; -} +} template static inline Mat_<_Tp>& operator *= (const Mat_<_Tp>& a, const Mat_<_Tp>& b) @@ -1530,11 +1530,11 @@ Mat_<_Tp>& operator *= (const Mat_<_Tp>& a, double s) { a.convertTo((Mat&)a, -1, s); return (Mat_<_Tp>&)a; -} +} static inline Mat& operator *= (const Mat& a, const MatExpr& b) { - b.op->augAssignMultiply(b, (Mat&)a); + b.op->augAssignMultiply(b, (Mat&)a); return (Mat&)a; } @@ -1543,8 +1543,8 @@ Mat_<_Tp>& operator *= (const Mat_<_Tp>& a, const MatExpr& b) { b.op->augAssignMultiply(b, (Mat&)a); return (Mat_<_Tp>&)a; -} - +} + static inline Mat& operator /= (const Mat& a, const Mat& b) { divide(a, b, (Mat&)a); @@ -1555,7 +1555,7 @@ static inline Mat& operator /= (const Mat& a, double s) { a.convertTo((Mat&)a, -1, 1./s); return (Mat&)a; -} +} template static inline Mat_<_Tp>& operator /= (const Mat_<_Tp>& a, const Mat_<_Tp>& b) @@ -1569,11 +1569,11 @@ Mat_<_Tp>& operator /= (const Mat_<_Tp>& a, double s) { a.convertTo((Mat&)a, -1, 1./s); return (Mat_<_Tp>&)a; -} +} static inline Mat& operator /= (const Mat& a, const MatExpr& b) { - b.op->augAssignDivide(b, (Mat&)a); + b.op->augAssignDivide(b, (Mat&)a); return (Mat&)a; } @@ -1596,22 +1596,22 @@ static inline Mat& operator &= (const Mat& a, const Scalar& s) { bitwise_and(a, s, (Mat&)a); return (Mat&)a; -} +} template static inline Mat_<_Tp>& operator &= (const Mat_<_Tp>& a, const Mat_<_Tp>& b) { bitwise_and(a, b, (Mat&)a); return (Mat_<_Tp>&)a; -} +} template static inline Mat_<_Tp>& operator &= (const Mat_<_Tp>& a, const Scalar& s) { bitwise_and(a, s, (Mat&)a); return (Mat_<_Tp>&)a; -} - +} + static inline Mat& operator |= (const Mat& a, const Mat& b) { bitwise_or(a, b, (Mat&)a); @@ -1622,22 +1622,22 @@ static inline Mat& operator |= (const Mat& a, const Scalar& s) { bitwise_or(a, s, (Mat&)a); return (Mat&)a; -} +} template static inline Mat_<_Tp>& operator |= (const Mat_<_Tp>& a, const Mat_<_Tp>& b) { bitwise_or(a, b, (Mat&)a); return (Mat_<_Tp>&)a; -} +} template static inline Mat_<_Tp>& operator |= (const Mat_<_Tp>& a, const Scalar& s) { bitwise_or(a, s, (Mat&)a); return (Mat_<_Tp>&)a; -} - +} + static inline Mat& operator ^= (const Mat& a, const Mat& b) { bitwise_xor(a, b, (Mat&)a); @@ -1648,39 +1648,39 @@ static inline Mat& operator ^= (const Mat& a, const Scalar& s) { bitwise_xor(a, s, (Mat&)a); return (Mat&)a; -} +} template static inline Mat_<_Tp>& operator ^= (const Mat_<_Tp>& a, const Mat_<_Tp>& b) { bitwise_xor(a, b, (Mat&)a); return (Mat_<_Tp>&)a; -} +} template static inline Mat_<_Tp>& operator ^= (const Mat_<_Tp>& a, const Scalar& s) { bitwise_xor(a, s, (Mat&)a); return (Mat_<_Tp>&)a; -} +} /////////////////////////////// Miscellaneous operations ////////////////////////////// - + template void split(const Mat& src, vector >& mv) { split(src, (vector&)mv ); } ////////////////////////////////////////////////////////////// - + template inline MatExpr Mat_<_Tp>::zeros(int rows, int cols) { return Mat::zeros(rows, cols, DataType<_Tp>::type); } - + template inline MatExpr Mat_<_Tp>::zeros(Size sz) { return Mat::zeros(sz, DataType<_Tp>::type); -} - +} + template inline MatExpr Mat_<_Tp>::ones(int rows, int cols) { return Mat::ones(rows, cols, DataType<_Tp>::type); @@ -1689,8 +1689,8 @@ template inline MatExpr Mat_<_Tp>::ones(int rows, int cols) template inline MatExpr Mat_<_Tp>::ones(Size sz) { return Mat::ones(sz, DataType<_Tp>::type); -} - +} + template inline MatExpr Mat_<_Tp>::eye(int rows, int cols) { return Mat::eye(rows, cols, DataType<_Tp>::type); @@ -1699,8 +1699,8 @@ template inline MatExpr Mat_<_Tp>::eye(int rows, int cols) template inline MatExpr Mat_<_Tp>::eye(Size sz) { return Mat::eye(sz, DataType<_Tp>::type); -} - +} + //////////////////////////////// Iterators & Comma initializers ////////////////////////////////// inline MatConstIterator::MatConstIterator() @@ -1742,7 +1742,7 @@ inline MatConstIterator::MatConstIterator(const Mat* _m, Point _pt) int idx[]={_pt.y, _pt.x}; seek(idx); } - + inline MatConstIterator::MatConstIterator(const MatConstIterator& it) : m(it.m), elemSize(it.elemSize), ptr(it.ptr), sliceStart(it.sliceStart), sliceEnd(it.sliceEnd) {} @@ -1755,7 +1755,7 @@ inline MatConstIterator& MatConstIterator::operator = (const MatConstIterator& i } inline uchar* MatConstIterator::operator *() const { return ptr; } - + inline MatConstIterator& MatConstIterator::operator += (ptrdiff_t ofs) { if( !m || ofs == 0 ) @@ -1778,7 +1778,7 @@ inline MatConstIterator& MatConstIterator::operator --() if( m && (ptr -= elemSize) < sliceStart ) { ptr += elemSize; - seek(-1, true); + seek(-1, true); } return *this; } @@ -1795,7 +1795,7 @@ inline MatConstIterator& MatConstIterator::operator ++() if( m && (ptr += elemSize) >= sliceEnd ) { ptr -= elemSize; - seek(1, true); + seek(1, true); } return *this; } @@ -1878,10 +1878,10 @@ template inline MatIterator_<_Tp>::MatIterator_(Mat_<_Tp>* _m, int template inline MatIterator_<_Tp>::MatIterator_(const Mat_<_Tp>* _m, Point _pt) : MatConstIterator_<_Tp>(_m, _pt) {} - + template inline MatIterator_<_Tp>::MatIterator_(const Mat_<_Tp>* _m, const int* _idx) : MatConstIterator_<_Tp>(_m, _idx) {} - + template inline MatIterator_<_Tp>::MatIterator_(const MatIterator_& it) : MatConstIterator_<_Tp>(it) {} @@ -1972,8 +1972,8 @@ operator == (const MatIterator_<_Tp>& a, const MatIterator_<_Tp>& b) template static inline bool operator != (const MatIterator_<_Tp>& a, const MatIterator_<_Tp>& b) -{ return a.m != b.m || a.ptr != b.ptr; } - +{ return a.m != b.m || a.ptr != b.ptr; } + static inline bool operator < (const MatConstIterator& a, const MatConstIterator& b) { return a.ptr < b.ptr; } @@ -1981,7 +1981,7 @@ operator < (const MatConstIterator& a, const MatConstIterator& b) static inline bool operator > (const MatConstIterator& a, const MatConstIterator& b) { return a.ptr > b.ptr; } - + static inline bool operator <= (const MatConstIterator& a, const MatConstIterator& b) { return a.ptr <= b.ptr; } @@ -2000,7 +2000,7 @@ static inline MatConstIterator operator + (ptrdiff_t ofs, const MatConstIterator static inline MatConstIterator operator - (const MatConstIterator& a, ptrdiff_t ofs) { MatConstIterator b = a; return b += -ofs; } - + template static inline MatConstIterator_<_Tp> operator + (const MatConstIterator_<_Tp>& a, ptrdiff_t ofs) { MatConstIterator t = (const MatConstIterator&)a + ofs; return (MatConstIterator_<_Tp>&)t; } @@ -2008,14 +2008,14 @@ operator + (const MatConstIterator_<_Tp>& a, ptrdiff_t ofs) template static inline MatConstIterator_<_Tp> operator + (ptrdiff_t ofs, const MatConstIterator_<_Tp>& a) { MatConstIterator t = (const MatConstIterator&)a + ofs; return (MatConstIterator_<_Tp>&)t; } - + template static inline MatConstIterator_<_Tp> operator - (const MatConstIterator_<_Tp>& a, ptrdiff_t ofs) { MatConstIterator t = (const MatConstIterator&)a - ofs; return (MatConstIterator_<_Tp>&)t; } inline uchar* MatConstIterator::operator [](ptrdiff_t i) const { return *(*this + i); } - + template inline _Tp MatConstIterator_<_Tp>::operator [](ptrdiff_t i) const { return *(_Tp*)MatConstIterator::operator [](i); } @@ -2030,7 +2030,7 @@ operator + (ptrdiff_t ofs, const MatIterator_<_Tp>& a) template static inline MatIterator_<_Tp> operator - (const MatIterator_<_Tp>& a, ptrdiff_t ofs) { MatConstIterator t = (const MatConstIterator&)a - ofs; return (MatIterator_<_Tp>&)t; } - + template inline _Tp& MatIterator_<_Tp>::operator [](ptrdiff_t i) const { return *(*this + i); } @@ -2066,8 +2066,8 @@ template inline MatCommaInitializer_<_Tp>::operator Mat_<_Tp>() co { CV_DbgAssert( this->it == ((const Mat_<_Tp>*)this->it.m)->end() ); return Mat_<_Tp>(*this->it.m); -} - +} + template static inline MatCommaInitializer_<_Tp> operator << (const Mat_<_Tp>& m, T2 val) { @@ -2123,12 +2123,12 @@ inline SparseMat SparseMat::clone() const } -inline void SparseMat::assignTo( SparseMat& m, int type ) const +inline void SparseMat::assignTo( SparseMat& m, int _type ) const { - if( type < 0 ) + if( _type < 0 ) m = *this; else - convertTo(m, type); + convertTo(m, _type); } inline void SparseMat::addref() @@ -2209,7 +2209,7 @@ inline size_t SparseMat::hash(const int* idx) const template inline _Tp& SparseMat::ref(int i0, size_t* hashval) { return *(_Tp*)((SparseMat*)this)->ptr(i0, true, hashval); } - + template inline _Tp& SparseMat::ref(int i0, int i1, size_t* hashval) { return *(_Tp*)((SparseMat*)this)->ptr(i0, i1, true, hashval); } @@ -2223,8 +2223,8 @@ template inline _Tp SparseMat::value(int i0, size_t* hashval) cons { const _Tp* p = (const _Tp*)((SparseMat*)this)->ptr(i0, false, hashval); return p ? *p : _Tp(); -} - +} + template inline _Tp SparseMat::value(int i0, int i1, size_t* hashval) const { const _Tp* p = (const _Tp*)((SparseMat*)this)->ptr(i0, i1, false, hashval); @@ -2245,7 +2245,7 @@ template inline _Tp SparseMat::value(const int* idx, size_t* hashv template inline const _Tp* SparseMat::find(int i0, size_t* hashval) const { return (const _Tp*)((SparseMat*)this)->ptr(i0, false, hashval); } - + template inline const _Tp* SparseMat::find(int i0, int i1, size_t* hashval) const { return (const _Tp*)((SparseMat*)this)->ptr(i0, i1, false, hashval); } @@ -2275,23 +2275,23 @@ inline SparseMatConstIterator SparseMat::begin() const inline SparseMatIterator SparseMat::end() { SparseMatIterator it(this); it.seekEnd(); return it; } - + inline SparseMatConstIterator SparseMat::end() const { SparseMatConstIterator it(this); it.seekEnd(); return it; } - + template inline SparseMatIterator_<_Tp> SparseMat::begin() { return SparseMatIterator_<_Tp>(this); } - + template inline SparseMatConstIterator_<_Tp> SparseMat::begin() const { return SparseMatConstIterator_<_Tp>(this); } - + template inline SparseMatIterator_<_Tp> SparseMat::end() { SparseMatIterator_<_Tp> it(this); it.seekEnd(); return it; } template inline SparseMatConstIterator_<_Tp> SparseMat::end() const { SparseMatConstIterator_<_Tp> it(this); it.seekEnd(); return it; } - - + + inline SparseMatConstIterator::SparseMatConstIterator() : m(0), hashidx(0), ptr(0) { @@ -2336,7 +2336,7 @@ inline SparseMatConstIterator SparseMatConstIterator::operator ++(int) return it; } - + inline void SparseMatConstIterator::seekEnd() { if( m && m->hdr ) @@ -2345,7 +2345,7 @@ inline void SparseMatConstIterator::seekEnd() ptr = 0; } } - + inline SparseMatIterator::SparseMatIterator() {} @@ -2482,8 +2482,8 @@ SparseMat_<_Tp>::ref(int i0, size_t* hashval) template inline _Tp SparseMat_<_Tp>::operator()(int i0, size_t* hashval) const -{ return SparseMat::value<_Tp>(i0, hashval); } - +{ return SparseMat::value<_Tp>(i0, hashval); } + template inline _Tp& SparseMat_<_Tp>::ref(int i0, int i1, size_t* hashval) { return SparseMat::ref<_Tp>(i0, i1, hashval); } @@ -2516,7 +2516,7 @@ template inline SparseMatConstIterator_<_Tp> SparseMat_<_Tp>::begi template inline SparseMatIterator_<_Tp> SparseMat_<_Tp>::end() { SparseMatIterator_<_Tp> it(this); it.seekEnd(); return it; } - + template inline SparseMatConstIterator_<_Tp> SparseMat_<_Tp>::end() const { SparseMatConstIterator_<_Tp> it(this); it.seekEnd(); return it; } @@ -2597,7 +2597,7 @@ SparseMatIterator_<_Tp>::operator ++(int) SparseMatConstIterator::operator ++(); return it; } - + } #endif diff --git a/modules/core/include/opencv2/core/opengl_interop.hpp b/modules/core/include/opencv2/core/opengl_interop.hpp index 338466b..d680d82 100644 --- a/modules/core/include/opencv2/core/opengl_interop.hpp +++ b/modules/core/include/opencv2/core/opengl_interop.hpp @@ -47,282 +47,287 @@ #include "opencv2/core/core.hpp" -namespace cv +namespace cv { - //! Smart pointer for OpenGL buffer memory with reference counting. - class CV_EXPORTS GlBuffer +//! Smart pointer for OpenGL buffer memory with reference counting. +class CV_EXPORTS GlBuffer +{ +public: + enum Usage { - public: - enum Usage - { - ARRAY_BUFFER = 0x8892, // buffer will use for OpenGL arrays (vertices, colors, normals, etc) - TEXTURE_BUFFER = 0x88EC // buffer will ise for OpenGL textures - }; - - //! create empty buffer - explicit GlBuffer(Usage usage); - - //! create buffer - GlBuffer(int rows, int cols, int type, Usage usage); - GlBuffer(Size size, int type, Usage usage); - - //! copy from host/device memory - GlBuffer(InputArray mat, Usage usage); - - void create(int rows, int cols, int type, Usage usage); - inline void create(Size size, int type, Usage usage) { create(size.height, size.width, type, usage); } - inline void create(int rows, int cols, int type) { create(rows, cols, type, usage()); } - inline void create(Size size, int type) { create(size.height, size.width, type, usage()); } - - void release(); - - //! copy from host/device memory - void copyFrom(InputArray mat); - - void bind() const; - void unbind() const; - - //! map to host memory - Mat mapHost(); - void unmapHost(); - - //! map to device memory - gpu::GpuMat mapDevice(); - void unmapDevice(); - - inline int rows() const { return rows_; } - inline int cols() const { return cols_; } - inline Size size() const { return Size(cols_, rows_); } - inline bool empty() const { return rows_ == 0 || cols_ == 0; } - - inline int type() const { return type_; } - inline int depth() const { return CV_MAT_DEPTH(type_); } - inline int channels() const { return CV_MAT_CN(type_); } - inline int elemSize() const { return CV_ELEM_SIZE(type_); } - inline int elemSize1() const { return CV_ELEM_SIZE1(type_); } - - inline Usage usage() const { return usage_; } - - class Impl; - private: - int rows_; - int cols_; - int type_; - Usage usage_; - - Ptr impl_; + ARRAY_BUFFER = 0x8892, // buffer will use for OpenGL arrays (vertices, colors, normals, etc) + TEXTURE_BUFFER = 0x88EC // buffer will ise for OpenGL textures }; - template <> CV_EXPORTS void Ptr::delete_obj(); + //! create empty buffer + explicit GlBuffer(Usage usage); + + //! create buffer + GlBuffer(int rows, int cols, int type, Usage usage); + GlBuffer(Size size, int type, Usage usage); + + //! copy from host/device memory + GlBuffer(InputArray mat, Usage usage); + + void create(int rows, int cols, int type, Usage usage); + void create(Size size, int type, Usage usage); + void create(int rows, int cols, int type); + void create(Size size, int type); + + void release(); + + //! copy from host/device memory + void copyFrom(InputArray mat); + + void bind() const; + void unbind() const; + + //! map to host memory + Mat mapHost(); + void unmapHost(); + + //! map to device memory + gpu::GpuMat mapDevice(); + void unmapDevice(); + + inline int rows() const { return rows_; } + inline int cols() const { return cols_; } + inline Size size() const { return Size(cols_, rows_); } + inline bool empty() const { return rows_ == 0 || cols_ == 0; } + + inline int type() const { return type_; } + inline int depth() const { return CV_MAT_DEPTH(type_); } + inline int channels() const { return CV_MAT_CN(type_); } + inline int elemSize() const { return CV_ELEM_SIZE(type_); } + inline int elemSize1() const { return CV_ELEM_SIZE1(type_); } + + inline Usage usage() const { return usage_; } + + class Impl; +private: + int rows_; + int cols_; + int type_; + Usage usage_; + + Ptr impl_; +}; + +template <> CV_EXPORTS void Ptr::delete_obj(); + +//! Smart pointer for OpenGL 2d texture memory with reference counting. +class CV_EXPORTS GlTexture +{ +public: + //! create empty texture + GlTexture(); + + //! create texture + GlTexture(int rows, int cols, int type); + GlTexture(Size size, int type); + + //! copy from host/device memory + explicit GlTexture(InputArray mat, bool bgra = true); + + void create(int rows, int cols, int type); + void create(Size size, int type); + void release(); + + //! copy from host/device memory + void copyFrom(InputArray mat, bool bgra = true); + + void bind() const; + void unbind() const; + + inline int rows() const { return rows_; } + inline int cols() const { return cols_; } + inline Size size() const { return Size(cols_, rows_); } + inline bool empty() const { return rows_ == 0 || cols_ == 0; } - //! Smart pointer for OpenGL 2d texture memory with reference counting. - class CV_EXPORTS GlTexture + inline int type() const { return type_; } + inline int depth() const { return CV_MAT_DEPTH(type_); } + inline int channels() const { return CV_MAT_CN(type_); } + inline int elemSize() const { return CV_ELEM_SIZE(type_); } + inline int elemSize1() const { return CV_ELEM_SIZE1(type_); } + + class Impl; +private: + int rows_; + int cols_; + int type_; + + Ptr impl_; + GlBuffer buf_; +}; + +template <> CV_EXPORTS void Ptr::delete_obj(); + +//! OpenGL Arrays +class CV_EXPORTS GlArrays +{ +public: + inline GlArrays() + : vertex_(GlBuffer::ARRAY_BUFFER), color_(GlBuffer::ARRAY_BUFFER), bgra_(true), normal_(GlBuffer::ARRAY_BUFFER), texCoord_(GlBuffer::ARRAY_BUFFER) { - public: - //! create empty texture - GlTexture(); - - //! create texture - GlTexture(int rows, int cols, int type); - GlTexture(Size size, int type); - - //! copy from host/device memory - explicit GlTexture(InputArray mat, bool bgra = true); - - void create(int rows, int cols, int type); - inline void create(Size size, int type) { create(size.height, size.width, type); } - void release(); - - //! copy from host/device memory - void copyFrom(InputArray mat, bool bgra = true); - - void bind() const; - void unbind() const; - - inline int rows() const { return rows_; } - inline int cols() const { return cols_; } - inline Size size() const { return Size(cols_, rows_); } - inline bool empty() const { return rows_ == 0 || cols_ == 0; } - - inline int type() const { return type_; } - inline int depth() const { return CV_MAT_DEPTH(type_); } - inline int channels() const { return CV_MAT_CN(type_); } - inline int elemSize() const { return CV_ELEM_SIZE(type_); } - inline int elemSize1() const { return CV_ELEM_SIZE1(type_); } - - class Impl; - private: - int rows_; - int cols_; - int type_; - - Ptr impl_; - GlBuffer buf_; - }; + } + + void setVertexArray(InputArray vertex); + inline void resetVertexArray() { vertex_.release(); } + + void setColorArray(InputArray color, bool bgra = true); + inline void resetColorArray() { color_.release(); } + + void setNormalArray(InputArray normal); + inline void resetNormalArray() { normal_.release(); } + + void setTexCoordArray(InputArray texCoord); + inline void resetTexCoordArray() { texCoord_.release(); } + + void bind() const; + void unbind() const; - template <> CV_EXPORTS void Ptr::delete_obj(); + inline int rows() const { return vertex_.rows(); } + inline int cols() const { return vertex_.cols(); } + inline Size size() const { return vertex_.size(); } + inline bool empty() const { return vertex_.empty(); } - //! OpenGL Arrays - class CV_EXPORTS GlArrays +private: + GlBuffer vertex_; + GlBuffer color_; + bool bgra_; + GlBuffer normal_; + GlBuffer texCoord_; +}; + +//! OpenGL Font +class CV_EXPORTS GlFont +{ +public: + enum Weight { - public: - inline GlArrays() - : vertex_(GlBuffer::ARRAY_BUFFER), color_(GlBuffer::ARRAY_BUFFER), bgra_(true), normal_(GlBuffer::ARRAY_BUFFER), texCoord_(GlBuffer::ARRAY_BUFFER) - { - } - - void setVertexArray(InputArray vertex); - inline void resetVertexArray() { vertex_.release(); } - - void setColorArray(InputArray color, bool bgra = true); - inline void resetColorArray() { color_.release(); } - - void setNormalArray(InputArray normal); - inline void resetNormalArray() { normal_.release(); } - - void setTexCoordArray(InputArray texCoord); - inline void resetTexCoordArray() { texCoord_.release(); } - - void bind() const; - void unbind() const; - - inline int rows() const { return vertex_.rows(); } - inline int cols() const { return vertex_.cols(); } - inline Size size() const { return vertex_.size(); } - inline bool empty() const { return vertex_.empty(); } - - private: - GlBuffer vertex_; - GlBuffer color_; - bool bgra_; - GlBuffer normal_; - GlBuffer texCoord_; + WEIGHT_LIGHT = 300, + WEIGHT_NORMAL = 400, + WEIGHT_SEMIBOLD = 600, + WEIGHT_BOLD = 700, + WEIGHT_BLACK = 900 }; - //! OpenGL Font - class CV_EXPORTS GlFont + enum Style { - public: - enum Weight - { - WEIGHT_LIGHT = 300, - WEIGHT_NORMAL = 400, - WEIGHT_SEMIBOLD = 600, - WEIGHT_BOLD = 700, - WEIGHT_BLACK = 900 - }; - - enum Style - { - STYLE_NORMAL = 0, - STYLE_ITALIC = 1, - STYLE_UNDERLINE = 2 - }; - - static Ptr get(const std::string& family, int height = 12, Weight weight = WEIGHT_NORMAL, Style style = STYLE_NORMAL); - - void draw(const char* str, int len) const; - - inline const std::string& family() const { return family_; } - inline int height() const { return height_; } - inline Weight weight() const { return weight_; } - inline Style style() const { return style_; } - - private: - GlFont(const std::string& family, int height, Weight weight, Style style); - - std::string family_; - int height_; - Weight weight_; - Style style_; - - unsigned int base_; - - GlFont(const GlFont&); - GlFont& operator =(const GlFont&); + STYLE_NORMAL = 0, + STYLE_ITALIC = 1, + STYLE_UNDERLINE = 2 }; - //! render functions - - //! render texture rectangle in window - CV_EXPORTS void render(const GlTexture& tex, - Rect_ wndRect = Rect_(0.0, 0.0, 1.0, 1.0), - Rect_ texRect = Rect_(0.0, 0.0, 1.0, 1.0)); - - //! render mode - namespace RenderMode { - enum { - POINTS = 0x0000, - LINES = 0x0001, - LINE_LOOP = 0x0002, - LINE_STRIP = 0x0003, - TRIANGLES = 0x0004, - TRIANGLE_STRIP = 0x0005, - TRIANGLE_FAN = 0x0006, - QUADS = 0x0007, - QUAD_STRIP = 0x0008, - POLYGON = 0x0009 - }; - } + static Ptr get(const std::string& family, int height = 12, Weight weight = WEIGHT_NORMAL, Style style = STYLE_NORMAL); + + void draw(const char* str, int len) const; + + inline const std::string& family() const { return family_; } + inline int height() const { return height_; } + inline Weight weight() const { return weight_; } + inline Style style() const { return style_; } + +private: + GlFont(const std::string& family, int height, Weight weight, Style style); + + std::string family_; + int height_; + Weight weight_; + Style style_; + + unsigned int base_; + + GlFont(const GlFont&); + GlFont& operator =(const GlFont&); +}; + +//! render functions + +//! render texture rectangle in window +CV_EXPORTS void render(const GlTexture& tex, + Rect_ wndRect = Rect_(0.0, 0.0, 1.0, 1.0), + Rect_ texRect = Rect_(0.0, 0.0, 1.0, 1.0)); + +//! render mode +namespace RenderMode { + enum { + POINTS = 0x0000, + LINES = 0x0001, + LINE_LOOP = 0x0002, + LINE_STRIP = 0x0003, + TRIANGLES = 0x0004, + TRIANGLE_STRIP = 0x0005, + TRIANGLE_FAN = 0x0006, + QUADS = 0x0007, + QUAD_STRIP = 0x0008, + POLYGON = 0x0009 + }; +} - //! render OpenGL arrays - CV_EXPORTS void render(const GlArrays& arr, int mode = RenderMode::POINTS, Scalar color = Scalar::all(255)); +//! render OpenGL arrays +CV_EXPORTS void render(const GlArrays& arr, int mode = RenderMode::POINTS, Scalar color = Scalar::all(255)); - CV_EXPORTS void render(const std::string& str, const Ptr& font, Scalar color, Point2d pos); +CV_EXPORTS void render(const std::string& str, const Ptr& font, Scalar color, Point2d pos); - //! OpenGL camera - class CV_EXPORTS GlCamera - { - public: - GlCamera(); +//! OpenGL camera +class CV_EXPORTS GlCamera +{ +public: + GlCamera(); - void lookAt(Point3d eye, Point3d center, Point3d up); - void setCameraPos(Point3d pos, double yaw, double pitch, double roll); + void lookAt(Point3d eye, Point3d center, Point3d up); + void setCameraPos(Point3d pos, double yaw, double pitch, double roll); - void setScale(Point3d scale); + void setScale(Point3d scale); - void setProjectionMatrix(const Mat& projectionMatrix, bool transpose = true); - void setPerspectiveProjection(double fov, double aspect, double zNear, double zFar); - void setOrthoProjection(double left, double right, double bottom, double top, double zNear, double zFar); + void setProjectionMatrix(const Mat& projectionMatrix, bool transpose = true); + void setPerspectiveProjection(double fov, double aspect, double zNear, double zFar); + void setOrthoProjection(double left, double right, double bottom, double top, double zNear, double zFar); - void setupProjectionMatrix() const; - void setupModelViewMatrix() const; + void setupProjectionMatrix() const; + void setupModelViewMatrix() const; - private: - Point3d eye_; - Point3d center_; - Point3d up_; +private: + Point3d eye_; + Point3d center_; + Point3d up_; - Point3d pos_; - double yaw_; - double pitch_; - double roll_; + Point3d pos_; + double yaw_; + double pitch_; + double roll_; - bool useLookAtParams_; + bool useLookAtParams_; - Point3d scale_; + Point3d scale_; - Mat projectionMatrix_; + Mat projectionMatrix_; - double fov_; - double aspect_; + double fov_; + double aspect_; - double left_; - double right_; - double bottom_; - double top_; + double left_; + double right_; + double bottom_; + double top_; - double zNear_; - double zFar_; + double zNear_; + double zFar_; - bool perspectiveProjection_; - }; + bool perspectiveProjection_; +}; - namespace gpu - { - //! set a CUDA device to use OpenGL interoperability - CV_EXPORTS void setGlDevice(int device = 0); - } +inline void GlBuffer::create(Size _size, int _type, Usage _usage) { create(_size.height, _size.width, _type, _usage); } +inline void GlBuffer::create(int _rows, int _cols, int _type) { create(_rows, _cols, _type, usage()); } +inline void GlBuffer::create(Size _size, int _type) { create(_size.height, _size.width, _type, usage()); } +inline void GlTexture::create(Size _size, int _type) { create(_size.height, _size.width, _type); } + +namespace gpu +{ + //! set a CUDA device to use OpenGL interoperability + CV_EXPORTS void setGlDevice(int device = 0); +} } // namespace cv #endif // __cplusplus diff --git a/modules/core/include/opencv2/core/operations.hpp b/modules/core/include/opencv2/core/operations.hpp index 1d8d42f..c7bc94c 100644 --- a/modules/core/include/opencv2/core/operations.hpp +++ b/modules/core/include/opencv2/core/operations.hpp @@ -55,7 +55,7 @@ #if defined __INTEL_COMPILER && !(defined WIN32 || defined _WIN32) // atomic increment on the linux version of the Intel(tm) compiler #define CV_XADD(addr,delta) _InterlockedExchangeAdd(const_cast(reinterpret_cast(addr)), delta) #elif defined __GNUC__ - + #if __GNUC__*10 + __GNUC_MINOR__ >= 42 #if !defined WIN32 && (defined __i486__ || defined __i586__ || \ @@ -74,7 +74,7 @@ #define CV_XADD __exchange_and_add #endif #endif - + #elif defined WIN32 || defined _WIN32 #define WIN32_MEAN_AND_LEAN #ifndef _WIN32_WINNT // This is needed for the declaration of TryEnterCriticalSection in winbase.h with Visual Studio 2005 (and older?) @@ -88,14 +88,14 @@ #else static inline int CV_XADD(int* addr, int delta) - { int tmp = *addr; *addr += delta; return tmp; } + { int tmp = *addr; *addr += delta; return tmp; } #endif #include namespace cv { - + using std::cos; using std::sin; using std::max; @@ -105,7 +105,7 @@ using std::log; using std::pow; using std::sqrt; - + /////////////// saturate_cast (used in image & signal processing) /////////////////// template static inline _Tp saturate_cast(uchar v) { return _Tp(v); } @@ -184,7 +184,7 @@ template<> inline int saturate_cast(double v) { return cvRound(v); } // we intentionally do not clip negative numbers, to make -1 become 0xffffffff etc. template<> inline unsigned saturate_cast(float v){ return cvRound(v); } template<> inline unsigned saturate_cast(double v) { return cvRound(v); } - + inline int fast_abs(uchar v) { return v; } inline int fast_abs(schar v) { return std::abs((int)v); } inline int fast_abs(ushort v) { return v; } @@ -284,7 +284,7 @@ template inline Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1 for(int i = 10; i < channels; i++) val[i] = _Tp(0); } - + template inline Matx<_Tp,m,n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, @@ -349,7 +349,7 @@ template inline _Tp Matx<_Tp, m, n>::dot(const Matx< return s; } - + template inline double Matx<_Tp, m, n>::ddot(const Matx<_Tp, m, n>& M) const { double s = 0; @@ -376,7 +376,7 @@ Matx<_Tp,m,n> Matx<_Tp,m,n>::randu(_Tp a, _Tp b) cv::randu(matM, Scalar(a), Scalar(b)); return M; } - + template inline Matx<_Tp,m,n> Matx<_Tp,m,n>::randn(_Tp a, _Tp b) { @@ -385,7 +385,7 @@ Matx<_Tp,m,n> Matx<_Tp,m,n>::randn(_Tp a, _Tp b) cv::randn(matM, Scalar(a), Scalar(b)); return M; } - + template template inline Matx<_Tp, m, n>::operator Matx() const { @@ -393,7 +393,7 @@ inline Matx<_Tp, m, n>::operator Matx() const for( int i = 0; i < m*n; i++ ) M.val[i] = saturate_cast(val[i]); return M; } - + template template inline Matx<_Tp, m1, n1> Matx<_Tp, m, n>::reshape() const @@ -423,7 +423,7 @@ Matx<_Tp, 1, n> Matx<_Tp, m, n>::row(int i) const return Matx<_Tp, 1, n>(&val[i*n]); } - + template inline Matx<_Tp, m, 1> Matx<_Tp, m, n>::col(int j) const { @@ -434,7 +434,7 @@ Matx<_Tp, m, 1> Matx<_Tp, m, n>::col(int j) const return v; } - + template inline typename Matx<_Tp, m, n>::diag_type Matx<_Tp, m, n>::diag() const { @@ -444,7 +444,7 @@ typename Matx<_Tp, m, n>::diag_type Matx<_Tp, m, n>::diag() const return d; } - + template inline const _Tp& Matx<_Tp, m, n>::operator ()(int i, int j) const { @@ -452,7 +452,7 @@ const _Tp& Matx<_Tp, m, n>::operator ()(int i, int j) const return this->val[i*n + j]; } - + template inline _Tp& Matx<_Tp, m, n>::operator ()(int i, int j) { @@ -476,23 +476,23 @@ _Tp& Matx<_Tp, m, n>::operator ()(int i) return val[i]; } - + template static inline Matx<_Tp1, m, n>& operator += (Matx<_Tp1, m, n>& a, const Matx<_Tp2, m, n>& b) { for( int i = 0; i < m*n; i++ ) a.val[i] = saturate_cast<_Tp1>(a.val[i] + b.val[i]); return a; -} +} + - template static inline Matx<_Tp1, m, n>& operator -= (Matx<_Tp1, m, n>& a, const Matx<_Tp2, m, n>& b) { for( int i = 0; i < m*n; i++ ) a.val[i] = saturate_cast<_Tp1>(a.val[i] - b.val[i]); return a; -} +} template inline @@ -502,31 +502,31 @@ Matx<_Tp,m,n>::Matx(const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b, Matx_Add val[i] = saturate_cast<_Tp>(a.val[i] + b.val[i]); } - + template inline Matx<_Tp,m,n>::Matx(const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b, Matx_SubOp) { for( int i = 0; i < m*n; i++ ) val[i] = saturate_cast<_Tp>(a.val[i] - b.val[i]); } - - + + template template inline Matx<_Tp,m,n>::Matx(const Matx<_Tp, m, n>& a, _T2 alpha, Matx_ScaleOp) { for( int i = 0; i < m*n; i++ ) val[i] = saturate_cast<_Tp>(a.val[i] * alpha); } - - + + template inline Matx<_Tp,m,n>::Matx(const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b, Matx_MulOp) { for( int i = 0; i < m*n; i++ ) val[i] = saturate_cast<_Tp>(a.val[i] * b.val[i]); } - - + + template template inline Matx<_Tp,m,n>::Matx(const Matx<_Tp, m, l>& a, const Matx<_Tp, l, n>& b, Matx_MatMulOp) { @@ -539,8 +539,8 @@ Matx<_Tp,m,n>::Matx(const Matx<_Tp, m, l>& a, const Matx<_Tp, l, n>& b, Matx_Mat val[i*n + j] = s; } } - - + + template inline Matx<_Tp,m,n>::Matx(const Matx<_Tp, n, m>& a, Matx_TOp) { @@ -549,20 +549,20 @@ Matx<_Tp,m,n>::Matx(const Matx<_Tp, n, m>& a, Matx_TOp) val[i*n + j] = a(j, i); } - + template static inline Matx<_Tp, m, n> operator + (const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b) { return Matx<_Tp, m, n>(a, b, Matx_AddOp()); } - - + + template static inline Matx<_Tp, m, n> operator - (const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b) { return Matx<_Tp, m, n>(a, b, Matx_SubOp()); -} - +} + template static inline Matx<_Tp, m, n>& operator *= (Matx<_Tp, m, n>& a, int alpha) @@ -570,15 +570,15 @@ Matx<_Tp, m, n>& operator *= (Matx<_Tp, m, n>& a, int alpha) for( int i = 0; i < m*n; i++ ) a.val[i] = saturate_cast<_Tp>(a.val[i] * alpha); return a; -} - +} + template static inline Matx<_Tp, m, n>& operator *= (Matx<_Tp, m, n>& a, float alpha) { for( int i = 0; i < m*n; i++ ) a.val[i] = saturate_cast<_Tp>(a.val[i] * alpha); return a; -} +} template static inline Matx<_Tp, m, n>& operator *= (Matx<_Tp, m, n>& a, double alpha) @@ -586,44 +586,44 @@ Matx<_Tp, m, n>& operator *= (Matx<_Tp, m, n>& a, double alpha) for( int i = 0; i < m*n; i++ ) a.val[i] = saturate_cast<_Tp>(a.val[i] * alpha); return a; -} +} template static inline Matx<_Tp, m, n> operator * (const Matx<_Tp, m, n>& a, int alpha) { return Matx<_Tp, m, n>(a, alpha, Matx_ScaleOp()); -} +} template static inline Matx<_Tp, m, n> operator * (const Matx<_Tp, m, n>& a, float alpha) { return Matx<_Tp, m, n>(a, alpha, Matx_ScaleOp()); -} +} template static inline Matx<_Tp, m, n> operator * (const Matx<_Tp, m, n>& a, double alpha) { return Matx<_Tp, m, n>(a, alpha, Matx_ScaleOp()); -} - +} + template static inline Matx<_Tp, m, n> operator * (int alpha, const Matx<_Tp, m, n>& a) { return Matx<_Tp, m, n>(a, alpha, Matx_ScaleOp()); -} +} template static inline Matx<_Tp, m, n> operator * (float alpha, const Matx<_Tp, m, n>& a) { return Matx<_Tp, m, n>(a, alpha, Matx_ScaleOp()); -} +} template static inline Matx<_Tp, m, n> operator * (double alpha, const Matx<_Tp, m, n>& a) { return Matx<_Tp, m, n>(a, alpha, Matx_ScaleOp()); -} - +} + template static inline Matx<_Tp, m, n> operator - (const Matx<_Tp, m, n>& a) { @@ -637,15 +637,15 @@ Matx<_Tp, m, n> operator * (const Matx<_Tp, m, l>& a, const Matx<_Tp, l, n>& b) return Matx<_Tp, m, n>(a, b, Matx_MatMulOp()); } - + template static inline Vec<_Tp, m> operator * (const Matx<_Tp, m, n>& a, const Vec<_Tp, n>& b) { Matx<_Tp, m, 1> c(a, b, Matx_MatMulOp()); return reinterpret_cast&>(c); } - - + + template static inline Point_<_Tp> operator * (const Matx<_Tp, 2, 2>& a, const Point_<_Tp>& b) { @@ -653,13 +653,13 @@ Point_<_Tp> operator * (const Matx<_Tp, 2, 2>& a, const Point_<_Tp>& b) return Point_<_Tp>(tmp.val[0], tmp.val[1]); } - + template static inline Point3_<_Tp> operator * (const Matx<_Tp, 3, 3>& a, const Point3_<_Tp>& b) { Matx<_Tp, 3, 1> tmp = a*Vec<_Tp,3>(b.x, b.y, b.z); return Point3_<_Tp>(tmp.val[0], tmp.val[1], tmp.val[2]); -} +} template static inline @@ -667,14 +667,14 @@ Point3_<_Tp> operator * (const Matx<_Tp, 3, 3>& a, const Point_<_Tp>& b) { Matx<_Tp, 3, 1> tmp = a*Vec<_Tp,3>(b.x, b.y, 1); return Point3_<_Tp>(tmp.val[0], tmp.val[1], tmp.val[2]); -} +} + - template static inline Matx<_Tp, 4, 1> operator * (const Matx<_Tp, 4, 4>& a, const Point3_<_Tp>& b) { return a*Matx<_Tp, 4, 1>(b.x, b.y, b.z, 1); -} +} template static inline @@ -684,7 +684,7 @@ Scalar operator * (const Matx<_Tp, 4, 4>& a, const Scalar& b) return reinterpret_cast(c); } - + static inline Scalar operator * (const Matx& a, const Scalar& b) { @@ -692,18 +692,18 @@ Scalar operator * (const Matx& a, const Scalar& b) return reinterpret_cast(c); } - + template inline Matx<_Tp, m, n> Matx<_Tp, m, n>::mul(const Matx<_Tp, m, n>& a) const { return Matx<_Tp, m, n>(*this, a, Matx_MulOp()); } - + CV_EXPORTS int LU(float* A, size_t astep, int m, float* b, size_t bstep, int n); CV_EXPORTS int LU(double* A, size_t astep, int m, double* b, size_t bstep, int n); CV_EXPORTS bool Cholesky(float* A, size_t astep, int m, float* b, size_t bstep, int n); -CV_EXPORTS bool Cholesky(double* A, size_t astep, int m, double* b, size_t bstep, int n); +CV_EXPORTS bool Cholesky(double* A, size_t astep, int m, double* b, size_t bstep, int n); template struct CV_EXPORTS Matx_DetOp @@ -719,7 +719,7 @@ template struct CV_EXPORTS Matx_DetOp return p; } }; - + template struct CV_EXPORTS Matx_DetOp<_Tp, 1> { @@ -748,13 +748,13 @@ template struct CV_EXPORTS Matx_DetOp<_Tp, 3> a(0,2)*(a(1,0)*a(2,1) - a(2,0)*a(1,1)); } }; - + template static inline double determinant(const Matx<_Tp, m, m>& a) { - return Matx_DetOp<_Tp, m>()(a); + return Matx_DetOp<_Tp, m>()(a); } - + template static inline double trace(const Matx<_Tp, m, n>& a) @@ -763,9 +763,9 @@ double trace(const Matx<_Tp, m, n>& a) for( int i = 0; i < std::min(m, n); i++ ) s += a(i,i); return s; -} +} + - template inline Matx<_Tp, n, m> Matx<_Tp, m, n>::t() const { @@ -778,19 +778,19 @@ template struct CV_EXPORTS Matx_FastInvOp bool operator()(const Matx<_Tp, m, m>& a, Matx<_Tp, m, m>& b, int method) const { Matx<_Tp, m, m> temp = a; - + // assume that b is all 0's on input => make it a unity matrix for( int i = 0; i < m; i++ ) b(i, i) = (_Tp)1; - + if( method == DECOMP_CHOLESKY ) return Cholesky(temp.val, m*sizeof(_Tp), m, b.val, m*sizeof(_Tp), m); - + return LU(temp.val, m*sizeof(_Tp), m, b.val, m*sizeof(_Tp), m) != 0; } }; - + template struct CV_EXPORTS Matx_FastInvOp<_Tp, 2> { bool operator()(const Matx<_Tp, 2, 2>& a, Matx<_Tp, 2, 2>& b, int) const @@ -807,7 +807,7 @@ template struct CV_EXPORTS Matx_FastInvOp<_Tp, 2> } }; - + template struct CV_EXPORTS Matx_FastInvOp<_Tp, 3> { bool operator()(const Matx<_Tp, 3, 3>& a, Matx<_Tp, 3, 3>& b, int) const @@ -819,11 +819,11 @@ template struct CV_EXPORTS Matx_FastInvOp<_Tp, 3> b(0,0) = (a(1,1) * a(2,2) - a(1,2) * a(2,1)) * d; b(0,1) = (a(0,2) * a(2,1) - a(0,1) * a(2,2)) * d; b(0,2) = (a(0,1) * a(1,2) - a(0,2) * a(1,1)) * d; - + b(1,0) = (a(1,2) * a(2,0) - a(1,0) * a(2,2)) * d; b(1,1) = (a(0,0) * a(2,2) - a(0,2) * a(2,0)) * d; b(1,2) = (a(0,2) * a(1,0) - a(0,0) * a(1,2)) * d; - + b(2,0) = (a(1,0) * a(2,1) - a(1,1) * a(2,0)) * d; b(2,1) = (a(0,1) * a(2,0) - a(0,0) * a(2,1)) * d; b(2,2) = (a(0,0) * a(1,1) - a(0,1) * a(1,0)) * d; @@ -831,7 +831,7 @@ template struct CV_EXPORTS Matx_FastInvOp<_Tp, 3> } }; - + template inline Matx<_Tp, n, m> Matx<_Tp, m, n>::inv(int method) const { @@ -857,7 +857,7 @@ template struct CV_EXPORTS Matx_FastSolveOp x = b; if( method == DECOMP_CHOLESKY ) return Cholesky(temp.val, m*sizeof(_Tp), m, x.val, n*sizeof(_Tp), n); - + return LU(temp.val, m*sizeof(_Tp), m, x.val, n*sizeof(_Tp), n) != 0; } }; @@ -878,7 +878,7 @@ template struct CV_EXPORTS Matx_FastSolveOp<_Tp, 2, 1> } }; - + template struct CV_EXPORTS Matx_FastSolveOp<_Tp, 3, 1> { bool operator()(const Matx<_Tp, 3, 3>& a, const Matx<_Tp, 3, 1>& b, @@ -891,19 +891,19 @@ template struct CV_EXPORTS Matx_FastSolveOp<_Tp, 3, 1> x(0) = d*(b(0)*(a(1,1)*a(2,2) - a(1,2)*a(2,1)) - a(0,1)*(b(1)*a(2,2) - a(1,2)*b(2)) + a(0,2)*(b(1)*a(2,1) - a(1,1)*b(2))); - + x(1) = d*(a(0,0)*(b(1)*a(2,2) - a(1,2)*b(2)) - b(0)*(a(1,0)*a(2,2) - a(1,2)*a(2,0)) + a(0,2)*(a(1,0)*b(2) - b(1)*a(2,0))); - + x(2) = d*(a(0,0)*(a(1,1)*b(2) - b(1)*a(2,1)) - a(0,1)*(a(1,0)*b(2) - b(1)*a(2,0)) + b(0)*(a(1,0)*a(2,1) - a(1,1)*a(2,0))); return true; } }; - - + + template template inline Matx<_Tp, n, l> Matx<_Tp, m, n>::solve(const Matx<_Tp, m, l>& rhs, int method) const { @@ -920,13 +920,13 @@ Matx<_Tp, n, l> Matx<_Tp, m, n>::solve(const Matx<_Tp, m, l>& rhs, int method) c return ok ? x : Matx<_Tp, n, l>::zeros(); } -template inline +template inline Vec<_Tp, n> Matx<_Tp, m, n>::solve(const Vec<_Tp, m>& rhs, int method) const { Matx<_Tp, n, 1> x = solve(reinterpret_cast&>(rhs), method); return reinterpret_cast&>(x); } - + template static inline _AccTp normL2Sqr(const _Tp* a, int n) { @@ -974,8 +974,8 @@ _AccTp normInf(const _Tp* a, int n) s = std::max(s, (_AccTp)fast_abs(a[i])); return s; } - - + + template static inline _AccTp normL2Sqr(const _Tp* a, const _Tp* b, int n) { @@ -984,13 +984,13 @@ _AccTp normL2Sqr(const _Tp* a, const _Tp* b, int n) #if CV_ENABLE_UNROLLED for(; i <= n - 4; i += 4 ) { - _AccTp v0 = a[i] - b[i], v1 = a[i+1] - b[i+1], v2 = a[i+2] - b[i+2], v3 = a[i+3] - b[i+3]; + _AccTp v0 = _AccTp(a[i] - b[i]), v1 = _AccTp(a[i+1] - b[i+1]), v2 = _AccTp(a[i+2] - b[i+2]), v3 = _AccTp(a[i+3] - b[i+3]); s += v0*v0 + v1*v1 + v2*v2 + v3*v3; } #endif for( ; i < n; i++ ) { - _AccTp v = (_AccTp)(a[i] - b[i]); + _AccTp v = _AccTp(a[i] - b[i]); s += v*v; } return s; @@ -1001,7 +1001,7 @@ CV_EXPORTS float normL1_(const float* a, const float* b, int n); CV_EXPORTS int normL1_(const uchar* a, const uchar* b, int n); CV_EXPORTS int normHamming(const uchar* a, const uchar* b, int n); CV_EXPORTS int normHamming(const uchar* a, const uchar* b, int n, int cellSize); - + template<> inline float normL2Sqr(const float* a, const float* b, int n) { if( n >= 8 ) @@ -1015,7 +1015,7 @@ template<> inline float normL2Sqr(const float* a, const float* b, int n) return s; } - + template static inline _AccTp normL1(const _Tp* a, const _Tp* b, int n) { @@ -1024,13 +1024,13 @@ _AccTp normL1(const _Tp* a, const _Tp* b, int n) #if CV_ENABLE_UNROLLED for(; i <= n - 4; i += 4 ) { - _AccTp v0 = a[i] - b[i], v1 = a[i+1] - b[i+1], v2 = a[i+2] - b[i+2], v3 = a[i+3] - b[i+3]; + _AccTp v0 = _AccTp(a[i] - b[i]), v1 = _AccTp(a[i+1] - b[i+1]), v2 = _AccTp(a[i+2] - b[i+2]), v3 = _AccTp(a[i+3] - b[i+3]); s += std::abs(v0) + std::abs(v1) + std::abs(v2) + std::abs(v3); } #endif for( ; i < n; i++ ) { - _AccTp v = (_AccTp)(a[i] - b[i]); + _AccTp v = _AccTp(a[i] - b[i]); s += std::abs(v); } return s; @@ -1052,7 +1052,7 @@ template<> inline float normL1(const float* a, const float* b, int n) template<> inline int normL1(const uchar* a, const uchar* b, int n) { return normL1_(a, b, n); -} +} template static inline _AccTp normInf(const _Tp* a, const _Tp* b, int n) @@ -1065,7 +1065,7 @@ _AccTp normInf(const _Tp* a, const _Tp* b, int n) } return s; } - + template static inline double norm(const Matx<_Tp, m, n>& M) @@ -1073,7 +1073,7 @@ double norm(const Matx<_Tp, m, n>& M) return std::sqrt(normL2Sqr<_Tp, double>(M.val, m*n)); } - + template static inline double norm(const Matx<_Tp, m, n>& M, int normType) { @@ -1081,8 +1081,8 @@ double norm(const Matx<_Tp, m, n>& M, int normType) normType == NORM_L1 ? (double)normL1<_Tp, DataType<_Tp>::work_type>(M.val, m*n) : std::sqrt((double)normL2Sqr<_Tp, DataType<_Tp>::work_type>(M.val, m*n)); } - - + + template static inline bool operator == (const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b) { @@ -1090,7 +1090,7 @@ bool operator == (const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b) if( a.val[i] != b.val[i] ) return false; return true; } - + template static inline bool operator != (const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b) { @@ -1123,7 +1123,7 @@ Matx<_Tp, m, n> MatxCommaInitializer<_Tp, m, n>::operator *() const { CV_DbgAssert( idx == n*m ); return *dst; -} +} /////////////////////////// short vector (Vec) ///////////////////////////// @@ -1175,11 +1175,11 @@ template inline Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v8, _Tp v9) : Matx<_Tp, cn, 1>(v0, v1, v2, v3, v4, v5, v6, v7, v8, v9) {} - + template inline Vec<_Tp, cn>::Vec(const _Tp* values) : Matx<_Tp, cn, 1>(values) {} - + template inline Vec<_Tp, cn>::Vec(const Vec<_Tp, cn>& m) : Matx<_Tp, cn, 1>(m.val) @@ -1198,8 +1198,8 @@ Vec<_Tp, cn>::Vec(const Matx<_Tp, cn, 1>& a, const Matx<_Tp, cn, 1>& b, Matx_Sub template template inline Vec<_Tp, cn>::Vec(const Matx<_Tp, cn, 1>& a, _T2 alpha, Matx_ScaleOp op) : Matx<_Tp, cn, 1>(a, alpha, op) -{} - +{} + template inline Vec<_Tp, cn> Vec<_Tp, cn>::all(_Tp alpha) { Vec v; @@ -1222,8 +1222,8 @@ template Vec<_Tp, 2> conjugate(const Vec<_Tp, 2>& v) template Vec<_Tp, 4> conjugate(const Vec<_Tp, 4>& v) { return Vec<_Tp, 4>(v[0], -v[1], -v[2], -v[3]); -} - +} + template<> inline Vec Vec::conj() const { return conjugate(*this); @@ -1243,13 +1243,13 @@ template<> inline Vec Vec::conj() const { return conjugate(*this); } - + template inline Vec<_Tp, cn> Vec<_Tp, cn>::cross(const Vec<_Tp, cn>& v) const { CV_Error(CV_StsError, "for arbitrary-size vector there is no cross-product defined"); return Vec<_Tp, cn>(); } - + template template inline Vec<_Tp, cn>::operator Vec() const { @@ -1272,7 +1272,7 @@ template inline const _Tp& Vec<_Tp, cn>::operator [](int i CV_DbgAssert( (unsigned)i < (unsigned)cn ); return this->val[i]; } - + template inline _Tp& Vec<_Tp, cn>::operator [](int i) { CV_DbgAssert( (unsigned)i < (unsigned)cn ); @@ -1289,15 +1289,15 @@ template inline _Tp& Vec<_Tp, cn>::operator ()(int i) { CV_DbgAssert( (unsigned)i < (unsigned)cn ); return this->val[i]; -} - +} + template static inline Vec<_Tp1, cn>& operator += (Vec<_Tp1, cn>& a, const Vec<_Tp2, cn>& b) { for( int i = 0; i < cn; i++ ) a.val[i] = saturate_cast<_Tp1>(a.val[i] + b.val[i]); return a; -} +} template static inline Vec<_Tp1, cn>& operator -= (Vec<_Tp1, cn>& a, const Vec<_Tp2, cn>& b) @@ -1305,8 +1305,8 @@ operator -= (Vec<_Tp1, cn>& a, const Vec<_Tp2, cn>& b) for( int i = 0; i < cn; i++ ) a.val[i] = saturate_cast<_Tp1>(a.val[i] - b.val[i]); return a; -} - +} + template static inline Vec<_Tp, cn> operator + (const Vec<_Tp, cn>& a, const Vec<_Tp, cn>& b) { @@ -1334,7 +1334,7 @@ Vec<_Tp, cn>& operator *= (Vec<_Tp, cn>& a, float alpha) a[i] = saturate_cast<_Tp>(a[i]*alpha); return a; } - + template static inline Vec<_Tp, cn>& operator *= (Vec<_Tp, cn>& a, double alpha) { @@ -1351,7 +1351,7 @@ Vec<_Tp, cn>& operator /= (Vec<_Tp, cn>& a, int alpha) a[i] = saturate_cast<_Tp>(a[i]*ialpha); return a; } - + template static inline Vec<_Tp, cn>& operator /= (Vec<_Tp, cn>& a, float alpha) { @@ -1368,8 +1368,8 @@ Vec<_Tp, cn>& operator /= (Vec<_Tp, cn>& a, double alpha) for( int i = 0; i < cn; i++ ) a[i] = saturate_cast<_Tp>(a[i]*ialpha); return a; -} - +} + template static inline Vec<_Tp, cn> operator * (const Vec<_Tp, cn>& a, int alpha) { @@ -1404,7 +1404,7 @@ template static inline Vec<_Tp, cn> operator * (double alpha, const Vec<_Tp, cn>& a) { return Vec<_Tp, cn>(a, alpha, Matx_ScaleOp()); -} +} template static inline Vec<_Tp, cn> operator / (const Vec<_Tp, cn>& a, int alpha) @@ -1416,14 +1416,14 @@ template static inline Vec<_Tp, cn> operator / (const Vec<_Tp, cn>& a, float alpha) { return Vec<_Tp, cn>(a, 1.f/alpha, Matx_ScaleOp()); -} +} template static inline Vec<_Tp, cn> operator / (const Vec<_Tp, cn>& a, double alpha) { return Vec<_Tp, cn>(a, 1./alpha, Matx_ScaleOp()); -} - +} + template static inline Vec<_Tp, cn> operator - (const Vec<_Tp, cn>& a) { @@ -1439,13 +1439,13 @@ template inline Vec<_Tp, 4> operator * (const Vec<_Tp, 4>& v1, con saturate_cast<_Tp>(v1[0]*v2[2] - v1[1]*v2[3] + v1[2]*v2[0] + v1[3]*v2[1]), saturate_cast<_Tp>(v1[0]*v2[3] + v1[1]*v2[2] - v1[2]*v2[1] + v1[3]*v2[0])); } - + template inline Vec<_Tp, 4>& operator *= (Vec<_Tp, 4>& v1, const Vec<_Tp, 4>& v2) { v1 = v1 * v2; return v1; } - + template<> inline Vec Vec::cross(const Vec& v) const { return Vec(val[1]*v.val[2] - val[2]*v.val[1], @@ -1465,14 +1465,14 @@ template inline Vec<_Tp, cn> normalize(const Vec<_Tp, cn>& double nv = norm(v); return v * (nv ? 1./nv : 0.); } - + template static inline VecCommaInitializer<_Tp, cn> operator << (const Vec<_Tp, cn>& vec, _T2 val) { VecCommaInitializer<_Tp, cn> commaInitializer((Vec<_Tp, cn>*)&vec); return (commaInitializer, val); } - + template inline VecCommaInitializer<_Tp, cn>::VecCommaInitializer(Vec<_Tp, cn>* _vec) : MatxCommaInitializer<_Tp, cn, 1>(_vec) @@ -1491,7 +1491,7 @@ Vec<_Tp, cn> VecCommaInitializer<_Tp, cn>::operator *() const { CV_DbgAssert( this->idx == cn ); return *this->dst; -} +} //////////////////////////////// Complex ////////////////////////////// @@ -1508,8 +1508,8 @@ bool operator == (const Complex<_Tp>& a, const Complex<_Tp>& b) template static inline bool operator != (const Complex<_Tp>& a, const Complex<_Tp>& b) -{ return a.re != b.re || a.im != b.im; } - +{ return a.re != b.re || a.im != b.im; } + template static inline Complex<_Tp> operator + (const Complex<_Tp>& a, const Complex<_Tp>& b) { return Complex<_Tp>( a.re + b.re, a.im + b.im ); } @@ -1637,7 +1637,7 @@ template inline double Point_<_Tp>::ddot(const Point_& pt) const template inline double Point_<_Tp>::cross(const Point_& pt) const { return (double)x*pt.y - (double)y*pt.x; } - + template static inline Point_<_Tp>& operator += (Point_<_Tp>& a, const Point_<_Tp>& b) { @@ -1676,8 +1676,8 @@ operator *= (Point_<_Tp>& a, double b) a.x = saturate_cast<_Tp>(a.x*b); a.y = saturate_cast<_Tp>(a.y*b); return a; -} - +} + template static inline double norm(const Point_<_Tp>& pt) { return std::sqrt((double)pt.x*pt.x + (double)pt.y*pt.y); } @@ -1701,7 +1701,7 @@ template static inline Point_<_Tp> operator * (const Point_<_Tp>& template static inline Point_<_Tp> operator * (int a, const Point_<_Tp>& b) { return Point_<_Tp>( saturate_cast<_Tp>(b.x*a), saturate_cast<_Tp>(b.y*a) ); } - + template static inline Point_<_Tp> operator * (const Point_<_Tp>& a, float b) { return Point_<_Tp>( saturate_cast<_Tp>(a.x*b), saturate_cast<_Tp>(a.y*b) ); } @@ -1712,8 +1712,8 @@ template static inline Point_<_Tp> operator * (const Point_<_Tp>& { return Point_<_Tp>( saturate_cast<_Tp>(a.x*b), saturate_cast<_Tp>(a.y*b) ); } template static inline Point_<_Tp> operator * (double a, const Point_<_Tp>& b) -{ return Point_<_Tp>( saturate_cast<_Tp>(b.x*a), saturate_cast<_Tp>(b.y*a) ); } - +{ return Point_<_Tp>( saturate_cast<_Tp>(b.x*a), saturate_cast<_Tp>(b.y*a) ); } + //////////////////////////////// 3D Point //////////////////////////////// template inline Point3_<_Tp>::Point3_() : x(0), y(0), z(0) {} @@ -1740,7 +1740,7 @@ template inline _Tp Point3_<_Tp>::dot(const Point3_& pt) const { return saturate_cast<_Tp>(x*pt.x + y*pt.y + z*pt.z); } template inline double Point3_<_Tp>::ddot(const Point3_& pt) const { return (double)x*pt.x + (double)y*pt.y + (double)z*pt.z; } - + template inline Point3_<_Tp> Point3_<_Tp>::cross(const Point3_<_Tp>& pt) const { return Point3_<_Tp>(y*pt.z - z*pt.y, z*pt.x - x*pt.z, x*pt.y - y*pt.x); @@ -1754,7 +1754,7 @@ operator += (Point3_<_Tp>& a, const Point3_<_Tp>& b) a.z = saturate_cast<_Tp>(a.z + b.z); return a; } - + template static inline Point3_<_Tp>& operator -= (Point3_<_Tp>& a, const Point3_<_Tp>& b) { @@ -1762,8 +1762,8 @@ operator -= (Point3_<_Tp>& a, const Point3_<_Tp>& b) a.y = saturate_cast<_Tp>(a.y - b.y); a.z = saturate_cast<_Tp>(a.z - b.z); return a; -} - +} + template static inline Point3_<_Tp>& operator *= (Point3_<_Tp>& a, int b) { @@ -1789,8 +1789,8 @@ operator *= (Point3_<_Tp>& a, double b) a.y = saturate_cast<_Tp>(a.y*b); a.z = saturate_cast<_Tp>(a.z*b); return a; -} - +} + template static inline double norm(const Point3_<_Tp>& pt) { return std::sqrt((double)pt.x*pt.x + (double)pt.y*pt.y + (double)pt.z*pt.z); } @@ -1799,7 +1799,7 @@ template static inline bool operator == (const Point3_<_Tp>& a, co template static inline bool operator != (const Point3_<_Tp>& a, const Point3_<_Tp>& b) { return a.x != b.x || a.y != b.y || a.z != b.z; } - + template static inline Point3_<_Tp> operator + (const Point3_<_Tp>& a, const Point3_<_Tp>& b) { return Point3_<_Tp>( saturate_cast<_Tp>(a.x + b.x), saturate_cast<_Tp>(a.y + b.y), @@ -1844,7 +1844,7 @@ template static inline Point3_<_Tp> operator * (double a, const Po { return Point3_<_Tp>( saturate_cast<_Tp>(b.x*a), saturate_cast<_Tp>(b.y*a), saturate_cast<_Tp>(b.z*a) ); } - + //////////////////////////////// Size //////////////////////////////// template inline Size_<_Tp>::Size_() @@ -1958,8 +1958,8 @@ template static inline bool operator == (const Rect_<_Tp>& a, cons template static inline bool operator != (const Rect_<_Tp>& a, const Rect_<_Tp>& b) { return a.x != b.x || a.y != b.y || a.width != b.width || a.height != b.height; -} - +} + template static inline Rect_<_Tp> operator + (const Rect_<_Tp>& a, const Point_<_Tp>& b) { return Rect_<_Tp>( a.x + b.x, a.y + b.y, a.width, a.height ); @@ -2002,7 +2002,7 @@ inline RotatedRect::operator CvBox2D() const CvBox2D box; box.center = center; box.size = size; box.angle = angle; return box; } - + //////////////////////////////// Scalar_ /////////////////////////////// template inline Scalar_<_Tp>::Scalar_() @@ -2117,7 +2117,7 @@ template static inline Scalar_<_Tp> operator - (const Scalar_<_Tp> saturate_cast<_Tp>(-a.val[2]), saturate_cast<_Tp>(-a.val[3])); } - + template static inline Scalar_<_Tp> operator * (const Scalar_<_Tp>& a, const Scalar_<_Tp>& b) { @@ -2126,14 +2126,14 @@ operator * (const Scalar_<_Tp>& a, const Scalar_<_Tp>& b) saturate_cast<_Tp>(a[0]*b[2] - a[1]*b[3] + a[2]*b[0] + a[3]*b[1]), saturate_cast<_Tp>(a[0]*b[3] + a[1]*b[2] - a[2]*b[1] + a[3]*b[0])); } - + template static inline Scalar_<_Tp>& operator *= (Scalar_<_Tp>& a, const Scalar_<_Tp>& b) { a = a*b; return a; -} - +} + template inline Scalar_<_Tp> Scalar_<_Tp>::conj() const { return Scalar_<_Tp>(saturate_cast<_Tp>(this->val[0]), @@ -2146,7 +2146,7 @@ template inline bool Scalar_<_Tp>::isReal() const { return this->val[1] == 0 && this->val[2] == 0 && this->val[3] == 0; } - + template static inline Scalar_<_Tp> operator / (const Scalar_<_Tp>& a, _Tp alpha) { @@ -2154,36 +2154,36 @@ Scalar_<_Tp> operator / (const Scalar_<_Tp>& a, _Tp alpha) saturate_cast<_Tp>(a.val[1] / alpha), saturate_cast<_Tp>(a.val[2] / alpha), saturate_cast<_Tp>(a.val[3] / alpha)); -} +} template static inline Scalar_ operator / (const Scalar_& a, float alpha) { float s = 1/alpha; return Scalar_(a.val[0]*s, a.val[1]*s, a.val[2]*s, a.val[3]*s); -} +} template static inline Scalar_ operator / (const Scalar_& a, double alpha) { double s = 1/alpha; return Scalar_(a.val[0]*s, a.val[1]*s, a.val[2]*s, a.val[3]*s); -} - +} + template static inline Scalar_<_Tp>& operator /= (Scalar_<_Tp>& a, _Tp alpha) { a = a/alpha; return a; } - + template static inline Scalar_<_Tp> operator / (_Tp a, const Scalar_<_Tp>& b) { _Tp s = a/(b[0]*b[0] + b[1]*b[1] + b[2]*b[2] + b[3]*b[3]); return b.conj()*s; -} - +} + template static inline Scalar_<_Tp> operator / (const Scalar_<_Tp>& a, const Scalar_<_Tp>& b) { @@ -2196,7 +2196,7 @@ Scalar_<_Tp>& operator /= (Scalar_<_Tp>& a, const Scalar_<_Tp>& b) a = a/b; return a; } - + //////////////////////////////// Range ///////////////////////////////// inline Range::Range() : start(0), end(0) {} @@ -2251,8 +2251,8 @@ static inline Range operator - (const Range& r1, int delta) inline Range::operator CvSlice() const { return *this != Range::all() ? cvSlice(start, end) : CV_WHOLE_SEQ; } - - + + //////////////////////////////// Vector //////////////////////////////// // template vector class. It is similar to STL's vector, @@ -2268,7 +2268,7 @@ public: typedef const _Tp* const_iterator; typedef _Tp& reference; typedef const _Tp& const_reference; - + struct CV_EXPORTS Hdr { Hdr() : data(0), datastart(0), refcount(0), size(0), capacity(0) {}; @@ -2278,7 +2278,7 @@ public: size_t size; size_t capacity; }; - + Vector() {} Vector(size_t _size) { resize(_size); } Vector(size_t _size, const _Tp& val) @@ -2289,15 +2289,15 @@ public: } Vector(_Tp* _data, size_t _size, bool _copyData=false) { set(_data, _size, _copyData); } - + template Vector(const Vec<_Tp, n>& vec) - { set((_Tp*)&vec.val[0], n, true); } - + { set((_Tp*)&vec.val[0], n, true); } + Vector(const std::vector<_Tp>& vec, bool _copyData=false) - { set(!vec.empty() ? (_Tp*)&vec[0] : 0, vec.size(), _copyData); } - + { set(!vec.empty() ? (_Tp*)&vec[0] : 0, vec.size(), _copyData); } + Vector(const Vector& d) { *this = d; } - + Vector(const Vector& d, const Range& r_) { Range r = r_ == Range::all() ? Range(0, d.size()) : r_; @@ -2313,7 +2313,7 @@ public: hdr.capacity = hdr.size = r.size(); } } - + Vector<_Tp>& operator = (const Vector& d) { if( this != &d ) @@ -2325,12 +2325,12 @@ public: } return *this; } - + ~Vector() { release(); } - + Vector<_Tp> clone() const { return hdr.data ? Vector<_Tp>(hdr.data, hdr.size, true) : Vector<_Tp>(); } - + void copyTo(Vector<_Tp>& vec) const { size_t i, sz = size(); @@ -2340,7 +2340,7 @@ public: for( i = 0; i < sz; i++ ) dst[i] = src[i]; } - + void copyTo(std::vector<_Tp>& vec) const { size_t i, sz = size(); @@ -2350,10 +2350,10 @@ public: for( i = 0; i < sz; i++ ) dst[i] = src[i]; } - + operator CvMat() const { return cvMat((int)size(), 1, type(), (void*)hdr.data); } - + _Tp& operator [] (size_t i) { CV_DbgAssert( i < size() ); return hdr.data[i]; } const _Tp& operator [] (size_t i) const { CV_DbgAssert( i < size() ); return hdr.data[i]; } Vector operator() (const Range& r) const { return Vector(*this, r); } @@ -2361,12 +2361,12 @@ public: const _Tp& back() const { CV_DbgAssert(!empty()); return hdr.data[hdr.size-1]; } _Tp& front() { CV_DbgAssert(!empty()); return hdr.data[0]; } const _Tp& front() const { CV_DbgAssert(!empty()); return hdr.data[0]; } - + _Tp* begin() { return hdr.data; } _Tp* end() { return hdr.data + hdr.size; } const _Tp* begin() const { return hdr.data; } const _Tp* end() const { return hdr.data + hdr.size; } - + void addref() { if( hdr.refcount ) CV_XADD(hdr.refcount, 1); } void release() { @@ -2377,7 +2377,7 @@ public: } hdr = Hdr(); } - + void set(_Tp* _data, size_t _size, bool _copyData=false) { if( !_copyData ) @@ -2395,7 +2395,7 @@ public: hdr.size = _size; } } - + void reserve(size_t newCapacity) { _Tp* newData; @@ -2414,7 +2414,7 @@ public: hdr.size = oldSize; hdr.refcount = newRefcount; } - + void resize(size_t newSize) { size_t i; @@ -2427,7 +2427,7 @@ public: hdr.data[i] = _Tp(); hdr.size = newSize; } - + Vector<_Tp>& push_back(const _Tp& elem) { if( hdr.size == hdr.capacity ) @@ -2435,25 +2435,25 @@ public: hdr.data[hdr.size++] = elem; return *this; } - + Vector<_Tp>& pop_back() { if( hdr.size > 0 ) --hdr.size; return *this; } - + size_t size() const { return hdr.size; } size_t capacity() const { return hdr.capacity; } bool empty() const { return hdr.size == 0; } void clear() { resize(0); } int type() const { return DataType<_Tp>::type; } - + protected: Hdr hdr; -}; +}; + - template inline typename DataType<_Tp>::work_type dot(const Vector<_Tp>& v1, const Vector<_Tp>& v2) { @@ -2475,7 +2475,7 @@ dot(const Vector<_Tp>& v1, const Vector<_Tp>& v2) } return s; } - + // Multiply-with-Carry RNG inline RNG::RNG() { state = 0xffffffff; } inline RNG::RNG(uint64 _state) { state = _state ? _state : 0xffffffff; } @@ -2533,7 +2533,7 @@ inline Point LineIterator::pos() const p.x = (int)(((ptr - ptr0) - p.y*step)/elemSize); return p; } - + /////////////////////////////// AutoBuffer //////////////////////////////////////// template inline AutoBuffer<_Tp, fixed_size>::AutoBuffer() @@ -2616,20 +2616,20 @@ template inline void Ptr<_Tp>::delete_obj() template inline Ptr<_Tp>::~Ptr() { release(); } -template inline Ptr<_Tp>::Ptr(const Ptr<_Tp>& ptr) +template inline Ptr<_Tp>::Ptr(const Ptr<_Tp>& _ptr) { - obj = ptr.obj; - refcount = ptr.refcount; + obj = _ptr.obj; + refcount = _ptr.refcount; addref(); } -template inline Ptr<_Tp>& Ptr<_Tp>::operator = (const Ptr<_Tp>& ptr) +template inline Ptr<_Tp>& Ptr<_Tp>::operator = (const Ptr<_Tp>& _ptr) { - int* _refcount = ptr.refcount; + int* _refcount = _ptr.refcount; if( _refcount ) CV_XADD(_refcount, 1); release(); - obj = ptr.obj; + obj = _ptr.obj; refcount = _refcount; return *this; } @@ -2653,7 +2653,7 @@ template template inline Ptr<_Tp2> Ptr<_Tp>::ptr() p.refcount = refcount; return p; } - + template template inline const Ptr<_Tp2> Ptr<_Tp>::ptr() const { Ptr<_Tp2> p; @@ -2665,7 +2665,7 @@ template template inline const Ptr<_Tp2> Ptr<_Tp>:: p.refcount = refcount; return p; } - + //// specializied implementations of Ptr::delete_obj() for classic OpenCV types template<> CV_EXPORTS void Ptr::delete_obj(); @@ -2674,7 +2674,7 @@ template<> CV_EXPORTS void Ptr::delete_obj(); template<> CV_EXPORTS void Ptr::delete_obj(); template<> CV_EXPORTS void Ptr::delete_obj(); template<> CV_EXPORTS void Ptr::delete_obj(); - + //////////////////////////////////////// XML & YAML I/O //////////////////////////////////// CV_EXPORTS_W void write( FileStorage& fs, const string& name, int value ); @@ -2870,11 +2870,11 @@ template static inline void write( FileStorage& fs, const string& { WriteStructContext ws(fs, name, CV_NODE_SEQ+(DataType<_Tp>::fmt != 0 ? CV_NODE_FLOW : 0)); write(fs, vec); -} - +} + CV_EXPORTS_W void write( FileStorage& fs, const string& name, const Mat& value ); CV_EXPORTS void write( FileStorage& fs, const string& name, const SparseMat& value ); - + template static inline FileStorage& operator << (FileStorage& fs, const _Tp& value) { if( !fs.isOpened() ) @@ -2923,7 +2923,7 @@ static inline void read(const FileNode& node, int& value, int default_value) CV_NODE_IS_INT(node.node->tag) ? node.node->data.i : CV_NODE_IS_REAL(node.node->tag) ? cvRound(node.node->data.f) : 0x7fffffff; } - + static inline void read(const FileNode& node, bool& value, bool default_value) { int temp; read(node, temp, (int)default_value); @@ -2953,7 +2953,7 @@ static inline void read(const FileNode& node, short& value, short default_value) int temp; read(node, temp, (int)default_value); value = saturate_cast(temp); } - + static inline void read(const FileNode& node, float& value, float default_value) { value = !node.node ? default_value : @@ -2975,7 +2975,7 @@ static inline void read(const FileNode& node, string& value, const string& defau CV_EXPORTS_W void read(const FileNode& node, Mat& mat, const Mat& default_mat=Mat() ); CV_EXPORTS void read(const FileNode& node, SparseMat& mat, const SparseMat& default_mat=SparseMat() ); - + inline FileNode::operator int() const { int value; @@ -3019,7 +3019,7 @@ public: } FileNodeIterator* it; }; - + template class CV_EXPORTS VecReaderProxy<_Tp,1> { public: @@ -3055,7 +3055,7 @@ read( const FileNode& node, vector<_Tp>& vec, const vector<_Tp>& default_value=v read( it, vec ); } } - + inline FileNodeIterator FileNode::begin() const { return FileNodeIterator(fs, node); @@ -3459,7 +3459,7 @@ partition( const vector<_Tp>& _vec, vector& labels, return nclasses; } - + ////////////////////////////////////////////////////////////////////////////// // bridge C++ => C Seq API @@ -3473,7 +3473,7 @@ CV_EXPORTS void seqRemove( CvSeq* seq, int index ); CV_EXPORTS void clearSeq( CvSeq* seq ); CV_EXPORTS schar* getSeqElem( const CvSeq* seq, int index ); CV_EXPORTS void seqRemoveSlice( CvSeq* seq, CvSlice slice ); -CV_EXPORTS void seqInsertSlice( CvSeq* seq, int before_index, const CvArr* from_arr ); +CV_EXPORTS void seqInsertSlice( CvSeq* seq, int before_index, const CvArr* from_arr ); template inline Seq<_Tp>::Seq() : seq(0) {} template inline Seq<_Tp>::Seq( const CvSeq* _seq ) : seq((CvSeq*)_seq) @@ -3528,8 +3528,8 @@ template inline void Seq<_Tp>::push_back(const _Tp* elem, size_t c { cvSeqPushMulti(seq, elem, (int)count, 0); } template inline void Seq<_Tp>::push_front(const _Tp* elem, size_t count) -{ cvSeqPushMulti(seq, elem, (int)count, 1); } - +{ cvSeqPushMulti(seq, elem, (int)count, 1); } + template inline _Tp& Seq<_Tp>::back() { return *(_Tp*)getSeqElem(seq, -1); } @@ -3558,23 +3558,23 @@ template inline void Seq<_Tp>::pop_back(_Tp* elem, size_t count) { seqPopMulti(seq, elem, (int)count, 0); } template inline void Seq<_Tp>::pop_front(_Tp* elem, size_t count) -{ seqPopMulti(seq, elem, (int)count, 1); } +{ seqPopMulti(seq, elem, (int)count, 1); } template inline void Seq<_Tp>::insert(int idx, const _Tp& elem) { seqInsert(seq, idx, &elem); } - + template inline void Seq<_Tp>::insert(int idx, const _Tp* elems, size_t count) { CvMat m = cvMat(1, count, DataType<_Tp>::type, elems); seqInsertSlice(seq, idx, &m); } - + template inline void Seq<_Tp>::remove(int idx) { seqRemove(seq, idx); } - + template inline void Seq<_Tp>::remove(const Range& r) { seqRemoveSlice(seq, r); } - + template inline void Seq<_Tp>::copyTo(vector<_Tp>& vec, const Range& range) const { size_t len = !seq ? 0 : range == Range::all() ? seq->total : range.end - range.start; @@ -3593,10 +3593,10 @@ template inline Seq<_Tp>::operator vector<_Tp>() const template inline SeqIterator<_Tp>::SeqIterator() { memset(this, 0, sizeof(*this)); } -template inline SeqIterator<_Tp>::SeqIterator(const Seq<_Tp>& seq, bool seekEnd) +template inline SeqIterator<_Tp>::SeqIterator(const Seq<_Tp>& _seq, bool seekEnd) { - cvStartReadSeq(seq.seq, this); - index = seekEnd ? seq.seq->total : 0; + cvStartReadSeq(_seq.seq, this); + index = seekEnd ? _seq.seq->total : 0; } template inline void SeqIterator<_Tp>::seek(size_t pos) @@ -3716,7 +3716,7 @@ public: delete obj; return 0; } - + static void write(CvFileStorage* _fs, const char* name, const void* ptr, CvAttrList) { if(ptr && _fs) @@ -3726,7 +3726,7 @@ public: ((const _ClsName*)ptr)->write(fs, string(name)); } } - + static void* clone(const void* ptr) { if(!ptr) @@ -3735,7 +3735,7 @@ public: } }; - + class CV_EXPORTS Formatter { public: @@ -3759,7 +3759,7 @@ struct CV_EXPORTS Formatted vector params; }; - + /** Writes a point to an output stream in Matlab notation */ template inline std::ostream& operator<<(std::ostream& out, const Point_<_Tp>& p) @@ -3774,7 +3774,7 @@ template inline std::ostream& operator<<(std::ostream& out, const { out << "[" << p.x << ", " << p.y << ", " << p.z << "]"; return out; -} +} static inline Formatted format(const Mat& mtx, const char* fmt, const vector& params=vector()) @@ -3800,7 +3800,7 @@ template static inline Formatted format(const vector Mat my_mat = Mat::eye(3,3,CV_32F); std::cout << my_mat; @endverbatim - */ + */ static inline std::ostream& operator << (std::ostream& out, const Mat& mtx) { Formatter::get()->write(out, mtx); @@ -3813,7 +3813,7 @@ static inline std::ostream& operator << (std::ostream& out, const Mat& mtx) Mat my_mat = Mat::eye(3,3,CV_32F); std::cout << my_mat; @endverbatim - */ + */ static inline std::ostream& operator << (std::ostream& out, const Formatted& fmtd) { fmtd.fmt->write(out, fmtd.mtx); @@ -3835,27 +3835,27 @@ template static inline std::ostream& operator << (std::ostream& ou Formatter::get()->write(out, Mat(vec)); return out; } - + template inline Ptr<_Tp> Algorithm::create(const string& name) { return _create(name).ptr<_Tp>(); } - -template inline typename ParamType<_Tp>::member_type Algorithm::get(const string& name) const + +template inline typename ParamType<_Tp>::member_type Algorithm::get(const string& _name) const { typename ParamType<_Tp>::member_type value; - info()->get(this, name.c_str(), ParamType<_Tp>::type, &value); + info()->get(this, _name.c_str(), ParamType<_Tp>::type, &value); return value; } -template inline typename ParamType<_Tp>::member_type Algorithm::get(const char* name) const +template inline typename ParamType<_Tp>::member_type Algorithm::get(const char* _name) const { typename ParamType<_Tp>::member_type value; - info()->get(this, name, ParamType<_Tp>::type, &value); + info()->get(this, _name, ParamType<_Tp>::type, &value); return value; -} - +} + } #endif // __cplusplus diff --git a/modules/core/include/opencv2/core/types_c.h b/modules/core/include/opencv2/core/types_c.h index e11f096..40f5da4 100644 --- a/modules/core/include/opencv2/core/types_c.h +++ b/modules/core/include/opencv2/core/types_c.h @@ -43,122 +43,132 @@ #ifndef __OPENCV_CORE_TYPES_H__ #define __OPENCV_CORE_TYPES_H__ -#if !defined _CRT_SECURE_NO_DEPRECATE && _MSC_VER > 1300 -#define _CRT_SECURE_NO_DEPRECATE /* to avoid multiple Visual Studio 2005 warnings */ +#if !defined _CRT_SECURE_NO_DEPRECATE && defined _MSC_VER +# if _MSC_VER > 1300 +# define _CRT_SECURE_NO_DEPRECATE /* to avoid multiple Visual Studio 2005 warnings */ +# endif #endif #ifndef SKIP_INCLUDES - #include - #include - #include - #include + +#include +#include +#include +#include #if !defined _MSC_VER && !defined __BORLANDC__ - #include +# include +#endif + +#if defined __ICL +# define CV_ICC __ICL +#elif defined __ICC +# define CV_ICC __ICC +#elif defined __ECL +# define CV_ICC __ECL +#elif defined __ECC +# define CV_ICC __ECC +#elif defined __INTEL_COMPILER +# define CV_ICC __INTEL_COMPILER +#endif + +#if defined CV_ICC && !defined CV_ENABLE_UNROLLED +# define CV_ENABLE_UNROLLED 0 +#else +# define CV_ENABLE_UNROLLED 1 +#endif + +#if (defined _M_X64 && defined _MSC_VER && _MSC_VER >= 1400) || (__GNUC__ >= 4 && defined __x86_64__) +# if defined WIN32 +# include +# endif +# if __SSE2__ || !defined __GNUC__ +# include +# endif +#endif + +#if defined __BORLANDC__ +# include +#else +# include +#endif + +#ifdef HAVE_IPL +# ifndef __IPL_H__ +# if defined WIN32 || defined _WIN32 +# include +# else +# include +# endif +# endif +#elif defined __IPL_H__ +# define HAVE_IPL #endif - #if defined __ICL - #define CV_ICC __ICL - #elif defined __ICC - #define CV_ICC __ICC - #elif defined __ECL - #define CV_ICC __ECL - #elif defined __ECC - #define CV_ICC __ECC - #elif defined __INTEL_COMPILER - #define CV_ICC __INTEL_COMPILER - #endif - - #if (_MSC_VER >= 1400 && defined _M_X64) || (__GNUC__ >= 4 && defined __x86_64__) - #if defined WIN32 - #include - #endif - #if __SSE2__ || !defined __GNUC__ - #include - #endif - #endif - - #if defined __BORLANDC__ - #include - #else - #include - #endif - - #ifdef HAVE_IPL - #ifndef __IPL_H__ - #if defined WIN32 || defined _WIN32 - #include - #else - #include - #endif - #endif - #elif defined __IPL_H__ - #define HAVE_IPL - #endif #endif // SKIP_INCLUDES #if defined WIN32 || defined _WIN32 - #define CV_CDECL __cdecl - #define CV_STDCALL __stdcall +# define CV_CDECL __cdecl +# define CV_STDCALL __stdcall #else - #define CV_CDECL - #define CV_STDCALL +# define CV_CDECL +# define CV_STDCALL #endif #ifndef CV_EXTERN_C - #ifdef __cplusplus - #define CV_EXTERN_C extern "C" - #define CV_DEFAULT(val) = val - #else - #define CV_EXTERN_C - #define CV_DEFAULT(val) - #endif +# ifdef __cplusplus +# define CV_EXTERN_C extern "C" +# define CV_DEFAULT(val) = val +# else +# define CV_EXTERN_C +# define CV_DEFAULT(val) +# endif #endif #ifndef CV_EXTERN_C_FUNCPTR - #ifdef __cplusplus - #define CV_EXTERN_C_FUNCPTR(x) extern "C" { typedef x; } - #else - #define CV_EXTERN_C_FUNCPTR(x) typedef x - #endif +# ifdef __cplusplus +# define CV_EXTERN_C_FUNCPTR(x) extern "C" { typedef x; } +# else +# define CV_EXTERN_C_FUNCPTR(x) typedef x +# endif #endif #ifndef CV_INLINE -#if defined __cplusplus - #define CV_INLINE inline -#elif (defined WIN32 || defined _WIN32 || defined WINCE) && !defined __GNUC__ - #define CV_INLINE __inline -#else - #define CV_INLINE static -#endif +# if defined __cplusplus +# define CV_INLINE inline +# elif (defined WIN32 || defined _WIN32 || defined WINCE) && !defined __GNUC__ +# define CV_INLINE __inline +# else +# define CV_INLINE static +# endif #endif /* CV_INLINE */ #if (defined WIN32 || defined _WIN32 || defined WINCE) && defined CVAPI_EXPORTS - #define CV_EXPORTS __declspec(dllexport) +# define CV_EXPORTS __declspec(dllexport) #else - #define CV_EXPORTS +# define CV_EXPORTS #endif #ifndef CVAPI - #define CVAPI(rettype) CV_EXTERN_C CV_EXPORTS rettype CV_CDECL +# define CVAPI(rettype) CV_EXTERN_C CV_EXPORTS rettype CV_CDECL #endif #if defined _MSC_VER || defined __BORLANDC__ -typedef __int64 int64; -typedef unsigned __int64 uint64; -#define CV_BIG_INT(n) n##I64 -#define CV_BIG_UINT(n) n##UI64 + typedef __int64 int64; + typedef unsigned __int64 uint64; +# define CV_BIG_INT(n) n##I64 +# define CV_BIG_UINT(n) n##UI64 #else -typedef int64_t int64; -typedef uint64_t uint64; -#define CV_BIG_INT(n) n##LL -#define CV_BIG_UINT(n) n##ULL + typedef int64_t int64; + typedef uint64_t uint64; +# define CV_BIG_INT(n) n##LL +# define CV_BIG_UINT(n) n##ULL #endif #ifndef HAVE_IPL -typedef unsigned char uchar; -typedef unsigned short ushort; + typedef unsigned char uchar; + typedef unsigned short ushort; #endif typedef signed char schar; @@ -203,7 +213,7 @@ Cv64suf; typedef int CVStatus; -enum { +enum { CV_StsOk= 0, /* everithing is ok */ CV_StsBackTrace= -1, /* pseudo error for back trace */ CV_StsError= -2, /* unknown /unspecified error */ @@ -241,8 +251,8 @@ enum { CV_StsInplaceNotSupported= -203, /* in-place operation is not supported */ CV_StsObjectNotFound= -204, /* request can't be completed */ CV_StsUnmatchedFormats= -205, /* formats of input/output arrays differ */ - CV_StsBadFlag= -206, /* flag is wrong or not supported */ - CV_StsBadPoint= -207, /* bad CvPoint */ + CV_StsBadFlag= -206, /* flag is wrong or not supported */ + CV_StsBadPoint= -207, /* bad CvPoint */ CV_StsBadMask= -208, /* bad format of mask (neither 8uC1 nor 8sC1)*/ CV_StsUnmatchedSizes= -209, /* sizes of input/output structures do not match */ CV_StsUnsupportedFormat= -210, /* the data format/type is not supported by the function*/ @@ -250,8 +260,8 @@ enum { CV_StsParseError= -212, /* invalid syntax/structure of the parsed file */ CV_StsNotImplemented= -213, /* the requested function/feature is not implemented */ CV_StsBadMemBlock= -214, /* an allocated block has been corrupted */ - CV_StsAssert= -215, /* assertion failed */ - CV_GpuNotSupported= -216, + CV_StsAssert= -215, /* assertion failed */ + CV_GpuNotSupported= -216, CV_GpuApiCallError= -217, CV_OpenGlNotSupported= -218, CV_OpenGlApiCallError= -219 @@ -262,7 +272,7 @@ enum { \****************************************************************************************/ #ifdef HAVE_TEGRA_OPTIMIZATION -# include "tegra_round.hpp" +# include "tegra_round.hpp" #endif #define CV_PI 3.1415926535897932384626433832795 @@ -271,11 +281,11 @@ enum { #define CV_SWAP(a,b,t) ((t) = (a), (a) = (b), (b) = (t)) #ifndef MIN -#define MIN(a,b) ((a) > (b) ? (b) : (a)) +# define MIN(a,b) ((a) > (b) ? (b) : (a)) #endif #ifndef MAX -#define MAX(a,b) ((a) < (b) ? (b) : (a)) +# define MAX(a,b) ((a) < (b) ? (b) : (a)) #endif /* min & max without jumps */ @@ -285,9 +295,9 @@ enum { /* absolute value without jumps */ #ifndef __cplusplus -#define CV_IABS(a) (((a) ^ ((a) < 0 ? -1 : 0)) - ((a) < 0 ? -1 : 0)) +# define CV_IABS(a) (((a) ^ ((a) < 0 ? -1 : 0)) - ((a) < 0 ? -1 : 0)) #else -#define CV_IABS(a) abs(a) +# define CV_IABS(a) abs(a) #endif #define CV_CMP(a,b) (((a) > (b)) - ((a) < (b))) #define CV_SIGN(a) CV_CMP((a),0) @@ -306,11 +316,11 @@ CV_INLINE int cvRound( double value ) } return t; #elif defined HAVE_LRINT || defined CV_ICC || defined __GNUC__ -# ifdef HAVE_TEGRA_OPTIMIZATION +# ifdef HAVE_TEGRA_OPTIMIZATION TEGRA_ROUND(value); -# else +# else return (int)lrint(value); -# endif +# endif #else // while this is not IEEE754-compliant rounding, it's usually a good enough approximation return (int)(value + (value >= 0 ? 0.5 : -0.5)); @@ -318,7 +328,7 @@ CV_INLINE int cvRound( double value ) } #if defined __SSE2__ || (defined _M_IX86_FP && 2 == _M_IX86_FP) -#include "emmintrin.h" +# include "emmintrin.h" #endif CV_INLINE int cvFloor( double value ) @@ -1886,6 +1896,6 @@ typedef struct CvModuleInfo } CvModuleInfo; -#endif /*_CXCORE_TYPES_H_*/ +#endif /*__OPENCV_CORE_TYPES_H__*/ /* End of file. */ diff --git a/modules/core/perf/perf_precomp.hpp b/modules/core/perf/perf_precomp.hpp index 901f889..22a4abd 100644 --- a/modules/core/perf/perf_precomp.hpp +++ b/modules/core/perf/perf_precomp.hpp @@ -1,9 +1,13 @@ +#ifdef __GNUC__ +# pragma GCC diagnostic ignored "-Wmissing-declarations" +#endif + #ifndef __OPENCV_PERF_PRECOMP_HPP__ #define __OPENCV_PERF_PRECOMP_HPP__ #include "opencv2/ts/ts.hpp" -#if GTEST_CREATE_SHARED_LIBRARY +#ifdef GTEST_CREATE_SHARED_LIBRARY #error no modules except ts should have GTEST_CREATE_SHARED_LIBRARY defined #endif diff --git a/modules/core/perf/perf_stat.cpp b/modules/core/perf/perf_stat.cpp index 418cb24..79e849e 100644 --- a/modules/core/perf/perf_stat.cpp +++ b/modules/core/perf/perf_stat.cpp @@ -28,11 +28,11 @@ PERF_TEST_P(Size_MatType, mean, TYPICAL_MATS) Mat src(sz, type); Scalar s; - + declare.in(src, WARMUP_RNG).out(s); - + TEST_CYCLE() s = mean(src); - + SANITY_CHECK(s, 1e-6); } @@ -44,11 +44,11 @@ PERF_TEST_P(Size_MatType, mean_mask, TYPICAL_MATS) Mat src(sz, type); Mat mask = Mat::ones(src.size(), CV_8U); Scalar s; - + declare.in(src, WARMUP_RNG).in(mask).out(s); - + TEST_CYCLE() s = mean(src, mask); - + SANITY_CHECK(s, 1e-6); } @@ -64,7 +64,7 @@ PERF_TEST_P(Size_MatType, meanStdDev, TYPICAL_MATS) declare.in(src, WARMUP_RNG).out(mean, dev); TEST_CYCLE() meanStdDev(src, mean, dev); - + SANITY_CHECK(mean, 1e-6); SANITY_CHECK(dev, 1e-6); } @@ -80,9 +80,9 @@ PERF_TEST_P(Size_MatType, meanStdDev_mask, TYPICAL_MATS) Scalar dev; declare.in(src, WARMUP_RNG).in(mask).out(mean, dev); - + TEST_CYCLE() meanStdDev(src, mean, dev, mask); - + SANITY_CHECK(mean, 1e-6); SANITY_CHECK(dev, 1e-6); } @@ -96,8 +96,8 @@ PERF_TEST_P(Size_MatType, countNonZero, testing::Combine( testing::Values( TYPIC int cnt = 0; declare.in(src, WARMUP_RNG); - + TEST_CYCLE() cnt = countNonZero(src); - + SANITY_CHECK(cnt); } diff --git a/modules/core/src/algorithm.cpp b/modules/core/src/algorithm.cpp index 6623c27..7c783ea 100644 --- a/modules/core/src/algorithm.cpp +++ b/modules/core/src/algorithm.cpp @@ -46,7 +46,7 @@ namespace cv { using std::pair; - + template struct sorted_vector { sorted_vector() {} @@ -54,7 +54,7 @@ template struct sorted_vector size_t size() const { return vec.size(); } _ValueTp& operator [](size_t idx) { return vec[idx]; } const _ValueTp& operator [](size_t idx) const { return vec[idx]; } - + void add(const _KeyTp& k, const _ValueTp& val) { pair<_KeyTp, _ValueTp> p(k, val); @@ -64,7 +64,7 @@ template struct sorted_vector std::swap(vec[i-1], vec[i]); CV_Assert( i == 0 || vec[i].first != vec[i-1].first ); } - + bool find(const _KeyTp& key, _ValueTp& value) const { size_t a = 0, b = vec.size(); @@ -76,7 +76,7 @@ template struct sorted_vector else b = c; } - + if( a < vec.size() && vec[a].first == key ) { value = vec[a].second; @@ -84,26 +84,26 @@ template struct sorted_vector } return false; } - + void get_keys(vector<_KeyTp>& keys) const { size_t i = 0, n = vec.size(); keys.resize(n); - + for( i = 0; i < n; i++ ) keys[i] = vec[i].first; } - + vector > vec; }; - + template inline const _ValueTp* findstr(const sorted_vector& vec, const char* key) { if( !key ) return 0; - + size_t a = 0, b = vec.vec.size(); while( b > a ) { @@ -113,13 +113,13 @@ template inline const _ValueTp* findstr(const sorted_vector& alglist() { static sorted_vector alglist_var; @@ -171,152 +171,152 @@ Ptr Algorithm::_create(const string& name) Algorithm::Algorithm() { } - + Algorithm::~Algorithm() { } - + string Algorithm::name() const { return info()->name(); } - -void Algorithm::set(const string& name, int value) + +void Algorithm::set(const string& parameter, int value) { - info()->set(this, name.c_str(), ParamType::type, &value); + info()->set(this, parameter.c_str(), ParamType::type, &value); } -void Algorithm::set(const string& name, double value) +void Algorithm::set(const string& parameter, double value) { - info()->set(this, name.c_str(), ParamType::type, &value); + info()->set(this, parameter.c_str(), ParamType::type, &value); } -void Algorithm::set(const string& name, bool value) +void Algorithm::set(const string& parameter, bool value) { - info()->set(this, name.c_str(), ParamType::type, &value); + info()->set(this, parameter.c_str(), ParamType::type, &value); } -void Algorithm::set(const string& name, const string& value) +void Algorithm::set(const string& parameter, const string& value) { - info()->set(this, name.c_str(), ParamType::type, &value); + info()->set(this, parameter.c_str(), ParamType::type, &value); } -void Algorithm::set(const string& name, const Mat& value) +void Algorithm::set(const string& parameter, const Mat& value) { - info()->set(this, name.c_str(), ParamType::type, &value); + info()->set(this, parameter.c_str(), ParamType::type, &value); } -void Algorithm::set(const string& name, const vector& value) +void Algorithm::set(const string& parameter, const vector& value) { - info()->set(this, name.c_str(), ParamType >::type, &value); -} - -void Algorithm::set(const string& name, const Ptr& value) + info()->set(this, parameter.c_str(), ParamType >::type, &value); +} + +void Algorithm::set(const string& parameter, const Ptr& value) { - info()->set(this, name.c_str(), ParamType::type, &value); + info()->set(this, parameter.c_str(), ParamType::type, &value); } -void Algorithm::set(const char* name, int value) +void Algorithm::set(const char* parameter, int value) { - info()->set(this, name, ParamType::type, &value); + info()->set(this, parameter, ParamType::type, &value); } -void Algorithm::set(const char* name, double value) +void Algorithm::set(const char* parameter, double value) { - info()->set(this, name, ParamType::type, &value); + info()->set(this, parameter, ParamType::type, &value); } -void Algorithm::set(const char* name, bool value) +void Algorithm::set(const char* parameter, bool value) { - info()->set(this, name, ParamType::type, &value); + info()->set(this, parameter, ParamType::type, &value); } -void Algorithm::set(const char* name, const string& value) +void Algorithm::set(const char* parameter, const string& value) { - info()->set(this, name, ParamType::type, &value); + info()->set(this, parameter, ParamType::type, &value); } -void Algorithm::set(const char* name, const Mat& value) +void Algorithm::set(const char* parameter, const Mat& value) { - info()->set(this, name, ParamType::type, &value); + info()->set(this, parameter, ParamType::type, &value); } -void Algorithm::set(const char* name, const vector& value) +void Algorithm::set(const char* parameter, const vector& value) { - info()->set(this, name, ParamType >::type, &value); -} - -void Algorithm::set(const char* name, const Ptr& value) + info()->set(this, parameter, ParamType >::type, &value); +} + +void Algorithm::set(const char* parameter, const Ptr& value) { - info()->set(this, name, ParamType::type, &value); + info()->set(this, parameter, ParamType::type, &value); } - -int Algorithm::getInt(const string& name) const + +int Algorithm::getInt(const string& parameter) const { - return get(name); + return get(parameter); } - -double Algorithm::getDouble(const string& name) const + +double Algorithm::getDouble(const string& parameter) const { - return get(name); + return get(parameter); } -bool Algorithm::getBool(const string& name) const +bool Algorithm::getBool(const string& parameter) const { - return get(name); + return get(parameter); } -string Algorithm::getString(const string& name) const +string Algorithm::getString(const string& parameter) const { - return get(name); + return get(parameter); } -Mat Algorithm::getMat(const string& name) const +Mat Algorithm::getMat(const string& parameter) const { - return get(name); + return get(parameter); } -vector Algorithm::getMatVector(const string& name) const +vector Algorithm::getMatVector(const string& parameter) const { - return get >(name); + return get >(parameter); } -Ptr Algorithm::getAlgorithm(const string& name) const +Ptr Algorithm::getAlgorithm(const string& parameter) const { - return get(name); + return get(parameter); } - -string Algorithm::paramHelp(const string& name) const + +string Algorithm::paramHelp(const string& parameter) const { - return info()->paramHelp(name.c_str()); + return info()->paramHelp(parameter.c_str()); } - -int Algorithm::paramType(const string& name) const + +int Algorithm::paramType(const string& parameter) const { - return info()->paramType(name.c_str()); + return info()->paramType(parameter.c_str()); } -int Algorithm::paramType(const char* name) const +int Algorithm::paramType(const char* parameter) const { - return info()->paramType(name); -} - + return info()->paramType(parameter); +} + void Algorithm::getParams(vector& names) const { info()->getParams(names); } - + void Algorithm::write(FileStorage& fs) const { info()->write(this, fs); } - + void Algorithm::read(const FileNode& fn) { info()->read(this, fn); -} +} + - AlgorithmInfo::AlgorithmInfo(const string& _name, Algorithm::Constructor create) { data = new AlgorithmInfoData; @@ -327,8 +327,8 @@ AlgorithmInfo::AlgorithmInfo(const string& _name, Algorithm::Constructor create) AlgorithmInfo::~AlgorithmInfo() { delete data; -} - +} + void AlgorithmInfo::write(const Algorithm* algo, FileStorage& fs) const { size_t i = 0, nparams = data->params.vec.size(); @@ -364,7 +364,7 @@ void AlgorithmInfo::read(Algorithm* algo, const FileNode& fn) const { size_t i = 0, nparams = data->params.vec.size(); AlgorithmInfo* info = algo->info(); - + for( i = 0; i < nparams; i++ ) { const Param& p = data->params.vec[i].second; @@ -414,13 +414,13 @@ void AlgorithmInfo::read(Algorithm* algo, const FileNode& fn) const else CV_Error( CV_StsUnsupportedFormat, "unknown/unsupported parameter type"); } -} +} string AlgorithmInfo::name() const { return data->_name; } - + union GetSetParam { int (Algorithm::*get_int)() const; @@ -430,7 +430,7 @@ union GetSetParam Mat (Algorithm::*get_mat)() const; vector (Algorithm::*get_mat_vector)() const; Ptr (Algorithm::*get_algo)() const; - + void (Algorithm::*set_int)(int); void (Algorithm::*set_bool)(bool); void (Algorithm::*set_double)(double); @@ -440,15 +440,15 @@ union GetSetParam void (Algorithm::*set_algo)(const Ptr&); }; -void AlgorithmInfo::set(Algorithm* algo, const char* name, int argType, const void* value, bool force) const +void AlgorithmInfo::set(Algorithm* algo, const char* parameter, int argType, const void* value, bool force) const { - const Param* p = findstr(data->params, name); + const Param* p = findstr(data->params, parameter); if( !p ) - CV_Error_( CV_StsBadArg, ("No parameter '%s' is found", name ? name : "") ); + CV_Error_( CV_StsBadArg, ("No parameter '%s' is found", parameter ? parameter : "") ); if( !force && p->readonly ) - CV_Error_( CV_StsError, ("Parameter '%s' is readonly", name)); + CV_Error_( CV_StsError, ("Parameter '%s' is readonly", parameter)); GetSetParam f; f.set_int = p->setter; @@ -531,23 +531,23 @@ void AlgorithmInfo::set(Algorithm* algo, const char* name, int argType, const vo else CV_Error(CV_StsBadArg, "Unknown/unsupported parameter type"); } - -void AlgorithmInfo::get(const Algorithm* algo, const char* name, int argType, void* value) const + +void AlgorithmInfo::get(const Algorithm* algo, const char* parameter, int argType, void* value) const { - const Param* p = findstr(data->params, name); + const Param* p = findstr(data->params, parameter); if( !p ) - CV_Error_( CV_StsBadArg, ("No parameter '%s' is found", name ? name : "") ); - + CV_Error_( CV_StsBadArg, ("No parameter '%s' is found", parameter ? parameter : "") ); + GetSetParam f; f.get_int = p->getter; - + if( argType == Param::INT || argType == Param::BOOLEAN || argType == Param::REAL ) { if( p->type == Param::INT ) { CV_Assert( argType == Param::INT || argType == Param::REAL ); int val = p->getter ? (algo->*f.get_int)() : *(int*)((uchar*)algo + p->offset); - + if( argType == Param::INT ) *(int*)value = val; else @@ -557,7 +557,7 @@ void AlgorithmInfo::get(const Algorithm* algo, const char* name, int argType, vo { CV_Assert( argType == Param::INT || argType == Param::BOOLEAN || argType == Param::REAL ); bool val = p->getter ? (algo->*f.get_bool)() : *(bool*)((uchar*)algo + p->offset); - + if( argType == Param::INT ) *(int*)value = (int)val; else if( argType == Param::BOOLEAN ) @@ -569,35 +569,35 @@ void AlgorithmInfo::get(const Algorithm* algo, const char* name, int argType, vo { CV_Assert( argType == Param::REAL ); double val = p->getter ? (algo->*f.get_double)() : *(double*)((uchar*)algo + p->offset); - + *(double*)value = val; } } else if( argType == Param::STRING ) { CV_Assert( p->type == Param::STRING ); - + *(string*)value = p->getter ? (algo->*f.get_string)() : *(string*)((uchar*)algo + p->offset); } else if( argType == Param::MAT ) { CV_Assert( p->type == Param::MAT ); - + *(Mat*)value = p->getter ? (algo->*f.get_mat)() : *(Mat*)((uchar*)algo + p->offset); } else if( argType == Param::MAT_VECTOR ) { CV_Assert( p->type == Param::MAT_VECTOR ); - + *(vector*)value = p->getter ? (algo->*f.get_mat_vector)() : *(vector*)((uchar*)algo + p->offset); } else if( argType == Param::ALGORITHM ) { CV_Assert( p->type == Param::ALGORITHM ); - + *(Ptr*)value = p->getter ? (algo->*f.get_algo)() : *(Ptr*)((uchar*)algo + p->offset); } @@ -605,21 +605,21 @@ void AlgorithmInfo::get(const Algorithm* algo, const char* name, int argType, vo CV_Error(CV_StsBadArg, "Unknown/unsupported parameter type"); } - -int AlgorithmInfo::paramType(const char* name) const + +int AlgorithmInfo::paramType(const char* parameter) const { - const Param* p = findstr(data->params, name); + const Param* p = findstr(data->params, parameter); if( !p ) - CV_Error_( CV_StsBadArg, ("No parameter '%s' is found", name ? name : "") ); + CV_Error_( CV_StsBadArg, ("No parameter '%s' is found", parameter ? parameter : "") ); return p->type; } - - -string AlgorithmInfo::paramHelp(const char* name) const + + +string AlgorithmInfo::paramHelp(const char* parameter) const { - const Param* p = findstr(data->params, name); + const Param* p = findstr(data->params, parameter); if( !p ) - CV_Error_( CV_StsBadArg, ("No parameter '%s' is found", name ? name : "") ); + CV_Error_( CV_StsBadArg, ("No parameter '%s' is found", parameter ? parameter : "") ); return p->help; } @@ -628,10 +628,10 @@ void AlgorithmInfo::getParams(vector& names) const { data->params.get_keys(names); } - - -void AlgorithmInfo::addParam_(Algorithm& algo, const char* name, int argType, - void* value, bool readOnly, + + +void AlgorithmInfo::addParam_(Algorithm& algo, const char* parameter, int argType, + void* value, bool readOnly, Algorithm::Getter getter, Algorithm::Setter setter, const string& help) { @@ -639,82 +639,82 @@ void AlgorithmInfo::addParam_(Algorithm& algo, const char* name, int argType, argType == Param::REAL || argType == Param::STRING || argType == Param::MAT || argType == Param::MAT_VECTOR || argType == Param::ALGORITHM ); - data->params.add(string(name), Param(argType, readOnly, + data->params.add(string(parameter), Param(argType, readOnly, (int)((size_t)value - (size_t)(void*)&algo), getter, setter, help)); } - - -void AlgorithmInfo::addParam(Algorithm& algo, const char* name, - int& value, bool readOnly, + + +void AlgorithmInfo::addParam(Algorithm& algo, const char* parameter, + int& value, bool readOnly, int (Algorithm::*getter)(), void (Algorithm::*setter)(int), const string& help) { - addParam_(algo, name, ParamType::type, &value, readOnly, + addParam_(algo, parameter, ParamType::type, &value, readOnly, (Algorithm::Getter)getter, (Algorithm::Setter)setter, help); } -void AlgorithmInfo::addParam(Algorithm& algo, const char* name, - bool& value, bool readOnly, +void AlgorithmInfo::addParam(Algorithm& algo, const char* parameter, + bool& value, bool readOnly, int (Algorithm::*getter)(), void (Algorithm::*setter)(int), const string& help) { - addParam_(algo, name, ParamType::type, &value, readOnly, + addParam_(algo, parameter, ParamType::type, &value, readOnly, (Algorithm::Getter)getter, (Algorithm::Setter)setter, help); } - -void AlgorithmInfo::addParam(Algorithm& algo, const char* name, - double& value, bool readOnly, + +void AlgorithmInfo::addParam(Algorithm& algo, const char* parameter, + double& value, bool readOnly, double (Algorithm::*getter)(), void (Algorithm::*setter)(double), const string& help) { - addParam_(algo, name, ParamType::type, &value, readOnly, + addParam_(algo, parameter, ParamType::type, &value, readOnly, (Algorithm::Getter)getter, (Algorithm::Setter)setter, help); } -void AlgorithmInfo::addParam(Algorithm& algo, const char* name, - string& value, bool readOnly, +void AlgorithmInfo::addParam(Algorithm& algo, const char* parameter, + string& value, bool readOnly, string (Algorithm::*getter)(), void (Algorithm::*setter)(const string&), const string& help) { - addParam_(algo, name, ParamType::type, &value, readOnly, + addParam_(algo, parameter, ParamType::type, &value, readOnly, (Algorithm::Getter)getter, (Algorithm::Setter)setter, help); } -void AlgorithmInfo::addParam(Algorithm& algo, const char* name, - Mat& value, bool readOnly, +void AlgorithmInfo::addParam(Algorithm& algo, const char* parameter, + Mat& value, bool readOnly, Mat (Algorithm::*getter)(), void (Algorithm::*setter)(const Mat&), const string& help) { - addParam_(algo, name, ParamType::type, &value, readOnly, + addParam_(algo, parameter, ParamType::type, &value, readOnly, (Algorithm::Getter)getter, (Algorithm::Setter)setter, help); } -void AlgorithmInfo::addParam(Algorithm& algo, const char* name, - vector& value, bool readOnly, +void AlgorithmInfo::addParam(Algorithm& algo, const char* parameter, + vector& value, bool readOnly, vector (Algorithm::*getter)(), void (Algorithm::*setter)(const vector&), const string& help) { - addParam_(algo, name, ParamType >::type, &value, readOnly, + addParam_(algo, parameter, ParamType >::type, &value, readOnly, (Algorithm::Getter)getter, (Algorithm::Setter)setter, help); -} - -void AlgorithmInfo::addParam(Algorithm& algo, const char* name, - Ptr& value, bool readOnly, +} + +void AlgorithmInfo::addParam(Algorithm& algo, const char* parameter, + Ptr& value, bool readOnly, Ptr (Algorithm::*getter)(), void (Algorithm::*setter)(const Ptr&), const string& help) { - addParam_(algo, name, ParamType::type, &value, readOnly, + addParam_(algo, parameter, ParamType::type, &value, readOnly, (Algorithm::Getter)getter, (Algorithm::Setter)setter, help); -} +} } - + /* End of file. */ diff --git a/modules/core/src/alloc.cpp b/modules/core/src/alloc.cpp index dfe7252..1944ed1 100644 --- a/modules/core/src/alloc.cpp +++ b/modules/core/src/alloc.cpp @@ -55,7 +55,9 @@ static void* OutOfMemoryError(size_t size) #if CV_USE_SYSTEM_MALLOC +#if defined WIN32 || defined _WIN32 void deleteThreadAllocData() {} +#endif void* fastMalloc( size_t size ) { @@ -66,14 +68,14 @@ void* fastMalloc( size_t size ) adata[-1] = udata; return adata; } - + void fastFree(void* ptr) { if(ptr) { uchar* udata = ((uchar**)ptr)[-1]; CV_DbgAssert(udata < (uchar*)ptr && - ((uchar*)ptr - udata) <= (ptrdiff_t)(sizeof(void*)+CV_MALLOC_ALIGN)); + ((uchar*)ptr - udata) <= (ptrdiff_t)(sizeof(void*)+CV_MALLOC_ALIGN)); free(udata); } } @@ -388,7 +390,7 @@ struct ThreadData #ifdef WIN32 #ifdef WINCE -# define TLS_OUT_OF_INDEXES ((DWORD)0xFFFFFFFF) +# define TLS_OUT_OF_INDEXES ((DWORD)0xFFFFFFFF) #endif //WINCE static DWORD tlsKey; @@ -535,7 +537,7 @@ void* fastMalloc( size_t size ) freePtr = block; if( !data ) { - block = gcPtr; + block = gcPtr; for( int k = 0; k < 2; k++ ) { SANITY_CHECK(block); @@ -620,7 +622,7 @@ void fastFree( void* ptr ) Block*& startPtr = tls->bins[idx][START]; Block*& freePtr = tls->bins[idx][FREE]; Block*& gcPtr = tls->bins[idx][GC]; - + if( block == block->next ) { CV_DbgAssert( startPtr == block && freePtr == block && gcPtr == block ); diff --git a/modules/core/src/arithm.cpp b/modules/core/src/arithm.cpp index a5c2111..52b3105 100644 --- a/modules/core/src/arithm.cpp +++ b/modules/core/src/arithm.cpp @@ -974,7 +974,7 @@ void convertAndUnrollScalar( const Mat& sc, int buftype, uchar* scbuf, size_t bl scbuf[i] = scbuf[i - esz]; } -void binary_op(InputArray _src1, InputArray _src2, OutputArray _dst, +static void binary_op(InputArray _src1, InputArray _src2, OutputArray _dst, InputArray _mask, const BinaryFunc* tab, bool bitwise) { int kind1 = _src1.kind(), kind2 = _src2.kind(); @@ -1216,7 +1216,7 @@ void cv::min(const Mat& src1, double src2, Mat& dst) namespace cv { -void arithm_op(InputArray _src1, InputArray _src2, OutputArray _dst, +static void arithm_op(InputArray _src1, InputArray _src2, OutputArray _dst, InputArray _mask, int dtype, BinaryFunc* tab, bool muldiv=false, void* usrdata=0) { int kind1 = _src1.kind(), kind2 = _src2.kind(); diff --git a/modules/core/src/cmdparser.cpp b/modules/core/src/cmdparser.cpp index aee5ff9..b5c838c 100644 --- a/modules/core/src/cmdparser.cpp +++ b/modules/core/src/cmdparser.cpp @@ -6,6 +6,7 @@ using namespace std; using namespace cv; +namespace { void helpParser() { printf("\nThe CommandLineParser class is designed for command line arguments parsing\n" @@ -89,6 +90,8 @@ string del_space(string name) return name; } +}//namespace + CommandLineParser::CommandLineParser(int argc, const char* const argv[], const char* keys) { std::string keys_buffer; diff --git a/modules/core/src/convert.cpp b/modules/core/src/convert.cpp index 090c1cb..3c5b381 100644 --- a/modules/core/src/convert.cpp +++ b/modules/core/src/convert.cpp @@ -88,7 +88,7 @@ split_( const T* src, T** dst, int len, int cn ) dst2[i] = src[j+2]; dst3[i] = src[j+3]; } } - + for( ; k < cn; k += 4 ) { T *dst0 = dst[k], *dst1 = dst[k+1], *dst2 = dst[k+2], *dst3 = dst[k+3]; @@ -99,7 +99,7 @@ split_( const T* src, T** dst, int len, int cn ) } } } - + template static void merge_( const T** src, T* dst, int len, int cn ) { @@ -139,7 +139,7 @@ merge_( const T** src, T* dst, int len, int cn ) dst[j+2] = src2[i]; dst[j+3] = src3[i]; } } - + for( ; k < cn; k += 4 ) { const T *src0 = src[k], *src1 = src[k+1], *src2 = src[k+2], *src3 = src[k+3]; @@ -165,7 +165,7 @@ static void split32s(const int* src, int** dst, int len, int cn ) { split_(src, dst, len, cn); } - + static void split64s(const int64* src, int64** dst, int len, int cn ) { split_(src, dst, len, cn); @@ -189,7 +189,7 @@ static void merge32s(const int** src, int* dst, int len, int cn ) static void merge64s(const int64** src, int64* dst, int len, int cn ) { merge_(src, dst, len, cn); -} +} typedef void (*SplitFunc)(const uchar* src, uchar** dst, int len, int cn); typedef void (*MergeFunc)(const uchar** src, uchar* dst, int len, int cn); @@ -205,9 +205,9 @@ static MergeFunc mergeTab[] = (MergeFunc)GET_OPTIMIZED(merge8u), (MergeFunc)GET_OPTIMIZED(merge8u), (MergeFunc)GET_OPTIMIZED(merge16u), (MergeFunc)GET_OPTIMIZED(merge16u), (MergeFunc)GET_OPTIMIZED(merge32s), (MergeFunc)GET_OPTIMIZED(merge32s), (MergeFunc)GET_OPTIMIZED(merge64s), 0 }; - + } - + void cv::split(const Mat& src, Mat* mv) { int k, depth = src.depth(), cn = src.channels(); @@ -219,30 +219,30 @@ void cv::split(const Mat& src, Mat* mv) SplitFunc func = splitTab[depth]; CV_Assert( func != 0 ); - + int esz = (int)src.elemSize(), esz1 = (int)src.elemSize1(); int blocksize0 = (BLOCK_SIZE + esz-1)/esz; AutoBuffer _buf((cn+1)*(sizeof(Mat*) + sizeof(uchar*)) + 16); const Mat** arrays = (const Mat**)(uchar*)_buf; uchar** ptrs = (uchar**)alignPtr(arrays + cn + 1, 16); - + arrays[0] = &src; for( k = 0; k < cn; k++ ) { mv[k].create(src.dims, src.size, depth); arrays[k+1] = &mv[k]; } - + NAryMatIterator it(arrays, ptrs, cn+1); int total = (int)it.size, blocksize = cn <= 4 ? total : std::min(total, blocksize0); - + for( size_t i = 0; i < it.nplanes; i++, ++it ) { for( int j = 0; j < total; j += blocksize ) { int bsz = std::min(total - j, blocksize); func( ptrs[0], &ptrs[1], bsz, cn ); - + if( j + blocksize < total ) { ptrs[0] += bsz*esz; @@ -252,45 +252,45 @@ void cv::split(const Mat& src, Mat* mv) } } } - + void cv::split(const Mat& m, vector& mv) { mv.resize(!m.empty() ? m.channels() : 0); if(!m.empty()) split(m, &mv[0]); } - + void cv::merge(const Mat* mv, size_t n, OutputArray _dst) { CV_Assert( mv && n > 0 ); - + int depth = mv[0].depth(); bool allch1 = true; int k, cn = 0; size_t i; - + for( i = 0; i < n; i++ ) { CV_Assert(mv[i].size == mv[0].size && mv[i].depth() == depth); allch1 = allch1 && mv[i].channels() == 1; cn += mv[i].channels(); } - + CV_Assert( 0 < cn && cn <= CV_CN_MAX ); _dst.create(mv[0].dims, mv[0].size, CV_MAKETYPE(depth, cn)); Mat dst = _dst.getMat(); - + if( n == 1 ) { mv[0].copyTo(dst); return; } - + if( !allch1 ) { AutoBuffer pairs(cn*2); int j, ni=0; - + for( i = 0, j = 0; i < n; i++, j += ni ) { ni = mv[i].channels(); @@ -303,33 +303,33 @@ void cv::merge(const Mat* mv, size_t n, OutputArray _dst) mixChannels( mv, n, &dst, 1, &pairs[0], cn ); return; } - + size_t esz = dst.elemSize(), esz1 = dst.elemSize1(); int blocksize0 = (int)((BLOCK_SIZE + esz-1)/esz); AutoBuffer _buf((cn+1)*(sizeof(Mat*) + sizeof(uchar*)) + 16); const Mat** arrays = (const Mat**)(uchar*)_buf; uchar** ptrs = (uchar**)alignPtr(arrays + cn + 1, 16); - + arrays[0] = &dst; for( k = 0; k < cn; k++ ) arrays[k+1] = &mv[k]; - + NAryMatIterator it(arrays, ptrs, cn+1); int total = (int)it.size, blocksize = cn <= 4 ? total : std::min(total, blocksize0); MergeFunc func = mergeTab[depth]; - + for( i = 0; i < it.nplanes; i++, ++it ) { for( int j = 0; j < total; j += blocksize ) { int bsz = std::min(total - j, blocksize); func( (const uchar**)&ptrs[1], ptrs[0], bsz, cn ); - + if( j + blocksize < total ) { ptrs[0] += bsz*esz; - for( int k = 0; k < cn; k++ ) - ptrs[k+1] += bsz*esz1; + for( int t = 0; t < cn; t++ ) + ptrs[t+1] += bsz*esz1; } } } @@ -338,7 +338,7 @@ void cv::merge(const Mat* mv, size_t n, OutputArray _dst) void cv::merge(const vector& mv, OutputArray _dst) { merge(!mv.empty() ? &mv[0] : 0, mv.size(), _dst); -} +} /****************************************************************************************\ * Generalized split/merge: mixing channels * @@ -378,7 +378,7 @@ mixChannels_( const T** src, const int* sdelta, } } - + static void mixChannels8u( const uchar** src, const int* sdelta, uchar** dst, const int* ddelta, int len, int npairs ) @@ -399,14 +399,14 @@ static void mixChannels32s( const int** src, const int* sdelta, { mixChannels_(src, sdelta, dst, ddelta, len, npairs); } - + static void mixChannels64s( const int64** src, const int* sdelta, int64** dst, const int* ddelta, int len, int npairs ) { mixChannels_(src, sdelta, dst, ddelta, len, npairs); } - + typedef void (*MixChannelsFunc)( const uchar** src, const int* sdelta, uchar** dst, const int* ddelta, int len, int npairs ); @@ -414,17 +414,17 @@ static MixChannelsFunc mixchTab[] = { (MixChannelsFunc)mixChannels8u, (MixChannelsFunc)mixChannels8u, (MixChannelsFunc)mixChannels16u, (MixChannelsFunc)mixChannels16u, (MixChannelsFunc)mixChannels32s, (MixChannelsFunc)mixChannels32s, - (MixChannelsFunc)mixChannels64s, 0 + (MixChannelsFunc)mixChannels64s, 0 }; - + } - + void cv::mixChannels( const Mat* src, size_t nsrcs, Mat* dst, size_t ndsts, const int* fromTo, size_t npairs ) { if( npairs == 0 ) return; CV_Assert( src && nsrcs > 0 && dst && ndsts > 0 && fromTo && npairs > 0 ); - + size_t i, j, k, esz1 = dst[0].elemSize1(); int depth = dst[0].depth(); @@ -435,13 +435,13 @@ void cv::mixChannels( const Mat* src, size_t nsrcs, Mat* dst, size_t ndsts, cons uchar** dsts = (uchar**)(srcs + npairs); int* tab = (int*)(dsts + npairs); int *sdelta = (int*)(tab + npairs*4), *ddelta = sdelta + npairs; - + for( i = 0; i < nsrcs; i++ ) arrays[i] = &src[i]; for( i = 0; i < ndsts; i++ ) arrays[i + nsrcs] = &dst[i]; ptrs[nsrcs + ndsts] = 0; - + for( i = 0; i < npairs; i++ ) { int i0 = fromTo[i*2], i1 = fromTo[i*2+1]; @@ -459,7 +459,7 @@ void cv::mixChannels( const Mat* src, size_t nsrcs, Mat* dst, size_t ndsts, cons tab[i*4] = (int)(nsrcs + ndsts); tab[i*4+1] = 0; sdelta[i] = 0; } - + for( j = 0; j < ndsts; i1 -= dst[j].channels(), j++ ) if( i1 < dst[j].channels() ) break; @@ -471,7 +471,7 @@ void cv::mixChannels( const Mat* src, size_t nsrcs, Mat* dst, size_t ndsts, cons NAryMatIterator it(arrays, ptrs, (int)(nsrcs + ndsts)); int total = (int)it.size, blocksize = std::min(total, (int)((BLOCK_SIZE + esz1-1)/esz1)); MixChannelsFunc func = mixchTab[depth]; - + for( i = 0; i < it.nplanes; i++, ++it ) { for( k = 0; k < npairs; k++ ) @@ -479,13 +479,13 @@ void cv::mixChannels( const Mat* src, size_t nsrcs, Mat* dst, size_t ndsts, cons srcs[k] = ptrs[tab[k*4]] + tab[k*4+1]; dsts[k] = ptrs[tab[k*4+2]] + tab[k*4+3]; } - - for( int j = 0; j < total; j += blocksize ) + + for( int t = 0; t < total; t += blocksize ) { - int bsz = std::min(total - j, blocksize); + int bsz = std::min(total - t, blocksize); func( srcs, sdelta, dsts, ddelta, bsz, (int)npairs ); - - if( j + blocksize < total ) + + if( t + blocksize < total ) for( k = 0; k < npairs; k++ ) { srcs[k] += blocksize*sdelta[k]*esz1; @@ -515,7 +515,7 @@ void cv::mixChannels(InputArrayOfArrays src, InputArrayOfArrays dst, int i; int nsrc = src_is_mat ? 1 : (int)src.total(); int ndst = dst_is_mat ? 1 : (int)dst.total(); - + CV_Assert(fromTo.size()%2 == 0 && nsrc > 0 && ndst > 0); cv::AutoBuffer _buf(nsrc + ndst); Mat* buf = _buf; @@ -559,7 +559,7 @@ cvtScaleAbs_( const T* src, size_t sstep, { sstep /= sizeof(src[0]); dstep /= sizeof(dst[0]); - + for( ; size.height--; src += sstep, dst += dstep ) { int x = 0; @@ -574,11 +574,11 @@ cvtScaleAbs_( const T* src, size_t sstep, t1 = saturate_cast
(std::abs(src[x+3]*scale + shift)); dst[x+2] = t0; dst[x+3] = t1; } - #endif + #endif for( ; x < size.width; x++ ) dst[x] = saturate_cast
(std::abs(src[x]*scale + shift)); } -} +} template static void @@ -588,7 +588,7 @@ cvtScale_( const T* src, size_t sstep, { sstep /= sizeof(src[0]); dstep /= sizeof(dst[0]); - + for( ; size.height--; src += sstep, dst += dstep ) { int x = 0; @@ -614,7 +614,7 @@ cvtScale_( const T* src, size_t sstep, template<> void cvtScale_( const short* src, size_t sstep, short* dst, size_t dstep, Size size, - float scale, float shift ) + float scale, float shift ) { sstep /= sizeof(src[0]); dstep /= sizeof(dst[0]); @@ -639,13 +639,13 @@ cvtScale_( const short* src, size_t sstep, r1 = _mm_cvtps_epi32(rf1); r0 = _mm_packs_epi32(r0, r1); _mm_storeu_si128((__m128i*)(dst + x), r0); - } + } } #endif for(; x < size.width; x++ ) dst[x] = saturate_cast(src[x]*scale + shift); - } + } } @@ -655,7 +655,7 @@ cvt_( const T* src, size_t sstep, { sstep /= sizeof(src[0]); dstep /= sizeof(dst[0]); - + for( ; size.height--; src += sstep, dst += dstep ) { int x = 0; @@ -683,7 +683,7 @@ cvt_( const float* src, size_t sstep, { sstep /= sizeof(src[0]); dstep /= sizeof(dst[0]); - + for( ; size.height--; src += sstep, dst += dstep ) { int x = 0; @@ -693,10 +693,10 @@ cvt_( const float* src, size_t sstep, { __m128 src128 = _mm_loadu_ps (src + x); __m128i src_int128 = _mm_cvtps_epi32 (src128); - - src128 = _mm_loadu_ps (src + x + 4); + + src128 = _mm_loadu_ps (src + x + 4); __m128i src1_int128 = _mm_cvtps_epi32 (src128); - + src1_int128 = _mm_packs_epi32(src_int128, src1_int128); _mm_storeu_si128((__m128i*)(dst + x),src1_int128); } @@ -714,11 +714,11 @@ cpy_( const T* src, size_t sstep, T* dst, size_t dstep, Size size ) { sstep /= sizeof(src[0]); dstep /= sizeof(dst[0]); - + for( ; size.height--; src += sstep, dst += dstep ) memcpy(dst, src, size.width*sizeof(src[0])); } - + #define DEF_CVT_SCALE_ABS_FUNC(suffix, tfunc, stype, dtype, wtype) \ static void cvtScaleAbs##suffix( const stype* src, size_t sstep, const uchar*, size_t, \ dtype* dst, size_t dstep, Size size, double* scale) \ @@ -732,8 +732,8 @@ dtype* dst, size_t dstep, Size size, double* scale) \ { \ cvtScale_(src, sstep, dst, dstep, size, (wtype)scale[0], (wtype)scale[1]); \ } - - + + #define DEF_CVT_FUNC(suffix, stype, dtype) \ static void cvt##suffix( const stype* src, size_t sstep, const uchar*, size_t, \ dtype* dst, size_t dstep, Size size, double*) \ @@ -747,15 +747,15 @@ stype* dst, size_t dstep, Size size, double*) \ { \ cpy_(src, sstep, dst, dstep, size); \ } - - + + DEF_CVT_SCALE_ABS_FUNC(8u, cvtScaleAbs_, uchar, uchar, float); DEF_CVT_SCALE_ABS_FUNC(8s8u, cvtScaleAbs_, schar, uchar, float); DEF_CVT_SCALE_ABS_FUNC(16u8u, cvtScaleAbs_, ushort, uchar, float); DEF_CVT_SCALE_ABS_FUNC(16s8u, cvtScaleAbs_, short, uchar, float); DEF_CVT_SCALE_ABS_FUNC(32s8u, cvtScaleAbs_, int, uchar, float); DEF_CVT_SCALE_ABS_FUNC(32f8u, cvtScaleAbs_, float, uchar, float); -DEF_CVT_SCALE_ABS_FUNC(64f8u, cvtScaleAbs_, double, uchar, float); +DEF_CVT_SCALE_ABS_FUNC(64f8u, cvtScaleAbs_, double, uchar, float); DEF_CVT_SCALE_FUNC(8u, uchar, uchar, float); DEF_CVT_SCALE_FUNC(8s8u, schar, uchar, float); @@ -763,7 +763,7 @@ DEF_CVT_SCALE_FUNC(16u8u, ushort, uchar, float); DEF_CVT_SCALE_FUNC(16s8u, short, uchar, float); DEF_CVT_SCALE_FUNC(32s8u, int, uchar, float); DEF_CVT_SCALE_FUNC(32f8u, float, uchar, float); -DEF_CVT_SCALE_FUNC(64f8u, double, uchar, float); +DEF_CVT_SCALE_FUNC(64f8u, double, uchar, float); DEF_CVT_SCALE_FUNC(8u8s, uchar, schar, float); DEF_CVT_SCALE_FUNC(8s, schar, schar, float); @@ -771,7 +771,7 @@ DEF_CVT_SCALE_FUNC(16u8s, ushort, schar, float); DEF_CVT_SCALE_FUNC(16s8s, short, schar, float); DEF_CVT_SCALE_FUNC(32s8s, int, schar, float); DEF_CVT_SCALE_FUNC(32f8s, float, schar, float); -DEF_CVT_SCALE_FUNC(64f8s, double, schar, float); +DEF_CVT_SCALE_FUNC(64f8s, double, schar, float); DEF_CVT_SCALE_FUNC(8u16u, uchar, ushort, float); DEF_CVT_SCALE_FUNC(8s16u, schar, ushort, float); @@ -779,7 +779,7 @@ DEF_CVT_SCALE_FUNC(16u, ushort, ushort, float); DEF_CVT_SCALE_FUNC(16s16u, short, ushort, float); DEF_CVT_SCALE_FUNC(32s16u, int, ushort, float); DEF_CVT_SCALE_FUNC(32f16u, float, ushort, float); -DEF_CVT_SCALE_FUNC(64f16u, double, ushort, float); +DEF_CVT_SCALE_FUNC(64f16u, double, ushort, float); DEF_CVT_SCALE_FUNC(8u16s, uchar, short, float); DEF_CVT_SCALE_FUNC(8s16s, schar, short, float); @@ -788,7 +788,7 @@ DEF_CVT_SCALE_FUNC(16s, short, short, float); DEF_CVT_SCALE_FUNC(32s16s, int, short, float); DEF_CVT_SCALE_FUNC(32f16s, float, short, float); DEF_CVT_SCALE_FUNC(64f16s, double, short, float); - + DEF_CVT_SCALE_FUNC(8u32s, uchar, int, float); DEF_CVT_SCALE_FUNC(8s32s, schar, int, float); DEF_CVT_SCALE_FUNC(16u32s, ushort, int, float); @@ -865,7 +865,7 @@ DEF_CVT_FUNC(16s64f, short, double); DEF_CVT_FUNC(32s64f, int, double); DEF_CVT_FUNC(32f64f, float, double); DEF_CPY_FUNC(64s, int64); - + static BinaryFunc cvtScaleAbsTab[] = { (BinaryFunc)cvtScaleAbs8u, (BinaryFunc)cvtScaleAbs8s8u, (BinaryFunc)cvtScaleAbs16u8u, @@ -956,7 +956,7 @@ static BinaryFunc cvtTab[][8] = 0, 0, 0, 0, 0, 0, 0, 0 } }; - + BinaryFunc getConvertFunc(int sdepth, int ddepth) { return cvtTab[CV_MAT_DEPTH(ddepth)][CV_MAT_DEPTH(sdepth)]; @@ -965,10 +965,10 @@ BinaryFunc getConvertFunc(int sdepth, int ddepth) BinaryFunc getConvertScaleFunc(int sdepth, int ddepth) { return cvtScaleTab[CV_MAT_DEPTH(ddepth)][CV_MAT_DEPTH(sdepth)]; -} - } - + +} + void cv::convertScaleAbs( InputArray _src, OutputArray _dst, double alpha, double beta ) { Mat src = _src.getMat(); @@ -978,7 +978,7 @@ void cv::convertScaleAbs( InputArray _src, OutputArray _dst, double alpha, doubl Mat dst = _dst.getMat(); BinaryFunc func = cvtScaleAbsTab[src.depth()]; CV_Assert( func != 0 ); - + if( src.dims <= 2 ) { Size sz = getContinuousSize(src, dst, cn); @@ -990,7 +990,7 @@ void cv::convertScaleAbs( InputArray _src, OutputArray _dst, double alpha, doubl uchar* ptrs[2]; NAryMatIterator it(arrays, ptrs); Size sz((int)it.size*cn, 1); - + for( size_t i = 0; i < it.nplanes; i++, ++it ) func( ptrs[0], 0, 0, 0, ptrs[1], 0, sz, scale ); } @@ -1013,12 +1013,12 @@ void cv::Mat::convertTo(OutputArray _dst, int _type, double alpha, double beta) } Mat src = *this; - + BinaryFunc func = noScale ? getConvertFunc(sdepth, ddepth) : getConvertScaleFunc(sdepth, ddepth); double scale[] = {alpha, beta}; int cn = channels(); CV_Assert( func != 0 ); - + if( dims <= 2 ) { _dst.create( size(), _type ); @@ -1034,7 +1034,7 @@ void cv::Mat::convertTo(OutputArray _dst, int _type, double alpha, double beta) uchar* ptrs[2]; NAryMatIterator it(arrays, ptrs); Size sz((int)(it.size*cn), 1); - + for( size_t i = 0; i < it.nplanes; i++, ++it ) func(ptrs[0], 0, 0, 0, ptrs[1], 0, sz, scale); } @@ -1096,10 +1096,10 @@ static void LUT8u_32f( const uchar* src, const float* lut, float* dst, int len, static void LUT8u_64f( const uchar* src, const double* lut, double* dst, int len, int cn, int lutcn ) { LUT8u_( src, lut, dst, len, cn, lutcn ); -} - +} + typedef void (*LUTFunc)( const uchar* src, const uchar* lut, uchar* dst, int len, int cn, int lutcn ); - + static LUTFunc lutTab[] = { (LUTFunc)LUT8u_8u, (LUTFunc)LUT8u_8s, (LUTFunc)LUT8u_16u, (LUTFunc)LUT8u_16s, @@ -1107,7 +1107,7 @@ static LUTFunc lutTab[] = }; } - + void cv::LUT( InputArray _src, InputArray _lut, OutputArray _dst, int interpolation ) { Mat src = _src.getMat(), lut = _lut.getMat(); @@ -1123,12 +1123,12 @@ void cv::LUT( InputArray _src, InputArray _lut, OutputArray _dst, int interpolat LUTFunc func = lutTab[lut.depth()]; CV_Assert( func != 0 ); - + const Mat* arrays[] = {&src, &dst, 0}; uchar* ptrs[2]; NAryMatIterator it(arrays, ptrs); int len = (int)it.size; - + for( size_t i = 0; i < it.nplanes; i++, ++it ) func(ptrs[0], lut.data, ptrs[1], len, cn, lutcn); } @@ -1138,7 +1138,7 @@ void cv::normalize( InputArray _src, OutputArray _dst, double a, double b, int norm_type, int rtype, InputArray _mask ) { Mat src = _src.getMat(), mask = _mask.getMat(); - + double scale = 1, shift = 0; if( norm_type == CV_MINMAX ) { @@ -1156,13 +1156,13 @@ void cv::normalize( InputArray _src, OutputArray _dst, double a, double b, } else CV_Error( CV_StsBadArg, "Unknown/unsupported norm type" ); - + if( rtype < 0 ) rtype = _dst.fixedType() ? _dst.depth() : src.depth(); - + _dst.create(src.dims, src.size, CV_MAKETYPE(rtype, src.channels())); Mat dst = _dst.getMat(); - + if( !mask.data ) src.convertTo( dst, rtype, scale, shift ); else @@ -1273,7 +1273,7 @@ cvConvertScale( const void* srcarr, void* dstarr, double scale, double shift ) { cv::Mat src = cv::cvarrToMat(srcarr), dst = cv::cvarrToMat(dstarr); - + CV_Assert( src.size == dst.size && src.channels() == dst.channels() ); src.convertTo(dst, dst.type(), scale, shift); } diff --git a/modules/core/src/copy.cpp b/modules/core/src/copy.cpp index c0d0501..24e6a51 100644 --- a/modules/core/src/copy.cpp +++ b/modules/core/src/copy.cpp @@ -59,7 +59,7 @@ copyMask_(const uchar* _src, size_t sstep, const uchar* mask, size_t mstep, ucha const T* src = (const T*)_src; T* dst = (T*)_dst; int x = 0; - #if CV_ENABLE_UNROLLED + #if CV_ENABLE_UNROLLED for( ; x <= size.width - 4; x += 4 ) { if( mask[x] ) @@ -96,16 +96,16 @@ copyMaskGeneric(const uchar* _src, size_t sstep, const uchar* mask, size_t mstep } } } - - + + #define DEF_COPY_MASK(suffix, type) \ static void copyMask##suffix(const uchar* src, size_t sstep, const uchar* mask, size_t mstep, \ uchar* dst, size_t dstep, Size size, void*) \ { \ copyMask_(src, sstep, mask, mstep, dst, dstep, size); \ } - - + + DEF_COPY_MASK(8u, uchar); DEF_COPY_MASK(16u, ushort); DEF_COPY_MASK(8uC3, Vec3b); @@ -116,7 +116,7 @@ DEF_COPY_MASK(32sC3, Vec3i); DEF_COPY_MASK(32sC4, Vec4i); DEF_COPY_MASK(32sC6, Vec6i); DEF_COPY_MASK(32sC8, Vec8i); - + BinaryFunc copyMaskTab[] = { 0, @@ -137,7 +137,7 @@ BinaryFunc copyMaskTab[] = 0, 0, 0, 0, 0, 0, 0, copyMask32sC8 }; - + BinaryFunc getCopyMaskFunc(size_t esz) { return esz <= 32 && copyMaskTab[esz] ? copyMaskTab[esz] : copyMaskGeneric; @@ -152,51 +152,51 @@ void Mat::copyTo( OutputArray _dst ) const convertTo( _dst, dtype ); return; } - + if( empty() ) { _dst.release(); return; } - + if( dims <= 2 ) { _dst.create( rows, cols, type() ); Mat dst = _dst.getMat(); if( data == dst.data ) return; - + if( rows > 0 && cols > 0 ) { const uchar* sptr = data; uchar* dptr = dst.data; - + // to handle the copying 1xn matrix => nx1 std vector. Size sz = size() == dst.size() ? getContinuousSize(*this, dst) : getContinuousSize(*this); size_t len = sz.width*elemSize(); - + for( ; sz.height--; sptr += step, dptr += dst.step ) memcpy( dptr, sptr, len ); } return; } - + _dst.create( dims, size, type() ); Mat dst = _dst.getMat(); if( data == dst.data ) return; - + if( total() != 0 ) { const Mat* arrays[] = { this, &dst }; uchar* ptrs[2]; NAryMatIterator it(arrays, ptrs, 2); - size_t size = it.size*elemSize(); - + size_t sz = it.size*elemSize(); + for( size_t i = 0; i < it.nplanes; i++, ++it ) - memcpy(ptrs[1], ptrs[0], size); + memcpy(ptrs[1], ptrs[0], sz); } } @@ -208,33 +208,33 @@ void Mat::copyTo( OutputArray _dst, InputArray _mask ) const copyTo(_dst); return; } - + int cn = channels(), mcn = mask.channels(); CV_Assert( mask.depth() == CV_8U && (mcn == 1 || mcn == cn) ); bool colorMask = mcn > 1; - + size_t esz = colorMask ? elemSize1() : elemSize(); BinaryFunc copymask = getCopyMaskFunc(esz); - + uchar* data0 = _dst.getMat().data; _dst.create( dims, size, type() ); Mat dst = _dst.getMat(); - + if( dst.data != data0 ) // do not leave dst uninitialized dst = Scalar(0); - + if( dims <= 2 ) { Size sz = getContinuousSize(*this, dst, mask, mcn); copymask(data, step, mask.data, mask.step, dst.data, dst.step, sz, &esz); return; } - + const Mat* arrays[] = { this, &dst, &mask, 0 }; uchar* ptrs[3]; NAryMatIterator it(arrays, ptrs); Size sz((int)(it.size*mcn), 1); - + for( size_t i = 0; i < it.nplanes; i++, ++it ) copymask(ptrs[0], 0, ptrs[2], 0, ptrs[1], 0, sz, &esz); } @@ -242,14 +242,14 @@ void Mat::copyTo( OutputArray _dst, InputArray _mask ) const Mat& Mat::operator = (const Scalar& s) { const Mat* arrays[] = { this }; - uchar* ptr; - NAryMatIterator it(arrays, &ptr, 1); - size_t size = it.size*elemSize(); - + uchar* dptr; + NAryMatIterator it(arrays, &dptr, 1); + size_t elsize = it.size*elemSize(); + if( s[0] == 0 && s[1] == 0 && s[2] == 0 && s[3] == 0 ) { for( size_t i = 0; i < it.nplanes; i++, ++it ) - memset( ptr, 0, size ); + memset( dptr, 0, elsize ); } else { @@ -258,50 +258,50 @@ Mat& Mat::operator = (const Scalar& s) double scalar[12]; scalarToRawData(s, scalar, type(), 12); size_t blockSize = 12*elemSize1(); - - for( size_t j = 0; j < size; j += blockSize ) + + for( size_t j = 0; j < elsize; j += blockSize ) { - size_t sz = MIN(blockSize, size - j); - memcpy( ptr + j, scalar, sz ); + size_t sz = MIN(blockSize, elsize - j); + memcpy( dptr + j, scalar, sz ); } } - + for( size_t i = 1; i < it.nplanes; i++ ) { ++it; - memcpy( ptr, data, size ); + memcpy( dptr, data, elsize ); } } return *this; } - + Mat& Mat::setTo(InputArray _value, InputArray _mask) { if( !data ) return *this; - + Mat value = _value.getMat(), mask = _mask.getMat(); - + CV_Assert( checkScalar(value, type(), _value.kind(), _InputArray::MAT )); CV_Assert( mask.empty() || mask.type() == CV_8U ); - + size_t esz = elemSize(); BinaryFunc copymask = getCopyMaskFunc(esz); - + const Mat* arrays[] = { this, !mask.empty() ? &mask : 0, 0 }; uchar* ptrs[2]={0,0}; NAryMatIterator it(arrays, ptrs); - int total = (int)it.size, blockSize0 = std::min(total, (int)((BLOCK_SIZE + esz-1)/esz)); + int totalsz = (int)it.size, blockSize0 = std::min(totalsz, (int)((BLOCK_SIZE + esz-1)/esz)); AutoBuffer _scbuf(blockSize0*esz + 32); uchar* scbuf = alignPtr((uchar*)_scbuf, (int)sizeof(double)); convertAndUnrollScalar( value, type(), scbuf, blockSize0 ); - + for( size_t i = 0; i < it.nplanes; i++, ++it ) { - for( int j = 0; j < total; j += blockSize0 ) + for( int j = 0; j < totalsz; j += blockSize0 ) { - Size sz(std::min(blockSize0, total - j), 1); + Size sz(std::min(blockSize0, totalsz - j), 1); size_t blockSize = sz.width*esz; if( ptrs[1] ) { @@ -323,7 +323,7 @@ flipHoriz( const uchar* src, size_t sstep, uchar* dst, size_t dstep, Size size, int i, j, limit = (int)(((size.width + 1)/2)*esz); AutoBuffer _tab(size.width*esz); int* tab = _tab; - + for( i = 0; i < size.width; i++ ) for( size_t k = 0; k < esz; k++ ) tab[i*esz + k] = (int)((size.width - i - 1)*esz + k); @@ -403,7 +403,7 @@ flipVert( const uchar* src0, size_t sstep, uchar* dst0, size_t dstep, Size size, void flip( InputArray _src, OutputArray _dst, int flip_mode ) { Mat src = _src.getMat(); - + CV_Assert( src.dims <= 2 ); _dst.create( src.size(), src.type() ); Mat dst = _dst.getMat(); @@ -413,7 +413,7 @@ void flip( InputArray _src, OutputArray _dst, int flip_mode ) flipVert( src.data, src.step, dst.data, dst.step, src.size(), esz ); else flipHoriz( src.data, src.step, dst.data, dst.step, src.size(), esz ); - + if( flip_mode < 0 ) flipHoriz( dst.data, dst.step, dst.data, dst.step, dst.size(), esz ); } @@ -423,7 +423,7 @@ void repeat(InputArray _src, int ny, int nx, OutputArray _dst) { Mat src = _src.getMat(); CV_Assert( src.dims <= 2 ); - + _dst.create(src.rows*ny, src.cols*nx, src.type()); Mat dst = _dst.getMat(); Size ssize = src.size(), dsize = dst.size(); @@ -493,25 +493,25 @@ cvCopy( const void* srcarr, void* dstarr, const void* maskarr ) } cv::Mat src = cv::cvarrToMat(srcarr, false, true, 1), dst = cv::cvarrToMat(dstarr, false, true, 1); CV_Assert( src.depth() == dst.depth() && src.size == dst.size ); - + int coi1 = 0, coi2 = 0; if( CV_IS_IMAGE(srcarr) ) coi1 = cvGetImageCOI((const IplImage*)srcarr); if( CV_IS_IMAGE(dstarr) ) coi2 = cvGetImageCOI((const IplImage*)dstarr); - + if( coi1 || coi2 ) { CV_Assert( (coi1 != 0 || src.channels() == 1) && (coi2 != 0 || dst.channels() == 1) ); - + int pair[] = { std::max(coi1-1, 0), std::max(coi2-1, 0) }; cv::mixChannels( &src, 1, &dst, 1, pair, 1 ); return; } else CV_Assert( src.channels() == dst.channels() ); - + if( !maskarr ) src.copyTo(dst); else @@ -548,12 +548,12 @@ cvFlip( const CvArr* srcarr, CvArr* dstarr, int flip_mode ) { cv::Mat src = cv::cvarrToMat(srcarr); cv::Mat dst; - + if (!dstarr) dst = src; else dst = cv::cvarrToMat(dstarr); - + CV_Assert( src.type() == dst.type() && src.size() == dst.size() ); cv::flip( src, dst, flip_mode ); } diff --git a/modules/core/src/datastructs.cpp b/modules/core/src/datastructs.cpp index 02f188c..9438fa2 100644 --- a/modules/core/src/datastructs.cpp +++ b/modules/core/src/datastructs.cpp @@ -3349,7 +3349,7 @@ cvTreeToNodeSeq( const void* first, int header_size, CvMemStorage* storage ) } } - + return allseq; } @@ -3531,9 +3531,9 @@ namespace cv // both cv (CvFeatureTree) and ml (kNN). // The algorithm is taken from: -// J.S. Beis and D.G. Lowe. Shape indexing using approximate nearest-neighbor search -// in highdimensional spaces. In Proc. IEEE Conf. Comp. Vision Patt. Recog., -// pages 1000--1006, 1997. http://citeseer.ist.psu.edu/beis97shape.html +// J.S. Beis and D.G. Lowe. Shape indexing using approximate nearest-neighbor search +// in highdimensional spaces. In Proc. IEEE Conf. Comp. Vision Patt. Recog., +// pages 1000--1006, 1997. http://citeseer.ist.psu.edu/beis97shape.html const int MAX_TREE_DEPTH = 32; @@ -3555,8 +3555,8 @@ KDTree::KDTree(InputArray _points, InputArray _labels, bool _copyData) maxDepth = -1; normType = NORM_L2; build(_points, _labels, _copyData); -} - +} + struct SubTree { SubTree() : first(0), last(0), nodeIdx(0), depth(0) {} @@ -3596,7 +3596,7 @@ medianPartition( size_t* ofs, int a, int b, const float* vals ) else a = i0; } - + float pivot = vals[ofs[middle]]; int less = 0, more = 0; for( k = a0; k < middle; k++ ) @@ -3632,7 +3632,7 @@ computeSums( const Mat& points, const size_t* ofs, int a, int b, double* sums ) } } - + void KDTree::build(InputArray _points, bool _copyData) { build(_points, noArray(), _copyData); @@ -3652,8 +3652,8 @@ void KDTree::build(InputArray __points, InputArray __labels, bool _copyData) points.release(); points.create(_points.size(), _points.type()); } - - int i, j, n = _points.rows, dims = _points.cols, top = 0; + + int i, j, n = _points.rows, ptdims = _points.cols, top = 0; const float* data = _points.ptr(0); float* dstdata = points.ptr(0); size_t step = _points.step1(); @@ -3661,7 +3661,7 @@ void KDTree::build(InputArray __points, InputArray __labels, bool _copyData) int ptpos = 0; labels.resize(n); const int* _labels_data = 0; - + if( !_labels.empty() ) { int nlabels = _labels.checkVector(1, CV_32S, true); @@ -3669,9 +3669,9 @@ void KDTree::build(InputArray __points, InputArray __labels, bool _copyData) _labels_data = (const int*)_labels.data; } - Mat sumstack(MAX_TREE_DEPTH*2, dims*2, CV_64F); + Mat sumstack(MAX_TREE_DEPTH*2, ptdims*2, CV_64F); SubTree stack[MAX_TREE_DEPTH*2]; - + vector _ptofs(n); size_t* ptofs = &_ptofs[0]; @@ -3682,7 +3682,7 @@ void KDTree::build(InputArray __points, InputArray __labels, bool _copyData) computeSums(points, ptofs, 0, n-1, sumstack.ptr(top)); stack[top++] = SubTree(0, n-1, 0, 0); int _maxDepth = 0; - + while( --top >= 0 ) { int first = stack[top].first, last = stack[top].last; @@ -3700,16 +3700,16 @@ void KDTree::build(InputArray __points, InputArray __labels, bool _copyData) { const float* src = data + ptofs[first]; float* dst = dstdata + idx*dstep; - for( j = 0; j < dims; j++ ) + for( j = 0; j < ptdims; j++ ) dst[j] = src[j]; } - labels[idx] = _labels_data ? _labels_data[idx0] : idx0; + labels[idx] = _labels_data ? _labels_data[idx0] : idx0; _maxDepth = std::max(_maxDepth, depth); continue; } // find the dimensionality with the biggest variance - for( j = 0; j < dims; j++ ) + for( j = 0; j < ptdims; j++ ) { double m = sums[j*2]*invCount; double varj = sums[j*2+1]*invCount - m*m; @@ -3729,9 +3729,9 @@ void KDTree::build(InputArray __points, InputArray __labels, bool _copyData) nodes[nidx].boundary = medianPartition(ptofs, first, last, data + dim); int middle = (first + last)/2; - double *lsums = (double*)sums, *rsums = lsums + dims*2; + double *lsums = (double*)sums, *rsums = lsums + ptdims*2; computeSums(points, ptofs, middle+1, last, rsums); - for( j = 0; j < dims*2; j++ ) + for( j = 0; j < ptdims*2; j++ ) lsums[j] = sums[j] - rsums[j]; stack[top++] = SubTree(first, middle, left, depth+1); stack[top++] = SubTree(middle+1, last, right, depth+1); @@ -3752,13 +3752,13 @@ struct PQueueElem int KDTree::findNearest(InputArray _vec, int K, int emax, OutputArray _neighborsIdx, OutputArray _neighbors, OutputArray _dist, OutputArray _labels) const - + { Mat vecmat = _vec.getMat(); CV_Assert( vecmat.isContinuous() && vecmat.type() == CV_32F && vecmat.total() == (size_t)points.cols ); const float* vec = vecmat.ptr(); K = std::min(K, points.rows); - int dims = points.cols; + int ptdims = points.cols; CV_Assert(K > 0 && (normType == NORM_L2 || normType == NORM_L1)); @@ -3776,7 +3776,7 @@ int KDTree::findNearest(InputArray _vec, int K, int emax, { float d, alt_d = 0.f; int nidx; - + if( e == 0 ) nidx = 0; else @@ -3803,7 +3803,7 @@ int KDTree::findNearest(InputArray _vec, int K, int emax, i = left; } } - + if( ncount == K && alt_d > dist[ncount-1] ) continue; } @@ -3813,21 +3813,21 @@ int KDTree::findNearest(InputArray _vec, int K, int emax, if( nidx < 0 ) break; const Node& n = nodes[nidx]; - + if( n.idx < 0 ) { i = ~n.idx; const float* row = points.ptr(i); if( normType == NORM_L2 ) - for( j = 0, d = 0.f; j < dims; j++ ) + for( j = 0, d = 0.f; j < ptdims; j++ ) { float t = vec[j] - row[j]; d += t*t; } else - for( j = 0, d = 0.f; j < dims; j++ ) + for( j = 0, d = 0.f; j < ptdims; j++ ) d += std::abs(vec[j] - row[j]); - + dist[ncount] = d; idx[ncount] = i; for( i = ncount-1; i >= 0; i-- ) @@ -3839,9 +3839,9 @@ int KDTree::findNearest(InputArray _vec, int K, int emax, } ncount += ncount < K; e++; - break; + break; } - + int alt; if( vec[n.idx] <= n.boundary ) { @@ -3853,7 +3853,7 @@ int KDTree::findNearest(InputArray _vec, int K, int emax, nidx = n.right; alt = n.left; } - + d = vec[n.idx] - n.boundary; if( normType == NORM_L2 ) d = d*d + alt_d; @@ -3898,22 +3898,22 @@ void KDTree::findOrthoRange(InputArray _lowerBound, OutputArray _neighbors, OutputArray _labels ) const { - int dims = points.cols; + int ptdims = points.cols; Mat lowerBound = _lowerBound.getMat(), upperBound = _upperBound.getMat(); CV_Assert( lowerBound.size == upperBound.size && lowerBound.isContinuous() && upperBound.isContinuous() && lowerBound.type() == upperBound.type() && lowerBound.type() == CV_32F && - lowerBound.total() == (size_t)dims ); + lowerBound.total() == (size_t)ptdims ); const float* L = lowerBound.ptr(); const float* R = upperBound.ptr(); - + vector idx; AutoBuffer _stack(MAX_TREE_DEPTH*2 + 1); int* stack = _stack; int top = 0; - + stack[top++] = 0; while( --top >= 0 ) @@ -3926,10 +3926,10 @@ void KDTree::findOrthoRange(InputArray _lowerBound, { int j, i = ~n.idx; const float* row = points.ptr(i); - for( j = 0; j < dims; j++ ) + for( j = 0; j < ptdims; j++ ) if( row[j] < L[j] || row[j] >= R[j] ) break; - if( j == dims ) + if( j == ptdims ) idx.push_back(i); continue; } @@ -3948,7 +3948,7 @@ void KDTree::findOrthoRange(InputArray _lowerBound, getPoints( idx, _neighbors, _labels ); } - + void KDTree::getPoints(InputArray _idx, OutputArray _pts, OutputArray _labels) const { Mat idxmat = _idx.getMat(), pts, labelsmat; @@ -3956,8 +3956,8 @@ void KDTree::getPoints(InputArray _idx, OutputArray _pts, OutputArray _labels) c (idxmat.cols == 1 || idxmat.rows == 1) ); const int* idx = idxmat.ptr(); int* dstlabels = 0; - - int dims = points.cols; + + int ptdims = points.cols; int i, nidx = (int)idxmat.total(); if( nidx == 0 ) { @@ -3965,13 +3965,13 @@ void KDTree::getPoints(InputArray _idx, OutputArray _pts, OutputArray _labels) c _labels.release(); return; } - + if( _pts.needed() ) { - _pts.create( nidx, dims, points.type()); + _pts.create( nidx, ptdims, points.type()); pts = _pts.getMat(); } - + if(_labels.needed()) { _labels.create(nidx, 1, CV_32S, -1, true); @@ -3980,14 +3980,14 @@ void KDTree::getPoints(InputArray _idx, OutputArray _pts, OutputArray _labels) c dstlabels = labelsmat.ptr(); } const int* srclabels = !labels.empty() ? &labels[0] : 0; - + for( i = 0; i < nidx; i++ ) { int k = idx[i]; CV_Assert( (unsigned)k < (unsigned)points.rows ); const float* src = points.ptr(k); if( pts.data ) - std::copy(src, src + dims, pts.ptr(i)); + std::copy(src, src + ptdims, pts.ptr(i)); if( dstlabels ) dstlabels[i] = srclabels ? srclabels[k] : k; } @@ -4007,9 +4007,9 @@ int KDTree::dims() const { return !points.empty() ? points.cols : 0; } - + //////////////////////////////////////////////////////////////////////////////// - + schar* seqPush( CvSeq* seq, const void* element ) { return cvSeqPush(seq, element); diff --git a/modules/core/src/drawing.cpp b/modules/core/src/drawing.cpp index 4a91b32..9e33408 100644 --- a/modules/core/src/drawing.cpp +++ b/modules/core/src/drawing.cpp @@ -169,7 +169,7 @@ LineIterator::LineIterator(const Mat& img, Point pt1, Point pt2, } int bt_pix0 = (int)img.elemSize(), bt_pix = bt_pix0; - size_t step = img.step; + size_t istep = img.step; int dx = pt2.x - pt1.x; int dy = pt2.y - pt1.y; @@ -188,11 +188,11 @@ LineIterator::LineIterator(const Mat& img, Point pt1, Point pt2, bt_pix = (bt_pix ^ s) - s; } - ptr = (uchar*)(img.data + pt1.y * step + pt1.x * bt_pix0); + ptr = (uchar*)(img.data + pt1.y * istep + pt1.x * bt_pix0); s = dy < 0 ? -1 : 0; dy = (dy ^ s) - s; - step = (step ^ s) - s; + istep = (istep ^ s) - s; s = dy > dx ? -1 : 0; @@ -201,9 +201,9 @@ LineIterator::LineIterator(const Mat& img, Point pt1, Point pt2, dy ^= dx & s; dx ^= dy & s; - bt_pix ^= step & s; - step ^= bt_pix & s; - bt_pix ^= step & s; + bt_pix ^= istep & s; + istep ^= bt_pix & s; + bt_pix ^= istep & s; if( connectivity == 8 ) { @@ -212,7 +212,7 @@ LineIterator::LineIterator(const Mat& img, Point pt1, Point pt2, err = dx - (dy + dy); plusDelta = dx + dx; minusDelta = -(dy + dy); - plusStep = (int)step; + plusStep = (int)istep; minusStep = bt_pix; count = dx + 1; } @@ -223,7 +223,7 @@ LineIterator::LineIterator(const Mat& img, Point pt1, Point pt2, err = 0; plusDelta = (dx + dx) + (dy + dy); minusDelta = -(dy + dy); - plusStep = (int)step - bt_pix; + plusStep = (int)istep - bt_pix; minusStep = bt_pix; count = dx + dy + 1; } diff --git a/modules/core/src/dxt.cpp b/modules/core/src/dxt.cpp index 80ed8cd..04e9010 100644 --- a/modules/core/src/dxt.cpp +++ b/modules/core/src/dxt.cpp @@ -46,8 +46,8 @@ namespace cv // On Win64 optimized versions of DFT and DCT fail the tests (fixed in VS2010) #if defined _MSC_VER && !defined CV_ICC && defined _M_X64 && _MSC_VER < 1600 -#pragma optimize("", off) -#pragma warning( disable : 4748 ) +# pragma optimize("", off) +# pragma warning(disable: 4748) #endif /****************************************************************************************\ diff --git a/modules/core/src/gpumat.cpp b/modules/core/src/gpumat.cpp index e5d9367..73f7405 100644 --- a/modules/core/src/gpumat.cpp +++ b/modules/core/src/gpumat.cpp @@ -524,30 +524,30 @@ cv::gpu::GpuMat::GpuMat(Size size_, int type_, void* data_, size_t step_) : dataend += step * (rows - 1) + minstep; } -cv::gpu::GpuMat::GpuMat(const GpuMat& m, Range rowRange, Range colRange) +cv::gpu::GpuMat::GpuMat(const GpuMat& m, Range _rowRange, Range _colRange) { flags = m.flags; step = m.step; refcount = m.refcount; data = m.data; datastart = m.datastart; dataend = m.dataend; - if (rowRange == Range::all()) + if (_rowRange == Range::all()) rows = m.rows; else { - CV_Assert(0 <= rowRange.start && rowRange.start <= rowRange.end && rowRange.end <= m.rows); + CV_Assert(0 <= _rowRange.start && _rowRange.start <= _rowRange.end && _rowRange.end <= m.rows); - rows = rowRange.size(); - data += step*rowRange.start; + rows = _rowRange.size(); + data += step*_rowRange.start; } - if (colRange == Range::all()) + if (_colRange == Range::all()) cols = m.cols; else { - CV_Assert(0 <= colRange.start && colRange.start <= colRange.end && colRange.end <= m.cols); + CV_Assert(0 <= _colRange.start && _colRange.start <= _colRange.end && _colRange.end <= m.cols); - cols = colRange.size(); - data += colRange.start*elemSize(); + cols = _colRange.size(); + data += _colRange.start*elemSize(); flags &= cols < m.cols ? ~Mat::CONTINUOUS_FLAG : -1; } diff --git a/modules/core/src/mathfuncs.cpp b/modules/core/src/mathfuncs.cpp index 9a95019..e391b15 100644 --- a/modules/core/src/mathfuncs.cpp +++ b/modules/core/src/mathfuncs.cpp @@ -53,7 +53,7 @@ static const float atan2_p1 = 0.9997878412794807f*(float)(180/CV_PI); static const float atan2_p3 = -0.3258083974640975f*(float)(180/CV_PI); static const float atan2_p5 = 0.1555786518463281f*(float)(180/CV_PI); static const float atan2_p7 = -0.04432655554792128f*(float)(180/CV_PI); - + float fastAtan2( float y, float x ) { float ax = std::abs(x), ay = std::abs(y); @@ -109,18 +109,18 @@ static void FastAtan2_32f(const float *Y, const float *X, float *angle, int len, a = _mm_mul_ps(_mm_add_ps(a, p5), c2); a = _mm_mul_ps(_mm_add_ps(a, p3), c2); a = _mm_mul_ps(_mm_add_ps(a, p1), c); - + __m128 b = _mm_sub_ps(_90, a); a = _mm_xor_ps(a, _mm_and_ps(_mm_xor_ps(a, b), mask)); - + b = _mm_sub_ps(_180, a); mask = _mm_cmplt_ps(x, z); a = _mm_xor_ps(a, _mm_and_ps(_mm_xor_ps(a, b), mask)); - + b = _mm_sub_ps(_360, a); mask = _mm_cmplt_ps(y, z); a = _mm_xor_ps(a, _mm_and_ps(_mm_xor_ps(a, b), mask)); - + a = _mm_mul_ps(a, scale4); _mm_storeu_ps(angle + i, a); } @@ -197,7 +197,7 @@ float cubeRoot( float value ) static void Magnitude_32f(const float* x, const float* y, float* mag, int len) { int i = 0; - + #if CV_SSE if( USE_SSE2 ) { @@ -223,8 +223,8 @@ static void Magnitude_32f(const float* x, const float* y, float* mag, int len) static void Magnitude_64f(const double* x, const double* y, double* mag, int len) { int i = 0; - -#if CV_SSE2 + +#if CV_SSE2 if( USE_SSE2 ) { for( ; i <= len - 4; i += 4 ) @@ -238,7 +238,7 @@ static void Magnitude_64f(const double* x, const double* y, double* mag, int len } } #endif - + for( ; i < len; i++ ) { double x0 = x[i], y0 = y[i]; @@ -246,14 +246,14 @@ static void Magnitude_64f(const double* x, const double* y, double* mag, int len } } - + static void InvSqrt_32f(const float* src, float* dst, int len) { int i = 0; - -#if CV_SSE + +#if CV_SSE if( USE_SSE2 ) - { + { __m128 _0_5 = _mm_set1_ps(0.5f), _1_5 = _mm_set1_ps(1.5f); if( (((size_t)src|(size_t)dst) & 15) == 0 ) for( ; i <= len - 8; i += 8 ) @@ -277,24 +277,24 @@ static void InvSqrt_32f(const float* src, float* dst, int len) } } #endif - + for( ; i < len; i++ ) dst[i] = 1/std::sqrt(src[i]); } - + static void InvSqrt_64f(const double* src, double* dst, int len) { for( int i = 0; i < len; i++ ) dst[i] = 1/std::sqrt(src[i]); -} - - +} + + static void Sqrt_32f(const float* src, float* dst, int len) { int i = 0; - -#if CV_SSE + +#if CV_SSE if( USE_SSE2 ) { if( (((size_t)src|(size_t)dst) & 15) == 0 ) @@ -312,18 +312,18 @@ static void Sqrt_32f(const float* src, float* dst, int len) _mm_storeu_ps(dst + i, t0); _mm_storeu_ps(dst + i + 4, t1); } } -#endif - +#endif + for( ; i < len; i++ ) dst[i] = std::sqrt(src[i]); } - + static void Sqrt_64f(const double* src, double* dst, int len) { int i = 0; - -#if CV_SSE2 + +#if CV_SSE2 if( USE_SSE2 ) { if( (((size_t)src|(size_t)dst) & 15) == 0 ) @@ -342,7 +342,7 @@ static void Sqrt_64f(const double* src, double* dst, int len) } } #endif - + for( ; i < len; i++ ) dst[i] = std::sqrt(src[i]); } @@ -359,12 +359,12 @@ void magnitude( InputArray src1, InputArray src2, OutputArray dst ) CV_Assert( X.size == Y.size && type == Y.type() && (depth == CV_32F || depth == CV_64F)); dst.create(X.dims, X.size, X.type()); Mat Mag = dst.getMat(); - + const Mat* arrays[] = {&X, &Y, &Mag, 0}; uchar* ptrs[3]; NAryMatIterator it(arrays, ptrs); int len = (int)it.size*cn; - + for( size_t i = 0; i < it.nplanes; i++, ++it ) { if( depth == CV_32F ) @@ -382,7 +382,7 @@ void magnitude( InputArray src1, InputArray src2, OutputArray dst ) } } - + void phase( InputArray src1, InputArray src2, OutputArray dst, bool angleInDegrees ) { Mat X = src1.getMat(), Y = src2.getMat(); @@ -390,7 +390,7 @@ void phase( InputArray src1, InputArray src2, OutputArray dst, bool angleInDegre CV_Assert( X.size == Y.size && type == Y.type() && (depth == CV_32F || depth == CV_64F)); dst.create( X.dims, X.size, type ); Mat Angle = dst.getMat(); - + const Mat* arrays[] = {&X, &Y, &Angle, 0}; uchar* ptrs[3]; NAryMatIterator it(arrays, ptrs); @@ -398,7 +398,7 @@ void phase( InputArray src1, InputArray src2, OutputArray dst, bool angleInDegre float* buf[2] = {0, 0}; int j, k, total = (int)(it.size*cn), blockSize = total; size_t esz1 = X.elemSize1(); - + if( depth == CV_64F ) { blockSize = std::min(blockSize, ((BLOCK_SIZE+cn-1)/cn)*cn); @@ -406,7 +406,7 @@ void phase( InputArray src1, InputArray src2, OutputArray dst, bool angleInDegre buf[0] = _buf; buf[1] = buf[0] + blockSize; } - + for( size_t i = 0; i < it.nplanes; i++, ++it ) { for( j = 0; j < total; j += blockSize ) @@ -427,7 +427,7 @@ void phase( InputArray src1, InputArray src2, OutputArray dst, bool angleInDegre buf[0][k] = (float)x[k]; buf[1][k] = (float)y[k]; } - + FastAtan2_32f( buf[1], buf[0], buf[0], len, angleInDegrees ); for( k = 0; k < len; k++ ) angle[k] = buf[0][k]; @@ -438,8 +438,8 @@ void phase( InputArray src1, InputArray src2, OutputArray dst, bool angleInDegre } } } - - + + void cartToPolar( InputArray src1, InputArray src2, OutputArray dst1, OutputArray dst2, bool angleInDegrees ) { @@ -449,7 +449,7 @@ void cartToPolar( InputArray src1, InputArray src2, dst1.create( X.dims, X.size, type ); dst2.create( X.dims, X.size, type ); Mat Mag = dst1.getMat(), Angle = dst2.getMat(); - + const Mat* arrays[] = {&X, &Y, &Mag, &Angle, 0}; uchar* ptrs[4]; NAryMatIterator it(arrays, ptrs); @@ -457,14 +457,14 @@ void cartToPolar( InputArray src1, InputArray src2, float* buf[2] = {0, 0}; int j, k, total = (int)(it.size*cn), blockSize = std::min(total, ((BLOCK_SIZE+cn-1)/cn)*cn); size_t esz1 = X.elemSize1(); - + if( depth == CV_64F ) { _buf.allocate(blockSize*2); buf[0] = _buf; buf[1] = buf[0] + blockSize; } - + for( size_t i = 0; i < it.nplanes; i++, ++it ) { for( j = 0; j < total; j += blockSize ) @@ -481,14 +481,14 @@ void cartToPolar( InputArray src1, InputArray src2, { const double *x = (const double*)ptrs[0], *y = (const double*)ptrs[1]; double *angle = (double*)ptrs[3]; - + Magnitude_64f(x, y, (double*)ptrs[2], len); for( k = 0; k < len; k++ ) { buf[0][k] = (float)x[k]; buf[1][k] = (float)y[k]; } - + FastAtan2_32f( buf[1], buf[0], buf[0], len, angleInDegrees ); for( k = 0; k < len; k++ ) angle[k] = buf[0][k]; @@ -595,7 +595,7 @@ void polarToCart( InputArray src1, InputArray src2, dst1.create( Angle.dims, Angle.size, type ); dst2.create( Angle.dims, Angle.size, type ); Mat X = dst1.getMat(), Y = dst2.getMat(); - + const Mat* arrays[] = {&Mag, &Angle, &X, &Y, 0}; uchar* ptrs[4]; NAryMatIterator it(arrays, ptrs); @@ -603,14 +603,14 @@ void polarToCart( InputArray src1, InputArray src2, float* buf[2] = {0, 0}; int j, k, total = (int)(it.size*cn), blockSize = std::min(total, ((BLOCK_SIZE+cn-1)/cn)*cn); size_t esz1 = Angle.elemSize1(); - + if( depth == CV_64F ) { _buf.allocate(blockSize*2); buf[0] = _buf; buf[1] = buf[0] + blockSize; } - + for( size_t i = 0; i < it.nplanes; i++, ++it ) { for( j = 0; j < total; j += blockSize ) @@ -620,7 +620,7 @@ void polarToCart( InputArray src1, InputArray src2, { const float *mag = (const float*)ptrs[0], *angle = (const float*)ptrs[1]; float *x = (float*)ptrs[2], *y = (float*)ptrs[3]; - + SinCos_32f( angle, y, x, len, angleInDegrees ); if( mag ) for( k = 0; k < len; k++ ) @@ -633,10 +633,10 @@ void polarToCart( InputArray src1, InputArray src2, { const double *mag = (const double*)ptrs[0], *angle = (const double*)ptrs[1]; double *x = (double*)ptrs[2], *y = (double*)ptrs[3]; - + for( k = 0; k < len; k++ ) buf[0][k] = (float)angle[k]; - + SinCos_32f( buf[0], buf[1], buf[0], len, angleInDegrees ); if( mag ) for( k = 0; k < len; k++ ) @@ -650,7 +650,7 @@ void polarToCart( InputArray src1, InputArray src2, x[k] = buf[0][k]; y[k] = buf[1][k]; } } - + if( ptrs[0] ) ptrs[0] += len*esz1; ptrs[1] += len*esz1; @@ -759,8 +759,8 @@ static const double expTab[] = { (!defined __APPLE__ && defined __GNUC__ && __GNUC__*100 + __GNUC_MINOR__ < 402) #undef CV_SSE2 #define CV_SSE2 0 -#endif - +#endif + static const double exp_prescale = 1.4426950408889634073599246810019 * (1 << EXPTAB_SCALE); static const double exp_postscale = 1./(1 << EXPTAB_SCALE); static const double exp_max_val = 3000.*(1 << EXPTAB_SCALE); // log10(DBL_MAX) < 3000 @@ -772,11 +772,11 @@ static void Exp_32f( const float *_x, float *y, int n ) A3 = (float)(.6931471805521448196800669615864773144641 / EXPPOLY_32F_A0), A2 = (float)(.2402265109513301490103372422686535526573 / EXPPOLY_32F_A0), A1 = (float)(.5550339366753125211915322047004666939128e-1 / EXPPOLY_32F_A0); - + #undef EXPPOLY #define EXPPOLY(x) \ (((((x) + A1)*(x) + A2)*(x) + A3)*(x) + A4) - + int i = 0; const Cv32suf* x = (const Cv32suf*)_x; Cv32suf buf[4]; @@ -788,90 +788,90 @@ static void Exp_32f( const float *_x, float *y, int n ) static const __m128 postscale4 = _mm_set1_ps((float)exp_postscale); static const __m128 maxval4 = _mm_set1_ps((float)(exp_max_val/exp_prescale)); static const __m128 minval4 = _mm_set1_ps((float)(-exp_max_val/exp_prescale)); - + static const __m128 mA1 = _mm_set1_ps(A1); static const __m128 mA2 = _mm_set1_ps(A2); static const __m128 mA3 = _mm_set1_ps(A3); static const __m128 mA4 = _mm_set1_ps(A4); bool y_aligned = (size_t)(void*)y % 16 == 0; - + ushort CV_DECL_ALIGNED(16) tab_idx[8]; - + for( ; i <= n - 8; i += 8 ) { __m128 xf0, xf1; xf0 = _mm_loadu_ps(&x[i].f); xf1 = _mm_loadu_ps(&x[i+4].f); __m128i xi0, xi1, xi2, xi3; - + xf0 = _mm_min_ps(_mm_max_ps(xf0, minval4), maxval4); xf1 = _mm_min_ps(_mm_max_ps(xf1, minval4), maxval4); - + __m128d xd0 = _mm_cvtps_pd(xf0); __m128d xd2 = _mm_cvtps_pd(_mm_movehl_ps(xf0, xf0)); __m128d xd1 = _mm_cvtps_pd(xf1); __m128d xd3 = _mm_cvtps_pd(_mm_movehl_ps(xf1, xf1)); - + xd0 = _mm_mul_pd(xd0, prescale2); xd2 = _mm_mul_pd(xd2, prescale2); xd1 = _mm_mul_pd(xd1, prescale2); xd3 = _mm_mul_pd(xd3, prescale2); - + xi0 = _mm_cvtpd_epi32(xd0); xi2 = _mm_cvtpd_epi32(xd2); - + xi1 = _mm_cvtpd_epi32(xd1); xi3 = _mm_cvtpd_epi32(xd3); - + xd0 = _mm_sub_pd(xd0, _mm_cvtepi32_pd(xi0)); xd2 = _mm_sub_pd(xd2, _mm_cvtepi32_pd(xi2)); xd1 = _mm_sub_pd(xd1, _mm_cvtepi32_pd(xi1)); xd3 = _mm_sub_pd(xd3, _mm_cvtepi32_pd(xi3)); - + xf0 = _mm_movelh_ps(_mm_cvtpd_ps(xd0), _mm_cvtpd_ps(xd2)); xf1 = _mm_movelh_ps(_mm_cvtpd_ps(xd1), _mm_cvtpd_ps(xd3)); - + xf0 = _mm_mul_ps(xf0, postscale4); xf1 = _mm_mul_ps(xf1, postscale4); xi0 = _mm_unpacklo_epi64(xi0, xi2); xi1 = _mm_unpacklo_epi64(xi1, xi3); xi0 = _mm_packs_epi32(xi0, xi1); - + _mm_store_si128((__m128i*)tab_idx, _mm_and_si128(xi0, _mm_set1_epi16(EXPTAB_MASK))); - + xi0 = _mm_add_epi16(_mm_srai_epi16(xi0, EXPTAB_SCALE), _mm_set1_epi16(127)); xi0 = _mm_max_epi16(xi0, _mm_setzero_si128()); xi0 = _mm_min_epi16(xi0, _mm_set1_epi16(255)); xi1 = _mm_unpackhi_epi16(xi0, _mm_setzero_si128()); xi0 = _mm_unpacklo_epi16(xi0, _mm_setzero_si128()); - + __m128d yd0 = _mm_unpacklo_pd(_mm_load_sd(expTab + tab_idx[0]), _mm_load_sd(expTab + tab_idx[1])); __m128d yd1 = _mm_unpacklo_pd(_mm_load_sd(expTab + tab_idx[2]), _mm_load_sd(expTab + tab_idx[3])); __m128d yd2 = _mm_unpacklo_pd(_mm_load_sd(expTab + tab_idx[4]), _mm_load_sd(expTab + tab_idx[5])); __m128d yd3 = _mm_unpacklo_pd(_mm_load_sd(expTab + tab_idx[6]), _mm_load_sd(expTab + tab_idx[7])); - + __m128 yf0 = _mm_movelh_ps(_mm_cvtpd_ps(yd0), _mm_cvtpd_ps(yd1)); __m128 yf1 = _mm_movelh_ps(_mm_cvtpd_ps(yd2), _mm_cvtpd_ps(yd3)); yf0 = _mm_mul_ps(yf0, _mm_castsi128_ps(_mm_slli_epi32(xi0, 23))); yf1 = _mm_mul_ps(yf1, _mm_castsi128_ps(_mm_slli_epi32(xi1, 23))); - + __m128 zf0 = _mm_add_ps(xf0, mA1); __m128 zf1 = _mm_add_ps(xf1, mA1); - + zf0 = _mm_add_ps(_mm_mul_ps(zf0, xf0), mA2); zf1 = _mm_add_ps(_mm_mul_ps(zf1, xf1), mA2); - + zf0 = _mm_add_ps(_mm_mul_ps(zf0, xf0), mA3); zf1 = _mm_add_ps(_mm_mul_ps(zf1, xf1), mA3); - + zf0 = _mm_add_ps(_mm_mul_ps(zf0, xf0), mA4); zf1 = _mm_add_ps(_mm_mul_ps(zf1, xf1), mA4); - + zf0 = _mm_mul_ps(zf0, yf0); zf1 = _mm_mul_ps(zf1, yf1); - + if( y_aligned ) { _mm_store_ps(y + i, zf0); @@ -893,77 +893,77 @@ static void Exp_32f( const float *_x, float *y, int n ) double x2 = x[i + 2].f * exp_prescale; double x3 = x[i + 3].f * exp_prescale; int val0, val1, val2, val3, t; - + if( ((x[i].i >> 23) & 255) > 127 + 10 ) x0 = x[i].i < 0 ? -exp_max_val : exp_max_val; - + if( ((x[i+1].i >> 23) & 255) > 127 + 10 ) x1 = x[i+1].i < 0 ? -exp_max_val : exp_max_val; - + if( ((x[i+2].i >> 23) & 255) > 127 + 10 ) x2 = x[i+2].i < 0 ? -exp_max_val : exp_max_val; - + if( ((x[i+3].i >> 23) & 255) > 127 + 10 ) x3 = x[i+3].i < 0 ? -exp_max_val : exp_max_val; - + val0 = cvRound(x0); val1 = cvRound(x1); val2 = cvRound(x2); val3 = cvRound(x3); - + x0 = (x0 - val0)*exp_postscale; x1 = (x1 - val1)*exp_postscale; x2 = (x2 - val2)*exp_postscale; x3 = (x3 - val3)*exp_postscale; - + t = (val0 >> EXPTAB_SCALE) + 127; t = !(t & ~255) ? t : t < 0 ? 0 : 255; buf[0].i = t << 23; - + t = (val1 >> EXPTAB_SCALE) + 127; t = !(t & ~255) ? t : t < 0 ? 0 : 255; buf[1].i = t << 23; - + t = (val2 >> EXPTAB_SCALE) + 127; t = !(t & ~255) ? t : t < 0 ? 0 : 255; buf[2].i = t << 23; - + t = (val3 >> EXPTAB_SCALE) + 127; t = !(t & ~255) ? t : t < 0 ? 0 : 255; buf[3].i = t << 23; - + x0 = buf[0].f * expTab[val0 & EXPTAB_MASK] * EXPPOLY( x0 ); x1 = buf[1].f * expTab[val1 & EXPTAB_MASK] * EXPPOLY( x1 ); - + y[i] = (float)x0; y[i + 1] = (float)x1; - + x2 = buf[2].f * expTab[val2 & EXPTAB_MASK] * EXPPOLY( x2 ); x3 = buf[3].f * expTab[val3 & EXPTAB_MASK] * EXPPOLY( x3 ); - + y[i + 2] = (float)x2; y[i + 3] = (float)x3; } - + for( ; i < n; i++ ) { double x0 = x[i].f * exp_prescale; int val0, t; - + if( ((x[i].i >> 23) & 255) > 127 + 10 ) x0 = x[i].i < 0 ? -exp_max_val : exp_max_val; - + val0 = cvRound(x0); t = (val0 >> EXPTAB_SCALE) + 127; t = !(t & ~255) ? t : t < 0 ? 0 : 255; - + buf[0].i = t << 23; x0 = (x0 - val0)*exp_postscale; - + y[i] = (float)(buf[0].f * expTab[val0 & EXPTAB_MASK] * EXPPOLY(x0)); } } - + static void Exp_64f( const double *_x, double *y, int n ) { @@ -974,14 +974,14 @@ static void Exp_64f( const double *_x, double *y, int n ) A2 = .55504108793649567998466049042729e-1 / EXPPOLY_32F_A0, A1 = .96180973140732918010002372686186e-2 / EXPPOLY_32F_A0, A0 = .13369713757180123244806654839424e-2 / EXPPOLY_32F_A0; - + #undef EXPPOLY #define EXPPOLY(x) (((((A0*(x) + A1)*(x) + A2)*(x) + A3)*(x) + A4)*(x) + A5) - + int i = 0; Cv64suf buf[4]; const Cv64suf* x = (const Cv64suf*)_x; - + #if CV_SSE2 if( USE_SSE2 ) { @@ -989,16 +989,16 @@ static void Exp_64f( const double *_x, double *y, int n ) static const __m128d postscale2 = _mm_set1_pd(exp_postscale); static const __m128d maxval2 = _mm_set1_pd(exp_max_val); static const __m128d minval2 = _mm_set1_pd(-exp_max_val); - + static const __m128d mA0 = _mm_set1_pd(A0); static const __m128d mA1 = _mm_set1_pd(A1); static const __m128d mA2 = _mm_set1_pd(A2); static const __m128d mA3 = _mm_set1_pd(A3); static const __m128d mA4 = _mm_set1_pd(A4); static const __m128d mA5 = _mm_set1_pd(A5); - + int CV_DECL_ALIGNED(16) tab_idx[4]; - + for( ; i <= n - 4; i += 4 ) { __m128d xf0 = _mm_loadu_pd(&x[i].f), xf1 = _mm_loadu_pd(&x[i+2].f); @@ -1007,15 +1007,15 @@ static void Exp_64f( const double *_x, double *y, int n ) xf1 = _mm_min_pd(_mm_max_pd(xf1, minval2), maxval2); xf0 = _mm_mul_pd(xf0, prescale2); xf1 = _mm_mul_pd(xf1, prescale2); - + xi0 = _mm_cvtpd_epi32(xf0); xi1 = _mm_cvtpd_epi32(xf1); xf0 = _mm_mul_pd(_mm_sub_pd(xf0, _mm_cvtepi32_pd(xi0)), postscale2); xf1 = _mm_mul_pd(_mm_sub_pd(xf1, _mm_cvtepi32_pd(xi1)), postscale2); - + xi0 = _mm_unpacklo_epi64(xi0, xi1); _mm_store_si128((__m128i*)tab_idx, _mm_and_si128(xi0, _mm_set1_epi32(EXPTAB_MASK))); - + xi0 = _mm_add_epi32(_mm_srai_epi32(xi0, EXPTAB_SCALE), _mm_set1_epi32(1023)); xi0 = _mm_packs_epi32(xi0, xi0); xi0 = _mm_max_epi16(xi0, _mm_setzero_si128()); @@ -1023,30 +1023,30 @@ static void Exp_64f( const double *_x, double *y, int n ) xi0 = _mm_unpacklo_epi16(xi0, _mm_setzero_si128()); xi1 = _mm_unpackhi_epi32(xi0, _mm_setzero_si128()); xi0 = _mm_unpacklo_epi32(xi0, _mm_setzero_si128()); - + __m128d yf0 = _mm_unpacklo_pd(_mm_load_sd(expTab + tab_idx[0]), _mm_load_sd(expTab + tab_idx[1])); __m128d yf1 = _mm_unpacklo_pd(_mm_load_sd(expTab + tab_idx[2]), _mm_load_sd(expTab + tab_idx[3])); yf0 = _mm_mul_pd(yf0, _mm_castsi128_pd(_mm_slli_epi64(xi0, 52))); yf1 = _mm_mul_pd(yf1, _mm_castsi128_pd(_mm_slli_epi64(xi1, 52))); - + __m128d zf0 = _mm_add_pd(_mm_mul_pd(mA0, xf0), mA1); __m128d zf1 = _mm_add_pd(_mm_mul_pd(mA0, xf1), mA1); - + zf0 = _mm_add_pd(_mm_mul_pd(zf0, xf0), mA2); zf1 = _mm_add_pd(_mm_mul_pd(zf1, xf1), mA2); - + zf0 = _mm_add_pd(_mm_mul_pd(zf0, xf0), mA3); zf1 = _mm_add_pd(_mm_mul_pd(zf1, xf1), mA3); - + zf0 = _mm_add_pd(_mm_mul_pd(zf0, xf0), mA4); zf1 = _mm_add_pd(_mm_mul_pd(zf1, xf1), mA4); - + zf0 = _mm_add_pd(_mm_mul_pd(zf0, xf0), mA5); zf1 = _mm_add_pd(_mm_mul_pd(zf1, xf1), mA5); - + zf0 = _mm_mul_pd(zf0, yf0); zf1 = _mm_mul_pd(zf1, yf1); - + _mm_storeu_pd(y + i, zf0); _mm_storeu_pd(y + i + 2, zf1); } @@ -1059,81 +1059,81 @@ static void Exp_64f( const double *_x, double *y, int n ) double x1 = x[i + 1].f * exp_prescale; double x2 = x[i + 2].f * exp_prescale; double x3 = x[i + 3].f * exp_prescale; - + double y0, y1, y2, y3; int val0, val1, val2, val3, t; - + t = (int)(x[i].i >> 52); if( (t & 2047) > 1023 + 10 ) x0 = t < 0 ? -exp_max_val : exp_max_val; - + t = (int)(x[i+1].i >> 52); if( (t & 2047) > 1023 + 10 ) x1 = t < 0 ? -exp_max_val : exp_max_val; - + t = (int)(x[i+2].i >> 52); if( (t & 2047) > 1023 + 10 ) x2 = t < 0 ? -exp_max_val : exp_max_val; - + t = (int)(x[i+3].i >> 52); if( (t & 2047) > 1023 + 10 ) x3 = t < 0 ? -exp_max_val : exp_max_val; - + val0 = cvRound(x0); val1 = cvRound(x1); val2 = cvRound(x2); val3 = cvRound(x3); - + x0 = (x0 - val0)*exp_postscale; x1 = (x1 - val1)*exp_postscale; x2 = (x2 - val2)*exp_postscale; x3 = (x3 - val3)*exp_postscale; - + t = (val0 >> EXPTAB_SCALE) + 1023; t = !(t & ~2047) ? t : t < 0 ? 0 : 2047; buf[0].i = (int64)t << 52; - + t = (val1 >> EXPTAB_SCALE) + 1023; t = !(t & ~2047) ? t : t < 0 ? 0 : 2047; buf[1].i = (int64)t << 52; - + t = (val2 >> EXPTAB_SCALE) + 1023; t = !(t & ~2047) ? t : t < 0 ? 0 : 2047; buf[2].i = (int64)t << 52; - + t = (val3 >> EXPTAB_SCALE) + 1023; t = !(t & ~2047) ? t : t < 0 ? 0 : 2047; buf[3].i = (int64)t << 52; - + y0 = buf[0].f * expTab[val0 & EXPTAB_MASK] * EXPPOLY( x0 ); y1 = buf[1].f * expTab[val1 & EXPTAB_MASK] * EXPPOLY( x1 ); - + y[i] = y0; y[i + 1] = y1; - + y2 = buf[2].f * expTab[val2 & EXPTAB_MASK] * EXPPOLY( x2 ); y3 = buf[3].f * expTab[val3 & EXPTAB_MASK] * EXPPOLY( x3 ); - + y[i + 2] = y2; y[i + 3] = y3; } - + for( ; i < n; i++ ) { double x0 = x[i].f * exp_prescale; int val0, t; - + t = (int)(x[i].i >> 52); if( (t & 2047) > 1023 + 10 ) x0 = t < 0 ? -exp_max_val : exp_max_val; - + val0 = cvRound(x0); t = (val0 >> EXPTAB_SCALE) + 1023; t = !(t & ~2047) ? t : t < 0 ? 0 : 2047; - + buf[0].i = (int64)t << 52; x0 = (x0 - val0)*exp_postscale; - + y[i] = buf[0].f * expTab[val0 & EXPTAB_MASK] * EXPPOLY( x0 ); } } @@ -1153,17 +1153,17 @@ void exp( InputArray _src, OutputArray _dst ) { Mat src = _src.getMat(); int type = src.type(), depth = src.depth(), cn = src.channels(); - + _dst.create( src.dims, src.size, type ); Mat dst = _dst.getMat(); - + CV_Assert( depth == CV_32F || depth == CV_64F ); - + const Mat* arrays[] = {&src, &dst, 0}; uchar* ptrs[2]; NAryMatIterator it(arrays, ptrs); int len = (int)(it.size*cn); - + for( size_t i = 0; i < it.nplanes; i++, ++it ) { if( depth == CV_32F ) @@ -1470,26 +1470,26 @@ static void Log_32f( const float *_x, float *y, int n ) static const __m128d ln2_2 = _mm_set1_pd(ln_2); static const __m128 _1_4 = _mm_set1_ps(1.f); static const __m128 shift4 = _mm_set1_ps(-1.f/512); - + static const __m128 mA0 = _mm_set1_ps(A0); static const __m128 mA1 = _mm_set1_ps(A1); static const __m128 mA2 = _mm_set1_ps(A2); - + int CV_DECL_ALIGNED(16) idx[4]; - + for( ; i <= n - 4; i += 4 ) - { + { __m128i h0 = _mm_loadu_si128((const __m128i*)(x + i)); __m128i yi0 = _mm_sub_epi32(_mm_and_si128(_mm_srli_epi32(h0, 23), _mm_set1_epi32(255)), _mm_set1_epi32(127)); __m128d yd0 = _mm_mul_pd(_mm_cvtepi32_pd(yi0), ln2_2); __m128d yd1 = _mm_mul_pd(_mm_cvtepi32_pd(_mm_unpackhi_epi64(yi0,yi0)), ln2_2); - + __m128i xi0 = _mm_or_si128(_mm_and_si128(h0, _mm_set1_epi32(LOGTAB_MASK2_32F)), _mm_set1_epi32(127 << 23)); - + h0 = _mm_and_si128(_mm_srli_epi32(h0, 23 - LOGTAB_SCALE - 1), _mm_set1_epi32(LOGTAB_MASK*2)); _mm_store_si128((__m128i*)idx, h0); h0 = _mm_cmpeq_epi32(h0, _mm_set1_epi32(510)); - + __m128d t0, t1, t2, t3, t4; t0 = _mm_load_pd(icvLogTab + idx[0]); t2 = _mm_load_pd(icvLogTab + idx[1]); @@ -1499,21 +1499,21 @@ static void Log_32f( const float *_x, float *y, int n ) t4 = _mm_load_pd(icvLogTab + idx[3]); t3 = _mm_unpackhi_pd(t2, t4); t2 = _mm_unpacklo_pd(t2, t4); - + yd0 = _mm_add_pd(yd0, t0); yd1 = _mm_add_pd(yd1, t2); - + __m128 yf0 = _mm_movelh_ps(_mm_cvtpd_ps(yd0), _mm_cvtpd_ps(yd1)); - + __m128 xf0 = _mm_sub_ps(_mm_castsi128_ps(xi0), _1_4); xf0 = _mm_mul_ps(xf0, _mm_movelh_ps(_mm_cvtpd_ps(t1), _mm_cvtpd_ps(t3))); xf0 = _mm_add_ps(xf0, _mm_and_ps(_mm_castsi128_ps(h0), shift4)); - + __m128 zf0 = _mm_mul_ps(xf0, mA0); zf0 = _mm_mul_ps(_mm_add_ps(zf0, mA1), xf0); zf0 = _mm_mul_ps(_mm_add_ps(zf0, mA2), xf0); yf0 = _mm_add_ps(yf0, zf0); - + _mm_storeu_ps(y + i, yf0); } } @@ -1626,10 +1626,10 @@ static void Log_64f( const double *x, double *y, int n ) static const __m128d ln2_2 = _mm_set1_pd(ln_2); static const __m128d _1_2 = _mm_set1_pd(1.); static const __m128d shift2 = _mm_set1_pd(-1./512); - + static const __m128i log_and_mask2 = _mm_set_epi32(LOGTAB_MASK2, 0xffffffff, LOGTAB_MASK2, 0xffffffff); static const __m128i log_or_mask2 = _mm_set_epi32(1023 << 20, 0, 1023 << 20, 0); - + static const __m128d mA0 = _mm_set1_pd(A0); static const __m128d mA1 = _mm_set1_pd(A1); static const __m128d mA2 = _mm_set1_pd(A2); @@ -1638,28 +1638,28 @@ static void Log_64f( const double *x, double *y, int n ) static const __m128d mA5 = _mm_set1_pd(A5); static const __m128d mA6 = _mm_set1_pd(A6); static const __m128d mA7 = _mm_set1_pd(A7); - + int CV_DECL_ALIGNED(16) idx[4]; - + for( ; i <= n - 4; i += 4 ) { __m128i h0 = _mm_loadu_si128((const __m128i*)(x + i)); __m128i h1 = _mm_loadu_si128((const __m128i*)(x + i + 2)); - + __m128d xd0 = _mm_castsi128_pd(_mm_or_si128(_mm_and_si128(h0, log_and_mask2), log_or_mask2)); __m128d xd1 = _mm_castsi128_pd(_mm_or_si128(_mm_and_si128(h1, log_and_mask2), log_or_mask2)); - + h0 = _mm_unpackhi_epi32(_mm_unpacklo_epi32(h0, h1), _mm_unpackhi_epi32(h0, h1)); - + __m128i yi0 = _mm_sub_epi32(_mm_and_si128(_mm_srli_epi32(h0, 20), _mm_set1_epi32(2047)), _mm_set1_epi32(1023)); __m128d yd0 = _mm_mul_pd(_mm_cvtepi32_pd(yi0), ln2_2); __m128d yd1 = _mm_mul_pd(_mm_cvtepi32_pd(_mm_unpackhi_epi64(yi0, yi0)), ln2_2); - + h0 = _mm_and_si128(_mm_srli_epi32(h0, 20 - LOGTAB_SCALE - 1), _mm_set1_epi32(LOGTAB_MASK * 2)); _mm_store_si128((__m128i*)idx, h0); h0 = _mm_cmpeq_epi32(h0, _mm_set1_epi32(510)); - + __m128d t0, t1, t2, t3, t4; t0 = _mm_load_pd(icvLogTab + idx[0]); t2 = _mm_load_pd(icvLogTab + idx[1]); @@ -1669,16 +1669,16 @@ static void Log_64f( const double *x, double *y, int n ) t4 = _mm_load_pd(icvLogTab + idx[3]); t3 = _mm_unpackhi_pd(t2, t4); t2 = _mm_unpacklo_pd(t2, t4); - + yd0 = _mm_add_pd(yd0, t0); yd1 = _mm_add_pd(yd1, t2); - + xd0 = _mm_mul_pd(_mm_sub_pd(xd0, _1_2), t1); xd1 = _mm_mul_pd(_mm_sub_pd(xd1, _1_2), t3); - + xd0 = _mm_add_pd(xd0, _mm_and_pd(_mm_castsi128_pd(_mm_unpacklo_epi32(h0, h0)), shift2)); xd1 = _mm_add_pd(xd1, _mm_and_pd(_mm_castsi128_pd(_mm_unpackhi_epi32(h0, h0)), shift2)); - + __m128d zd0 = _mm_mul_pd(xd0, mA0); __m128d zd1 = _mm_mul_pd(xd1, mA0); zd0 = _mm_mul_pd(_mm_add_pd(zd0, mA1), xd0); @@ -1695,10 +1695,10 @@ static void Log_64f( const double *x, double *y, int n ) zd1 = _mm_mul_pd(_mm_add_pd(zd1, mA6), xd1); zd0 = _mm_mul_pd(_mm_add_pd(zd0, mA7), xd0); zd1 = _mm_mul_pd(_mm_add_pd(zd1, mA7), xd1); - + yd0 = _mm_add_pd(yd0, zd0); yd1 = _mm_add_pd(yd1, zd1); - + _mm_storeu_pd(y + i, yd0); _mm_storeu_pd(y + i + 2, yd1); } @@ -1769,7 +1769,7 @@ static void Log_64f( const double *x, double *y, int n ) y[i + 2] = y2; y[i + 3] = y3; } - + for( ; i < n; i++ ) { int h0 = X[i].i.hi; @@ -1798,17 +1798,17 @@ void log( InputArray _src, OutputArray _dst ) { Mat src = _src.getMat(); int type = src.type(), depth = src.depth(), cn = src.channels(); - + _dst.create( src.dims, src.size, type ); Mat dst = _dst.getMat(); - + CV_Assert( depth == CV_32F || depth == CV_64F ); - + const Mat* arrays[] = {&src, &dst, 0}; uchar* ptrs[2]; NAryMatIterator it(arrays, ptrs); int len = (int)(it.size*cn); - + for( size_t i = 0; i < it.nplanes; i++, ++it ) { if( depth == CV_32F ) @@ -1816,7 +1816,7 @@ void log( InputArray _src, OutputArray _dst ) else Log_64f( (const double*)ptrs[0], (double*)ptrs[1], len ); } -} +} /****************************************************************************************\ * P O W E R * @@ -1844,63 +1844,63 @@ iPow_( const T* src, T* dst, int len, int power ) } } - -void iPow8u(const uchar* src, uchar* dst, int len, int power) + +static void iPow8u(const uchar* src, uchar* dst, int len, int power) { iPow_(src, dst, len, power); } -void iPow8s(const schar* src, schar* dst, int len, int power) +static void iPow8s(const schar* src, schar* dst, int len, int power) { iPow_(src, dst, len, power); } - -void iPow16u(const ushort* src, ushort* dst, int len, int power) + +static void iPow16u(const ushort* src, ushort* dst, int len, int power) { iPow_(src, dst, len, power); } -void iPow16s(const short* src, short* dst, int len, int power) +static void iPow16s(const short* src, short* dst, int len, int power) { iPow_(src, dst, len, power); } - -void iPow32s(const int* src, int* dst, int len, int power) + +static void iPow32s(const int* src, int* dst, int len, int power) { iPow_(src, dst, len, power); } -void iPow32f(const float* src, float* dst, int len, int power) +static void iPow32f(const float* src, float* dst, int len, int power) { iPow_(src, dst, len, power); } -void iPow64f(const double* src, double* dst, int len, int power) +static void iPow64f(const double* src, double* dst, int len, int power) { iPow_(src, dst, len, power); } - + typedef void (*IPowFunc)( const uchar* src, uchar* dst, int len, int power ); - + static IPowFunc ipowTab[] = { (IPowFunc)iPow8u, (IPowFunc)iPow8s, (IPowFunc)iPow16u, (IPowFunc)iPow16s, (IPowFunc)iPow32s, (IPowFunc)iPow32f, (IPowFunc)iPow64f, 0 }; - + void pow( InputArray _src, double power, OutputArray _dst ) { Mat src = _src.getMat(); int type = src.type(), depth = src.depth(), cn = src.channels(); - + _dst.create( src.dims, src.size, type ); Mat dst = _dst.getMat(); - + int ipower = cvRound(power); bool is_ipower = false; - + if( fabs(ipower - power) < DBL_EPSILON ) { if( ipower < 0 ) @@ -1911,7 +1911,7 @@ void pow( InputArray _src, double power, OutputArray _dst ) ipower = -ipower; src = dst; } - + switch( ipower ) { case 0: @@ -1929,17 +1929,17 @@ void pow( InputArray _src, double power, OutputArray _dst ) } else CV_Assert( depth == CV_32F || depth == CV_64F ); - + const Mat* arrays[] = {&src, &dst, 0}; uchar* ptrs[2]; NAryMatIterator it(arrays, ptrs); int len = (int)(it.size*cn); - + if( is_ipower ) { IPowFunc func = ipowTab[depth]; CV_Assert( func != 0 ); - + for( size_t i = 0; i < it.nplanes; i++, ++it ) func( ptrs[0], ptrs[1], len, ipower ); } @@ -1948,7 +1948,7 @@ void pow( InputArray _src, double power, OutputArray _dst ) MathFunc func = power < 0 ? (depth == CV_32F ? (MathFunc)InvSqrt_32f : (MathFunc)InvSqrt_64f) : (depth == CV_32F ? (MathFunc)Sqrt_32f : (MathFunc)Sqrt_64f); - + for( size_t i = 0; i < it.nplanes; i++, ++it ) func( ptrs[0], ptrs[1], len ); } @@ -1956,7 +1956,7 @@ void pow( InputArray _src, double power, OutputArray _dst ) { int j, k, blockSize = std::min(len, ((BLOCK_SIZE + cn-1)/cn)*cn); size_t esz1 = src.elemSize1(); - + for( size_t i = 0; i < it.nplanes; i++, ++it ) { for( j = 0; j < len; j += blockSize ) @@ -1966,7 +1966,7 @@ void pow( InputArray _src, double power, OutputArray _dst ) { const float* x = (const float*)ptrs[0]; float* y = (float*)ptrs[1]; - + Log_32f(x, y, bsz); for( k = 0; k < bsz; k++ ) y[k] = (float)(y[k]*power); @@ -1976,7 +1976,7 @@ void pow( InputArray _src, double power, OutputArray _dst ) { const double* x = (const double*)ptrs[0]; double* y = (double*)ptrs[1]; - + Log_64f(x, y, bsz); for( k = 0; k < bsz; k++ ) y[k] *= power; @@ -2036,8 +2036,8 @@ template<> struct mat_type_assotiations template bool checkIntegerRange(cv::Mat src, Point& bad_pt, int minVal, int maxVal, double& bad_value) { - typedef mat_type_assotiations type_ass; - + typedef mat_type_assotiations type_ass; + if (minVal < type_ass::min_allowable && maxVal > type_ass::max_allowable) { return true; @@ -2051,23 +2051,23 @@ bool checkIntegerRange(cv::Mat src, Point& bad_pt, int minVal, int maxVal, doubl for (int j = 0; j < as_one_channel.rows; ++j) for (int i = 0; i < as_one_channel.cols; ++i) - { + { if (as_one_channel.at(j ,i) < minVal || as_one_channel.at(j ,i) > maxVal) - { - bad_pt.y = j ; + { + bad_pt.y = j ; bad_pt.x = i % src.channels(); bad_value = as_one_channel.at(j ,i); return false; } } bad_value = 0.0; - + return true; } -typedef bool (*check_range_function)(cv::Mat src, Point& bad_pt, int minVal, int maxVal, double& bad_value); +typedef bool (*check_range_function)(cv::Mat src, Point& bad_pt, int minVal, int maxVal, double& bad_value); -check_range_function check_range_functions[] = +check_range_function check_range_functions[] = { &checkIntegerRange, &checkIntegerRange, @@ -2085,7 +2085,7 @@ bool checkRange(InputArray _src, bool quiet, Point* pt, double minVal, double ma const Mat* arrays[] = {&src, 0}; Mat planes[1]; NAryMatIterator it(arrays, planes); - + for ( size_t i = 0; i < it.nplanes; i++, ++it ) { if (!checkRange( it.planes[0], quiet, pt, minVal, maxVal )) @@ -2096,7 +2096,7 @@ bool checkRange(InputArray _src, bool quiet, Point* pt, double minVal, double ma } return true; } - + int depth = src.depth(); Point badPt(-1, -1); double badValue = 0; @@ -2185,19 +2185,19 @@ bool checkRange(InputArray _src, bool quiet, Point* pt, double minVal, double ma return badPt.x < 0; } - + void patchNaNs( InputOutputArray _a, double _val ) { Mat a = _a.getMat(); CV_Assert( a.depth() == CV_32F ); - + const Mat* arrays[] = {&a, 0}; int* ptrs[1]; NAryMatIterator it(arrays, (uchar**)ptrs); size_t len = it.size*a.channels(); Cv32suf val; val.f = (float)_val; - + for( size_t i = 0; i < it.nplanes; i++, ++it ) { int* tptr = ptrs[0]; @@ -2207,22 +2207,22 @@ void patchNaNs( InputOutputArray _a, double _val ) } } - + void exp(const float* src, float* dst, int n) { Exp_32f(src, dst, n); } - + void log(const float* src, float* dst, int n) { Log_32f(src, dst, n); } - + void fastAtan2(const float* y, const float* x, float* dst, int n, bool angleInDegrees) { FastAtan2_32f(y, x, dst, n, angleInDegrees); } - + void magnitude(const float* x, const float* y, float* dst, int n) { Magnitude_32f(x, y, dst, n); @@ -2343,26 +2343,26 @@ int cv::solveCubic( InputArray _coeffs, OutputArray _roots ) const int n0 = 3; Mat coeffs = _coeffs.getMat(); int ctype = coeffs.type(); - + CV_Assert( ctype == CV_32F || ctype == CV_64F ); CV_Assert( (coeffs.size() == Size(n0, 1) || coeffs.size() == Size(n0+1, 1) || coeffs.size() == Size(1, n0) || coeffs.size() == Size(1, n0+1)) ); - + _roots.create(n0, 1, ctype, -1, true, DEPTH_MASK_FLT); Mat roots = _roots.getMat(); - + int i = -1, n = 0; double a0 = 1., a1, a2, a3; double x0 = 0., x1 = 0., x2 = 0.; int ncoeffs = coeffs.rows + coeffs.cols - 1; - + if( ctype == CV_32FC1 ) { if( ncoeffs == 4 ) a0 = coeffs.at(++i); - + a1 = coeffs.at(i+1); a2 = coeffs.at(i+2); a3 = coeffs.at(i+3); @@ -2371,12 +2371,12 @@ int cv::solveCubic( InputArray _coeffs, OutputArray _roots ) { if( ncoeffs == 4 ) a0 = coeffs.at(++i); - + a1 = coeffs.at(i+1); a2 = coeffs.at(i+2); a3 = coeffs.at(i+3); } - + if( a0 == 0 ) { if( a1 == 0 ) @@ -2419,12 +2419,12 @@ int cv::solveCubic( InputArray _coeffs, OutputArray _roots ) a1 *= a0; a2 *= a0; a3 *= a0; - + double Q = (a1 * a1 - 3 * a2) * (1./9); double R = (2 * a1 * a1 * a1 - 9 * a1 * a2 + 27 * a3) * (1./54); double Qcubed = Q * Q * Q; double d = Qcubed - R * R; - + if( d >= 0 ) { double theta = acos(R / sqrt(Qcubed)); @@ -2448,7 +2448,7 @@ int cv::solveCubic( InputArray _coeffs, OutputArray _roots ) n = 1; } } - + if( roots.type() == CV_32FC1 ) { roots.at(0) = (float)x0; @@ -2461,7 +2461,7 @@ int cv::solveCubic( InputArray _coeffs, OutputArray _roots ) roots.at(1) = x1; roots.at(2) = x2; } - + return n; } @@ -2476,15 +2476,15 @@ double cv::solvePoly( InputArray _coeffs0, OutputArray _roots0, int maxIters ) Mat coeffs0 = _coeffs0.getMat(); int ctype = _coeffs0.type(); int cdepth = CV_MAT_DEPTH(ctype); - + CV_Assert( CV_MAT_DEPTH(ctype) >= CV_32F && CV_MAT_CN(ctype) <= 2 ); CV_Assert( coeffs0.rows == 1 || coeffs0.cols == 1 ); - + int n = coeffs0.cols + coeffs0.rows - 2; - _roots0.create(n, 1, CV_MAKETYPE(cdepth, 2), -1, true, DEPTH_MASK_FLT); + _roots0.create(n, 1, CV_MAKETYPE(cdepth, 2), -1, true, DEPTH_MASK_FLT); Mat roots0 = _roots0.getMat(); - + AutoBuffer buf(n*2+2); C *coeffs = buf, *roots = coeffs + n + 1; Mat coeffs1(coeffs0.size(), CV_MAKETYPE(CV_64F, coeffs0.channels()), coeffs0.channels() == 2 ? coeffs : roots); diff --git a/modules/core/src/matmul.cpp b/modules/core/src/matmul.cpp index a55fab3..fe138f0 100644 --- a/modules/core/src/matmul.cpp +++ b/modules/core/src/matmul.cpp @@ -63,7 +63,7 @@ GEMM_CopyBlock( const uchar* src, size_t src_step, for( ; size.height--; src += src_step, dst += dst_step ) { - j=0; + j=0; #if CV_ENABLE_UNROLLED for( ; j <= size.width - 4; j += 4 ) { @@ -345,7 +345,7 @@ GEMMSingleMul( const T* a_data, size_t a_step, for( k = 0; k < n; k++, b_data += b_step ) { WT al(a_data[k]); - j=0; + j=0; #if CV_ENABLE_UNROLLED for(; j <= m - 4; j += 4 ) { @@ -513,8 +513,8 @@ GEMMStore( const T* c_data, size_t c_step, if( _c_data ) { c_data = _c_data; - j=0; - #if CV_ENABLE_UNROLLED + j=0; + #if CV_ENABLE_UNROLLED for(; j <= d_size.width - 4; j += 4, c_data += 4*c_step1 ) { WT t0 = alpha*d_buf[j]; @@ -539,8 +539,8 @@ GEMMStore( const T* c_data, size_t c_step, } else { - j = 0; - #if CV_ENABLE_UNROLLED + j = 0; + #if CV_ENABLE_UNROLLED for( ; j <= d_size.width - 4; j += 4 ) { WT t0 = alpha*d_buf[j]; @@ -552,7 +552,7 @@ GEMMStore( const T* c_data, size_t c_step, d_data[j+2] = T(t0); d_data[j+3] = T(t1); } - #endif + #endif for( ; j < d_size.width; j++ ) d_data[j] = T(alpha*d_buf[j]); } @@ -597,7 +597,7 @@ static void GEMMSingleMul_64f( const double* a_data, size_t a_step, alpha, beta, flags); } - + static void GEMMSingleMul_32fc( const Complexf* a_data, size_t a_step, const Complexf* b_data, size_t b_step, const Complexf* c_data, size_t c_step, @@ -620,7 +620,7 @@ static void GEMMSingleMul_64fc( const Complexd* a_data, size_t a_step, GEMMSingleMul(a_data, a_step, b_data, b_step, c_data, c_step, d_data, d_step, a_size, d_size, alpha, beta, flags); -} +} static void GEMMBlockMul_32f( const float* a_data, size_t a_step, const float* b_data, size_t b_step, @@ -696,7 +696,7 @@ static void GEMMStore_64fc( const Complexd* c_data, size_t c_step, } void cv::gemm( InputArray matA, InputArray matB, double alpha, - InputArray matC, double beta, OutputArray matD, int flags ) + InputArray matC, double beta, OutputArray _matD, int flags ) { const int block_lin_size = 128; const int block_size = block_lin_size * block_lin_size; @@ -741,8 +741,8 @@ void cv::gemm( InputArray matA, InputArray matB, double alpha, ((flags&GEMM_3_T) != 0 && C.rows == d_size.width && C.cols == d_size.height))); } - matD.create( d_size.height, d_size.width, type ); - Mat D = matD.getMat(); + _matD.create( d_size.height, d_size.width, type ); + Mat D = _matD.getMat(); if( (flags & GEMM_3_T) != 0 && C.data == D.data ) { transpose( C, C ); @@ -2008,7 +2008,7 @@ static void scaleAdd_32f(const float* src1, const float* src2, float* dst, t1 = src1[i+3]*alpha + src2[i+3]; dst[i+2] = t0; dst[i+3] = t1; } - for(; i < len; i++ ) + for(; i < len; i++ ) dst[i] = src1[i]*alpha + src2[i]; } @@ -2035,7 +2035,7 @@ static void scaleAdd_64f(const double* src1, const double* src2, double* dst, } else #endif - //vz why do we need unroll here? + //vz why do we need unroll here? for( ; i <= len - 4; i += 4 ) { double t0, t1; @@ -2046,7 +2046,7 @@ static void scaleAdd_64f(const double* src1, const double* src2, double* dst, t1 = src1[i+3]*alpha + src2[i+3]; dst[i+2] = t0; dst[i+3] = t1; } - for(; i < len; i++ ) + for(; i < len; i++ ) dst[i] = src1[i]*alpha + src2[i]; } @@ -2072,7 +2072,7 @@ void cv::scaleAdd( InputArray _src1, double alpha, InputArray _src2, OutputArray float falpha = (float)alpha; void* palpha = depth == CV_32F ? (void*)&falpha : (void*)α - ScaleAddFunc func = depth == CV_32F ? (ScaleAddFunc)scaleAdd_32f : (ScaleAddFunc)scaleAdd_64f; + ScaleAddFunc func = depth == CV_32F ? (ScaleAddFunc)scaleAdd_32f : (ScaleAddFunc)scaleAdd_64f; if( src1.isContinuous() && src2.isContinuous() && dst.isContinuous() ) { @@ -2134,12 +2134,12 @@ void cv::calcCovarMatrix( const Mat* data, int nsamples, Mat& covar, Mat& _mean, _mean = mean.reshape(1, size.height); } -void cv::calcCovarMatrix( InputArray _data, OutputArray _covar, InputOutputArray _mean, int flags, int ctype ) +void cv::calcCovarMatrix( InputArray _src, OutputArray _covar, InputOutputArray _mean, int flags, int ctype ) { - if(_data.kind() == _InputArray::STD_VECTOR_MAT) + if(_src.kind() == _InputArray::STD_VECTOR_MAT) { std::vector src; - _data.getMatVector(src); + _src.getMatVector(src); CV_Assert( src.size() > 0 ); @@ -2185,7 +2185,7 @@ void cv::calcCovarMatrix( InputArray _data, OutputArray _covar, InputOutputArray return; } - Mat data = _data.getMat(), mean; + Mat data = _src.getMat(), mean; CV_Assert( ((flags & CV_COVAR_ROWS) != 0) ^ ((flags & CV_COVAR_COLS) != 0) ); bool takeRows = (flags & CV_COVAR_ROWS) != 0; int type = data.type(); @@ -2209,7 +2209,7 @@ void cv::calcCovarMatrix( InputArray _data, OutputArray _covar, InputOutputArray else { ctype = std::max(CV_MAT_DEPTH(ctype >= 0 ? ctype : type), CV_32F); - reduce( _data, _mean, takeRows ? 0 : 1, CV_REDUCE_AVG, ctype ); + reduce( _src, _mean, takeRows ? 0 : 1, CV_REDUCE_AVG, ctype ); mean = _mean.getMat(); } @@ -2223,7 +2223,7 @@ void cv::calcCovarMatrix( InputArray _data, OutputArray _covar, InputOutputArray double cv::Mahalanobis( InputArray _v1, InputArray _v2, InputArray _icovar ) { - Mat v1 = _v1.getMat(), v2 = _v2.getMat(), icovar = _icovar.getMat(); + Mat v1 = _v1.getMat(), v2 = _v2.getMat(), icovar = _icovar.getMat(); int type = v1.type(), depth = v1.depth(); Size sz = v1.size(); int i, j, len = sz.width*sz.height*v1.channels(); @@ -2261,7 +2261,7 @@ double cv::Mahalanobis( InputArray _v1, InputArray _v2, InputArray _icovar ) { double row_sum = 0; j = 0; - #if CV_ENABLE_UNROLLED + #if CV_ENABLE_UNROLLED for(; j <= len - 4; j += 4 ) row_sum += diff[j]*mat[j] + diff[j+1]*mat[j+1] + diff[j+2]*mat[j+2] + diff[j+3]*mat[j+3]; @@ -2292,7 +2292,7 @@ double cv::Mahalanobis( InputArray _v1, InputArray _v2, InputArray _icovar ) { double row_sum = 0; j = 0; - #if CV_ENABLE_UNROLLED + #if CV_ENABLE_UNROLLED for(; j <= len - 4; j += 4 ) row_sum += diff[j]*mat[j] + diff[j+1]*mat[j+1] + diff[j+2]*mat[j+2] + diff[j+3]*mat[j+3]; @@ -2642,7 +2642,7 @@ dotProd_(const T* src1, const T* src2, int len) { int i = 0; double result = 0; - #if CV_ENABLE_UNROLLED + #if CV_ENABLE_UNROLLED for( ; i <= len - 4; i += 4 ) result += (double)src1[i]*src2[i] + (double)src1[i+1]*src2[i+1] + (double)src1[i+2]*src2[i+2] + (double)src1[i+3]*src2[i+3]; @@ -2674,7 +2674,7 @@ static double dotProd_8u(const uchar* src1, const uchar* src2, int len) { blockSize = std::min(len0 - i, blockSize0); __m128i s = _mm_setzero_si128(); - j = 0; + j = 0; for( ; j <= blockSize - 16; j += 16 ) { __m128i b0 = _mm_loadu_si128((const __m128i*)(src1 + j)); @@ -2806,9 +2806,9 @@ double Mat::dot(InputArray _mat) const PCA::PCA() {} -PCA::PCA(InputArray data, InputArray mean, int flags, int maxComponents) +PCA::PCA(InputArray data, InputArray _mean, int flags, int maxComponents) { - operator()(data, mean, flags, maxComponents); + operator()(data, _mean, flags, maxComponents); } PCA& PCA::operator()(InputArray _data, InputArray __mean, int flags, int maxComponents) @@ -2964,7 +2964,7 @@ void cv::PCACompute(InputArray data, InputOutputArray mean, pca.mean.copyTo(mean); pca.eigenvectors.copyTo(eigenvectors); } - + void cv::PCAProject(InputArray data, InputArray mean, InputArray eigenvectors, OutputArray result) { diff --git a/modules/core/src/matop.cpp b/modules/core/src/matop.cpp index e4ee189..c7fb841 100644 --- a/modules/core/src/matop.cpp +++ b/modules/core/src/matop.cpp @@ -59,82 +59,82 @@ public: bool elementWise(const MatExpr& /*expr*/) const { return true; } void assign(const MatExpr& expr, Mat& m, int type=-1) const; - + static void makeExpr(MatExpr& res, const Mat& m); }; static MatOp_Identity g_MatOp_Identity; - + class MatOp_AddEx : public MatOp { public: MatOp_AddEx() {} virtual ~MatOp_AddEx() {} - + bool elementWise(const MatExpr& /*expr*/) const { return true; } void assign(const MatExpr& expr, Mat& m, int type=-1) const; - + void add(const MatExpr& e1, const Scalar& s, MatExpr& res) const; void subtract(const Scalar& s, const MatExpr& expr, MatExpr& res) const; void multiply(const MatExpr& e1, double s, MatExpr& res) const; void divide(double s, const MatExpr& e, MatExpr& res) const; - + void transpose(const MatExpr& e1, MatExpr& res) const; void abs(const MatExpr& expr, MatExpr& res) const; - + static void makeExpr(MatExpr& res, const Mat& a, const Mat& b, double alpha, double beta, const Scalar& s=Scalar()); }; static MatOp_AddEx g_MatOp_AddEx; - + class MatOp_Bin : public MatOp { public: MatOp_Bin() {} virtual ~MatOp_Bin() {} - + bool elementWise(const MatExpr& /*expr*/) const { return true; } void assign(const MatExpr& expr, Mat& m, int type=-1) const; - + void multiply(const MatExpr& e1, double s, MatExpr& res) const; void divide(double s, const MatExpr& e, MatExpr& res) const; - + static void makeExpr(MatExpr& res, char op, const Mat& a, const Mat& b, double scale=1); static void makeExpr(MatExpr& res, char op, const Mat& a, const Scalar& s); }; static MatOp_Bin g_MatOp_Bin; - + class MatOp_Cmp : public MatOp { public: MatOp_Cmp() {} virtual ~MatOp_Cmp() {} - + bool elementWise(const MatExpr& /*expr*/) const { return true; } void assign(const MatExpr& expr, Mat& m, int type=-1) const; - + static void makeExpr(MatExpr& res, int cmpop, const Mat& a, const Mat& b); static void makeExpr(MatExpr& res, int cmpop, const Mat& a, double alpha); }; - + static MatOp_Cmp g_MatOp_Cmp; - + class MatOp_GEMM : public MatOp { public: MatOp_GEMM() {} virtual ~MatOp_GEMM() {} - + bool elementWise(const MatExpr& /*expr*/) const { return false; } void assign(const MatExpr& expr, Mat& m, int type=-1) const; - + void add(const MatExpr& e1, const MatExpr& e2, MatExpr& res) const; void subtract(const MatExpr& e1, const MatExpr& e2, MatExpr& res) const; void multiply(const MatExpr& e, double s, MatExpr& res) const; - + void transpose(const MatExpr& expr, MatExpr& res) const; - + static void makeExpr(MatExpr& res, int flags, const Mat& a, const Mat& b, double alpha=1, const Mat& c=Mat(), double beta=1); }; @@ -146,14 +146,14 @@ class MatOp_Invert : public MatOp public: MatOp_Invert() {} virtual ~MatOp_Invert() {} - + bool elementWise(const MatExpr& /*expr*/) const { return false; } void assign(const MatExpr& expr, Mat& m, int type=-1) const; - + void matmul(const MatExpr& expr1, const MatExpr& expr2, MatExpr& res) const; - + static void makeExpr(MatExpr& res, int method, const Mat& m); -}; +}; static MatOp_Invert g_MatOp_Invert; @@ -162,13 +162,13 @@ class MatOp_T : public MatOp public: MatOp_T() {} virtual ~MatOp_T() {} - + bool elementWise(const MatExpr& /*expr*/) const { return false; } void assign(const MatExpr& expr, Mat& m, int type=-1) const; - + void multiply(const MatExpr& e1, double s, MatExpr& res) const; void transpose(const MatExpr& expr, MatExpr& res) const; - + static void makeExpr(MatExpr& res, const Mat& a, double alpha=1); }; @@ -179,10 +179,10 @@ class MatOp_Solve : public MatOp public: MatOp_Solve() {} virtual ~MatOp_Solve() {} - + bool elementWise(const MatExpr& /*expr*/) const { return false; } void assign(const MatExpr& expr, Mat& m, int type=-1) const; - + static void makeExpr(MatExpr& res, int method, const Mat& a, const Mat& b); }; @@ -193,17 +193,17 @@ class MatOp_Initializer : public MatOp public: MatOp_Initializer() {} virtual ~MatOp_Initializer() {} - + bool elementWise(const MatExpr& /*expr*/) const { return false; } void assign(const MatExpr& expr, Mat& m, int type=-1) const; - + void multiply(const MatExpr& e, double s, MatExpr& res) const; - + static void makeExpr(MatExpr& res, int method, Size sz, int type, double alpha=1); }; static MatOp_Initializer g_MatOp_Initializer; - + static inline bool isIdentity(const MatExpr& e) { return e.op == &g_MatOp_Identity; } static inline bool isAddEx(const MatExpr& e) { return e.op == &g_MatOp_AddEx; } static inline bool isScaled(const MatExpr& e) { return isAddEx(e) && (!e.b.data || e.beta == 0) && e.s == Scalar(); } @@ -216,14 +216,14 @@ static inline bool isSolve(const MatExpr& e) { return e.op == &g_MatOp_Solve; } static inline bool isGEMM(const MatExpr& e) { return e.op == &g_MatOp_GEMM; } static inline bool isMatProd(const MatExpr& e) { return e.op == &g_MatOp_GEMM && (!e.c.data || e.beta == 0); } static inline bool isInitializer(const MatExpr& e) { return e.op == &g_MatOp_Initializer; } - + ///////////////////////////////////////////////////////////////////////////////////////////////////// - + bool MatOp::elementWise(const MatExpr& /*expr*/) const { return false; } - + void MatOp::roi(const MatExpr& expr, const Range& rowRange, const Range& colRange, MatExpr& e) const { if( elementWise(expr) ) @@ -244,7 +244,7 @@ void MatOp::roi(const MatExpr& expr, const Range& rowRange, const Range& colRang e = MatExpr(&g_MatOp_Identity, 0, m(rowRange, colRange), Mat(), Mat()); } } - + void MatOp::diag(const MatExpr& expr, int d, MatExpr& e) const { if( elementWise(expr) ) @@ -266,7 +266,7 @@ void MatOp::diag(const MatExpr& expr, int d, MatExpr& e) const } } - + void MatOp::augAssignAdd(const MatExpr& expr, Mat& m) const { Mat temp; @@ -274,7 +274,7 @@ void MatOp::augAssignAdd(const MatExpr& expr, Mat& m) const m += temp; } - + void MatOp::augAssignSubtract(const MatExpr& expr, Mat& m) const { Mat temp; @@ -282,7 +282,7 @@ void MatOp::augAssignSubtract(const MatExpr& expr, Mat& m) const m -= temp; } - + void MatOp::augAssignMultiply(const MatExpr& expr, Mat& m) const { Mat temp; @@ -290,15 +290,15 @@ void MatOp::augAssignMultiply(const MatExpr& expr, Mat& m) const m *= temp; } - + void MatOp::augAssignDivide(const MatExpr& expr, Mat& m) const { Mat temp; expr.op->assign(expr, temp); m /= temp; } - - + + void MatOp::augAssignAnd(const MatExpr& expr, Mat& m) const { Mat temp; @@ -306,7 +306,7 @@ void MatOp::augAssignAnd(const MatExpr& expr, Mat& m) const m &= temp; } - + void MatOp::augAssignOr(const MatExpr& expr, Mat& m) const { Mat temp; @@ -314,14 +314,14 @@ void MatOp::augAssignOr(const MatExpr& expr, Mat& m) const m |= temp; } - + void MatOp::augAssignXor(const MatExpr& expr, Mat& m) const { Mat temp; expr.op->assign(expr, temp); m /= temp; } - + void MatOp::add(const MatExpr& e1, const MatExpr& e2, MatExpr& res) const { @@ -338,13 +338,13 @@ void MatOp::add(const MatExpr& e1, const MatExpr& e2, MatExpr& res) const } else e1.op->assign(e1, m1); - + if( isAddEx(e2) && (!e2.b.data || e2.beta == 0) ) { m2 = e2.a; beta = e2.alpha; s += e2.s; - } + } else e2.op->assign(e2, m2); MatOp_AddEx::makeExpr(res, m1, m2, alpha, beta, s); @@ -353,7 +353,7 @@ void MatOp::add(const MatExpr& e1, const MatExpr& e2, MatExpr& res) const e2.op->add(e1, e2, res); } - + void MatOp::add(const MatExpr& expr1, const Scalar& s, MatExpr& res) const { Mat m1; @@ -361,7 +361,7 @@ void MatOp::add(const MatExpr& expr1, const Scalar& s, MatExpr& res) const MatOp_AddEx::makeExpr(res, m1, Mat(), 1, 0, s); } - + void MatOp::subtract(const MatExpr& e1, const MatExpr& e2, MatExpr& res) const { if( this == e2.op ) @@ -377,13 +377,13 @@ void MatOp::subtract(const MatExpr& e1, const MatExpr& e2, MatExpr& res) const } else e1.op->assign(e1, m1); - + if( isAddEx(e2) && (!e2.b.data || e2.beta == 0) ) { m2 = e2.a; beta = -e2.alpha; s -= e2.s; - } + } else e2.op->assign(e2, m2); MatOp_AddEx::makeExpr(res, m1, m2, alpha, beta, s); @@ -392,7 +392,7 @@ void MatOp::subtract(const MatExpr& e1, const MatExpr& e2, MatExpr& res) const e2.op->subtract(e1, e2, res); } - + void MatOp::subtract(const Scalar& s, const MatExpr& expr, MatExpr& res) const { Mat m; @@ -400,13 +400,13 @@ void MatOp::subtract(const Scalar& s, const MatExpr& expr, MatExpr& res) const MatOp_AddEx::makeExpr(res, m, Mat(), -1, 0, s); } - + void MatOp::multiply(const MatExpr& e1, const MatExpr& e2, MatExpr& res, double scale) const { if( this == e2.op ) { Mat m1, m2; - + if( isReciprocal(e1) ) { if( isScaled(e2) ) @@ -429,7 +429,7 @@ void MatOp::multiply(const MatExpr& e1, const MatExpr& e2, MatExpr& res, double } else e1.op->assign(e1, m1); - + if( isScaled(e2) ) { m2 = e2.a; @@ -443,23 +443,23 @@ void MatOp::multiply(const MatExpr& e1, const MatExpr& e2, MatExpr& res, double } else e2.op->assign(e2, m2); - + MatOp_Bin::makeExpr(res, op, m1, m2, scale); } } else e2.op->multiply(e1, e2, res, scale); } - - + + void MatOp::multiply(const MatExpr& expr, double s, MatExpr& res) const { Mat m; expr.op->assign(expr, m); - MatOp_AddEx::makeExpr(res, m, Mat(), s, 0); + MatOp_AddEx::makeExpr(res, m, Mat(), s, 0); } - - + + void MatOp::divide(const MatExpr& e1, const MatExpr& e2, MatExpr& res, double scale) const { if( this == e2.op ) @@ -470,7 +470,7 @@ void MatOp::divide(const MatExpr& e1, const MatExpr& e2, MatExpr& res, double sc { Mat m1, m2; char op = '/'; - + if( isScaled(e1) ) { m1 = e1.a; @@ -478,7 +478,7 @@ void MatOp::divide(const MatExpr& e1, const MatExpr& e2, MatExpr& res, double sc } else e1.op->assign(e1, m1); - + if( isScaled(e2) ) { m2 = e2.a; @@ -499,7 +499,7 @@ void MatOp::divide(const MatExpr& e1, const MatExpr& e2, MatExpr& res, double sc e2.op->divide(e1, e2, res, scale); } - + void MatOp::divide(double s, const MatExpr& expr, MatExpr& res) const { Mat m; @@ -507,7 +507,7 @@ void MatOp::divide(double s, const MatExpr& expr, MatExpr& res) const MatOp_Bin::makeExpr(res, '/', m, Mat(), s); } - + void MatOp::abs(const MatExpr& expr, MatExpr& res) const { Mat m; @@ -515,7 +515,7 @@ void MatOp::abs(const MatExpr& expr, MatExpr& res) const MatOp_Bin::makeExpr(res, 'a', m, Mat()); } - + void MatOp::transpose(const MatExpr& expr, MatExpr& res) const { Mat m; @@ -523,7 +523,7 @@ void MatOp::transpose(const MatExpr& expr, MatExpr& res) const MatOp_T::makeExpr(res, m, 1); } - + void MatOp::matmul(const MatExpr& e1, const MatExpr& e2, MatExpr& res) const { if( this == e2.op ) @@ -531,7 +531,7 @@ void MatOp::matmul(const MatExpr& e1, const MatExpr& e2, MatExpr& res) const double scale = 1; int flags = 0; Mat m1, m2; - + if( isT(e1) ) { flags = CV_GEMM_A_T; @@ -545,7 +545,7 @@ void MatOp::matmul(const MatExpr& e1, const MatExpr& e2, MatExpr& res) const } else e1.op->assign(e1, m1); - + if( isT(e2) ) { flags |= CV_GEMM_B_T; @@ -559,22 +559,22 @@ void MatOp::matmul(const MatExpr& e1, const MatExpr& e2, MatExpr& res) const } else e2.op->assign(e2, m2); - + MatOp_GEMM::makeExpr(res, flags, m1, m2, scale); } else e2.op->matmul(e1, e2, res); } - + void MatOp::invert(const MatExpr& expr, int method, MatExpr& res) const { Mat m; expr.op->assign(expr, m); MatOp_Invert::makeExpr(res, method, m); } - - + + Size MatOp::size(const MatExpr& expr) const { return !expr.a.empty() ? expr.a.size() : expr.b.empty() ? expr.b.size() : expr.c.size(); @@ -583,14 +583,14 @@ Size MatOp::size(const MatExpr& expr) const int MatOp::type(const MatExpr& expr) const { return !expr.a.empty() ? expr.a.type() : expr.b.empty() ? expr.b.type() : expr.c.type(); -} - +} + ////////////////////////////////////////////////////////////////////////////////////////////////// MatExpr::MatExpr(const Mat& m) : op(&g_MatOp_Identity), flags(0), a(m), b(Mat()), c(Mat()), alpha(1), beta(0), s(Scalar()) { } - + MatExpr MatExpr::row(int y) const { MatExpr e; @@ -670,42 +670,42 @@ MatExpr operator + (const Mat& a, const Mat& b) MatOp_AddEx::makeExpr(e, a, b, 1, 1); return e; } - + MatExpr operator + (const Mat& a, const Scalar& s) { MatExpr e; MatOp_AddEx::makeExpr(e, a, Mat(), 1, 0, s); return e; } - + MatExpr operator + (const Scalar& s, const Mat& a) { MatExpr e; MatOp_AddEx::makeExpr(e, a, Mat(), 1, 0, s); return e; -} - +} + MatExpr operator + (const MatExpr& e, const Mat& m) { MatExpr en; e.op->add(e, MatExpr(m), en); return en; } - + MatExpr operator + (const Mat& m, const MatExpr& e) { MatExpr en; e.op->add(e, MatExpr(m), en); return en; -} - +} + MatExpr operator + (const MatExpr& e, const Scalar& s) { MatExpr en; e.op->add(e, s, en); return en; } - + MatExpr operator + (const Scalar& s, const MatExpr& e) { MatExpr en; @@ -726,49 +726,49 @@ MatExpr operator - (const Mat& a, const Mat& b) MatOp_AddEx::makeExpr(e, a, b, 1, -1); return e; } - + MatExpr operator - (const Mat& a, const Scalar& s) { MatExpr e; MatOp_AddEx::makeExpr(e, a, Mat(), 1, 0, -s); return e; } - + MatExpr operator - (const Scalar& s, const Mat& a) { MatExpr e; MatOp_AddEx::makeExpr(e, a, Mat(), -1, 0, s); return e; } - + MatExpr operator - (const MatExpr& e, const Mat& m) { MatExpr en; e.op->subtract(e, MatExpr(m), en); return en; } - + MatExpr operator - (const Mat& m, const MatExpr& e) { MatExpr en; e.op->subtract(MatExpr(m), e, en); return en; } - + MatExpr operator - (const MatExpr& e, const Scalar& s) { MatExpr en; e.op->add(e, -s, en); return en; } - + MatExpr operator - (const Scalar& s, const MatExpr& e) { MatExpr en; e.op->subtract(s, e, en); return en; } - + MatExpr operator - (const MatExpr& e1, const MatExpr& e2) { MatExpr en; @@ -782,7 +782,7 @@ MatExpr operator - (const Mat& m) MatOp_AddEx::makeExpr(e, m, Mat(), -1, 0); return e; } - + MatExpr operator - (const MatExpr& e) { MatExpr en; @@ -852,49 +852,49 @@ MatExpr operator / (const Mat& a, const Mat& b) MatOp_Bin::makeExpr(e, '/', a, b); return e; } - + MatExpr operator / (const Mat& a, double s) { MatExpr e; MatOp_AddEx::makeExpr(e, a, Mat(), 1./s, 0); return e; } - + MatExpr operator / (double s, const Mat& a) { MatExpr e; MatOp_Bin::makeExpr(e, '/', a, Mat(), s); return e; } - + MatExpr operator / (const MatExpr& e, const Mat& m) { MatExpr en; e.op->divide(e, MatExpr(m), en); return en; } - + MatExpr operator / (const Mat& m, const MatExpr& e) { MatExpr en; e.op->divide(MatExpr(m), e, en); return en; } - + MatExpr operator / (const MatExpr& e, double s) { MatExpr en; e.op->multiply(e, 1./s, en); return en; } - + MatExpr operator / (double s, const MatExpr& e) { MatExpr en; e.op->divide(s, e, en); return en; } - + MatExpr operator / (const MatExpr& e1, const MatExpr& e2) { MatExpr en; @@ -908,14 +908,14 @@ MatExpr operator < (const Mat& a, const Mat& b) MatOp_Cmp::makeExpr(e, CV_CMP_LT, a, b); return e; } - + MatExpr operator < (const Mat& a, double s) { MatExpr e; MatOp_Cmp::makeExpr(e, CV_CMP_LT, a, s); return e; } - + MatExpr operator < (double s, const Mat& a) { MatExpr e; @@ -985,7 +985,7 @@ MatExpr operator != (double s, const Mat& a) MatOp_Cmp::makeExpr(e, CV_CMP_NE, a, s); return e; } - + MatExpr operator >= (const Mat& a, const Mat& b) { MatExpr e; @@ -1026,22 +1026,22 @@ MatExpr operator > (double s, const Mat& a) MatExpr e; MatOp_Cmp::makeExpr(e, CV_CMP_LT, a, s); return e; -} - +} + MatExpr min(const Mat& a, const Mat& b) { MatExpr e; MatOp_Bin::makeExpr(e, 'm', a, b); return e; } - + MatExpr min(const Mat& a, double s) { MatExpr e; MatOp_Bin::makeExpr(e, 'm', a, s); return e; } - + MatExpr min(double s, const Mat& a) { MatExpr e; @@ -1055,14 +1055,14 @@ MatExpr max(const Mat& a, const Mat& b) MatOp_Bin::makeExpr(e, 'M', a, b); return e; } - + MatExpr max(const Mat& a, double s) { MatExpr e; MatOp_Bin::makeExpr(e, 'M', a, s); return e; } - + MatExpr max(double s, const Mat& a) { MatExpr e; @@ -1076,35 +1076,35 @@ MatExpr operator & (const Mat& a, const Mat& b) MatOp_Bin::makeExpr(e, '&', a, b); return e; } - + MatExpr operator & (const Mat& a, const Scalar& s) { MatExpr e; MatOp_Bin::makeExpr(e, '&', a, s); return e; } - + MatExpr operator & (const Scalar& s, const Mat& a) { MatExpr e; MatOp_Bin::makeExpr(e, '&', a, s); return e; } - + MatExpr operator | (const Mat& a, const Mat& b) { MatExpr e; MatOp_Bin::makeExpr(e, '|', a, b); return e; } - + MatExpr operator | (const Mat& a, const Scalar& s) { MatExpr e; MatOp_Bin::makeExpr(e, '|', a, s); return e; } - + MatExpr operator | (const Scalar& s, const Mat& a) { MatExpr e; @@ -1154,7 +1154,7 @@ MatExpr abs(const MatExpr& e) return en; } - + Size MatExpr::size() const { if( isT(*this) || isInv(*this) ) @@ -1167,8 +1167,8 @@ Size MatExpr::size() const return a.size(); return op ? op->size(*this) : Size(); } - - + + int MatExpr::type() const { if( isInitializer(*this) ) @@ -1177,31 +1177,31 @@ int MatExpr::type() const return CV_8U; return op ? op->type(*this) : -1; } - - + + ///////////////////////////////////////////////////////////////////////////////////////////////////// - -void MatOp_Identity::assign(const MatExpr& e, Mat& m, int type) const + +void MatOp_Identity::assign(const MatExpr& e, Mat& m, int _type) const { - if( type == -1 || type == e.a.type() ) + if( _type == -1 || _type == e.a.type() ) m = e.a; else { - CV_Assert( CV_MAT_CN(type) == e.a.channels() ); - e.a.convertTo(m, type); + CV_Assert( CV_MAT_CN(_type) == e.a.channels() ); + e.a.convertTo(m, _type); } } inline void MatOp_Identity::makeExpr(MatExpr& res, const Mat& m) { res = MatExpr(&g_MatOp_Identity, 0, m, Mat(), Mat(), 1, 0); -} - +} + ///////////////////////////////////////////////////////////////////////////////////////////////////// -void MatOp_AddEx::assign(const MatExpr& e, Mat& m, int type) const -{ - Mat temp, &dst = type == -1 || e.a.type() == type ? m : temp; +void MatOp_AddEx::assign(const MatExpr& e, Mat& m, int _type) const +{ + Mat temp, &dst = _type == -1 || e.a.type() == _type ? m : temp; if( e.b.data ) { if( e.s == Scalar() || !e.s.isReal() ) @@ -1224,7 +1224,7 @@ void MatOp_AddEx::assign(const MatExpr& e, Mat& m, int type) const } else cv::addWeighted(e.a, e.alpha, e.b, e.beta, 0, dst); - + if( !e.s.isReal() ) cv::add(dst, e.s, dst); } @@ -1233,7 +1233,7 @@ void MatOp_AddEx::assign(const MatExpr& e, Mat& m, int type) const } else if( e.s.isReal() && (dst.data != m.data || fabs(e.alpha) != 1)) { - e.a.convertTo(m, type, e.alpha, e.s[0]); + e.a.convertTo(m, _type, e.alpha, e.s[0]); return; } else if( e.alpha == 1 ) @@ -1245,19 +1245,19 @@ void MatOp_AddEx::assign(const MatExpr& e, Mat& m, int type) const e.a.convertTo(dst, e.a.type(), e.alpha); cv::add(dst, e.s, dst); } - + if( dst.data != m.data ) dst.convertTo(m, m.type()); } - + void MatOp_AddEx::add(const MatExpr& e, const Scalar& s, MatExpr& res) const { res = e; res.s += s; } - + void MatOp_AddEx::subtract(const Scalar& s, const MatExpr& e, MatExpr& res) const { res = e; @@ -1265,7 +1265,7 @@ void MatOp_AddEx::subtract(const Scalar& s, const MatExpr& e, MatExpr& res) cons res.beta = -res.beta; res.s = s - res.s; } - + void MatOp_AddEx::multiply(const MatExpr& e, double s, MatExpr& res) const { res = e; @@ -1273,7 +1273,7 @@ void MatOp_AddEx::multiply(const MatExpr& e, double s, MatExpr& res) const res.beta *= s; res.s *= s; } - + void MatOp_AddEx::divide(double s, const MatExpr& e, MatExpr& res) const { if( isScaled(e) ) @@ -1290,7 +1290,7 @@ void MatOp_AddEx::transpose(const MatExpr& e, MatExpr& res) const else MatOp::transpose(e, res); } - + void MatOp_AddEx::abs(const MatExpr& e, MatExpr& res) const { if( (!e.b.data || e.beta == 0) && fabs(e.alpha) == 1 ) @@ -1300,18 +1300,18 @@ void MatOp_AddEx::abs(const MatExpr& e, MatExpr& res) const else MatOp::abs(e, res); } - + inline void MatOp_AddEx::makeExpr(MatExpr& res, const Mat& a, const Mat& b, double alpha, double beta, const Scalar& s) { res = MatExpr(&g_MatOp_AddEx, 0, a, b, Mat(), alpha, beta, s); } - + ////////////////////////////////////////////////////////////////////////////////////////////////////////////////// - -void MatOp_Bin::assign(const MatExpr& e, Mat& m, int type) const + +void MatOp_Bin::assign(const MatExpr& e, Mat& m, int _type) const { - Mat temp, &dst = type == -1 || e.a.type() == type ? m : temp; - + Mat temp, &dst = _type == -1 || e.a.type() == _type ? m : temp; + if( e.flags == '*' ) cv::multiply(e.a, e.b, dst, e.alpha); else if( e.flags == '/' && e.b.data ) @@ -1346,9 +1346,9 @@ void MatOp_Bin::assign(const MatExpr& e, Mat& m, int type) const cv::absdiff(e.a, e.s, dst); else CV_Error(CV_StsError, "Unknown operation"); - + if( dst.data != m.data ) - dst.convertTo(m, type); + dst.convertTo(m, _type); } void MatOp_Bin::multiply(const MatExpr& e, double s, MatExpr& res) const @@ -1379,42 +1379,42 @@ inline void MatOp_Bin::makeExpr(MatExpr& res, char op, const Mat& a, const Scala { res = MatExpr(&g_MatOp_Bin, op, a, Mat(), Mat(), 1, 0, s); } - + /////////////////////////////////////////////////////////////////////////////////////////////////////// - -void MatOp_Cmp::assign(const MatExpr& e, Mat& m, int type) const + +void MatOp_Cmp::assign(const MatExpr& e, Mat& m, int _type) const { - Mat temp, &dst = type == -1 || type == CV_8U ? m : temp; - + Mat temp, &dst = _type == -1 || _type == CV_8U ? m : temp; + if( e.b.data ) cv::compare(e.a, e.b, dst, e.flags); else cv::compare(e.a, e.alpha, dst, e.flags); - + if( dst.data != m.data ) - dst.convertTo(m, type); + dst.convertTo(m, _type); } inline void MatOp_Cmp::makeExpr(MatExpr& res, int cmpop, const Mat& a, const Mat& b) { res = MatExpr(&g_MatOp_Cmp, cmpop, a, b, Mat(), 1, 1); } - + inline void MatOp_Cmp::makeExpr(MatExpr& res, int cmpop, const Mat& a, double alpha) { res = MatExpr(&g_MatOp_Cmp, cmpop, a, Mat(), Mat(), alpha, 1); } ///////////////////////////////////////////////////////////////////////////////////////////////////////// - -void MatOp_T::assign(const MatExpr& e, Mat& m, int type) const + +void MatOp_T::assign(const MatExpr& e, Mat& m, int _type) const { - Mat temp, &dst = type == -1 || type == e.a.type() ? m : temp; - + Mat temp, &dst = _type == -1 || _type == e.a.type() ? m : temp; + cv::transpose(e.a, dst); - + if( dst.data != m.data || e.alpha != 1 ) - dst.convertTo(m, type, e.alpha); + dst.convertTo(m, _type, e.alpha); } void MatOp_T::multiply(const MatExpr& e, double s, MatExpr& res) const @@ -1422,7 +1422,7 @@ void MatOp_T::multiply(const MatExpr& e, double s, MatExpr& res) const res = e; res.alpha *= s; } - + void MatOp_T::transpose(const MatExpr& e, MatExpr& res) const { if( e.alpha == 1 ) @@ -1430,28 +1430,28 @@ void MatOp_T::transpose(const MatExpr& e, MatExpr& res) const else MatOp_AddEx::makeExpr(res, e.a, Mat(), e.alpha, 0); } - + inline void MatOp_T::makeExpr(MatExpr& res, const Mat& a, double alpha) { res = MatExpr(&g_MatOp_T, 0, a, Mat(), Mat(), alpha, 0); } ///////////////////////////////////////////////////////////////////////////////////////////////////////// - -void MatOp_GEMM::assign(const MatExpr& e, Mat& m, int type) const + +void MatOp_GEMM::assign(const MatExpr& e, Mat& m, int _type) const { - Mat temp, &dst = type == -1 || type == e.a.type() ? m : temp; - + Mat temp, &dst = _type == -1 || _type == e.a.type() ? m : temp; + cv::gemm(e.a, e.b, e.alpha, e.c, e.beta, dst, e.flags); if( dst.data != m.data ) - dst.convertTo(m, type); + dst.convertTo(m, _type); } void MatOp_GEMM::add(const MatExpr& e1, const MatExpr& e2, MatExpr& res) const { bool i1 = isIdentity(e1), i2 = isIdentity(e2); double alpha1 = i1 ? 1 : e1.alpha, alpha2 = i2 ? 1 : e2.alpha; - + if( isMatProd(e1) && (i2 || isScaled(e2) || isT(e2)) ) MatOp_GEMM::makeExpr(res, (e1.flags & ~CV_GEMM_C_T)|(isT(e2) ? CV_GEMM_C_T : 0), e1.a, e1.b, alpha1, e2.a, alpha2); @@ -1463,12 +1463,12 @@ void MatOp_GEMM::add(const MatExpr& e1, const MatExpr& e2, MatExpr& res) const else e2.op->add(e1, e2, res); } - + void MatOp_GEMM::subtract(const MatExpr& e1, const MatExpr& e2, MatExpr& res) const { bool i1 = isIdentity(e1), i2 = isIdentity(e2); double alpha1 = i1 ? 1 : e1.alpha, alpha2 = i2 ? 1 : e2.alpha; - + if( isMatProd(e1) && (i2 || isScaled(e2) || isT(e2)) ) MatOp_GEMM::makeExpr(res, (e1.flags & ~CV_GEMM_C_T)|(isT(e2) ? CV_GEMM_C_T : 0), e1.a, e1.b, alpha1, e2.a, -alpha2); @@ -1487,7 +1487,7 @@ void MatOp_GEMM::multiply(const MatExpr& e, double s, MatExpr& res) const res.alpha *= s; res.beta *= s; } - + void MatOp_GEMM::transpose(const MatExpr& e, MatExpr& res) const { res = e; @@ -1502,14 +1502,14 @@ inline void MatOp_GEMM::makeExpr(MatExpr& res, int flags, const Mat& a, const Ma } /////////////////////////////////////////////////////////////////////////////////////////////////////// - -void MatOp_Invert::assign(const MatExpr& e, Mat& m, int type) const + +void MatOp_Invert::assign(const MatExpr& e, Mat& m, int _type) const { - Mat temp, &dst = type == -1 || type == e.a.type() ? m : temp; - + Mat temp, &dst = _type == -1 || _type == e.a.type() ? m : temp; + cv::invert(e.a, dst, e.flags); if( dst.data != m.data ) - dst.convertTo(m, type); + dst.convertTo(m, _type); } void MatOp_Invert::matmul(const MatExpr& e1, const MatExpr& e2, MatExpr& res) const @@ -1528,14 +1528,14 @@ inline void MatOp_Invert::makeExpr(MatExpr& res, int method, const Mat& m) } ///////////////////////////////////////////////////////////////////////////////////////////////////////// - -void MatOp_Solve::assign(const MatExpr& e, Mat& m, int type) const + +void MatOp_Solve::assign(const MatExpr& e, Mat& m, int _type) const { - Mat temp, &dst = type == -1 || type == e.a.type() ? m : temp; - + Mat temp, &dst = _type == -1 || _type == e.a.type() ? m : temp; + cv::solve(e.a, e.b, dst, e.flags); if( dst.data != m.data ) - dst.convertTo(m, type); + dst.convertTo(m, _type); } inline void MatOp_Solve::makeExpr(MatExpr& res, int method, const Mat& a, const Mat& b) @@ -1544,12 +1544,12 @@ inline void MatOp_Solve::makeExpr(MatExpr& res, int method, const Mat& a, const } ////////////////////////////////////////////////////////////////////////////////////////////////////////// - -void MatOp_Initializer::assign(const MatExpr& e, Mat& m, int type) const + +void MatOp_Initializer::assign(const MatExpr& e, Mat& m, int _type) const { - if( type == -1 ) - type = e.a.type(); - m.create(e.a.size(), type); + if( _type == -1 ) + _type = e.a.type(); + m.create(e.a.size(), _type); if( e.flags == 'I' ) setIdentity(m, Scalar(e.alpha)); else if( e.flags == '0' ) @@ -1565,13 +1565,13 @@ void MatOp_Initializer::multiply(const MatExpr& e, double s, MatExpr& res) const res = e; res.alpha *= s; } - + inline void MatOp_Initializer::makeExpr(MatExpr& res, int method, Size sz, int type, double alpha) { res = MatExpr(&g_MatOp_Initializer, method, Mat(sz, type, (void*)0), Mat(), Mat(), alpha, 0); -} +} + - /////////////////////////////////////////////////////////////////////////////////////////////////////////// MatExpr Mat::t() const @@ -1580,14 +1580,14 @@ MatExpr Mat::t() const MatOp_T::makeExpr(e, *this); return e; } - + MatExpr Mat::inv(int method) const { MatExpr e; MatOp_Invert::makeExpr(e, method, *this); return e; } - + MatExpr Mat::mul(InputArray m, double scale) const { @@ -1608,21 +1608,21 @@ MatExpr Mat::zeros(int rows, int cols, int type) MatOp_Initializer::makeExpr(e, '0', Size(cols, rows), type); return e; } - + MatExpr Mat::zeros(Size size, int type) { MatExpr e; MatOp_Initializer::makeExpr(e, '0', size, type); return e; } - + MatExpr Mat::ones(int rows, int cols, int type) { MatExpr e; MatOp_Initializer::makeExpr(e, '1', Size(cols, rows), type); return e; } - + MatExpr Mat::ones(Size size, int type) { MatExpr e; @@ -1643,7 +1643,7 @@ MatExpr Mat::eye(Size size, int type) MatOp_Initializer::makeExpr(e, 'I', size, type); return e; } - + } /* End of file. */ diff --git a/modules/core/src/matrix.cpp b/modules/core/src/matrix.cpp index 845bd89..03cc8fb 100644 --- a/modules/core/src/matrix.cpp +++ b/modules/core/src/matrix.cpp @@ -62,18 +62,18 @@ void swap( Mat& a, Mat& b ) std::swap(a.dataend, b.dataend); std::swap(a.datalimit, b.datalimit); std::swap(a.allocator, b.allocator); - + std::swap(a.size.p, b.size.p); std::swap(a.step.p, b.step.p); std::swap(a.step.buf[0], b.step.buf[0]); std::swap(a.step.buf[1], b.step.buf[1]); - + if( a.step.p == b.step.buf ) { a.step.p = a.step.buf; a.size.p = &a.rows; } - + if( b.step.p == a.step.buf ) { b.step.p = b.step.buf; @@ -102,11 +102,11 @@ static inline void setSize( Mat& m, int _dims, const int* _sz, m.rows = m.cols = -1; } } - + m.dims = _dims; if( !_sz ) return; - + size_t esz = CV_ELEM_SIZE(m.flags), total = esz; int i; for( i = _dims-1; i >= 0; i-- ) @@ -114,7 +114,7 @@ static inline void setSize( Mat& m, int _dims, const int* _sz, int s = _sz[i]; CV_Assert( s >= 0 ); m.size.p[i] = s; - + if( _steps ) m.step.p[i] = i < _dims-1 ? _steps[i] : esz; else if( autoSteps ) @@ -126,7 +126,7 @@ static inline void setSize( Mat& m, int _dims, const int* _sz, total = (size_t)total1; } } - + if( _dims == 1 ) { m.dims = 2; @@ -134,7 +134,7 @@ static inline void setSize( Mat& m, int _dims, const int* _sz, m.step[1] = esz; } } - + static void updateContinuityFlag(Mat& m) { int i, j; @@ -143,20 +143,20 @@ static void updateContinuityFlag(Mat& m) if( m.size[i] > 1 ) break; } - + for( j = m.dims-1; j > i; j-- ) { if( m.step[j]*m.size[j] < m.step[j-1] ) break; } - + int64 t = (int64)m.step[0]*m.size[0]; if( j <= i && t == (int)t ) m.flags |= Mat::CONTINUOUS_FLAG; else m.flags &= ~Mat::CONTINUOUS_FLAG; } - + static void finalizeHdr(Mat& m) { updateContinuityFlag(m); @@ -178,14 +178,14 @@ static void finalizeHdr(Mat& m) else m.dataend = m.datalimit = 0; } - - + + void Mat::create(int d, const int* _sizes, int _type) { int i; CV_Assert(0 <= d && _sizes && d <= CV_MAX_DIM && _sizes); _type = CV_MAT_TYPE(_type); - + if( data && (d == dims || (d == 1 && dims <= 2)) && _type == type() ) { if( d == 2 && rows == _sizes[0] && cols == _sizes[1] ) @@ -196,13 +196,13 @@ void Mat::create(int d, const int* _sizes, int _type) if( i == d && (d > 1 || size[1] == 1)) return; } - + release(); if( d == 0 ) return; flags = (_type & CV_MAT_TYPE_MASK) | MAGIC_VAL; setSize(*this, d, _sizes, 0, true); - + if( total() > 0 ) { #ifdef HAVE_TGPU @@ -210,24 +210,24 @@ void Mat::create(int d, const int* _sizes, int _type) #endif if( !allocator ) { - size_t total = alignSize(step.p[0]*size.p[0], (int)sizeof(*refcount)); - data = datastart = (uchar*)fastMalloc(total + (int)sizeof(*refcount)); - refcount = (int*)(data + total); + size_t totalsize = alignSize(step.p[0]*size.p[0], (int)sizeof(*refcount)); + data = datastart = (uchar*)fastMalloc(totalsize + (int)sizeof(*refcount)); + refcount = (int*)(data + totalsize); *refcount = 1; } else { #ifdef HAVE_TGPU - try + try { allocator->allocate(dims, size, _type, refcount, datastart, data, step.p); CV_Assert( step[dims-1] == (size_t)CV_ELEM_SIZE(flags) ); }catch(...) { allocator = 0; - size_t total = alignSize(step.p[0]*size.p[0], (int)sizeof(*refcount)); - data = datastart = (uchar*)fastMalloc(total + (int)sizeof(*refcount)); - refcount = (int*)(data + total); + size_t totalSize = alignSize(step.p[0]*size.p[0], (int)sizeof(*refcount)); + data = datastart = (uchar*)fastMalloc(totalSize + (int)sizeof(*refcount)); + refcount = (int*)(data + totalSize); *refcount = 1; } #else @@ -236,7 +236,7 @@ void Mat::create(int d, const int* _sizes, int _type) #endif } } - + finalizeHdr(*this); } @@ -249,7 +249,7 @@ void Mat::copySize(const Mat& m) step[i] = m.step[i]; } } - + void Mat::deallocate() { if( allocator ) @@ -261,51 +261,51 @@ void Mat::deallocate() } } - -Mat::Mat(const Mat& m, const Range& rowRange, const Range& colRange) : size(&rows) + +Mat::Mat(const Mat& m, const Range& _rowRange, const Range& _colRange) : size(&rows) { initEmpty(); CV_Assert( m.dims >= 2 ); if( m.dims > 2 ) { AutoBuffer rs(m.dims); - rs[0] = rowRange; - rs[1] = colRange; + rs[0] = _rowRange; + rs[1] = _colRange; for( int i = 2; i < m.dims; i++ ) rs[i] = Range::all(); *this = m(rs); return; } - + *this = m; - if( rowRange != Range::all() && rowRange != Range(0,rows) ) + if( _rowRange != Range::all() && _rowRange != Range(0,rows) ) { - CV_Assert( 0 <= rowRange.start && rowRange.start <= rowRange.end && rowRange.end <= m.rows ); - rows = rowRange.size(); - data += step*rowRange.start; + CV_Assert( 0 <= _rowRange.start && _rowRange.start <= _rowRange.end && _rowRange.end <= m.rows ); + rows = _rowRange.size(); + data += step*_rowRange.start; flags |= SUBMATRIX_FLAG; } - - if( colRange != Range::all() && colRange != Range(0,cols) ) + + if( _colRange != Range::all() && _colRange != Range(0,cols) ) { - CV_Assert( 0 <= colRange.start && colRange.start <= colRange.end && colRange.end <= m.cols ); - cols = colRange.size(); - data += colRange.start*elemSize(); + CV_Assert( 0 <= _colRange.start && _colRange.start <= _colRange.end && _colRange.end <= m.cols ); + cols = _colRange.size(); + data += _colRange.start*elemSize(); flags &= cols < m.cols ? ~CONTINUOUS_FLAG : -1; flags |= SUBMATRIX_FLAG; } - + if( rows == 1 ) flags |= CONTINUOUS_FLAG; - + if( rows <= 0 || cols <= 0 ) { release(); rows = cols = 0; } } - - + + Mat::Mat(const Mat& m, const Rect& roi) : flags(m.flags), dims(2), rows(roi.height), cols(roi.width), data(m.data + roi.y*m.step[0]), refcount(m.refcount), @@ -315,7 +315,7 @@ Mat::Mat(const Mat& m, const Rect& roi) CV_Assert( m.dims <= 2 ); flags &= roi.width < m.cols ? ~CONTINUOUS_FLAG : -1; flags |= roi.height == 1 ? CONTINUOUS_FLAG : 0; - + size_t esz = CV_ELEM_SIZE(flags); data += roi.x*esz; CV_Assert( 0 <= roi.x && 0 <= roi.width && roi.x + roi.width <= m.cols && @@ -324,9 +324,9 @@ Mat::Mat(const Mat& m, const Rect& roi) CV_XADD(refcount, 1); if( roi.width < m.cols || roi.height < m.rows ) flags |= SUBMATRIX_FLAG; - + step[0] = m.step[0]; step[1] = esz; - + if( rows <= 0 || cols <= 0 ) { release(); @@ -334,7 +334,7 @@ Mat::Mat(const Mat& m, const Rect& roi) } } - + Mat::Mat(int _dims, const int* _sizes, int _type, void* _data, const size_t* _steps) : size(&rows) { initEmpty(); @@ -343,13 +343,13 @@ Mat::Mat(int _dims, const int* _sizes, int _type, void* _data, const size_t* _st setSize(*this, _dims, _sizes, _steps, true); finalizeHdr(*this); } - - + + Mat::Mat(const Mat& m, const Range* ranges) : size(&rows) { initEmpty(); int i, d = m.dims; - + CV_Assert(ranges); for( i = 0; i < d; i++ ) { @@ -369,8 +369,8 @@ Mat::Mat(const Mat& m, const Range* ranges) : size(&rows) } updateContinuityFlag(*this); } - - + + Mat::Mat(const CvMatND* m, bool copyData) : size(&rows) { initEmpty(); @@ -380,14 +380,14 @@ Mat::Mat(const CvMatND* m, bool copyData) : size(&rows) flags |= CV_MAT_TYPE(m->type); int _sizes[CV_MAX_DIM]; size_t _steps[CV_MAX_DIM]; - + int i, d = m->dims; for( i = 0; i < d; i++ ) { _sizes[i] = m->dim[i].size; _steps[i] = m->dim[i].step; } - + setSize(*this, d, _sizes, _steps); finalizeHdr(*this); @@ -397,15 +397,15 @@ Mat::Mat(const CvMatND* m, bool copyData) : size(&rows) temp.copyTo(*this); } } - - + + Mat Mat::diag(int d) const { CV_Assert( dims <= 2 ); Mat m = *this; size_t esz = elemSize(); int len; - + if( d >= 0 ) { len = std::min(cols - d, rows); @@ -417,30 +417,30 @@ Mat Mat::diag(int d) const m.data -= step[0]*d; } CV_DbgAssert( len > 0 ); - + m.size[0] = m.rows = len; m.size[1] = m.cols = 1; m.step[0] += (len > 1 ? esz : 0); - + if( m.rows > 1 ) m.flags &= ~CONTINUOUS_FLAG; else m.flags |= CONTINUOUS_FLAG; - + if( size() != Size(1,1) ) m.flags |= SUBMATRIX_FLAG; - + return m; } - + Mat::Mat(const CvMat* m, bool copyData) : size(&rows) { initEmpty(); - + if( !m ) return; - + if( !copyData ) { flags = MAGIC_VAL + (m->type & (CV_MAT_TYPE_MASK|CV_MAT_CONT_FLAG)); @@ -462,25 +462,25 @@ Mat::Mat(const CvMat* m, bool copyData) : size(&rows) } } - + Mat::Mat(const IplImage* img, bool copyData) : size(&rows) { initEmpty(); - + if( !img ) return; - + dims = 2; CV_DbgAssert(CV_IS_IMAGE(img) && img->imageData != 0); - - int depth = IPL2CV_DEPTH(img->depth); + + int imgdepth = IPL2CV_DEPTH(img->depth); size_t esz; step[0] = img->widthStep; if(!img->roi) { CV_Assert(img->dataOrder == IPL_DATA_ORDER_PIXEL); - flags = MAGIC_VAL + CV_MAKETYPE(depth, img->nChannels); + flags = MAGIC_VAL + CV_MAKETYPE(imgdepth, img->nChannels); rows = img->height; cols = img->width; datastart = data = (uchar*)img->imageData; esz = CV_ELEM_SIZE(flags); @@ -489,12 +489,12 @@ Mat::Mat(const IplImage* img, bool copyData) : size(&rows) { CV_Assert(img->dataOrder == IPL_DATA_ORDER_PIXEL || img->roi->coi != 0); bool selectedPlane = img->roi->coi && img->dataOrder == IPL_DATA_ORDER_PLANE; - flags = MAGIC_VAL + CV_MAKETYPE(depth, selectedPlane ? 1 : img->nChannels); + flags = MAGIC_VAL + CV_MAKETYPE(imgdepth, selectedPlane ? 1 : img->nChannels); rows = img->roi->height; cols = img->roi->width; esz = CV_ELEM_SIZE(flags); data = datastart = (uchar*)img->imageData + - (selectedPlane ? (img->roi->coi - 1)*step*img->height : 0) + - img->roi->yOffset*step[0] + img->roi->xOffset*esz; + (selectedPlane ? (img->roi->coi - 1)*step*img->height : 0) + + img->roi->yOffset*step[0] + img->roi->xOffset*esz; } datalimit = datastart + step.p[0]*rows; dataend = datastart + step.p[0]*(rows-1) + esz*cols; @@ -517,7 +517,7 @@ Mat::Mat(const IplImage* img, bool copyData) : size(&rows) } } - + Mat::operator IplImage() const { CV_Assert( dims <= 2 ); @@ -527,11 +527,11 @@ Mat::operator IplImage() const return img; } - + void Mat::pop_back(size_t nelems) { CV_Assert( nelems <= (size_t)size.p[0] ); - + if( isSubmatrix() ) *this = rowRange(0, size.p[0] - (int)nelems); else @@ -547,14 +547,14 @@ void Mat::pop_back(size_t nelems) }*/ } } - - + + void Mat::push_back_(const void* elem) { int r = size.p[0]; if( isSubmatrix() || dataend + step.p[0] > datalimit ) reserve( std::max(r + 1, (r*3+1)/2) ); - + size_t esz = elemSize(); memcpy(data + r*step.p[0], elem, esz); size.p[0] = r + 1; @@ -566,22 +566,22 @@ void Mat::push_back_(const void* elem) void Mat::reserve(size_t nelems) { const size_t MIN_SIZE = 64; - + CV_Assert( (int)nelems >= 0 ); if( !isSubmatrix() && data + step.p[0]*nelems <= datalimit ) return; - + int r = size.p[0]; - + if( (size_t)r >= nelems ) return; - + size.p[0] = std::max((int)nelems, 1); size_t newsize = total()*elemSize(); - + if( newsize < MIN_SIZE ) size.p[0] = (int)((MIN_SIZE + newsize - 1)*nelems/newsize); - + Mat m(dims, size.p, type()); size.p[0] = r; if( r > 0 ) @@ -589,42 +589,42 @@ void Mat::reserve(size_t nelems) Mat mpart = m.rowRange(0, r); copyTo(mpart); } - + *this = m; size.p[0] = r; dataend = data + step.p[0]*r; } - + void Mat::resize(size_t nelems) { int saveRows = size.p[0]; if( saveRows == (int)nelems ) return; CV_Assert( (int)nelems >= 0 ); - + if( isSubmatrix() || data + step.p[0]*nelems > datalimit ) reserve(nelems); - + size.p[0] = (int)nelems; dataend += (size.p[0] - saveRows)*step.p[0]; - + //updateContinuityFlag(*this); -} +} + - void Mat::resize(size_t nelems, const Scalar& s) { int saveRows = size.p[0]; resize(nelems); - + if( size.p[0] > saveRows ) { Mat part = rowRange(saveRows, size.p[0]); part = s; } -} - +} + void Mat::push_back(const Mat& elems) { int r = size.p[0], delta = elems.size.p[0]; @@ -636,11 +636,11 @@ void Mat::push_back(const Mat& elems) push_back(tmp); return; } - if( !data ) - { - *this = elems.clone(); - return; - } + if( !data ) + { + *this = elems.clone(); + return; + } size.p[0] = elems.size.p[0]; bool eq = size == elems.size; @@ -649,15 +649,15 @@ void Mat::push_back(const Mat& elems) CV_Error(CV_StsUnmatchedSizes, ""); if( type() != elems.type() ) CV_Error(CV_StsUnmatchedFormats, ""); - + if( isSubmatrix() || dataend + step.p[0]*delta > datalimit ) reserve( std::max(r + delta, (r*3+1)/2) ); - + size.p[0] += delta; dataend += step.p[0]*delta; - + //updateContinuityFlag(*this); - + if( isContinuous() && elems.isContinuous() ) memcpy(data + r*step.p[0], elems.data, elems.total()*elems.elemSize()); else @@ -667,7 +667,7 @@ void Mat::push_back(const Mat& elems) } } - + Mat cvarrToMat(const CvArr* arr, bool copyData, bool /*allowND*/, int coiMode) { @@ -703,7 +703,7 @@ void Mat::locateROI( Size& wholeSize, Point& ofs ) const CV_Assert( dims <= 2 && step[0] > 0 ); size_t esz = elemSize(), minstep; ptrdiff_t delta1 = data - datastart, delta2 = dataend - datastart; - + if( delta1 == 0 ) ofs.x = ofs.y = 0; else @@ -735,17 +735,17 @@ Mat& Mat::adjustROI( int dtop, int dbottom, int dleft, int dright ) else flags &= ~CONTINUOUS_FLAG; return *this; -} +} } - + void cv::extractImageCOI(const CvArr* arr, OutputArray _ch, int coi) { Mat mat = cvarrToMat(arr, false, true, 1); _ch.create(mat.dims, mat.size, mat.depth()); Mat ch = _ch.getMat(); if(coi < 0) - { + { CV_Assert( CV_IS_IMAGE(arr) ); coi = cvGetImageCOI((const IplImage*)arr)-1; } @@ -753,12 +753,12 @@ void cv::extractImageCOI(const CvArr* arr, OutputArray _ch, int coi) int _pairs[] = { coi, 0 }; mixChannels( &mat, 1, &ch, 1, _pairs, 1 ); } - + void cv::insertImageCOI(InputArray _ch, CvArr* arr, int coi) { Mat ch = _ch.getMat(), mat = cvarrToMat(arr, false, true, 1); if(coi < 0) - { + { CV_Assert( CV_IS_IMAGE(arr) ); coi = cvGetImageCOI((const IplImage*)arr)-1; } @@ -766,7 +766,7 @@ void cv::insertImageCOI(InputArray _ch, CvArr* arr, int coi) int _pairs[] = { 0, coi }; mixChannels( &ch, 1, &mat, 1, _pairs, 1 ); } - + namespace cv { @@ -774,7 +774,7 @@ Mat Mat::reshape(int new_cn, int new_rows) const { int cn = channels(); Mat hdr = *this; - + if( dims > 2 && new_rows == 0 && new_cn != 0 && size[dims-1]*cn % new_cn == 0 ) { hdr.flags = (hdr.flags & ~CV_MAT_CN_MASK) | ((new_cn-1) << CV_CN_SHIFT); @@ -782,9 +782,9 @@ Mat Mat::reshape(int new_cn, int new_rows) const hdr.size[dims-1] = hdr.size[dims-1]*cn / new_cn; return hdr; } - + CV_Assert( dims <= 2 ); - + if( new_cn == 0 ) new_cn = cn; @@ -825,7 +825,7 @@ Mat Mat::reshape(int new_cn, int new_rows) const return hdr; } - + int Mat::checkVector(int _elemChannels, int _depth, bool _requireContinuous) const { return (depth() == _depth || _depth <= 0) && @@ -911,12 +911,15 @@ void scalarToRawData(const Scalar& s, void* _buf, int type, int unroll_to) } } - + /*************************************************************************************************\ Input/Output Array \*************************************************************************************************/ _InputArray::_InputArray() : flags(0), obj(0) {} +#ifdef OPENCV_CAN_BREAK_BINARY_COMPATIBILITY +_InputArray::~_InputArray() {} +#endif _InputArray::_InputArray(const Mat& m) : flags(MAT), obj((void*)&m) {} _InputArray::_InputArray(const vector& vec) : flags(STD_VECTOR_MAT), obj((void*)&vec) {} _InputArray::_InputArray(const double& val) : flags(FIXED_TYPE + FIXED_SIZE + MATX + CV_64F), obj((void*)&val), sz(Size(1,1)) {} @@ -924,11 +927,11 @@ _InputArray::_InputArray(const MatExpr& expr) : flags(FIXED_TYPE + FIXED_SIZE + _InputArray::_InputArray(const GlBuffer& buf) : flags(FIXED_TYPE + FIXED_SIZE + OPENGL_BUFFER), obj((void*)&buf) {} _InputArray::_InputArray(const GlTexture& tex) : flags(FIXED_TYPE + FIXED_SIZE + OPENGL_TEXTURE), obj((void*)&tex) {} _InputArray::_InputArray(const gpu::GpuMat& d_mat) : flags(GPU_MAT), obj((void*)&d_mat) {} - + Mat _InputArray::getMat(int i) const { int k = kind(); - + if( k == MAT ) { const Mat* m = (const Mat*)obj; @@ -936,115 +939,115 @@ Mat _InputArray::getMat(int i) const return *m; return m->row(i); } - + if( k == EXPR ) { CV_Assert( i < 0 ); return (Mat)*((const MatExpr*)obj); } - + if( k == MATX ) { CV_Assert( i < 0 ); return Mat(sz, flags, obj); } - + if( k == STD_VECTOR ) { CV_Assert( i < 0 ); int t = CV_MAT_TYPE(flags); const vector& v = *(const vector*)obj; - + return !v.empty() ? Mat(size(), t, (void*)&v[0]) : Mat(); } - + if( k == NONE ) return Mat(); - + if( k == STD_VECTOR_VECTOR ) { int t = type(i); const vector >& vv = *(const vector >*)obj; CV_Assert( 0 <= i && i < (int)vv.size() ); const vector& v = vv[i]; - + return !v.empty() ? Mat(size(i), t, (void*)&v[0]) : Mat(); } - + CV_Assert( k == STD_VECTOR_MAT ); //if( k == STD_VECTOR_MAT ) { const vector& v = *(const vector*)obj; CV_Assert( 0 <= i && i < (int)v.size() ); - + return v[i]; - } + } } - - + + void _InputArray::getMatVector(vector& mv) const { int k = kind(); - + if( k == MAT ) { const Mat& m = *(const Mat*)obj; int i, n = (int)m.size[0]; mv.resize(n); - + for( i = 0; i < n; i++ ) mv[i] = m.dims == 2 ? Mat(1, m.cols, m.type(), (void*)m.ptr(i)) : Mat(m.dims-1, &m.size[1], m.type(), (void*)m.ptr(i), &m.step[1]); return; } - + if( k == EXPR ) { Mat m = *(const MatExpr*)obj; int i, n = m.size[0]; mv.resize(n); - + for( i = 0; i < n; i++ ) mv[i] = m.row(i); return; } - + if( k == MATX ) { size_t i, n = sz.height, esz = CV_ELEM_SIZE(flags); mv.resize(n); - + for( i = 0; i < n; i++ ) mv[i] = Mat(1, sz.width, CV_MAT_TYPE(flags), (uchar*)obj + esz*sz.width*i); return; } - + if( k == STD_VECTOR ) { const vector& v = *(const vector*)obj; - + size_t i, n = v.size(), esz = CV_ELEM_SIZE(flags); int t = CV_MAT_DEPTH(flags), cn = CV_MAT_CN(flags); mv.resize(n); - + for( i = 0; i < n; i++ ) mv[i] = Mat(1, cn, t, (void*)(&v[0] + esz*i)); return; } - + if( k == NONE ) { mv.clear(); return; } - + if( k == STD_VECTOR_VECTOR ) { const vector >& vv = *(const vector >*)obj; int i, n = (int)vv.size(); int t = CV_MAT_TYPE(flags); mv.resize(n); - + for( i = 0; i < n; i++ ) { const vector& v = vv[i]; @@ -1052,7 +1055,7 @@ void _InputArray::getMatVector(vector& mv) const } return; } - + CV_Assert( k == STD_VECTOR_MAT ); //if( k == STD_VECTOR_MAT ) { @@ -1098,34 +1101,34 @@ gpu::GpuMat _InputArray::getGpuMat() const return *d_mat; } } - + int _InputArray::kind() const { return flags & KIND_MASK; } - + Size _InputArray::size(int i) const { int k = kind(); - + if( k == MAT ) { CV_Assert( i < 0 ); return ((const Mat*)obj)->size(); } - + if( k == EXPR ) { CV_Assert( i < 0 ); return ((const MatExpr*)obj)->size(); } - + if( k == MATX ) { CV_Assert( i < 0 ); return sz; } - + if( k == STD_VECTOR ) { CV_Assert( i < 0 ); @@ -1134,10 +1137,10 @@ Size _InputArray::size(int i) const size_t szb = v.size(), szi = iv.size(); return szb == szi ? Size((int)szb, 1) : Size((int)(szb/CV_ELEM_SIZE(flags)), 1); } - + if( k == NONE ) return Size(); - + if( k == STD_VECTOR_VECTOR ) { const vector >& vv = *(const vector >*)obj; @@ -1145,18 +1148,18 @@ Size _InputArray::size(int i) const return vv.empty() ? Size() : Size((int)vv.size(), 1); CV_Assert( i < (int)vv.size() ); const vector >& ivv = *(const vector >*)obj; - + size_t szb = vv[i].size(), szi = ivv[i].size(); return szb == szi ? Size((int)szb, 1) : Size((int)(szb/CV_ELEM_SIZE(flags)), 1); } - + if( k == STD_VECTOR_MAT ) { const vector& vv = *(const vector*)obj; if( i < 0 ) return vv.empty() ? Size() : Size((int)vv.size(), 1); CV_Assert( i < (int)vv.size() ); - + return vv[i].size(); } @@ -1187,106 +1190,109 @@ size_t _InputArray::total(int i) const { return size(i).area(); } - + int _InputArray::type(int i) const { int k = kind(); - + if( k == MAT ) return ((const Mat*)obj)->type(); - + if( k == EXPR ) return ((const MatExpr*)obj)->type(); - + if( k == MATX || k == STD_VECTOR || k == STD_VECTOR_VECTOR ) return CV_MAT_TYPE(flags); - + if( k == NONE ) return -1; - + if( k == STD_VECTOR_MAT ) { const vector& vv = *(const vector*)obj; CV_Assert( i < (int)vv.size() ); - + return vv[i >= 0 ? i : 0].type(); } - + if( k == OPENGL_BUFFER ) return ((const GlBuffer*)obj)->type(); - + if( k == OPENGL_TEXTURE ) return ((const GlTexture*)obj)->type(); - + CV_Assert( k == GPU_MAT ); //if( k == GPU_MAT ) return ((const gpu::GpuMat*)obj)->type(); } - + int _InputArray::depth(int i) const { return CV_MAT_DEPTH(type(i)); } - + int _InputArray::channels(int i) const { return CV_MAT_CN(type(i)); } - + bool _InputArray::empty() const { int k = kind(); - + if( k == MAT ) return ((const Mat*)obj)->empty(); - + if( k == EXPR ) return false; - + if( k == MATX ) return false; - + if( k == STD_VECTOR ) { const vector& v = *(const vector*)obj; return v.empty(); } - + if( k == NONE ) return true; - + if( k == STD_VECTOR_VECTOR ) { const vector >& vv = *(const vector >*)obj; return vv.empty(); } - + if( k == STD_VECTOR_MAT ) { const vector& vv = *(const vector*)obj; return vv.empty(); } - + if( k == OPENGL_BUFFER ) return ((const GlBuffer*)obj)->empty(); - + if( k == OPENGL_TEXTURE ) return ((const GlTexture*)obj)->empty(); - + CV_Assert( k == GPU_MAT ); //if( k == GPU_MAT ) return ((const gpu::GpuMat*)obj)->empty(); } - - + + _OutputArray::_OutputArray() {} +#ifdef OPENCV_CAN_BREAK_BINARY_COMPATIBILITY +_OutputArray::~_OutputArray() {} +#endif _OutputArray::_OutputArray(Mat& m) : _InputArray(m) {} _OutputArray::_OutputArray(vector& vec) : _InputArray(vec) {} _OutputArray::_OutputArray(const Mat& m) : _InputArray(m) {flags |= FIXED_SIZE|FIXED_TYPE;} _OutputArray::_OutputArray(const vector& vec) : _InputArray(vec) {flags |= FIXED_SIZE;} - + bool _OutputArray::fixedSize() const { return (flags & FIXED_SIZE) == FIXED_SIZE; @@ -1296,40 +1302,40 @@ bool _OutputArray::fixedType() const { return (flags & FIXED_TYPE) == FIXED_TYPE; } - -void _OutputArray::create(Size _sz, int type, int i, bool allowTransposed, int fixedDepthMask) const + +void _OutputArray::create(Size _sz, int mtype, int i, bool allowTransposed, int fixedDepthMask) const { int k = kind(); if( k == MAT && i < 0 && !allowTransposed && fixedDepthMask == 0 ) { CV_Assert(!fixedSize() || ((Mat*)obj)->size.operator()() == _sz); - CV_Assert(!fixedType() || ((Mat*)obj)->type() == type); - ((Mat*)obj)->create(_sz, type); + CV_Assert(!fixedType() || ((Mat*)obj)->type() == mtype); + ((Mat*)obj)->create(_sz, mtype); return; } - int sz[] = {_sz.height, _sz.width}; - create(2, sz, type, i, allowTransposed, fixedDepthMask); + int sizes[] = {_sz.height, _sz.width}; + create(2, sizes, mtype, i, allowTransposed, fixedDepthMask); } -void _OutputArray::create(int rows, int cols, int type, int i, bool allowTransposed, int fixedDepthMask) const +void _OutputArray::create(int rows, int cols, int mtype, int i, bool allowTransposed, int fixedDepthMask) const { int k = kind(); if( k == MAT && i < 0 && !allowTransposed && fixedDepthMask == 0 ) { CV_Assert(!fixedSize() || ((Mat*)obj)->size.operator()() == Size(cols, rows)); - CV_Assert(!fixedType() || ((Mat*)obj)->type() == type); - ((Mat*)obj)->create(rows, cols, type); + CV_Assert(!fixedType() || ((Mat*)obj)->type() == mtype); + ((Mat*)obj)->create(rows, cols, mtype); return; } - int sz[] = {rows, cols}; - create(2, sz, type, i, allowTransposed, fixedDepthMask); + int sizes[] = {rows, cols}; + create(2, sizes, mtype, i, allowTransposed, fixedDepthMask); } - -void _OutputArray::create(int dims, const int* size, int type, int i, bool allowTransposed, int fixedDepthMask) const + +void _OutputArray::create(int dims, const int* sizes, int mtype, int i, bool allowTransposed, int fixedDepthMask) const { int k = kind(); - type = CV_MAT_TYPE(type); - + mtype = CV_MAT_TYPE(mtype); + if( k == MAT ) { CV_Assert( i < 0 ); @@ -1341,45 +1347,45 @@ void _OutputArray::create(int dims, const int* size, int type, int i, bool allow CV_Assert(!fixedType() && !fixedSize()); m.release(); } - + if( dims == 2 && m.dims == 2 && m.data && - m.type() == type && m.rows == size[1] && m.cols == size[0] ) + m.type() == mtype && m.rows == sizes[1] && m.cols == sizes[0] ) return; } if(fixedType()) { - if(CV_MAT_CN(type) == m.channels() && ((1 << CV_MAT_TYPE(flags)) & fixedDepthMask) != 0 ) - type = m.type(); + if(CV_MAT_CN(mtype) == m.channels() && ((1 << CV_MAT_TYPE(flags)) & fixedDepthMask) != 0 ) + mtype = m.type(); else - CV_Assert(CV_MAT_TYPE(type) == m.type()); + CV_Assert(CV_MAT_TYPE(mtype) == m.type()); } if(fixedSize()) { CV_Assert(m.dims == dims); for(int j = 0; j < dims; ++j) - CV_Assert(m.size[j] == size[j]); + CV_Assert(m.size[j] == sizes[j]); } - m.create(dims, size, type); + m.create(dims, sizes, mtype); return; } - + if( k == MATX ) { CV_Assert( i < 0 ); int type0 = CV_MAT_TYPE(flags); - CV_Assert( type == type0 || (CV_MAT_CN(type) == 1 && ((1 << type0) & fixedDepthMask) != 0) ); - CV_Assert( dims == 2 && ((size[0] == sz.height && size[1] == sz.width) || - (allowTransposed && size[0] == sz.width && size[1] == sz.height))); + CV_Assert( mtype == type0 || (CV_MAT_CN(mtype) == 1 && ((1 << type0) & fixedDepthMask) != 0) ); + CV_Assert( dims == 2 && ((sizes[0] == sz.height && sizes[1] == sz.width) || + (allowTransposed && sizes[0] == sz.width && sizes[1] == sz.height))); return; } - + if( k == STD_VECTOR || k == STD_VECTOR_VECTOR ) { - CV_Assert( dims == 2 && (size[0] == 1 || size[1] == 1 || size[0]*size[1] == 0) ); - size_t len = size[0]*size[1] > 0 ? size[0] + size[1] - 1 : 0; + CV_Assert( dims == 2 && (sizes[0] == 1 || sizes[1] == 1 || sizes[0]*sizes[1] == 0) ); + size_t len = sizes[0]*sizes[1] > 0 ? sizes[0] + sizes[1] - 1 : 0; vector* v = (vector*)obj; - + if( k == STD_VECTOR_VECTOR ) { vector >& vv = *(vector >*)obj; @@ -1394,10 +1400,10 @@ void _OutputArray::create(int dims, const int* size, int type, int i, bool allow } else CV_Assert( i < 0 ); - + int type0 = CV_MAT_TYPE(flags); - CV_Assert( type == type0 || (CV_MAT_CN(type) == CV_MAT_CN(type0) && ((1 << type0) & fixedDepthMask) != 0) ); - + CV_Assert( mtype == type0 || (CV_MAT_CN(mtype) == CV_MAT_CN(type0) && ((1 << type0) & fixedDepthMask) != 0) ); + int esz = CV_ELEM_SIZE(type0); CV_Assert(!fixedSize() || len == ((vector*)v)->size() / esz); switch( esz ) @@ -1455,42 +1461,42 @@ void _OutputArray::create(int dims, const int* size, int type, int i, bool allow } return; } - + if( k == NONE ) { - CV_Error(CV_StsNullPtr, "create() called for the missing output array" ); + CV_Error(CV_StsNullPtr, "create() called for the missing output array" ); return; } - + CV_Assert( k == STD_VECTOR_MAT ); //if( k == STD_VECTOR_MAT ) { vector& v = *(vector*)obj; - + if( i < 0 ) { - CV_Assert( dims == 2 && (size[0] == 1 || size[1] == 1 || size[0]*size[1] == 0) ); - size_t len = size[0]*size[1] > 0 ? size[0] + size[1] - 1 : 0, len0 = v.size(); - + CV_Assert( dims == 2 && (sizes[0] == 1 || sizes[1] == 1 || sizes[0]*sizes[1] == 0) ); + size_t len = sizes[0]*sizes[1] > 0 ? sizes[0] + sizes[1] - 1 : 0, len0 = v.size(); + CV_Assert(!fixedSize() || len == len0); v.resize(len); if( fixedType() ) { - int type = CV_MAT_TYPE(flags); + int _type = CV_MAT_TYPE(flags); for( size_t j = len0; j < len; j++ ) { - if( v[i].type() == type ) + if( v[i].type() == _type ) continue; CV_Assert( v[i].empty() ); - v[i].flags = (v[i].flags & ~CV_MAT_TYPE_MASK) | type; + v[i].flags = (v[i].flags & ~CV_MAT_TYPE_MASK) | _type; } } return; } - + CV_Assert( i < (int)v.size() ); Mat& m = v[i]; - + if( allowTransposed ) { if( !m.isContinuous() ) @@ -1498,78 +1504,78 @@ void _OutputArray::create(int dims, const int* size, int type, int i, bool allow CV_Assert(!fixedType() && !fixedSize()); m.release(); } - + if( dims == 2 && m.dims == 2 && m.data && - m.type() == type && m.rows == size[1] && m.cols == size[0] ) + m.type() == mtype && m.rows == sizes[1] && m.cols == sizes[0] ) return; } if(fixedType()) { - if(CV_MAT_CN(type) == m.channels() && ((1 << CV_MAT_TYPE(flags)) & fixedDepthMask) != 0 ) - type = m.type(); + if(CV_MAT_CN(mtype) == m.channels() && ((1 << CV_MAT_TYPE(flags)) & fixedDepthMask) != 0 ) + mtype = m.type(); else - CV_Assert(!fixedType() || (CV_MAT_CN(type) == m.channels() && ((1 << CV_MAT_TYPE(flags)) & fixedDepthMask) != 0)); + CV_Assert(!fixedType() || (CV_MAT_CN(mtype) == m.channels() && ((1 << CV_MAT_TYPE(flags)) & fixedDepthMask) != 0)); } if(fixedSize()) { CV_Assert(m.dims == dims); for(int j = 0; j < dims; ++j) - CV_Assert(m.size[j] == size[j]); + CV_Assert(m.size[j] == sizes[j]); } - m.create(dims, size, type); + m.create(dims, sizes, mtype); } } - + void _OutputArray::release() const { CV_Assert(!fixedSize()); int k = kind(); - + if( k == MAT ) { ((Mat*)obj)->release(); return; } - + if( k == NONE ) return; - + if( k == STD_VECTOR ) { create(Size(), CV_MAT_TYPE(flags)); return; } - + if( k == STD_VECTOR_VECTOR ) { ((vector >*)obj)->clear(); return; } - + CV_Assert( k == STD_VECTOR_MAT ); //if( k == STD_VECTOR_MAT ) { ((vector*)obj)->clear(); - } + } } void _OutputArray::clear() const { int k = kind(); - + if( k == MAT ) { CV_Assert(!fixedSize()); ((Mat*)obj)->resize(0); return; } - + release(); } - + bool _OutputArray::needed() const { return kind() != NONE; @@ -1594,7 +1600,7 @@ Mat& _OutputArray::getMatRef(int i) const static _OutputArray _none; OutputArray noArray() { return _none; } - + } /*************************************************************************************************\ @@ -1608,7 +1614,7 @@ void cv::hconcat(const Mat* src, size_t nsrc, OutputArray _dst) _dst.release(); return; } - + int totalCols = 0, cols = 0; size_t i; for( i = 0; i < nsrc; i++ ) @@ -1627,13 +1633,13 @@ void cv::hconcat(const Mat* src, size_t nsrc, OutputArray _dst) cols += src[i].cols; } } - + void cv::hconcat(InputArray src1, InputArray src2, OutputArray dst) { Mat src[] = {src1.getMat(), src2.getMat()}; hconcat(src, 2, dst); } - + void cv::hconcat(InputArray _src, OutputArray dst) { vector src; @@ -1648,7 +1654,7 @@ void cv::vconcat(const Mat* src, size_t nsrc, OutputArray _dst) _dst.release(); return; } - + int totalRows = 0, rows = 0; size_t i; for( i = 0; i < nsrc; i++ ) @@ -1667,12 +1673,12 @@ void cv::vconcat(const Mat* src, size_t nsrc, OutputArray _dst) rows += src[i].rows; } } - + void cv::vconcat(InputArray src1, InputArray src2, OutputArray dst) { Mat src[] = {src1.getMat(), src2.getMat()}; vconcat(src, 2, dst); -} +} void cv::vconcat(InputArray _src, OutputArray dst) { @@ -1680,14 +1686,14 @@ void cv::vconcat(InputArray _src, OutputArray dst) _src.getMatVector(src); vconcat(!src.empty() ? &src[0] : 0, src.size(), dst); } - + //////////////////////////////////////// set identity //////////////////////////////////////////// void cv::setIdentity( InputOutputArray _m, const Scalar& s ) { Mat m = _m.getMat(); CV_Assert( m.dims <= 2 ); int i, j, rows = m.rows, cols = m.cols, type = m.type(); - + if( type == CV_32FC1 ) { float* data = (float*)m.data; @@ -1721,15 +1727,15 @@ void cv::setIdentity( InputOutputArray _m, const Scalar& s ) } } -//////////////////////////////////////////// trace /////////////////////////////////////////// - +//////////////////////////////////////////// trace /////////////////////////////////////////// + cv::Scalar cv::trace( InputArray _m ) { Mat m = _m.getMat(); CV_Assert( m.dims <= 2 ); int i, type = m.type(); int nm = std::min(m.rows, m.cols); - + if( type == CV_32FC1 ) { const float* ptr = (const float*)m.data; @@ -1739,7 +1745,7 @@ cv::Scalar cv::trace( InputArray _m ) _s += ptr[i*step]; return _s; } - + if( type == CV_64FC1 ) { const double* ptr = (const double*)m.data; @@ -1749,7 +1755,7 @@ cv::Scalar cv::trace( InputArray _m ) _s += ptr[i*step]; return _s; } - + return cv::sum(m.diag()); } @@ -1763,27 +1769,27 @@ transpose_( const uchar* src, size_t sstep, uchar* dst, size_t dstep, Size sz ) { int i=0, j, m = sz.width, n = sz.height; - #if CV_ENABLE_UNROLLED + #if CV_ENABLE_UNROLLED for(; i <= m - 4; i += 4 ) { T* d0 = (T*)(dst + dstep*i); T* d1 = (T*)(dst + dstep*(i+1)); T* d2 = (T*)(dst + dstep*(i+2)); T* d3 = (T*)(dst + dstep*(i+3)); - + for( j = 0; j <= n - 4; j += 4 ) { const T* s0 = (const T*)(src + i*sizeof(T) + sstep*j); const T* s1 = (const T*)(src + i*sizeof(T) + sstep*(j+1)); const T* s2 = (const T*)(src + i*sizeof(T) + sstep*(j+2)); const T* s3 = (const T*)(src + i*sizeof(T) + sstep*(j+3)); - + d0[j] = s0[0]; d0[j+1] = s1[0]; d0[j+2] = s2[0]; d0[j+3] = s3[0]; d1[j] = s0[1]; d1[j+1] = s1[1]; d1[j+2] = s2[1]; d1[j+3] = s3[1]; d2[j] = s0[2]; d2[j+1] = s1[2]; d2[j+2] = s2[2]; d2[j+3] = s3[2]; d3[j] = s0[3]; d3[j+1] = s1[3]; d3[j+2] = s2[3]; d3[j+3] = s3[3]; } - + for( ; j < n; j++ ) { const T* s0 = (const T*)(src + i*sizeof(T) + j*sstep); @@ -1795,14 +1801,14 @@ transpose_( const uchar* src, size_t sstep, uchar* dst, size_t dstep, Size sz ) { T* d0 = (T*)(dst + dstep*i); j = 0; - #if CV_ENABLE_UNROLLED + #if CV_ENABLE_UNROLLED for(; j <= n - 4; j += 4 ) { const T* s0 = (const T*)(src + i*sizeof(T) + sstep*j); const T* s1 = (const T*)(src + i*sizeof(T) + sstep*(j+1)); const T* s2 = (const T*)(src + i*sizeof(T) + sstep*(j+2)); const T* s3 = (const T*)(src + i*sizeof(T) + sstep*(j+3)); - + d0[j] = s0[0]; d0[j+1] = s1[0]; d0[j+2] = s2[0]; d0[j+3] = s3[0]; } #endif @@ -1826,10 +1832,10 @@ transposeI_( uchar* data, size_t step, int n ) std::swap( row[j], *(T*)(data1 + step*j) ); } } - + typedef void (*TransposeFunc)( const uchar* src, size_t sstep, uchar* dst, size_t dstep, Size sz ); typedef void (*TransposeInplaceFunc)( uchar* data, size_t step, int n ); - + #define DEF_TRANSPOSE_FUNC(suffix, type) \ static void transpose_##suffix( const uchar* src, size_t sstep, uchar* dst, size_t dstep, Size sz ) \ { transpose_(src, sstep, dst, dstep, sz); } \ @@ -1863,7 +1869,7 @@ static TransposeInplaceFunc transposeInplaceTab[] = }; } - + void cv::transpose( InputArray _src, OutputArray _dst ) { Mat src = _src.getMat(); @@ -1872,7 +1878,7 @@ void cv::transpose( InputArray _src, OutputArray _dst ) _dst.create(src.cols, src.rows, src.type()); Mat dst = _dst.getMat(); - + if( dst.data == src.data ) { TransposeInplaceFunc func = transposeInplaceTab[esz]; @@ -1892,7 +1898,7 @@ void cv::completeSymm( InputOutputArray _m, bool LtoR ) { Mat m = _m.getMat(); CV_Assert( m.dims <= 2 ); - + int i, j, nrows = m.rows, type = m.type(); int j0 = 0, j1 = nrows; CV_Assert( m.rows == m.cols ); @@ -1923,14 +1929,14 @@ void cv::completeSymm( InputOutputArray _m, bool LtoR ) CV_Error( CV_StsUnsupportedFormat, "" ); } - + cv::Mat cv::Mat::cross(InputArray _m) const { Mat m = _m.getMat(); - int t = type(), d = CV_MAT_DEPTH(t); - CV_Assert( dims <= 2 && m.dims <= 2 && size() == m.size() && t == m.type() && + int tp = type(), d = CV_MAT_DEPTH(tp); + CV_Assert( dims <= 2 && m.dims <= 2 && size() == m.size() && tp == m.type() && ((rows == 3 && cols == 1) || (cols*channels() == 3 && rows == 1))); - Mat result(rows, cols, t); + Mat result(rows, cols, tp); if( d == CV_32F ) { @@ -1985,7 +1991,7 @@ reduceR_( const Mat& srcmat, Mat& dstmat ) { src += srcstep; i = 0; - #if CV_ENABLE_UNROLLED + #if CV_ENABLE_UNROLLED for(; i <= size.width - 4; i += 4 ) { WT s0, s1; @@ -2044,7 +2050,7 @@ reduceC_( const Mat& srcmat, Mat& dstmat ) dst[k] = (ST)a0; } } - } + } } typedef void (*ReduceFunc)( const Mat& src, Mat& dst ); @@ -2110,7 +2116,7 @@ void cv::reduce(InputArray _src, OutputArray _dst, int dim, int op, int dtype) _dst.create(dim == 0 ? 1 : src.rows, dim == 0 ? src.cols : 1, CV_MAKETYPE(dtype >= 0 ? dtype : stype, cn)); Mat dst = _dst.getMat(), temp = dst; - + CV_Assert( op == CV_REDUCE_SUM || op == CV_REDUCE_MAX || op == CV_REDUCE_MIN || op == CV_REDUCE_AVG ); CV_Assert( src.channels() == dst.channels() ); @@ -2240,8 +2246,8 @@ void cv::reduce(InputArray _src, OutputArray _dst, int dim, int op, int dtype) if( op0 == CV_REDUCE_AVG ) temp.convertTo(dst, dst.type(), 1./(dim == 0 ? src.rows : src.cols)); } - - + + //////////////////////////////////////// sort /////////////////////////////////////////// namespace cv @@ -2255,7 +2261,7 @@ template static void sort_( const Mat& src, Mat& dst, int flags ) bool sortRows = (flags & 1) == CV_SORT_EVERY_ROW; bool inplace = src.data == dst.data; bool sortDescending = (flags & CV_SORT_DESCENDING) != 0; - + if( sortRows ) n = src.rows, len = src.cols; else @@ -2306,7 +2312,7 @@ template static void sortIdx_( const Mat& src, Mat& dst, int flags ) bool sortDescending = (flags & CV_SORT_DESCENDING) != 0; CV_Assert( src.data != dst.data ); - + if( sortRows ) n = src.rows, len = src.cols; else @@ -2348,7 +2354,7 @@ template static void sortIdx_( const Mat& src, Mat& dst, int flags ) typedef void (*SortFunc)(const Mat& src, Mat& dst, int flags); } - + void cv::sort( InputArray _src, OutputArray _dst, int flags ) { static SortFunc tab[] = @@ -2374,7 +2380,7 @@ void cv::sortIdx( InputArray _src, OutputArray _dst, int flags ) Mat src = _src.getMat(); SortFunc func = tab[src.depth()]; CV_Assert( src.dims <= 2 && src.channels() == 1 && func != 0 ); - + Mat dst = _dst.getMat(); if( dst.data == src.data ) _dst.release(); @@ -2382,8 +2388,8 @@ void cv::sortIdx( InputArray _src, OutputArray _dst, int flags ) dst = _dst.getMat(); func( src, dst, flags ); } - - + + ////////////////////////////////////////// kmeans //////////////////////////////////////////// namespace cv @@ -2421,7 +2427,7 @@ static void generateCentersPP(const Mat& _data, Mat& _out_centers, dist[i] = normL2Sqr_(data + step*i, data + step*centers[0], dims); sum0 += dist[i]; } - + for( k = 1; k < K; k++ ) { double bestSum = DBL_MAX; @@ -2439,7 +2445,7 @@ static void generateCentersPP(const Mat& _data, Mat& _out_centers, tdist2[i] = std::min(normL2Sqr_(data + step*i, data + step*ci, dims), dist[i]); s += tdist2[i]; } - + if( s < bestSum ) { bestSum = s; @@ -2462,7 +2468,7 @@ static void generateCentersPP(const Mat& _data, Mat& _out_centers, } } - + double cv::kmeans( InputArray _data, int K, InputOutputArray _bestLabels, TermCriteria criteria, int attempts, @@ -2480,7 +2486,7 @@ double cv::kmeans( InputArray _data, int K, CV_Assert( N >= K ); _bestLabels.create(N, 1, CV_32S, -1, true); - + Mat _labels, best_labels = _bestLabels.getMat(); if( flags & CV_KMEANS_USE_INITIAL_LABELS ) { @@ -2566,7 +2572,7 @@ double cv::kmeans( InputArray _data, int K, for( i = 0; i < N; i++ ) CV_Assert( (unsigned)labels[i] < (unsigned)K ); } - + // compute centers centers = Scalar(0); for( k = 0; k < K; k++ ) @@ -2577,8 +2583,8 @@ double cv::kmeans( InputArray _data, int K, sample = data.ptr(i); k = labels[i]; float* center = centers.ptr(k); - j=0; - #if CV_ENABLE_UNROLLED + j=0; + #if CV_ENABLE_UNROLLED for(; j <= dims - 4; j += 4 ) { float t0 = center[j] + sample[j]; @@ -2601,7 +2607,7 @@ double cv::kmeans( InputArray _data, int K, if( iter > 0 ) max_center_shift = 0; - + for( k = 0; k < K; k++ ) { if( counters[k] != 0 ) @@ -2617,8 +2623,8 @@ double cv::kmeans( InputArray _data, int K, if( counters[max_k] < counters[k1] ) max_k = k1; } - - double max_dist = 0; + + double max_dist = 0; int farthest_i = -1; float* new_center = centers.ptr(k); float* old_center = centers.ptr(max_k); @@ -2626,26 +2632,26 @@ double cv::kmeans( InputArray _data, int K, float scale = 1.f/counters[max_k]; for( j = 0; j < dims; j++ ) _old_center[j] = old_center[j]*scale; - + for( i = 0; i < N; i++ ) { if( labels[i] != max_k ) continue; sample = data.ptr(i); double dist = normL2Sqr_(sample, _old_center, dims); - + if( max_dist <= dist ) { max_dist = dist; farthest_i = i; } } - + counters[max_k]--; counters[k]++; labels[farthest_i] = k; sample = data.ptr(farthest_i); - + for( j = 0; j < dims; j++ ) { old_center[j] -= sample[j]; @@ -2661,7 +2667,7 @@ double cv::kmeans( InputArray _data, int K, float scale = 1.f/counters[k]; for( j = 0; j < dims; j++ ) center[j] *= scale; - + if( iter > 0 ) { double dist = 0; @@ -2675,7 +2681,7 @@ double cv::kmeans( InputArray _data, int K, } } } - + if( ++iter == MAX(criteria.maxCount, 2) || max_center_shift <= criteria.epsilon ) break; @@ -2759,7 +2765,7 @@ CV_IMPL void cvReduce( const CvArr* srcarr, CvArr* dstarr, int dim, int op ) { cv::Mat src = cv::cvarrToMat(srcarr), dst = cv::cvarrToMat(dstarr); - + if( dim < 0 ) dim = src.rows > dst.rows ? 0 : src.cols > dst.cols ? 1 : dst.cols == 1; @@ -2769,7 +2775,7 @@ cvReduce( const CvArr* srcarr, CvArr* dstarr, int dim, int op ) if( (dim == 0 && (dst.cols != src.cols || dst.rows != 1)) || (dim == 1 && (dst.rows != src.rows || dst.cols != 1)) ) CV_Error( CV_StsBadSize, "The output array size is incorrect" ); - + if( src.channels() != dst.channels() ) CV_Error( CV_StsUnmatchedFormats, "Input and output arrays must have the same number of channels" ); @@ -2781,14 +2787,14 @@ CV_IMPL CvArr* cvRange( CvArr* arr, double start, double end ) { int ok = 0; - + CvMat stub, *mat = (CvMat*)arr; double delta; int type, step; double val = start; int i, j; int rows, cols; - + if( !CV_IS_MAT(mat) ) mat = cvGetMat( mat, &stub); @@ -2843,8 +2849,8 @@ cvRange( CvArr* arr, double start, double end ) CV_IMPL void cvSort( const CvArr* _src, CvArr* _dst, CvArr* _idx, int flags ) { - cv::Mat src = cv::cvarrToMat(_src), dst, idx; - + cv::Mat src = cv::cvarrToMat(_src); + if( _idx ) { cv::Mat idx0 = cv::cvarrToMat(_idx), idx = idx0; @@ -2884,7 +2890,7 @@ cvKMeans2( const CvArr* _samples, int cluster_count, CvArr* _labels, CV_Assert( labels.isContinuous() && labels.type() == CV_32S && (labels.cols == 1 || labels.rows == 1) && labels.cols + labels.rows - 1 == data.rows ); - + double compactness = cv::kmeans(data, cluster_count, labels, termcrit, attempts, flags, _centers ? cv::_OutputArray(centers) : cv::_OutputArray() ); if( _compactness ) @@ -2932,26 +2938,26 @@ NAryMatIterator::NAryMatIterator(const Mat** _arrays, Mat* _planes, int _narrays : arrays(0), planes(0), ptrs(0), narrays(0), nplanes(0), size(0), iterdepth(0), idx(0) { init(_arrays, _planes, 0, _narrays); -} - +} + NAryMatIterator::NAryMatIterator(const Mat** _arrays, uchar** _ptrs, int _narrays) : arrays(0), planes(0), ptrs(0), narrays(0), nplanes(0), size(0), iterdepth(0), idx(0) { init(_arrays, 0, _ptrs, _narrays); } - + void NAryMatIterator::init(const Mat** _arrays, Mat* _planes, uchar** _ptrs, int _narrays) { CV_Assert( _arrays && (_ptrs || _planes) ); int i, j, d1=0, i0 = -1, d = -1; - + arrays = _arrays; ptrs = _ptrs; planes = _planes; narrays = _narrays; nplanes = 0; size = 0; - + if( narrays < 0 ) { for( i = 0; _arrays[i] != 0; i++ ) @@ -2968,15 +2974,15 @@ void NAryMatIterator::init(const Mat** _arrays, Mat* _planes, uchar** _ptrs, int const Mat& A = *arrays[i]; if( ptrs ) ptrs[i] = A.data; - + if( !A.data ) continue; - + if( i0 < 0 ) { i0 = i; d = A.dims; - + // find the first dimensionality which is different from 1; // in any of the arrays the first "d1" step do not affect the continuity for( d1 = 0; d1 < d; d1++ ) @@ -3010,16 +3016,16 @@ void NAryMatIterator::init(const Mat** _arrays, Mat* _planes, uchar** _ptrs, int iterdepth = j; if( iterdepth == d1 ) iterdepth = 0; - + nplanes = 1; for( j = iterdepth-1; j >= 0; j-- ) nplanes *= arrays[i0]->size[j]; } else iterdepth = 0; - + idx = 0; - + if( !planes ) return; @@ -3027,14 +3033,14 @@ void NAryMatIterator::init(const Mat** _arrays, Mat* _planes, uchar** _ptrs, int { CV_Assert(arrays[i] != 0); const Mat& A = *arrays[i]; - + if( !A.data ) { planes[i] = Mat(); continue; } - - planes[i] = Mat(1, (int)size, A.type(), A.data); + + planes[i] = Mat(1, (int)size, A.type(), A.data); } } @@ -3044,7 +3050,7 @@ NAryMatIterator& NAryMatIterator::operator ++() if( idx >= nplanes-1 ) return *this; ++idx; - + if( iterdepth == 1 ) { if( ptrs ) @@ -3087,7 +3093,7 @@ NAryMatIterator& NAryMatIterator::operator ++() planes[i].data = data; } } - + return *this; } @@ -3107,7 +3113,7 @@ Point MatConstIterator::pos() const if( !m ) return Point(); CV_DbgAssert(m->dims <= 2); - + ptrdiff_t ofs = ptr - m->data; int y = (int)(ofs/m->step[0]); return Point((int)((ofs - y*m->step[0])/elemSize), y); @@ -3147,7 +3153,7 @@ ptrdiff_t MatConstIterator::lpos() const } return result; } - + void MatConstIterator::seek(ptrdiff_t ofs, bool relative) { if( m->isContinuous() ) @@ -3159,7 +3165,7 @@ void MatConstIterator::seek(ptrdiff_t ofs, bool relative) ptr = sliceEnd; return; } - + int d = m->dims; if( d == 2 ) { @@ -3178,20 +3184,20 @@ void MatConstIterator::seek(ptrdiff_t ofs, bool relative) sliceStart + (ofs - y*m->cols)*elemSize; return; } - + if( relative ) ofs += lpos(); - + if( ofs < 0 ) ofs = 0; - + int szi = m->size[d-1]; ptrdiff_t t = ofs/szi; int v = (int)(ofs - t*szi); ofs = t; ptr = m->data + v*elemSize; sliceStart = m->data; - + for( int i = d-2; i >= 0; i-- ) { szi = m->size[i]; @@ -3200,14 +3206,14 @@ void MatConstIterator::seek(ptrdiff_t ofs, bool relative) ofs = t; sliceStart += v*m->step[i]; } - + sliceEnd = sliceStart + m->size[d-1]*elemSize; if( ofs > 0 ) ptr = sliceEnd; else ptr = sliceStart + (ptr - m->data); } - + void MatConstIterator::seek(const int* _idx, bool relative) { int i, d = m->dims; @@ -3232,8 +3238,8 @@ ptrdiff_t operator - (const MatConstIterator& b, const MatConstIterator& a) return (b.ptr - a.ptr)/b.elemSize; return b.lpos() - a.lpos(); -} - +} + //////////////////////////////// SparseMat //////////////////////////////// template void @@ -3260,7 +3266,7 @@ convertScaleData_(const void* _from, void* _to, int cn, double alpha, double bet to[i] = saturate_cast(from[i]*alpha + beta); } -ConvertData getConvertData(int fromType, int toType) +static ConvertData getConvertData(int fromType, int toType) { static ConvertData tab[][8] = {{ convertData_, convertData_, @@ -3305,7 +3311,7 @@ ConvertData getConvertData(int fromType, int toType) return func; } -ConvertScaleData getConvertScaleData(int fromType, int toType) +static ConvertScaleData getConvertScaleData(int fromType, int toType) { static ConvertScaleData tab[][8] = {{ convertScaleData_, convertScaleData_, @@ -3382,7 +3388,7 @@ SparseMat::Hdr::Hdr( int _dims, const int* _sizes, int _type ) sizeof(int)*std::max(dims - CV_MAX_DIM, 0), CV_ELEM_SIZE1(_type)); nodeSize = alignSize(valueOffset + CV_ELEM_SIZE(_type), (int)sizeof(size_t)); - + int i; for( i = 0; i < dims; i++ ) size[i] = _sizes[i]; @@ -3408,22 +3414,22 @@ SparseMat::SparseMat(const Mat& m) int i, idx[CV_MAX_DIM] = {0}, d = m.dims, lastSize = m.size[d - 1]; size_t esz = m.elemSize(); - uchar* ptr = m.data; + uchar* dptr = m.data; for(;;) { - for( i = 0; i < lastSize; i++, ptr += esz ) + for( i = 0; i < lastSize; i++, dptr += esz ) { - if( isZeroElem(ptr, esz) ) + if( isZeroElem(dptr, esz) ) continue; idx[d-1] = i; uchar* to = newNode(idx, hash(idx)); - copyElem( ptr, to, esz ); + copyElem( dptr, to, esz ); } - + for( i = d - 2; i >= 0; i-- ) { - ptr += m.step[i] - m.size[i+1]*m.step[i+1]; + dptr += m.step[i] - m.size[i+1]*m.step[i+1]; if( ++idx[i] < m.size[i] ) break; idx[i] = 0; @@ -3432,7 +3438,7 @@ SparseMat::SparseMat(const Mat& m) break; } } - + SparseMat::SparseMat(const CvSparseMat* m) : flags(MAGIC_VAL), hdr(0) { @@ -3525,11 +3531,11 @@ void SparseMat::convertTo( SparseMat& m, int rtype, double alpha ) const m = temp; return; } - + CV_Assert(hdr != 0); if( hdr != m.hdr ) m.create( hdr->dims, hdr->size, rtype ); - + SparseMatConstIterator from = begin(); size_t i, N = nzcount(); @@ -3540,7 +3546,7 @@ void SparseMat::convertTo( SparseMat& m, int rtype, double alpha ) const { const Node* n = from.node(); uchar* to = hdr == m.hdr ? from.ptr : m.newNode(n->idx, n->hashval); - cvtfunc( from.ptr, to, cn ); + cvtfunc( from.ptr, to, cn ); } } else @@ -3550,7 +3556,7 @@ void SparseMat::convertTo( SparseMat& m, int rtype, double alpha ) const { const Node* n = from.node(); uchar* to = hdr == m.hdr ? from.ptr : m.newNode(n->idx, n->hashval); - cvtfunc( from.ptr, to, cn, alpha, 0 ); + cvtfunc( from.ptr, to, cn, alpha, 0 ); } } } @@ -3562,7 +3568,7 @@ void SparseMat::convertTo( Mat& m, int rtype, double alpha, double beta ) const if( rtype < 0 ) rtype = type(); rtype = CV_MAKETYPE(rtype, cn); - + CV_Assert( hdr ); m.create( dims(), hdr->size, rtype ); m = Scalar(beta); @@ -3629,7 +3635,7 @@ uchar* SparseMat::ptr(int i0, bool createMissing, size_t* hashval) return &value(elem); nidx = elem->next; } - + if( createMissing ) { int idx[] = { i0 }; @@ -3637,7 +3643,7 @@ uchar* SparseMat::ptr(int i0, bool createMissing, size_t* hashval) } return 0; } - + uchar* SparseMat::ptr(int i0, int i1, bool createMissing, size_t* hashval) { CV_Assert( hdr && hdr->dims == 2 ); @@ -3810,7 +3816,7 @@ uchar* SparseMat::newNode(const int* idx, size_t hashval) resizeHashTab(std::max(hsize*2, (size_t)8)); hsize = hdr->hashtab.size(); } - + if( !hdr->freeList ) { size_t i, nsz = hdr->nodeSize, psize = hdr->pool.size(), @@ -3841,7 +3847,7 @@ uchar* SparseMat::newNode(const int* idx, size_t hashval) *((double*)p) = 0.; else memset(p, 0, esz); - + return p; } @@ -3913,14 +3919,14 @@ SparseMatConstIterator& SparseMatConstIterator::operator ++() double norm( const SparseMat& src, int normType ) { SparseMatConstIterator it = src.begin(); - + size_t i, N = src.nzcount(); normType &= NORM_TYPE_MASK; int type = src.type(); double result = 0; - + CV_Assert( normType == NORM_INF || normType == NORM_L1 || normType == NORM_L2 ); - + if( type == CV_32F ) { if( normType == NORM_INF ) @@ -3932,7 +3938,7 @@ double norm( const SparseMat& src, int normType ) else for( i = 0; i < N; i++, ++it ) { - double v = *(const float*)it.ptr; + double v = *(const float*)it.ptr; result += v*v; } } @@ -3947,25 +3953,25 @@ double norm( const SparseMat& src, int normType ) else for( i = 0; i < N; i++, ++it ) { - double v = *(const double*)it.ptr; + double v = *(const double*)it.ptr; result += v*v; } } else CV_Error( CV_StsUnsupportedFormat, "Only 32f and 64f are supported" ); - + if( normType == NORM_L2 ) result = std::sqrt(result); return result; } - + void minMaxLoc( const SparseMat& src, double* _minval, double* _maxval, int* _minidx, int* _maxidx ) { SparseMatConstIterator it = src.begin(); size_t i, N = src.nzcount(), d = src.hdr ? src.hdr->dims : 0; int type = src.type(); const int *minidx = 0, *maxidx = 0; - + if( type == CV_32F ) { float minval = FLT_MAX, maxval = -FLT_MAX; @@ -4012,7 +4018,7 @@ void minMaxLoc( const SparseMat& src, double* _minval, double* _maxval, int* _mi } else CV_Error( CV_StsUnsupportedFormat, "Only 32f and 64f are supported" ); - + if( _minidx ) for( i = 0; i < d; i++ ) _minidx[i] = minidx[i]; @@ -4021,7 +4027,7 @@ void minMaxLoc( const SparseMat& src, double* _minval, double* _maxval, int* _mi _maxidx[i] = maxidx[i]; } - + void normalize( const SparseMat& src, SparseMat& dst, double a, int norm_type ) { double scale = 1; @@ -4032,18 +4038,18 @@ void normalize( const SparseMat& src, SparseMat& dst, double a, int norm_type ) } else CV_Error( CV_StsBadArg, "Unknown/unsupported norm type" ); - + src.convertTo( dst, -1, scale ); } ////////////////////// RotatedRect ////////////////////// - + void RotatedRect::points(Point2f pt[]) const { double _angle = angle*CV_PI/180.; float b = (float)cos(_angle)*0.5f; float a = (float)sin(_angle)*0.5f; - + pt[0].x = center.x - a*size.height - b*size.width; pt[0].y = center.y + b*size.height - a*size.width; pt[1].x = center.x + a*size.height - b*size.width; @@ -4065,8 +4071,8 @@ Rect RotatedRect::boundingRect() const r.width -= r.x - 1; r.height -= r.y - 1; return r; -} - } - + +} + /* End of file. */ diff --git a/modules/core/src/opengl_interop.cpp b/modules/core/src/opengl_interop.cpp index de8ad87..08cd0b5 100644 --- a/modules/core/src/opengl_interop.cpp +++ b/modules/core/src/opengl_interop.cpp @@ -163,11 +163,11 @@ void icvSetOpenGlFuncTab(const CvOpenGlFuncTab* tab) void cv::gpu::setGlDevice(int device) { #ifndef HAVE_CUDA - (void)device; + (void)device; throw_nocuda; #else #ifndef HAVE_OPENGL - (void)device; + (void)device; throw_nogl; #else if (!glFuncTab()->isGlContextInitialized()) @@ -287,7 +287,7 @@ class cv::GlBuffer::Impl { public: static const Ptr& empty(); - + Impl(int rows, int cols, int type, unsigned int target); Impl(const Mat& m, unsigned int target); ~Impl(); @@ -311,7 +311,7 @@ public: private: Impl(); - + unsigned int buffer_; #ifdef HAVE_CUDA @@ -484,57 +484,57 @@ inline void cv::GlBuffer::Impl::unmapDevice(cudaStream_t stream) #endif // HAVE_OPENGL -cv::GlBuffer::GlBuffer(Usage usage) : rows_(0), cols_(0), type_(0), usage_(usage) +cv::GlBuffer::GlBuffer(Usage _usage) : rows_(0), cols_(0), type_(0), usage_(_usage) { #ifndef HAVE_OPENGL - (void)usage; + (void)_usage; throw_nogl; #else impl_ = Impl::empty(); #endif } -cv::GlBuffer::GlBuffer(int rows, int cols, int type, Usage usage) : rows_(0), cols_(0), type_(0), usage_(usage) +cv::GlBuffer::GlBuffer(int _rows, int _cols, int _type, Usage _usage) : rows_(0), cols_(0), type_(0), usage_(_usage) { #ifndef HAVE_OPENGL - (void)rows; - (void)cols; - (void)type; - (void)usage; + (void)_rows; + (void)_cols; + (void)_type; + (void)_usage; throw_nogl; #else - impl_ = new Impl(rows, cols, type, usage); - rows_ = rows; - cols_ = cols; - type_ = type; + impl_ = new Impl(_rows, _cols, _type, _usage); + rows_ = _rows; + cols_ = _cols; + type_ = _type; #endif } -cv::GlBuffer::GlBuffer(Size size, int type, Usage usage) : rows_(0), cols_(0), type_(0), usage_(usage) +cv::GlBuffer::GlBuffer(Size _size, int _type, Usage _usage) : rows_(0), cols_(0), type_(0), usage_(_usage) { #ifndef HAVE_OPENGL - (void)size; - (void)type; - (void)usage; + (void)_size; + (void)_type; + (void)_usage; throw_nogl; #else - impl_ = new Impl(size.height, size.width, type, usage); - rows_ = size.height; - cols_ = size.width; - type_ = type; + impl_ = new Impl(_size.height, _size.width, _type, _usage); + rows_ = _size.height; + cols_ = _size.width; + type_ = _type; #endif } -cv::GlBuffer::GlBuffer(InputArray mat_, Usage usage) : rows_(0), cols_(0), type_(0), usage_(usage) +cv::GlBuffer::GlBuffer(InputArray mat_, Usage _usage) : rows_(0), cols_(0), type_(0), usage_(_usage) { #ifndef HAVE_OPENGL - (void)mat_; - (void)usage; + (void)mat_; + (void)_usage; throw_nogl; #else int kind = mat_.kind(); - Size size = mat_.size(); - int type = mat_.type(); + Size _size = mat_.size(); + int _type = mat_.type(); if (kind == _InputArray::GPU_MAT) { @@ -542,38 +542,38 @@ cv::GlBuffer::GlBuffer(InputArray mat_, Usage usage) : rows_(0), cols_(0), type_ throw_nocuda; #else GpuMat d_mat = mat_.getGpuMat(); - impl_ = new Impl(d_mat.rows, d_mat.cols, d_mat.type(), usage); + impl_ = new Impl(d_mat.rows, d_mat.cols, d_mat.type(), _usage); impl_->copyFrom(d_mat); #endif } else { Mat mat = mat_.getMat(); - impl_ = new Impl(mat, usage); + impl_ = new Impl(mat, _usage); } - rows_ = size.height; - cols_ = size.width; - type_ = type; + rows_ = _size.height; + cols_ = _size.width; + type_ = _type; #endif } -void cv::GlBuffer::create(int rows, int cols, int type, Usage usage) +void cv::GlBuffer::create(int _rows, int _cols, int _type, Usage _usage) { #ifndef HAVE_OPENGL - (void)rows; - (void)cols; - (void)type; - (void)usage; + (void)_rows; + (void)_cols; + (void)_type; + (void)_usage; throw_nogl; #else - if (rows_ != rows || cols_ != cols || type_ != type || usage_ != usage) + if (rows_ != _rows || cols_ != _cols || type_ != _type || usage_ != _usage) { - impl_ = new Impl(rows, cols, type, usage); - rows_ = rows; - cols_ = cols; - type_ = type; - usage_ = usage; + impl_ = new Impl(_rows, _cols, _type, _usage); + rows_ = _rows; + cols_ = _cols; + type_ = _type; + usage_ = _usage; } #endif } @@ -590,14 +590,14 @@ void cv::GlBuffer::release() void cv::GlBuffer::copyFrom(InputArray mat_) { #ifndef HAVE_OPENGL - (void)mat_; + (void)mat_; throw_nogl; #else int kind = mat_.kind(); - Size size = mat_.size(); - int type = mat_.type(); + Size _size = mat_.size(); + int _type = mat_.type(); - create(size, type); + create(_size, _type); switch (kind) { @@ -728,7 +728,7 @@ public: private: Impl(); - + GLuint tex_; }; @@ -926,45 +926,45 @@ cv::GlTexture::GlTexture() : rows_(0), cols_(0), type_(0), buf_(GlBuffer::TEXTUR #endif } -cv::GlTexture::GlTexture(int rows, int cols, int type) : rows_(0), cols_(0), type_(0), buf_(GlBuffer::TEXTURE_BUFFER) +cv::GlTexture::GlTexture(int _rows, int _cols, int _type) : rows_(0), cols_(0), type_(0), buf_(GlBuffer::TEXTURE_BUFFER) { #ifndef HAVE_OPENGL - (void)rows; - (void)cols; - (void)type; + (void)_rows; + (void)_cols; + (void)_type; throw_nogl; #else - impl_ = new Impl(rows, cols, type); - rows_ = rows; - cols_ = cols; - type_ = type; + impl_ = new Impl(_rows, _cols, _type); + rows_ = _rows; + cols_ = _cols; + type_ = _type; #endif } -cv::GlTexture::GlTexture(Size size, int type) : rows_(0), cols_(0), type_(0), buf_(GlBuffer::TEXTURE_BUFFER) +cv::GlTexture::GlTexture(Size _size, int _type) : rows_(0), cols_(0), type_(0), buf_(GlBuffer::TEXTURE_BUFFER) { #ifndef HAVE_OPENGL - (void)size; - (void)type; + (void)_size; + (void)_type; throw_nogl; #else - impl_ = new Impl(size.height, size.width, type); - rows_ = size.height; - cols_ = size.width; - type_ = type; + impl_ = new Impl(_size.height, _size.width, _type); + rows_ = _size.height; + cols_ = _size.width; + type_ = _type; #endif } cv::GlTexture::GlTexture(InputArray mat_, bool bgra) : rows_(0), cols_(0), type_(0), buf_(GlBuffer::TEXTURE_BUFFER) { #ifndef HAVE_OPENGL - (void)mat_; - (void)bgra; + (void)mat_; + (void)bgra; throw_nogl; -#else +#else int kind = mat_.kind(); - Size size = mat_.size(); - int type = mat_.type(); + Size _size = mat_.size(); + int _type = mat_.type(); switch (kind) { @@ -994,26 +994,26 @@ cv::GlTexture::GlTexture(InputArray mat_, bool bgra) : rows_(0), cols_(0), type_ } } - rows_ = size.height; - cols_ = size.width; - type_ = type; + rows_ = _size.height; + cols_ = _size.width; + type_ = _type; #endif } -void cv::GlTexture::create(int rows, int cols, int type) +void cv::GlTexture::create(int _rows, int _cols, int _type) { #ifndef HAVE_OPENGL - (void)rows; - (void)cols; - (void)type; + (void)_rows; + (void)_cols; + (void)_type; throw_nogl; #else - if (rows_ != rows || cols_ != cols || type_ != type) + if (rows_ != _rows || cols_ != _cols || type_ != _type) { - impl_ = new Impl(rows, cols, type); - rows_ = rows; - cols_ = cols; - type_ = type; + impl_ = new Impl(_rows, _cols, _type); + rows_ = _rows; + cols_ = _cols; + type_ = _type; } #endif } @@ -1030,15 +1030,15 @@ void cv::GlTexture::release() void cv::GlTexture::copyFrom(InputArray mat_, bool bgra) { #ifndef HAVE_OPENGL - (void)mat_; - (void)bgra; + (void)mat_; + (void)bgra; throw_nogl; #else int kind = mat_.kind(); - Size size = mat_.size(); - int type = mat_.type(); + Size _size = mat_.size(); + int _type = mat_.type(); - create(size, type); + create(_size, _type); switch(kind) { @@ -1244,8 +1244,8 @@ void cv::GlArrays::unbind() const //////////////////////////////////////////////////////////////////////// // GlFont -cv::GlFont::GlFont(const string& family, int height, Weight weight, Style style) - : family_(family), height_(height), weight_(weight), style_(style), base_(0) +cv::GlFont::GlFont(const string& _family, int _height, Weight _weight, Style _style) + : family_(_family), height_(_height), weight_(_weight), style_(_style), base_(0) { #ifndef HAVE_OPENGL throw_nogl; @@ -1253,7 +1253,7 @@ cv::GlFont::GlFont(const string& family, int height, Weight weight, Style style) base_ = glGenLists(256); CV_CheckGlError(); - glFuncTab()->generateBitmapFont(family, height, weight, (style & STYLE_ITALIC) != 0, (style & STYLE_UNDERLINE) != 0, 0, 256, base_); + glFuncTab()->generateBitmapFont(family_, height_, weight_, (style_ & STYLE_ITALIC) != 0, (style_ & STYLE_UNDERLINE) != 0, 0, 256, base_); #endif } @@ -1283,7 +1283,7 @@ namespace class FontCompare : public unary_function, bool> { public: - inline FontCompare(const string& family, int height, GlFont::Weight weight, GlFont::Style style) + inline FontCompare(const string& family, int height, GlFont::Weight weight, GlFont::Style style) : family_(family), height_(height), weight_(weight), style_(style) { } @@ -1304,10 +1304,10 @@ namespace Ptr cv::GlFont::get(const std::string& family, int height, Weight weight, Style style) { #ifndef HAVE_OPENGL - (void)family; - (void)height; - (void)weight; - (void)style; + (void)family; + (void)height; + (void)weight; + (void)style; throw_nogl; return Ptr(); #else @@ -1333,9 +1333,9 @@ Ptr cv::GlFont::get(const std::string& family, int height, Weight weight void cv::render(const GlTexture& tex, Rect_ wndRect, Rect_ texRect) { #ifndef HAVE_OPENGL - (void)tex; - (void)wndRect; - (void)texRect; + (void)tex; + (void)wndRect; + (void)texRect; throw_nogl; #else if (!tex.empty()) @@ -1368,9 +1368,9 @@ void cv::render(const GlTexture& tex, Rect_ wndRect, Rect_ texRe void cv::render(const GlArrays& arr, int mode, Scalar color) { #ifndef HAVE_OPENGL - (void)arr; - (void)mode; - (void)color; + (void)arr; + (void)mode; + (void)color; throw_nogl; #else glColor3d(color[0] / 255.0, color[1] / 255.0, color[2] / 255.0); @@ -1386,10 +1386,10 @@ void cv::render(const GlArrays& arr, int mode, Scalar color) void cv::render(const string& str, const Ptr& font, Scalar color, Point2d pos) { #ifndef HAVE_OPENGL - (void)str; - (void)font; - (void)color; - (void)pos; + (void)str; + (void)font; + (void)color; + (void)pos; throw_nogl; #else glPushAttrib(GL_DEPTH_BUFFER_BIT); @@ -1544,9 +1544,9 @@ void cv::GlCamera::setupModelViewMatrix() const bool icvCheckGlError(const char* file, const int line, const char* func) { #ifndef HAVE_OPENGL - (void)file; - (void)line; - (void)func; + (void)file; + (void)line; + (void)func; return true; #else GLenum err = glGetError(); diff --git a/modules/core/src/out.cpp b/modules/core/src/out.cpp index 366f5cf..6817fca 100644 --- a/modules/core/src/out.cpp +++ b/modules/core/src/out.cpp @@ -116,13 +116,13 @@ static void writeMat(std::ostream& out, const Mat& m, char rowsep, char elembrac { CV_Assert(m.dims <= 2); int type = m.type(); - + char crowbrace = getCloseBrace(rowsep); char orowbrace = crowbrace ? rowsep : '\0'; - + if( orowbrace || isspace(rowsep) ) rowsep = '\0'; - + for( int i = 0; i < m.rows; i++ ) { if(orowbrace) @@ -151,7 +151,7 @@ public: writeMat(out, m, ';', ' ', m.cols == 1); out << "]"; } - + void write(std::ostream& out, const void* data, int nelems, int type, const int*, int) const { writeElems(out, data, nelems, type, ' '); @@ -168,7 +168,7 @@ public: writeMat(out, m, m.cols > 1 ? '[' : ' ', '[', m.cols*m.channels() == 1); out << "]"; } - + void write(std::ostream& out, const void* data, int nelems, int type, const int*, int) const { writeElems(out, data, nelems, type, '['); @@ -190,7 +190,7 @@ public: writeMat(out, m, m.cols > 1 ? '[' : ' ', '[', m.cols*m.channels() == 1); out << "], type='" << numpyTypes[m.depth()] << "')"; } - + void write(std::ostream& out, const void* data, int nelems, int type, const int*, int) const { writeElems(out, data, nelems, type, '['); @@ -208,7 +208,7 @@ public: if(m.rows > 1) out << "\n"; } - + void write(std::ostream& out, const void* data, int nelems, int type, const int*, int) const { writeElems(out, data, nelems, type, ' '); @@ -226,7 +226,7 @@ public: writeMat(out, m, ',', ' ', m.cols==1); out << "}"; } - + void write(std::ostream& out, const void* data, int nelems, int type, const int*, int) const { writeElems(out, data, nelems, type, ' '); @@ -243,7 +243,7 @@ static CFormatter cFormatter; static const Formatter* g_defaultFormatter0 = &matlabFormatter; static const Formatter* g_defaultFormatter = &matlabFormatter; -bool my_streq(const char* a, const char* b) +static bool my_streq(const char* a, const char* b) { size_t i, alen = strlen(a), blen = strlen(b); if( alen != blen ) @@ -280,7 +280,7 @@ const Formatter* Formatter::setDefault(const Formatter* fmt) g_defaultFormatter = fmt; return prevFmt; } - + Formatted::Formatted(const Mat& _m, const Formatter* _fmt, const vector& _params) { @@ -288,12 +288,12 @@ Formatted::Formatted(const Mat& _m, const Formatter* _fmt, fmt = _fmt ? _fmt : Formatter::get(); std::copy(_params.begin(), _params.end(), back_inserter(params)); } - + Formatted::Formatted(const Mat& _m, const Formatter* _fmt, const int* _params) { mtx = _m; fmt = _fmt ? _fmt : Formatter::get(); - + if( _params ) { int i, maxParams = 100; diff --git a/modules/core/src/precomp.hpp b/modules/core/src/precomp.hpp index 0c9b3a7..3046b23 100644 --- a/modules/core/src/precomp.hpp +++ b/modules/core/src/precomp.hpp @@ -43,11 +43,6 @@ #ifndef __OPENCV_PRECOMP_H__ #define __OPENCV_PRECOMP_H__ -#if defined _MSC_VER && _MSC_VER >= 1200 - // disable warnings related to inline functions - #pragma warning( disable: 4251 4711 4710 4514 ) -#endif - #ifdef HAVE_CVCONFIG_H #include "cvconfig.h" #endif diff --git a/modules/core/src/stat.cpp b/modules/core/src/stat.cpp index 499c44c..b5b08fb 100644 --- a/modules/core/src/stat.cpp +++ b/modules/core/src/stat.cpp @@ -54,7 +54,7 @@ template static inline Scalar rawToScalar(const T& v) for( i = 0; i < n; i++ ) s.val[i] = ((T1*)&v)[i]; return s; -} +} /****************************************************************************************\ * sum * @@ -72,7 +72,7 @@ static int sum_(const T* src0, const uchar* mask, ST* dst, int len, int cn ) { ST s0 = dst[0]; - #if CV_ENABLE_UNROLLED + #if CV_ENABLE_UNROLLED for(; i <= len - 4; i += 4, src += cn*4 ) s0 += src[0] + src[cn] + src[cn*2] + src[cn*3]; #endif @@ -104,7 +104,7 @@ static int sum_(const T* src0, const uchar* mask, ST* dst, int len, int cn ) dst[1] = s1; dst[2] = s2; } - + for( ; k < cn; k += 4 ) { src = src0 + k; @@ -121,7 +121,7 @@ static int sum_(const T* src0, const uchar* mask, ST* dst, int len, int cn ) } return len; } - + int i, nzm = 0; if( cn == 1 ) { @@ -155,7 +155,7 @@ static int sum_(const T* src0, const uchar* mask, ST* dst, int len, int cn ) if( mask[i] ) { int k = 0; - #if CV_ENABLE_UNROLLED + #if CV_ENABLE_UNROLLED for( ; k <= cn - 4; k += 4 ) { ST s0, s1; @@ -212,7 +212,7 @@ template static int countNonZero_(const T* src, int len ) { int i=0, nz = 0; - #if CV_ENABLE_UNROLLED + #if CV_ENABLE_UNROLLED for(; i <= len - 4; i += 4 ) nz += (src[i] != 0) + (src[i+1] != 0) + (src[i+2] != 0) + (src[i+3] != 0); #endif @@ -251,12 +251,12 @@ template static int sumsqr_(const T* src0, const uchar* mask, ST* sum, SQT* sqsum, int len, int cn ) { const T* src = src0; - + if( !mask ) { int i; int k = cn % 4; - + if( k == 1 ) { ST s0 = sum[0]; @@ -296,7 +296,7 @@ static int sumsqr_(const T* src0, const uchar* mask, ST* sum, SQT* sqsum, int le sum[0] = s0; sum[1] = s1; sum[2] = s2; sqsum[0] = sq0; sqsum[1] = sq1; sqsum[2] = sq2; } - + for( ; k < cn; k += 4 ) { src = src0 + k; @@ -319,7 +319,7 @@ static int sumsqr_(const T* src0, const uchar* mask, ST* sum, SQT* sqsum, int le } return len; } - + int i, nzm = 0; if( cn == 1 ) @@ -368,7 +368,7 @@ static int sumsqr_(const T* src0, const uchar* mask, ST* sum, SQT* sqsum, int le } } return nzm; -} +} static int sqsum8u( const uchar* src, const uchar* mask, int* sum, int* sqsum, int len, int cn ) @@ -407,9 +407,9 @@ cv::Scalar cv::sum( InputArray _src ) Mat src = _src.getMat(); int k, cn = src.channels(), depth = src.depth(); SumFunc func = sumTab[depth]; - + CV_Assert( cn <= 4 && func != 0 ); - + const Mat* arrays[] = {&src, 0}; uchar* ptrs[1]; NAryMatIterator it(arrays, ptrs); @@ -420,7 +420,7 @@ cv::Scalar cv::sum( InputArray _src ) int* buf = (int*)&s[0]; size_t esz = 0; bool blockSum = depth < CV_32S; - + if( blockSum ) { intSumBlockSize = depth <= CV_8S ? (1 << 23) : (1 << 15); @@ -459,30 +459,30 @@ int cv::countNonZero( InputArray _src ) { Mat src = _src.getMat(); CountNonZeroFunc func = countNonZeroTab[src.depth()]; - + CV_Assert( src.channels() == 1 && func != 0 ); - + const Mat* arrays[] = {&src, 0}; uchar* ptrs[1]; NAryMatIterator it(arrays, ptrs); int total = (int)it.size, nz = 0; - + for( size_t i = 0; i < it.nplanes; i++, ++it ) nz += func( ptrs[0], total ); - + return nz; -} +} cv::Scalar cv::mean( InputArray _src, InputArray _mask ) { Mat src = _src.getMat(), mask = _mask.getMat(); CV_Assert( mask.empty() || mask.type() == CV_8U ); - + int k, cn = src.channels(), depth = src.depth(); SumFunc func = sumTab[depth]; - + CV_Assert( cn <= 4 && func != 0 ); - + const Mat* arrays[] = {&src, &mask, 0}; uchar* ptrs[2]; NAryMatIterator it(arrays, ptrs); @@ -493,19 +493,19 @@ cv::Scalar cv::mean( InputArray _src, InputArray _mask ) int* buf = (int*)&s[0]; bool blockSum = depth <= CV_16S; size_t esz = 0, nz0 = 0; - + if( blockSum ) { intSumBlockSize = depth <= CV_8S ? (1 << 23) : (1 << 15); blockSize = std::min(blockSize, intSumBlockSize); _buf.allocate(cn); buf = _buf; - + for( k = 0; k < cn; k++ ) buf[k] = 0; esz = src.elemSize(); } - + for( size_t i = 0; i < it.nplanes; i++, ++it ) { for( j = 0; j < total; j += blockSize ) @@ -529,19 +529,19 @@ cv::Scalar cv::mean( InputArray _src, InputArray _mask ) } } return s*(nz0 ? 1./nz0 : 0); -} +} void cv::meanStdDev( InputArray _src, OutputArray _mean, OutputArray _sdv, InputArray _mask ) { Mat src = _src.getMat(), mask = _mask.getMat(); CV_Assert( mask.empty() || mask.type() == CV_8U ); - + int k, cn = src.channels(), depth = src.depth(); SumSqrFunc func = sumSqrTab[depth]; - + CV_Assert( func != 0 ); - + const Mat* arrays[] = {&src, &mask, 0}; uchar* ptrs[2]; NAryMatIterator it(arrays, ptrs); @@ -552,10 +552,10 @@ void cv::meanStdDev( InputArray _src, OutputArray _mean, OutputArray _sdv, Input int *sbuf = (int*)s, *sqbuf = (int*)sq; bool blockSum = depth <= CV_16S, blockSqSum = depth <= CV_8S; size_t esz = 0; - + for( k = 0; k < cn; k++ ) s[k] = sq[k] = 0; - + if( blockSum ) { intSumBlockSize = 1 << 15; @@ -567,7 +567,7 @@ void cv::meanStdDev( InputArray _src, OutputArray _mean, OutputArray _sdv, Input sbuf[k] = sqbuf[k] = 0; esz = src.elemSize(); } - + for( size_t i = 0; i < it.nplanes; i++, ++it ) { for( j = 0; j < total; j += blockSize ) @@ -598,14 +598,14 @@ void cv::meanStdDev( InputArray _src, OutputArray _mean, OutputArray _sdv, Input ptrs[1] += bsz; } } - + double scale = nz0 ? 1./nz0 : 0.; for( k = 0; k < cn; k++ ) { s[k] *= scale; sq[k] = std::sqrt(std::max(sq[k]*scale - s[k]*s[k], 0.)); } - + for( j = 0; j < 2; j++ ) { const double* sptr = j == 0 ? s : sq; @@ -640,7 +640,7 @@ minMaxIdx_( const T* src, const uchar* mask, WT* _minVal, WT* _maxVal, { WT minVal = *_minVal, maxVal = *_maxVal; size_t minIdx = *_minIdx, maxIdx = *_maxIdx; - + if( !mask ) { for( int i = 0; i < len; i++ ) @@ -708,7 +708,7 @@ static void minMaxIdx_32f(const float* src, const uchar* mask, float* minval, fl static void minMaxIdx_64f(const double* src, const uchar* mask, double* minval, double* maxval, size_t* minidx, size_t* maxidx, int len, size_t startidx ) -{ minMaxIdx_(src, mask, minval, maxval, minidx, maxidx, len, startidx ); } +{ minMaxIdx_(src, mask, minval, maxval, minidx, maxidx, len, startidx ); } typedef void (*MinMaxIdxFunc)(const uchar*, const uchar*, int*, int*, size_t*, size_t*, int, size_t); @@ -749,16 +749,16 @@ void cv::minMaxIdx(InputArray _src, double* minVal, { Mat src = _src.getMat(), mask = _mask.getMat(); int depth = src.depth(), cn = src.channels(); - + CV_Assert( (cn == 1 && (mask.empty() || mask.type() == CV_8U)) || (cn >= 1 && mask.empty() && !minIdx && !maxIdx) ); MinMaxIdxFunc func = minmaxTab[depth]; CV_Assert( func != 0 ); - + const Mat* arrays[] = {&src, &mask, 0}; uchar* ptrs[2]; NAryMatIterator it(arrays, ptrs); - + size_t minidx = 0, maxidx = 0; int iminval = INT_MAX, imaxval = INT_MIN; float fminval = FLT_MAX, fmaxval = -FLT_MAX; @@ -766,39 +766,39 @@ void cv::minMaxIdx(InputArray _src, double* minVal, size_t startidx = 1; int *minval = &iminval, *maxval = &imaxval; int planeSize = (int)it.size*cn; - + if( depth == CV_32F ) minval = (int*)&fminval, maxval = (int*)&fmaxval; else if( depth == CV_64F ) minval = (int*)&dminval, maxval = (int*)&dmaxval; - + for( size_t i = 0; i < it.nplanes; i++, ++it, startidx += planeSize ) func( ptrs[0], ptrs[1], minval, maxval, &minidx, &maxidx, planeSize, startidx ); - + if( minidx == 0 ) dminval = dmaxval = 0; else if( depth == CV_32F ) dminval = fminval, dmaxval = fmaxval; else if( depth <= CV_32S ) dminval = iminval, dmaxval = imaxval; - + if( minVal ) *minVal = dminval; if( maxVal ) *maxVal = dmaxval; - + if( minIdx ) ofs2idx(src, minidx, minIdx); if( maxIdx ) ofs2idx(src, maxidx, maxIdx); -} +} void cv::minMaxLoc( InputArray _img, double* minVal, double* maxVal, Point* minLoc, Point* maxLoc, InputArray mask ) { Mat img = _img.getMat(); CV_Assert(img.dims <= 2); - + minMaxIdx(_img, minVal, maxVal, (int*)minLoc, (int*)maxLoc, mask); if( minLoc ) std::swap(minLoc->x, minLoc->y); @@ -821,7 +821,7 @@ float normL2Sqr_(const float* a, const float* b, int n) { float CV_DECL_ALIGNED(16) buf[4]; __m128 d0 = _mm_setzero_ps(), d1 = _mm_setzero_ps(); - + for( ; j <= n - 8; j += 8 ) { __m128 t0 = _mm_sub_ps(_mm_loadu_ps(a + j), _mm_loadu_ps(b + j)); @@ -834,14 +834,14 @@ float normL2Sqr_(const float* a, const float* b, int n) } else #endif - { + { for( ; j <= n - 4; j += 4 ) { float t0 = a[j] - b[j], t1 = a[j+1] - b[j+1], t2 = a[j+2] - b[j+2], t3 = a[j+3] - b[j+3]; d += t0*t0 + t1*t1 + t2*t2 + t3*t3; } } - + for( ; j < n; j++ ) { float t = a[j] - b[j]; @@ -861,7 +861,7 @@ float normL1_(const float* a, const float* b, int n) static const int CV_DECL_ALIGNED(16) absbuf[4] = {0x7fffffff, 0x7fffffff, 0x7fffffff, 0x7fffffff}; __m128 d0 = _mm_setzero_ps(), d1 = _mm_setzero_ps(); __m128 absmask = _mm_load_ps((const float*)absbuf); - + for( ; j <= n - 8; j += 8 ) { __m128 t0 = _mm_sub_ps(_mm_loadu_ps(a + j), _mm_loadu_ps(b + j)); @@ -894,12 +894,12 @@ int normL1_(const uchar* a, const uchar* b, int n) if( USE_SSE2 ) { __m128i d0 = _mm_setzero_si128(); - + for( ; j <= n - 16; j += 16 ) { __m128i t0 = _mm_loadu_si128((const __m128i*)(a + j)); __m128i t1 = _mm_loadu_si128((const __m128i*)(b + j)); - + d0 = _mm_add_epi32(d0, _mm_sad_epu8(t0, t1)); } @@ -907,7 +907,7 @@ int normL1_(const uchar* a, const uchar* b, int n) { __m128i t0 = _mm_cvtsi32_si128(*(const int*)(a + j)); __m128i t1 = _mm_cvtsi32_si128(*(const int*)(b + j)); - + d0 = _mm_add_epi32(d0, _mm_sad_epu8(t0, t1)); } d = _mm_cvtsi128_si32(_mm_add_epi32(d0, _mm_unpackhi_epi64(d0, d0))); @@ -926,7 +926,7 @@ int normL1_(const uchar* a, const uchar* b, int n) return d; } -static const uchar popCountTable[] = +static const uchar popCountTable[] = { 0, 1, 1, 2, 1, 2, 2, 3, 1, 2, 2, 3, 2, 3, 3, 4, 1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5, 1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5, 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, @@ -962,7 +962,7 @@ static const uchar popCountTable4[] = 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2 }; -int normHamming(const uchar* a, int n) +static int normHamming(const uchar* a, int n) { int i = 0, result = 0; #if CV_NEON @@ -989,7 +989,7 @@ int normHamming(const uchar* a, int n) result += popCountTable[a[i]]; return result; } - + int normHamming(const uchar* a, const uchar* b, int n) { int i = 0, result = 0; @@ -1020,7 +1020,7 @@ int normHamming(const uchar* a, const uchar* b, int n) return result; } -int normHamming(const uchar* a, int n, int cellSize) +static int normHamming(const uchar* a, int n, int cellSize) { if( cellSize == 1 ) return normHamming(a, n); @@ -1039,8 +1039,8 @@ int normHamming(const uchar* a, int n, int cellSize) for( ; i < n; i++ ) result += tab[a[i]]; return result; -} - +} + int normHamming(const uchar* a, const uchar* b, int n, int cellSize) { if( cellSize == 1 ) @@ -1053,7 +1053,7 @@ int normHamming(const uchar* a, const uchar* b, int n, int cellSize) else CV_Error( CV_StsBadSize, "bad cell size (not 1, 2 or 4) in normHamming" ); int i = 0, result = 0; - #if CV_ENABLE_UNROLLED + #if CV_ENABLE_UNROLLED for( ; i <= n - 4; i += 4 ) result += tab[a[i] ^ b[i]] + tab[a[i+1] ^ b[i+1]] + tab[a[i+2] ^ b[i+2]] + tab[a[i+3] ^ b[i+3]]; @@ -1128,7 +1128,7 @@ normL2_(const T* src, const uchar* mask, ST* _result, int len, int cn) } *_result = result; return 0; -} +} template int normDiffInf_(const T* src1, const T* src2, const uchar* mask, ST* _result, int len, int cn) @@ -1194,7 +1194,7 @@ normDiffL2_(const T* src1, const T* src2, const uchar* mask, ST* _result, int le } *_result = result; return 0; -} +} #define CV_DEF_NORM_FUNC(L, suffix, type, ntype) \ @@ -1219,7 +1219,7 @@ CV_DEF_NORM_ALL(64f, double, double, double, double) typedef int (*NormFunc)(const uchar*, const uchar*, uchar*, int, int); -typedef int (*NormDiffFunc)(const uchar*, const uchar*, const uchar*, uchar*, int, int); +typedef int (*NormDiffFunc)(const uchar*, const uchar*, const uchar*, uchar*, int, int); static NormFunc normTab[3][8] = { @@ -1265,11 +1265,11 @@ double cv::norm( InputArray _src, int normType, InputArray _mask ) { Mat src = _src.getMat(), mask = _mask.getMat(); int depth = src.depth(), cn = src.channels(); - + normType &= 7; CV_Assert( normType == NORM_INF || normType == NORM_L1 || normType == NORM_L2 || normType == NORM_L2SQR || ((normType == NORM_HAMMING || normType == NORM_HAMMING2) && src.type() == CV_8U) ); - + if( src.isContinuous() && mask.empty() ) { size_t len = src.total()*cn; @@ -1278,7 +1278,7 @@ double cv::norm( InputArray _src, int normType, InputArray _mask ) if( depth == CV_32F ) { const float* data = src.ptr(); - + if( normType == NORM_L2 ) { double result = 0; @@ -1307,18 +1307,18 @@ double cv::norm( InputArray _src, int normType, InputArray _mask ) if( depth == CV_8U ) { const uchar* data = src.ptr(); - + if( normType == NORM_HAMMING ) return normHamming(data, (int)len); - + if( normType == NORM_HAMMING2 ) return normHamming(data, (int)len, 2); } } } - + CV_Assert( mask.empty() || mask.type() == CV_8U ); - + if( normType == NORM_HAMMING || normType == NORM_HAMMING2 ) { if( !mask.empty() ) @@ -1328,22 +1328,22 @@ double cv::norm( InputArray _src, int normType, InputArray _mask ) return norm(temp, normType); } int cellSize = normType == NORM_HAMMING ? 1 : 2; - + const Mat* arrays[] = {&src, 0}; uchar* ptrs[1]; NAryMatIterator it(arrays, ptrs); int total = (int)it.size; int result = 0; - + for( size_t i = 0; i < it.nplanes; i++, ++it ) result += normHamming(ptrs[0], total, cellSize); - + return result; } - + NormFunc func = normTab[normType >> 1][depth]; CV_Assert( func != 0 ); - + const Mat* arrays[] = {&src, &mask, 0}; uchar* ptrs[2]; union @@ -1361,7 +1361,7 @@ double cv::norm( InputArray _src, int normType, InputArray _mask ) int isum = 0; int *ibuf = &result.i; size_t esz = 0; - + if( blockSum ) { intSumBlockSize = (normType == NORM_L1 && depth <= CV_8S ? (1 << 23) : (1 << 15))/cn; @@ -1369,7 +1369,7 @@ double cv::norm( InputArray _src, int normType, InputArray _mask ) ibuf = &isum; esz = src.elemSize(); } - + for( size_t i = 0; i < it.nplanes; i++, ++it ) { for( j = 0; j < total; j += blockSize ) @@ -1388,7 +1388,7 @@ double cv::norm( InputArray _src, int normType, InputArray _mask ) ptrs[1] += bsz; } } - + if( normType == NORM_INF ) { if( depth == CV_64F ) @@ -1400,7 +1400,7 @@ double cv::norm( InputArray _src, int normType, InputArray _mask ) } else if( normType == NORM_L2 ) result.d = std::sqrt(result.d); - + return result.d; } @@ -1409,16 +1409,16 @@ double cv::norm( InputArray _src1, InputArray _src2, int normType, InputArray _m { if( normType & CV_RELATIVE ) return norm(_src1, _src2, normType & ~CV_RELATIVE, _mask)/(norm(_src2, normType, _mask) + DBL_EPSILON); - + Mat src1 = _src1.getMat(), src2 = _src2.getMat(), mask = _mask.getMat(); int depth = src1.depth(), cn = src1.channels(); - + CV_Assert( src1.size == src2.size && src1.type() == src2.type() ); - + normType &= 7; CV_Assert( normType == NORM_INF || normType == NORM_L1 || normType == NORM_L2 || normType == NORM_L2SQR || ((normType == NORM_HAMMING || normType == NORM_HAMMING2) && src1.type() == CV_8U) ); - + if( src1.isContinuous() && src2.isContinuous() && mask.empty() ) { size_t len = src1.total()*src1.channels(); @@ -1428,7 +1428,7 @@ double cv::norm( InputArray _src1, InputArray _src2, int normType, InputArray _m { const float* data1 = src1.ptr(); const float* data2 = src2.ptr(); - + if( normType == NORM_L2 ) { double result = 0; @@ -1456,9 +1456,9 @@ double cv::norm( InputArray _src1, InputArray _src2, int normType, InputArray _m } } } - + CV_Assert( mask.empty() || mask.type() == CV_8U ); - + if( normType == NORM_HAMMING || normType == NORM_HAMMING2 ) { if( !mask.empty() ) @@ -1469,22 +1469,22 @@ double cv::norm( InputArray _src1, InputArray _src2, int normType, InputArray _m return norm(temp, normType); } int cellSize = normType == NORM_HAMMING ? 1 : 2; - + const Mat* arrays[] = {&src1, &src2, 0}; uchar* ptrs[2]; NAryMatIterator it(arrays, ptrs); int total = (int)it.size; int result = 0; - + for( size_t i = 0; i < it.nplanes; i++, ++it ) result += normHamming(ptrs[0], ptrs[1], total, cellSize); - + return result; } - + NormDiffFunc func = normDiffTab[normType >> 1][depth]; CV_Assert( func != 0 ); - + const Mat* arrays[] = {&src1, &src2, &mask, 0}; uchar* ptrs[3]; union @@ -1503,7 +1503,7 @@ double cv::norm( InputArray _src1, InputArray _src2, int normType, InputArray _m unsigned isum = 0; unsigned *ibuf = &result.u; size_t esz = 0; - + if( blockSum ) { intSumBlockSize = normType == NORM_L1 && depth <= CV_8S ? (1 << 23) : (1 << 15); @@ -1511,7 +1511,7 @@ double cv::norm( InputArray _src1, InputArray _src2, int normType, InputArray _m ibuf = &isum; esz = src1.elemSize(); } - + for( size_t i = 0; i < it.nplanes; i++, ++it ) { for( j = 0; j < total; j += blockSize ) @@ -1531,7 +1531,7 @@ double cv::norm( InputArray _src1, InputArray _src2, int normType, InputArray _m ptrs[2] += bsz; } } - + if( normType == NORM_INF ) { if( depth == CV_64F ) @@ -1543,7 +1543,7 @@ double cv::norm( InputArray _src1, InputArray _src2, int normType, InputArray _m } else if( normType == NORM_L2 ) result.d = std::sqrt(result.d); - + return result.d; } @@ -1692,7 +1692,7 @@ static void batchDistL2_32f(const float* src1, const float* src2, size_t step2, typedef void (*BatchDistFunc)(const uchar* src1, const uchar* src2, size_t step2, int nvecs, int len, uchar* dist, const uchar* mask); - + struct BatchDistInvoker { BatchDistInvoker( const Mat& _src1, const Mat& _src2, @@ -1709,26 +1709,26 @@ struct BatchDistInvoker update = _update; func = _func; } - + void operator()(const BlockedRange& range) const { AutoBuffer buf(src2->rows); int* bufptr = buf; - + for( int i = range.begin(); i < range.end(); i++ ) { func(src1->ptr(i), src2->ptr(), src2->step, src2->rows, src2->cols, K > 0 ? (uchar*)bufptr : dist->ptr(i), mask->data ? mask->ptr(i) : 0); - + if( K > 0 ) { int* nidxptr = nidx->ptr(i); // since positive float's can be compared just like int's, // we handle both CV_32S and CV_32F cases with a single branch int* distptr = (int*)dist->ptr(i); - + int j, k; - + for( j = 0; j < src2->rows; j++ ) { int d = bufptr[j]; @@ -1746,7 +1746,7 @@ struct BatchDistInvoker } } } - + const Mat *src1; const Mat *src2; Mat *dist; @@ -1756,9 +1756,9 @@ struct BatchDistInvoker int update; BatchDistFunc func; }; - + } - + void cv::batchDistance( InputArray _src1, InputArray _src2, OutputArray _dist, int dtype, OutputArray _nidx, int normType, int K, InputArray _mask, @@ -1769,7 +1769,7 @@ void cv::batchDistance( InputArray _src1, InputArray _src2, CV_Assert( type == src2.type() && src1.cols == src2.cols && (type == CV_32F || type == CV_8U)); CV_Assert( _nidx.needed() == (K > 0) ); - + if( dtype == -1 ) { dtype = normType == NORM_HAMMING || normType == NORM_HAMMING2 ? CV_32S : CV_32F; @@ -1777,7 +1777,7 @@ void cv::batchDistance( InputArray _src1, InputArray _src2, CV_Assert( (type == CV_8U && dtype == CV_32S) || dtype == CV_32F); K = std::min(K, src2.rows); - + _dist.create(src1.rows, (K > 0 ? K : src2.rows), dtype); Mat dist = _dist.getMat(), nidx; if( _nidx.needed() ) @@ -1785,19 +1785,19 @@ void cv::batchDistance( InputArray _src1, InputArray _src2, _nidx.create(dist.size(), CV_32S); nidx = _nidx.getMat(); } - + if( update == 0 && K > 0 ) { dist = Scalar::all(dtype == CV_32S ? (double)INT_MAX : (double)FLT_MAX); nidx = Scalar::all(-1); } - + if( crosscheck ) { CV_Assert( K == 1 && update == 0 && mask.empty() ); Mat tdist, tidx; batchDistance(src2, src1, tdist, dtype, tidx, normType, K, mask, 0, false); - + // if an idx-th element from src1 appeared to be the nearest to i-th element of src2, // we update the minimum mutual distance between idx-th element of src1 and the whole src2 set. // As a result, if nidx[idx] = i*, it means that idx-th element of src1 is the nearest @@ -1832,7 +1832,7 @@ void cv::batchDistance( InputArray _src1, InputArray _src2, } return; } - + BatchDistFunc func = 0; if( type == CV_8U ) { @@ -1860,12 +1860,12 @@ void cv::batchDistance( InputArray _src1, InputArray _src2, else if( normType == NORM_L2 ) func = (BatchDistFunc)batchDistL2_32f; } - + if( func == 0 ) CV_Error_(CV_StsUnsupportedFormat, ("The combination of type=%d, dtype=%d and normType=%d is not supported", type, dtype, normType)); - + parallel_for(BlockedRange(0, src1.rows), BatchDistInvoker(src1, src2, dist, nidx, K, mask, update, func)); } diff --git a/modules/core/src/system.cpp b/modules/core/src/system.cpp index b8c46fa..0c75202 100644 --- a/modules/core/src/system.cpp +++ b/modules/core/src/system.cpp @@ -88,7 +88,7 @@ #if defined __linux__ || defined __APPLE__ #include #include -#include +#include #if defined ANDROID #include #else @@ -111,7 +111,7 @@ Exception::~Exception() throw() {} /*! \return the error description and the context as a text string. - */ + */ const char* Exception::what() const throw() { return msg.c_str(); } void Exception::formatMessage() @@ -121,7 +121,7 @@ void Exception::formatMessage() else msg = format("%s:%d: error: (%d) %s\n", file.c_str(), line, code, err.c_str()); } - + struct HWFeatures { enum { MAX_FEATURE = CV_HARDWARE_MAX_FEATURE }; @@ -374,7 +374,7 @@ int getThreadNum(void) #endif } -#if ANDROID +#ifdef ANDROID static inline int getNumberOfCPUsImpl() { FILE* cpuPossible = fopen("/sys/devices/system/cpu/possible", "r"); @@ -408,7 +408,7 @@ static inline int getNumberOfCPUsImpl() sscanf(pos, "%d-%d", &rstart, &rend); cpusAvailable += rend - rstart + 1; } - + } return cpusAvailable ? cpusAvailable : 1; } @@ -419,9 +419,9 @@ int getNumberOfCPUs(void) #if defined WIN32 || defined _WIN32 SYSTEM_INFO sysinfo; GetSystemInfo( &sysinfo ); - + return (int)sysinfo.dwNumberOfProcessors; -#elif ANDROID +#elif defined ANDROID static int ncpus = getNumberOfCPUsImpl(); printf("CPUS= %d\n", ncpus); return ncpus; @@ -430,24 +430,24 @@ int getNumberOfCPUs(void) #elif defined __APPLE__ int numCPU=0; int mib[4]; - size_t len = sizeof(numCPU); - + size_t len = sizeof(numCPU); + /* set the mib for hw.ncpu */ mib[0] = CTL_HW; mib[1] = HW_AVAILCPU; // alternatively, try HW_NCPU; - + /* get the number of CPUs from the system */ sysctl(mib, 2, &numCPU, &len, NULL, 0); - - if( numCPU < 1 ) + + if( numCPU < 1 ) { mib[1] = HW_NCPU; sysctl( mib, 2, &numCPU, &len, NULL, 0 ); - + if( numCPU < 1 ) numCPU = 1; } - + return (int)numCPU; #else return 1; @@ -475,7 +475,7 @@ string tempfile( const char* suffix ) { char buf[L_tmpnam]; char* name = 0; -#if ANDROID +#ifdef ANDROID strcpy(buf, "/sdcard/__opencv_temp_XXXXXX"); name = mktemp(buf); #else @@ -869,6 +869,8 @@ cvGetModuleInfo( const char* name, const char **version, const char **plugin_lis } #if defined BUILD_SHARED_LIBS && defined CVAPI_EXPORTS && defined WIN32 && !defined WINCE +BOOL WINAPI DllMain( HINSTANCE, DWORD fdwReason, LPVOID ); + BOOL WINAPI DllMain( HINSTANCE, DWORD fdwReason, LPVOID ) { if( fdwReason == DLL_THREAD_DETACH || fdwReason == DLL_PROCESS_DETACH ) diff --git a/modules/core/test/test_arithm.cpp b/modules/core/test/test_arithm.cpp index b20d28a..fc07bd8 100644 --- a/modules/core/test/test_arithm.cpp +++ b/modules/core/test/test_arithm.cpp @@ -1264,7 +1264,7 @@ struct NormOp : public BaseElemWiseOp dst.at(0,0) = cvtest::norm(src[0], normType, mask); dst.at(0,1) = cvtest::norm(src[0], src[1], normType, mask); } - void generateScalars(int, RNG& rng) + void generateScalars(int, RNG& /*rng*/) { } double getMaxErr(int) diff --git a/modules/core/test/test_ds.cpp b/modules/core/test/test_ds.cpp index bd12fd5..7a5c6f1 100644 --- a/modules/core/test/test_ds.cpp +++ b/modules/core/test/test_ds.cpp @@ -45,11 +45,11 @@ static void cvTsClearSimpleSeq( CvTsSimpleSeq* seq ) static void cvTsSimpleSeqShiftAndCopy( CvTsSimpleSeq* seq, int from_idx, int to_idx, void* elem=0 ) { int elem_size = seq->elem_size; - + if( from_idx == to_idx ) return; assert( (from_idx > to_idx && !elem) || (from_idx < to_idx && elem) ); - + if( from_idx < seq->count ) { memmove( seq->array + to_idx*elem_size, seq->array + from_idx*elem_size, @@ -64,7 +64,7 @@ static void cvTsSimpleSeqInvert( CvTsSimpleSeq* seq ) { int i, k, len = seq->count, elem_size = seq->elem_size; schar *data = seq->array, t; - + for( i = 0; i < len/2; i++ ) { schar* a = data + i*elem_size; @@ -92,7 +92,7 @@ static void cvTsClearSimpleSet( CvTsSimpleSet* set_header ) { int i; int elem_size = set_header->elem_size; - + for( i = 0; i < set_header->max_count; i++ ) { set_header->array[i*elem_size] = 0; @@ -111,7 +111,7 @@ static CvTsSimpleSet* cvTsCreateSimpleSet( int max_count, int elem_size ) set_header->max_count = max_count; set_header->free_stack = (int*)(set_header + 1); set_header->array = (schar*)(set_header->free_stack + max_count); - + cvTsClearSimpleSet( set_header ); return set_header; } @@ -135,7 +135,7 @@ static int cvTsSimpleSetAdd( CvTsSimpleSet* set_header, void* elem ) { int idx, idx2; assert( set_header->free_count > 0 ); - + idx = set_header->free_stack[--set_header->free_count]; idx2 = idx * set_header->elem_size; assert( set_header->array[idx2] == 0 ); @@ -143,7 +143,7 @@ static int cvTsSimpleSetAdd( CvTsSimpleSet* set_header, void* elem ) if( set_header->elem_size > 1 ) memcpy( set_header->array + idx2 + 1, elem, set_header->elem_size - 1 ); set_header->count = MAX( set_header->count, idx + 1 ); - + return idx; } @@ -153,7 +153,7 @@ static void cvTsSimpleSetRemove( CvTsSimpleSet* set_header, int index ) assert( set_header->free_count < set_header->max_count && 0 <= index && index < set_header->max_count ); assert( set_header->array[index * set_header->elem_size] == 1 ); - + set_header->free_stack[set_header->free_count++] = index; set_header->array[index * set_header->elem_size] = 0; } @@ -184,7 +184,7 @@ static CvTsSimpleGraph* cvTsCreateSimpleGraph( int max_vtx_count, int vtx_size, int edge_size, int oriented ) { CvTsSimpleGraph* graph; - + assert( max_vtx_count > 1 && vtx_size >= 0 && edge_size >= 0 ); graph = (CvTsSimpleGraph*)cvAlloc( sizeof(*graph) + max_vtx_count * max_vtx_count * (edge_size + 1)); @@ -192,7 +192,7 @@ static CvTsSimpleGraph* cvTsCreateSimpleGraph( int max_vtx_count, int vtx_size, graph->edge_size = edge_size + 1; graph->matrix = (char*)(graph + 1); graph->oriented = oriented; - + cvTsClearSimpleGraph( graph ); return graph; } @@ -219,7 +219,7 @@ static void cvTsSimpleGraphRemoveVertex( CvTsSimpleGraph* graph, int index ) int i, max_vtx_count = graph->vtx->max_count; int edge_size = graph->edge_size; cvTsSimpleSetRemove( graph->vtx, index ); - + /* remove all the corresponding edges */ for( i = 0; i < max_vtx_count; i++ ) { @@ -232,10 +232,10 @@ static void cvTsSimpleGraphRemoveVertex( CvTsSimpleGraph* graph, int index ) static void cvTsSimpleGraphAddEdge( CvTsSimpleGraph* graph, int idx1, int idx2, void* edge ) { int i, t, n = graph->oriented ? 1 : 2; - + assert( cvTsSimpleSetFind( graph->vtx, idx1 ) && cvTsSimpleSetFind( graph->vtx, idx2 )); - + for( i = 0; i < n; i++ ) { int ofs = (idx1*graph->vtx->max_count + idx2)*graph->edge_size; @@ -243,7 +243,7 @@ static void cvTsSimpleGraphAddEdge( CvTsSimpleGraph* graph, int idx1, int idx2, graph->matrix[ofs] = 1; if( graph->edge_size > 1 ) memcpy( graph->matrix + ofs + 1, edge, graph->edge_size - 1 ); - + CV_SWAP( idx1, idx2, t ); } } @@ -252,10 +252,10 @@ static void cvTsSimpleGraphAddEdge( CvTsSimpleGraph* graph, int idx1, int idx2, static void cvTsSimpleGraphRemoveEdge( CvTsSimpleGraph* graph, int idx1, int idx2 ) { int i, t, n = graph->oriented ? 1 : 2; - + assert( cvTsSimpleSetFind( graph->vtx, idx1 ) && cvTsSimpleSetFind( graph->vtx, idx2 )); - + for( i = 0; i < n; i++ ) { int ofs = (idx1*graph->vtx->max_count + idx2)*graph->edge_size; @@ -290,13 +290,13 @@ static int cvTsSimpleGraphVertexDegree( CvTsSimpleGraph* graph, int index ) int edge_size = graph->edge_size; int max_vtx_count = graph->vtx->max_count; assert( cvTsSimpleGraphFindVertex( graph, index ) != 0 ); - + for( i = 0; i < max_vtx_count; i++ ) { count += graph->matrix[(i*max_vtx_count + index)*edge_size] + graph->matrix[(index*max_vtx_count + i)*edge_size]; } - + if( !graph->oriented ) { assert( count % 2 == 0 ); @@ -323,7 +323,7 @@ public: virtual ~Core_DynStructBaseTest(); bool can_do_fast_forward(); void clear(); - + protected: int read_params( CvFileStorage* fs ); void run_func(void); @@ -332,7 +332,7 @@ protected: const char* file, int line ); int test_seq_block_consistence( int _struct_idx, CvSeq* seq, int total ); void update_progressbar(); - + int struct_count, max_struct_size, iterations, generations; int min_log_storage_block_size, max_log_storage_block_size; int min_log_elem_size, max_log_elem_size; @@ -358,7 +358,7 @@ Core_DynStructBaseTest::Core_DynStructBaseTest() iterations = max_struct_size*2; gen = struct_idx = iter = -1; test_progress = -1; - + storage = 0; } @@ -391,33 +391,33 @@ int Core_DynStructBaseTest::read_params( CvFileStorage* fs ) double sqrt_scale = sqrt(ts->get_test_case_count_scale()); if( code < 0 ) return code; - + struct_count = cvReadInt( find_param( fs, "struct_count" ), struct_count ); max_struct_size = cvReadInt( find_param( fs, "max_struct_size" ), max_struct_size ); generations = cvReadInt( find_param( fs, "generations" ), generations ); iterations = cvReadInt( find_param( fs, "iterations" ), iterations ); generations = cvRound(generations*sqrt_scale); iterations = cvRound(iterations*sqrt_scale); - + min_log_storage_block_size = cvReadInt( find_param( fs, "min_log_storage_block_size" ), min_log_storage_block_size ); max_log_storage_block_size = cvReadInt( find_param( fs, "max_log_storage_block_size" ), max_log_storage_block_size ); min_log_elem_size = cvReadInt( find_param( fs, "min_log_elem_size" ), min_log_elem_size ); max_log_elem_size = cvReadInt( find_param( fs, "max_log_elem_size" ), max_log_elem_size ); - + struct_count = cvtest::clipInt( struct_count, 1, 100 ); max_struct_size = cvtest::clipInt( max_struct_size, 1, 1<<20 ); generations = cvtest::clipInt( generations, 1, 100 ); iterations = cvtest::clipInt( iterations, 100, 1<<20 ); - + min_log_storage_block_size = cvtest::clipInt( min_log_storage_block_size, 7, 20 ); max_log_storage_block_size = cvtest::clipInt( max_log_storage_block_size, min_log_storage_block_size, 20 ); - + min_log_elem_size = cvtest::clipInt( min_log_elem_size, 0, 8 ); max_log_elem_size = cvtest::clipInt( max_log_elem_size, min_log_elem_size, 10 ); - + return 0; } @@ -425,14 +425,14 @@ int Core_DynStructBaseTest::read_params( CvFileStorage* fs ) void Core_DynStructBaseTest::update_progressbar() { int64 t; - + if( test_progress < 0 ) { test_progress = 0; cpu_freq = cv::getTickFrequency(); start_time = cv::getTickCount(); } - + t = cv::getTickCount(); test_progress = update_progress( test_progress, 0, 0, (double)(t - start_time)/cpu_freq ); } @@ -453,16 +453,16 @@ int Core_DynStructBaseTest::test_seq_block_consistence( int _struct_idx, CvSeq* { int sum = 0; struct_idx = _struct_idx; - + CV_TS_SEQ_CHECK_CONDITION( seq != 0, "Null sequence pointer" ); - + if( seq->first ) { CvSeqBlock* block = seq->first; CvSeqBlock* prev_block = block->prev; - + int delta_idx = seq->first->start_index; - + for( ;; ) { CV_TS_SEQ_CHECK_CONDITION( sum == block->start_index - delta_idx && @@ -474,15 +474,15 @@ int Core_DynStructBaseTest::test_seq_block_consistence( int _struct_idx, CvSeq* block = block->next; if( block == seq->first ) break; } - + CV_TS_SEQ_CHECK_CONDITION( block->prev->count * seq->elem_size + block->prev->data <= seq->block_max, "block->data or block_max pointer are incorrect" ); } - + CV_TS_SEQ_CHECK_CONDITION( seq->total == sum && sum == total, "total number of elements is incorrect" ); - + return 0; } @@ -495,7 +495,7 @@ public: Core_SeqBaseTest(); void clear(); void run( int ); - + protected: int test_multi_create(); int test_get_seq_elem( int _struct_idx, int iters ); @@ -524,20 +524,20 @@ int Core_SeqBaseTest::test_multi_create() vector index(struct_count); int cur_count, elem_size; RNG& rng = ts->get_rng(); - + for( int i = 0; i < struct_count; i++ ) { double t; CvTsSimpleSeq* sseq; - + pos[i] = -1; index[i] = i; - + t = cvtest::randReal(rng)*(max_log_elem_size - min_log_elem_size) + min_log_elem_size; elem_size = cvRound( exp(t * CV_LOG2) ); elem_size = MIN( elem_size, (int)(storage->block_size - sizeof(void*) - sizeof(CvSeqBlock) - sizeof(CvMemBlock)) ); - + cvTsReleaseSimpleSeq( (CvTsSimpleSeq**)&simple_struct[i] ); simple_struct[i] = sseq = cvTsCreateSimpleSeq( max_struct_size, elem_size ); cxcore_struct[i] = 0; @@ -545,7 +545,7 @@ int Core_SeqBaseTest::test_multi_create() Mat m( 1, MAX(sseq->count,1)*elem_size, CV_8UC1, sseq->array ); cvtest::randUni( rng, m, Scalar::all(0), Scalar::all(256) ); } - + for( cur_count = struct_count; cur_count > 0; cur_count-- ) { for(;;) @@ -553,13 +553,13 @@ int Core_SeqBaseTest::test_multi_create() int k = cvtest::randInt( rng ) % cur_count; struct_idx = index[k]; CvTsSimpleSeq* sseq = (CvTsSimpleSeq*)simple_struct[struct_idx]; - + if( pos[struct_idx] < 0 ) { int hdr_size = (cvtest::randInt(rng) % 10)*4 + sizeof(CvSeq); hdr_size = MIN( hdr_size, (int)(storage->block_size - sizeof(CvMemBlock)) ); elem_size = sseq->elem_size; - + if( cvtest::randInt(rng) % 2 ) { cvStartWriteSeq( 0, hdr_size, elem_size, storage, &writer[struct_idx] ); @@ -570,11 +570,11 @@ int Core_SeqBaseTest::test_multi_create() s = cvCreateSeq( 0, hdr_size, elem_size, storage ); cvStartAppendToSeq( s, &writer[struct_idx] ); } - + cvSetSeqBlockSize( writer[struct_idx].seq, cvtest::randInt( rng ) % 10000 ); pos[struct_idx] = 0; } - + update_progressbar(); if( pos[struct_idx] == sseq->count ) { @@ -584,7 +584,7 @@ int Core_SeqBaseTest::test_multi_create() index[k] = index[k+1]; break; } - + { schar* el = cvTsSimpleSeqElem( sseq, pos[struct_idx] ); CV_WRITE_SEQ_ELEM_VAR( el, writer[struct_idx] ); @@ -592,7 +592,7 @@ int Core_SeqBaseTest::test_multi_create() pos[struct_idx]++; } } - + return 0; } @@ -600,16 +600,16 @@ int Core_SeqBaseTest::test_multi_create() int Core_SeqBaseTest::test_get_seq_elem( int _struct_idx, int iters ) { RNG& rng = ts->get_rng(); - + CvSeq* seq = (CvSeq*)cxcore_struct[_struct_idx]; CvTsSimpleSeq* sseq = (CvTsSimpleSeq*)simple_struct[_struct_idx]; struct_idx = _struct_idx; - + assert( seq->total == sseq->count ); - + if( sseq->count == 0 ) return 0; - + for( int i = 0; i < iters; i++ ) { int idx = cvtest::randInt(rng) % (sseq->count*3) - sseq->count*3/2; @@ -618,7 +618,7 @@ int Core_SeqBaseTest::test_get_seq_elem( int _struct_idx, int iters ) int bad_range = (unsigned)idx0 >= (unsigned)(sseq->count); schar* elem; elem = cvGetSeqElem( seq, idx ); - + if( bad_range ) { CV_TS_SEQ_CHECK_CONDITION( elem == 0, @@ -630,13 +630,13 @@ int Core_SeqBaseTest::test_get_seq_elem( int _struct_idx, int iters ) CV_TS_SEQ_CHECK_CONDITION( elem != 0 && !memcmp( elem, cvTsSimpleSeqElem(sseq, idx0), sseq->elem_size ), "cvGetSeqElem returns wrong element" ); - + idx = cvSeqElemIdx(seq, elem ); CV_TS_SEQ_CHECK_CONDITION( idx >= 0 && idx == idx0, "cvSeqElemIdx is incorrect" ); } } - + return 0; } @@ -651,43 +651,43 @@ int Core_SeqBaseTest::test_get_seq_reading( int _struct_idx, int iters ) CvSeqReader reader; vector _elem(sseq->elem_size); schar* elem = &_elem[0]; - + assert( total == sseq->count ); this->struct_idx = _struct_idx; - + int pos = cvtest::randInt(rng) % 2; cvStartReadSeq( seq, &reader, pos ); - + if( total == 0 ) { CV_TS_SEQ_CHECK_CONDITION( reader.ptr == 0, "Empty sequence reader pointer is not NULL" ); return 0; } - + pos = pos ? seq->total - 1 : 0; - + CV_TS_SEQ_CHECK_CONDITION( pos == cvGetSeqReaderPos(&reader), "initial reader position is wrong" ); - + for( iter = 0; iter < iters; iter++ ) { int op = cvtest::randInt(rng) % max_val; - + if( op >= max_val - 2 ) { int new_pos, new_pos0; int bad_range; int is_relative = op == max_val - 1; - + new_pos = cvtest::randInt(rng) % (total*2) - total; new_pos0 = new_pos + (is_relative ? pos : 0 ); - + if( new_pos0 < 0 ) new_pos0 += total; if( new_pos0 >= total ) new_pos0 -= total; - + bad_range = (unsigned)new_pos0 >= (unsigned)total; cvSetSeqReaderPos( &reader, new_pos, is_relative ); - + if( !bad_range ) { CV_TS_SEQ_CHECK_CONDITION( new_pos0 == cvGetSeqReaderPos( &reader ), @@ -704,7 +704,7 @@ int Core_SeqBaseTest::test_get_seq_reading( int _struct_idx, int iters ) { int direction = (op % 3) - 1; memcpy( elem, reader.ptr, sseq->elem_size ); - + if( direction > 0 ) { CV_NEXT_SEQ_ELEM( sseq->elem_size, reader ); @@ -713,18 +713,18 @@ int Core_SeqBaseTest::test_get_seq_reading( int _struct_idx, int iters ) { CV_PREV_SEQ_ELEM( sseq->elem_size, reader ); } - + CV_TS_SEQ_CHECK_CONDITION( memcmp(elem, cvTsSimpleSeqElem(sseq, pos), sseq->elem_size) == 0, "reading is incorrect" ); pos += direction; if( -pos > 0 ) pos += total; if( pos >= total ) pos -= total; - + CV_TS_SEQ_CHECK_CONDITION( pos == cvGetSeqReaderPos( &reader ), "reader doesn't move correctly after reading" ); } } - + return 0; } @@ -735,14 +735,14 @@ int Core_SeqBaseTest::test_seq_ops( int iters ) int max_elem_size = 0; schar* elem2 = 0; RNG& rng = ts->get_rng(); - + for( int i = 0; i < struct_count; i++ ) max_elem_size = MAX( max_elem_size, ((CvSeq*)cxcore_struct[i])->elem_size ); - + vector elem_buf(max_struct_size*max_elem_size); schar* elem = (schar*)&elem_buf[0]; Mat elem_mat; - + for( iter = 0; iter < iters; iter++ ) { struct_idx = cvtest::randInt(rng) % struct_count; @@ -751,7 +751,7 @@ int Core_SeqBaseTest::test_seq_ops( int iters ) CvTsSimpleSeq* sseq = (CvTsSimpleSeq*)simple_struct[struct_idx]; int elem_size = sseq->elem_size; int whence = 0, pos = 0, count = 0; - + switch( op ) { case 0: @@ -759,10 +759,10 @@ int Core_SeqBaseTest::test_seq_ops( int iters ) case 2: // push/pushfront/insert if( sseq->count == sseq->max_count ) break; - + elem_mat = Mat(1, elem_size, CV_8U, elem); cvtest::randUni( rng, elem_mat, cvScalarAll(0), cvScalarAll(255) ); - + whence = op - 1; if( whence < 0 ) { @@ -779,7 +779,7 @@ int Core_SeqBaseTest::test_seq_ops( int iters ) pos = cvtest::randInt(rng) % (sseq->count + 1); cvSeqInsert( seq, pos, elem ); } - + cvTsSimpleSeqShiftAndCopy( sseq, pos, pos + 1, elem ); elem2 = cvGetSeqElem( seq, pos ); CV_TS_SEQ_CHECK_CONDITION( elem2 != 0, "The inserted element could not be retrieved" ); @@ -787,13 +787,13 @@ int Core_SeqBaseTest::test_seq_ops( int iters ) memcmp(elem2, cvTsSimpleSeqElem(sseq,pos), elem_size) == 0, "The inserted sequence element is wrong" ); break; - + case 3: case 4: case 5: // pop/popfront/remove if( sseq->count == 0 ) break; - + whence = op - 4; if( whence < 0 ) { @@ -810,19 +810,19 @@ int Core_SeqBaseTest::test_seq_ops( int iters ) pos = cvtest::randInt(rng) % sseq->count; cvSeqRemove( seq, pos ); } - + if( whence != 0 ) CV_TS_SEQ_CHECK_CONDITION( seq->total == sseq->count - 1 && memcmp( elem, cvTsSimpleSeqElem(sseq,pos), elem_size) == 0, "The popped sequence element isn't correct" ); - + cvTsSimpleSeqShiftAndCopy( sseq, pos + 1, pos ); - + if( sseq->count > 0 ) { elem2 = cvGetSeqElem( seq, pos < sseq->count ? pos : -1 ); CV_TS_SEQ_CHECK_CONDITION( elem2 != 0, "GetSeqElem fails after removing the element" ); - + CV_TS_SEQ_CHECK_CONDITION( memcmp( elem2, cvTsSimpleSeqElem(sseq, pos - (pos == sseq->count)), elem_size) == 0, "The first shifted element is not correct after removing another element" ); @@ -833,17 +833,17 @@ int Core_SeqBaseTest::test_seq_ops( int iters ) "The sequence doesn't become empty after the final remove" ); } break; - + case 6: case 7: case 8: // push [front] multi/insert slice if( sseq->count == sseq->max_count ) break; - + count = cvtest::randInt( rng ) % (sseq->max_count - sseq->count + 1); elem_mat = Mat(1, MAX(count,1) * elem_size, CV_8U, elem); cvtest::randUni( rng, elem_mat, cvScalarAll(0), cvScalarAll(255) ); - + whence = op - 7; pos = whence < 0 ? 0 : whence > 0 ? sseq->count : cvtest::randInt(rng) % (sseq->count+1); if( whence != 0 ) @@ -858,11 +858,11 @@ int Core_SeqBaseTest::test_seq_ops( int iters ) sseq->elem_size, elem, count, &header, &block ); - + cvSeqInsertSlice( seq, pos, &header ); } cvTsSimpleSeqShiftAndCopy( sseq, pos, pos + count, elem ); - + if( sseq->count > 0 ) { // choose the random element among the added @@ -879,22 +879,22 @@ int Core_SeqBaseTest::test_seq_ops( int iters ) "Adding no elements to empty sequence fails" ); } break; - + case 9: case 10: case 11: // pop [front] multi if( sseq->count == 0 ) break; - + count = cvtest::randInt(rng) % (sseq->count+1); whence = op - 10; pos = whence < 0 ? 0 : whence > 0 ? sseq->count - count : cvtest::randInt(rng) % (sseq->count - count + 1); - + if( whence != 0 ) { cvSeqPopMulti( seq, elem, count, whence < 0 ); - + if( count > 0 ) { CV_TS_SEQ_CHECK_CONDITION( memcmp(elem, @@ -906,10 +906,10 @@ int Core_SeqBaseTest::test_seq_ops( int iters ) { cvSeqRemoveSlice( seq, cvSlice(pos, pos + count) ); } - + CV_TS_SEQ_CHECK_CONDITION( seq->total == sseq->count - count, "The popmulti left a wrong number of elements in the sequence" ); - + cvTsSimpleSeqShiftAndCopy( sseq, pos + count, pos, 0 ); if( sseq->count > 0 ) { @@ -929,15 +929,15 @@ int Core_SeqBaseTest::test_seq_ops( int iters ) { CvMemStoragePos storage_pos; cvSaveMemStoragePos( storage, &storage_pos ); - + int copy_data = cvtest::randInt(rng) % 2; count = cvtest::randInt(rng) % (seq->total + 1); pos = cvtest::randInt(rng) % (seq->total - count + 1); CvSeq* seq_slice = cvSeqSlice( seq, cvSlice(pos, pos + count), storage, copy_data ); - + CV_TS_SEQ_CHECK_CONDITION( seq_slice && seq_slice->total == count, "cvSeqSlice returned incorrect slice" ); - + if( count > 0 ) { int test_idx = cvtest::randInt(rng) % count; @@ -949,7 +949,7 @@ int Core_SeqBaseTest::test_seq_ops( int iters ) CV_TS_SEQ_CHECK_CONDITION( (elem2 == elem3) ^ copy_data, "copy_data flag is handled incorrectly" ); } - + cvRestoreMemStoragePos( storage, &storage_pos ); } break; @@ -963,16 +963,16 @@ int Core_SeqBaseTest::test_seq_ops( int iters ) assert(0); return -1; } - + if( test_seq_block_consistence(struct_idx, seq, sseq->count) < 0 ) return -1; - + if( test_get_seq_elem(struct_idx, 7) < 0 ) return -1; - + update_progressbar(); } - + return 0; } @@ -984,44 +984,44 @@ void Core_SeqBaseTest::run( int ) RNG& rng = ts->get_rng(); int i; double t; - + clear(); test_progress = -1; - + simple_struct.resize(struct_count, 0); cxcore_struct.resize(struct_count, 0); - + for( gen = 0; gen < generations; gen++ ) { struct_idx = iter = -1; - + if( !storage ) { t = cvtest::randReal(rng)*(max_log_storage_block_size - min_log_storage_block_size) + min_log_storage_block_size; storage = cvCreateMemStorage( cvRound( exp(t * CV_LOG2) ) ); } - + iter = struct_idx = -1; test_multi_create(); - + for( i = 0; i < struct_count; i++ ) { if( test_seq_block_consistence(i, (CvSeq*)cxcore_struct[i], ((CvTsSimpleSeq*)simple_struct[i])->count) < 0 ) return; - + if( test_get_seq_elem( i, MAX(iterations/3,7) ) < 0 ) return; - + if( test_get_seq_reading( i, MAX(iterations/3,7) ) < 0 ) return; update_progressbar(); } - + if( test_seq_ops( iterations ) < 0 ) return; - + if( cvtest::randInt(rng) % 2 ) storage.release(); else @@ -1041,7 +1041,7 @@ class Core_SeqSortInvTest : public Core_SeqBaseTest public: Core_SeqSortInvTest(); void run( int ); - + protected: }; @@ -1072,73 +1072,73 @@ void Core_SeqSortInvTest::run( int ) double t; schar *elem0, *elem, *elem2; vector buffer; - + clear(); test_progress = -1; - + simple_struct.resize(struct_count, 0); cxcore_struct.resize(struct_count, 0); - + for( gen = 0; gen < generations; gen++ ) { struct_idx = iter = -1; - + if( storage.empty() ) { t = cvtest::randReal(rng)*(max_log_storage_block_size - min_log_storage_block_size) + min_log_storage_block_size; storage = cvCreateMemStorage( cvRound( exp(t * CV_LOG2) ) ); } - + for( iter = 0; iter < iterations/10; iter++ ) { int max_size = 0; test_multi_create(); - + for( i = 0; i < struct_count; i++ ) { CvTsSimpleSeq* sseq = (CvTsSimpleSeq*)simple_struct[i]; max_size = MAX( max_size, sseq->count*sseq->elem_size ); } - + buffer.resize(max_size); - + for( i = 0; i < struct_count; i++ ) { CvSeq* seq = (CvSeq*)cxcore_struct[i]; CvTsSimpleSeq* sseq = (CvTsSimpleSeq*)simple_struct[i]; CvSlice slice = CV_WHOLE_SEQ; - + //printf("%d. %d. %d-th size = %d\n", gen, iter, i, sseq->count ); - + cvSeqInvert( seq ); cvTsSimpleSeqInvert( sseq ); - + if( test_seq_block_consistence( i, seq, sseq->count ) < 0 ) return; - + if( sseq->count > 0 && cvtest::randInt(rng) % 2 == 0 ) { slice.end_index = cvtest::randInt(rng) % sseq->count + 1; slice.start_index = cvtest::randInt(rng) % (sseq->count - slice.end_index + 1); slice.end_index += slice.start_index; } - + cvCvtSeqToArray( seq, &buffer[0], slice ); - + slice.end_index = MIN( slice.end_index, sseq->count ); CV_TS_SEQ_CHECK_CONDITION( sseq->count == 0 || memcmp( &buffer[0], sseq->array + slice.start_index*sseq->elem_size, (slice.end_index - slice.start_index)*sseq->elem_size ) == 0, "cvSeqInvert returned wrong result" ); - + for( k = 0; k < (sseq->count > 0 ? 10 : 0); k++ ) { int idx0 = cvtest::randInt(rng) % sseq->count, idx = 0; elem0 = cvTsSimpleSeqElem( sseq, idx0 ); elem = cvGetSeqElem( seq, idx0 ); elem2 = cvSeqSearch( seq, elem0, k % 2 ? icvCmpSeqElems : 0, 0, &idx, seq ); - + CV_TS_SEQ_CHECK_CONDITION( elem != 0 && memcmp( elem0, elem, seq->elem_size ) == 0, "cvSeqInvert gives incorrect result" ); @@ -1147,18 +1147,18 @@ void Core_SeqSortInvTest::run( int ) elem2 == cvGetSeqElem( seq, idx ), "cvSeqSearch failed (linear search)" ); } - + cvSeqSort( seq, icvCmpSeqElems, seq ); - + if( test_seq_block_consistence( i, seq, sseq->count ) < 0 ) return; - + if( sseq->count > 0 ) { // !!! This is not thread-safe !!! icvCmpSeqElems2_elem_size = sseq->elem_size; qsort( sseq->array, sseq->count, sseq->elem_size, icvCmpSeqElems2 ); - + if( cvtest::randInt(rng) % 2 == 0 ) { slice.end_index = cvtest::randInt(rng) % sseq->count + 1; @@ -1166,20 +1166,20 @@ void Core_SeqSortInvTest::run( int ) slice.end_index += slice.start_index; } } - + cvCvtSeqToArray( seq, &buffer[0], slice ); CV_TS_SEQ_CHECK_CONDITION( sseq->count == 0 || memcmp( &buffer[0], sseq->array + slice.start_index*sseq->elem_size, (slice.end_index - slice.start_index)*sseq->elem_size ) == 0, "cvSeqSort returned wrong result" ); - + for( k = 0; k < (sseq->count > 0 ? 10 : 0); k++ ) { int idx0 = cvtest::randInt(rng) % sseq->count, idx = 0; elem0 = cvTsSimpleSeqElem( sseq, idx0 ); elem = cvGetSeqElem( seq, idx0 ); elem2 = cvSeqSearch( seq, elem0, icvCmpSeqElems, 1, &idx, seq ); - + CV_TS_SEQ_CHECK_CONDITION( elem != 0 && memcmp( elem0, elem, seq->elem_size ) == 0, "cvSeqSort gives incorrect result" ); @@ -1189,10 +1189,10 @@ void Core_SeqSortInvTest::run( int ) "cvSeqSearch failed (binary search)" ); } } - + cvClearMemStorage( storage ); } - + storage.release(); } } @@ -1210,7 +1210,7 @@ public: Core_SetTest(); void clear(); void run( int ); - + protected: //int test_seq_block_consistence( int struct_idx ); int test_set_ops( int iters ); @@ -1239,17 +1239,17 @@ int Core_SetTest::test_set_ops( int iters ) schar* elem_data = 0; RNG& rng = ts->get_rng(); //int max_active_count = 0, mean_active_count = 0; - + for( int i = 0; i < struct_count; i++ ) max_elem_size = MAX( max_elem_size, ((CvSeq*)cxcore_struct[i])->elem_size ); - + vector elem_buf(max_elem_size); Mat elem_mat; - + for( iter = 0; iter < iters; iter++ ) { struct_idx = cvtest::randInt(rng) % struct_count; - + CvSet* cvset = (CvSet*)cxcore_struct[struct_idx]; CvTsSimpleSet* sset = (CvTsSimpleSet*)simple_struct[struct_idx]; int pure_elem_size = sset->elem_size - 1; @@ -1259,13 +1259,13 @@ int Core_SetTest::test_set_ops( int iters ) CvSetElem* first_free = cvset->free_elems; CvSetElem* next_free = first_free ? first_free->next_free : 0; int pass_data = 0; - + if( iter > iters/10 && cvtest::randInt(rng)%200 == 0 ) // clear set { - int prev_count = cvset->total; + prev_count = cvset->total; cvClearSet( cvset ); cvTsClearSimpleSet( sset ); - + CV_TS_SEQ_CHECK_CONDITION( cvset->active_count == 0 && cvset->total == 0 && cvset->first == 0 && cvset->free_elems == 0 && (cvset->free_blocks != 0 || prev_count == 0), @@ -1276,11 +1276,11 @@ int Core_SetTest::test_set_ops( int iters ) { if( sset->free_count == 0 ) continue; - + elem_mat = Mat(1, cvset->elem_size, CV_8U, &elem_buf[0]); cvtest::randUni( rng, elem_mat, cvScalarAll(0), cvScalarAll(255) ); elem = (CvSetElem*)&elem_buf[0]; - + if( by_ptr ) { elem2 = cvSetNew( cvset ); @@ -1293,21 +1293,21 @@ int Core_SetTest::test_set_ops( int iters ) CV_TS_SEQ_CHECK_CONDITION( elem2 != 0 && elem2->flags == idx, "cvSetAdd returned NULL pointer or a wrong index" ); } - + elem_data = (schar*)elem + sizeof(int); - + if( !pass_data ) memcpy( (schar*)elem2 + sizeof(int), elem_data, pure_elem_size ); - + idx = elem2->flags; idx0 = cvTsSimpleSetAdd( sset, elem_data ); elem3 = cvGetSetElem( cvset, idx ); - + CV_TS_SEQ_CHECK_CONDITION( CV_IS_SET_ELEM(elem3) && idx == idx0 && elem3 == elem2 && (!pass_data || memcmp( (char*)elem3 + sizeof(int), elem_data, pure_elem_size) == 0), "The added element is not correct" ); - + CV_TS_SEQ_CHECK_CONDITION( (!first_free || elem3 == first_free) && (!next_free || cvset->free_elems == next_free) && cvset->active_count == prev_count + 1, @@ -1316,19 +1316,19 @@ int Core_SetTest::test_set_ops( int iters ) else if( op == 2 || op == 3 ) // remove element { idx = cvtest::randInt(rng) % sset->max_count; - + if( sset->free_count == sset->max_count || idx >= sset->count ) continue; - + elem_data = cvTsSimpleSetFind(sset, idx); if( elem_data == 0 ) continue; - + elem = cvGetSetElem( cvset, idx ); CV_TS_SEQ_CHECK_CONDITION( CV_IS_SET_ELEM(elem) && elem->flags == idx && memcmp((char*)elem + sizeof(int), elem_data, pure_elem_size) == 0, "cvGetSetElem returned wrong element" ); - + if( by_ptr ) { cvSetRemoveByPtr( cvset, elem ); @@ -1337,32 +1337,32 @@ int Core_SetTest::test_set_ops( int iters ) { cvSetRemove( cvset, idx ); } - + cvTsSimpleSetRemove( sset, idx ); - + CV_TS_SEQ_CHECK_CONDITION( !CV_IS_SET_ELEM(elem) && !cvGetSetElem(cvset, idx) && (elem->flags & CV_SET_ELEM_IDX_MASK) == idx, "cvSetRemove[ByPtr] didn't release the element properly" ); - + CV_TS_SEQ_CHECK_CONDITION( elem->next_free == first_free && cvset->free_elems == elem && cvset->active_count == prev_count - 1, "The free node list has not been updated properly" ); } - + //max_active_count = MAX( max_active_count, cvset->active_count ); //mean_active_count += cvset->active_count; CV_TS_SEQ_CHECK_CONDITION( cvset->active_count == sset->max_count - sset->free_count && cvset->total >= cvset->active_count && (cvset->total == 0 || cvset->total >= prev_total), "The total number of cvset elements is not correct" ); - + // CvSet and simple set do not neccessary have the same "total" (active & free) number, // so pass "set->total" to skip that check test_seq_block_consistence( struct_idx, (CvSeq*)cvset, cvset->total ); update_progressbar(); } - + return 0; } @@ -1373,19 +1373,19 @@ void Core_SetTest::run( int ) { RNG& rng = ts->get_rng(); double t; - + clear(); test_progress = -1; - + simple_struct.resize(struct_count, 0); cxcore_struct.resize(struct_count, 0); - + for( gen = 0; gen < generations; gen++ ) { struct_idx = iter = -1; t = cvtest::randReal(rng)*(max_log_storage_block_size - min_log_storage_block_size) + min_log_storage_block_size; storage = cvCreateMemStorage( cvRound( exp(t * CV_LOG2) ) ); - + for( int i = 0; i < struct_count; i++ ) { t = cvtest::randReal(rng)*(max_log_elem_size - min_log_elem_size) + min_log_elem_size; @@ -1395,15 +1395,15 @@ void Core_SetTest::run( int ) elem_size = MAX( elem_size, (int)sizeof(CvSetElem) ); elem_size = MIN( elem_size, (int)(storage->block_size - sizeof(void*) - sizeof(CvMemBlock) - sizeof(CvSeqBlock)) ); pure_elem_size = MIN( pure_elem_size, elem_size-(int)sizeof(CvSetElem) ); - + cvTsReleaseSimpleSet( (CvTsSimpleSet**)&simple_struct[i] ); simple_struct[i] = cvTsCreateSimpleSet( max_struct_size, pure_elem_size ); cxcore_struct[i] = cvCreateSet( 0, sizeof(CvSet), elem_size, storage ); } - + if( test_set_ops( iterations*100 ) < 0 ) return; - + storage.release(); } } @@ -1421,7 +1421,7 @@ public: Core_GraphTest(); void clear(); void run( int ); - + protected: //int test_seq_block_consistence( int struct_idx ); int test_graph_ops( int iters ); @@ -1451,17 +1451,17 @@ int Core_GraphTest::test_graph_ops( int iters ) CvGraphEdge* edge = 0, *edge2 = 0; RNG& rng = ts->get_rng(); //int max_active_count = 0, mean_active_count = 0; - + for( i = 0; i < struct_count; i++ ) { CvGraph* graph = (CvGraph*)cxcore_struct[i]; max_elem_size = MAX( max_elem_size, graph->elem_size ); max_elem_size = MAX( max_elem_size, graph->edges->elem_size ); } - + vector elem_buf(max_elem_size); Mat elem_mat; - + for( iter = 0; iter < iters; iter++ ) { struct_idx = cvtest::randInt(rng) % struct_count; @@ -1479,55 +1479,55 @@ int Core_GraphTest::test_graph_ops( int iters ) int op = cvtest::randInt(rng) % max_op; int pass_data = 0, vtx_degree0 = 0, vtx_degree = 0; CvSetElem *first_free, *next_free; - + if( cvtest::randInt(rng) % 200 == 0 ) // clear graph { - int prev_vtx_count = graph->total, prev_edge_count = graph->edges->total; - + int prev_vtx_count2 = graph->total, prev_edge_count2 = graph->edges->total; + cvClearGraph( graph ); cvTsClearSimpleGraph( sgraph ); - + CV_TS_SEQ_CHECK_CONDITION( graph->active_count == 0 && graph->total == 0 && graph->first == 0 && graph->free_elems == 0 && - (graph->free_blocks != 0 || prev_vtx_count == 0), + (graph->free_blocks != 0 || prev_vtx_count2 == 0), "The graph is not empty after clearing" ); - + CV_TS_SEQ_CHECK_CONDITION( edges->active_count == 0 && edges->total == 0 && edges->first == 0 && edges->free_elems == 0 && - (edges->free_blocks != 0 || prev_edge_count == 0), + (edges->free_blocks != 0 || prev_edge_count2 == 0), "The graph is not empty after clearing" ); } else if( op == 0 ) // add vertex { if( sgraph->vtx->free_count == 0 ) continue; - + first_free = graph->free_elems; next_free = first_free ? first_free->next_free : 0; - + if( pure_vtx_size ) { elem_mat = Mat(1, graph->elem_size, CV_8U, &elem_buf[0]); cvtest::randUni( rng, elem_mat, cvScalarAll(0), cvScalarAll(255) ); } - + vtx = (CvGraphVtx*)&elem_buf[0]; idx0 = cvTsSimpleGraphAddVertex( sgraph, vtx + 1 ); - + pass_data = cvtest::randInt(rng) % 2; idx = cvGraphAddVtx( graph, pass_data ? vtx : 0, &vtx2 ); - + if( !pass_data && pure_vtx_size > 0 ) memcpy( vtx2 + 1, vtx + 1, pure_vtx_size ); - + vtx3 = cvGetGraphVtx( graph, idx ); - + CV_TS_SEQ_CHECK_CONDITION( (CV_IS_SET_ELEM(vtx3) && vtx3->flags == idx && vtx3->first == 0) || (idx == idx0 && vtx3 == vtx2 && (!pass_data || pure_vtx_size == 0 || memcmp(vtx3 + 1, vtx + 1, pure_vtx_size) == 0)), "The added element is not correct" ); - + CV_TS_SEQ_CHECK_CONDITION( (!first_free || first_free == (CvSetElem*)vtx3) && (!next_free || graph->free_elems == next_free) && graph->active_count == prev_vtx_count + 1, @@ -1538,19 +1538,19 @@ int Core_GraphTest::test_graph_ops( int iters ) idx = cvtest::randInt(rng) % sgraph->vtx->max_count; if( sgraph->vtx->free_count == sgraph->vtx->max_count || idx >= sgraph->vtx->count ) continue; - + vtx_data = cvTsSimpleGraphFindVertex(sgraph, idx); if( vtx_data == 0 ) continue; - + vtx_degree0 = cvTsSimpleGraphVertexDegree( sgraph, idx ); first_free = graph->free_elems; - + vtx = cvGetGraphVtx( graph, idx ); CV_TS_SEQ_CHECK_CONDITION( CV_IS_SET_ELEM(vtx) && vtx->flags == idx && (pure_vtx_size == 0 || memcmp( vtx + 1, vtx_data, pure_vtx_size) == 0), "cvGetGraphVtx returned wrong element" ); - + if( cvtest::randInt(rng) % 2 ) { vtx_degree = cvGraphVtxDegreeByPtr( graph, vtx ); @@ -1561,20 +1561,20 @@ int Core_GraphTest::test_graph_ops( int iters ) vtx_degree = cvGraphVtxDegree( graph, idx ); cvGraphRemoveVtx( graph, idx ); } - + cvTsSimpleGraphRemoveVertex( sgraph, idx ); - + CV_TS_SEQ_CHECK_CONDITION( vtx_degree == vtx_degree0, "Number of incident edges is different in two graph representations" ); - + CV_TS_SEQ_CHECK_CONDITION( !CV_IS_SET_ELEM(vtx) && !cvGetGraphVtx(graph, idx) && (vtx->flags & CV_SET_ELEM_IDX_MASK) == idx, "cvGraphRemoveVtx[ByPtr] didn't release the vertex properly" ); - + CV_TS_SEQ_CHECK_CONDITION( graph->edges->active_count == prev_edge_count - vtx_degree, "cvGraphRemoveVtx[ByPtr] didn't remove all the incident edges " "(or removed some extra)" ); - + CV_TS_SEQ_CHECK_CONDITION( ((CvSetElem*)vtx)->next_free == first_free && graph->free_elems == (CvSetElem*)vtx && graph->active_count == prev_vtx_count - 1, @@ -1584,10 +1584,10 @@ int Core_GraphTest::test_graph_ops( int iters ) { int v_idx[2] = {0,0}, res = 0; int v_prev_degree[2] = {0,0}, v_degree[2] = {0,0}; - + if( sgraph->vtx->free_count >= sgraph->vtx->max_count-1 ) continue; - + for( i = 0, k = 0; i < 10; i++ ) { int j = cvtest::randInt(rng) % sgraph->vtx->count; @@ -1605,34 +1605,34 @@ int Core_GraphTest::test_graph_ops( int iters ) } } } - + if( k < 2 ) continue; - + first_free = graph->edges->free_elems; next_free = first_free ? first_free->next_free : 0; - + edge = cvFindGraphEdge( graph, v_idx[0], v_idx[1] ); CV_TS_SEQ_CHECK_CONDITION( edge == 0, "Extra edge appeared in the graph" ); - + if( pure_edge_size > 0 ) { elem_mat = Mat(1, graph->edges->elem_size, CV_8U, &elem_buf[0]); cvtest::randUni( rng, elem_mat, cvScalarAll(0), cvScalarAll(255) ); } edge = (CvGraphEdge*)&elem_buf[0]; - + // assign some default weight that is easy to check for // consistensy, 'cause an edge weight is not stored // in the simple graph edge->weight = (float)(v_idx[0] + v_idx[1]); pass_data = cvtest::randInt(rng) % 2; - + vtx = cvGetGraphVtx( graph, v_idx[0] ); vtx2 = cvGetGraphVtx( graph, v_idx[1] ); CV_TS_SEQ_CHECK_CONDITION( vtx != 0 && vtx2 != 0 && vtx->flags == v_idx[0] && vtx2->flags == v_idx[1], "Some of the vertices are missing" ); - + if( cvtest::randInt(rng) % 2 ) { v_prev_degree[0] = cvGraphVtxDegreeByPtr( graph, vtx ); @@ -1649,27 +1649,27 @@ int Core_GraphTest::test_graph_ops( int iters ) v_degree[0] = cvGraphVtxDegree( graph, v_idx[0] ); v_degree[1] = cvGraphVtxDegree( graph, v_idx[1] ); } - + //edge3 = (CvGraphEdge*)cvGetSetElem( graph->edges, idx ); CV_TS_SEQ_CHECK_CONDITION( res == 1 && edge2 != 0 && CV_IS_SET_ELEM(edge2) && ((edge2->vtx[0] == vtx && edge2->vtx[1] == vtx2) || (!CV_IS_GRAPH_ORIENTED(graph) && edge2->vtx[0] == vtx2 && edge2->vtx[1] == vtx)) && (!pass_data || pure_edge_size == 0 || memcmp( edge2 + 1, edge + 1, pure_edge_size ) == 0), "The edge has been added incorrectly" ); - + if( !pass_data ) { if( pure_edge_size > 0 ) memcpy( edge2 + 1, edge + 1, pure_edge_size ); edge2->weight = edge->weight; } - + CV_TS_SEQ_CHECK_CONDITION( v_degree[0] == v_prev_degree[0] + 1 && v_degree[1] == v_prev_degree[1] + 1, "The vertices lists have not been updated properly" ); - + cvTsSimpleGraphAddEdge( sgraph, v_idx[0], v_idx[1], edge + 1 ); - + CV_TS_SEQ_CHECK_CONDITION( (!first_free || first_free == (CvSetElem*)edge2) && (!next_free || graph->edges->free_elems == next_free) && graph->edges->active_count == prev_edge_count + 1, @@ -1679,10 +1679,10 @@ int Core_GraphTest::test_graph_ops( int iters ) { int v_idx[2] = {0,0}, by_ptr; int v_prev_degree[2] = {0,0}, v_degree[2] = {0,0}; - + if( sgraph->vtx->free_count >= sgraph->vtx->max_count-1 ) continue; - + edge_data = 0; for( i = 0, k = 0; i < 10; i++ ) { @@ -1704,18 +1704,18 @@ int Core_GraphTest::test_graph_ops( int iters ) } } } - + if( k < 2 ) continue; - + by_ptr = cvtest::randInt(rng) % 2; first_free = graph->edges->free_elems; - + vtx = cvGetGraphVtx( graph, v_idx[0] ); vtx2 = cvGetGraphVtx( graph, v_idx[1] ); CV_TS_SEQ_CHECK_CONDITION( vtx != 0 && vtx2 != 0 && vtx->flags == v_idx[0] && vtx2->flags == v_idx[1], "Some of the vertices are missing" ); - + if( by_ptr ) { edge = cvFindGraphEdgeByPtr( graph, vtx, vtx2 ); @@ -1728,15 +1728,15 @@ int Core_GraphTest::test_graph_ops( int iters ) v_prev_degree[0] = cvGraphVtxDegree( graph, v_idx[0] ); v_prev_degree[1] = cvGraphVtxDegree( graph, v_idx[1] ); } - + idx = edge->flags; - + CV_TS_SEQ_CHECK_CONDITION( edge != 0 && edge->weight == v_idx[0] + v_idx[1] && ((edge->vtx[0] == vtx && edge->vtx[1] == vtx2) || (!CV_IS_GRAPH_ORIENTED(graph) && edge->vtx[1] == vtx && edge->vtx[0] == vtx2)) && (pure_edge_size == 0 || memcmp(edge + 1, edge_data, pure_edge_size) == 0), "An edge is missing or incorrect" ); - + if( by_ptr ) { cvGraphRemoveEdgeByPtr( graph, vtx, vtx2 ); @@ -1751,41 +1751,41 @@ int Core_GraphTest::test_graph_ops( int iters ) v_degree[0] = cvGraphVtxDegree( graph, v_idx[0] ); v_degree[1] = cvGraphVtxDegree( graph, v_idx[1] ); } - + CV_TS_SEQ_CHECK_CONDITION( !edge2 && !CV_IS_SET_ELEM(edge), "The edge has not been removed from the edge set" ); - + CV_TS_SEQ_CHECK_CONDITION( v_degree[0] == v_prev_degree[0] - 1 && v_degree[1] == v_prev_degree[1] - 1, "The vertices lists have not been updated properly" ); - + cvTsSimpleGraphRemoveEdge( sgraph, v_idx[0], v_idx[1] ); - + CV_TS_SEQ_CHECK_CONDITION( graph->edges->free_elems == (CvSetElem*)edge && graph->edges->free_elems->next_free == first_free && graph->edges->active_count == prev_edge_count - 1, "The free edge list has not been modified properly" ); } - + //max_active_count = MAX( max_active_count, graph->active_count ); //mean_active_count += graph->active_count; - + CV_TS_SEQ_CHECK_CONDITION( graph->active_count == sgraph->vtx->max_count - sgraph->vtx->free_count && graph->total >= graph->active_count && (graph->total == 0 || graph->total >= prev_vtx_total), "The total number of graph vertices is not correct" ); - + CV_TS_SEQ_CHECK_CONDITION( graph->edges->total >= graph->edges->active_count && (graph->edges->total == 0 || graph->edges->total >= prev_edge_total), "The total number of graph vertices is not correct" ); - + // CvGraph and simple graph do not neccessary have the same "total" (active & free) number, // so pass "graph->total" (or "graph->edges->total") to skip that check test_seq_block_consistence( struct_idx, (CvSeq*)graph, graph->total ); test_seq_block_consistence( struct_idx, (CvSeq*)graph->edges, graph->edges->total ); update_progressbar(); } - + return 0; } @@ -1797,22 +1797,22 @@ void Core_GraphTest::run( int ) RNG& rng = ts->get_rng(); int i, k; double t; - + clear(); test_progress = -1; - + simple_struct.resize(struct_count, 0); cxcore_struct.resize(struct_count, 0); - + for( gen = 0; gen < generations; gen++ ) { struct_idx = iter = -1; t = cvtest::randReal(rng)*(max_log_storage_block_size - min_log_storage_block_size) + min_log_storage_block_size; int block_size = cvRound( exp(t * CV_LOG2) ); block_size = MAX(block_size, (int)(sizeof(CvGraph) + sizeof(CvMemBlock) + sizeof(CvSeqBlock))); - + storage = cvCreateMemStorage(block_size); - + for( i = 0; i < struct_count; i++ ) { int pure_elem_size[2], elem_size[2]; @@ -1830,7 +1830,7 @@ void Core_GraphTest::run( int ) pure_elem_size[k] = pe; elem_size[k] = e; } - + cvTsReleaseSimpleGraph( (CvTsSimpleGraph**)&simple_struct[i] ); simple_struct[i] = cvTsCreateSimpleGraph( max_struct_size/4, pure_elem_size[0], pure_elem_size[1], is_oriented ); @@ -1838,10 +1838,10 @@ void Core_GraphTest::run( int ) sizeof(CvGraph), elem_size[0], elem_size[1], storage ); } - + if( test_graph_ops( iterations*10 ) < 0 ) return; - + storage.release(); } } @@ -1858,7 +1858,7 @@ class Core_GraphScanTest : public Core_DynStructBaseTest public: Core_GraphScanTest(); void run( int ); - + protected: //int test_seq_block_consistence( int struct_idx ); int create_random_graph( int ); @@ -1879,29 +1879,29 @@ int Core_GraphScanTest::create_random_graph( int _struct_idx ) int i, vtx_count = cvtest::randInt(rng) % max_struct_size; int edge_count = cvtest::randInt(rng) % MAX(vtx_count*20, 1); CvGraph* graph; - + struct_idx = _struct_idx; cxcore_struct[_struct_idx] = graph = cvCreateGraph(is_oriented ? CV_ORIENTED_GRAPH : CV_GRAPH, sizeof(CvGraph), sizeof(CvGraphVtx), sizeof(CvGraphEdge), storage ); - + for( i = 0; i < vtx_count; i++ ) cvGraphAddVtx( graph ); - + assert( graph->active_count == vtx_count ); - + for( i = 0; i < edge_count; i++ ) { int j = cvtest::randInt(rng) % vtx_count; int k = cvtest::randInt(rng) % vtx_count; - + if( j != k ) cvGraphAddEdge( graph, j, k ); } - + assert( graph->active_count == vtx_count && graph->edges->active_count <= edge_count ); - + return 0; } @@ -1915,12 +1915,12 @@ void Core_GraphScanTest::run( int ) vector vtx_mask, edge_mask; double t; int i; - + clear(); test_progress = -1; - + cxcore_struct.resize(struct_count, 0); - + for( gen = 0; gen < generations; gen++ ) { struct_idx = iter = -1; @@ -1930,49 +1930,49 @@ void Core_GraphScanTest::run( int ) storage_blocksize = MAX(storage_blocksize, (int)(sizeof(CvGraphEdge) + sizeof(CvMemBlock) + sizeof(CvSeqBlock))); storage_blocksize = MAX(storage_blocksize, (int)(sizeof(CvGraphVtx) + sizeof(CvMemBlock) + sizeof(CvSeqBlock))); storage = cvCreateMemStorage(storage_blocksize); - + if( gen == 0 ) { // special regression test for one sample graph. // !!! ATTENTION !!! The test relies on the particular order of the inserted edges // (LIFO: the edge inserted last goes first in the list of incident edges). // if it is changed, the test will have to be modified. - + int vtx_count = -1, edge_count = 0, edges[][3] = { {0,4,'f'}, {0,1,'t'}, {1,4,'t'}, {1,2,'t'}, {2,3,'t'}, {4,3,'c'}, {3,1,'b'}, {5,7,'t'}, {7,5,'b'}, {5,6,'t'}, {6,0,'c'}, {7,6,'c'}, {6,4,'c'}, {-1,-1,0} }; - + CvGraph* graph = cvCreateGraph( CV_ORIENTED_GRAPH, sizeof(CvGraph), sizeof(CvGraphVtx), sizeof(CvGraphEdge), storage ); - + for( i = 0; edges[i][0] >= 0; i++ ) { vtx_count = MAX( vtx_count, edges[i][0] ); vtx_count = MAX( vtx_count, edges[i][1] ); } vtx_count++; - + for( i = 0; i < vtx_count; i++ ) cvGraphAddVtx( graph ); - + for( i = 0; edges[i][0] >= 0; i++ ) { CvGraphEdge* edge; cvGraphAddEdge( graph, edges[i][0], edges[i][1], 0, &edge ); edge->weight = (float)edges[i][2]; } - + edge_count = i; scanner = cvCreateGraphScanner( graph, 0, CV_GRAPH_ALL_ITEMS ); - + for(;;) { int code, a = -1, b = -1; const char* event = ""; code = cvNextGraphItem( scanner ); - + switch( code ) { case CV_GRAPH_VERTEX: @@ -2023,16 +2023,9 @@ void Core_GraphScanTest::run( int ) event = "End of procedure"; break; default: -#if _MSC_VER >= 1200 - #pragma warning( push ) - #pragma warning( disable : 4127 ) -#endif CV_TS_SEQ_CHECK_CONDITION( 0, "Invalid code appeared during graph scan" ); -#if _MSC_VER >= 1200 - #pragma warning( pop ) -#endif } - + ts->printf( cvtest::TS::LOG, "%s", event ); if( a >= 0 ) { @@ -2041,48 +2034,48 @@ void Core_GraphScanTest::run( int ) else ts->printf( cvtest::TS::LOG, ": %d", a ); } - + ts->printf( cvtest::TS::LOG, "\n" ); - + if( code < 0 ) break; } - + CV_TS_SEQ_CHECK_CONDITION( vtx_count == 0 && edge_count == 0, "Not every vertex/edge has been visited" ); update_progressbar(); } - + // for a random graph the test just checks that every graph vertex and // every edge is vitisted during the scan for( iter = 0; iter < iterations; iter++ ) { create_random_graph(0); CvGraph* graph = (CvGraph*)cxcore_struct[0]; - + // iterate twice to check that scanner doesn't damage the graph for( i = 0; i < 2; i++ ) { CvGraphVtx* start_vtx = cvtest::randInt(rng) % 2 || graph->active_count == 0 ? 0 : cvGetGraphVtx( graph, cvtest::randInt(rng) % graph->active_count ); - + scanner = cvCreateGraphScanner( graph, start_vtx, CV_GRAPH_ALL_ITEMS ); - + vtx_mask.resize(0); vtx_mask.resize(graph->active_count, 0); edge_mask.resize(0); edge_mask.resize(graph->edges->active_count, 0); - + for(;;) { int code = cvNextGraphItem( scanner ); - + if( code == CV_GRAPH_OVER ) break; else if( code & CV_GRAPH_ANY_EDGE ) { int edge_idx = scanner->edge->flags & CV_SET_ELEM_IDX_MASK; - + CV_TS_SEQ_CHECK_CONDITION( edge_idx < graph->edges->active_count && edge_mask[edge_idx] == 0, "The edge is not found or visited for the second time" ); @@ -2091,16 +2084,16 @@ void Core_GraphScanTest::run( int ) else if( code & CV_GRAPH_VERTEX ) { int vtx_idx = scanner->vtx->flags & CV_SET_ELEM_IDX_MASK; - + CV_TS_SEQ_CHECK_CONDITION( vtx_idx < graph->active_count && vtx_mask[vtx_idx] == 0, "The vtx is not found or visited for the second time" ); vtx_mask[vtx_idx] = 1; } } - + cvReleaseGraphScanner( &scanner ); - + CV_TS_SEQ_CHECK_CONDITION( cvtest::norm(Mat(vtx_mask),CV_L1) == graph->active_count && cvtest::norm(Mat(edge_mask),CV_L1) == graph->edges->active_count, "Some vertices or edges have not been visited" ); @@ -2108,14 +2101,14 @@ void Core_GraphScanTest::run( int ) } cvClearMemStorage( storage ); } - + storage.release(); } } catch(int) { } - + cvReleaseGraphScanner( &scanner ); } diff --git a/modules/core/test/test_mat.cpp b/modules/core/test/test_mat.cpp index f588ae4..6b5d09f 100644 --- a/modules/core/test/test_mat.cpp +++ b/modules/core/test/test_mat.cpp @@ -22,25 +22,25 @@ void testReduce( const Mat& src, Mat& sum, Mat& avg, Mat& max, Mat& min, int dim assert( src.channels() == 1 ); if( dim == 0 ) // row { - sum.create( 1, src.cols, CV_64FC1 ); + sum.create( 1, src.cols, CV_64FC1 ); max.create( 1, src.cols, CV_64FC1 ); min.create( 1, src.cols, CV_64FC1 ); } else { - sum.create( src.rows, 1, CV_64FC1 ); + sum.create( src.rows, 1, CV_64FC1 ); max.create( src.rows, 1, CV_64FC1 ); min.create( src.rows, 1, CV_64FC1 ); } sum.setTo(Scalar(0)); max.setTo(Scalar(-DBL_MAX)); min.setTo(Scalar(DBL_MAX)); - + const Mat_& src_ = src; Mat_& sum_ = (Mat_&)sum; Mat_& min_ = (Mat_&)min; Mat_& max_ = (Mat_&)max; - + if( dim == 0 ) { for( int ri = 0; ri < src.rows; ri++ ) @@ -128,7 +128,7 @@ int Core_ReduceTest::checkOp( const Mat& src, int dstType, int opType, const Mat else if ( dstType == CV_32S ) eps = 0.6; } - + assert( opRes.type() == CV_64FC1 ); Mat _dst, dst, diff; reduce( src, _dst, dim, opType, dstType ); @@ -151,7 +151,7 @@ int Core_ReduceTest::checkOp( const Mat& src, int dstType, int opType, const Mat getMatTypeStr( src.type(), srcTypeStr ); getMatTypeStr( dstType, dstTypeStr ); const char* dimStr = dim == 0 ? "ROWS" : "COLS"; - + sprintf( msg, "bad accuracy with srcType = %s, dstType = %s, opType = %s, dim = %s", srcTypeStr.c_str(), dstTypeStr.c_str(), opTypeStr, dimStr ); ts->printf( cvtest::TS::LOG, msg ); @@ -164,10 +164,10 @@ int Core_ReduceTest::checkCase( int srcType, int dstType, int dim, Size sz ) { int code = cvtest::TS::OK, tempCode; Mat src, sum, avg, max, min; - + src.create( sz, srcType ); randu( src, Scalar(0), Scalar(100) ); - + if( srcType == CV_8UC1 ) testReduce( src, sum, avg, max, min, dim ); else if( srcType == CV_8SC1 ) @@ -182,110 +182,108 @@ int Core_ReduceTest::checkCase( int srcType, int dstType, int dim, Size sz ) testReduce( src, sum, avg, max, min, dim ); else if( srcType == CV_64FC1 ) testReduce( src, sum, avg, max, min, dim ); - else + else assert( 0 ); - + // 1. sum tempCode = checkOp( src, dstType, CV_REDUCE_SUM, sum, dim ); code = tempCode != cvtest::TS::OK ? tempCode : code; - + // 2. avg tempCode = checkOp( src, dstType, CV_REDUCE_AVG, avg, dim ); code = tempCode != cvtest::TS::OK ? tempCode : code; - + // 3. max tempCode = checkOp( src, dstType, CV_REDUCE_MAX, max, dim ); code = tempCode != cvtest::TS::OK ? tempCode : code; - + // 4. min tempCode = checkOp( src, dstType, CV_REDUCE_MIN, min, dim ); code = tempCode != cvtest::TS::OK ? tempCode : code; - + return code; } int Core_ReduceTest::checkDim( int dim, Size sz ) { int code = cvtest::TS::OK, tempCode; - + // CV_8UC1 tempCode = checkCase( CV_8UC1, CV_8UC1, dim, sz ); code = tempCode != cvtest::TS::OK ? tempCode : code; - + tempCode = checkCase( CV_8UC1, CV_32SC1, dim, sz ); code = tempCode != cvtest::TS::OK ? tempCode : code; - + tempCode = checkCase( CV_8UC1, CV_32FC1, dim, sz ); code = tempCode != cvtest::TS::OK ? tempCode : code; - + tempCode = checkCase( CV_8UC1, CV_64FC1, dim, sz ); code = tempCode != cvtest::TS::OK ? tempCode : code; - + // CV_16UC1 tempCode = checkCase( CV_16UC1, CV_32FC1, dim, sz ); code = tempCode != cvtest::TS::OK ? tempCode : code; - + tempCode = checkCase( CV_16UC1, CV_64FC1, dim, sz ); code = tempCode != cvtest::TS::OK ? tempCode : code; - + // CV_16SC1 tempCode = checkCase( CV_16SC1, CV_32FC1, dim, sz ); code = tempCode != cvtest::TS::OK ? tempCode : code; - + tempCode = checkCase( CV_16SC1, CV_64FC1, dim, sz ); code = tempCode != cvtest::TS::OK ? tempCode : code; - + // CV_32FC1 tempCode = checkCase( CV_32FC1, CV_32FC1, dim, sz ); code = tempCode != cvtest::TS::OK ? tempCode : code; - + tempCode = checkCase( CV_32FC1, CV_64FC1, dim, sz ); code = tempCode != cvtest::TS::OK ? tempCode : code; - + // CV_64FC1 tempCode = checkCase( CV_64FC1, CV_64FC1, dim, sz ); code = tempCode != cvtest::TS::OK ? tempCode : code; - + return code; } int Core_ReduceTest::checkSize( Size sz ) { int code = cvtest::TS::OK, tempCode; - + tempCode = checkDim( 0, sz ); // rows code = tempCode != cvtest::TS::OK ? tempCode : code; - - tempCode = checkDim( 1, sz ); // cols + + tempCode = checkDim( 1, sz ); // cols code = tempCode != cvtest::TS::OK ? tempCode : code; - + return code; } void Core_ReduceTest::run( int ) { int code = cvtest::TS::OK, tempCode; - + tempCode = checkSize( Size(1,1) ); code = tempCode != cvtest::TS::OK ? tempCode : code; - + tempCode = checkSize( Size(1,100) ); code = tempCode != cvtest::TS::OK ? tempCode : code; - + tempCode = checkSize( Size(100,1) ); code = tempCode != cvtest::TS::OK ? tempCode : code; - + tempCode = checkSize( Size(1000,500) ); code = tempCode != cvtest::TS::OK ? tempCode : code; - + ts->set_failed_test_info( code ); } #define CHECK_C -Size sz(200, 500); - class Core_PCATest : public cvtest::BaseTest { public: @@ -293,41 +291,43 @@ public: protected: void run(int) { + const Size sz(200, 500); + double diffPrjEps, diffBackPrjEps, prjEps, backPrjEps, evalEps, evecEps; int maxComponents = 100; Mat rPoints(sz, CV_32FC1), rTestPoints(sz, CV_32FC1); - RNG& rng = ts->get_rng(); - + RNG& rng = ts->get_rng(); + rng.fill( rPoints, RNG::UNIFORM, Scalar::all(0.0), Scalar::all(1.0) ); rng.fill( rTestPoints, RNG::UNIFORM, Scalar::all(0.0), Scalar::all(1.0) ); - + PCA rPCA( rPoints, Mat(), CV_PCA_DATA_AS_ROW, maxComponents ), cPCA; - + // 1. check C++ PCA & ROW Mat rPrjTestPoints = rPCA.project( rTestPoints ); Mat rBackPrjTestPoints = rPCA.backProject( rPrjTestPoints ); - + Mat avg(1, sz.width, CV_32FC1 ); reduce( rPoints, avg, 0, CV_REDUCE_AVG ); Mat Q = rPoints - repeat( avg, rPoints.rows, 1 ), Qt = Q.t(), eval, evec; Q = Qt * Q; Q = Q /(float)rPoints.rows; - + eigen( Q, eval, evec ); /*SVD svd(Q); evec = svd.vt; eval = svd.w;*/ - + Mat subEval( maxComponents, 1, eval.type(), eval.data ), subEvec( maxComponents, evec.cols, evec.type(), evec.data ); - + #ifdef CHECK_C Mat prjTestPoints, backPrjTestPoints, cPoints = rPoints.t(), cTestPoints = rTestPoints.t(); CvMat _points, _testPoints, _avg, _eval, _evec, _prjTestPoints, _backPrjTestPoints; #endif - + // check eigen() double eigenEps = 1e-6; double err; @@ -335,7 +335,7 @@ protected: { Mat v = evec.row(i).t(); Mat Qv = Q * v; - + Mat lv = eval.at(i,0) * v; err = norm( Qv, lv ); if( err > eigenEps ) @@ -370,7 +370,7 @@ protected: absdiff(rPCA.eigenvectors, subEvec, tmp); double mval = 0; Point mloc; minMaxLoc(tmp, 0, &mval, 0, &mloc); - + ts->printf( cvtest::TS::LOG, "pca.eigenvectors is incorrect (CV_PCA_DATA_AS_ROW); err = %f\n", err ); ts->printf( cvtest::TS::LOG, "max diff is %g at (i=%d, j=%d) (%g vs %g)\n", mval, mloc.y, mloc.x, rPCA.eigenvectors.at(mloc.y, mloc.x), @@ -380,7 +380,7 @@ protected: } } } - + prjEps = 1.265, backPrjEps = 1.265; for( int i = 0; i < rTestPoints.rows; i++ ) { @@ -404,7 +404,7 @@ protected: return; } } - + // 2. check C++ PCA & COL cPCA( rPoints.t(), Mat(), CV_PCA_DATA_AS_COL, maxComponents ); diffPrjEps = 1, diffBackPrjEps = 1; @@ -423,7 +423,7 @@ protected: ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY ); return; } - + #ifdef CHECK_C // 3. check C PCA & ROW _points = rPoints; @@ -435,11 +435,11 @@ protected: backPrjTestPoints.create(rPoints.size(), rPoints.type() ); _prjTestPoints = prjTestPoints; _backPrjTestPoints = backPrjTestPoints; - + cvCalcPCA( &_points, &_avg, &_eval, &_evec, CV_PCA_DATA_AS_ROW ); cvProjectPCA( &_testPoints, &_avg, &_evec, &_prjTestPoints ); cvBackProjectPCA( &_prjTestPoints, &_avg, &_evec, &_backPrjTestPoints ); - + err = norm(prjTestPoints, rPrjTestPoints, CV_RELATIVE_L2); if( err > diffPrjEps ) { @@ -454,7 +454,7 @@ protected: ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY ); return; } - + // 3. check C PCA & COL _points = cPoints; _testPoints = cTestPoints; @@ -463,11 +463,11 @@ protected: evec = evec.t(); _evec = evec; prjTestPoints = prjTestPoints.t(); _prjTestPoints = prjTestPoints; backPrjTestPoints = backPrjTestPoints.t(); _backPrjTestPoints = backPrjTestPoints; - + cvCalcPCA( &_points, &_avg, &_eval, &_evec, CV_PCA_DATA_AS_COL ); cvProjectPCA( &_testPoints, &_avg, &_evec, &_prjTestPoints ); cvBackProjectPCA( &_prjTestPoints, &_avg, &_evec, &_backPrjTestPoints ); - + err = norm(cv::abs(prjTestPoints), cv::abs(rPrjTestPoints.t()), CV_RELATIVE_L2 ); if( err > diffPrjEps ) { @@ -490,9 +490,9 @@ class Core_ArrayOpTest : public cvtest::BaseTest { public: Core_ArrayOpTest(); - ~Core_ArrayOpTest(); + ~Core_ArrayOpTest(); protected: - void run(int); + void run(int); }; @@ -536,7 +536,7 @@ static double getValue(SparseMat& M, const int* idx, RNG& rng) d == 3 ? M.hash(idx[0], idx[1], idx[2]) : M.hash(idx); phv = &hv; } - + const uchar* ptr = d == 2 ? M.ptr(idx[0], idx[1], false, phv) : d == 3 ? M.ptr(idx[0], idx[1], idx[2], false, phv) : M.ptr(idx, false, phv); @@ -560,7 +560,7 @@ static void eraseValue(SparseMat& M, const int* idx, RNG& rng) d == 3 ? M.hash(idx[0], idx[1], idx[2]) : M.hash(idx); phv = &hv; } - + if( d == 2 ) M.erase(idx[0], idx[1], phv); else if( d == 3 ) @@ -584,7 +584,7 @@ static void setValue(SparseMat& M, const int* idx, double value, RNG& rng) d == 3 ? M.hash(idx[0], idx[1], idx[2]) : M.hash(idx); phv = &hv; } - + uchar* ptr = d == 2 ? M.ptr(idx[0], idx[1], true, phv) : d == 3 ? M.ptr(idx[0], idx[1], idx[2], true, phv) : M.ptr(idx, true, phv); @@ -599,7 +599,7 @@ static void setValue(SparseMat& M, const int* idx, double value, RNG& rng) void Core_ArrayOpTest::run( int /* start_from */) { int errcount = 0; - + // dense matrix operations { int sz3[] = {5, 10, 15}; @@ -608,7 +608,7 @@ void Core_ArrayOpTest::run( int /* start_from */) RNG rng; rng.fill(A, CV_RAND_UNI, Scalar::all(-10), Scalar::all(10)); rng.fill(B, CV_RAND_UNI, Scalar::all(-10), Scalar::all(10)); - + int idx0[] = {3,4,5}, idx1[] = {0, 9, 7}; float val0 = 130; Scalar val1(-1000, 30, 3, 8); @@ -617,12 +617,12 @@ void Core_ArrayOpTest::run( int /* start_from */) cvSetND(&matB, idx0, val1); cvSet3D(&matB, idx1[0], idx1[1], idx1[2], -val1); Ptr matC = cvCloneMatND(&matB); - + if( A.at(idx0[0], idx0[1], idx0[2]) != val0 || A.at(idx1[0], idx1[1], idx1[2]) != -val0 || cvGetReal3D(&matA, idx0[0], idx0[1], idx0[2]) != val0 || cvGetRealND(&matA, idx1) != -val0 || - + Scalar(B.at(idx0[0], idx0[1], idx0[2])) != val1 || Scalar(B.at(idx1[0], idx1[1], idx1[2])) != -val1 || Scalar(cvGet3D(matC, idx0[0], idx0[1], idx0[2])) != val1 || @@ -633,7 +633,7 @@ void Core_ArrayOpTest::run( int /* start_from */) errcount++; } } - + RNG rng; const int MAX_DIM = 5, MAX_DIM_SZ = 10; // sparse matrix operations @@ -647,7 +647,7 @@ void Core_ArrayOpTest::run( int /* start_from */) vector all_vals2; string sidx, min_sidx, max_sidx; double min_val=0, max_val=0; - + int p = 1; for( k = 0; k < dims; k++ ) { @@ -656,7 +656,7 @@ void Core_ArrayOpTest::run( int /* start_from */) } SparseMat M( dims, size, depth ); map M0; - + int nz0 = (unsigned)rng % max(p/5,10); nz0 = min(max(nz0, 1), p); all_vals.resize(nz0); @@ -676,12 +676,12 @@ void Core_ArrayOpTest::run( int /* start_from */) _all_vals2.convertTo(_all_vals2_f, CV_32F); _all_vals2_f.convertTo(_all_vals2, CV_64F); } - + minMaxLoc(_all_vals, &min_val, &max_val); double _norm0 = norm(_all_vals, CV_C); double _norm1 = norm(_all_vals, CV_L1); double _norm2 = norm(_all_vals, CV_L2); - + for( i = 0; i < nz0; i++ ) { for(;;) @@ -708,18 +708,18 @@ void Core_ArrayOpTest::run( int /* start_from */) break; } } - + Ptr M2 = (CvSparseMat*)M; MatND Md; M.copyTo(Md); SparseMat M3; SparseMat(Md).convertTo(M3, Md.type(), 2); - + int nz1 = (int)M.nzcount(), nz2 = (int)M3.nzcount(); double norm0 = norm(M, CV_C); double norm1 = norm(M, CV_L1); double norm2 = norm(M, CV_L2); double eps = depth == CV_32F ? FLT_EPSILON*100 : DBL_EPSILON*1000; - + if( nz1 != nz0 || nz2 != nz0) { errcount++; @@ -727,7 +727,7 @@ void Core_ArrayOpTest::run( int /* start_from */) si, nz1, nz2, nz0 ); break; } - + if( fabs(norm0 - _norm0) > fabs(_norm0)*eps || fabs(norm1 - _norm1) > fabs(_norm1)*eps || fabs(norm2 - _norm2) > fabs(_norm2)*eps ) @@ -737,10 +737,10 @@ void Core_ArrayOpTest::run( int /* start_from */) si, norm0, norm1, norm2, _norm0, _norm1, _norm2 ); break; } - + int n = (unsigned)rng % max(p/5,10); n = min(max(n, 1), p) + nz0; - + for( i = 0; i < n; i++ ) { double val1, val2, val3, val0; @@ -760,7 +760,7 @@ void Core_ArrayOpTest::run( int /* start_from */) val1 = getValue(M, idx, rng); val2 = getValue(M2, idx); val3 = getValue(M3, idx, rng); - + if( val1 != val0 || val2 != val0 || fabs(val3 - val0*2) > fabs(val0*2)*FLT_EPSILON ) { errcount++; @@ -768,7 +768,7 @@ void Core_ArrayOpTest::run( int /* start_from */) break; } } - + for( i = 0; i < n; i++ ) { double val1, val2; @@ -792,9 +792,9 @@ void Core_ArrayOpTest::run( int /* start_from */) errcount++; ts->printf(cvtest::TS::LOG, "SparseMat: after deleting M[%s], it is =%g/%g (while it should be 0)\n", sidx.c_str(), val1, val2 ); break; - } + } } - + int nz = (int)M.nzcount(); if( nz != 0 ) { @@ -802,7 +802,7 @@ void Core_ArrayOpTest::run( int /* start_from */) ts->printf(cvtest::TS::LOG, "The number of non-zero elements after removing all the elements = %d (while it should be 0)\n", nz ); break; } - + int idx1[MAX_DIM], idx2[MAX_DIM]; double val1 = 0, val2 = 0; M3 = SparseMat(Md); @@ -816,7 +816,7 @@ void Core_ArrayOpTest::run( int /* start_from */) min_val, max_val, min_sidx.c_str(), max_sidx.c_str()); break; } - + minMaxIdx(Md, &val1, &val2, idx1, idx2); s1 = idx2string(idx1, dims), s2 = idx2string(idx2, dims); if( (min_val < 0 && (val1 != min_val || s1 != min_sidx)) || @@ -829,7 +829,7 @@ void Core_ArrayOpTest::run( int /* start_from */) break; } } - + ts->set_failed_test_info(errcount == 0 ? cvtest::TS::OK : cvtest::TS::FAIL_INVALID_OUTPUT); } diff --git a/modules/core/test/test_precomp.hpp b/modules/core/test/test_precomp.hpp index 887f915..f00212f 100644 --- a/modules/core/test/test_precomp.hpp +++ b/modules/core/test/test_precomp.hpp @@ -1,3 +1,7 @@ +#ifdef __GNUC__ +# pragma GCC diagnostic ignored "-Wmissing-declarations" +#endif + #ifndef __OPENCV_TEST_PRECOMP_HPP__ #define __OPENCV_TEST_PRECOMP_HPP__ diff --git a/modules/core/test/test_rand.cpp b/modules/core/test/test_rand.cpp index 616264d..8fab165 100644 --- a/modules/core/test/test_rand.cpp +++ b/modules/core/test/test_rand.cpp @@ -27,7 +27,7 @@ static double chi2_p95(int n) 36.42f, 37.65f, 38.89f, 40.11f, 41.34f, 42.56f, 43.77f }; static const double xp = 1.64; CV_Assert(n >= 1); - + if( n <= 30 ) return chi2_tab95[n-1]; return n + sqrt((double)2*n)*xp + 0.6666666666666*(xp*xp - 1); @@ -40,12 +40,12 @@ bool Core_RandTest::check_pdf(const Mat& hist, double scale, const int* H = (const int*)hist.data; float* H0 = ((float*)hist0.data); int i, hsz = hist.cols; - + double sum = 0; for( i = 0; i < hsz; i++ ) sum += H[i]; CV_Assert( fabs(1./sum - scale) < FLT_EPSILON ); - + if( dist_type == CV_RAND_UNI ) { float scale0 = (float)(1./hsz); @@ -54,19 +54,19 @@ bool Core_RandTest::check_pdf(const Mat& hist, double scale, } else { - double sum = 0, r = (hsz-1.)/2; + double sum2 = 0, r = (hsz-1.)/2; double alpha = 2*sqrt(2.)/r, beta = -alpha*r; for( i = 0; i < hsz; i++ ) { double x = i*alpha + beta; H0[i] = (float)exp(-x*x); - sum += H0[i]; + sum2 += H0[i]; } - sum = 1./sum; + sum2 = 1./sum2; for( i = 0; i < hsz; i++ ) - H0[i] = (float)(H0[i]*sum); + H0[i] = (float)(H0[i]*sum2); } - + double chi2 = 0; for( i = 0; i < hsz; i++ ) { @@ -76,7 +76,7 @@ bool Core_RandTest::check_pdf(const Mat& hist, double scale, chi2 += (a - b)*(a - b)/(a + b); } realval = chi2; - + double chi2_pval = chi2_p95(hsz - 1 - (dist_type == CV_RAND_NORMAL ? 2 : 0)); refval = chi2_pval*0.01; return realval <= refval; @@ -87,22 +87,22 @@ void Core_RandTest::run( int ) static int _ranges[][2] = {{ 0, 256 }, { -128, 128 }, { 0, 65536 }, { -32768, 32768 }, { -1000000, 1000000 }, { -1000, 1000 }, { -1000, 1000 }}; - + const int MAX_SDIM = 10; const int N = 2000000; const int maxSlice = 1000; const int MAX_HIST_SIZE = 1000; int progress = 0; - + RNG& rng = ts->get_rng(); RNG tested_rng = theRNG(); test_case_count = 200; - + for( int idx = 0; idx < test_case_count; idx++ ) { progress = update_progress( progress, idx, test_case_count, 0 ); ts->update_context( this, idx, false ); - + int depth = cvtest::randInt(rng) % (CV_64F+1); int c, cn = (cvtest::randInt(rng) % 4) + 1; int type = CV_MAKETYPE(depth, cn); @@ -113,15 +113,15 @@ void Core_RandTest::run( int ) double eps = 1.e-4; if (depth == CV_64F) eps = 1.e-7; - + bool do_sphere_test = dist_type == CV_RAND_UNI; Mat arr[2], hist[4]; int W[] = {0,0,0,0}; - + arr[0].create(1, SZ, type); arr[1].create(1, SZ, type); bool fast_algo = dist_type == CV_RAND_UNI && depth < CV_32F; - + for( c = 0; c < cn; c++ ) { int a, b, hsz; @@ -137,7 +137,7 @@ void Core_RandTest::run( int ) while( abs(a-b) <= 1 ); if( a > b ) std::swap(a, b); - + unsigned r = (unsigned)(b - a); fast_algo = fast_algo && r <= 256 && (r & (r-1)) == 0; hsz = min((unsigned)(b - a), (unsigned)MAX_HIST_SIZE); @@ -149,7 +149,7 @@ void Core_RandTest::run( int ) int meanrange = vrange/16; int mindiv = MAX(vrange/20, 5); int maxdiv = MIN(vrange/8, 10000); - + a = cvtest::randInt(rng) % meanrange - meanrange/2 + (_ranges[depth][0] + _ranges[depth][1])/2; b = cvtest::randInt(rng) % (maxdiv - mindiv) + mindiv; @@ -157,9 +157,9 @@ void Core_RandTest::run( int ) } A[c] = a; B[c] = b; - hist[c].create(1, hsz, CV_32S); + hist[c].create(1, hsz, CV_32S); } - + cv::RNG saved_rng = tested_rng; int maxk = fast_algo ? 0 : 1; for( k = 0; k <= maxk; k++ ) @@ -173,14 +173,14 @@ void Core_RandTest::run( int ) tested_rng.fill(aslice, dist_type, A, B); } } - + if( maxk >= 1 && norm(arr[0], arr[1], NORM_INF) > eps) { ts->printf( cvtest::TS::LOG, "RNG output depends on the array lengths (some generated numbers get lost?)" ); ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT ); return; } - + for( c = 0; c < cn; c++ ) { const uchar* data = arr[0].data; @@ -190,9 +190,9 @@ void Core_RandTest::run( int ) double maxVal = dist_type == CV_RAND_UNI ? B[c] : A[c] + B[c]*4; double scale = HSZ/(maxVal - minVal); double delta = -minVal*scale; - + hist[c] = Scalar::all(0); - + for( i = c; i < SZ*cn; i += cn ) { double val = depth == CV_8U ? ((const uchar*)data)[i] : @@ -221,7 +221,7 @@ void Core_RandTest::run( int ) } } } - + if( dist_type == CV_RAND_UNI && W[c] != SZ ) { ts->printf( cvtest::TS::LOG, "Uniform RNG gave values out of the range [%g,%g) on channel %d/%d\n", @@ -237,7 +237,7 @@ void Core_RandTest::run( int ) return; } double refval = 0, realval = 0; - + if( !check_pdf(hist[c], 1./W[c], dist_type, refval, realval) ) { ts->printf( cvtest::TS::LOG, "RNG failed Chi-square test " @@ -247,13 +247,13 @@ void Core_RandTest::run( int ) return; } } - + // Monte-Carlo test. Compute volume of SDIM-dimensional sphere // inscribed in [-1,1]^SDIM cube. if( do_sphere_test ) { int SDIM = cvtest::randInt(rng) % (MAX_SDIM-1) + 2; - int N0 = (SZ*cn/SDIM), N = 0; + int N0 = (SZ*cn/SDIM), n = 0; double r2 = 0; const uchar* data = arr[0].data; double scale[4], delta[4]; @@ -262,7 +262,7 @@ void Core_RandTest::run( int ) scale[c] = 2./(B[c] - A[c]); delta[c] = -A[c]*scale[c] - 1; } - + for( i = k = c = 0; i <= SZ*cn - SDIM; i++, k++, c++ ) { double val = depth == CV_8U ? ((const uchar*)data)[i] : @@ -276,20 +276,20 @@ void Core_RandTest::run( int ) r2 += val*val; if( k == SDIM-1 ) { - N += r2 <= 1; + n += r2 <= 1; r2 = 0; k = -1; } } - - double V = ((double)N/N0)*(1 << SDIM); - + + double V = ((double)n/N0)*(1 << SDIM); + // the theoretically computed volume int sdim = SDIM % 2; double V0 = sdim + 1; for( sdim += 2; sdim <= SDIM; sdim += 2 ) V0 *= 2*CV_PI/sdim; - + if( fabs(V - V0) > 0.3*fabs(V0) ) { ts->printf( cvtest::TS::LOG, "RNG failed %d-dim sphere volume test (got %g instead of %g)\n", @@ -309,7 +309,7 @@ class Core_RandRangeTest : public cvtest::BaseTest { public: Core_RandRangeTest() {} - ~Core_RandRangeTest() {} + ~Core_RandRangeTest() {} protected: void run(int) { @@ -319,7 +319,7 @@ protected: theRNG().fill(af, RNG::UNIFORM, -DBL_MAX, DBL_MAX); int n0 = 0, n255 = 0, nx = 0; int nfmin = 0, nfmax = 0, nfx = 0; - + for( int i = 0; i < a.rows; i++ ) for( int j = 0; j < a.cols; j++ ) { diff --git a/modules/features2d/perf/perf_precomp.hpp b/modules/features2d/perf/perf_precomp.hpp index c986237..b8608bf 100644 --- a/modules/features2d/perf/perf_precomp.hpp +++ b/modules/features2d/perf/perf_precomp.hpp @@ -1,3 +1,7 @@ +#ifdef __GNUC__ +# pragma GCC diagnostic ignored "-Wmissing-declarations" +#endif + #ifndef __OPENCV_PERF_PRECOMP_HPP__ #define __OPENCV_PERF_PRECOMP_HPP__ @@ -5,7 +9,7 @@ #include "opencv2/highgui/highgui.hpp" #include "opencv2/features2d/features2d.hpp" -#if GTEST_CREATE_SHARED_LIBRARY +#ifdef GTEST_CREATE_SHARED_LIBRARY #error no modules except ts should have GTEST_CREATE_SHARED_LIBRARY defined #endif diff --git a/modules/features2d/src/bagofwords.cpp b/modules/features2d/src/bagofwords.cpp index 384d2bc..9770064 100755 --- a/modules/features2d/src/bagofwords.cpp +++ b/modules/features2d/src/bagofwords.cpp @@ -110,10 +110,10 @@ Mat BOWKMeansTrainer::cluster() const BOWKMeansTrainer::~BOWKMeansTrainer() {} -Mat BOWKMeansTrainer::cluster( const Mat& descriptors ) const +Mat BOWKMeansTrainer::cluster( const Mat& _descriptors ) const { Mat labels, vocabulary; - kmeans( descriptors, clusterCount, labels, termcrit, attempts, flags, vocabulary ); + kmeans( _descriptors, clusterCount, labels, termcrit, attempts, flags, vocabulary ); return vocabulary; } diff --git a/modules/features2d/src/brief.cpp b/modules/features2d/src/brief.cpp index 7247b92..95ed868 100644 --- a/modules/features2d/src/brief.cpp +++ b/modules/features2d/src/brief.cpp @@ -61,7 +61,7 @@ inline int smoothedSum(const Mat& sum, const KeyPoint& pt, int y, int x) + sum.at(img_y - HALF_KERNEL, img_x - HALF_KERNEL); } -void pixelTests16(const Mat& sum, const std::vector& keypoints, Mat& descriptors) +static void pixelTests16(const Mat& sum, const std::vector& keypoints, Mat& descriptors) { for (int i = 0; i < (int)keypoints.size(); ++i) { @@ -71,7 +71,7 @@ void pixelTests16(const Mat& sum, const std::vector& keypoints, Mat& d } } -void pixelTests32(const Mat& sum, const std::vector& keypoints, Mat& descriptors) +static void pixelTests32(const Mat& sum, const std::vector& keypoints, Mat& descriptors) { for (int i = 0; i < (int)keypoints.size(); ++i) { @@ -82,7 +82,7 @@ void pixelTests32(const Mat& sum, const std::vector& keypoints, Mat& d } } -void pixelTests64(const Mat& sum, const std::vector& keypoints, Mat& descriptors) +static void pixelTests64(const Mat& sum, const std::vector& keypoints, Mat& descriptors) { for (int i = 0; i < (int)keypoints.size(); ++i) { @@ -127,8 +127,8 @@ int BriefDescriptorExtractor::descriptorType() const void BriefDescriptorExtractor::read( const FileNode& fn) { - int descriptorSize = fn["descriptorSize"]; - switch (descriptorSize) + int dSize = fn["descriptorSize"]; + switch (dSize) { case 16: test_fn_ = pixelTests16; @@ -142,7 +142,7 @@ void BriefDescriptorExtractor::read( const FileNode& fn) default: CV_Error(CV_StsBadArg, "descriptorSize must be 16, 32, or 64"); } - bytes_ = descriptorSize; + bytes_ = dSize; } void BriefDescriptorExtractor::write( FileStorage& fs) const diff --git a/modules/features2d/src/descriptors.cpp b/modules/features2d/src/descriptors.cpp index 7d8d9fc..2fb43b7 100644 --- a/modules/features2d/src/descriptors.cpp +++ b/modules/features2d/src/descriptors.cpp @@ -56,7 +56,7 @@ DescriptorExtractor::~DescriptorExtractor() {} void DescriptorExtractor::compute( const Mat& image, vector& keypoints, Mat& descriptors ) const -{ +{ if( image.empty() || keypoints.empty() ) { descriptors.release(); @@ -102,7 +102,7 @@ Ptr DescriptorExtractor::create(const string& descriptorExt string type = descriptorExtractorType.substr(pos); return new OpponentColorDescriptorExtractor(DescriptorExtractor::create(type)); } - + return Algorithm::create("Feature2D." + descriptorExtractorType); } @@ -117,7 +117,7 @@ OpponentColorDescriptorExtractor::OpponentColorDescriptorExtractor( const Ptr& opponentChannels ) +static void convertBGRImageToOpponentColorSpace( const Mat& bgrImage, vector& opponentChannels ) { if( bgrImage.type() != CV_8UC3 ) CV_Error( CV_StsBadArg, "input image must be an BGR image of type CV_8UC3" ); @@ -223,11 +223,11 @@ void OpponentColorDescriptorExtractor::computeImpl( const Mat& bgrImage, vector< vector outKeypoints; outKeypoints.reserve( keypoints.size() ); - int descriptorSize = descriptorExtractor->descriptorSize(); - Mat mergedDescriptors( maxKeypointsCount, 3*descriptorSize, descriptorExtractor->descriptorType() ); + int dSize = descriptorExtractor->descriptorSize(); + Mat mergedDescriptors( maxKeypointsCount, 3*dSize, descriptorExtractor->descriptorType() ); int mergedCount = 0; // cp - current channel position - size_t cp[] = {0, 0, 0}; + size_t cp[] = {0, 0, 0}; while( cp[0] < channelKeypoints[0].size() && cp[1] < channelKeypoints[1].size() && cp[2] < channelKeypoints[2].size() ) @@ -250,7 +250,7 @@ void OpponentColorDescriptorExtractor::computeImpl( const Mat& bgrImage, vector< // merge descriptors for( int ci = 0; ci < N; ci++ ) { - Mat dst = mergedDescriptors(Range(mergedCount, mergedCount+1), Range(ci*descriptorSize, (ci+1)*descriptorSize)); + Mat dst = mergedDescriptors(Range(mergedCount, mergedCount+1), Range(ci*dSize, (ci+1)*dSize)); channelDescriptors[ci].row( idxs[ci][cp[ci]] ).copyTo( dst ); cp[ci]++; } diff --git a/modules/features2d/src/detectors.cpp b/modules/features2d/src/detectors.cpp index 8b23242..70e795a 100644 --- a/modules/features2d/src/detectors.cpp +++ b/modules/features2d/src/detectors.cpp @@ -45,7 +45,7 @@ using namespace std; namespace cv { - + /* * FeatureDetector */ @@ -95,19 +95,19 @@ Ptr FeatureDetector::create( const string& detectorType ) return new GridAdaptedFeatureDetector(FeatureDetector::create( detectorType.substr(strlen("Grid")))); } - + if( detectorType.find("Pyramid") == 0 ) { return new PyramidAdaptedFeatureDetector(FeatureDetector::create( detectorType.substr(strlen("Pyramid")))); } - + if( detectorType.find("Dynamic") == 0 ) { return new DynamicAdaptedFeatureDetector(AdjusterAdapter::create( detectorType.substr(strlen("Dynamic")))); } - + if( detectorType.compare( "HARRIS" ) == 0 ) { Ptr fd = FeatureDetector::create("GFTT"); @@ -149,13 +149,13 @@ void GFTTDetector::detectImpl( const Mat& image, vector& keypoints, co /* * DenseFeatureDetector */ -DenseFeatureDetector::DenseFeatureDetector( float _initFeatureScale, int _featureScaleLevels, - float _featureScaleMul, int _initXyStep, - int _initImgBound, bool _varyXyStepWithScale, - bool _varyImgBoundWithScale ) : - initFeatureScale(_initFeatureScale), featureScaleLevels(_featureScaleLevels), - featureScaleMul(_featureScaleMul), initXyStep(_initXyStep), initImgBound(_initImgBound), - varyXyStepWithScale(_varyXyStepWithScale), varyImgBoundWithScale(_varyImgBoundWithScale) +DenseFeatureDetector::DenseFeatureDetector( float _initFeatureScale, int _featureScaleLevels, + float _featureScaleMul, int _initXyStep, + int _initImgBound, bool _varyXyStepWithScale, + bool _varyImgBoundWithScale ) : + initFeatureScale(_initFeatureScale), featureScaleLevels(_featureScaleLevels), + featureScaleMul(_featureScaleMul), initXyStep(_initXyStep), initImgBound(_initImgBound), + varyXyStepWithScale(_varyXyStepWithScale), varyImgBoundWithScale(_varyImgBoundWithScale) {} @@ -203,7 +203,7 @@ struct ResponseComparator } }; -void keepStrongest( int N, vector& keypoints ) +static void keepStrongest( int N, vector& keypoints ) { if( (int)keypoints.size() > N ) { diff --git a/modules/features2d/src/draw.cpp b/modules/features2d/src/draw.cpp index ec14911..c123526 100755 --- a/modules/features2d/src/draw.cpp +++ b/modules/features2d/src/draw.cpp @@ -156,11 +156,11 @@ static void _prepareImgAndDrawKeypoints( const Mat& img1, const vector // draw keypoints if( !(flags & DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS) ) { - Mat outImg1 = outImg( Rect(0, 0, img1.cols, img1.rows) ); - drawKeypoints( outImg1, keypoints1, outImg1, singlePointColor, flags + DrawMatchesFlags::DRAW_OVER_OUTIMG ); + Mat _outImg1 = outImg( Rect(0, 0, img1.cols, img1.rows) ); + drawKeypoints( _outImg1, keypoints1, _outImg1, singlePointColor, flags + DrawMatchesFlags::DRAW_OVER_OUTIMG ); - Mat outImg2 = outImg( Rect(img1.cols, 0, img2.cols, img2.rows) ); - drawKeypoints( outImg2, keypoints2, outImg2, singlePointColor, flags + DrawMatchesFlags::DRAW_OVER_OUTIMG ); + Mat _outImg2 = outImg( Rect(img1.cols, 0, img2.cols, img2.rows) ); + drawKeypoints( _outImg2, keypoints2, _outImg2, singlePointColor, flags + DrawMatchesFlags::DRAW_OVER_OUTIMG ); } } @@ -178,9 +178,9 @@ static inline void _drawMatch( Mat& outImg, Mat& outImg1, Mat& outImg2 , pt2 = kp2.pt, dpt2 = Point2f( std::min(pt2.x+outImg1.cols, float(outImg.cols-1)), pt2.y ); - line( outImg, - Point(cvRound(pt1.x*draw_multiplier), cvRound(pt1.y*draw_multiplier)), - Point(cvRound(dpt2.x*draw_multiplier), cvRound(dpt2.y*draw_multiplier)), + line( outImg, + Point(cvRound(pt1.x*draw_multiplier), cvRound(pt1.y*draw_multiplier)), + Point(cvRound(dpt2.x*draw_multiplier), cvRound(dpt2.y*draw_multiplier)), color, 1, CV_AA, draw_shift_bits ); } diff --git a/modules/features2d/src/features2d_init.cpp b/modules/features2d/src/features2d_init.cpp index 6f9e07c..631f5c7 100644 --- a/modules/features2d/src/features2d_init.cpp +++ b/modules/features2d/src/features2d_init.cpp @@ -42,8 +42,7 @@ #include "precomp.hpp" -namespace cv -{ +using namespace cv; /////////////////////// AlgorithmInfo for various detector & descriptors //////////////////////////// @@ -54,7 +53,7 @@ namespace cv CV_INIT_ALGORITHM(BriefDescriptorExtractor, "Feature2D.BRIEF", obj.info()->addParam(obj, "bytes", obj.bytes_)); - + /////////////////////////////////////////////////////////////////////////////////////////////////////////// CV_INIT_ALGORITHM(FastFeatureDetector, "Feature2D.FAST", @@ -69,7 +68,7 @@ CV_INIT_ALGORITHM(StarDetector, "Feature2D.STAR", obj.info()->addParam(obj, "lineThresholdProjected", obj.lineThresholdProjected); obj.info()->addParam(obj, "lineThresholdBinarized", obj.lineThresholdBinarized); obj.info()->addParam(obj, "suppressNonmaxSize", obj.suppressNonmaxSize)); - + /////////////////////////////////////////////////////////////////////////////////////////////////////////// CV_INIT_ALGORITHM(MSER, "Feature2D.MSER", @@ -81,8 +80,8 @@ CV_INIT_ALGORITHM(MSER, "Feature2D.MSER", obj.info()->addParam(obj, "maxEvolution", obj.maxEvolution); obj.info()->addParam(obj, "areaThreshold", obj.areaThreshold); obj.info()->addParam(obj, "minMargin", obj.minMargin); - obj.info()->addParam(obj, "edgeBlurSize", obj.edgeBlurSize)); - + obj.info()->addParam(obj, "edgeBlurSize", obj.edgeBlurSize)); + /////////////////////////////////////////////////////////////////////////////////////////////////////////// CV_INIT_ALGORITHM(ORB, "Feature2D.ORB", @@ -96,7 +95,7 @@ CV_INIT_ALGORITHM(ORB, "Feature2D.ORB", obj.info()->addParam(obj, "scoreType", obj.scoreType)); /////////////////////////////////////////////////////////////////////////////////////////////////////////// - + CV_INIT_ALGORITHM(GFTTDetector, "Feature2D.GFTT", obj.info()->addParam(obj, "nfeatures", obj.nfeatures); obj.info()->addParam(obj, "qualityLevel", obj.qualityLevel); @@ -105,15 +104,18 @@ CV_INIT_ALGORITHM(GFTTDetector, "Feature2D.GFTT", obj.info()->addParam(obj, "k", obj.k)); /////////////////////////////////////////////////////////////////////////////////////////////////////////// - + class CV_EXPORTS HarrisDetector : public GFTTDetector { public: HarrisDetector( int maxCorners=1000, double qualityLevel=0.01, double minDistance=1, - int blockSize=3, bool useHarrisDetector=true, double k=0.04 ) - : GFTTDetector( maxCorners, qualityLevel, minDistance, blockSize, useHarrisDetector, k ) {} + int blockSize=3, bool useHarrisDetector=true, double k=0.04 ); AlgorithmInfo* info() const; -}; +}; + +inline HarrisDetector::HarrisDetector( int _maxCorners, double _qualityLevel, double _minDistance, + int _blockSize, bool _useHarrisDetector, double _k ) + : GFTTDetector( _maxCorners, _qualityLevel, _minDistance, _blockSize, _useHarrisDetector, _k ) {} CV_INIT_ALGORITHM(HarrisDetector, "Feature2D.HARRIS", obj.info()->addParam(obj, "nfeatures", obj.nfeatures); @@ -122,7 +124,7 @@ CV_INIT_ALGORITHM(HarrisDetector, "Feature2D.HARRIS", obj.info()->addParam(obj, "useHarrisDetector", obj.useHarrisDetector); obj.info()->addParam(obj, "k", obj.k)); -//////////////////////////////////////////////////////////////////////////////////////////////////////////// +//////////////////////////////////////////////////////////////////////////////////////////////////////////// CV_INIT_ALGORITHM(DenseFeatureDetector, "Feature2D.Dense", obj.info()->addParam(obj, "initFeatureScale", obj.initFeatureScale); @@ -139,17 +141,18 @@ CV_INIT_ALGORITHM(GridAdaptedFeatureDetector, "Feature2D.Grid", obj.info()->addParam(obj, "gridRows", obj.gridRows); obj.info()->addParam(obj, "gridCols", obj.gridCols)); -bool initModule_features2d(void) +bool cv::initModule_features2d(void) { - Ptr brief = createBriefDescriptorExtractor(), orb = createORB(), - star = createStarDetector(), fastd = createFastFeatureDetector(), mser = createMSER(), - dense = createDenseFeatureDetector(), gftt = createGFTTDetector(), - harris = createHarrisDetector(), grid = createGridAdaptedFeatureDetector(); - - return brief->info() != 0 && orb->info() != 0 && star->info() != 0 && - fastd->info() != 0 && mser->info() != 0 && dense->info() != 0 && - gftt->info() != 0 && harris->info() != 0 && grid->info() != 0; + bool all = true; + all &= !BriefDescriptorExtractor_info_auto.name().empty(); + all &= !FastFeatureDetector_info_auto.name().empty(); + all &= !StarDetector_info_auto.name().empty(); + all &= !MSER_info_auto.name().empty(); + all &= !ORB_info_auto.name().empty(); + all &= !GFTTDetector_info_auto.name().empty(); + all &= !HarrisDetector_info_auto.name().empty(); + all &= !DenseFeatureDetector_info_auto.name().empty(); + all &= !GridAdaptedFeatureDetector_info_auto.name().empty(); + + return all; } - -} - diff --git a/modules/features2d/src/keypoint.cpp b/modules/features2d/src/keypoint.cpp index c89799d..7e9af4b 100644 --- a/modules/features2d/src/keypoint.cpp +++ b/modules/features2d/src/keypoint.cpp @@ -274,6 +274,7 @@ public: private: const Mat mask; + MaskPredicate& operator=(const MaskPredicate&); }; void KeyPointsFilter::runByPixelsMask( vector& keypoints, const Mat& mask ) diff --git a/modules/features2d/src/matchers.cpp b/modules/features2d/src/matchers.cpp index 61a6ced..f1a97dc 100755 --- a/modules/features2d/src/matchers.cpp +++ b/modules/features2d/src/matchers.cpp @@ -174,7 +174,7 @@ int DescriptorMatcher::DescriptorCollection::size() const /* * DescriptorMatcher */ -void convertMatches( const vector >& knnMatches, vector& matches ) +static void convertMatches( const vector >& knnMatches, vector& matches ) { matches.clear(); matches.reserve( knnMatches.size() ); @@ -539,7 +539,7 @@ void FlannBasedMatcher::read( const FileNode& fn) for(int i = 0; i < (int)ip.size(); ++i) { CV_Assert(ip[i].type() == FileNode::MAP); - std::string name = (std::string)ip[i]["name"]; + std::string _name = (std::string)ip[i]["name"]; int type = (int)ip[i]["type"]; switch(type) @@ -549,19 +549,19 @@ void FlannBasedMatcher::read( const FileNode& fn) case CV_16U: case CV_16S: case CV_32S: - indexParams->setInt(name, (int) ip[i]["value"]); + indexParams->setInt(_name, (int) ip[i]["value"]); break; case CV_32F: - indexParams->setFloat(name, (float) ip[i]["value"]); + indexParams->setFloat(_name, (float) ip[i]["value"]); break; case CV_64F: - indexParams->setDouble(name, (double) ip[i]["value"]); + indexParams->setDouble(_name, (double) ip[i]["value"]); break; case CV_USRTYPE1: - indexParams->setString(name, (std::string) ip[i]["value"]); + indexParams->setString(_name, (std::string) ip[i]["value"]); break; case CV_MAKETYPE(CV_USRTYPE1,2): - indexParams->setBool(name, (int) ip[i]["value"] != 0); + indexParams->setBool(_name, (int) ip[i]["value"] != 0); break; case CV_MAKETYPE(CV_USRTYPE1,3): indexParams->setAlgorithm((int) ip[i]["value"]); @@ -578,7 +578,7 @@ void FlannBasedMatcher::read( const FileNode& fn) for(int i = 0; i < (int)sp.size(); ++i) { CV_Assert(sp[i].type() == FileNode::MAP); - std::string name = (std::string)sp[i]["name"]; + std::string _name = (std::string)sp[i]["name"]; int type = (int)sp[i]["type"]; switch(type) @@ -588,19 +588,19 @@ void FlannBasedMatcher::read( const FileNode& fn) case CV_16U: case CV_16S: case CV_32S: - searchParams->setInt(name, (int) sp[i]["value"]); + searchParams->setInt(_name, (int) sp[i]["value"]); break; case CV_32F: - searchParams->setFloat(name, (float) ip[i]["value"]); + searchParams->setFloat(_name, (float) ip[i]["value"]); break; case CV_64F: - searchParams->setDouble(name, (double) ip[i]["value"]); + searchParams->setDouble(_name, (double) ip[i]["value"]); break; case CV_USRTYPE1: - searchParams->setString(name, (std::string) ip[i]["value"]); + searchParams->setString(_name, (std::string) ip[i]["value"]); break; case CV_MAKETYPE(CV_USRTYPE1,2): - searchParams->setBool(name, (int) ip[i]["value"] != 0); + searchParams->setBool(_name, (int) ip[i]["value"] != 0); break; case CV_MAKETYPE(CV_USRTYPE1,3): searchParams->setAlgorithm((int) ip[i]["value"]); diff --git a/modules/features2d/src/mser.cpp b/modules/features2d/src/mser.cpp index f7bd7a8..c2a2749 100644 --- a/modules/features2d/src/mser.cpp +++ b/modules/features2d/src/mser.cpp @@ -539,8 +539,8 @@ static void extractMSER_8UC1_Pass( int* ioptr, } *imgptr += 0x10000; } - int i = (int)(imgptr-ioptr); - ptsptr->pt = cvPoint( i&stepmask, i>>stepgap ); + int imsk = (int)(imgptr-ioptr); + ptsptr->pt = cvPoint( imsk&stepmask, imsk>>stepgap ); // get the current location accumulateMSERComp( comptr, ptsptr ); ptsptr++; diff --git a/modules/features2d/src/orb.cpp b/modules/features2d/src/orb.cpp index 32b4849..19491f1 100644 --- a/modules/features2d/src/orb.cpp +++ b/modules/features2d/src/orb.cpp @@ -555,9 +555,9 @@ static inline float getScale(int level, int firstLevel, double scaleFactor) * @param detector_params parameters to use */ ORB::ORB(int _nfeatures, float _scaleFactor, int _nlevels, int _edgeThreshold, - int _firstLevel, int WTA_K, int _scoreType, int _patchSize) : + int _firstLevel, int _WTA_K, int _scoreType, int _patchSize) : nfeatures(_nfeatures), scaleFactor(_scaleFactor), nlevels(_nlevels), - edgeThreshold(_edgeThreshold), firstLevel(_firstLevel), WTA_K(WTA_K), + edgeThreshold(_edgeThreshold), firstLevel(_firstLevel), WTA_K(_WTA_K), scoreType(_scoreType), patchSize(_patchSize) {} @@ -653,8 +653,8 @@ static void computeKeyPoints(const vector& imagePyramid, for (int level = 0; level < nlevels; ++level) { - int nfeatures = nfeaturesPerLevel[level]; - allKeypoints[level].reserve(nfeatures*2); + int featuresNum = nfeaturesPerLevel[level]; + allKeypoints[level].reserve(featuresNum*2); vector & keypoints = allKeypoints[level]; @@ -668,14 +668,14 @@ static void computeKeyPoints(const vector& imagePyramid, if( scoreType == ORB::HARRIS_SCORE ) { // Keep more points than necessary as FAST does not give amazing corners - KeyPointsFilter::retainBest(keypoints, 2 * nfeatures); + KeyPointsFilter::retainBest(keypoints, 2 * featuresNum); // Compute the Harris cornerness (better scoring than FAST) HarrisResponses(imagePyramid[level], keypoints, 7, HARRIS_K); } //cull to the final desired level, using the new Harris scores or the original FAST scores. - KeyPointsFilter::retainBest(keypoints, nfeatures); + KeyPointsFilter::retainBest(keypoints, featuresNum); float sf = getScale(level, firstLevel, scaleFactor); @@ -738,7 +738,7 @@ void ORB::operator()( InputArray _image, InputArray _mask, vector& _ke if( image.type() != CV_8UC1 ) cvtColor(_image, image, CV_BGR2GRAY); - int nlevels = this->nlevels; + int levelsNum = this->nlevels; if( !do_keypoints ) { @@ -751,15 +751,15 @@ void ORB::operator()( InputArray _image, InputArray _mask, vector& _ke // // In short, ultimately the descriptor should // ignore octave parameter and deal only with the keypoint size. - nlevels = 0; + levelsNum = 0; for( size_t i = 0; i < _keypoints.size(); i++ ) - nlevels = std::max(nlevels, std::max(_keypoints[i].octave, 0)); - nlevels++; + levelsNum = std::max(levelsNum, std::max(_keypoints[i].octave, 0)); + levelsNum++; } // Pre-compute the scale pyramids - vector imagePyramid(nlevels), maskPyramid(nlevels); - for (int level = 0; level < nlevels; ++level) + vector imagePyramid(levelsNum), maskPyramid(levelsNum); + for (int level = 0; level < levelsNum; ++level) { float scale = 1/getScale(level, firstLevel, scaleFactor); Size sz(cvRound(image.cols*scale), cvRound(image.rows*scale)); @@ -839,13 +839,13 @@ void ORB::operator()( InputArray _image, InputArray _mask, vector& _ke KeyPointsFilter::runByImageBorder(_keypoints, image.size(), edgeThreshold); // Cluster the input keypoints depending on the level they were computed at - allKeypoints.resize(nlevels); + allKeypoints.resize(levelsNum); for (vector::iterator keypoint = _keypoints.begin(), keypointEnd = _keypoints.end(); keypoint != keypointEnd; ++keypoint) allKeypoints[keypoint->octave].push_back(*keypoint); // Make sure we rescale the coordinates - for (int level = 0; level < nlevels; ++level) + for (int level = 0; level < levelsNum; ++level) { if (level == firstLevel) continue; @@ -864,7 +864,7 @@ void ORB::operator()( InputArray _image, InputArray _mask, vector& _ke if( do_descriptors ) { int nkeypoints = 0; - for (int level = 0; level < nlevels; ++level) + for (int level = 0; level < levelsNum; ++level) nkeypoints += (int)allKeypoints[level].size(); if( nkeypoints == 0 ) _descriptors.release(); @@ -897,7 +897,7 @@ void ORB::operator()( InputArray _image, InputArray _mask, vector& _ke _keypoints.clear(); int offset = 0; - for (int level = 0; level < nlevels; ++level) + for (int level = 0; level < levelsNum; ++level) { // Get the features and compute their orientation vector& keypoints = allKeypoints[level]; diff --git a/modules/features2d/src/precomp.hpp b/modules/features2d/src/precomp.hpp index 1678c69..4265ecd 100644 --- a/modules/features2d/src/precomp.hpp +++ b/modules/features2d/src/precomp.hpp @@ -43,10 +43,6 @@ #ifndef __OPENCV_PRECOMP_H__ #define __OPENCV_PRECOMP_H__ -#if _MSC_VER >= 1200 -#pragma warning( disable: 4251 4512 4710 4711 4514 4996 ) -#endif - #ifdef HAVE_CVCONFIG_H #include "cvconfig.h" #endif diff --git a/modules/features2d/src/stardetector.cpp b/modules/features2d/src/stardetector.cpp index 3775ebd..0ceb3f8 100644 --- a/modules/features2d/src/stardetector.cpp +++ b/modules/features2d/src/stardetector.cpp @@ -48,13 +48,13 @@ static void computeIntegralImages( const Mat& matI, Mat& matS, Mat& matT, Mat& _FT ) { CV_Assert( matI.type() == CV_8U ); - + int x, y, rows = matI.rows, cols = matI.cols; - + matS.create(rows + 1, cols + 1, CV_32S); matT.create(rows + 1, cols + 1, CV_32S); _FT.create(rows + 1, cols + 1, CV_32S); - + const uchar* I = matI.ptr(); int *S = matS.ptr(), *T = matT.ptr(), *FT = _FT.ptr(); int istep = (int)matI.step, step = (int)(matS.step/sizeof(S[0])); @@ -121,29 +121,28 @@ StarDetectorComputeResponses( const Mat& img, Mat& responses, Mat& sizes, int ma StarFeature f[MAX_PATTERN]; Mat sum, tilted, flatTilted; - int y, i=0, rows = img.rows, cols = img.cols; + int y, rows = img.rows, cols = img.cols; int border, npatterns=0, maxIdx=0; CV_Assert( img.type() == CV_8UC1 ); - + responses.create( img.size(), CV_32F ); sizes.create( img.size(), CV_16S ); - while( pairs[i][0] >= 0 && ! - ( sizes0[pairs[i][0]] >= maxSize - || sizes0[pairs[i+1][0]] + sizes0[pairs[i+1][0]]/2 >= std::min(rows, cols) ) ) + while( pairs[npatterns][0] >= 0 && ! + ( sizes0[pairs[npatterns][0]] >= maxSize + || sizes0[pairs[npatterns+1][0]] + sizes0[pairs[npatterns+1][0]]/2 >= std::min(rows, cols) ) ) { - ++i; + ++npatterns; } - - npatterns = i; + npatterns += (pairs[npatterns-1][0] >= 0); maxIdx = pairs[npatterns-1][0]; - + computeIntegralImages( img, sum, tilted, flatTilted ); int step = (int)(sum.step/sum.elemSize()); - for( i = 0; i <= maxIdx; i++ ) + for(int i = 0; i <= maxIdx; i++ ) { int ur_size = sizes0[i], t_size = sizes0[i] + sizes0[i]/2; int ur_area = (2*ur_size + 1)*(2*ur_size + 1); @@ -169,24 +168,24 @@ StarDetectorComputeResponses( const Mat& img, Mat& responses, Mat& sizes, int ma sizes1[maxIdx] = -sizes1[maxIdx]; border = sizes0[maxIdx] + sizes0[maxIdx]/2; - for( i = 0; i < npatterns; i++ ) + for(int i = 0; i < npatterns; i++ ) { int innerArea = f[pairs[i][1]].area; int outerArea = f[pairs[i][0]].area - innerArea; invSizes[i][0] = 1.f/outerArea; invSizes[i][1] = 1.f/innerArea; } - + #if CV_SSE2 if( useSIMD ) { - for( i = 0; i < npatterns; i++ ) + for(int i = 0; i < npatterns; i++ ) { _mm_store_ps((float*)&invSizes4[i][0], _mm_set1_ps(invSizes[i][0])); _mm_store_ps((float*)&invSizes4[i][1], _mm_set1_ps(invSizes[i][1])); } - for( i = 0; i <= maxIdx; i++ ) + for(int i = 0; i <= maxIdx; i++ ) _mm_store_ps((float*)&sizes1_4[i], _mm_set1_ps((float)sizes1[i])); } #endif @@ -197,7 +196,7 @@ StarDetectorComputeResponses( const Mat& img, Mat& responses, Mat& sizes, int ma float* r_ptr2 = responses.ptr(rows - 1 - y); short* s_ptr = sizes.ptr(y); short* s_ptr2 = sizes.ptr(rows - 1 - y); - + memset( r_ptr, 0, cols*sizeof(r_ptr[0])); memset( r_ptr2, 0, cols*sizeof(r_ptr2[0])); memset( s_ptr, 0, cols*sizeof(s_ptr[0])); @@ -206,10 +205,10 @@ StarDetectorComputeResponses( const Mat& img, Mat& responses, Mat& sizes, int ma for( y = border; y < rows - border; y++ ) { - int x = border, i; + int x = border; float* r_ptr = responses.ptr(y); short* s_ptr = sizes.ptr(y); - + memset( r_ptr, 0, border*sizeof(r_ptr[0])); memset( s_ptr, 0, border*sizeof(s_ptr[0])); memset( r_ptr + cols - border, 0, border*sizeof(r_ptr[0])); @@ -226,7 +225,7 @@ StarDetectorComputeResponses( const Mat& img, Mat& responses, Mat& sizes, int ma __m128 bestResponse = _mm_setzero_ps(); __m128 bestSize = _mm_setzero_ps(); - for( i = 0; i <= maxIdx; i++ ) + for(int i = 0; i <= maxIdx; i++ ) { const int** p = (const int**)&f[i].p[0]; __m128i r0 = _mm_sub_epi32(_mm_loadu_si128((const __m128i*)(p[0]+ofs)), @@ -241,7 +240,7 @@ StarDetectorComputeResponses( const Mat& img, Mat& responses, Mat& sizes, int ma _mm_store_ps((float*)&vals[i], _mm_cvtepi32_ps(r0)); } - for( i = 0; i < npatterns; i++ ) + for(int i = 0; i < npatterns; i++ ) { __m128 inner_sum = vals[pairs[i][1]]; __m128 outer_sum = _mm_sub_ps(vals[pairs[i][0]], inner_sum); @@ -260,7 +259,7 @@ StarDetectorComputeResponses( const Mat& img, Mat& responses, Mat& sizes, int ma _mm_packs_epi32(_mm_cvtps_epi32(bestSize),_mm_setzero_si128())); } } -#endif +#endif for( ; x < cols - border; x++ ) { int ofs = y*step + x; @@ -268,13 +267,13 @@ StarDetectorComputeResponses( const Mat& img, Mat& responses, Mat& sizes, int ma float bestResponse = 0; int bestSize = 0; - for( i = 0; i <= maxIdx; i++ ) + for(int i = 0; i <= maxIdx; i++ ) { const int** p = (const int**)&f[i].p[0]; vals[i] = p[0][ofs] - p[1][ofs] - p[2][ofs] + p[3][ofs] + p[4][ofs] - p[5][ofs] - p[6][ofs] + p[7][ofs]; } - for( i = 0; i < npatterns; i++ ) + for(int i = 0; i < npatterns; i++ ) { int inner_sum = vals[pairs[i][1]]; int outer_sum = vals[pairs[i][0]] - inner_sum; @@ -306,7 +305,7 @@ static bool StarDetectorSuppressLines( const Mat& responses, const Mat& sizes, P int x, y, delta = sz/4, radius = delta*4; float Lxx = 0, Lyy = 0, Lxy = 0; int Lxxb = 0, Lyyb = 0, Lxyb = 0; - + for( y = pt.y - radius; y <= pt.y + radius; y += delta ) for( x = pt.x - radius; x <= pt.x + radius; x += delta ) { @@ -314,7 +313,7 @@ static bool StarDetectorSuppressLines( const Mat& responses, const Mat& sizes, P float Ly = r_ptr[(y+1)*rstep + x] - r_ptr[(y-1)*rstep + x]; Lxx += Lx*Lx; Lyy += Ly*Ly; Lxy += Lx*Ly; } - + if( (Lxx + Lyy)*(Lxx + Lyy) >= lineThresholdProjected*(Lxx*Lyy - Lxy*Lxy) ) return true; @@ -415,7 +414,7 @@ StarDetectorSuppressNonmax( const Mat& responses, const Mat& sizes, ; } } - + StarDetector::StarDetector(int _maxSize, int _responseThreshold, int _lineThresholdProjected, int _lineThresholdBinarized, @@ -431,10 +430,10 @@ void StarDetector::detectImpl( const Mat& image, vector& keypoints, co { Mat grayImage = image; if( image.type() != CV_8U ) cvtColor( image, grayImage, CV_BGR2GRAY ); - + (*this)(grayImage, keypoints); KeyPointsFilter::runByPixelsMask( keypoints, mask ); -} +} void StarDetector::operator()(const Mat& img, vector& keypoints) const { @@ -446,5 +445,5 @@ void StarDetector::operator()(const Mat& img, vector& keypoints) const responseThreshold, lineThresholdProjected, lineThresholdBinarized, suppressNonmaxSize ); } - + } diff --git a/modules/features2d/test/test_features2d.cpp b/modules/features2d/test/test_features2d.cpp index 272acca..10805d5 100644 --- a/modules/features2d/test/test_features2d.cpp +++ b/modules/features2d/test/test_features2d.cpp @@ -493,23 +493,6 @@ private: CV_DescriptorExtractorTest& operator=(const CV_DescriptorExtractorTest&) { return *this; } }; -/*template -class CV_CalonderDescriptorExtractorTest : public CV_DescriptorExtractorTest -{ -public: - CV_CalonderDescriptorExtractorTest( const char* testName, float _normDif, float _prevTime ) : - CV_DescriptorExtractorTest( testName, _normDif, Ptr(), _prevTime ) - {} - -protected: - virtual void createDescriptorExtractor() - { - CV_DescriptorExtractorTest::dextractor = - new CalonderDescriptorExtractor( string(CV_DescriptorExtractorTest::ts->get_data_path()) + - FEATURES2D_DIR + "/calonder_classifier.rtc"); - } -};*/ - /****************************************************************************************\ * Algorithmic tests for descriptor matchers * \****************************************************************************************/ @@ -928,7 +911,7 @@ void CV_DescriptorMatcherTest::radiusMatchTest( const Mat& query, const Mat& tra dmatcher->radiusMatch( query, matches, radius, masks ); - int curRes = cvtest::TS::OK; + //int curRes = cvtest::TS::OK; if( (int)matches.size() != queryDescCount ) { ts->printf(cvtest::TS::LOG, "Incorrect matches count while test radiusMatch() function (1).\n"); @@ -968,7 +951,7 @@ void CV_DescriptorMatcherTest::radiusMatchTest( const Mat& query, const Mat& tra } if( (float)badCount > (float)queryDescCount*badPart ) { - curRes = cvtest::TS::FAIL_INVALID_OUTPUT; + //curRes = cvtest::TS::FAIL_INVALID_OUTPUT; ts->printf( cvtest::TS::LOG, "%f - too large bad matches part while test radiusMatch() function (2).\n", (float)badCount/(float)queryDescCount ); ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY ); @@ -1059,24 +1042,6 @@ TEST( Features2d_DescriptorExtractor_BRIEF, regression ) test.safe_run(); } -#if CV_SSE2 -TEST( Features2d_DescriptorExtractor_Calonder_uchar, regression ) -{ - CV_CalonderDescriptorExtractorTest > test( "descriptor-calonder-uchar", - std::numeric_limits::epsilon() + 1, - 0.0132175f ); - test.safe_run(); -} - -TEST( Features2d_DescriptorExtractor_Calonder_float, regression ) -{ - CV_CalonderDescriptorExtractorTest > test( "descriptor-calonder-float", - std::numeric_limits::epsilon(), - 0.0221308f ); - test.safe_run(); -} -#endif // CV_SSE2 - /* * Matchers */ diff --git a/modules/features2d/test/test_precomp.hpp b/modules/features2d/test/test_precomp.hpp index b787504..29be5d4 100644 --- a/modules/features2d/test/test_precomp.hpp +++ b/modules/features2d/test/test_precomp.hpp @@ -1,3 +1,7 @@ +#ifdef __GNUC__ +# pragma GCC diagnostic ignored "-Wmissing-declarations" +#endif + #ifndef __OPENCV_TEST_PRECOMP_HPP__ #define __OPENCV_TEST_PRECOMP_HPP__ diff --git a/modules/flann/include/opencv2/flann/allocator.h b/modules/flann/include/opencv2/flann/allocator.h index 6ca44fc..26091d0 100644 --- a/modules/flann/include/opencv2/flann/allocator.h +++ b/modules/flann/include/opencv2/flann/allocator.h @@ -92,9 +92,9 @@ public: /** Default constructor. Initializes a new pool. */ - PooledAllocator(int blocksize = BLOCKSIZE) + PooledAllocator(int blockSize = BLOCKSIZE) { - this->blocksize = blocksize; + blocksize = blockSize; remaining = 0; base = NULL; @@ -122,7 +122,7 @@ public: */ void* allocateMemory(int size) { - int blocksize; + int blockSize; /* Round size up to a multiple of wordsize. The following expression only works for WORDSIZE that is a power of 2, by masking last bits of @@ -138,11 +138,11 @@ public: wastedMemory += remaining; /* Allocate new storage. */ - blocksize = (size + sizeof(void*) + (WORDSIZE-1) > BLOCKSIZE) ? + blockSize = (size + sizeof(void*) + (WORDSIZE-1) > BLOCKSIZE) ? size + sizeof(void*) + (WORDSIZE-1) : BLOCKSIZE; // use the standard C malloc to allocate memory - void* m = ::malloc(blocksize); + void* m = ::malloc(blockSize); if (!m) { fprintf(stderr,"Failed to allocate memory.\n"); return NULL; @@ -155,7 +155,7 @@ public: int shift = 0; //int shift = (WORDSIZE - ( (((size_t)m) + sizeof(void*)) & (WORDSIZE-1))) & (WORDSIZE-1); - remaining = blocksize - sizeof(void*) - shift; + remaining = blockSize - sizeof(void*) - shift; loc = ((char*)m + sizeof(void*) + shift); } void* rloc = loc; diff --git a/modules/flann/include/opencv2/flann/any.h b/modules/flann/include/opencv2/flann/any.h index 34a7120..3febc68 100644 --- a/modules/flann/include/opencv2/flann/any.h +++ b/modules/flann/include/opencv2/flann/any.h @@ -46,6 +46,10 @@ struct base_any_policy virtual ::size_t get_size() = 0; virtual const std::type_info& type() = 0; virtual void print(std::ostream& out, void* const* src) = 0; + +#ifdef OPENCV_CAN_BREAK_BINARY_COMPATIBILITY + virtual ~base_any_policy() {} +#endif }; template diff --git a/modules/flann/include/opencv2/flann/defines.h b/modules/flann/include/opencv2/flann/defines.h index 7bd8964..178f07b 100644 --- a/modules/flann/include/opencv2/flann/defines.h +++ b/modules/flann/include/opencv2/flann/defines.h @@ -65,7 +65,7 @@ #undef FLANN_PLATFORM_32_BIT #undef FLANN_PLATFORM_64_BIT -#if __amd64__ || __x86_64__ || _WIN64 || _M_X64 +#if defined __amd64__ || defined __x86_64__ || defined _WIN64 || defined _M_X64 #define FLANN_PLATFORM_64_BIT #else #define FLANN_PLATFORM_32_BIT diff --git a/modules/flann/include/opencv2/flann/dynamic_bitset.h b/modules/flann/include/opencv2/flann/dynamic_bitset.h index 064ec39..bfd39ce 100644 --- a/modules/flann/include/opencv2/flann/dynamic_bitset.h +++ b/modules/flann/include/opencv2/flann/dynamic_bitset.h @@ -35,6 +35,9 @@ #ifndef OPENCV_FLANN_DYNAMIC_BITSET_H_ #define OPENCV_FLANN_DYNAMIC_BITSET_H_ +#ifndef FLANN_USE_BOOST +# define FLANN_USE_BOOST 0 +#endif //#define FLANN_USE_BOOST 1 #if FLANN_USE_BOOST #include @@ -63,9 +66,9 @@ public: /** @param only constructor we use in our code * @param the size of the bitset (in bits) */ - DynamicBitset(size_t size) + DynamicBitset(size_t sz) { - resize(size); + resize(sz); reset(); } @@ -113,10 +116,10 @@ public: /** @param resize the bitset so that it contains at least size bits * @param size */ - void resize(size_t size) + void resize(size_t sz) { - size_ = size; - bitset_.resize(size / cell_bit_size_ + 1); + size_ = sz; + bitset_.resize(sz / cell_bit_size_ + 1); } /** @param set a bit to true diff --git a/modules/flann/include/opencv2/flann/heap.h b/modules/flann/include/opencv2/flann/heap.h index dc0a6f7..92a6ea6 100644 --- a/modules/flann/include/opencv2/flann/heap.h +++ b/modules/flann/include/opencv2/flann/heap.h @@ -67,12 +67,12 @@ public: * Constructor. * * Params: - * size = heap size + * sz = heap size */ - Heap(int size) + Heap(int sz) { - length = size; + length = sz; heap.reserve(length); count = 0; } diff --git a/modules/flann/include/opencv2/flann/hierarchical_clustering_index.h b/modules/flann/include/opencv2/flann/hierarchical_clustering_index.h index 103d24d..3b61bd2 100644 --- a/modules/flann/include/opencv2/flann/hierarchical_clustering_index.h +++ b/modules/flann/include/opencv2/flann/hierarchical_clustering_index.h @@ -106,7 +106,7 @@ private: * indices_length = length of indices vector * */ - void chooseCentersRandom(int k, int* indices, int indices_length, int* centers, int& centers_length) + void chooseCentersRandom(int k, int* dsindices, int indices_length, int* centers, int& centers_length) { UniqueRandom r(indices_length); @@ -122,7 +122,7 @@ private: return; } - centers[index] = indices[rnd]; + centers[index] = dsindices[rnd]; for (int j=0; j=0 && rnd < n); - centers[0] = indices[rnd]; + centers[0] = dsindices[rnd]; int index; for (index=1; index=0 && index < n); - centers[0] = indices[index]; + centers[0] = dsindices[index]; for (int i = 0; i < n; i++) { - closestDistSq[i] = distance(dataset[indices[i]], dataset[indices[index]], dataset.cols); + closestDistSq[i] = distance(dataset[dsindices[i]], dataset[dsindices[index]], dataset.cols); currentPot += closestDistSq[i]; } @@ -237,7 +237,7 @@ private: // Compute the new potential double newPot = 0; - for (int i = 0; i < n; i++) newPot += std::min( distance(dataset[indices[i]], dataset[indices[index]], dataset.cols), closestDistSq[i] ); + for (int i = 0; i < n; i++) newPot += std::min( distance(dataset[dsindices[i]], dataset[dsindices[index]], dataset.cols), closestDistSq[i] ); // Store the best result if ((bestNewPot < 0)||(newPot < bestNewPot)) { @@ -247,9 +247,9 @@ private: } // Add the appropriate center - centers[centerCount] = indices[bestNewIndex]; + centers[centerCount] = dsindices[bestNewIndex]; currentPot = bestNewPot; - for (int i = 0; i < n; i++) closestDistSq[i] = std::min( distance(dataset[indices[i]], dataset[indices[bestNewIndex]], dataset.cols), closestDistSq[i] ); + for (int i = 0; i < n; i++) closestDistSq[i] = std::min( distance(dataset[dsindices[i]], dataset[dsindices[bestNewIndex]], dataset.cols), closestDistSq[i] ); } centers_length = centerCount; @@ -518,11 +518,11 @@ private: - void computeLabels(int* indices, int indices_length, int* centers, int centers_length, int* labels, DistanceType& cost) + void computeLabels(int* dsindices, int indices_length, int* centers, int centers_length, int* labels, DistanceType& cost) { cost = 0; for (int i=0; isize = indices_length; node->level = level; if (indices_length < leaf_size_) { // leaf node - node->indices = indices; + node->indices = dsindices; std::sort(node->indices,node->indices+indices_length); node->childs = NULL; return; @@ -563,10 +563,10 @@ private: std::vector labels(indices_length); int centers_length; - (this->*chooseCenters)(branching, indices, indices_length, ¢ers[0], centers_length); + (this->*chooseCenters)(branching, dsindices, indices_length, ¢ers[0], centers_length); if (centers_lengthindices = indices; + node->indices = dsindices; std::sort(node->indices,node->indices+indices_length); node->childs = NULL; return; @@ -575,7 +575,7 @@ private: // assign points to clusters DistanceType cost; - computeLabels(indices, indices_length, ¢ers[0], centers_length, &labels[0], cost); + computeLabels(dsindices, indices_length, ¢ers[0], centers_length, &labels[0], cost); node->childs = pool.allocate(branching); int start = 0; @@ -583,7 +583,7 @@ private: for (int i=0; ichilds[i] = pool.allocate(); node->childs[i]->pivot = centers[i]; node->childs[i]->indices = NULL; - computeClustering(node->childs[i],indices+start, end-start, branching, level+1); + computeClustering(node->childs[i],dsindices+start, end-start, branching, level+1); start=end; } } diff --git a/modules/flann/include/opencv2/flann/index_testing.h b/modules/flann/include/opencv2/flann/index_testing.h index ab80dd7..d764004 100644 --- a/modules/flann/include/opencv2/flann/index_testing.h +++ b/modules/flann/include/opencv2/flann/index_testing.h @@ -164,7 +164,7 @@ float test_index_precision(NNIndex& index, const Matrix& index, const Matrix #include // TODO as soon as we use C++0x, use the code in USE_UNORDERED_MAP +#ifdef __GXX_EXPERIMENTAL_CXX0X__ +# define USE_UNORDERED_MAP 1 +#else +# define USE_UNORDERED_MAP 0 +#endif #if USE_UNORDERED_MAP #include #else diff --git a/modules/flann/src/flann.cpp b/modules/flann/src/flann.cpp index 85ccdc7..36ee669 100644 --- a/modules/flann/src/flann.cpp +++ b/modules/flann/src/flann.cpp @@ -27,10 +27,6 @@ *************************************************************************/ #include "precomp.hpp" - -#ifdef _MSC_VER -#pragma warning(disable: 4996) -#endif #include "opencv2/flann/flann.hpp" namespace cvflann diff --git a/modules/flann/src/precomp.hpp b/modules/flann/src/precomp.hpp index cebe286..72731af 100644 --- a/modules/flann/src/precomp.hpp +++ b/modules/flann/src/precomp.hpp @@ -5,10 +5,6 @@ #include #include -#ifdef _MSC_VER -#pragma warning(disable: 4996) -#endif - #ifdef HAVE_CVCONFIG_H # include "cvconfig.h" #endif diff --git a/modules/gpu/CMakeLists.txt b/modules/gpu/CMakeLists.txt index bc16c5b..7f776d6 100644 --- a/modules/gpu/CMakeLists.txt +++ b/modules/gpu/CMakeLists.txt @@ -22,32 +22,31 @@ source_group("Device" FILES ${lib_device_hdrs}) source_group("Device\\Detail" FILES ${lib_device_hdrs_detail}) if (HAVE_CUDA) - file(GLOB_RECURSE ncv_srcs "src/nvidia/*.cpp") + file(GLOB_RECURSE ncv_srcs "src/nvidia/*.cpp") file(GLOB_RECURSE ncv_cuda "src/nvidia/*.cu") file(GLOB_RECURSE ncv_hdrs "src/nvidia/*.hpp" "src/nvidia/*.h") set(ncv_files ${ncv_srcs} ${ncv_hdrs} ${ncv_cuda}) source_group("Src\\NVidia" FILES ${ncv_files}) ocv_include_directories("src/nvidia" "src/nvidia/core" "src/nvidia/NPP_staging" ${CUDA_INCLUDE_DIRS}) + ocv_warnings_disable(CMAKE_CXX_FLAGS -Wundef -Wmissing-declarations /wd4211 /wd4201 /wd4100 /wd4505 /wd4408) #set (CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS} "-keep") #set (CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS} "-Xcompiler;/EHsc-;") - + if(MSVC) if(NOT ENABLE_NOISY_WARNINGS) - set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /wd4211 /wd4201 /wd4100 /wd4505 /wd4408") - foreach(var CMAKE_CXX_FLAGS CMAKE_CXX_FLAGS_RELEASE CMAKE_CXX_FLAGS_DEBUG) string(REPLACE "/W4" "/W3" ${var} "${${var}}") endforeach() - + set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS} -Xcompiler /wd4251) endif() endif() - OCV_CUDA_COMPILE(cuda_objs ${lib_cuda} ${ncv_cuda}) + ocv_cuda_compile(cuda_objs ${lib_cuda} ${ncv_cuda}) #CUDA_BUILD_CLEAN_TARGET() - + set(cuda_link_libs ${CUDA_LIBRARIES} ${CUDA_npp_LIBRARY}) else() set(lib_cuda "") @@ -60,9 +59,9 @@ ocv_set_module_sources( HEADERS ${lib_hdrs} SOURCES ${lib_int_hdrs} ${lib_cuda_hdrs} ${lib_device_hdrs} ${lib_device_hdrs_detail} ${lib_srcs} ${lib_cuda} ${ncv_files} ${cuda_objs} ) - + ocv_create_module(${cuda_link_libs}) - + if(HAVE_CUDA) if(HAVE_CUFFT) CUDA_ADD_CUFFT_TO_TARGET(${the_module}) @@ -71,10 +70,10 @@ if(HAVE_CUDA) if(HAVE_CUBLAS) CUDA_ADD_CUBLAS_TO_TARGET(${the_module}) endif() - + install(FILES src/nvidia/NPP_staging/NPP_staging.hpp src/nvidia/core/NCV.hpp - DESTINATION ${OPENCV_INCLUDE_PREFIX}/opencv2/${name} - COMPONENT main) + DESTINATION ${OPENCV_INCLUDE_PREFIX}/opencv2/${name} + COMPONENT main) endif() ocv_add_precompiled_headers(${the_module}) @@ -84,11 +83,11 @@ ocv_add_precompiled_headers(${the_module}) ################################################################################################################ file(GLOB test_srcs "test/*.cpp") file(GLOB test_hdrs "test/*.hpp" "test/*.h") + +set(nvidia "") if(HAVE_CUDA) - file(GLOB nvidia "test/nvidia/*.cpp" "test/nvidia/*.hpp" "test/nvidia/*.h") + file(GLOB nvidia "test/nvidia/*.cpp" "test/nvidia/*.hpp" "test/nvidia/*.h") set(nvidia FILES "Src\\\\\\\\NVidia" ${nvidia}) # 8 ugly backslashes :'( -else() - set(nvidia "") endif() ocv_add_accuracy_tests(FILES "Include" ${test_hdrs} diff --git a/modules/gpu/include/opencv2/gpu/gpu.hpp b/modules/gpu/include/opencv2/gpu/gpu.hpp index 5c21edd..2438a3a 100644 --- a/modules/gpu/include/opencv2/gpu/gpu.hpp +++ b/modules/gpu/include/opencv2/gpu/gpu.hpp @@ -1698,15 +1698,7 @@ class CV_EXPORTS GoodFeaturesToTrackDetector_GPU { public: explicit GoodFeaturesToTrackDetector_GPU(int maxCorners = 1000, double qualityLevel = 0.01, double minDistance = 0.0, - int blockSize = 3, bool useHarrisDetector = false, double harrisK = 0.04) - { - this->maxCorners = maxCorners; - this->qualityLevel = qualityLevel; - this->minDistance = minDistance; - this->blockSize = blockSize; - this->useHarrisDetector = useHarrisDetector; - this->harrisK = harrisK; - } + int blockSize = 3, bool useHarrisDetector = false, double harrisK = 0.04); //! return 1 rows matrix with CV_32FC2 type void operator ()(const GpuMat& image, GpuMat& corners, const GpuMat& mask = GpuMat()); @@ -1738,6 +1730,18 @@ private: GpuMat tmpCorners_; }; +inline GoodFeaturesToTrackDetector_GPU::GoodFeaturesToTrackDetector_GPU(int maxCorners_, double qualityLevel_, double minDistance_, + int blockSize_, bool useHarrisDetector_, double harrisK_) +{ + maxCorners = maxCorners_; + qualityLevel = qualityLevel_; + minDistance = minDistance_; + blockSize = blockSize_; + useHarrisDetector = useHarrisDetector_; + harrisK = harrisK_; +} + + class CV_EXPORTS PyrLKOpticalFlow { public: diff --git a/modules/gpu/perf/perf_precomp.hpp b/modules/gpu/perf/perf_precomp.hpp index ef2839b..1b569f7 100644 --- a/modules/gpu/perf/perf_precomp.hpp +++ b/modules/gpu/perf/perf_precomp.hpp @@ -1,3 +1,7 @@ +#ifdef __GNUC__ +# pragma GCC diagnostic ignored "-Wmissing-declarations" +#endif + #ifndef __OPENCV_PERF_PRECOMP_HPP__ #define __OPENCV_PERF_PRECOMP_HPP__ @@ -11,7 +15,7 @@ #include "opencv2/gpu/gpu.hpp" #include "perf_utility.hpp" -#if GTEST_CREATE_SHARED_LIBRARY +#ifdef GTEST_CREATE_SHARED_LIBRARY #error no modules except ts should have GTEST_CREATE_SHARED_LIBRARY defined #endif diff --git a/modules/gpu/src/color.cpp b/modules/gpu/src/color.cpp index 5fcd076..a47758c 100644 --- a/modules/gpu/src/color.cpp +++ b/modules/gpu/src/color.cpp @@ -52,157 +52,7 @@ void cv::gpu::swapChannels(GpuMat&, const int[], Stream&) { throw_nogpu(); } #else /* !defined (HAVE_CUDA) */ -namespace cv { namespace gpu { namespace device -{ -#define OPENCV_GPU_DECLARE_CVTCOLOR_ONE(name) \ - void name(const DevMem2Db& src, const DevMem2Db& dst, cudaStream_t stream); - -#define OPENCV_GPU_DECLARE_CVTCOLOR_ALL(name) \ - OPENCV_GPU_DECLARE_CVTCOLOR_ONE(name ## _8u) \ - OPENCV_GPU_DECLARE_CVTCOLOR_ONE(name ## _16u) \ - OPENCV_GPU_DECLARE_CVTCOLOR_ONE(name ## _32f) - -#define OPENCV_GPU_DECLARE_CVTCOLOR_8U32F(name) \ - OPENCV_GPU_DECLARE_CVTCOLOR_ONE(name ## _8u) \ - OPENCV_GPU_DECLARE_CVTCOLOR_ONE(name ## _32f) \ - OPENCV_GPU_DECLARE_CVTCOLOR_ONE(name ## _full_8u) \ - OPENCV_GPU_DECLARE_CVTCOLOR_ONE(name ## _full_32f) - - OPENCV_GPU_DECLARE_CVTCOLOR_ALL(bgr_to_rgb) - OPENCV_GPU_DECLARE_CVTCOLOR_ALL(bgr_to_bgra) - OPENCV_GPU_DECLARE_CVTCOLOR_ALL(bgr_to_rgba) - OPENCV_GPU_DECLARE_CVTCOLOR_ALL(bgra_to_bgr) - OPENCV_GPU_DECLARE_CVTCOLOR_ALL(bgra_to_rgb) - OPENCV_GPU_DECLARE_CVTCOLOR_ALL(bgra_to_rgba) - - OPENCV_GPU_DECLARE_CVTCOLOR_ONE(bgr_to_bgr555) - OPENCV_GPU_DECLARE_CVTCOLOR_ONE(bgr_to_bgr565) - OPENCV_GPU_DECLARE_CVTCOLOR_ONE(rgb_to_bgr555) - OPENCV_GPU_DECLARE_CVTCOLOR_ONE(rgb_to_bgr565) - OPENCV_GPU_DECLARE_CVTCOLOR_ONE(bgra_to_bgr555) - OPENCV_GPU_DECLARE_CVTCOLOR_ONE(bgra_to_bgr565) - OPENCV_GPU_DECLARE_CVTCOLOR_ONE(rgba_to_bgr555) - OPENCV_GPU_DECLARE_CVTCOLOR_ONE(rgba_to_bgr565) - - OPENCV_GPU_DECLARE_CVTCOLOR_ONE(bgr555_to_rgb) - OPENCV_GPU_DECLARE_CVTCOLOR_ONE(bgr565_to_rgb) - OPENCV_GPU_DECLARE_CVTCOLOR_ONE(bgr555_to_bgr) - OPENCV_GPU_DECLARE_CVTCOLOR_ONE(bgr565_to_bgr) - OPENCV_GPU_DECLARE_CVTCOLOR_ONE(bgr555_to_rgba) - OPENCV_GPU_DECLARE_CVTCOLOR_ONE(bgr565_to_rgba) - OPENCV_GPU_DECLARE_CVTCOLOR_ONE(bgr555_to_bgra) - OPENCV_GPU_DECLARE_CVTCOLOR_ONE(bgr565_to_bgra) - - OPENCV_GPU_DECLARE_CVTCOLOR_ALL(gray_to_bgr) - OPENCV_GPU_DECLARE_CVTCOLOR_ALL(gray_to_bgra) - - OPENCV_GPU_DECLARE_CVTCOLOR_ONE(gray_to_bgr555) - OPENCV_GPU_DECLARE_CVTCOLOR_ONE(gray_to_bgr565) - - OPENCV_GPU_DECLARE_CVTCOLOR_ONE(bgr555_to_gray) - OPENCV_GPU_DECLARE_CVTCOLOR_ONE(bgr565_to_gray) - - OPENCV_GPU_DECLARE_CVTCOLOR_ALL(rgb_to_gray) - OPENCV_GPU_DECLARE_CVTCOLOR_ALL(bgr_to_gray) - OPENCV_GPU_DECLARE_CVTCOLOR_ALL(rgba_to_gray) - OPENCV_GPU_DECLARE_CVTCOLOR_ALL(bgra_to_gray) - - OPENCV_GPU_DECLARE_CVTCOLOR_ALL(rgb_to_yuv) - OPENCV_GPU_DECLARE_CVTCOLOR_ALL(rgba_to_yuv) - OPENCV_GPU_DECLARE_CVTCOLOR_ALL(rgb_to_yuv4) - OPENCV_GPU_DECLARE_CVTCOLOR_ALL(rgba_to_yuv4) - OPENCV_GPU_DECLARE_CVTCOLOR_ALL(bgr_to_yuv) - OPENCV_GPU_DECLARE_CVTCOLOR_ALL(bgra_to_yuv) - OPENCV_GPU_DECLARE_CVTCOLOR_ALL(bgr_to_yuv4) - OPENCV_GPU_DECLARE_CVTCOLOR_ALL(bgra_to_yuv4) - - OPENCV_GPU_DECLARE_CVTCOLOR_ALL(yuv_to_rgb) - OPENCV_GPU_DECLARE_CVTCOLOR_ALL(yuv_to_rgba) - OPENCV_GPU_DECLARE_CVTCOLOR_ALL(yuv4_to_rgb) - OPENCV_GPU_DECLARE_CVTCOLOR_ALL(yuv4_to_rgba) - OPENCV_GPU_DECLARE_CVTCOLOR_ALL(yuv_to_bgr) - OPENCV_GPU_DECLARE_CVTCOLOR_ALL(yuv_to_bgra) - OPENCV_GPU_DECLARE_CVTCOLOR_ALL(yuv4_to_bgr) - OPENCV_GPU_DECLARE_CVTCOLOR_ALL(yuv4_to_bgra) - - OPENCV_GPU_DECLARE_CVTCOLOR_ALL(rgb_to_YCrCb) - OPENCV_GPU_DECLARE_CVTCOLOR_ALL(rgba_to_YCrCb) - OPENCV_GPU_DECLARE_CVTCOLOR_ALL(rgb_to_YCrCb4) - OPENCV_GPU_DECLARE_CVTCOLOR_ALL(rgba_to_YCrCb4) - OPENCV_GPU_DECLARE_CVTCOLOR_ALL(bgr_to_YCrCb) - OPENCV_GPU_DECLARE_CVTCOLOR_ALL(bgra_to_YCrCb) - OPENCV_GPU_DECLARE_CVTCOLOR_ALL(bgr_to_YCrCb4) - OPENCV_GPU_DECLARE_CVTCOLOR_ALL(bgra_to_YCrCb4) - - OPENCV_GPU_DECLARE_CVTCOLOR_ALL(YCrCb_to_rgb) - OPENCV_GPU_DECLARE_CVTCOLOR_ALL(YCrCb_to_rgba) - OPENCV_GPU_DECLARE_CVTCOLOR_ALL(YCrCb4_to_rgb) - OPENCV_GPU_DECLARE_CVTCOLOR_ALL(YCrCb4_to_rgba) - OPENCV_GPU_DECLARE_CVTCOLOR_ALL(YCrCb_to_bgr) - OPENCV_GPU_DECLARE_CVTCOLOR_ALL(YCrCb_to_bgra) - OPENCV_GPU_DECLARE_CVTCOLOR_ALL(YCrCb4_to_bgr) - OPENCV_GPU_DECLARE_CVTCOLOR_ALL(YCrCb4_to_bgra) - - OPENCV_GPU_DECLARE_CVTCOLOR_ALL(rgb_to_xyz) - OPENCV_GPU_DECLARE_CVTCOLOR_ALL(rgba_to_xyz) - OPENCV_GPU_DECLARE_CVTCOLOR_ALL(rgb_to_xyz4) - OPENCV_GPU_DECLARE_CVTCOLOR_ALL(rgba_to_xyz4) - OPENCV_GPU_DECLARE_CVTCOLOR_ALL(bgr_to_xyz) - OPENCV_GPU_DECLARE_CVTCOLOR_ALL(bgra_to_xyz) - OPENCV_GPU_DECLARE_CVTCOLOR_ALL(bgr_to_xyz4) - OPENCV_GPU_DECLARE_CVTCOLOR_ALL(bgra_to_xyz4) - - OPENCV_GPU_DECLARE_CVTCOLOR_ALL(xyz_to_rgb) - OPENCV_GPU_DECLARE_CVTCOLOR_ALL(xyz4_to_rgb) - OPENCV_GPU_DECLARE_CVTCOLOR_ALL(xyz_to_rgba) - OPENCV_GPU_DECLARE_CVTCOLOR_ALL(xyz4_to_rgba) - OPENCV_GPU_DECLARE_CVTCOLOR_ALL(xyz_to_bgr) - OPENCV_GPU_DECLARE_CVTCOLOR_ALL(xyz4_to_bgr) - OPENCV_GPU_DECLARE_CVTCOLOR_ALL(xyz_to_bgra) - OPENCV_GPU_DECLARE_CVTCOLOR_ALL(xyz4_to_bgra) - - OPENCV_GPU_DECLARE_CVTCOLOR_8U32F(rgb_to_hsv) - OPENCV_GPU_DECLARE_CVTCOLOR_8U32F(rgba_to_hsv) - OPENCV_GPU_DECLARE_CVTCOLOR_8U32F(rgb_to_hsv4) - OPENCV_GPU_DECLARE_CVTCOLOR_8U32F(rgba_to_hsv4) - OPENCV_GPU_DECLARE_CVTCOLOR_8U32F(bgr_to_hsv) - OPENCV_GPU_DECLARE_CVTCOLOR_8U32F(bgra_to_hsv) - OPENCV_GPU_DECLARE_CVTCOLOR_8U32F(bgr_to_hsv4) - OPENCV_GPU_DECLARE_CVTCOLOR_8U32F(bgra_to_hsv4) - - OPENCV_GPU_DECLARE_CVTCOLOR_8U32F(hsv_to_rgb) - OPENCV_GPU_DECLARE_CVTCOLOR_8U32F(hsv_to_rgba) - OPENCV_GPU_DECLARE_CVTCOLOR_8U32F(hsv4_to_rgb) - OPENCV_GPU_DECLARE_CVTCOLOR_8U32F(hsv4_to_rgba) - OPENCV_GPU_DECLARE_CVTCOLOR_8U32F(hsv_to_bgr) - OPENCV_GPU_DECLARE_CVTCOLOR_8U32F(hsv_to_bgra) - OPENCV_GPU_DECLARE_CVTCOLOR_8U32F(hsv4_to_bgr) - OPENCV_GPU_DECLARE_CVTCOLOR_8U32F(hsv4_to_bgra) - - OPENCV_GPU_DECLARE_CVTCOLOR_8U32F(rgb_to_hls) - OPENCV_GPU_DECLARE_CVTCOLOR_8U32F(rgba_to_hls) - OPENCV_GPU_DECLARE_CVTCOLOR_8U32F(rgb_to_hls4) - - OPENCV_GPU_DECLARE_CVTCOLOR_8U32F(rgba_to_hls4) - OPENCV_GPU_DECLARE_CVTCOLOR_8U32F(bgr_to_hls) - OPENCV_GPU_DECLARE_CVTCOLOR_8U32F(bgra_to_hls) - OPENCV_GPU_DECLARE_CVTCOLOR_8U32F(bgr_to_hls4) - OPENCV_GPU_DECLARE_CVTCOLOR_8U32F(bgra_to_hls4) - - OPENCV_GPU_DECLARE_CVTCOLOR_8U32F(hls_to_rgb) - OPENCV_GPU_DECLARE_CVTCOLOR_8U32F(hls_to_rgba) - OPENCV_GPU_DECLARE_CVTCOLOR_8U32F(hls4_to_rgb) - OPENCV_GPU_DECLARE_CVTCOLOR_8U32F(hls4_to_rgba) - OPENCV_GPU_DECLARE_CVTCOLOR_8U32F(hls_to_bgr) - OPENCV_GPU_DECLARE_CVTCOLOR_8U32F(hls_to_bgra) - OPENCV_GPU_DECLARE_CVTCOLOR_8U32F(hls4_to_bgr) - OPENCV_GPU_DECLARE_CVTCOLOR_8U32F(hls4_to_bgra) - - #undef OPENCV_GPU_DECLARE_CVTCOLOR_ONE - #undef OPENCV_GPU_DECLARE_CVTCOLOR_ALL - #undef OPENCV_GPU_DECLARE_CVTCOLOR_8U32F -}}} - +#include using namespace ::cv::gpu::device; namespace diff --git a/modules/gpu/src/cuda/color.cu b/modules/gpu/src/cuda/color.cu index 1dc03c4..5184222 100644 --- a/modules/gpu/src/cuda/color.cu +++ b/modules/gpu/src/cuda/color.cu @@ -40,9 +40,10 @@ // //M*/ -#include "internal_shared.hpp" -#include "opencv2/gpu/device/transform.hpp" -#include "opencv2/gpu/device/color.hpp" +#include +#include +#include +#include namespace cv { namespace gpu { namespace device { diff --git a/modules/gpu/src/cuda/matrix_reductions.cu b/modules/gpu/src/cuda/matrix_reductions.cu index fbf5ce5..d6b6d94 100644 --- a/modules/gpu/src/cuda/matrix_reductions.cu +++ b/modules/gpu/src/cuda/matrix_reductions.cu @@ -87,7 +87,9 @@ namespace cv { namespace gpu { namespace device __device__ __forceinline__ bool operator()(int y, int x) const { return true; - } + } + __device__ __forceinline__ MaskTrue(){} + __device__ __forceinline__ MaskTrue(const MaskTrue& mask_){} }; ////////////////////////////////////////////////////////////////////////////// @@ -1795,6 +1797,9 @@ namespace cv { namespace gpu { namespace device return 0; } + __device__ __forceinline__ SumReductor(const SumReductor& other){} + __device__ __forceinline__ SumReductor(){} + __device__ __forceinline__ S operator ()(volatile S a, volatile S b) const { return a + b; @@ -1813,6 +1818,9 @@ namespace cv { namespace gpu { namespace device return 0; } + __device__ __forceinline__ AvgReductor(const AvgReductor& other){} + __device__ __forceinline__ AvgReductor(){} + __device__ __forceinline__ S operator ()(volatile S a, volatile S b) const { return a + b; @@ -1831,6 +1839,9 @@ namespace cv { namespace gpu { namespace device return numeric_limits::max(); } + __device__ __forceinline__ MinReductor(const MinReductor& other){} + __device__ __forceinline__ MinReductor(){} + template __device__ __forceinline__ T operator ()(volatile T a, volatile T b) const { return saturate_cast(::min(a, b)); @@ -1853,6 +1864,9 @@ namespace cv { namespace gpu { namespace device return numeric_limits::min(); } + __device__ __forceinline__ MaxReductor(const MaxReductor& other){} + __device__ __forceinline__ MaxReductor(){} + template __device__ __forceinline__ int operator ()(volatile T a, volatile T b) const { return ::max(a, b); diff --git a/modules/gpu/src/cuda/surf.cu b/modules/gpu/src/cuda/surf.cu index ac7b6c2..e65f4aa 100644 --- a/modules/gpu/src/cuda/surf.cu +++ b/modules/gpu/src/cuda/surf.cu @@ -116,7 +116,7 @@ namespace cv { namespace gpu { namespace device template __device__ float icvCalcHaarPatternSum(const float src[][5], int oldSize, int newSize, int y, int x) { - #if __CUDA_ARCH__ >= 200 + #if __CUDA_ARCH__ && __CUDA_ARCH__ >= 200 typedef double real_t; #else typedef float real_t; @@ -248,7 +248,7 @@ namespace cv { namespace gpu { namespace device template __global__ void icvFindMaximaInLayer(const PtrStepf det, const PtrStepf trace, int4* maxPosBuffer, unsigned int* maxCounter) { - #if __CUDA_ARCH__ >= 110 + #if __CUDA_ARCH__ && __CUDA_ARCH__ >= 110 extern __shared__ float N9[]; @@ -371,7 +371,7 @@ namespace cv { namespace gpu { namespace device float* featureX, float* featureY, int* featureLaplacian, int* featureOctave, float* featureSize, float* featureHessian, unsigned int* featureCounter) { - #if __CUDA_ARCH__ >= 110 + #if __CUDA_ARCH__ && __CUDA_ARCH__ >= 110 const int4 maxPos = maxPosBuffer[blockIdx.x]; diff --git a/modules/gpu/src/cvt_colot_internal.h b/modules/gpu/src/cvt_colot_internal.h new file mode 100644 index 0000000..5ba17a2 --- /dev/null +++ b/modules/gpu/src/cvt_colot_internal.h @@ -0,0 +1,197 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef __cvt_color_internal_h__ +#define __cvt_color_internal_h__ + +namespace cv { namespace gpu { namespace device +{ +#define OPENCV_GPU_DECLARE_CVTCOLOR_ONE(name) \ + void name(const DevMem2Db& src, const DevMem2Db& dst, cudaStream_t stream); + +#define OPENCV_GPU_DECLARE_CVTCOLOR_ALL(name) \ + OPENCV_GPU_DECLARE_CVTCOLOR_ONE(name ## _8u) \ + OPENCV_GPU_DECLARE_CVTCOLOR_ONE(name ## _16u) \ + OPENCV_GPU_DECLARE_CVTCOLOR_ONE(name ## _32f) + +#define OPENCV_GPU_DECLARE_CVTCOLOR_8U32F(name) \ + OPENCV_GPU_DECLARE_CVTCOLOR_ONE(name ## _8u) \ + OPENCV_GPU_DECLARE_CVTCOLOR_ONE(name ## _32f) \ + OPENCV_GPU_DECLARE_CVTCOLOR_ONE(name ## _full_8u) \ + OPENCV_GPU_DECLARE_CVTCOLOR_ONE(name ## _full_32f) + + OPENCV_GPU_DECLARE_CVTCOLOR_ALL(bgr_to_rgb) + OPENCV_GPU_DECLARE_CVTCOLOR_ALL(bgr_to_bgra) + OPENCV_GPU_DECLARE_CVTCOLOR_ALL(bgr_to_rgba) + OPENCV_GPU_DECLARE_CVTCOLOR_ALL(bgra_to_bgr) + OPENCV_GPU_DECLARE_CVTCOLOR_ALL(bgra_to_rgb) + OPENCV_GPU_DECLARE_CVTCOLOR_ALL(bgra_to_rgba) + + OPENCV_GPU_DECLARE_CVTCOLOR_ONE(bgr_to_bgr555) + OPENCV_GPU_DECLARE_CVTCOLOR_ONE(bgr_to_bgr565) + OPENCV_GPU_DECLARE_CVTCOLOR_ONE(rgb_to_bgr555) + OPENCV_GPU_DECLARE_CVTCOLOR_ONE(rgb_to_bgr565) + OPENCV_GPU_DECLARE_CVTCOLOR_ONE(bgra_to_bgr555) + OPENCV_GPU_DECLARE_CVTCOLOR_ONE(bgra_to_bgr565) + OPENCV_GPU_DECLARE_CVTCOLOR_ONE(rgba_to_bgr555) + OPENCV_GPU_DECLARE_CVTCOLOR_ONE(rgba_to_bgr565) + + OPENCV_GPU_DECLARE_CVTCOLOR_ONE(bgr555_to_rgb) + OPENCV_GPU_DECLARE_CVTCOLOR_ONE(bgr565_to_rgb) + OPENCV_GPU_DECLARE_CVTCOLOR_ONE(bgr555_to_bgr) + OPENCV_GPU_DECLARE_CVTCOLOR_ONE(bgr565_to_bgr) + OPENCV_GPU_DECLARE_CVTCOLOR_ONE(bgr555_to_rgba) + OPENCV_GPU_DECLARE_CVTCOLOR_ONE(bgr565_to_rgba) + OPENCV_GPU_DECLARE_CVTCOLOR_ONE(bgr555_to_bgra) + OPENCV_GPU_DECLARE_CVTCOLOR_ONE(bgr565_to_bgra) + + OPENCV_GPU_DECLARE_CVTCOLOR_ALL(gray_to_bgr) + OPENCV_GPU_DECLARE_CVTCOLOR_ALL(gray_to_bgra) + + OPENCV_GPU_DECLARE_CVTCOLOR_ONE(gray_to_bgr555) + OPENCV_GPU_DECLARE_CVTCOLOR_ONE(gray_to_bgr565) + + OPENCV_GPU_DECLARE_CVTCOLOR_ONE(bgr555_to_gray) + OPENCV_GPU_DECLARE_CVTCOLOR_ONE(bgr565_to_gray) + + OPENCV_GPU_DECLARE_CVTCOLOR_ALL(rgb_to_gray) + OPENCV_GPU_DECLARE_CVTCOLOR_ALL(bgr_to_gray) + OPENCV_GPU_DECLARE_CVTCOLOR_ALL(rgba_to_gray) + OPENCV_GPU_DECLARE_CVTCOLOR_ALL(bgra_to_gray) + + OPENCV_GPU_DECLARE_CVTCOLOR_ALL(rgb_to_yuv) + OPENCV_GPU_DECLARE_CVTCOLOR_ALL(rgba_to_yuv) + OPENCV_GPU_DECLARE_CVTCOLOR_ALL(rgb_to_yuv4) + OPENCV_GPU_DECLARE_CVTCOLOR_ALL(rgba_to_yuv4) + OPENCV_GPU_DECLARE_CVTCOLOR_ALL(bgr_to_yuv) + OPENCV_GPU_DECLARE_CVTCOLOR_ALL(bgra_to_yuv) + OPENCV_GPU_DECLARE_CVTCOLOR_ALL(bgr_to_yuv4) + OPENCV_GPU_DECLARE_CVTCOLOR_ALL(bgra_to_yuv4) + + OPENCV_GPU_DECLARE_CVTCOLOR_ALL(yuv_to_rgb) + OPENCV_GPU_DECLARE_CVTCOLOR_ALL(yuv_to_rgba) + OPENCV_GPU_DECLARE_CVTCOLOR_ALL(yuv4_to_rgb) + OPENCV_GPU_DECLARE_CVTCOLOR_ALL(yuv4_to_rgba) + OPENCV_GPU_DECLARE_CVTCOLOR_ALL(yuv_to_bgr) + OPENCV_GPU_DECLARE_CVTCOLOR_ALL(yuv_to_bgra) + OPENCV_GPU_DECLARE_CVTCOLOR_ALL(yuv4_to_bgr) + OPENCV_GPU_DECLARE_CVTCOLOR_ALL(yuv4_to_bgra) + + OPENCV_GPU_DECLARE_CVTCOLOR_ALL(rgb_to_YCrCb) + OPENCV_GPU_DECLARE_CVTCOLOR_ALL(rgba_to_YCrCb) + OPENCV_GPU_DECLARE_CVTCOLOR_ALL(rgb_to_YCrCb4) + OPENCV_GPU_DECLARE_CVTCOLOR_ALL(rgba_to_YCrCb4) + OPENCV_GPU_DECLARE_CVTCOLOR_ALL(bgr_to_YCrCb) + OPENCV_GPU_DECLARE_CVTCOLOR_ALL(bgra_to_YCrCb) + OPENCV_GPU_DECLARE_CVTCOLOR_ALL(bgr_to_YCrCb4) + OPENCV_GPU_DECLARE_CVTCOLOR_ALL(bgra_to_YCrCb4) + + OPENCV_GPU_DECLARE_CVTCOLOR_ALL(YCrCb_to_rgb) + OPENCV_GPU_DECLARE_CVTCOLOR_ALL(YCrCb_to_rgba) + OPENCV_GPU_DECLARE_CVTCOLOR_ALL(YCrCb4_to_rgb) + OPENCV_GPU_DECLARE_CVTCOLOR_ALL(YCrCb4_to_rgba) + OPENCV_GPU_DECLARE_CVTCOLOR_ALL(YCrCb_to_bgr) + OPENCV_GPU_DECLARE_CVTCOLOR_ALL(YCrCb_to_bgra) + OPENCV_GPU_DECLARE_CVTCOLOR_ALL(YCrCb4_to_bgr) + OPENCV_GPU_DECLARE_CVTCOLOR_ALL(YCrCb4_to_bgra) + + OPENCV_GPU_DECLARE_CVTCOLOR_ALL(rgb_to_xyz) + OPENCV_GPU_DECLARE_CVTCOLOR_ALL(rgba_to_xyz) + OPENCV_GPU_DECLARE_CVTCOLOR_ALL(rgb_to_xyz4) + OPENCV_GPU_DECLARE_CVTCOLOR_ALL(rgba_to_xyz4) + OPENCV_GPU_DECLARE_CVTCOLOR_ALL(bgr_to_xyz) + OPENCV_GPU_DECLARE_CVTCOLOR_ALL(bgra_to_xyz) + OPENCV_GPU_DECLARE_CVTCOLOR_ALL(bgr_to_xyz4) + OPENCV_GPU_DECLARE_CVTCOLOR_ALL(bgra_to_xyz4) + + OPENCV_GPU_DECLARE_CVTCOLOR_ALL(xyz_to_rgb) + OPENCV_GPU_DECLARE_CVTCOLOR_ALL(xyz4_to_rgb) + OPENCV_GPU_DECLARE_CVTCOLOR_ALL(xyz_to_rgba) + OPENCV_GPU_DECLARE_CVTCOLOR_ALL(xyz4_to_rgba) + OPENCV_GPU_DECLARE_CVTCOLOR_ALL(xyz_to_bgr) + OPENCV_GPU_DECLARE_CVTCOLOR_ALL(xyz4_to_bgr) + OPENCV_GPU_DECLARE_CVTCOLOR_ALL(xyz_to_bgra) + OPENCV_GPU_DECLARE_CVTCOLOR_ALL(xyz4_to_bgra) + + OPENCV_GPU_DECLARE_CVTCOLOR_8U32F(rgb_to_hsv) + OPENCV_GPU_DECLARE_CVTCOLOR_8U32F(rgba_to_hsv) + OPENCV_GPU_DECLARE_CVTCOLOR_8U32F(rgb_to_hsv4) + OPENCV_GPU_DECLARE_CVTCOLOR_8U32F(rgba_to_hsv4) + OPENCV_GPU_DECLARE_CVTCOLOR_8U32F(bgr_to_hsv) + OPENCV_GPU_DECLARE_CVTCOLOR_8U32F(bgra_to_hsv) + OPENCV_GPU_DECLARE_CVTCOLOR_8U32F(bgr_to_hsv4) + OPENCV_GPU_DECLARE_CVTCOLOR_8U32F(bgra_to_hsv4) + + OPENCV_GPU_DECLARE_CVTCOLOR_8U32F(hsv_to_rgb) + OPENCV_GPU_DECLARE_CVTCOLOR_8U32F(hsv_to_rgba) + OPENCV_GPU_DECLARE_CVTCOLOR_8U32F(hsv4_to_rgb) + OPENCV_GPU_DECLARE_CVTCOLOR_8U32F(hsv4_to_rgba) + OPENCV_GPU_DECLARE_CVTCOLOR_8U32F(hsv_to_bgr) + OPENCV_GPU_DECLARE_CVTCOLOR_8U32F(hsv_to_bgra) + OPENCV_GPU_DECLARE_CVTCOLOR_8U32F(hsv4_to_bgr) + OPENCV_GPU_DECLARE_CVTCOLOR_8U32F(hsv4_to_bgra) + + OPENCV_GPU_DECLARE_CVTCOLOR_8U32F(rgb_to_hls) + OPENCV_GPU_DECLARE_CVTCOLOR_8U32F(rgba_to_hls) + OPENCV_GPU_DECLARE_CVTCOLOR_8U32F(rgb_to_hls4) + + OPENCV_GPU_DECLARE_CVTCOLOR_8U32F(rgba_to_hls4) + OPENCV_GPU_DECLARE_CVTCOLOR_8U32F(bgr_to_hls) + OPENCV_GPU_DECLARE_CVTCOLOR_8U32F(bgra_to_hls) + OPENCV_GPU_DECLARE_CVTCOLOR_8U32F(bgr_to_hls4) + OPENCV_GPU_DECLARE_CVTCOLOR_8U32F(bgra_to_hls4) + + OPENCV_GPU_DECLARE_CVTCOLOR_8U32F(hls_to_rgb) + OPENCV_GPU_DECLARE_CVTCOLOR_8U32F(hls_to_rgba) + OPENCV_GPU_DECLARE_CVTCOLOR_8U32F(hls4_to_rgb) + OPENCV_GPU_DECLARE_CVTCOLOR_8U32F(hls4_to_rgba) + OPENCV_GPU_DECLARE_CVTCOLOR_8U32F(hls_to_bgr) + OPENCV_GPU_DECLARE_CVTCOLOR_8U32F(hls_to_bgra) + OPENCV_GPU_DECLARE_CVTCOLOR_8U32F(hls4_to_bgr) + OPENCV_GPU_DECLARE_CVTCOLOR_8U32F(hls4_to_bgra) + + #undef OPENCV_GPU_DECLARE_CVTCOLOR_ONE + #undef OPENCV_GPU_DECLARE_CVTCOLOR_ALL + #undef OPENCV_GPU_DECLARE_CVTCOLOR_8U32F +}}} + +#endif diff --git a/modules/gpu/src/nvidia/NCVHaarObjectDetection.cu b/modules/gpu/src/nvidia/NCVHaarObjectDetection.cu index fded861..781d411 100644 --- a/modules/gpu/src/nvidia/NCVHaarObjectDetection.cu +++ b/modules/gpu/src/nvidia/NCVHaarObjectDetection.cu @@ -231,7 +231,7 @@ __device__ Ncv32u d_outMaskPosition; __device__ void compactBlockWriteOutAnchorParallel(Ncv32u threadPassFlag, Ncv32u threadElem, Ncv32u *vectorOut) { -#if __CUDA_ARCH__ >= 110 +#if __CUDA_ARCH__ && __CUDA_ARCH__ >= 110 __shared__ Ncv32u shmem[NUM_THREADS_ANCHORSPARALLEL * 2]; __shared__ Ncv32u numPassed; @@ -587,7 +587,7 @@ __global__ void applyHaarClassifierClassifierParallel(Ncv32u *d_IImg, Ncv32u IIm } else { -#if __CUDA_ARCH__ >= 110 +#if __CUDA_ARCH__ && __CUDA_ARCH__ >= 110 if (bPass && !threadIdx.x) { Ncv32u outMaskOffset = atomicAdd(&d_outMaskPosition, 1); diff --git a/modules/gpu/src/nvidia/core/NCV.hpp b/modules/gpu/src/nvidia/core/NCV.hpp index bab90f8..f89a364 100644 --- a/modules/gpu/src/nvidia/core/NCV.hpp +++ b/modules/gpu/src/nvidia/core/NCV.hpp @@ -142,7 +142,7 @@ struct NcvRect8u Ncv8u width; Ncv8u height; __host__ __device__ NcvRect8u() : x(0), y(0), width(0), height(0) {}; - __host__ __device__ NcvRect8u(Ncv8u x, Ncv8u y, Ncv8u width, Ncv8u height) : x(x), y(y), width(width), height(height) {} + __host__ __device__ NcvRect8u(Ncv8u x_, Ncv8u y_, Ncv8u width_, Ncv8u height_) : x(x_), y(y_), width(width_), height(height_) {} }; @@ -153,7 +153,8 @@ struct NcvRect32s Ncv32s width; ///< Rectangle width. Ncv32s height; ///< Rectangle height. __host__ __device__ NcvRect32s() : x(0), y(0), width(0), height(0) {}; - __host__ __device__ NcvRect32s(Ncv32s x, Ncv32s y, Ncv32s width, Ncv32s height) : x(x), y(y), width(width), height(height) {} + __host__ __device__ NcvRect32s(Ncv32s x_, Ncv32s y_, Ncv32s width_, Ncv32s height_) + : x(x_), y(y_), width(width_), height(height_) {} }; @@ -164,7 +165,8 @@ struct NcvRect32u Ncv32u width; ///< Rectangle width. Ncv32u height; ///< Rectangle height. __host__ __device__ NcvRect32u() : x(0), y(0), width(0), height(0) {}; - __host__ __device__ NcvRect32u(Ncv32u x, Ncv32u y, Ncv32u width, Ncv32u height) : x(x), y(y), width(width), height(height) {} + __host__ __device__ NcvRect32u(Ncv32u x_, Ncv32u y_, Ncv32u width_, Ncv32u height_) + : x(x_), y(y_), width(width_), height(height_) {} }; @@ -173,7 +175,7 @@ struct NcvSize32s Ncv32s width; ///< Rectangle width. Ncv32s height; ///< Rectangle height. __host__ __device__ NcvSize32s() : width(0), height(0) {}; - __host__ __device__ NcvSize32s(Ncv32s width, Ncv32s height) : width(width), height(height) {} + __host__ __device__ NcvSize32s(Ncv32s width_, Ncv32s height_) : width(width_), height(height_) {} }; @@ -182,7 +184,7 @@ struct NcvSize32u Ncv32u width; ///< Rectangle width. Ncv32u height; ///< Rectangle height. __host__ __device__ NcvSize32u() : width(0), height(0) {}; - __host__ __device__ NcvSize32u(Ncv32u width, Ncv32u height) : width(width), height(height) {} + __host__ __device__ NcvSize32u(Ncv32u width_, Ncv32u height_) : width(width_), height(height_) {} __host__ __device__ bool operator == (const NcvSize32u &another) const {return this->width == another.width && this->height == another.height;} }; @@ -192,7 +194,7 @@ struct NcvPoint2D32s Ncv32s x; ///< Point X. Ncv32s y; ///< Point Y. __host__ __device__ NcvPoint2D32s() : x(0), y(0) {}; - __host__ __device__ NcvPoint2D32s(Ncv32s x, Ncv32s y) : x(x), y(y) {} + __host__ __device__ NcvPoint2D32s(Ncv32s x_, Ncv32s y_) : x(x_), y(y_) {} }; @@ -201,7 +203,7 @@ struct NcvPoint2D32u Ncv32u x; ///< Point X. Ncv32u y; ///< Point Y. __host__ __device__ NcvPoint2D32u() : x(0), y(0) {}; - __host__ __device__ NcvPoint2D32u(Ncv32u x, Ncv32u y) : x(x), y(y) {} + __host__ __device__ NcvPoint2D32u(Ncv32u x_, Ncv32u y_) : x(x_), y(y_) {} }; @@ -625,9 +627,9 @@ class NCVVectorAlloc : public NCVVector public: - NCVVectorAlloc(INCVMemAllocator &allocator, Ncv32u length) + NCVVectorAlloc(INCVMemAllocator &allocator_, Ncv32u length) : - allocator(allocator) + allocator(allocator_) { NCVStatus ncvStat; @@ -839,7 +841,7 @@ class NCVMatrixAlloc : public NCVMatrix NCVMatrixAlloc& operator=(const NCVMatrixAlloc &); public: - NCVMatrixAlloc(INCVMemAllocator &allocator, Ncv32u width, Ncv32u height, Ncv32u pitch=0) + NCVMatrixAlloc(INCVMemAllocator &allocator, Ncv32u width, Ncv32u height, Ncv32u _pitch=0) : allocator(allocator) { @@ -851,12 +853,12 @@ public: Ncv32u widthBytes = width * sizeof(T); Ncv32u pitchBytes = alignUp(widthBytes, allocator.alignment()); - if (pitch != 0) + if (_pitch != 0) { - ncvAssertPrintReturn(pitch >= pitchBytes && - (pitch & (allocator.alignment() - 1)) == 0, + ncvAssertPrintReturn(_pitch >= pitchBytes && + (_pitch & (allocator.alignment() - 1)) == 0, "NCVMatrixAlloc ctor:: incorrect pitch passed", ); - pitchBytes = pitch; + pitchBytes = _pitch; } Ncv32u requiredAllocSize = pitchBytes * height; @@ -1020,4 +1022,4 @@ NCV_EXPORTS NCVStatus ncvDrawRects_32u_device(Ncv32u *d_dst, Ncv32u dstStride, N -#endif // _ncv_hpp_ \ No newline at end of file +#endif // _ncv_hpp_ diff --git a/modules/gpu/src/nvidia/core/NCVRuntimeTemplates.hpp b/modules/gpu/src/nvidia/core/NCVRuntimeTemplates.hpp index 2fcc2c5..a13d344 100644 --- a/modules/gpu/src/nvidia/core/NCVRuntimeTemplates.hpp +++ b/modules/gpu/src/nvidia/core/NCVRuntimeTemplates.hpp @@ -41,7 +41,7 @@ #ifndef _ncvruntimetemplates_hpp_ #define _ncvruntimetemplates_hpp_ -#if _MSC_VER >= 1200 +#if defined _MSC_VER &&_MSC_VER >= 1200 #pragma warning( disable: 4800 ) #endif diff --git a/modules/gpu/src/opencv2/gpu/device/datamov_utils.hpp b/modules/gpu/src/opencv2/gpu/device/datamov_utils.hpp index 50b9c7e..bd5c49f 100644 --- a/modules/gpu/src/opencv2/gpu/device/datamov_utils.hpp +++ b/modules/gpu/src/opencv2/gpu/device/datamov_utils.hpp @@ -47,7 +47,7 @@ namespace cv { namespace gpu { namespace device { - #if __CUDA_ARCH__ >= 200 + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 200 // for Fermi memory space is detected automatically template struct ForceGlob diff --git a/modules/gpu/src/opencv2/gpu/device/detail/color_detail.hpp b/modules/gpu/src/opencv2/gpu/device/detail/color_detail.hpp index 2f1a65a..900958f 100644 --- a/modules/gpu/src/opencv2/gpu/device/detail/color_detail.hpp +++ b/modules/gpu/src/opencv2/gpu/device/detail/color_detail.hpp @@ -63,6 +63,7 @@ namespace cv { namespace gpu { namespace device static __device__ __forceinline__ T max() { return numeric_limits::max(); } static __device__ __forceinline__ T half() { return (T)(max()/2 + 1); } }; + template<> struct ColorChannel { typedef float worktype_f; @@ -73,14 +74,17 @@ namespace cv { namespace gpu { namespace device template static __device__ __forceinline__ void setAlpha(typename TypeVec::vec_type& vec, T val) { } + template static __device__ __forceinline__ void setAlpha(typename TypeVec::vec_type& vec, T val) { vec.w = val; } + template static __device__ __forceinline__ T getAlpha(const typename TypeVec::vec_type& vec) { return ColorChannel::max(); } + template static __device__ __forceinline__ T getAlpha(const typename TypeVec::vec_type& vec) { return vec.w; @@ -101,7 +105,8 @@ namespace cv { namespace gpu { namespace device namespace color_detail { - template struct RGB2RGB : unary_function::vec_type, typename TypeVec::vec_type> + template struct RGB2RGB + : unary_function::vec_type, typename TypeVec::vec_type> { __device__ typename TypeVec::vec_type operator()(const typename TypeVec::vec_type& src) const { @@ -114,6 +119,12 @@ namespace cv { namespace gpu { namespace device return dst; } + + __device__ __forceinline__ RGB2RGB() + : unary_function::vec_type, typename TypeVec::vec_type>(){} + + __device__ __forceinline__ RGB2RGB(const RGB2RGB& other_) + :unary_function::vec_type, typename TypeVec::vec_type>(){} }; template <> struct RGB2RGB : unary_function @@ -129,6 +140,9 @@ namespace cv { namespace gpu { namespace device return dst; } + + __device__ __forceinline__ RGB2RGB():unary_function(){} + __device__ __forceinline__ RGB2RGB(const RGB2RGB& other_):unary_function(){} }; } @@ -153,6 +167,7 @@ namespace cv { namespace gpu { namespace device { return (ushort)(((&src.x)[bidx] >> 3) | ((src.y & ~3) << 3) | (((&src.x)[bidx^2] & ~7) << 8)); } + static __device__ __forceinline__ ushort cvt(uint src) { uint b = 0xffu & (src >> (bidx * 8)); @@ -161,12 +176,14 @@ namespace cv { namespace gpu { namespace device return (ushort)((b >> 3) | ((g & ~3) << 3) | ((r & ~7) << 8)); } }; + template struct RGB2RGB5x5Converter<5, bidx> { static __device__ __forceinline__ ushort cvt(const uchar3& src) { return (ushort)(((&src.x)[bidx] >> 3) | ((src.y & ~7) << 2) | (((&src.x)[bidx^2] & ~7) << 7)); } + static __device__ __forceinline__ ushort cvt(uint src) { uint b = 0xffu & (src >> (bidx * 8)); @@ -178,19 +195,27 @@ namespace cv { namespace gpu { namespace device }; template struct RGB2RGB5x5; + template struct RGB2RGB5x5<3, bidx,green_bits> : unary_function { __device__ __forceinline__ ushort operator()(const uchar3& src) const { return RGB2RGB5x5Converter::cvt(src); } + + __device__ __forceinline__ RGB2RGB5x5():unary_function(){} + __device__ __forceinline__ RGB2RGB5x5(const RGB2RGB5x5& other_):unary_function(){} }; + template struct RGB2RGB5x5<4, bidx,green_bits> : unary_function { __device__ __forceinline__ ushort operator()(uint src) const { return RGB2RGB5x5Converter::cvt(src); } + + __device__ __forceinline__ RGB2RGB5x5():unary_function(){} + __device__ __forceinline__ RGB2RGB5x5(const RGB2RGB5x5& other_):unary_function(){} }; } @@ -207,6 +232,7 @@ namespace cv { namespace gpu { namespace device namespace color_detail { template struct RGB5x52RGBConverter; + template struct RGB5x52RGBConverter<5, bidx> { static __device__ __forceinline__ void cvt(uint src, uchar3& dst) @@ -215,6 +241,7 @@ namespace cv { namespace gpu { namespace device dst.y = (src >> 2) & ~7; (&dst.x)[bidx ^ 2] = (src >> 7) & ~7; } + static __device__ __forceinline__ void cvt(uint src, uint& dst) { dst = 0; @@ -225,6 +252,7 @@ namespace cv { namespace gpu { namespace device dst |= ((src & 0x8000) * 0xffu) << 24; } }; + template struct RGB5x52RGBConverter<6, bidx> { static __device__ __forceinline__ void cvt(uint src, uchar3& dst) @@ -233,6 +261,7 @@ namespace cv { namespace gpu { namespace device dst.y = (src >> 3) & ~3; (&dst.x)[bidx ^ 2] = (src >> 8) & ~7; } + static __device__ __forceinline__ void cvt(uint src, uint& dst) { dst = 0xffu << 24; @@ -244,6 +273,7 @@ namespace cv { namespace gpu { namespace device }; template struct RGB5x52RGB; + template struct RGB5x52RGB<3, bidx, green_bits> : unary_function { __device__ __forceinline__ uchar3 operator()(ushort src) const @@ -252,7 +282,11 @@ namespace cv { namespace gpu { namespace device RGB5x52RGBConverter::cvt(src, dst); return dst; } + __device__ __forceinline__ RGB5x52RGB():unary_function(){} + __device__ __forceinline__ RGB5x52RGB(const RGB5x52RGB& other_):unary_function(){} + }; + template struct RGB5x52RGB<4, bidx, green_bits> : unary_function { __device__ __forceinline__ uint operator()(ushort src) const @@ -261,6 +295,8 @@ namespace cv { namespace gpu { namespace device RGB5x52RGBConverter::cvt(src, dst); return dst; } + __device__ __forceinline__ RGB5x52RGB():unary_function(){} + __device__ __forceinline__ RGB5x52RGB(const RGB5x52RGB& other_):unary_function(){} }; } @@ -289,7 +325,11 @@ namespace cv { namespace gpu { namespace device return dst; } + __device__ __forceinline__ Gray2RGB():unary_function::vec_type>(){} + __device__ __forceinline__ Gray2RGB(const Gray2RGB& other_) + : unary_function::vec_type>(){} }; + template <> struct Gray2RGB : unary_function { __device__ __forceinline__ uint operator()(uint src) const @@ -302,6 +342,8 @@ namespace cv { namespace gpu { namespace device return dst; } + __device__ __forceinline__ Gray2RGB():unary_function(){} + __device__ __forceinline__ Gray2RGB(const Gray2RGB& other_):unary_function(){} }; } @@ -325,6 +367,7 @@ namespace cv { namespace gpu { namespace device return (ushort)((t >> 3) | ((t & ~3) << 3) | ((t & ~7) << 8)); } }; + template<> struct Gray2RGB5x5Converter<5> { static __device__ __forceinline__ ushort cvt(uint t) @@ -340,6 +383,9 @@ namespace cv { namespace gpu { namespace device { return Gray2RGB5x5Converter::cvt(src); } + + __device__ __forceinline__ Gray2RGB5x5():unary_function(){} + __device__ __forceinline__ Gray2RGB5x5(const Gray2RGB5x5& other_):unary_function(){} }; } @@ -365,6 +411,7 @@ namespace cv { namespace gpu { namespace device return (uchar)CV_DESCALE(((t << 3) & 0xf8) * B2Y + ((t >> 3) & 0xfc) * G2Y + ((t >> 8) & 0xf8) * R2Y, yuv_shift); } }; + template <> struct RGB5x52GrayConverter<5> { static __device__ __forceinline__ uchar cvt(uint t) @@ -379,6 +426,8 @@ namespace cv { namespace gpu { namespace device { return RGB5x52GrayConverter::cvt(src); } + __device__ __forceinline__ RGB5x52Gray() : unary_function(){} + __device__ __forceinline__ RGB5x52Gray(const RGB5x52Gray& other_) : unary_function(){} }; } @@ -398,6 +447,7 @@ namespace cv { namespace gpu { namespace device { return (T)CV_DESCALE((unsigned)(src[bidx] * B2Y + src[1] * G2Y + src[bidx^2] * R2Y), yuv_shift); } + template static __device__ __forceinline__ uchar RGB2GrayConvert(uint src) { uint b = 0xffu & (src >> (bidx * 8)); @@ -405,6 +455,7 @@ namespace cv { namespace gpu { namespace device uint r = 0xffu & (src >> ((bidx ^ 2) * 8)); return CV_DESCALE((uint)(b * B2Y + g * G2Y + r * R2Y), yuv_shift); } + template static __device__ __forceinline__ float RGB2GrayConvert(const float* src) { return src[bidx] * 0.114f + src[1] * 0.587f + src[bidx^2] * 0.299f; @@ -416,13 +467,19 @@ namespace cv { namespace gpu { namespace device { return RGB2GrayConvert(&src.x); } + __device__ __forceinline__ RGB2Gray() : unary_function::vec_type, T>(){} + __device__ __forceinline__ RGB2Gray(const RGB2Gray& other_) + : unary_function::vec_type, T>(){} }; + template struct RGB2Gray : unary_function { __device__ __forceinline__ uchar operator()(uint src) const { return RGB2GrayConvert(src); } + __device__ __forceinline__ RGB2Gray() : unary_function(){} + __device__ __forceinline__ RGB2Gray(const RGB2Gray& other_) : unary_function(){} }; } @@ -463,7 +520,8 @@ namespace cv { namespace gpu { namespace device dst.z = (src[bidx] - dst.x) * c_RGB2YUVCoeffs_f[4] + ColorChannel::half(); } - template struct RGB2YUV : unary_function::vec_type, typename TypeVec::vec_type> + template struct RGB2YUV + : unary_function::vec_type, typename TypeVec::vec_type> { __device__ __forceinline__ typename TypeVec::vec_type operator ()(const typename TypeVec::vec_type& src) const { @@ -471,6 +529,10 @@ namespace cv { namespace gpu { namespace device RGB2YUVConvert(&src.x, dst); return dst; } + __device__ __forceinline__ RGB2YUV() + : unary_function::vec_type, typename TypeVec::vec_type>(){} + __device__ __forceinline__ RGB2YUV(const RGB2YUV& other_) + : unary_function::vec_type, typename TypeVec::vec_type>(){} }; } @@ -492,13 +554,17 @@ namespace cv { namespace gpu { namespace device template static __device__ void YUV2RGBConvert(const T& src, D* dst) { const int b = src.x + CV_DESCALE((src.z - ColorChannel::half()) * c_YUV2RGBCoeffs_i[3], yuv_shift); - const int g = src.x + CV_DESCALE((src.z - ColorChannel::half()) * c_YUV2RGBCoeffs_i[2] + (src.y - ColorChannel::half()) * c_YUV2RGBCoeffs_i[1], yuv_shift); + + const int g = src.x + CV_DESCALE((src.z - ColorChannel::half()) * c_YUV2RGBCoeffs_i[2] + + (src.y - ColorChannel::half()) * c_YUV2RGBCoeffs_i[1], yuv_shift); + const int r = src.x + CV_DESCALE((src.y - ColorChannel::half()) * c_YUV2RGBCoeffs_i[0], yuv_shift); dst[bidx] = saturate_cast(b); dst[1] = saturate_cast(g); dst[bidx^2] = saturate_cast(r); } + template static __device__ uint YUV2RGBConvert(uint src) { const int x = 0xff & (src); @@ -506,7 +572,10 @@ namespace cv { namespace gpu { namespace device const int z = 0xff & (src >> 16); const int b = x + CV_DESCALE((z - ColorChannel::half()) * c_YUV2RGBCoeffs_i[3], yuv_shift); - const int g = x + CV_DESCALE((z - ColorChannel::half()) * c_YUV2RGBCoeffs_i[2] + (y - ColorChannel::half()) * c_YUV2RGBCoeffs_i[1], yuv_shift); + + const int g = x + CV_DESCALE((z - ColorChannel::half()) * c_YUV2RGBCoeffs_i[2] + + (y - ColorChannel::half()) * c_YUV2RGBCoeffs_i[1], yuv_shift); + const int r = x + CV_DESCALE((y - ColorChannel::half()) * c_YUV2RGBCoeffs_i[0], yuv_shift); uint dst = 0xffu << 24; @@ -517,14 +586,19 @@ namespace cv { namespace gpu { namespace device return dst; } + template static __device__ __forceinline__ void YUV2RGBConvert(const T& src, float* dst) { dst[bidx] = src.x + (src.z - ColorChannel::half()) * c_YUV2RGBCoeffs_f[3]; - dst[1] = src.x + (src.z - ColorChannel::half()) * c_YUV2RGBCoeffs_f[2] + (src.y - ColorChannel::half()) * c_YUV2RGBCoeffs_f[1]; + + dst[1] = src.x + (src.z - ColorChannel::half()) * c_YUV2RGBCoeffs_f[2] + + (src.y - ColorChannel::half()) * c_YUV2RGBCoeffs_f[1]; + dst[bidx^2] = src.x + (src.y - ColorChannel::half()) * c_YUV2RGBCoeffs_f[0]; } - template struct YUV2RGB : unary_function::vec_type, typename TypeVec::vec_type> + template struct YUV2RGB + : unary_function::vec_type, typename TypeVec::vec_type> { __device__ __forceinline__ typename TypeVec::vec_type operator ()(const typename TypeVec::vec_type& src) const { @@ -535,13 +609,20 @@ namespace cv { namespace gpu { namespace device return dst; } + __device__ __forceinline__ YUV2RGB() + : unary_function::vec_type, typename TypeVec::vec_type>(){} + __device__ __forceinline__ YUV2RGB(const YUV2RGB& other_) + : unary_function::vec_type, typename TypeVec::vec_type>(){} }; + template struct YUV2RGB : unary_function { __device__ __forceinline__ uint operator ()(uint src) const { return YUV2RGBConvert(src); } + __device__ __forceinline__ YUV2RGB() : unary_function(){} + __device__ __forceinline__ YUV2RGB(const YUV2RGB& other_) : unary_function(){} }; } @@ -574,6 +655,7 @@ namespace cv { namespace gpu { namespace device dst.y = saturate_cast(Cr); dst.z = saturate_cast(Cb); } + template static __device__ uint RGB2YCrCbConvert(uint src) { const int delta = ColorChannel::half() * (1 << yuv_shift); @@ -590,6 +672,7 @@ namespace cv { namespace gpu { namespace device return dst; } + template static __device__ __forceinline__ void RGB2YCrCbConvert(const float* src, D& dst) { dst.x = src[0] * c_RGB2YCrCbCoeffs_f[bidx^2] + src[1] * c_RGB2YCrCbCoeffs_f[1] + src[2] * c_RGB2YCrCbCoeffs_f[bidx]; @@ -597,7 +680,8 @@ namespace cv { namespace gpu { namespace device dst.z = (src[bidx] - dst.x) * c_RGB2YCrCbCoeffs_f[4] + ColorChannel::half(); } - template struct RGB2YCrCb : unary_function::vec_type, typename TypeVec::vec_type> + template struct RGB2YCrCb + : unary_function::vec_type, typename TypeVec::vec_type> { __device__ __forceinline__ typename TypeVec::vec_type operator ()(const typename TypeVec::vec_type& src) const { @@ -605,13 +689,21 @@ namespace cv { namespace gpu { namespace device RGB2YCrCbConvert(&src.x, dst); return dst; } + __device__ __forceinline__ RGB2YCrCb() + : unary_function::vec_type, typename TypeVec::vec_type>(){} + __device__ __forceinline__ RGB2YCrCb(const RGB2YCrCb& other_) + : unary_function::vec_type, typename TypeVec::vec_type>(){} }; + template struct RGB2YCrCb : unary_function { __device__ __forceinline__ uint operator ()(uint src) const { return RGB2YCrCbConvert(src); } + + __device__ __forceinline__ RGB2YCrCb() : unary_function(){} + __device__ __forceinline__ RGB2YCrCb(const RGB2YCrCb& other_) : unary_function(){} }; } @@ -640,6 +732,7 @@ namespace cv { namespace gpu { namespace device dst[1] = saturate_cast(g); dst[bidx^2] = saturate_cast(r); } + template static __device__ uint YCrCb2RGBConvert(uint src) { const int x = 0xff & (src); @@ -658,6 +751,7 @@ namespace cv { namespace gpu { namespace device return dst; } + template __device__ __forceinline__ void YCrCb2RGBConvert(const T& src, float* dst) { dst[bidx] = src.x + (src.z - ColorChannel::half()) * c_YCrCb2RGBCoeffs_f[3]; @@ -665,7 +759,8 @@ namespace cv { namespace gpu { namespace device dst[bidx^2] = src.x + (src.y - ColorChannel::half()) * c_YCrCb2RGBCoeffs_f[0]; } - template struct YCrCb2RGB : unary_function::vec_type, typename TypeVec::vec_type> + template struct YCrCb2RGB + : unary_function::vec_type, typename TypeVec::vec_type> { __device__ __forceinline__ typename TypeVec::vec_type operator ()(const typename TypeVec::vec_type& src) const { @@ -676,13 +771,20 @@ namespace cv { namespace gpu { namespace device return dst; } + __device__ __forceinline__ YCrCb2RGB() + : unary_function::vec_type, typename TypeVec::vec_type>(){} + __device__ __forceinline__ YCrCb2RGB(const YCrCb2RGB& other_) + : unary_function::vec_type, typename TypeVec::vec_type>(){} }; + template struct YCrCb2RGB : unary_function { __device__ __forceinline__ uint operator ()(uint src) const { return YCrCb2RGBConvert(src); } + __device__ __forceinline__ YCrCb2RGB() : unary_function(){} + __device__ __forceinline__ YCrCb2RGB(const YCrCb2RGB& other_) : unary_function(){} }; } @@ -709,6 +811,7 @@ namespace cv { namespace gpu { namespace device dst.y = saturate_cast(CV_DESCALE(src[bidx^2] * c_RGB2XYZ_D65i[3] + src[1] * c_RGB2XYZ_D65i[4] + src[bidx] * c_RGB2XYZ_D65i[5], xyz_shift)); dst.z = saturate_cast(CV_DESCALE(src[bidx^2] * c_RGB2XYZ_D65i[6] + src[1] * c_RGB2XYZ_D65i[7] + src[bidx] * c_RGB2XYZ_D65i[8], xyz_shift)); } + template static __device__ __forceinline__ uint RGB2XYZConvert(uint src) { const uint b = 0xffu & (src >> (bidx * 8)); @@ -727,6 +830,7 @@ namespace cv { namespace gpu { namespace device return dst; } + template static __device__ __forceinline__ void RGB2XYZConvert(const float* src, D& dst) { dst.x = src[bidx^2] * c_RGB2XYZ_D65f[0] + src[1] * c_RGB2XYZ_D65f[1] + src[bidx] * c_RGB2XYZ_D65f[2]; @@ -734,7 +838,8 @@ namespace cv { namespace gpu { namespace device dst.z = src[bidx^2] * c_RGB2XYZ_D65f[6] + src[1] * c_RGB2XYZ_D65f[7] + src[bidx] * c_RGB2XYZ_D65f[8]; } - template struct RGB2XYZ : unary_function::vec_type, typename TypeVec::vec_type> + template struct RGB2XYZ + : unary_function::vec_type, typename TypeVec::vec_type> { __device__ __forceinline__ typename TypeVec::vec_type operator()(const typename TypeVec::vec_type& src) const { @@ -744,13 +849,20 @@ namespace cv { namespace gpu { namespace device return dst; } + __device__ __forceinline__ RGB2XYZ() + : unary_function::vec_type, typename TypeVec::vec_type>(){} + __device__ __forceinline__ RGB2XYZ(const RGB2XYZ& other_) + : unary_function::vec_type, typename TypeVec::vec_type>(){} }; + template struct RGB2XYZ : unary_function { __device__ __forceinline__ uint operator()(uint src) const { return RGB2XYZConvert(src); } + __device__ __forceinline__ RGB2XYZ() : unary_function(){} + __device__ __forceinline__ RGB2XYZ(const RGB2XYZ& other_) : unary_function(){} }; } @@ -775,6 +887,7 @@ namespace cv { namespace gpu { namespace device dst[1] = saturate_cast(CV_DESCALE(src.x * c_XYZ2sRGB_D65i[3] + src.y * c_XYZ2sRGB_D65i[4] + src.z * c_XYZ2sRGB_D65i[5], xyz_shift)); dst[bidx] = saturate_cast(CV_DESCALE(src.x * c_XYZ2sRGB_D65i[6] + src.y * c_XYZ2sRGB_D65i[7] + src.z * c_XYZ2sRGB_D65i[8], xyz_shift)); } + template static __device__ __forceinline__ uint XYZ2RGBConvert(uint src) { const int x = 0xff & src; @@ -793,6 +906,7 @@ namespace cv { namespace gpu { namespace device return dst; } + template static __device__ __forceinline__ void XYZ2RGBConvert(const T& src, float* dst) { dst[bidx^2] = src.x * c_XYZ2sRGB_D65f[0] + src.y * c_XYZ2sRGB_D65f[1] + src.z * c_XYZ2sRGB_D65f[2]; @@ -800,7 +914,8 @@ namespace cv { namespace gpu { namespace device dst[bidx] = src.x * c_XYZ2sRGB_D65f[6] + src.y * c_XYZ2sRGB_D65f[7] + src.z * c_XYZ2sRGB_D65f[8]; } - template struct XYZ2RGB : unary_function::vec_type, typename TypeVec::vec_type> + template struct XYZ2RGB + : unary_function::vec_type, typename TypeVec::vec_type> { __device__ __forceinline__ typename TypeVec::vec_type operator()(const typename TypeVec::vec_type& src) const { @@ -811,13 +926,20 @@ namespace cv { namespace gpu { namespace device return dst; } + __device__ __forceinline__ XYZ2RGB() + : unary_function::vec_type, typename TypeVec::vec_type>(){} + __device__ __forceinline__ XYZ2RGB(const XYZ2RGB& other_) + : unary_function::vec_type, typename TypeVec::vec_type>(){} }; + template struct XYZ2RGB : unary_function { __device__ __forceinline__ uint operator()(uint src) const { return XYZ2RGBConvert(src); } + __device__ __forceinline__ XYZ2RGB() : unary_function(){} + __device__ __forceinline__ XYZ2RGB(const XYZ2RGB& other_) : unary_function(){} }; } @@ -867,6 +989,7 @@ namespace cv { namespace gpu { namespace device dst.y = (uchar)s; dst.z = (uchar)v; } + template static __device__ uint RGB2HSVConvert(uint src) { const int hsv_shift = 12; @@ -902,6 +1025,7 @@ namespace cv { namespace gpu { namespace device return dst; } + template static __device__ void RGB2HSVConvert(const float* src, D& dst) { const float hscale = hr * (1.f / 360.f); @@ -931,7 +1055,8 @@ namespace cv { namespace gpu { namespace device dst.z = v; } - template struct RGB2HSV : unary_function::vec_type, typename TypeVec::vec_type> + template struct RGB2HSV + : unary_function::vec_type, typename TypeVec::vec_type> { __device__ __forceinline__ typename TypeVec::vec_type operator()(const typename TypeVec::vec_type& src) const { @@ -941,13 +1066,20 @@ namespace cv { namespace gpu { namespace device return dst; } + __device__ __forceinline__ RGB2HSV() + : unary_function::vec_type, typename TypeVec::vec_type>(){} + __device__ __forceinline__ RGB2HSV(const RGB2HSV& other_) + : unary_function::vec_type, typename TypeVec::vec_type>(){} }; + template struct RGB2HSV : unary_function { __device__ __forceinline__ uint operator()(uint src) const { return RGB2HSVConvert(src); } + __device__ __forceinline__ RGB2HSV():unary_function(){} + __device__ __forceinline__ RGB2HSV(const RGB2HSV& other_):unary_function(){} }; } @@ -1023,6 +1155,7 @@ namespace cv { namespace gpu { namespace device dst[1] = g; dst[bidx^2] = r; } + template static __device__ void HSV2RGBConvert(const T& src, uchar* dst) { float3 buf; @@ -1037,6 +1170,7 @@ namespace cv { namespace gpu { namespace device dst[1] = saturate_cast(buf.y * 255.f); dst[2] = saturate_cast(buf.z * 255.f); } + template static __device__ uint HSV2RGBConvert(uint src) { float3 buf; @@ -1056,7 +1190,8 @@ namespace cv { namespace gpu { namespace device return dst; } - template struct HSV2RGB : unary_function::vec_type, typename TypeVec::vec_type> + template struct HSV2RGB + : unary_function::vec_type, typename TypeVec::vec_type> { __device__ __forceinline__ typename TypeVec::vec_type operator()(const typename TypeVec::vec_type& src) const { @@ -1067,13 +1202,20 @@ namespace cv { namespace gpu { namespace device return dst; } + __device__ __forceinline__ HSV2RGB() + : unary_function::vec_type, typename TypeVec::vec_type>(){} + __device__ __forceinline__ HSV2RGB(const HSV2RGB& other_) + : unary_function::vec_type, typename TypeVec::vec_type>(){} }; + template struct HSV2RGB : unary_function { __device__ __forceinline__ uint operator()(uint src) const { return HSV2RGBConvert(src); } + __device__ __forceinline__ HSV2RGB():unary_function(){} + __device__ __forceinline__ HSV2RGB(const HSV2RGB& other_):unary_function(){} }; } @@ -1149,6 +1291,7 @@ namespace cv { namespace gpu { namespace device dst.y = l; dst.z = s; } + template static __device__ void RGB2HLSConvert(const uchar* src, D& dst) { float3 buf; @@ -1163,6 +1306,7 @@ namespace cv { namespace gpu { namespace device dst.y = saturate_cast(buf.y*255.f); dst.z = saturate_cast(buf.z*255.f); } + template static __device__ uint RGB2HLSConvert(uint src) { float3 buf; @@ -1182,7 +1326,8 @@ namespace cv { namespace gpu { namespace device return dst; } - template struct RGB2HLS : unary_function::vec_type, typename TypeVec::vec_type> + template struct RGB2HLS + : unary_function::vec_type, typename TypeVec::vec_type> { __device__ __forceinline__ typename TypeVec::vec_type operator()(const typename TypeVec::vec_type& src) const { @@ -1192,13 +1337,20 @@ namespace cv { namespace gpu { namespace device return dst; } + __device__ __forceinline__ RGB2HLS() + : unary_function::vec_type, typename TypeVec::vec_type>(){} + __device__ __forceinline__ RGB2HLS(const RGB2HLS& other_) + : unary_function::vec_type, typename TypeVec::vec_type>(){} }; + template struct RGB2HLS : unary_function { __device__ __forceinline__ uint operator()(uint src) const { return RGB2HLSConvert(src); } + __device__ __forceinline__ RGB2HLS() : unary_function(){} + __device__ __forceinline__ RGB2HLS(const RGB2HLS& other_) : unary_function(){} }; } @@ -1280,6 +1432,7 @@ namespace cv { namespace gpu { namespace device dst[1] = g; dst[bidx^2] = r; } + template static __device__ void HLS2RGBConvert(const T& src, uchar* dst) { float3 buf; @@ -1294,6 +1447,7 @@ namespace cv { namespace gpu { namespace device dst[1] = saturate_cast(buf.y * 255.f); dst[2] = saturate_cast(buf.z * 255.f); } + template static __device__ uint HLS2RGBConvert(uint src) { float3 buf; @@ -1313,7 +1467,8 @@ namespace cv { namespace gpu { namespace device return dst; } - template struct HLS2RGB : unary_function::vec_type, typename TypeVec::vec_type> + template struct HLS2RGB + : unary_function::vec_type, typename TypeVec::vec_type> { __device__ __forceinline__ typename TypeVec::vec_type operator()(const typename TypeVec::vec_type& src) const { @@ -1324,13 +1479,20 @@ namespace cv { namespace gpu { namespace device return dst; } + __device__ __forceinline__ HLS2RGB() + : unary_function::vec_type, typename TypeVec::vec_type>(){} + __device__ __forceinline__ HLS2RGB(const HLS2RGB& other_) + : unary_function::vec_type, typename TypeVec::vec_type>(){} }; + template struct HLS2RGB : unary_function { __device__ __forceinline__ uint operator()(uint src) const { return HLS2RGBConvert(src); } + __device__ __forceinline__ HLS2RGB() : unary_function(){} + __device__ __forceinline__ HLS2RGB(const HLS2RGB& other_) : unary_function(){} }; } diff --git a/modules/gpu/src/opencv2/gpu/device/functional.hpp b/modules/gpu/src/opencv2/gpu/device/functional.hpp index d21f728..435fe65 100644 --- a/modules/gpu/src/opencv2/gpu/device/functional.hpp +++ b/modules/gpu/src/opencv2/gpu/device/functional.hpp @@ -56,158 +56,224 @@ namespace cv { namespace gpu { namespace device using thrust::binary_function; // Arithmetic Operations - template struct plus : binary_function { - __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType a, typename TypeTraits::ParameterType b) const + __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const { return a + b; } + __device__ __forceinline__ plus(const plus& other):binary_function(){} + __device__ __forceinline__ plus():binary_function(){} }; + template struct minus : binary_function { - __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType a, typename TypeTraits::ParameterType b) const + __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const { return a - b; } + __device__ __forceinline__ minus(const minus& other):binary_function(){} + __device__ __forceinline__ minus():binary_function(){} }; + template struct multiplies : binary_function { - __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType a, typename TypeTraits::ParameterType b) const + __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const { return a * b; } + __device__ __forceinline__ multiplies(const multiplies& other):binary_function(){} + __device__ __forceinline__ multiplies():binary_function(){} }; + template struct divides : binary_function { - __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType a, typename TypeTraits::ParameterType b) const + __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const { return a / b; } + __device__ __forceinline__ divides(const divides& other):binary_function(){} + __device__ __forceinline__ divides():binary_function(){} }; + template struct modulus : binary_function { - __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType a, typename TypeTraits::ParameterType b) const + __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const { return a % b; } + __device__ __forceinline__ modulus(const modulus& other):binary_function(){} + __device__ __forceinline__ modulus():binary_function(){} }; + template struct negate : unary_function { __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType a) const { return -a; } + __device__ __forceinline__ negate(const negate& other):unary_function(){} + __device__ __forceinline__ negate():unary_function(){} }; // Comparison Operations - template struct equal_to : binary_function { - __device__ __forceinline__ bool operator ()(typename TypeTraits::ParameterType a, typename TypeTraits::ParameterType b) const + __device__ __forceinline__ bool operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const { return a == b; } + __device__ __forceinline__ equal_to(const equal_to& other):binary_function(){} + __device__ __forceinline__ equal_to():binary_function(){} }; + template struct not_equal_to : binary_function { - __device__ __forceinline__ bool operator ()(typename TypeTraits::ParameterType a, typename TypeTraits::ParameterType b) const + __device__ __forceinline__ bool operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const { return a != b; } + __device__ __forceinline__ not_equal_to(const not_equal_to& other):binary_function(){} + __device__ __forceinline__ not_equal_to():binary_function(){} }; + template struct greater : binary_function { - __device__ __forceinline__ bool operator ()(typename TypeTraits::ParameterType a, typename TypeTraits::ParameterType b) const + __device__ __forceinline__ bool operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const { return a > b; } + __device__ __forceinline__ greater(const greater& other):binary_function(){} + __device__ __forceinline__ greater():binary_function(){} }; + template struct less : binary_function { - __device__ __forceinline__ bool operator ()(typename TypeTraits::ParameterType a, typename TypeTraits::ParameterType b) const + __device__ __forceinline__ bool operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const { return a < b; } + __device__ __forceinline__ less(const less& other):binary_function(){} + __device__ __forceinline__ less():binary_function(){} }; + template struct greater_equal : binary_function { - __device__ __forceinline__ bool operator ()(typename TypeTraits::ParameterType a, typename TypeTraits::ParameterType b) const + __device__ __forceinline__ bool operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const { return a >= b; } + __device__ __forceinline__ greater_equal(const greater_equal& other):binary_function(){} + __device__ __forceinline__ greater_equal():binary_function(){} }; + template struct less_equal : binary_function { - __device__ __forceinline__ bool operator ()(typename TypeTraits::ParameterType a, typename TypeTraits::ParameterType b) const + __device__ __forceinline__ bool operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const { return a <= b; } + __device__ __forceinline__ less_equal(const less_equal& other):binary_function(){} + __device__ __forceinline__ less_equal():binary_function(){} }; // Logical Operations - template struct logical_and : binary_function { - __device__ __forceinline__ bool operator ()(typename TypeTraits::ParameterType a, typename TypeTraits::ParameterType b) const + __device__ __forceinline__ bool operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const { return a && b; } + __device__ __forceinline__ logical_and(const logical_and& other):binary_function(){} + __device__ __forceinline__ logical_and():binary_function(){} }; + template struct logical_or : binary_function { - __device__ __forceinline__ bool operator ()(typename TypeTraits::ParameterType a, typename TypeTraits::ParameterType b) const + __device__ __forceinline__ bool operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const { return a || b; } + __device__ __forceinline__ logical_or(const logical_or& other):binary_function(){} + __device__ __forceinline__ logical_or():binary_function(){} }; + template struct logical_not : unary_function { __device__ __forceinline__ bool operator ()(typename TypeTraits::ParameterType a) const { return !a; } + __device__ __forceinline__ logical_not(const logical_not& other):unary_function(){} + __device__ __forceinline__ logical_not():unary_function(){} }; // Bitwise Operations - template struct bit_and : binary_function { - __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType a, typename TypeTraits::ParameterType b) const + __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const { return a & b; } + __device__ __forceinline__ bit_and(const bit_and& other):binary_function(){} + __device__ __forceinline__ bit_and():binary_function(){} }; + template struct bit_or : binary_function { - __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType a, typename TypeTraits::ParameterType b) const + __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const { return a | b; } + __device__ __forceinline__ bit_or(const bit_or& other):binary_function(){} + __device__ __forceinline__ bit_or():binary_function(){} }; + template struct bit_xor : binary_function { - __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType a, typename TypeTraits::ParameterType b) const + __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const { return a ^ b; } + __device__ __forceinline__ bit_xor(const bit_xor& other):binary_function(){} + __device__ __forceinline__ bit_xor():binary_function(){} }; + template struct bit_not : unary_function { __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType v) const { return ~v; } + __device__ __forceinline__ bit_not(const bit_not& other):unary_function(){} + __device__ __forceinline__ bit_not():unary_function(){} }; // Generalized Identity Operations - template struct identity : unary_function { __device__ __forceinline__ typename TypeTraits::ParameterType operator()(typename TypeTraits::ParameterType x) const { return x; } + __device__ __forceinline__ identity(const identity& other):unary_function(){} + __device__ __forceinline__ identity():unary_function(){} }; template struct project1st : binary_function @@ -216,13 +282,18 @@ namespace cv { namespace gpu { namespace device { return lhs; } + __device__ __forceinline__ project1st(const project1st& other):binary_function(){} + __device__ __forceinline__ project1st():binary_function(){} }; + template struct project2nd : binary_function { __device__ __forceinline__ typename TypeTraits::ParameterType operator()(typename TypeTraits::ParameterType lhs, typename TypeTraits::ParameterType rhs) const { return rhs; } + __device__ __forceinline__ project2nd(const project2nd& other):binary_function(){} + __device__ __forceinline__ project2nd():binary_function(){} }; // Min/Max Operations @@ -231,6 +302,8 @@ namespace cv { namespace gpu { namespace device template <> struct name : binary_function \ { \ __device__ __forceinline__ type operator()(type lhs, type rhs) const {return op(lhs, rhs);} \ + __device__ __forceinline__ name(const name& other):binary_function(){}\ + __device__ __forceinline__ name():binary_function(){}\ }; template struct maximum : binary_function @@ -239,6 +312,8 @@ namespace cv { namespace gpu { namespace device { return lhs < rhs ? rhs : lhs; } + __device__ __forceinline__ maximum(const maximum& other):binary_function(){} + __device__ __forceinline__ maximum():binary_function(){} }; OPENCV_GPU_IMPLEMENT_MINMAX(maximum, uchar, ::max) @@ -257,6 +332,8 @@ namespace cv { namespace gpu { namespace device { return lhs < rhs ? lhs : rhs; } + __device__ __forceinline__ minimum(const minimum& other):binary_function(){} + __device__ __forceinline__ minimum():binary_function(){} }; OPENCV_GPU_IMPLEMENT_MINMAX(minimum, uchar, ::min) @@ -272,7 +349,7 @@ namespace cv { namespace gpu { namespace device #undef OPENCV_GPU_IMPLEMENT_MINMAX // Math functions - +///bound========================================= #define OPENCV_GPU_IMPLEMENT_UN_FUNCTOR(name, func) \ template struct name ## _func : unary_function \ { \ @@ -342,17 +419,17 @@ namespace cv { namespace gpu { namespace device }; // Saturate Cast Functor - template struct saturate_cast_func : unary_function { __device__ __forceinline__ D operator ()(typename TypeTraits::ParameterType v) const { return saturate_cast(v); } + __device__ __forceinline__ saturate_cast_func(const saturate_cast_func& other):unary_function(){} + __device__ __forceinline__ saturate_cast_func():unary_function(){} }; // Threshold Functors - template struct thresh_binary_func : unary_function { __host__ __device__ __forceinline__ thresh_binary_func(T thresh_, T maxVal_) : thresh(thresh_), maxVal(maxVal_) {} @@ -361,10 +438,15 @@ namespace cv { namespace gpu { namespace device { return (src > thresh) * maxVal; } + __device__ __forceinline__ thresh_binary_func(const thresh_binary_func& other) + : unary_function(), thresh(other.thresh), maxVal(other.maxVal){} + + __device__ __forceinline__ thresh_binary_func():unary_function(){} const T thresh; const T maxVal; }; + template struct thresh_binary_inv_func : unary_function { __host__ __device__ __forceinline__ thresh_binary_inv_func(T thresh_, T maxVal_) : thresh(thresh_), maxVal(maxVal_) {} @@ -373,10 +455,15 @@ namespace cv { namespace gpu { namespace device { return (src <= thresh) * maxVal; } + __device__ __forceinline__ thresh_binary_inv_func(const thresh_binary_inv_func& other) + : unary_function(), thresh(other.thresh), maxVal(other.maxVal){} + + __device__ __forceinline__ thresh_binary_inv_func():unary_function(){} const T thresh; const T maxVal; }; + template struct thresh_trunc_func : unary_function { explicit __host__ __device__ __forceinline__ thresh_trunc_func(T thresh_, T maxVal_ = 0) : thresh(thresh_) {} @@ -386,8 +473,14 @@ namespace cv { namespace gpu { namespace device return minimum()(src, thresh); } + __device__ __forceinline__ thresh_trunc_func(const thresh_trunc_func& other) + : unary_function(), thresh(other.thresh){} + + __device__ __forceinline__ thresh_trunc_func():unary_function(){} + const T thresh; }; + template struct thresh_to_zero_func : unary_function { explicit __host__ __device__ __forceinline__ thresh_to_zero_func(T thresh_, T maxVal_ = 0) : thresh(thresh_) {} @@ -396,9 +489,14 @@ namespace cv { namespace gpu { namespace device { return (src > thresh) * src; } + __device__ __forceinline__ thresh_to_zero_func(const thresh_to_zero_func& other) + : unary_function(), thresh(other.thresh){} + + __device__ __forceinline__ thresh_to_zero_func():unary_function(){} const T thresh; }; + template struct thresh_to_zero_inv_func : unary_function { explicit __host__ __device__ __forceinline__ thresh_to_zero_inv_func(T thresh_, T maxVal_ = 0) : thresh(thresh_) {} @@ -407,12 +505,15 @@ namespace cv { namespace gpu { namespace device { return (src <= thresh) * src; } + __device__ __forceinline__ thresh_to_zero_inv_func(const thresh_to_zero_inv_func& other) + : unary_function(), thresh(other.thresh){} + + __device__ __forceinline__ thresh_to_zero_inv_func():unary_function(){} const T thresh; }; - +//bound!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! ============> // Function Object Adaptors - template struct unary_negate : unary_function { explicit __host__ __device__ __forceinline__ unary_negate(const Predicate& p) : pred(p) {} diff --git a/modules/gpu/src/opencv2/gpu/device/saturate_cast.hpp b/modules/gpu/src/opencv2/gpu/device/saturate_cast.hpp index 35575a2..d9fa5ce 100644 --- a/modules/gpu/src/opencv2/gpu/device/saturate_cast.hpp +++ b/modules/gpu/src/opencv2/gpu/device/saturate_cast.hpp @@ -84,7 +84,7 @@ namespace cv { namespace gpu { namespace device } template<> __device__ __forceinline__ uchar saturate_cast(double v) { - #if __CUDA_ARCH__ >= 130 + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 130 int iv = __double2int_rn(v); return saturate_cast(iv); #else @@ -120,7 +120,7 @@ namespace cv { namespace gpu { namespace device } template<> __device__ __forceinline__ schar saturate_cast(double v) { - #if __CUDA_ARCH__ >= 130 + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 130 int iv = __double2int_rn(v); return saturate_cast(iv); #else @@ -151,7 +151,7 @@ namespace cv { namespace gpu { namespace device } template<> __device__ __forceinline__ ushort saturate_cast(double v) { - #if __CUDA_ARCH__ >= 130 + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 130 int iv = __double2int_rn(v); return saturate_cast(iv); #else @@ -178,7 +178,7 @@ namespace cv { namespace gpu { namespace device } template<> __device__ __forceinline__ short saturate_cast(double v) { - #if __CUDA_ARCH__ >= 130 + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 130 int iv = __double2int_rn(v); return saturate_cast(iv); #else @@ -192,7 +192,7 @@ namespace cv { namespace gpu { namespace device } template<> __device__ __forceinline__ int saturate_cast(double v) { - #if __CUDA_ARCH__ >= 130 + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 130 return __double2int_rn(v); #else return saturate_cast((float)v); @@ -205,7 +205,7 @@ namespace cv { namespace gpu { namespace device } template<> __device__ __forceinline__ uint saturate_cast(double v) { - #if __CUDA_ARCH__ >= 130 + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 130 return __double2uint_rn(v); #else return saturate_cast((float)v); @@ -213,4 +213,4 @@ namespace cv { namespace gpu { namespace device } }}} -#endif /* __OPENCV_GPU_SATURATE_CAST_HPP__ */ \ No newline at end of file +#endif /* __OPENCV_GPU_SATURATE_CAST_HPP__ */ diff --git a/modules/gpu/src/opencv2/gpu/device/transform.hpp b/modules/gpu/src/opencv2/gpu/device/transform.hpp index 89eed7e..a0e79df 100644 --- a/modules/gpu/src/opencv2/gpu/device/transform.hpp +++ b/modules/gpu/src/opencv2/gpu/device/transform.hpp @@ -50,14 +50,14 @@ namespace cv { namespace gpu { namespace device { template - static inline void transform(DevMem2D_ src, DevMem2D_ dst, UnOp op, Mask mask, cudaStream_t stream) + static inline void transform(DevMem2D_ src, DevMem2D_ dst, UnOp op, const Mask& mask, cudaStream_t stream) { typedef TransformFunctorTraits ft; transform_detail::TransformDispatcher::cn == 1 && VecTraits::cn == 1 && ft::smart_shift != 1>::call(src, dst, op, mask, stream); } template - static inline void transform(DevMem2D_ src1, DevMem2D_ src2, DevMem2D_ dst, BinOp op, Mask mask, cudaStream_t stream) + static inline void transform(DevMem2D_ src1, DevMem2D_ src2, DevMem2D_ dst, BinOp op, const Mask& mask, cudaStream_t stream) { typedef TransformFunctorTraits ft; transform_detail::TransformDispatcher::cn == 1 && VecTraits::cn == 1 && VecTraits::cn == 1 && ft::smart_shift != 1>::call(src1, src2, dst, op, mask, stream); diff --git a/modules/gpu/src/opencv2/gpu/device/utility.hpp b/modules/gpu/src/opencv2/gpu/device/utility.hpp index cb702b7..cb4db80 100644 --- a/modules/gpu/src/opencv2/gpu/device/utility.hpp +++ b/modules/gpu/src/opencv2/gpu/device/utility.hpp @@ -70,6 +70,7 @@ namespace cv { namespace gpu { namespace device struct SingleMask { explicit __host__ __device__ __forceinline__ SingleMask(PtrStepb mask_) : mask(mask_) {} + __host__ __device__ __forceinline__ SingleMask(const SingleMask& mask_): mask(mask_.mask){} __device__ __forceinline__ bool operator()(int y, int x) const { @@ -81,7 +82,10 @@ namespace cv { namespace gpu { namespace device struct SingleMaskChannels { - __host__ __device__ __forceinline__ SingleMaskChannels(PtrStepb mask_, int channels_) : mask(mask_), channels(channels_) {} + __host__ __device__ __forceinline__ SingleMaskChannels(PtrStepb mask_, int channels_) + : mask(mask_), channels(channels_) {} + __host__ __device__ __forceinline__ SingleMaskChannels(const SingleMaskChannels& mask_) + :mask(mask_.mask), channels(mask_.channels){} __device__ __forceinline__ bool operator()(int y, int x) const { @@ -94,7 +98,11 @@ namespace cv { namespace gpu { namespace device struct MaskCollection { - explicit __host__ __device__ __forceinline__ MaskCollection(PtrStepb* maskCollection_) : maskCollection(maskCollection_) {} + explicit __host__ __device__ __forceinline__ MaskCollection(PtrStepb* maskCollection_) + : maskCollection(maskCollection_) {} + + __device__ __forceinline__ MaskCollection(const MaskCollection& masks_) + : maskCollection(masks_.maskCollection), curMask(masks_.curMask){} __device__ __forceinline__ void next() { @@ -117,6 +125,9 @@ namespace cv { namespace gpu { namespace device struct WithOutMask { + __device__ __forceinline__ WithOutMask(){} + __device__ __forceinline__ WithOutMask(const WithOutMask& mask){} + __device__ __forceinline__ void next() const { } diff --git a/modules/gpu/src/precomp.hpp b/modules/gpu/src/precomp.hpp index adb2188..cb0b04b 100644 --- a/modules/gpu/src/precomp.hpp +++ b/modules/gpu/src/precomp.hpp @@ -43,7 +43,7 @@ #ifndef __OPENCV_PRECOMP_H__ #define __OPENCV_PRECOMP_H__ -#if _MSC_VER >= 1200 +#if defined _MSC_VER && _MSC_VER >= 1200 #pragma warning( disable: 4251 4710 4711 4514 4996 ) #endif diff --git a/modules/gpu/test/main_test_nvidia.h b/modules/gpu/test/main_test_nvidia.h new file mode 100644 index 0000000..d1c3620 --- /dev/null +++ b/modules/gpu/test/main_test_nvidia.h @@ -0,0 +1,25 @@ +#ifndef __main_test_nvidia_h__ +#define __main_test_nvidia_h__ + +#include + +enum OutputLevel +{ + OutputLevelNone, + OutputLevelCompact, + OutputLevelFull +}; + +bool nvidia_NPPST_Integral_Image(const std::string& test_data_path, OutputLevel outputLevel); +bool nvidia_NPPST_Squared_Integral_Image(const std::string& test_data_path, OutputLevel outputLevel); +bool nvidia_NPPST_RectStdDev(const std::string& test_data_path, OutputLevel outputLevel); +bool nvidia_NPPST_Resize(const std::string& test_data_path, OutputLevel outputLevel); +bool nvidia_NPPST_Vector_Operations(const std::string& test_data_path, OutputLevel outputLevel); +bool nvidia_NPPST_Transpose(const std::string& test_data_path, OutputLevel outputLevel); +bool nvidia_NCV_Vector_Operations(const std::string& test_data_path, OutputLevel outputLevel); +bool nvidia_NCV_Haar_Cascade_Loader(const std::string& test_data_path, OutputLevel outputLevel); +bool nvidia_NCV_Haar_Cascade_Application(const std::string& test_data_path, OutputLevel outputLevel); +bool nvidia_NCV_Hypotheses_Filtration(const std::string& test_data_path, OutputLevel outputLevel); +bool nvidia_NCV_Visualization(const std::string& test_data_path, OutputLevel outputLevel); + +#endif diff --git a/modules/gpu/test/nvidia/NCVAutoTestLister.hpp b/modules/gpu/test/nvidia/NCVAutoTestLister.hpp index bdaa8fa..72eb8af 100644 --- a/modules/gpu/test/nvidia/NCVAutoTestLister.hpp +++ b/modules/gpu/test/nvidia/NCVAutoTestLister.hpp @@ -14,13 +14,13 @@ #include #include "NCVTest.hpp" - -enum OutputLevel -{ - OutputLevelNone, - OutputLevelCompact, - OutputLevelFull -}; +#include +//enum OutputLevel +//{ +// OutputLevelNone, +// OutputLevelCompact, +// OutputLevelFull +//}; class NCVAutoTestLister { diff --git a/modules/gpu/test/nvidia/NCVTest.hpp b/modules/gpu/test/nvidia/NCVTest.hpp index 00be0f0..94ec46c 100644 --- a/modules/gpu/test/nvidia/NCVTest.hpp +++ b/modules/gpu/test/nvidia/NCVTest.hpp @@ -11,7 +11,9 @@ #ifndef _ncvtest_hpp_ #define _ncvtest_hpp_ -#pragma warning( disable : 4201 4408 4127 4100) +#if defined _MSC_VER +# pragma warning( disable : 4201 4408 4127 4100) +#endif #include #include @@ -36,6 +38,7 @@ class INCVTest public: virtual bool executeTest(NCVTestReport &report) = 0; virtual std::string getName() const = 0; + virtual ~INCVTest(){} }; diff --git a/modules/gpu/test/nvidia/main_nvidia.cpp b/modules/gpu/test/nvidia/main_nvidia.cpp index 1f1e24a..9957fda 100644 --- a/modules/gpu/test/nvidia/main_nvidia.cpp +++ b/modules/gpu/test/nvidia/main_nvidia.cpp @@ -21,11 +21,15 @@ #include "NCVAutoTestLister.hpp" #include "NCVTestSourceProvider.hpp" +#include + static std::string path; +namespace { template -void generateIntegralTests(NCVAutoTestLister &testLister, NCVTestSourceProvider &src, +void generateIntegralTests(NCVAutoTestLister &testLister, + NCVTestSourceProvider &src, Ncv32u maxWidth, Ncv32u maxHeight) { for (Ncv32f _i=1.0; _i(testName, src, 2, i)); } - //test VGA testLister.add(new TestIntegralImage("LinIntImg_VGA", src, 640, 480)); - - //TODO: add tests of various resolutions up to 4096x4096 } - void generateSquaredIntegralTests(NCVAutoTestLister &testLister, NCVTestSourceProvider &src, Ncv32u maxWidth, Ncv32u maxHeight) { @@ -68,13 +68,9 @@ void generateSquaredIntegralTests(NCVAutoTestLister &testLister, NCVTestSourcePr testLister.add(new TestIntegralImageSquared(testName, src, 32, i)); } - //test VGA testLister.add(new TestIntegralImageSquared("SqLinIntImg_VGA", src, 640, 480)); - - //TODO: add tests of various resolutions up to 4096x4096 } - void generateRectStdDevTests(NCVAutoTestLister &testLister, NCVTestSourceProvider &src, Ncv32u maxWidth, Ncv32u maxHeight) { @@ -91,17 +87,12 @@ void generateRectStdDevTests(NCVAutoTestLister &testLister, NCVTestSourceProvide testLister.add(new TestRectStdDev(testName, src, i-1, i*2-1, rect, 2.5, true)); } - //test VGA testLister.add(new TestRectStdDev("RectStdDev_VGA", src, 640, 480, rect, 1, true)); - - //TODO: add tests of various resolutions up to 4096x4096 } - template void generateResizeTests(NCVAutoTestLister &testLister, NCVTestSourceProvider &src) { - //test VGA for (Ncv32u i=1; i<480; i+=3) { char testName[80]; @@ -110,7 +101,6 @@ void generateResizeTests(NCVAutoTestLister &testLister, NCVTestSourceProvider testLister.add(new TestResize(testName, src, 640, 480, i, false)); } - //test HD for (Ncv32u i=1; i<1080; i+=5) { char testName[80]; @@ -118,11 +108,8 @@ void generateResizeTests(NCVAutoTestLister &testLister, NCVTestSourceProvider testLister.add(new TestResize(testName, src, 1920, 1080, i, true)); testLister.add(new TestResize(testName, src, 1920, 1080, i, false)); } - - //TODO: add tests of various resolutions up to 4096x4096 } - void generateNPPSTVectorTests(NCVAutoTestLister &testLister, NCVTestSourceProvider &src, Ncv32u maxLength) { //compaction @@ -186,9 +173,10 @@ void generateTransposeTests(NCVAutoTestLister &testLister, NCVTestSourceProvider testLister.add(new TestTranspose("TestTranspose_reg_0", src, 1072, 375)); } - template -void generateDrawRectsTests(NCVAutoTestLister &testLister, NCVTestSourceProvider &src, NCVTestSourceProvider &src32u, +void generateDrawRectsTests(NCVAutoTestLister &testLister, + NCVTestSourceProvider &src, + NCVTestSourceProvider &src32u, Ncv32u maxWidth, Ncv32u maxHeight) { for (Ncv32f _i=16.0; _i("DrawRects_VGA", src, src32u, 640, 480, 640*480/1000, (T)0xFF)); - - //TODO: add tests of various resolutions up to 4096x4096 } - void generateVectorTests(NCVAutoTestLister &testLister, NCVTestSourceProvider &src, Ncv32u maxLength) { //growth @@ -237,7 +222,6 @@ void generateVectorTests(NCVAutoTestLister &testLister, NCVTestSourceProvider &src, Ncv32u maxLength) { for (Ncv32f _i=1.0; _i &src, Ncv32u maxWidth, Ncv32u maxHeight) { @@ -285,7 +268,6 @@ void generateHaarApplicationTests(NCVAutoTestLister &testLister, NCVTestSourcePr static void devNullOutput(const std::string& msg) { - } bool nvidia_NPPST_Integral_Image(const std::string& test_data_path, OutputLevel outputLevel) @@ -304,6 +286,8 @@ bool nvidia_NPPST_Integral_Image(const std::string& test_data_path, OutputLevel return testListerII.invoke(); } +} + bool nvidia_NPPST_Squared_Integral_Image(const std::string& test_data_path, OutputLevel outputLevel) { path = test_data_path; diff --git a/modules/gpu/test/precomp.hpp b/modules/gpu/test/precomp.hpp index f03db7b..d65ce73 100644 --- a/modules/gpu/test/precomp.hpp +++ b/modules/gpu/test/precomp.hpp @@ -39,6 +39,10 @@ // //M*/ +#ifdef __GNUC__ +# pragma GCC diagnostic ignored "-Wmissing-declarations" +#endif + #ifndef __OPENCV_TEST_PRECOMP_HPP__ #define __OPENCV_TEST_PRECOMP_HPP__ diff --git a/modules/gpu/test/test_filters.cpp b/modules/gpu/test/test_filters.cpp index 2e441ee..9df6ee2 100644 --- a/modules/gpu/test/test_filters.cpp +++ b/modules/gpu/test/test_filters.cpp @@ -513,7 +513,6 @@ PARAM_TEST_CASE(Filter2D, cv::gpu::DeviceInfo, cv::Size, MatType, KSize, Anchor, bool useRoi; cv::Mat img; - cv::Mat kernel; virtual void SetUp() { diff --git a/modules/gpu/test/test_imgproc.cpp b/modules/gpu/test/test_imgproc.cpp index 63b4527..388badf 100644 --- a/modules/gpu/test/test_imgproc.cpp +++ b/modules/gpu/test/test_imgproc.cpp @@ -1,307 +1,301 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// Intel License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000, Intel Corporation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of Intel Corporation may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#include "precomp.hpp" - -namespace { - -/////////////////////////////////////////////////////////////////////////////////////////////////////// -// Integral - -PARAM_TEST_CASE(Integral, cv::gpu::DeviceInfo, cv::Size, UseRoi) -{ - cv::gpu::DeviceInfo devInfo; - cv::Size size; - bool useRoi; - - virtual void SetUp() - { - devInfo = GET_PARAM(0); - size = GET_PARAM(1); - useRoi = GET_PARAM(2); - - cv::gpu::setDevice(devInfo.deviceID()); - } -}; - -TEST_P(Integral, Accuracy) -{ - cv::Mat src = randomMat(size, CV_8UC1); - - cv::gpu::GpuMat dst = createMat(cv::Size(src.cols + 1, src.rows + 1), CV_32SC1, useRoi); - cv::gpu::integral(loadMat(src, useRoi), dst); - - cv::Mat dst_gold; - cv::integral(src, dst_gold, CV_32S); - - EXPECT_MAT_NEAR(dst_gold, dst, 0.0); -} - -INSTANTIATE_TEST_CASE_P(GPU_ImgProc, Integral, testing::Combine( - ALL_DEVICES, - DIFFERENT_SIZES, - WHOLE_SUBMAT)); - -/////////////////////////////////////////////////////////////////////////////////////////////////////// -// HistEven - -struct HistEven : testing::TestWithParam -{ - cv::gpu::DeviceInfo devInfo; - - virtual void SetUp() - { - devInfo = GetParam(); - - cv::gpu::setDevice(devInfo.deviceID()); - } -}; - -TEST_P(HistEven, Accuracy) -{ - cv::Mat img = readImage("stereobm/aloe-L.png"); - ASSERT_FALSE(img.empty()); - - cv::Mat hsv; - cv::cvtColor(img, hsv, CV_BGR2HSV); - - int hbins = 30; - float hranges[] = {0.0f, 180.0f}; - - std::vector srcs; - cv::gpu::split(loadMat(hsv), srcs); - - cv::gpu::GpuMat hist; - cv::gpu::histEven(srcs[0], hist, hbins, (int)hranges[0], (int)hranges[1]); - - cv::MatND histnd; - int histSize[] = {hbins}; - const float* ranges[] = {hranges}; - int channels[] = {0}; - cv::calcHist(&hsv, 1, channels, cv::Mat(), histnd, 1, histSize, ranges); - - cv::Mat hist_gold = histnd; - hist_gold = hist_gold.t(); - hist_gold.convertTo(hist_gold, CV_32S); - - EXPECT_MAT_NEAR(hist_gold, hist, 0.0); -} - -INSTANTIATE_TEST_CASE_P(GPU_ImgProc, HistEven, ALL_DEVICES); - -/////////////////////////////////////////////////////////////////////////////////////////////////////// -// CalcHist - -void calcHistGold(const cv::Mat& src, cv::Mat& hist) -{ - hist.create(1, 256, CV_32SC1); - hist.setTo(cv::Scalar::all(0)); - - int* hist_row = hist.ptr(); - for (int y = 0; y < src.rows; ++y) - { - const uchar* src_row = src.ptr(y); - - for (int x = 0; x < src.cols; ++x) - ++hist_row[src_row[x]]; - } -} - -PARAM_TEST_CASE(CalcHist, cv::gpu::DeviceInfo, cv::Size) -{ - cv::gpu::DeviceInfo devInfo; - - cv::Size size; - cv::Mat src; - cv::Mat hist_gold; - - virtual void SetUp() - { - devInfo = GET_PARAM(0); - size = GET_PARAM(1); - - cv::gpu::setDevice(devInfo.deviceID()); - } -}; - -TEST_P(CalcHist, Accuracy) -{ - cv::Mat src = randomMat(size, CV_8UC1); - - cv::gpu::GpuMat hist; - cv::gpu::calcHist(loadMat(src), hist); - - cv::Mat hist_gold; - calcHistGold(src, hist_gold); - - EXPECT_MAT_NEAR(hist_gold, hist, 0.0); -} - -INSTANTIATE_TEST_CASE_P(GPU_ImgProc, CalcHist, testing::Combine( - ALL_DEVICES, - DIFFERENT_SIZES)); - -/////////////////////////////////////////////////////////////////////////////////////////////////////// -// EqualizeHist - -PARAM_TEST_CASE(EqualizeHist, cv::gpu::DeviceInfo, cv::Size) -{ - cv::gpu::DeviceInfo devInfo; - cv::Size size; - - virtual void SetUp() - { - devInfo = GET_PARAM(0); - size = GET_PARAM(1); - - cv::gpu::setDevice(devInfo.deviceID()); - } -}; - -TEST_P(EqualizeHist, Accuracy) -{ - cv::Mat src = randomMat(size, CV_8UC1); - - cv::gpu::GpuMat dst; - cv::gpu::equalizeHist(loadMat(src), dst); - - cv::Mat dst_gold; - cv::equalizeHist(src, dst_gold); - - EXPECT_MAT_NEAR(dst_gold, dst, 3.0); -} - -INSTANTIATE_TEST_CASE_P(GPU_ImgProc, EqualizeHist, testing::Combine( - ALL_DEVICES, - DIFFERENT_SIZES)); - -//////////////////////////////////////////////////////////////////////// -// ColumnSum - -PARAM_TEST_CASE(ColumnSum, cv::gpu::DeviceInfo, cv::Size) -{ - cv::gpu::DeviceInfo devInfo; - cv::Size size; - - cv::Mat src; - - virtual void SetUp() - { - devInfo = GET_PARAM(0); - size = GET_PARAM(1); - - cv::gpu::setDevice(devInfo.deviceID()); - } -}; - -TEST_P(ColumnSum, Accuracy) -{ - cv::Mat src = randomMat(size, CV_32FC1); - - cv::gpu::GpuMat d_dst; - cv::gpu::columnSum(loadMat(src), d_dst); - - cv::Mat dst(d_dst); - - for (int j = 0; j < src.cols; ++j) - { - float gold = src.at(0, j); - float res = dst.at(0, j); - ASSERT_NEAR(res, gold, 1e-5); - } - - for (int i = 1; i < src.rows; ++i) - { - for (int j = 0; j < src.cols; ++j) - { - float gold = src.at(i, j) += src.at(i - 1, j); - float res = dst.at(i, j); - ASSERT_NEAR(res, gold, 1e-5); - } - } -} - -INSTANTIATE_TEST_CASE_P(GPU_ImgProc, ColumnSum, testing::Combine( - ALL_DEVICES, - DIFFERENT_SIZES)); - -//////////////////////////////////////////////////////// -// Canny - -IMPLEMENT_PARAM_CLASS(AppertureSize, int); -IMPLEMENT_PARAM_CLASS(L2gradient, bool); - -PARAM_TEST_CASE(Canny, cv::gpu::DeviceInfo, AppertureSize, L2gradient, UseRoi) -{ - cv::gpu::DeviceInfo devInfo; - int apperture_size; - bool useL2gradient; - bool useRoi; - - cv::Mat edges_gold; - - virtual void SetUp() - { - devInfo = GET_PARAM(0); - apperture_size = GET_PARAM(1); - useL2gradient = GET_PARAM(2); - useRoi = GET_PARAM(3); - - cv::gpu::setDevice(devInfo.deviceID()); - } -}; - -TEST_P(Canny, Accuracy) -{ - cv::Mat img = readImage("stereobm/aloe-L.png", cv::IMREAD_GRAYSCALE); - ASSERT_FALSE(img.empty()); - - double low_thresh = 50.0; - double high_thresh = 100.0; +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// Intel License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000, Intel Corporation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of Intel Corporation may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#include "precomp.hpp" + +namespace { + +/////////////////////////////////////////////////////////////////////////////////////////////////////// +// Integral + +PARAM_TEST_CASE(Integral, cv::gpu::DeviceInfo, cv::Size, UseRoi) +{ + cv::gpu::DeviceInfo devInfo; + cv::Size size; + bool useRoi; + + virtual void SetUp() + { + devInfo = GET_PARAM(0); + size = GET_PARAM(1); + useRoi = GET_PARAM(2); + + cv::gpu::setDevice(devInfo.deviceID()); + } +}; + +TEST_P(Integral, Accuracy) +{ + cv::Mat src = randomMat(size, CV_8UC1); + + cv::gpu::GpuMat dst = createMat(cv::Size(src.cols + 1, src.rows + 1), CV_32SC1, useRoi); + cv::gpu::integral(loadMat(src, useRoi), dst); + + cv::Mat dst_gold; + cv::integral(src, dst_gold, CV_32S); + + EXPECT_MAT_NEAR(dst_gold, dst, 0.0); +} + +INSTANTIATE_TEST_CASE_P(GPU_ImgProc, Integral, testing::Combine( + ALL_DEVICES, + DIFFERENT_SIZES, + WHOLE_SUBMAT)); + +/////////////////////////////////////////////////////////////////////////////////////////////////////// +// HistEven + +struct HistEven : testing::TestWithParam +{ + cv::gpu::DeviceInfo devInfo; + + virtual void SetUp() + { + devInfo = GetParam(); + + cv::gpu::setDevice(devInfo.deviceID()); + } +}; + +TEST_P(HistEven, Accuracy) +{ + cv::Mat img = readImage("stereobm/aloe-L.png"); + ASSERT_FALSE(img.empty()); + + cv::Mat hsv; + cv::cvtColor(img, hsv, CV_BGR2HSV); + + int hbins = 30; + float hranges[] = {0.0f, 180.0f}; + + std::vector srcs; + cv::gpu::split(loadMat(hsv), srcs); + + cv::gpu::GpuMat hist; + cv::gpu::histEven(srcs[0], hist, hbins, (int)hranges[0], (int)hranges[1]); + + cv::MatND histnd; + int histSize[] = {hbins}; + const float* ranges[] = {hranges}; + int channels[] = {0}; + cv::calcHist(&hsv, 1, channels, cv::Mat(), histnd, 1, histSize, ranges); + + cv::Mat hist_gold = histnd; + hist_gold = hist_gold.t(); + hist_gold.convertTo(hist_gold, CV_32S); + + EXPECT_MAT_NEAR(hist_gold, hist, 0.0); +} + +INSTANTIATE_TEST_CASE_P(GPU_ImgProc, HistEven, ALL_DEVICES); + +/////////////////////////////////////////////////////////////////////////////////////////////////////// +// CalcHist + +void calcHistGold(const cv::Mat& src, cv::Mat& hist) +{ + hist.create(1, 256, CV_32SC1); + hist.setTo(cv::Scalar::all(0)); + + int* hist_row = hist.ptr(); + for (int y = 0; y < src.rows; ++y) + { + const uchar* src_row = src.ptr(y); + + for (int x = 0; x < src.cols; ++x) + ++hist_row[src_row[x]]; + } +} + +PARAM_TEST_CASE(CalcHist, cv::gpu::DeviceInfo, cv::Size) +{ + cv::gpu::DeviceInfo devInfo; + + cv::Size size; + + virtual void SetUp() + { + devInfo = GET_PARAM(0); + size = GET_PARAM(1); + + cv::gpu::setDevice(devInfo.deviceID()); + } +}; + +TEST_P(CalcHist, Accuracy) +{ + cv::Mat src = randomMat(size, CV_8UC1); + + cv::gpu::GpuMat hist; + cv::gpu::calcHist(loadMat(src), hist); + + cv::Mat hist_gold; + calcHistGold(src, hist_gold); + + EXPECT_MAT_NEAR(hist_gold, hist, 0.0); +} + +INSTANTIATE_TEST_CASE_P(GPU_ImgProc, CalcHist, testing::Combine( + ALL_DEVICES, + DIFFERENT_SIZES)); + +/////////////////////////////////////////////////////////////////////////////////////////////////////// +// EqualizeHist + +PARAM_TEST_CASE(EqualizeHist, cv::gpu::DeviceInfo, cv::Size) +{ + cv::gpu::DeviceInfo devInfo; + cv::Size size; + + virtual void SetUp() + { + devInfo = GET_PARAM(0); + size = GET_PARAM(1); + + cv::gpu::setDevice(devInfo.deviceID()); + } +}; + +TEST_P(EqualizeHist, Accuracy) +{ + cv::Mat src = randomMat(size, CV_8UC1); + + cv::gpu::GpuMat dst; + cv::gpu::equalizeHist(loadMat(src), dst); + + cv::Mat dst_gold; + cv::equalizeHist(src, dst_gold); + + EXPECT_MAT_NEAR(dst_gold, dst, 3.0); +} + +INSTANTIATE_TEST_CASE_P(GPU_ImgProc, EqualizeHist, testing::Combine( + ALL_DEVICES, + DIFFERENT_SIZES)); + +//////////////////////////////////////////////////////////////////////// +// ColumnSum + +PARAM_TEST_CASE(ColumnSum, cv::gpu::DeviceInfo, cv::Size) +{ + cv::gpu::DeviceInfo devInfo; + cv::Size size; + + virtual void SetUp() + { + devInfo = GET_PARAM(0); + size = GET_PARAM(1); + + cv::gpu::setDevice(devInfo.deviceID()); + } +}; + +TEST_P(ColumnSum, Accuracy) +{ + cv::Mat src = randomMat(size, CV_32FC1); + + cv::gpu::GpuMat d_dst; + cv::gpu::columnSum(loadMat(src), d_dst); + + cv::Mat dst(d_dst); + + for (int j = 0; j < src.cols; ++j) + { + float gold = src.at(0, j); + float res = dst.at(0, j); + ASSERT_NEAR(res, gold, 1e-5); + } + + for (int i = 1; i < src.rows; ++i) + { + for (int j = 0; j < src.cols; ++j) + { + float gold = src.at(i, j) += src.at(i - 1, j); + float res = dst.at(i, j); + ASSERT_NEAR(res, gold, 1e-5); + } + } +} + +INSTANTIATE_TEST_CASE_P(GPU_ImgProc, ColumnSum, testing::Combine( + ALL_DEVICES, + DIFFERENT_SIZES)); + +//////////////////////////////////////////////////////// +// Canny + +IMPLEMENT_PARAM_CLASS(AppertureSize, int); +IMPLEMENT_PARAM_CLASS(L2gradient, bool); + +PARAM_TEST_CASE(Canny, cv::gpu::DeviceInfo, AppertureSize, L2gradient, UseRoi) +{ + cv::gpu::DeviceInfo devInfo; + int apperture_size; + bool useL2gradient; + bool useRoi; + + virtual void SetUp() + { + devInfo = GET_PARAM(0); + apperture_size = GET_PARAM(1); + useL2gradient = GET_PARAM(2); + useRoi = GET_PARAM(3); + + cv::gpu::setDevice(devInfo.deviceID()); + } +}; + +TEST_P(Canny, Accuracy) +{ + cv::Mat img = readImage("stereobm/aloe-L.png", cv::IMREAD_GRAYSCALE); + ASSERT_FALSE(img.empty()); + + double low_thresh = 50.0; + double high_thresh = 100.0; if (!supportFeature(devInfo, cv::gpu::SHARED_ATOMICS)) { try - { - cv::gpu::GpuMat edges; + { + cv::gpu::GpuMat edges; cv::gpu::Canny(loadMat(img), edges, low_thresh, high_thresh, apperture_size, useL2gradient); } catch (const cv::Exception& e) @@ -310,832 +304,824 @@ TEST_P(Canny, Accuracy) } } else - { - cv::gpu::GpuMat edges; - cv::gpu::Canny(loadMat(img, useRoi), edges, low_thresh, high_thresh, apperture_size, useL2gradient); - - cv::Mat edges_gold; - cv::Canny(img, edges_gold, low_thresh, high_thresh, apperture_size, useL2gradient); - - EXPECT_MAT_SIMILAR(edges_gold, edges, 1e-2); - } -} - -INSTANTIATE_TEST_CASE_P(GPU_ImgProc, Canny, testing::Combine( - ALL_DEVICES, - testing::Values(AppertureSize(3), AppertureSize(5)), - testing::Values(L2gradient(false), L2gradient(true)), - WHOLE_SUBMAT)); - -//////////////////////////////////////////////////////////////////////////////// -// MeanShift - -struct MeanShift : testing::TestWithParam -{ - cv::gpu::DeviceInfo devInfo; - - cv::Mat img; - - int spatialRad; - int colorRad; - - virtual void SetUp() - { - devInfo = GetParam(); - - cv::gpu::setDevice(devInfo.deviceID()); - - img = readImageType("meanshift/cones.png", CV_8UC4); - ASSERT_FALSE(img.empty()); - - spatialRad = 30; - colorRad = 30; - } -}; - -TEST_P(MeanShift, Filtering) -{ - cv::Mat img_template; - if (supportFeature(devInfo, cv::gpu::FEATURE_SET_COMPUTE_20)) - img_template = readImage("meanshift/con_result.png"); - else - img_template = readImage("meanshift/con_result_CC1X.png"); - ASSERT_FALSE(img_template.empty()); - - cv::gpu::GpuMat d_dst; - cv::gpu::meanShiftFiltering(loadMat(img), d_dst, spatialRad, colorRad); - - ASSERT_EQ(CV_8UC4, d_dst.type()); - - cv::Mat dst(d_dst); - - cv::Mat result; - cv::cvtColor(dst, result, CV_BGRA2BGR); - - EXPECT_MAT_NEAR(img_template, result, 0.0); -} - -TEST_P(MeanShift, Proc) -{ - cv::FileStorage fs; - if (supportFeature(devInfo, cv::gpu::FEATURE_SET_COMPUTE_20)) - fs.open(std::string(cvtest::TS::ptr()->get_data_path()) + "meanshift/spmap.yaml", cv::FileStorage::READ); - else - fs.open(std::string(cvtest::TS::ptr()->get_data_path()) + "meanshift/spmap_CC1X.yaml", cv::FileStorage::READ); - ASSERT_TRUE(fs.isOpened()); - - cv::Mat spmap_template; - fs["spmap"] >> spmap_template; - ASSERT_FALSE(spmap_template.empty()); - - cv::gpu::GpuMat rmap_filtered; - cv::gpu::meanShiftFiltering(loadMat(img), rmap_filtered, spatialRad, colorRad); - - cv::gpu::GpuMat rmap; - cv::gpu::GpuMat spmap; - cv::gpu::meanShiftProc(loadMat(img), rmap, spmap, spatialRad, colorRad); - - ASSERT_EQ(CV_8UC4, rmap.type()); - - EXPECT_MAT_NEAR(rmap_filtered, rmap, 0.0); - EXPECT_MAT_NEAR(spmap_template, spmap, 0.0); -} - -INSTANTIATE_TEST_CASE_P(GPU_ImgProc, MeanShift, ALL_DEVICES); - -//////////////////////////////////////////////////////////////////////////////// -// MeanShiftSegmentation - -IMPLEMENT_PARAM_CLASS(MinSize, int); - -PARAM_TEST_CASE(MeanShiftSegmentation, cv::gpu::DeviceInfo, MinSize) -{ - cv::gpu::DeviceInfo devInfo; - int minsize; - - virtual void SetUp() - { - devInfo = GET_PARAM(0); - minsize = GET_PARAM(1); - - cv::gpu::setDevice(devInfo.deviceID()); - } -}; - -TEST_P(MeanShiftSegmentation, Regression) -{ - cv::Mat img = readImageType("meanshift/cones.png", CV_8UC4); - ASSERT_FALSE(img.empty()); - - std::ostringstream path; - path << "meanshift/cones_segmented_sp10_sr10_minsize" << minsize; - if (supportFeature(devInfo, cv::gpu::FEATURE_SET_COMPUTE_20)) - path << ".png"; - else - path << "_CC1X.png"; - cv::Mat dst_gold = readImage(path.str()); - ASSERT_FALSE(dst_gold.empty()); - - cv::Mat dst; - cv::gpu::meanShiftSegmentation(loadMat(img), dst, 10, 10, minsize); - - cv::Mat dst_rgb; - cv::cvtColor(dst, dst_rgb, CV_BGRA2BGR); - - EXPECT_MAT_SIMILAR(dst_gold, dst_rgb, 1e-3); -} - -INSTANTIATE_TEST_CASE_P(GPU_ImgProc, MeanShiftSegmentation, testing::Combine( - ALL_DEVICES, - testing::Values(MinSize(0), MinSize(4), MinSize(20), MinSize(84), MinSize(340), MinSize(1364)))); - -//////////////////////////////////////////////////////////////////////////// -// Blend - -template -void blendLinearGold(const cv::Mat& img1, const cv::Mat& img2, const cv::Mat& weights1, const cv::Mat& weights2, cv::Mat& result_gold) -{ - result_gold.create(img1.size(), img1.type()); - - int cn = img1.channels(); - - for (int y = 0; y < img1.rows; ++y) - { - const float* weights1_row = weights1.ptr(y); - const float* weights2_row = weights2.ptr(y); - const T* img1_row = img1.ptr(y); - const T* img2_row = img2.ptr(y); - T* result_gold_row = result_gold.ptr(y); - - for (int x = 0; x < img1.cols * cn; ++x) - { - float w1 = weights1_row[x / cn]; - float w2 = weights2_row[x / cn]; - result_gold_row[x] = static_cast((img1_row[x] * w1 + img2_row[x] * w2) / (w1 + w2 + 1e-5f)); - } - } -} - -PARAM_TEST_CASE(Blend, cv::gpu::DeviceInfo, cv::Size, MatType, UseRoi) -{ - cv::gpu::DeviceInfo devInfo; - cv::Size size; - int type; - bool useRoi; - - virtual void SetUp() - { - devInfo = GET_PARAM(0); - size = GET_PARAM(1); - type = GET_PARAM(2); - useRoi = GET_PARAM(3); - - cv::gpu::setDevice(devInfo.deviceID()); - } -}; - -TEST_P(Blend, Accuracy) -{ - int depth = CV_MAT_DEPTH(type); - - cv::Mat img1 = randomMat(size, type, 0.0, depth == CV_8U ? 255.0 : 1.0); - cv::Mat img2 = randomMat(size, type, 0.0, depth == CV_8U ? 255.0 : 1.0); - cv::Mat weights1 = randomMat(size, CV_32F, 0, 1); - cv::Mat weights2 = randomMat(size, CV_32F, 0, 1); - - cv::gpu::GpuMat result; - cv::gpu::blendLinear(loadMat(img1, useRoi), loadMat(img2, useRoi), loadMat(weights1, useRoi), loadMat(weights2, useRoi), result); - - cv::Mat result_gold; - if (depth == CV_8U) - blendLinearGold(img1, img2, weights1, weights2, result_gold); - else - blendLinearGold(img1, img2, weights1, weights2, result_gold); - - EXPECT_MAT_NEAR(result_gold, result, CV_MAT_DEPTH(type) == CV_8U ? 1.0 : 1e-5); -} - -INSTANTIATE_TEST_CASE_P(GPU_ImgProc, Blend, testing::Combine( - ALL_DEVICES, - DIFFERENT_SIZES, - testing::Values(MatType(CV_8UC1), MatType(CV_8UC3), MatType(CV_8UC4), MatType(CV_32FC1), MatType(CV_32FC3), MatType(CV_32FC4)), - WHOLE_SUBMAT)); - -//////////////////////////////////////////////////////// -// Convolve - -void convolveDFT(const cv::Mat& A, const cv::Mat& B, cv::Mat& C, bool ccorr = false) -{ - // reallocate the output array if needed - C.create(std::abs(A.rows - B.rows) + 1, std::abs(A.cols - B.cols) + 1, A.type()); - cv::Size dftSize; - - // compute the size of DFT transform - dftSize.width = cv::getOptimalDFTSize(A.cols + B.cols - 1); - dftSize.height = cv::getOptimalDFTSize(A.rows + B.rows - 1); - - // allocate temporary buffers and initialize them with 0s - cv::Mat tempA(dftSize, A.type(), cv::Scalar::all(0)); - cv::Mat tempB(dftSize, B.type(), cv::Scalar::all(0)); - - // copy A and B to the top-left corners of tempA and tempB, respectively - cv::Mat roiA(tempA, cv::Rect(0, 0, A.cols, A.rows)); - A.copyTo(roiA); - cv::Mat roiB(tempB, cv::Rect(0, 0, B.cols, B.rows)); - B.copyTo(roiB); - - // now transform the padded A & B in-place; - // use "nonzeroRows" hint for faster processing - cv::dft(tempA, tempA, 0, A.rows); - cv::dft(tempB, tempB, 0, B.rows); - - // multiply the spectrums; - // the function handles packed spectrum representations well - cv::mulSpectrums(tempA, tempB, tempA, 0, ccorr); - - // transform the product back from the frequency domain. - // Even though all the result rows will be non-zero, - // you need only the first C.rows of them, and thus you - // pass nonzeroRows == C.rows - cv::dft(tempA, tempA, cv::DFT_INVERSE + cv::DFT_SCALE, C.rows); - - // now copy the result back to C. - tempA(cv::Rect(0, 0, C.cols, C.rows)).copyTo(C); -} - -IMPLEMENT_PARAM_CLASS(KSize, int); -IMPLEMENT_PARAM_CLASS(Ccorr, bool); - -PARAM_TEST_CASE(Convolve, cv::gpu::DeviceInfo, cv::Size, KSize, Ccorr) -{ - cv::gpu::DeviceInfo devInfo; - cv::Size size; - int ksize; - bool ccorr; - - cv::Mat src; - cv::Mat kernel; - - cv::Mat dst_gold; - - virtual void SetUp() - { - devInfo = GET_PARAM(0); - size = GET_PARAM(1); - ksize = GET_PARAM(2); - ccorr = GET_PARAM(3); - - cv::gpu::setDevice(devInfo.deviceID()); - } -}; - -TEST_P(Convolve, Accuracy) -{ - cv::Mat src = randomMat(size, CV_32FC1, 0.0, 100.0); - cv::Mat kernel = randomMat(cv::Size(ksize, ksize), CV_32FC1, 0.0, 1.0); - - cv::gpu::GpuMat dst; - cv::gpu::convolve(loadMat(src), loadMat(kernel), dst, ccorr); - - cv::Mat dst_gold; - convolveDFT(src, kernel, dst_gold, ccorr); - - EXPECT_MAT_NEAR(dst, dst_gold, 1e-1); -} - -INSTANTIATE_TEST_CASE_P(GPU_ImgProc, Convolve, testing::Combine( - ALL_DEVICES, - DIFFERENT_SIZES, - testing::Values(KSize(3), KSize(7), KSize(11), KSize(17), KSize(19), KSize(23), KSize(45)), - testing::Values(Ccorr(false), Ccorr(true)))); - -//////////////////////////////////////////////////////////////////////////////// -// MatchTemplate8U - -CV_ENUM(TemplateMethod, cv::TM_SQDIFF, cv::TM_SQDIFF_NORMED, cv::TM_CCORR, cv::TM_CCORR_NORMED, cv::TM_CCOEFF, cv::TM_CCOEFF_NORMED) -#define ALL_TEMPLATE_METHODS testing::Values(TemplateMethod(cv::TM_SQDIFF), TemplateMethod(cv::TM_SQDIFF_NORMED), TemplateMethod(cv::TM_CCORR), TemplateMethod(cv::TM_CCORR_NORMED), TemplateMethod(cv::TM_CCOEFF), TemplateMethod(cv::TM_CCOEFF_NORMED)) - -IMPLEMENT_PARAM_CLASS(TemplateSize, cv::Size); - -PARAM_TEST_CASE(MatchTemplate8U, cv::gpu::DeviceInfo, cv::Size, TemplateSize, Channels, TemplateMethod) -{ - cv::gpu::DeviceInfo devInfo; - cv::Size size; - cv::Size templ_size; - int cn; - int method; - - virtual void SetUp() - { - devInfo = GET_PARAM(0); - size = GET_PARAM(1); - templ_size = GET_PARAM(2); - cn = GET_PARAM(3); - method = GET_PARAM(4); - - cv::gpu::setDevice(devInfo.deviceID()); - } -}; - -TEST_P(MatchTemplate8U, Accuracy) -{ - cv::Mat image = randomMat(size, CV_MAKETYPE(CV_8U, cn)); - cv::Mat templ = randomMat(templ_size, CV_MAKETYPE(CV_8U, cn)); - - cv::gpu::GpuMat dst; - cv::gpu::matchTemplate(loadMat(image), loadMat(templ), dst, method); - - cv::Mat dst_gold; - cv::matchTemplate(image, templ, dst_gold, method); - - EXPECT_MAT_NEAR(dst_gold, dst, templ_size.area() * 1e-1); -} - -INSTANTIATE_TEST_CASE_P(GPU_ImgProc, MatchTemplate8U, testing::Combine( - ALL_DEVICES, - DIFFERENT_SIZES, - testing::Values(TemplateSize(cv::Size(5, 5)), TemplateSize(cv::Size(16, 16)), TemplateSize(cv::Size(30, 30))), - testing::Values(Channels(1), Channels(3), Channels(4)), - ALL_TEMPLATE_METHODS)); - -//////////////////////////////////////////////////////////////////////////////// -// MatchTemplate32F - -PARAM_TEST_CASE(MatchTemplate32F, cv::gpu::DeviceInfo, cv::Size, TemplateSize, Channels, TemplateMethod) -{ - cv::gpu::DeviceInfo devInfo; - cv::Size size; - cv::Size templ_size; - int cn; - int method; - - int n, m, h, w; - cv::Mat image, templ; - - cv::Mat dst_gold; - - virtual void SetUp() - { - devInfo = GET_PARAM(0); - size = GET_PARAM(1); - templ_size = GET_PARAM(2); - cn = GET_PARAM(3); - method = GET_PARAM(4); - - cv::gpu::setDevice(devInfo.deviceID()); - } -}; - -TEST_P(MatchTemplate32F, Regression) -{ - cv::Mat image = randomMat(size, CV_MAKETYPE(CV_32F, cn)); - cv::Mat templ = randomMat(templ_size, CV_MAKETYPE(CV_32F, cn)); - - cv::gpu::GpuMat dst; - cv::gpu::matchTemplate(loadMat(image), loadMat(templ), dst, method); - - cv::Mat dst_gold; - cv::matchTemplate(image, templ, dst_gold, method); - - EXPECT_MAT_NEAR(dst_gold, dst, templ_size.area() * 1e-1); -} - -INSTANTIATE_TEST_CASE_P(GPU_ImgProc, MatchTemplate32F, testing::Combine( - ALL_DEVICES, - DIFFERENT_SIZES, - testing::Values(TemplateSize(cv::Size(5, 5)), TemplateSize(cv::Size(16, 16)), TemplateSize(cv::Size(30, 30))), - testing::Values(Channels(1), Channels(3), Channels(4)), - testing::Values(TemplateMethod(cv::TM_SQDIFF), TemplateMethod(cv::TM_CCORR)))); - -//////////////////////////////////////////////////////////////////////////////// -// MatchTemplateBlackSource - -PARAM_TEST_CASE(MatchTemplateBlackSource, cv::gpu::DeviceInfo, TemplateMethod) -{ - cv::gpu::DeviceInfo devInfo; - int method; - - virtual void SetUp() - { - devInfo = GET_PARAM(0); - method = GET_PARAM(1); - - cv::gpu::setDevice(devInfo.deviceID()); - } -}; - -TEST_P(MatchTemplateBlackSource, Accuracy) -{ - cv::Mat image = readImage("matchtemplate/black.png"); - ASSERT_FALSE(image.empty()); - - cv::Mat pattern = readImage("matchtemplate/cat.png"); - ASSERT_FALSE(pattern.empty()); - - cv::gpu::GpuMat d_dst; - cv::gpu::matchTemplate(loadMat(image), loadMat(pattern), d_dst, method); - - cv::Mat dst(d_dst); - - double maxValue; - cv::Point maxLoc; - cv::minMaxLoc(dst, NULL, &maxValue, NULL, &maxLoc); - - cv::Point maxLocGold = cv::Point(284, 12); - - ASSERT_EQ(maxLocGold, maxLoc); -} - -INSTANTIATE_TEST_CASE_P(GPU_ImgProc, MatchTemplateBlackSource, testing::Combine( - ALL_DEVICES, - testing::Values(TemplateMethod(cv::TM_CCOEFF_NORMED), TemplateMethod(cv::TM_CCORR_NORMED)))); - -//////////////////////////////////////////////////////////////////////////////// -// MatchTemplate_CCOEF_NORMED - -PARAM_TEST_CASE(MatchTemplate_CCOEF_NORMED, cv::gpu::DeviceInfo, std::pair) -{ - cv::gpu::DeviceInfo devInfo; - std::string imageName; - std::string patternName; - - virtual void SetUp() - { - devInfo = GET_PARAM(0); - imageName = GET_PARAM(1).first; - patternName = GET_PARAM(1).second; - - cv::gpu::setDevice(devInfo.deviceID()); - } -}; - -TEST_P(MatchTemplate_CCOEF_NORMED, Accuracy) -{ - cv::Mat image = readImage(imageName); - ASSERT_FALSE(image.empty()); - - cv::Mat pattern = readImage(patternName); - ASSERT_FALSE(pattern.empty()); - - cv::gpu::GpuMat d_dst; - cv::gpu::matchTemplate(loadMat(image), loadMat(pattern), d_dst, CV_TM_CCOEFF_NORMED); - - cv::Mat dst(d_dst); - - cv::Point minLoc, maxLoc; - double minVal, maxVal; - cv::minMaxLoc(dst, &minVal, &maxVal, &minLoc, &maxLoc); - - cv::Mat dstGold; - cv::matchTemplate(image, pattern, dstGold, CV_TM_CCOEFF_NORMED); - - double minValGold, maxValGold; - cv::Point minLocGold, maxLocGold; - cv::minMaxLoc(dstGold, &minValGold, &maxValGold, &minLocGold, &maxLocGold); - - ASSERT_EQ(minLocGold, minLoc); - ASSERT_EQ(maxLocGold, maxLoc); - ASSERT_LE(maxVal, 1.0); - ASSERT_GE(minVal, -1.0); -} - -INSTANTIATE_TEST_CASE_P(GPU_ImgProc, MatchTemplate_CCOEF_NORMED, testing::Combine( - ALL_DEVICES, - testing::Values(std::make_pair(std::string("matchtemplate/source-0.png"), std::string("matchtemplate/target-0.png"))))); - -//////////////////////////////////////////////////////////////////////////////// -// MatchTemplate_CanFindBigTemplate - -struct MatchTemplate_CanFindBigTemplate : testing::TestWithParam -{ - cv::gpu::DeviceInfo devInfo; - - virtual void SetUp() - { - devInfo = GetParam(); - - cv::gpu::setDevice(devInfo.deviceID()); - } -}; - -TEST_P(MatchTemplate_CanFindBigTemplate, SQDIFF_NORMED) -{ - cv::Mat scene = readImage("matchtemplate/scene.jpg"); - ASSERT_FALSE(scene.empty()); - - cv::Mat templ = readImage("matchtemplate/template.jpg"); - ASSERT_FALSE(templ.empty()); - - cv::gpu::GpuMat d_result; - cv::gpu::matchTemplate(loadMat(scene), loadMat(templ), d_result, CV_TM_SQDIFF_NORMED); - - cv::Mat result(d_result); - - double minVal; - cv::Point minLoc; - cv::minMaxLoc(result, &minVal, 0, &minLoc, 0); - - ASSERT_GE(minVal, 0); - ASSERT_LT(minVal, 1e-3); - ASSERT_EQ(344, minLoc.x); - ASSERT_EQ(0, minLoc.y); -} - -TEST_P(MatchTemplate_CanFindBigTemplate, SQDIFF) -{ - cv::Mat scene = readImage("matchtemplate/scene.jpg"); - ASSERT_FALSE(scene.empty()); - - cv::Mat templ = readImage("matchtemplate/template.jpg"); - ASSERT_FALSE(templ.empty()); - - cv::gpu::GpuMat d_result; - cv::gpu::matchTemplate(loadMat(scene), loadMat(templ), d_result, CV_TM_SQDIFF); - - cv::Mat result(d_result); - - double minVal; - cv::Point minLoc; - cv::minMaxLoc(result, &minVal, 0, &minLoc, 0); - - ASSERT_GE(minVal, 0); - ASSERT_EQ(344, minLoc.x); - ASSERT_EQ(0, minLoc.y); -} - -INSTANTIATE_TEST_CASE_P(GPU_ImgProc, MatchTemplate_CanFindBigTemplate, ALL_DEVICES); - -//////////////////////////////////////////////////////////////////////////// -// MulSpectrums - -CV_FLAGS(DftFlags, 0, cv::DFT_INVERSE, cv::DFT_SCALE, cv::DFT_ROWS, cv::DFT_COMPLEX_OUTPUT, cv::DFT_REAL_OUTPUT) - -PARAM_TEST_CASE(MulSpectrums, cv::gpu::DeviceInfo, cv::Size, DftFlags) -{ - cv::gpu::DeviceInfo devInfo; - cv::Size size; - int flag; - - cv::Mat a, b; - - virtual void SetUp() - { - devInfo = GET_PARAM(0); - size = GET_PARAM(1); - flag = GET_PARAM(2); - - cv::gpu::setDevice(devInfo.deviceID()); - - a = randomMat(size, CV_32FC2); - b = randomMat(size, CV_32FC2); - } -}; - -TEST_P(MulSpectrums, Simple) -{ - cv::gpu::GpuMat c; - cv::gpu::mulSpectrums(loadMat(a), loadMat(b), c, flag, false); - - cv::Mat c_gold; - cv::mulSpectrums(a, b, c_gold, flag, false); - - EXPECT_MAT_NEAR(c_gold, c, 1e-2); -} - -TEST_P(MulSpectrums, Scaled) -{ - float scale = 1.f / size.area(); - - cv::gpu::GpuMat c; - cv::gpu::mulAndScaleSpectrums(loadMat(a), loadMat(b), c, flag, scale, false); - - cv::Mat c_gold; - cv::mulSpectrums(a, b, c_gold, flag, false); - c_gold.convertTo(c_gold, c_gold.type(), scale); - - EXPECT_MAT_NEAR(c_gold, c, 1e-2); -} - -INSTANTIATE_TEST_CASE_P(GPU_ImgProc, MulSpectrums, testing::Combine( - ALL_DEVICES, - DIFFERENT_SIZES, - testing::Values(DftFlags(0), DftFlags(cv::DFT_ROWS)))); - -//////////////////////////////////////////////////////////////////////////// -// Dft - -struct Dft : testing::TestWithParam -{ - cv::gpu::DeviceInfo devInfo; - - virtual void SetUp() - { - devInfo = GetParam(); - - cv::gpu::setDevice(devInfo.deviceID()); - } -}; - -void testC2C(const std::string& hint, int cols, int rows, int flags, bool inplace) -{ - SCOPED_TRACE(hint); - - cv::Mat a = randomMat(cv::Size(cols, rows), CV_32FC2, 0.0, 10.0); - - cv::Mat b_gold; - cv::dft(a, b_gold, flags); - - cv::gpu::GpuMat d_b; - cv::gpu::GpuMat d_b_data; - if (inplace) - { - d_b_data.create(1, a.size().area(), CV_32FC2); - d_b = cv::gpu::GpuMat(a.rows, a.cols, CV_32FC2, d_b_data.ptr(), a.cols * d_b_data.elemSize()); - } - cv::gpu::dft(loadMat(a), d_b, cv::Size(cols, rows), flags); - - EXPECT_TRUE(!inplace || d_b.ptr() == d_b_data.ptr()); - ASSERT_EQ(CV_32F, d_b.depth()); - ASSERT_EQ(2, d_b.channels()); - EXPECT_MAT_NEAR(b_gold, cv::Mat(d_b), rows * cols * 1e-4); -} - -TEST_P(Dft, C2C) -{ - int cols = randomInt(2, 100); - int rows = randomInt(2, 100); - - for (int i = 0; i < 2; ++i) - { - bool inplace = i != 0; - - testC2C("no flags", cols, rows, 0, inplace); - testC2C("no flags 0 1", cols, rows + 1, 0, inplace); - testC2C("no flags 1 0", cols, rows + 1, 0, inplace); - testC2C("no flags 1 1", cols + 1, rows, 0, inplace); - testC2C("DFT_INVERSE", cols, rows, cv::DFT_INVERSE, inplace); - testC2C("DFT_ROWS", cols, rows, cv::DFT_ROWS, inplace); - testC2C("single col", 1, rows, 0, inplace); - testC2C("single row", cols, 1, 0, inplace); - testC2C("single col inversed", 1, rows, cv::DFT_INVERSE, inplace); - testC2C("single row inversed", cols, 1, cv::DFT_INVERSE, inplace); - testC2C("single row DFT_ROWS", cols, 1, cv::DFT_ROWS, inplace); - testC2C("size 1 2", 1, 2, 0, inplace); - testC2C("size 2 1", 2, 1, 0, inplace); - } -} - -void testR2CThenC2R(const std::string& hint, int cols, int rows, bool inplace) -{ - SCOPED_TRACE(hint); - - cv::Mat a = randomMat(cv::Size(cols, rows), CV_32FC1, 0.0, 10.0); - - cv::gpu::GpuMat d_b, d_c; - cv::gpu::GpuMat d_b_data, d_c_data; - if (inplace) - { - if (a.cols == 1) - { - d_b_data.create(1, (a.rows / 2 + 1) * a.cols, CV_32FC2); - d_b = cv::gpu::GpuMat(a.rows / 2 + 1, a.cols, CV_32FC2, d_b_data.ptr(), a.cols * d_b_data.elemSize()); - } - else - { - d_b_data.create(1, a.rows * (a.cols / 2 + 1), CV_32FC2); - d_b = cv::gpu::GpuMat(a.rows, a.cols / 2 + 1, CV_32FC2, d_b_data.ptr(), (a.cols / 2 + 1) * d_b_data.elemSize()); - } - d_c_data.create(1, a.size().area(), CV_32F); - d_c = cv::gpu::GpuMat(a.rows, a.cols, CV_32F, d_c_data.ptr(), a.cols * d_c_data.elemSize()); - } - - cv::gpu::dft(loadMat(a), d_b, cv::Size(cols, rows), 0); - cv::gpu::dft(d_b, d_c, cv::Size(cols, rows), cv::DFT_REAL_OUTPUT | cv::DFT_SCALE); - - EXPECT_TRUE(!inplace || d_b.ptr() == d_b_data.ptr()); - EXPECT_TRUE(!inplace || d_c.ptr() == d_c_data.ptr()); - ASSERT_EQ(CV_32F, d_c.depth()); - ASSERT_EQ(1, d_c.channels()); - - cv::Mat c(d_c); - EXPECT_MAT_NEAR(a, c, rows * cols * 1e-5); -} - -TEST_P(Dft, R2CThenC2R) -{ - int cols = randomInt(2, 100); - int rows = randomInt(2, 100); - - testR2CThenC2R("sanity", cols, rows, false); - testR2CThenC2R("sanity 0 1", cols, rows + 1, false); - testR2CThenC2R("sanity 1 0", cols + 1, rows, false); - testR2CThenC2R("sanity 1 1", cols + 1, rows + 1, false); - testR2CThenC2R("single col", 1, rows, false); - testR2CThenC2R("single col 1", 1, rows + 1, false); - testR2CThenC2R("single row", cols, 1, false); - testR2CThenC2R("single row 1", cols + 1, 1, false); - - testR2CThenC2R("sanity", cols, rows, true); - testR2CThenC2R("sanity 0 1", cols, rows + 1, true); - testR2CThenC2R("sanity 1 0", cols + 1, rows, true); - testR2CThenC2R("sanity 1 1", cols + 1, rows + 1, true); - testR2CThenC2R("single row", cols, 1, true); - testR2CThenC2R("single row 1", cols + 1, 1, true); -} - -INSTANTIATE_TEST_CASE_P(GPU_ImgProc, Dft, ALL_DEVICES); - -/////////////////////////////////////////////////////////////////////////////////////////////////////// -// CornerHarris - -IMPLEMENT_PARAM_CLASS(BlockSize, int); -IMPLEMENT_PARAM_CLASS(ApertureSize, int); - -PARAM_TEST_CASE(CornerHarris, cv::gpu::DeviceInfo, MatType, BorderType, BlockSize, ApertureSize) -{ - cv::gpu::DeviceInfo devInfo; - int type; - int borderType; - int blockSize; - int apertureSize; - - virtual void SetUp() - { - devInfo = GET_PARAM(0); - type = GET_PARAM(1); - borderType = GET_PARAM(2); - blockSize = GET_PARAM(3); - apertureSize = GET_PARAM(4); - - cv::gpu::setDevice(devInfo.deviceID()); - } -}; - -TEST_P(CornerHarris, Accuracy) -{ - cv::Mat src = readImageType("stereobm/aloe-L.png", type); - ASSERT_FALSE(src.empty()); - - double k = randomDouble(0.1, 0.9); - - cv::gpu::GpuMat dst; - cv::gpu::cornerHarris(loadMat(src), dst, blockSize, apertureSize, k, borderType); - - cv::Mat dst_gold; - cv::cornerHarris(src, dst_gold, blockSize, apertureSize, k, borderType); - - EXPECT_MAT_NEAR(dst_gold, dst, 0.02); -} - -INSTANTIATE_TEST_CASE_P(GPU_ImgProc, CornerHarris, testing::Combine( - ALL_DEVICES, - testing::Values(MatType(CV_8UC1), MatType(CV_32FC1)), - testing::Values(BorderType(cv::BORDER_REFLECT101), BorderType(cv::BORDER_REPLICATE), BorderType(cv::BORDER_REFLECT)), - testing::Values(BlockSize(3), BlockSize(5), BlockSize(7)), - testing::Values(ApertureSize(0), ApertureSize(3), ApertureSize(5), ApertureSize(7)))); - -/////////////////////////////////////////////////////////////////////////////////////////////////////// -// cornerMinEigen - -PARAM_TEST_CASE(CornerMinEigen, cv::gpu::DeviceInfo, MatType, BorderType, BlockSize, ApertureSize) -{ - cv::gpu::DeviceInfo devInfo; - int type; - int borderType; - int blockSize; - int apertureSize; - - virtual void SetUp() - { - devInfo = GET_PARAM(0); - type = GET_PARAM(1); - borderType = GET_PARAM(2); - blockSize = GET_PARAM(3); - apertureSize = GET_PARAM(4); - - cv::gpu::setDevice(devInfo.deviceID()); - } -}; - -TEST_P(CornerMinEigen, Accuracy) -{ - cv::Mat src = readImageType("stereobm/aloe-L.png", type); - ASSERT_FALSE(src.empty()); - - cv::gpu::GpuMat dst; - cv::gpu::cornerMinEigenVal(loadMat(src), dst, blockSize, apertureSize, borderType); - - cv::Mat dst_gold; - cv::cornerMinEigenVal(src, dst_gold, blockSize, apertureSize, borderType); - - EXPECT_MAT_NEAR(dst_gold, dst, 0.02); -} - -INSTANTIATE_TEST_CASE_P(GPU_ImgProc, CornerMinEigen, testing::Combine( - ALL_DEVICES, - testing::Values(MatType(CV_8UC1), MatType(CV_32FC1)), - testing::Values(BorderType(cv::BORDER_REFLECT101), BorderType(cv::BORDER_REPLICATE), BorderType(cv::BORDER_REFLECT)), - testing::Values(BlockSize(3), BlockSize(5), BlockSize(7)), - testing::Values(ApertureSize(0), ApertureSize(3), ApertureSize(5), ApertureSize(7)))); - -} // namespace + { + cv::gpu::GpuMat edges; + cv::gpu::Canny(loadMat(img, useRoi), edges, low_thresh, high_thresh, apperture_size, useL2gradient); + + cv::Mat edges_gold; + cv::Canny(img, edges_gold, low_thresh, high_thresh, apperture_size, useL2gradient); + + EXPECT_MAT_SIMILAR(edges_gold, edges, 1e-2); + } +} + +INSTANTIATE_TEST_CASE_P(GPU_ImgProc, Canny, testing::Combine( + ALL_DEVICES, + testing::Values(AppertureSize(3), AppertureSize(5)), + testing::Values(L2gradient(false), L2gradient(true)), + WHOLE_SUBMAT)); + +//////////////////////////////////////////////////////////////////////////////// +// MeanShift + +struct MeanShift : testing::TestWithParam +{ + cv::gpu::DeviceInfo devInfo; + + cv::Mat img; + + int spatialRad; + int colorRad; + + virtual void SetUp() + { + devInfo = GetParam(); + + cv::gpu::setDevice(devInfo.deviceID()); + + img = readImageType("meanshift/cones.png", CV_8UC4); + ASSERT_FALSE(img.empty()); + + spatialRad = 30; + colorRad = 30; + } +}; + +TEST_P(MeanShift, Filtering) +{ + cv::Mat img_template; + if (supportFeature(devInfo, cv::gpu::FEATURE_SET_COMPUTE_20)) + img_template = readImage("meanshift/con_result.png"); + else + img_template = readImage("meanshift/con_result_CC1X.png"); + ASSERT_FALSE(img_template.empty()); + + cv::gpu::GpuMat d_dst; + cv::gpu::meanShiftFiltering(loadMat(img), d_dst, spatialRad, colorRad); + + ASSERT_EQ(CV_8UC4, d_dst.type()); + + cv::Mat dst(d_dst); + + cv::Mat result; + cv::cvtColor(dst, result, CV_BGRA2BGR); + + EXPECT_MAT_NEAR(img_template, result, 0.0); +} + +TEST_P(MeanShift, Proc) +{ + cv::FileStorage fs; + if (supportFeature(devInfo, cv::gpu::FEATURE_SET_COMPUTE_20)) + fs.open(std::string(cvtest::TS::ptr()->get_data_path()) + "meanshift/spmap.yaml", cv::FileStorage::READ); + else + fs.open(std::string(cvtest::TS::ptr()->get_data_path()) + "meanshift/spmap_CC1X.yaml", cv::FileStorage::READ); + ASSERT_TRUE(fs.isOpened()); + + cv::Mat spmap_template; + fs["spmap"] >> spmap_template; + ASSERT_FALSE(spmap_template.empty()); + + cv::gpu::GpuMat rmap_filtered; + cv::gpu::meanShiftFiltering(loadMat(img), rmap_filtered, spatialRad, colorRad); + + cv::gpu::GpuMat rmap; + cv::gpu::GpuMat spmap; + cv::gpu::meanShiftProc(loadMat(img), rmap, spmap, spatialRad, colorRad); + + ASSERT_EQ(CV_8UC4, rmap.type()); + + EXPECT_MAT_NEAR(rmap_filtered, rmap, 0.0); + EXPECT_MAT_NEAR(spmap_template, spmap, 0.0); +} + +INSTANTIATE_TEST_CASE_P(GPU_ImgProc, MeanShift, ALL_DEVICES); + +//////////////////////////////////////////////////////////////////////////////// +// MeanShiftSegmentation + +IMPLEMENT_PARAM_CLASS(MinSize, int); + +PARAM_TEST_CASE(MeanShiftSegmentation, cv::gpu::DeviceInfo, MinSize) +{ + cv::gpu::DeviceInfo devInfo; + int minsize; + + virtual void SetUp() + { + devInfo = GET_PARAM(0); + minsize = GET_PARAM(1); + + cv::gpu::setDevice(devInfo.deviceID()); + } +}; + +TEST_P(MeanShiftSegmentation, Regression) +{ + cv::Mat img = readImageType("meanshift/cones.png", CV_8UC4); + ASSERT_FALSE(img.empty()); + + std::ostringstream path; + path << "meanshift/cones_segmented_sp10_sr10_minsize" << minsize; + if (supportFeature(devInfo, cv::gpu::FEATURE_SET_COMPUTE_20)) + path << ".png"; + else + path << "_CC1X.png"; + cv::Mat dst_gold = readImage(path.str()); + ASSERT_FALSE(dst_gold.empty()); + + cv::Mat dst; + cv::gpu::meanShiftSegmentation(loadMat(img), dst, 10, 10, minsize); + + cv::Mat dst_rgb; + cv::cvtColor(dst, dst_rgb, CV_BGRA2BGR); + + EXPECT_MAT_SIMILAR(dst_gold, dst_rgb, 1e-3); +} + +INSTANTIATE_TEST_CASE_P(GPU_ImgProc, MeanShiftSegmentation, testing::Combine( + ALL_DEVICES, + testing::Values(MinSize(0), MinSize(4), MinSize(20), MinSize(84), MinSize(340), MinSize(1364)))); + +//////////////////////////////////////////////////////////////////////////// +// Blend + +template +void blendLinearGold(const cv::Mat& img1, const cv::Mat& img2, const cv::Mat& weights1, const cv::Mat& weights2, cv::Mat& result_gold) +{ + result_gold.create(img1.size(), img1.type()); + + int cn = img1.channels(); + + for (int y = 0; y < img1.rows; ++y) + { + const float* weights1_row = weights1.ptr(y); + const float* weights2_row = weights2.ptr(y); + const T* img1_row = img1.ptr(y); + const T* img2_row = img2.ptr(y); + T* result_gold_row = result_gold.ptr(y); + + for (int x = 0; x < img1.cols * cn; ++x) + { + float w1 = weights1_row[x / cn]; + float w2 = weights2_row[x / cn]; + result_gold_row[x] = static_cast((img1_row[x] * w1 + img2_row[x] * w2) / (w1 + w2 + 1e-5f)); + } + } +} + +PARAM_TEST_CASE(Blend, cv::gpu::DeviceInfo, cv::Size, MatType, UseRoi) +{ + cv::gpu::DeviceInfo devInfo; + cv::Size size; + int type; + bool useRoi; + + virtual void SetUp() + { + devInfo = GET_PARAM(0); + size = GET_PARAM(1); + type = GET_PARAM(2); + useRoi = GET_PARAM(3); + + cv::gpu::setDevice(devInfo.deviceID()); + } +}; + +TEST_P(Blend, Accuracy) +{ + int depth = CV_MAT_DEPTH(type); + + cv::Mat img1 = randomMat(size, type, 0.0, depth == CV_8U ? 255.0 : 1.0); + cv::Mat img2 = randomMat(size, type, 0.0, depth == CV_8U ? 255.0 : 1.0); + cv::Mat weights1 = randomMat(size, CV_32F, 0, 1); + cv::Mat weights2 = randomMat(size, CV_32F, 0, 1); + + cv::gpu::GpuMat result; + cv::gpu::blendLinear(loadMat(img1, useRoi), loadMat(img2, useRoi), loadMat(weights1, useRoi), loadMat(weights2, useRoi), result); + + cv::Mat result_gold; + if (depth == CV_8U) + blendLinearGold(img1, img2, weights1, weights2, result_gold); + else + blendLinearGold(img1, img2, weights1, weights2, result_gold); + + EXPECT_MAT_NEAR(result_gold, result, CV_MAT_DEPTH(type) == CV_8U ? 1.0 : 1e-5); +} + +INSTANTIATE_TEST_CASE_P(GPU_ImgProc, Blend, testing::Combine( + ALL_DEVICES, + DIFFERENT_SIZES, + testing::Values(MatType(CV_8UC1), MatType(CV_8UC3), MatType(CV_8UC4), MatType(CV_32FC1), MatType(CV_32FC3), MatType(CV_32FC4)), + WHOLE_SUBMAT)); + +//////////////////////////////////////////////////////// +// Convolve + +void convolveDFT(const cv::Mat& A, const cv::Mat& B, cv::Mat& C, bool ccorr = false) +{ + // reallocate the output array if needed + C.create(std::abs(A.rows - B.rows) + 1, std::abs(A.cols - B.cols) + 1, A.type()); + cv::Size dftSize; + + // compute the size of DFT transform + dftSize.width = cv::getOptimalDFTSize(A.cols + B.cols - 1); + dftSize.height = cv::getOptimalDFTSize(A.rows + B.rows - 1); + + // allocate temporary buffers and initialize them with 0s + cv::Mat tempA(dftSize, A.type(), cv::Scalar::all(0)); + cv::Mat tempB(dftSize, B.type(), cv::Scalar::all(0)); + + // copy A and B to the top-left corners of tempA and tempB, respectively + cv::Mat roiA(tempA, cv::Rect(0, 0, A.cols, A.rows)); + A.copyTo(roiA); + cv::Mat roiB(tempB, cv::Rect(0, 0, B.cols, B.rows)); + B.copyTo(roiB); + + // now transform the padded A & B in-place; + // use "nonzeroRows" hint for faster processing + cv::dft(tempA, tempA, 0, A.rows); + cv::dft(tempB, tempB, 0, B.rows); + + // multiply the spectrums; + // the function handles packed spectrum representations well + cv::mulSpectrums(tempA, tempB, tempA, 0, ccorr); + + // transform the product back from the frequency domain. + // Even though all the result rows will be non-zero, + // you need only the first C.rows of them, and thus you + // pass nonzeroRows == C.rows + cv::dft(tempA, tempA, cv::DFT_INVERSE + cv::DFT_SCALE, C.rows); + + // now copy the result back to C. + tempA(cv::Rect(0, 0, C.cols, C.rows)).copyTo(C); +} + +IMPLEMENT_PARAM_CLASS(KSize, int); +IMPLEMENT_PARAM_CLASS(Ccorr, bool); + +PARAM_TEST_CASE(Convolve, cv::gpu::DeviceInfo, cv::Size, KSize, Ccorr) +{ + cv::gpu::DeviceInfo devInfo; + cv::Size size; + int ksize; + bool ccorr; + + virtual void SetUp() + { + devInfo = GET_PARAM(0); + size = GET_PARAM(1); + ksize = GET_PARAM(2); + ccorr = GET_PARAM(3); + + cv::gpu::setDevice(devInfo.deviceID()); + } +}; + +TEST_P(Convolve, Accuracy) +{ + cv::Mat src = randomMat(size, CV_32FC1, 0.0, 100.0); + cv::Mat kernel = randomMat(cv::Size(ksize, ksize), CV_32FC1, 0.0, 1.0); + + cv::gpu::GpuMat dst; + cv::gpu::convolve(loadMat(src), loadMat(kernel), dst, ccorr); + + cv::Mat dst_gold; + convolveDFT(src, kernel, dst_gold, ccorr); + + EXPECT_MAT_NEAR(dst, dst_gold, 1e-1); +} + +INSTANTIATE_TEST_CASE_P(GPU_ImgProc, Convolve, testing::Combine( + ALL_DEVICES, + DIFFERENT_SIZES, + testing::Values(KSize(3), KSize(7), KSize(11), KSize(17), KSize(19), KSize(23), KSize(45)), + testing::Values(Ccorr(false), Ccorr(true)))); + +//////////////////////////////////////////////////////////////////////////////// +// MatchTemplate8U + +CV_ENUM(TemplateMethod, cv::TM_SQDIFF, cv::TM_SQDIFF_NORMED, cv::TM_CCORR, cv::TM_CCORR_NORMED, cv::TM_CCOEFF, cv::TM_CCOEFF_NORMED) +#define ALL_TEMPLATE_METHODS testing::Values(TemplateMethod(cv::TM_SQDIFF), TemplateMethod(cv::TM_SQDIFF_NORMED), TemplateMethod(cv::TM_CCORR), TemplateMethod(cv::TM_CCORR_NORMED), TemplateMethod(cv::TM_CCOEFF), TemplateMethod(cv::TM_CCOEFF_NORMED)) + +IMPLEMENT_PARAM_CLASS(TemplateSize, cv::Size); + +PARAM_TEST_CASE(MatchTemplate8U, cv::gpu::DeviceInfo, cv::Size, TemplateSize, Channels, TemplateMethod) +{ + cv::gpu::DeviceInfo devInfo; + cv::Size size; + cv::Size templ_size; + int cn; + int method; + + virtual void SetUp() + { + devInfo = GET_PARAM(0); + size = GET_PARAM(1); + templ_size = GET_PARAM(2); + cn = GET_PARAM(3); + method = GET_PARAM(4); + + cv::gpu::setDevice(devInfo.deviceID()); + } +}; + +TEST_P(MatchTemplate8U, Accuracy) +{ + cv::Mat image = randomMat(size, CV_MAKETYPE(CV_8U, cn)); + cv::Mat templ = randomMat(templ_size, CV_MAKETYPE(CV_8U, cn)); + + cv::gpu::GpuMat dst; + cv::gpu::matchTemplate(loadMat(image), loadMat(templ), dst, method); + + cv::Mat dst_gold; + cv::matchTemplate(image, templ, dst_gold, method); + + EXPECT_MAT_NEAR(dst_gold, dst, templ_size.area() * 1e-1); +} + +INSTANTIATE_TEST_CASE_P(GPU_ImgProc, MatchTemplate8U, testing::Combine( + ALL_DEVICES, + DIFFERENT_SIZES, + testing::Values(TemplateSize(cv::Size(5, 5)), TemplateSize(cv::Size(16, 16)), TemplateSize(cv::Size(30, 30))), + testing::Values(Channels(1), Channels(3), Channels(4)), + ALL_TEMPLATE_METHODS)); + +//////////////////////////////////////////////////////////////////////////////// +// MatchTemplate32F + +PARAM_TEST_CASE(MatchTemplate32F, cv::gpu::DeviceInfo, cv::Size, TemplateSize, Channels, TemplateMethod) +{ + cv::gpu::DeviceInfo devInfo; + cv::Size size; + cv::Size templ_size; + int cn; + int method; + + int n, m, h, w; + + virtual void SetUp() + { + devInfo = GET_PARAM(0); + size = GET_PARAM(1); + templ_size = GET_PARAM(2); + cn = GET_PARAM(3); + method = GET_PARAM(4); + + cv::gpu::setDevice(devInfo.deviceID()); + } +}; + +TEST_P(MatchTemplate32F, Regression) +{ + cv::Mat image = randomMat(size, CV_MAKETYPE(CV_32F, cn)); + cv::Mat templ = randomMat(templ_size, CV_MAKETYPE(CV_32F, cn)); + + cv::gpu::GpuMat dst; + cv::gpu::matchTemplate(loadMat(image), loadMat(templ), dst, method); + + cv::Mat dst_gold; + cv::matchTemplate(image, templ, dst_gold, method); + + EXPECT_MAT_NEAR(dst_gold, dst, templ_size.area() * 1e-1); +} + +INSTANTIATE_TEST_CASE_P(GPU_ImgProc, MatchTemplate32F, testing::Combine( + ALL_DEVICES, + DIFFERENT_SIZES, + testing::Values(TemplateSize(cv::Size(5, 5)), TemplateSize(cv::Size(16, 16)), TemplateSize(cv::Size(30, 30))), + testing::Values(Channels(1), Channels(3), Channels(4)), + testing::Values(TemplateMethod(cv::TM_SQDIFF), TemplateMethod(cv::TM_CCORR)))); + +//////////////////////////////////////////////////////////////////////////////// +// MatchTemplateBlackSource + +PARAM_TEST_CASE(MatchTemplateBlackSource, cv::gpu::DeviceInfo, TemplateMethod) +{ + cv::gpu::DeviceInfo devInfo; + int method; + + virtual void SetUp() + { + devInfo = GET_PARAM(0); + method = GET_PARAM(1); + + cv::gpu::setDevice(devInfo.deviceID()); + } +}; + +TEST_P(MatchTemplateBlackSource, Accuracy) +{ + cv::Mat image = readImage("matchtemplate/black.png"); + ASSERT_FALSE(image.empty()); + + cv::Mat pattern = readImage("matchtemplate/cat.png"); + ASSERT_FALSE(pattern.empty()); + + cv::gpu::GpuMat d_dst; + cv::gpu::matchTemplate(loadMat(image), loadMat(pattern), d_dst, method); + + cv::Mat dst(d_dst); + + double maxValue; + cv::Point maxLoc; + cv::minMaxLoc(dst, NULL, &maxValue, NULL, &maxLoc); + + cv::Point maxLocGold = cv::Point(284, 12); + + ASSERT_EQ(maxLocGold, maxLoc); +} + +INSTANTIATE_TEST_CASE_P(GPU_ImgProc, MatchTemplateBlackSource, testing::Combine( + ALL_DEVICES, + testing::Values(TemplateMethod(cv::TM_CCOEFF_NORMED), TemplateMethod(cv::TM_CCORR_NORMED)))); + +//////////////////////////////////////////////////////////////////////////////// +// MatchTemplate_CCOEF_NORMED + +PARAM_TEST_CASE(MatchTemplate_CCOEF_NORMED, cv::gpu::DeviceInfo, std::pair) +{ + cv::gpu::DeviceInfo devInfo; + std::string imageName; + std::string patternName; + + virtual void SetUp() + { + devInfo = GET_PARAM(0); + imageName = GET_PARAM(1).first; + patternName = GET_PARAM(1).second; + + cv::gpu::setDevice(devInfo.deviceID()); + } +}; + +TEST_P(MatchTemplate_CCOEF_NORMED, Accuracy) +{ + cv::Mat image = readImage(imageName); + ASSERT_FALSE(image.empty()); + + cv::Mat pattern = readImage(patternName); + ASSERT_FALSE(pattern.empty()); + + cv::gpu::GpuMat d_dst; + cv::gpu::matchTemplate(loadMat(image), loadMat(pattern), d_dst, CV_TM_CCOEFF_NORMED); + + cv::Mat dst(d_dst); + + cv::Point minLoc, maxLoc; + double minVal, maxVal; + cv::minMaxLoc(dst, &minVal, &maxVal, &minLoc, &maxLoc); + + cv::Mat dstGold; + cv::matchTemplate(image, pattern, dstGold, CV_TM_CCOEFF_NORMED); + + double minValGold, maxValGold; + cv::Point minLocGold, maxLocGold; + cv::minMaxLoc(dstGold, &minValGold, &maxValGold, &minLocGold, &maxLocGold); + + ASSERT_EQ(minLocGold, minLoc); + ASSERT_EQ(maxLocGold, maxLoc); + ASSERT_LE(maxVal, 1.0); + ASSERT_GE(minVal, -1.0); +} + +INSTANTIATE_TEST_CASE_P(GPU_ImgProc, MatchTemplate_CCOEF_NORMED, testing::Combine( + ALL_DEVICES, + testing::Values(std::make_pair(std::string("matchtemplate/source-0.png"), std::string("matchtemplate/target-0.png"))))); + +//////////////////////////////////////////////////////////////////////////////// +// MatchTemplate_CanFindBigTemplate + +struct MatchTemplate_CanFindBigTemplate : testing::TestWithParam +{ + cv::gpu::DeviceInfo devInfo; + + virtual void SetUp() + { + devInfo = GetParam(); + + cv::gpu::setDevice(devInfo.deviceID()); + } +}; + +TEST_P(MatchTemplate_CanFindBigTemplate, SQDIFF_NORMED) +{ + cv::Mat scene = readImage("matchtemplate/scene.jpg"); + ASSERT_FALSE(scene.empty()); + + cv::Mat templ = readImage("matchtemplate/template.jpg"); + ASSERT_FALSE(templ.empty()); + + cv::gpu::GpuMat d_result; + cv::gpu::matchTemplate(loadMat(scene), loadMat(templ), d_result, CV_TM_SQDIFF_NORMED); + + cv::Mat result(d_result); + + double minVal; + cv::Point minLoc; + cv::minMaxLoc(result, &minVal, 0, &minLoc, 0); + + ASSERT_GE(minVal, 0); + ASSERT_LT(minVal, 1e-3); + ASSERT_EQ(344, minLoc.x); + ASSERT_EQ(0, minLoc.y); +} + +TEST_P(MatchTemplate_CanFindBigTemplate, SQDIFF) +{ + cv::Mat scene = readImage("matchtemplate/scene.jpg"); + ASSERT_FALSE(scene.empty()); + + cv::Mat templ = readImage("matchtemplate/template.jpg"); + ASSERT_FALSE(templ.empty()); + + cv::gpu::GpuMat d_result; + cv::gpu::matchTemplate(loadMat(scene), loadMat(templ), d_result, CV_TM_SQDIFF); + + cv::Mat result(d_result); + + double minVal; + cv::Point minLoc; + cv::minMaxLoc(result, &minVal, 0, &minLoc, 0); + + ASSERT_GE(minVal, 0); + ASSERT_EQ(344, minLoc.x); + ASSERT_EQ(0, minLoc.y); +} + +INSTANTIATE_TEST_CASE_P(GPU_ImgProc, MatchTemplate_CanFindBigTemplate, ALL_DEVICES); + +//////////////////////////////////////////////////////////////////////////// +// MulSpectrums + +CV_FLAGS(DftFlags, 0, cv::DFT_INVERSE, cv::DFT_SCALE, cv::DFT_ROWS, cv::DFT_COMPLEX_OUTPUT, cv::DFT_REAL_OUTPUT) + +PARAM_TEST_CASE(MulSpectrums, cv::gpu::DeviceInfo, cv::Size, DftFlags) +{ + cv::gpu::DeviceInfo devInfo; + cv::Size size; + int flag; + + cv::Mat a, b; + + virtual void SetUp() + { + devInfo = GET_PARAM(0); + size = GET_PARAM(1); + flag = GET_PARAM(2); + + cv::gpu::setDevice(devInfo.deviceID()); + + a = randomMat(size, CV_32FC2); + b = randomMat(size, CV_32FC2); + } +}; + +TEST_P(MulSpectrums, Simple) +{ + cv::gpu::GpuMat c; + cv::gpu::mulSpectrums(loadMat(a), loadMat(b), c, flag, false); + + cv::Mat c_gold; + cv::mulSpectrums(a, b, c_gold, flag, false); + + EXPECT_MAT_NEAR(c_gold, c, 1e-2); +} + +TEST_P(MulSpectrums, Scaled) +{ + float scale = 1.f / size.area(); + + cv::gpu::GpuMat c; + cv::gpu::mulAndScaleSpectrums(loadMat(a), loadMat(b), c, flag, scale, false); + + cv::Mat c_gold; + cv::mulSpectrums(a, b, c_gold, flag, false); + c_gold.convertTo(c_gold, c_gold.type(), scale); + + EXPECT_MAT_NEAR(c_gold, c, 1e-2); +} + +INSTANTIATE_TEST_CASE_P(GPU_ImgProc, MulSpectrums, testing::Combine( + ALL_DEVICES, + DIFFERENT_SIZES, + testing::Values(DftFlags(0), DftFlags(cv::DFT_ROWS)))); + +//////////////////////////////////////////////////////////////////////////// +// Dft + +struct Dft : testing::TestWithParam +{ + cv::gpu::DeviceInfo devInfo; + + virtual void SetUp() + { + devInfo = GetParam(); + + cv::gpu::setDevice(devInfo.deviceID()); + } +}; + +void testC2C(const std::string& hint, int cols, int rows, int flags, bool inplace) +{ + SCOPED_TRACE(hint); + + cv::Mat a = randomMat(cv::Size(cols, rows), CV_32FC2, 0.0, 10.0); + + cv::Mat b_gold; + cv::dft(a, b_gold, flags); + + cv::gpu::GpuMat d_b; + cv::gpu::GpuMat d_b_data; + if (inplace) + { + d_b_data.create(1, a.size().area(), CV_32FC2); + d_b = cv::gpu::GpuMat(a.rows, a.cols, CV_32FC2, d_b_data.ptr(), a.cols * d_b_data.elemSize()); + } + cv::gpu::dft(loadMat(a), d_b, cv::Size(cols, rows), flags); + + EXPECT_TRUE(!inplace || d_b.ptr() == d_b_data.ptr()); + ASSERT_EQ(CV_32F, d_b.depth()); + ASSERT_EQ(2, d_b.channels()); + EXPECT_MAT_NEAR(b_gold, cv::Mat(d_b), rows * cols * 1e-4); +} + +TEST_P(Dft, C2C) +{ + int cols = randomInt(2, 100); + int rows = randomInt(2, 100); + + for (int i = 0; i < 2; ++i) + { + bool inplace = i != 0; + + testC2C("no flags", cols, rows, 0, inplace); + testC2C("no flags 0 1", cols, rows + 1, 0, inplace); + testC2C("no flags 1 0", cols, rows + 1, 0, inplace); + testC2C("no flags 1 1", cols + 1, rows, 0, inplace); + testC2C("DFT_INVERSE", cols, rows, cv::DFT_INVERSE, inplace); + testC2C("DFT_ROWS", cols, rows, cv::DFT_ROWS, inplace); + testC2C("single col", 1, rows, 0, inplace); + testC2C("single row", cols, 1, 0, inplace); + testC2C("single col inversed", 1, rows, cv::DFT_INVERSE, inplace); + testC2C("single row inversed", cols, 1, cv::DFT_INVERSE, inplace); + testC2C("single row DFT_ROWS", cols, 1, cv::DFT_ROWS, inplace); + testC2C("size 1 2", 1, 2, 0, inplace); + testC2C("size 2 1", 2, 1, 0, inplace); + } +} + +void testR2CThenC2R(const std::string& hint, int cols, int rows, bool inplace) +{ + SCOPED_TRACE(hint); + + cv::Mat a = randomMat(cv::Size(cols, rows), CV_32FC1, 0.0, 10.0); + + cv::gpu::GpuMat d_b, d_c; + cv::gpu::GpuMat d_b_data, d_c_data; + if (inplace) + { + if (a.cols == 1) + { + d_b_data.create(1, (a.rows / 2 + 1) * a.cols, CV_32FC2); + d_b = cv::gpu::GpuMat(a.rows / 2 + 1, a.cols, CV_32FC2, d_b_data.ptr(), a.cols * d_b_data.elemSize()); + } + else + { + d_b_data.create(1, a.rows * (a.cols / 2 + 1), CV_32FC2); + d_b = cv::gpu::GpuMat(a.rows, a.cols / 2 + 1, CV_32FC2, d_b_data.ptr(), (a.cols / 2 + 1) * d_b_data.elemSize()); + } + d_c_data.create(1, a.size().area(), CV_32F); + d_c = cv::gpu::GpuMat(a.rows, a.cols, CV_32F, d_c_data.ptr(), a.cols * d_c_data.elemSize()); + } + + cv::gpu::dft(loadMat(a), d_b, cv::Size(cols, rows), 0); + cv::gpu::dft(d_b, d_c, cv::Size(cols, rows), cv::DFT_REAL_OUTPUT | cv::DFT_SCALE); + + EXPECT_TRUE(!inplace || d_b.ptr() == d_b_data.ptr()); + EXPECT_TRUE(!inplace || d_c.ptr() == d_c_data.ptr()); + ASSERT_EQ(CV_32F, d_c.depth()); + ASSERT_EQ(1, d_c.channels()); + + cv::Mat c(d_c); + EXPECT_MAT_NEAR(a, c, rows * cols * 1e-5); +} + +TEST_P(Dft, R2CThenC2R) +{ + int cols = randomInt(2, 100); + int rows = randomInt(2, 100); + + testR2CThenC2R("sanity", cols, rows, false); + testR2CThenC2R("sanity 0 1", cols, rows + 1, false); + testR2CThenC2R("sanity 1 0", cols + 1, rows, false); + testR2CThenC2R("sanity 1 1", cols + 1, rows + 1, false); + testR2CThenC2R("single col", 1, rows, false); + testR2CThenC2R("single col 1", 1, rows + 1, false); + testR2CThenC2R("single row", cols, 1, false); + testR2CThenC2R("single row 1", cols + 1, 1, false); + + testR2CThenC2R("sanity", cols, rows, true); + testR2CThenC2R("sanity 0 1", cols, rows + 1, true); + testR2CThenC2R("sanity 1 0", cols + 1, rows, true); + testR2CThenC2R("sanity 1 1", cols + 1, rows + 1, true); + testR2CThenC2R("single row", cols, 1, true); + testR2CThenC2R("single row 1", cols + 1, 1, true); +} + +INSTANTIATE_TEST_CASE_P(GPU_ImgProc, Dft, ALL_DEVICES); + +/////////////////////////////////////////////////////////////////////////////////////////////////////// +// CornerHarris + +IMPLEMENT_PARAM_CLASS(BlockSize, int); +IMPLEMENT_PARAM_CLASS(ApertureSize, int); + +PARAM_TEST_CASE(CornerHarris, cv::gpu::DeviceInfo, MatType, BorderType, BlockSize, ApertureSize) +{ + cv::gpu::DeviceInfo devInfo; + int type; + int borderType; + int blockSize; + int apertureSize; + + virtual void SetUp() + { + devInfo = GET_PARAM(0); + type = GET_PARAM(1); + borderType = GET_PARAM(2); + blockSize = GET_PARAM(3); + apertureSize = GET_PARAM(4); + + cv::gpu::setDevice(devInfo.deviceID()); + } +}; + +TEST_P(CornerHarris, Accuracy) +{ + cv::Mat src = readImageType("stereobm/aloe-L.png", type); + ASSERT_FALSE(src.empty()); + + double k = randomDouble(0.1, 0.9); + + cv::gpu::GpuMat dst; + cv::gpu::cornerHarris(loadMat(src), dst, blockSize, apertureSize, k, borderType); + + cv::Mat dst_gold; + cv::cornerHarris(src, dst_gold, blockSize, apertureSize, k, borderType); + + EXPECT_MAT_NEAR(dst_gold, dst, 0.02); +} + +INSTANTIATE_TEST_CASE_P(GPU_ImgProc, CornerHarris, testing::Combine( + ALL_DEVICES, + testing::Values(MatType(CV_8UC1), MatType(CV_32FC1)), + testing::Values(BorderType(cv::BORDER_REFLECT101), BorderType(cv::BORDER_REPLICATE), BorderType(cv::BORDER_REFLECT)), + testing::Values(BlockSize(3), BlockSize(5), BlockSize(7)), + testing::Values(ApertureSize(0), ApertureSize(3), ApertureSize(5), ApertureSize(7)))); + +/////////////////////////////////////////////////////////////////////////////////////////////////////// +// cornerMinEigen + +PARAM_TEST_CASE(CornerMinEigen, cv::gpu::DeviceInfo, MatType, BorderType, BlockSize, ApertureSize) +{ + cv::gpu::DeviceInfo devInfo; + int type; + int borderType; + int blockSize; + int apertureSize; + + virtual void SetUp() + { + devInfo = GET_PARAM(0); + type = GET_PARAM(1); + borderType = GET_PARAM(2); + blockSize = GET_PARAM(3); + apertureSize = GET_PARAM(4); + + cv::gpu::setDevice(devInfo.deviceID()); + } +}; + +TEST_P(CornerMinEigen, Accuracy) +{ + cv::Mat src = readImageType("stereobm/aloe-L.png", type); + ASSERT_FALSE(src.empty()); + + cv::gpu::GpuMat dst; + cv::gpu::cornerMinEigenVal(loadMat(src), dst, blockSize, apertureSize, borderType); + + cv::Mat dst_gold; + cv::cornerMinEigenVal(src, dst_gold, blockSize, apertureSize, borderType); + + EXPECT_MAT_NEAR(dst_gold, dst, 0.02); +} + +INSTANTIATE_TEST_CASE_P(GPU_ImgProc, CornerMinEigen, testing::Combine( + ALL_DEVICES, + testing::Values(MatType(CV_8UC1), MatType(CV_32FC1)), + testing::Values(BorderType(cv::BORDER_REFLECT101), BorderType(cv::BORDER_REPLICATE), BorderType(cv::BORDER_REFLECT)), + testing::Values(BlockSize(3), BlockSize(5), BlockSize(7)), + testing::Values(ApertureSize(0), ApertureSize(3), ApertureSize(5), ApertureSize(7)))); + +} // namespace diff --git a/modules/gpu/test/test_nvidia.cpp b/modules/gpu/test/test_nvidia.cpp index 1b9879c..7ce3192 100644 --- a/modules/gpu/test/test_nvidia.cpp +++ b/modules/gpu/test/test_nvidia.cpp @@ -39,6 +39,7 @@ // //M*/ +#include #include "precomp.hpp" #ifdef HAVE_CUDA @@ -46,24 +47,12 @@ using namespace cvtest; using namespace testing; -enum OutputLevel -{ - OutputLevelNone, - OutputLevelCompact, - OutputLevelFull -}; - -bool nvidia_NPPST_Integral_Image(const std::string& test_data_path, OutputLevel outputLevel); -bool nvidia_NPPST_Squared_Integral_Image(const std::string& test_data_path, OutputLevel outputLevel); -bool nvidia_NPPST_RectStdDev(const std::string& test_data_path, OutputLevel outputLevel); -bool nvidia_NPPST_Resize(const std::string& test_data_path, OutputLevel outputLevel); -bool nvidia_NPPST_Vector_Operations(const std::string& test_data_path, OutputLevel outputLevel); -bool nvidia_NPPST_Transpose(const std::string& test_data_path, OutputLevel outputLevel); -bool nvidia_NCV_Vector_Operations(const std::string& test_data_path, OutputLevel outputLevel); -bool nvidia_NCV_Haar_Cascade_Loader(const std::string& test_data_path, OutputLevel outputLevel); -bool nvidia_NCV_Haar_Cascade_Application(const std::string& test_data_path, OutputLevel outputLevel); -bool nvidia_NCV_Hypotheses_Filtration(const std::string& test_data_path, OutputLevel outputLevel); -bool nvidia_NCV_Visualization(const std::string& test_data_path, OutputLevel outputLevel); +//enum OutputLevel +//{ +// OutputLevelNone, +// OutputLevelCompact, +// OutputLevelFull +//}; struct NVidiaTest : TestWithParam { @@ -86,12 +75,12 @@ struct NCV : NVidiaTest {}; OutputLevel nvidiaTestOutputLevel = OutputLevelCompact; -TEST_P(NPPST, Integral) -{ - bool res = nvidia_NPPST_Integral_Image(path, nvidiaTestOutputLevel); +//TEST_P(NPPST, Integral) +//{ +// bool res = nvidia_NPPST_Integral_Image(path, nvidiaTestOutputLevel); - ASSERT_TRUE(res); -} +// ASSERT_TRUE(res); +//} TEST_P(NPPST, SquaredIntegral) { diff --git a/modules/gpu/test/test_objdetect.cpp b/modules/gpu/test/test_objdetect.cpp index e8284a2..4b5a2fa 100644 --- a/modules/gpu/test/test_objdetect.cpp +++ b/modules/gpu/test/test_objdetect.cpp @@ -69,16 +69,16 @@ struct HOG : testing::TestWithParam, cv::gpu::HOGDescriptor } #ifdef DUMP - void dump(const cv::Mat& block_hists, const std::vector& locations) + void dump(const cv::Mat& blockHists, const std::vector& locations) { - f.write((char*)&block_hists.rows, sizeof(block_hists.rows)); - f.write((char*)&block_hists.cols, sizeof(block_hists.cols)); + f.write((char*)&blockHists.rows, sizeof(blockHists.rows)); + f.write((char*)&blockHists.cols, sizeof(blockHists.cols)); - for (int i = 0; i < block_hists.rows; ++i) + for (int i = 0; i < blockHists.rows; ++i) { - for (int j = 0; j < block_hists.cols; ++j) + for (int j = 0; j < blockHists.cols; ++j) { - float val = block_hists.at(i, j); + float val = blockHists.at(i, j); f.write((char*)&val, sizeof(val)); } } @@ -90,21 +90,21 @@ struct HOG : testing::TestWithParam, cv::gpu::HOGDescriptor f.write((char*)&locations[i], sizeof(locations[i])); } #else - void compare(const cv::Mat& block_hists, const std::vector& locations) + void compare(const cv::Mat& blockHists, const std::vector& locations) { int rows, cols; f.read((char*)&rows, sizeof(rows)); f.read((char*)&cols, sizeof(cols)); - ASSERT_EQ(rows, block_hists.rows); - ASSERT_EQ(cols, block_hists.cols); + ASSERT_EQ(rows, blockHists.rows); + ASSERT_EQ(cols, blockHists.cols); - for (int i = 0; i < block_hists.rows; ++i) + for (int i = 0; i < blockHists.rows; ++i) { - for (int j = 0; j < block_hists.cols; ++j) + for (int j = 0; j < blockHists.cols; ++j) { float val; f.read((char*)&val, sizeof(val)); - ASSERT_NEAR(val, block_hists.at(i, j), 1e-3); + ASSERT_NEAR(val, blockHists.at(i, j), 1e-3); } } diff --git a/modules/highgui/CMakeLists.txt b/modules/highgui/CMakeLists.txt index ecd0276..2323120 100644 --- a/modules/highgui/CMakeLists.txt +++ b/modules/highgui/CMakeLists.txt @@ -76,11 +76,20 @@ if(HAVE_QT) endif() include(${QT_USE_FILE}) + if(QT_INCLUDE_DIR) + ocv_include_directories(${QT_INCLUDE_DIR}) + endif() + QT4_ADD_RESOURCES(_RCC_OUTFILES src/window_QT.qrc) QT4_WRAP_CPP(_MOC_OUTFILES src/window_QT.h) list(APPEND HIGHGUI_LIBRARIES ${QT_LIBRARIES} ${QT_QTTEST_LIBRARY}) list(APPEND highgui_srcs src/window_QT.cpp ${_MOC_OUTFILES} ${_RCC_OUTFILES} ) + + ocv_check_flag_support(CXX -Wno-missing-declarations HAVE_CXX_WNO_MISSING_DECLARATIONS) + if(HAVE_CXX_WNO_MISSING_DECLARATIONS) + set_source_files_properties(${_RCC_OUTFILES} PROPERTIES COMPILE_FLAGS -Wno-missing-declarations) + endif() elseif(WIN32) list(APPEND highgui_srcs src/window_w32.cpp) elseif(HAVE_GTK) @@ -166,7 +175,6 @@ if(WITH_IMAGEIO) if(IOS) list(APPEND HIGHGUI_LIBRARIES "-framework ImageIO") endif() - #TODO: check if need to link with some framework on OS X: -framework ApplicationServices ?? endif(WITH_IMAGEIO) if(WITH_AVFOUNDATION) @@ -186,7 +194,7 @@ endif() if(WIN32) link_directories("${OpenCV_SOURCE_DIR}/3rdparty/lib") # for ffmpeg wrapper only - include_directories(AFTER "${OpenCV_SOURCE_DIR}/3rdparty/include") # for directshow in VS2005 and multi-monitor support on MinGW + include_directories(AFTER SYSTEM "${OpenCV_SOURCE_DIR}/3rdparty/include") # for directshow in VS2005 and multi-monitor support on MinGW endif() if(UNIX) @@ -220,10 +228,7 @@ endif() set_target_properties(${the_module} PROPERTIES LINK_INTERFACE_LIBRARIES "") ocv_add_precompiled_headers(${the_module}) - -if(CMAKE_COMPILER_IS_GNUCXX AND NOT ENABLE_NOISY_WARNINGS) - set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-deprecated-declarations") -endif() +ocv_warnings_disable(CMAKE_CXX_FLAGS -Wno-deprecated-declarations) if(WIN32 AND WITH_FFMPEG) #copy ffmpeg dll to the output folder diff --git a/modules/highgui/include/opencv2/highgui/highgui_c.h b/modules/highgui/include/opencv2/highgui/highgui_c.h index 7937df4..ddd3003 100644 --- a/modules/highgui/include/opencv2/highgui/highgui_c.h +++ b/modules/highgui/include/opencv2/highgui/highgui_c.h @@ -79,7 +79,7 @@ CVAPI(void) cvDisplayStatusBar(const char* name, const char* text, int delayms C CVAPI(void) cvSaveWindowParameters(const char* name); CVAPI(void) cvLoadWindowParameters(const char* name); CVAPI(int) cvStartLoop(int (*pt2Func)(int argc, char *argv[]), int argc, char* argv[]); -CVAPI(void) cvStopLoop(); +CVAPI(void) cvStopLoop( void ); typedef void (CV_CDECL *CvButtonCallback)(int state, void* userdata); enum {CV_PUSH_BUTTON = 0, CV_CHECKBOX = 1, CV_RADIOBOX = 2}; @@ -90,7 +90,7 @@ CVAPI(int) cvCreateButton( const char* button_name CV_DEFAULT(NULL),CvButtonCall /* this function is used to set some external parameters in case of X Window */ CVAPI(int) cvInitSystem( int argc, char** argv ); -CVAPI(int) cvStartWindowThread(); +CVAPI(int) cvStartWindowThread( void ); // --------- YV --------- enum @@ -100,16 +100,16 @@ enum CV_WND_PROP_AUTOSIZE = 1, //to change/get window's autosize property CV_WND_PROP_ASPECTRATIO= 2, //to change/get window's aspectratio property CV_WND_PROP_OPENGL = 3, //to change/get window's opengl support - + //These 2 flags are used by cvNamedWindow and cvSet/GetWindowProperty CV_WINDOW_NORMAL = 0x00000000, //the user can resize the window (no constraint) / also use to switch a fullscreen window to a normal size CV_WINDOW_AUTOSIZE = 0x00000001, //the user cannot resize the window, the size is constrainted by the image displayed CV_WINDOW_OPENGL = 0x00001000, //window with opengl support - + //Those flags are only for Qt CV_GUI_EXPANDED = 0x00000000, //status bar and tool bar CV_GUI_NORMAL = 0x00000010, //old fashious way - + //These 3 flags are used by cvNamedWindow and cvSet/GetWindowProperty CV_WINDOW_FULLSCREEN = 1,//change the window to fullscreen CV_WINDOW_FREERATIO = 0x00000100,//the image expends as much as it can (no ratio constraint) @@ -303,10 +303,10 @@ enum CV_CAP_OPENNI_ASUS =910, // OpenNI (for Asus Xtion) CV_CAP_ANDROID =1000, // Android - + CV_CAP_XIAPI =1100, // XIMEA Camera API - - CV_CAP_AVFOUNDATION = 1200 // AVFoundation framework for iOS (OS X Lion will have the same API) + + CV_CAP_AVFOUNDATION = 1200 // AVFoundation framework for iOS (OS X Lion will have the same API) }; /* start capturing frames from camera: index = camera_index + domain_offset (CV_CAP_*) */ @@ -367,15 +367,15 @@ enum CV_CAP_PROP_TRIGGER_DELAY =25, CV_CAP_PROP_WHITE_BALANCE_RED_V =26, CV_CAP_PROP_ZOOM =27, - CV_CAP_PROP_FOCUS =28, - CV_CAP_PROP_GUID =29, - CV_CAP_PROP_ISO_SPEED =30, + CV_CAP_PROP_FOCUS =28, + CV_CAP_PROP_GUID =29, + CV_CAP_PROP_ISO_SPEED =30, CV_CAP_PROP_MAX_DC1394 =31, - CV_CAP_PROP_BACKLIGHT =32, - CV_CAP_PROP_PAN =33, - CV_CAP_PROP_TILT =34, - CV_CAP_PROP_ROLL =35, - CV_CAP_PROP_IRIS =36, + CV_CAP_PROP_BACKLIGHT =32, + CV_CAP_PROP_PAN =33, + CV_CAP_PROP_TILT =34, + CV_CAP_PROP_ROLL =35, + CV_CAP_PROP_IRIS =36, CV_CAP_PROP_SETTINGS =37, CV_CAP_PROP_AUTOGRAB =1024, // property for highgui class CvCapture_Android only @@ -409,24 +409,24 @@ enum CV_CAP_OPENNI_DEPTH_GENERATOR_FOCAL_LENGTH = CV_CAP_OPENNI_DEPTH_GENERATOR + CV_CAP_PROP_OPENNI_FOCAL_LENGTH, CV_CAP_OPENNI_DEPTH_GENERATOR_REGISTRATION = CV_CAP_OPENNI_DEPTH_GENERATOR + CV_CAP_PROP_OPENNI_REGISTRATION, CV_CAP_OPENNI_DEPTH_GENERATOR_REGISTRATION_ON = CV_CAP_OPENNI_DEPTH_GENERATOR_REGISTRATION, - + // Properties of cameras available through GStreamer interface CV_CAP_GSTREAMER_QUEUE_LENGTH = 200, // default is 1 CV_CAP_PROP_PVAPI_MULTICASTIP = 300, // ip for anable multicast master mode. 0 for disable multicast - + // Properties of cameras available through XIMEA SDK interface - CV_CAP_PROP_XI_DOWNSAMPLING = 400, // Change image resolution by binning or skipping. + CV_CAP_PROP_XI_DOWNSAMPLING = 400, // Change image resolution by binning or skipping. CV_CAP_PROP_XI_DATA_FORMAT = 401, // Output data format. CV_CAP_PROP_XI_OFFSET_X = 402, // Horizontal offset from the origin to the area of interest (in pixels). CV_CAP_PROP_XI_OFFSET_Y = 403, // Vertical offset from the origin to the area of interest (in pixels). CV_CAP_PROP_XI_TRG_SOURCE = 404, // Defines source of trigger. CV_CAP_PROP_XI_TRG_SOFTWARE = 405, // Generates an internal trigger. PRM_TRG_SOURCE must be set to TRG_SOFTWARE. - CV_CAP_PROP_XI_GPI_SELECTOR = 406, // Selects general purpose input + CV_CAP_PROP_XI_GPI_SELECTOR = 406, // Selects general purpose input CV_CAP_PROP_XI_GPI_MODE = 407, // Set general purpose input mode CV_CAP_PROP_XI_GPI_LEVEL = 408, // Get general purpose level - CV_CAP_PROP_XI_GPO_SELECTOR = 409, // Selects general purpose output + CV_CAP_PROP_XI_GPO_SELECTOR = 409, // Selects general purpose output CV_CAP_PROP_XI_GPO_MODE = 410, // Set general purpose output mode - CV_CAP_PROP_XI_LED_SELECTOR = 411, // Selects camera signalling LED + CV_CAP_PROP_XI_LED_SELECTOR = 411, // Selects camera signalling LED CV_CAP_PROP_XI_LED_MODE = 412, // Define camera signalling LED functionality CV_CAP_PROP_XI_MANUAL_WB = 413, // Calculates White Balance(must be called during acquisition) CV_CAP_PROP_XI_AUTO_WB = 414, // Automatic white balance @@ -436,7 +436,7 @@ enum CV_CAP_PROP_XI_AG_MAX_LIMIT = 418, // Maximum limit of gain in AEAG procedure CV_CAP_PROP_XI_AEAG_LEVEL = 419, // Average intensity of output signal AEAG should achieve(in %) CV_CAP_PROP_XI_TIMEOUT = 420, // Image capture timeout in milliseconds - + // Properties for Android cameras CV_CAP_PROP_ANDROID_FLASH_MODE = 8001, CV_CAP_PROP_ANDROID_FOCUS_MODE = 8002, @@ -532,7 +532,7 @@ CVAPI(double) cvGetCaptureProperty( CvCapture* capture, int property_id ); CVAPI(int) cvSetCaptureProperty( CvCapture* capture, int property_id, double value ); // Return the type of the capturer (eg, CV_CAP_V4W, CV_CAP_UNICAP), which is unknown if created with CV_CAP_ANY -CVAPI(int) cvGetCaptureDomain( CvCapture* capture); +CVAPI(int) cvGetCaptureDomain( CvCapture* capture); /* "black box" video file writer structure */ typedef struct CvVideoWriter CvVideoWriter; diff --git a/modules/highgui/perf/perf_precomp.hpp b/modules/highgui/perf/perf_precomp.hpp index 2ffa9ee..f77c1c4 100644 --- a/modules/highgui/perf/perf_precomp.hpp +++ b/modules/highgui/perf/perf_precomp.hpp @@ -1,10 +1,14 @@ +#ifdef __GNUC__ +# pragma GCC diagnostic ignored "-Wmissing-declarations" +#endif + #ifndef __OPENCV_PERF_PRECOMP_HPP__ #define __OPENCV_PERF_PRECOMP_HPP__ #include "opencv2/ts/ts.hpp" #include "opencv2/highgui/highgui.hpp" -#if GTEST_CREATE_SHARED_LIBRARY +#ifdef GTEST_CREATE_SHARED_LIBRARY #error no modules except ts should have GTEST_CREATE_SHARED_LIBRARY defined #endif diff --git a/modules/highgui/src/cap.cpp b/modules/highgui/src/cap.cpp index 8dc25f3..fdc40d1 100644 --- a/modules/highgui/src/cap.cpp +++ b/modules/highgui/src/cap.cpp @@ -41,13 +41,9 @@ #include "precomp.hpp" -#if _MSC_VER >= 1200 -#pragma warning( disable: 4711 ) -#endif - #if defined _M_X64 && defined _MSC_VER && !defined CV_ICC #pragma optimize("",off) -#pragma warning( disable: 4748 ) +#pragma warning(disable: 4748) #endif namespace cv @@ -282,7 +278,7 @@ CV_IMPL CvCapture * cvCreateCameraCapture (int index) return capture; break; #endif - + #ifdef HAVE_PVAPI case CV_CAP_PVAPI: capture = cvCreateCameraCapture_PvAPI (index); @@ -306,7 +302,7 @@ CV_IMPL CvCapture * cvCreateCameraCapture (int index) return capture; break; #endif - + #ifdef HAVE_XIMEA case CV_CAP_XIAPI: capture = cvCreateCameraCapture_XIMEA (index); @@ -354,7 +350,7 @@ CV_IMPL CvCapture * cvCreateFileCapture (const char * filename) if (! result) result = cvCreateFileCapture_QT (filename); #endif - + #ifdef HAVE_AVFOUNDATION if (! result) result = cvCreateFileCapture_AVFoundation (filename); @@ -364,7 +360,7 @@ CV_IMPL CvCapture * cvCreateFileCapture (const char * filename) if (! result) result = cvCreateFileCapture_OpenNI (filename); #endif - + if (! result) result = cvCreateFileCapture_Images (filename); @@ -378,29 +374,29 @@ CV_IMPL CvCapture * cvCreateFileCapture (const char * filename) CV_IMPL CvVideoWriter* cvCreateVideoWriter( const char* filename, int fourcc, double fps, CvSize frameSize, int is_color ) { - //CV_FUNCNAME( "cvCreateVideoWriter" ); + //CV_FUNCNAME( "cvCreateVideoWriter" ); - CvVideoWriter *result = 0; + CvVideoWriter *result = 0; - if(!fourcc || !fps) - result = cvCreateVideoWriter_Images(filename); + if(!fourcc || !fps) + result = cvCreateVideoWriter_Images(filename); - if(!result) - result = cvCreateVideoWriter_FFMPEG_proxy (filename, fourcc, fps, frameSize, is_color); + if(!result) + result = cvCreateVideoWriter_FFMPEG_proxy (filename, fourcc, fps, frameSize, is_color); -/* #ifdef HAVE_XINE - if(!result) - result = cvCreateVideoWriter_XINE(filename, fourcc, fps, frameSize, is_color); - #endif +/* #ifdef HAVE_XINE + if(!result) + result = cvCreateVideoWriter_XINE(filename, fourcc, fps, frameSize, is_color); + #endif */ -#ifdef HAVE_AVFOUNDATION +#ifdef HAVE_AVFOUNDATION if (! result) result = cvCreateVideoWriter_AVFoundation(filename, fourcc, fps, frameSize, is_color); #endif #ifdef HAVE_QUICKTIME - if(!result) - result = cvCreateVideoWriter_QT(filename, fourcc, fps, frameSize, is_color); + if(!result) + result = cvCreateVideoWriter_QT(filename, fourcc, fps, frameSize, is_color); #endif #ifdef HAVE_GSTREAMER @@ -408,10 +404,10 @@ CV_IMPL CvVideoWriter* cvCreateVideoWriter( const char* filename, int fourcc, result = cvCreateVideoWriter_GStreamer(filename, fourcc, fps, frameSize, is_color); #endif - if(!result) - result = cvCreateVideoWriter_Images(filename); + if(!result) + result = cvCreateVideoWriter_Images(filename); - return result; + return result; } CV_IMPL int cvWriteFrame( CvVideoWriter* writer, const IplImage* image ) @@ -434,12 +430,12 @@ namespace cv VideoCapture::VideoCapture() {} - + VideoCapture::VideoCapture(const string& filename) { open(filename); } - + VideoCapture::VideoCapture(int device) { open(device); @@ -449,21 +445,21 @@ VideoCapture::~VideoCapture() { cap.release(); } - + bool VideoCapture::open(const string& filename) { cap = cvCreateFileCapture(filename.c_str()); return isOpened(); } - + bool VideoCapture::open(int device) { cap = cvCreateCameraCapture(device); return isOpened(); } - + bool VideoCapture::isOpened() const { return !cap.empty(); } - + void VideoCapture::release() { cap.release(); @@ -473,7 +469,7 @@ bool VideoCapture::grab() { return cvGrabFrame(cap) != 0; } - + bool VideoCapture::retrieve(Mat& image, int channel) { IplImage* _img = cvRetrieveFrame(cap, channel); @@ -500,18 +496,18 @@ bool VideoCapture::read(Mat& image) image.release(); return !image.empty(); } - + VideoCapture& VideoCapture::operator >> (Mat& image) { read(image); return *this; } - + bool VideoCapture::set(int propId, double value) { return cvSetCaptureProperty(cap, propId, value) != 0; } - + double VideoCapture::get(int propId) { return cvGetCaptureProperty(cap, propId); @@ -519,7 +515,7 @@ double VideoCapture::get(int propId) VideoWriter::VideoWriter() {} - + VideoWriter::VideoWriter(const string& filename, int fourcc, double fps, Size frameSize, bool isColor) { open(filename, fourcc, fps, frameSize, isColor); @@ -528,13 +524,13 @@ VideoWriter::VideoWriter(const string& filename, int fourcc, double fps, Size fr void VideoWriter::release() { writer.release(); -} - +} + VideoWriter::~VideoWriter() { release(); } - + bool VideoWriter::open(const string& filename, int fourcc, double fps, Size frameSize, bool isColor) { writer = cvCreateVideoWriter(filename.c_str(), fourcc, fps, frameSize, isColor); @@ -544,18 +540,18 @@ bool VideoWriter::open(const string& filename, int fourcc, double fps, Size fram bool VideoWriter::isOpened() const { return !writer.empty(); -} +} void VideoWriter::write(const Mat& image) { IplImage _img = image; cvWriteFrame(writer, &_img); } - + VideoWriter& VideoWriter::operator << (const Mat& image) { write(image); - return *this; + return *this; } } diff --git a/modules/highgui/src/cap_cmu.cpp b/modules/highgui/src/cap_cmu.cpp index 9cf0d86..357b106 100644 --- a/modules/highgui/src/cap_cmu.cpp +++ b/modules/highgui/src/cap_cmu.cpp @@ -45,7 +45,7 @@ /****************** Capturing video from camera via CMU lib *******************/ -#if HAVE_CMU1394 +#ifdef HAVE_CMU1394 // This firewire capability added by Philip Gruebele (pgruebele@cox.net). // For this to work you need to install the CMU firewire DCAM drivers, diff --git a/modules/highgui/src/cap_dc1394_v2.cpp b/modules/highgui/src/cap_dc1394_v2.cpp index ec60373..8821095 100644 --- a/modules/highgui/src/cap_dc1394_v2.cpp +++ b/modules/highgui/src/cap_dc1394_v2.cpp @@ -126,8 +126,6 @@ static dc1394error_t adaptBufferStereoLocal(dc1394video_frame_t *in, dc1394video static dc1394error_t dc1394_deinterlace_stereo_frames_fixed(dc1394video_frame_t *in, dc1394video_frame_t *out, dc1394stereo_method_t method) { - dc1394error_t err; - if((in->color_coding == DC1394_COLOR_CODING_RAW16) || (in->color_coding == DC1394_COLOR_CODING_MONO16) || (in->color_coding == DC1394_COLOR_CODING_YUV422)) @@ -136,14 +134,14 @@ static dc1394error_t dc1394_deinterlace_stereo_frames_fixed(dc1394video_frame_t { case DC1394_STEREO_METHOD_INTERLACED: - err = adaptBufferStereoLocal(in, out); + adaptBufferStereoLocal(in, out); //FIXED by AB: // dc1394_deinterlace_stereo(in->image, out->image, in->size[0], in->size[1]); dc1394_deinterlace_stereo(in->image, out->image, out->size[0], out->size[1]); break; case DC1394_STEREO_METHOD_FIELD: - err = adaptBufferStereoLocal(in, out); + adaptBufferStereoLocal(in, out); memcpy(out->image, in->image, out->image_bytes); break; } diff --git a/modules/highgui/src/cap_dshow.cpp b/modules/highgui/src/cap_dshow.cpp index dec1fdb..84cfbc6 100644 --- a/modules/highgui/src/cap_dshow.cpp +++ b/modules/highgui/src/cap_dshow.cpp @@ -89,7 +89,8 @@ Thanks to: #include "precomp.hpp" -#if _MSC_VER >= 100 +#if defined _MSC_VER && _MSC_VER >= 100 +//'sprintf': name was marked as #pragma deprecated #pragma warning(disable: 4995) #endif @@ -103,17 +104,18 @@ Thanks to: #include //Include Directshow stuff here so we don't worry about needing all the h files. -#if _MSC_VER >= 1500 -#include "DShow.h" -#include "strmif.h" -#include "Aviriff.h" -#include "dvdmedia.h" -#include "bdaiface.h" +#if defined _MSC_VER && _MSC_VER >= 1500 +# include "DShow.h" +# include "strmif.h" +# include "Aviriff.h" +# include "dvdmedia.h" +# include "bdaiface.h" #else -#ifdef _MSC_VER -#define __extension__ -typedef BOOL WINBOOL; +# ifdef _MSC_VER +# define __extension__ + typedef BOOL WINBOOL; #endif + #include "dshow/dshow.h" #include "dshow/dvdmedia.h" #include "dshow/bdatypes.h" @@ -133,6 +135,8 @@ public: virtual HRESULT STDMETHODCALLTYPE Clone( /* [out] */ IEnumPIDMap **ppIEnumPIDMap) = 0; + + virtual ~IEnumPIDMap() {} }; interface IMPEG2PIDMap : public IUnknown @@ -148,6 +152,8 @@ interface IMPEG2PIDMap : public IUnknown virtual HRESULT STDMETHODCALLTYPE EnumPIDMap( /* [out] */ IEnumPIDMap **pIEnumPIDMap) = 0; + + virtual ~IMPEG2PIDMap() {} }; #endif @@ -234,6 +240,7 @@ interface ISampleGrabberCB : public IUnknown BYTE *pBuffer, LONG BufferLen) = 0; + virtual ~ISampleGrabberCB() {} }; interface ISampleGrabber : public IUnknown @@ -261,6 +268,7 @@ interface ISampleGrabber : public IUnknown ISampleGrabberCB *pCallback, LONG WhichMethodToCallback) = 0; + virtual ~ISampleGrabber() {} }; #ifndef HEADER @@ -519,12 +527,12 @@ class videoInput{ //Manual control over settings thanks..... //These are experimental for now. - bool setVideoSettingFilter(int deviceID, long Property, long lValue, long Flags = NULL, bool useDefaultValue = false); - bool setVideoSettingFilterPct(int deviceID, long Property, float pctValue, long Flags = NULL); + bool setVideoSettingFilter(int deviceID, long Property, long lValue, long Flags = 0, bool useDefaultValue = false); + bool setVideoSettingFilterPct(int deviceID, long Property, float pctValue, long Flags = 0); bool getVideoSettingFilter(int deviceID, long Property, long &min, long &max, long &SteppingDelta, long ¤tValue, long &flags, long &defaultValue); - bool setVideoSettingCamera(int deviceID, long Property, long lValue, long Flags = NULL, bool useDefaultValue = false); - bool setVideoSettingCameraPct(int deviceID, long Property, float pctValue, long Flags = NULL); + bool setVideoSettingCamera(int deviceID, long Property, long lValue, long Flags = 0, bool useDefaultValue = false); + bool setVideoSettingCameraPct(int deviceID, long Property, float pctValue, long Flags = 0); bool getVideoSettingCamera(int deviceID, long Property, long &min, long &max, long &SteppingDelta, long ¤tValue, long &flags, long &defaultValue); //bool setVideoSettingCam(int deviceID, long Property, long lValue, long Flags = NULL, bool useDefaultValue = false); @@ -597,7 +605,7 @@ class videoInput{ /////////////////////////// HANDY FUNCTIONS ///////////////////////////// -void MyFreeMediaType(AM_MEDIA_TYPE& mt){ +static void MyFreeMediaType(AM_MEDIA_TYPE& mt){ if (mt.cbFormat != 0) { CoTaskMemFree((PVOID)mt.pbFormat); @@ -612,7 +620,7 @@ void MyFreeMediaType(AM_MEDIA_TYPE& mt){ } } -void MyDeleteMediaType(AM_MEDIA_TYPE *pmt) +static void MyDeleteMediaType(AM_MEDIA_TYPE *pmt) { if (pmt != NULL) { @@ -642,7 +650,7 @@ public: //------------------------------------------------ - ~SampleGrabberCallback(){ + virtual ~SampleGrabberCallback(){ ptrBuffer = NULL; DeleteCriticalSection(&critSection); CloseHandle(hEvent); @@ -849,7 +857,7 @@ void videoDevice::NukeDownstream(IBaseFilter *pBF){ // ---------------------------------------------------------------------- void videoDevice::destroyGraph(){ - HRESULT hr = NULL; + HRESULT hr = 0; //int FuncRetval=0; //int NumFilters=0; @@ -867,7 +875,7 @@ void videoDevice::destroyGraph(){ IBaseFilter * pFilter = NULL; if (pEnum->Next(1, &pFilter, &cFetched) == S_OK) { - FILTER_INFO FilterInfo={0}; + FILTER_INFO FilterInfo; memset(&FilterInfo, 0, sizeof(FilterInfo)); hr = pFilter->QueryFilterInfo(&FilterInfo); FilterInfo.pGraph->Release(); @@ -1163,10 +1171,10 @@ bool videoInput::setupDevice(int deviceNumber){ // // ---------------------------------------------------------------------- -bool videoInput::setupDevice(int deviceNumber, int connection){ +bool videoInput::setupDevice(int deviceNumber, int _connection){ if(deviceNumber >= VI_MAX_CAMERAS || VDList[deviceNumber]->readyToCapture) return false; - setPhyCon(deviceNumber, connection); + setPhyCon(deviceNumber, _connection); if(setup(deviceNumber))return true; return false; } @@ -1213,11 +1221,11 @@ bool videoInput::setupDeviceFourcc(int deviceNumber, int w, int h,int fourcc){ // // ---------------------------------------------------------------------- -bool videoInput::setupDevice(int deviceNumber, int w, int h, int connection){ +bool videoInput::setupDevice(int deviceNumber, int w, int h, int _connection){ if(deviceNumber >= VI_MAX_CAMERAS || VDList[deviceNumber]->readyToCapture) return false; setAttemptCaptureSize(deviceNumber,w,h); - setPhyCon(deviceNumber, connection); + setPhyCon(deviceNumber, _connection); if(setup(deviceNumber))return true; return false; } @@ -1620,14 +1628,15 @@ void __cdecl videoInput::basicThread(void * objPtr){ void videoInput::showSettingsWindow(int id){ if(isDeviceSetup(id)){ - HANDLE myTempThread; + //HANDLE myTempThread; //we reconnect to the device as we have freed our reference to it //why have we freed our reference? because there seemed to be an issue //with some mpeg devices if we didn't HRESULT hr = getDevice(&VDList[id]->pVideoInputFilter, id, VDList[id]->wDeviceName, VDList[id]->nDeviceName); if(hr == S_OK){ - myTempThread = (HANDLE)_beginthread(basicThread, 0, (void *)&VDList[id]); + //myTempThread = (HANDLE) + _beginthread(basicThread, 0, (void *)&VDList[id]); } } } @@ -1705,7 +1714,7 @@ bool videoInput::setVideoSettingFilterPct(int deviceID, long Property, float pct float halfStep = (float)stepAmnt * 0.5f; if( mod < halfStep ) rasterValue -= mod; else rasterValue += stepAmnt - mod; - printf("RASTER - pctValue is %f - value is %i - step is %i - mod is %i - rasterValue is %i\n", pctValue, value, stepAmnt, mod, rasterValue); + printf("RASTER - pctValue is %f - value is %li - step is %li - mod is %li - rasterValue is %li\n", pctValue, value, stepAmnt, mod, rasterValue); } return setVideoSettingFilter(deviceID, Property, rasterValue, Flags, false); @@ -1795,7 +1804,7 @@ bool videoInput::setVideoSettingCameraPct(int deviceID, long Property, float pct float halfStep = (float)stepAmnt * 0.5f; if( mod < halfStep ) rasterValue -= mod; else rasterValue += stepAmnt - mod; - printf("RASTER - pctValue is %f - value is %i - step is %i - mod is %i - rasterValue is %i\n", pctValue, value, stepAmnt, mod, rasterValue); + printf("RASTER - pctValue is %f - value is %li - step is %li - mod is %li - rasterValue is %li\n", pctValue, value, stepAmnt, mod, rasterValue); } return setVideoSettingCamera(deviceID, Property, rasterValue, Flags, false); @@ -1920,7 +1929,7 @@ bool videoInput::restartDevice(int id){ stopDevice(id); //set our fps if needed - if( avgFrameTime != -1){ + if( avgFrameTime != (unsigned long)-1){ VDList[id]->requestedFrameTime = avgFrameTime; } @@ -2300,7 +2309,7 @@ static void findClosestSizeAndSubtype(videoDevice * VD, int widthIn, int heightI //find perfect match or closest size int nearW = 9999999; int nearH = 9999999; - bool foundClosestMatch = true; + //bool foundClosestMatch = true; int iCount = 0; int iSize = 0; @@ -2360,7 +2369,7 @@ static void findClosestSizeAndSubtype(videoDevice * VD, int widthIn, int heightI //see if we have an exact match! if(exactMatchX && exactMatchY){ - foundClosestMatch = false; + //foundClosestMatch = false; exactMatch = true; widthOut = widthIn; @@ -2937,7 +2946,7 @@ HRESULT videoInput::ShowFilterPropertyPages(IBaseFilter *pFilter){ return hr; } -HRESULT videoInput::ShowStreamPropertyPages(IAMStreamConfig *pStream){ +HRESULT videoInput::ShowStreamPropertyPages(IAMStreamConfig * /*pStream*/){ HRESULT hr = NOERROR; return hr; @@ -3027,11 +3036,11 @@ HRESULT videoInput::routeCrossbar(ICaptureGraphBuilder2 **ppBuild, IBaseFilter * LONG lInpin, lOutpin; hr = Crossbar->get_PinCounts(&lOutpin , &lInpin); - BOOL IPin=TRUE; LONG pIndex=0 , pRIndex=0 , pType=0; + BOOL iPin=TRUE; LONG pIndex=0 , pRIndex=0 , pType=0; while( pIndex < lInpin) { - hr = Crossbar->get_CrossbarPinInfo( IPin , pIndex , &pRIndex , &pType); + hr = Crossbar->get_CrossbarPinInfo( iPin , pIndex , &pRIndex , &pType); if( pType == conType){ if(verbose)printf("SETUP: Found Physical Interface"); diff --git a/modules/highgui/src/cap_ffmpeg.cpp b/modules/highgui/src/cap_ffmpeg.cpp index be89b9c..0cc60e3 100644 --- a/modules/highgui/src/cap_ffmpeg.cpp +++ b/modules/highgui/src/cap_ffmpeg.cpp @@ -123,7 +123,7 @@ icvInitFFMPEG(void) icvReleaseVideoWriter_FFMPEG_p = (CvReleaseVideoWriter_Plugin)cvReleaseVideoWriter_FFMPEG; icvWriteFrame_FFMPEG_p = (CvWriteFrame_Plugin)cvWriteFrame_FFMPEG; #endif - + ffmpegInitialized = 1; } } @@ -151,7 +151,7 @@ public: { unsigned char* data = 0; int step=0, width=0, height=0, cn=0; - + if(!ffmpegCapture || !icvRetrieveFrame_FFMPEG_p(ffmpegCapture,&data,&step,&width,&height,&cn)) return 0; @@ -193,7 +193,7 @@ CvCapture* cvCreateFileCapture_FFMPEG_proxy(const char * filename) return cvCreateFileCapture_VFW(filename); #else return 0; -#endif +#endif } @@ -247,5 +247,5 @@ CvVideoWriter* cvCreateVideoWriter_FFMPEG_proxy( const char* filename, int fourc return cvCreateVideoWriter_VFW(filename, fourcc, fps, frameSize, isColor); #else return 0; -#endif +#endif } diff --git a/modules/highgui/src/cap_ffmpeg_impl.hpp b/modules/highgui/src/cap_ffmpeg_impl.hpp index e27ae43..8019bbc 100644 --- a/modules/highgui/src/cap_ffmpeg_impl.hpp +++ b/modules/highgui/src/cap_ffmpeg_impl.hpp @@ -66,7 +66,7 @@ extern "C" { #ifndef HAVE_FFMPEG_SWSCALE #error "libswscale is necessary to build the newer OpenCV ffmpeg wrapper" #endif - + // if the header path is not specified explicitly, let's deduce it #if !defined HAVE_FFMPEG_AVCODEC_H && !defined HAVE_LIBAVCODEC_AVCODEC_H @@ -140,7 +140,7 @@ extern "C" { #define AV_NOPTS_VALUE_ ((int64_t)AV_NOPTS_VALUE) #endif -int get_number_of_cpus(void) +static int get_number_of_cpus(void) { #if LIBAVFORMAT_BUILD < CALC_FFMPEG_VERSION(52, 111, 0) return 1; @@ -210,7 +210,7 @@ struct CvCapture_FFMPEG void seek(int64_t frame_number); void seek(double sec); - bool slowSeek( int framenumber ); + bool slowSeek( int framenumber ); int64_t get_total_frames(); double get_duration_sec(); @@ -225,8 +225,8 @@ struct CvCapture_FFMPEG AVCodec * avcodec; int video_stream; AVStream * video_st; - AVFrame * picture; - AVFrame rgb_picture; + AVFrame * picture; + AVFrame rgb_picture; int64_t picture_pts; AVPacket packet; @@ -274,7 +274,7 @@ void CvCapture_FFMPEG::close() sws_freeContext(img_convert_ctx); img_convert_ctx = 0; } - + if( picture ) av_free(picture); @@ -293,9 +293,9 @@ void CvCapture_FFMPEG::close() if( ic ) { #if LIBAVFORMAT_BUILD < CALC_FFMPEG_VERSION(53, 24, 2) - av_close_input_file(ic); + av_close_input_file(ic); #else - avformat_close_input(&ic); + avformat_close_input(&ic); #endif ic = NULL; @@ -337,7 +337,7 @@ static void icvInitFFMPEG_internal() av_register_all(); av_log_set_level(AV_LOG_ERROR); - + initialized = true; } } @@ -345,18 +345,18 @@ static void icvInitFFMPEG_internal() bool CvCapture_FFMPEG::open( const char* _filename ) { icvInitFFMPEG_internal(); - + unsigned i; bool valid = false; close(); - + #if LIBAVFORMAT_BUILD >= CALC_FFMPEG_VERSION(52, 111, 0) int err = avformat_open_input(&ic, _filename, NULL, NULL); #else int err = av_open_input_file(&ic, _filename, NULL, 0, NULL); -#endif - +#endif + if (err < 0) { CV_WARN("Error opening file"); goto exit_func; @@ -438,13 +438,13 @@ bool CvCapture_FFMPEG::grabFrame() const int max_number_of_attempts = 1 << 16; if( !ic || !video_st ) return false; - + if( ic->streams[video_stream]->nb_frames > 0 && frame_number > ic->streams[video_stream]->nb_frames ) return false; av_free_packet (&packet); - + picture_pts = AV_NOPTS_VALUE_; // get the next frame @@ -463,7 +463,7 @@ bool CvCapture_FFMPEG::grabFrame() break; continue; } - + // Decode video frame #if LIBAVFORMAT_BUILD >= CALC_FFMPEG_VERSION(53, 2, 0) avcodec_decode_video2(video_st->codec, picture, &got_picture, &packet); @@ -498,7 +498,7 @@ bool CvCapture_FFMPEG::grabFrame() if( valid && first_frame_number < 0 ) first_frame_number = dts_to_frame_number(picture_pts); - + // return if we have a new picture or not return valid; } @@ -518,7 +518,7 @@ bool CvCapture_FFMPEG::retrieveFrame(int, unsigned char** data, int* step, int* { if( img_convert_ctx ) sws_freeContext(img_convert_ctx); - + frame.width = video_st->codec->width; frame.height = video_st->codec->height; @@ -629,7 +629,7 @@ double CvCapture_FFMPEG::get_fps() { fps = r2d(ic->streams[video_stream]->avg_frame_rate); } -#endif +#endif if (fps < eps_zero) { @@ -666,12 +666,12 @@ void CvCapture_FFMPEG::seek(int64_t _frame_number) { _frame_number = std::min(_frame_number, get_total_frames()); int delta = 16; - + // if we have not grabbed a single frame before first seek, let's read the first frame // and get some valuable information during the process if( first_frame_number < 0 ) grabFrame(); - + for(;;) { int64_t _frame_number_temp = std::max(_frame_number-delta, (int64_t)0); @@ -684,13 +684,13 @@ void CvCapture_FFMPEG::seek(int64_t _frame_number) if( _frame_number > 0 ) { grabFrame(); - + if( _frame_number > 1 ) { frame_number = dts_to_frame_number(picture_pts) - first_frame_number; //printf("_frame_number = %d, frame_number = %d, delta = %d\n", // (int)_frame_number, (int)frame_number, delta); - + if( frame_number < 0 || frame_number > _frame_number-1 ) { if( _frame_number_temp == 0 || delta >= INT_MAX/4 ) @@ -771,7 +771,7 @@ struct CvVideoWriter_FFMPEG void init(); - AVOutputFormat * fmt; + AVOutputFormat * fmt; AVFormatContext * oc; uint8_t * outbuf; uint32_t outbuf_size; @@ -1010,7 +1010,7 @@ static AVStream *icv_add_video_stream_FFMPEG(AVFormatContext *oc, static const int OPENCV_NO_FRAMES_WRITTEN_CODE = 1000; -int icv_av_write_frame_FFMPEG( AVFormatContext * oc, AVStream * video_st, uint8_t * outbuf, uint32_t outbuf_size, AVFrame * picture ) +static int icv_av_write_frame_FFMPEG( AVFormatContext * oc, AVStream * video_st, uint8_t * outbuf, uint32_t outbuf_size, AVFrame * picture ) { #if LIBAVFORMAT_BUILD > 4628 AVCodecContext * c = video_st->codec; @@ -1046,7 +1046,7 @@ int icv_av_write_frame_FFMPEG( AVFormatContext * oc, AVStream * video_st, uint8_ #if LIBAVFORMAT_BUILD > 4752 if(c->coded_frame->pts != (int64_t)AV_NOPTS_VALUE) - pkt.pts = av_rescale_q(c->coded_frame->pts, c->time_base, video_st->time_base); + pkt.pts = av_rescale_q(c->coded_frame->pts, c->time_base, video_st->time_base); #else pkt.pts = c->coded_frame->pts; #endif @@ -1069,7 +1069,7 @@ int icv_av_write_frame_FFMPEG( AVFormatContext * oc, AVStream * video_st, uint8_ bool CvVideoWriter_FFMPEG::writeFrame( const unsigned char* data, int step, int width, int height, int cn, int origin ) { bool ret = false; - + if( (width & -2) != frame_width || (height & -2) != frame_height || !data ) return false; width = frame_width; @@ -1180,7 +1180,7 @@ void CvVideoWriter_FFMPEG::close() // nothing to do if already released if ( !picture ) return; - + /* no more frame to compress. The codec has a latency of a few frames if using B frames, so we get the last frames by passing the same picture again */ @@ -1200,7 +1200,7 @@ void CvVideoWriter_FFMPEG::close() } av_write_trailer(oc); } - + if( img_convert_ctx ) { sws_freeContext(img_convert_ctx); @@ -1272,7 +1272,7 @@ bool CvVideoWriter_FFMPEG::open( const char * filename, int fourcc, double fps, int width, int height, bool is_color ) { icvInitFFMPEG_internal(); - + CodecID codec_id = CODEC_ID_NONE; int err, codec_pix_fmt; double bitrate_scale = 1; @@ -1284,7 +1284,7 @@ bool CvVideoWriter_FFMPEG::open( const char * filename, int fourcc, return false; if(fps <= 0) return false; - + // we allow frames of odd width or height, but in this case we truncate // the rightmost column/the bottom row. Probably, this should be handled more elegantly, // but some internal functions inside FFMPEG swscale require even width/height. @@ -1363,7 +1363,7 @@ bool CvVideoWriter_FFMPEG::open( const char * filename, int fourcc, codec_pix_fmt = PIX_FMT_YUV420P; break; } - + double bitrate = MIN(bitrate_scale*fps*width*height, (double)INT_MAX/2); // TODO -- safe to ignore output audio stream? @@ -1480,8 +1480,8 @@ bool CvVideoWriter_FFMPEG::open( const char * filename, int fourcc, err=avformat_write_header(oc, NULL); #else err=av_write_header( oc ); -#endif - +#endif + if(err < 0) { close(); diff --git a/modules/highgui/src/cap_gstreamer.cpp b/modules/highgui/src/cap_gstreamer.cpp index bb9efa1..fd55435 100644 --- a/modules/highgui/src/cap_gstreamer.cpp +++ b/modules/highgui/src/cap_gstreamer.cpp @@ -283,7 +283,7 @@ void CvCapture_GStreamer::newPad(GstElement *uridecodebin, sinkpad = gst_element_get_static_pad (color, "sink"); - + // printf("linking dynamic pad to colourconverter %p %p\n", uridecodebin, pad); gst_pad_link (pad, sinkpad); @@ -357,13 +357,13 @@ bool CvCapture_GStreamer::open( int type, const char* filename ) if(manualpipeline) { GstIterator *it = gst_bin_iterate_sinks(GST_BIN(uridecodebin)); if(gst_iterator_next(it, (gpointer *)&sink) != GST_ITERATOR_OK) { - CV_ERROR(CV_StsError, "GStreamer: cannot find appsink in manual pipeline\n"); - return false; + CV_ERROR(CV_StsError, "GStreamer: cannot find appsink in manual pipeline\n"); + return false; } - pipeline = uridecodebin; + pipeline = uridecodebin; } else { - pipeline = gst_pipeline_new (NULL); + pipeline = gst_pipeline_new (NULL); color = gst_element_factory_make("ffmpegcolorspace", NULL); sink = gst_element_factory_make("appsink", NULL); @@ -381,16 +381,12 @@ bool CvCapture_GStreamer::open( int type, const char* filename ) gst_app_sink_set_max_buffers (GST_APP_SINK(sink), 1); gst_app_sink_set_drop (GST_APP_SINK(sink), stream); - { - GstCaps* caps; - caps = gst_caps_new_simple("video/x-raw-rgb", - "red_mask", G_TYPE_INT, 0x0000FF, - "green_mask", G_TYPE_INT, 0x00FF00, - "blue_mask", G_TYPE_INT, 0xFF0000, - NULL); - gst_app_sink_set_caps(GST_APP_SINK(sink), caps); + gst_app_sink_set_caps(GST_APP_SINK(sink), gst_caps_new_simple("video/x-raw-rgb", + "red_mask", G_TYPE_INT, 0x0000FF, + "green_mask", G_TYPE_INT, 0x00FF00, + "blue_mask", G_TYPE_INT, 0xFF0000, + NULL)); gst_caps_unref(caps); - } if(gst_element_set_state(GST_ELEMENT(pipeline), GST_STATE_READY) == GST_STATE_CHANGE_FAILURE) { diff --git a/modules/highgui/src/cap_libv4l.cpp b/modules/highgui/src/cap_libv4l.cpp index 233e6f6..c27224d 100644 --- a/modules/highgui/src/cap_libv4l.cpp +++ b/modules/highgui/src/cap_libv4l.cpp @@ -779,9 +779,9 @@ static int _capture_V4L2 (CvCaptureCAM_V4L *capture, char *deviceName) return -1; } else { buffer_number--; - fprintf (stderr, "Insufficient buffer memory on %s -- decreaseing buffers\n", deviceName); + fprintf (stderr, "Insufficient buffer memory on %s -- decreaseing buffers\n", deviceName); - goto try_again; + goto try_again; } } @@ -824,8 +824,8 @@ static int _capture_V4L2 (CvCaptureCAM_V4L *capture, char *deviceName) if (capture->buffers[MAX_V4L_BUFFERS].start) { free(capture->buffers[MAX_V4L_BUFFERS].start); capture->buffers[MAX_V4L_BUFFERS].start = NULL; - } - + } + capture->buffers[MAX_V4L_BUFFERS].start = malloc(buf.length); capture->buffers[MAX_V4L_BUFFERS].length = buf.length; }; @@ -1080,11 +1080,11 @@ static int read_frame_v4l2(CvCaptureCAM_V4L* capture) { #ifdef USE_TEMP_BUFFER memcpy(capture->buffers[MAX_V4L_BUFFERS].start, - capture->buffers[buf.index].start, - capture->buffers[MAX_V4L_BUFFERS].length ); + capture->buffers[buf.index].start, + capture->buffers[MAX_V4L_BUFFERS].length ); capture->bufferIndex = MAX_V4L_BUFFERS; //printf("got data in buff %d, len=%d, flags=0x%X, seq=%d, used=%d)\n", - // buf.index, buf.length, buf.flags, buf.sequence, buf.bytesused); + // buf.index, buf.length, buf.flags, buf.sequence, buf.bytesused); #else capture->bufferIndex = buf.index; #endif @@ -1211,9 +1211,9 @@ static int icvGrabFrameCAM_V4L(CvCaptureCAM_V4L* capture) { capture->mmaps[capture->bufferIndex].format = capture->imageProperties.palette; if (v4l1_ioctl (capture->deviceHandle, VIDIOCMCAPTURE, - &capture->mmaps[capture->bufferIndex]) == -1) { - /* capture is on the way, so just exit */ - return 1; + &capture->mmaps[capture->bufferIndex]) == -1) { + /* capture is on the way, so just exit */ + return 1; } ++capture->bufferIndex; @@ -1273,11 +1273,11 @@ static IplImage* icvRetrieveFrameCAM_V4L( CvCaptureCAM_V4L* capture, int) { if (capture->is_v4l2_device == 1) { - if(capture->buffers[capture->bufferIndex].start){ - memcpy((char *)capture->frame.imageData, - (char *)capture->buffers[capture->bufferIndex].start, - capture->frame.imageSize); - } + if(capture->buffers[capture->bufferIndex].start){ + memcpy((char *)capture->frame.imageData, + (char *)capture->buffers[capture->bufferIndex].start, + capture->frame.imageSize); + } } else #endif /* HAVE_CAMV4L2 */ @@ -1353,7 +1353,7 @@ static double icvGetPropertyCAM_V4L (CvCaptureCAM_V4L* capture, sprintf(name, ""); capture->control.id = property_id; } - + if(v4l2_ioctl(capture->deviceHandle, VIDIOC_G_CTRL, &capture->control) == 0) { /* all went well */ is_v4l2_device = 1; @@ -1519,7 +1519,7 @@ static int icvSetControl (CvCaptureCAM_V4L* capture, int property_id, double val CLEAR (capture->control); CLEAR (capture->queryctrl); - + /* get current values */ switch (property_id) { case CV_CAP_PROP_BRIGHTNESS: @@ -1688,8 +1688,8 @@ static void icvCloseCAM_V4L( CvCaptureCAM_V4L* capture ){ if (xioctl(capture->deviceHandle, VIDIOC_STREAMOFF, &capture->type) < 0) { perror ("Unable to stop the stream."); } - for (unsigned int n_buffers = 0; n_buffers < capture->req.count; ++n_buffers) { - if (-1 == v4l2_munmap (capture->buffers[n_buffers].start, capture->buffers[n_buffers].length)) { + for (unsigned int n_buffers2 = 0; n_buffers2 < capture->req.count; ++n_buffers2) { + if (-1 == v4l2_munmap (capture->buffers[n_buffers2].start, capture->buffers[n_buffers2].length)) { perror ("munmap"); } } diff --git a/modules/highgui/src/cap_openni.cpp b/modules/highgui/src/cap_openni.cpp index 739adf8..b440e0a 100644 --- a/modules/highgui/src/cap_openni.cpp +++ b/modules/highgui/src/cap_openni.cpp @@ -50,6 +50,20 @@ #include #include + +#ifndef i386 +# define i386 0 +#endif +#ifndef __arm__ +# define __arm__ 0 +#endif +#ifndef _ARC +# define _ARC 0 +#endif +#ifndef __APPLE__ +# define __APPLE__ 0 +#endif + #include "XnCppWrapper.h" const std::string XMLConfig = @@ -85,12 +99,12 @@ const std::string XMLConfig = class ApproximateSyncGrabber { public: - ApproximateSyncGrabber( xn::Context &context, - xn::DepthGenerator &depthGenerator, - xn::ImageGenerator &imageGenerator, - int maxBufferSize, bool isCircleBuffer, int maxTimeDuration ) : - context(context), depthGenerator(depthGenerator), imageGenerator(imageGenerator), - maxBufferSize(maxBufferSize), isCircleBuffer(isCircleBuffer), maxTimeDuration(maxTimeDuration) + ApproximateSyncGrabber( xn::Context &_context, + xn::DepthGenerator &_depthGenerator, + xn::ImageGenerator &_imageGenerator, + int _maxBufferSize, bool _isCircleBuffer, int _maxTimeDuration ) : + context(_context), depthGenerator(_depthGenerator), imageGenerator(_imageGenerator), + maxBufferSize(_maxBufferSize), isCircleBuffer(_isCircleBuffer), maxTimeDuration(_maxTimeDuration) { task = 0; @@ -165,10 +179,12 @@ private: class ApproximateSynchronizerBase { public: - ApproximateSynchronizerBase( ApproximateSyncGrabber& approxSyncGrabber ) : - approxSyncGrabber(approxSyncGrabber), isDepthFilled(false), isImageFilled(false) + ApproximateSynchronizerBase( ApproximateSyncGrabber& _approxSyncGrabber ) : + approxSyncGrabber(_approxSyncGrabber), isDepthFilled(false), isImageFilled(false) {} + virtual ~ApproximateSynchronizerBase() {} + virtual bool isSpinContinue() const = 0; virtual void pushDepthMetaData( xn::DepthMetaData& depthMetaData ) = 0; virtual void pushImageMetaData( xn::ImageMetaData& imageMetaData ) = 0; @@ -183,8 +199,8 @@ private: if( status != XN_STATUS_OK ) continue; - xn::DepthMetaData depth; - xn::ImageMetaData image; + //xn::DepthMetaData depth; + //xn::ImageMetaData image; approxSyncGrabber.depthGenerator.GetMetaData(depth); approxSyncGrabber.imageGenerator.GetMetaData(image); @@ -242,8 +258,8 @@ private: class ApproximateSynchronizer: public ApproximateSynchronizerBase { public: - ApproximateSynchronizer( ApproximateSyncGrabber& approxSyncGrabber ) : - ApproximateSynchronizerBase(approxSyncGrabber) + ApproximateSynchronizer( ApproximateSyncGrabber& _approxSyncGrabber ) : + ApproximateSynchronizerBase(_approxSyncGrabber) {} virtual bool isSpinContinue() const @@ -410,7 +426,7 @@ class CvCapture_OpenNI : public CvCapture { public: enum { DEVICE_DEFAULT=0, DEVICE_MS_KINECT=0, DEVICE_ASUS_XTION=1, DEVICE_MAX=1 }; - + static const int INVALID_PIXEL_VAL = 0; static const int INVALID_COORDINATE_VAL = 0; @@ -445,6 +461,8 @@ protected: static const int outputMapsTypesCount = 7; + static XnMapOutputMode defaultMapOutputMode(); + IplImage* retrieveDepthMap(); IplImage* retrievePointCloudMap(); IplImage* retrieveDisparityMap(); @@ -508,7 +526,7 @@ bool CvCapture_OpenNI::isOpened() const return isContextOpened; } -XnMapOutputMode defaultMapOutputMode() +XnMapOutputMode CvCapture_OpenNI::defaultMapOutputMode() { XnMapOutputMode mode; mode.nXRes = XN_VGA_X_RES; @@ -517,17 +535,16 @@ XnMapOutputMode defaultMapOutputMode() return mode; } - CvCapture_OpenNI::CvCapture_OpenNI( int index ) { int deviceType = DEVICE_DEFAULT; XnStatus status; - + isContextOpened = false; maxBufferSize = DEFAULT_MAX_BUFFER_SIZE; isCircleBuffer = DEFAULT_IS_CIRCLE_BUFFER; maxTimeDuration = DEFAULT_MAX_TIME_DURATION; - + if( index >= 10 ) { deviceType = index / 10; @@ -1201,7 +1218,7 @@ IplImage* CvCapture_OpenNI::retrievePointCloudMap() return outputMaps[CV_CAP_OPENNI_POINT_CLOUD_MAP].getIplImagePtr(); } -void computeDisparity_32F( const xn::DepthMetaData& depthMetaData, cv::Mat& disp, XnDouble baseline, XnUInt64 F, +static void computeDisparity_32F( const xn::DepthMetaData& depthMetaData, cv::Mat& disp, XnDouble baseline, XnUInt64 F, XnUInt64 noSampleValue, XnUInt64 shadowValue ) { cv::Mat depth; diff --git a/modules/highgui/src/cap_v4l.cpp b/modules/highgui/src/cap_v4l.cpp index 5f9e111..b970fa0 100644 --- a/modules/highgui/src/cap_v4l.cpp +++ b/modules/highgui/src/cap_v4l.cpp @@ -1736,7 +1736,7 @@ mjpeg_to_rgb24 (int width, int height, * */ -void bayer2rgb24(long int WIDTH, long int HEIGHT, unsigned char *src, unsigned char *dst) +static void bayer2rgb24(long int WIDTH, long int HEIGHT, unsigned char *src, unsigned char *dst) { long int i; unsigned char *rawpt, *scanpt; @@ -1814,7 +1814,7 @@ void bayer2rgb24(long int WIDTH, long int HEIGHT, unsigned char *src, unsigned c // at least for 046d:092f Logitech, Inc. QuickCam Express Plus to work //see: http://www.siliconimaging.com/RGB%20Bayer.htm //and 4.6 at http://tldp.org/HOWTO/html_single/libdc1394-HOWTO/ -void sgbrg2rgb24(long int WIDTH, long int HEIGHT, unsigned char *src, unsigned char *dst) +static void sgbrg2rgb24(long int WIDTH, long int HEIGHT, unsigned char *src, unsigned char *dst) { long int i; unsigned char *rawpt, *scanpt; @@ -1921,7 +1921,7 @@ static int init_done = 0; present at the MSB of byte x. */ -void sonix_decompress_init(void) +static void sonix_decompress_init(void) { int i; int is_abs, val, len; @@ -1999,7 +1999,7 @@ void sonix_decompress_init(void) Returns <0 if operation failed. */ -int sonix_decompress(int width, int height, unsigned char *inp, unsigned char *outp) +static int sonix_decompress(int width, int height, unsigned char *inp, unsigned char *outp) { int row, col; int val; @@ -2769,9 +2769,9 @@ static void icvCloseCAM_V4L( CvCaptureCAM_V4L* capture ){ perror ("Unable to stop the stream."); } - for (unsigned int n_buffers = 0; n_buffers < capture->req.count; ++n_buffers) + for (unsigned int n_buffers_ = 0; n_buffers_ < capture->req.count; ++n_buffers_) { - if (-1 == munmap (capture->buffers[n_buffers].start, capture->buffers[n_buffers].length)) { + if (-1 == munmap (capture->buffers[n_buffers_].start, capture->buffers[n_buffers_].length)) { perror ("munmap"); } } diff --git a/modules/highgui/src/cap_vfw.cpp b/modules/highgui/src/cap_vfw.cpp index 059be19..2debbc1 100644 --- a/modules/highgui/src/cap_vfw.cpp +++ b/modules/highgui/src/cap_vfw.cpp @@ -43,16 +43,12 @@ #include -#if _MSC_VER >= 1200 -#pragma warning( disable: 4711 ) -#endif - #ifdef __GNUC__ #define WM_CAP_FIRSTA (WM_USER) #define capSendMessage(hwnd,m,w,l) (IsWindow(hwnd)?SendMessage(hwnd,m,w,l):0) #endif -#if defined _M_X64 +#if defined _M_X64 && defined _MSC_VER #pragma optimize("",off) #pragma warning(disable: 4748) #endif @@ -177,13 +173,13 @@ bool CvCaptureAVI_VFW::open( const char* filename ) { size.width = aviinfo.rcFrame.right - aviinfo.rcFrame.left; size.height = aviinfo.rcFrame.bottom - aviinfo.rcFrame.top; - BITMAPINFOHEADER bmih = icvBitmapHeader( size.width, size.height, 24 ); + BITMAPINFOHEADER bmihdr = icvBitmapHeader( size.width, size.height, 24 ); film_range.start_index = (int)aviinfo.dwStart; film_range.end_index = film_range.start_index + (int)aviinfo.dwLength; fps = (double)aviinfo.dwRate/aviinfo.dwScale; pos = film_range.start_index; - getframe = AVIStreamGetFrameOpen( avistream, &bmih ); + getframe = AVIStreamGetFrameOpen( avistream, &bmihdr ); if( getframe != 0 ) return true; } diff --git a/modules/highgui/src/grfmt_jpeg.cpp b/modules/highgui/src/grfmt_jpeg.cpp index 183a38e..c1f7518 100644 --- a/modules/highgui/src/grfmt_jpeg.cpp +++ b/modules/highgui/src/grfmt_jpeg.cpp @@ -45,7 +45,8 @@ #ifdef HAVE_JPEG #ifdef _MSC_VER -#pragma warning(disable: 4324 4611) +//interaction between '_setjmp' and C++ object destruction is non-portable +#pragma warning(disable: 4611) #endif #include @@ -69,11 +70,18 @@ extern "C" { namespace cv { +#ifdef _MSC_VER +# pragma warning(push) +# pragma warning(disable:4324) //structure was padded due to __declspec(align()) +#endif struct JpegErrorMgr { struct jpeg_error_mgr pub; jmp_buf setjmp_buffer; }; +#ifdef _MSC_VER +# pragma warning(pop) +#endif struct JpegSource { @@ -126,8 +134,7 @@ skip_input_data(j_decompress_ptr cinfo, long num_bytes) } -GLOBAL(void) -jpeg_buffer_src(j_decompress_ptr cinfo, JpegSource* source) +static void jpeg_buffer_src(j_decompress_ptr cinfo, JpegSource* source) { cinfo->src = &source->pub; @@ -498,8 +505,7 @@ empty_output_buffer (j_compress_ptr cinfo) return TRUE; } -GLOBAL(void) -jpeg_buffer_dest(j_compress_ptr cinfo, JpegDestination* destination) +static void jpeg_buffer_dest(j_compress_ptr cinfo, JpegDestination* destination) { cinfo->dest = &destination->pub; diff --git a/modules/highgui/src/grfmt_png.cpp b/modules/highgui/src/grfmt_png.cpp index dac017f..7b6665c 100644 --- a/modules/highgui/src/grfmt_png.cpp +++ b/modules/highgui/src/grfmt_png.cpp @@ -60,7 +60,7 @@ #include "grfmt_png.hpp" #if defined _MSC_VER && _MSC_VER >= 1200 - // disable warnings related to _setjmp + // interaction between '_setjmp' and C++ object destruction is non-portable #pragma warning( disable: 4611 ) #endif @@ -157,22 +157,22 @@ bool PngDecoder::readHeader() if( !m_buf.empty() || m_f ) { - png_uint_32 width, height; + png_uint_32 wdth, hght; int bit_depth, color_type; png_read_info( png_ptr, info_ptr ); - png_get_IHDR( png_ptr, info_ptr, &width, &height, + png_get_IHDR( png_ptr, info_ptr, &wdth, &hght, &bit_depth, &color_type, 0, 0, 0 ); - m_width = (int)width; - m_height = (int)height; + m_width = (int)wdth; + m_height = (int)hght; m_color_type = color_type; m_bit_depth = bit_depth; if( bit_depth <= 8 || bit_depth == 16 ) { - switch(color_type) + switch(color_type) { case PNG_COLOR_TYPE_RGB: case PNG_COLOR_TYPE_PALETTE: @@ -224,7 +224,7 @@ bool PngDecoder::readData( Mat& img ) else if( !isBigEndian() ) png_set_swap( png_ptr ); - if(img.channels() < 4) + if(img.channels() < 4) { /* observation: png_read_image() writes 400 bytes beyond * end of data when reading a 400x118 color png @@ -247,7 +247,7 @@ bool PngDecoder::readData( Mat& img ) #else png_set_gray_1_2_4_to_8( png_ptr ); #endif - + if( CV_MAT_CN(m_type) > 1 && color ) png_set_bgr( png_ptr ); // convert RGB to BGR else if( color ) @@ -330,7 +330,7 @@ bool PngEncoder::write( const Mat& img, const vector& params ) if( params[i] == CV_IMWRITE_PNG_STRATEGY ) { compression_strategy = params[i+1]; - compression_strategy = MIN(MAX(compression_strategy, 0), Z_FIXED); + compression_strategy = MIN(MAX(compression_strategy, 0), Z_FIXED); } } diff --git a/modules/highgui/src/grfmt_tiff.cpp b/modules/highgui/src/grfmt_tiff.cpp index 686acf0..eba54bc 100644 --- a/modules/highgui/src/grfmt_tiff.cpp +++ b/modules/highgui/src/grfmt_tiff.cpp @@ -115,19 +115,19 @@ bool TiffDecoder::readHeader() if( tif ) { - int width = 0, height = 0, photometric = 0; + int wdth = 0, hght = 0, photometric = 0; m_tif = tif; - if( TIFFGetField( tif, TIFFTAG_IMAGEWIDTH, &width ) && - TIFFGetField( tif, TIFFTAG_IMAGELENGTH, &height ) && + if( TIFFGetField( tif, TIFFTAG_IMAGEWIDTH, &wdth ) && + TIFFGetField( tif, TIFFTAG_IMAGELENGTH, &hght ) && TIFFGetField( tif, TIFFTAG_PHOTOMETRIC, &photometric )) { int bpp=8, ncn = photometric > 1 ? 3 : 1; TIFFGetField( tif, TIFFTAG_BITSPERSAMPLE, &bpp ); TIFFGetField( tif, TIFFTAG_SAMPLESPERPIXEL, &ncn ); - - m_width = width; - m_height = height; + + m_width = wdth; + m_height = hght; if( bpp > 8 && ((photometric != 2 && photometric != 1) || (ncn != 1 && ncn != 3 && ncn != 4))) @@ -169,7 +169,7 @@ bool TiffDecoder::readData( Mat& img ) bool color = img.channels() > 1; uchar* data = img.data; int step = (int)img.step; - + if( img.depth() != CV_8U && img.depth() != CV_16U && img.depth() != CV_32F && img.depth() != CV_64F ) return false; @@ -422,9 +422,9 @@ bool TiffEncoder::writeLibTiff( const Mat& img, const vector& /*params*/) default: { return false; - } + } } - + const int bitsPerByte = 8; size_t fileStep = (width * channels * bitsPerChannel) / bitsPerByte; int rowsPerStrip = (int)((1 << 13)/fileStep); @@ -443,7 +443,7 @@ bool TiffEncoder::writeLibTiff( const Mat& img, const vector& /*params*/) { return false; } - + // defaults for now, maybe base them on params in the future int compression = COMPRESSION_LZW; int predictor = PREDICTOR_HORIZONTAL; @@ -516,7 +516,7 @@ bool TiffEncoder::writeLibTiff( const Mat& img, const vector& /*params*/) return false; } } - + TIFFClose(pTiffHandle); return true; } @@ -546,7 +546,7 @@ bool TiffEncoder::write( const Mat& img, const vector& /*params*/) if( !strm.open(*m_buf) ) return false; } - else + else { #ifdef HAVE_TIFF return writeLibTiff(img, params); diff --git a/modules/highgui/src/loadsave.cpp b/modules/highgui/src/loadsave.cpp index b903b03..9250fff 100644 --- a/modules/highgui/src/loadsave.cpp +++ b/modules/highgui/src/loadsave.cpp @@ -57,7 +57,7 @@ namespace cv static vector decoders; static vector encoders; -ImageDecoder findDecoder( const string& filename ) +static ImageDecoder findDecoder( const string& filename ) { size_t i, maxlen = 0; for( i = 0; i < decoders.size(); i++ ) @@ -83,7 +83,7 @@ ImageDecoder findDecoder( const string& filename ) return ImageDecoder(); } -ImageDecoder findDecoder( const Mat& buf ) +static ImageDecoder findDecoder( const Mat& buf ) { size_t i, maxlen = 0; @@ -110,7 +110,7 @@ ImageDecoder findDecoder( const Mat& buf ) return ImageDecoder(); } -ImageEncoder findEncoder( const string& _ext ) +static ImageEncoder findEncoder( const string& _ext ) { if( _ext.size() <= 1 ) return ImageEncoder(); @@ -395,7 +395,7 @@ Mat imdecode( InputArray _buf, int flags ) imdecode_( buf, flags, LOAD_MAT, &img ); return img; } - + bool imencode( const string& ext, InputArray _image, vector& buf, const vector& params ) { diff --git a/modules/highgui/src/precomp.hpp b/modules/highgui/src/precomp.hpp index 415f5e8..eca4ce1 100644 --- a/modules/highgui/src/precomp.hpp +++ b/modules/highgui/src/precomp.hpp @@ -42,10 +42,6 @@ #ifndef __HIGHGUI_H_ #define __HIGHGUI_H_ -#if _MSC_VER >= 1200 -#pragma warning( disable: 4251 ) -#endif - #include "cvconfig.h" #include "opencv2/highgui/highgui.hpp" diff --git a/modules/highgui/src/window.cpp b/modules/highgui/src/window.cpp index 9e107d4..8d85f94 100644 --- a/modules/highgui/src/window.cpp +++ b/modules/highgui/src/window.cpp @@ -724,10 +724,10 @@ CV_IMPL void cvSaveWindowParameters(const char* name) CV_NO_GUI_ERROR("cvSaveWindowParameters"); } -CV_IMPL void cvLoadWindowParameterss(const char* name) -{ - CV_NO_GUI_ERROR("cvLoadWindowParameters"); -} +// CV_IMPL void cvLoadWindowParameterss(const char* name) +// { +// CV_NO_GUI_ERROR("cvLoadWindowParameters"); +// } CV_IMPL int cvCreateButton(const char*, void (*)(int, void*), void*, int, int) { diff --git a/modules/highgui/src/window_QT.cpp b/modules/highgui/src/window_QT.cpp index 30bf973..ebc90de 100755 --- a/modules/highgui/src/window_QT.cpp +++ b/modules/highgui/src/window_QT.cpp @@ -1,4 +1,4 @@ -//IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +//IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. // By downloading, copying, installing or using the software you agree to this license. // If you do not agree to this license, do not download, install, @@ -78,181 +78,181 @@ static CvWinProperties* global_control_panel = NULL; CV_IMPL CvFont cvFontQt(const char* nameFont, int pointSize,CvScalar color,int weight,int style, int spacing) { - /* - //nameFont <- only Qt - //CvScalar color <- only Qt (blue_component, green_component, red\_component[, alpha_component]) - int font_face;//<- style in Qt - const int* ascii; - const int* greek; - const int* cyrillic; - float hscale, vscale; - float shear; - int thickness;//<- weight in Qt - float dx;//spacing letter in Qt (0 default) in pixel - int line_type;//<- pointSize in Qt - */ - CvFont f = {nameFont,color,style,NULL,NULL,NULL,0,0,0,weight,spacing,pointSize}; - return f; + /* + //nameFont <- only Qt + //CvScalar color <- only Qt (blue_component, green_component, red\_component[, alpha_component]) + int font_face;//<- style in Qt + const int* ascii; + const int* greek; + const int* cyrillic; + float hscale, vscale; + float shear; + int thickness;//<- weight in Qt + float dx;//spacing letter in Qt (0 default) in pixel + int line_type;//<- pointSize in Qt + */ + CvFont f = {nameFont,color,style,NULL,NULL,NULL,0,0,0,weight,spacing,pointSize}; + return f; } CV_IMPL void cvAddText(const CvArr* img, const char* text, CvPoint org, CvFont* font) { - if (!guiMainThread) - CV_Error( CV_StsNullPtr, "NULL guiReceiver (please create a window)" ); + if (!guiMainThread) + CV_Error( CV_StsNullPtr, "NULL guiReceiver (please create a window)" ); - QMetaObject::invokeMethod(guiMainThread, - "putText", - Qt::AutoConnection, - Q_ARG(void*, (void*) img), - Q_ARG(QString,QString(text)), - Q_ARG(QPoint, QPoint(org.x,org.y)), - Q_ARG(void*,(void*) font)); + QMetaObject::invokeMethod(guiMainThread, + "putText", + Qt::AutoConnection, + Q_ARG(void*, (void*) img), + Q_ARG(QString,QString(text)), + Q_ARG(QPoint, QPoint(org.x,org.y)), + Q_ARG(void*,(void*) font)); } double cvGetRatioWindow_QT(const char* name) { - if (!guiMainThread) - CV_Error( CV_StsNullPtr, "NULL guiReceiver (please create a window)" ); + if (!guiMainThread) + CV_Error( CV_StsNullPtr, "NULL guiReceiver (please create a window)" ); - double result = -1; - QMetaObject::invokeMethod(guiMainThread, - "getRatioWindow", - //Qt::DirectConnection, - Qt::AutoConnection, - Q_RETURN_ARG(double, result), - Q_ARG(QString, QString(name))); + double result = -1; + QMetaObject::invokeMethod(guiMainThread, + "getRatioWindow", + //Qt::DirectConnection, + Qt::AutoConnection, + Q_RETURN_ARG(double, result), + Q_ARG(QString, QString(name))); - return result; + return result; } void cvSetRatioWindow_QT(const char* name,double prop_value) { - if (!guiMainThread) - CV_Error( CV_StsNullPtr, "NULL guiReceiver (please create a window)" ); + if (!guiMainThread) + CV_Error( CV_StsNullPtr, "NULL guiReceiver (please create a window)" ); - QMetaObject::invokeMethod(guiMainThread, - "setRatioWindow", - Qt::AutoConnection, - Q_ARG(QString, QString(name)), - Q_ARG(double, prop_value)); + QMetaObject::invokeMethod(guiMainThread, + "setRatioWindow", + Qt::AutoConnection, + Q_ARG(QString, QString(name)), + Q_ARG(double, prop_value)); } double cvGetPropWindow_QT(const char* name) { - if (!guiMainThread) - CV_Error( CV_StsNullPtr, "NULL guiReceiver (please create a window)" ); + if (!guiMainThread) + CV_Error( CV_StsNullPtr, "NULL guiReceiver (please create a window)" ); - double result = -1; - QMetaObject::invokeMethod(guiMainThread, - "getPropWindow", - //Qt::DirectConnection, - Qt::AutoConnection, - Q_RETURN_ARG(double, result), - Q_ARG(QString, QString(name))); + double result = -1; + QMetaObject::invokeMethod(guiMainThread, + "getPropWindow", + //Qt::DirectConnection, + Qt::AutoConnection, + Q_RETURN_ARG(double, result), + Q_ARG(QString, QString(name))); - return result; + return result; } void cvSetPropWindow_QT(const char* name,double prop_value) { - if (!guiMainThread) - CV_Error( CV_StsNullPtr, "NULL guiReceiver (please create a window)" ); + if (!guiMainThread) + CV_Error( CV_StsNullPtr, "NULL guiReceiver (please create a window)" ); - QMetaObject::invokeMethod(guiMainThread, - "setPropWindow", - Qt::AutoConnection, - Q_ARG(QString, QString(name)), - Q_ARG(double, prop_value)); + QMetaObject::invokeMethod(guiMainThread, + "setPropWindow", + Qt::AutoConnection, + Q_ARG(QString, QString(name)), + Q_ARG(double, prop_value)); } void cvSetModeWindow_QT(const char* name, double prop_value) { - if (!guiMainThread) - CV_Error( CV_StsNullPtr, "NULL guiReceiver (please create a window)" ); + if (!guiMainThread) + CV_Error( CV_StsNullPtr, "NULL guiReceiver (please create a window)" ); - QMetaObject::invokeMethod(guiMainThread, - "toggleFullScreen", - Qt::AutoConnection, - Q_ARG(QString, QString(name)), - Q_ARG(double, prop_value)); + QMetaObject::invokeMethod(guiMainThread, + "toggleFullScreen", + Qt::AutoConnection, + Q_ARG(QString, QString(name)), + Q_ARG(double, prop_value)); } double cvGetModeWindow_QT(const char* name) { - if (!guiMainThread) - CV_Error( CV_StsNullPtr, "NULL guiReceiver (please create a window)" ); + if (!guiMainThread) + CV_Error( CV_StsNullPtr, "NULL guiReceiver (please create a window)" ); - double result = -1; + double result = -1; - QMetaObject::invokeMethod(guiMainThread, - "isFullScreen", - Qt::AutoConnection, - Q_RETURN_ARG(double, result), - Q_ARG(QString, QString(name))); + QMetaObject::invokeMethod(guiMainThread, + "isFullScreen", + Qt::AutoConnection, + Q_RETURN_ARG(double, result), + Q_ARG(QString, QString(name))); - return result; + return result; } CV_IMPL void cvDisplayOverlay(const char* name, const char* text, int delayms) { - if (!guiMainThread) - CV_Error( CV_StsNullPtr, "NULL guiReceiver (please create a window)" ); + if (!guiMainThread) + CV_Error( CV_StsNullPtr, "NULL guiReceiver (please create a window)" ); - QMetaObject::invokeMethod(guiMainThread, - "displayInfo", - Qt::AutoConnection, - //Qt::DirectConnection, - Q_ARG(QString, QString(name)), - Q_ARG(QString, QString(text)), - Q_ARG(int, delayms)); + QMetaObject::invokeMethod(guiMainThread, + "displayInfo", + Qt::AutoConnection, + //Qt::DirectConnection, + Q_ARG(QString, QString(name)), + Q_ARG(QString, QString(text)), + Q_ARG(int, delayms)); } CV_IMPL void cvSaveWindowParameters(const char* name) { - if (!guiMainThread) - CV_Error( CV_StsNullPtr, "NULL guiReceiver (please create a window)" ); + if (!guiMainThread) + CV_Error( CV_StsNullPtr, "NULL guiReceiver (please create a window)" ); - QMetaObject::invokeMethod(guiMainThread, - "saveWindowParameters", - Qt::AutoConnection, - Q_ARG(QString, QString(name))); + QMetaObject::invokeMethod(guiMainThread, + "saveWindowParameters", + Qt::AutoConnection, + Q_ARG(QString, QString(name))); } CV_IMPL void cvLoadWindowParameters(const char* name) { - if (!guiMainThread) - CV_Error( CV_StsNullPtr, "NULL guiReceiver (please create a window)" ); + if (!guiMainThread) + CV_Error( CV_StsNullPtr, "NULL guiReceiver (please create a window)" ); - QMetaObject::invokeMethod(guiMainThread, - "loadWindowParameters", - Qt::AutoConnection, - Q_ARG(QString, QString(name))); + QMetaObject::invokeMethod(guiMainThread, + "loadWindowParameters", + Qt::AutoConnection, + Q_ARG(QString, QString(name))); } CV_IMPL void cvDisplayStatusBar(const char* name, const char* text, int delayms) { - if (!guiMainThread) - CV_Error( CV_StsNullPtr, "NULL guiReceiver (please create a window)" ); + if (!guiMainThread) + CV_Error( CV_StsNullPtr, "NULL guiReceiver (please create a window)" ); - QMetaObject::invokeMethod(guiMainThread, - "displayStatusBar", - Qt::AutoConnection, - //Qt::DirectConnection, - Q_ARG(QString, QString(name)), - Q_ARG(QString, QString(text)), - Q_ARG(int, delayms)); + QMetaObject::invokeMethod(guiMainThread, + "displayStatusBar", + Qt::AutoConnection, + //Qt::DirectConnection, + Q_ARG(QString, QString(name)), + Q_ARG(QString, QString(text)), + Q_ARG(int, delayms)); } @@ -337,19 +337,19 @@ CV_IMPL int cvWaitKey(int delay) //We recommend not using this function for now CV_IMPL int cvStartLoop(int (*pt2Func)(int argc, char *argv[]), int argc, char* argv[]) { - multiThreads = true; - QFuture future = QtConcurrent::run(pt2Func, argc, argv); - return guiMainThread->start(); + multiThreads = true; + QFuture future = QtConcurrent::run(pt2Func, argc, argv); + return guiMainThread->start(); } CV_IMPL void cvStopLoop() { - qApp->exit(); + qApp->exit(); } -CvWindow* icvFindWindowByName(QString name) +static CvWindow* icvFindWindowByName(QString name) { CvWindow* window = 0; @@ -357,100 +357,100 @@ CvWindow* icvFindWindowByName(QString name) //that can be grabbed here and crash the code at 'w->param_name==name'. foreach (QWidget* widget, QApplication::topLevelWidgets()) { - if (widget->isWindow() && !widget->parentWidget())//is a window without parent - { - CvWinModel* temp = (CvWinModel*) widget; + if (widget->isWindow() && !widget->parentWidget())//is a window without parent + { + CvWinModel* temp = (CvWinModel*) widget; - if (temp->type == type_CvWindow) - { - CvWindow* w = (CvWindow*) temp; + if (temp->type == type_CvWindow) + { + CvWindow* w = (CvWindow*) temp; if (w->windowTitle() == name) - { - window = w; - break; - } - } - } - } + { + window = w; + break; + } + } + } + } return window; } -CvBar* icvFindBarByName(QBoxLayout* layout, QString name_bar, typeBar type) +static CvBar* icvFindBarByName(QBoxLayout* layout, QString name_bar, typeBar type) { if (!layout) - return NULL; + return NULL; int stop_index = layout->layout()->count(); for (int i = 0; i < stop_index; ++i) { - CvBar* t = (CvBar*) layout->layout()->itemAt(i); + CvBar* t = (CvBar*) layout->layout()->itemAt(i); - if (t->type == type && t->name_bar == name_bar) - return t; + if (t->type == type && t->name_bar == name_bar) + return t; } return NULL; } -CvTrackbar* icvFindTrackBarByName(const char* name_trackbar, const char* name_window, QBoxLayout* layout = NULL) +static CvTrackbar* icvFindTrackBarByName(const char* name_trackbar, const char* name_window, QBoxLayout* layout = NULL) { QString nameQt(name_trackbar); if (!name_window && global_control_panel) //window name is null and we have a control panel - layout = global_control_panel->myLayout; + layout = global_control_panel->myLayout; if (!layout) { - QPointer w = icvFindWindowByName(QLatin1String(name_window)); + QPointer w = icvFindWindowByName(QLatin1String(name_window)); - if (!w) - CV_Error(CV_StsNullPtr, "NULL window handler"); + if (!w) + CV_Error(CV_StsNullPtr, "NULL window handler"); - if (w->param_gui_mode == CV_GUI_NORMAL) - return (CvTrackbar*) icvFindBarByName(w->myBarLayout, nameQt, type_CvTrackbar); + if (w->param_gui_mode == CV_GUI_NORMAL) + return (CvTrackbar*) icvFindBarByName(w->myBarLayout, nameQt, type_CvTrackbar); - if (w->param_gui_mode == CV_GUI_EXPANDED) - { - CvBar* result = icvFindBarByName(w->myBarLayout, nameQt, type_CvTrackbar); + if (w->param_gui_mode == CV_GUI_EXPANDED) + { + CvBar* result = icvFindBarByName(w->myBarLayout, nameQt, type_CvTrackbar); - if (result) - return (CvTrackbar*) result; + if (result) + return (CvTrackbar*) result; - return (CvTrackbar*) icvFindBarByName(global_control_panel->myLayout, nameQt, type_CvTrackbar); - } + return (CvTrackbar*) icvFindBarByName(global_control_panel->myLayout, nameQt, type_CvTrackbar); + } - return NULL; + return NULL; } else { - //layout was specified - return (CvTrackbar*) icvFindBarByName(layout, nameQt, type_CvTrackbar); + //layout was specified + return (CvTrackbar*) icvFindBarByName(layout, nameQt, type_CvTrackbar); } } - -CvButtonbar* icvFindButtonBarByName(const char* button_name, QBoxLayout* layout) +/* +static CvButtonbar* icvFindButtonBarByName(const char* button_name, QBoxLayout* layout) { QString nameQt(button_name); return (CvButtonbar*) icvFindBarByName(layout, nameQt, type_CvButtonbar); } +*/ - -int icvInitSystem(int* c, char** v) +static int icvInitSystem(int* c, char** v) { //"For any GUI application using Qt, there is precisely one QApplication object" if (!QApplication::instance()) { - new QApplication(*c, v); + new QApplication(*c, v); - qDebug() << "init done"; + qDebug() << "init done"; #ifdef HAVE_QT_OPENGL - qDebug() << "opengl support available"; + qDebug() << "opengl support available"; #endif } @@ -460,212 +460,212 @@ int icvInitSystem(int* c, char** v) CV_IMPL int cvInitSystem(int, char**) { - icvInitSystem(¶meterSystemC, parameterSystemV); - return 0; + icvInitSystem(¶meterSystemC, parameterSystemV); + return 0; } CV_IMPL int cvNamedWindow(const char* name, int flags) { - if (!guiMainThread) - guiMainThread = new GuiReceiver; + if (!guiMainThread) + guiMainThread = new GuiReceiver; - if (multiThreads) - QMetaObject::invokeMethod(guiMainThread, - "createWindow", - Qt::BlockingQueuedConnection, - Q_ARG(QString, QString(name)), - Q_ARG(int, flags)); - else - guiMainThread->createWindow(QString(name), flags); + if (multiThreads) + QMetaObject::invokeMethod(guiMainThread, + "createWindow", + Qt::BlockingQueuedConnection, + Q_ARG(QString, QString(name)), + Q_ARG(int, flags)); + else + guiMainThread->createWindow(QString(name), flags); - return 1; //Dummy value + return 1; //Dummy value } CV_IMPL void cvDestroyWindow(const char* name) { - if (!guiMainThread) - CV_Error( CV_StsNullPtr, "NULL guiReceiver (please create a window)" ); + if (!guiMainThread) + CV_Error( CV_StsNullPtr, "NULL guiReceiver (please create a window)" ); - QMetaObject::invokeMethod(guiMainThread, - "destroyWindow", - //Qt::BlockingQueuedConnection, - Qt::AutoConnection, - Q_ARG(QString, QString(name))); + QMetaObject::invokeMethod(guiMainThread, + "destroyWindow", + //Qt::BlockingQueuedConnection, + Qt::AutoConnection, + Q_ARG(QString, QString(name))); } CV_IMPL void cvDestroyAllWindows() { - if (!guiMainThread) - CV_Error( CV_StsNullPtr, "NULL guiReceiver (please create a window)" ); + if (!guiMainThread) + CV_Error( CV_StsNullPtr, "NULL guiReceiver (please create a window)" ); - QMetaObject::invokeMethod(guiMainThread, - "destroyAllWindow", - //Qt::BlockingQueuedConnection, - Qt::AutoConnection); + QMetaObject::invokeMethod(guiMainThread, + "destroyAllWindow", + //Qt::BlockingQueuedConnection, + Qt::AutoConnection); } CV_IMPL void* cvGetWindowHandle(const char* name) { - if (!name) - CV_Error( CV_StsNullPtr, "NULL name string" ); + if (!name) + CV_Error( CV_StsNullPtr, "NULL name string" ); - return (void*) icvFindWindowByName(QLatin1String(name)); + return (void*) icvFindWindowByName(QLatin1String(name)); } CV_IMPL const char* cvGetWindowName(void* window_handle) { - if( !window_handle ) - CV_Error( CV_StsNullPtr, "NULL window handler" ); + if( !window_handle ) + CV_Error( CV_StsNullPtr, "NULL window handler" ); - return ((CvWindow*)window_handle)->windowTitle().toLatin1().data(); + return ((CvWindow*)window_handle)->windowTitle().toLatin1().data(); } CV_IMPL void cvMoveWindow(const char* name, int x, int y) { - if (!guiMainThread) - CV_Error( CV_StsNullPtr, "NULL guiReceiver (please create a window)" ); + if (!guiMainThread) + CV_Error( CV_StsNullPtr, "NULL guiReceiver (please create a window)" ); - QMetaObject::invokeMethod(guiMainThread, - "moveWindow", - //Qt::BlockingQueuedConnection, - Qt::AutoConnection, - Q_ARG(QString, QString(name)), - Q_ARG(int, x), - Q_ARG(int, y)); + QMetaObject::invokeMethod(guiMainThread, + "moveWindow", + //Qt::BlockingQueuedConnection, + Qt::AutoConnection, + Q_ARG(QString, QString(name)), + Q_ARG(int, x), + Q_ARG(int, y)); } CV_IMPL void cvResizeWindow(const char* name, int width, int height) { - if (!guiMainThread) - CV_Error( CV_StsNullPtr, "NULL guiReceiver (please create a window)" ); + if (!guiMainThread) + CV_Error( CV_StsNullPtr, "NULL guiReceiver (please create a window)" ); - QMetaObject::invokeMethod(guiMainThread, - "resizeWindow", - //Qt::BlockingQueuedConnection, - Qt::AutoConnection, - Q_ARG(QString, QString(name)), - Q_ARG(int, width), - Q_ARG(int, height)); + QMetaObject::invokeMethod(guiMainThread, + "resizeWindow", + //Qt::BlockingQueuedConnection, + Qt::AutoConnection, + Q_ARG(QString, QString(name)), + Q_ARG(int, width), + Q_ARG(int, height)); } CV_IMPL int cvCreateTrackbar2(const char* name_bar, const char* window_name, int* val, int count, CvTrackbarCallback2 on_notify, void* userdata) { - if (!guiMainThread) - CV_Error( CV_StsNullPtr, "NULL guiReceiver (please create a window)" ); + if (!guiMainThread) + CV_Error( CV_StsNullPtr, "NULL guiReceiver (please create a window)" ); - QMetaObject::invokeMethod(guiMainThread, - "addSlider2", - Qt::AutoConnection, - Q_ARG(QString, QString(name_bar)), - Q_ARG(QString, QString(window_name)), - Q_ARG(void*, (void*)val), - Q_ARG(int, count), - Q_ARG(void*, (void*)on_notify), - Q_ARG(void*, (void*)userdata)); + QMetaObject::invokeMethod(guiMainThread, + "addSlider2", + Qt::AutoConnection, + Q_ARG(QString, QString(name_bar)), + Q_ARG(QString, QString(window_name)), + Q_ARG(void*, (void*)val), + Q_ARG(int, count), + Q_ARG(void*, (void*)on_notify), + Q_ARG(void*, (void*)userdata)); - return 1; //dummy value + return 1; //dummy value } CV_IMPL int cvStartWindowThread() { - return 0; + return 0; } CV_IMPL int cvCreateTrackbar(const char* name_bar, const char* window_name, int* value, int count, CvTrackbarCallback on_change) { - if (!guiMainThread) - CV_Error( CV_StsNullPtr, "NULL guiReceiver (please create a window)" ); + if (!guiMainThread) + CV_Error( CV_StsNullPtr, "NULL guiReceiver (please create a window)" ); - QMetaObject::invokeMethod(guiMainThread, - "addSlider", - Qt::AutoConnection, - Q_ARG(QString, QString(name_bar)), - Q_ARG(QString, QString(window_name)), - Q_ARG(void*, (void*)value), - Q_ARG(int, count), - Q_ARG(void*, (void*)on_change)); + QMetaObject::invokeMethod(guiMainThread, + "addSlider", + Qt::AutoConnection, + Q_ARG(QString, QString(name_bar)), + Q_ARG(QString, QString(window_name)), + Q_ARG(void*, (void*)value), + Q_ARG(int, count), + Q_ARG(void*, (void*)on_change)); - return 1; //dummy value + return 1; //dummy value } CV_IMPL int cvCreateButton(const char* button_name, CvButtonCallback on_change, void* userdata, int button_type, int initial_button_state) { - if (!guiMainThread) - CV_Error( CV_StsNullPtr, "NULL guiReceiver (please create a window)" ); + if (!guiMainThread) + CV_Error( CV_StsNullPtr, "NULL guiReceiver (please create a window)" ); - if (initial_button_state < 0 || initial_button_state > 1) - return 0; + if (initial_button_state < 0 || initial_button_state > 1) + return 0; - QMetaObject::invokeMethod(guiMainThread, - "addButton", - Qt::AutoConnection, - Q_ARG(QString, QString(button_name)), - Q_ARG(int, button_type), - Q_ARG(int, initial_button_state), - Q_ARG(void*, (void*)on_change), - Q_ARG(void*, userdata)); + QMetaObject::invokeMethod(guiMainThread, + "addButton", + Qt::AutoConnection, + Q_ARG(QString, QString(button_name)), + Q_ARG(int, button_type), + Q_ARG(int, initial_button_state), + Q_ARG(void*, (void*)on_change), + Q_ARG(void*, userdata)); - return 1;//dummy value + return 1;//dummy value } CV_IMPL int cvGetTrackbarPos(const char* name_bar, const char* window_name) { - int result = -1; + int result = -1; - QPointer t = icvFindTrackBarByName(name_bar, window_name); + QPointer t = icvFindTrackBarByName(name_bar, window_name); - if (t) - result = t->slider->value(); + if (t) + result = t->slider->value(); - return result; + return result; } CV_IMPL void cvSetTrackbarPos(const char* name_bar, const char* window_name, int pos) { - QPointer t = icvFindTrackBarByName(name_bar, window_name); + QPointer t = icvFindTrackBarByName(name_bar, window_name); - if (t) - t->slider->setValue(pos); + if (t) + t->slider->setValue(pos); } /* assign callback for mouse events */ CV_IMPL void cvSetMouseCallback(const char* window_name, CvMouseCallback on_mouse, void* param) { - QPointer w = icvFindWindowByName(QLatin1String(window_name)); + QPointer w = icvFindWindowByName(QLatin1String(window_name)); - if (!w) - CV_Error(CV_StsNullPtr, "NULL window handler"); + if (!w) + CV_Error(CV_StsNullPtr, "NULL window handler"); - w->setMouseCallBack(on_mouse, param); + w->setMouseCallBack(on_mouse, param); } CV_IMPL void cvShowImage(const char* name, const CvArr* arr) { - if (!guiMainThread) - guiMainThread = new GuiReceiver; + if (!guiMainThread) + guiMainThread = new GuiReceiver; - QMetaObject::invokeMethod(guiMainThread, - "showImage", - //Qt::BlockingQueuedConnection, - Qt::DirectConnection, - Q_ARG(QString, QString(name)), - Q_ARG(void*, (void*)arr)); + QMetaObject::invokeMethod(guiMainThread, + "showImage", + //Qt::BlockingQueuedConnection, + Qt::DirectConnection, + Q_ARG(QString, QString(name)), + Q_ARG(void*, (void*)arr)); } @@ -673,53 +673,53 @@ CV_IMPL void cvShowImage(const char* name, const CvArr* arr) CV_IMPL void cvSetOpenGlDrawCallback(const char* window_name, CvOpenGlDrawCallback callback, void* userdata) { - if (!guiMainThread) - CV_Error( CV_StsNullPtr, "NULL guiReceiver (please create a window)" ); + if (!guiMainThread) + CV_Error( CV_StsNullPtr, "NULL guiReceiver (please create a window)" ); - QMetaObject::invokeMethod(guiMainThread, - "setOpenGlDrawCallback", - Qt::AutoConnection, - Q_ARG(QString, QString(window_name)), - Q_ARG(void*, (void*)callback), - Q_ARG(void*, userdata)); + QMetaObject::invokeMethod(guiMainThread, + "setOpenGlDrawCallback", + Qt::AutoConnection, + Q_ARG(QString, QString(window_name)), + Q_ARG(void*, (void*)callback), + Q_ARG(void*, userdata)); } void icvSetOpenGlCleanCallback(const char* window_name, CvOpenGlCleanCallback callback, void* userdata) { - if (!guiMainThread) - CV_Error( CV_StsNullPtr, "NULL guiReceiver (please create a window)" ); + if (!guiMainThread) + CV_Error( CV_StsNullPtr, "NULL guiReceiver (please create a window)" ); - QMetaObject::invokeMethod(guiMainThread, - "setOpenGlCleanCallback", - Qt::AutoConnection, - Q_ARG(QString, QString(window_name)), - Q_ARG(void*, (void*)callback), - Q_ARG(void*, userdata)); + QMetaObject::invokeMethod(guiMainThread, + "setOpenGlCleanCallback", + Qt::AutoConnection, + Q_ARG(QString, QString(window_name)), + Q_ARG(void*, (void*)callback), + Q_ARG(void*, userdata)); } CV_IMPL void cvSetOpenGlContext(const char* window_name) { - if (!guiMainThread) - CV_Error( CV_StsNullPtr, "NULL guiReceiver (please create a window)" ); + if (!guiMainThread) + CV_Error( CV_StsNullPtr, "NULL guiReceiver (please create a window)" ); - QMetaObject::invokeMethod(guiMainThread, - "setOpenGlContext", - Qt::AutoConnection, - Q_ARG(QString, QString(window_name))); + QMetaObject::invokeMethod(guiMainThread, + "setOpenGlContext", + Qt::AutoConnection, + Q_ARG(QString, QString(window_name))); } CV_IMPL void cvUpdateWindow(const char* window_name) { - if (!guiMainThread) - CV_Error( CV_StsNullPtr, "NULL guiReceiver (please create a window)" ); + if (!guiMainThread) + CV_Error( CV_StsNullPtr, "NULL guiReceiver (please create a window)" ); - QMetaObject::invokeMethod(guiMainThread, - "updateWindow", - Qt::AutoConnection, - Q_ARG(QString, QString(window_name))); + QMetaObject::invokeMethod(guiMainThread, + "updateWindow", + Qt::AutoConnection, + Q_ARG(QString, QString(window_name))); } #endif @@ -727,18 +727,18 @@ CV_IMPL void cvUpdateWindow(const char* window_name) double cvGetOpenGlProp_QT(const char* name) { - double result = -1; + double result = -1; - if (guiMainThread) + if (guiMainThread) { - QMetaObject::invokeMethod(guiMainThread, - "isOpenGl", - Qt::AutoConnection, - Q_RETURN_ARG(double, result), - Q_ARG(QString, QString(name))); + QMetaObject::invokeMethod(guiMainThread, + "isOpenGl", + Qt::AutoConnection, + Q_RETURN_ARG(double, result), + Q_ARG(QString, QString(name))); } - return result; + return result; } @@ -748,108 +748,108 @@ double cvGetOpenGlProp_QT(const char* name) GuiReceiver::GuiReceiver() : bTimeOut(false), nb_windows(0) { - doesExternalQAppExist = (QApplication::instance() != 0); - icvInitSystem(¶meterSystemC, parameterSystemV); + doesExternalQAppExist = (QApplication::instance() != 0); + icvInitSystem(¶meterSystemC, parameterSystemV); - timer = new QTimer(this); - QObject::connect(timer, SIGNAL(timeout()), this, SLOT(timeOut())); - timer->setSingleShot(true); + timer = new QTimer(this); + QObject::connect(timer, SIGNAL(timeout()), this, SLOT(timeOut())); + timer->setSingleShot(true); } void GuiReceiver::isLastWindow() { - if (--nb_windows <= 0) - { - delete guiMainThread;//delete global_control_panel too - guiMainThread = NULL; + if (--nb_windows <= 0) + { + delete guiMainThread;//delete global_control_panel too + guiMainThread = NULL; - if (!doesExternalQAppExist) - { - qApp->quit(); - } - } + if (!doesExternalQAppExist) + { + qApp->quit(); + } + } } GuiReceiver::~GuiReceiver() -{ - if (global_control_panel) - { - delete global_control_panel; - global_control_panel = NULL; - } +{ + if (global_control_panel) + { + delete global_control_panel; + global_control_panel = NULL; + } } void GuiReceiver::putText(void* arr, QString text, QPoint org, void* arg2) { - CV_Assert(arr); + CV_Assert(arr); - CvMat* mat, stub; - mat = cvGetMat(arr, &stub); + CvMat* mat, stub; + mat = cvGetMat(arr, &stub); - int nbChannelOriginImage = cvGetElemType(mat); - if (nbChannelOriginImage != CV_8UC3) return; //for now, font works only with 8UC3 + int nbChannelOriginImage = cvGetElemType(mat); + if (nbChannelOriginImage != CV_8UC3) return; //for now, font works only with 8UC3 - QImage qimg(mat->data.ptr, mat->cols, mat->rows, mat->step, QImage::Format_RGB888); + QImage qimg(mat->data.ptr, mat->cols, mat->rows, mat->step, QImage::Format_RGB888); - CvFont* font = (CvFont*)arg2; + CvFont* font = (CvFont*)arg2; - QPainter qp(&qimg); - if (font) - { - QFont f(font->nameFont, font->line_type/*PointSize*/, font->thickness/*weight*/); - f.setStyle((QFont::Style) font->font_face/*style*/); - f.setLetterSpacing(QFont::AbsoluteSpacing, font->dx/*spacing*/); - //cvScalar(blue_component, green_component, red_component[, alpha_component]) - //Qt map non-transparent to 0xFF and transparent to 0 - //OpenCV scalar is the reverse, so 255-font->color.val[3] - qp.setPen(QColor(font->color.val[2], font->color.val[1], font->color.val[0], 255 - font->color.val[3])); - qp.setFont(f); - } - qp.drawText(org, text); - qp.end(); + QPainter qp(&qimg); + if (font) + { + QFont f(font->nameFont, font->line_type/*PointSize*/, font->thickness/*weight*/); + f.setStyle((QFont::Style) font->font_face/*style*/); + f.setLetterSpacing(QFont::AbsoluteSpacing, font->dx/*spacing*/); + //cvScalar(blue_component, green_component, red_component[, alpha_component]) + //Qt map non-transparent to 0xFF and transparent to 0 + //OpenCV scalar is the reverse, so 255-font->color.val[3] + qp.setPen(QColor(font->color.val[2], font->color.val[1], font->color.val[0], 255 - font->color.val[3])); + qp.setFont(f); + } + qp.drawText(org, text); + qp.end(); } void GuiReceiver::saveWindowParameters(QString name) { - QPointer w = icvFindWindowByName(name); + QPointer w = icvFindWindowByName(name); - if (w) - w->writeSettings(); + if (w) + w->writeSettings(); } void GuiReceiver::loadWindowParameters(QString name) { - QPointer w = icvFindWindowByName(name); + QPointer w = icvFindWindowByName(name); - if (w) - w->readSettings(); + if (w) + w->readSettings(); } double GuiReceiver::getRatioWindow(QString name) { - QPointer w = icvFindWindowByName(name); + QPointer w = icvFindWindowByName(name); - if (!w) - return -1; + if (!w) + return -1; - return w->getRatio(); + return w->getRatio(); } void GuiReceiver::setRatioWindow(QString name, double arg2) { - QPointer w = icvFindWindowByName( name.toLatin1().data() ); + QPointer w = icvFindWindowByName( name.toLatin1().data() ); + + if (!w) + return; - if (!w) - return; - - int flags = (int) arg2; + int flags = (int) arg2; w->setRatio(flags); } @@ -857,23 +857,23 @@ void GuiReceiver::setRatioWindow(QString name, double arg2) double GuiReceiver::getPropWindow(QString name) { - QPointer w = icvFindWindowByName(name); + QPointer w = icvFindWindowByName(name); - if (!w) - return -1; + if (!w) + return -1; - return (double) w->getPropWindow(); + return (double) w->getPropWindow(); } void GuiReceiver::setPropWindow(QString name, double arg2) { - QPointer w = icvFindWindowByName(name); + QPointer w = icvFindWindowByName(name); - if (!w) - return; + if (!w) + return; - int flags = (int) arg2; + int flags = (int) arg2; w->setPropWindow(flags); } @@ -881,10 +881,10 @@ void GuiReceiver::setPropWindow(QString name, double arg2) double GuiReceiver::isFullScreen(QString name) { - QPointer w = icvFindWindowByName(name); + QPointer w = icvFindWindowByName(name); - if (!w) - return -1; + if (!w) + return -1; return w->isFullScreen() ? CV_WINDOW_FULLSCREEN : CV_WINDOW_NORMAL; } @@ -892,12 +892,12 @@ double GuiReceiver::isFullScreen(QString name) void GuiReceiver::toggleFullScreen(QString name, double arg2) { - QPointer w = icvFindWindowByName(name); + QPointer w = icvFindWindowByName(name); - if (!w) - return; + if (!w) + return; - int flags = (int) arg2; + int flags = (int) arg2; w->toggleFullScreen(flags); } @@ -905,53 +905,53 @@ void GuiReceiver::toggleFullScreen(QString name, double arg2) void GuiReceiver::createWindow(QString name, int flags) { - if (!qApp) - CV_Error(CV_StsNullPtr, "NULL session handler" ); + if (!qApp) + CV_Error(CV_StsNullPtr, "NULL session handler" ); - // Check the name in the storage - if (icvFindWindowByName(name.toLatin1().data())) - { - return; - } + // Check the name in the storage + if (icvFindWindowByName(name.toLatin1().data())) + { + return; + } - nb_windows++; - new CvWindow(name, flags); + nb_windows++; + new CvWindow(name, flags); } void GuiReceiver::timeOut() { - bTimeOut = true; + bTimeOut = true; } void GuiReceiver::displayInfo(QString name, QString text, int delayms) { - QPointer w = icvFindWindowByName(name); + QPointer w = icvFindWindowByName(name); - if (w) - w->displayInfo(text, delayms); + if (w) + w->displayInfo(text, delayms); } void GuiReceiver::displayStatusBar(QString name, QString text, int delayms) { - QPointer w = icvFindWindowByName(name); + QPointer w = icvFindWindowByName(name); - if (w) - w->displayStatusBar(text, delayms); + if (w) + w->displayStatusBar(text, delayms); } void GuiReceiver::showImage(QString name, void* arr) { - QPointer w = icvFindWindowByName(name); + QPointer w = icvFindWindowByName(name); - if (!w) //as observed in the previous implementation (W32, GTK or Carbon), create a new window is the pointer returned is null - { - cvNamedWindow(name.toLatin1().data()); - w = icvFindWindowByName(name); - } + if (!w) //as observed in the previous implementation (W32, GTK or Carbon), create a new window is the pointer returned is null + { + cvNamedWindow(name.toLatin1().data()); + w = icvFindWindowByName(name); + } if (!w || !arr) return; // keep silence here. @@ -960,253 +960,253 @@ void GuiReceiver::showImage(QString name, void* arr) { CvMat* mat, stub; - mat = cvGetMat(arr, &stub); + mat = cvGetMat(arr, &stub); cv::Mat im(mat); cv::imshow(name.toStdString(), im); } else { - w->updateImage(arr); + w->updateImage(arr); } - if (w->isHidden()) - w->show(); + if (w->isHidden()) + w->show(); } void GuiReceiver::destroyWindow(QString name) { - QPointer w = icvFindWindowByName(name); + QPointer w = icvFindWindowByName(name); - if (w) - { - w->close(); + if (w) + { + w->close(); - //in not-multiThreads mode, looks like the window is hidden but not deleted - //so I do it manually - //otherwise QApplication do it for me if the exec command was executed (in multiThread mode) - if (!multiThreads) - delete w; - } + //in not-multiThreads mode, looks like the window is hidden but not deleted + //so I do it manually + //otherwise QApplication do it for me if the exec command was executed (in multiThread mode) + if (!multiThreads) + delete w; + } } void GuiReceiver::destroyAllWindow() { - if (!qApp) - CV_Error(CV_StsNullPtr, "NULL session handler" ); - - if (multiThreads) - { - // WARNING: this could even close windows from an external parent app - //#TODO check externalQAppExists and in case it does, close windows carefully, - // i.e. apply the className-check from below... - qApp->closeAllWindows(); - } - else - { - bool isWidgetDeleted = true; - while(isWidgetDeleted) - { - isWidgetDeleted = false; - QWidgetList list = QApplication::topLevelWidgets(); - for (int i = 0; i < list.count(); i++) - { - QObject *obj = list.at(i); - if (obj->metaObject()->className() == QString("CvWindow")) - { - delete obj; - isWidgetDeleted = true; - break; - } - } - } - } + if (!qApp) + CV_Error(CV_StsNullPtr, "NULL session handler" ); + + if (multiThreads) + { + // WARNING: this could even close windows from an external parent app + //#TODO check externalQAppExists and in case it does, close windows carefully, + // i.e. apply the className-check from below... + qApp->closeAllWindows(); + } + else + { + bool isWidgetDeleted = true; + while(isWidgetDeleted) + { + isWidgetDeleted = false; + QWidgetList list = QApplication::topLevelWidgets(); + for (int i = 0; i < list.count(); i++) + { + QObject *obj = list.at(i); + if (obj->metaObject()->className() == QString("CvWindow")) + { + delete obj; + isWidgetDeleted = true; + break; + } + } + } + } } void GuiReceiver::moveWindow(QString name, int x, int y) { - QPointer w = icvFindWindowByName(name); + QPointer w = icvFindWindowByName(name); - if (w) - w->move(x, y); + if (w) + w->move(x, y); } void GuiReceiver::resizeWindow(QString name, int width, int height) { - QPointer w = icvFindWindowByName(name); + QPointer w = icvFindWindowByName(name); - if (w) - { - w->showNormal(); + if (w) + { + w->showNormal(); w->setViewportSize(QSize(width, height)); - } + } } void GuiReceiver::enablePropertiesButtonEachWindow() { - //For each window, enable window property button - foreach (QWidget* widget, QApplication::topLevelWidgets()) - { - if (widget->isWindow() && !widget->parentWidget()) //is a window without parent - { - CvWinModel* temp = (CvWinModel*) widget; - if (temp->type == type_CvWindow) - { - CvWindow* w = (CvWindow*) widget; + //For each window, enable window property button + foreach (QWidget* widget, QApplication::topLevelWidgets()) + { + if (widget->isWindow() && !widget->parentWidget()) //is a window without parent + { + CvWinModel* temp = (CvWinModel*) widget; + if (temp->type == type_CvWindow) + { + CvWindow* w = (CvWindow*) widget; - //active window properties button - w->enablePropertiesButton(); - } - } - } + //active window properties button + w->enablePropertiesButton(); + } + } + } } void GuiReceiver::addButton(QString button_name, int button_type, int initial_button_state, void* on_change, void* userdata) { - if (!global_control_panel) - return; + if (!global_control_panel) + return; - QPointer b; + QPointer b; - if (global_control_panel->myLayout->count() == 0) //if that is the first button attach to the control panel, create a new button bar - { - b = CvWindow::createButtonBar(button_name); //the bar has the name of the first button attached to it - enablePropertiesButtonEachWindow(); + if (global_control_panel->myLayout->count() == 0) //if that is the first button attach to the control panel, create a new button bar + { + b = CvWindow::createButtonBar(button_name); //the bar has the name of the first button attached to it + enablePropertiesButtonEachWindow(); - } + } else { - CvBar* lastbar = (CvBar*) global_control_panel->myLayout->itemAt(global_control_panel->myLayout->count() - 1); + CvBar* lastbar = (CvBar*) global_control_panel->myLayout->itemAt(global_control_panel->myLayout->count() - 1); - if (lastbar->type == type_CvTrackbar) //if last bar is a trackbar, create a new buttonbar, else, attach to the current bar - b = CvWindow::createButtonBar(button_name); //the bar has the name of the first button attached to it - else - b = (CvButtonbar*) lastbar; + if (lastbar->type == type_CvTrackbar) //if last bar is a trackbar, create a new buttonbar, else, attach to the current bar + b = CvWindow::createButtonBar(button_name); //the bar has the name of the first button attached to it + else + b = (CvButtonbar*) lastbar; - } + } - b->addButton(button_name, (CvButtonCallback) on_change, userdata, button_type, initial_button_state); + b->addButton(button_name, (CvButtonCallback) on_change, userdata, button_type, initial_button_state); } void GuiReceiver::addSlider2(QString bar_name, QString window_name, void* value, int count, void* on_change, void *userdata) { - QBoxLayout *layout = NULL; - QPointer w; + QBoxLayout *layout = NULL; + QPointer w; if (!window_name.isEmpty()) - { - w = icvFindWindowByName(window_name); + { + w = icvFindWindowByName(window_name); - if (!w) - return; - } + if (!w) + return; + } else { - if (global_control_panel) - layout = global_control_panel->myLayout; - } + if (global_control_panel) + layout = global_control_panel->myLayout; + } - QPointer t = icvFindTrackBarByName(bar_name.toLatin1().data(), window_name.toLatin1().data(), layout); + QPointer t = icvFindTrackBarByName(bar_name.toLatin1().data(), window_name.toLatin1().data(), layout); - if (t) //trackbar exists - return; + if (t) //trackbar exists + return; - if (!value) - CV_Error(CV_StsNullPtr, "NULL value pointer" ); + if (!value) + CV_Error(CV_StsNullPtr, "NULL value pointer" ); - if (count <= 0) //count is the max value of the slider, so must be bigger than 0 - CV_Error(CV_StsNullPtr, "Max value of the slider must be bigger than 0" ); + if (count <= 0) //count is the max value of the slider, so must be bigger than 0 + CV_Error(CV_StsNullPtr, "Max value of the slider must be bigger than 0" ); - CvWindow::addSlider2(w, bar_name, (int*)value, count, (CvTrackbarCallback2) on_change, userdata); + CvWindow::addSlider2(w, bar_name, (int*)value, count, (CvTrackbarCallback2) on_change, userdata); } void GuiReceiver::addSlider(QString bar_name, QString window_name, void* value, int count, void* on_change) { - QBoxLayout *layout = NULL; - QPointer w; + QBoxLayout *layout = NULL; + QPointer w; - if (!window_name.isEmpty()) - { - w = icvFindWindowByName(window_name); + if (!window_name.isEmpty()) + { + w = icvFindWindowByName(window_name); - if (!w) - return; - } + if (!w) + return; + } else { - if (global_control_panel) - layout = global_control_panel->myLayout; - } + if (global_control_panel) + layout = global_control_panel->myLayout; + } - QPointer t = icvFindTrackBarByName(bar_name.toLatin1().data(), window_name.toLatin1().data(), layout); + QPointer t = icvFindTrackBarByName(bar_name.toLatin1().data(), window_name.toLatin1().data(), layout); - if (t) //trackbar exists - return; + if (t) //trackbar exists + return; - if (!value) - CV_Error(CV_StsNullPtr, "NULL value pointer" ); + if (!value) + CV_Error(CV_StsNullPtr, "NULL value pointer" ); - if (count <= 0) //count is the max value of the slider, so must be bigger than 0 - CV_Error(CV_StsNullPtr, "Max value of the slider must be bigger than 0" ); + if (count <= 0) //count is the max value of the slider, so must be bigger than 0 + CV_Error(CV_StsNullPtr, "Max value of the slider must be bigger than 0" ); - CvWindow::addSlider(w, bar_name, (int*)value, count, (CvTrackbarCallback) on_change); + CvWindow::addSlider(w, bar_name, (int*)value, count, (CvTrackbarCallback) on_change); } int GuiReceiver::start() { - return qApp->exec(); + return qApp->exec(); } void GuiReceiver::setOpenGlDrawCallback(QString name, void* callback, void* userdata) { - QPointer w = icvFindWindowByName(name); + QPointer w = icvFindWindowByName(name); - if (w) - w->setOpenGlDrawCallback((CvOpenGlDrawCallback) callback, userdata); + if (w) + w->setOpenGlDrawCallback((CvOpenGlDrawCallback) callback, userdata); } void GuiReceiver::setOpenGlCleanCallback(QString name, void* callback, void* userdata) { - QPointer w = icvFindWindowByName(name); + QPointer w = icvFindWindowByName(name); - if (w) - w->setOpenGlCleanCallback((CvOpenGlCleanCallback) callback, userdata); + if (w) + w->setOpenGlCleanCallback((CvOpenGlCleanCallback) callback, userdata); } void GuiReceiver::setOpenGlContext(QString name) { - QPointer w = icvFindWindowByName(name); + QPointer w = icvFindWindowByName(name); - if (w) - w->makeCurrentOpenGlContext(); + if (w) + w->makeCurrentOpenGlContext(); } void GuiReceiver::updateWindow(QString name) { - QPointer w = icvFindWindowByName(name); + QPointer w = icvFindWindowByName(name); - if (w) - w->updateGl(); + if (w) + w->updateGl(); } double GuiReceiver::isOpenGl(QString name) { double result = -1; - QPointer w = icvFindWindowByName(name); + QPointer w = icvFindWindowByName(name); - if (w) - result = (double) w->isOpenGl(); + if (w) + result = (double) w->isOpenGl(); return result; } @@ -1216,127 +1216,127 @@ double GuiReceiver::isOpenGl(QString name) // CvTrackbar -CvTrackbar::CvTrackbar(CvWindow* arg, QString name, int* value, int count, CvTrackbarCallback2 on_change, void* data) +CvTrackbar::CvTrackbar(CvWindow* arg, QString name, int* value, int _count, CvTrackbarCallback2 on_change, void* data) { - callback = NULL; - callback2 = on_change; - userdata = data; + callback = NULL; + callback2 = on_change; + userdata = data; - create(arg, name, value, count); + create(arg, name, value, _count); } -CvTrackbar::CvTrackbar(CvWindow* arg, QString name, int* value, int count, CvTrackbarCallback on_change) +CvTrackbar::CvTrackbar(CvWindow* arg, QString name, int* value, int _count, CvTrackbarCallback on_change) { - callback = on_change; - callback2 = NULL; - userdata = NULL; + callback = on_change; + callback2 = NULL; + userdata = NULL; - create(arg, name, value, count); + create(arg, name, value, _count); } -void CvTrackbar::create(CvWindow* arg, QString name, int* value, int count) +void CvTrackbar::create(CvWindow* arg, QString name, int* value, int _count) { - type = type_CvTrackbar; - myparent = arg; - name_bar = name; - setObjectName(name_bar); - dataSlider = value; + type = type_CvTrackbar; + myparent = arg; + name_bar = name; + setObjectName(name_bar); + dataSlider = value; - slider = new QSlider(Qt::Horizontal); - slider->setFocusPolicy(Qt::StrongFocus); - slider->setMinimum(0); - slider->setMaximum(count); - slider->setPageStep(5); - slider->setValue(*value); - slider->setTickPosition(QSlider::TicksBelow); + slider = new QSlider(Qt::Horizontal); + slider->setFocusPolicy(Qt::StrongFocus); + slider->setMinimum(0); + slider->setMaximum(_count); + slider->setPageStep(5); + slider->setValue(*value); + slider->setTickPosition(QSlider::TicksBelow); - //Change style of the Slider - //slider->setStyleSheet(str_Trackbar_css); + //Change style of the Slider + //slider->setStyleSheet(str_Trackbar_css); - QFile qss(":/stylesheet-trackbar"); - if (qss.open(QFile::ReadOnly)) - { - slider->setStyleSheet(QLatin1String(qss.readAll())); - qss.close(); - } + QFile qss(":/stylesheet-trackbar"); + if (qss.open(QFile::ReadOnly)) + { + slider->setStyleSheet(QLatin1String(qss.readAll())); + qss.close(); + } - //this next line does not work if we change the style with a stylesheet, why ? (bug in QT ?) - //slider->setTickPosition(QSlider::TicksBelow); - label = new QPushButton; - label->setFlat(true); - setLabel(slider->value()); + //this next line does not work if we change the style with a stylesheet, why ? (bug in QT ?) + //slider->setTickPosition(QSlider::TicksBelow); + label = new QPushButton; + label->setFlat(true); + setLabel(slider->value()); - QObject::connect(slider, SIGNAL(valueChanged(int)), this, SLOT(update(int))); + QObject::connect(slider, SIGNAL(valueChanged(int)), this, SLOT(update(int))); - QObject::connect(label, SIGNAL(clicked()), this, SLOT(createDialog())); + QObject::connect(label, SIGNAL(clicked()), this, SLOT(createDialog())); - //label->setStyleSheet("QPushButton:disabled {color: black}"); + //label->setStyleSheet("QPushButton:disabled {color: black}"); - addWidget(label, Qt::AlignLeft);//name + value - addWidget(slider, Qt::AlignCenter);//slider + addWidget(label, Qt::AlignLeft);//name + value + addWidget(slider, Qt::AlignCenter);//slider } void CvTrackbar::createDialog() { - bool ok = false; + bool ok = false; - //crash if I access the values directly and give them to QInputDialog, so do a copy first. - int value = slider->value(); - int step = slider->singleStep(); - int min = slider->minimum(); - int max = slider->maximum(); + //crash if I access the values directly and give them to QInputDialog, so do a copy first. + int value = slider->value(); + int step = slider->singleStep(); + int min = slider->minimum(); + int max = slider->maximum(); - int i = + int i = #if QT_VERSION >= 0x040500 - QInputDialog::getInt + QInputDialog::getInt #else - QInputDialog::getInteger + QInputDialog::getInteger #endif - (this->parentWidget(), - tr("Slider %1").arg(name_bar), - tr("New value:"), - value, - min, - max, - step, - &ok); + (this->parentWidget(), + tr("Slider %1").arg(name_bar), + tr("New value:"), + value, + min, + max, + step, + &ok); - if (ok) - slider->setValue(i); + if (ok) + slider->setValue(i); } void CvTrackbar::update(int myvalue) { - setLabel(myvalue); + setLabel(myvalue); - *dataSlider = myvalue; - if (callback) - { - callback(myvalue); - return; - } + *dataSlider = myvalue; + if (callback) + { + callback(myvalue); + return; + } - if (callback2) - { - callback2(myvalue, userdata); - return; - } + if (callback2) + { + callback2(myvalue, userdata); + return; + } } void CvTrackbar::setLabel(int myvalue) { - QString nameNormalized = name_bar.leftJustified( 10, ' ', true ); - QString valueMaximum = QString("%1").arg(slider->maximum()); - QString str = QString("%1 (%2/%3)").arg(nameNormalized).arg(myvalue,valueMaximum.length(),10,QChar('0')).arg(valueMaximum); - label->setText(str); + QString nameNormalized = name_bar.leftJustified( 10, ' ', true ); + QString valueMaximum = QString("%1").arg(slider->maximum()); + QString str = QString("%1 (%2/%3)").arg(nameNormalized).arg(myvalue,valueMaximum.length(),10,QChar('0')).arg(valueMaximum); + label->setText(str); } @@ -1347,52 +1347,52 @@ void CvTrackbar::setLabel(int myvalue) //here CvButtonbar class CvButtonbar::CvButtonbar(QWidget* arg, QString arg2) { - type = type_CvButtonbar; - myparent = arg; - name_bar = arg2; - setObjectName(name_bar); + type = type_CvButtonbar; + myparent = arg; + name_bar = arg2; + setObjectName(name_bar); - group_button = new QButtonGroup(this); + group_button = new QButtonGroup(this); } void CvButtonbar::setLabel() { - QString nameNormalized = name_bar.leftJustified(10, ' ', true); - label->setText(nameNormalized); + QString nameNormalized = name_bar.leftJustified(10, ' ', true); + label->setText(nameNormalized); } void CvButtonbar::addButton(QString name, CvButtonCallback call, void* userdata, int button_type, int initial_button_state) { - QString button_name = name; + QString button_name = name; - if (button_name == "") - button_name = tr("button %1").arg(this->count()); + if (button_name == "") + button_name = tr("button %1").arg(this->count()); - QPointer button; + QPointer button; - if (button_type == CV_PUSH_BUTTON) - button = (QAbstractButton*) new CvPushButton(this, button_name,call, userdata); + if (button_type == CV_PUSH_BUTTON) + button = (QAbstractButton*) new CvPushButton(this, button_name,call, userdata); - if (button_type == CV_CHECKBOX) - button = (QAbstractButton*) new CvCheckBox(this, button_name,call, userdata, initial_button_state); + if (button_type == CV_CHECKBOX) + button = (QAbstractButton*) new CvCheckBox(this, button_name,call, userdata, initial_button_state); - if (button_type == CV_RADIOBOX) - { - button = (QAbstractButton*) new CvRadioButton(this, button_name,call, userdata, initial_button_state); - group_button->addButton(button); - } + if (button_type == CV_RADIOBOX) + { + button = (QAbstractButton*) new CvRadioButton(this, button_name,call, userdata, initial_button_state); + group_button->addButton(button); + } - if (button) - { - if (button_type == CV_PUSH_BUTTON) - QObject::connect(button, SIGNAL(clicked(bool)), button, SLOT(callCallBack(bool))); - else - QObject::connect(button, SIGNAL(toggled(bool)), button, SLOT(callCallBack(bool))); + if (button) + { + if (button_type == CV_PUSH_BUTTON) + QObject::connect(button, SIGNAL(clicked(bool)), button, SLOT(callCallBack(bool))); + else + QObject::connect(button, SIGNAL(toggled(bool)), button, SLOT(callCallBack(bool))); - addWidget(button, Qt::AlignCenter); - } + addWidget(button, Qt::AlignCenter); + } } @@ -1403,68 +1403,68 @@ void CvButtonbar::addButton(QString name, CvButtonCallback call, void* userdata, //buttons here CvPushButton::CvPushButton(CvButtonbar* arg1, QString arg2, CvButtonCallback arg3, void* arg4) { - myparent = arg1; - button_name = arg2; - callback = arg3; - userdata = arg4; + myparent = arg1; + button_name = arg2; + callback = arg3; + userdata = arg4; - setObjectName(button_name); - setText(button_name); + setObjectName(button_name); + setText(button_name); - if (isChecked()) - callCallBack(true); + if (isChecked()) + callCallBack(true); } void CvPushButton::callCallBack(bool checked) { - if (callback) - callback(checked, userdata); + if (callback) + callback(checked, userdata); } CvCheckBox::CvCheckBox(CvButtonbar* arg1, QString arg2, CvButtonCallback arg3, void* arg4, int initial_button_state) { - myparent = arg1; - button_name = arg2; - callback = arg3; - userdata = arg4; + myparent = arg1; + button_name = arg2; + callback = arg3; + userdata = arg4; - setObjectName(button_name); - setCheckState((initial_button_state == 1 ? Qt::Checked : Qt::Unchecked)); - setText(button_name); + setObjectName(button_name); + setCheckState((initial_button_state == 1 ? Qt::Checked : Qt::Unchecked)); + setText(button_name); - if (isChecked()) - callCallBack(true); + if (isChecked()) + callCallBack(true); } void CvCheckBox::callCallBack(bool checked) { - if (callback) - callback(checked, userdata); + if (callback) + callback(checked, userdata); } CvRadioButton::CvRadioButton(CvButtonbar* arg1, QString arg2, CvButtonCallback arg3, void* arg4, int initial_button_state) { - myparent = arg1; - button_name = arg2; - callback = arg3; - userdata = arg4; + myparent = arg1; + button_name = arg2; + callback = arg3; + userdata = arg4; - setObjectName(button_name); - setChecked(initial_button_state); - setText(button_name); + setObjectName(button_name); + setChecked(initial_button_state); + setText(button_name); - if (isChecked()) - callCallBack(true); + if (isChecked()) + callCallBack(true); } void CvRadioButton::callCallBack(bool checked) { - if (callback) - callback(checked, userdata); + if (callback) + callback(checked, userdata); } @@ -1473,69 +1473,69 @@ void CvRadioButton::callCallBack(bool checked) //here CvWinProperties class -CvWinProperties::CvWinProperties(QString name_paraWindow, QObject* parent) +CvWinProperties::CvWinProperties(QString name_paraWindow, QObject* /*parent*/) { - //setParent(parent); - type = type_CvWinProperties; - setWindowFlags(Qt::Tool); - setContentsMargins(0, 0, 0, 0); - setWindowTitle(name_paraWindow); - setObjectName(name_paraWindow); - resize(100, 50); + //setParent(parent); + type = type_CvWinProperties; + setWindowFlags(Qt::Tool); + setContentsMargins(0, 0, 0, 0); + setWindowTitle(name_paraWindow); + setObjectName(name_paraWindow); + resize(100, 50); - myLayout = new QBoxLayout(QBoxLayout::TopToBottom); - myLayout->setObjectName(QString::fromUtf8("boxLayout")); - myLayout->setContentsMargins(0, 0, 0, 0); - myLayout->setSpacing(0); - myLayout->setMargin(0); - myLayout->setSizeConstraint(QLayout::SetFixedSize); - setLayout(myLayout); + myLayout = new QBoxLayout(QBoxLayout::TopToBottom); + myLayout->setObjectName(QString::fromUtf8("boxLayout")); + myLayout->setContentsMargins(0, 0, 0, 0); + myLayout->setSpacing(0); + myLayout->setMargin(0); + myLayout->setSizeConstraint(QLayout::SetFixedSize); + setLayout(myLayout); - hide(); + hide(); } void CvWinProperties::closeEvent(QCloseEvent* e) { - e->accept(); //intersept the close event (not sure I really need it) - //an hide event is also sent. I will intercept it and do some processing + e->accept(); //intersept the close event (not sure I really need it) + //an hide event is also sent. I will intercept it and do some processing } -void CvWinProperties::showEvent(QShowEvent* event) +void CvWinProperties::showEvent(QShowEvent* evnt) { - //why -1,-1 ?: do this trick because the first time the code is run, - //no value pos was saved so we let Qt move the window in the middle of its parent (event ignored). - //then hide will save the last position and thus, we want to retreive it (event accepted). - QPoint mypos(-1, -1); - QSettings settings("OpenCV2", windowTitle()); - mypos = settings.value("pos", mypos).toPoint(); + //why -1,-1 ?: do this trick because the first time the code is run, + //no value pos was saved so we let Qt move the window in the middle of its parent (event ignored). + //then hide will save the last position and thus, we want to retreive it (event accepted). + QPoint mypos(-1, -1); + QSettings settings("OpenCV2", windowTitle()); + mypos = settings.value("pos", mypos).toPoint(); - if (mypos.x() >= 0) - { - move(mypos); - event->accept(); - } - else + if (mypos.x() >= 0) + { + move(mypos); + evnt->accept(); + } + else { - event->ignore(); - } + evnt->ignore(); + } } -void CvWinProperties::hideEvent(QHideEvent* event) +void CvWinProperties::hideEvent(QHideEvent* evnt) { - QSettings settings("OpenCV2", windowTitle()); - settings.setValue("pos", pos()); //there is an offset of 6 pixels (so the window's position is wrong -- why ?) - event->accept(); + QSettings settings("OpenCV2", windowTitle()); + settings.setValue("pos", pos()); //there is an offset of 6 pixels (so the window's position is wrong -- why ?) + evnt->accept(); } CvWinProperties::~CvWinProperties() { - //clear the setting pos - QSettings settings("OpenCV2", windowTitle()); - settings.remove("pos"); + //clear the setting pos + QSettings settings("OpenCV2", windowTitle()); + settings.remove("pos"); } @@ -1545,102 +1545,102 @@ CvWinProperties::~CvWinProperties() CvWindow::CvWindow(QString name, int arg2) { - type = type_CvWindow; - moveToThread(qApp->instance()->thread()); + type = type_CvWindow; + moveToThread(qApp->instance()->thread()); - param_flags = arg2 & 0x0000000F; - param_gui_mode = arg2 & 0x000000F0; - param_ratio_mode = arg2 & 0x00000F00; + param_flags = arg2 & 0x0000000F; + param_gui_mode = arg2 & 0x000000F0; + param_ratio_mode = arg2 & 0x00000F00; - //setAttribute(Qt::WA_DeleteOnClose); //in other case, does not release memory - setContentsMargins(0, 0, 0, 0); - setWindowTitle(name); + //setAttribute(Qt::WA_DeleteOnClose); //in other case, does not release memory + setContentsMargins(0, 0, 0, 0); + setWindowTitle(name); setObjectName(name); setFocus( Qt::PopupFocusReason ); //#1695 arrow keys are not recieved without the explicit focus - resize(400, 300); - setMinimumSize(1, 1); + resize(400, 300); + setMinimumSize(1, 1); - //1: create control panel - if (!global_control_panel) - global_control_panel = createParameterWindow(); + //1: create control panel + if (!global_control_panel) + global_control_panel = createParameterWindow(); - //2: Layouts - createBarLayout(); - createGlobalLayout(); + //2: Layouts + createBarLayout(); + createGlobalLayout(); - //3: my view + //3: my view #ifndef HAVE_QT_OPENGL if (arg2 & CV_WINDOW_OPENGL) CV_Error( CV_OpenGlNotSupported, "Library was built without OpenGL support" ); - mode_display = CV_MODE_NORMAL; + mode_display = CV_MODE_NORMAL; #else mode_display = arg2 & CV_WINDOW_OPENGL ? CV_MODE_OPENGL : CV_MODE_NORMAL; if (mode_display == CV_MODE_OPENGL) param_gui_mode = CV_GUI_NORMAL; #endif - createView(); + createView(); - //4: shortcuts and actions - //5: toolBar and statusbar - if (param_gui_mode == CV_GUI_EXPANDED) - { + //4: shortcuts and actions + //5: toolBar and statusbar + if (param_gui_mode == CV_GUI_EXPANDED) + { createActions(); createShortcuts(); - createToolBar(); - createStatusBar(); - } + createToolBar(); + createStatusBar(); + } - //Now attach everything - if (myToolBar) - myGlobalLayout->addWidget(myToolBar, Qt::AlignCenter); + //Now attach everything + if (myToolBar) + myGlobalLayout->addWidget(myToolBar, Qt::AlignCenter); - myGlobalLayout->addWidget(myView->getWidget(), Qt::AlignCenter); + myGlobalLayout->addWidget(myView->getWidget(), Qt::AlignCenter); - myGlobalLayout->addLayout(myBarLayout, Qt::AlignCenter); + myGlobalLayout->addLayout(myBarLayout, Qt::AlignCenter); - if (myStatusBar) - myGlobalLayout->addWidget(myStatusBar, Qt::AlignCenter); + if (myStatusBar) + myGlobalLayout->addWidget(myStatusBar, Qt::AlignCenter); - setLayout(myGlobalLayout); - show(); + setLayout(myGlobalLayout); + show(); } CvWindow::~CvWindow() { - if (guiMainThread) - guiMainThread->isLastWindow(); + if (guiMainThread) + guiMainThread->isLastWindow(); } void CvWindow::setMouseCallBack(CvMouseCallback callback, void* param) { - myView->setMouseCallBack(callback, param); + myView->setMouseCallBack(callback, param); } void CvWindow::writeSettings() { - //organisation and application's name - QSettings settings("OpenCV2", QFileInfo(QApplication::applicationFilePath()).fileName()); + //organisation and application's name + QSettings settings("OpenCV2", QFileInfo(QApplication::applicationFilePath()).fileName()); - settings.setValue("pos", pos()); - settings.setValue("size", size()); - settings.setValue("mode_resize" ,param_flags); - settings.setValue("mode_gui", param_gui_mode); + settings.setValue("pos", pos()); + settings.setValue("size", size()); + settings.setValue("mode_resize" ,param_flags); + settings.setValue("mode_gui", param_gui_mode); myView->writeSettings(settings); - icvSaveTrackbars(&settings); + icvSaveTrackbars(&settings); - if (global_control_panel) - { - icvSaveControlPanel(); - settings.setValue("posPanel", global_control_panel->pos()); - } + if (global_control_panel) + { + icvSaveControlPanel(); + settings.setValue("posPanel", global_control_panel->pos()); + } } @@ -1648,30 +1648,30 @@ void CvWindow::writeSettings() //TODO: load CV_GUI flag (done) and act accordingly (create win property if needed and attach trackbars) void CvWindow::readSettings() { - //organisation and application's name - QSettings settings("OpenCV2", QFileInfo(QApplication::applicationFilePath()).fileName()); + //organisation and application's name + QSettings settings("OpenCV2", QFileInfo(QApplication::applicationFilePath()).fileName()); - QPoint pos = settings.value("pos", QPoint(200, 200)).toPoint(); - QSize size = settings.value("size", QSize(400, 400)).toSize(); + QPoint _pos = settings.value("pos", QPoint(200, 200)).toPoint(); + QSize _size = settings.value("size", QSize(400, 400)).toSize(); - param_flags = settings.value("mode_resize", param_flags).toInt(); - param_gui_mode = settings.value("mode_gui", param_gui_mode).toInt(); + param_flags = settings.value("mode_resize", param_flags).toInt(); + param_gui_mode = settings.value("mode_gui", param_gui_mode).toInt(); - param_flags = settings.value("mode_resize", param_flags).toInt(); + param_flags = settings.value("mode_resize", param_flags).toInt(); - myView->readSettings(settings); + myView->readSettings(settings); - //trackbar here - icvLoadTrackbars(&settings); + //trackbar here + icvLoadTrackbars(&settings); - resize(size); - move(pos); + resize(_size); + move(_pos); - if (global_control_panel) - { - icvLoadControlPanel(); - global_control_panel->move(settings.value("posPanel", global_control_panel->pos()).toPoint()); - } + if (global_control_panel) + { + icvLoadControlPanel(); + global_control_panel->move(settings.value("posPanel", global_control_panel->pos()).toPoint()); + } } @@ -1687,30 +1687,30 @@ void CvWindow::setRatio(int flags) } -int CvWindow::getPropWindow() -{ - return param_flags; +int CvWindow::getPropWindow() +{ + return param_flags; } void CvWindow::setPropWindow(int flags) { if (param_flags == flags) //nothing to do - return; + return; switch(flags) { case CV_WINDOW_NORMAL: - myGlobalLayout->setSizeConstraint(QLayout::SetMinAndMaxSize); - param_flags = flags; + myGlobalLayout->setSizeConstraint(QLayout::SetMinAndMaxSize); + param_flags = flags; - break; + break; case CV_WINDOW_AUTOSIZE: - myGlobalLayout->setSizeConstraint(QLayout::SetFixedSize); - param_flags = flags; + myGlobalLayout->setSizeConstraint(QLayout::SetFixedSize); + param_flags = flags; - break; + break; default: ; @@ -1722,36 +1722,36 @@ void CvWindow::toggleFullScreen(int flags) { if (isFullScreen() && flags == CV_WINDOW_NORMAL) { - showTools(); - showNormal(); - return; + showTools(); + showNormal(); + return; } if (!isFullScreen() && flags == CV_WINDOW_FULLSCREEN) { - hideTools(); - showFullScreen(); - return; + hideTools(); + showFullScreen(); + return; } } void CvWindow::updateImage(void* arr) { - myView->updateImage(arr); + myView->updateImage(arr); } void CvWindow::displayInfo(QString text, int delayms) { - myView->startDisplayInfo(text, delayms); + myView->startDisplayInfo(text, delayms); } void CvWindow::displayStatusBar(QString text, int delayms) { if (myStatusBar) - myStatusBar->showMessage(text, delayms); + myStatusBar->showMessage(text, delayms); } @@ -1763,74 +1763,74 @@ void CvWindow::enablePropertiesButton() CvButtonbar* CvWindow::createButtonBar(QString name_bar) { - QPointer t = new CvButtonbar(global_control_panel, name_bar); - t->setAlignment(Qt::AlignHCenter); + QPointer t = new CvButtonbar(global_control_panel, name_bar); + t->setAlignment(Qt::AlignHCenter); - QPointer myLayout = global_control_panel->myLayout; + QPointer myLayout = global_control_panel->myLayout; - myLayout->insertLayout(myLayout->count(), t); + myLayout->insertLayout(myLayout->count(), t); - return t; + return t; } void CvWindow::addSlider(CvWindow* w, QString name, int* value, int count, CvTrackbarCallback on_change) { - QPointer t = new CvTrackbar(w, name, value, count, on_change); - t->setAlignment(Qt::AlignHCenter); + QPointer t = new CvTrackbar(w, name, value, count, on_change); + t->setAlignment(Qt::AlignHCenter); - QPointer myLayout; + QPointer myLayout; - if (w) - { - myLayout = w->myBarLayout; - } - else - { - myLayout = global_control_panel->myLayout; + if (w) + { + myLayout = w->myBarLayout; + } + else + { + myLayout = global_control_panel->myLayout; - //if first one, enable control panel - if (myLayout->count() == 0) - guiMainThread->enablePropertiesButtonEachWindow(); - } + //if first one, enable control panel + if (myLayout->count() == 0) + guiMainThread->enablePropertiesButtonEachWindow(); + } - myLayout->insertLayout(myLayout->count(), t); + myLayout->insertLayout(myLayout->count(), t); } void CvWindow::addSlider2(CvWindow* w, QString name, int* value, int count, CvTrackbarCallback2 on_change, void* userdata) { - QPointer t = new CvTrackbar(w, name, value, count, on_change, userdata); - t->setAlignment(Qt::AlignHCenter); + QPointer t = new CvTrackbar(w, name, value, count, on_change, userdata); + t->setAlignment(Qt::AlignHCenter); - QPointer myLayout; + QPointer myLayout; - if (w) - { - myLayout = w->myBarLayout; - } - else - { - myLayout = global_control_panel->myLayout; + if (w) + { + myLayout = w->myBarLayout; + } + else + { + myLayout = global_control_panel->myLayout; - //if first one, enable control panel - if (myLayout->count() == 0) - guiMainThread->enablePropertiesButtonEachWindow(); - } + //if first one, enable control panel + if (myLayout->count() == 0) + guiMainThread->enablePropertiesButtonEachWindow(); + } - myLayout->insertLayout(myLayout->count(), t); + myLayout->insertLayout(myLayout->count(), t); } void CvWindow::setOpenGlDrawCallback(CvOpenGlDrawCallback callback, void* userdata) { - myView->setOpenGlDrawCallback(callback, userdata); + myView->setOpenGlDrawCallback(callback, userdata); } void CvWindow::setOpenGlCleanCallback(CvOpenGlCleanCallback callback, void* userdata) { - myView->setOpenGlCleanCallback(callback, userdata); + myView->setOpenGlCleanCallback(callback, userdata); } @@ -1852,36 +1852,36 @@ bool CvWindow::isOpenGl() } -void CvWindow::setViewportSize(QSize size) +void CvWindow::setViewportSize(QSize _size) { - myView->getWidget()->resize(size); - myView->setSize(size); + myView->getWidget()->resize(_size); + myView->setSize(_size); } void CvWindow::createBarLayout() { - myBarLayout = new QBoxLayout(QBoxLayout::TopToBottom); - myBarLayout->setObjectName(QString::fromUtf8("barLayout")); - myBarLayout->setContentsMargins(0, 0, 0, 0); - myBarLayout->setSpacing(0); - myBarLayout->setMargin(0); + myBarLayout = new QBoxLayout(QBoxLayout::TopToBottom); + myBarLayout->setObjectName(QString::fromUtf8("barLayout")); + myBarLayout->setContentsMargins(0, 0, 0, 0); + myBarLayout->setSpacing(0); + myBarLayout->setMargin(0); } void CvWindow::createGlobalLayout() { - myGlobalLayout = new QBoxLayout(QBoxLayout::TopToBottom); - myGlobalLayout->setObjectName(QString::fromUtf8("boxLayout")); - myGlobalLayout->setContentsMargins(0, 0, 0, 0); - myGlobalLayout->setSpacing(0); - myGlobalLayout->setMargin(0); - setMinimumSize(1, 1); + myGlobalLayout = new QBoxLayout(QBoxLayout::TopToBottom); + myGlobalLayout->setObjectName(QString::fromUtf8("boxLayout")); + myGlobalLayout->setContentsMargins(0, 0, 0, 0); + myGlobalLayout->setSpacing(0); + myGlobalLayout->setMargin(0); + setMinimumSize(1, 1); - if (param_flags == CV_WINDOW_AUTOSIZE) - myGlobalLayout->setSizeConstraint(QLayout::SetFixedSize); - else if (param_flags == CV_WINDOW_NORMAL) - myGlobalLayout->setSizeConstraint(QLayout::SetMinAndMaxSize); + if (param_flags == CV_WINDOW_AUTOSIZE) + myGlobalLayout->setSizeConstraint(QLayout::SetFixedSize); + else if (param_flags == CV_WINDOW_NORMAL) + myGlobalLayout->setSizeConstraint(QLayout::SetMinAndMaxSize); } @@ -1889,369 +1889,369 @@ void CvWindow::createView() { #ifdef HAVE_QT_OPENGL if (isOpenGl()) - myView = new OpenGlViewPort(this); + myView = new OpenGlViewPort(this); else #endif - myView = new DefaultViewPort(this, param_ratio_mode); + myView = new DefaultViewPort(this, param_ratio_mode); } void CvWindow::createActions() { - vect_QActions.resize(10); + vect_QActions.resize(10); QWidget* view = myView->getWidget(); - //if the shortcuts are changed in window_QT.h, we need to update the tooltip manually - vect_QActions[0] = new QAction(QIcon(":/left-icon"), "Panning left (CTRL+arrowLEFT)", this); - vect_QActions[0]->setIconVisibleInMenu(true); - QObject::connect(vect_QActions[0], SIGNAL(triggered()), view, SLOT(siftWindowOnLeft())); + //if the shortcuts are changed in window_QT.h, we need to update the tooltip manually + vect_QActions[0] = new QAction(QIcon(":/left-icon"), "Panning left (CTRL+arrowLEFT)", this); + vect_QActions[0]->setIconVisibleInMenu(true); + QObject::connect(vect_QActions[0], SIGNAL(triggered()), view, SLOT(siftWindowOnLeft())); - vect_QActions[1] = new QAction(QIcon(":/right-icon"), "Panning right (CTRL+arrowRIGHT)", this); - vect_QActions[1]->setIconVisibleInMenu(true); - QObject::connect(vect_QActions[1], SIGNAL(triggered()), view, SLOT(siftWindowOnRight())); + vect_QActions[1] = new QAction(QIcon(":/right-icon"), "Panning right (CTRL+arrowRIGHT)", this); + vect_QActions[1]->setIconVisibleInMenu(true); + QObject::connect(vect_QActions[1], SIGNAL(triggered()), view, SLOT(siftWindowOnRight())); - vect_QActions[2] = new QAction(QIcon(":/up-icon"), "Panning up (CTRL+arrowUP)", this); - vect_QActions[2]->setIconVisibleInMenu(true); - QObject::connect(vect_QActions[2], SIGNAL(triggered()), view, SLOT(siftWindowOnUp())); + vect_QActions[2] = new QAction(QIcon(":/up-icon"), "Panning up (CTRL+arrowUP)", this); + vect_QActions[2]->setIconVisibleInMenu(true); + QObject::connect(vect_QActions[2], SIGNAL(triggered()), view, SLOT(siftWindowOnUp())); - vect_QActions[3] = new QAction(QIcon(":/down-icon"), "Panning down (CTRL+arrowDOWN)", this); - vect_QActions[3]->setIconVisibleInMenu(true); - QObject::connect(vect_QActions[3], SIGNAL(triggered()), view, SLOT(siftWindowOnDown()) ); + vect_QActions[3] = new QAction(QIcon(":/down-icon"), "Panning down (CTRL+arrowDOWN)", this); + vect_QActions[3]->setIconVisibleInMenu(true); + QObject::connect(vect_QActions[3], SIGNAL(triggered()), view, SLOT(siftWindowOnDown()) ); - vect_QActions[4] = new QAction(QIcon(":/zoom_x1-icon"), "Zoom x1 (CTRL+P)", this); - vect_QActions[4]->setIconVisibleInMenu(true); - QObject::connect(vect_QActions[4], SIGNAL(triggered()), view, SLOT(resetZoom())); + vect_QActions[4] = new QAction(QIcon(":/zoom_x1-icon"), "Zoom x1 (CTRL+P)", this); + vect_QActions[4]->setIconVisibleInMenu(true); + QObject::connect(vect_QActions[4], SIGNAL(triggered()), view, SLOT(resetZoom())); - vect_QActions[5] = new QAction(QIcon(":/imgRegion-icon"), tr("Zoom x%1 (see label) (CTRL+X)").arg(threshold_zoom_img_region), this); - vect_QActions[5]->setIconVisibleInMenu(true); - QObject::connect(vect_QActions[5], SIGNAL(triggered()), view, SLOT(imgRegion())); + vect_QActions[5] = new QAction(QIcon(":/imgRegion-icon"), tr("Zoom x%1 (see label) (CTRL+X)").arg(threshold_zoom_img_region), this); + vect_QActions[5]->setIconVisibleInMenu(true); + QObject::connect(vect_QActions[5], SIGNAL(triggered()), view, SLOT(imgRegion())); - vect_QActions[6] = new QAction(QIcon(":/zoom_in-icon"), "Zoom in (CTRL++)", this); - vect_QActions[6]->setIconVisibleInMenu(true); - QObject::connect(vect_QActions[6], SIGNAL(triggered()), view, SLOT(ZoomIn())); + vect_QActions[6] = new QAction(QIcon(":/zoom_in-icon"), "Zoom in (CTRL++)", this); + vect_QActions[6]->setIconVisibleInMenu(true); + QObject::connect(vect_QActions[6], SIGNAL(triggered()), view, SLOT(ZoomIn())); - vect_QActions[7] = new QAction(QIcon(":/zoom_out-icon"), "Zoom out (CTRL+-)", this); - vect_QActions[7]->setIconVisibleInMenu(true); - QObject::connect(vect_QActions[7], SIGNAL(triggered()), view, SLOT(ZoomOut())); + vect_QActions[7] = new QAction(QIcon(":/zoom_out-icon"), "Zoom out (CTRL+-)", this); + vect_QActions[7]->setIconVisibleInMenu(true); + QObject::connect(vect_QActions[7], SIGNAL(triggered()), view, SLOT(ZoomOut())); - vect_QActions[8] = new QAction(QIcon(":/save-icon"), "Save current image (CTRL+S)", this); - vect_QActions[8]->setIconVisibleInMenu(true); - QObject::connect(vect_QActions[8], SIGNAL(triggered()), view, SLOT(saveView())); + vect_QActions[8] = new QAction(QIcon(":/save-icon"), "Save current image (CTRL+S)", this); + vect_QActions[8]->setIconVisibleInMenu(true); + QObject::connect(vect_QActions[8], SIGNAL(triggered()), view, SLOT(saveView())); - vect_QActions[9] = new QAction(QIcon(":/properties-icon"), "Display properties window (CTRL+P)", this); - vect_QActions[9]->setIconVisibleInMenu(true); - QObject::connect(vect_QActions[9], SIGNAL(triggered()), this, SLOT(displayPropertiesWin())); + vect_QActions[9] = new QAction(QIcon(":/properties-icon"), "Display properties window (CTRL+P)", this); + vect_QActions[9]->setIconVisibleInMenu(true); + QObject::connect(vect_QActions[9], SIGNAL(triggered()), this, SLOT(displayPropertiesWin())); - if (global_control_panel->myLayout->count() == 0) - vect_QActions[9]->setDisabled(true); + if (global_control_panel->myLayout->count() == 0) + vect_QActions[9]->setDisabled(true); } void CvWindow::createShortcuts() { - vect_QShortcuts.resize(10); + vect_QShortcuts.resize(10); QWidget* view = myView->getWidget(); - vect_QShortcuts[0] = new QShortcut(shortcut_panning_left, this); - QObject::connect(vect_QShortcuts[0], SIGNAL(activated()), view, SLOT(siftWindowOnLeft())); + vect_QShortcuts[0] = new QShortcut(shortcut_panning_left, this); + QObject::connect(vect_QShortcuts[0], SIGNAL(activated()), view, SLOT(siftWindowOnLeft())); - vect_QShortcuts[1] = new QShortcut(shortcut_panning_right, this); - QObject::connect(vect_QShortcuts[1], SIGNAL(activated()), view, SLOT(siftWindowOnRight())); + vect_QShortcuts[1] = new QShortcut(shortcut_panning_right, this); + QObject::connect(vect_QShortcuts[1], SIGNAL(activated()), view, SLOT(siftWindowOnRight())); - vect_QShortcuts[2] = new QShortcut(shortcut_panning_up, this); - QObject::connect(vect_QShortcuts[2], SIGNAL(activated()), view, SLOT(siftWindowOnUp())); + vect_QShortcuts[2] = new QShortcut(shortcut_panning_up, this); + QObject::connect(vect_QShortcuts[2], SIGNAL(activated()), view, SLOT(siftWindowOnUp())); - vect_QShortcuts[3] = new QShortcut(shortcut_panning_down, this); - QObject::connect(vect_QShortcuts[3], SIGNAL(activated()), view, SLOT(siftWindowOnDown())); + vect_QShortcuts[3] = new QShortcut(shortcut_panning_down, this); + QObject::connect(vect_QShortcuts[3], SIGNAL(activated()), view, SLOT(siftWindowOnDown())); - vect_QShortcuts[4] = new QShortcut(shortcut_zoom_normal, this); - QObject::connect(vect_QShortcuts[4], SIGNAL(activated()), view, SLOT(resetZoom())); + vect_QShortcuts[4] = new QShortcut(shortcut_zoom_normal, this); + QObject::connect(vect_QShortcuts[4], SIGNAL(activated()), view, SLOT(resetZoom())); - vect_QShortcuts[5] = new QShortcut(shortcut_zoom_imgRegion, this); - QObject::connect(vect_QShortcuts[5], SIGNAL(activated()), view, SLOT(imgRegion())); + vect_QShortcuts[5] = new QShortcut(shortcut_zoom_imgRegion, this); + QObject::connect(vect_QShortcuts[5], SIGNAL(activated()), view, SLOT(imgRegion())); - vect_QShortcuts[6] = new QShortcut(shortcut_zoom_in, this); - QObject::connect(vect_QShortcuts[6], SIGNAL(activated()), view, SLOT(ZoomIn())); + vect_QShortcuts[6] = new QShortcut(shortcut_zoom_in, this); + QObject::connect(vect_QShortcuts[6], SIGNAL(activated()), view, SLOT(ZoomIn())); - vect_QShortcuts[7] = new QShortcut(shortcut_zoom_out, this); - QObject::connect(vect_QShortcuts[7], SIGNAL(activated()), view, SLOT(ZoomOut())); + vect_QShortcuts[7] = new QShortcut(shortcut_zoom_out, this); + QObject::connect(vect_QShortcuts[7], SIGNAL(activated()), view, SLOT(ZoomOut())); - vect_QShortcuts[8] = new QShortcut(shortcut_save_img, this); - QObject::connect(vect_QShortcuts[8], SIGNAL(activated()), view, SLOT(saveView())); + vect_QShortcuts[8] = new QShortcut(shortcut_save_img, this); + QObject::connect(vect_QShortcuts[8], SIGNAL(activated()), view, SLOT(saveView())); - vect_QShortcuts[9] = new QShortcut(shortcut_properties_win, this); - QObject::connect(vect_QShortcuts[9], SIGNAL(activated()), this, SLOT(displayPropertiesWin())); + vect_QShortcuts[9] = new QShortcut(shortcut_properties_win, this); + QObject::connect(vect_QShortcuts[9], SIGNAL(activated()), this, SLOT(displayPropertiesWin())); } void CvWindow::createToolBar() { - myToolBar = new QToolBar(this); - myToolBar->setFloatable(false); //is not a window - myToolBar->setFixedHeight(28); - myToolBar->setMinimumWidth(1); + myToolBar = new QToolBar(this); + myToolBar->setFloatable(false); //is not a window + myToolBar->setFixedHeight(28); + myToolBar->setMinimumWidth(1); - foreach (QAction *a, vect_QActions) - myToolBar->addAction(a); + foreach (QAction *a, vect_QActions) + myToolBar->addAction(a); } void CvWindow::createStatusBar() { - myStatusBar = new QStatusBar(this); - myStatusBar->setSizeGripEnabled(false); - myStatusBar->setFixedHeight(20); - myStatusBar->setMinimumWidth(1); - myStatusBar_msg = new QLabel; + myStatusBar = new QStatusBar(this); + myStatusBar->setSizeGripEnabled(false); + myStatusBar->setFixedHeight(20); + myStatusBar->setMinimumWidth(1); + myStatusBar_msg = new QLabel; - //I comment this because if we change the style, myview (the picture) - //will not be the correct size anymore (will lost 2 pixel because of the borders) + //I comment this because if we change the style, myview (the picture) + //will not be the correct size anymore (will lost 2 pixel because of the borders) - //myStatusBar_msg->setFrameStyle(QFrame::Raised); + //myStatusBar_msg->setFrameStyle(QFrame::Raised); - myStatusBar_msg->setAlignment(Qt::AlignHCenter); - myStatusBar->addWidget(myStatusBar_msg); + myStatusBar_msg->setAlignment(Qt::AlignHCenter); + myStatusBar->addWidget(myStatusBar_msg); } void CvWindow::hideTools() { - if (myToolBar) - myToolBar->hide(); + if (myToolBar) + myToolBar->hide(); - if (myStatusBar) - myStatusBar->hide(); + if (myStatusBar) + myStatusBar->hide(); - if (global_control_panel) - global_control_panel->hide(); + if (global_control_panel) + global_control_panel->hide(); } void CvWindow::showTools() { - if (myToolBar) - myToolBar->show(); + if (myToolBar) + myToolBar->show(); - if (myStatusBar) - myStatusBar->show(); + if (myStatusBar) + myStatusBar->show(); } CvWinProperties* CvWindow::createParameterWindow() { - QString name_paraWindow = QFileInfo(QApplication::applicationFilePath()).fileName() + " settings"; + QString name_paraWindow = QFileInfo(QApplication::applicationFilePath()).fileName() + " settings"; - CvWinProperties* result = new CvWinProperties(name_paraWindow, guiMainThread); + CvWinProperties* result = new CvWinProperties(name_paraWindow, guiMainThread); - return result; + return result; } void CvWindow::displayPropertiesWin() { - if (global_control_panel->isHidden()) - global_control_panel->show(); - else - global_control_panel->hide(); + if (global_control_panel->isHidden()) + global_control_panel->show(); + else + global_control_panel->hide(); } //Need more test here ! -void CvWindow::keyPressEvent(QKeyEvent *event) +void CvWindow::keyPressEvent(QKeyEvent *evnt) { - //see http://doc.trolltech.com/4.6/qt.html#Key-enum - int key = event->key(); + //see http://doc.trolltech.com/4.6/qt.html#Key-enum + int key = evnt->key(); Qt::Key qtkey = static_cast(key); char asciiCode = QTest::keyToAscii(qtkey); if (asciiCode != 0) key = static_cast(asciiCode); else - key = event->nativeVirtualKey(); //same codes as returned by GTK-based backend + key = evnt->nativeVirtualKey(); //same codes as returned by GTK-based backend - //control plus (Z, +, -, up, down, left, right) are used for zoom/panning functions - if (event->modifiers() != Qt::ControlModifier) + //control plus (Z, +, -, up, down, left, right) are used for zoom/panning functions + if (evnt->modifiers() != Qt::ControlModifier) { - mutexKey.lock(); - last_key = key; - mutexKey.unlock(); - key_pressed.wakeAll(); - //event->accept(); - } + mutexKey.lock(); + last_key = key; + mutexKey.unlock(); + key_pressed.wakeAll(); + //evnt->accept(); + } - QWidget::keyPressEvent(event); + QWidget::keyPressEvent(evnt); } void CvWindow::icvLoadControlPanel() { - QSettings settings("OpenCV2", QFileInfo(QApplication::applicationFilePath()).fileName() + " control panel"); - - int size = settings.beginReadArray("bars"); + QSettings settings("OpenCV2", QFileInfo(QApplication::applicationFilePath()).fileName() + " control panel"); + + int bsize = settings.beginReadArray("bars"); - if (size == global_control_panel->myLayout->layout()->count()) + if (bsize == global_control_panel->myLayout->layout()->count()) { - for (int i = 0; i < size; ++i) + for (int i = 0; i < bsize; ++i) { - CvBar* t = (CvBar*) global_control_panel->myLayout->layout()->itemAt(i); - settings.setArrayIndex(i); - if (t->type == type_CvTrackbar) - { - if (t->name_bar == settings.value("namebar").toString()) - { - ((CvTrackbar*)t)->slider->setValue(settings.value("valuebar").toInt()); - } - } - if (t->type == type_CvButtonbar) - { - int subsize = settings.beginReadArray(QString("buttonbar")+i); + CvBar* t = (CvBar*) global_control_panel->myLayout->layout()->itemAt(i); + settings.setArrayIndex(i); + if (t->type == type_CvTrackbar) + { + if (t->name_bar == settings.value("namebar").toString()) + { + ((CvTrackbar*)t)->slider->setValue(settings.value("valuebar").toInt()); + } + } + if (t->type == type_CvButtonbar) + { + int subsize = settings.beginReadArray(QString("buttonbar")+i); + + if ( subsize == ((CvButtonbar*)t)->layout()->count() ) + icvLoadButtonbar((CvButtonbar*)t,&settings); - if ( subsize == ((CvButtonbar*)t)->layout()->count() ) - icvLoadButtonbar((CvButtonbar*)t,&settings); - - settings.endArray(); - } - } + settings.endArray(); + } + } } - settings.endArray(); + settings.endArray(); } void CvWindow::icvSaveControlPanel() { - QSettings settings("OpenCV2", QFileInfo(QApplication::applicationFilePath()).fileName()+" control panel"); + QSettings settings("OpenCV2", QFileInfo(QApplication::applicationFilePath()).fileName()+" control panel"); - settings.beginWriteArray("bars"); + settings.beginWriteArray("bars"); - for (int i = 0; i < global_control_panel->myLayout->layout()->count(); ++i) + for (int i = 0; i < global_control_panel->myLayout->layout()->count(); ++i) { - CvBar* t = (CvBar*) global_control_panel->myLayout->layout()->itemAt(i); - settings.setArrayIndex(i); - if (t->type == type_CvTrackbar) - { - settings.setValue("namebar", QString(t->name_bar)); - settings.setValue("valuebar",((CvTrackbar*)t)->slider->value()); - } - if (t->type == type_CvButtonbar) - { - settings.beginWriteArray(QString("buttonbar")+i); - icvSaveButtonbar((CvButtonbar*)t,&settings); - settings.endArray(); - } - } + CvBar* t = (CvBar*) global_control_panel->myLayout->layout()->itemAt(i); + settings.setArrayIndex(i); + if (t->type == type_CvTrackbar) + { + settings.setValue("namebar", QString(t->name_bar)); + settings.setValue("valuebar",((CvTrackbar*)t)->slider->value()); + } + if (t->type == type_CvButtonbar) + { + settings.beginWriteArray(QString("buttonbar")+i); + icvSaveButtonbar((CvButtonbar*)t,&settings); + settings.endArray(); + } + } - settings.endArray(); + settings.endArray(); } void CvWindow::icvSaveButtonbar(CvButtonbar* b, QSettings* settings) { - for (int i = 0, count = b->layout()->count(); i < count; ++i) + for (int i = 0, count = b->layout()->count(); i < count; ++i) { - settings->setArrayIndex(i); + settings->setArrayIndex(i); - QWidget* temp = (QWidget*) b->layout()->itemAt(i)->widget(); + QWidget* temp = (QWidget*) b->layout()->itemAt(i)->widget(); QString myclass(QLatin1String(temp->metaObject()->className())); - if (myclass == "CvPushButton") - { - CvPushButton* button = (CvPushButton*) temp; - settings->setValue("namebutton", button->text()); - settings->setValue("valuebutton", int(button->isChecked())); - } - else if (myclass == "CvCheckBox") - { - CvCheckBox* button = (CvCheckBox*) temp; - settings->setValue("namebutton", button->text()); - settings->setValue("valuebutton", int(button->isChecked())); - } - else if (myclass == "CvRadioButton") - { - CvRadioButton* button = (CvRadioButton*) temp; - settings->setValue("namebutton", button->text()); - settings->setValue("valuebutton", int(button->isChecked())); - } - } + if (myclass == "CvPushButton") + { + CvPushButton* button = (CvPushButton*) temp; + settings->setValue("namebutton", button->text()); + settings->setValue("valuebutton", int(button->isChecked())); + } + else if (myclass == "CvCheckBox") + { + CvCheckBox* button = (CvCheckBox*) temp; + settings->setValue("namebutton", button->text()); + settings->setValue("valuebutton", int(button->isChecked())); + } + else if (myclass == "CvRadioButton") + { + CvRadioButton* button = (CvRadioButton*) temp; + settings->setValue("namebutton", button->text()); + settings->setValue("valuebutton", int(button->isChecked())); + } + } } void CvWindow::icvLoadButtonbar(CvButtonbar* b, QSettings* settings) { - for (int i = 0, count = b->layout()->count(); i < count; ++i) - { - settings->setArrayIndex(i); + for (int i = 0, count = b->layout()->count(); i < count; ++i) + { + settings->setArrayIndex(i); - QWidget* temp = (QWidget*) b->layout()->itemAt(i)->widget(); - QString myclass(QLatin1String(temp->metaObject()->className())); + QWidget* temp = (QWidget*) b->layout()->itemAt(i)->widget(); + QString myclass(QLatin1String(temp->metaObject()->className())); - if (myclass == "CvPushButton") - { - CvPushButton* button = (CvPushButton*) temp; + if (myclass == "CvPushButton") + { + CvPushButton* button = (CvPushButton*) temp; - if (button->text() == settings->value("namebutton").toString()) - button->setChecked(settings->value("valuebutton").toInt()); - } - else if (myclass == "CvCheckBox") - { - CvCheckBox* button = (CvCheckBox*) temp; + if (button->text() == settings->value("namebutton").toString()) + button->setChecked(settings->value("valuebutton").toInt()); + } + else if (myclass == "CvCheckBox") + { + CvCheckBox* button = (CvCheckBox*) temp; - if (button->text() == settings->value("namebutton").toString()) - button->setChecked(settings->value("valuebutton").toInt()); - } - else if (myclass == "CvRadioButton") - { - CvRadioButton* button = (CvRadioButton*) temp; + if (button->text() == settings->value("namebutton").toString()) + button->setChecked(settings->value("valuebutton").toInt()); + } + else if (myclass == "CvRadioButton") + { + CvRadioButton* button = (CvRadioButton*) temp; - if (button->text() == settings->value("namebutton").toString()) - button->setChecked(settings->value("valuebutton").toInt()); - } + if (button->text() == settings->value("namebutton").toString()) + button->setChecked(settings->value("valuebutton").toInt()); + } - } + } } void CvWindow::icvLoadTrackbars(QSettings* settings) { - int size = settings->beginReadArray("trackbars"); + int bsize = settings->beginReadArray("trackbars"); - //trackbar are saved in the same order, so no need to use icvFindTrackbarByName + //trackbar are saved in the same order, so no need to use icvFindTrackbarByName - if (myBarLayout->layout()->count() == size) //if not the same number, the window saved and loaded is not the same (nb trackbar not equal) + if (myBarLayout->layout()->count() == bsize) //if not the same number, the window saved and loaded is not the same (nb trackbar not equal) { - for (int i = 0; i < size; ++i) - { - settings->setArrayIndex(i); + for (int i = 0; i < bsize; ++i) + { + settings->setArrayIndex(i); - CvTrackbar* t = (CvTrackbar*) myBarLayout->layout()->itemAt(i); + CvTrackbar* t = (CvTrackbar*) myBarLayout->layout()->itemAt(i); - if (t->name_bar == settings->value("name").toString()) - t->slider->setValue(settings->value("value").toInt()); + if (t->name_bar == settings->value("name").toString()) + t->slider->setValue(settings->value("value").toInt()); - } + } } - settings->endArray(); + settings->endArray(); } void CvWindow::icvSaveTrackbars(QSettings* settings) { - settings->beginWriteArray("trackbars"); + settings->beginWriteArray("trackbars"); - for (int i = 0; i < myBarLayout->layout()->count(); ++i) + for (int i = 0; i < myBarLayout->layout()->count(); ++i) { - settings->setArrayIndex(i); + settings->setArrayIndex(i); - CvTrackbar* t = (CvTrackbar*) myBarLayout->layout()->itemAt(i); + CvTrackbar* t = (CvTrackbar*) myBarLayout->layout()->itemAt(i); - settings->setValue("name", t->name_bar); - settings->setValue("value", t->slider->value()); - } + settings->setValue("name", t->name_bar); + settings->setValue("value", t->slider->value()); + } - settings->endArray(); + settings->endArray(); } @@ -2261,44 +2261,44 @@ void CvWindow::icvSaveTrackbars(QSettings* settings) DefaultViewPort::DefaultViewPort(CvWindow* arg, int arg2) : QGraphicsView(arg), image2Draw_mat(0) { - centralWidget = arg; + centralWidget = arg; param_keepRatio = arg2; - setContentsMargins(0, 0, 0, 0); - setMinimumSize(1, 1); + setContentsMargins(0, 0, 0, 0); + setMinimumSize(1, 1); setAlignment(Qt::AlignHCenter); - setObjectName(QString::fromUtf8("graphicsView")); + setObjectName(QString::fromUtf8("graphicsView")); - timerDisplay = new QTimer(this); - timerDisplay->setSingleShot(true); - connect(timerDisplay, SIGNAL(timeout()), this, SLOT(stopDisplayInfo())); + timerDisplay = new QTimer(this); + timerDisplay->setSingleShot(true); + connect(timerDisplay, SIGNAL(timeout()), this, SLOT(stopDisplayInfo())); - drawInfo = false; - positionGrabbing = QPointF(0, 0); - positionCorners = QRect(0, 0, size().width(), size().height()); + drawInfo = false; + positionGrabbing = QPointF(0, 0); + positionCorners = QRect(0, 0, size().width(), size().height()); - on_mouse = 0; + on_mouse = 0; on_mouse_param = 0; - mouseCoordinate = QPoint(-1, -1); + mouseCoordinate = QPoint(-1, -1); - //no border - setStyleSheet( "QGraphicsView { border-style: none; }" ); + //no border + setStyleSheet( "QGraphicsView { border-style: none; }" ); image2Draw_mat = cvCreateMat(viewport()->height(), viewport()->width(), CV_8UC3); cvZero(image2Draw_mat); nbChannelOriginImage = 0; - setInteractive(false); - setMouseTracking(true); //receive mouse event everytime + setInteractive(false); + setMouseTracking(true); //receive mouse event everytime } DefaultViewPort::~DefaultViewPort() { - if (image2Draw_mat) - cvReleaseMat(&image2Draw_mat); + if (image2Draw_mat) + cvReleaseMat(&image2Draw_mat); } @@ -2310,9 +2310,9 @@ QWidget* DefaultViewPort::getWidget() void DefaultViewPort::setMouseCallBack(CvMouseCallback m, void* param) { - on_mouse = m; + on_mouse = m; - on_mouse_param = param; + on_mouse_param = param; } void DefaultViewPort::writeSettings(QSettings& settings) @@ -2354,63 +2354,63 @@ double DefaultViewPort::getRatio() void DefaultViewPort::setRatio(int flags) { if (getRatio() == flags) //nothing to do - return; + return; - //if valid flags - if (flags == CV_WINDOW_FREERATIO || flags == CV_WINDOW_KEEPRATIO) + //if valid flags + if (flags == CV_WINDOW_FREERATIO || flags == CV_WINDOW_KEEPRATIO) { centralWidget->param_ratio_mode = flags; - param_keepRatio = flags; - updateGeometry(); - viewport()->update(); + param_keepRatio = flags; + updateGeometry(); + viewport()->update(); } } void DefaultViewPort::updateImage(const CvArr* arr) { - CV_Assert(arr); + CV_Assert(arr); - CvMat* mat, stub; - int origin = 0; + CvMat* mat, stub; + int origin = 0; - if (CV_IS_IMAGE_HDR(arr)) - origin = ((IplImage*)arr)->origin; + if (CV_IS_IMAGE_HDR(arr)) + origin = ((IplImage*)arr)->origin; - mat = cvGetMat(arr, &stub); + mat = cvGetMat(arr, &stub); - if (!image2Draw_mat || !CV_ARE_SIZES_EQ(image2Draw_mat, mat)) - { + if (!image2Draw_mat || !CV_ARE_SIZES_EQ(image2Draw_mat, mat)) + { if (image2Draw_mat) - cvReleaseMat(&image2Draw_mat); + cvReleaseMat(&image2Draw_mat); - //the image in ipl (to do a deep copy with cvCvtColor) - image2Draw_mat = cvCreateMat(mat->rows, mat->cols, CV_8UC3); - image2Draw_qt = QImage(image2Draw_mat->data.ptr, image2Draw_mat->cols, image2Draw_mat->rows, image2Draw_mat->step, QImage::Format_RGB888); + //the image in ipl (to do a deep copy with cvCvtColor) + image2Draw_mat = cvCreateMat(mat->rows, mat->cols, CV_8UC3); + image2Draw_qt = QImage(image2Draw_mat->data.ptr, image2Draw_mat->cols, image2Draw_mat->rows, image2Draw_mat->step, QImage::Format_RGB888); - //use to compute mouse coordinate, I need to update the ratio here and in resizeEvent - ratioX = width() / float(image2Draw_mat->cols); - ratioY = height() / float(image2Draw_mat->rows); + //use to compute mouse coordinate, I need to update the ratio here and in resizeEvent + ratioX = width() / float(image2Draw_mat->cols); + ratioY = height() / float(image2Draw_mat->rows); - updateGeometry(); - } + updateGeometry(); + } - nbChannelOriginImage = cvGetElemType(mat); + nbChannelOriginImage = cvGetElemType(mat); - cvConvertImage(mat, image2Draw_mat, (origin != 0 ? CV_CVTIMG_FLIP : 0) + CV_CVTIMG_SWAP_RB); + cvConvertImage(mat, image2Draw_mat, (origin != 0 ? CV_CVTIMG_FLIP : 0) + CV_CVTIMG_SWAP_RB); - viewport()->update(); + viewport()->update(); } void DefaultViewPort::startDisplayInfo(QString text, int delayms) { - if (timerDisplay->isActive()) - stopDisplayInfo(); + if (timerDisplay->isActive()) + stopDisplayInfo(); - infoText = text; - if (delayms > 0) timerDisplay->start(delayms); - drawInfo = true; + infoText = text; + if (delayms > 0) timerDisplay->start(delayms); + drawInfo = true; } @@ -2441,381 +2441,381 @@ void DefaultViewPort::updateGl() //Note: move 2 percent of the window void DefaultViewPort::siftWindowOnLeft() { - float delta = 2 * width() / (100.0 * param_matrixWorld.m11()); - moveView(QPointF(delta, 0)); + float delta = 2 * width() / (100.0 * param_matrixWorld.m11()); + moveView(QPointF(delta, 0)); } //Note: move 2 percent of the window void DefaultViewPort::siftWindowOnRight() { - float delta = -2 * width() / (100.0 * param_matrixWorld.m11()); - moveView(QPointF(delta, 0)); + float delta = -2 * width() / (100.0 * param_matrixWorld.m11()); + moveView(QPointF(delta, 0)); } //Note: move 2 percent of the window void DefaultViewPort::siftWindowOnUp() { - float delta = 2 * height() / (100.0 * param_matrixWorld.m11()); - moveView(QPointF(0, delta)); + float delta = 2 * height() / (100.0 * param_matrixWorld.m11()); + moveView(QPointF(0, delta)); } //Note: move 2 percent of the window void DefaultViewPort::siftWindowOnDown() { - float delta = -2 * height() / (100.0 * param_matrixWorld.m11()); - moveView(QPointF(0, delta)); + float delta = -2 * height() / (100.0 * param_matrixWorld.m11()); + moveView(QPointF(0, delta)); } void DefaultViewPort::imgRegion() { - scaleView((threshold_zoom_img_region / param_matrixWorld.m11() - 1) * 5, QPointF(size().width() / 2, size().height() / 2)); + scaleView((threshold_zoom_img_region / param_matrixWorld.m11() - 1) * 5, QPointF(size().width() / 2, size().height() / 2)); } void DefaultViewPort::resetZoom() { - param_matrixWorld.reset(); - controlImagePosition(); + param_matrixWorld.reset(); + controlImagePosition(); } void DefaultViewPort::ZoomIn() { - scaleView(0.5, QPointF(size().width() / 2, size().height() / 2)); + scaleView(0.5, QPointF(size().width() / 2, size().height() / 2)); } void DefaultViewPort::ZoomOut() { - scaleView(-0.5, QPointF(size().width() / 2, size().height() / 2)); + scaleView(-0.5, QPointF(size().width() / 2, size().height() / 2)); } //can save as JPG, JPEG, BMP, PNG void DefaultViewPort::saveView() { - QDate date_d = QDate::currentDate(); - QString date_s = date_d.toString("dd.MM.yyyy"); + QDate date_d = QDate::currentDate(); + QString date_s = date_d.toString("dd.MM.yyyy"); QString name_s = centralWidget->windowTitle() + "_screenshot_" + date_s; - QString fileName = QFileDialog::getSaveFileName(this, tr("Save File %1").arg(name_s), name_s + ".png", tr("Images (*.png *.jpg *.bmp *.jpeg)")); + QString fileName = QFileDialog::getSaveFileName(this, tr("Save File %1").arg(name_s), name_s + ".png", tr("Images (*.png *.jpg *.bmp *.jpeg)")); + + if (!fileName.isEmpty()) //save the picture + { + QString extension = fileName.right(3); - if (!fileName.isEmpty()) //save the picture - { - QString extension = fileName.right(3); + // (no need anymore) create the image resized to receive the 'screenshot' + // image2Draw_qt_resized = QImage(viewport()->width(), viewport()->height(),QImage::Format_RGB888); - // (no need anymore) create the image resized to receive the 'screenshot' - // image2Draw_qt_resized = QImage(viewport()->width(), viewport()->height(),QImage::Format_RGB888); - - QPainter saveimage(&image2Draw_qt_resized); - this->render(&saveimage); + QPainter saveimage(&image2Draw_qt_resized); + this->render(&saveimage); - // Save it.. - if (QString::compare(extension, "png", Qt::CaseInsensitive) == 0) - { - image2Draw_qt_resized.save(fileName, "PNG"); - return; - } + // Save it.. + if (QString::compare(extension, "png", Qt::CaseInsensitive) == 0) + { + image2Draw_qt_resized.save(fileName, "PNG"); + return; + } - if (QString::compare(extension, "jpg", Qt::CaseInsensitive) == 0) - { - image2Draw_qt_resized.save(fileName, "JPG"); - return; - } + if (QString::compare(extension, "jpg", Qt::CaseInsensitive) == 0) + { + image2Draw_qt_resized.save(fileName, "JPG"); + return; + } - if (QString::compare(extension, "bmp", Qt::CaseInsensitive) == 0) - { - image2Draw_qt_resized.save(fileName, "BMP"); - return; - } + if (QString::compare(extension, "bmp", Qt::CaseInsensitive) == 0) + { + image2Draw_qt_resized.save(fileName, "BMP"); + return; + } - if (QString::compare(extension, "jpeg", Qt::CaseInsensitive) == 0) - { - image2Draw_qt_resized.save(fileName, "JPEG"); - return; - } + if (QString::compare(extension, "jpeg", Qt::CaseInsensitive) == 0) + { + image2Draw_qt_resized.save(fileName, "JPEG"); + return; + } - CV_Error(CV_StsNullPtr, "file extension not recognized, please choose between JPG, JPEG, BMP or PNG"); - } + CV_Error(CV_StsNullPtr, "file extension not recognized, please choose between JPG, JPEG, BMP or PNG"); + } } -void DefaultViewPort::contextMenuEvent(QContextMenuEvent* event) +void DefaultViewPort::contextMenuEvent(QContextMenuEvent* evnt) { - if (centralWidget->vect_QActions.size() > 0) - { - QMenu menu(this); + if (centralWidget->vect_QActions.size() > 0) + { + QMenu menu(this); - foreach (QAction *a, centralWidget->vect_QActions) - menu.addAction(a); + foreach (QAction *a, centralWidget->vect_QActions) + menu.addAction(a); - menu.exec(event->globalPos()); - } + menu.exec(evnt->globalPos()); + } } -void DefaultViewPort::resizeEvent(QResizeEvent* event) +void DefaultViewPort::resizeEvent(QResizeEvent* evnt) { controlImagePosition(); //use to compute mouse coordinate, I need to update the ratio here and in resizeEvent ratioX = width() / float(image2Draw_mat->cols); ratioY = height() / float(image2Draw_mat->rows); - + if (param_keepRatio == CV_WINDOW_KEEPRATIO)//to keep the same aspect ratio { - QSize newSize = QSize(image2Draw_mat->cols, image2Draw_mat->rows); - newSize.scale(event->size(), Qt::KeepAspectRatio); + QSize newSize = QSize(image2Draw_mat->cols, image2Draw_mat->rows); + newSize.scale(evnt->size(), Qt::KeepAspectRatio); - //imageWidth/imageHeight = newWidth/newHeight +/- epsilon - //ratioX = ratioY +/- epsilon - //||ratioX - ratioY|| = epsilon - if (fabs(ratioX - ratioY) * 100 > ratioX) //avoid infinity loop / epsilon = 1% of ratioX - { - resize(newSize); + //imageWidth/imageHeight = newWidth/newHeight +/- epsilon + //ratioX = ratioY +/- epsilon + //||ratioX - ratioY|| = epsilon + if (fabs(ratioX - ratioY) * 100 > ratioX) //avoid infinity loop / epsilon = 1% of ratioX + { + resize(newSize); - //move to the middle - //newSize get the delta offset to place the picture in the middle of its parent - newSize = (event->size() - newSize) / 2; + //move to the middle + //newSize get the delta offset to place the picture in the middle of its parent + newSize = (evnt->size() - newSize) / 2; - //if the toolbar is displayed, avoid drawing myview on top of it - if (centralWidget->myToolBar) - if(!centralWidget->myToolBar->isHidden()) - newSize += QSize(0, centralWidget->myToolBar->height()); + //if the toolbar is displayed, avoid drawing myview on top of it + if (centralWidget->myToolBar) + if(!centralWidget->myToolBar->isHidden()) + newSize += QSize(0, centralWidget->myToolBar->height()); - move(newSize.width(), newSize.height()); - } + move(newSize.width(), newSize.height()); + } } - return QGraphicsView::resizeEvent(event); + return QGraphicsView::resizeEvent(evnt); } -void DefaultViewPort::wheelEvent(QWheelEvent* event) +void DefaultViewPort::wheelEvent(QWheelEvent* evnt) { - scaleView(event->delta() / 240.0, event->pos()); - viewport()->update(); + scaleView(evnt->delta() / 240.0, evnt->pos()); + viewport()->update(); } -void DefaultViewPort::mousePressEvent(QMouseEvent* event) +void DefaultViewPort::mousePressEvent(QMouseEvent* evnt) { - int cv_event = -1, flags = 0; - QPoint pt = event->pos(); + int cv_event = -1, flags = 0; + QPoint pt = evnt->pos(); - //icvmouseHandler: pass parameters for cv_event, flags - icvmouseHandler(event, mouse_down, cv_event, flags); - icvmouseProcessing(QPointF(pt), cv_event, flags); + //icvmouseHandler: pass parameters for cv_event, flags + icvmouseHandler(evnt, mouse_down, cv_event, flags); + icvmouseProcessing(QPointF(pt), cv_event, flags); - if (param_matrixWorld.m11()>1) - { - setCursor(Qt::ClosedHandCursor); - positionGrabbing = event->pos(); - } + if (param_matrixWorld.m11()>1) + { + setCursor(Qt::ClosedHandCursor); + positionGrabbing = evnt->pos(); + } - QWidget::mousePressEvent(event); + QWidget::mousePressEvent(evnt); } -void DefaultViewPort::mouseReleaseEvent(QMouseEvent* event) +void DefaultViewPort::mouseReleaseEvent(QMouseEvent* evnt) { - int cv_event = -1, flags = 0; - QPoint pt = event->pos(); + int cv_event = -1, flags = 0; + QPoint pt = evnt->pos(); - //icvmouseHandler: pass parameters for cv_event, flags - icvmouseHandler(event, mouse_up, cv_event, flags); - icvmouseProcessing(QPointF(pt), cv_event, flags); + //icvmouseHandler: pass parameters for cv_event, flags + icvmouseHandler(evnt, mouse_up, cv_event, flags); + icvmouseProcessing(QPointF(pt), cv_event, flags); - if (param_matrixWorld.m11()>1) - setCursor(Qt::OpenHandCursor); + if (param_matrixWorld.m11()>1) + setCursor(Qt::OpenHandCursor); - QWidget::mouseReleaseEvent(event); + QWidget::mouseReleaseEvent(evnt); } -void DefaultViewPort::mouseDoubleClickEvent(QMouseEvent* event) +void DefaultViewPort::mouseDoubleClickEvent(QMouseEvent* evnt) { - int cv_event = -1, flags = 0; - QPoint pt = event->pos(); + int cv_event = -1, flags = 0; + QPoint pt = evnt->pos(); - //icvmouseHandler: pass parameters for cv_event, flags - icvmouseHandler(event, mouse_dbclick, cv_event, flags); - icvmouseProcessing(QPointF(pt), cv_event, flags); + //icvmouseHandler: pass parameters for cv_event, flags + icvmouseHandler(evnt, mouse_dbclick, cv_event, flags); + icvmouseProcessing(QPointF(pt), cv_event, flags); - QWidget::mouseDoubleClickEvent(event); + QWidget::mouseDoubleClickEvent(evnt); } -void DefaultViewPort::mouseMoveEvent(QMouseEvent* event) +void DefaultViewPort::mouseMoveEvent(QMouseEvent* evnt) { - int cv_event = CV_EVENT_MOUSEMOVE, flags = 0; - QPoint pt = event->pos(); + int cv_event = CV_EVENT_MOUSEMOVE, flags = 0; + QPoint pt = evnt->pos(); - //icvmouseHandler: pass parameters for cv_event, flags - icvmouseHandler(event, mouse_move, cv_event, flags); - icvmouseProcessing(QPointF(pt), cv_event, flags); + //icvmouseHandler: pass parameters for cv_event, flags + icvmouseHandler(evnt, mouse_move, cv_event, flags); + icvmouseProcessing(QPointF(pt), cv_event, flags); - if (param_matrixWorld.m11() > 1 && event->buttons() == Qt::LeftButton) - { - QPointF dxy = (pt - positionGrabbing)/param_matrixWorld.m11(); - positionGrabbing = event->pos(); - moveView(dxy); - } + if (param_matrixWorld.m11() > 1 && evnt->buttons() == Qt::LeftButton) + { + QPointF dxy = (pt - positionGrabbing)/param_matrixWorld.m11(); + positionGrabbing = evnt->pos(); + moveView(dxy); + } - //I update the statusbar here because if the user does a cvWaitkey(0) (like with inpaint.cpp) - //the status bar will only be repaint when a click occurs. - if (centralWidget->myStatusBar) - viewport()->update(); + //I update the statusbar here because if the user does a cvWaitkey(0) (like with inpaint.cpp) + //the status bar will only be repaint when a click occurs. + if (centralWidget->myStatusBar) + viewport()->update(); - QWidget::mouseMoveEvent(event); + QWidget::mouseMoveEvent(evnt); } -void DefaultViewPort::paintEvent(QPaintEvent* event) +void DefaultViewPort::paintEvent(QPaintEvent* evnt) { - QPainter myPainter(viewport()); - myPainter.setWorldTransform(param_matrixWorld); + QPainter myPainter(viewport()); + myPainter.setWorldTransform(param_matrixWorld); - draw2D(&myPainter); + draw2D(&myPainter); - //Now disable matrixWorld for overlay display - myPainter.setWorldMatrixEnabled(false); + //Now disable matrixWorld for overlay display + myPainter.setWorldMatrixEnabled(false); - //in mode zoom/panning - if (param_matrixWorld.m11() > 1) - { - if (param_matrixWorld.m11() >= threshold_zoom_img_region) - { - if (centralWidget->param_flags == CV_WINDOW_NORMAL) - startDisplayInfo("WARNING: The values displayed are the resized image's values. If you want the original image's values, use CV_WINDOW_AUTOSIZE", 1000); + //in mode zoom/panning + if (param_matrixWorld.m11() > 1) + { + if (param_matrixWorld.m11() >= threshold_zoom_img_region) + { + if (centralWidget->param_flags == CV_WINDOW_NORMAL) + startDisplayInfo("WARNING: The values displayed are the resized image's values. If you want the original image's values, use CV_WINDOW_AUTOSIZE", 1000); - drawImgRegion(&myPainter); - } + drawImgRegion(&myPainter); + } - drawViewOverview(&myPainter); - } + drawViewOverview(&myPainter); + } - //for information overlay - if (drawInfo) - drawInstructions(&myPainter); + //for information overlay + if (drawInfo) + drawInstructions(&myPainter); - //for statusbar - if (centralWidget->myStatusBar) - drawStatusBar(); + //for statusbar + if (centralWidget->myStatusBar) + drawStatusBar(); - QGraphicsView::paintEvent(event); + QGraphicsView::paintEvent(evnt); } void DefaultViewPort::stopDisplayInfo() { - timerDisplay->stop(); - drawInfo = false; + timerDisplay->stop(); + drawInfo = false; } inline bool DefaultViewPort::isSameSize(IplImage* img1, IplImage* img2) { - return img1->width == img2->width && img1->height == img2->height; + return img1->width == img2->width && img1->height == img2->height; } void DefaultViewPort::controlImagePosition() { - qreal left, top, right, bottom; - - //after check top-left, bottom right corner to avoid getting "out" during zoom/panning - param_matrixWorld.map(0,0,&left,&top); - - if (left > 0) - { - param_matrixWorld.translate(-left,0); - left = 0; - } - if (top > 0) - { - param_matrixWorld.translate(0,-top); - top = 0; - } - //------- - - QSize sizeImage = size(); - param_matrixWorld.map(sizeImage.width(),sizeImage.height(),&right,&bottom); - if (right < sizeImage.width()) - { - param_matrixWorld.translate(sizeImage.width()-right,0); - right = sizeImage.width(); - } - if (bottom < sizeImage.height()) - { - param_matrixWorld.translate(0,sizeImage.height()-bottom); - bottom = sizeImage.height(); - } - - //save corner position - positionCorners.setTopLeft(QPoint(left,top)); - positionCorners.setBottomRight(QPoint(right,bottom)); - //save also the inv matrix - matrixWorld_inv = param_matrixWorld.inverted(); - - //viewport()->update(); + qreal left, top, right, bottom; + + //after check top-left, bottom right corner to avoid getting "out" during zoom/panning + param_matrixWorld.map(0,0,&left,&top); + + if (left > 0) + { + param_matrixWorld.translate(-left,0); + left = 0; + } + if (top > 0) + { + param_matrixWorld.translate(0,-top); + top = 0; + } + //------- + + QSize sizeImage = size(); + param_matrixWorld.map(sizeImage.width(),sizeImage.height(),&right,&bottom); + if (right < sizeImage.width()) + { + param_matrixWorld.translate(sizeImage.width()-right,0); + right = sizeImage.width(); + } + if (bottom < sizeImage.height()) + { + param_matrixWorld.translate(0,sizeImage.height()-bottom); + bottom = sizeImage.height(); + } + + //save corner position + positionCorners.setTopLeft(QPoint(left,top)); + positionCorners.setBottomRight(QPoint(right,bottom)); + //save also the inv matrix + matrixWorld_inv = param_matrixWorld.inverted(); + + //viewport()->update(); } void DefaultViewPort::moveView(QPointF delta) { - param_matrixWorld.translate(delta.x(),delta.y()); - controlImagePosition(); - viewport()->update(); + param_matrixWorld.translate(delta.x(),delta.y()); + controlImagePosition(); + viewport()->update(); } //factor is -0.5 (zoom out) or 0.5 (zoom in) void DefaultViewPort::scaleView(qreal factor,QPointF center) { - factor/=5;//-0.1 <-> 0.1 - factor+=1;//0.9 <-> 1.1 + factor/=5;//-0.1 <-> 0.1 + factor+=1;//0.9 <-> 1.1 - //limit zoom out --- - if (param_matrixWorld.m11()==1 && factor < 1) - return; + //limit zoom out --- + if (param_matrixWorld.m11()==1 && factor < 1) + return; - if (param_matrixWorld.m11()*factor<1) - factor = 1/param_matrixWorld.m11(); + if (param_matrixWorld.m11()*factor<1) + factor = 1/param_matrixWorld.m11(); - //limit zoom int --- - if (param_matrixWorld.m11()>100 && factor > 1) - return; + //limit zoom int --- + if (param_matrixWorld.m11()>100 && factor > 1) + return; - //inverse the transform - int a, b; - matrixWorld_inv.map(center.x(),center.y(),&a,&b); + //inverse the transform + int a, b; + matrixWorld_inv.map(center.x(),center.y(),&a,&b); - param_matrixWorld.translate(a-factor*a,b-factor*b); - param_matrixWorld.scale(factor,factor); + param_matrixWorld.translate(a-factor*a,b-factor*b); + param_matrixWorld.scale(factor,factor); - controlImagePosition(); + controlImagePosition(); - //display new zoom - if (centralWidget->myStatusBar) - centralWidget->displayStatusBar(tr("Zoom: %1%").arg(param_matrixWorld.m11()*100),1000); + //display new zoom + if (centralWidget->myStatusBar) + centralWidget->displayStatusBar(tr("Zoom: %1%").arg(param_matrixWorld.m11()*100),1000); - if (param_matrixWorld.m11()>1) - setCursor(Qt::OpenHandCursor); - else - unsetCursor(); + if (param_matrixWorld.m11()>1) + setCursor(Qt::OpenHandCursor); + else + unsetCursor(); } //up, down, dclick, move -void DefaultViewPort::icvmouseHandler(QMouseEvent *event, type_mouse_event category, int &cv_event, int &flags) +void DefaultViewPort::icvmouseHandler(QMouseEvent *evnt, type_mouse_event category, int &cv_event, int &flags) { - Qt::KeyboardModifiers modifiers = event->modifiers(); - Qt::MouseButtons buttons = event->buttons(); - + Qt::KeyboardModifiers modifiers = evnt->modifiers(); + Qt::MouseButtons buttons = evnt->buttons(); + flags = 0; if(modifiers & Qt::ShiftModifier) flags |= CV_EVENT_FLAG_SHIFTKEY; @@ -2832,7 +2832,7 @@ void DefaultViewPort::icvmouseHandler(QMouseEvent *event, type_mouse_event categ flags |= CV_EVENT_FLAG_MBUTTON; cv_event = CV_EVENT_MOUSEMOVE; - switch(event->button()) + switch(evnt->button()) { case Qt::LeftButton: cv_event = tableMouseButtons[category][0]; @@ -2853,208 +2853,208 @@ void DefaultViewPort::icvmouseHandler(QMouseEvent *event, type_mouse_event categ void DefaultViewPort::icvmouseProcessing(QPointF pt, int cv_event, int flags) { - //to convert mouse coordinate - qreal pfx, pfy; - matrixWorld_inv.map(pt.x(),pt.y(),&pfx,&pfy); - - mouseCoordinate.rx()=floor(pfx/ratioX); - mouseCoordinate.ry()=floor(pfy/ratioY); + //to convert mouse coordinate + qreal pfx, pfy; + matrixWorld_inv.map(pt.x(),pt.y(),&pfx,&pfy); + + mouseCoordinate.rx()=floor(pfx/ratioX); + mouseCoordinate.ry()=floor(pfy/ratioY); - if (on_mouse) - on_mouse( cv_event, mouseCoordinate.x(), + if (on_mouse) + on_mouse( cv_event, mouseCoordinate.x(), mouseCoordinate.y(), flags, on_mouse_param ); } QSize DefaultViewPort::sizeHint() const { - if(image2Draw_mat) - return QSize(image2Draw_mat->cols, image2Draw_mat->rows); - else - return QGraphicsView::sizeHint(); + if(image2Draw_mat) + return QSize(image2Draw_mat->cols, image2Draw_mat->rows); + else + return QGraphicsView::sizeHint(); } void DefaultViewPort::draw2D(QPainter *painter) { - image2Draw_qt = QImage(image2Draw_mat->data.ptr, image2Draw_mat->cols, image2Draw_mat->rows,image2Draw_mat->step,QImage::Format_RGB888); - image2Draw_qt_resized = image2Draw_qt.scaled(viewport()->width(),viewport()->height(),Qt::IgnoreAspectRatio,Qt::FastTransformation);//Qt::SmoothTransformation); - painter->drawImage(0,0,image2Draw_qt_resized); + image2Draw_qt = QImage(image2Draw_mat->data.ptr, image2Draw_mat->cols, image2Draw_mat->rows,image2Draw_mat->step,QImage::Format_RGB888); + image2Draw_qt_resized = image2Draw_qt.scaled(viewport()->width(),viewport()->height(),Qt::IgnoreAspectRatio,Qt::FastTransformation);//Qt::SmoothTransformation); + painter->drawImage(0,0,image2Draw_qt_resized); } //only if CV_8UC1 or CV_8UC3 void DefaultViewPort::drawStatusBar() { - if (nbChannelOriginImage!=CV_8UC1 && nbChannelOriginImage!=CV_8UC3) - return; - - if (mouseCoordinate.x()>=0 && - mouseCoordinate.y()>=0 && - mouseCoordinate.x()=0 && mouseCoordinate.y()>=0) - { - QRgb rgbValue = image2Draw_qt.pixel(mouseCoordinate); - - if (nbChannelOriginImage==CV_8UC3 ) - { - centralWidget->myStatusBar_msg->setText(tr("(x=%1, y=%2) ~ ") - .arg(mouseCoordinate.x()) - .arg(mouseCoordinate.y())+ - tr("R:%3 ").arg(qRed(rgbValue))+//.arg(value.val[0])+ - tr("G:%4 ").arg(qGreen(rgbValue))+//.arg(value.val[1])+ - tr("B:%5").arg(qBlue(rgbValue))//.arg(value.val[2]) - ); - } - - if (nbChannelOriginImage==CV_8UC1) - { - //all the channel have the same value (because of cvconvertimage), so only the r channel is dsplayed - centralWidget->myStatusBar_msg->setText(tr("(x=%1, y=%2) ~ ") - .arg(mouseCoordinate.x()) - .arg(mouseCoordinate.y())+ - tr("L:%3 ").arg(qRed(rgbValue)) - ); - } - } + if (nbChannelOriginImage!=CV_8UC1 && nbChannelOriginImage!=CV_8UC3) + return; + + if (mouseCoordinate.x()>=0 && + mouseCoordinate.y()>=0 && + mouseCoordinate.x()=0 && mouseCoordinate.y()>=0) + { + QRgb rgbValue = image2Draw_qt.pixel(mouseCoordinate); + + if (nbChannelOriginImage==CV_8UC3 ) + { + centralWidget->myStatusBar_msg->setText(tr("(x=%1, y=%2) ~ ") + .arg(mouseCoordinate.x()) + .arg(mouseCoordinate.y())+ + tr("R:%3 ").arg(qRed(rgbValue))+//.arg(value.val[0])+ + tr("G:%4 ").arg(qGreen(rgbValue))+//.arg(value.val[1])+ + tr("B:%5").arg(qBlue(rgbValue))//.arg(value.val[2]) + ); + } + + if (nbChannelOriginImage==CV_8UC1) + { + //all the channel have the same value (because of cvconvertimage), so only the r channel is dsplayed + centralWidget->myStatusBar_msg->setText(tr("(x=%1, y=%2) ~ ") + .arg(mouseCoordinate.x()) + .arg(mouseCoordinate.y())+ + tr("L:%3 ").arg(qRed(rgbValue)) + ); + } + } } //accept only CV_8UC1 and CV_8UC8 image for now void DefaultViewPort::drawImgRegion(QPainter *painter) { - if (nbChannelOriginImage!=CV_8UC1 && nbChannelOriginImage!=CV_8UC3) - return; - - qreal offsetX = param_matrixWorld.dx()/param_matrixWorld.m11(); - offsetX = offsetX - floor(offsetX); - qreal offsetY = param_matrixWorld.dy()/param_matrixWorld.m11(); - offsetY = offsetY - floor(offsetY); - - QSize view = size(); - QVarLengthArray linesX; - for (qreal x = offsetX*param_matrixWorld.m11(); x < view.width(); x += param_matrixWorld.m11() ) - linesX.append(QLineF(x, 0, x, view.height())); + if (nbChannelOriginImage!=CV_8UC1 && nbChannelOriginImage!=CV_8UC3) + return; - QVarLengthArray linesY; - for (qreal y = offsetY*param_matrixWorld.m11(); y < view.height(); y += param_matrixWorld.m11() ) - linesY.append(QLineF(0, y, view.width(), y)); + qreal offsetX = param_matrixWorld.dx()/param_matrixWorld.m11(); + offsetX = offsetX - floor(offsetX); + qreal offsetY = param_matrixWorld.dy()/param_matrixWorld.m11(); + offsetY = offsetY - floor(offsetY); + QSize view = size(); + QVarLengthArray linesX; + for (qreal _x = offsetX*param_matrixWorld.m11(); _x < view.width(); _x += param_matrixWorld.m11() ) + linesX.append(QLineF(_x, 0, _x, view.height())); - QFont f = painter->font(); - int original_font_size = f.pointSize(); - //change font size - //f.setPointSize(4+(param_matrixWorld.m11()-threshold_zoom_img_region)/5); - f.setPixelSize(10+(param_matrixWorld.m11()-threshold_zoom_img_region)/5); - painter->setFont(f); - QString val; - QRgb rgbValue; + QVarLengthArray linesY; + for (qreal _y = offsetY*param_matrixWorld.m11(); _y < view.height(); _y += param_matrixWorld.m11() ) + linesY.append(QLineF(0, _y, view.width(), _y)); - QPointF point1;//sorry, I do not know how to name it - QPointF point2;//idem - for (int j=-1;jfont(); + int original_font_size = f.pointSize(); + //change font size + //f.setPointSize(4+(param_matrixWorld.m11()-threshold_zoom_img_region)/5); + f.setPixelSize(10+(param_matrixWorld.m11()-threshold_zoom_img_region)/5); + painter->setFont(f); + QString val; + QRgb rgbValue; - matrixWorld_inv.map(point1.x(),point1.y(),&point2.rx(),&point2.ry()); + QPointF point1;//sorry, I do not know how to name it + QPointF point2;//idem - point2.rx()= (long) (point2.x() + 0.5); - point2.ry()= (long) (point2.y() + 0.5); - - if (point2.x() >= 0 && point2.y() >= 0) - rgbValue = image2Draw_qt_resized.pixel(QPoint(point2.x(),point2.y())); - else - rgbValue = qRgb(0,0,0); + for (int j=-1;jsetPen(QPen(Qt::black, 1)); - painter->drawText(QRect(point1.x(),point1.y(),param_matrixWorld.m11(),param_matrixWorld.m11()/2), - Qt::AlignCenter, val); - */ + matrixWorld_inv.map(point1.x(),point1.y(),&point2.rx(),&point2.ry()); - val = tr("%1").arg(qRed(rgbValue)); - painter->setPen(QPen(Qt::red, 1)); - painter->drawText(QRect(point1.x(),point1.y(),param_matrixWorld.m11(),param_matrixWorld.m11()/3), - Qt::AlignCenter, val); + point2.rx()= (long) (point2.x() + 0.5); + point2.ry()= (long) (point2.y() + 0.5); - val = tr("%1").arg(qGreen(rgbValue)); - painter->setPen(QPen(Qt::green, 1)); - painter->drawText(QRect(point1.x(),point1.y()+param_matrixWorld.m11()/3,param_matrixWorld.m11(),param_matrixWorld.m11()/3), - Qt::AlignCenter, val); + if (point2.x() >= 0 && point2.y() >= 0) + rgbValue = image2Draw_qt_resized.pixel(QPoint(point2.x(),point2.y())); + else + rgbValue = qRgb(0,0,0); - val = tr("%1").arg(qBlue(rgbValue)); - painter->setPen(QPen(Qt::blue, 1)); - painter->drawText(QRect(point1.x(),point1.y()+2*param_matrixWorld.m11()/3,param_matrixWorld.m11(),param_matrixWorld.m11()/3), - Qt::AlignCenter, val); + if (nbChannelOriginImage==CV_8UC3) + { + //for debug + /* + val = tr("%1 %2").arg(point2.x()).arg(point2.y()); + painter->setPen(QPen(Qt::black, 1)); + painter->drawText(QRect(point1.x(),point1.y(),param_matrixWorld.m11(),param_matrixWorld.m11()/2), + Qt::AlignCenter, val); + */ + + val = tr("%1").arg(qRed(rgbValue)); + painter->setPen(QPen(Qt::red, 1)); + painter->drawText(QRect(point1.x(),point1.y(),param_matrixWorld.m11(),param_matrixWorld.m11()/3), + Qt::AlignCenter, val); + + val = tr("%1").arg(qGreen(rgbValue)); + painter->setPen(QPen(Qt::green, 1)); + painter->drawText(QRect(point1.x(),point1.y()+param_matrixWorld.m11()/3,param_matrixWorld.m11(),param_matrixWorld.m11()/3), + Qt::AlignCenter, val); + + val = tr("%1").arg(qBlue(rgbValue)); + painter->setPen(QPen(Qt::blue, 1)); + painter->drawText(QRect(point1.x(),point1.y()+2*param_matrixWorld.m11()/3,param_matrixWorld.m11(),param_matrixWorld.m11()/3), + Qt::AlignCenter, val); - } + } - if (nbChannelOriginImage==CV_8UC1) - { + if (nbChannelOriginImage==CV_8UC1) + { - val = tr("%1").arg(qRed(rgbValue)); - painter->drawText(QRect(point1.x(),point1.y(),param_matrixWorld.m11(),param_matrixWorld.m11()), - Qt::AlignCenter, val); - } - } + val = tr("%1").arg(qRed(rgbValue)); + painter->drawText(QRect(point1.x(),point1.y(),param_matrixWorld.m11(),param_matrixWorld.m11()), + Qt::AlignCenter, val); + } + } - painter->setPen(QPen(Qt::black, 1)); - painter->drawLines(linesX.data(), linesX.size()); - painter->drawLines(linesY.data(), linesY.size()); + painter->setPen(QPen(Qt::black, 1)); + painter->drawLines(linesX.data(), linesX.size()); + painter->drawLines(linesY.data(), linesY.size()); - //restore font size - f.setPointSize(original_font_size); - painter->setFont(f); + //restore font size + f.setPointSize(original_font_size); + painter->setFont(f); } void DefaultViewPort::drawViewOverview(QPainter *painter) { - QSize viewSize = size(); - viewSize.scale ( 100, 100,Qt::KeepAspectRatio ); + QSize viewSize = size(); + viewSize.scale ( 100, 100,Qt::KeepAspectRatio ); - const int margin = 5; + const int margin = 5; - //draw the image's location - painter->setBrush(QColor(0, 0, 0, 127)); - painter->setPen(Qt::darkGreen); - painter->drawRect(QRect(width()-viewSize.width()-margin, 0,viewSize.width(),viewSize.height())); + //draw the image's location + painter->setBrush(QColor(0, 0, 0, 127)); + painter->setPen(Qt::darkGreen); + painter->drawRect(QRect(width()-viewSize.width()-margin, 0,viewSize.width(),viewSize.height())); - //daw the view's location inside the image - qreal ratioSize = 1/param_matrixWorld.m11(); - qreal ratioWindow = (qreal)(viewSize.height())/(qreal)(size().height()); - painter->setPen(Qt::darkBlue); - painter->drawRect(QRectF(width()-viewSize.width()-positionCorners.left()*ratioSize*ratioWindow-margin, - -positionCorners.top()*ratioSize*ratioWindow, - (viewSize.width()-1)*ratioSize, - (viewSize.height()-1)*ratioSize) - ); + //daw the view's location inside the image + qreal ratioSize = 1/param_matrixWorld.m11(); + qreal ratioWindow = (qreal)(viewSize.height())/(qreal)(size().height()); + painter->setPen(Qt::darkBlue); + painter->drawRect(QRectF(width()-viewSize.width()-positionCorners.left()*ratioSize*ratioWindow-margin, + -positionCorners.top()*ratioSize*ratioWindow, + (viewSize.width()-1)*ratioSize, + (viewSize.height()-1)*ratioSize) + ); } void DefaultViewPort::drawInstructions(QPainter *painter) { - QFontMetrics metrics = QFontMetrics(font()); - int border = qMax(4, metrics.leading()); + QFontMetrics metrics = QFontMetrics(font()); + int border = qMax(4, metrics.leading()); - QRect rect = metrics.boundingRect(0, 0, width() - 2*border, int(height()*0.125), - Qt::AlignCenter | Qt::TextWordWrap, infoText); - painter->setRenderHint(QPainter::TextAntialiasing); - painter->fillRect(QRect(0, 0, width(), rect.height() + 2*border), - QColor(0, 0, 0, 127)); - painter->setPen(Qt::white); - painter->fillRect(QRect(0, 0, width(), rect.height() + 2*border), - QColor(0, 0, 0, 127)); + QRect qrect = metrics.boundingRect(0, 0, width() - 2*border, int(height()*0.125), + Qt::AlignCenter | Qt::TextWordWrap, infoText); + painter->setRenderHint(QPainter::TextAntialiasing); + painter->fillRect(QRect(0, 0, width(), qrect.height() + 2*border), + QColor(0, 0, 0, 127)); + painter->setPen(Qt::white); + painter->fillRect(QRect(0, 0, width(), qrect.height() + 2*border), + QColor(0, 0, 0, 127)); - painter->drawText((width() - rect.width())/2, border, - rect.width(), rect.height(), - Qt::AlignCenter | Qt::TextWordWrap, infoText); + painter->drawText((width() - qrect.width())/2, border, + qrect.width(), qrect.height(), + Qt::AlignCenter | Qt::TextWordWrap, infoText); } @@ -3068,7 +3068,7 @@ void DefaultViewPort::setSize(QSize size_) #ifdef HAVE_QT_OPENGL -OpenGlViewPort::OpenGlViewPort(QWidget* parent) : QGLWidget(parent), size(-1, -1) +OpenGlViewPort::OpenGlViewPort(QWidget* _parent) : QGLWidget(_parent), size(-1, -1) { mouseCallback = 0; mouseData = 0; @@ -3202,7 +3202,7 @@ public: void generateBitmapFont(const std::string& family, int height, int weight, bool italic, bool underline, int start, int count, int base) const; bool isGlContextInitialized() const; - + PFNGLGENBUFFERSPROC glGenBuffersExt; PFNGLDELETEBUFFERSPROC glDeleteBuffersExt; @@ -3352,7 +3352,7 @@ void GlFuncTab_QT::unmapBuffer(unsigned int target) const void GlFuncTab_QT::generateBitmapFont(const std::string& family, int height, int weight, bool italic, bool underline, int start, int count, int base) const { - CV_FUNCNAME( "GlFuncTab_QT::generateBitmapFont" ); + //CV_FUNCNAME( "GlFuncTab_QT::generateBitmapFont" ); QFont font(QString(family.c_str()), height, weight, italic); @@ -3381,26 +3381,26 @@ void OpenGlViewPort::initializeGL() glHint(GL_PERSPECTIVE_CORRECTION_HINT, GL_NICEST); #ifdef Q_WS_WIN - std::auto_ptr glFuncTab(new GlFuncTab_QT(getDC())); + std::auto_ptr qglFuncTab(new GlFuncTab_QT(getDC())); #else - std::auto_ptr glFuncTab(new GlFuncTab_QT); + std::auto_ptr qglFuncTab(new GlFuncTab_QT); #endif // Load extensions - glFuncTab->glGenBuffersExt = (PFNGLGENBUFFERSPROC)context()->getProcAddress("glGenBuffers"); - glFuncTab->glDeleteBuffersExt = (PFNGLDELETEBUFFERSPROC)context()->getProcAddress("glDeleteBuffers"); - glFuncTab->glBufferDataExt = (PFNGLBUFFERDATAPROC)context()->getProcAddress("glBufferData"); - glFuncTab->glBufferSubDataExt = (PFNGLBUFFERSUBDATAPROC)context()->getProcAddress("glBufferSubData"); - glFuncTab->glBindBufferExt = (PFNGLBINDBUFFERPROC)context()->getProcAddress("glBindBuffer"); - glFuncTab->glMapBufferExt = (PFNGLMAPBUFFERPROC)context()->getProcAddress("glMapBuffer"); - glFuncTab->glUnmapBufferExt = (PFNGLUNMAPBUFFERPROC)context()->getProcAddress("glUnmapBuffer"); + qglFuncTab->glGenBuffersExt = (PFNGLGENBUFFERSPROC)context()->getProcAddress("glGenBuffers"); + qglFuncTab->glDeleteBuffersExt = (PFNGLDELETEBUFFERSPROC)context()->getProcAddress("glDeleteBuffers"); + qglFuncTab->glBufferDataExt = (PFNGLBUFFERDATAPROC)context()->getProcAddress("glBufferData"); + qglFuncTab->glBufferSubDataExt = (PFNGLBUFFERSUBDATAPROC)context()->getProcAddress("glBufferSubData"); + qglFuncTab->glBindBufferExt = (PFNGLBINDBUFFERPROC)context()->getProcAddress("glBindBuffer"); + qglFuncTab->glMapBufferExt = (PFNGLMAPBUFFERPROC)context()->getProcAddress("glMapBuffer"); + qglFuncTab->glUnmapBufferExt = (PFNGLUNMAPBUFFERPROC)context()->getProcAddress("glUnmapBuffer"); - glFuncTab->initialized = true; + qglFuncTab->initialized = true; - this->glFuncTab = glFuncTab.release(); + glFuncTab = qglFuncTab.release(); - icvSetOpenGlFuncTab(this->glFuncTab); + icvSetOpenGlFuncTab(glFuncTab); } void OpenGlViewPort::resizeGL(int w, int h) @@ -3420,102 +3420,102 @@ void OpenGlViewPort::paintGL() CV_CheckGlError(); } -void OpenGlViewPort::mousePressEvent(QMouseEvent* event) +void OpenGlViewPort::mousePressEvent(QMouseEvent* evnt) { - int cv_event = -1, flags = 0; - QPoint pt = event->pos(); + int cv_event = -1, flags = 0; + QPoint pt = evnt->pos(); - icvmouseHandler(event, mouse_down, cv_event, flags); - icvmouseProcessing(QPointF(pt), cv_event, flags); + icvmouseHandler(evnt, mouse_down, cv_event, flags); + icvmouseProcessing(QPointF(pt), cv_event, flags); - QGLWidget::mousePressEvent(event); + QGLWidget::mousePressEvent(evnt); } -void OpenGlViewPort::mouseReleaseEvent(QMouseEvent* event) +void OpenGlViewPort::mouseReleaseEvent(QMouseEvent* evnt) { - int cv_event = -1, flags = 0; - QPoint pt = event->pos(); + int cv_event = -1, flags = 0; + QPoint pt = evnt->pos(); - icvmouseHandler(event, mouse_up, cv_event, flags); - icvmouseProcessing(QPointF(pt), cv_event, flags); + icvmouseHandler(evnt, mouse_up, cv_event, flags); + icvmouseProcessing(QPointF(pt), cv_event, flags); - QGLWidget::mouseReleaseEvent(event); + QGLWidget::mouseReleaseEvent(evnt); } -void OpenGlViewPort::mouseDoubleClickEvent(QMouseEvent* event) +void OpenGlViewPort::mouseDoubleClickEvent(QMouseEvent* evnt) { - int cv_event = -1, flags = 0; - QPoint pt = event->pos(); + int cv_event = -1, flags = 0; + QPoint pt = evnt->pos(); - icvmouseHandler(event, mouse_dbclick, cv_event, flags); - icvmouseProcessing(QPointF(pt), cv_event, flags); + icvmouseHandler(evnt, mouse_dbclick, cv_event, flags); + icvmouseProcessing(QPointF(pt), cv_event, flags); - QGLWidget::mouseDoubleClickEvent(event); + QGLWidget::mouseDoubleClickEvent(evnt); } -void OpenGlViewPort::mouseMoveEvent(QMouseEvent* event) +void OpenGlViewPort::mouseMoveEvent(QMouseEvent* evnt) { - int cv_event = CV_EVENT_MOUSEMOVE, flags = 0; - QPoint pt = event->pos(); + int cv_event = CV_EVENT_MOUSEMOVE, flags = 0; + QPoint pt = evnt->pos(); - //icvmouseHandler: pass parameters for cv_event, flags - icvmouseHandler(event, mouse_move, cv_event, flags); - icvmouseProcessing(QPointF(pt), cv_event, flags); + //icvmouseHandler: pass parameters for cv_event, flags + icvmouseHandler(evnt, mouse_move, cv_event, flags); + icvmouseProcessing(QPointF(pt), cv_event, flags); - QGLWidget::mouseMoveEvent(event); + QGLWidget::mouseMoveEvent(evnt); } -void OpenGlViewPort::icvmouseHandler(QMouseEvent* event, type_mouse_event category, int& cv_event, int& flags) +void OpenGlViewPort::icvmouseHandler(QMouseEvent* evnt, type_mouse_event category, int& cv_event, int& flags) { - Qt::KeyboardModifiers modifiers = event->modifiers(); - Qt::MouseButtons buttons = event->buttons(); - + Qt::KeyboardModifiers modifiers = evnt->modifiers(); + Qt::MouseButtons buttons = evnt->buttons(); + flags = 0; if (modifiers & Qt::ShiftModifier) - flags |= CV_EVENT_FLAG_SHIFTKEY; - if (modifiers & Qt::ControlModifier) - flags |= CV_EVENT_FLAG_CTRLKEY; - if (modifiers & Qt::AltModifier) - flags |= CV_EVENT_FLAG_ALTKEY; + flags |= CV_EVENT_FLAG_SHIFTKEY; + if (modifiers & Qt::ControlModifier) + flags |= CV_EVENT_FLAG_CTRLKEY; + if (modifiers & Qt::AltModifier) + flags |= CV_EVENT_FLAG_ALTKEY; if (buttons & Qt::LeftButton) - flags |= CV_EVENT_FLAG_LBUTTON; - if (buttons & Qt::RightButton) - flags |= CV_EVENT_FLAG_RBUTTON; + flags |= CV_EVENT_FLAG_LBUTTON; + if (buttons & Qt::RightButton) + flags |= CV_EVENT_FLAG_RBUTTON; if (buttons & Qt::MidButton) - flags |= CV_EVENT_FLAG_MBUTTON; + flags |= CV_EVENT_FLAG_MBUTTON; cv_event = CV_EVENT_MOUSEMOVE; - switch (event->button()) - { - case Qt::LeftButton: - cv_event = tableMouseButtons[category][0]; - flags |= CV_EVENT_FLAG_LBUTTON; - break; - - case Qt::RightButton: - cv_event = tableMouseButtons[category][1]; - flags |= CV_EVENT_FLAG_RBUTTON; - break; - - case Qt::MidButton: - cv_event = tableMouseButtons[category][2]; - flags |= CV_EVENT_FLAG_MBUTTON; - break; - - default: + switch (evnt->button()) + { + case Qt::LeftButton: + cv_event = tableMouseButtons[category][0]; + flags |= CV_EVENT_FLAG_LBUTTON; + break; + + case Qt::RightButton: + cv_event = tableMouseButtons[category][1]; + flags |= CV_EVENT_FLAG_RBUTTON; + break; + + case Qt::MidButton: + cv_event = tableMouseButtons[category][2]; + flags |= CV_EVENT_FLAG_MBUTTON; + break; + + default: ; - } + } } void OpenGlViewPort::icvmouseProcessing(QPointF pt, int cv_event, int flags) { - if (mouseCallback) - mouseCallback(cv_event, pt.x(), pt.y(), flags, mouseData); + if (mouseCallback) + mouseCallback(cv_event, pt.x(), pt.y(), flags, mouseData); } diff --git a/modules/highgui/src/window_gtk.cpp b/modules/highgui/src/window_gtk.cpp index 33cc72a..5beb785 100644 --- a/modules/highgui/src/window_gtk.cpp +++ b/modules/highgui/src/window_gtk.cpp @@ -55,16 +55,6 @@ #include #endif -/*#if _MSC_VER >= 1200 -#pragma warning( disable: 4505 ) -#pragma comment(lib,"gtk-win32-2.0.lib") -#pragma comment(lib,"glib-2.0.lib") -#pragma comment(lib,"gobject-2.0.lib") -#pragma comment(lib,"gdk-win32-2.0.lib") -#pragma comment(lib,"gdk_pixbuf-2.0.lib") -#endif*/ - - // TODO Fix the initial window size when flags=0. Right now the initial window is by default // 320x240 size. A better default would be actual size of the image. Problem // is determining desired window size with trackbars while still allowing resizing. @@ -1372,17 +1362,17 @@ cvDestroyAllWindows( void ) CV_UNLOCK_MUTEX(); } -CvSize icvCalcOptimalWindowSize( CvWindow * window, CvSize new_image_size){ - CvSize window_size; - GtkWidget * toplevel = gtk_widget_get_toplevel( window->frame ); - gdk_drawable_get_size( GDK_DRAWABLE(toplevel->window), - &window_size.width, &window_size.height ); +// CvSize icvCalcOptimalWindowSize( CvWindow * window, CvSize new_image_size){ +// CvSize window_size; +// GtkWidget * toplevel = gtk_widget_get_toplevel( window->frame ); +// gdk_drawable_get_size( GDK_DRAWABLE(toplevel->window), +// &window_size.width, &window_size.height ); - window_size.width = window_size.width + new_image_size.width - window->widget->allocation.width; - window_size.height = window_size.height + new_image_size.height - window->widget->allocation.height; +// window_size.width = window_size.width + new_image_size.width - window->widget->allocation.width; +// window_size.height = window_size.height + new_image_size.height - window->widget->allocation.height; - return window_size; -} +// return window_size; +// } CV_IMPL void cvShowImage( const char* name, const CvArr* arr ) diff --git a/modules/highgui/src/window_w32.cpp b/modules/highgui/src/window_w32.cpp index b97be0f..29cdda8 100644 --- a/modules/highgui/src/window_w32.cpp +++ b/modules/highgui/src/window_w32.cpp @@ -43,10 +43,6 @@ #if defined WIN32 || defined _WIN32 -#if _MSC_VER >= 1200 -#pragma warning( disable: 4710 ) -#endif - #define COMPILE_MULTIMON_STUBS // Required for multi-monitor support #ifndef _MULTIMON_USE_SECURE_CRT # define _MULTIMON_USE_SECURE_CRT 0 // some MinGW platforms have no strncpy_s @@ -61,6 +57,10 @@ #ifndef __inout # define __inout #endif + +#ifdef __GNUC__ +# pragma GCC diagnostic ignored "-Wmissing-declarations" +#endif #include #include @@ -166,7 +166,7 @@ typedef struct CvWindow HGDIOBJ image; int last_key; int flags; - int status;//0 normal, 1 fullscreen (YV) + int status;//0 normal, 1 fullscreen (YV) CvMouseCallback on_mouse; void* on_mouse_param; @@ -360,7 +360,7 @@ icvSaveWindowPos( const char* name, CvRect rect ) char rootKey[1024]; strcpy( szKey, icvWindowPosRootKey ); strcat( szKey, name ); - + if( RegOpenKeyEx( HKEY_CURRENT_USER,szKey,0,KEY_READ,&hkey) != ERROR_SUCCESS ) { HKEY hroot; @@ -405,7 +405,7 @@ icvSaveWindowPos( const char* name, CvRect rect ) if( RegOpenKeyEx( HKEY_CURRENT_USER,szKey,0,KEY_WRITE,&hkey) != ERROR_SUCCESS ) return; } - + RegSetValueEx(hkey, "Left", 0, REG_DWORD, (BYTE*)&rect.x, sizeof(rect.x)); RegSetValueEx(hkey, "Top", 0, REG_DWORD, (BYTE*)&rect.y, sizeof(rect.y)); RegSetValueEx(hkey, "Width", 0, REG_DWORD, (BYTE*)&rect.width, sizeof(rect.width)); @@ -415,9 +415,9 @@ icvSaveWindowPos( const char* name, CvRect rect ) double cvGetModeWindow_W32(const char* name)//YV { - double result = -1; - - CV_FUNCNAME( "cvGetModeWindow_W32" ); + double result = -1; + + CV_FUNCNAME( "cvGetModeWindow_W32" ); __BEGIN__; @@ -429,75 +429,75 @@ double cvGetModeWindow_W32(const char* name)//YV window = icvFindWindowByName( name ); if (!window) EXIT; // keep silence here - + result = window->status; - + __END__; - return result; + return result; } void cvSetModeWindow_W32( const char* name, double prop_value)//Yannick Verdie { - CV_FUNCNAME( "cvSetModeWindow_W32" ); + CV_FUNCNAME( "cvSetModeWindow_W32" ); - __BEGIN__; + __BEGIN__; - CvWindow* window; + CvWindow* window; - if(!name) - CV_ERROR( CV_StsNullPtr, "NULL name string" ); + if(!name) + CV_ERROR( CV_StsNullPtr, "NULL name string" ); - window = icvFindWindowByName( name ); - if( !window ) - CV_ERROR( CV_StsNullPtr, "NULL window" ); + window = icvFindWindowByName( name ); + if( !window ) + CV_ERROR( CV_StsNullPtr, "NULL window" ); - if(window->flags & CV_WINDOW_AUTOSIZE)//if the flag CV_WINDOW_AUTOSIZE is set - EXIT; + if(window->flags & CV_WINDOW_AUTOSIZE)//if the flag CV_WINDOW_AUTOSIZE is set + EXIT; - { - DWORD dwStyle = (DWORD)GetWindowLongPtr(window->frame, GWL_STYLE); - CvRect position; + { + DWORD dwStyle = (DWORD)GetWindowLongPtr(window->frame, GWL_STYLE); + CvRect position; - if (window->status==CV_WINDOW_FULLSCREEN && prop_value==CV_WINDOW_NORMAL) - { - icvLoadWindowPos(window->name,position ); - SetWindowLongPtr(window->frame, GWL_STYLE, dwStyle | WS_CAPTION | WS_THICKFRAME); + if (window->status==CV_WINDOW_FULLSCREEN && prop_value==CV_WINDOW_NORMAL) + { + icvLoadWindowPos(window->name,position ); + SetWindowLongPtr(window->frame, GWL_STYLE, dwStyle | WS_CAPTION | WS_THICKFRAME); - SetWindowPos(window->frame, HWND_TOP, position.x, position.y , position.width,position.height, SWP_NOZORDER | SWP_FRAMECHANGED); - window->status=CV_WINDOW_NORMAL; + SetWindowPos(window->frame, HWND_TOP, position.x, position.y , position.width,position.height, SWP_NOZORDER | SWP_FRAMECHANGED); + window->status=CV_WINDOW_NORMAL; - EXIT; - } + EXIT; + } - if (window->status==CV_WINDOW_NORMAL && prop_value==CV_WINDOW_FULLSCREEN) - { - //save dimension - RECT rect; - GetWindowRect(window->frame, &rect); - CvRect RectCV = cvRect(rect.left, rect.top,rect.right - rect.left, rect.bottom - rect.top); - icvSaveWindowPos(window->name,RectCV ); + if (window->status==CV_WINDOW_NORMAL && prop_value==CV_WINDOW_FULLSCREEN) + { + //save dimension + RECT rect; + GetWindowRect(window->frame, &rect); + CvRect RectCV = cvRect(rect.left, rect.top,rect.right - rect.left, rect.bottom - rect.top); + icvSaveWindowPos(window->name,RectCV ); - //Look at coordinate for fullscreen - HMONITOR hMonitor; - MONITORINFO mi; - hMonitor = MonitorFromRect(&rect, MONITOR_DEFAULTTONEAREST); + //Look at coordinate for fullscreen + HMONITOR hMonitor; + MONITORINFO mi; + hMonitor = MonitorFromRect(&rect, MONITOR_DEFAULTTONEAREST); - mi.cbSize = sizeof(mi); - GetMonitorInfo(hMonitor, &mi); + mi.cbSize = sizeof(mi); + GetMonitorInfo(hMonitor, &mi); - //fullscreen - position.x=mi.rcMonitor.left;position.y=mi.rcMonitor.top; - position.width=mi.rcMonitor.right - mi.rcMonitor.left;position.height=mi.rcMonitor.bottom - mi.rcMonitor.top; - SetWindowLongPtr(window->frame, GWL_STYLE, dwStyle & ~WS_CAPTION & ~WS_THICKFRAME); + //fullscreen + position.x=mi.rcMonitor.left;position.y=mi.rcMonitor.top; + position.width=mi.rcMonitor.right - mi.rcMonitor.left;position.height=mi.rcMonitor.bottom - mi.rcMonitor.top; + SetWindowLongPtr(window->frame, GWL_STYLE, dwStyle & ~WS_CAPTION & ~WS_THICKFRAME); - SetWindowPos(window->frame, HWND_TOP, position.x, position.y , position.width,position.height, SWP_NOZORDER | SWP_FRAMECHANGED); - window->status=CV_WINDOW_FULLSCREEN; + SetWindowPos(window->frame, HWND_TOP, position.x, position.y , position.width,position.height, SWP_NOZORDER | SWP_FRAMECHANGED); + window->status=CV_WINDOW_FULLSCREEN; - EXIT; - } - } + EXIT; + } + } - __END__; + __END__; } double cvGetPropWindowAutoSize_W32(const char* name) @@ -526,9 +526,9 @@ double cvGetPropWindowAutoSize_W32(const char* name) double cvGetRatioWindow_W32(const char* name) { - double result = -1; - - CV_FUNCNAME( "cvGetRatioWindow_W32" ); + double result = -1; + + CV_FUNCNAME( "cvGetRatioWindow_W32" ); __BEGIN__; @@ -540,20 +540,20 @@ double cvGetRatioWindow_W32(const char* name) window = icvFindWindowByName( name ); if (!window) EXIT; // keep silence here - + result = static_cast(window->width) / window->height; - + __END__; - return result; + return result; } double cvGetOpenGlProp_W32(const char* name) { - double result = -1; + double result = -1; -#ifdef HAVE_OPENGL - CV_FUNCNAME( "cvGetOpenGlProp_W32" ); +#ifdef HAVE_OPENGL + CV_FUNCNAME( "cvGetOpenGlProp_W32" ); __BEGIN__; @@ -565,14 +565,14 @@ double cvGetOpenGlProp_W32(const char* name) window = icvFindWindowByName( name ); if (!window) EXIT; // keep silence here - + result = window->useGl; - + __END__; #endif - (void)name; + (void)name; - return result; + return result; } @@ -626,7 +626,7 @@ namespace void generateBitmapFont(const std::string& family, int height, int weight, bool italic, bool underline, int start, int count, int base) const; bool isGlContextInitialized() const; - + PFNGLGENBUFFERSPROC glGenBuffersExt; PFNGLDELETEBUFFERSPROC glDeleteBuffersExt; @@ -787,8 +787,8 @@ namespace weight, // font weight italic ? TRUE : FALSE, // Italic underline ? TRUE : FALSE, // Underline - FALSE, // StrikeOut - ANSI_CHARSET, // CharSet + FALSE, // StrikeOut + ANSI_CHARSET, // CharSet OUT_TT_PRECIS, // OutPrecision CLIP_DEFAULT_PRECIS, // ClipPrecision ANTIALIASED_QUALITY, // Quality @@ -870,12 +870,12 @@ namespace 0, // Shift Bit Ignored 0, // No Accumulation Buffer 0, 0, 0, 0, // Accumulation Bits Ignored - 32, // 32 Bit Z-Buffer (Depth Buffer) + 32, // 32 Bit Z-Buffer (Depth Buffer) 0, // No Stencil Buffer 0, // No Auxiliary Buffer PFD_MAIN_PLANE, // Main Drawing Layer 0, // Reserved - 0, 0, 0 // Layer Masks Ignored + 0, 0, 0 // Layer Masks Ignored }; hGLDC = GetDC(hWnd); @@ -903,7 +903,7 @@ namespace void releaseGlContext(CvWindow* window) { - CV_FUNCNAME( "releaseGlContext" ); + //CV_FUNCNAME( "releaseGlContext" ); __BEGIN__; @@ -915,7 +915,7 @@ namespace window->hGLRC = NULL; } - if (window->dc) + if (window->dc) { ReleaseDC(window->hwnd, window->dc); window->dc = NULL; @@ -935,7 +935,7 @@ namespace if (!wglMakeCurrent(window->dc, window->hGLRC)) CV_ERROR( CV_OpenGlApiCallError, "Can't Activate The GL Rendering Context" ); - glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT); + glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT); if (window->glDrawCallback) window->glDrawCallback(window->glDrawData); @@ -1009,7 +1009,7 @@ CV_IMPL int cvNamedWindow( const char* name, int flags ) ShowWindow(mainhWnd, SW_SHOW); - //YV- remove one border by changing the style + //YV- remove one border by changing the style hWnd = CreateWindow("HighGUI class", "", (defStyle & ~WS_SIZEBOX) | WS_CHILD, CW_USEDEFAULT, 0, rect.width, rect.height, mainhWnd, 0, hg_hinstance, 0); if( !hWnd ) CV_ERROR( CV_StsError, "Frame window can not be created" ); @@ -1400,16 +1400,16 @@ cvShowImage( const char* name, const CvArr* arr ) CV_ERROR( CV_StsNullPtr, "NULL name" ); window = icvFindWindowByName(name); - if(!window) - { + if(!window) + { #ifndef HAVE_OPENGL - cvNamedWindow(name, CV_WINDOW_AUTOSIZE); + cvNamedWindow(name, CV_WINDOW_AUTOSIZE); #else - cvNamedWindow(name, CV_WINDOW_AUTOSIZE | CV_WINDOW_OPENGL); + cvNamedWindow(name, CV_WINDOW_AUTOSIZE | CV_WINDOW_OPENGL); #endif - window = icvFindWindowByName(name); - } + window = icvFindWindowByName(name); + } if( !window || !arr ) EXIT; // keep silence here. @@ -1467,6 +1467,7 @@ cvShowImage( const char* name, const CvArr* arr ) __END__; } +#if 0 CV_IMPL void cvShowImageHWND(HWND w_hWnd, const CvArr* arr) { @@ -1494,7 +1495,7 @@ cvShowImageHWND(HWND w_hWnd, const CvArr* arr) if( CV_IS_IMAGE_HDR( arr ) ) origin = ((IplImage*)arr)->origin; - CV_CALL( image = cvGetMat( arr, &stub ) ); + CV_CALL( image = cvGetMat( arr, &stub ) ); if ( hdc ) { @@ -1512,7 +1513,7 @@ cvShowImageHWND(HWND w_hWnd, const CvArr* arr) dst_ptr = bmp.bmBits; } - if( size.cx != image->width || size.cy != image->height || channels != channels0 ) + if( size.cx != image->width || size.cy != image->height || channels != channels0 ) { changed_size = true; @@ -1544,6 +1545,7 @@ cvShowImageHWND(HWND w_hWnd, const CvArr* arr) __END__; } +#endif CV_IMPL void cvResizeWindow(const char* name, int width, int height ) { @@ -1666,7 +1668,7 @@ MainWindowProc( HWND hwnd, UINT uMsg, WPARAM wParam, LPARAM lParam ) { // Snap window to screen edges with multi-monitor support. // Adi Shavit LPWINDOWPOS pos = (LPWINDOWPOS)lParam; - + RECT rect; GetWindowRect(window->frame, &rect); @@ -1679,15 +1681,15 @@ MainWindowProc( HWND hwnd, UINT uMsg, WPARAM wParam, LPARAM lParam ) const int SNAP_DISTANCE = 15; - if (abs(pos->x - mi.rcMonitor.left) <= SNAP_DISTANCE) + if (abs(pos->x - mi.rcMonitor.left) <= SNAP_DISTANCE) pos->x = mi.rcMonitor.left; // snap to left edge - else + else if (abs(pos->x + pos->cx - mi.rcMonitor.right) <= SNAP_DISTANCE) pos->x = mi.rcMonitor.right - pos->cx; // snap to right edge if (abs(pos->y - mi.rcMonitor.top) <= SNAP_DISTANCE) pos->y = mi.rcMonitor.top; // snap to top edge - else + else if (abs(pos->y + pos->cy - mi.rcMonitor.bottom) <= SNAP_DISTANCE) pos->y = mi.rcMonitor.bottom - pos->cy; // snap to bottom edge } @@ -1848,9 +1850,9 @@ static LRESULT CALLBACK HighGUIProc( HWND hwnd, UINT uMsg, WPARAM wParam, LPARAM EndPaint(hwnd, &paint); } #ifdef HAVE_OPENGL - else if(window->useGl) + else if(window->useGl) { - drawGl(window); + drawGl(window); return DefWindowProc(hwnd, uMsg, wParam, lParam); } #endif @@ -1901,18 +1903,18 @@ static LRESULT CALLBACK WindowProc( HWND hwnd, UINT uMsg, WPARAM wParam, LPARAM if( hg_on_preprocess ) { int was_processed = 0; - int ret = hg_on_preprocess(hwnd, uMsg, wParam, lParam, &was_processed); + int rethg = hg_on_preprocess(hwnd, uMsg, wParam, lParam, &was_processed); if( was_processed ) - return ret; + return rethg; } ret = HighGUIProc(hwnd, uMsg, wParam, lParam); if(hg_on_postprocess) { int was_processed = 0; - int ret = hg_on_postprocess(hwnd, uMsg, wParam, lParam, &was_processed); + int rethg = hg_on_postprocess(hwnd, uMsg, wParam, lParam, &was_processed); if( was_processed ) - return ret; + return rethg; } return ret; diff --git a/modules/highgui/test/test_drawing.cpp b/modules/highgui/test/test_drawing.cpp index 4b45a7b..09590b3 100644 --- a/modules/highgui/test/test_drawing.cpp +++ b/modules/highgui/test/test_drawing.cpp @@ -60,9 +60,9 @@ protected: void CV_DrawingTest::run( int ) { Mat testImg, valImg; - const string name = "drawing/image.jpg"; + const string fname = "drawing/image.jpg"; string path = ts->get_data_path(), filename; - filename = path + name; + filename = path + fname; draw( testImg ); @@ -415,30 +415,30 @@ class CV_FillConvexPolyTest : public cvtest::BaseTest { public: CV_FillConvexPolyTest() {} - ~CV_FillConvexPolyTest() {} + ~CV_FillConvexPolyTest() {} protected: void run(int) { vector line1; vector line2; - + line1.push_back(Point(1, 1)); line1.push_back(Point(5, 1)); line1.push_back(Point(5, 8)); line1.push_back(Point(1, 8)); - + line2.push_back(Point(2, 2)); line2.push_back(Point(10, 2)); line2.push_back(Point(10, 16)); line2.push_back(Point(2, 16)); - + Mat gray0(10,10,CV_8U, Scalar(0)); fillConvexPoly(gray0, line1, Scalar(255), 8, 0); int nz1 = countNonZero(gray0); - + fillConvexPoly(gray0, line2, Scalar(0), 8, 1); int nz2 = countNonZero(gray0)/255; - + CV_Assert( nz1 == 40 && nz2 == 0 ); } }; diff --git a/modules/highgui/test/test_gui.cpp b/modules/highgui/test/test_gui.cpp index 00fa20e..11aa3f9 100644 --- a/modules/highgui/test/test_gui.cpp +++ b/modules/highgui/test/test_gui.cpp @@ -43,7 +43,7 @@ #include "test_precomp.hpp" #include "opencv2/highgui/highgui.hpp" -#if defined HAVE_GTK || defined HAVE_QT || defined WIN32 || defined _WIN32 || HAVE_CARBON || HAVE_COCOA +#if defined HAVE_GTK || defined HAVE_QT || defined WIN32 || defined _WIN32 || defined HAVE_CARBON || defined HAVE_COCOA using namespace cv; using namespace std; diff --git a/modules/highgui/test/test_precomp.hpp b/modules/highgui/test/test_precomp.hpp index bbf924e..91aff1f 100644 --- a/modules/highgui/test/test_precomp.hpp +++ b/modules/highgui/test/test_precomp.hpp @@ -1,3 +1,7 @@ +#ifdef __GNUC__ +# pragma GCC diagnostic ignored "-Wmissing-declarations" +#endif + #ifndef __OPENCV_TEST_PRECOMP_HPP__ #define __OPENCV_TEST_PRECOMP_HPP__ @@ -40,7 +44,7 @@ /*defined(HAVE_OPENNI) || too specialized */ \ defined(HAVE_FFMPEG) || \ defined(WIN32) /* assume that we have ffmpeg */ - + # define BUILD_WITH_VIDEO_INPUT_SUPPORT 1 #else # define BUILD_WITH_VIDEO_INPUT_SUPPORT 0 @@ -61,19 +65,19 @@ namespace cvtest { string fourccToString(int fourcc); - + struct VideoFormat { VideoFormat() { fourcc = -1; } VideoFormat(const string& _ext, int _fourcc) : ext(_ext), fourcc(_fourcc) {} bool empty() const { return ext.empty(); } - + string ext; int fourcc; }; - + extern const VideoFormat g_specific_fmt_list[]; - + } #endif diff --git a/modules/highgui/test/test_video_io.cpp b/modules/highgui/test/test_video_io.cpp index 51c6e26..3134b8e 100644 --- a/modules/highgui/test/test_video_io.cpp +++ b/modules/highgui/test/test_video_io.cpp @@ -53,7 +53,7 @@ string fourccToString(int fourcc) { return format("%c%c%c%c", fourcc & 255, (fourcc >> 8) & 255, (fourcc >> 16) & 255, (fourcc >> 24) & 255); } - + const VideoFormat g_specific_fmt_list[] = { VideoFormat("avi", CV_FOURCC('X', 'V', 'I', 'D')), @@ -63,11 +63,11 @@ const VideoFormat g_specific_fmt_list[] = VideoFormat("mkv", CV_FOURCC('X', 'V', 'I', 'D')), VideoFormat("mkv", CV_FOURCC('M', 'P', 'E', 'G')), VideoFormat("mkv", CV_FOURCC('M', 'J', 'P', 'G')), - + VideoFormat("mov", CV_FOURCC('m', 'p', '4', 'v')), VideoFormat() }; - + } class CV_HighGuiTest : public cvtest::BaseTest @@ -246,7 +246,7 @@ void CV_HighGuiTest::VideoTest(const string& dir, const cvtest::VideoFormat& fmt if (!img) break; - + frames.push_back(Mat(img).clone()); if (writer == 0) @@ -393,7 +393,7 @@ void CV_HighGuiTest::SpecificVideoTest(const string& dir, const cvtest::VideoFor { string ext = fmt.ext; int fourcc = fmt.fourcc; - + string fourcc_str = cvtest::fourccToString(fourcc); const string video_file = "video_" + fourcc_str + "." + ext; @@ -403,7 +403,7 @@ void CV_HighGuiTest::SpecificVideoTest(const string& dir, const cvtest::VideoFor if (!writer.isOpened()) { // call it repeatedly for easier debugging - VideoWriter writer(video_file, fourcc, 25, frame_size, true); + VideoWriter writer2(video_file, fourcc, 25, frame_size, true); ts->printf(ts->LOG, "Creating a video in %s...\n", video_file.c_str()); ts->printf(ts->LOG, "Cannot create VideoWriter object with codec %s.\n", fourcc_str.c_str()); ts->set_failed_test_info(ts->FAIL_MISMATCH); @@ -412,7 +412,7 @@ void CV_HighGuiTest::SpecificVideoTest(const string& dir, const cvtest::VideoFor const size_t IMAGE_COUNT = 30; vector images; - + for( size_t i = 0; i < IMAGE_COUNT; ++i ) { string file_path = format("%s../python/images/QCIF_%02d.bmp", dir.c_str(), i); @@ -432,7 +432,7 @@ void CV_HighGuiTest::SpecificVideoTest(const string& dir, const cvtest::VideoFor if (img.at(k, l) == Vec3b::all(0)) img.at(k, l) = Vec3b(0, 255, 0); else img.at(k, l) = Vec3b(0, 0, 255); - + resize(img, img, frame_size, 0.0, 0.0, INTER_CUBIC); images.push_back(img); diff --git a/modules/highgui/test/test_video_pos.cpp b/modules/highgui/test/test_video_pos.cpp index 36d8551..d350b45 100755 --- a/modules/highgui/test/test_video_pos.cpp +++ b/modules/highgui/test/test_video_pos.cpp @@ -1,180 +1,180 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// - // - // IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. - // - // By downloading, copying, installing or using the software you agree to this license. - // If you do not agree to this license, do not download, install, - // copy or use the software. - // - // - // License Agreement - // For Open Source Computer Vision Library - // - // Copyright (C) 2000-2008, Intel Corporation, all rights reserved. - // Copyright (C) 2009, Willow Garage Inc., all rights reserved. - // Third party copyrights are property of their respective owners. - // - // Redistribution and use in source and binary forms, with or without modification, - // are permitted provided that the following conditions are met: - // - // * Redistribution's of source code must retain the above copyright notice, - // this list of conditions and the following disclaimer. - // - // * Redistribution's in binary form must reproduce the above copyright notice, - // this list of conditions and the following disclaimer in the documentation - // and/or other materials provided with the distribution. - // - // * The name of the copyright holders may not be used to endorse or promote products - // derived from this software without specific prior written permission. - // - // This software is provided by the copyright holders and contributors "as is" and - // any express or implied warranties, including, but not limited to, the implied - // warranties of merchantability and fitness for a particular purpose are disclaimed. - // In no event shall the Intel Corporation or contributors be liable for any direct, - // indirect, incidental, special, exemplary, or consequential damages - // (including, but not limited to, procurement of substitute goods or services; - // loss of use, data, or profits; or business interruption) however caused - // and on any theory of liability, whether in contract, strict liability, - // or tort (including negligence or otherwise) arising in any way out of - // the use of this software, even if advised of the possibility of such damage. - // - //M*/ - -#include "test_precomp.hpp" -#include "opencv2/highgui/highgui.hpp" - -using namespace cv; -using namespace std; - -class CV_PositioningTest : public cvtest::BaseTest -{ -public: - CV_PositioningTest() - { - framesize = Size(640, 480); - } - - Mat drawFrame(int i) - { - Mat mat = Mat::zeros(framesize, CV_8UC3); - - mat = Scalar(fabs(cos(i*0.08)*255), fabs(sin(i*0.05)*255), i); - putText(mat, format("%03d", i), Point(10, 350), 0, 10, Scalar(128, 255, 255), 15); - return mat; - } - - string getFilename(const cvtest::VideoFormat& fmt) - { - return format("test_video_%s.%s", cvtest::fourccToString(fmt.fourcc).c_str(), fmt.ext.c_str()); - } - - bool CreateTestVideo(const cvtest::VideoFormat& fmt, int framecount) - { - string filename = getFilename(fmt); - - VideoWriter writer(filename, fmt.fourcc, 25, framesize, true); - if( !writer.isOpened() ) - return false; - - for (int i = 0; i < framecount; ++i) - { - Mat img = drawFrame(i); - writer << img; - } - return true; - } - - void run(int) - { - int n_frames = 100; - - for( int testcase = 0; ; testcase++ ) - { - const cvtest::VideoFormat& fmt = cvtest::g_specific_fmt_list[testcase]; - if( fmt.empty() ) - break; - string filename = getFilename(fmt); - ts->printf(ts->LOG, "\nFile: %s\n", filename.c_str()); - - if( !CreateTestVideo(fmt, n_frames) ) - { - ts->printf(ts->LOG, "\nError: cannot create video file"); - ts->set_failed_test_info(ts->FAIL_INVALID_OUTPUT); - return; - } - - VideoCapture cap(filename); - - if (!cap.isOpened()) - { - ts->printf(ts->LOG, "\nError: cannot read video file."); - ts->set_failed_test_info(ts->FAIL_INVALID_TEST_DATA); - return; - } - - int N0 = cap.get(CV_CAP_PROP_FRAME_COUNT); - cap.set(CV_CAP_PROP_POS_FRAMES, 0); - int N = cap.get(CV_CAP_PROP_FRAME_COUNT); - - if (N != n_frames || N != N0) - { - ts->printf(ts->LOG, "\nError: returned frame count (N0=%d, N=%d) is different from the reference number %d\n", N0, N, n_frames); - ts->set_failed_test_info(ts->FAIL_INVALID_OUTPUT); - return; - } - - for (int k = 0; k < N; ++k) - { - int idx = theRNG().uniform(0, N); - - if( !cap.set(CV_CAP_PROP_POS_FRAMES, idx) ) - { - ts->printf(ts->LOG, "\nError: cannot seek to frame %d.\n", idx); - ts->set_failed_test_info(ts->FAIL_INVALID_OUTPUT); - return; - } - - int idx1 = (int)cap.get(CV_CAP_PROP_POS_FRAMES); - - Mat img; cap >> img; - Mat img0 = drawFrame(idx); - - if( idx != idx1 ) - { - ts->printf(ts->LOG, "\nError: the current position (%d) after seek is different from specified (%d)\n", - idx1, idx); - ts->printf(ts->LOG, "Saving both frames ...\n"); - ts->set_failed_test_info(ts->FAIL_INVALID_OUTPUT); - imwrite("opencv_test_highgui_postest_actual.png", img); - imwrite("opencv_test_highgui_postest_expected.png", img0); - return; - } - - if (img.empty()) - { - ts->printf(ts->LOG, "\nError: cannot read a frame at position %d.\n", idx); - ts->set_failed_test_info(ts->FAIL_INVALID_OUTPUT); - return; - } - - double err = PSNR(img, img0); - - if( err < 20 ) - { - ts->printf(ts->LOG, "The frame read after positioning to %d is incorrect (PSNR=%g)\n", idx, err); - ts->printf(ts->LOG, "Saving both frames ...\n"); - ts->set_failed_test_info(ts->FAIL_INVALID_OUTPUT); - imwrite("opencv_test_highgui_postest_actual.png", img); - imwrite("opencv_test_highgui_postest_expected.png", img0); - return; - } - } - } - } - - Size framesize; -}; - -#if BUILD_WITH_VIDEO_INPUT_SUPPORT && BUILD_WITH_VIDEO_OUTPUT_SUPPORT -TEST(Highgui_Video, seek_random_synthetic) { CV_PositioningTest test; test.safe_run(); } -#endif +/*M/////////////////////////////////////////////////////////////////////////////////////// + // + // IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. + // + // By downloading, copying, installing or using the software you agree to this license. + // If you do not agree to this license, do not download, install, + // copy or use the software. + // + // + // License Agreement + // For Open Source Computer Vision Library + // + // Copyright (C) 2000-2008, Intel Corporation, all rights reserved. + // Copyright (C) 2009, Willow Garage Inc., all rights reserved. + // Third party copyrights are property of their respective owners. + // + // Redistribution and use in source and binary forms, with or without modification, + // are permitted provided that the following conditions are met: + // + // * Redistribution's of source code must retain the above copyright notice, + // this list of conditions and the following disclaimer. + // + // * Redistribution's in binary form must reproduce the above copyright notice, + // this list of conditions and the following disclaimer in the documentation + // and/or other materials provided with the distribution. + // + // * The name of the copyright holders may not be used to endorse or promote products + // derived from this software without specific prior written permission. + // + // This software is provided by the copyright holders and contributors "as is" and + // any express or implied warranties, including, but not limited to, the implied + // warranties of merchantability and fitness for a particular purpose are disclaimed. + // In no event shall the Intel Corporation or contributors be liable for any direct, + // indirect, incidental, special, exemplary, or consequential damages + // (including, but not limited to, procurement of substitute goods or services; + // loss of use, data, or profits; or business interruption) however caused + // and on any theory of liability, whether in contract, strict liability, + // or tort (including negligence or otherwise) arising in any way out of + // the use of this software, even if advised of the possibility of such damage. + // + //M*/ + +#include "test_precomp.hpp" +#include "opencv2/highgui/highgui.hpp" + +using namespace cv; +using namespace std; + +class CV_PositioningTest : public cvtest::BaseTest +{ +public: + CV_PositioningTest() + { + framesize = Size(640, 480); + } + + Mat drawFrame(int i) + { + Mat mat = Mat::zeros(framesize, CV_8UC3); + + mat = Scalar(fabs(cos(i*0.08)*255), fabs(sin(i*0.05)*255), i); + putText(mat, format("%03d", i), Point(10, 350), 0, 10, Scalar(128, 255, 255), 15); + return mat; + } + + string getFilename(const cvtest::VideoFormat& fmt) + { + return format("test_video_%s.%s", cvtest::fourccToString(fmt.fourcc).c_str(), fmt.ext.c_str()); + } + + bool CreateTestVideo(const cvtest::VideoFormat& fmt, int framecount) + { + string filename = getFilename(fmt); + + VideoWriter writer(filename, fmt.fourcc, 25, framesize, true); + if( !writer.isOpened() ) + return false; + + for (int i = 0; i < framecount; ++i) + { + Mat img = drawFrame(i); + writer << img; + } + return true; + } + + void run(int) + { + int n_frames = 100; + + for( int testcase = 0; ; testcase++ ) + { + const cvtest::VideoFormat& fmt = cvtest::g_specific_fmt_list[testcase]; + if( fmt.empty() ) + break; + string filename = getFilename(fmt); + ts->printf(ts->LOG, "\nFile: %s\n", filename.c_str()); + + if( !CreateTestVideo(fmt, n_frames) ) + { + ts->printf(ts->LOG, "\nError: cannot create video file"); + ts->set_failed_test_info(ts->FAIL_INVALID_OUTPUT); + return; + } + + VideoCapture cap(filename); + + if (!cap.isOpened()) + { + ts->printf(ts->LOG, "\nError: cannot read video file."); + ts->set_failed_test_info(ts->FAIL_INVALID_TEST_DATA); + return; + } + + int N0 = (int)cap.get(CV_CAP_PROP_FRAME_COUNT); + cap.set(CV_CAP_PROP_POS_FRAMES, 0); + int N = (int)cap.get(CV_CAP_PROP_FRAME_COUNT); + + if (N != n_frames || N != N0) + { + ts->printf(ts->LOG, "\nError: returned frame count (N0=%d, N=%d) is different from the reference number %d\n", N0, N, n_frames); + ts->set_failed_test_info(ts->FAIL_INVALID_OUTPUT); + return; + } + + for (int k = 0; k < N; ++k) + { + int idx = theRNG().uniform(0, N); + + if( !cap.set(CV_CAP_PROP_POS_FRAMES, idx) ) + { + ts->printf(ts->LOG, "\nError: cannot seek to frame %d.\n", idx); + ts->set_failed_test_info(ts->FAIL_INVALID_OUTPUT); + return; + } + + int idx1 = (int)cap.get(CV_CAP_PROP_POS_FRAMES); + + Mat img; cap >> img; + Mat img0 = drawFrame(idx); + + if( idx != idx1 ) + { + ts->printf(ts->LOG, "\nError: the current position (%d) after seek is different from specified (%d)\n", + idx1, idx); + ts->printf(ts->LOG, "Saving both frames ...\n"); + ts->set_failed_test_info(ts->FAIL_INVALID_OUTPUT); + imwrite("opencv_test_highgui_postest_actual.png", img); + imwrite("opencv_test_highgui_postest_expected.png", img0); + return; + } + + if (img.empty()) + { + ts->printf(ts->LOG, "\nError: cannot read a frame at position %d.\n", idx); + ts->set_failed_test_info(ts->FAIL_INVALID_OUTPUT); + return; + } + + double err = PSNR(img, img0); + + if( err < 20 ) + { + ts->printf(ts->LOG, "The frame read after positioning to %d is incorrect (PSNR=%g)\n", idx, err); + ts->printf(ts->LOG, "Saving both frames ...\n"); + ts->set_failed_test_info(ts->FAIL_INVALID_OUTPUT); + imwrite("opencv_test_highgui_postest_actual.png", img); + imwrite("opencv_test_highgui_postest_expected.png", img0); + return; + } + } + } + } + + Size framesize; +}; + +#if BUILD_WITH_VIDEO_INPUT_SUPPORT && BUILD_WITH_VIDEO_OUTPUT_SUPPORT +TEST(Highgui_Video, seek_random_synthetic) { CV_PositioningTest test; test.safe_run(); } +#endif diff --git a/modules/imgproc/perf/perf_precomp.hpp b/modules/imgproc/perf/perf_precomp.hpp index bc6d2be..d85331e 100644 --- a/modules/imgproc/perf/perf_precomp.hpp +++ b/modules/imgproc/perf/perf_precomp.hpp @@ -1,3 +1,7 @@ +#ifdef __GNUC__ +# pragma GCC diagnostic ignored "-Wmissing-declarations" +#endif + #ifndef __OPENCV_PERF_PRECOMP_HPP__ #define __OPENCV_PERF_PRECOMP_HPP__ @@ -5,7 +9,7 @@ #include "opencv2/imgproc/imgproc.hpp" #include "opencv2/highgui/highgui.hpp" -#if GTEST_CREATE_SHARED_LIBRARY +#ifdef GTEST_CREATE_SHARED_LIBRARY #error no modules except ts should have GTEST_CREATE_SHARED_LIBRARY defined #endif diff --git a/modules/imgproc/src/_list.h b/modules/imgproc/src/_list.h index a19f7e2..29acdb4 100644 --- a/modules/imgproc/src/_list.h +++ b/modules/imgproc/src/_list.h @@ -98,6 +98,7 @@ typedef struct _list _CVLIST; _LIST_INLINE CVPOS prefix##get_tail_pos_##type(_CVLIST*);\ _LIST_INLINE type* prefix##get_next_##type(CVPOS*);\ _LIST_INLINE type* prefix##get_prev_##type(CVPOS*);\ + _LIST_INLINE int prefix##is_pos_##type(CVPOS pos);\ /* Modification functions*/\ _LIST_INLINE void prefix##clear_list_##type(_CVLIST*);\ _LIST_INLINE CVPOS prefix##add_head_##type(_CVLIST*, type*);\ @@ -151,8 +152,8 @@ typedef struct _list _CVLIST; }\ element->m_next = ((element_type*)l->m_head_free.m_pos);\ l->m_head_free.m_pos = element; - - + + /*#define GET_FIRST_FREE(l) ((ELEMENT_##type*)(l->m_head_free.m_pos))*/ #define IMPLEMENT_LIST(type, prefix)\ diff --git a/modules/imgproc/src/color.cpp b/modules/imgproc/src/color.cpp index f887837..42f7009 100644 --- a/modules/imgproc/src/color.cpp +++ b/modules/imgproc/src/color.cpp @@ -63,18 +63,18 @@ 44141 Dortmund Germany www.md-it.de - + Redistribution and use in source and binary forms, with or without modification, are permitted provided - that the following conditions are met: + that the following conditions are met: Redistributions of source code must retain - the above copyright notice, this list of conditions and the following disclaimer. + the above copyright notice, this list of conditions and the following disclaimer. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation - and/or other materials provided with the distribution. + and/or other materials provided with the distribution. The name of Contributor may not be used to endorse or promote products - derived from this software without specific prior written permission. + derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, @@ -102,14 +102,14 @@ template static void splineBuild(const _Tp* f, int n, _Tp* tab) _Tp cn = 0; int i; tab[0] = tab[1] = (_Tp)0; - + for(i = 1; i < n-1; i++) { _Tp t = 3*(f[i+1] - 2*f[i] + f[i-1]); _Tp l = 1/(4 - tab[(i-1)*4]); tab[i*4] = l; tab[i*4+1] = (t - tab[(i-1)*4+1])*l; } - + for(i = n-1; i >= 0; i--) { _Tp c = tab[i*4+1] - tab[i*4]*cn; @@ -129,14 +129,14 @@ template static inline _Tp splineInterpolate(_Tp x, const _Tp* tab x -= ix; tab += ix*4; return ((tab[3]*x + tab[2])*x + tab[1])*x + tab[0]; -} +} + - template struct ColorChannel { typedef float worktype_f; static _Tp max() { return std::numeric_limits<_Tp>::max(); } - static _Tp half() { return (_Tp)(max()/2 + 1); } + static _Tp half() { return (_Tp)(max()/2 + 1); } }; template<> struct ColorChannel @@ -153,7 +153,7 @@ template<> struct ColorChannel static double half() { return 0.5; } };*/ - + ///////////////////////////// Top-level template function //////////////////////////////// template void CvtColorLoop(const Mat& srcmat, Mat& dstmat, const Cvt& cvt) @@ -163,24 +163,24 @@ template void CvtColorLoop(const Mat& srcmat, Mat& dstmat, const Cvt& const uchar* src = srcmat.data; uchar* dst = dstmat.data; size_t srcstep = srcmat.step, dststep = dstmat.step; - + if( srcmat.isContinuous() && dstmat.isContinuous() ) { sz.width *= sz.height; sz.height = 1; - } - + } + for( ; sz.height--; src += srcstep, dst += dststep ) cvt((const _Tp*)src, (_Tp*)dst, sz.width); } - - + + ////////////////// Various 3/4-channel to 3/4-channel RGB transformations ///////////////// - + template struct RGB2RGB { typedef _Tp channel_type; - + RGB2RGB(int _srccn, int _dstcn, int _blueIdx) : srccn(_srccn), dstcn(_dstcn), blueIdx(_blueIdx) {} void operator()(const _Tp* src, _Tp* dst, int n) const { @@ -214,19 +214,19 @@ template struct RGB2RGB } } } - + int srccn, dstcn, blueIdx; }; - + /////////// Transforming 16-bit (565 or 555) RGB to/from 24/32-bit (888[8]) RGB ////////// struct RGB5x52RGB { typedef uchar channel_type; - + RGB5x52RGB(int _dstcn, int _blueIdx, int _greenBits) - : dstcn(_dstcn), blueIdx(_blueIdx), greenBits(_greenBits) {} - + : dstcn(_dstcn), blueIdx(_blueIdx), greenBits(_greenBits) {} + void operator()(const uchar* src, uchar* dst, int n) const { int dcn = dstcn, bidx = blueIdx; @@ -251,18 +251,18 @@ struct RGB5x52RGB dst[3] = t & 0x8000 ? 255 : 0; } } - + int dstcn, blueIdx, greenBits; }; - + struct RGB2RGB5x5 { typedef uchar channel_type; - + RGB2RGB5x5(int _srccn, int _blueIdx, int _greenBits) - : srccn(_srccn), blueIdx(_blueIdx), greenBits(_greenBits) {} - + : srccn(_srccn), blueIdx(_blueIdx), greenBits(_greenBits) {} + void operator()(const uchar* src, uchar* dst, int n) const { int scn = srccn, bidx = blueIdx; @@ -283,17 +283,17 @@ struct RGB2RGB5x5 ((src[bidx^2]&~7) << 7)|(src[3] ? 0x8000 : 0)); } } - + int srccn, blueIdx, greenBits; }; - + ///////////////////////////////// Color to/from Grayscale //////////////////////////////// template struct Gray2RGB { typedef _Tp channel_type; - + Gray2RGB(int _dstcn) : dstcn(_dstcn) {} void operator()(const _Tp* src, _Tp* dst, int n) const { @@ -312,15 +312,15 @@ struct Gray2RGB } } } - + int dstcn; }; - + struct Gray2RGB5x5 { typedef uchar channel_type; - + Gray2RGB5x5(int _greenBits) : greenBits(_greenBits) {} void operator()(const uchar* src, uchar* dst, int n) const { @@ -344,7 +344,7 @@ struct Gray2RGB5x5 #undef R2Y #undef G2Y #undef B2Y - + enum { yuv_shift = 14, @@ -359,7 +359,7 @@ enum struct RGB5x52Gray { typedef uchar channel_type; - + RGB5x52Gray(int _greenBits) : greenBits(_greenBits) {} void operator()(const uchar* src, uchar* dst, int n) const { @@ -387,7 +387,7 @@ struct RGB5x52Gray template struct RGB2Gray { typedef _Tp channel_type; - + RGB2Gray(int _srccn, int blueIdx, const float* _coeffs) : srccn(_srccn) { static const float coeffs0[] = { 0.299f, 0.587f, 0.114f }; @@ -395,7 +395,7 @@ template struct RGB2Gray if(blueIdx == 0) std::swap(coeffs[0], coeffs[2]); } - + void operator()(const _Tp* src, _Tp* dst, int n) const { int scn = srccn; @@ -407,19 +407,19 @@ template struct RGB2Gray float coeffs[3]; }; - + template<> struct RGB2Gray { typedef uchar channel_type; - + RGB2Gray(int _srccn, int blueIdx, const int* coeffs) : srccn(_srccn) { const int coeffs0[] = { R2Y, G2Y, B2Y }; if(!coeffs) coeffs = coeffs0; - + int b = 0, g = 0, r = (1 << (yuv_shift-1)); int db = coeffs[blueIdx^2], dg = coeffs[1], dr = coeffs[blueIdx]; - + for( int i = 0; i < 256; i++, b += db, g += dg, r += dr ) { tab[i] = b; @@ -430,19 +430,19 @@ template<> struct RGB2Gray void operator()(const uchar* src, uchar* dst, int n) const { int scn = srccn; - const int* _tab = tab; + const int* _tab = tab; for(int i = 0; i < n; i++, src += scn) dst[i] = (uchar)((_tab[src[0]] + _tab[src[1]+256] + _tab[src[2]+512]) >> yuv_shift); } - int srccn, blueIdx; + int srccn; int tab[256*3]; }; - + template<> struct RGB2Gray { typedef ushort channel_type; - + RGB2Gray(int _srccn, int blueIdx, const int* _coeffs) : srccn(_srccn) { static const int coeffs0[] = { R2Y, G2Y, B2Y }; @@ -450,7 +450,7 @@ template<> struct RGB2Gray if( blueIdx == 0 ) std::swap(coeffs[0], coeffs[2]); } - + void operator()(const ushort* src, ushort* dst, int n) const { int scn = srccn, cb = coeffs[0], cg = coeffs[1], cr = coeffs[2]; @@ -461,25 +461,25 @@ template<> struct RGB2Gray int coeffs[3]; }; - + ///////////////////////////////////// RGB <-> YCrCb ////////////////////////////////////// template struct RGB2YCrCb_f { typedef _Tp channel_type; - + RGB2YCrCb_f(int _srccn, int _blueIdx, const float* _coeffs) : srccn(_srccn), blueIdx(_blueIdx) - { - static const float coeffs0[] = {0.299f, 0.587f, 0.114f, 0.713f, 0.564f}; - memcpy(coeffs, _coeffs ? _coeffs : coeffs0, 5*sizeof(coeffs[0])); - if(blueIdx==0) std::swap(coeffs[0], coeffs[2]); - } - + { + static const float coeffs0[] = {0.299f, 0.587f, 0.114f, 0.713f, 0.564f}; + memcpy(coeffs, _coeffs ? _coeffs : coeffs0, 5*sizeof(coeffs[0])); + if(blueIdx==0) std::swap(coeffs[0], coeffs[2]); + } + void operator()(const _Tp* src, _Tp* dst, int n) const { int scn = srccn, bidx = blueIdx; const _Tp delta = ColorChannel<_Tp>::half(); - float C0 = coeffs[0], C1 = coeffs[1], C2 = coeffs[2], C3 = coeffs[3], C4 = coeffs[4]; + float C0 = coeffs[0], C1 = coeffs[1], C2 = coeffs[2], C3 = coeffs[3], C4 = coeffs[4]; n *= 3; for(int i = 0; i < n; i += 3, src += scn) { @@ -490,25 +490,25 @@ template struct RGB2YCrCb_f } } int srccn, blueIdx; - float coeffs[5]; + float coeffs[5]; }; template struct RGB2YCrCb_i { typedef _Tp channel_type; - + RGB2YCrCb_i(int _srccn, int _blueIdx, const int* _coeffs) - : srccn(_srccn), blueIdx(_blueIdx) - { - static const int coeffs0[] = {R2Y, G2Y, B2Y, 11682, 9241}; - memcpy(coeffs, _coeffs ? _coeffs : coeffs0, 5*sizeof(coeffs[0])); - if(blueIdx==0) std::swap(coeffs[0], coeffs[2]); - } + : srccn(_srccn), blueIdx(_blueIdx) + { + static const int coeffs0[] = {R2Y, G2Y, B2Y, 11682, 9241}; + memcpy(coeffs, _coeffs ? _coeffs : coeffs0, 5*sizeof(coeffs[0])); + if(blueIdx==0) std::swap(coeffs[0], coeffs[2]); + } void operator()(const _Tp* src, _Tp* dst, int n) const { int scn = srccn, bidx = blueIdx; - int C0 = coeffs[0], C1 = coeffs[1], C2 = coeffs[2], C3 = coeffs[3], C4 = coeffs[4]; + int C0 = coeffs[0], C1 = coeffs[1], C2 = coeffs[2], C3 = coeffs[3], C4 = coeffs[4]; int delta = ColorChannel<_Tp>::half()*(1 << yuv_shift); n *= 3; for(int i = 0; i < n; i += 3, src += scn) @@ -522,20 +522,20 @@ template struct RGB2YCrCb_i } } int srccn, blueIdx; - int coeffs[5]; -}; + int coeffs[5]; +}; template struct YCrCb2RGB_f { typedef _Tp channel_type; - + YCrCb2RGB_f(int _dstcn, int _blueIdx, const float* _coeffs) - : dstcn(_dstcn), blueIdx(_blueIdx) - { - static const float coeffs0[] = {1.403f, -0.714f, -0.344f, 1.773f}; - memcpy(coeffs, _coeffs ? _coeffs : coeffs0, 4*sizeof(coeffs[0])); - } + : dstcn(_dstcn), blueIdx(_blueIdx) + { + static const float coeffs0[] = {1.403f, -0.714f, -0.344f, 1.773f}; + memcpy(coeffs, _coeffs ? _coeffs : coeffs0, 4*sizeof(coeffs[0])); + } void operator()(const _Tp* src, _Tp* dst, int n) const { int dcn = dstcn, bidx = blueIdx; @@ -547,32 +547,32 @@ template struct YCrCb2RGB_f _Tp Y = src[i]; _Tp Cr = src[i+1]; _Tp Cb = src[i+2]; - + _Tp b = saturate_cast<_Tp>(Y + (Cb - delta)*C3); _Tp g = saturate_cast<_Tp>(Y + (Cb - delta)*C2 + (Cr - delta)*C1); _Tp r = saturate_cast<_Tp>(Y + (Cr - delta)*C0); - + dst[bidx] = b; dst[1] = g; dst[bidx^2] = r; if( dcn == 4 ) dst[3] = alpha; } } int dstcn, blueIdx; - float coeffs[4]; -}; + float coeffs[4]; +}; template struct YCrCb2RGB_i { typedef _Tp channel_type; - + YCrCb2RGB_i(int _dstcn, int _blueIdx, const int* _coeffs) : dstcn(_dstcn), blueIdx(_blueIdx) { - static const int coeffs0[] = {22987, -11698, -5636, 29049}; - memcpy(coeffs, _coeffs ? _coeffs : coeffs0, 4*sizeof(coeffs[0])); + static const int coeffs0[] = {22987, -11698, -5636, 29049}; + memcpy(coeffs, _coeffs ? _coeffs : coeffs0, 4*sizeof(coeffs[0])); } - + void operator()(const _Tp* src, _Tp* dst, int n) const { int dcn = dstcn, bidx = blueIdx; @@ -584,11 +584,11 @@ template struct YCrCb2RGB_i _Tp Y = src[i]; _Tp Cr = src[i+1]; _Tp Cb = src[i+2]; - + int b = Y + CV_DESCALE((Cb - delta)*C3, yuv_shift); int g = Y + CV_DESCALE((Cb - delta)*C2 + (Cr - delta)*C1, yuv_shift); int r = Y + CV_DESCALE((Cr - delta)*C0, yuv_shift); - + dst[bidx] = saturate_cast<_Tp>(b); dst[1] = saturate_cast<_Tp>(g); dst[bidx^2] = saturate_cast<_Tp>(r); @@ -598,9 +598,9 @@ template struct YCrCb2RGB_i } int dstcn, blueIdx; int coeffs[4]; -}; +}; + - ////////////////////////////////////// RGB <-> XYZ /////////////////////////////////////// static const float sRGB2XYZ_D65[] = @@ -609,18 +609,18 @@ static const float sRGB2XYZ_D65[] = 0.212671f, 0.715160f, 0.072169f, 0.019334f, 0.119193f, 0.950227f }; - + static const float XYZ2sRGB_D65[] = { 3.240479f, -1.53715f, -0.498535f, -0.969256f, 1.875991f, 0.041556f, 0.055648f, -0.204043f, 1.057311f }; - + template struct RGB2XYZ_f { typedef _Tp channel_type; - + RGB2XYZ_f(int _srccn, int blueIdx, const float* _coeffs) : srccn(_srccn) { memcpy(coeffs, _coeffs ? _coeffs : sRGB2XYZ_D65, 9*sizeof(coeffs[0])); @@ -637,13 +637,13 @@ template struct RGB2XYZ_f float C0 = coeffs[0], C1 = coeffs[1], C2 = coeffs[2], C3 = coeffs[3], C4 = coeffs[4], C5 = coeffs[5], C6 = coeffs[6], C7 = coeffs[7], C8 = coeffs[8]; - + n *= 3; for(int i = 0; i < n; i += 3, src += scn) { - _Tp X = saturate_cast<_Tp>(src[0]*C0 + src[1]*C1 + src[2]*C2); - _Tp Y = saturate_cast<_Tp>(src[0]*C3 + src[1]*C4 + src[2]*C5); - _Tp Z = saturate_cast<_Tp>(src[0]*C6 + src[1]*C7 + src[2]*C8); + _Tp X = saturate_cast<_Tp>(src[0]*C0 + src[1]*C1 + src[2]*C2); + _Tp Y = saturate_cast<_Tp>(src[0]*C3 + src[1]*C4 + src[2]*C5); + _Tp Z = saturate_cast<_Tp>(src[0]*C6 + src[1]*C7 + src[2]*C8); dst[i] = X; dst[i+1] = Y; dst[i+2] = Z; } } @@ -655,13 +655,13 @@ template struct RGB2XYZ_f template struct RGB2XYZ_i { typedef _Tp channel_type; - + RGB2XYZ_i(int _srccn, int blueIdx, const float* _coeffs) : srccn(_srccn) { static const int coeffs0[] = { - 1689, 1465, 739, - 871, 2929, 296, + 1689, 1465, 739, + 871, 2929, 296, 79, 488, 3892 }; for( int i = 0; i < 9; i++ ) @@ -692,12 +692,12 @@ template struct RGB2XYZ_i int srccn; int coeffs[9]; }; - - + + template struct XYZ2RGB_f { typedef _Tp channel_type; - + XYZ2RGB_f(int _dstcn, int _blueIdx, const float* _coeffs) : dstcn(_dstcn), blueIdx(_blueIdx) { @@ -709,23 +709,23 @@ template struct XYZ2RGB_f std::swap(coeffs[2], coeffs[8]); } } - + void operator()(const _Tp* src, _Tp* dst, int n) const { int dcn = dstcn; - _Tp alpha = ColorChannel<_Tp>::max(); + _Tp alpha = ColorChannel<_Tp>::max(); float C0 = coeffs[0], C1 = coeffs[1], C2 = coeffs[2], C3 = coeffs[3], C4 = coeffs[4], C5 = coeffs[5], C6 = coeffs[6], C7 = coeffs[7], C8 = coeffs[8]; n *= 3; for(int i = 0; i < n; i += 3, dst += dcn) { - _Tp B = saturate_cast<_Tp>(src[i]*C0 + src[i+1]*C1 + src[i+2]*C2); - _Tp G = saturate_cast<_Tp>(src[i]*C3 + src[i+1]*C4 + src[i+2]*C5); - _Tp R = saturate_cast<_Tp>(src[i]*C6 + src[i+1]*C7 + src[i+2]*C8); + _Tp B = saturate_cast<_Tp>(src[i]*C0 + src[i+1]*C1 + src[i+2]*C2); + _Tp G = saturate_cast<_Tp>(src[i]*C3 + src[i+1]*C4 + src[i+2]*C5); + _Tp R = saturate_cast<_Tp>(src[i]*C6 + src[i+1]*C7 + src[i+2]*C8); dst[0] = B; dst[1] = G; dst[2] = R; - if( dcn == 4 ) - dst[3] = alpha; + if( dcn == 4 ) + dst[3] = alpha; } } int dstcn, blueIdx; @@ -736,19 +736,19 @@ template struct XYZ2RGB_f template struct XYZ2RGB_i { typedef _Tp channel_type; - + XYZ2RGB_i(int _dstcn, int _blueIdx, const int* _coeffs) : dstcn(_dstcn), blueIdx(_blueIdx) { static const int coeffs0[] = { - 13273, -6296, -2042, - -3970, 7684, 170, + 13273, -6296, -2042, + -3970, 7684, 170, 228, -836, 4331 }; for(int i = 0; i < 9; i++) coeffs[i] = _coeffs ? cvRound(_coeffs[i]*(1 << xyz_shift)) : coeffs0[i]; - + if(blueIdx == 0) { std::swap(coeffs[0], coeffs[6]); @@ -772,13 +772,13 @@ template struct XYZ2RGB_i dst[0] = saturate_cast<_Tp>(B); dst[1] = saturate_cast<_Tp>(G); dst[2] = saturate_cast<_Tp>(R); if( dcn == 4 ) - dst[3] = alpha; + dst[3] = alpha; } } int dstcn, blueIdx; int coeffs[9]; }; - + ////////////////////////////////////// RGB <-> HSV /////////////////////////////////////// @@ -786,27 +786,27 @@ template struct XYZ2RGB_i struct RGB2HSV_b { typedef uchar channel_type; - + RGB2HSV_b(int _srccn, int _blueIdx, int _hrange) : srccn(_srccn), blueIdx(_blueIdx), hrange(_hrange) { CV_Assert( hrange == 180 || hrange == 256 ); } - + void operator()(const uchar* src, uchar* dst, int n) const { int i, bidx = blueIdx, scn = srccn; const int hsv_shift = 12; - + static int sdiv_table[256]; static int hdiv_table180[256]; static int hdiv_table256[256]; static volatile bool initialized = false; - + int hr = hrange; const int* hdiv_table = hr == 180 ? hdiv_table180 : hdiv_table256; n *= 3; - + if( !initialized ) { sdiv_table[0] = hdiv_table180[0] = hdiv_table256[0] = 0; @@ -818,65 +818,65 @@ struct RGB2HSV_b } initialized = true; } - + for( i = 0; i < n; i += 3, src += scn ) { int b = src[bidx], g = src[1], r = src[bidx^2]; int h, s, v = b; int vmin = b, diff; int vr, vg; - + CV_CALC_MAX_8U( v, g ); CV_CALC_MAX_8U( v, r ); CV_CALC_MIN_8U( vmin, g ); CV_CALC_MIN_8U( vmin, r ); - + diff = v - vmin; vr = v == r ? -1 : 0; vg = v == g ? -1 : 0; - + s = (diff * sdiv_table[v] + (1 << (hsv_shift-1))) >> hsv_shift; h = (vr & (g - b)) + (~vr & ((vg & (b - r + 2 * diff)) + ((~vg) & (r - g + 4 * diff)))); h = (h * hdiv_table[diff] + (1 << (hsv_shift-1))) >> hsv_shift; h += h < 0 ? hr : 0; - + dst[i] = saturate_cast(h); dst[i+1] = (uchar)s; dst[i+2] = (uchar)v; } } - + int srccn, blueIdx, hrange; -}; +}; + - struct RGB2HSV_f { typedef float channel_type; - + RGB2HSV_f(int _srccn, int _blueIdx, float _hrange) : srccn(_srccn), blueIdx(_blueIdx), hrange(_hrange) {} - + void operator()(const float* src, float* dst, int n) const { int i, bidx = blueIdx, scn = srccn; float hscale = hrange*(1.f/360.f); n *= 3; - + for( i = 0; i < n; i += 3, src += scn ) { float b = src[bidx], g = src[1], r = src[bidx^2]; float h, s, v; - + float vmin, diff; - + v = vmin = r; if( v < g ) v = g; if( v < b ) v = b; if( vmin > g ) vmin = g; if( vmin > b ) vmin = b; - + diff = v - vmin; s = diff/(float)(fabs(v) + FLT_EPSILON); diff = (float)(60./(diff + FLT_EPSILON)); @@ -886,15 +886,15 @@ struct RGB2HSV_f h = (b - r)*diff + 120.f; else h = (r - g)*diff + 240.f; - + if( h < 0 ) h += 360.f; - + dst[i] = h*hscale; dst[i+1] = s; dst[i+2] = v; } } - + int srccn, blueIdx; float hrange; }; @@ -903,17 +903,17 @@ struct RGB2HSV_f struct HSV2RGB_f { typedef float channel_type; - + HSV2RGB_f(int _dstcn, int _blueIdx, float _hrange) : dstcn(_dstcn), blueIdx(_blueIdx), hscale(6.f/_hrange) {} - + void operator()(const float* src, float* dst, int n) const { int i, bidx = blueIdx, dcn = dstcn; float _hscale = hscale; float alpha = ColorChannel::max(); n *= 3; - + for( i = 0; i < n; i += 3, dst += dcn ) { float h = src[i], s = src[i+1], v = src[i+2]; @@ -939,7 +939,7 @@ struct HSV2RGB_f tab[1] = v*(1.f - s); tab[2] = v*(1.f - s*h); tab[3] = v*(1.f - s*(1.f - h)); - + b = tab[sector_data[sector][0]]; g = tab[sector_data[sector][1]]; r = tab[sector_data[sector][2]]; @@ -956,26 +956,26 @@ struct HSV2RGB_f int dstcn, blueIdx; float hscale; }; - + struct HSV2RGB_b { typedef uchar channel_type; - + HSV2RGB_b(int _dstcn, int _blueIdx, int _hrange) : dstcn(_dstcn), cvt(3, _blueIdx, (float)_hrange) {} - + void operator()(const uchar* src, uchar* dst, int n) const { int i, j, dcn = dstcn; uchar alpha = ColorChannel::max(); float buf[3*BLOCK_SIZE]; - + for( i = 0; i < n; i += BLOCK_SIZE, src += BLOCK_SIZE*3 ) { int dn = std::min(n - i, (int)BLOCK_SIZE); - + for( j = 0; j < dn*3; j += 3 ) { buf[j] = src[j]; @@ -983,7 +983,7 @@ struct HSV2RGB_b buf[j+2] = src[j+2]*(1.f/255.f); } cvt(buf, buf, dn); - + for( j = 0; j < dn*3; j += 3, dst += dcn ) { dst[0] = saturate_cast(buf[j]*255.f); @@ -994,84 +994,84 @@ struct HSV2RGB_b } } } - + int dstcn; HSV2RGB_f cvt; }; - + ///////////////////////////////////// RGB <-> HLS //////////////////////////////////////// struct RGB2HLS_f { typedef float channel_type; - + RGB2HLS_f(int _srccn, int _blueIdx, float _hrange) : srccn(_srccn), blueIdx(_blueIdx), hrange(_hrange) {} - + void operator()(const float* src, float* dst, int n) const { int i, bidx = blueIdx, scn = srccn; float hscale = hrange*(1.f/360.f); n *= 3; - + for( i = 0; i < n; i += 3, src += scn ) { float b = src[bidx], g = src[1], r = src[bidx^2]; float h = 0.f, s = 0.f, l; float vmin, vmax, diff; - + vmax = vmin = r; if( vmax < g ) vmax = g; if( vmax < b ) vmax = b; if( vmin > g ) vmin = g; if( vmin > b ) vmin = b; - + diff = vmax - vmin; l = (vmax + vmin)*0.5f; - + if( diff > FLT_EPSILON ) { s = l < 0.5f ? diff/(vmax + vmin) : diff/(2 - vmax - vmin); diff = 60.f/diff; - + if( vmax == r ) h = (g - b)*diff; else if( vmax == g ) h = (b - r)*diff + 120.f; else h = (r - g)*diff + 240.f; - + if( h < 0.f ) h += 360.f; } - + dst[i] = h*hscale; dst[i+1] = l; dst[i+2] = s; } } - + int srccn, blueIdx; float hrange; }; - - + + struct RGB2HLS_b { typedef uchar channel_type; - + RGB2HLS_b(int _srccn, int _blueIdx, int _hrange) : srccn(_srccn), cvt(3, _blueIdx, (float)_hrange) {} - + void operator()(const uchar* src, uchar* dst, int n) const { int i, j, scn = srccn; float buf[3*BLOCK_SIZE]; - + for( i = 0; i < n; i += BLOCK_SIZE, dst += BLOCK_SIZE*3 ) { int dn = std::min(n - i, (int)BLOCK_SIZE); - + for( j = 0; j < dn*3; j += 3, src += scn ) { buf[j] = src[0]*(1.f/255.f); @@ -1079,7 +1079,7 @@ struct RGB2HLS_b buf[j+2] = src[2]*(1.f/255.f); } cvt(buf, buf, dn); - + for( j = 0; j < dn*3; j += 3 ) { dst[j] = saturate_cast(buf[j]); @@ -1088,31 +1088,31 @@ struct RGB2HLS_b } } } - + int srccn; RGB2HLS_f cvt; }; - + struct HLS2RGB_f { typedef float channel_type; - + HLS2RGB_f(int _dstcn, int _blueIdx, float _hrange) : dstcn(_dstcn), blueIdx(_blueIdx), hscale(6.f/_hrange) {} - + void operator()(const float* src, float* dst, int n) const { int i, bidx = blueIdx, dcn = dstcn; float _hscale = hscale; float alpha = ColorChannel::max(); n *= 3; - + for( i = 0; i < n; i += 3, dst += dcn ) { float h = src[i], l = src[i+1], s = src[i+2]; float b, g, r; - + if( s == 0 ) b = g = r = l; else @@ -1121,30 +1121,30 @@ struct HLS2RGB_f {{1,3,0}, {1,0,2}, {3,0,1}, {0,2,1}, {0,1,3}, {2,1,0}}; float tab[4]; int sector; - + float p2 = l <= 0.5f ? l*(1 + s) : l + s - l*s; float p1 = 2*l - p2; - + h *= _hscale; if( h < 0 ) do h += 6; while( h < 0 ); else if( h >= 6 ) do h -= 6; while( h >= 6 ); - + assert( 0 <= h && h < 6 ); sector = cvFloor(h); h -= sector; - + tab[0] = p2; tab[1] = p1; tab[2] = p1 + (p2 - p1)*(1-h); tab[3] = p1 + (p2 - p1)*h; - + b = tab[sector_data[sector][0]]; g = tab[sector_data[sector][1]]; r = tab[sector_data[sector][2]]; } - + dst[bidx] = b; dst[1] = g; dst[bidx^2] = r; @@ -1152,30 +1152,30 @@ struct HLS2RGB_f dst[3] = alpha; } } - + int dstcn, blueIdx; float hscale; }; - + struct HLS2RGB_b { typedef uchar channel_type; - + HLS2RGB_b(int _dstcn, int _blueIdx, int _hrange) : dstcn(_dstcn), cvt(3, _blueIdx, (float)_hrange) {} - + void operator()(const uchar* src, uchar* dst, int n) const { int i, j, dcn = dstcn; uchar alpha = ColorChannel::max(); float buf[3*BLOCK_SIZE]; - + for( i = 0; i < n; i += BLOCK_SIZE, src += BLOCK_SIZE*3 ) { int dn = std::min(n - i, (int)BLOCK_SIZE); - + for( j = 0; j < dn*3; j += 3 ) { buf[j] = src[j]; @@ -1183,7 +1183,7 @@ struct HLS2RGB_b buf[j+2] = src[j+2]*(1.f/255.f); } cvt(buf, buf, dn); - + for( j = 0; j < dn*3; j += 3, dst += dcn ) { dst[0] = saturate_cast(buf[j]*255.f); @@ -1194,12 +1194,12 @@ struct HLS2RGB_b } } } - + int dstcn; HLS2RGB_f cvt; }; - + ///////////////////////////////////// RGB <-> L*a*b* ///////////////////////////////////// static const float D65[] = { 0.950456f, 1.f, 1.088754f }; @@ -1210,15 +1210,15 @@ static const float LabCbrtTabScale = LAB_CBRT_TAB_SIZE/1.5f; static float sRGBGammaTab[GAMMA_TAB_SIZE*4], sRGBInvGammaTab[GAMMA_TAB_SIZE*4]; static const float GammaTabScale = (float)GAMMA_TAB_SIZE; - -static ushort sRGBGammaTab_b[256], linearGammaTab_b[256]; + +static ushort sRGBGammaTab_b[256], linearGammaTab_b[256]; #undef lab_shift #define lab_shift xyz_shift #define gamma_shift 3 #define lab_shift2 (lab_shift + gamma_shift) #define LAB_CBRT_TAB_SIZE_B (256*3/2*(1<(255.f*(1 << gamma_shift)*(x <= 0.04045f ? x*(1.f/12.92f) : (float)pow((double)(x + 0.055)*(1./1.055), 2.4))); linearGammaTab_b[i] = (ushort)(i*(1 << gamma_shift)); } - + for(i = 0; i < LAB_CBRT_TAB_SIZE_B; i++) { float x = i*(1.f/(255.f*(1 << gamma_shift))); @@ -1262,14 +1262,14 @@ static void initLabTabs() struct RGB2Lab_b { typedef uchar channel_type; - + RGB2Lab_b(int _srccn, int blueIdx, const float* _coeffs, const float* _whitept, bool _srgb) : srccn(_srccn), srgb(_srgb) { static volatile int _3 = 3; initLabTabs(); - + if(!_coeffs) _coeffs = sRGB2XYZ_D65; if(!_whitept) _whitept = D65; float scale[] = @@ -1278,18 +1278,18 @@ struct RGB2Lab_b (float)(1 << lab_shift), (1 << lab_shift)/_whitept[2] }; - + for( int i = 0; i < _3; i++ ) { coeffs[i*3+(blueIdx^2)] = cvRound(_coeffs[i*3]*scale[i]); coeffs[i*3+1] = cvRound(_coeffs[i*3+1]*scale[i]); coeffs[i*3+blueIdx] = cvRound(_coeffs[i*3+2]*scale[i]); - + CV_Assert( coeffs[i] >= 0 && coeffs[i*3+1] >= 0 && coeffs[i*3+2] >= 0 && coeffs[i*3] + coeffs[i*3+1] + coeffs[i*3+2] < 2*(1 << lab_shift) ); } } - + void operator()(const uchar* src, uchar* dst, int n) const { const int Lscale = (116*255+50)/100; @@ -1300,45 +1300,45 @@ struct RGB2Lab_b C3 = coeffs[3], C4 = coeffs[4], C5 = coeffs[5], C6 = coeffs[6], C7 = coeffs[7], C8 = coeffs[8]; n *= 3; - + for( i = 0; i < n; i += 3, src += scn ) { int R = tab[src[0]], G = tab[src[1]], B = tab[src[2]]; int fX = LabCbrtTab_b[CV_DESCALE(R*C0 + G*C1 + B*C2, lab_shift)]; int fY = LabCbrtTab_b[CV_DESCALE(R*C3 + G*C4 + B*C5, lab_shift)]; int fZ = LabCbrtTab_b[CV_DESCALE(R*C6 + G*C7 + B*C8, lab_shift)]; - + int L = CV_DESCALE( Lscale*fY + Lshift, lab_shift2 ); int a = CV_DESCALE( 500*(fX - fY) + 128*(1 << lab_shift2), lab_shift2 ); int b = CV_DESCALE( 200*(fY - fZ) + 128*(1 << lab_shift2), lab_shift2 ); - + dst[i] = saturate_cast(L); dst[i+1] = saturate_cast(a); dst[i+2] = saturate_cast(b); } } - + int srccn; int coeffs[9]; bool srgb; }; - - + + struct RGB2Lab_f { typedef float channel_type; - + RGB2Lab_f(int _srccn, int blueIdx, const float* _coeffs, const float* _whitept, bool _srgb) : srccn(_srccn), srgb(_srgb) { volatile int _3 = 3; initLabTabs(); - + if(!_coeffs) _coeffs = sRGB2XYZ_D65; if(!_whitept) _whitept = D65; float scale[] = { LabCbrtTabScale/_whitept[0], LabCbrtTabScale, LabCbrtTabScale/_whitept[2] }; - + for( int i = 0; i < _3; i++ ) { coeffs[i*3+(blueIdx^2)] = _coeffs[i*3]*scale[i]; @@ -1348,7 +1348,7 @@ struct RGB2Lab_f coeffs[i*3] + coeffs[i*3+1] + coeffs[i*3+2] < 1.5f*LabCbrtTabScale ); } } - + void operator()(const float* src, float* dst, int n) const { int i, scn = srccn; @@ -1358,7 +1358,7 @@ struct RGB2Lab_f C3 = coeffs[3], C4 = coeffs[4], C5 = coeffs[5], C6 = coeffs[6], C7 = coeffs[7], C8 = coeffs[8]; n *= 3; - + for( i = 0; i < n; i += 3, src += scn ) { float R = src[0], G = src[1], B = src[2]; @@ -1368,37 +1368,37 @@ struct RGB2Lab_f G = splineInterpolate(G*gscale, gammaTab, GAMMA_TAB_SIZE); B = splineInterpolate(B*gscale, gammaTab, GAMMA_TAB_SIZE); } - float fX = splineInterpolate(R*C0 + G*C1 + B*C2, LabCbrtTab, LAB_CBRT_TAB_SIZE); + float fX = splineInterpolate(R*C0 + G*C1 + B*C2, LabCbrtTab, LAB_CBRT_TAB_SIZE); float fY = splineInterpolate(R*C3 + G*C4 + B*C5, LabCbrtTab, LAB_CBRT_TAB_SIZE); float fZ = splineInterpolate(R*C6 + G*C7 + B*C8, LabCbrtTab, LAB_CBRT_TAB_SIZE); - + float L = 116.f*fY - 16.f; float a = 500.f*(fX - fY); float b = 200.f*(fY - fZ); - + dst[i] = L; dst[i+1] = a; dst[i+2] = b; } } - + int srccn; float coeffs[9]; bool srgb; }; - + struct Lab2RGB_f { typedef float channel_type; - + Lab2RGB_f( int _dstcn, int blueIdx, const float* _coeffs, const float* _whitept, bool _srgb ) : dstcn(_dstcn), srgb(_srgb) { initLabTabs(); - + if(!_coeffs) _coeffs = XYZ2sRGB_D65; if(!_whitept) _whitept = D65; - + for( int i = 0; i < 3; i++ ) { coeffs[i+(blueIdx^2)*3] = _coeffs[i]*_whitept[i]; @@ -1406,7 +1406,7 @@ struct Lab2RGB_f coeffs[i+blueIdx*3] = _coeffs[i+6]*_whitept[i]; } } - + void operator()(const float* src, float* dst, int n) const { int i, dcn = dstcn; @@ -1417,7 +1417,7 @@ struct Lab2RGB_f C6 = coeffs[6], C7 = coeffs[7], C8 = coeffs[8]; float alpha = ColorChannel::max(); n *= 3; - + for( i = 0; i < n; i += 3, dst += dcn ) { float L = src[i], a = src[i+1], b = src[i+2]; @@ -1427,48 +1427,48 @@ struct Lab2RGB_f Y = Y*Y*Y; X = X*X*X; Z = Z*Z*Z; - + float R = X*C0 + Y*C1 + Z*C2; float G = X*C3 + Y*C4 + Z*C5; float B = X*C6 + Y*C7 + Z*C8; - + if( gammaTab ) { R = splineInterpolate(R*gscale, gammaTab, GAMMA_TAB_SIZE); G = splineInterpolate(G*gscale, gammaTab, GAMMA_TAB_SIZE); B = splineInterpolate(B*gscale, gammaTab, GAMMA_TAB_SIZE); } - + dst[0] = R; dst[1] = G; dst[2] = B; if( dcn == 4 ) dst[3] = alpha; } } - + int dstcn; float coeffs[9]; bool srgb; }; - + struct Lab2RGB_b { typedef uchar channel_type; - + Lab2RGB_b( int _dstcn, int blueIdx, const float* _coeffs, const float* _whitept, bool _srgb ) : dstcn(_dstcn), cvt(3, blueIdx, _coeffs, _whitept, _srgb ) {} - + void operator()(const uchar* src, uchar* dst, int n) const { int i, j, dcn = dstcn; uchar alpha = ColorChannel::max(); float buf[3*BLOCK_SIZE]; - + for( i = 0; i < n; i += BLOCK_SIZE, src += BLOCK_SIZE*3 ) { int dn = std::min(n - i, (int)BLOCK_SIZE); - + for( j = 0; j < dn*3; j += 3 ) { buf[j] = src[j]*(100.f/255.f); @@ -1476,7 +1476,7 @@ struct Lab2RGB_b buf[j+2] = (float)(src[j+2] - 128); } cvt(buf, buf, dn); - + for( j = 0; j < dn*3; j += 3, dst += dcn ) { dst[0] = saturate_cast(buf[j]*255.f); @@ -1487,28 +1487,28 @@ struct Lab2RGB_b } } } - + int dstcn; Lab2RGB_f cvt; }; - - + + ///////////////////////////////////// RGB <-> L*u*v* ///////////////////////////////////// struct RGB2Luv_f { typedef float channel_type; - + RGB2Luv_f( int _srccn, int blueIdx, const float* _coeffs, const float* whitept, bool _srgb ) : srccn(_srccn), srgb(_srgb) { - volatile int i; + volatile int i; initLabTabs(); - + if(!_coeffs) _coeffs = sRGB2XYZ_D65; if(!whitept) whitept = D65; - + for( i = 0; i < 3; i++ ) { coeffs[i*3] = _coeffs[i*3]; @@ -1519,14 +1519,14 @@ struct RGB2Luv_f CV_Assert( coeffs[i*3] >= 0 && coeffs[i*3+1] >= 0 && coeffs[i*3+2] >= 0 && coeffs[i*3] + coeffs[i*3+1] + coeffs[i*3+2] < 1.5f ); } - + float d = 1.f/(whitept[0] + whitept[1]*15 + whitept[2]*3); un = 4*whitept[0]*d; vn = 9*whitept[1]*d; - + CV_Assert(whitept[1] == 1.f); } - + void operator()(const float* src, float* dst, int n) const { int i, scn = srccn; @@ -1537,7 +1537,7 @@ struct RGB2Luv_f C6 = coeffs[6], C7 = coeffs[7], C8 = coeffs[8]; float _un = 13*un, _vn = 13*vn; n *= 3; - + for( i = 0; i < n; i += 3, src += scn ) { float R = src[0], G = src[1], B = src[2]; @@ -1547,55 +1547,55 @@ struct RGB2Luv_f G = splineInterpolate(G*gscale, gammaTab, GAMMA_TAB_SIZE); B = splineInterpolate(B*gscale, gammaTab, GAMMA_TAB_SIZE); } - + float X = R*C0 + G*C1 + B*C2; float Y = R*C3 + G*C4 + B*C5; float Z = R*C6 + G*C7 + B*C8; - + float L = splineInterpolate(Y*LabCbrtTabScale, LabCbrtTab, LAB_CBRT_TAB_SIZE); L = 116.f*L - 16.f; - - float d = (4*13) / std::max(X + 15 * Y + 3 * Z, FLT_EPSILON); + + float d = (4*13) / std::max(X + 15 * Y + 3 * Z, FLT_EPSILON); float u = L*(X*d - _un); float v = L*((9*0.25f)*Y*d - _vn); - + dst[i] = L; dst[i+1] = u; dst[i+2] = v; } } - + int srccn; float coeffs[9], un, vn; bool srgb; }; - + struct Luv2RGB_f { typedef float channel_type; - + Luv2RGB_f( int _dstcn, int blueIdx, const float* _coeffs, const float* whitept, bool _srgb ) : dstcn(_dstcn), srgb(_srgb) { initLabTabs(); - + if(!_coeffs) _coeffs = XYZ2sRGB_D65; if(!whitept) whitept = D65; - + for( int i = 0; i < 3; i++ ) { coeffs[i+(blueIdx^2)*3] = _coeffs[i]; coeffs[i+3] = _coeffs[i+3]; coeffs[i+blueIdx*3] = _coeffs[i+6]; } - + float d = 1.f/(whitept[0] + whitept[1]*15 + whitept[2]*3); un = 4*whitept[0]*d; vn = 9*whitept[1]*d; - + CV_Assert(whitept[1] == 1.f); } - + void operator()(const float* src, float* dst, int n) const { int i, dcn = dstcn; @@ -1607,7 +1607,7 @@ struct Luv2RGB_f float alpha = ColorChannel::max(); float _un = un, _vn = vn; n *= 3; - + for( i = 0; i < n; i += 3, dst += dcn ) { float L = src[i], u = src[i+1], v = src[i+2], d, X, Y, Z; @@ -1618,48 +1618,48 @@ struct Luv2RGB_f v = v*d + _vn; float iv = 1.f/v; X = 2.25f * u * Y * iv ; - Z = (12 - 3 * u - 20 * v) * Y * 0.25f * iv; - + Z = (12 - 3 * u - 20 * v) * Y * 0.25f * iv; + float R = X*C0 + Y*C1 + Z*C2; float G = X*C3 + Y*C4 + Z*C5; float B = X*C6 + Y*C7 + Z*C8; - + if( gammaTab ) { R = splineInterpolate(R*gscale, gammaTab, GAMMA_TAB_SIZE); G = splineInterpolate(G*gscale, gammaTab, GAMMA_TAB_SIZE); B = splineInterpolate(B*gscale, gammaTab, GAMMA_TAB_SIZE); } - + dst[0] = R; dst[1] = G; dst[2] = B; if( dcn == 4 ) dst[3] = alpha; } } - + int dstcn; float coeffs[9], un, vn; bool srgb; }; - + struct RGB2Luv_b { typedef uchar channel_type; - + RGB2Luv_b( int _srccn, int blueIdx, const float* _coeffs, const float* _whitept, bool _srgb ) : srccn(_srccn), cvt(3, blueIdx, _coeffs, _whitept, _srgb) {} - + void operator()(const uchar* src, uchar* dst, int n) const { int i, j, scn = srccn; float buf[3*BLOCK_SIZE]; - + for( i = 0; i < n; i += BLOCK_SIZE, dst += BLOCK_SIZE*3 ) { int dn = std::min(n - i, (int)BLOCK_SIZE); - + for( j = 0; j < dn*3; j += 3, src += scn ) { buf[j] = src[0]*(1.f/255.f); @@ -1667,7 +1667,7 @@ struct RGB2Luv_b buf[j+2] = (float)(src[2]*(1.f/255.f)); } cvt(buf, buf, dn); - + for( j = 0; j < dn*3; j += 3 ) { dst[j] = saturate_cast(buf[j]*2.55f); @@ -1676,30 +1676,30 @@ struct RGB2Luv_b } } } - + int srccn; RGB2Luv_f cvt; }; - + struct Luv2RGB_b { typedef uchar channel_type; - + Luv2RGB_b( int _dstcn, int blueIdx, const float* _coeffs, const float* _whitept, bool _srgb ) : dstcn(_dstcn), cvt(3, blueIdx, _coeffs, _whitept, _srgb ) {} - + void operator()(const uchar* src, uchar* dst, int n) const { int i, j, dcn = dstcn; uchar alpha = ColorChannel::max(); float buf[3*BLOCK_SIZE]; - + for( i = 0; i < n; i += BLOCK_SIZE, src += BLOCK_SIZE*3 ) { int dn = std::min(n - i, (int)BLOCK_SIZE); - + for( j = 0; j < dn*3; j += 3 ) { buf[j] = src[j]*(100.f/255.f); @@ -1707,7 +1707,7 @@ struct Luv2RGB_b buf[j+2] = (float)(src[j+2]*1.003921568627451f - 140.f); } cvt(buf, buf, dn); - + for( j = 0; j < dn*3; j += 3, dst += dcn ) { dst[0] = saturate_cast(buf[j]*255.f); @@ -1718,12 +1718,12 @@ struct Luv2RGB_b } } } - + int dstcn; Luv2RGB_f cvt; }; - + //////////////////////////// Bayer Pattern -> RGB conversion ///////////////////////////// template @@ -1734,13 +1734,13 @@ public: { return 0; } - + int bayer2RGB(const T*, int, T*, int, int) const { return 0; } -}; - +}; + #if CV_SSE2 class SIMDBayerInterpolator_8u { @@ -1749,34 +1749,34 @@ public: { use_simd = checkHardwareSupport(CV_CPU_SSE2); } - + int bayer2Gray(const uchar* bayer, int bayer_step, uchar* dst, int width, int bcoeff, int gcoeff, int rcoeff) const { if( !use_simd ) return 0; - + __m128i _b2y = _mm_set1_epi16((short)(rcoeff*2)); __m128i _g2y = _mm_set1_epi16((short)(gcoeff*2)); __m128i _r2y = _mm_set1_epi16((short)(bcoeff*2)); const uchar* bayer_end = bayer + width; - + for( ; bayer <= bayer_end - 18; bayer += 14, dst += 14 ) { __m128i r0 = _mm_loadu_si128((const __m128i*)bayer); __m128i r1 = _mm_loadu_si128((const __m128i*)(bayer+bayer_step)); __m128i r2 = _mm_loadu_si128((const __m128i*)(bayer+bayer_step*2)); - + __m128i b1 = _mm_add_epi16(_mm_srli_epi16(_mm_slli_epi16(r0, 8), 7), _mm_srli_epi16(_mm_slli_epi16(r2, 8), 7)); __m128i b0 = _mm_add_epi16(b1, _mm_srli_si128(b1, 2)); b1 = _mm_slli_epi16(_mm_srli_si128(b1, 2), 1); - + __m128i g0 = _mm_add_epi16(_mm_srli_epi16(r0, 7), _mm_srli_epi16(r2, 7)); __m128i g1 = _mm_srli_epi16(_mm_slli_epi16(r1, 8), 7); g0 = _mm_add_epi16(g0, _mm_add_epi16(g1, _mm_srli_si128(g1, 2))); g1 = _mm_slli_epi16(_mm_srli_si128(g1, 2), 2); - + r0 = _mm_srli_epi16(r1, 8); r1 = _mm_slli_epi16(_mm_add_epi16(r0, _mm_srli_si128(r0, 2)), 2); r0 = _mm_slli_epi16(r0, 3); @@ -1792,10 +1792,10 @@ public: g0 = _mm_unpacklo_epi8(g0, g1); _mm_storeu_si128((__m128i*)dst, g0); } - + return (int)(bayer - (bayer_end - width)); } - + int bayer2RGB(const uchar* bayer, int bayer_step, uchar* dst, int width, int blue) const { if( !use_simd ) @@ -1809,82 +1809,82 @@ public: __m128i mask = _mm_set1_epi16(blue < 0 ? -1 : 0), z = _mm_setzero_si128(); __m128i masklo = _mm_set1_epi16(0x00ff); const uchar* bayer_end = bayer + width; - + for( ; bayer <= bayer_end - 18; bayer += 14, dst += 42 ) { __m128i r0 = _mm_loadu_si128((const __m128i*)bayer); __m128i r1 = _mm_loadu_si128((const __m128i*)(bayer+bayer_step)); __m128i r2 = _mm_loadu_si128((const __m128i*)(bayer+bayer_step*2)); - + __m128i b1 = _mm_add_epi16(_mm_and_si128(r0, masklo), _mm_and_si128(r2, masklo)); __m128i b0 = _mm_add_epi16(b1, _mm_srli_si128(b1, 2)); b1 = _mm_srli_si128(b1, 2); b1 = _mm_srli_epi16(_mm_add_epi16(b1, delta1), 1); b0 = _mm_srli_epi16(_mm_add_epi16(b0, delta2), 2); b0 = _mm_packus_epi16(b0, b1); - + __m128i g0 = _mm_add_epi16(_mm_srli_epi16(r0, 8), _mm_srli_epi16(r2, 8)); __m128i g1 = _mm_and_si128(r1, masklo); g0 = _mm_add_epi16(g0, _mm_add_epi16(g1, _mm_srli_si128(g1, 2))); g1 = _mm_srli_si128(g1, 2); g0 = _mm_srli_epi16(_mm_add_epi16(g0, delta2), 2); g0 = _mm_packus_epi16(g0, g1); - + r0 = _mm_srli_epi16(r1, 8); r1 = _mm_add_epi16(r0, _mm_srli_si128(r0, 2)); r1 = _mm_srli_epi16(_mm_add_epi16(r1, delta1), 1); r0 = _mm_packus_epi16(r0, r1); - + b1 = _mm_and_si128(_mm_xor_si128(b0, r0), mask); b0 = _mm_xor_si128(b0, b1); r0 = _mm_xor_si128(r0, b1); - + // b1 g1 b1 g1 ... b1 = _mm_unpackhi_epi8(b0, g0); // b0 g0 b2 g2 b4 g4 .... b0 = _mm_unpacklo_epi8(b0, g0); - + // r1 0 r3 0 ... r1 = _mm_unpackhi_epi8(r0, z); // r0 0 r2 0 r4 0 ... r0 = _mm_unpacklo_epi8(r0, z); - + // 0 b0 g0 r0 0 b2 g2 r2 0 ... g0 = _mm_slli_si128(_mm_unpacklo_epi16(b0, r0), 1); // 0 b8 g8 r8 0 b10 g10 r10 0 ... g1 = _mm_slli_si128(_mm_unpackhi_epi16(b0, r0), 1); - + // b1 g1 r1 0 b3 g3 r3 .... r0 = _mm_unpacklo_epi16(b1, r1); // b9 g9 r9 0 ... r1 = _mm_unpackhi_epi16(b1, r1); - + b0 = _mm_srli_si128(_mm_unpacklo_epi32(g0, r0), 1); b1 = _mm_srli_si128(_mm_unpackhi_epi32(g0, r0), 1); - + _mm_storel_epi64((__m128i*)(dst-1+0), b0); _mm_storel_epi64((__m128i*)(dst-1+6*1), _mm_srli_si128(b0, 8)); _mm_storel_epi64((__m128i*)(dst-1+6*2), b1); _mm_storel_epi64((__m128i*)(dst-1+6*3), _mm_srli_si128(b1, 8)); - + g0 = _mm_srli_si128(_mm_unpacklo_epi32(g1, r1), 1); g1 = _mm_srli_si128(_mm_unpackhi_epi32(g1, r1), 1); - + _mm_storel_epi64((__m128i*)(dst-1+6*4), g0); _mm_storel_epi64((__m128i*)(dst-1+6*5), _mm_srli_si128(g0, 8)); - + _mm_storel_epi64((__m128i*)(dst-1+6*6), g1); } - + return (int)(bayer - (bayer_end - width)); } - + bool use_simd; }; #else typedef SIMDBayerStubInterpolator_ SIMDBayerInterpolator_8u; #endif - + template static void Bayer2Gray_( const Mat& srcmat, Mat& dstmat, int code ) { @@ -1893,7 +1893,7 @@ static void Bayer2Gray_( const Mat& srcmat, Mat& dstmat, int code ) const int G2Y = 9617; const int B2Y = 1868; const int SHIFT = 14; - + const T* bayer0 = (const T*)srcmat.data; int bayer_step = (int)(srcmat.step/sizeof(T)); T* dst0 = (T*)dstmat.data; @@ -1902,58 +1902,58 @@ static void Bayer2Gray_( const Mat& srcmat, Mat& dstmat, int code ) int bcoeff = B2Y, rcoeff = R2Y; int start_with_green = code == CV_BayerGB2GRAY || code == CV_BayerGR2GRAY; bool brow = true; - + if( code != CV_BayerBG2GRAY && code != CV_BayerGB2GRAY ) { brow = false; std::swap(bcoeff, rcoeff); } - + dst0 += dst_step + 1; size.height -= 2; size.width -= 2; - + for( ; size.height-- > 0; bayer0 += bayer_step, dst0 += dst_step ) { unsigned t0, t1, t2; const T* bayer = bayer0; T* dst = dst0; const T* bayer_end = bayer + size.width; - + if( size.width <= 0 ) { dst[-1] = dst[size.width] = 0; continue; } - + if( start_with_green ) { t0 = (bayer[1] + bayer[bayer_step*2+1])*rcoeff; t1 = (bayer[bayer_step] + bayer[bayer_step+2])*bcoeff; t2 = bayer[bayer_step+1]*(2*G2Y); - + dst[0] = (T)CV_DESCALE(t0 + t1 + t2, SHIFT+1); bayer++; dst++; } - + int delta = vecOp.bayer2Gray(bayer, bayer_step, dst, size.width, bcoeff, G2Y, rcoeff); bayer += delta; dst += delta; - + for( ; bayer <= bayer_end - 2; bayer += 2, dst += 2 ) { t0 = (bayer[0] + bayer[2] + bayer[bayer_step*2] + bayer[bayer_step*2+2])*rcoeff; t1 = (bayer[1] + bayer[bayer_step] + bayer[bayer_step+2] + bayer[bayer_step*2+1])*G2Y; t2 = bayer[bayer_step+1]*(4*bcoeff); dst[0] = (T)CV_DESCALE(t0 + t1 + t2, SHIFT+2); - + t0 = (bayer[2] + bayer[bayer_step*2+2])*rcoeff; t1 = (bayer[bayer_step+1] + bayer[bayer_step+3])*bcoeff; t2 = bayer[bayer_step+2]*(2*G2Y); dst[1] = (T)CV_DESCALE(t0 + t1 + t2, SHIFT+1); } - + if( bayer < bayer_end ) { t0 = (bayer[0] + bayer[2] + bayer[bayer_step*2] + bayer[bayer_step*2+2])*rcoeff; @@ -1963,15 +1963,15 @@ static void Bayer2Gray_( const Mat& srcmat, Mat& dstmat, int code ) bayer++; dst++; } - + dst0[-1] = dst0[0]; dst0[size.width] = dst0[size.width-1]; - + brow = !brow; std::swap(bcoeff, rcoeff); start_with_green = !start_with_green; } - + size = dstmat.size(); dst0 = (T*)dstmat.data; if( size.height > 2 ) @@ -1987,7 +1987,7 @@ static void Bayer2Gray_( const Mat& srcmat, Mat& dstmat, int code ) } } -template +template static void Bayer2RGB_( const Mat& srcmat, Mat& dstmat, int code ) { SIMDInterpolator vecOp; @@ -1998,25 +1998,25 @@ static void Bayer2RGB_( const Mat& srcmat, Mat& dstmat, int code ) Size size = srcmat.size(); int blue = code == CV_BayerBG2BGR || code == CV_BayerGB2BGR ? -1 : 1; int start_with_green = code == CV_BayerGB2BGR || code == CV_BayerGR2BGR; - + dst0 += dst_step + 3 + 1; size.height -= 2; size.width -= 2; - + for( ; size.height-- > 0; bayer0 += bayer_step, dst0 += dst_step ) { int t0, t1; const T* bayer = bayer0; T* dst = dst0; const T* bayer_end = bayer + size.width; - + if( size.width <= 0 ) { dst[-4] = dst[-3] = dst[-2] = dst[size.width*3-1] = dst[size.width*3] = dst[size.width*3+1] = 0; continue; } - + if( start_with_green ) { t0 = (bayer[1] + bayer[bayer_step*2+1] + 1) >> 1; @@ -2027,11 +2027,11 @@ static void Bayer2RGB_( const Mat& srcmat, Mat& dstmat, int code ) bayer++; dst += 3; } - + int delta = vecOp.bayer2RGB(bayer, bayer_step, dst, size.width, blue); bayer += delta; dst += delta*3; - + if( blue > 0 ) { for( ; bayer <= bayer_end - 2; bayer += 2, dst += 6 ) @@ -2043,7 +2043,7 @@ static void Bayer2RGB_( const Mat& srcmat, Mat& dstmat, int code ) dst[-1] = (T)t0; dst[0] = (T)t1; dst[1] = bayer[bayer_step+1]; - + t0 = (bayer[2] + bayer[bayer_step*2+2] + 1) >> 1; t1 = (bayer[bayer_step+1] + bayer[bayer_step+3] + 1) >> 1; dst[2] = (T)t0; @@ -2062,7 +2062,7 @@ static void Bayer2RGB_( const Mat& srcmat, Mat& dstmat, int code ) dst[1] = (T)t0; dst[0] = (T)t1; dst[-1] = bayer[bayer_step+1]; - + t0 = (bayer[2] + bayer[bayer_step*2+2] + 1) >> 1; t1 = (bayer[bayer_step+1] + bayer[bayer_step+3] + 1) >> 1; dst[4] = (T)t0; @@ -2070,7 +2070,7 @@ static void Bayer2RGB_( const Mat& srcmat, Mat& dstmat, int code ) dst[2] = (T)t1; } } - + if( bayer < bayer_end ) { t0 = (bayer[0] + bayer[2] + bayer[bayer_step*2] + @@ -2083,18 +2083,18 @@ static void Bayer2RGB_( const Mat& srcmat, Mat& dstmat, int code ) bayer++; dst += 3; } - + dst0[-4] = dst0[-1]; dst0[-3] = dst0[0]; dst0[-2] = dst0[1]; dst0[size.width*3-1] = dst0[size.width*3-4]; dst0[size.width*3] = dst0[size.width*3-3]; dst0[size.width*3+1] = dst0[size.width*3-2]; - + blue = -blue; start_with_green = !start_with_green; } - + size = dstmat.size(); dst0 = (T*)dstmat.data; if( size.height > 2 ) @@ -2110,9 +2110,9 @@ static void Bayer2RGB_( const Mat& srcmat, Mat& dstmat, int code ) } } - + /////////////////// Demosaicing using Variable Number of Gradients /////////////////////// - + static void Bayer2RGB_VNG_8u( const Mat& srcmat, Mat& dstmat, int code ) { const uchar* bayer = srcmat.data; @@ -2120,45 +2120,45 @@ static void Bayer2RGB_VNG_8u( const Mat& srcmat, Mat& dstmat, int code ) uchar* dst = dstmat.data; int dststep = (int)dstmat.step; Size size = srcmat.size(); - + int blueIdx = code == CV_BayerBG2BGR_VNG || code == CV_BayerGB2BGR_VNG ? 0 : 2; bool greenCell0 = code != CV_BayerBG2BGR_VNG && code != CV_BayerRG2BGR_VNG; - + // for too small images use the simple interpolation algorithm if( MIN(size.width, size.height) < 8 ) { Bayer2RGB_( srcmat, dstmat, code ); return; } - + const int brows = 3, bcn = 7; - int N = size.width, N2 = N*2, N3 = N*3, N4 = N*4, N5 = N*5, N6 = N*6, N7 = N*7; + int N = size.width, N2 = N*2, N3 = N*3, N4 = N*4, N5 = N*5, N6 = N*6, N7 = N*7; int i, bufstep = N7*bcn; cv::AutoBuffer _buf(bufstep*brows); ushort* buf = (ushort*)_buf; - + bayer += bstep*2; - + #if CV_SSE2 bool haveSSE = cv::checkHardwareSupport(CV_CPU_SSE2); #define _mm_absdiff_epu16(a,b) _mm_adds_epu16(_mm_subs_epu16(a, b), _mm_subs_epu16(b, a)) #endif - + for( int y = 2; y < size.height - 4; y++ ) { uchar* dstrow = dst + dststep*y + 6; const uchar* srow; - + for( int dy = (y == 2 ? -1 : 1); dy <= 1; dy++ ) { ushort* brow = buf + ((y + dy - 1)%brows)*bufstep + 1; srow = bayer + (y+dy)*bstep + 1; - + for( i = 0; i < bcn; i++ ) brow[N*i-1] = brow[(N-2) + N*i] = 0; - + i = 1; - + #if CV_SSE2 if( haveSSE ) { @@ -2166,20 +2166,20 @@ static void Bayer2RGB_VNG_8u( const Mat& srcmat, Mat& dstmat, int code ) for( ; i <= N-9; i += 8, srow += 8, brow += 8 ) { __m128i s1, s2, s3, s4, s6, s7, s8, s9; - + s1 = _mm_unpacklo_epi8(_mm_loadl_epi64((__m128i*)(srow-1-bstep)),z); s2 = _mm_unpacklo_epi8(_mm_loadl_epi64((__m128i*)(srow-bstep)),z); s3 = _mm_unpacklo_epi8(_mm_loadl_epi64((__m128i*)(srow+1-bstep)),z); - + s4 = _mm_unpacklo_epi8(_mm_loadl_epi64((__m128i*)(srow-1)),z); s6 = _mm_unpacklo_epi8(_mm_loadl_epi64((__m128i*)(srow+1)),z); - + s7 = _mm_unpacklo_epi8(_mm_loadl_epi64((__m128i*)(srow-1+bstep)),z); s8 = _mm_unpacklo_epi8(_mm_loadl_epi64((__m128i*)(srow+bstep)),z); s9 = _mm_unpacklo_epi8(_mm_loadl_epi64((__m128i*)(srow+1+bstep)),z); - + __m128i b0, b1, b2, b3, b4, b5, b6; - + b0 = _mm_adds_epu16(_mm_slli_epi16(_mm_absdiff_epu16(s2,s8),1), _mm_adds_epu16(_mm_absdiff_epu16(s1, s7), _mm_absdiff_epu16(s3, s9))); @@ -2188,26 +2188,26 @@ static void Bayer2RGB_VNG_8u( const Mat& srcmat, Mat& dstmat, int code ) _mm_absdiff_epu16(s7, s9))); b2 = _mm_slli_epi16(_mm_absdiff_epu16(s3,s7),1); b3 = _mm_slli_epi16(_mm_absdiff_epu16(s1,s9),1); - + _mm_storeu_si128((__m128i*)brow, b0); _mm_storeu_si128((__m128i*)(brow + N), b1); _mm_storeu_si128((__m128i*)(brow + N2), b2); _mm_storeu_si128((__m128i*)(brow + N3), b3); - + b4 = _mm_adds_epu16(b2,_mm_adds_epu16(_mm_absdiff_epu16(s2, s4), _mm_absdiff_epu16(s6, s8))); b5 = _mm_adds_epu16(b3,_mm_adds_epu16(_mm_absdiff_epu16(s2, s6), _mm_absdiff_epu16(s4, s8))); b6 = _mm_adds_epu16(_mm_adds_epu16(s2, s4), _mm_adds_epu16(s6, s8)); b6 = _mm_srli_epi16(b6, 1); - + _mm_storeu_si128((__m128i*)(brow + N4), b4); _mm_storeu_si128((__m128i*)(brow + N5), b5); _mm_storeu_si128((__m128i*)(brow + N6), b6); } } #endif - + for( ; i < N-1; i++, srow++, brow++ ) { brow[0] = (ushort)(std::abs(srow[-1-bstep] - srow[-1+bstep]) + @@ -2225,21 +2225,21 @@ static void Bayer2RGB_VNG_8u( const Mat& srcmat, Mat& dstmat, int code ) brow[N6] = (ushort)((srow[-bstep] + srow[-1] + srow[1] + srow[+bstep])>>1); } } - + const ushort* brow0 = buf + ((y - 2) % brows)*bufstep + 2; const ushort* brow1 = buf + ((y - 1) % brows)*bufstep + 2; const ushort* brow2 = buf + (y % brows)*bufstep + 2; static const float scale[] = { 0.f, 0.5f, 0.25f, 0.1666666666667f, 0.125f, 0.1f, 0.08333333333f, 0.0714286f, 0.0625f }; srow = bayer + y*bstep + 2; bool greenCell = greenCell0; - + i = 2; #if CV_SSE2 int limit = !haveSSE ? N-2 : greenCell ? std::min(3, N-2) : 2; #else int limit = N - 2; #endif - + do { for( ; i < limit; i++, srow++, brow0++, brow1++, brow2++, dstrow += 3 ) @@ -2251,18 +2251,18 @@ static void Bayer2RGB_VNG_8u( const Mat& srcmat, Mat& dstmat, int code ) int minGrad = std::min(std::min(std::min(gradN, gradS), gradW), gradE); int maxGrad = std::max(std::max(std::max(gradN, gradS), gradW), gradE); int R, G, B; - + if( !greenCell ) { int gradNE = brow0[N4+1] + brow1[N4]; int gradSW = brow1[N4] + brow2[N4-1]; int gradNW = brow0[N5-1] + brow1[N5]; int gradSE = brow1[N5] + brow2[N5+1]; - + minGrad = std::min(std::min(std::min(std::min(minGrad, gradNE), gradSW), gradNW), gradSE); maxGrad = std::max(std::max(std::max(std::max(maxGrad, gradNE), gradSW), gradNW), gradSE); int T = minGrad + maxGrad/2; - + int Rs = 0, Gs = 0, Bs = 0, ng = 0; if( gradN < T ) { @@ -2322,7 +2322,7 @@ static void Bayer2RGB_VNG_8u( const Mat& srcmat, Mat& dstmat, int code ) } R = srow[0]; G = R + cvRound((Gs - Rs)*scale[ng]); - B = R + cvRound((Bs - Rs)*scale[ng]); + B = R + cvRound((Bs - Rs)*scale[ng]); } else { @@ -2330,11 +2330,11 @@ static void Bayer2RGB_VNG_8u( const Mat& srcmat, Mat& dstmat, int code ) int gradSW = brow1[N2] + brow1[N2-1] + brow2[N2] + brow2[N2-1]; int gradNW = brow0[N3] + brow0[N3-1] + brow1[N3] + brow1[N3-1]; int gradSE = brow1[N3] + brow1[N3+1] + brow2[N3] + brow2[N3+1]; - + minGrad = std::min(std::min(std::min(std::min(minGrad, gradNE), gradSW), gradNW), gradSE); maxGrad = std::max(std::max(std::max(std::max(maxGrad, gradNE), gradSW), gradNW), gradSE); int T = minGrad + maxGrad/2; - + int Rs = 0, Gs = 0, Bs = 0, ng = 0; if( gradN < T ) { @@ -2405,17 +2405,17 @@ static void Bayer2RGB_VNG_8u( const Mat& srcmat, Mat& dstmat, int code ) #if CV_SSE2 if( !haveSSE ) break; - + __m128i emask = _mm_set1_epi32(0x0000ffff), omask = _mm_set1_epi32(0xffff0000), z = _mm_setzero_si128(); __m128 _0_5 = _mm_set1_ps(0.5f); - + #define _mm_merge_epi16(a, b) _mm_or_si128(_mm_and_si128(a, emask), _mm_and_si128(b, omask)) //(aA_aA_aA_aA) * (bB_bB_bB_bB) => (bA_bA_bA_bA) #define _mm_cvtloepi16_ps(a) _mm_cvtepi32_ps(_mm_srai_epi32(_mm_unpacklo_epi16(a,a), 16)) //(1,2,3,4,5,6,7,8) => (1f,2f,3f,4f) #define _mm_cvthiepi16_ps(a) _mm_cvtepi32_ps(_mm_srai_epi32(_mm_unpackhi_epi16(a,a), 16)) //(1,2,3,4,5,6,7,8) => (5f,6f,7f,8f) #define _mm_loadl_u8_s16(ptr, offset) _mm_unpacklo_epi8(_mm_loadl_epi64((__m128i*)((ptr) + (offset))), z) //load 8 uchars to 8 shorts - + // process 8 pixels at once for( ; i <= N - 10; i += 8, srow += 8, brow0 += 8, brow1 += 8, brow2 += 8 ) { @@ -2430,28 +2430,28 @@ static void Bayer2RGB_VNG_8u( const Mat& srcmat, Mat& dstmat, int code ) //int gradE = brow1[N+1] + brow1[N]; __m128i gradE = _mm_adds_epi16(_mm_loadu_si128((__m128i*)(brow1+N+1)), _mm_loadu_si128((__m128i*)(brow1+N))); - + //int minGrad = std::min(std::min(std::min(gradN, gradS), gradW), gradE); //int maxGrad = std::max(std::max(std::max(gradN, gradS), gradW), gradE); __m128i minGrad = _mm_min_epi16(_mm_min_epi16(gradN, gradS), _mm_min_epi16(gradW, gradE)); __m128i maxGrad = _mm_max_epi16(_mm_max_epi16(gradN, gradS), _mm_max_epi16(gradW, gradE)); - + __m128i grad0, grad1; - + //int gradNE = brow0[N4+1] + brow1[N4]; //int gradNE = brow0[N2] + brow0[N2+1] + brow1[N2] + brow1[N2+1]; grad0 = _mm_adds_epi16(_mm_loadu_si128((__m128i*)(brow0+N4+1)), _mm_loadu_si128((__m128i*)(brow1+N4))); grad1 = _mm_adds_epi16( _mm_adds_epi16(_mm_loadu_si128((__m128i*)(brow0+N2)), _mm_loadu_si128((__m128i*)(brow0+N2+1))), _mm_adds_epi16(_mm_loadu_si128((__m128i*)(brow1+N2)), _mm_loadu_si128((__m128i*)(brow1+N2+1)))); __m128i gradNE = _mm_merge_epi16(grad0, grad1); - + //int gradSW = brow1[N4] + brow2[N4-1]; //int gradSW = brow1[N2] + brow1[N2-1] + brow2[N2] + brow2[N2-1]; grad0 = _mm_adds_epi16(_mm_loadu_si128((__m128i*)(brow2+N4-1)), _mm_loadu_si128((__m128i*)(brow1+N4))); grad1 = _mm_adds_epi16(_mm_adds_epi16(_mm_loadu_si128((__m128i*)(brow2+N2)), _mm_loadu_si128((__m128i*)(brow2+N2-1))), _mm_adds_epi16(_mm_loadu_si128((__m128i*)(brow1+N2)), _mm_loadu_si128((__m128i*)(brow1+N2-1)))); __m128i gradSW = _mm_merge_epi16(grad0, grad1); - + minGrad = _mm_min_epi16(_mm_min_epi16(minGrad, gradNE), gradSW); maxGrad = _mm_max_epi16(_mm_max_epi16(maxGrad, gradNE), gradSW); @@ -2461,22 +2461,22 @@ static void Bayer2RGB_VNG_8u( const Mat& srcmat, Mat& dstmat, int code ) grad1 = _mm_adds_epi16(_mm_adds_epi16(_mm_loadu_si128((__m128i*)(brow0+N3)), _mm_loadu_si128((__m128i*)(brow0+N3-1))), _mm_adds_epi16(_mm_loadu_si128((__m128i*)(brow1+N3)), _mm_loadu_si128((__m128i*)(brow1+N3-1)))); __m128i gradNW = _mm_merge_epi16(grad0, grad1); - + //int gradSE = brow1[N5] + brow2[N5+1]; //int gradSE = brow1[N3] + brow1[N3+1] + brow2[N3] + brow2[N3+1]; grad0 = _mm_adds_epi16(_mm_loadu_si128((__m128i*)(brow2+N5+1)), _mm_loadu_si128((__m128i*)(brow1+N5))); grad1 = _mm_adds_epi16(_mm_adds_epi16(_mm_loadu_si128((__m128i*)(brow2+N3)), _mm_loadu_si128((__m128i*)(brow2+N3+1))), _mm_adds_epi16(_mm_loadu_si128((__m128i*)(brow1+N3)), _mm_loadu_si128((__m128i*)(brow1+N3+1)))); __m128i gradSE = _mm_merge_epi16(grad0, grad1); - + minGrad = _mm_min_epi16(_mm_min_epi16(minGrad, gradNW), gradSE); maxGrad = _mm_max_epi16(_mm_max_epi16(maxGrad, gradNW), gradSE); - + //int T = minGrad + maxGrad/2; __m128i T = _mm_adds_epi16(_mm_srli_epi16(maxGrad, 1), minGrad); __m128i RGs = z, GRs = z, Bs = z, ng = z; - + __m128i x0 = _mm_loadl_u8_s16(srow, +0 ); __m128i x1 = _mm_loadl_u8_s16(srow, -1 - bstep ); __m128i x2 = _mm_loadl_u8_s16(srow, -1 - bstep*2); @@ -2496,39 +2496,39 @@ static void Bayer2RGB_VNG_8u( const Mat& srcmat, Mat& dstmat, int code ) __m128i x16 = _mm_loadl_u8_s16(srow, -2 - bstep ); __m128i t0, t1, mask; - + // gradN *********************************************** mask = _mm_cmpgt_epi16(T, gradN); // mask = T>gradN ng = _mm_sub_epi16(ng, mask); // ng += (T>gradN) - + t0 = _mm_slli_epi16(x3, 1); // srow[-bstep]*2 t1 = _mm_adds_epi16(_mm_loadl_u8_s16(srow, -bstep*2), x0); // srow[-bstep*2] + srow[0] - + // RGs += (srow[-bstep*2] + srow[0]) * (T>gradN) RGs = _mm_adds_epi16(RGs, _mm_and_si128(t1, mask)); // GRs += {srow[-bstep]*2; (srow[-bstep*2-1] + srow[-bstep*2+1])} * (T>gradN) GRs = _mm_adds_epi16(GRs, _mm_and_si128(_mm_merge_epi16(t0, _mm_adds_epi16(x2,x4)), mask)); // Bs += {(srow[-bstep-1]+srow[-bstep+1]); srow[-bstep]*2 } * (T>gradN) Bs = _mm_adds_epi16(Bs, _mm_and_si128(_mm_merge_epi16(_mm_adds_epi16(x1,x5), t0), mask)); - + // gradNE ********************************************** mask = _mm_cmpgt_epi16(T, gradNE); // mask = T>gradNE ng = _mm_sub_epi16(ng, mask); // ng += (T>gradNE) t0 = _mm_slli_epi16(x5, 1); // srow[-bstep+1]*2 t1 = _mm_adds_epi16(_mm_loadl_u8_s16(srow, -bstep*2+2), x0); // srow[-bstep*2+2] + srow[0] - + // RGs += {(srow[-bstep*2+2] + srow[0]); srow[-bstep+1]*2} * (T>gradNE) RGs = _mm_adds_epi16(RGs, _mm_and_si128(_mm_merge_epi16(t1, t0), mask)); // GRs += {brow0[N6+1]; (srow[-bstep*2+1] + srow[1])} * (T>gradNE) GRs = _mm_adds_epi16(GRs, _mm_and_si128(_mm_merge_epi16(_mm_loadu_si128((__m128i*)(brow0+N6+1)), _mm_adds_epi16(x4,x7)), mask)); // Bs += {srow[-bstep+1]*2; (srow[-bstep] + srow[-bstep+2])} * (T>gradNE) Bs = _mm_adds_epi16(Bs, _mm_and_si128(_mm_merge_epi16(t0,_mm_adds_epi16(x3,x6)), mask)); - + // gradE *********************************************** mask = _mm_cmpgt_epi16(T, gradE); // mask = T>gradE ng = _mm_sub_epi16(ng, mask); // ng += (T>gradE) - + t0 = _mm_slli_epi16(x7, 1); // srow[1]*2 t1 = _mm_adds_epi16(_mm_loadl_u8_s16(srow, 2), x0); // srow[2] + srow[0] @@ -2538,11 +2538,11 @@ static void Bayer2RGB_VNG_8u( const Mat& srcmat, Mat& dstmat, int code ) GRs = _mm_adds_epi16(GRs, _mm_and_si128(t0, mask)); // Bs += {(srow[-bstep+1]+srow[bstep+1]); (srow[-bstep+2]+srow[bstep+2])} * (T>gradE) Bs = _mm_adds_epi16(Bs, _mm_and_si128(_mm_merge_epi16(_mm_adds_epi16(x5,x9), _mm_adds_epi16(x6,x8)), mask)); - + // gradSE ********************************************** mask = _mm_cmpgt_epi16(T, gradSE); // mask = T>gradSE ng = _mm_sub_epi16(ng, mask); // ng += (T>gradSE) - + t0 = _mm_slli_epi16(x9, 1); // srow[bstep+1]*2 t1 = _mm_adds_epi16(_mm_loadl_u8_s16(srow, bstep*2+2), x0); // srow[bstep*2+2] + srow[0] @@ -2552,11 +2552,11 @@ static void Bayer2RGB_VNG_8u( const Mat& srcmat, Mat& dstmat, int code ) GRs = _mm_adds_epi16(GRs, _mm_and_si128(_mm_merge_epi16(_mm_loadu_si128((__m128i*)(brow2+N6+1)), _mm_adds_epi16(x7,x10)), mask)); // Bs += {srow[-bstep+1]*2; (srow[bstep+2]+srow[bstep])} * (T>gradSE) Bs = _mm_adds_epi16(Bs, _mm_and_si128(_mm_merge_epi16(_mm_slli_epi16(x5, 1), _mm_adds_epi16(x8,x11)), mask)); - + // gradS *********************************************** mask = _mm_cmpgt_epi16(T, gradS); // mask = T>gradS ng = _mm_sub_epi16(ng, mask); // ng += (T>gradS) - + t0 = _mm_slli_epi16(x11, 1); // srow[bstep]*2 t1 = _mm_adds_epi16(_mm_loadl_u8_s16(srow,bstep*2), x0); // srow[bstep*2]+srow[0] @@ -2566,11 +2566,11 @@ static void Bayer2RGB_VNG_8u( const Mat& srcmat, Mat& dstmat, int code ) GRs = _mm_adds_epi16(GRs, _mm_and_si128(_mm_merge_epi16(t0, _mm_adds_epi16(x10,x12)), mask)); // Bs += {(srow[bstep+1]+srow[bstep-1]); srow[bstep]*2} * (T>gradS) Bs = _mm_adds_epi16(Bs, _mm_and_si128(_mm_merge_epi16(_mm_adds_epi16(x9,x13), t0), mask)); - + // gradSW ********************************************** mask = _mm_cmpgt_epi16(T, gradSW); // mask = T>gradSW ng = _mm_sub_epi16(ng, mask); // ng += (T>gradSW) - + t0 = _mm_slli_epi16(x13, 1); // srow[bstep-1]*2 t1 = _mm_adds_epi16(_mm_loadl_u8_s16(srow, bstep*2-2), x0); // srow[bstep*2-2]+srow[0] @@ -2580,11 +2580,11 @@ static void Bayer2RGB_VNG_8u( const Mat& srcmat, Mat& dstmat, int code ) GRs = _mm_adds_epi16(GRs, _mm_and_si128(_mm_merge_epi16(_mm_loadu_si128((__m128i*)(brow2+N6-1)), _mm_adds_epi16(x12,x15)), mask)); // Bs += {srow[bstep-1]*2; (srow[bstep]+srow[bstep-2])} * (T>gradSW) Bs = _mm_adds_epi16(Bs, _mm_and_si128(_mm_merge_epi16(t0,_mm_adds_epi16(x11,x14)), mask)); - + // gradW *********************************************** mask = _mm_cmpgt_epi16(T, gradW); // mask = T>gradW ng = _mm_sub_epi16(ng, mask); // ng += (T>gradW) - + t0 = _mm_slli_epi16(x15, 1); // srow[-1]*2 t1 = _mm_adds_epi16(_mm_loadl_u8_s16(srow, -2), x0); // srow[-2]+srow[0] @@ -2594,11 +2594,11 @@ static void Bayer2RGB_VNG_8u( const Mat& srcmat, Mat& dstmat, int code ) GRs = _mm_adds_epi16(GRs, _mm_and_si128(t0, mask)); // Bs += {(srow[-bstep-1]+srow[bstep-1]); (srow[bstep-2]+srow[-bstep-2])} * (T>gradW) Bs = _mm_adds_epi16(Bs, _mm_and_si128(_mm_merge_epi16(_mm_adds_epi16(x1,x13), _mm_adds_epi16(x14,x16)), mask)); - + // gradNW ********************************************** mask = _mm_cmpgt_epi16(T, gradNW); // mask = T>gradNW ng = _mm_sub_epi16(ng, mask); // ng += (T>gradNW) - + t0 = _mm_slli_epi16(x1, 1); // srow[-bstep-1]*2 t1 = _mm_adds_epi16(_mm_loadl_u8_s16(srow,-bstep*2-2), x0); // srow[-bstep*2-2]+srow[0] @@ -2612,49 +2612,49 @@ static void Bayer2RGB_VNG_8u( const Mat& srcmat, Mat& dstmat, int code ) __m128 ngf0, ngf1; ngf0 = _mm_div_ps(_0_5, _mm_cvtloepi16_ps(ng)); ngf1 = _mm_div_ps(_0_5, _mm_cvthiepi16_ps(ng)); - + // now interpolate r, g & b t0 = _mm_sub_epi16(GRs, RGs); t1 = _mm_sub_epi16(Bs, RGs); - + t0 = _mm_add_epi16(x0, _mm_packs_epi32( _mm_cvtps_epi32(_mm_mul_ps(_mm_cvtloepi16_ps(t0), ngf0)), _mm_cvtps_epi32(_mm_mul_ps(_mm_cvthiepi16_ps(t0), ngf1)))); - + t1 = _mm_add_epi16(x0, _mm_packs_epi32( _mm_cvtps_epi32(_mm_mul_ps(_mm_cvtloepi16_ps(t1), ngf0)), _mm_cvtps_epi32(_mm_mul_ps(_mm_cvthiepi16_ps(t1), ngf1)))); - + x1 = _mm_merge_epi16(x0, t0); x2 = _mm_merge_epi16(t0, x0); - + uchar R[8], G[8], B[8]; - + _mm_storel_epi64(blueIdx ? (__m128i*)B : (__m128i*)R, _mm_packus_epi16(x1, z)); _mm_storel_epi64((__m128i*)G, _mm_packus_epi16(x2, z)); _mm_storel_epi64(blueIdx ? (__m128i*)R : (__m128i*)B, _mm_packus_epi16(t1, z)); - + for( int j = 0; j < 8; j++, dstrow += 3 ) { dstrow[0] = B[j]; dstrow[1] = G[j]; dstrow[2] = R[j]; } } #endif - + limit = N - 2; } while( i < N - 2 ); - + for( i = 0; i < 6; i++ ) { dst[dststep*y + 5 - i] = dst[dststep*y + 8 - i]; dst[dststep*y + (N - 2)*3 + i] = dst[dststep*y + (N - 3)*3 + i]; } - + greenCell0 = !greenCell0; blueIdx ^= 2; } - + for( i = 0; i < size.width*3; i++ ) { dst[i] = dst[i + dststep] = dst[i + dststep*2]; @@ -2831,14 +2831,14 @@ struct YUV420p2RGB888Invoker { const int rangeBegin = range.begin() * 2; const int rangeEnd = range.end() * 2; - + size_t uvsteps[2] = {width/2, stride - width/2}; int usIdx = ustepIdx, vsIdx = vstepIdx; const uchar* y1 = my1 + rangeBegin * stride; const uchar* u1 = mu + (range.begin() / 2) * stride; const uchar* v1 = mv + (range.begin() / 2) * stride; - + if(range.begin() % 2 == 1) { u1 += uvsteps[(usIdx++) & 1]; @@ -2906,7 +2906,7 @@ struct YUV420p2RGBA8888Invoker const uchar* y1 = my1 + rangeBegin * stride; const uchar* u1 = mu + (range.begin() / 2) * stride; const uchar* v1 = mv + (range.begin() / 2) * stride; - + if(range.begin() % 2 == 1) { u1 += uvsteps[(usIdx++) & 1]; @@ -3139,9 +3139,9 @@ void cv::cvtColor( InputArray _src, OutputArray _dst, int code, int dcn ) Mat src = _src.getMat(), dst; Size sz = src.size(); int scn = src.channels(), depth = src.depth(), bidx; - + CV_Assert( depth == CV_8U || depth == CV_16U || depth == CV_32F ); - + switch( code ) { case CV_BGR2BGRA: case CV_RGB2BGRA: case CV_BGRA2BGR: @@ -3149,10 +3149,10 @@ void cv::cvtColor( InputArray _src, OutputArray _dst, int code, int dcn ) CV_Assert( scn == 3 || scn == 4 ); dcn = code == CV_BGR2BGRA || code == CV_RGB2BGRA || code == CV_BGRA2RGBA ? 4 : 3; bidx = code == CV_BGR2BGRA || code == CV_BGRA2BGR ? 0 : 2; - + _dst.create( sz, CV_MAKETYPE(depth, dcn)); dst = _dst.getMat(); - + if( depth == CV_8U ) { #ifdef HAVE_TEGRA_OPTIMIZATION @@ -3165,7 +3165,7 @@ void cv::cvtColor( InputArray _src, OutputArray _dst, int code, int dcn ) else CvtColorLoop(src, dst, RGB2RGB(scn, dcn, bidx)); break; - + case CV_BGR2BGR565: case CV_BGR2BGR555: case CV_RGB2BGR565: case CV_RGB2BGR555: case CV_BGRA2BGR565: case CV_BGRA2BGR555: case CV_RGBA2BGR565: case CV_RGBA2BGR555: CV_Assert( (scn == 3 || scn == 4) && depth == CV_8U ); @@ -3177,7 +3177,7 @@ void cv::cvtColor( InputArray _src, OutputArray _dst, int code, int dcn ) if(tegra::cvtRGB2RGB565(src, dst, code == CV_RGB2BGR565 || code == CV_RGBA2BGR565 ? 0 : 2)) break; #endif - + CvtColorLoop(src, dst, RGB2RGB5x5(scn, code == CV_BGR2BGR565 || code == CV_BGR2BGR555 || code == CV_BGRA2BGR565 || code == CV_BGRA2BGR555 ? 0 : 2, @@ -3185,14 +3185,14 @@ void cv::cvtColor( InputArray _src, OutputArray _dst, int code, int dcn ) code == CV_BGRA2BGR565 || code == CV_RGBA2BGR565 ? 6 : 5 // green bits )); break; - + case CV_BGR5652BGR: case CV_BGR5552BGR: case CV_BGR5652RGB: case CV_BGR5552RGB: case CV_BGR5652BGRA: case CV_BGR5552BGRA: case CV_BGR5652RGBA: case CV_BGR5552RGBA: if(dcn <= 0) dcn = (code==CV_BGR5652BGRA || code==CV_BGR5552BGRA || code==CV_BGR5652RGBA || code==CV_BGR5552RGBA) ? 4 : 3; CV_Assert( (dcn == 3 || dcn == 4) && scn == 2 && depth == CV_8U ); _dst.create(sz, CV_MAKETYPE(depth, dcn)); dst = _dst.getMat(); - + CvtColorLoop(src, dst, RGB5x52RGB(dcn, code == CV_BGR5652BGR || code == CV_BGR5552BGR || code == CV_BGR5652BGRA || code == CV_BGR5552BGRA ? 0 : 2, // blue idx @@ -3200,14 +3200,14 @@ void cv::cvtColor( InputArray _src, OutputArray _dst, int code, int dcn ) code == CV_BGR5652BGRA || code == CV_BGR5652RGBA ? 6 : 5 // green bits )); break; - + case CV_BGR2GRAY: case CV_BGRA2GRAY: case CV_RGB2GRAY: case CV_RGBA2GRAY: CV_Assert( scn == 3 || scn == 4 ); _dst.create(sz, CV_MAKETYPE(depth, 1)); dst = _dst.getMat(); - + bidx = code == CV_BGR2GRAY || code == CV_BGRA2GRAY ? 0 : 2; - + if( depth == CV_8U ) { #ifdef HAVE_TEGRA_OPTIMIZATION @@ -3220,21 +3220,21 @@ void cv::cvtColor( InputArray _src, OutputArray _dst, int code, int dcn ) else CvtColorLoop(src, dst, RGB2Gray(scn, bidx, 0)); break; - + case CV_BGR5652GRAY: case CV_BGR5552GRAY: CV_Assert( scn == 2 && depth == CV_8U ); _dst.create(sz, CV_8UC1); dst = _dst.getMat(); - + CvtColorLoop(src, dst, RGB5x52Gray(code == CV_BGR5652GRAY ? 6 : 5)); break; - + case CV_GRAY2BGR: case CV_GRAY2BGRA: if( dcn <= 0 ) dcn = (code==CV_GRAY2BGRA) ? 4 : 3; CV_Assert( scn == 1 && (dcn == 3 || dcn == 4)); _dst.create(sz, CV_MAKETYPE(depth, dcn)); dst = _dst.getMat(); - + if( depth == CV_8U ) { #ifdef HAVE_TEGRA_OPTIMIZATION @@ -3247,15 +3247,15 @@ void cv::cvtColor( InputArray _src, OutputArray _dst, int code, int dcn ) else CvtColorLoop(src, dst, Gray2RGB(dcn)); break; - + case CV_GRAY2BGR565: case CV_GRAY2BGR555: CV_Assert( scn == 1 && depth == CV_8U ); _dst.create(sz, CV_8UC2); dst = _dst.getMat(); - + CvtColorLoop(src, dst, Gray2RGB5x5(code == CV_GRAY2BGR565 ? 6 : 5)); break; - + case CV_BGR2YCrCb: case CV_RGB2YCrCb: case CV_BGR2YUV: case CV_RGB2YUV: { @@ -3265,10 +3265,10 @@ void cv::cvtColor( InputArray _src, OutputArray _dst, int code, int dcn ) static const int yuv_i[] = { B2Y, G2Y, R2Y, 8061, 14369 }; const float* coeffs_f = code == CV_BGR2YCrCb || code == CV_RGB2YCrCb ? 0 : yuv_f; const int* coeffs_i = code == CV_BGR2YCrCb || code == CV_RGB2YCrCb ? 0 : yuv_i; - + _dst.create(sz, CV_MAKETYPE(depth, 3)); dst = _dst.getMat(); - + if( depth == CV_8U ) { #ifdef HAVE_TEGRA_OPTIMIZATION @@ -3283,7 +3283,7 @@ void cv::cvtColor( InputArray _src, OutputArray _dst, int code, int dcn ) CvtColorLoop(src, dst, RGB2YCrCb_f(scn, bidx, coeffs_f)); } break; - + case CV_YCrCb2BGR: case CV_YCrCb2RGB: case CV_YUV2BGR: case CV_YUV2RGB: { @@ -3291,13 +3291,13 @@ void cv::cvtColor( InputArray _src, OutputArray _dst, int code, int dcn ) CV_Assert( scn == 3 && (dcn == 3 || dcn == 4) ); bidx = code == CV_YCrCb2BGR || code == CV_YUV2RGB ? 0 : 2; static const float yuv_f[] = { 2.032f, -0.395f, -0.581f, 1.140f }; - static const int yuv_i[] = { 33292, -6472, -9519, 18678 }; + static const int yuv_i[] = { 33292, -6472, -9519, 18678 }; const float* coeffs_f = code == CV_YCrCb2BGR || code == CV_YCrCb2RGB ? 0 : yuv_f; const int* coeffs_i = code == CV_YCrCb2BGR || code == CV_YCrCb2RGB ? 0 : yuv_i; - + _dst.create(sz, CV_MAKETYPE(depth, dcn)); dst = _dst.getMat(); - + if( depth == CV_8U ) CvtColorLoop(src, dst, YCrCb2RGB_i(dcn, bidx, coeffs_i)); else if( depth == CV_16U ) @@ -3306,14 +3306,14 @@ void cv::cvtColor( InputArray _src, OutputArray _dst, int code, int dcn ) CvtColorLoop(src, dst, YCrCb2RGB_f(dcn, bidx, coeffs_f)); } break; - + case CV_BGR2XYZ: case CV_RGB2XYZ: CV_Assert( scn == 3 || scn == 4 ); bidx = code == CV_BGR2XYZ ? 0 : 2; - + _dst.create(sz, CV_MAKETYPE(depth, 3)); dst = _dst.getMat(); - + if( depth == CV_8U ) CvtColorLoop(src, dst, RGB2XYZ_i(scn, bidx, 0)); else if( depth == CV_16U ) @@ -3321,15 +3321,15 @@ void cv::cvtColor( InputArray _src, OutputArray _dst, int code, int dcn ) else CvtColorLoop(src, dst, RGB2XYZ_f(scn, bidx, 0)); break; - + case CV_XYZ2BGR: case CV_XYZ2RGB: if( dcn <= 0 ) dcn = 3; CV_Assert( scn == 3 && (dcn == 3 || dcn == 4) ); bidx = code == CV_XYZ2BGR ? 0 : 2; - + _dst.create(sz, CV_MAKETYPE(depth, dcn)); dst = _dst.getMat(); - + if( depth == CV_8U ) CvtColorLoop(src, dst, XYZ2RGB_i(dcn, bidx, 0)); else if( depth == CV_16U ) @@ -3337,7 +3337,7 @@ void cv::cvtColor( InputArray _src, OutputArray _dst, int code, int dcn ) else CvtColorLoop(src, dst, XYZ2RGB_f(dcn, bidx, 0)); break; - + case CV_BGR2HSV: case CV_RGB2HSV: case CV_BGR2HSV_FULL: case CV_RGB2HSV_FULL: case CV_BGR2HLS: case CV_RGB2HLS: case CV_BGR2HLS_FULL: case CV_RGB2HLS_FULL: { @@ -3346,15 +3346,15 @@ void cv::cvtColor( InputArray _src, OutputArray _dst, int code, int dcn ) code == CV_BGR2HSV_FULL || code == CV_BGR2HLS_FULL ? 0 : 2; int hrange = depth == CV_32F ? 360 : code == CV_BGR2HSV || code == CV_RGB2HSV || code == CV_BGR2HLS || code == CV_RGB2HLS ? 180 : 256; - + _dst.create(sz, CV_MAKETYPE(depth, 3)); dst = _dst.getMat(); - + if( code == CV_BGR2HSV || code == CV_RGB2HSV || code == CV_BGR2HSV_FULL || code == CV_RGB2HSV_FULL ) { #ifdef HAVE_TEGRA_OPTIMIZATION - if(tegra::cvtRGB2HSV(src, dst, bidx, hrange)) + if(tegra::cvtRGB2HSV(src, dst, bidx, hrange)) break; #endif if( depth == CV_8U ) @@ -3371,7 +3371,7 @@ void cv::cvtColor( InputArray _src, OutputArray _dst, int code, int dcn ) } } break; - + case CV_HSV2BGR: case CV_HSV2RGB: case CV_HSV2BGR_FULL: case CV_HSV2RGB_FULL: case CV_HLS2BGR: case CV_HLS2RGB: case CV_HLS2BGR_FULL: case CV_HLS2RGB_FULL: { @@ -3381,10 +3381,10 @@ void cv::cvtColor( InputArray _src, OutputArray _dst, int code, int dcn ) code == CV_HSV2BGR_FULL || code == CV_HLS2BGR_FULL ? 0 : 2; int hrange = depth == CV_32F ? 360 : code == CV_HSV2BGR || code == CV_HSV2RGB || code == CV_HLS2BGR || code == CV_HLS2RGB ? 180 : 255; - + _dst.create(sz, CV_MAKETYPE(depth, dcn)); dst = _dst.getMat(); - + if( code == CV_HSV2BGR || code == CV_HSV2RGB || code == CV_HSV2BGR_FULL || code == CV_HSV2RGB_FULL ) { @@ -3402,7 +3402,7 @@ void cv::cvtColor( InputArray _src, OutputArray _dst, int code, int dcn ) } } break; - + case CV_BGR2Lab: case CV_RGB2Lab: case CV_LBGR2Lab: case CV_LRGB2Lab: case CV_BGR2Luv: case CV_RGB2Luv: case CV_LBGR2Luv: case CV_LRGB2Luv: { @@ -3411,10 +3411,10 @@ void cv::cvtColor( InputArray _src, OutputArray _dst, int code, int dcn ) code == CV_LBGR2Lab || code == CV_LBGR2Luv ? 0 : 2; bool srgb = code == CV_BGR2Lab || code == CV_RGB2Lab || code == CV_BGR2Luv || code == CV_RGB2Luv; - + _dst.create(sz, CV_MAKETYPE(depth, 3)); dst = _dst.getMat(); - + if( code == CV_BGR2Lab || code == CV_RGB2Lab || code == CV_LBGR2Lab || code == CV_LRGB2Lab ) { @@ -3432,7 +3432,7 @@ void cv::cvtColor( InputArray _src, OutputArray _dst, int code, int dcn ) } } break; - + case CV_Lab2BGR: case CV_Lab2RGB: case CV_Lab2LBGR: case CV_Lab2LRGB: case CV_Luv2BGR: case CV_Luv2RGB: case CV_Luv2LBGR: case CV_Luv2LRGB: { @@ -3442,10 +3442,10 @@ void cv::cvtColor( InputArray _src, OutputArray _dst, int code, int dcn ) code == CV_Lab2LBGR || code == CV_Luv2LBGR ? 0 : 2; bool srgb = code == CV_Lab2BGR || code == CV_Lab2RGB || code == CV_Luv2BGR || code == CV_Luv2RGB; - + _dst.create(sz, CV_MAKETYPE(depth, dcn)); dst = _dst.getMat(); - + if( code == CV_Lab2BGR || code == CV_Lab2RGB || code == CV_Lab2LBGR || code == CV_Lab2LRGB ) { @@ -3463,14 +3463,14 @@ void cv::cvtColor( InputArray _src, OutputArray _dst, int code, int dcn ) } } break; - + case CV_BayerBG2GRAY: case CV_BayerGB2GRAY: case CV_BayerRG2GRAY: case CV_BayerGR2GRAY: if(dcn <= 0) dcn = 1; CV_Assert( scn == 1 && dcn == 1 ); - + _dst.create(sz, depth); dst = _dst.getMat(); - + if( depth == CV_8U ) Bayer2Gray_(src, dst, code); else if( depth == CV_16U ) @@ -3478,15 +3478,15 @@ void cv::cvtColor( InputArray _src, OutputArray _dst, int code, int dcn ) else CV_Error(CV_StsUnsupportedFormat, "Bayer->Gray demosaicing only supports 8u and 16u types"); break; - + case CV_BayerBG2BGR: case CV_BayerGB2BGR: case CV_BayerRG2BGR: case CV_BayerGR2BGR: case CV_BayerBG2BGR_VNG: case CV_BayerGB2BGR_VNG: case CV_BayerRG2BGR_VNG: case CV_BayerGR2BGR_VNG: if(dcn <= 0) dcn = 3; CV_Assert( scn == 1 && dcn == 3 ); - + _dst.create(sz, CV_MAKETYPE(depth, dcn)); dst = _dst.getMat(); - + if( code == CV_BayerBG2BGR || code == CV_BayerGB2BGR || code == CV_BayerRG2BGR || code == CV_BayerGR2BGR ) { @@ -3508,14 +3508,14 @@ void cv::cvtColor( InputArray _src, OutputArray _dst, int code, int dcn ) { // http://www.fourcc.org/yuv.php#NV21 == yuv420sp -> a plane of 8 bit Y samples followed by an interleaved V/U plane containing 8 bit 2x2 subsampled chroma samples // http://www.fourcc.org/yuv.php#NV12 -> a plane of 8 bit Y samples followed by an interleaved U/V plane containing 8 bit 2x2 subsampled colour difference samples - + if (dcn <= 0) dcn = (code==CV_YUV420sp2BGRA || code==CV_YUV420sp2RGBA || code==CV_YUV2BGRA_NV12 || code==CV_YUV2RGBA_NV12) ? 4 : 3; - const int bidx = (code==CV_YUV2BGR_NV21 || code==CV_YUV2BGRA_NV21 || code==CV_YUV2BGR_NV12 || code==CV_YUV2BGRA_NV12) ? 0 : 2; - const int uidx = (code==CV_YUV2BGR_NV21 || code==CV_YUV2BGRA_NV21 || code==CV_YUV2RGB_NV21 || code==CV_YUV2RGBA_NV21) ? 1 : 0; - + const int bIdx = (code==CV_YUV2BGR_NV21 || code==CV_YUV2BGRA_NV21 || code==CV_YUV2BGR_NV12 || code==CV_YUV2BGRA_NV12) ? 0 : 2; + const int uIdx = (code==CV_YUV2BGR_NV21 || code==CV_YUV2BGRA_NV21 || code==CV_YUV2RGB_NV21 || code==CV_YUV2RGBA_NV21) ? 1 : 0; + CV_Assert( dcn == 3 || dcn == 4 ); CV_Assert( sz.width % 2 == 0 && sz.height % 3 == 0 && depth == CV_8U ); - + Size dstSz(sz.width, sz.height * 2 / 3); _dst.create(dstSz, CV_MAKETYPE(depth, dcn)); dst = _dst.getMat(); @@ -3523,8 +3523,8 @@ void cv::cvtColor( InputArray _src, OutputArray _dst, int code, int dcn ) int srcstep = (int)src.step; const uchar* y = src.ptr(); const uchar* uv = y + srcstep * dstSz.height; - - switch(dcn*100 + bidx * 10 + uidx) + + switch(dcn*100 + bIdx * 10 + uIdx) { case 300: cvtYUV420sp2RGB<0, 0> (dst, srcstep, y, uv); break; case 301: cvtYUV420sp2RGB<0, 1> (dst, srcstep, y, uv); break; @@ -3543,29 +3543,29 @@ void cv::cvtColor( InputArray _src, OutputArray _dst, int code, int dcn ) { //http://www.fourcc.org/yuv.php#YV12 == yuv420p -> It comprises an NxM Y plane followed by (N/2)x(M/2) V and U planes. //http://www.fourcc.org/yuv.php#IYUV == I420 -> It comprises an NxN Y plane followed by (N/2)x(N/2) U and V planes - + if (dcn <= 0) dcn = (code==CV_YUV2BGRA_YV12 || code==CV_YUV2RGBA_YV12 || code==CV_YUV2RGBA_IYUV || code==CV_YUV2BGRA_IYUV) ? 4 : 3; - const int bidx = (code==CV_YUV2BGR_YV12 || code==CV_YUV2BGRA_YV12 || code==CV_YUV2BGR_IYUV || code==CV_YUV2BGRA_IYUV) ? 0 : 2; - const int uidx = (code==CV_YUV2BGR_YV12 || code==CV_YUV2RGB_YV12 || code==CV_YUV2BGRA_YV12 || code==CV_YUV2RGBA_YV12) ? 1 : 0; - + const int bIdx = (code==CV_YUV2BGR_YV12 || code==CV_YUV2BGRA_YV12 || code==CV_YUV2BGR_IYUV || code==CV_YUV2BGRA_IYUV) ? 0 : 2; + const int uIdx = (code==CV_YUV2BGR_YV12 || code==CV_YUV2RGB_YV12 || code==CV_YUV2BGRA_YV12 || code==CV_YUV2RGBA_YV12) ? 1 : 0; + CV_Assert( dcn == 3 || dcn == 4 ); CV_Assert( sz.width % 2 == 0 && sz.height % 3 == 0 && depth == CV_8U ); Size dstSz(sz.width, sz.height * 2 / 3); _dst.create(dstSz, CV_MAKETYPE(depth, dcn)); dst = _dst.getMat(); - + int srcstep = (int)src.step; const uchar* y = src.ptr(); const uchar* u = y + srcstep * dstSz.height; const uchar* v = y + srcstep * (dstSz.height + dstSz.height/4) + (dstSz.width/2) * ((dstSz.height % 4)/2); - + int ustepIdx = 0; int vstepIdx = dstSz.height % 4 == 2 ? 1 : 0; - - if(uidx == 1) { std::swap(u ,v), std::swap(ustepIdx, vstepIdx); }; - - switch(dcn*10 + bidx) + + if(uIdx == 1) { std::swap(u ,v), std::swap(ustepIdx, vstepIdx); }; + + switch(dcn*10 + bIdx) { case 30: cvtYUV420p2RGB<0>(dst, srcstep, y, u, v, ustepIdx, vstepIdx); break; case 32: cvtYUV420p2RGB<2>(dst, srcstep, y, u, v, ustepIdx, vstepIdx); break; @@ -3578,14 +3578,14 @@ void cv::cvtColor( InputArray _src, OutputArray _dst, int code, int dcn ) case CV_YUV2GRAY_420: { if (dcn <= 0) dcn = 1; - + CV_Assert( dcn == 1 ); CV_Assert( sz.width % 2 == 0 && sz.height % 3 == 0 && depth == CV_8U ); Size dstSz(sz.width, sz.height * 2 / 3); _dst.create(dstSz, CV_MAKETYPE(depth, dcn)); dst = _dst.getMat(); - + src(Range(0, dstSz.height), Range::all()).copyTo(dst); } break; @@ -3595,20 +3595,20 @@ void cv::cvtColor( InputArray _src, OutputArray _dst, int code, int dcn ) { //http://www.fourcc.org/yuv.php#UYVY //http://www.fourcc.org/yuv.php#YUY2 - //http://www.fourcc.org/yuv.php#YVYU - + //http://www.fourcc.org/yuv.php#YVYU + if (dcn <= 0) dcn = (code==CV_YUV2RGBA_UYVY || code==CV_YUV2BGRA_UYVY || code==CV_YUV2RGBA_YUY2 || code==CV_YUV2BGRA_YUY2 || code==CV_YUV2RGBA_YVYU || code==CV_YUV2BGRA_YVYU) ? 4 : 3; - const int bidx = (code==CV_YUV2BGR_UYVY || code==CV_YUV2BGRA_UYVY || code==CV_YUV2BGR_YUY2 || code==CV_YUV2BGRA_YUY2 || code==CV_YUV2BGR_YVYU || code==CV_YUV2BGRA_YVYU) ? 0 : 2; - const int ycn = (code==CV_YUV2RGB_UYVY || code==CV_YUV2BGR_UYVY || code==CV_YUV2RGBA_UYVY || code==CV_YUV2BGRA_UYVY) ? 1 : 0; - const int uidx = (code==CV_YUV2RGB_YVYU || code==CV_YUV2BGR_YVYU || code==CV_YUV2RGBA_YVYU || code==CV_YUV2BGRA_YVYU) ? 1 : 0; - + const int bIdx = (code==CV_YUV2BGR_UYVY || code==CV_YUV2BGRA_UYVY || code==CV_YUV2BGR_YUY2 || code==CV_YUV2BGRA_YUY2 || code==CV_YUV2BGR_YVYU || code==CV_YUV2BGRA_YVYU) ? 0 : 2; + const int ycn = (code==CV_YUV2RGB_UYVY || code==CV_YUV2BGR_UYVY || code==CV_YUV2RGBA_UYVY || code==CV_YUV2BGRA_UYVY) ? 1 : 0; + const int uIdx = (code==CV_YUV2RGB_YVYU || code==CV_YUV2BGR_YVYU || code==CV_YUV2RGBA_YVYU || code==CV_YUV2BGRA_YVYU) ? 1 : 0; + CV_Assert( dcn == 3 || dcn == 4 ); CV_Assert( scn == 2 && depth == CV_8U ); _dst.create(sz, CV_8UC(dcn)); dst = _dst.getMat(); - - switch(dcn*1000 + bidx*100 + uidx*10 + ycn) + + switch(dcn*1000 + bIdx*100 + uIdx*10 + ycn) { case 3000: cvtYUV422toRGB<0,0,0>(dst, (int)src.step, src.ptr()); break; case 3001: cvtYUV422toRGB<0,0,1>(dst, (int)src.step, src.ptr()); break; @@ -3633,7 +3633,7 @@ void cv::cvtColor( InputArray _src, OutputArray _dst, int code, int dcn ) case CV_YUV2GRAY_UYVY: case CV_YUV2GRAY_YUY2: { if (dcn <= 0) dcn = 1; - + CV_Assert( dcn == 1 ); CV_Assert( scn == 2 && depth == CV_8U ); @@ -3644,13 +3644,13 @@ void cv::cvtColor( InputArray _src, OutputArray _dst, int code, int dcn ) CV_Error( CV_StsBadFlag, "Unknown/unsupported color conversion code" ); } } - + CV_IMPL void cvCvtColor( const CvArr* srcarr, CvArr* dstarr, int code ) { cv::Mat src = cv::cvarrToMat(srcarr), dst0 = cv::cvarrToMat(dstarr), dst = dst0; CV_Assert( src.depth() == dst.depth() ); - + cv::cvtColor(src, dst, code, dst.channels()); CV_Assert( dst.data == dst0.data ); } diff --git a/modules/imgproc/src/filter.cpp b/modules/imgproc/src/filter.cpp index 6a998e5..a2bfa6a 100644 --- a/modules/imgproc/src/filter.cpp +++ b/modules/imgproc/src/filter.cpp @@ -48,11 +48,11 @@ /* Various border types, image boundaries are denoted with '|' - + * BORDER_REPLICATE: aaaaaa|abcdefgh|hhhhhhh * BORDER_REFLECT: fedcba|abcdefgh|hgfedcb * BORDER_REFLECT_101: gfedcb|abcdefgh|gfedcba - * BORDER_WRAP: cdefgh|abcdefgh|abcdefg + * BORDER_WRAP: cdefgh|abcdefgh|abcdefg * BORDER_CONSTANT: iiiiii|abcdefgh|iiiiiii with some specified 'i' */ int cv::borderInterpolate( int p, int len, int borderType ) @@ -113,7 +113,7 @@ FilterEngine::FilterEngine() wholeSize = Size(-1,-1); } - + FilterEngine::FilterEngine( const Ptr& _filter2D, const Ptr& _rowFilter, @@ -125,7 +125,7 @@ FilterEngine::FilterEngine( const Ptr& _filter2D, init(_filter2D, _rowFilter, _columnFilter, _srcType, _dstType, _bufType, _rowBorderType, _columnBorderType, _borderValue); } - + FilterEngine::~FilterEngine() { } @@ -141,24 +141,24 @@ void FilterEngine::init( const Ptr& _filter2D, _srcType = CV_MAT_TYPE(_srcType); _bufType = CV_MAT_TYPE(_bufType); _dstType = CV_MAT_TYPE(_dstType); - + srcType = _srcType; int srcElemSize = (int)getElemSize(srcType); dstType = _dstType; bufType = _bufType; - + filter2D = _filter2D; rowFilter = _rowFilter; columnFilter = _columnFilter; if( _columnBorderType < 0 ) _columnBorderType = _rowBorderType; - + rowBorderType = _rowBorderType; columnBorderType = _columnBorderType; - + CV_Assert( columnBorderType != BORDER_WRAP ); - + if( isSeparable() ) { CV_Assert( !rowFilter.empty() && !columnFilter.empty() ); @@ -175,7 +175,7 @@ void FilterEngine::init( const Ptr& _filter2D, CV_Assert( 0 <= anchor.x && anchor.x < ksize.width && 0 <= anchor.y && anchor.y < ksize.height ); - borderElemSize = srcElemSize/(CV_MAT_DEPTH(srcType) >= CV_32S ? sizeof(int) : 1); + borderElemSize = srcElemSize/(CV_MAT_DEPTH(srcType) >= CV_32S ? sizeof(int) : 1); int borderLength = std::max(ksize.width - 1, 1); borderTab.resize(borderLength*borderElemSize); @@ -198,7 +198,7 @@ static const int VEC_ALIGN = CV_MALLOC_ALIGN; int FilterEngine::start(Size _wholeSize, Rect _roi, int _maxBufRows) { int i, j; - + wholeSize = _wholeSize; roi = _roi; CV_Assert( roi.x >= 0 && roi.y >= 0 && roi.width >= 0 && roi.height >= 0 && @@ -226,7 +226,7 @@ int FilterEngine::start(Size _wholeSize, Rect _roi, int _maxBufRows) int n = (int)constBorderValue.size(), N; N = (maxWidth + ksize.width - 1)*esz; tdst = isSeparable() ? &srcRow[0] : dst; - + for( i = 0; i < N; i += n ) { n = std::min( n, N - i ); @@ -237,7 +237,7 @@ int FilterEngine::start(Size _wholeSize, Rect _roi, int _maxBufRows) if( isSeparable() ) (*rowFilter)(&srcRow[0], dst, maxWidth, cn); } - + int maxBufStep = bufElemSize*(int)alignSize(maxWidth + (!isSeparable() ? ksize.width - 1 : 0),VEC_ALIGN); ringBuf.resize(maxBufStep*rows.size()+VEC_ALIGN); @@ -265,10 +265,10 @@ int FilterEngine::start(Size _wholeSize, Rect _roi, int _maxBufRows) else { int xofs1 = std::min(roi.x, anchor.x) - roi.x; - + int btab_esz = borderElemSize, wholeWidth = wholeSize.width; int* btab = (int*)&borderTab[0]; - + for( i = 0; i < dx1; i++ ) { int p0 = (borderInterpolate(i-dx1, wholeWidth, rowBorderType) + xofs1)*btab_esz; @@ -301,20 +301,20 @@ int FilterEngine::start(const Mat& src, const Rect& _srcRoi, bool isolated, int maxBufRows) { Rect srcRoi = _srcRoi; - + if( srcRoi == Rect(0,0,-1,-1) ) srcRoi = Rect(0,0,src.cols,src.rows); - + CV_Assert( srcRoi.x >= 0 && srcRoi.y >= 0 && srcRoi.width >= 0 && srcRoi.height >= 0 && srcRoi.x + srcRoi.width <= src.cols && srcRoi.y + srcRoi.height <= src.rows ); Point ofs; - Size wholeSize(src.cols, src.rows); + Size wsz(src.cols, src.rows); if( !isolated ) - src.locateROI( wholeSize, ofs ); - start( wholeSize, srcRoi + ofs, maxBufRows ); + src.locateROI( wsz, ofs ); + start( wsz, srcRoi + ofs, maxBufRows ); return startY - ofs.y; } @@ -334,7 +334,7 @@ int FilterEngine::proceed( const uchar* src, int srcstep, int count, uchar* dst, int dststep ) { CV_Assert( wholeSize.width > 0 && wholeSize.height > 0 ); - + const int *btab = &borderTab[0]; int esz = (int)getElemSize(srcType), btab_esz = borderElemSize; uchar** brows = &rows[0]; @@ -365,7 +365,7 @@ int FilterEngine::proceed( const uchar* src, int srcstep, int count, int bi = (startY - startY0 + rowCount) % bufRows; uchar* brow = alignPtr(&ringBuf[0], VEC_ALIGN) + bi*bufStep; uchar* row = isSep ? &srcRow[0] : brow; - + if( ++rowCount > bufRows ) { --rowCount; @@ -394,7 +394,7 @@ int FilterEngine::proceed( const uchar* src, int srcstep, int count, row[i + (width1 - _dx2)*esz] = src[btab[i+_dx1*esz]]; } } - + if( isSep ) (*rowFilter)(row, brow, width, CV_MAT_CN(srcType)); } @@ -434,11 +434,11 @@ void FilterEngine::apply(const Mat& src, Mat& dst, const Rect& _srcRoi, Point dstOfs, bool isolated) { CV_Assert( src.type() == srcType && dst.type() == dstType ); - + Rect srcRoi = _srcRoi; if( srcRoi == Rect(0,0,-1,-1) ) srcRoi = Rect(0,0,src.cols,src.rows); - + if( srcRoi.area() == 0 ) return; @@ -560,7 +560,7 @@ struct RowVec_8u32s { if( !checkHardwareSupport(CV_CPU_SSE2) ) return 0; - + int i = 0, k, _ksize = kernel.rows + kernel.cols - 1; int* dst = (int*)_dst; const int* _kx = (const int*)kernel.data; @@ -593,7 +593,7 @@ struct RowVec_8u32s s2 = _mm_add_epi32(s2, _mm_unpacklo_epi16(x2, x3)); s3 = _mm_add_epi32(s3, _mm_unpackhi_epi16(x2, x3)); } - + _mm_store_si128((__m128i*)(dst + i), s0); _mm_store_si128((__m128i*)(dst + i + 4), s1); _mm_store_si128((__m128i*)(dst + i + 8), s2); @@ -652,7 +652,7 @@ struct SymmRowSmallVec_8u32s { if( !checkHardwareSupport(CV_CPU_SSE2) ) return 0; - + int i = 0, j, k, _ksize = kernel.rows + kernel.cols - 1; int* dst = (int*)_dst; bool symmetrical = (symmetryType & KERNEL_SYMMETRICAL) != 0; @@ -973,7 +973,7 @@ struct SymmColumnVec_32s8u { if( !checkHardwareSupport(CV_CPU_SSE2) ) return 0; - + int ksize2 = (kernel.rows + kernel.cols - 1)/2; const float* ky = (const float*)kernel.data + ksize2; int i = 0, k; @@ -1121,7 +1121,7 @@ struct SymmColumnSmallVec_32s16s { if( !checkHardwareSupport(CV_CPU_SSE2) ) return 0; - + int ksize2 = (kernel.rows + kernel.cols - 1)/2; const float* ky = (const float*)kernel.data + ksize2; int i = 0; @@ -1237,9 +1237,9 @@ struct SymmColumnSmallVec_32s16s Mat kernel; }; - + /////////////////////////////////////// 16s ////////////////////////////////// - + struct RowVec_16s32f { RowVec_16s32f() {} @@ -1248,17 +1248,17 @@ struct RowVec_16s32f kernel = _kernel; sse2_supported = checkHardwareSupport(CV_CPU_SSE2); } - + int operator()(const uchar* _src, uchar* _dst, int width, int cn) const { if( !sse2_supported ) return 0; - + int i = 0, k, _ksize = kernel.rows + kernel.cols - 1; float* dst = (float*)_dst; const float* _kx = (const float*)kernel.data; width *= cn; - + for( ; i <= width - 8; i += 8 ) { const short* src = (const short*)_src + i; @@ -1267,7 +1267,7 @@ struct RowVec_16s32f { f = _mm_load_ss(_kx+k); f = _mm_shuffle_ps(f, f, 0); - + __m128i x0i = _mm_loadu_si128((const __m128i*)src); __m128i x1i = _mm_srai_epi32(_mm_unpackhi_epi16(x0i, x0i), 16); x0i = _mm_srai_epi32(_mm_unpacklo_epi16(x0i, x0i), 16); @@ -1281,12 +1281,12 @@ struct RowVec_16s32f } return i; } - + Mat kernel; bool sse2_supported; }; - - + + struct SymmColumnVec_32f16s { SymmColumnVec_32f16s() { symmetryType=0; } @@ -1298,12 +1298,12 @@ struct SymmColumnVec_32f16s CV_Assert( (symmetryType & (KERNEL_SYMMETRICAL | KERNEL_ASYMMETRICAL)) != 0 ); sse2_supported = checkHardwareSupport(CV_CPU_SSE2); } - + int operator()(const uchar** _src, uchar* _dst, int width) const { if( !sse2_supported ) return 0; - + int ksize2 = (kernel.rows + kernel.cols - 1)/2; const float* ky = (const float*)kernel.data + ksize2; int i = 0, k; @@ -1312,7 +1312,7 @@ struct SymmColumnVec_32f16s const float *S, *S2; short* dst = (short*)_dst; __m128 d4 = _mm_set1_ps(delta); - + if( symmetrical ) { for( ; i <= width - 16; i += 16 ) @@ -1330,7 +1330,7 @@ struct SymmColumnVec_32f16s s3 = _mm_load_ps(S+12); s2 = _mm_add_ps(_mm_mul_ps(s2, f), d4); s3 = _mm_add_ps(_mm_mul_ps(s3, f), d4); - + for( k = 1; k <= ksize2; k++ ) { S = src[k] + i; @@ -1346,23 +1346,23 @@ struct SymmColumnVec_32f16s s2 = _mm_add_ps(s2, _mm_mul_ps(x0, f)); s3 = _mm_add_ps(s3, _mm_mul_ps(x1, f)); } - + __m128i s0i = _mm_cvtps_epi32(s0); __m128i s1i = _mm_cvtps_epi32(s1); __m128i s2i = _mm_cvtps_epi32(s2); __m128i s3i = _mm_cvtps_epi32(s3); - + _mm_storeu_si128((__m128i*)(dst + i), _mm_packs_epi32(s0i, s1i)); _mm_storeu_si128((__m128i*)(dst + i + 8), _mm_packs_epi32(s2i, s3i)); } - + for( ; i <= width - 4; i += 4 ) { __m128 f = _mm_load_ss(ky); f = _mm_shuffle_ps(f, f, 0); __m128 x0, s0 = _mm_load_ps(src[0] + i); s0 = _mm_add_ps(_mm_mul_ps(s0, f), d4); - + for( k = 1; k <= ksize2; k++ ) { f = _mm_load_ss(ky+k); @@ -1372,7 +1372,7 @@ struct SymmColumnVec_32f16s x0 = _mm_add_ps(_mm_load_ps(src[k]+i), _mm_load_ps(src[-k] + i)); s0 = _mm_add_ps(s0, _mm_mul_ps(x0, f)); } - + __m128i s0i = _mm_cvtps_epi32(s0); _mm_storel_epi64((__m128i*)(dst + i), _mm_packs_epi32(s0i, s0i)); } @@ -1384,7 +1384,7 @@ struct SymmColumnVec_32f16s __m128 f, s0 = d4, s1 = d4, s2 = d4, s3 = d4; __m128 x0, x1; S = src[0] + i; - + for( k = 1; k <= ksize2; k++ ) { S = src[k] + i; @@ -1400,20 +1400,20 @@ struct SymmColumnVec_32f16s s2 = _mm_add_ps(s2, _mm_mul_ps(x0, f)); s3 = _mm_add_ps(s3, _mm_mul_ps(x1, f)); } - + __m128i s0i = _mm_cvtps_epi32(s0); __m128i s1i = _mm_cvtps_epi32(s1); __m128i s2i = _mm_cvtps_epi32(s2); __m128i s3i = _mm_cvtps_epi32(s3); - + _mm_storeu_si128((__m128i*)(dst + i), _mm_packs_epi32(s0i, s1i)); _mm_storeu_si128((__m128i*)(dst + i + 8), _mm_packs_epi32(s2i, s3i)); } - + for( ; i <= width - 4; i += 4 ) { __m128 f, x0, s0 = d4; - + for( k = 1; k <= ksize2; k++ ) { f = _mm_load_ss(ky+k); @@ -1421,21 +1421,21 @@ struct SymmColumnVec_32f16s x0 = _mm_sub_ps(_mm_load_ps(src[k]+i), _mm_load_ps(src[-k] + i)); s0 = _mm_add_ps(s0, _mm_mul_ps(x0, f)); } - + __m128i s0i = _mm_cvtps_epi32(s0); _mm_storel_epi64((__m128i*)(dst + i), _mm_packs_epi32(s0i, s0i)); } } - + return i; } - + int symmetryType; float delta; Mat kernel; bool sse2_supported; -}; - +}; + /////////////////////////////////////// 32f ////////////////////////////////// @@ -1451,7 +1451,7 @@ struct RowVec_32f { if( !checkHardwareSupport(CV_CPU_SSE) ) return 0; - + int i = 0, k, _ksize = kernel.rows + kernel.cols - 1; float* dst = (float*)_dst; const float* _kx = (const float*)kernel.data; @@ -1494,7 +1494,7 @@ struct SymmRowSmallVec_32f { if( !checkHardwareSupport(CV_CPU_SSE) ) return 0; - + int i = 0, _ksize = kernel.rows + kernel.cols - 1; float* dst = (float*)_dst; const float* src = (const float*)_src + (_ksize/2)*cn; @@ -1594,12 +1594,12 @@ struct SymmRowSmallVec_32f y0 = _mm_mul_ps(_mm_add_ps(y0, y2), k1); x0 = _mm_add_ps(x0, _mm_mul_ps(x1, k0)); y0 = _mm_add_ps(y0, _mm_mul_ps(y1, k0)); - + x2 = _mm_add_ps(_mm_loadu_ps(src + cn*2), _mm_loadu_ps(src - cn*2)); y2 = _mm_add_ps(_mm_loadu_ps(src + cn*2 + 4), _mm_loadu_ps(src - cn*2 + 4)); x0 = _mm_add_ps(x0, _mm_mul_ps(x2, k2)); y0 = _mm_add_ps(y0, _mm_mul_ps(y2, k2)); - + _mm_store_ps(dst + i, x0); _mm_store_ps(dst + i + 4, y0); } @@ -1654,12 +1654,12 @@ struct SymmRowSmallVec_32f x0 = _mm_mul_ps(_mm_sub_ps(x0, x2), k1); y0 = _mm_mul_ps(_mm_sub_ps(y0, y2), k1); - + x2 = _mm_sub_ps(_mm_loadu_ps(src + cn*2), _mm_loadu_ps(src - cn*2)); y2 = _mm_sub_ps(_mm_loadu_ps(src + cn*2 + 4), _mm_loadu_ps(src - cn*2 + 4)); x0 = _mm_add_ps(x0, _mm_mul_ps(x2, k2)); y0 = _mm_add_ps(y0, _mm_mul_ps(y2, k2)); - + _mm_store_ps(dst + i, x0); _mm_store_ps(dst + i + 4, y0); } @@ -1689,7 +1689,7 @@ struct SymmColumnVec_32f { if( !checkHardwareSupport(CV_CPU_SSE) ) return 0; - + int ksize2 = (kernel.rows + kernel.cols - 1)/2; const float* ky = (const float*)kernel.data + ksize2; int i = 0, k; @@ -1829,7 +1829,7 @@ struct SymmColumnSmallVec_32f { if( !checkHardwareSupport(CV_CPU_SSE) ) return 0; - + int ksize2 = (kernel.rows + kernel.cols - 1)/2; const float* ky = (const float*)kernel.data + ksize2; int i = 0; @@ -1963,7 +1963,7 @@ struct FilterVec_8u { if( !checkHardwareSupport(CV_CPU_SSE2) ) return 0; - + const float* kf = (const float*)&coeffs[0]; int i = 0, k, nz = _nz; __m128 d4 = _mm_set1_ps(delta); @@ -2046,7 +2046,7 @@ struct FilterVec_8u16s { if( !checkHardwareSupport(CV_CPU_SSE2) ) return 0; - + const float* kf = (const float*)&coeffs[0]; short* dst = (short*)_dst; int i = 0, k, nz = _nz; @@ -2127,7 +2127,7 @@ struct FilterVec_32f { if( !checkHardwareSupport(CV_CPU_SSE) ) return 0; - + const float* kf = (const float*)&coeffs[0]; const float** src = (const float**)_src; float* dst = (float*)_dst; @@ -2217,7 +2217,7 @@ template struct RowFilter : public BaseRo (kernel.rows == 1 || kernel.cols == 1)); vecOp = _vecOp; } - + void operator()(const uchar* src, uchar* dst, int width, int cn) { int _ksize = ksize; @@ -2242,7 +2242,7 @@ template struct RowFilter : public BaseRo s0 += f*S[0]; s1 += f*S[1]; s2 += f*S[2]; s3 += f*S[3]; } - + D[i] = s0; D[i+1] = s1; D[i+2] = s2; D[i+3] = s3; } @@ -2275,7 +2275,7 @@ template struct SymmRowSmallFilter : symmetryType = _symmetryType; CV_Assert( (symmetryType & (KERNEL_SYMMETRICAL | KERNEL_ASYMMETRICAL)) != 0 && this->ksize <= 5 ); } - + void operator()(const uchar* src, uchar* dst, int width, int cn) { int ksize2 = this->ksize/2, ksize2n = ksize2*cn; @@ -2397,7 +2397,7 @@ template struct ColumnFilter : public BaseColumnFilte { typedef typename CastOp::type1 ST; typedef typename CastOp::rtype DT; - + ColumnFilter( const Mat& _kernel, int _anchor, double _delta, const CastOp& _castOp=CastOp(), const VecOp& _vecOp=VecOp() ) @@ -2427,7 +2427,7 @@ template struct ColumnFilter : public BaseColumnFilte { DT* D = (DT*)dst; i = vecOp(src, dst, width); - #if CV_ENABLE_UNROLLED + #if CV_ENABLE_UNROLLED for( ; i <= width - 4; i += 4 ) { ST f = ky[0]; @@ -2574,7 +2574,7 @@ struct SymmColumnSmallFilter : public SymmColumnFilter { typedef typename CastOp::type1 ST; typedef typename CastOp::rtype DT; - + SymmColumnSmallFilter( const Mat& _kernel, int _anchor, double _delta, int _symmetryType, const CastOp& _castOp=CastOp(), @@ -2610,7 +2610,7 @@ struct SymmColumnSmallFilter : public SymmColumnFilter { if( is_1_2_1 ) { - #if CV_ENABLE_UNROLLED + #if CV_ENABLE_UNROLLED for( ; i <= width - 4; i += 4 ) { ST s0 = S0[i] + S1[i]*2 + S2[i] + _delta; @@ -2624,7 +2624,7 @@ struct SymmColumnSmallFilter : public SymmColumnFilter D[i+3] = castOp(s1); } #else - for( ; i < width; i ++ ) + for( ; i < width; i ++ ) { ST s0 = S0[i] + S1[i]*2 + S2[i] + _delta; D[i] = castOp(s0); @@ -2633,7 +2633,7 @@ struct SymmColumnSmallFilter : public SymmColumnFilter } else if( is_1_m2_1 ) { - #if CV_ENABLE_UNROLLED + #if CV_ENABLE_UNROLLED for( ; i <= width - 4; i += 4 ) { ST s0 = S0[i] - S1[i]*2 + S2[i] + _delta; @@ -2647,7 +2647,7 @@ struct SymmColumnSmallFilter : public SymmColumnFilter D[i+3] = castOp(s1); } #else - for( ; i < width; i ++ ) + for( ; i < width; i ++ ) { ST s0 = S0[i] - S1[i]*2 + S2[i] + _delta; D[i] = castOp(s0); @@ -2700,7 +2700,7 @@ struct SymmColumnSmallFilter : public SymmColumnFilter D[i+3] = castOp(s1); } #else - for( ; i < width; i ++ ) + for( ; i < width; i ++ ) { ST s0 = S2[i] - S0[i] + _delta; D[i] = castOp(s0); @@ -2763,7 +2763,7 @@ template struct FixedPtCastEx }; } - + cv::Ptr cv::getLinearRowFilter( int srcType, int bufType, InputArray _kernel, int anchor, int symmetryType ) @@ -2785,7 +2785,7 @@ cv::Ptr cv::getLinearRowFilter( int srcType, int bufType, return Ptr(new SymmRowSmallFilter (kernel, anchor, symmetryType, SymmRowSmallVec_32f(kernel, symmetryType))); } - + if( sdepth == CV_8U && ddepth == CV_32S ) return Ptr(new RowFilter (kernel, anchor, RowVec_8u32s(kernel))); @@ -2820,7 +2820,7 @@ cv::Ptr cv::getLinearRowFilter( int srcType, int bufType, cv::Ptr cv::getLinearColumnFilter( int bufType, int dstType, InputArray _kernel, int anchor, - int symmetryType, double delta, + int symmetryType, double delta, int bits ) { Mat kernel = _kernel.getMat(); @@ -3045,7 +3045,7 @@ template struct Filter2D : public BaseFi { typedef typename CastOp::type1 KT; typedef typename CastOp::rtype DT; - + Filter2D( const Mat& _kernel, Point _anchor, double _delta, const CastOp& _castOp=CastOp(), const VecOp& _vecOp=VecOp() ) @@ -3143,7 +3143,7 @@ cv::Ptr cv::getLinearFilter(int srcType, int dstType, kernel = _kernel; else _kernel.convertTo(kernel, kdepth, _kernel.type() == CV_32S ? 1./(1 << bits) : 1.); - + if( sdepth == CV_8U && ddepth == CV_8U ) return Ptr(new Filter2D, FilterVec_8u> (kernel, anchor, delta, Cast(), FilterVec_8u(kernel, 0, delta))); @@ -3203,7 +3203,7 @@ cv::Ptr cv::createLinearFilter( int _srcType, int _dstType, { Mat _kernel = filter_kernel.getMat(); _srcType = CV_MAT_TYPE(_srcType); - _dstType = CV_MAT_TYPE(_dstType); + _dstType = CV_MAT_TYPE(_dstType); int cn = CV_MAT_CN(_srcType); CV_Assert( cn == CV_MAT_CN(_dstType) ); @@ -3211,14 +3211,14 @@ cv::Ptr cv::createLinearFilter( int _srcType, int _dstType, int bits = 0; /*int sdepth = CV_MAT_DEPTH(_srcType), ddepth = CV_MAT_DEPTH(_dstType); - int ktype = _kernel.depth() == CV_32S ? KERNEL_INTEGER : getKernelType(_kernel, _anchor); + int ktype = _kernel.depth() == CV_32S ? KERNEL_INTEGER : getKernelType(_kernel, _anchor); if( sdepth == CV_8U && (ddepth == CV_8U || ddepth == CV_16S) && _kernel.rows*_kernel.cols <= (1 << 10) ) { bits = (ktype & KERNEL_INTEGER) ? 0 : 11; _kernel.convertTo(kernel, CV_32S, 1 << bits); }*/ - + Ptr _filter2D = getLinearFilter(_srcType, _dstType, kernel, _anchor, _delta, bits); @@ -3233,7 +3233,7 @@ void cv::filter2D( InputArray _src, OutputArray _dst, int ddepth, double delta, int borderType ) { Mat src = _src.getMat(), kernel = _kernel.getMat(); - + if( ddepth < 0 ) ddepth = src.depth(); @@ -3279,7 +3279,7 @@ void cv::sepFilter2D( InputArray _src, OutputArray _dst, int ddepth, double delta, int borderType ) { Mat src = _src.getMat(), kernelX = _kernelX.getMat(), kernelY = _kernelY.getMat(); - + if( ddepth < 0 ) ddepth = src.depth(); diff --git a/modules/imgproc/src/floodfill.cpp b/modules/imgproc/src/floodfill.cpp index c77640b..e970a31 100644 --- a/modules/imgproc/src/floodfill.cpp +++ b/modules/imgproc/src/floodfill.cpp @@ -233,7 +233,7 @@ typedef DiffC3 Diff32sC3; typedef DiffC1 Diff32fC1; typedef DiffC3 Diff32fC3; -cv::Vec3i& operator += (cv::Vec3i& a, const cv::Vec3b& b) +static cv::Vec3i& operator += (cv::Vec3i& a, const cv::Vec3b& b) { a[0] += b[0]; a[1] += b[1]; @@ -440,7 +440,7 @@ cvFloodFill( CvArr* arr, CvPoint seed_point, { cv::Ptr tempMask; cv::AutoBuffer buffer; - + if( comp ) memset( comp, 0, sizeof(*comp) ); @@ -491,16 +491,16 @@ cvFloodFill( CvArr* arr, CvPoint seed_point, { /*int elem_size = CV_ELEM_SIZE(type); const uchar* seed_ptr = img->data.ptr + img->step*seed_point.y + elem_size*seed_point.x; - + // check if the new value is different from the current value at the seed point. // if they are exactly the same, use the generic version with mask to avoid infinite loops. for( i = 0; i < elem_size; i++ ) if( seed_ptr[i] != ((uchar*)nv_buf)[i] ) break; - + if( i == elem_size ) return;*/ - + if( type == CV_8UC1 ) icvFloodFill_CnIR(img->data.ptr, img->step, size, seed_point, nv_buf.b[0], comp, flags, buffer, buffer_size); @@ -632,7 +632,7 @@ int cv::floodFill( InputOutputArray _image, Point seedPoint, } int cv::floodFill( InputOutputArray _image, InputOutputArray _mask, - Point seedPoint, Scalar newVal, Rect* rect, + Point seedPoint, Scalar newVal, Rect* rect, Scalar loDiff, Scalar upDiff, int flags ) { CvConnectedComp ccomp; diff --git a/modules/imgproc/src/gcgraph.hpp b/modules/imgproc/src/gcgraph.hpp index 7f0f501..59c9744 100644 --- a/modules/imgproc/src/gcgraph.hpp +++ b/modules/imgproc/src/gcgraph.hpp @@ -64,7 +64,7 @@ private: int ts; int dist; TWeight weight; - uchar t; + uchar t; }; class Edge { @@ -174,7 +174,7 @@ TWeight GCGraph::maxFlow() v->t = v->weight < 0; } else - v->parent = 0; + v->parent = 0; } first = first->next; last->next = nilNode; @@ -290,14 +290,14 @@ TWeight GCGraph::maxFlow() curr_ts++; while( !orphans.empty() ) { - Vtx* v = orphans.back(); + Vtx* v2 = orphans.back(); orphans.pop_back(); int d, minDist = INT_MAX; e0 = 0; - vt = v->t; + vt = v2->t; - for( ei = v->first; ei != 0; ei = edgePtr[ei].next ) + for( ei = v2->first; ei != 0; ei = edgePtr[ei].next ) { if( edgePtr[ei^(vt^1)].weight == 0 ) continue; @@ -344,16 +344,16 @@ TWeight GCGraph::maxFlow() } } - if( (v->parent = e0) > 0 ) + if( (v2->parent = e0) > 0 ) { - v->ts = curr_ts; - v->dist = minDist; + v2->ts = curr_ts; + v2->dist = minDist; continue; } /* no parent is found */ - v->ts = 0; - for( ei = v->first; ei != 0; ei = edgePtr[ei].next ) + v2->ts = 0; + for( ei = v2->first; ei != 0; ei = edgePtr[ei].next ) { u = vtxPtr+edgePtr[ei].dst; ej = u->parent; @@ -364,7 +364,7 @@ TWeight GCGraph::maxFlow() u->next = nilNode; last = last->next = u; } - if( ej > 0 && vtxPtr+edgePtr[ej].dst == v ) + if( ej > 0 && vtxPtr+edgePtr[ej].dst == v2 ) { orphans.push_back(u); u->parent = ORPHAN; diff --git a/modules/imgproc/src/grabcut.cpp b/modules/imgproc/src/grabcut.cpp index 27a535c..98dbf74 100644 --- a/modules/imgproc/src/grabcut.cpp +++ b/modules/imgproc/src/grabcut.cpp @@ -230,7 +230,7 @@ void GMM::calcInverseCovAndDeterm( int ci ) Calculate beta - parameter of GrabCut algorithm. beta = 1/(2*avg(sqr(||color[i] - color[j]||))) */ -double calcBeta( const Mat& img ) +static double calcBeta( const Mat& img ) { double beta = 0; for( int y = 0; y < img.rows; y++ ) @@ -272,7 +272,7 @@ double calcBeta( const Mat& img ) Calculate weights of noterminal vertices of graph. beta and gamma - parameters of GrabCut algorithm. */ -void calcNWeights( const Mat& img, Mat& leftW, Mat& upleftW, Mat& upW, Mat& uprightW, double beta, double gamma ) +static void calcNWeights( const Mat& img, Mat& leftW, Mat& upleftW, Mat& upW, Mat& uprightW, double beta, double gamma ) { const double gammaDivSqrt2 = gamma / std::sqrt(2.0f); leftW.create( img.rows, img.cols, CV_64FC1 ); @@ -319,7 +319,7 @@ void calcNWeights( const Mat& img, Mat& leftW, Mat& upleftW, Mat& upW, Mat& upri /* Check size, type and element values of mask matrix. */ -void checkMask( const Mat& img, const Mat& mask ) +static void checkMask( const Mat& img, const Mat& mask ) { if( mask.empty() ) CV_Error( CV_StsBadArg, "mask is empty" ); @@ -342,7 +342,7 @@ void checkMask( const Mat& img, const Mat& mask ) /* Initialize mask using rectangular. */ -void initMaskWithRect( Mat& mask, Size imgSize, Rect rect ) +static void initMaskWithRect( Mat& mask, Size imgSize, Rect rect ) { mask.create( imgSize, CV_8UC1 ); mask.setTo( GC_BGD ); @@ -358,7 +358,7 @@ void initMaskWithRect( Mat& mask, Size imgSize, Rect rect ) /* Initialize GMM background and foreground models using kmeans algorithm. */ -void initGMMs( const Mat& img, const Mat& mask, GMM& bgdGMM, GMM& fgdGMM ) +static void initGMMs( const Mat& img, const Mat& mask, GMM& bgdGMM, GMM& fgdGMM ) { const int kMeansItCount = 10; const int kMeansType = KMEANS_PP_CENTERS; @@ -398,7 +398,7 @@ void initGMMs( const Mat& img, const Mat& mask, GMM& bgdGMM, GMM& fgdGMM ) /* Assign GMMs components for each pixel. */ -void assignGMMsComponents( const Mat& img, const Mat& mask, const GMM& bgdGMM, const GMM& fgdGMM, Mat& compIdxs ) +static void assignGMMsComponents( const Mat& img, const Mat& mask, const GMM& bgdGMM, const GMM& fgdGMM, Mat& compIdxs ) { Point p; for( p.y = 0; p.y < img.rows; p.y++ ) @@ -415,7 +415,7 @@ void assignGMMsComponents( const Mat& img, const Mat& mask, const GMM& bgdGMM, c /* Learn GMMs parameters. */ -void learnGMMs( const Mat& img, const Mat& mask, const Mat& compIdxs, GMM& bgdGMM, GMM& fgdGMM ) +static void learnGMMs( const Mat& img, const Mat& mask, const Mat& compIdxs, GMM& bgdGMM, GMM& fgdGMM ) { bgdGMM.initLearning(); fgdGMM.initLearning(); @@ -443,7 +443,7 @@ void learnGMMs( const Mat& img, const Mat& mask, const Mat& compIdxs, GMM& bgdGM /* Construct GCGraph */ -void constructGCGraph( const Mat& img, const Mat& mask, const GMM& bgdGMM, const GMM& fgdGMM, double lambda, +static void constructGCGraph( const Mat& img, const Mat& mask, const GMM& bgdGMM, const GMM& fgdGMM, double lambda, const Mat& leftW, const Mat& upleftW, const Mat& upW, const Mat& uprightW, GCGraph& graph ) { @@ -506,7 +506,7 @@ void constructGCGraph( const Mat& img, const Mat& mask, const GMM& bgdGMM, const /* Estimate segmentation using MaxFlow algorithm */ -void estimateSegmentation( GCGraph& graph, Mat& mask ) +static void estimateSegmentation( GCGraph& graph, Mat& mask ) { graph.maxFlow(); Point p; @@ -533,7 +533,7 @@ void cv::grabCut( InputArray _img, InputOutputArray _mask, Rect rect, Mat& mask = _mask.getMatRef(); Mat& bgdModel = _bgdModel.getMatRef(); Mat& fgdModel = _fgdModel.getMatRef(); - + if( img.empty() ) CV_Error( CV_StsBadArg, "image is empty" ); if( img.type() != CV_8UC3 ) diff --git a/modules/imgproc/src/histogram.cpp b/modules/imgproc/src/histogram.cpp index 546c215..edcb240 100644 --- a/modules/imgproc/src/histogram.cpp +++ b/modules/imgproc/src/histogram.cpp @@ -7,8 +7,8 @@ // copy or use the software. // // -// Intel License Agreement -// For Open Source Computer Vision Library +// Intel License Agreement +// For Open Source Computer Vision Library // // Copyright (C) 2000, Intel Corporation, all rights reserved. // Third party copyrights are property of their respective owners. @@ -46,7 +46,7 @@ namespace cv template<> void Ptr::delete_obj() { cvReleaseHist(&obj); } - + ////////////////// Helper functions ////////////////////// static const size_t OUT_OF_RANGE = (size_t)1 << (sizeof(size_t)*8 - 2); @@ -60,7 +60,7 @@ calcHistLookupTables_8u( const Mat& hist, const SparseMat& shist, int i, j; _tab.resize((high-low)*dims); size_t* tab = &_tab[0]; - + if( uniform ) { for( i = 0; i < dims; i++ ) @@ -69,7 +69,7 @@ calcHistLookupTables_8u( const Mat& hist, const SparseMat& shist, double b = uniranges[i*2+1]; int sz = !issparse ? hist.size[i] : shist.size(i); size_t step = !issparse ? hist.step[i] : 1; - + for( j = low; j < high; j++ ) { int idx = cvFloor(j*a + b); @@ -78,7 +78,7 @@ calcHistLookupTables_8u( const Mat& hist, const SparseMat& shist, written_idx = idx*step; else written_idx = OUT_OF_RANGE; - + tab[i*(high - low) + j - low] = written_idx; } } @@ -91,12 +91,12 @@ calcHistLookupTables_8u( const Mat& hist, const SparseMat& shist, int idx = -1, sz = !issparse ? hist.size[i] : shist.size(i); size_t written_idx = OUT_OF_RANGE; size_t step = !issparse ? hist.step[i] : 1; - + for(j = low;;) { for( ; j < limit; j++ ) tab[i*(high - low) + j - low] = written_idx; - + if( (unsigned)(++idx) < (unsigned)sz ) { limit = std::min(cvCeil(ranges[i][idx+1]), high); @@ -122,14 +122,14 @@ static void histPrepareImages( const Mat* images, int nimages, const int* channe { int i, j, c; CV_Assert( channels != 0 || nimages == dims ); - + imsize = images[0].size(); int depth = images[0].depth(), esz1 = (int)images[0].elemSize1(); bool isContinuous = true; - + ptrs.resize(dims + 1); deltas.resize((dims + 1)*2); - + for( i = 0; i < dims; i++ ) { if(!channels) @@ -147,7 +147,7 @@ static void histPrepareImages( const Mat* images, int nimages, const int* channe break; CV_Assert( j < nimages ); } - + CV_Assert( images[j].size() == imsize && images[j].depth() == depth ); if( !images[j].isContinuous() ) isContinuous = false; @@ -155,7 +155,7 @@ static void histPrepareImages( const Mat* images, int nimages, const int* channe deltas[i*2] = images[j].channels(); deltas[i*2+1] = (int)(images[j].step/esz1 - imsize.width*deltas[i*2]); } - + if( mask.data ) { CV_Assert( mask.size() == imsize && mask.channels() == 1 ); @@ -164,17 +164,17 @@ static void histPrepareImages( const Mat* images, int nimages, const int* channe deltas[dims*2] = 1; deltas[dims*2 + 1] = (int)(mask.step/mask.elemSize1()); } - + if( isContinuous ) { imsize.width *= imsize.height; imsize.height = 1; } - + if( !ranges ) { CV_Assert( depth == CV_8U ); - + uniranges.resize( dims*2 ); for( i = 0; i < dims; i++ ) { @@ -198,16 +198,16 @@ static void histPrepareImages( const Mat* images, int nimages, const int* channe { for( i = 0; i < dims; i++ ) { - size_t j, n = histSize[i]; - for( j = 0; j < n; j++ ) - CV_Assert( ranges[i][j] < ranges[i][j+1] ); + size_t n = histSize[i]; + for(size_t k = 0; k < n; k++ ) + CV_Assert( ranges[i][k] < ranges[i][k+1] ); } } } - - -////////////////////////////////// C A L C U L A T E H I S T O G R A M //////////////////////////////////// - + + +////////////////////////////////// C A L C U L A T E H I S T O G R A M //////////////////////////////////// + template static void calcHist_( vector& _ptrs, const vector& _deltas, Size imsize, Mat& hist, int dims, const float** _ranges, @@ -221,23 +221,23 @@ calcHist_( vector& _ptrs, const vector& _deltas, int mstep = _deltas[dims*2 + 1]; int size[CV_MAX_DIM]; size_t hstep[CV_MAX_DIM]; - + for( i = 0; i < dims; i++ ) { size[i] = hist.size[i]; hstep[i] = hist.step[i]; } - + if( uniform ) { const double* uniranges = &_uniranges[0]; - + if( dims == 1 ) { double a = uniranges[0], b = uniranges[1]; int sz = size[0], d0 = deltas[0], step0 = deltas[1]; const T* p0 = (const T*)ptrs[0]; - + for( ; imsize.height--; p0 += step0, mask += mstep ) { if( !mask ) @@ -266,7 +266,7 @@ calcHist_( vector& _ptrs, const vector& _deltas, size_t hstep0 = hstep[0]; const T* p0 = (const T*)ptrs[0]; const T* p1 = (const T*)ptrs[1]; - + for( ; imsize.height--; p0 += step0, p1 += step1, mask += mstep ) { if( !mask ) @@ -300,8 +300,8 @@ calcHist_( vector& _ptrs, const vector& _deltas, size_t hstep0 = hstep[0], hstep1 = hstep[1]; const T* p0 = (const T*)ptrs[0]; const T* p1 = (const T*)ptrs[1]; - const T* p2 = (const T*)ptrs[2]; - + const T* p2 = (const T*)ptrs[2]; + for( ; imsize.height--; p0 += step0, p1 += step1, p2 += step2, mask += mstep ) { if( !mask ) @@ -345,7 +345,7 @@ calcHist_( vector& _ptrs, const vector& _deltas, ptrs[i] += deltas[i*2]; Hptr += idx*hstep[i]; } - + if( i == dims ) ++*((int*)Hptr); else @@ -366,7 +366,7 @@ calcHist_( vector& _ptrs, const vector& _deltas, ptrs[i] += deltas[i*2]; Hptr += idx*hstep[i]; } - + if( i == dims ) ++*((int*)Hptr); else @@ -384,45 +384,45 @@ calcHist_( vector& _ptrs, const vector& _deltas, const float* ranges[CV_MAX_DIM]; for( i = 0; i < dims; i++ ) ranges[i] = &_ranges[i][0]; - + for( ; imsize.height--; mask += mstep ) { for( x = 0; x < imsize.width; x++ ) { uchar* Hptr = H; i = 0; - + if( !mask || mask[x] ) for( ; i < dims; i++ ) { float v = (float)*ptrs[i]; const float* R = ranges[i]; int idx = -1, sz = size[i]; - + while( v >= R[idx+1] && ++idx < sz ) ; // nop - + if( (unsigned)idx >= (unsigned)sz ) break; ptrs[i] += deltas[i*2]; Hptr += idx*hstep[i]; } - + if( i == dims ) ++*((int*)Hptr); else for( ; i < dims; i++ ) ptrs[i] += deltas[i*2]; } - + for( i = 0; i < dims; i++ ) ptrs[i] += deltas[i*2 + 1]; } - } + } } - - + + static void calcHist_8u( vector& _ptrs, const vector& _deltas, Size imsize, Mat& hist, int dims, const float** _ranges, @@ -431,20 +431,20 @@ calcHist_8u( vector& _ptrs, const vector& _deltas, uchar** ptrs = &_ptrs[0]; const int* deltas = &_deltas[0]; uchar* H = hist.data; - int i, x; + int x; const uchar* mask = _ptrs[dims]; int mstep = _deltas[dims*2 + 1]; vector _tab; - + calcHistLookupTables_8u( hist, SparseMat(), dims, _ranges, _uniranges, uniform, false, _tab ); const size_t* tab = &_tab[0]; - + if( dims == 1 ) { int d0 = deltas[0], step0 = deltas[1]; int matH[256] = {0}; const uchar* p0 = (const uchar*)ptrs[0]; - + for( ; imsize.height--; p0 += step0, mask += mstep ) { if( !mask ) @@ -460,7 +460,7 @@ calcHist_8u( vector& _ptrs, const vector& _deltas, } p0 += x; } - else + else for( x = 0; x <= imsize.width - 4; x += 4 ) { int t0 = p0[0], t1 = p0[d0]; @@ -470,7 +470,7 @@ calcHist_8u( vector& _ptrs, const vector& _deltas, matH[t0]++; matH[t1]++; p0 += d0*2; } - + for( ; x < imsize.width; x++, p0 += d0 ) matH[*p0]++; } @@ -479,8 +479,8 @@ calcHist_8u( vector& _ptrs, const vector& _deltas, if( mask[x] ) matH[*p0]++; } - - for( i = 0; i < 256; i++ ) + + for(int i = 0; i < 256; i++ ) { size_t hidx = tab[i]; if( hidx < OUT_OF_RANGE ) @@ -493,7 +493,7 @@ calcHist_8u( vector& _ptrs, const vector& _deltas, d1 = deltas[2], step1 = deltas[3]; const uchar* p0 = (const uchar*)ptrs[0]; const uchar* p1 = (const uchar*)ptrs[1]; - + for( ; imsize.height--; p0 += step0, p1 += step1, mask += mstep ) { if( !mask ) @@ -517,11 +517,11 @@ calcHist_8u( vector& _ptrs, const vector& _deltas, int d0 = deltas[0], step0 = deltas[1], d1 = deltas[2], step1 = deltas[3], d2 = deltas[4], step2 = deltas[5]; - + const uchar* p0 = (const uchar*)ptrs[0]; const uchar* p1 = (const uchar*)ptrs[1]; const uchar* p2 = (const uchar*)ptrs[2]; - + for( ; imsize.height--; p0 += step0, p1 += step1, p2 += step2, mask += mstep ) { if( !mask ) @@ -548,7 +548,8 @@ calcHist_8u( vector& _ptrs, const vector& _deltas, for( x = 0; x < imsize.width; x++ ) { uchar* Hptr = H; - for( i = 0; i < dims; i++ ) + int i = 0; + for( ; i < dims; i++ ) { size_t idx = tab[*ptrs[i] + i*256]; if( idx >= OUT_OF_RANGE ) @@ -556,7 +557,7 @@ calcHist_8u( vector& _ptrs, const vector& _deltas, Hptr += idx; ptrs[i] += deltas[i*2]; } - + if( i == dims ) ++*((int*)Hptr); else @@ -577,14 +578,14 @@ calcHist_8u( vector& _ptrs, const vector& _deltas, Hptr += idx; ptrs[i] += deltas[i*2]; } - + if( i == dims ) ++*((int*)Hptr); else for( ; i < dims; i++ ) ptrs[i] += deltas[i*2]; } - for( i = 0; i < dims; i++ ) + for(int i = 0; i < dims; i++ ) ptrs[i] += deltas[i*2 + 1]; } } @@ -597,31 +598,31 @@ void cv::calcHist( const Mat* images, int nimages, const int* channels, const float** ranges, bool uniform, bool accumulate ) { Mat mask = _mask.getMat(); - + CV_Assert(dims > 0 && histSize); - + uchar* histdata = _hist.getMat().data; _hist.create(dims, histSize, CV_32F); Mat hist = _hist.getMat(), ihist = hist; ihist.flags = (ihist.flags & ~CV_MAT_TYPE_MASK)|CV_32S; - + if( !accumulate || histdata != hist.data ) hist = Scalar(0.); else hist.convertTo(ihist, CV_32S); - + vector ptrs; vector deltas; vector uniranges; Size imsize; - + CV_Assert( !mask.data || mask.type() == CV_8UC1 ); histPrepareImages( images, nimages, channels, mask, dims, hist.size, ranges, uniform, ptrs, deltas, imsize, uniranges ); const double* _uniranges = uniform ? &uniranges[0] : 0; - + int depth = images[0].depth(); - + if( depth == CV_8U ) calcHist_8u(ptrs, deltas, imsize, ihist, dims, ranges, _uniranges, uniform ); else if( depth == CV_16U ) @@ -630,7 +631,7 @@ void cv::calcHist( const Mat* images, int nimages, const int* channels, calcHist_(ptrs, deltas, imsize, ihist, dims, ranges, _uniranges, uniform ); else CV_Error(CV_StsUnsupportedFormat, ""); - + ihist.convertTo(hist, CV_32F); } @@ -650,11 +651,11 @@ calcSparseHist_( vector& _ptrs, const vector& _deltas, int mstep = _deltas[dims*2 + 1]; const int* size = hist.hdr->size; int idx[CV_MAX_DIM]; - + if( uniform ) { const double* uniranges = &_uniranges[0]; - + for( ; imsize.height--; mask += mstep ) { for( x = 0; x < imsize.width; x++ ) @@ -668,7 +669,7 @@ calcSparseHist_( vector& _ptrs, const vector& _deltas, break; ptrs[i] += deltas[i*2]; } - + if( i == dims ) ++*(int*)hist.ptr(idx, true); else @@ -685,43 +686,43 @@ calcSparseHist_( vector& _ptrs, const vector& _deltas, const float* ranges[CV_MAX_DIM]; for( i = 0; i < dims; i++ ) ranges[i] = &_ranges[i][0]; - + for( ; imsize.height--; mask += mstep ) { for( x = 0; x < imsize.width; x++ ) { i = 0; - + if( !mask || mask[x] ) for( ; i < dims; i++ ) { float v = (float)*ptrs[i]; const float* R = ranges[i]; int j = -1, sz = size[i]; - + while( v >= R[j+1] && ++j < sz ) ; // nop - + if( (unsigned)j >= (unsigned)sz ) break; - ptrs[i] += deltas[i*2]; + ptrs[i] += deltas[i*2]; idx[i] = j; } - + if( i == dims ) ++*(int*)hist.ptr(idx, true); else for( ; i < dims; i++ ) ptrs[i] += deltas[i*2]; } - + for( i = 0; i < dims; i++ ) ptrs[i] += deltas[i*2 + 1]; } - } -} + } +} + - static void calcSparseHist_8u( vector& _ptrs, const vector& _deltas, Size imsize, SparseMat& hist, int dims, const float** _ranges, @@ -729,15 +730,15 @@ calcSparseHist_8u( vector& _ptrs, const vector& _deltas, { uchar** ptrs = (uchar**)&_ptrs[0]; const int* deltas = &_deltas[0]; - int i, x; + int x; const uchar* mask = _ptrs[dims]; int mstep = _deltas[dims*2 + 1]; int idx[CV_MAX_DIM]; vector _tab; - + calcHistLookupTables_8u( Mat(), hist, dims, _ranges, _uniranges, uniform, true, _tab ); const size_t* tab = &_tab[0]; - + for( ; imsize.height--; mask += mstep ) { for( x = 0; x < imsize.width; x++ ) @@ -752,25 +753,25 @@ calcSparseHist_8u( vector& _ptrs, const vector& _deltas, ptrs[i] += deltas[i*2]; idx[i] = (int)hidx; } - + if( i == dims ) ++*(int*)hist.ptr(idx,true); else for( ; i < dims; i++ ) ptrs[i] += deltas[i*2]; } - for( i = 0; i < dims; i++ ) + for(int i = 0; i < dims; i++ ) ptrs[i] += deltas[i*2 + 1]; } -} - +} + static void calcHist( const Mat* images, int nimages, const int* channels, const Mat& mask, SparseMat& hist, int dims, const int* histSize, const float** ranges, bool uniform, bool accumulate, bool keepInt ) { size_t i, N; - + if( !accumulate ) hist.create(dims, histSize, CV_32F); else @@ -782,17 +783,17 @@ static void calcHist( const Mat* images, int nimages, const int* channels, val->i = cvRound(val->f); } } - + vector ptrs; vector deltas; vector uniranges; Size imsize; - + CV_Assert( !mask.data || mask.type() == CV_8UC1 ); histPrepareImages( images, nimages, channels, mask, dims, hist.hdr->size, ranges, uniform, ptrs, deltas, imsize, uniranges ); const double* _uniranges = uniform ? &uniranges[0] : 0; - + int depth = images[0].depth(); if( depth == CV_8U ) calcSparseHist_8u(ptrs, deltas, imsize, hist, dims, ranges, _uniranges, uniform ); @@ -802,7 +803,7 @@ static void calcHist( const Mat* images, int nimages, const int* channels, calcSparseHist_(ptrs, deltas, imsize, hist, dims, ranges, _uniranges, uniform ); else CV_Error(CV_StsUnsupportedFormat, ""); - + if( !keepInt ) { SparseMatIterator it = hist.begin(); @@ -813,9 +814,9 @@ static void calcHist( const Mat* images, int nimages, const int* channels, } } } - + } - + void cv::calcHist( const Mat* images, int nimages, const int* channels, InputArray _mask, SparseMat& hist, int dims, const int* histSize, const float** ranges, bool uniform, bool accumulate ) @@ -834,7 +835,7 @@ void cv::calcHist( InputArrayOfArrays images, const vector& channels, { int i, dims = (int)histSize.size(), rsz = (int)ranges.size(), csz = (int)channels.size(); int nimages = (int)images.total(); - + CV_Assert(nimages > 0 && dims > 0); CV_Assert(rsz == dims*2 || (rsz == 0 && images.depth(0) == CV_8U)); CV_Assert(csz == 0 || csz == dims); @@ -844,19 +845,19 @@ void cv::calcHist( InputArrayOfArrays images, const vector& channels, for( i = 0; i < rsz/2; i++ ) _ranges[i] = (float*)&ranges[i*2]; } - + AutoBuffer buf(nimages); for( i = 0; i < nimages; i++ ) buf[i] = images.getMat(i); - + calcHist(&buf[0], nimages, csz ? &channels[0] : 0, mask, hist, dims, &histSize[0], rsz ? (const float**)_ranges : 0, true, accumulate); } -/////////////////////////////////////// B A C K P R O J E C T //////////////////////////////////// - +/////////////////////////////////////// B A C K P R O J E C T //////////////////////////////////// + namespace cv { @@ -873,23 +874,23 @@ calcBackProj_( vector& _ptrs, const vector& _deltas, int bpstep = _deltas[dims*2 + 1]; int size[CV_MAX_DIM]; size_t hstep[CV_MAX_DIM]; - + for( i = 0; i < dims; i++ ) { size[i] = hist.size[i]; hstep[i] = hist.step[i]; } - + if( uniform ) { const double* uniranges = &_uniranges[0]; - + if( dims == 1 ) { double a = uniranges[0], b = uniranges[1]; int sz = size[0], d0 = deltas[0], step0 = deltas[1]; const T* p0 = (const T*)ptrs[0]; - + for( ; imsize.height--; p0 += step0, bproj += bpstep ) { for( x = 0; x < imsize.width; x++, p0 += d0 ) @@ -909,7 +910,7 @@ calcBackProj_( vector& _ptrs, const vector& _deltas, size_t hstep0 = hstep[0]; const T* p0 = (const T*)ptrs[0]; const T* p1 = (const T*)ptrs[1]; - + for( ; imsize.height--; p0 += step0, p1 += step1, bproj += bpstep ) { for( x = 0; x < imsize.width; x++, p0 += d0, p1 += d1 ) @@ -934,8 +935,8 @@ calcBackProj_( vector& _ptrs, const vector& _deltas, size_t hstep0 = hstep[0], hstep1 = hstep[1]; const T* p0 = (const T*)ptrs[0]; const T* p1 = (const T*)ptrs[1]; - const T* p2 = (const T*)ptrs[2]; - + const T* p2 = (const T*)ptrs[2]; + for( ; imsize.height--; p0 += step0, p1 += step1, p2 += step2, bproj += bpstep ) { for( x = 0; x < imsize.width; x++, p0 += d0, p1 += d1, p2 += d2 ) @@ -965,7 +966,7 @@ calcBackProj_( vector& _ptrs, const vector& _deltas, ptrs[i] += deltas[i*2]; Hptr += idx*hstep[i]; } - + if( i == dims ) bproj[x] = saturate_cast(*(float*)Hptr*scale); else @@ -986,7 +987,7 @@ calcBackProj_( vector& _ptrs, const vector& _deltas, const float* ranges[CV_MAX_DIM]; for( i = 0; i < dims; i++ ) ranges[i] = &_ranges[i][0]; - + for( ; imsize.height--; bproj += bpstep ) { for( x = 0; x < imsize.width; x++ ) @@ -997,17 +998,17 @@ calcBackProj_( vector& _ptrs, const vector& _deltas, float v = (float)*ptrs[i]; const float* R = ranges[i]; int idx = -1, sz = size[i]; - + while( v >= R[idx+1] && ++idx < sz ) ; // nop - + if( (unsigned)idx >= (unsigned)sz ) break; ptrs[i] += deltas[i*2]; Hptr += idx*hstep[i]; } - + if( i == dims ) bproj[x] = saturate_cast(*(float*)Hptr*scale); else @@ -1017,11 +1018,11 @@ calcBackProj_( vector& _ptrs, const vector& _deltas, ptrs[i] += deltas[i*2]; } } - + for( i = 0; i < dims; i++ ) ptrs[i] += deltas[i*2 + 1]; } - } + } } @@ -1037,23 +1038,23 @@ calcBackProj_8u( vector& _ptrs, const vector& _deltas, uchar* bproj = _ptrs[dims]; int bpstep = _deltas[dims*2 + 1]; vector _tab; - + calcHistLookupTables_8u( hist, SparseMat(), dims, _ranges, _uniranges, uniform, false, _tab ); const size_t* tab = &_tab[0]; - + if( dims == 1 ) { int d0 = deltas[0], step0 = deltas[1]; uchar matH[256] = {0}; const uchar* p0 = (const uchar*)ptrs[0]; - + for( i = 0; i < 256; i++ ) { size_t hidx = tab[i]; if( hidx < OUT_OF_RANGE ) matH[i] = saturate_cast(*(float*)(H + hidx)*scale); } - + for( ; imsize.height--; p0 += step0, bproj += bpstep ) { if( d0 == 1 ) @@ -1067,7 +1068,7 @@ calcBackProj_8u( vector& _ptrs, const vector& _deltas, } p0 += x; } - else + else for( x = 0; x <= imsize.width - 4; x += 4 ) { uchar t0 = matH[p0[0]], t1 = matH[p0[d0]]; @@ -1077,7 +1078,7 @@ calcBackProj_8u( vector& _ptrs, const vector& _deltas, bproj[x+2] = t0; bproj[x+3] = t1; p0 += d0*2; } - + for( ; x < imsize.width; x++, p0 += d0 ) bproj[x] = matH[*p0]; } @@ -1088,7 +1089,7 @@ calcBackProj_8u( vector& _ptrs, const vector& _deltas, d1 = deltas[2], step1 = deltas[3]; const uchar* p0 = (const uchar*)ptrs[0]; const uchar* p1 = (const uchar*)ptrs[1]; - + for( ; imsize.height--; p0 += step0, p1 += step1, bproj += bpstep ) { for( x = 0; x < imsize.width; x++, p0 += d0, p1 += d1 ) @@ -1106,7 +1107,7 @@ calcBackProj_8u( vector& _ptrs, const vector& _deltas, const uchar* p0 = (const uchar*)ptrs[0]; const uchar* p1 = (const uchar*)ptrs[1]; const uchar* p2 = (const uchar*)ptrs[2]; - + for( ; imsize.height--; p0 += step0, p1 += step1, p2 += step2, bproj += bpstep ) { for( x = 0; x < imsize.width; x++, p0 += d0, p1 += d1, p2 += d2 ) @@ -1131,7 +1132,7 @@ calcBackProj_8u( vector& _ptrs, const vector& _deltas, ptrs[i] += deltas[i*2]; Hptr += idx; } - + if( i == dims ) bproj[x] = saturate_cast(*(float*)Hptr*scale); else @@ -1145,10 +1146,10 @@ calcBackProj_8u( vector& _ptrs, const vector& _deltas, ptrs[i] += deltas[i*2 + 1]; } } -} +} } - + void cv::calcBackProject( const Mat* images, int nimages, const int* channels, InputArray _hist, OutputArray _backProject, const float** ranges, double scale, bool uniform ) @@ -1159,14 +1160,14 @@ void cv::calcBackProject( const Mat* images, int nimages, const int* channels, vector uniranges; Size imsize; int dims = hist.dims == 2 && hist.size[1] == 1 ? 1 : hist.dims; - + CV_Assert( dims > 0 && hist.data ); _backProject.create( images[0].size(), images[0].depth() ); Mat backProject = _backProject.getMat(); histPrepareImages( images, nimages, channels, backProject, dims, hist.size, ranges, uniform, ptrs, deltas, imsize, uniranges ); const double* _uniranges = uniform ? &uniranges[0] : 0; - + int depth = images[0].depth(); if( depth == CV_8U ) calcBackProj_8u(ptrs, deltas, imsize, hist, dims, ranges, _uniranges, (float)scale, uniform); @@ -1195,7 +1196,7 @@ calcSparseBackProj_( vector& _ptrs, const vector& _deltas, const int* size = hist.hdr->size; int idx[CV_MAX_DIM]; const SparseMat_& hist_ = (const SparseMat_&)hist; - + if( uniform ) { const double* uniranges = &_uniranges[0]; @@ -1210,7 +1211,7 @@ calcSparseBackProj_( vector& _ptrs, const vector& _deltas, break; ptrs[i] += deltas[i*2]; } - + if( i == dims ) bproj[x] = saturate_cast(hist_(idx)*scale); else @@ -1230,7 +1231,7 @@ calcSparseBackProj_( vector& _ptrs, const vector& _deltas, const float* ranges[CV_MAX_DIM]; for( i = 0; i < dims; i++ ) ranges[i] = &_ranges[i][0]; - + for( ; imsize.height--; bproj += bpstep ) { for( x = 0; x < imsize.width; x++ ) @@ -1240,16 +1241,16 @@ calcSparseBackProj_( vector& _ptrs, const vector& _deltas, float v = (float)*ptrs[i]; const float* R = ranges[i]; int j = -1, sz = size[i]; - + while( v >= R[j+1] && ++j < sz ) ; // nop - + if( (unsigned)j >= (unsigned)sz ) break; idx[i] = j; ptrs[i] += deltas[i*2]; } - + if( i == dims ) bproj[x] = saturate_cast(hist_(idx)*scale); else @@ -1259,11 +1260,11 @@ calcSparseBackProj_( vector& _ptrs, const vector& _deltas, ptrs[i] += deltas[i*2]; } } - + for( i = 0; i < dims; i++ ) ptrs[i] += deltas[i*2 + 1]; } - } + } } @@ -1279,10 +1280,10 @@ calcSparseBackProj_8u( vector& _ptrs, const vector& _deltas, int bpstep = _deltas[dims*2 + 1]; vector _tab; int idx[CV_MAX_DIM]; - + calcHistLookupTables_8u( Mat(), hist, dims, _ranges, _uniranges, uniform, true, _tab ); const size_t* tab = &_tab[0]; - + for( ; imsize.height--; bproj += bpstep ) { for( x = 0; x < imsize.width; x++ ) @@ -1295,7 +1296,7 @@ calcSparseBackProj_8u( vector& _ptrs, const vector& _deltas, idx[i] = (int)hidx; ptrs[i] += deltas[i*2]; } - + if( i == dims ) bproj[x] = saturate_cast(hist.value(idx)*scale); else @@ -1308,7 +1309,7 @@ calcSparseBackProj_8u( vector& _ptrs, const vector& _deltas, for( i = 0; i < dims; i++ ) ptrs[i] += deltas[i*2 + 1]; } -} +} } @@ -1321,7 +1322,7 @@ void cv::calcBackProject( const Mat* images, int nimages, const int* channels, vector uniranges; Size imsize; int dims = hist.dims(); - + CV_Assert( dims > 0 ); _backProject.create( images[0].size(), images[0].depth() ); Mat backProject = _backProject.getMat(); @@ -1329,7 +1330,7 @@ void cv::calcBackProject( const Mat* images, int nimages, const int* channels, dims, hist.hdr->size, ranges, uniform, ptrs, deltas, imsize, uniranges ); const double* _uniranges = uniform ? &uniranges[0] : 0; - + int depth = images[0].depth(); if( depth == CV_8U ) calcSparseBackProj_8u(ptrs, deltas, imsize, hist, dims, ranges, @@ -1344,7 +1345,7 @@ void cv::calcBackProject( const Mat* images, int nimages, const int* channels, CV_Error(CV_StsUnsupportedFormat, ""); } - + void cv::calcBackProject( InputArrayOfArrays images, const vector& channels, InputArray hist, OutputArray dst, const vector& ranges, @@ -1374,17 +1375,17 @@ void cv::calcBackProject( InputArrayOfArrays images, const vector& channels for( i = 0; i < rsz/2; i++ ) _ranges[i] = (float*)&ranges[i*2]; } - + AutoBuffer buf(nimages); for( i = 0; i < nimages; i++ ) buf[i] = images.getMat(i); - + calcBackProject(&buf[0], nimages, csz ? &channels[0] : 0, hist, dst, rsz ? (const float**)_ranges : 0, scale, true); } - -////////////////// C O M P A R E H I S T O G R A M S //////////////////////// + +////////////////// C O M P A R E H I S T O G R A M S //////////////////////// double cv::compareHist( InputArray _H1, InputArray _H2, int method ) { @@ -1394,19 +1395,19 @@ double cv::compareHist( InputArray _H1, InputArray _H2, int method ) NAryMatIterator it(arrays, planes); double result = 0; int j, len = (int)it.size; - + CV_Assert( H1.type() == H2.type() && H1.type() == CV_32F ); - + double s1 = 0, s2 = 0, s11 = 0, s12 = 0, s22 = 0; - + CV_Assert( it.planes[0].isContinuous() && it.planes[1].isContinuous() ); - + for( size_t i = 0; i < it.nplanes; i++, ++it ) { const float* h1 = (const float*)it.planes[0].data; const float* h2 = (const float*)it.planes[1].data; len = it.planes[0].rows*it.planes[0].cols; - + if( method == CV_COMP_CHISQR ) { for( j = 0; j < len; j++ ) @@ -1423,7 +1424,7 @@ double cv::compareHist( InputArray _H1, InputArray _H2, int method ) { double a = h1[j]; double b = h2[j]; - + s12 += a*b; s1 += a; s11 += a*a; @@ -1450,7 +1451,7 @@ double cv::compareHist( InputArray _H1, InputArray _H2, int method ) else CV_Error( CV_StsBadArg, "Unknown comparison method" ); } - + if( method == CV_COMP_CORREL ) { size_t total = H1.total(); @@ -1465,27 +1466,27 @@ double cv::compareHist( InputArray _H1, InputArray _H2, int method ) s1 = fabs(s1) > FLT_EPSILON ? 1./std::sqrt(s1) : 1.; result = std::sqrt(std::max(1. - result*s1, 0.)); } - + return result; } - + double cv::compareHist( const SparseMat& H1, const SparseMat& H2, int method ) { double result = 0; int i, dims = H1.dims(); - + CV_Assert( dims > 0 && dims == H2.dims() && H1.type() == H2.type() && H1.type() == CV_32F ); for( i = 0; i < dims; i++ ) CV_Assert( H1.size(i) == H2.size(i) ); - + const SparseMat *PH1 = &H1, *PH2 = &H2; if( PH1->nzcount() > PH2->nzcount() && method != CV_COMP_CHISQR ) std::swap(PH1, PH2); - + SparseMatConstIterator it = PH1->begin(); int N1 = (int)PH1->nzcount(), N2 = (int)PH2->nzcount(); - + if( method == CV_COMP_CHISQR ) { for( i = 0; i < N1; i++, ++it ) @@ -1502,7 +1503,7 @@ double cv::compareHist( const SparseMat& H1, const SparseMat& H2, int method ) else if( method == CV_COMP_CORREL ) { double s1 = 0, s2 = 0, s11 = 0, s12 = 0, s22 = 0; - + for( i = 0; i < N1; i++, ++it ) { double v1 = it.value(); @@ -1511,7 +1512,7 @@ double cv::compareHist( const SparseMat& H1, const SparseMat& H2, int method ) s1 += v1; s11 += v1*v1; } - + it = PH2->begin(); for( i = 0; i < N2; i++, ++it ) { @@ -1519,7 +1520,7 @@ double cv::compareHist( const SparseMat& H1, const SparseMat& H2, int method ) s2 += v2; s22 += v2*v2; } - + size_t total = 1; for( i = 0; i < H1.dims(); i++ ) total *= H1.size(i); @@ -1542,7 +1543,7 @@ double cv::compareHist( const SparseMat& H1, const SparseMat& H2, int method ) else if( method == CV_COMP_BHATTACHARYYA ) { double s1 = 0, s2 = 0; - + for( i = 0; i < N1; i++, ++it ) { double v1 = it.value(); @@ -1551,22 +1552,22 @@ double cv::compareHist( const SparseMat& H1, const SparseMat& H2, int method ) result += std::sqrt(v1*v2); s1 += v1; } - + it = PH2->begin(); for( i = 0; i < N2; i++, ++it ) s2 += it.value(); - + s1 *= s2; s1 = fabs(s1) > FLT_EPSILON ? 1./std::sqrt(s1) : 1.; result = std::sqrt(std::max(1. - result*s1, 0.)); } else CV_Error( CV_StsBadArg, "Unknown comparison method" ); - + return result; } - + const int CV_HIST_DEFAULT_TYPE = CV_32F; /* Creates new histogram */ @@ -1583,7 +1584,7 @@ cvCreateHist( int dims, int *sizes, CvHistType type, float** ranges, int uniform hist = (CvHistogram *)cvAlloc( sizeof( CvHistogram )); hist->type = CV_HIST_MAGIC_VAL + ((int)type & 1); - if (uniform) hist->type|= CV_HIST_UNIFORM_FLAG; + if (uniform) hist->type|= CV_HIST_UNIFORM_FLAG; hist->thresh2 = 0; hist->bins = 0; if( type == CV_HIST_ARRAY ) @@ -1652,7 +1653,7 @@ cvReleaseHist( CvHistogram **hist ) cvReleaseData( temp->bins ); temp->bins = 0; } - + if( temp->thresh2 ) cvFree( &temp->thresh2 ); cvFree( &temp ); @@ -1686,7 +1687,7 @@ cvThreshHist( CvHistogram* hist, double thresh ) CvSparseMat* mat = (CvSparseMat*)hist->bins; CvSparseMatIterator iterator; CvSparseNode *node; - + for( node = cvInitSparseMatIterator( mat, &iterator ); node != 0; node = cvGetNextSparseNode( &iterator )) { @@ -1722,7 +1723,7 @@ cvNormalizeHist( CvHistogram* hist, double factor ) CvSparseMatIterator iterator; CvSparseNode *node; float scale; - + for( node = cvInitSparseMatIterator( mat, &iterator ); node != 0; node = cvGetNextSparseNode( &iterator )) { @@ -1749,7 +1750,7 @@ cvGetMinMaxHistValue( const CvHistogram* hist, int* idx_min, int* idx_max ) { double minVal, maxVal; - int i, dims, size[CV_MAX_DIM]; + int dims, size[CV_MAX_DIM]; if( !CV_IS_HIST(hist) ) CV_Error( CV_StsBadArg, "Invalid histogram header" ); @@ -1782,9 +1783,8 @@ cvGetMinMaxHistValue( const CvHistogram* hist, { int imin = minPt.y*mat.cols + minPt.x; int imax = maxPt.y*mat.cols + maxPt.x; - int i; - - for( i = dims - 1; i >= 0; i-- ) + + for(int i = dims - 1; i >= 0; i-- ) { if( idx_min ) { @@ -1844,7 +1844,7 @@ cvGetMinMaxHistValue( const CvHistogram* hist, minVal = maxVal = 0; } - for( i = 0; i < dims; i++ ) + for(int i = 0; i < dims; i++ ) { if( idx_min ) idx_min[i] = _idx_min ? _idx_min[i] : -1; @@ -1869,7 +1869,7 @@ cvCompareHist( const CvHistogram* hist1, { int i; int size1[CV_MAX_DIM], size2[CV_MAX_DIM], total = 1; - + if( !CV_IS_HIST(hist1) || !CV_IS_HIST(hist2) ) CV_Error( CV_StsBadArg, "Invalid histogram header[s]" ); @@ -1881,14 +1881,14 @@ cvCompareHist( const CvHistogram* hist1, cv::Mat H1((const CvMatND*)hist1->bins), H2((const CvMatND*)hist2->bins); return cv::compareHist(H1, H2, method); } - + int dims1 = cvGetDims( hist1->bins, size1 ); int dims2 = cvGetDims( hist2->bins, size2 ); - + if( dims1 != dims2 ) CV_Error( CV_StsUnmatchedSizes, "The histograms have different numbers of dimensions" ); - + for( i = 0; i < dims1; i++ ) { if( size1[i] != size2[i] ) @@ -1928,7 +1928,7 @@ cvCompareHist( const CvHistogram* hist1, double s2 = 0, s22 = 0; double s12 = 0; double num, denom2, scale = 1./total; - + for( node1 = cvInitSparseMatIterator( mat1, &iterator ); node1 != 0; node1 = cvGetNextSparseNode( &iterator )) { @@ -1977,7 +1977,7 @@ cvCompareHist( const CvHistogram* hist1, else if( method == CV_COMP_BHATTACHARYYA ) { double s1 = 0, s2 = 0; - + for( node1 = cvInitSparseMatIterator( mat1, &iterator ); node1 != 0; node1 = cvGetNextSparseNode( &iterator )) { @@ -2006,7 +2006,7 @@ cvCompareHist( const CvHistogram* hist1, } else CV_Error( CV_StsBadArg, "Unknown comparison method" ); - + return result; } @@ -2021,7 +2021,7 @@ cvCopyHist( const CvHistogram* src, CvHistogram** _dst ) float* ranges[CV_MAX_DIM]; float** thresh = 0; CvHistogram* dst; - + if( !_dst ) CV_Error( CV_StsNullPtr, "Destination double pointer is NULL" ); @@ -2038,7 +2038,7 @@ cvCopyHist( const CvHistogram* src, CvHistogram** _dst ) if( dst && is_sparse == CV_IS_SPARSE_MAT(dst->bins)) { dims2 = cvGetDims( dst->bins, size2 ); - + if( dims1 == dims2 ) { for( i = 0; i < dims1; i++ ) @@ -2089,7 +2089,7 @@ cvSetHistBinRanges( CvHistogram* hist, float** ranges, int uniform ) dims = cvGetDims( hist->bins, size ); for( i = 0; i < dims; i++ ) total += size[i]+1; - + if( uniform ) { for( i = 0; i < dims; i++ ) @@ -2120,7 +2120,7 @@ cvSetHistBinRanges( CvHistogram* hist, float** ranges, int uniform ) if( !ranges[i] ) CV_Error( CV_StsNullPtr, "One of elements is NULL" ); - + for( j = 0; j <= size[i]; j++ ) { float val = ranges[i][j]; @@ -2151,18 +2151,18 @@ cvCalcArrHist( CvArr** img, CvHistogram* hist, int accumulate, const CvArr* mask int size[CV_MAX_DIM]; int i, dims = cvGetDims( hist->bins, size); bool uniform = CV_IS_UNIFORM_HIST(hist); - + cv::vector images(dims); for( i = 0; i < dims; i++ ) images[i] = cv::cvarrToMat(img[i]); - + cv::Mat _mask; if( mask ) _mask = cv::cvarrToMat(mask); - + const float* uranges[CV_MAX_DIM] = {0}; const float** ranges = 0; - + if( hist->type & CV_HIST_RANGES_FLAG ) { ranges = (const float**)hist->thresh2; @@ -2173,7 +2173,7 @@ cvCalcArrHist( CvArr** img, CvHistogram* hist, int accumulate, const CvArr* mask ranges = uranges; } } - + if( !CV_IS_SPARSE_HIST(hist) ) { cv::Mat H((const CvMatND*)hist->bins); @@ -2183,16 +2183,16 @@ cvCalcArrHist( CvArr** img, CvHistogram* hist, int accumulate, const CvArr* mask else { CvSparseMat* sparsemat = (CvSparseMat*)hist->bins; - + if( !accumulate ) cvZero( hist->bins ); cv::SparseMat sH(sparsemat); cv::calcHist( &images[0], (int)images.size(), 0, _mask, sH, sH.dims(), sH.dims() > 0 ? sH.hdr->size : 0, ranges, uniform, accumulate != 0, true ); - + if( accumulate ) cvZero( sparsemat ); - + cv::SparseMatConstIterator it = sH.begin(); int nz = (int)sH.nzcount(); for( i = 0; i < nz; i++, ++it ) @@ -2212,11 +2212,11 @@ cvCalcArrBackProject( CvArr** img, CvArr* dst, const CvHistogram* hist ) int size[CV_MAX_DIM]; int i, dims = cvGetDims( hist->bins, size ); - + bool uniform = CV_IS_UNIFORM_HIST(hist); const float* uranges[CV_MAX_DIM] = {0}; const float** ranges = 0; - + if( hist->type & CV_HIST_RANGES_FLAG ) { ranges = (const float**)hist->thresh2; @@ -2227,15 +2227,15 @@ cvCalcArrBackProject( CvArr** img, CvArr* dst, const CvHistogram* hist ) ranges = uranges; } } - + cv::vector images(dims); for( i = 0; i < dims; i++ ) images[i] = cv::cvarrToMat(img[i]); - + cv::Mat _dst = cv::cvarrToMat(dst); - + CV_Assert( _dst.size() == images[0].size() && _dst.depth() == images[0].depth() ); - + if( !CV_IS_SPARSE_HIST(hist) ) { cv::Mat H((const CvMatND*)hist->bins); @@ -2331,13 +2331,13 @@ CV_IMPL void cvCalcBayesianProb( CvHistogram** src, int count, CvHistogram** dst ) { int i; - + if( !src || !dst ) CV_Error( CV_StsNullPtr, "NULL histogram array pointer" ); if( count < 2 ) CV_Error( CV_StsOutOfRange, "Too small number of histograms" ); - + for( i = 0; i < count; i++ ) { if( !CV_IS_HIST(src[i]) || !CV_IS_HIST(dst[i]) ) @@ -2346,7 +2346,7 @@ cvCalcBayesianProb( CvHistogram** src, int count, CvHistogram** dst ) if( !CV_IS_MATND(src[i]->bins) || !CV_IS_MATND(dst[i]->bins) ) CV_Error( CV_StsBadArg, "The function supports dense histograms only" ); } - + cvZero( dst[0]->bins ); // dst[0] = src[0] + ... + src[count-1] for( i = 0; i < count; i++ ) @@ -2409,7 +2409,7 @@ CV_IMPL void cvEqualizeHist( const CvArr* srcarr, CvArr* dstarr ) { CvMat sstub, *src = cvGetMat(srcarr, &sstub); CvMat dstub, *dst = cvGetMat(dstarr, &dstub); - + CV_Assert( CV_ARE_SIZES_EQ(src, dst) && CV_ARE_TYPES_EQ(src, dst) && CV_MAT_TYPE(src->type) == CV_8UC1 ); CvSize size = cvGetMatSize(src); @@ -2422,14 +2422,14 @@ CV_IMPL void cvEqualizeHist( const CvArr* srcarr, CvArr* dstarr ) const int hist_sz = 256; int hist[hist_sz]; memset(hist, 0, sizeof(hist)); - + for( y = 0; y < size.height; y++ ) { const uchar* sptr = src->data.ptr + src->step*y; for( x = 0; x < size.width; x++ ) hist[sptr[x]]++; } - + float scale = 255.f/(size.width*size.height); int sum = 0; uchar lut[hist_sz+1]; @@ -2511,7 +2511,7 @@ static void *icvReadHist( CvFileStorage * fs, CvFileNode * node ) h->mat.refcount = mat->refcount; // increase refcount so freeing temp header doesn't free data - cvIncRefData( mat ); + cvIncRefData( mat ); // free temporary header cvReleaseMatND( &mat ); diff --git a/modules/imgproc/src/hough.cpp b/modules/imgproc/src/hough.cpp index a2ada39..6b5c2e4 100644 --- a/modules/imgproc/src/hough.cpp +++ b/modules/imgproc/src/hough.cpp @@ -92,8 +92,6 @@ icvHoughLinesStandard( const CvMat* img, float rho, float theta, int step, width, height; int numangle, numrho; int total = 0; - float ang; - int r, n; int i, j; float irho = 1 / rho; double scale; @@ -114,10 +112,11 @@ icvHoughLinesStandard( const CvMat* img, float rho, float theta, _tabCos.allocate(numangle); int *accum = _accum, *sort_buf = _sort_buf; float *tabSin = _tabSin, *tabCos = _tabCos; - + memset( accum, 0, sizeof(accum[0]) * (numangle+2) * (numrho+2) ); - for( ang = 0, n = 0; n < numangle; ang += theta, n++ ) + float ang = 0; + for(int n = 0; n < numangle; ang += theta, n++ ) { tabSin[n] = (float)(sin(ang) * irho); tabCos[n] = (float)(cos(ang) * irho); @@ -128,17 +127,17 @@ icvHoughLinesStandard( const CvMat* img, float rho, float theta, for( j = 0; j < width; j++ ) { if( image[i * step + j] != 0 ) - for( n = 0; n < numangle; n++ ) + for(int n = 0; n < numangle; n++ ) { - r = cvRound( j * tabCos[n] + i * tabSin[n] ); + int r = cvRound( j * tabCos[n] + i * tabSin[n] ); r += (numrho - 1) / 2; accum[(n+1) * (numrho+2) + r+1]++; } } // stage 2. find local maximums - for( r = 0; r < numrho; r++ ) - for( n = 0; n < numangle; n++ ) + for(int r = 0; r < numrho; r++ ) + for(int n = 0; n < numangle; n++ ) { int base = (n+1) * (numrho+2) + r+1; if( accum[base] > threshold && @@ -170,10 +169,6 @@ icvHoughLinesStandard( const CvMat* img, float rho, float theta, * Multi-Scale variant of Classical Hough Transform * \****************************************************************************************/ -#if defined _MSC_VER && _MSC_VER >= 1200 -#pragma warning( disable: 4714 ) -#endif - //DECLARE_AND_IMPLEMENT_LIST( _index, h_ ); IMPLEMENT_LIST( _index, h_ ) @@ -249,7 +244,7 @@ icvHoughLinesSDiv( const CvMat* img, /* Precalculating sin */ _sinTable.resize( 5 * tn * stn ); sinTable = &_sinTable[0]; - + for( index = 0; index < 5 * tn * stn; index++ ) sinTable[index] = (float)cos( stheta * index * 0.2f ); @@ -449,7 +444,7 @@ icvHoughLinesSDiv( const CvMat* img, h_get_next__index( &pos ); } } - + h_destroy_list__index(list); } @@ -529,7 +524,7 @@ icvHoughLinesProbabilistic( CvMat* image, // choose random point out of the remaining ones int idx = cvRandInt(&rng) % count; int max_val = threshold-1, max_n = 0; - CvPoint* pt = (CvPoint*)cvGetSeqElem( seq, idx ); + CvPoint* point = (CvPoint*)cvGetSeqElem( seq, idx ); CvPoint line_end[2] = {{0,0}, {0,0}}; float a, b; int* adata = (int*)accum.data; @@ -537,11 +532,11 @@ icvHoughLinesProbabilistic( CvMat* image, int good_line; const int shift = 16; - i = pt->y; - j = pt->x; + i = point->y; + j = point->x; // "remove" it by overriding it with the last element - *pt = *(CvPoint*)cvGetSeqElem( seq, count-1 ); + *point = *(CvPoint*)cvGetSeqElem( seq, count-1 ); // check if it has been excluded already (i.e. belongs to some other line) if( !mdata0[i*width + j] ) @@ -756,7 +751,7 @@ cvHoughLines2( CvArr* src_image, void* lineStorage, int method, } else CV_Error( CV_StsBadArg, "Destination is not CvMemStorage* nor CvMat*" ); - + iparam1 = cvRound(param1); iparam2 = cvRound(param2); @@ -842,7 +837,7 @@ icvHoughCirclesGradient( CvMat* img, float dp, float min_dist, acols = accum->cols - 2; adata = accum->data.i; astep = accum->step/sizeof(adata[0]); - // Accumulate circle evidence for each edge pixel + // Accumulate circle evidence for each edge pixel for( y = 0; y < rows; y++ ) { const uchar* edges_row = edges->data.ptr + y*edges->step; @@ -852,7 +847,7 @@ icvHoughCirclesGradient( CvMat* img, float dp, float min_dist, for( x = 0; x < cols; x++ ) { float vx, vy; - int sx, sy, x0, y0, x1, y1, r, k; + int sx, sy, x0, y0, x1, y1, r; CvPoint pt; vx = dx_row[x]; @@ -868,8 +863,8 @@ icvHoughCirclesGradient( CvMat* img, float dp, float min_dist, x0 = cvRound((x*idp)*ONE); y0 = cvRound((y*idp)*ONE); - // Step from min_radius to max_radius in both directions of the gradient - for( k = 0; k < 2; k++ ) + // Step from min_radius to max_radius in both directions of the gradient + for(int k1 = 0; k1 < 2; k1++ ) { x1 = x0 + min_radius * sx; y1 = y0 + min_radius * sy; @@ -894,7 +889,7 @@ icvHoughCirclesGradient( CvMat* img, float dp, float min_dist, nz_count = nz->total; if( !nz_count ) return; - //Find possible circle centers + //Find possible circle centers for( y = 1; y < arows - 1; y++ ) { for( x = 1; x < acols - 1; x++ ) @@ -924,19 +919,19 @@ icvHoughCirclesGradient( CvMat* img, float dp, float min_dist, dr = dp; min_dist = MAX( min_dist, dp ); min_dist *= min_dist; - // For each found possible center - // Estimate radius and check support + // For each found possible center + // Estimate radius and check support for( i = 0; i < centers->total; i++ ) { int ofs = *(int*)cvGetSeqElem( centers, i ); y = ofs/(acols+2); x = ofs - (y)*(acols+2); - //Calculate circle's center in pixels + //Calculate circle's center in pixels float cx = (float)((x + 0.5f)*dp), cy = (float)(( y + 0.5f )*dp); float start_dist, dist_sum; - float r_best = 0, c[3]; + float r_best = 0; int max_count = 0; - // Check distance with previously detected circles + // Check distance with previously detected circles for( j = 0; j < circles->total; j++ ) { float* c = (float*)cvGetSeqElem( circles, j ); @@ -946,7 +941,7 @@ icvHoughCirclesGradient( CvMat* img, float dp, float min_dist, if( j < circles->total ) continue; - // Estimate best radius + // Estimate best radius cvStartReadSeq( nz, &reader ); for( j = k = 0; j < nz_count; j++ ) { @@ -982,7 +977,7 @@ icvHoughCirclesGradient( CvMat* img, float dp, float min_dist, { float r_cur = ddata[sort_buf[(j + start_idx)/2]]; if( (start_idx - j)*r_best >= max_count*r_cur || - (r_best < FLT_EPSILON && start_idx - j >= max_count) ) + (r_best < FLT_EPSILON && start_idx - j >= max_count) ) { r_best = r_cur; max_count = start_idx - j; @@ -993,9 +988,10 @@ icvHoughCirclesGradient( CvMat* img, float dp, float min_dist, } dist_sum += d; } - // Check if the circle has enough support + // Check if the circle has enough support if( max_count > acc_threshold ) { + float c[3]; c[0] = cx; c[1] = cy; c[2] = (float)r_best; @@ -1103,9 +1099,9 @@ static void seqToMat(const CvSeq* seq, OutputArray _arr) else _arr.release(); } - + } - + void cv::HoughLines( InputArray _image, OutputArray _lines, double rho, double theta, int threshold, double srn, double stn ) diff --git a/modules/imgproc/src/imgwarp.cpp b/modules/imgproc/src/imgwarp.cpp index be21ae9..24e97b8 100644 --- a/modules/imgproc/src/imgwarp.cpp +++ b/modules/imgproc/src/imgwarp.cpp @@ -97,7 +97,6 @@ static inline void interpolateLanczos4( float x, float* coeffs ) static const double cs[][2]= {{1, 0}, {-s45, -s45}, {0, 1}, {s45, -s45}, {-1, 0}, {s45, s45}, {0, -1}, {-s45, s45}}; - int i; if( x < FLT_EPSILON ) { for( int i = 0; i < 8; i++ ) @@ -108,7 +107,7 @@ static inline void interpolateLanczos4( float x, float* coeffs ) float sum = 0; double y0=-(x+3)*CV_PI*0.25, s0 = sin(y0), c0=cos(y0); - for( i = 0; i < 8; i++ ) + for(int i = 0; i < 8; i++ ) { double y = -(x+3-i)*CV_PI*0.25; coeffs[i] = (float)((cs[i][0]*s0 + cs[i][1]*c0)/(y*y)); @@ -116,7 +115,7 @@ static inline void interpolateLanczos4( float x, float* coeffs ) } sum = 1.f/sum; - for( i = 0; i < 8; i++ ) + for(int i = 0; i < 8; i++ ) coeffs[i] *= sum; } @@ -1091,14 +1090,14 @@ static void resizeGeneric_( const Mat& src, Mat& dst, const T* srows[MAX_ESIZE]={0}; WT* rows[MAX_ESIZE]={0}; int prev_sy[MAX_ESIZE]; - int k, dy; + int dy; xmin *= cn; xmax *= cn; HResize hresize; VResize vresize; - for( k = 0; k < ksize; k++ ) + for(int k = 0; k < ksize; k++ ) { prev_sy[k] = -1; rows[k] = (WT*)_buffer + bufstep*k; @@ -1107,9 +1106,9 @@ static void resizeGeneric_( const Mat& src, Mat& dst, // image resize is a separable operation. In case of not too strong for( dy = 0; dy < dsize.height; dy++, beta += ksize ) { - int sy0 = yofs[dy], k, k0=ksize, k1=0, ksize2 = ksize/2; + int sy0 = yofs[dy], k0=ksize, k1=0, ksize2 = ksize/2; - for( k = 0; k < ksize; k++ ) + for(int k = 0; k < ksize; k++ ) { int sy = clip(sy0 - ksize2 + 1 + k, 0, ssize.height); for( k1 = std::max(k1, k); k1 < ksize; k1++ ) @@ -1522,7 +1521,7 @@ void cv::resize( InputArray _src, OutputArray _dst, Size dsize, assert( k < ssize.width*2 ); xofs[k].di = dx*cn; xofs[k].si = sx*cn; - xofs[k++].alpha = 1.f / min(scale_x, src.cols - fsx1); + xofs[k++].alpha = float(1.0 / min(scale_x, src.cols - fsx1)); } if( fsx2 - sx2 > 1e-3 ) @@ -2374,25 +2373,25 @@ static void remapLanczos4( const Mat& _src, Mat& _dst, const Mat& _xy, for( i = 0; i < 8; i++, w += 8 ) { int yi = y[i]; - const T* S = S0 + yi*sstep; + const T* S1 = S0 + yi*sstep; if( yi < 0 ) continue; if( x[0] >= 0 ) - sum += (S[x[0]] - cv)*w[0]; + sum += (S1[x[0]] - cv)*w[0]; if( x[1] >= 0 ) - sum += (S[x[1]] - cv)*w[1]; + sum += (S1[x[1]] - cv)*w[1]; if( x[2] >= 0 ) - sum += (S[x[2]] - cv)*w[2]; + sum += (S1[x[2]] - cv)*w[2]; if( x[3] >= 0 ) - sum += (S[x[3]] - cv)*w[3]; + sum += (S1[x[3]] - cv)*w[3]; if( x[4] >= 0 ) - sum += (S[x[4]] - cv)*w[4]; + sum += (S1[x[4]] - cv)*w[4]; if( x[5] >= 0 ) - sum += (S[x[5]] - cv)*w[5]; + sum += (S1[x[5]] - cv)*w[5]; if( x[6] >= 0 ) - sum += (S[x[6]] - cv)*w[6]; + sum += (S1[x[6]] - cv)*w[6]; if( x[7] >= 0 ) - sum += (S[x[7]] - cv)*w[7]; + sum += (S1[x[7]] - cv)*w[7]; } D[k] = castOp(sum); } @@ -2966,8 +2965,8 @@ void cv::warpAffine( InputArray _src, OutputArray _dst, remap( src, dpart, _XY, Mat(), interpolation, borderType, borderValue ); else { - Mat matA(bh, bw, CV_16U, A); - remap( src, dpart, _XY, matA, interpolation, borderType, borderValue ); + Mat _matA(bh, bw, CV_16U, A); + remap( src, dpart, _XY, _matA, interpolation, borderType, borderValue ); } } } @@ -3064,8 +3063,8 @@ void cv::warpPerspective( InputArray _src, OutputArray _dst, InputArray _M0, remap( src, dpart, _XY, Mat(), interpolation, borderType, borderValue ); else { - Mat matA(bh, bw, CV_16U, A); - remap( src, dpart, _XY, matA, interpolation, borderType, borderValue ); + Mat _matA(bh, bw, CV_16U, A); + remap( src, dpart, _XY, _matA, interpolation, borderType, borderValue ); } } } diff --git a/modules/imgproc/src/moments.cpp b/modules/imgproc/src/moments.cpp index aa1a569..784a61b 100644 --- a/modules/imgproc/src/moments.cpp +++ b/modules/imgproc/src/moments.cpp @@ -106,7 +106,7 @@ static void icvContourMoments( CvSeq* contour, CvMoments* moments ) yi_1 = ((CvPoint2D32f*)(reader.ptr))->y; } CV_NEXT_SEQ_ELEM( contour->elem_size, reader ); - + xi_12 = xi_1 * xi_1; yi_12 = yi_1 * yi_1; @@ -208,7 +208,7 @@ static void momentsInTile( const cv::Mat& img, double* moments ) const T* ptr = (const T*)(img.data + y*img.step); WT x0 = 0, x1 = 0, x2 = 0; MT x3 = 0; - + for( x = 0; x < size.width; x++ ) { WT p = ptr[x]; @@ -248,21 +248,21 @@ template<> void momentsInTile( const cv::Mat& img, double* mome typedef int WT; typedef int MT; cv::Size size = img.size(); - int x, y; + int y; MT mom[10] = {0,0,0,0,0,0,0,0,0,0}; bool useSIMD = cv::checkHardwareSupport(CV_CPU_SSE2); - + for( y = 0; y < size.height; y++ ) { const T* ptr = img.ptr(y); int x0 = 0, x1 = 0, x2 = 0, x3 = 0, x = 0; - + if( useSIMD ) { __m128i qx_init = _mm_setr_epi16(0, 1, 2, 3, 4, 5, 6, 7); __m128i dx = _mm_set1_epi16(8); __m128i z = _mm_setzero_si128(), qx0 = z, qx1 = z, qx2 = z, qx3 = z, qx = qx_init; - + for( ; x <= size.width - 8; x += 8 ) { __m128i p = _mm_unpacklo_epi8(_mm_loadl_epi64((const __m128i*)(ptr + x)), z); @@ -272,34 +272,34 @@ template<> void momentsInTile( const cv::Mat& img, double* mome qx1 = _mm_add_epi32(qx1, _mm_madd_epi16(p, qx)); qx2 = _mm_add_epi32(qx2, _mm_madd_epi16(p, sx)); qx3 = _mm_add_epi32(qx3, _mm_madd_epi16(px, sx)); - + qx = _mm_add_epi16(qx, dx); } int CV_DECL_ALIGNED(16) buf[4]; _mm_store_si128((__m128i*)buf, qx0); x0 = buf[0] + buf[1] + buf[2] + buf[3]; _mm_store_si128((__m128i*)buf, qx1); - x1 = buf[0] + buf[1] + buf[2] + buf[3]; + x1 = buf[0] + buf[1] + buf[2] + buf[3]; _mm_store_si128((__m128i*)buf, qx2); x2 = buf[0] + buf[1] + buf[2] + buf[3]; _mm_store_si128((__m128i*)buf, qx3); x3 = buf[0] + buf[1] + buf[2] + buf[3]; } - + for( ; x < size.width; x++ ) { WT p = ptr[x]; WT xp = x * p, xxp; - + x0 += p; x1 += xp; xxp = xp * x; x2 += xxp; x3 += xxp * x; } - + WT py = y * x0, sy = y*y; - + mom[9] += ((MT)py) * sy; // m03 mom[8] += ((MT)x1) * sy; // m12 mom[7] += ((MT)x2) * y; // m21 @@ -311,8 +311,8 @@ template<> void momentsInTile( const cv::Mat& img, double* mome mom[1] += x1; // m10 mom[0] += x0; // m00 } - - for( x = 0; x < 10; x++ ) + + for(int x = 0; x < 10; x++ ) moments[x] = (double)mom[x]; } @@ -366,7 +366,7 @@ CV_IMPL void cvMoments( const void* array, CvMoments* moments, int binary ) type = CV_MAT_TYPE( mat->type ); depth = CV_MAT_DEPTH( type ); cn = CV_MAT_CN( type ); - + cv::Size size = cvGetMatSize( mat ); if( cn > 1 && coi == 0 ) @@ -387,14 +387,14 @@ CV_IMPL void cvMoments( const void* array, CvMoments* moments, int binary ) func = momentsInTile; else CV_Error( CV_StsUnsupportedFormat, "" ); - + cv::Mat src0(mat); for( int y = 0; y < size.height; y += TILE_SIZE ) { cv::Size tileSize; tileSize.height = std::min(TILE_SIZE, size.height - y); - + for( int x = 0; x < size.width; x += TILE_SIZE ) { tileSize.width = std::min(TILE_SIZE, size.width - x); @@ -413,20 +413,20 @@ CV_IMPL void cvMoments( const void* array, CvMoments* moments, int binary ) cv::compare( src, 0, tmp, CV_CMP_NE ); src = tmp; } - + double mom[10]; func( src, mom ); - + if(binary) { double s = 1./255; for( int k = 0; k < 10; k++ ) mom[k] *= s; } - + double xm = x * mom[0], ym = y * mom[0]; - // accumulate moments computed in each tile + // accumulate moments computed in each tile // + m00 ( = m00' ) moments->m00 += mom[0]; @@ -451,7 +451,7 @@ CV_IMPL void cvMoments( const void* array, CvMoments* moments, int binary ) // + m21 ( = m21' + x*(2*m11' + 2*y*m10' + x*m01' + x*y*m00') + y*m20') moments->m21 += mom[7] + x * (2 * (mom[4] + y * mom[1]) + x * (mom[2] + ym)) + y * mom[3]; - + // + m12 ( = m12' + y*(2*m11' + 2*x*m01' + y*m10' + x*y*m00') + x*m02') moments->m12 += mom[8] + y * (2 * (mom[4] + x * mom[2]) + y * (mom[1] + xm)) + x * mom[5]; @@ -601,9 +601,9 @@ Moments::operator CvMoments() const return m; } - + } - + cv::Moments cv::moments( InputArray _array, bool binaryImage ) { CvMoments om; diff --git a/modules/imgproc/src/phasecorr.cpp b/modules/imgproc/src/phasecorr.cpp index f78af82..71582cb 100644 --- a/modules/imgproc/src/phasecorr.cpp +++ b/modules/imgproc/src/phasecorr.cpp @@ -406,42 +406,42 @@ static void fftShift(InputOutputArray _out) merge(planes, out); } -Point2d weightedCentroid(InputArray _src, cv::Point peakLocation, cv::Size weightBoxSize) +static Point2d weightedCentroid(InputArray _src, cv::Point peakLocation, cv::Size weightBoxSize) { Mat src = _src.getMat(); - + int type = src.type(); CV_Assert( type == CV_32FC1 || type == CV_64FC1 ); - + int minr = peakLocation.y - (weightBoxSize.height >> 1); int maxr = peakLocation.y + (weightBoxSize.height >> 1); int minc = peakLocation.x - (weightBoxSize.width >> 1); int maxc = peakLocation.x + (weightBoxSize.width >> 1); - + Point2d centroid; double sumIntensity = 0.0; - + // clamp the values to min and max if needed. if(minr < 0) { minr = 0; } - + if(minc < 0) { minc = 0; } - + if(maxr > src.rows - 1) { maxr = src.rows - 1; } - + if(maxc > src.cols - 1) { maxc = src.cols - 1; } - + if(type == CV_32FC1) { const float* dataIn = (const float*)src.data; @@ -454,7 +454,7 @@ Point2d weightedCentroid(InputArray _src, cv::Point peakLocation, cv::Size weigh centroid.y += (double)y*dataIn[x]; sumIntensity += (double)dataIn[x]; } - + dataIn += src.cols; } } @@ -470,19 +470,19 @@ Point2d weightedCentroid(InputArray _src, cv::Point peakLocation, cv::Size weigh centroid.y += (double)y*dataIn[x]; sumIntensity += dataIn[x]; } - + dataIn += src.cols; } } - + sumIntensity += DBL_EPSILON; // prevent div0 problems... - + centroid.x /= sumIntensity; centroid.y /= sumIntensity; - + return centroid; } - + } cv::Point2d cv::phaseCorrelate(InputArray _src1, InputArray _src2, InputArray _window) diff --git a/modules/imgproc/src/precomp.hpp b/modules/imgproc/src/precomp.hpp index 40ac883..fef5f75 100644 --- a/modules/imgproc/src/precomp.hpp +++ b/modules/imgproc/src/precomp.hpp @@ -43,11 +43,6 @@ #ifndef __OPENCV_PRECOMP_H__ #define __OPENCV_PRECOMP_H__ -#if defined _MSC_VER && _MSC_VER >= 1200 - // disable warnings related to inline functions - #pragma warning( disable: 4251 4711 4710 4514 ) -#endif - #ifdef HAVE_CVCONFIG_H #include "cvconfig.h" #endif diff --git a/modules/imgproc/src/rotcalipers.cpp b/modules/imgproc/src/rotcalipers.cpp index 4ea8f75..2171ec1 100644 --- a/modules/imgproc/src/rotcalipers.cpp +++ b/modules/imgproc/src/rotcalipers.cpp @@ -64,18 +64,18 @@ icvMinAreaState; // Parameters: // points - convex hull vertices ( any orientation ) // n - number of vertices -// mode - concrete application of algorithm -// can be CV_CALIPERS_MAXDIST or -// CV_CALIPERS_MINAREARECT +// mode - concrete application of algorithm +// can be CV_CALIPERS_MAXDIST or +// CV_CALIPERS_MINAREARECT // left, bottom, right, top - indexes of extremal points // out - output info. -// In case CV_CALIPERS_MAXDIST it points to float value - +// In case CV_CALIPERS_MAXDIST it points to float value - // maximal height of polygon. // In case CV_CALIPERS_MINAREARECT -// ((CvPoint2D32f*)out)[0] - corner +// ((CvPoint2D32f*)out)[0] - corner // ((CvPoint2D32f*)out)[1] - vector1 // ((CvPoint2D32f*)out)[0] - corner2 -// +// // ^ // | // vector2 | @@ -94,15 +94,15 @@ icvRotatingCalipers( CvPoint2D32f* points, int n, int mode, float* out ) { float minarea = FLT_MAX; float max_dist = 0; - char buffer[32]; + char buffer[32] = {}; int i, k; CvPoint2D32f* vect = (CvPoint2D32f*)cvAlloc( n * sizeof(vect[0]) ); float* inv_vect_length = (float*)cvAlloc( n * sizeof(inv_vect_length[0]) ); int left = 0, bottom = 0, right = 0, top = 0; int seq[4] = { -1, -1, -1, -1 }; - /* rotating calipers sides will always have coordinates - (a,b) (-b,a) (-a,-b) (b, -a) + /* rotating calipers sides will always have coordinates + (a,b) (-b,a) (-a,-b) (b, -a) */ /* this is a first base bector (a,b) initialized by (1,0) */ float orientation = 0; @@ -111,14 +111,14 @@ icvRotatingCalipers( CvPoint2D32f* points, int n, int mode, float* out ) float left_x, right_x, top_y, bottom_y; CvPoint2D32f pt0 = points[0]; - + left_x = right_x = pt0.x; top_y = bottom_y = pt0.y; - + for( i = 0; i < n; i++ ) { double dx, dy; - + if( pt0.x < left_x ) left_x = pt0.x, left = i; @@ -132,7 +132,7 @@ icvRotatingCalipers( CvPoint2D32f* points, int n, int mode, float* out ) bottom_y = pt0.y, bottom = i; CvPoint2D32f pt = points[(i+1) & (i+1 < n ? -1 : 0)]; - + dx = pt.x - pt0.x; dy = pt.y - pt0.y; @@ -149,7 +149,7 @@ icvRotatingCalipers( CvPoint2D32f* points, int n, int mode, float* out ) { double ax = vect[n-1].x; double ay = vect[n-1].y; - + for( i = 0; i < n; i++ ) { double bx = vect[i].x; @@ -218,7 +218,7 @@ icvRotatingCalipers( CvPoint2D32f* points, int n, int mode, float* out ) base_b = lead_y; break; case 1: - base_a = lead_y; + base_a = lead_y; base_b = -lead_x; break; case 2: @@ -231,12 +231,12 @@ icvRotatingCalipers( CvPoint2D32f* points, int n, int mode, float* out ) break; default: assert(0); } - } + } /* change base point of main edge */ seq[main_element] += 1; seq[main_element] = (seq[main_element] == n) ? 0 : seq[main_element]; - + switch (mode) { case CV_CALIPERS_MAXHEIGHT: @@ -351,7 +351,7 @@ cvMinAreaRect2( const CvArr* array, CvMemStorage* storage ) CvBox2D box; cv::AutoBuffer _points; CvPoint2D32f* points; - + memset(&box, 0, sizeof(box)); int i, n; @@ -409,7 +409,7 @@ cvMinAreaRect2( const CvArr* array, CvMemStorage* storage ) CV_READ_SEQ_ELEM( points[i], reader ); } } - + if( n > 2 ) { icvRotatingCalipers( points, n, CV_CALIPERS_MINAREARECT, (float*)out ); diff --git a/modules/imgproc/src/shapedescr.cpp b/modules/imgproc/src/shapedescr.cpp index efc11b0..36c0c6c 100644 --- a/modules/imgproc/src/shapedescr.cpp +++ b/modules/imgproc/src/shapedescr.cpp @@ -49,7 +49,7 @@ cvArcLength( const void *array, CvSlice slice, int is_closed ) int i, j = 0, count; const int N = 16; float buf[N]; - CvMat buffer = cvMat( 1, N, CV_32F, buf ); + CvMat buffer = cvMat( 1, N, CV_32F, buf ); CvSeqReader reader; CvContour contour_header; CvSeq* contour = 0; @@ -74,7 +74,7 @@ cvArcLength( const void *array, CvSlice slice, int is_closed ) if( contour->total > 1 ) { int is_float = CV_SEQ_ELTYPE( contour ) == CV_32FC2; - + cvStartReadSeq( contour, &reader, 0 ); cvSetSeqReaderPos( &reader, slice.start_index ); count = cvSliceLength( slice, contour ); @@ -110,7 +110,7 @@ cvArcLength( const void *array, CvSlice slice, int is_closed ) CV_NEXT_SEQ_ELEM( contour->elem_size, reader ); // Bugfix by Axel at rubico.com 2010-03-22, affects closed slices only // wraparound not handled by CV_NEXT_SEQ_ELEM - if( is_closed && i == count - 2 ) + if( is_closed && i == count - 2 ) cvSetSeqReaderPos( &reader, slice.start_index ); buffer.data.fl[j] = dx * dx + dy * dy; @@ -287,7 +287,7 @@ cvMinEnclosingCircle( const void* array, CvPoint2D32f * _center, float *_radius *_radius = 0; CvSeqReader reader; - int i, k, count; + int k, count; CvPoint2D32f pts[8]; CvContour contour_header; CvSeqBlock block; @@ -324,7 +324,7 @@ cvMinEnclosingCircle( const void* array, CvPoint2D32f * _center, float *_radius pt_left = pt_right = pt_top = pt_bottom = (CvPoint *)(reader.ptr); CV_READ_SEQ_ELEM( pt, reader ); - for( i = 1; i < count; i++ ) + for(int i = 1; i < count; i++ ) { CvPoint* pt_ptr = (CvPoint*)reader.ptr; CV_READ_SEQ_ELEM( pt, reader ); @@ -351,7 +351,7 @@ cvMinEnclosingCircle( const void* array, CvPoint2D32f * _center, float *_radius pt_left = pt_right = pt_top = pt_bottom = (CvPoint2D32f *) (reader.ptr); CV_READ_SEQ_ELEM( pt, reader ); - for( i = 1; i < count; i++ ) + for(int i = 1; i < count; i++ ) { CvPoint2D32f* pt_ptr = (CvPoint2D32f*)reader.ptr; CV_READ_SEQ_ELEM( pt, reader ); @@ -375,14 +375,14 @@ cvMinEnclosingCircle( const void* array, CvPoint2D32f * _center, float *_radius for( k = 0; k < max_iters; k++ ) { double min_delta = 0, delta; - CvPoint2D32f ptfl, farAway = { 0, 0}; - /*only for first iteration because the alg is repared at the loop's foot*/ - if(k==0) - icvFindEnslosingCicle4pts_32f( pts, ¢er, &radius ); + CvPoint2D32f ptfl, farAway = { 0, 0}; + /*only for first iteration because the alg is repared at the loop's foot*/ + if(k==0) + icvFindEnslosingCicle4pts_32f( pts, ¢er, &radius ); cvStartReadSeq( sequence, &reader, 0 ); - for( i = 0; i < count; i++ ) + for(int i = 0; i < count; i++ ) { if( !is_float ) { @@ -406,22 +406,22 @@ cvMinEnclosingCircle( const void* array, CvPoint2D32f * _center, float *_radius if( result ) break; - CvPoint2D32f ptsCopy[4]; - /* find good replacement partner for the point which is at most far away, - starting with the one that lays in the actual circle (i=3) */ - for(int i = 3; i >=0; i-- ) - { - for(int j = 0; j < 4; j++ ) - { - ptsCopy[j]=(i != j)? pts[j]: farAway; - } - - icvFindEnslosingCicle4pts_32f(ptsCopy, ¢er, &radius ); - if( icvIsPtInCircle( pts[i], center, radius )>=0){ // replaced one again in the new circle? - pts[i] = farAway; - break; - } - } + CvPoint2D32f ptsCopy[4]; + /* find good replacement partner for the point which is at most far away, + starting with the one that lays in the actual circle (i=3) */ + for(int i = 3; i >=0; i-- ) + { + for(int j = 0; j < 4; j++ ) + { + ptsCopy[j]=(i != j)? pts[j]: farAway; + } + + icvFindEnslosingCicle4pts_32f(ptsCopy, ¢er, &radius ); + if( icvIsPtInCircle( pts[i], center, radius )>=0){ // replaced one again in the new circle? + pts[i] = farAway; + break; + } + } } if( !result ) @@ -429,7 +429,7 @@ cvMinEnclosingCircle( const void* array, CvPoint2D32f * _center, float *_radius cvStartReadSeq( sequence, &reader, 0 ); radius = 0.f; - for( i = 0; i < count; i++ ) + for(int i = 0; i < count; i++ ) { CvPoint2D32f ptfl; float t, dx, dy; @@ -486,7 +486,7 @@ icvContourArea( const CvSeq* contour, double *area ) yi_1 = ((CvPoint2D32f*)(reader.ptr))->y; } CV_NEXT_SEQ_ELEM( contour->elem_size, reader ); - + while( lpt-- > 0 ) { double dxy, xi, yi; @@ -520,7 +520,7 @@ icvContourArea( const CvSeq* contour, double *area ) /****************************************************************************************\ - copy data from one buffer to other buffer + copy data from one buffer to other buffer \****************************************************************************************/ @@ -797,9 +797,9 @@ cvFitEllipse2( const CvArr* array ) n = ptseq->total; if( n < 5 ) CV_Error( CV_StsBadSize, "Number of points should be >= 5" ); - + /* - * New fitellipse algorithm, contributed by Dr. Daniel Weiss + * New fitellipse algorithm, contributed by Dr. Daniel Weiss */ CvPoint2D32f c = {0,0}; double gfp[5], rp[5], t; @@ -818,7 +818,7 @@ cvFitEllipse2( const CvArr* array ) cvStartReadSeq( ptseq, &reader ); is_float = CV_SEQ_ELTYPE(ptseq) == CV_32FC2; - + for( i = 0; i < n; i++ ) { CvPoint2D32f p; @@ -857,7 +857,7 @@ cvFitEllipse2( const CvArr* array ) Ad[i*5 + 3] = p.x; Ad[i*5 + 4] = p.y; } - + cvSolve( &A, &b, &x, CV_SVD ); // now use general-form parameters A - E to find the ellipse center: @@ -1069,7 +1069,7 @@ cvBoundingRect( CvArr* array, int update ) xmin = ymin = 0; } else if( ptseq->total ) - { + { int is_float = CV_SEQ_ELTYPE(ptseq) == CV_32FC2; cvStartReadSeq( ptseq, &reader, 0 ); @@ -1082,12 +1082,12 @@ cvBoundingRect( CvArr* array, int update ) ymin = ymax = pt.y; for( i = 1; i < ptseq->total; i++ ) - { + { CV_READ_SEQ_ELEM( pt, reader ); - + if( xmin > pt.x ) xmin = pt.x; - + if( xmax < pt.x ) xmax = pt.x; @@ -1108,14 +1108,14 @@ cvBoundingRect( CvArr* array, int update ) ymin = ymax = CV_TOGGLE_FLT(pt.y); for( i = 1; i < ptseq->total; i++ ) - { + { CV_READ_SEQ_ELEM( pt, reader ); pt.x = CV_TOGGLE_FLT(pt.x); pt.y = CV_TOGGLE_FLT(pt.y); - + if( xmin > pt.x ) xmin = pt.x; - + if( xmax < pt.x ) xmax = pt.x; @@ -1144,7 +1144,7 @@ cvBoundingRect( CvArr* array, int update ) if( update ) ((CvContour*)ptseq)->rect = rect; - + return rect; } diff --git a/modules/imgproc/src/smooth.cpp b/modules/imgproc/src/smooth.cpp index eda4c85..d93ac5e 100644 --- a/modules/imgproc/src/smooth.cpp +++ b/modules/imgproc/src/smooth.cpp @@ -73,13 +73,13 @@ template struct RowSum : public BaseRowFilter ksize = _ksize; anchor = _anchor; } - + void operator()(const uchar* src, uchar* dst, int width, int cn) { const T* S = (const T*)src; ST* D = (ST*)dst; int i = 0, k, ksz_cn = ksize*cn; - + width = (width - 1)*cn; for( k = 0; k < cn; k++, S++, D++ ) { @@ -108,7 +108,7 @@ template struct ColumnSum : public BaseColumnFilter } void reset() { sumCount = 0; } - + void operator()(const uchar** src, uchar* dst, int dststep, int count, int width) { int i; @@ -198,7 +198,7 @@ template struct ColumnSum : public BaseColumnFilter } - + cv::Ptr cv::getRowSumFilter(int srcType, int sumType, int ksize, int anchor) { int sdepth = CV_MAT_DEPTH(srcType), ddepth = CV_MAT_DEPTH(sumType); @@ -325,7 +325,7 @@ void cv::blur( InputArray src, OutputArray dst, Size ksize, Point anchor, int borderType ) { boxFilter( src, dst, -1, ksize, anchor, true, borderType ); -} +} /****************************************************************************************\ Gaussian Blur @@ -422,7 +422,7 @@ void cv::GaussianBlur( InputArray _src, OutputArray _dst, Size ksize, Mat src = _src.getMat(); _dst.create( src.size(), src.type() ); Mat dst = _dst.getMat(); - + if( borderType != BORDER_CONSTANT ) { if( src.rows == 1 ) @@ -453,11 +453,6 @@ void cv::GaussianBlur( InputArray _src, OutputArray _dst, Size ksize, namespace cv { - -#if _MSC_VER >= 1200 -#pragma warning( disable: 4244 ) -#endif - typedef ushort HT; /** @@ -479,7 +474,7 @@ typedef struct #if CV_SSE2 #define MEDIAN_HAVE_SIMD 1 - + static inline void histogram_add_simd( const HT x[16], HT y[16] ) { const __m128i* rx = (const __m128i*)x; @@ -499,12 +494,12 @@ static inline void histogram_sub_simd( const HT x[16], HT y[16] ) _mm_store_si128(ry+0, r0); _mm_store_si128(ry+1, r1); } - + #else #define MEDIAN_HAVE_SIMD 0 #endif - + static inline void histogram_add( const HT x[16], HT y[16] ) { int i; @@ -569,7 +564,7 @@ medianBlur_8u_O1( const Mat& _src, Mat& _dst, int ksize ) for( c = 0; c < cn; c++ ) { for( j = 0; j < n; j++ ) - COP( c, j, src[cn*j+c], += r+2 ); + COP( c, j, src[cn*j+c], += (cv::HT)(r+2) ); for( i = 1; i < r; i++ ) { @@ -628,7 +623,7 @@ medianBlur_8u_O1( const Mat& _src, Mat& _dst, int ksize ) if ( luc[c][k] <= j-r ) { memset( &H[c].fine[k], 0, 16 * sizeof(HT) ); - for ( luc[c][k] = j-r; luc[c][k] < MIN(j+r+1,n); ++luc[c][k] ) + for ( luc[c][k] = cv::HT(j-r); luc[c][k] < MIN(j+r+1,n); ++luc[c][k] ) histogram_add_simd( &h_fine[16*(n*(16*c+k)+luc[c][k])], H[c].fine[k] ); if ( luc[c][k] < j+r+1 ) @@ -667,14 +662,14 @@ medianBlur_8u_O1( const Mat& _src, Mat& _dst, int ksize ) { for( j = 0; j < 2*r; ++j ) histogram_add( &h_coarse[16*(n*c+j)], H[c].coarse ); - + for( j = r; j < n-r; j++ ) { int t = 2*r*r + 2*r, b, sum = 0; HT* segment; - + histogram_add( &h_coarse[16*(n*c + std::min(j+r,n-1))], H[c].coarse ); - + // Find median at coarse level for ( k = 0; k < 16 ; ++k ) { @@ -686,14 +681,14 @@ medianBlur_8u_O1( const Mat& _src, Mat& _dst, int ksize ) } } assert( k < 16 ); - + /* Update corresponding histogram segment */ if ( luc[c][k] <= j-r ) { memset( &H[c].fine[k], 0, 16 * sizeof(HT) ); - for ( luc[c][k] = j-r; luc[c][k] < MIN(j+r+1,n); ++luc[c][k] ) + for ( luc[c][k] = cv::HT(j-r); luc[c][k] < MIN(j+r+1,n); ++luc[c][k] ) histogram_add( &h_fine[16*(n*(16*c+k)+luc[c][k])], H[c].fine[k] ); - + if ( luc[c][k] < j+r+1 ) { histogram_muladd( j+r+1 - n, &h_fine[16*(n*(16*c+k)+(n-1))], &H[c].fine[k][0] ); @@ -708,9 +703,9 @@ medianBlur_8u_O1( const Mat& _src, Mat& _dst, int ksize ) histogram_add( &h_fine[16*(n*(16*c+k)+MIN(luc[c][k],n-1))], H[c].fine[k] ); } } - + histogram_sub( &h_coarse[16*(n*c+MAX(j-r,0))], H[c].coarse ); - + /* Find median in segment */ segment = H[c].fine[k]; for ( b = 0; b < 16 ; b++ ) @@ -733,11 +728,6 @@ medianBlur_8u_O1( const Mat& _src, Mat& _dst, int ksize ) #undef COP } - -#if _MSC_VER >= 1200 -#pragma warning( default: 4244 ) -#endif - static void medianBlur_8u_Om( const Mat& _src, Mat& _dst, int m ) { @@ -910,7 +900,7 @@ struct MinMax16u b = std::max(b, t); } }; - + struct MinMax16s { typedef short value_type; @@ -974,7 +964,7 @@ struct MinMaxVec16u } }; - + struct MinMaxVec16s { typedef short value_type; @@ -988,9 +978,9 @@ struct MinMaxVec16s a = _mm_min_epi16(a, b); b = _mm_max_epi16(b, t); } -}; +}; + - struct MinMaxVec32f { typedef float value_type; @@ -1033,7 +1023,7 @@ medianBlur_SortNet( const Mat& _src, Mat& _dst, int m ) Op op; VecOp vop; volatile bool useSIMD = checkHardwareSupport(CV_CPU_SSE2); - + if( m == 3 ) { if( size.width == 1 || size.height == 1 ) @@ -1055,7 +1045,7 @@ medianBlur_SortNet( const Mat& _src, Mat& _dst, int m ) } return; } - + size.width *= cn; for( i = 0; i < size.height; i++, dst += dstep ) { @@ -1155,7 +1145,7 @@ medianBlur_SortNet( const Mat& _src, Mat& _dst, int m ) p[k*5+2] = rowk[j]; p[k*5+3] = rowk[j3]; p[k*5+4] = rowk[j4]; } - + op(p[1], p[2]); op(p[0], p[1]); op(p[1], p[2]); op(p[4], p[5]); op(p[3], p[4]); op(p[4], p[5]); op(p[0], p[3]); op(p[2], p[5]); op(p[2], p[3]); op(p[1], p[4]); op(p[1], p[2]); op(p[3], p[4]); op(p[7], p[8]); op(p[6], p[7]); op(p[7], p[8]); @@ -1195,7 +1185,7 @@ medianBlur_SortNet( const Mat& _src, Mat& _dst, int m ) p[k*5+2] = vop.load(rowk+j); p[k*5+3] = vop.load(rowk+j+cn); p[k*5+4] = vop.load(rowk+j+cn*2); } - + vop(p[1], p[2]); vop(p[0], p[1]); vop(p[1], p[2]); vop(p[4], p[5]); vop(p[3], p[4]); vop(p[4], p[5]); vop(p[0], p[3]); vop(p[2], p[5]); vop(p[2], p[3]); vop(p[1], p[4]); vop(p[1], p[2]); vop(p[3], p[4]); vop(p[7], p[8]); vop(p[6], p[7]); vop(p[7], p[8]); @@ -1229,13 +1219,13 @@ medianBlur_SortNet( const Mat& _src, Mat& _dst, int m ) } } - + void cv::medianBlur( InputArray _src0, OutputArray _dst, int ksize ) { Mat src0 = _src0.getMat(); _dst.create( src0.size(), src0.type() ); Mat dst = _dst.getMat(); - + if( ksize <= 1 ) { src0.copyTo(dst); @@ -1248,13 +1238,13 @@ void cv::medianBlur( InputArray _src0, OutputArray _dst, int ksize ) if (tegra::medianBlur(src0, dst, ksize)) return; #endif - + bool useSortNet = ksize == 3 || (ksize == 5 #if !CV_SSE2 && src0.depth() > CV_8U #endif ); - + Mat src; if( useSortNet ) { @@ -1315,7 +1305,7 @@ bilateralFilter_8u( const Mat& src, Mat& dst, int d, sigma_color = 1; if( sigma_space <= 0 ) sigma_space = 1; - + double gauss_color_coeff = -0.5/(sigma_color*sigma_color); double gauss_space_coeff = -0.5/(sigma_space*sigma_space); @@ -1422,7 +1412,7 @@ bilateralFilter_32f( const Mat& src, Mat& dst, int d, sigma_color = 1; if( sigma_space <= 0 ) sigma_space = 1; - + double gauss_color_coeff = -0.5/(sigma_color*sigma_color); double gauss_space_coeff = -0.5/(sigma_space*sigma_space); @@ -1433,9 +1423,9 @@ bilateralFilter_32f( const Mat& src, Mat& dst, int d, radius = MAX(radius, 1); d = radius*2 + 1; // compute the min/max range for the input image (even if multichannel) - + minMaxLoc( src.reshape(1), &minValSrc, &maxValSrc ); - + // temporary copy of the image with borders for easy processing Mat temp; copyMakeBorder( src, temp, radius, radius, radius, radius, borderType ); @@ -1454,7 +1444,7 @@ bilateralFilter_32f( const Mat& src, Mat& dst, int d, float* expLUT = &_expLUT[0]; scale_index = kExpNumBins/len; - + // initialize the exp LUT for( i = 0; i < kExpNumBins+2; i++ ) { @@ -1467,7 +1457,7 @@ bilateralFilter_32f( const Mat& src, Mat& dst, int d, else expLUT[i] = 0.f; } - + // initialize space-related bilateral filter coefficients for( i = -radius, maxk = 0; i <= radius; i++ ) for( j = -radius; j <= radius; j++ ) @@ -1481,7 +1471,7 @@ bilateralFilter_32f( const Mat& src, Mat& dst, int d, for( i = 0; i < size.height; i++ ) { - const float* sptr = (const float*)(temp.data + (i+radius)*temp.step) + radius*cn; + const float* sptr = (const float*)(temp.data + (i+radius)*temp.step) + radius*cn; float* dptr = (float*)(dst.data + i*dst.step); if( cn == 1 ) @@ -1493,11 +1483,11 @@ bilateralFilter_32f( const Mat& src, Mat& dst, int d, for( k = 0; k < maxk; k++ ) { float val = sptr[j + space_ofs[k]]; - float alpha = (float)(std::abs(val - val0)*scale_index); + float alpha = (float)(std::abs(val - val0)*scale_index); int idx = cvFloor(alpha); alpha -= idx; float w = space_weight[k]*(expLUT[idx] + alpha*(expLUT[idx+1] - expLUT[idx])); - sum += val*w; + sum += val*w; wsum += w; } dptr[j] = (float)(sum/wsum); @@ -1514,7 +1504,7 @@ bilateralFilter_32f( const Mat& src, Mat& dst, int d, { const float* sptr_k = sptr + j + space_ofs[k]; float b = sptr_k[0], g = sptr_k[1], r = sptr_k[2]; - float alpha = (float)((std::abs(b - b0) + + float alpha = (float)((std::abs(b - b0) + std::abs(g - g0) + std::abs(r - r0))*scale_index); int idx = cvFloor(alpha); alpha -= idx; @@ -1541,7 +1531,7 @@ void cv::bilateralFilter( InputArray _src, OutputArray _dst, int d, Mat src = _src.getMat(); _dst.create( src.size(), src.type() ); Mat dst = _dst.getMat(); - + if( src.depth() == CV_8U ) bilateralFilter_8u( src, dst, d, sigmaColor, sigmaSpace, borderType ); else if( src.depth() == CV_32F ) diff --git a/modules/imgproc/src/sumpixels.cpp b/modules/imgproc/src/sumpixels.cpp index 3a57a78..b441970 100644 --- a/modules/imgproc/src/sumpixels.cpp +++ b/modules/imgproc/src/sumpixels.cpp @@ -134,7 +134,7 @@ void integral_( const T* src, size_t _srcstep, ST* sum, size_t _sumstep, if( size.width == cn ) buf[cn] = 0; - + if( sqsum ) { sqsum[-cn] = 0; @@ -148,7 +148,7 @@ void integral_( const T* src, size_t _srcstep, ST* sum, size_t _sumstep, sum += sumstep - cn; tilted += tiltedstep - cn; buf += -cn; - + if( sqsum ) sqsum += sqsumstep - cn; @@ -197,7 +197,7 @@ void integral_( const T* src, size_t _srcstep, ST* sum, size_t _sumstep, tilted[x] = t0 + t1 + tilted[x - tiltedstep - cn]; buf[x] = t0; } - + if( sqsum ) sqsum++; } @@ -205,10 +205,10 @@ void integral_( const T* src, size_t _srcstep, ST* sum, size_t _sumstep, } } - + #define DEF_INTEGRAL_FUNC(suffix, T, ST, QT) \ -void integral_##suffix( T* src, size_t srcstep, ST* sum, size_t sumstep, QT* sqsum, size_t sqsumstep, \ - ST* tilted, size_t tiltedstep, Size size, int cn ) \ +static void integral_##suffix( T* src, size_t srcstep, ST* sum, size_t sumstep, QT* sqsum, size_t sqsumstep, \ + ST* tilted, size_t tiltedstep, Size size, int cn ) \ { integral_(src, srcstep, sum, sumstep, sqsum, sqsumstep, tilted, tiltedstep, size, cn); } DEF_INTEGRAL_FUNC(8u32s, uchar, int, double) @@ -217,7 +217,7 @@ DEF_INTEGRAL_FUNC(8u64f, uchar, double, double) DEF_INTEGRAL_FUNC(32f, float, float, double) DEF_INTEGRAL_FUNC(32f64f, float, double, double) DEF_INTEGRAL_FUNC(64f, double, double, double) - + typedef void (*IntegralFunc)(const uchar* src, size_t srcstep, uchar* sum, size_t sumstep, uchar* sqsum, size_t sqsumstep, uchar* tilted, size_t tstep, Size size, int cn ); @@ -236,19 +236,19 @@ void cv::integral( InputArray _src, OutputArray _sum, OutputArray _sqsum, Output sdepth = CV_MAT_DEPTH(sdepth); _sum.create( isize, CV_MAKETYPE(sdepth, cn) ); sum = _sum.getMat(); - + if( _tilted.needed() ) { _tilted.create( isize, CV_MAKETYPE(sdepth, cn) ); tilted = _tilted.getMat(); } - + if( _sqsum.needed() ) { _sqsum.create( isize, CV_MAKETYPE(CV_64F, cn) ); sqsum = _sqsum.getMat(); } - + IntegralFunc func = 0; if( depth == CV_8U && sdepth == CV_32S ) @@ -269,7 +269,7 @@ void cv::integral( InputArray _src, OutputArray _sum, OutputArray _sqsum, Output func( src.data, src.step, sum.data, sum.step, sqsum.data, sqsum.step, tilted.data, tilted.step, src.size(), cn ); } - + void cv::integral( InputArray src, OutputArray sum, int sdepth ) { integral( src, sum, noArray(), noArray(), sdepth ); diff --git a/modules/imgproc/src/undistort.cpp b/modules/imgproc/src/undistort.cpp index d84cfef..fc13b50 100644 --- a/modules/imgproc/src/undistort.cpp +++ b/modules/imgproc/src/undistort.cpp @@ -48,7 +48,7 @@ cv::Mat cv::getDefaultNewCameraMatrix( InputArray _cameraMatrix, Size imgsize, Mat cameraMatrix = _cameraMatrix.getMat(); if( !centerPrincipalPoint && cameraMatrix.type() == CV_64F ) return cameraMatrix; - + Mat newCameraMatrix; cameraMatrix.convertTo(newCameraMatrix, CV_64F); if( centerPrincipalPoint ) @@ -65,7 +65,7 @@ void cv::initUndistortRectifyMap( InputArray _cameraMatrix, InputArray _distCoef { Mat cameraMatrix = _cameraMatrix.getMat(), distCoeffs = _distCoeffs.getMat(); Mat matR = _matR.getMat(), newCameraMatrix = _newCameraMatrix.getMat(); - + if( m1type <= 0 ) m1type = CV_16SC2; CV_Assert( m1type == CV_16SC2 || m1type == CV_32FC1 || m1type == CV_32FC2 ); @@ -106,7 +106,7 @@ void cv::initUndistortRectifyMap( InputArray _cameraMatrix, InputArray _distCoef double u0 = A(0, 2), v0 = A(1, 2); double fx = A(0, 0), fy = A(1, 1); - CV_Assert( distCoeffs.size() == Size(1, 4) || distCoeffs.size() == Size(4, 1) || + CV_Assert( distCoeffs.size() == Size(1, 4) || distCoeffs.size() == Size(4, 1) || distCoeffs.size() == Size(1, 5) || distCoeffs.size() == Size(5, 1) || distCoeffs.size() == Size(1, 8) || distCoeffs.size() == Size(8, 1)); @@ -166,10 +166,10 @@ void cv::undistort( InputArray _src, OutputArray _dst, InputArray _cameraMatrix, { Mat src = _src.getMat(), cameraMatrix = _cameraMatrix.getMat(); Mat distCoeffs = _distCoeffs.getMat(), newCameraMatrix = _newCameraMatrix.getMat(); - + _dst.create( src.size(), src.type() ); Mat dst = _dst.getMat(); - + CV_Assert( dst.data != src.data ); int stripe_size0 = std::min(std::max(1, (1 << 12) / std::max(src.cols, 1)), src.rows); @@ -289,11 +289,11 @@ void cvUndistortPoints( const CvMat* _src, CvMat* _dst, const CvMat* _cameraMatr (_distCoeffs->rows == 1 || _distCoeffs->cols == 1) && (_distCoeffs->rows*_distCoeffs->cols == 4 || _distCoeffs->rows*_distCoeffs->cols == 5 || - _distCoeffs->rows*_distCoeffs->cols == 8)); + _distCoeffs->rows*_distCoeffs->cols == 8)); _Dk = cvMat( _distCoeffs->rows, _distCoeffs->cols, CV_MAKETYPE(CV_64F,CV_MAT_CN(_distCoeffs->type)), k); - + cvConvert( _distCoeffs, &_Dk ); iters = 5; } @@ -389,13 +389,13 @@ void cv::undistortPoints( InputArray _src, OutputArray _dst, { Mat src = _src.getMat(), cameraMatrix = _cameraMatrix.getMat(); Mat distCoeffs = _distCoeffs.getMat(), R = _Rmat.getMat(), P = _Pmat.getMat(); - + CV_Assert( src.isContinuous() && (src.depth() == CV_32F || src.depth() == CV_64F) && ((src.rows == 1 && src.channels() == 2) || src.cols*src.channels() == 2)); - + _dst.create(src.size(), src.type(), -1, true); Mat dst = _dst.getMat(); - + CvMat _csrc = src, _cdst = dst, _ccameraMatrix = cameraMatrix; CvMat matR, matP, _cdistCoeffs, *pR=0, *pP=0, *pD=0; if( R.data ) @@ -416,11 +416,11 @@ static Point2f mapPointSpherical(const Point2f& p, float alpha, Vec4d* J, int pr double beta = 1 + 2*alpha; double v = x*x + y*y + 1, iv = 1/v; double u = sqrt(beta*v + alpha*alpha); - + double k = (u - alpha)*iv; double kv = (v*beta/u - (u - alpha)*2)*iv*iv; double kx = kv*x, ky = kv*y; - + if( projType == PROJ_SPHERICAL_ORTHO ) { if(J) @@ -433,7 +433,7 @@ static Point2f mapPointSpherical(const Point2f& p, float alpha, Vec4d* J, int pr double iR = 1/(alpha + 1); double x1 = std::max(std::min(x*k*iR, 1.), -1.); double y1 = std::max(std::min(y*k*iR, 1.), -1.); - + if(J) { double fx1 = iR/sqrt(1 - x1*x1); @@ -446,35 +446,35 @@ static Point2f mapPointSpherical(const Point2f& p, float alpha, Vec4d* J, int pr return Point2f(); } - + static Point2f invMapPointSpherical(Point2f _p, float alpha, int projType) { static int avgiter = 0, avgn = 0; - + double eps = 1e-12; Vec2d p(_p.x, _p.y), q(_p.x, _p.y), err; Vec4d J; int i, maxiter = 5; - + for( i = 0; i < maxiter; i++ ) { Point2f p1 = mapPointSpherical(Point2f((float)q[0], (float)q[1]), alpha, &J, projType); err = Vec2d(p1.x, p1.y) - p; if( err[0]*err[0] + err[1]*err[1] < eps ) break; - + Vec4d JtJ(J[0]*J[0] + J[2]*J[2], J[0]*J[1] + J[2]*J[3], J[0]*J[1] + J[2]*J[3], J[1]*J[1] + J[3]*J[3]); double d = JtJ[0]*JtJ[3] - JtJ[1]*JtJ[2]; d = d ? 1./d : 0; Vec4d iJtJ(JtJ[3]*d, -JtJ[1]*d, -JtJ[2]*d, JtJ[0]*d); Vec2d JtErr(J[0]*err[0] + J[2]*err[1], J[1]*err[0] + J[3]*err[1]); - + q -= Vec2d(iJtJ[0]*JtErr[0] + iJtJ[1]*JtErr[1], iJtJ[2]*JtErr[0] + iJtJ[3]*JtErr[1]); //Matx22d J(kx*x + k, kx*y, ky*x, ky*y + k); //q -= Vec2d((J.t()*J).inv()*(J.t()*err)); } - + if( i < maxiter ) { avgiter += i; @@ -482,12 +482,12 @@ static Point2f invMapPointSpherical(Point2f _p, float alpha, int projType) if( avgn == 1500 ) printf("avg iters = %g\n", (double)avgiter/avgn); } - + return i < maxiter ? Point2f((float)q[0], (float)q[1]) : Point2f(-FLT_MAX, -FLT_MAX); } } - + float cv::initWideAngleProjMap( InputArray _cameraMatrix0, InputArray _distCoeffs0, Size imageSize, int destImageWidth, int m1type, OutputArray _map1, OutputArray _map2, int projType, double _alpha ) @@ -500,40 +500,40 @@ float cv::initWideAngleProjMap( InputArray _cameraMatrix0, InputArray _distCoeff Point2f dcenter((destImageWidth-1)*0.5f, 0.f); float xmin = FLT_MAX, xmax = -FLT_MAX, ymin = FLT_MAX, ymax = -FLT_MAX; int N = 9; - std::vector u(1), v(1); - Mat _u(u), I = Mat::eye(3,3,CV_64F); + std::vector uvec(1), vvec(1); + Mat I = Mat::eye(3,3,CV_64F); float alpha = (float)_alpha; - + int ndcoeffs = distCoeffs0.cols*distCoeffs0.rows*distCoeffs0.channels(); CV_Assert((distCoeffs0.cols == 1 || distCoeffs0.rows == 1) && (ndcoeffs == 4 || ndcoeffs == 5 || ndcoeffs == 8)); CV_Assert(cameraMatrix0.size() == Size(3,3)); distCoeffs0.convertTo(distCoeffs,CV_64F); cameraMatrix0.convertTo(cameraMatrix,CV_64F); - + alpha = std::min(alpha, 0.999f); - + for( int i = 0; i < N; i++ ) for( int j = 0; j < N; j++ ) { Point2f p((float)j*imageSize.width/(N-1), (float)i*imageSize.height/(N-1)); - u[0] = p; - undistortPoints(_u, v, cameraMatrix, distCoeffs, I, I); - Point2f q = mapPointSpherical(v[0], alpha, 0, projType); + uvec[0] = p; + undistortPoints(uvec, vvec, cameraMatrix, distCoeffs, I, I); + Point2f q = mapPointSpherical(vvec[0], alpha, 0, projType); if( xmin > q.x ) xmin = q.x; if( xmax < q.x ) xmax = q.x; if( ymin > q.y ) ymin = q.y; if( ymax < q.y ) ymax = q.y; } - + float scale = (float)std::min(dcenter.x/fabs(xmax), dcenter.x/fabs(xmin)); Size dsize(destImageWidth, cvCeil(std::max(scale*fabs(ymin)*2, scale*fabs(ymax)*2))); dcenter.y = (dsize.height - 1)*0.5f; - + Mat mapxy(dsize, CV_32FC2); double k1 = k[0], k2 = k[1], k3 = k[2], p1 = k[3], p2 = k[4], k4 = k[5], k5 = k[6], k6 = k[7]; double fx = cameraMatrix.at(0,0), fy = cameraMatrix.at(1,1), cx = scenter.x, cy = scenter.y; - + for( int y = 0; y < dsize.height; y++ ) { Point2f* mxy = mapxy.ptr(y); @@ -551,11 +551,11 @@ float cv::initWideAngleProjMap( InputArray _cameraMatrix0, InputArray _distCoeff double kr = 1 + ((k3*r2 + k2)*r2 + k1)*r2/(1 + ((k6*r2 + k5)*r2 + k4)*r2); double u = fx*(q.x*kr + p1*_2xy + p2*(r2 + 2*x2)) + cx; double v = fy*(q.y*kr + p1*(r2 + 2*y2) + p2*_2xy) + cy; - + mxy[x] = Point2f((float)u, (float)v); } } - + if(m1type == CV_32FC2) { _map1.create(mapxy.size(), mapxy.type()); @@ -565,7 +565,7 @@ float cv::initWideAngleProjMap( InputArray _cameraMatrix0, InputArray _distCoeff } else convertMaps(mapxy, Mat(), _map1, _map2, m1type, false); - + return scale; } diff --git a/modules/imgproc/test/test_contours.cpp b/modules/imgproc/test/test_contours.cpp index 3e07488..a8c9a72 100644 --- a/modules/imgproc/test/test_contours.cpp +++ b/modules/imgproc/test/test_contours.cpp @@ -266,7 +266,7 @@ void CV_FindContourTest::run_func() // the whole testing is done here, run_func() is not utilized in this test int CV_FindContourTest::validate_test_results( int /*test_case_idx*/ ) { - int i, code = cvtest::TS::OK; + int code = cvtest::TS::OK; cvCmpS( img[0], 0, img[0], CV_CMP_GT ); @@ -284,7 +284,7 @@ int CV_FindContourTest::validate_test_results( int /*test_case_idx*/ ) Mat _img[4]; for( int i = 0; i < 4; i++ ) _img[i] = cvarrToMat(img[i]); - + code = cvtest::cmpEps2(ts, _img[0], _img[3], 0, true, "Comparing original image with the map of filled contours" ); if( code < 0 ) @@ -303,7 +303,7 @@ int CV_FindContourTest::validate_test_results( int /*test_case_idx*/ ) CvTreeNodeIterator iterator2; int count3; - for( i = 0; i < 2; i++ ) + for(int i = 0; i < 2; i++ ) { CvTreeNodeIterator iterator; cvInitTreeNodeIterator( &iterator, i == 0 ? contours : contours2, INT_MAX ); @@ -353,7 +353,7 @@ int CV_FindContourTest::validate_test_results( int /*test_case_idx*/ ) goto _exit_; } - for( i = 0; i < seq1->total; i++ ) + for(int i = 0; i < seq1->total; i++ ) { CvPoint pt1; CvPoint pt2; diff --git a/modules/imgproc/test/test_convhull.cpp b/modules/imgproc/test/test_convhull.cpp index 84fa0cf..19f536a 100644 --- a/modules/imgproc/test/test_convhull.cpp +++ b/modules/imgproc/test/test_convhull.cpp @@ -193,7 +193,7 @@ protected: void* result; double low_high_range; CvScalar low, high; - + bool test_cpp; }; @@ -254,7 +254,7 @@ int CV_BaseShapeDescrTest::read_params( CvFileStorage* fs ) } -void CV_BaseShapeDescrTest::generate_point_set( void* points ) +void CV_BaseShapeDescrTest::generate_point_set( void* pointsSet ) { RNG& rng = ts->get_rng(); int i, k, n, total, point_type; @@ -269,16 +269,16 @@ void CV_BaseShapeDescrTest::generate_point_set( void* points ) } memset( &reader, 0, sizeof(reader) ); - if( CV_IS_SEQ(points) ) + if( CV_IS_SEQ(pointsSet) ) { - CvSeq* ptseq = (CvSeq*)points; + CvSeq* ptseq = (CvSeq*)pointsSet; total = ptseq->total; point_type = CV_SEQ_ELTYPE(ptseq); cvStartReadSeq( ptseq, &reader ); } else { - CvMat* ptm = (CvMat*)points; + CvMat* ptm = (CvMat*)pointsSet; assert( CV_IS_MAT(ptm) && CV_IS_MAT_CONT(ptm->type) ); total = ptm->rows + ptm->cols - 1; point_type = CV_MAT_TYPE(ptm->type); @@ -362,7 +362,7 @@ int CV_BaseShapeDescrTest::prepare_test_case( int test_case_idx ) } generate_point_set( points ); - + test_cpp = (cvtest::randInt(rng) & 16) == 0; return 1; } @@ -614,16 +614,16 @@ int CV_ConvHullTest::validate_test_results( int test_case_idx ) for( i = 0; i < point_count; i++ ) { int idx = 0, on_edge = 0; - double result = cvTsPointPolygonTest( p[i], h, hull_count, &idx, &on_edge ); + double pptresult = cvTsPointPolygonTest( p[i], h, hull_count, &idx, &on_edge ); - if( result < 0 ) + if( pptresult < 0 ) { ts->printf( cvtest::TS::LOG, "The point #%d is outside of the convex hull\n", i ); code = cvtest::TS::FAIL_BAD_ACCURACY; goto _exit_; } - if( result < FLT_EPSILON && !on_edge ) + if( pptresult < FLT_EPSILON && !on_edge ) mask->data.ptr[idx] = (uchar)1; } @@ -735,15 +735,15 @@ int CV_MinAreaRectTest::validate_test_results( int test_case_idx ) for( i = 0; i < point_count; i++ ) { int idx = 0, on_edge = 0; - double result = cvTsPointPolygonTest( p[i], box_pt, 4, &idx, &on_edge ); - if( result < -eps ) + double pptresult = cvTsPointPolygonTest( p[i], box_pt, 4, &idx, &on_edge ); + if( pptresult < -eps ) { ts->printf( cvtest::TS::LOG, "The point #%d is outside of the box\n", i ); code = cvtest::TS::FAIL_BAD_ACCURACY; goto _exit_; } - if( result < eps ) + if( pptresult < eps ) { for( j = 0; j < 4; j++ ) { @@ -997,7 +997,7 @@ CV_FitEllipseTest::CV_FitEllipseTest() } -void CV_FitEllipseTest::generate_point_set( void* points ) +void CV_FitEllipseTest::generate_point_set( void* pointsSet ) { RNG& rng = ts->get_rng(); int i, total, point_type; @@ -1020,16 +1020,16 @@ void CV_FitEllipseTest::generate_point_set( void* points ) } memset( &reader, 0, sizeof(reader) ); - if( CV_IS_SEQ(points) ) + if( CV_IS_SEQ(pointsSet) ) { - CvSeq* ptseq = (CvSeq*)points; + CvSeq* ptseq = (CvSeq*)pointsSet; total = ptseq->total; point_type = CV_SEQ_ELTYPE(ptseq); cvStartReadSeq( ptseq, &reader ); } else { - CvMat* ptm = (CvMat*)points; + CvMat* ptm = (CvMat*)pointsSet; assert( CV_IS_MAT(ptm) && CV_IS_MAT_CONT(ptm->type) ); total = ptm->rows + ptm->cols - 1; point_type = CV_MAT_TYPE(ptm->type); @@ -1171,7 +1171,7 @@ class CV_FitEllipseSmallTest : public cvtest::BaseTest { public: CV_FitEllipseSmallTest() {} - ~CV_FitEllipseSmallTest() {} + ~CV_FitEllipseSmallTest() {} protected: void run(int) { @@ -1188,7 +1188,7 @@ protected: c[0].push_back(Point(8, 6)*scale+ofs); c[0].push_back(Point(8, 2)*scale+ofs); c[0].push_back(Point(6, 0)*scale+ofs); - + RotatedRect e = fitEllipse(c[0]); CV_Assert( fabs(e.center.x - 4) <= 1. && fabs(e.center.y - 4) <= 1. && @@ -1226,7 +1226,7 @@ CV_FitLineTest::CV_FitLineTest() } -void CV_FitLineTest::generate_point_set( void* points ) +void CV_FitLineTest::generate_point_set( void* pointsSet ) { RNG& rng = ts->get_rng(); int i, k, n, total, point_type; @@ -1250,16 +1250,16 @@ void CV_FitLineTest::generate_point_set( void* points ) memset( &reader, 0, sizeof(reader) ); - if( CV_IS_SEQ(points) ) + if( CV_IS_SEQ(pointsSet) ) { - CvSeq* ptseq = (CvSeq*)points; + CvSeq* ptseq = (CvSeq*)pointsSet; total = ptseq->total; point_type = CV_MAT_DEPTH(CV_SEQ_ELTYPE(ptseq)); cvStartReadSeq( ptseq, &reader ); } else { - CvMat* ptm = (CvMat*)points; + CvMat* ptm = (CvMat*)pointsSet; assert( CV_IS_MAT(ptm) && CV_IS_MAT_CONT(ptm->type) ); total = ptm->rows + ptm->cols - 1; point_type = CV_MAT_DEPTH(CV_MAT_TYPE(ptm->type)); @@ -1498,7 +1498,7 @@ CV_ContourMomentsTest::CV_ContourMomentsTest() } -void CV_ContourMomentsTest::generate_point_set( void* points ) +void CV_ContourMomentsTest::generate_point_set( void* pointsSet ) { RNG& rng = ts->get_rng(); float max_sz; @@ -1518,7 +1518,7 @@ void CV_ContourMomentsTest::generate_point_set( void* points ) max_r_scale = cvtest::randReal(rng)*max_max_r_scale*0.01; angle = cvtest::randReal(rng)*360; - cvTsGenerateTousledBlob( center, axes, max_r_scale, angle, points, rng ); + cvTsGenerateTousledBlob( center, axes, max_r_scale, angle, pointsSet, rng ); if( points1 ) points1->flags = CV_SEQ_MAGIC_VAL + CV_SEQ_POLYGON; @@ -1614,8 +1614,8 @@ class CV_PerimeterAreaSliceTest : public cvtest::BaseTest { public: CV_PerimeterAreaSliceTest(); - ~CV_PerimeterAreaSliceTest(); -protected: + ~CV_PerimeterAreaSliceTest(); +protected: void run(int); }; @@ -1629,7 +1629,7 @@ void CV_PerimeterAreaSliceTest::run( int ) Ptr storage = cvCreateMemStorage(); RNG& rng = theRNG(); const double min_r = 90, max_r = 120; - + for( int i = 0; i < 100; i++ ) { ts->update_context( this, i, true ); @@ -1640,7 +1640,7 @@ void CV_PerimeterAreaSliceTest::run( int ) CvPoint center; center.x = rng.uniform(cvCeil(max_r), cvFloor(640-max_r)); center.y = rng.uniform(cvCeil(max_r), cvFloor(480-max_r)); - + for( int j = 0; j < n; j++ ) { CvPoint pt; @@ -1650,7 +1650,7 @@ void CV_PerimeterAreaSliceTest::run( int ) pt.y = cvRound(center.y - r*sin(phi)); cvSeqPush(contour, &pt); } - + CvSlice slice; for(;;) { @@ -1664,14 +1664,14 @@ void CV_PerimeterAreaSliceTest::run( int ) /*printf( "%d. (%d, %d) of %d, length = %d, length1 = %d\n", i, slice.start_index, slice.end_index, contour->total, cvSliceLength(slice, contour), cslice->total ); - + double area0 = cvContourArea(cslice); - double area1 = cvContourArea(contour, slice); + double area1 = cvContourArea(contour, slice); if( area0 != area1 ) { ts->printf(cvtest::TS::LOG, "The contour area slice is computed differently (%g vs %g)\n", area0, area1 ); - ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY ); + ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY ); return; }*/ @@ -1681,7 +1681,7 @@ void CV_PerimeterAreaSliceTest::run( int ) { ts->printf(cvtest::TS::LOG, "The contour arc length is computed differently (%g vs %g)\n", len0, len1 ); - ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY ); + ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY ); return; } } diff --git a/modules/imgproc/test/test_filter.cpp b/modules/imgproc/test/test_filter.cpp index 49f0f6b..0260bdf 100644 --- a/modules/imgproc/test/test_filter.cpp +++ b/modules/imgproc/test/test_filter.cpp @@ -253,46 +253,46 @@ void CV_MorphologyBaseTest::prepare_to_validation( int /*test_case_idx*/ ) Mat _ielement(element->nRows, element->nCols, CV_32S, element->values); Mat _element; _ielement.convertTo(_element, CV_8U); - Point anchor(element->anchorX, element->anchorY); - int border = BORDER_REPLICATE; + Point _anchor(element->anchorX, element->anchorY); + int _border = BORDER_REPLICATE; if( optype == CV_MOP_ERODE ) { - cvtest::erode( src, dst, _element, anchor, border ); + cvtest::erode( src, dst, _element, _anchor, _border ); } else if( optype == CV_MOP_DILATE ) { - cvtest::dilate( src, dst, _element, anchor, border ); + cvtest::dilate( src, dst, _element, _anchor, _border ); } else { Mat temp; if( optype == CV_MOP_OPEN ) { - cvtest::erode( src, temp, _element, anchor, border ); - cvtest::dilate( temp, dst, _element, anchor, border ); + cvtest::erode( src, temp, _element, _anchor, _border ); + cvtest::dilate( temp, dst, _element, _anchor, _border ); } else if( optype == CV_MOP_CLOSE ) { - cvtest::dilate( src, temp, _element, anchor, border ); - cvtest::erode( temp, dst, _element, anchor, border ); + cvtest::dilate( src, temp, _element, _anchor, _border ); + cvtest::erode( temp, dst, _element, _anchor, _border ); } else if( optype == CV_MOP_GRADIENT ) { - cvtest::erode( src, temp, _element, anchor, border ); - cvtest::dilate( src, dst, _element, anchor, border ); + cvtest::erode( src, temp, _element, _anchor, _border ); + cvtest::dilate( src, dst, _element, _anchor, _border ); cvtest::add( dst, 1, temp, -1, Scalar::all(0), dst, dst.type() ); } else if( optype == CV_MOP_TOPHAT ) { - cvtest::erode( src, temp, _element, anchor, border ); - cvtest::dilate( temp, dst, _element, anchor, border ); + cvtest::erode( src, temp, _element, _anchor, _border ); + cvtest::dilate( temp, dst, _element, _anchor, _border ); cvtest::add( src, 1, dst, -1, Scalar::all(0), dst, dst.type() ); } else if( optype == CV_MOP_BLACKHAT ) { - cvtest::dilate( src, temp, _element, anchor, border ); - cvtest::erode( temp, dst, _element, anchor, border ); + cvtest::dilate( src, temp, _element, _anchor, _border ); + cvtest::erode( temp, dst, _element, _anchor, _border ); cvtest::add( dst, 1, src, -1, Scalar::all(0), dst, dst.type() ); } else diff --git a/modules/imgproc/test/test_floodfill.cpp b/modules/imgproc/test/test_floodfill.cpp index f185ec5..e46e9e1 100644 --- a/modules/imgproc/test/test_floodfill.cpp +++ b/modules/imgproc/test/test_floodfill.cpp @@ -56,7 +56,7 @@ protected: void prepare_to_validation( int ); void fill_array( int test_case_idx, int i, int j, Mat& arr ); - + /*int write_default_params(CvFileStorage* fs); void get_timing_test_array_types_and_sizes( int test_case_idx, vector >& sizes, vector >& types CvSize** whole_sizes, bool *are_images ); @@ -94,7 +94,7 @@ void CV_FloodFillTest::get_test_array_types_and_sizes( int test_case_idx, RNG& rng = ts->get_rng(); int depth, cn; int i; - double buf[8]; + double buff[8]; cvtest::ArrayTest::get_test_array_types_and_sizes( test_case_idx, sizes, types ); depth = cvtest::randInt(rng) % 3; @@ -111,7 +111,7 @@ void CV_FloodFillTest::get_test_array_types_and_sizes( int test_case_idx, types[INPUT_OUTPUT][1] = types[REF_INPUT_OUTPUT][1] = CV_8UC1; types[OUTPUT][0] = types[REF_OUTPUT][0] = CV_64FC1; sizes[OUTPUT][0] = sizes[REF_OUTPUT][0] = cvSize(9,1); - + if( !use_mask ) sizes[INPUT_OUTPUT][1] = sizes[REF_INPUT_OUTPUT][1] = cvSize(0,0); else @@ -119,7 +119,7 @@ void CV_FloodFillTest::get_test_array_types_and_sizes( int test_case_idx, CvSize sz = sizes[INPUT_OUTPUT][0]; sizes[INPUT_OUTPUT][1] = sizes[REF_INPUT_OUTPUT][1] = cvSize(sz.width+2,sz.height+2); } - + seed_pt.x = cvtest::randInt(rng) % sizes[INPUT_OUTPUT][0].width; seed_pt.y = cvtest::randInt(rng) % sizes[INPUT_OUTPUT][0].height; @@ -127,7 +127,7 @@ void CV_FloodFillTest::get_test_array_types_and_sizes( int test_case_idx, l_diff = u_diff = Scalar::all(0.); else { - Mat m( 1, 8, CV_16S, buf ); + Mat m( 1, 8, CV_16S, buff ); rng.fill( m, RNG::NORMAL, Scalar::all(0), Scalar::all(32) ); for( i = 0; i < 4; i++ ) { @@ -139,7 +139,7 @@ void CV_FloodFillTest::get_test_array_types_and_sizes( int test_case_idx, new_val = Scalar::all(0.); for( i = 0; i < cn; i++ ) new_val.val[i] = cvtest::randReal(rng)*255; - + test_cpp = (cvtest::randInt(rng) & 256) == 0; } @@ -153,13 +153,13 @@ double CV_FloodFillTest::get_success_error_level( int /*test_case_idx*/, int i, void CV_FloodFillTest::fill_array( int test_case_idx, int i, int j, Mat& arr ) { RNG& rng = ts->get_rng(); - + if( i != INPUT && i != INPUT_OUTPUT ) { cvtest::ArrayTest::fill_array( test_case_idx, i, j, arr ); return; } - + if( j == 0 ) { Mat tmp = arr; @@ -191,7 +191,7 @@ void CV_FloodFillTest::run_func() int flags = connectivity + (mask_only ? CV_FLOODFILL_MASK_ONLY : 0) + (range_type == 1 ? CV_FLOODFILL_FIXED_RANGE : 0) + (new_mask_val << 8); double* odata = test_mat[OUTPUT][0].ptr(); - + if(!test_cpp) { CvConnectedComp comp; @@ -255,7 +255,7 @@ cvTsFloodFill( CvMat* _img, CvPoint seed_pt, CvScalar new_val, int cols = _img->cols, rows = _img->rows; int u0 = 0, u1 = 0, u2 = 0; double s0 = 0, s1 = 0, s2 = 0; - + if( CV_MAT_DEPTH(_img->type) == CV_8U || CV_MAT_DEPTH(_img->type) == CV_32S ) { tmp = cvCreateMat( rows, cols, CV_MAKETYPE(CV_32F,CV_MAT_CN(_img->type)) ); @@ -395,7 +395,7 @@ cvTsFloodFill( CvMat* _img, CvPoint seed_pt, CvScalar new_val, cvSeqPush( seq, &p ); } } - } + } } r.x = r.width = seed_pt.x; diff --git a/modules/imgproc/test/test_histograms.cpp b/modules/imgproc/test/test_histograms.cpp index 2f55a5f..3ac1e94b 100644 --- a/modules/imgproc/test/test_histograms.cpp +++ b/modules/imgproc/test/test_histograms.cpp @@ -59,7 +59,7 @@ protected: int prepare_test_case( int test_case_idx ); int validate_test_results( int test_case_idx ); virtual void init_hist( int test_case_idx, int i ); - + virtual void get_hist_params( int test_case_idx ); virtual float** get_hist_ranges( int test_case_idx ); @@ -73,7 +73,7 @@ protected: int uniform; int gen_random_hist; double gen_hist_max_val, gen_hist_sparse_nz_ratio; - + int init_ranges; int img_type; int img_max_log_size; @@ -127,7 +127,7 @@ int CV_BaseHistTest::read_params( CvFileStorage* fs ) max_log_size = cvtest::clipInt( max_log_size, 1, 20 ); img_max_log_size = cvReadInt( find_param( fs, "max_log_array_size" ), img_max_log_size ); img_max_log_size = cvtest::clipInt( img_max_log_size, 1, 9 ); - + max_cdims = cvReadInt( find_param( fs, "max_cdims" ), max_cdims ); max_cdims = cvtest::clipInt( max_cdims, 1, 6 ); @@ -146,13 +146,13 @@ void CV_BaseHistTest::get_hist_params( int /*test_case_idx*/ ) max_dim_size = cvRound(pow(hist_size,1./cdims)); total_size = 1; uniform = cvtest::randInt(rng) % 2; - hist_type = cvtest::randInt(rng) % 2 ? CV_HIST_SPARSE : CV_HIST_ARRAY; - + hist_type = cvtest::randInt(rng) % 2 ? CV_HIST_SPARSE : CV_HIST_ARRAY; + for( i = 0; i < cdims; i++ ) { dims[i] = cvtest::randInt(rng) % (max_dim_size + 2) + 2; if( !uniform ) - dims[i] = MIN(dims[i], max_ni_dim_size); + dims[i] = MIN(dims[i], max_ni_dim_size); total_size *= dims[i]; } @@ -178,12 +178,12 @@ void CV_BaseHistTest::get_hist_params( int /*test_case_idx*/ ) float** CV_BaseHistTest::get_hist_ranges( int /*test_case_idx*/ ) { double _low = low + range_delta, _high = high - range_delta; - + if( !init_ranges ) return 0; - + ranges.resize(cdims); - + if( uniform ) { _ranges.resize(cdims*2); @@ -200,7 +200,7 @@ float** CV_BaseHistTest::get_hist_ranges( int /*test_case_idx*/ ) for( i = 0; i < cdims; i++ ) dims_sum += dims[i] + 1; _ranges.resize(dims_sum); - + for( i = 0; i < cdims; i++ ) { int j, n = dims[i]; @@ -212,7 +212,7 @@ float** CV_BaseHistTest::get_hist_ranges( int /*test_case_idx*/ ) if( (pow(q,(double)n)-1)/(q-1.) >= _high-_low ) break; } - + if( j == 0 ) { delta = (_high-_low)/n; @@ -223,9 +223,9 @@ float** CV_BaseHistTest::get_hist_ranges( int /*test_case_idx*/ ) q = 1 + j*0.1; delta = cvFloor((_high-_low)*(q-1)/(pow(q,(double)n) - 1)); delta = MAX(delta, 1.); - } + } val = _low; - + for( j = 0; j <= n; j++ ) { _ranges[j+ofs] = (float)MIN(val,_high); @@ -236,7 +236,7 @@ float** CV_BaseHistTest::get_hist_ranges( int /*test_case_idx*/ ) ofs += n + 1; } } - + return &ranges[0]; } @@ -246,7 +246,7 @@ void CV_BaseHistTest::init_hist( int /*test_case_idx*/, int hist_i ) if( gen_random_hist ) { RNG& rng = ts->get_rng(); - + if( hist_type == CV_HIST_ARRAY ) { Mat h = cvarrToMat(hist[hist_i]->bins); @@ -255,13 +255,13 @@ void CV_BaseHistTest::init_hist( int /*test_case_idx*/, int hist_i ) else { CvArr* arr = hist[hist_i]->bins; - int i, j, total_size = 1, nz_count; + int i, j, totalSize = 1, nz_count; int idx[CV_MAX_DIM]; for( i = 0; i < cdims; i++ ) - total_size *= dims[i]; + totalSize *= dims[i]; - nz_count = cvtest::randInt(rng) % MAX( total_size/4, 100 ); - nz_count = MIN( nz_count, total_size ); + nz_count = cvtest::randInt(rng) % MAX( totalSize/4, 100 ); + nz_count = MIN( nz_count, totalSize ); // a zero number of non-zero elements should be allowed for( i = 0; i < nz_count; i++ ) @@ -286,7 +286,7 @@ int CV_BaseHistTest::prepare_test_case( int test_case_idx ) get_hist_params( test_case_idx ); r = get_hist_ranges( test_case_idx ); hist.resize(hist_count); - + for( i = 0; i < hist_count; i++ ) { hist[i] = cvCreateHist( cdims, dims, hist_type, r, uniform ); @@ -323,7 +323,7 @@ protected: int prepare_test_case( int test_case_idx ); int validate_test_results( int test_case_idx ); void init_hist( int test_case_idx, int i ); - + CvMat* indices; CvMat* values; CvMat* values0; @@ -376,7 +376,7 @@ int CV_QueryHistTest::prepare_test_case( int test_case_idx ) iters = (cvtest::randInt(rng) % MAX(total_size/10,100)) + 1; iters = MIN( iters, total_size*9/10 + 1 ); - + indices = cvCreateMat( 1, iters*cdims, CV_32S ); values = cvCreateMat( 1, iters, CV_32F ); values0 = cvCreateMat( 1, iters, CV_32F ); @@ -422,7 +422,7 @@ int CV_QueryHistTest::prepare_test_case( int test_case_idx ) if( GET_BIT(lin_idx) ) values0->data.fl[i] = (float)(lin_idx+1); } - + cvReleaseMat( &bit_mask ); } @@ -539,7 +539,7 @@ int CV_QueryHistTest::validate_test_results( int /*test_case_idx*/ ) { int code = cvtest::TS::OK; int i, j, iters = values->cols; - + for( i = 0; i < iters; i++ ) { float v = values->data.fl[i], v0 = values0->data.fl[i]; @@ -613,7 +613,7 @@ void CV_MinMaxHistTest::init_hist(int test_case_idx, int hist_i) } if( !eq || total_size == 1 ) break; - } + } min_val0 = (float)(-cvtest::randReal(rng)*10 - FLT_EPSILON); max_val0 = (float)(cvtest::randReal(rng)*10 + FLT_EPSILON + gen_hist_max_val); @@ -644,7 +644,7 @@ void CV_MinMaxHistTest::run_func(void) int CV_MinMaxHistTest::validate_test_results( int /*test_case_idx*/ ) { int code = cvtest::TS::OK; - + if( cvIsNaN(min_val) || cvIsInf(min_val) || cvIsNaN(max_val) || cvIsInf(max_val) ) { @@ -728,7 +728,7 @@ void CV_NormHistTest::run_func(void) if( hist_type != CV_HIST_ARRAY && test_cpp ) { cv::SparseMat h((CvSparseMat*)hist[0]->bins); - cv::normalize(h, h, factor, CV_L1); + cv::normalize(h, h, factor, CV_L1); cvReleaseSparseMat((CvSparseMat**)&hist[0]->bins); hist[0]->bins = (CvSparseMat*)h; } @@ -741,7 +741,7 @@ int CV_NormHistTest::validate_test_results( int /*test_case_idx*/ ) { int code = cvtest::TS::OK; double sum = 0; - + if( hist_type == CV_HIST_ARRAY ) { int i; @@ -755,7 +755,7 @@ int CV_NormHistTest::validate_test_results( int /*test_case_idx*/ ) CvSparseMat* sparse = (CvSparseMat*)hist[0]->bins; CvSparseMatIterator iterator; CvSparseNode *node; - + for( node = cvInitSparseMatIterator( sparse, &iterator ); node != 0; node = cvGetNextSparseNode( &iterator )) { @@ -839,7 +839,7 @@ int CV_ThreshHistTest::prepare_test_case( int test_case_idx ) if( hist_type == CV_HIST_ARRAY ) { orig_nz_count = total_size; - + values = cvCreateMat( 1, total_size, CV_32F ); memcpy( values->data.fl, cvPtr1D( hist[0]->bins, 0 ), total_size*sizeof(float) ); } @@ -859,7 +859,7 @@ int CV_ThreshHistTest::prepare_test_case( int test_case_idx ) node != 0; node = cvGetNextSparseNode( &iterator ), i++ ) { const int* idx = CV_NODE_IDX(sparse,node); - + OPENCV_ASSERT( i < orig_nz_count, "CV_ThreshHistTest::prepare_test_case", "Buffer overflow" ); values->data.fl[i] = *(float*)CV_NODE_VAL(sparse,node); @@ -924,7 +924,7 @@ int CV_ThreshHistTest::validate_test_results( int /*test_case_idx*/ ) } } } - + if( code > 0 && hist_type == CV_HIST_SPARSE ) { if( sparse->heap->active_count > 0 ) @@ -1003,7 +1003,7 @@ int CV_CompareHistTest::validate_test_results( int /*test_case_idx*/ ) { float* ptr0 = (float*)cvPtr1D( hist[0]->bins, 0 ); float* ptr1 = (float*)cvPtr1D( hist[1]->bins, 0 ); - + for( i = 0; i < total_size; i++ ) { double v0 = ptr0[i], v1 = ptr1[i]; @@ -1031,7 +1031,7 @@ int CV_CompareHistTest::validate_test_results( int /*test_case_idx*/ ) const int* idx = CV_NODE_IDX(sparse0, node); double v0 = *(float*)CV_NODE_VAL(sparse0, node); double v1 = (float)cvGetRealND(sparse1, idx); - + result0[CV_COMP_CORREL] += v0*v1; result0[CV_COMP_INTERSECT] += MIN(v0,v1); if( fabs(v0) > DBL_EPSILON ) @@ -1134,7 +1134,7 @@ CV_CalcHistTest::~CV_CalcHistTest() void CV_CalcHistTest::clear() { int i; - + for( i = 0; i <= CV_MAX_DIM; i++ ) cvReleaseImage( &images[i] ); @@ -1160,7 +1160,7 @@ int CV_CalcHistTest::prepare_test_case( int test_case_idx ) img_type == CV_8U ? IPL_DEPTH_8U : IPL_DEPTH_32F, nch ); channels[i] = cvtest::randInt(rng) % nch; Mat images_i = cvarrToMat(images[i]); - + cvtest::randUni( rng, images_i, Scalar::all(low), Scalar::all(high) ); } else if( i == CV_MAX_DIM && cvtest::randInt(rng) % 2 ) @@ -1168,7 +1168,7 @@ int CV_CalcHistTest::prepare_test_case( int test_case_idx ) // create mask images[i] = cvCreateImage( img_size, IPL_DEPTH_8U, 1 ); Mat images_i = cvarrToMat(images[i]); - + // make ~25% pixels in the mask non-zero cvtest::randUni( rng, images_i, Scalar::all(-2), Scalar::all(2) ); } @@ -1230,7 +1230,7 @@ cvTsCalcHist( IplImage** _images, CvHistogram* hist, IplImage* _mask, int* chann { float val[CV_MAX_DIM]; int idx[CV_MAX_DIM]; - + if( mptr && !mptr[x] ) continue; if( img_depth == IPL_DEPTH_8U ) @@ -1288,7 +1288,7 @@ int CV_CalcHistTest::validate_test_results( int /*test_case_idx*/ ) { ts->printf( cvtest::TS::LOG, "The histogram does not match to the reference one\n" ); code = cvtest::TS::FAIL_BAD_ACCURACY; - + } if( code < 0 ) @@ -1345,7 +1345,7 @@ CV_CalcBackProjectTest::~CV_CalcBackProjectTest() void CV_CalcBackProjectTest::clear() { int i; - + for( i = 0; i < CV_MAX_DIM+3; i++ ) cvReleaseImage( &images[i] ); @@ -1399,7 +1399,7 @@ int CV_CalcBackProjectTest::prepare_test_case( int test_case_idx ) { int idx = cvtest::randInt(rng) % img_len; double val = cvtest::randReal(rng)*(high - low) + low; - + if( img_type == CV_8U ) ((uchar*)data)[idx] = (uchar)cvRound(val); else @@ -1453,7 +1453,7 @@ cvTsCalcBackProject( IplImage** images, IplImage* dst, CvHistogram* hist, int* c float val[CV_MAX_DIM]; float bin_val = 0; int idx[CV_MAX_DIM]; - + if( img_depth == IPL_DEPTH_8U ) for( k = 0; k < cdims; k++ ) val[k] = plane[k].ptr[x*nch[k]]; @@ -1569,7 +1569,7 @@ CV_CalcBackProjectPatchTest::~CV_CalcBackProjectPatchTest() void CV_CalcBackProjectPatchTest::clear() { int i; - + for( i = 0; i < CV_MAX_DIM+2; i++ ) cvReleaseImage( &images[i] ); @@ -1627,7 +1627,7 @@ int CV_CalcBackProjectPatchTest::prepare_test_case( int test_case_idx ) { int idx = cvtest::randInt(rng) % img_len; double val = cvtest::randReal(rng)*(high - low) + low; - + if( img_type == CV_8U ) ((uchar*)data)[idx] = (uchar)cvRound(val); else @@ -1652,7 +1652,7 @@ cvTsCalcBackProjectPatch( IplImage** images, IplImage* dst, CvSize patch_size, double factor, int* channels ) { CvHistogram* model = 0; - + IplImage imgstub[CV_MAX_DIM], *img[CV_MAX_DIM]; IplROI roi; int i, dims; @@ -1679,7 +1679,7 @@ cvTsCalcBackProjectPatch( IplImage** images, IplImage* dst, CvSize patch_size, for( x = 0; x < size.width; x++ ) { double result; - + roi.xOffset = x; roi.yOffset = y; roi.width = patch_size.width; @@ -1703,7 +1703,7 @@ int CV_CalcBackProjectPatchTest::validate_test_results( int /*test_case_idx*/ ) cvTsCalcBackProjectPatch( images, images[CV_MAX_DIM+1], patch_size, hist[0], method, factor, channels ); - + Mat a = cvarrToMat(images[CV_MAX_DIM]), b = cvarrToMat(images[CV_MAX_DIM+1]); code = cvtest::cmpEps2( ts, a, b, err_level, true, "BackProjectPatch result" ); @@ -1756,7 +1756,7 @@ void CV_BayesianProbTest::init_hist( int test_case_idx, int hist_i ) int CV_BayesianProbTest::prepare_test_case( int test_case_idx ) { RNG& rng = ts->get_rng(); - + hist_count = (cvtest::randInt(rng) % (MAX_HIST/2-1) + 2)*2; hist_count = MIN( hist_count, MAX_HIST ); int code = CV_BaseHistTest::prepare_test_case( test_case_idx ); @@ -1833,5 +1833,5 @@ TEST(Imgproc_Hist_MinMaxVal, accuracy) { CV_MinMaxHistTest test; test.safe_run() TEST(Imgproc_Hist_CalcBackProject, accuracy) { CV_CalcBackProjectTest test; test.safe_run(); } TEST(Imgproc_Hist_CalcBackProjectPatch, accuracy) { CV_CalcBackProjectPatchTest test; test.safe_run(); } TEST(Imgproc_Hist_BayesianProb, accuracy) { CV_BayesianProbTest test; test.safe_run(); } - + /* End Of File */ diff --git a/modules/imgproc/test/test_imgwarp.cpp b/modules/imgproc/test/test_imgwarp.cpp index afc0bc5..c43ecd4 100644 --- a/modules/imgproc/test/test_imgwarp.cpp +++ b/modules/imgproc/test/test_imgwarp.cpp @@ -135,7 +135,7 @@ int CV_ImgWarpBaseTest::prepare_test_case( int test_case_idx ) if( test_mat[INPUT_OUTPUT][0].cols >= img.cols && test_mat[INPUT_OUTPUT][0].rows >= img.rows ) space_scale = spatial_scale_zoom; - + for( i = 0; i < img.rows; i++ ) { uchar* ptr = img.ptr(i); @@ -192,7 +192,7 @@ int CV_ImgWarpBaseTest::prepare_test_case( int test_case_idx ) }*/ cv::Mat src(1, cols*cn, CV_32F, &buffer[0]); cv::Mat dst(1, cols*cn, depth, ptr); - src.convertTo(dst, dst.type()); + src.convertTo(dst, dst.type()); } return code; @@ -279,7 +279,7 @@ void CV_ResizeTest::prepare_to_validation( int /*test_case_idx*/ ) CvMat* x_idx = cvCreateMat( 1, dst->cols, CV_32SC1 ); CvMat* y_idx = cvCreateMat( 1, dst->rows, CV_32SC1 ); int* x_tab = x_idx->data.i; - int elem_size = CV_ELEM_SIZE(src->type); + int elem_size = CV_ELEM_SIZE(src->type); int drows = dst->rows, dcols = dst->cols; if( interpolation == CV_INTER_NN ) @@ -302,7 +302,7 @@ void CV_ResizeTest::prepare_to_validation( int /*test_case_idx*/ ) { double scale_x = (double)src->cols/dcols; double scale_y = (double)src->rows/drows; - + for( j = 0; j < dcols; j++ ) { double f = ((j+0.5)*scale_x - 0.5); @@ -322,7 +322,7 @@ void CV_ResizeTest::prepare_to_validation( int /*test_case_idx*/ ) { uchar* dptr = dst->data.ptr + dst->step*i; const uchar* sptr0 = src->data.ptr + src->step*y_idx->data.i[i]; - + for( j = 0; j < dcols; j++, dptr += elem_size ) { const uchar* sptr = sptr0 + x_tab[j]; @@ -394,7 +394,7 @@ static void test_remap( const Mat& src, Mat& dst, const Mat& mapx, const Mat& ma xs -= ixs; ys -= iys; - + switch( depth ) { case CV_8U: @@ -508,7 +508,7 @@ int CV_WarpAffineTest::prepare_test_case( int test_case_idx ) RNG& rng = ts->get_rng(); int code = CV_ImgWarpBaseTest::prepare_test_case( test_case_idx ); const Mat& src = test_mat[INPUT][0]; - const Mat& dst = test_mat[INPUT_OUTPUT][0]; + const Mat& dst = test_mat[INPUT_OUTPUT][0]; Mat& mat = test_mat[INPUT][1]; CvPoint2D32f center; double scale, angle; @@ -516,8 +516,8 @@ int CV_WarpAffineTest::prepare_test_case( int test_case_idx ) if( code <= 0 ) return code; - double buf[6]; - Mat tmp( 2, 3, mat.type(), buf ); + double buffer[6]; + Mat tmp( 2, 3, mat.type(), buffer ); center.x = (float)((cvtest::randReal(rng)*1.2 - 0.1)*src.cols); center.y = (float)((cvtest::randReal(rng)*1.2 - 0.1)*src.rows); @@ -619,7 +619,7 @@ int CV_WarpPerspectiveTest::prepare_test_case( int test_case_idx ) RNG& rng = ts->get_rng(); int code = CV_ImgWarpBaseTest::prepare_test_case( test_case_idx ); const CvMat& src = test_mat[INPUT][0]; - const CvMat& dst = test_mat[INPUT_OUTPUT][0]; + const CvMat& dst = test_mat[INPUT_OUTPUT][0]; Mat& mat = test_mat[INPUT][1]; Point2f s[4], d[4]; int i; @@ -636,17 +636,17 @@ int CV_WarpPerspectiveTest::prepare_test_case( int test_case_idx ) s[3] = Point2f(0,src.rows-1.f); d[3] = Point2f(0,dst.rows-1.f); - float buf[16]; - Mat tmp( 1, 16, CV_32FC1, buf ); + float bufer[16]; + Mat tmp( 1, 16, CV_32FC1, bufer ); rng.fill( tmp, CV_RAND_NORMAL, Scalar::all(0.), Scalar::all(0.1) ); for( i = 0; i < 4; i++ ) { - s[i].x += buf[i*4]*src.cols/2; - s[i].y += buf[i*4+1]*src.rows/2; - d[i].x += buf[i*4+2]*dst.cols/2; - d[i].y += buf[i*4+3]*dst.rows/2; + s[i].x += bufer[i*4]*src.cols/2; + s[i].y += bufer[i*4+1]*src.rows/2; + d[i].x += bufer[i*4+2]*dst.cols/2; + d[i].y += bufer[i*4+3]*dst.rows/2; } cv::getPerspectiveTransform( s, d ).convertTo( mat, mat.depth() ); @@ -675,11 +675,11 @@ void CV_WarpPerspectiveTest::prepare_to_validation( int /*test_case_idx*/ ) double xs = x*m[0] + y*m[1] + m[2]; double ys = x*m[3] + y*m[4] + m[5]; double ds = x*m[6] + y*m[7] + m[8]; - + ds = ds ? 1./ds : 0; xs *= ds; ys *= ds; - + mapx.at(y, x) = (float)xs; mapy.at(y, x) = (float)ys; } @@ -806,15 +806,15 @@ protected: void fill_array( int test_case_idx, int i, int j, Mat& arr ); private: - bool useCPlus; - cv::Mat input0; - cv::Mat input1; - cv::Mat input2; - cv::Mat input_new_cam; - cv::Mat input_output; - - bool zero_new_cam; - bool zero_distortion; + bool useCPlus; + cv::Mat input0; + cv::Mat input1; + cv::Mat input2; + cv::Mat input_new_cam; + cv::Mat input_output; + + bool zero_new_cam; + bool zero_distortion; }; @@ -823,7 +823,7 @@ CV_UndistortTest::CV_UndistortTest() : CV_ImgWarpBaseTest( false ) //spatial_scale_zoom = spatial_scale_decimate; test_array[INPUT].push_back(NULL); test_array[INPUT].push_back(NULL); - test_array[INPUT].push_back(NULL); + test_array[INPUT].push_back(NULL); spatial_scale_decimate = spatial_scale_zoom; } @@ -834,14 +834,14 @@ void CV_UndistortTest::get_test_array_types_and_sizes( int test_case_idx, vector RNG& rng = ts->get_rng(); CV_ImgWarpBaseTest::get_test_array_types_and_sizes( test_case_idx, sizes, types ); int type = types[INPUT][0]; - type = CV_MAKETYPE( CV_8U, CV_MAT_CN(type) ); + type = CV_MAKETYPE( CV_8U, CV_MAT_CN(type) ); types[INPUT][0] = types[INPUT_OUTPUT][0] = types[REF_INPUT_OUTPUT][0] = type; types[INPUT][1] = cvtest::randInt(rng)%2 ? CV_64F : CV_32F; types[INPUT][2] = cvtest::randInt(rng)%2 ? CV_64F : CV_32F; sizes[INPUT][1] = cvSize(3,3); sizes[INPUT][2] = cvtest::randInt(rng)%2 ? cvSize(4,1) : cvSize(1,4); - types[INPUT][3] = types[INPUT][1]; - sizes[INPUT][3] = sizes[INPUT][1]; + types[INPUT][3] = types[INPUT][1]; + sizes[INPUT][3] = sizes[INPUT][1]; interpolation = CV_INTER_LINEAR; } @@ -855,22 +855,22 @@ void CV_UndistortTest::fill_array( int test_case_idx, int i, int j, Mat& arr ) void CV_UndistortTest::run_func() { - if (!useCPlus) - { + if (!useCPlus) + { CvMat a = test_mat[INPUT][1], k = test_mat[INPUT][2]; - cvUndistort2( test_array[INPUT][0], test_array[INPUT_OUTPUT][0], &a, &k); - } - else - { - if (zero_distortion) - { - cv::undistort(input0,input_output,input1,cv::Mat()); - } - else - { - cv::undistort(input0,input_output,input1,input2); - } - } + cvUndistort2( test_array[INPUT][0], test_array[INPUT_OUTPUT][0], &a, &k); + } + else + { + if (zero_distortion) + { + cv::undistort(input0,input_output,input1,cv::Mat()); + } + else + { + cv::undistort(input0,input_output,input1,input2); + } + } } @@ -888,10 +888,10 @@ int CV_UndistortTest::prepare_test_case( int test_case_idx ) const Mat& src = test_mat[INPUT][0]; double k[4], a[9] = {0,0,0,0,0,0,0,0,1}; - double new_cam[9] = {0,0,0,0,0,0,0,0,1}; + double new_cam[9] = {0,0,0,0,0,0,0,0,1}; double sz = MAX(src.rows, src.cols); - - Mat& _new_cam0 = test_mat[INPUT][3]; + + Mat& _new_cam0 = test_mat[INPUT][3]; Mat _new_cam(test_mat[INPUT][3].rows,test_mat[INPUT][3].cols,CV_64F,new_cam); Mat& _a0 = test_mat[INPUT][1]; Mat _a(3,3,CV_64F,a); @@ -925,21 +925,21 @@ int CV_UndistortTest::prepare_test_case( int test_case_idx ) _a.convertTo(_a0, _a0.depth()); - zero_distortion = (cvtest::randInt(rng)%2) == 0 ? false : true; + zero_distortion = (cvtest::randInt(rng)%2) == 0 ? false : true; _k.convertTo(_k0, _k0.depth()); - zero_new_cam = (cvtest::randInt(rng)%2) == 0 ? false : true; + zero_new_cam = (cvtest::randInt(rng)%2) == 0 ? false : true; _new_cam.convertTo(_new_cam0, _new_cam0.depth()); - //Testing C++ code - useCPlus = ((cvtest::randInt(rng) % 2)!=0); - if (useCPlus) - { - input0 = test_mat[INPUT][0]; - input1 = test_mat[INPUT][1]; - input2 = test_mat[INPUT][2]; - input_new_cam = test_mat[INPUT][3]; - } + //Testing C++ code + useCPlus = ((cvtest::randInt(rng) % 2)!=0); + if (useCPlus) + { + input0 = test_mat[INPUT][0]; + input1 = test_mat[INPUT][1]; + input2 = test_mat[INPUT][2]; + input_new_cam = test_mat[INPUT][3]; + } return code; } @@ -947,11 +947,11 @@ int CV_UndistortTest::prepare_test_case( int test_case_idx ) void CV_UndistortTest::prepare_to_validation( int /*test_case_idx*/ ) { - if (useCPlus) - { + if (useCPlus) + { Mat& output = test_mat[INPUT_OUTPUT][0]; input_output.convertTo(output, output.type()); - } + } Mat& src = test_mat[INPUT][0]; Mat& dst = test_mat[REF_INPUT_OUTPUT][0]; Mat& dst0 = test_mat[INPUT_OUTPUT][0]; @@ -978,7 +978,7 @@ protected: void fill_array( int test_case_idx, int i, int j, Mat& arr ); private: - bool dualChannel; + bool dualChannel; }; @@ -1003,8 +1003,8 @@ void CV_UndistortMapTest::get_test_array_types_and_sizes( int test_case_idx, vec CvSize sz = sizes[OUTPUT][0]; types[INPUT][0] = types[INPUT][1] = depth; - dualChannel = cvtest::randInt(rng)%2 == 0; - types[OUTPUT][0] = types[OUTPUT][1] = + dualChannel = cvtest::randInt(rng)%2 == 0; + types[OUTPUT][0] = types[OUTPUT][1] = types[REF_OUTPUT][0] = types[REF_OUTPUT][1] = dualChannel ? CV_32FC2 : CV_32F; sizes[INPUT][0] = cvSize(3,3); sizes[INPUT][1] = cvtest::randInt(rng)%2 ? cvSize(4,1) : cvSize(1,4); @@ -1026,11 +1026,11 @@ void CV_UndistortMapTest::fill_array( int test_case_idx, int i, int j, Mat& arr void CV_UndistortMapTest::run_func() { CvMat a = test_mat[INPUT][0], k = test_mat[INPUT][1]; - - if (!dualChannel ) - cvInitUndistortMap( &a, &k, test_array[OUTPUT][0], test_array[OUTPUT][1] ); - else - cvInitUndistortMap( &a, &k, test_array[OUTPUT][0], 0 ); + + if (!dualChannel ) + cvInitUndistortMap( &a, &k, test_array[OUTPUT][0], test_array[OUTPUT][1] ); + else + cvInitUndistortMap( &a, &k, test_array[OUTPUT][0], 0 ); } @@ -1069,11 +1069,11 @@ int CV_UndistortMapTest::prepare_test_case( int test_case_idx ) _a.convertTo(_a0, _a0.depth()); _k.convertTo(_k0, _k0.depth()); - if (dualChannel) - { + if (dualChannel) + { test_mat[REF_OUTPUT][1] = Scalar::all(0); - test_mat[OUTPUT][1] = Scalar::all(0); - } + test_mat[OUTPUT][1] = Scalar::all(0); + } return code; } @@ -1102,7 +1102,7 @@ test_getQuadrangeSubPix( const Mat& src, Mat& dst, double* a ) { int sstep = (int)(src.step / sizeof(float)); int scols = src.cols, srows = src.rows; - + CV_Assert( src.depth() == CV_32F && src.type() == dst.type() ); int cn = dst.channels(); @@ -1167,11 +1167,11 @@ void CV_GetRectSubPixTest::get_test_array_types_and_sizes( int test_case_idx, ve int src_depth = cvtest::randInt(rng) % 2, dst_depth; int cn = cvtest::randInt(rng) % 2 ? 3 : 1; CvSize src_size, dst_size; - + dst_depth = src_depth = src_depth == 0 ? CV_8U : CV_32F; if( src_depth < CV_32F && cvtest::randInt(rng) % 2 ) dst_depth = CV_32F; - + types[INPUT][0] = CV_MAKETYPE(src_depth,cn); types[INPUT_OUTPUT][0] = types[REF_INPUT_OUTPUT][0] = CV_MAKETYPE(dst_depth,cn); @@ -1181,11 +1181,11 @@ void CV_GetRectSubPixTest::get_test_array_types_and_sizes( int test_case_idx, ve dst_size.width = MIN(dst_size.width,src_size.width); dst_size.height = MIN(dst_size.width,src_size.height); sizes[INPUT_OUTPUT][0] = sizes[REF_INPUT_OUTPUT][0] = dst_size; - + center.x = (float)(cvtest::randReal(rng)*src_size.width); center.y = (float)(cvtest::randReal(rng)*src_size.height); interpolation = CV_INTER_LINEAR; - + test_cpp = (cvtest::randInt(rng) & 256) == 0; } @@ -1274,11 +1274,11 @@ void CV_GetQuadSubPixTest::get_test_array_types_and_sizes( int test_case_idx, ve RNG& rng = ts->get_rng(); int msz, src_depth = cvtest::randInt(rng) % 2, dst_depth; int cn = cvtest::randInt(rng) % 2 ? 3 : 1; - + dst_depth = src_depth = src_depth == 0 ? CV_8U : CV_32F; if( src_depth < CV_32F && cvtest::randInt(rng) % 2 ) dst_depth = CV_32F; - + types[INPUT][0] = CV_MAKETYPE(src_depth,cn); types[INPUT_OUTPUT][0] = types[REF_INPUT_OUTPUT][0] = CV_MAKETYPE(dst_depth,cn); @@ -1333,7 +1333,7 @@ int CV_GetQuadSubPixTest::prepare_test_case( int test_case_idx ) center.y = (float)((cvtest::randReal(rng)*1.2 - 0.1)*src.rows); angle = cvtest::randReal(rng)*360; scale = cvtest::randReal(rng)*0.2 + 0.9; - + // y = Ax + b -> x = A^-1(y - b) = A^-1*y - A^-1*b scale = 1./scale; angle = angle*(CV_PI/180.); @@ -1413,7 +1413,7 @@ TEST(Imgproc_fitLine_vector_2d, regression) points_vector.push_back(p21); points_vector.push_back(p22); - points_vector.push_back(p23); + points_vector.push_back(p23); std::vector line; diff --git a/modules/imgproc/test/test_precomp.hpp b/modules/imgproc/test/test_precomp.hpp index f28d167..f58def6 100644 --- a/modules/imgproc/test/test_precomp.hpp +++ b/modules/imgproc/test/test_precomp.hpp @@ -1,3 +1,7 @@ +#ifdef __GNUC__ +# pragma GCC diagnostic ignored "-Wmissing-declarations" +#endif + #ifndef __OPENCV_TEST_PRECOMP_HPP__ #define __OPENCV_TEST_PRECOMP_HPP__ diff --git a/modules/imgproc/test/test_thresh.cpp b/modules/imgproc/test/test_thresh.cpp index 0b8d381..80f06b8 100644 --- a/modules/imgproc/test/test_thresh.cpp +++ b/modules/imgproc/test/test_thresh.cpp @@ -91,7 +91,8 @@ void CV_ThreshTest::get_test_array_types_and_sizes( int test_case_idx, } else if( depth == CV_16S ) { - float min_val = SHRT_MIN-100.f, max_val = SHRT_MAX+100.f; + float min_val = SHRT_MIN-100.f; + max_val = SHRT_MAX+100.f; thresh_val = (float)(cvtest::randReal(rng)*(max_val - min_val) + min_val); max_val = (float)(cvtest::randReal(rng)*(max_val - min_val) + min_val); if( cvtest::randInt(rng)%4 == 0 ) diff --git a/modules/java/CMakeLists.txt b/modules/java/CMakeLists.txt index 2bcc397..23b93c5 100644 --- a/modules/java/CMakeLists.txt +++ b/modules/java/CMakeLists.txt @@ -162,6 +162,8 @@ else() endif() add_dependencies(${the_module} ${api_target}) +ocv_warnings_disable(CMAKE_CXX_FLAGS -Wmissing-declarations) + # Additional target properties set_target_properties(${the_module} PROPERTIES OUTPUT_NAME "${the_module}" diff --git a/modules/java/src/cpp/jni_part.cpp b/modules/java/src/cpp/jni_part.cpp index def3b3d..4a0df4f 100644 --- a/modules/java/src/cpp/jni_part.cpp +++ b/modules/java/src/cpp/jni_part.cpp @@ -1,5 +1,23 @@ #include +#include "opencv2/opencv_modules.hpp" + +#ifdef HAVE_OPENCV_NONFREE +# include "opencv2/nonfree/nonfree.hpp" +#endif + +#ifdef HAVE_OPENCV_FEATURES2D +# include "opencv2/features2d/features2d.hpp" +#endif + +#ifdef HAVE_OPENCV_VIDEO +# include "opencv2/video/video.hpp" +#endif + +#ifdef HAVE_OPENCV_ML +# include "opencv2/ml/ml.hpp" +#endif + extern "C" { JNIEXPORT jint JNICALL @@ -9,6 +27,23 @@ JNI_OnLoad(JavaVM* vm, void* reserved) if (vm->GetEnv((void**) &env, JNI_VERSION_1_6) != JNI_OK) return -1; + bool init = true; +#ifdef HAVE_OPENCV_NONFREE + init &= cv::initModule_nonfree(); +#endif +#ifdef HAVE_OPENCV_FEATURES2D + init &= cv::initModule_features2d(); +#endif +#ifdef HAVE_OPENCV_VIDEO + init &= cv::initModule_video(); +#endif +#ifdef HAVE_OPENCV_ML + init &= cv::initModule_ml(); +#endif + + if(!init) + return -1; + /* get class with (*env)->FindClass */ /* register methods with (*env)->RegisterNatives */ @@ -21,16 +56,4 @@ JNI_OnUnload(JavaVM *vm, void *reserved) //do nothing } -} // extern "C" - -#include "opencv2/opencv_modules.hpp" - -#if HAVE_OPENCV_MODULES_NONFREE -#include "opencv2/nonfree/nonfree.hpp" -static bool makeUseOfNonfree = initModule_nonfree(); -#endif - -#if HAVE_OPENCV_MODULES_FEATURES2D -#include "opencv2/features2d/features2d.hpp" -static bool makeUseOfNonfree = initModule_features2d(); -#endif \ No newline at end of file +} // extern "C" \ No newline at end of file diff --git a/modules/legacy/include/opencv2/legacy/blobtrack.hpp b/modules/legacy/include/opencv2/legacy/blobtrack.hpp index 3afce31..7a2695a 100644 --- a/modules/legacy/include/opencv2/legacy/blobtrack.hpp +++ b/modules/legacy/include/opencv2/legacy/blobtrack.hpp @@ -49,7 +49,7 @@ #include "opencv2/core/core_c.h" #include -#if _MSC_VER >= 1200 || defined __BORLANDC__ +#if (defined _MSC_VER && _MSC_VER >= 1200) || defined __BORLANDC__ #define cv_stricmp stricmp #define cv_strnicmp strnicmp #if defined WINCE diff --git a/modules/legacy/include/opencv2/legacy/legacy.hpp b/modules/legacy/include/opencv2/legacy/legacy.hpp index 8b8b78d..fea7e8c 100644 --- a/modules/legacy/include/opencv2/legacy/legacy.hpp +++ b/modules/legacy/include/opencv2/legacy/legacy.hpp @@ -1368,9 +1368,9 @@ class CV_EXPORTS CvImage { public: CvImage() : image(0), refcount(0) {} - CvImage( CvSize size, int depth, int channels ) + CvImage( CvSize _size, int _depth, int _channels ) { - image = cvCreateImage( size, depth, channels ); + image = cvCreateImage( _size, _depth, _channels ); refcount = image ? new int(1) : 0; } @@ -1404,12 +1404,12 @@ public: CvImage clone() { return CvImage(image ? cvCloneImage(image) : 0); } - void create( CvSize size, int depth, int channels ) + void create( CvSize _size, int _depth, int _channels ) { if( !image || !refcount || - image->width != size.width || image->height != size.height || - image->depth != depth || image->nChannels != channels ) - attach( cvCreateImage( size, depth, channels )); + image->width != _size.width || image->height != _size.height || + image->depth != _depth || image->nChannels != _channels ) + attach( cvCreateImage( _size, _depth, _channels )); } void release() { detach(); } @@ -1468,9 +1468,9 @@ public: int coi() const { return !image || !image->roi ? 0 : image->roi->coi; } - void set_roi(CvRect roi) { cvSetImageROI(image,roi); } + void set_roi(CvRect _roi) { cvSetImageROI(image,_roi); } void reset_roi() { cvResetImageROI(image); } - void set_coi(int coi) { cvSetImageCOI(image,coi); } + void set_coi(int _coi) { cvSetImageCOI(image,_coi); } int depth() const { return image ? image->depth : 0; } int channels() const { return image ? image->nChannels : 0; } int pix_size() const { return image ? ((image->depth & 255)>>3)*image->nChannels : 0; } @@ -1532,18 +1532,18 @@ class CV_EXPORTS CvMatrix { public: CvMatrix() : matrix(0) {} - CvMatrix( int rows, int cols, int type ) - { matrix = cvCreateMat( rows, cols, type ); } + CvMatrix( int _rows, int _cols, int _type ) + { matrix = cvCreateMat( _rows, _cols, _type ); } - CvMatrix( int rows, int cols, int type, CvMat* hdr, - void* data=0, int step=CV_AUTOSTEP ) - { matrix = cvInitMatHeader( hdr, rows, cols, type, data, step ); } + CvMatrix( int _rows, int _cols, int _type, CvMat* hdr, + void* _data=0, int _step=CV_AUTOSTEP ) + { matrix = cvInitMatHeader( hdr, _rows, _cols, _type, _data, _step ); } CvMatrix( int rows, int cols, int type, CvMemStorage* storage, bool alloc_data=true ); - CvMatrix( int rows, int cols, int type, void* data, int step=CV_AUTOSTEP ) - { matrix = cvCreateMatHeader( rows, cols, type ); - cvSetData( matrix, data, step ); } + CvMatrix( int _rows, int _cols, int _type, void* _data, int _step=CV_AUTOSTEP ) + { matrix = cvCreateMatHeader( _rows, _cols, _type ); + cvSetData( matrix, _data, _step ); } CvMatrix( CvMat* m ) { matrix = m; } @@ -1578,12 +1578,12 @@ public: addref(); } - void create( int rows, int cols, int type ) + void create( int _rows, int _cols, int _type ) { if( !matrix || !matrix->refcount || - matrix->rows != rows || matrix->cols != cols || - CV_MAT_TYPE(matrix->type) != type ) - set( cvCreateMat( rows, cols, type ), false ); + matrix->rows != _rows || matrix->cols != _cols || + CV_MAT_TYPE(matrix->type) != _type ) + set( cvCreateMat( _rows, _cols, _type ), false ); } void addref() const @@ -1647,8 +1647,8 @@ public: const uchar* data() const { return matrix ? matrix->data.ptr : 0; } int step() const { return matrix ? matrix->step : 0; } - void set_data( void* data, int step=CV_AUTOSTEP ) - { cvSetData( matrix, data, step ); } + void set_data( void* _data, int _step=CV_AUTOSTEP ) + { cvSetData( matrix, _data, _step ); } uchar* row(int i) { return !matrix ? 0 : matrix->data.ptr + i*matrix->step; } const uchar* row(int i) const @@ -2035,8 +2035,8 @@ struct CV_EXPORTS BaseKeypoint : x(0), y(0), image(NULL) {} - BaseKeypoint(int x, int y, IplImage* image) - : x(x), y(y), image(image) + BaseKeypoint(int _x, int _y, IplImage* _image) + : x(_x), y(_y), image(_image) {} }; @@ -2847,7 +2847,7 @@ template class CV_EXPORTS BruteForceMatcher : public BFMatcher { public: - BruteForceMatcher( Distance d = Distance() ) : BFMatcher(Distance::normType, false) {} + BruteForceMatcher( Distance d = Distance() ) : BFMatcher(Distance::normType, false) {(void)d;} virtual ~BruteForceMatcher() {} }; @@ -3409,7 +3409,7 @@ typedef struct CvBGCodeBookModel CvBGCodeBookElem* freeList; } CvBGCodeBookModel; -CVAPI(CvBGCodeBookModel*) cvCreateBGCodeBookModel(); +CVAPI(CvBGCodeBookModel*) cvCreateBGCodeBookModel( void ); CVAPI(void) cvReleaseBGCodeBookModel( CvBGCodeBookModel** model ); CVAPI(void) cvBGCodeBookUpdate( CvBGCodeBookModel* model, const CvArr* image, diff --git a/modules/legacy/src/3dtracker.cpp b/modules/legacy/src/3dtracker.cpp index e7dd622..536a534 100644 --- a/modules/legacy/src/3dtracker.cpp +++ b/modules/legacy/src/3dtracker.cpp @@ -41,18 +41,9 @@ #include "precomp.hpp" -#if _MSC_VER >= 1200 -#pragma warning(disable:4786) // Disable MSVC warnings in the standard library. -#pragma warning(disable:4100) -#pragma warning(disable:4512) -#endif #include #include #include -#if _MSC_VER >= 1200 -#pragma warning(default:4100) -#pragma warning(default:4512) -#endif #define ARRAY_SIZEOF(a) (sizeof(a)/sizeof((a)[0])) @@ -148,7 +139,7 @@ CV_IMPL CvBool cv3dTrackerCalibrateCameras(int num_cameras, cvReleaseImage(&gray_img); CV_CALL(gray_img = cvCreateImage(image_size, IPL_DEPTH_8U, 1)); } - + CV_CALL(cvCvtColor(samples[c], gray_img, CV_BGR2GRAY)); img = gray_img; @@ -172,7 +163,7 @@ CV_IMPL CvBool cv3dTrackerCalibrateCameras(int num_cameras, etalon_size, points, &count) != 0; if (count == 0) continue; - + // If found is true, it means all the points were found (count = num_points). // If found is false but count is non-zero, it means that not all points were found. @@ -258,7 +249,7 @@ CV_IMPL CvBool cv3dTrackerCalibrateCameras(int num_cameras, { 0.f, 1.f, 0.f, 0.f }, { 0.f, 0.f, 1.f, 0.f }, { transVect[0], transVect[1], transVect[2], 1.f } }; - + float rmat[4][4] = { { rotMatr[0], rotMatr[1], rotMatr[2], 0.f }, { rotMatr[3], rotMatr[4], rotMatr[5], 0.f }, { rotMatr[6], rotMatr[7], rotMatr[8], 0.f }, @@ -267,7 +258,7 @@ CV_IMPL CvBool cv3dTrackerCalibrateCameras(int num_cameras, MultMatrix(camera_info[c].mat, tmat, rmat); - // change the transformation of the cameras to put them in the world coordinate + // change the transformation of the cameras to put them in the world coordinate // system we want to work with. // Start with an identity matrix; then fill in the values to accomplish diff --git a/modules/legacy/src/_kdtree.hpp b/modules/legacy/src/_kdtree.hpp index b46c995..ba9097a 100644 --- a/modules/legacy/src/_kdtree.hpp +++ b/modules/legacy/src/_kdtree.hpp @@ -53,13 +53,9 @@ #include "assert.h" #include "math.h" -#if _MSC_VER >= 1400 -#pragma warning(disable: 4512) // suppress "assignment operator could not be generated" -#endif - -// J.S. Beis and D.G. Lowe. Shape indexing using approximate nearest-neighbor search -// in highdimensional spaces. In Proc. IEEE Conf. Comp. Vision Patt. Recog., -// pages 1000--1006, 1997. http://citeseer.ist.psu.edu/beis97shape.html +// J.S. Beis and D.G. Lowe. Shape indexing using approximate nearest-neighbor search +// in highdimensional spaces. In Proc. IEEE Conf. Comp. Vision Patt. Recog., +// pages 1000--1006, 1997. http://citeseer.ist.psu.edu/beis97shape.html #undef __deref #undef __valuetype @@ -72,23 +68,23 @@ public: private: struct node { - int dim; // split dimension; >=0 for nodes, -1 for leaves - __valuetype value; // if leaf, value of leaf - int left, right; // node indices of left and right branches - scalar_type boundary; // left if deref(value,dim)<=boundary, otherwise right + int dim; // split dimension; >=0 for nodes, -1 for leaves + __valuetype value; // if leaf, value of leaf + int left, right; // node indices of left and right branches + scalar_type boundary; // left if deref(value,dim)<=boundary, otherwise right }; typedef std::vector < node > node_array; - __deref deref; // requires operator() (__valuetype lhs,int dim) + __deref deref; // requires operator() (__valuetype lhs,int dim) - node_array nodes; // node storage - int point_dim; // dimension of points (the k in kd-tree) - int root_node; // index of root node, -1 if empty tree + node_array nodes; // node storage + int point_dim; // dimension of points (the k in kd-tree) + int root_node; // index of root node, -1 if empty tree // for given set of point indices, compute dimension of highest variance template < class __instype, class __valuector > int dimension_of_highest_variance(__instype * first, __instype * last, - __valuector ctor) { + __valuector ctor) { assert(last - first > 0); accum_type maxvar = -std::numeric_limits < accum_type >::max(); @@ -96,32 +92,32 @@ private: for (int j = 0; j < point_dim; ++j) { accum_type mean = 0; for (__instype * k = first; k < last; ++k) - mean += deref(ctor(*k), j); + mean += deref(ctor(*k), j); mean /= last - first; accum_type var = 0; for (__instype * k = first; k < last; ++k) { - accum_type diff = accum_type(deref(ctor(*k), j)) - mean; - var += diff * diff; + accum_type diff = accum_type(deref(ctor(*k), j)) - mean; + var += diff * diff; } var /= last - first; assert(maxj != -1 || var >= maxvar); if (var >= maxvar) { - maxvar = var; - maxj = j; + maxvar = var; + maxj = j; } } return maxj; } - // given point indices and dimension, find index of median; (almost) modifies [first,last) + // given point indices and dimension, find index of median; (almost) modifies [first,last) // such that points_in[first,median]<=point[median], points_in(median,last)>point[median]. // implemented as partial quicksort; expected linear perf. template < class __instype, class __valuector > __instype * median_partition(__instype * first, __instype * last, - int dim, __valuector ctor) { + int dim, __valuector ctor) { assert(last - first > 0); __instype *k = first + (last - first) / 2; median_partition(first, last, k, dim, ctor); @@ -140,17 +136,19 @@ private: bool operator() (const __instype & lhs) const { return deref(ctor(lhs), dim) <= deref(ctor(pivot), dim); } + private: + median_pr& operator=(const median_pr&); }; template < class __instype, class __valuector > - void median_partition(__instype * first, __instype * last, - __instype * k, int dim, __valuector ctor) { + void median_partition(__instype * first, __instype * last, + __instype * k, int dim, __valuector ctor) { int pivot = (int)((last - first) / 2); std::swap(first[pivot], last[-1]); __instype *middle = std::partition(first, last - 1, - median_pr < __instype, __valuector > - (last[-1], dim, deref, ctor)); + median_pr < __instype, __valuector > + (last[-1], dim, deref, ctor)); std::swap(*middle, last[-1]); if (middle < k) @@ -170,36 +168,36 @@ private: __instype *median = median_partition(first, last, dim, ctor); __instype *split = median; - for (; split != last && deref(ctor(*split), dim) == - deref(ctor(*median), dim); ++split); + for (; split != last && deref(ctor(*split), dim) == + deref(ctor(*median), dim); ++split); if (split == last) { // leaf - int nexti = -1; - for (--split; split >= first; --split) { - int i = (int)nodes.size(); - node & n = *nodes.insert(nodes.end(), node()); - n.dim = -1; - n.value = ctor(*split); - n.left = -1; - n.right = nexti; - nexti = i; - } - - return nexti; + int nexti = -1; + for (--split; split >= first; --split) { + int i = (int)nodes.size(); + node & n = *nodes.insert(nodes.end(), node()); + n.dim = -1; + n.value = ctor(*split); + n.left = -1; + n.right = nexti; + nexti = i; + } + + return nexti; } else { // node - int i = (int)nodes.size(); - // note that recursive insert may invalidate this ref - node & n = *nodes.insert(nodes.end(), node()); + int i = (int)nodes.size(); + // note that recursive insert may invalidate this ref + node & n = *nodes.insert(nodes.end(), node()); - n.dim = dim; - n.boundary = deref(ctor(*median), dim); + n.dim = dim; + n.boundary = deref(ctor(*median), dim); - int left = insert(first, split, ctor); - nodes[i].left = left; - int right = insert(split, last, ctor); - nodes[i].right = right; + int left = insert(first, split, ctor); + nodes[i].left = left; + int right = insert(split, last, ctor); + nodes[i].right = right; - return i; + return i; } } } @@ -214,21 +212,21 @@ private: if (n.dim >= 0) { // node if (deref(p, n.dim) <= n.boundary) // left - r = remove(&n.left, p); + r = remove(&n.left, p); else // right - r = remove(&n.right, p); + r = remove(&n.right, p); // if terminal, remove this node if (n.left == -1 && n.right == -1) - *i = -1; + *i = -1; return r; } else { // leaf if (n.value == p) { - *i = n.right; - return true; + *i = n.right; + return true; } else - return remove(&n.right, p); + return remove(&n.right, p); } } @@ -245,14 +243,14 @@ public: } // given points, initialize a balanced tree CvKDTree(__valuetype * first, __valuetype * last, int _point_dim, - __deref _deref = __deref()) + __deref _deref = __deref()) : deref(_deref) { set_data(first, last, _point_dim, identity_ctor()); } // given points, initialize a balanced tree template < class __instype, class __valuector > CvKDTree(__instype * first, __instype * last, int _point_dim, - __valuector ctor, __deref _deref = __deref()) + __valuector ctor, __deref _deref = __deref()) : deref(_deref) { set_data(first, last, _point_dim, ctor); } @@ -266,7 +264,7 @@ public: } template < class __instype, class __valuector > void set_data(__instype * first, __instype * last, int _point_dim, - __valuector ctor) { + __valuector ctor) { point_dim = _point_dim; nodes.clear(); nodes.reserve(last - first); @@ -292,9 +290,9 @@ public: std::cout << " "; const node & n = nodes[i]; if (n.dim >= 0) { - std::cout << "node " << i << ", left " << nodes[i].left << ", right " << - nodes[i].right << ", dim " << nodes[i].dim << ", boundary " << - nodes[i].boundary << std::endl; + std::cout << "node " << i << ", left " << nodes[i].left << ", right " << + nodes[i].right << ", dim " << nodes[i].dim << ", boundary " << + nodes[i].boundary << std::endl; print(n.left, indent + 3); print(n.right, indent + 3); } else @@ -304,9 +302,9 @@ public: //////////////////////////////////////////////////////////////////////////////////////// // bbf search public: - struct bbf_nn { // info on found neighbors (approx k nearest) - const __valuetype *p; // nearest neighbor - accum_type dist; // distance from d to query point + struct bbf_nn { // info on found neighbors (approx k nearest) + const __valuetype *p; // nearest neighbor + accum_type dist; // distance from d to query point bbf_nn(const __valuetype & _p, accum_type _dist) : p(&_p), dist(_dist) { } @@ -316,9 +314,9 @@ public: }; typedef std::vector < bbf_nn > bbf_nn_pqueue; private: - struct bbf_node { // info on branches not taken - int node; // corresponding node - accum_type dist; // minimum distance from bounds to query point + struct bbf_node { // info on branches not taken + int node; // corresponding node + accum_type dist; // minimum distance from bounds to query point bbf_node(int _node, accum_type _dist) : node(_node), dist(_dist) { } @@ -346,10 +344,10 @@ private: int bbf_branch(int i, const __desctype * d, bbf_pqueue & pq) const { const node & n = nodes[i]; // push bbf_node with bounds of alternate branch, then branch - if (d[n.dim] <= n.boundary) { // left + if (d[n.dim] <= n.boundary) { // left pq_alternate(n.right, pq, n.boundary - d[n.dim]); return n.left; - } else { // right + } else { // right pq_alternate(n.left, pq, d[n.dim] - n.boundary); return n.right; } @@ -366,11 +364,11 @@ private: } // called per candidate nearest neighbor; constructs new bbf_nn for - // candidate and adds it to priority queue of all candidates; if + // candidate and adds it to priority queue of all candidates; if // queue len exceeds k, drops the point furthest from query point d. template < class __desctype > - void bbf_new_nn(bbf_nn_pqueue & nn_pq, int k, - const __desctype * d, const __valuetype & p) const { + void bbf_new_nn(bbf_nn_pqueue & nn_pq, int k, + const __desctype * d, const __valuetype & p) const { bbf_nn nn(p, distance(d, p)); if ((int) nn_pq.size() < k) { nn_pq.push_back(nn); @@ -384,14 +382,14 @@ private: } public: - // finds (with high probability) the k nearest neighbors of d, + // finds (with high probability) the k nearest neighbors of d, // searching at most emax leaves/bins. - // ret_nn_pq is an array containing the (at most) k nearest neighbors + // ret_nn_pq is an array containing the (at most) k nearest neighbors // (see bbf_nn structure def above). template < class __desctype > - int find_nn_bbf(const __desctype * d, - int k, int emax, - bbf_nn_pqueue & ret_nn_pq) const { + int find_nn_bbf(const __desctype * d, + int k, int emax, + bbf_nn_pqueue & ret_nn_pq) const { assert(k > 0); ret_nn_pq.clear(); @@ -411,17 +409,17 @@ public: int i; for (i = bbf.node; - i != -1 && nodes[i].dim >= 0; - i = bbf_branch(i, d, tmp_pq)); + i != -1 && nodes[i].dim >= 0; + i = bbf_branch(i, d, tmp_pq)); if (i != -1) { - // add points in leaf/bin to ret_nn_pq - do { - bbf_new_nn(ret_nn_pq, k, d, nodes[i].value); - } while (-1 != (i = nodes[i].right)); + // add points in leaf/bin to ret_nn_pq + do { + bbf_new_nn(ret_nn_pq, k, d, nodes[i].value); + } while (-1 != (i = nodes[i].right)); - --emax; + --emax; } } @@ -433,27 +431,27 @@ public: // orthogonal range search private: void find_ortho_range(int i, scalar_type * bounds_min, - scalar_type * bounds_max, - std::vector < __valuetype > &inbounds) const { + scalar_type * bounds_max, + std::vector < __valuetype > &inbounds) const { if (i == -1) return; const node & n = nodes[i]; if (n.dim >= 0) { // node if (bounds_min[n.dim] <= n.boundary) - find_ortho_range(n.left, bounds_min, bounds_max, inbounds); + find_ortho_range(n.left, bounds_min, bounds_max, inbounds); if (bounds_max[n.dim] > n.boundary) - find_ortho_range(n.right, bounds_min, bounds_max, inbounds); + find_ortho_range(n.right, bounds_min, bounds_max, inbounds); } else { // leaf do { - inbounds.push_back(nodes[i].value); + inbounds.push_back(nodes[i].value); } while (-1 != (i = nodes[i].right)); } } public: // return all points that lie within the given bounds; inbounds is cleared int find_ortho_range(scalar_type * bounds_min, - scalar_type * bounds_max, - std::vector < __valuetype > &inbounds) const { + scalar_type * bounds_max, + std::vector < __valuetype > &inbounds) const { inbounds.clear(); find_ortho_range(root_node, bounds_min, bounds_max, inbounds); return (int)inbounds.size(); diff --git a/modules/legacy/src/blobtrackanalysishist.cpp b/modules/legacy/src/blobtrackanalysishist.cpp index bc52422..0e349f9 100644 --- a/modules/legacy/src/blobtrackanalysishist.cpp +++ b/modules/legacy/src/blobtrackanalysishist.cpp @@ -46,369 +46,365 @@ typedef struct DefBlobFVN { - CvBlob blob; - CvBlob BlobSeq[BLOB_NUM]; - int state; - int LastFrame; - int FrameNum; + CvBlob blob; + CvBlob BlobSeq[BLOB_NUM]; + int state; + int LastFrame; + int FrameNum; } DefBlobFVN; class CvBlobTrackFVGenN: public CvBlobTrackFVGen { private: - CvBlobSeq m_BlobList; - CvMemStorage* m_pMem; - CvSeq* m_pFVSeq; - float m_FVMax[MAX_FV_SIZE]; - float m_FVMin[MAX_FV_SIZE]; - float m_FVVar[MAX_FV_SIZE]; - int m_Dim; - CvBlob m_BlobSeq[BLOB_NUM]; - int m_Frame; - int m_State; - int m_LastFrame; - int m_ClearFlag; - void Clear() - { - if(m_pMem) - { - cvClearMemStorage(m_pMem); - m_pFVSeq = cvCreateSeq(0,sizeof(CvSeq),sizeof(float)*(m_Dim+1), m_pMem); - m_ClearFlag = 1; - } - } + CvBlobSeq m_BlobList; + CvMemStorage* m_pMem; + CvSeq* m_pFVSeq; + float m_FVMax[MAX_FV_SIZE]; + float m_FVMin[MAX_FV_SIZE]; + float m_FVVar[MAX_FV_SIZE]; + int m_Dim; + CvBlob m_BlobSeq[BLOB_NUM]; + int m_Frame; + int m_State; + int m_LastFrame; + int m_ClearFlag; + void Clear() + { + if(m_pMem) + { + cvClearMemStorage(m_pMem); + m_pFVSeq = cvCreateSeq(0,sizeof(CvSeq),sizeof(float)*(m_Dim+1), m_pMem); + m_ClearFlag = 1; + } + } public: - CvBlobTrackFVGenN(int dim = 2 ):m_BlobList(sizeof(DefBlobFVN)) - { - int i; - assert(dim <= MAX_FV_SIZE); - m_Dim = dim; - for(i=0; iblob = pBlob[0]; - - /* Shift: */ - for(i=(BLOB_NUM-1); i>0; --i) - { - pFVBlob->BlobSeq[i] = pFVBlob->BlobSeq[i-1]; - } - - pFVBlob->BlobSeq[0] = pBlob[0]; - - if(m_Dim>0) - { /* Calculate FV position: */ - FV[0] = CV_BLOB_X(pBlob); - FV[1] = CV_BLOB_Y(pBlob); - } - - if(m_Dim<=2) - { /* Add new FV if position is enough: */ - *(int*)(FV+m_Dim) = CV_BLOB_ID(pBlob); - cvSeqPush( m_pFVSeq, FV ); - } - else if(pFVBlob->FrameNum > BLOB_NUM) - { /* Calculate velocity for more complex FV: */ - float AverVx = 0; - float AverVy = 0; - { /* Average velocity: */ - CvBlob* pBlobSeq = pFVBlob->BlobSeq; - int i; - for(i=1;i4) - { /* State duration: */ - float T = (CV_BLOB_WX(pBlob)+CV_BLOB_WY(pBlob))*0.01f; - - if( fabs(AverVx) < T && fabs(AverVy) < T) - pFVBlob->state++; - else - pFVBlob->state=0; - FV[4] = (float)pFVBlob->state; - } /* State duration. */ - - /* Add new FV: */ - *(int*)(FV+m_Dim) = CV_BLOB_ID(pBlob); - cvSeqPush( m_pFVSeq, FV ); - - } /* If velocity is calculated. */ - - pFVBlob->FrameNum++; - pFVBlob->LastFrame = m_Frame; - }; /* AddBlob */ - - void Process(IplImage* pImg, IplImage* /*pFG*/) - { - int i; - if(!m_ClearFlag) Clear(); - for(i=m_BlobList.GetBlobNum(); i>0; --i) - { /* Delete unused blob: */ - DefBlobFVN* pFVBlob = (DefBlobFVN*)m_BlobList.GetBlob(i-1); - if(pFVBlob->LastFrame < m_Frame) - { - m_BlobList.DelBlob(i-1); - } - } /* Check next blob in list. */ - - m_FVMin[0] = 0; - m_FVMin[1] = 0; - m_FVMax[0] = (float)(pImg->width-1); - m_FVMax[1] = (float)(pImg->height-1); - m_FVVar[0] = m_FVMax[0]*0.01f; - m_FVVar[1] = m_FVMax[1]*0.01f; - m_FVVar[2] = (float)(pImg->width-1)/1440.0f; - m_FVMax[2] = (float)(pImg->width-1)*0.02f; - m_FVMin[2] = -m_FVMax[2]; - m_FVVar[3] = (float)(pImg->width-1)/1440.0f; - m_FVMax[3] = (float)(pImg->height-1)*0.02f; - m_FVMin[3] = -m_FVMax[3]; - m_FVMax[4] = 25*32.0f; /* max state is 32 sec */ - m_FVMin[4] = 0; - m_FVVar[4] = 10; - - m_Frame++; - m_ClearFlag = 0; - }; - virtual void Release(){delete this;}; - virtual int GetFVSize(){return m_Dim;}; - virtual int GetFVNum() - { - return m_pFVSeq->total; - }; - - virtual float* GetFV(int index, int* pFVID) - { - float* pFV = (float*)cvGetSeqElem( m_pFVSeq, index ); - if(pFVID)pFVID[0] = *(int*)(pFV+m_Dim); - return pFV; - }; - virtual float* GetFVMin(){return m_FVMin;}; /* returned pointer to array of minimal values of FV, if return 0 then FVrange is not exist */ - virtual float* GetFVMax(){return m_FVMax;}; /* returned pointer to array of maximal values of FV, if return 0 then FVrange is not exist */ - virtual float* GetFVVar(){return m_FVVar;}; /* returned pointer to array of maximal values of FV, if return 0 then FVrange is not exist */ + CvBlobTrackFVGenN(int dim = 2 ):m_BlobList(sizeof(DefBlobFVN)) + { + int i; + assert(dim <= MAX_FV_SIZE); + m_Dim = dim; + for(i=0; iblob = pBlob[0]; + + /* Shift: */ + for(int i=(BLOB_NUM-1); i>0; --i) + { + pFVBlob->BlobSeq[i] = pFVBlob->BlobSeq[i-1]; + } + + pFVBlob->BlobSeq[0] = pBlob[0]; + + if(m_Dim>0) + { /* Calculate FV position: */ + FV[0] = CV_BLOB_X(pBlob); + FV[1] = CV_BLOB_Y(pBlob); + } + + if(m_Dim<=2) + { /* Add new FV if position is enough: */ + *(int*)(FV+m_Dim) = CV_BLOB_ID(pBlob); + cvSeqPush( m_pFVSeq, FV ); + } + else if(pFVBlob->FrameNum > BLOB_NUM) + { /* Calculate velocity for more complex FV: */ + float AverVx = 0; + float AverVy = 0; + { /* Average velocity: */ + CvBlob* pBlobSeq = pFVBlob->BlobSeq; + for(int i=1;i4) + { /* State duration: */ + float T = (CV_BLOB_WX(pBlob)+CV_BLOB_WY(pBlob))*0.01f; + + if( fabs(AverVx) < T && fabs(AverVy) < T) + pFVBlob->state++; + else + pFVBlob->state=0; + FV[4] = (float)pFVBlob->state; + } /* State duration. */ + + /* Add new FV: */ + *(int*)(FV+m_Dim) = CV_BLOB_ID(pBlob); + cvSeqPush( m_pFVSeq, FV ); + + } /* If velocity is calculated. */ + + pFVBlob->FrameNum++; + pFVBlob->LastFrame = m_Frame; + }; /* AddBlob */ + + void Process(IplImage* pImg, IplImage* /*pFG*/) + { + int i; + if(!m_ClearFlag) Clear(); + for(i=m_BlobList.GetBlobNum(); i>0; --i) + { /* Delete unused blob: */ + DefBlobFVN* pFVBlob = (DefBlobFVN*)m_BlobList.GetBlob(i-1); + if(pFVBlob->LastFrame < m_Frame) + { + m_BlobList.DelBlob(i-1); + } + } /* Check next blob in list. */ + + m_FVMin[0] = 0; + m_FVMin[1] = 0; + m_FVMax[0] = (float)(pImg->width-1); + m_FVMax[1] = (float)(pImg->height-1); + m_FVVar[0] = m_FVMax[0]*0.01f; + m_FVVar[1] = m_FVMax[1]*0.01f; + m_FVVar[2] = (float)(pImg->width-1)/1440.0f; + m_FVMax[2] = (float)(pImg->width-1)*0.02f; + m_FVMin[2] = -m_FVMax[2]; + m_FVVar[3] = (float)(pImg->width-1)/1440.0f; + m_FVMax[3] = (float)(pImg->height-1)*0.02f; + m_FVMin[3] = -m_FVMax[3]; + m_FVMax[4] = 25*32.0f; /* max state is 32 sec */ + m_FVMin[4] = 0; + m_FVVar[4] = 10; + + m_Frame++; + m_ClearFlag = 0; + }; + virtual void Release(){delete this;}; + virtual int GetFVSize(){return m_Dim;}; + virtual int GetFVNum() + { + return m_pFVSeq->total; + }; + + virtual float* GetFV(int index, int* pFVID) + { + float* pFV = (float*)cvGetSeqElem( m_pFVSeq, index ); + if(pFVID)pFVID[0] = *(int*)(pFV+m_Dim); + return pFV; + }; + virtual float* GetFVMin(){return m_FVMin;}; /* returned pointer to array of minimal values of FV, if return 0 then FVrange is not exist */ + virtual float* GetFVMax(){return m_FVMax;}; /* returned pointer to array of maximal values of FV, if return 0 then FVrange is not exist */ + virtual float* GetFVVar(){return m_FVVar;}; /* returned pointer to array of maximal values of FV, if return 0 then FVrange is not exist */ };/* CvBlobTrackFVGenN */ -CvBlobTrackFVGen* cvCreateFVGenP(){return (CvBlobTrackFVGen*)new CvBlobTrackFVGenN(2);} -CvBlobTrackFVGen* cvCreateFVGenPV(){return (CvBlobTrackFVGen*)new CvBlobTrackFVGenN(4);} -CvBlobTrackFVGen* cvCreateFVGenPVS(){return (CvBlobTrackFVGen*)new CvBlobTrackFVGenN(5);} +inline CvBlobTrackFVGen* cvCreateFVGenP(){return (CvBlobTrackFVGen*)new CvBlobTrackFVGenN(2);} +inline CvBlobTrackFVGen* cvCreateFVGenPV(){return (CvBlobTrackFVGen*)new CvBlobTrackFVGenN(4);} +inline CvBlobTrackFVGen* cvCreateFVGenPVS(){return (CvBlobTrackFVGen*)new CvBlobTrackFVGenN(5);} #undef MAX_FV_SIZE #define MAX_FV_SIZE 4 class CvBlobTrackFVGenSS: public CvBlobTrackFVGen { private: - CvBlobSeq m_BlobList; - CvMemStorage* m_pMem; - CvSeq* m_pFVSeq; - float m_FVMax[MAX_FV_SIZE]; - float m_FVMin[MAX_FV_SIZE]; - float m_FVVar[MAX_FV_SIZE]; - int m_Dim; - CvBlob m_BlobSeq[BLOB_NUM]; - int m_Frame; - int m_State; - int m_LastFrame; - int m_ClearFlag; - void Clear() - { - cvClearMemStorage(m_pMem); - m_pFVSeq = cvCreateSeq(0,sizeof(CvSeq),sizeof(float)*(m_Dim+1), m_pMem); - m_ClearFlag = 1; - } + CvBlobSeq m_BlobList; + CvMemStorage* m_pMem; + CvSeq* m_pFVSeq; + float m_FVMax[MAX_FV_SIZE]; + float m_FVMin[MAX_FV_SIZE]; + float m_FVVar[MAX_FV_SIZE]; + int m_Dim; + CvBlob m_BlobSeq[BLOB_NUM]; + int m_Frame; + int m_State; + int m_LastFrame; + int m_ClearFlag; + void Clear() + { + cvClearMemStorage(m_pMem); + m_pFVSeq = cvCreateSeq(0,sizeof(CvSeq),sizeof(float)*(m_Dim+1), m_pMem); + m_ClearFlag = 1; + } public: - CvBlobTrackFVGenSS(int dim = 2 ):m_BlobList(sizeof(DefBlobFVN)) - { - int i; - assert(dim <= MAX_FV_SIZE); - m_Dim = dim; - for(i=0;i0; --i) - { - pFVBlob->BlobSeq[i] = pFVBlob->BlobSeq[i-1]; - } - - pFVBlob->BlobSeq[0] = pBlob[0]; - - if(pFVBlob->FrameNum > BLOB_NUM) - { /* Average velocity: */ - CvBlob* pBlobSeq = pFVBlob->BlobSeq; - float T = (CV_BLOB_WX(pBlob)+CV_BLOB_WY(pBlob))*0.01f; - float AverVx = 0; - float AverVy = 0; - int i; - for(i=1; istate++; - else - pFVBlob->state=0; - } - - if(pFVBlob->state == 5) - { /* Object is stopped: */ - float FV[MAX_FV_SIZE]; - FV[0] = pFVBlob->blob.x; - FV[1] = pFVBlob->blob.y; - FV[2] = pFVBlob->BlobSeq[0].x; - FV[3] = pFVBlob->BlobSeq[0].y; - *(int*)(FV+m_Dim) = CV_BLOB_ID(pBlob); - cvSeqPush( m_pFVSeq, FV ); - } /* Object is stopped. */ - - pFVBlob->FrameNum++; - pFVBlob->LastFrame = m_Frame; - }; /* AddBlob */ - void Process(IplImage* pImg, IplImage* /*pFG*/) - { - int i; - - if(!m_ClearFlag) Clear(); - - for(i=m_BlobList.GetBlobNum();i>0;--i) - { /* Delete unused blob: */ - DefBlobFVN* pFVBlob = (DefBlobFVN*)m_BlobList.GetBlob(i-1); - if(pFVBlob->LastFrame < m_Frame) - { - float FV[MAX_FV_SIZE+1]; - FV[0] = pFVBlob->blob.x; - FV[1] = pFVBlob->blob.y; - FV[2] = pFVBlob->BlobSeq[0].x; - FV[3] = pFVBlob->BlobSeq[0].y; - *(int*)(FV+m_Dim) = CV_BLOB_ID(pFVBlob); - cvSeqPush( m_pFVSeq, FV ); - m_BlobList.DelBlob(i-1); - } - } /* Check next blob in list. */ - - /* Set max min range: */ - m_FVMin[0] = 0; - m_FVMin[1] = 0; - m_FVMin[2] = 0; - m_FVMin[3] = 0; - m_FVMax[0] = (float)(pImg->width-1); - m_FVMax[1] = (float)(pImg->height-1); - m_FVMax[2] = (float)(pImg->width-1); - m_FVMax[3] = (float)(pImg->height-1); - m_FVVar[0] = m_FVMax[0]*0.01f; - m_FVVar[1] = m_FVMax[1]*0.01f; - m_FVVar[2] = m_FVMax[2]*0.01f; - m_FVVar[3] = m_FVMax[3]*0.01f; - - m_Frame++; - m_ClearFlag = 0; - }; - virtual void Release(){delete this;}; - virtual int GetFVSize(){return m_Dim;}; - virtual int GetFVNum() - { - return m_pFVSeq->total; - }; - - virtual float* GetFV(int index, int* pFVID) - { - float* pFV = (float*)cvGetSeqElem( m_pFVSeq, index ); - if(pFVID)pFVID[0] = *(int*)(pFV+m_Dim); - return pFV; - }; - - virtual float* GetFVMin(){return m_FVMin;}; /* returned pointer to array of minimal values of FV, if return 0 then FVrange is not exist */ - virtual float* GetFVMax(){return m_FVMax;}; /* returned pointer to array of maximal values of FV, if return 0 then FVrange is not exist */ - virtual float* GetFVVar(){return m_FVVar;}; /* returned pointer to array of maximal values of FV, if return 0 then FVrange is not exist */ + CvBlobTrackFVGenSS(int dim = 2 ):m_BlobList(sizeof(DefBlobFVN)) + { + int i; + assert(dim <= MAX_FV_SIZE); + m_Dim = dim; + for(i=0;i0; --i) + { + pFVBlob->BlobSeq[i] = pFVBlob->BlobSeq[i-1]; + } + + pFVBlob->BlobSeq[0] = pBlob[0]; + + if(pFVBlob->FrameNum > BLOB_NUM) + { /* Average velocity: */ + CvBlob* pBlobSeq = pFVBlob->BlobSeq; + float T = (CV_BLOB_WX(pBlob)+CV_BLOB_WY(pBlob))*0.01f; + float AverVx = 0; + float AverVy = 0; + for(int i=1; istate++; + else + pFVBlob->state=0; + } + + if(pFVBlob->state == 5) + { /* Object is stopped: */ + float FV[MAX_FV_SIZE]; + FV[0] = pFVBlob->blob.x; + FV[1] = pFVBlob->blob.y; + FV[2] = pFVBlob->BlobSeq[0].x; + FV[3] = pFVBlob->BlobSeq[0].y; + *(int*)(FV+m_Dim) = CV_BLOB_ID(pBlob); + cvSeqPush( m_pFVSeq, FV ); + } /* Object is stopped. */ + + pFVBlob->FrameNum++; + pFVBlob->LastFrame = m_Frame; + }; /* AddBlob */ + void Process(IplImage* pImg, IplImage* /*pFG*/) + { + int i; + + if(!m_ClearFlag) Clear(); + + for(i=m_BlobList.GetBlobNum();i>0;--i) + { /* Delete unused blob: */ + DefBlobFVN* pFVBlob = (DefBlobFVN*)m_BlobList.GetBlob(i-1); + if(pFVBlob->LastFrame < m_Frame) + { + float FV[MAX_FV_SIZE+1]; + FV[0] = pFVBlob->blob.x; + FV[1] = pFVBlob->blob.y; + FV[2] = pFVBlob->BlobSeq[0].x; + FV[3] = pFVBlob->BlobSeq[0].y; + *(int*)(FV+m_Dim) = CV_BLOB_ID(pFVBlob); + cvSeqPush( m_pFVSeq, FV ); + m_BlobList.DelBlob(i-1); + } + } /* Check next blob in list. */ + + /* Set max min range: */ + m_FVMin[0] = 0; + m_FVMin[1] = 0; + m_FVMin[2] = 0; + m_FVMin[3] = 0; + m_FVMax[0] = (float)(pImg->width-1); + m_FVMax[1] = (float)(pImg->height-1); + m_FVMax[2] = (float)(pImg->width-1); + m_FVMax[3] = (float)(pImg->height-1); + m_FVVar[0] = m_FVMax[0]*0.01f; + m_FVVar[1] = m_FVMax[1]*0.01f; + m_FVVar[2] = m_FVMax[2]*0.01f; + m_FVVar[3] = m_FVMax[3]*0.01f; + + m_Frame++; + m_ClearFlag = 0; + }; + virtual void Release(){delete this;}; + virtual int GetFVSize(){return m_Dim;}; + virtual int GetFVNum() + { + return m_pFVSeq->total; + }; + + virtual float* GetFV(int index, int* pFVID) + { + float* pFV = (float*)cvGetSeqElem( m_pFVSeq, index ); + if(pFVID)pFVID[0] = *(int*)(pFV+m_Dim); + return pFV; + }; + + virtual float* GetFVMin(){return m_FVMin;}; /* returned pointer to array of minimal values of FV, if return 0 then FVrange is not exist */ + virtual float* GetFVMax(){return m_FVMax;}; /* returned pointer to array of maximal values of FV, if return 0 then FVrange is not exist */ + virtual float* GetFVVar(){return m_FVVar;}; /* returned pointer to array of maximal values of FV, if return 0 then FVrange is not exist */ };/* CvBlobTrackFVGenSS */ -CvBlobTrackFVGen* cvCreateFVGenSS(){return (CvBlobTrackFVGen*)new CvBlobTrackFVGenSS;} +inline CvBlobTrackFVGen* cvCreateFVGenSS(){return (CvBlobTrackFVGen*)new CvBlobTrackFVGenSS;} /*======================= TRAJECTORY ANALYZER MODULES =====================*/ /* Trajectory Analyser module */ @@ -418,696 +414,692 @@ CvBlobTrackFVGen* cvCreateFVGenSS(){return (CvBlobTrackFVGen*)new CvBlobTrackFVG class DefMat { private: - CvSparseMatIterator m_SparseIterator; - CvSparseNode* m_pSparseNode; - int* m_IDXs; - int m_Dim; + CvSparseMatIterator m_SparseIterator; + CvSparseNode* m_pSparseNode; + int* m_IDXs; + int m_Dim; public: - CvSparseMat* m_pSparse; - CvMatND* m_pND; - int m_Volume; - int m_Max; - DefMat(int dim = 0, int* sizes = NULL, int type = SPARSE) - { - /* Create sparse or ND matrix but not both: */ - m_pSparseNode = NULL; - m_pSparse = NULL; - m_pND = NULL; - m_Volume = 0; - m_Max = 0; - m_IDXs = NULL; - m_Dim = 0; - if(dim>0 && sizes != 0) - Realloc(dim, sizes, type); - } - ~DefMat() - { - if(m_pSparse)cvReleaseSparseMat(&m_pSparse); - if(m_pND)cvReleaseMatND(&m_pND); - if(m_IDXs) cvFree(&m_IDXs); - } - - void Realloc(int dim, int* sizes, int type = SPARSE) - { - if(m_pSparse)cvReleaseSparseMat(&m_pSparse); - if(m_pND)cvReleaseMatND(&m_pND); - - if(type == BYSIZE ) - { - int size = 0; - int i; - for(size=1,i=0;i (2<<20)) - { /* if size > 1M */ - type = SPARSE; - } - else - { - type = ND; - } - } /* Define matrix type. */ - - if(type == SPARSE) - { - m_pSparse = cvCreateSparseMat( dim, sizes, CV_32SC1 ); - m_Dim = dim; - } - if(type == ND ) - { - m_pND = cvCreateMatND( dim, sizes, CV_32SC1 ); - cvZero(m_pND); - m_IDXs = (int*)cvAlloc(sizeof(int)*dim); - m_Dim = dim; - } - m_Volume = 0; - m_Max = 0; - } - void Save(const char* File) - { - if(m_pSparse)cvSave(File, m_pSparse ); - if(m_pND)cvSave(File, m_pND ); - } - void Save(CvFileStorage* fs, const char* name) - { - if(m_pSparse) - { - cvWrite(fs, name, m_pSparse ); - } - else if(m_pND) - { - cvWrite(fs, name, m_pND ); - } - } - void Load(const char* File) - { - CvFileStorage* fs = cvOpenFileStorage( File, NULL, CV_STORAGE_READ ); - if(fs) - { - void* ptr; - if(m_pSparse) cvReleaseSparseMat(&m_pSparse); - if(m_pND) cvReleaseMatND(&m_pND); - m_Volume = 0; - m_Max = 0; - ptr = cvLoad(File); - if(ptr && CV_IS_MATND_HDR(ptr)) m_pND = (CvMatND*)ptr; - if(ptr && CV_IS_SPARSE_MAT_HDR(ptr)) m_pSparse = (CvSparseMat*)ptr; - cvReleaseFileStorage(&fs); - } - AfterLoad(); - } /* Load. */ - - void Load(CvFileStorage* fs, CvFileNode* node, const char* name) - { - CvFileNode* n = cvGetFileNodeByName(fs,node,name); - void* ptr = n?cvRead(fs,n):NULL; - if(ptr) - { - if(m_pSparse) cvReleaseSparseMat(&m_pSparse); - if(m_pND) cvReleaseMatND(&m_pND); - m_Volume = 0; - m_Max = 0; - if(CV_IS_MATND_HDR(ptr)) m_pND = (CvMatND*)ptr; - if(CV_IS_SPARSE_MAT_HDR(ptr)) m_pSparse = (CvSparseMat*)ptr; - } - else - { - printf("WARNING!!! Can't load %s matrix\n",name); - } - AfterLoad(); - } /* Load. */ - - void AfterLoad() - { - m_Volume = 0; - m_Max = 0; - if(m_pSparse) - { /* Calculate Volume of loaded hist: */ - CvSparseMatIterator mat_iterator; - CvSparseNode* node = cvInitSparseMatIterator( m_pSparse, &mat_iterator ); - - for( ; node != 0; node = cvGetNextSparseNode( &mat_iterator )) - { - int val = *(int*)CV_NODE_VAL( m_pSparse, node ); /* get value of the element + CvSparseMat* m_pSparse; + CvMatND* m_pND; + int m_Volume; + int m_Max; + DefMat(int dim = 0, int* sizes = NULL, int type = SPARSE) + { + /* Create sparse or ND matrix but not both: */ + m_pSparseNode = NULL; + m_pSparse = NULL; + m_pND = NULL; + m_Volume = 0; + m_Max = 0; + m_IDXs = NULL; + m_Dim = 0; + if(dim>0 && sizes != 0) + Realloc(dim, sizes, type); + } + ~DefMat() + { + if(m_pSparse)cvReleaseSparseMat(&m_pSparse); + if(m_pND)cvReleaseMatND(&m_pND); + if(m_IDXs) cvFree(&m_IDXs); + } + + void Realloc(int dim, int* sizes, int type = SPARSE) + { + if(m_pSparse)cvReleaseSparseMat(&m_pSparse); + if(m_pND)cvReleaseMatND(&m_pND); + + if(type == BYSIZE ) + { + int size = 0; + int i; + for(size=1,i=0;i (2<<20)) + { /* if size > 1M */ + type = SPARSE; + } + else + { + type = ND; + } + } /* Define matrix type. */ + + if(type == SPARSE) + { + m_pSparse = cvCreateSparseMat( dim, sizes, CV_32SC1 ); + m_Dim = dim; + } + if(type == ND ) + { + m_pND = cvCreateMatND( dim, sizes, CV_32SC1 ); + cvZero(m_pND); + m_IDXs = (int*)cvAlloc(sizeof(int)*dim); + m_Dim = dim; + } + m_Volume = 0; + m_Max = 0; + } + void Save(const char* File) + { + if(m_pSparse)cvSave(File, m_pSparse ); + if(m_pND)cvSave(File, m_pND ); + } + void Save(CvFileStorage* fs, const char* name) + { + if(m_pSparse) + { + cvWrite(fs, name, m_pSparse ); + } + else if(m_pND) + { + cvWrite(fs, name, m_pND ); + } + } + void Load(const char* File) + { + CvFileStorage* fs = cvOpenFileStorage( File, NULL, CV_STORAGE_READ ); + if(fs) + { + void* ptr; + if(m_pSparse) cvReleaseSparseMat(&m_pSparse); + if(m_pND) cvReleaseMatND(&m_pND); + m_Volume = 0; + m_Max = 0; + ptr = cvLoad(File); + if(ptr && CV_IS_MATND_HDR(ptr)) m_pND = (CvMatND*)ptr; + if(ptr && CV_IS_SPARSE_MAT_HDR(ptr)) m_pSparse = (CvSparseMat*)ptr; + cvReleaseFileStorage(&fs); + } + AfterLoad(); + } /* Load. */ + + void Load(CvFileStorage* fs, CvFileNode* node, const char* name) + { + CvFileNode* n = cvGetFileNodeByName(fs,node,name); + void* ptr = n?cvRead(fs,n):NULL; + if(ptr) + { + if(m_pSparse) cvReleaseSparseMat(&m_pSparse); + if(m_pND) cvReleaseMatND(&m_pND); + m_Volume = 0; + m_Max = 0; + if(CV_IS_MATND_HDR(ptr)) m_pND = (CvMatND*)ptr; + if(CV_IS_SPARSE_MAT_HDR(ptr)) m_pSparse = (CvSparseMat*)ptr; + } + else + { + printf("WARNING!!! Can't load %s matrix\n",name); + } + AfterLoad(); + } /* Load. */ + + void AfterLoad() + { + m_Volume = 0; + m_Max = 0; + if(m_pSparse) + { /* Calculate Volume of loaded hist: */ + CvSparseMatIterator mat_iterator; + CvSparseNode* node = cvInitSparseMatIterator( m_pSparse, &mat_iterator ); + + for( ; node != 0; node = cvGetNextSparseNode( &mat_iterator )) + { + int val = *(int*)CV_NODE_VAL( m_pSparse, node ); /* get value of the element (assume that the type is CV_32SC1) */ - m_Volume += val; - if(m_Max < val)m_Max = val; - } - } /* Calculate Volume of loaded hist. */ - - if(m_pND) - { /* Calculate Volume of loaded hist: */ - CvMat mat; - double max_val; - double vol; - cvGetMat( m_pND, &mat, NULL, 1 ); - - vol = cvSum(&mat).val[0]; - m_Volume = cvRound(vol); - cvMinMaxLoc( &mat, NULL, &max_val); - m_Max = cvRound(max_val); - /* MUST BE WRITTEN LATER */ - } /* Calculate Volume of loaded hist. */ - } /* AfterLoad. */ - - int* GetPtr(int* indx) - { - if(m_pSparse) return (int*)cvPtrND( m_pSparse, indx, NULL, 1, NULL); - if(m_pND) return (int*)cvPtrND( m_pND, indx, NULL, 1, NULL); - return NULL; - } /* GetPtr. */ - - int GetVal(int* indx) - { - int* p = GetPtr(indx); - if(p)return p[0]; - return -1; - } /* GetVal. */ - - int Add(int* indx, int val) - { - int NewVal; - int* pVal = GetPtr(indx); - if(pVal == NULL) return -1; - pVal[0] += val; - NewVal = pVal[0]; - m_Volume += val; - if(m_Max < NewVal)m_Max = NewVal; - return NewVal; - } /* Add. */ - - void Add(DefMat* pMatAdd) - { - int* pIDXS = NULL; - int Val = 0; - for(Val = pMatAdd->GetNext(&pIDXS, 1 );pIDXS;Val=pMatAdd->GetNext(&pIDXS, 0 )) - { - Add(pIDXS,Val); - } - } /* Add. */ - - int SetMax(int* indx, int val) - { - int NewVal; - int* pVal = GetPtr(indx); - if(pVal == NULL) return -1; - if(val > pVal[0]) - { - m_Volume += val-pVal[0]; - pVal[0] = val; - } - NewVal = pVal[0]; - if(m_Max < NewVal)m_Max = NewVal; - return NewVal; - } /* Add. */ - - int GetNext(int** pIDXS, int init = 0) - { - int Val = 0; - pIDXS[0] = NULL; - if(m_pSparse) - { - m_pSparseNode = (init || m_pSparseNode==NULL)? - cvInitSparseMatIterator( m_pSparse, &m_SparseIterator ): - cvGetNextSparseNode( &m_SparseIterator ); - - if(m_pSparseNode) - { - int* pVal = (int*)CV_NODE_VAL( m_pSparse, m_pSparseNode ); - if(pVal)Val = pVal[0]; - pIDXS[0] = CV_NODE_IDX( m_pSparse, m_pSparseNode ); - } - }/* Sparse matrix. */ - - if(m_pND) - { - int i; - if(init) - { - for(i=0;i0) - break; - m_IDXs[i] = cvGetDimSize( m_pND, i )-1; - } - if(i==m_Dim) - { - pIDXS[0] = NULL; - } - else - { - pIDXS[0] = m_IDXs; - Val = GetVal(m_IDXs); - } - - } /* Get next ND. */ - - } /* Sparse matrix. */ - - return Val; - - }; /* GetNext. */ + m_Volume += val; + if(m_Max < val)m_Max = val; + } + } /* Calculate Volume of loaded hist. */ + + if(m_pND) + { /* Calculate Volume of loaded hist: */ + CvMat mat; + double max_val; + double vol; + cvGetMat( m_pND, &mat, NULL, 1 ); + + vol = cvSum(&mat).val[0]; + m_Volume = cvRound(vol); + cvMinMaxLoc( &mat, NULL, &max_val); + m_Max = cvRound(max_val); + /* MUST BE WRITTEN LATER */ + } /* Calculate Volume of loaded hist. */ + } /* AfterLoad. */ + + int* GetPtr(int* indx) + { + if(m_pSparse) return (int*)cvPtrND( m_pSparse, indx, NULL, 1, NULL); + if(m_pND) return (int*)cvPtrND( m_pND, indx, NULL, 1, NULL); + return NULL; + } /* GetPtr. */ + + int GetVal(int* indx) + { + int* p = GetPtr(indx); + if(p)return p[0]; + return -1; + } /* GetVal. */ + + int Add(int* indx, int val) + { + int NewVal; + int* pVal = GetPtr(indx); + if(pVal == NULL) return -1; + pVal[0] += val; + NewVal = pVal[0]; + m_Volume += val; + if(m_Max < NewVal)m_Max = NewVal; + return NewVal; + } /* Add. */ + + void Add(DefMat* pMatAdd) + { + int* pIDXS = NULL; + int Val = 0; + for(Val = pMatAdd->GetNext(&pIDXS, 1 );pIDXS;Val=pMatAdd->GetNext(&pIDXS, 0 )) + { + Add(pIDXS,Val); + } + } /* Add. */ + + int SetMax(int* indx, int val) + { + int NewVal; + int* pVal = GetPtr(indx); + if(pVal == NULL) return -1; + if(val > pVal[0]) + { + m_Volume += val-pVal[0]; + pVal[0] = val; + } + NewVal = pVal[0]; + if(m_Max < NewVal)m_Max = NewVal; + return NewVal; + } /* Add. */ + + int GetNext(int** pIDXS, int init = 0) + { + int Val = 0; + pIDXS[0] = NULL; + if(m_pSparse) + { + m_pSparseNode = (init || m_pSparseNode==NULL)? + cvInitSparseMatIterator( m_pSparse, &m_SparseIterator ): + cvGetNextSparseNode( &m_SparseIterator ); + + if(m_pSparseNode) + { + int* pVal = (int*)CV_NODE_VAL( m_pSparse, m_pSparseNode ); + if(pVal)Val = pVal[0]; + pIDXS[0] = CV_NODE_IDX( m_pSparse, m_pSparseNode ); + } + }/* Sparse matrix. */ + + if(m_pND) + { + int i; + if(init) + { + for(i=0;i0) + break; + m_IDXs[i] = cvGetDimSize( m_pND, i )-1; + } + if(i==m_Dim) + { + pIDXS[0] = NULL; + } + else + { + pIDXS[0] = m_IDXs; + Val = GetVal(m_IDXs); + } + + } /* Get next ND. */ + + } /* Sparse matrix. */ + + return Val; + + }; /* GetNext. */ }; #define FV_NUM 10 #define FV_SIZE 10 typedef struct DefTrackFG { - CvBlob blob; - // CvBlobTrackFVGen* pFVGen; - int LastFrame; - float state; - DefMat* pHist; + CvBlob blob; + // CvBlobTrackFVGen* pFVGen; + int LastFrame; + float state; + DefMat* pHist; } DefTrackFG; class CvBlobTrackAnalysisHist : public CvBlobTrackAnalysis { - /*---------------- Internal functions: --------------------*/ + /*---------------- Internal functions: --------------------*/ private: - int m_BinNumParam; - int m_SmoothRadius; - const char* m_SmoothKernel; - float m_AbnormalThreshold; - int m_TrackNum; - int m_Frame; - int m_BinNum; - char m_DataFileName[1024]; - int m_Dim; - int* m_Sizes; - DefMat m_HistMat; - int m_HistVolumeSaved; - int* m_pFVi; - int* m_pFViVar; - int* m_pFViVarRes; - CvBlobSeq m_TrackFGList; - //CvBlobTrackFVGen* (*m_CreateFVGen)(); - CvBlobTrackFVGen* m_pFVGen; - void SaveHist() - { - if(m_DataFileName[0]) - { - m_HistMat.Save(m_DataFileName); - m_HistVolumeSaved = m_HistMat.m_Volume; - } - }; - void LoadHist() - { - if(m_DataFileName[0])m_HistMat.Load(m_DataFileName); - m_HistVolumeSaved = m_HistMat.m_Volume; - } - void AllocData() - { /* AllocData: */ - m_pFVi = (int*)cvAlloc(sizeof(int)*m_Dim); - m_pFViVar = (int*)cvAlloc(sizeof(int)*m_Dim); - m_pFViVarRes = (int*)cvAlloc(sizeof(int)*m_Dim); - m_Sizes = (int*)cvAlloc(sizeof(int)*m_Dim); - - { /* Create init sparce matrix: */ - int i; - for(i=0;i0;--i) - { - //DefTrackFG* pF = (DefTrackFG*)m_TrackFGList.GetBlob(i-1); - // pF->pFVGen->Release(); - m_TrackFGList.DelBlob(i-1); - } - cvFree(&m_pFVi); - cvFree(&m_pFViVar); - cvFree(&m_pFViVarRes); - cvFree(&m_Sizes); - } /* FreeData. */ - - virtual void ParamUpdate() - { - if(m_BinNum != m_BinNumParam) - { - FreeData(); - m_BinNum = m_BinNumParam; - AllocData(); - } - } + int m_BinNumParam; + int m_SmoothRadius; + const char* m_SmoothKernel; + float m_AbnormalThreshold; + int m_TrackNum; + int m_Frame; + int m_BinNum; + char m_DataFileName[1024]; + int m_Dim; + int* m_Sizes; + DefMat m_HistMat; + int m_HistVolumeSaved; + int* m_pFVi; + int* m_pFViVar; + int* m_pFViVarRes; + CvBlobSeq m_TrackFGList; + //CvBlobTrackFVGen* (*m_CreateFVGen)(); + CvBlobTrackFVGen* m_pFVGen; + void SaveHist() + { + if(m_DataFileName[0]) + { + m_HistMat.Save(m_DataFileName); + m_HistVolumeSaved = m_HistMat.m_Volume; + } + }; + void LoadHist() + { + if(m_DataFileName[0])m_HistMat.Load(m_DataFileName); + m_HistVolumeSaved = m_HistMat.m_Volume; + } + void AllocData() + { /* AllocData: */ + m_pFVi = (int*)cvAlloc(sizeof(int)*m_Dim); + m_pFViVar = (int*)cvAlloc(sizeof(int)*m_Dim); + m_pFViVarRes = (int*)cvAlloc(sizeof(int)*m_Dim); + m_Sizes = (int*)cvAlloc(sizeof(int)*m_Dim); + + { /* Create init sparce matrix: */ + int i; + for(i=0;i0;--i) + { + //DefTrackFG* pF = (DefTrackFG*)m_TrackFGList.GetBlob(i-1); + // pF->pFVGen->Release(); + m_TrackFGList.DelBlob(i-1); + } + cvFree(&m_pFVi); + cvFree(&m_pFViVar); + cvFree(&m_pFViVarRes); + cvFree(&m_Sizes); + } /* FreeData. */ + + virtual void ParamUpdate() + { + if(m_BinNum != m_BinNumParam) + { + FreeData(); + m_BinNum = m_BinNumParam; + AllocData(); + } + } public: - CvBlobTrackAnalysisHist(CvBlobTrackFVGen* (*createFVGen)()):m_TrackFGList(sizeof(DefTrackFG)) - { - m_pFVGen = createFVGen(); - m_Dim = m_pFVGen->GetFVSize(); - m_Frame = 0; - m_pFVi = 0; - m_TrackNum = 0; - m_BinNum = 32; - m_DataFileName[0] = 0; - - m_AbnormalThreshold = 0.02f; - AddParam("AbnormalThreshold",&m_AbnormalThreshold); - CommentParam("AbnormalThreshold","If trajectory histogram value is lesst then then trajectory is abnormal"); - - m_SmoothRadius = 1; - AddParam("SmoothRadius",&m_SmoothRadius); - CommentParam("AbnormalThreshold","Radius (in bins) for histogram smoothing"); - - m_SmoothKernel = "L"; - AddParam("SmoothKernel",&m_SmoothKernel); - CommentParam("SmoothKernel","L - Linear, G - Gaussian"); - - - m_BinNumParam = m_BinNum; - AddParam("BinNum",&m_BinNumParam); - CommentParam("BinNum","Number of bin for each dimention of feature vector"); - - AllocData(); - SetModuleName("Hist"); - - } /* Constructor. */ - - ~CvBlobTrackAnalysisHist() - { - SaveHist(); - FreeData(); - m_pFVGen->Release(); - } /* Destructor. */ - - /*----------------- Interface: --------------------*/ - virtual void AddBlob(CvBlob* pBlob) - { - DefTrackFG* pF = (DefTrackFG*)m_TrackFGList.GetBlobByID(CV_BLOB_ID(pBlob)); - if(pF == NULL) - { /* create new filter */ - DefTrackFG F; - F.state = 0; - F.blob = pBlob[0]; - F.LastFrame = m_Frame; - // F.pFVGen = m_CreateFVGen(); - F.pHist = new DefMat(m_Dim,m_Sizes,SPARSE); - m_TrackFGList.AddBlob((CvBlob*)&F); - pF = (DefTrackFG*)m_TrackFGList.GetBlobByID(CV_BLOB_ID(pBlob)); - } - - assert(pF); - pF->blob = pBlob[0]; - pF->LastFrame = m_Frame; - m_pFVGen->AddBlob(pBlob); - }; - virtual void Process(IplImage* pImg, IplImage* pFG) - { - int i; - m_pFVGen->Process(pImg, pFG); - int SK = m_SmoothKernel[0]; - - for(i=0; iGetFVNum(); ++i) - { - int BlobID = 0; - float* pFV = m_pFVGen->GetFV(i,&BlobID); - float* pFVMax = m_pFVGen->GetFVMax(); - float* pFVMin = m_pFVGen->GetFVMin(); - DefTrackFG* pF = (DefTrackFG*)m_TrackFGList.GetBlobByID(BlobID); - int HistVal = 1; - - if(pFV==NULL) break; - - pF->LastFrame = m_Frame; - - { /* Binarize FV: */ - int j; - for(j=0; jf0); - index = cvRound((m_BinNum-1)*(pFV[j]-f0)/(f1-f0)); - if(index<0)index=0; - if(index>=m_BinNum)index=m_BinNum-1; - m_pFVi[j] = index; - } - } - - HistVal = m_HistMat.GetVal(m_pFVi);/* get bin value*/ - pF->state = 0; - { /* Calculate state: */ - float T = m_HistMat.m_Max*m_AbnormalThreshold; /* calc threshold */ - - if(m_TrackNum>0) T = 256.0f * m_TrackNum*m_AbnormalThreshold; - if(T>0) - { - pF->state = (T - HistVal)/(T*0.2f) + 0.5f; - } - if(pF->state<0)pF->state=0; - if(pF->state>1)pF->state=1; - } - - { /* If it is a new FV then add it to trajectory histogram: */ - int i,flag = 1; - int r = m_SmoothRadius; - - // printf("BLob %3d NEW FV [", CV_BLOB_ID(pF)); - // for(i=0;i=m_BinNum) good= 0; - dist += m_pFViVar[i]*m_pFViVar[i]; - }/* Calculate next dimension. */ - - if(SK=='G' || SK=='g') - { - double dist2 = dist/(r*r); - HistAdd = cvRound(256*exp(-dist2)); /* Hist Add for (dist=1) = 25.6*/ - } - else if(SK=='L' || SK=='l') - { - dist = (float)(sqrt(dist)/(r+1)); - HistAdd = cvRound(256*(1-dist)); - } - else - { - HistAdd = 255; /* Flat smoothing. */ - } - - if(good && HistAdd>0) - { /* Update histogram: */ - assert(pF->pHist); - pF->pHist->SetMax(m_pFViVarRes, HistAdd); - } /* Update histogram. */ - - for(i=0; i0; --i) - { /* Add histogram and delete blob from list: */ - DefTrackFG* pF = (DefTrackFG*)m_TrackFGList.GetBlob(i-1); - if(pF->LastFrame+3 < m_Frame && pF->pHist) - { - m_HistMat.Add(pF->pHist); - delete pF->pHist; - m_TrackNum++; - m_TrackFGList.DelBlob(i-1); - } - }/* next blob */ - } - - m_Frame++; - - if(m_Wnd) - { /* Debug output: */ - int* idxs = NULL; - int Val = 0; - IplImage* pI = cvCloneImage(pImg); - - cvZero(pI); - - for(Val = m_HistMat.GetNext(&idxs,1); idxs; Val=m_HistMat.GetNext(&idxs,0)) - { /* Draw all elements: */ - float vf; - int x,y; - - if(!idxs) break; - if(Val == 0) continue; - - vf = (float)Val/(m_HistMat.m_Max?m_HistMat.m_Max:1); - x = cvRound((float)(pI->width-1)*(float)idxs[0] / (float)m_BinNum); - y = cvRound((float)(pI->height-1)*(float)idxs[1] / (float)m_BinNum); - - cvCircle(pI, cvPoint(x,y), cvRound(vf*pI->height/(m_BinNum*2)),CV_RGB(255,0,0),CV_FILLED); - if(m_Dim > 3) - { - int dx = -2*(idxs[2]-m_BinNum/2); - int dy = -2*(idxs[3]-m_BinNum/2); - cvLine(pI,cvPoint(x,y),cvPoint(x+dx,y+dy),CV_RGB(0,cvRound(vf*255),1)); - } - if( m_Dim==4 && - m_pFVGen->GetFVMax()[0]==m_pFVGen->GetFVMax()[2] && - m_pFVGen->GetFVMax()[1]==m_pFVGen->GetFVMax()[3]) - { - int x = cvRound((float)(pI->width-1)*(float)idxs[2] / (float)m_BinNum); - int y = cvRound((float)(pI->height-1)*(float)idxs[3] / (float)m_BinNum); - cvCircle(pI, cvPoint(x,y), cvRound(vf*pI->height/(m_BinNum*2)),CV_RGB(0,0,255),CV_FILLED); - } - } /* Draw all elements. */ - - for(i=m_TrackFGList.GetBlobNum();i>0;--i) - { - DefTrackFG* pF = (DefTrackFG*)m_TrackFGList.GetBlob(i-1); - DefMat* pHist = pF?pF->pHist:NULL; - - if(pHist==NULL) continue; - - for(Val = pHist->GetNext(&idxs,1);idxs;Val=pHist->GetNext(&idxs,0)) - { /* Draw all elements: */ - float vf; - int x,y; - - if(!idxs) break; - if(Val == 0) continue; - - vf = (float)Val/(pHist->m_Max?pHist->m_Max:1); - x = cvRound((float)(pI->width-1)*(float)idxs[0] / (float)m_BinNum); - y = cvRound((float)(pI->height-1)*(float)idxs[1] / (float)m_BinNum); - - cvCircle(pI, cvPoint(x,y), cvRound(2*vf),CV_RGB(0,0,cvRound(255*vf)),CV_FILLED); - if(m_Dim > 3) - { - int dx = -2*(idxs[2]-m_BinNum/2); - int dy = -2*(idxs[3]-m_BinNum/2); - cvLine(pI,cvPoint(x,y),cvPoint(x+dx,y+dy),CV_RGB(0,0,255)); - } - if( m_Dim==4 && - m_pFVGen->GetFVMax()[0]==m_pFVGen->GetFVMax()[2] && - m_pFVGen->GetFVMax()[1]==m_pFVGen->GetFVMax()[3]) - { /* if SS feature vector */ - int x = cvRound((float)(pI->width-1)*(float)idxs[2] / (float)m_BinNum); - int y = cvRound((float)(pI->height-1)*(float)idxs[3] / (float)m_BinNum); - cvCircle(pI, cvPoint(x,y), cvRound(vf*pI->height/(m_BinNum*2)),CV_RGB(0,0,255),CV_FILLED); - } - } /* Draw all elements. */ - } /* Next track. */ - - //cvNamedWindow("Hist",0); - //cvShowImage("Hist", pI); - cvReleaseImage(&pI); - } - }; - - float GetState(int BlobID) - { - DefTrackFG* pF = (DefTrackFG*)m_TrackFGList.GetBlobByID(BlobID); - return pF?pF->state:0.0f; - }; - - /* Return 0 if trajectory is normal; + CvBlobTrackAnalysisHist(CvBlobTrackFVGen* (*createFVGen)()):m_TrackFGList(sizeof(DefTrackFG)) + { + m_pFVGen = createFVGen(); + m_Dim = m_pFVGen->GetFVSize(); + m_Frame = 0; + m_pFVi = 0; + m_TrackNum = 0; + m_BinNum = 32; + m_DataFileName[0] = 0; + + m_AbnormalThreshold = 0.02f; + AddParam("AbnormalThreshold",&m_AbnormalThreshold); + CommentParam("AbnormalThreshold","If trajectory histogram value is lesst then then trajectory is abnormal"); + + m_SmoothRadius = 1; + AddParam("SmoothRadius",&m_SmoothRadius); + CommentParam("AbnormalThreshold","Radius (in bins) for histogram smoothing"); + + m_SmoothKernel = "L"; + AddParam("SmoothKernel",&m_SmoothKernel); + CommentParam("SmoothKernel","L - Linear, G - Gaussian"); + + + m_BinNumParam = m_BinNum; + AddParam("BinNum",&m_BinNumParam); + CommentParam("BinNum","Number of bin for each dimention of feature vector"); + + AllocData(); + SetModuleName("Hist"); + + } /* Constructor. */ + + ~CvBlobTrackAnalysisHist() + { + SaveHist(); + FreeData(); + m_pFVGen->Release(); + } /* Destructor. */ + + /*----------------- Interface: --------------------*/ + virtual void AddBlob(CvBlob* pBlob) + { + DefTrackFG* pF = (DefTrackFG*)m_TrackFGList.GetBlobByID(CV_BLOB_ID(pBlob)); + if(pF == NULL) + { /* create new filter */ + DefTrackFG F; + F.state = 0; + F.blob = pBlob[0]; + F.LastFrame = m_Frame; + // F.pFVGen = m_CreateFVGen(); + F.pHist = new DefMat(m_Dim,m_Sizes,SPARSE); + m_TrackFGList.AddBlob((CvBlob*)&F); + pF = (DefTrackFG*)m_TrackFGList.GetBlobByID(CV_BLOB_ID(pBlob)); + } + + assert(pF); + pF->blob = pBlob[0]; + pF->LastFrame = m_Frame; + m_pFVGen->AddBlob(pBlob); + }; + virtual void Process(IplImage* pImg, IplImage* pFG) + { + m_pFVGen->Process(pImg, pFG); + int SK = m_SmoothKernel[0]; + + for(int i=0; iGetFVNum(); ++i) + { + int BlobID = 0; + float* pFV = m_pFVGen->GetFV(i,&BlobID); + float* pFVMax = m_pFVGen->GetFVMax(); + float* pFVMin = m_pFVGen->GetFVMin(); + DefTrackFG* pF = (DefTrackFG*)m_TrackFGList.GetBlobByID(BlobID); + int HistVal = 1; + + if(pFV==NULL) break; + + pF->LastFrame = m_Frame; + + { /* Binarize FV: */ + int j; + for(j=0; jf0); + index = cvRound((m_BinNum-1)*(pFV[j]-f0)/(f1-f0)); + if(index<0)index=0; + if(index>=m_BinNum)index=m_BinNum-1; + m_pFVi[j] = index; + } + } + + HistVal = m_HistMat.GetVal(m_pFVi);/* get bin value*/ + pF->state = 0; + { /* Calculate state: */ + float T = m_HistMat.m_Max*m_AbnormalThreshold; /* calc threshold */ + + if(m_TrackNum>0) T = 256.0f * m_TrackNum*m_AbnormalThreshold; + if(T>0) + { + pF->state = (T - HistVal)/(T*0.2f) + 0.5f; + } + if(pF->state<0)pF->state=0; + if(pF->state>1)pF->state=1; + } + + { /* If it is a new FV then add it to trajectory histogram: */ + int flag = 1; + int r = m_SmoothRadius; + + // printf("BLob %3d NEW FV [", CV_BLOB_ID(pF)); + // for(i=0;i=m_BinNum) good= 0; + dist += m_pFViVar[k]*m_pFViVar[k]; + }/* Calculate next dimension. */ + + if(SK=='G' || SK=='g') + { + double dist2 = dist/(r*r); + HistAdd = cvRound(256*exp(-dist2)); /* Hist Add for (dist=1) = 25.6*/ + } + else if(SK=='L' || SK=='l') + { + dist = (float)(sqrt(dist)/(r+1)); + HistAdd = cvRound(256*(1-dist)); + } + else + { + HistAdd = 255; /* Flat smoothing. */ + } + + if(good && HistAdd>0) + { /* Update histogram: */ + assert(pF->pHist); + pF->pHist->SetMax(m_pFViVarRes, HistAdd); + } /* Update histogram. */ + + int idx = 0; + for( ; idx0; --i) + { /* Add histogram and delete blob from list: */ + DefTrackFG* pF = (DefTrackFG*)m_TrackFGList.GetBlob(i-1); + if(pF->LastFrame+3 < m_Frame && pF->pHist) + { + m_HistMat.Add(pF->pHist); + delete pF->pHist; + m_TrackNum++; + m_TrackFGList.DelBlob(i-1); + } + }/* next blob */ + } + + m_Frame++; + + if(m_Wnd) + { /* Debug output: */ + int* idxs = NULL; + int Val = 0; + IplImage* pI = cvCloneImage(pImg); + + cvZero(pI); + + for(Val = m_HistMat.GetNext(&idxs,1); idxs; Val=m_HistMat.GetNext(&idxs,0)) + { /* Draw all elements: */ + if(!idxs) break; + if(Val == 0) continue; + + float vf = (float)Val/(m_HistMat.m_Max?m_HistMat.m_Max:1); + int x = cvRound((float)(pI->width-1)*(float)idxs[0] / (float)m_BinNum); + int y = cvRound((float)(pI->height-1)*(float)idxs[1] / (float)m_BinNum); + + cvCircle(pI, cvPoint(x,y), cvRound(vf*pI->height/(m_BinNum*2)),CV_RGB(255,0,0),CV_FILLED); + if(m_Dim > 3) + { + int dx = -2*(idxs[2]-m_BinNum/2); + int dy = -2*(idxs[3]-m_BinNum/2); + cvLine(pI,cvPoint(x,y),cvPoint(x+dx,y+dy),CV_RGB(0,cvRound(vf*255),1)); + } + if( m_Dim==4 && + m_pFVGen->GetFVMax()[0]==m_pFVGen->GetFVMax()[2] && + m_pFVGen->GetFVMax()[1]==m_pFVGen->GetFVMax()[3]) + { + int x1 = cvRound((float)(pI->width-1)*(float)idxs[2] / (float)m_BinNum); + int y1 = cvRound((float)(pI->height-1)*(float)idxs[3] / (float)m_BinNum); + cvCircle(pI, cvPoint(x1,y1), cvRound(vf*pI->height/(m_BinNum*2)),CV_RGB(0,0,255),CV_FILLED); + } + } /* Draw all elements. */ + + for(int i=m_TrackFGList.GetBlobNum();i>0;--i) + { + DefTrackFG* pF = (DefTrackFG*)m_TrackFGList.GetBlob(i-1); + DefMat* pHist = pF?pF->pHist:NULL; + + if(pHist==NULL) continue; + + for(Val = pHist->GetNext(&idxs,1);idxs;Val=pHist->GetNext(&idxs,0)) + { /* Draw all elements: */ + float vf; + int x,y; + + if(!idxs) break; + if(Val == 0) continue; + + vf = (float)Val/(pHist->m_Max?pHist->m_Max:1); + x = cvRound((float)(pI->width-1)*(float)idxs[0] / (float)m_BinNum); + y = cvRound((float)(pI->height-1)*(float)idxs[1] / (float)m_BinNum); + + cvCircle(pI, cvPoint(x,y), cvRound(2*vf),CV_RGB(0,0,cvRound(255*vf)),CV_FILLED); + if(m_Dim > 3) + { + int dx = -2*(idxs[2]-m_BinNum/2); + int dy = -2*(idxs[3]-m_BinNum/2); + cvLine(pI,cvPoint(x,y),cvPoint(x+dx,y+dy),CV_RGB(0,0,255)); + } + if( m_Dim==4 && + m_pFVGen->GetFVMax()[0]==m_pFVGen->GetFVMax()[2] && + m_pFVGen->GetFVMax()[1]==m_pFVGen->GetFVMax()[3]) + { /* if SS feature vector */ + int x1 = cvRound((float)(pI->width-1)*(float)idxs[2] / (float)m_BinNum); + int y1 = cvRound((float)(pI->height-1)*(float)idxs[3] / (float)m_BinNum); + cvCircle(pI, cvPoint(x1,y1), cvRound(vf*pI->height/(m_BinNum*2)),CV_RGB(0,0,255),CV_FILLED); + } + } /* Draw all elements. */ + } /* Next track. */ + + //cvNamedWindow("Hist",0); + //cvShowImage("Hist", pI); + cvReleaseImage(&pI); + } + }; + + float GetState(int BlobID) + { + DefTrackFG* pF = (DefTrackFG*)m_TrackFGList.GetBlobByID(BlobID); + return pF?pF->state:0.0f; + }; + + /* Return 0 if trajectory is normal; rreturn >0 if trajectory abnormal. */ - virtual const char* GetStateDesc(int BlobID) - { - if(GetState(BlobID)>0.5) return "abnormal"; - return NULL; - } - - virtual void SetFileName(char* DataBaseName) - { - if(m_HistMat.m_Volume!=m_HistVolumeSaved)SaveHist(); - m_DataFileName[0] = m_DataFileName[1000] = 0; - - if(DataBaseName) - { - strncpy(m_DataFileName,DataBaseName,1000); - strcat(m_DataFileName, ".yml"); - } - LoadHist(); - }; - - virtual void SaveState(CvFileStorage* fs) - { - int b, bN = m_TrackFGList.GetBlobNum(); - cvWriteInt(fs,"BlobNum",bN); - cvStartWriteStruct(fs,"BlobList",CV_NODE_SEQ); - - for(b=0; bblob), "ffffi"); - cvWriteInt(fs,"LastFrame",pF->LastFrame); - cvWriteReal(fs,"State",pF->state); - pF->pHist->Save(fs, "Hist"); - cvEndWriteStruct(fs); - } - cvEndWriteStruct(fs); - m_HistMat.Save(fs, "Hist"); - }; - - virtual void LoadState(CvFileStorage* fs, CvFileNode* node) - { - CvFileNode* pBLN = cvGetFileNodeByName(fs,node,"BlobList"); - - if(pBLN && CV_NODE_IS_SEQ(pBLN->tag)) - { - int b, bN = pBLN->data.seq->total; - for(b=0; bdata.seq,b); - - assert(pBN); - cvReadStructByName(fs, pBN, "Blob", &Blob, "ffffi"); - AddBlob(&Blob); - pF = (DefTrackFG*)m_TrackFGList.GetBlobByID(Blob.ID); - if(pF==NULL) continue; - assert(pF); - pF->state = (float)cvReadIntByName(fs,pBN,"State",cvRound(pF->state)); - assert(pF->pHist); - pF->pHist->Load(fs,pBN,"Hist"); - } - } - - m_HistMat.Load(fs, node, "Hist"); - }; /* LoadState */ - - - virtual void Release(){ delete this; }; + virtual const char* GetStateDesc(int BlobID) + { + if(GetState(BlobID)>0.5) return "abnormal"; + return NULL; + } + + virtual void SetFileName(char* DataBaseName) + { + if(m_HistMat.m_Volume!=m_HistVolumeSaved)SaveHist(); + m_DataFileName[0] = m_DataFileName[1000] = 0; + + if(DataBaseName) + { + strncpy(m_DataFileName,DataBaseName,1000); + strcat(m_DataFileName, ".yml"); + } + LoadHist(); + }; + + virtual void SaveState(CvFileStorage* fs) + { + int b, bN = m_TrackFGList.GetBlobNum(); + cvWriteInt(fs,"BlobNum",bN); + cvStartWriteStruct(fs,"BlobList",CV_NODE_SEQ); + + for(b=0; bblob), "ffffi"); + cvWriteInt(fs,"LastFrame",pF->LastFrame); + cvWriteReal(fs,"State",pF->state); + pF->pHist->Save(fs, "Hist"); + cvEndWriteStruct(fs); + } + cvEndWriteStruct(fs); + m_HistMat.Save(fs, "Hist"); + }; + + virtual void LoadState(CvFileStorage* fs, CvFileNode* node) + { + CvFileNode* pBLN = cvGetFileNodeByName(fs,node,"BlobList"); + + if(pBLN && CV_NODE_IS_SEQ(pBLN->tag)) + { + int b, bN = pBLN->data.seq->total; + for(b=0; bdata.seq,b); + + assert(pBN); + cvReadStructByName(fs, pBN, "Blob", &Blob, "ffffi"); + AddBlob(&Blob); + pF = (DefTrackFG*)m_TrackFGList.GetBlobByID(Blob.ID); + if(pF==NULL) continue; + assert(pF); + pF->state = (float)cvReadIntByName(fs,pBN,"State",cvRound(pF->state)); + assert(pF->pHist); + pF->pHist->Load(fs,pBN,"Hist"); + } + } + + m_HistMat.Load(fs, node, "Hist"); + }; /* LoadState */ + + + virtual void Release(){ delete this; }; }; @@ -1127,390 +1119,390 @@ CvBlobTrackAnalysis* cvCreateModuleBlobTrackAnalysisHistSS() typedef struct DefTrackSVM { - CvBlob blob; - // CvBlobTrackFVGen* pFVGen; - int LastFrame; - float state; - CvBlob BlobLast; - CvSeq* pFVSeq; - CvMemStorage* pMem; + CvBlob blob; + // CvBlobTrackFVGen* pFVGen; + int LastFrame; + float state; + CvBlob BlobLast; + CvSeq* pFVSeq; + CvMemStorage* pMem; } DefTrackSVM; class CvBlobTrackAnalysisSVM : public CvBlobTrackAnalysis { - /*---------------- Internal functions: --------------------*/ + /*---------------- Internal functions: --------------------*/ private: - CvMemStorage* m_pMem; - int m_TrackNum; - int m_Frame; - char m_DataFileName[1024]; - int m_Dim; - float* m_pFV; - //CvStatModel* m_pStatModel; - void* m_pStatModel; - CvBlobSeq m_Tracks; - CvMat* m_pTrainData; - int m_LastTrainDataSize; - // CvBlobTrackFVGen* (*m_CreateFVGen)(); - CvBlobTrackFVGen* m_pFVGen; - float m_NU; - float m_RBFWidth; - IplImage* m_pStatImg; /* for debug purpose */ - CvSize m_ImgSize; - void RetrainStatModel() - { - ///////// !!!!! TODO !!!!! Repair ///////////// + CvMemStorage* m_pMem; + int m_TrackNum; + int m_Frame; + char m_DataFileName[1024]; + int m_Dim; + float* m_pFV; + //CvStatModel* m_pStatModel; + void* m_pStatModel; + CvBlobSeq m_Tracks; + CvMat* m_pTrainData; + int m_LastTrainDataSize; + // CvBlobTrackFVGen* (*m_CreateFVGen)(); + CvBlobTrackFVGen* m_pFVGen; + float m_NU; + float m_RBFWidth; + IplImage* m_pStatImg; /* for debug purpose */ + CvSize m_ImgSize; + void RetrainStatModel() + { + ///////// !!!!! TODO !!!!! Repair ///////////// #if 0 - float nu = 0; - CvSVMModelParams SVMParams = {0}; - CvStatModel* pM = NULL; - - - memset(&SVMParams,0,sizeof(SVMParams)); - SVMParams.svm_type = CV_SVM_ONE_CLASS; - SVMParams.kernel_type = CV_SVM_RBF; - SVMParams.gamma = 2.0/(m_RBFWidth*m_RBFWidth); - SVMParams.nu = m_NU; - SVMParams.degree = 3; - SVMParams.criteria = cvTermCriteria(CV_TERMCRIT_EPS, 100, 1e-3 ); - SVMParams.C = 1; - SVMParams.p = 0.1; - - - if(m_pTrainData == NULL) return; - { - int64 TickCount = cvGetTickCount(); - printf("Frame: %d\n Retrain SVM\nData Size = %d\n",m_Frame, m_pTrainData->rows); - pM = cvTrainSVM( m_pTrainData,CV_ROW_SAMPLE, NULL, (CvStatModelParams*)&SVMParams, NULL, NULL); - TickCount = cvGetTickCount() - TickCount ; - printf("SV Count = %d\n",((CvSVMModel*)pM)->sv_total); - printf("Processing Time = %.1f(ms)\n",TickCount/(1000*cvGetTickFrequency())); - - } - if(pM==NULL) return; - if(m_pStatModel) cvReleaseStatModel(&m_pStatModel); - m_pStatModel = pM; - - if(m_pTrainData && m_Wnd) - { - float MaxVal = 0; - IplImage* pW = cvCreateImage(m_ImgSize,IPL_DEPTH_32F,1); - IplImage* pI = cvCreateImage(m_ImgSize,IPL_DEPTH_8U,1); - float* pFVVar = m_pFVGen->GetFVVar(); - int i; - cvZero(pW); - - for(i=0; irows; ++i) - { /* Draw all elements: */ - float* pFV = (float*)(m_pTrainData->data.ptr + m_pTrainData->step*i); - int x = cvRound(pFV[0]*pFVVar[0]); - int y = cvRound(pFV[1]*pFVVar[1]); - float r; - - if(x<0)x=0; - if(x>=pW->width)x=pW->width-1; - if(y<0)y=0; - if(y>=pW->height)y=pW->height-1; - - r = ((float*)(pW->imageData + y*pW->widthStep))[x]; - r++; - ((float*)(pW->imageData + y*pW->widthStep))[x] = r; - - if(r>MaxVal)MaxVal=r; - } /* Next point. */ - - if(MaxVal>0)cvConvertScale(pW,pI,255/MaxVal,0); - cvNamedWindow("SVMData",0); - cvShowImage("SVMData",pI); - cvSaveImage("SVMData.bmp",pI); - cvReleaseImage(&pW); - cvReleaseImage(&pI); - } /* Prepare for debug. */ - - if(m_pStatModel && m_Wnd && m_Dim == 2) - { - float* pFVVar = m_pFVGen->GetFVVar(); - int x,y; - if(m_pStatImg==NULL) - { - m_pStatImg = cvCreateImage(m_ImgSize,IPL_DEPTH_8U,1); - } - cvZero(m_pStatImg); - - for(y=0; yheight; y+=1) for(x=0; xwidth; x+=1) - { /* Draw all elements: */ - float res; - uchar* pData = (uchar*)m_pStatImg->imageData + x + y*m_pStatImg->widthStep; - CvMat FVmat; - float xy[2] = {x/pFVVar[0],y/pFVVar[1]}; - cvInitMatHeader( &FVmat, 1, 2, CV_32F, xy ); - res = cvStatModelPredict( m_pStatModel, &FVmat, NULL ); - pData[0]=((res>0.5)?255:0); - } /* Next point. */ - - cvNamedWindow("SVMMask",0); - cvShowImage("SVMMask",m_pStatImg); - cvSaveImage("SVMMask.bmp",m_pStatImg); - } /* Prepare for debug. */ + float nu = 0; + CvSVMModelParams SVMParams = {0}; + CvStatModel* pM = NULL; + + + memset(&SVMParams,0,sizeof(SVMParams)); + SVMParams.svm_type = CV_SVM_ONE_CLASS; + SVMParams.kernel_type = CV_SVM_RBF; + SVMParams.gamma = 2.0/(m_RBFWidth*m_RBFWidth); + SVMParams.nu = m_NU; + SVMParams.degree = 3; + SVMParams.criteria = cvTermCriteria(CV_TERMCRIT_EPS, 100, 1e-3 ); + SVMParams.C = 1; + SVMParams.p = 0.1; + + + if(m_pTrainData == NULL) return; + { + int64 TickCount = cvGetTickCount(); + printf("Frame: %d\n Retrain SVM\nData Size = %d\n",m_Frame, m_pTrainData->rows); + pM = cvTrainSVM( m_pTrainData,CV_ROW_SAMPLE, NULL, (CvStatModelParams*)&SVMParams, NULL, NULL); + TickCount = cvGetTickCount() - TickCount ; + printf("SV Count = %d\n",((CvSVMModel*)pM)->sv_total); + printf("Processing Time = %.1f(ms)\n",TickCount/(1000*cvGetTickFrequency())); + + } + if(pM==NULL) return; + if(m_pStatModel) cvReleaseStatModel(&m_pStatModel); + m_pStatModel = pM; + + if(m_pTrainData && m_Wnd) + { + float MaxVal = 0; + IplImage* pW = cvCreateImage(m_ImgSize,IPL_DEPTH_32F,1); + IplImage* pI = cvCreateImage(m_ImgSize,IPL_DEPTH_8U,1); + float* pFVVar = m_pFVGen->GetFVVar(); + int i; + cvZero(pW); + + for(i=0; irows; ++i) + { /* Draw all elements: */ + float* pFV = (float*)(m_pTrainData->data.ptr + m_pTrainData->step*i); + int x = cvRound(pFV[0]*pFVVar[0]); + int y = cvRound(pFV[1]*pFVVar[1]); + float r; + + if(x<0)x=0; + if(x>=pW->width)x=pW->width-1; + if(y<0)y=0; + if(y>=pW->height)y=pW->height-1; + + r = ((float*)(pW->imageData + y*pW->widthStep))[x]; + r++; + ((float*)(pW->imageData + y*pW->widthStep))[x] = r; + + if(r>MaxVal)MaxVal=r; + } /* Next point. */ + + if(MaxVal>0)cvConvertScale(pW,pI,255/MaxVal,0); + cvNamedWindow("SVMData",0); + cvShowImage("SVMData",pI); + cvSaveImage("SVMData.bmp",pI); + cvReleaseImage(&pW); + cvReleaseImage(&pI); + } /* Prepare for debug. */ + + if(m_pStatModel && m_Wnd && m_Dim == 2) + { + float* pFVVar = m_pFVGen->GetFVVar(); + int x,y; + if(m_pStatImg==NULL) + { + m_pStatImg = cvCreateImage(m_ImgSize,IPL_DEPTH_8U,1); + } + cvZero(m_pStatImg); + + for(y=0; yheight; y+=1) for(x=0; xwidth; x+=1) + { /* Draw all elements: */ + float res; + uchar* pData = (uchar*)m_pStatImg->imageData + x + y*m_pStatImg->widthStep; + CvMat FVmat; + float xy[2] = {x/pFVVar[0],y/pFVVar[1]}; + cvInitMatHeader( &FVmat, 1, 2, CV_32F, xy ); + res = cvStatModelPredict( m_pStatModel, &FVmat, NULL ); + pData[0]=((res>0.5)?255:0); + } /* Next point. */ + + cvNamedWindow("SVMMask",0); + cvShowImage("SVMMask",m_pStatImg); + cvSaveImage("SVMMask.bmp",m_pStatImg); + } /* Prepare for debug. */ #endif - }; - void SaveStatModel() - { - if(m_DataFileName[0]) - { - if(m_pTrainData)cvSave(m_DataFileName, m_pTrainData); - } - }; - void LoadStatModel() - { - if(m_DataFileName[0]) - { - CvMat* pTrainData = (CvMat*)cvLoad(m_DataFileName); - if(CV_IS_MAT(pTrainData) && pTrainData->width == m_Dim) - { - if(m_pTrainData) cvReleaseMat(&m_pTrainData); - m_pTrainData = pTrainData; - RetrainStatModel(); - } - } - } + }; + void SaveStatModel() + { + if(m_DataFileName[0]) + { + if(m_pTrainData)cvSave(m_DataFileName, m_pTrainData); + } + }; + void LoadStatModel() + { + if(m_DataFileName[0]) + { + CvMat* pTrainData = (CvMat*)cvLoad(m_DataFileName); + if(CV_IS_MAT(pTrainData) && pTrainData->width == m_Dim) + { + if(m_pTrainData) cvReleaseMat(&m_pTrainData); + m_pTrainData = pTrainData; + RetrainStatModel(); + } + } + } public: - CvBlobTrackAnalysisSVM(CvBlobTrackFVGen* (*createFVGen)()):m_Tracks(sizeof(DefTrackSVM)) - { - m_pFVGen = createFVGen(); - m_Dim = m_pFVGen->GetFVSize(); - m_pFV = (float*)cvAlloc(sizeof(float)*m_Dim); - m_Frame = 0; - m_TrackNum = 0; - m_pTrainData = NULL; - m_pStatModel = NULL; - m_DataFileName[0] = 0; - m_pStatImg = NULL; - m_LastTrainDataSize = 0; - - m_NU = 0.2f; - AddParam("Nu",&m_NU); - CommentParam("Nu","Parameters that tunes SVM border elastic"); - - m_RBFWidth = 1; - AddParam("RBFWidth",&m_RBFWidth); - CommentParam("RBFWidth","Parameters that tunes RBF kernel function width."); - - SetModuleName("SVM"); - - } /* Constructor. */ - - ~CvBlobTrackAnalysisSVM() - { - int i; - SaveStatModel(); - for(i=m_Tracks.GetBlobNum();i>0;--i) - { - DefTrackSVM* pF = (DefTrackSVM*)m_Tracks.GetBlob(i-1); - if(pF->pMem) cvReleaseMemStorage(&pF->pMem); - //pF->pFVGen->Release(); - } - if(m_pStatImg)cvReleaseImage(&m_pStatImg); - cvFree(&m_pFV); - } /* Destructor. */ - - /*----------------- Interface: --------------------*/ - virtual void AddBlob(CvBlob* pBlob) - { - DefTrackSVM* pF = (DefTrackSVM*)m_Tracks.GetBlobByID(CV_BLOB_ID(pBlob)); - - m_pFVGen->AddBlob(pBlob); - - if(pF == NULL) - { /* Create new record: */ - DefTrackSVM F; - F.state = 0; - F.blob = pBlob[0]; - F.LastFrame = m_Frame; - //F.pFVGen = m_CreateFVGen(); - F.pMem = cvCreateMemStorage(); - F.pFVSeq = cvCreateSeq(0,sizeof(CvSeq),sizeof(float)*m_Dim,F.pMem); - - F.BlobLast.x = -1; - F.BlobLast.y = -1; - F.BlobLast.w = -1; - F.BlobLast.h = -1; - m_Tracks.AddBlob((CvBlob*)&F); - pF = (DefTrackSVM*)m_Tracks.GetBlobByID(CV_BLOB_ID(pBlob)); - } - - assert(pF); - pF->blob = pBlob[0]; - pF->LastFrame = m_Frame; - }; - - virtual void Process(IplImage* pImg, IplImage* pFG) - { - int i; - float* pFVVar = m_pFVGen->GetFVVar(); - - m_pFVGen->Process(pImg, pFG); - m_ImgSize = cvSize(pImg->width,pImg->height); - - for(i=m_pFVGen->GetFVNum(); i>0; --i) - { - int BlobID = 0; - float* pFV = m_pFVGen->GetFV(i,&BlobID); - DefTrackSVM* pF = (DefTrackSVM*)m_Tracks.GetBlobByID(BlobID); - - if(pF && pFV) - { /* Process: */ - float dx,dy; - CvMat FVmat; - - pF->state = 0; - - if(m_pStatModel) - { - int j; - for(j=0; jstate = cvStatModelPredict( m_pStatModel, &FVmat, NULL )<0.5; - pF->state = 1.f; - } - - dx = (pF->blob.x - pF->BlobLast.x); - dy = (pF->blob.y - pF->BlobLast.y); - - if(pF->BlobLast.x<0 || (dx*dx+dy*dy) >= 2*2) - { /* Add feature vector to train data base: */ - pF->BlobLast = pF->blob; - cvSeqPush(pF->pFVSeq,pFV); - } - } /* Process one blob. */ - } /* Next FV. */ - - for(i=m_Tracks.GetBlobNum(); i>0; --i) - { /* Check each blob record: */ - DefTrackSVM* pF = (DefTrackSVM*)m_Tracks.GetBlob(i-1); - - if(pF->LastFrame+3 < m_Frame ) - { /* Retrain stat model and delete blob filter: */ - int mult = 1+m_Dim; - int old_height = m_pTrainData?m_pTrainData->height:0; - int height = old_height + pF->pFVSeq->total*mult; - CvMat* pTrainData = cvCreateMat(height, m_Dim, CV_32F); - int j; - if(m_pTrainData && pTrainData) - { /* Create new train data matrix: */ - int h = pTrainData->height; - pTrainData->height = MIN(pTrainData->height, m_pTrainData->height); - cvCopy(m_pTrainData,pTrainData); - pTrainData->height = h; - } - - for(j=0; jpFVSeq->total; ++j) - { /* Copy new data to train data: */ - float* pFVVar = m_pFVGen->GetFVVar(); - float* pFV = (float*)cvGetSeqElem(pF->pFVSeq,j); - int k; - - for(k=0; k0) - { /* Variate: */ - for(t=0; tpMem); - m_TrackNum++; - m_Tracks.DelBlob(i-1); - - } /* End delete. */ - } /* Next track. */ - - /* Retrain data each 1 minute if new data exist: */ - if(m_Frame%(25*60) == 0 && m_pTrainData && m_pTrainData->rows > m_LastTrainDataSize) - { - RetrainStatModel(); - } - - m_Frame++; - - if(m_Wnd && m_Dim==2) - { /* Debug output: */ - int x,y; - IplImage* pI = cvCloneImage(pImg); - - if(m_pStatModel && m_pStatImg) - - for(y=0; yheight; y+=2) - { - uchar* pStatData = (uchar*)m_pStatImg->imageData + y*m_pStatImg->widthStep; - uchar* pData = (uchar*)pI->imageData + y*pI->widthStep; - - for(x=0;xwidth;x+=2) - { /* Draw all elements: */ - int d = pStatData[x]; - d = (d<<8) | (d^0xff); - *(ushort*)(pData + x*3) = (ushort)d; - } - } /* Next line. */ - - //cvNamedWindow("SVMMap",0); - //cvShowImage("SVMMap", pI); - cvReleaseImage(&pI); - } /* Debug output. */ - }; - float GetState(int BlobID) - { - DefTrackSVM* pF = (DefTrackSVM*)m_Tracks.GetBlobByID(BlobID); - return pF?pF->state:0.0f; - }; - - /* Return 0 if trajectory is normal; + CvBlobTrackAnalysisSVM(CvBlobTrackFVGen* (*createFVGen)()):m_Tracks(sizeof(DefTrackSVM)) + { + m_pFVGen = createFVGen(); + m_Dim = m_pFVGen->GetFVSize(); + m_pFV = (float*)cvAlloc(sizeof(float)*m_Dim); + m_Frame = 0; + m_TrackNum = 0; + m_pTrainData = NULL; + m_pStatModel = NULL; + m_DataFileName[0] = 0; + m_pStatImg = NULL; + m_LastTrainDataSize = 0; + + m_NU = 0.2f; + AddParam("Nu",&m_NU); + CommentParam("Nu","Parameters that tunes SVM border elastic"); + + m_RBFWidth = 1; + AddParam("RBFWidth",&m_RBFWidth); + CommentParam("RBFWidth","Parameters that tunes RBF kernel function width."); + + SetModuleName("SVM"); + + } /* Constructor. */ + + ~CvBlobTrackAnalysisSVM() + { + int i; + SaveStatModel(); + for(i=m_Tracks.GetBlobNum();i>0;--i) + { + DefTrackSVM* pF = (DefTrackSVM*)m_Tracks.GetBlob(i-1); + if(pF->pMem) cvReleaseMemStorage(&pF->pMem); + //pF->pFVGen->Release(); + } + if(m_pStatImg)cvReleaseImage(&m_pStatImg); + cvFree(&m_pFV); + } /* Destructor. */ + + /*----------------- Interface: --------------------*/ + virtual void AddBlob(CvBlob* pBlob) + { + DefTrackSVM* pF = (DefTrackSVM*)m_Tracks.GetBlobByID(CV_BLOB_ID(pBlob)); + + m_pFVGen->AddBlob(pBlob); + + if(pF == NULL) + { /* Create new record: */ + DefTrackSVM F; + F.state = 0; + F.blob = pBlob[0]; + F.LastFrame = m_Frame; + //F.pFVGen = m_CreateFVGen(); + F.pMem = cvCreateMemStorage(); + F.pFVSeq = cvCreateSeq(0,sizeof(CvSeq),sizeof(float)*m_Dim,F.pMem); + + F.BlobLast.x = -1; + F.BlobLast.y = -1; + F.BlobLast.w = -1; + F.BlobLast.h = -1; + m_Tracks.AddBlob((CvBlob*)&F); + pF = (DefTrackSVM*)m_Tracks.GetBlobByID(CV_BLOB_ID(pBlob)); + } + + assert(pF); + pF->blob = pBlob[0]; + pF->LastFrame = m_Frame; + }; + + virtual void Process(IplImage* pImg, IplImage* pFG) + { + int i; + float* pFVVar = m_pFVGen->GetFVVar(); + + m_pFVGen->Process(pImg, pFG); + m_ImgSize = cvSize(pImg->width,pImg->height); + + for(i=m_pFVGen->GetFVNum(); i>0; --i) + { + int BlobID = 0; + float* pFV = m_pFVGen->GetFV(i,&BlobID); + DefTrackSVM* pF = (DefTrackSVM*)m_Tracks.GetBlobByID(BlobID); + + if(pF && pFV) + { /* Process: */ + float dx,dy; + CvMat FVmat; + + pF->state = 0; + + if(m_pStatModel) + { + int j; + for(j=0; jstate = cvStatModelPredict( m_pStatModel, &FVmat, NULL )<0.5; + pF->state = 1.f; + } + + dx = (pF->blob.x - pF->BlobLast.x); + dy = (pF->blob.y - pF->BlobLast.y); + + if(pF->BlobLast.x<0 || (dx*dx+dy*dy) >= 2*2) + { /* Add feature vector to train data base: */ + pF->BlobLast = pF->blob; + cvSeqPush(pF->pFVSeq,pFV); + } + } /* Process one blob. */ + } /* Next FV. */ + + for(i=m_Tracks.GetBlobNum(); i>0; --i) + { /* Check each blob record: */ + DefTrackSVM* pF = (DefTrackSVM*)m_Tracks.GetBlob(i-1); + + if(pF->LastFrame+3 < m_Frame ) + { /* Retrain stat model and delete blob filter: */ + int mult = 1+m_Dim; + int old_height = m_pTrainData?m_pTrainData->height:0; + int height = old_height + pF->pFVSeq->total*mult; + CvMat* pTrainData = cvCreateMat(height, m_Dim, CV_32F); + int j; + if(m_pTrainData && pTrainData) + { /* Create new train data matrix: */ + int h = pTrainData->height; + pTrainData->height = MIN(pTrainData->height, m_pTrainData->height); + cvCopy(m_pTrainData,pTrainData); + pTrainData->height = h; + } + + for(j=0; jpFVSeq->total; ++j) + { /* Copy new data to train data: */ + float* pFVvar = m_pFVGen->GetFVVar(); + float* pFV = (float*)cvGetSeqElem(pF->pFVSeq,j); + int k; + + for(k=0; k0) + { /* Variate: */ + for(t=0; tpMem); + m_TrackNum++; + m_Tracks.DelBlob(i-1); + + } /* End delete. */ + } /* Next track. */ + + /* Retrain data each 1 minute if new data exist: */ + if(m_Frame%(25*60) == 0 && m_pTrainData && m_pTrainData->rows > m_LastTrainDataSize) + { + RetrainStatModel(); + } + + m_Frame++; + + if(m_Wnd && m_Dim==2) + { /* Debug output: */ + int x,y; + IplImage* pI = cvCloneImage(pImg); + + if(m_pStatModel && m_pStatImg) + + for(y=0; yheight; y+=2) + { + uchar* pStatData = (uchar*)m_pStatImg->imageData + y*m_pStatImg->widthStep; + uchar* pData = (uchar*)pI->imageData + y*pI->widthStep; + + for(x=0;xwidth;x+=2) + { /* Draw all elements: */ + int d = pStatData[x]; + d = (d<<8) | (d^0xff); + *(ushort*)(pData + x*3) = (ushort)d; + } + } /* Next line. */ + + //cvNamedWindow("SVMMap",0); + //cvShowImage("SVMMap", pI); + cvReleaseImage(&pI); + } /* Debug output. */ + }; + float GetState(int BlobID) + { + DefTrackSVM* pF = (DefTrackSVM*)m_Tracks.GetBlobByID(BlobID); + return pF?pF->state:0.0f; + }; + + /* Return 0 if trajectory is normal; return >0 if trajectory abnormal. */ - virtual const char* GetStateDesc(int BlobID) - { - if(GetState(BlobID)>0.5) return "abnormal"; - return NULL; - } - - virtual void SetFileName(char* DataBaseName) - { - if(m_pTrainData)SaveStatModel(); - m_DataFileName[0] = m_DataFileName[1000] = 0; - if(DataBaseName) - { - strncpy(m_DataFileName,DataBaseName,1000); - strcat(m_DataFileName, ".yml"); - } - LoadStatModel(); - }; - - - virtual void Release(){ delete this; }; + virtual const char* GetStateDesc(int BlobID) + { + if(GetState(BlobID)>0.5) return "abnormal"; + return NULL; + } + + virtual void SetFileName(char* DataBaseName) + { + if(m_pTrainData)SaveStatModel(); + m_DataFileName[0] = m_DataFileName[1000] = 0; + if(DataBaseName) + { + strncpy(m_DataFileName,DataBaseName,1000); + strcat(m_DataFileName, ".yml"); + } + LoadStatModel(); + }; + + + virtual void Release(){ delete this; }; }; /* CvBlobTrackAnalysisSVM. */ - +#if 0 CvBlobTrackAnalysis* cvCreateModuleBlobTrackAnalysisSVMP() {return (CvBlobTrackAnalysis*) new CvBlobTrackAnalysisSVM(cvCreateFVGenP);} @@ -1522,3 +1514,4 @@ CvBlobTrackAnalysis* cvCreateModuleBlobTrackAnalysisSVMPVS() CvBlobTrackAnalysis* cvCreateModuleBlobTrackAnalysisSVMSS() {return (CvBlobTrackAnalysis*) new CvBlobTrackAnalysisSVM(cvCreateFVGenSS);} +#endif diff --git a/modules/legacy/src/blobtrackanalysistrackdist.cpp b/modules/legacy/src/blobtrackanalysistrackdist.cpp index 3c8a346..be34ab1 100644 --- a/modules/legacy/src/blobtrackanalysistrackdist.cpp +++ b/modules/legacy/src/blobtrackanalysistrackdist.cpp @@ -350,11 +350,10 @@ public: virtual void Process(IplImage* pImg, IplImage* /*pFG*/) { - int i; double MinTv = pImg->width/1440.0; /* minimal threshold for speed difference */ double MinTv2 = MinTv*MinTv; - for(i=m_Tracks.GetBlobNum(); i>0; --i) + for(int i=m_Tracks.GetBlobNum(); i>0; --i) { DefTrackForDist* pF = (DefTrackForDist*)m_Tracks.GetBlob(i-1); pF->state = 0; @@ -466,14 +465,13 @@ public: if(m_Wnd) { /* Debug output: */ - int i; if(m_pDebugImg==NULL) m_pDebugImg = cvCloneImage(pImg); else cvCopy(pImg, m_pDebugImg); - for(i=m_TrackDataBase.GetBlobNum(); i>0; --i) + for(int i=m_TrackDataBase.GetBlobNum(); i>0; --i) { /* Draw all elements in track data base: */ int j; DefTrackForDist* pF = (DefTrackForDist*)m_TrackDataBase.GetBlob(i-1); @@ -497,7 +495,7 @@ public: pF->close = 0; } /* Draw all elements in track data base. */ - for(i=m_Tracks.GetBlobNum(); i>0; --i) + for(int i=m_Tracks.GetBlobNum(); i>0; --i) { /* Draw all elements for all trajectories: */ DefTrackForDist* pF = (DefTrackForDist*)m_Tracks.GetBlob(i-1); int j; diff --git a/modules/legacy/src/blobtrackingauto.cpp b/modules/legacy/src/blobtrackingauto.cpp index 450bcc9..82e92bd 100644 --- a/modules/legacy/src/blobtrackingauto.cpp +++ b/modules/legacy/src/blobtrackingauto.cpp @@ -206,7 +206,6 @@ CvBlobTrackerAuto1::~CvBlobTrackerAuto1() void CvBlobTrackerAuto1::Process(IplImage* pImg, IplImage* pMask) { int CurBlobNum = 0; - int i; IplImage* pFG = pMask; /* Bump frame counter: */ @@ -268,15 +267,14 @@ void CvBlobTrackerAuto1::Process(IplImage* pImg, IplImage* pMask) TIME_BEGIN() if(m_pBT) { - int i; m_pBT->Process(pImg, pFG); - for(i=m_BlobList.GetBlobNum(); i>0; --i) + for(int i=m_BlobList.GetBlobNum(); i>0; --i) { /* Update data of tracked blob list: */ CvBlob* pB = m_BlobList.GetBlob(i-1); int BlobID = CV_BLOB_ID(pB); - int i = m_pBT->GetBlobIndexByID(BlobID); - m_pBT->ProcessBlob(i, pB, pImg, pFG); + int idx = m_pBT->GetBlobIndexByID(BlobID); + m_pBT->ProcessBlob(idx, pB, pImg, pFG); pB->ID = BlobID; } CurBlobNum = m_pBT->GetBlobNum(); @@ -286,9 +284,7 @@ void CvBlobTrackerAuto1::Process(IplImage* pImg, IplImage* pMask) /* This part should be removed: */ if(m_BTReal && m_pBT) { /* Update blob list (detect new blob for real blob tracker): */ - int i; - - for(i=m_pBT->GetBlobNum(); i>0; --i) + for(int i=m_pBT->GetBlobNum(); i>0; --i) { /* Update data of tracked blob list: */ CvBlob* pB = m_pBT->GetBlob(i-1); if(pB && m_BlobList.GetBlobByID(CV_BLOB_ID(pB)) == NULL ) @@ -301,7 +297,7 @@ void CvBlobTrackerAuto1::Process(IplImage* pImg, IplImage* pMask) } /* Next blob. */ /* Delete blobs: */ - for(i=m_BlobList.GetBlobNum(); i>0; --i) + for(int i=m_BlobList.GetBlobNum(); i>0; --i) { /* Update tracked-blob list: */ CvBlob* pB = m_BlobList.GetBlob(i-1); if(pB && m_pBT->GetBlobByID(CV_BLOB_ID(pB)) == NULL ) @@ -315,15 +311,14 @@ void CvBlobTrackerAuto1::Process(IplImage* pImg, IplImage* pMask) TIME_BEGIN() if(m_pBTPostProc) { /* Post-processing module: */ - int i; - for(i=m_BlobList.GetBlobNum(); i>0; --i) + for(int i=m_BlobList.GetBlobNum(); i>0; --i) { /* Update tracked-blob list: */ CvBlob* pB = m_BlobList.GetBlob(i-1); m_pBTPostProc->AddBlob(pB); } m_pBTPostProc->Process(); - for(i=m_BlobList.GetBlobNum(); i>0; --i) + for(int i=m_BlobList.GetBlobNum(); i>0; --i) { /* Update tracked-blob list: */ CvBlob* pB = m_BlobList.GetBlob(i-1); int BlobID = CV_BLOB_ID(pB); @@ -423,12 +418,12 @@ void CvBlobTrackerAuto1::Process(IplImage* pImg, IplImage* pMask) if(m_pBD->DetectNewBlob(pImg, pFG, &NewBlobList, &m_BlobList)) { /* Add new blob to tracker and blob list: */ int i; - IplImage* pMask = pFG; + IplImage* pmask = pFG; /*if(0)if(NewBlobList.GetBlobNum()>0 && pFG ) {// erode FG mask (only for FG_0 and MS1||MS2) - pMask = cvCloneImage(pFG); - cvErode(pFG,pMask,NULL,2); + pmask = cvCloneImage(pFG); + cvErode(pFG,pmask,NULL,2); }*/ for(i=0; iw >= CV_BLOB_MINW && pBN->h >= CV_BLOB_MINH) { - CvBlob* pB = m_pBT->AddBlob(pBN, pImg, pMask ); + CvBlob* pB = m_pBT->AddBlob(pBN, pImg, pmask ); if(pB) { NewB.blob = pB[0]; @@ -449,7 +444,7 @@ void CvBlobTrackerAuto1::Process(IplImage* pImg, IplImage* pMask) } } /* Add next blob from list of detected blob. */ - if(pMask != pFG) cvReleaseImage(&pMask); + if(pmask != pFG) cvReleaseImage(&pmask); } /* Create and add new blobs and trackers. */ @@ -460,7 +455,7 @@ void CvBlobTrackerAuto1::Process(IplImage* pImg, IplImage* pMask) TIME_BEGIN() if(m_pBTGen) { /* Run track generator: */ - for(i=m_BlobList.GetBlobNum(); i>0; --i) + for(int i=m_BlobList.GetBlobNum(); i>0; --i) { /* Update data of tracked blob list: */ CvBlob* pB = m_BlobList.GetBlob(i-1); m_pBTGen->AddBlob(pB); diff --git a/modules/legacy/src/blobtrackingcc.cpp b/modules/legacy/src/blobtrackingcc.cpp index 753628d..c2279a2 100644 --- a/modules/legacy/src/blobtrackingcc.cpp +++ b/modules/legacy/src/blobtrackingcc.cpp @@ -301,8 +301,8 @@ public: { /* Find a neighbour on current frame * for each blob from previous frame: */ - CvBlob* pB = m_BlobList.GetBlob(i-1); - DefBlobTracker* pBT = (DefBlobTracker*)pB; + CvBlob* pBl = m_BlobList.GetBlob(i-1); + DefBlobTracker* pBT = (DefBlobTracker*)pBl; //int BlobID = CV_BLOB_ID(pB); //CvBlob* pBBest = NULL; //double DistBest = -1; diff --git a/modules/legacy/src/blobtrackingccwithcr.cpp b/modules/legacy/src/blobtrackingccwithcr.cpp index 2341498..ad00b94 100644 --- a/modules/legacy/src/blobtrackingccwithcr.cpp +++ b/modules/legacy/src/blobtrackingccwithcr.cpp @@ -175,7 +175,6 @@ public: { CvSeq* cnts; CvSeq* cnt; - int i; //CvMat* pMC = NULL; if(m_BlobList.GetBlobNum() <= 0 ) return; @@ -219,7 +218,7 @@ public: cvReleaseImage(&pBin); } - for(i=m_BlobList.GetBlobNum(); i>0; --i) + for(int i=m_BlobList.GetBlobNum(); i>0; --i) { /* Predict new blob position. */ CvBlob* pB = NULL; DefBlobTrackerCR* pBT = (DefBlobTrackerCR*)m_BlobList.GetBlob(i-1); @@ -237,11 +236,10 @@ public: if(m_BlobList.GetBlobNum()>0 && m_BlobListNew.GetBlobNum()>0) { /* Resolve new blob to old: */ - int i,j; int NOld = m_BlobList.GetBlobNum(); int NNew = m_BlobListNew.GetBlobNum(); - for(i=0; iCollision = 0; @@ -249,12 +247,12 @@ public: } /* Set 0 collision. */ /* Create correspondence records: */ - for(j=0; j0; --i) + for(int i=m_BlobList.GetBlobNum(); i>0; --i) { /* Track each blob. */ CvBlob* pB = m_BlobList.GetBlob(i-1); DefBlobTrackerCR* pBT = (DefBlobTrackerCR*)pB; int BlobID = CV_BLOB_ID(pB); //CvBlob* pBBest = NULL; //double DistBest = -1; - int j; if(pBT->pResolver) { @@ -309,7 +306,7 @@ public: CvBlob* pBBest = NULL; double DistBest = -1; double CMax = 0; - for(j=pBT->pBlobHyp->GetBlobNum();j>0;--j) + for(int j=pBT->pBlobHyp->GetBlobNum();j>0;--j) { /* Find best CC: */ CvBlob* pBNew = pBT->pBlobHyp->GetBlob(j-1); if(pBT->pResolver) @@ -354,8 +351,7 @@ public: if(m_Wnd) { IplImage* pI = cvCloneImage(pImg); - int i; - for(i=m_BlobListNew.GetBlobNum(); i>0; --i) + for(int i=m_BlobListNew.GetBlobNum(); i>0; --i) { /* Draw each new CC: */ CvBlob* pB = m_BlobListNew.GetBlob(i-1); CvPoint p = cvPointFrom32f(CV_BLOB_CENTER(pB)); @@ -369,7 +365,7 @@ public: CV_RGB(255,255,0), 1 ); } - for(i=m_BlobList.GetBlobNum(); i>0; --i) + for(int i=m_BlobList.GetBlobNum(); i>0; --i) { /* Draw each new CC: */ DefBlobTrackerCR* pF = (DefBlobTrackerCR*)m_BlobList.GetBlob(i-1); CvBlob* pB = &(pF->BlobPredict); diff --git a/modules/legacy/src/blobtrackingkalman.cpp b/modules/legacy/src/blobtrackingkalman.cpp index 2e41ea1..3a0f03a 100644 --- a/modules/legacy/src/blobtrackingkalman.cpp +++ b/modules/legacy/src/blobtrackingkalman.cpp @@ -162,12 +162,15 @@ public: } }; /* class CvBlobTrackerOneKalman */ +#if 0 static CvBlobTrackerOne* cvCreateModuleBlobTrackerOneKalman() { return (CvBlobTrackerOne*) new CvBlobTrackerOneKalman; } + CvBlobTracker* cvCreateBlobTrackerKalman() { return cvCreateBlobTrackerList(cvCreateModuleBlobTrackerOneKalman); } +#endif diff --git a/modules/legacy/src/blobtrackingmsfg.cpp b/modules/legacy/src/blobtrackingmsfg.cpp index 2d1dafb..8444964 100644 --- a/modules/legacy/src/blobtrackingmsfg.cpp +++ b/modules/legacy/src/blobtrackingmsfg.cpp @@ -716,7 +716,7 @@ void CvBlobTrackerOneMSFG::CollectHist(IplImage* pImg, IplImage* pMask, CvBlob* }; /* CollectHist */ #endif -CvBlobTrackerOne* cvCreateBlobTrackerOneMSFG() +static CvBlobTrackerOne* cvCreateBlobTrackerOneMSFG() { return (CvBlobTrackerOne*) new CvBlobTrackerOneMSFG; } @@ -739,7 +739,7 @@ public: }; }; -CvBlobTrackerOne* cvCreateBlobTrackerOneMS() +static CvBlobTrackerOne* cvCreateBlobTrackerOneMS() { return (CvBlobTrackerOne*) new CvBlobTrackerOneMS; } @@ -1169,6 +1169,7 @@ public: }; /* CvBlobTrackerOneMSPF */ +CvBlobTrackerOne* cvCreateBlobTrackerOneMSPF(); CvBlobTrackerOne* cvCreateBlobTrackerOneMSPF() { return (CvBlobTrackerOne*) new CvBlobTrackerOneMSPF; diff --git a/modules/legacy/src/blobtrackingmsfgs.cpp b/modules/legacy/src/blobtrackingmsfgs.cpp index 9adf56a..3e1c7a0 100644 --- a/modules/legacy/src/blobtrackingmsfgs.cpp +++ b/modules/legacy/src/blobtrackingmsfgs.cpp @@ -47,7 +47,7 @@ typedef float DefHistType; #define DefHistTypeMat CV_32F #define HIST_INDEX(_pData) (((_pData)[0]>>m_ByteShift) + (((_pData)[1]>>(m_ByteShift))<>m_ByteShift)<<(m_BinBit*2))) -void calcKernelEpanechnikov(CvMat* pK) +static void calcKernelEpanechnikov(CvMat* pK) { /* Allocate kernel for histogramm creation: */ int x,y; int w = pK->width; @@ -395,7 +395,7 @@ public: { /* Mean shift in scale space: */ float news = 0; - float sum = 0; + float sum1 = 0; float scale; Center = cvPoint(cvRound(m_Blob.x),cvRound(m_Blob.y)); @@ -407,13 +407,13 @@ public: { double W = cvDotProduct(m_Weights, m_KernelMeanShiftG[si]);; int s = si-SCALE_RANGE; - sum += (float)fabs(W); + sum1 += (float)fabs(W); news += (float)(s*W); } - if(sum>0) + if(sum1>0) { - news /= sum; + news /= sum1; } scale = (float)pow((double)SCALE_BASE,(double)news); @@ -445,7 +445,7 @@ public: virtual void Release(){delete this;}; }; /*CvBlobTrackerOneMSFGS*/ -CvBlobTrackerOne* cvCreateBlobTrackerOneMSFGS() +static CvBlobTrackerOne* cvCreateBlobTrackerOneMSFGS() { return (CvBlobTrackerOne*) new CvBlobTrackerOneMSFGS; } diff --git a/modules/legacy/src/blobtrackpostprockalman.cpp b/modules/legacy/src/blobtrackpostprockalman.cpp index 8eea2c6..debe89d 100644 --- a/modules/legacy/src/blobtrackpostprockalman.cpp +++ b/modules/legacy/src/blobtrackpostprockalman.cpp @@ -188,7 +188,7 @@ void CvBlobTrackPostProcKalman::Release() delete this; } -CvBlobTrackPostProcOne* cvCreateModuleBlobTrackPostProcKalmanOne() +static CvBlobTrackPostProcOne* cvCreateModuleBlobTrackPostProcKalmanOne() { return (CvBlobTrackPostProcOne*) new CvBlobTrackPostProcKalman; } diff --git a/modules/legacy/src/blobtrackpostproclinear.cpp b/modules/legacy/src/blobtrackpostproclinear.cpp index df50e0f..2192cf0 100644 --- a/modules/legacy/src/blobtrackpostproclinear.cpp +++ b/modules/legacy/src/blobtrackpostproclinear.cpp @@ -74,9 +74,9 @@ public: { float WSum = 0; int i; - int index = m_Frame % TIME_WND; + int idx = m_Frame % TIME_WND; int size = MIN((m_Frame+1), TIME_WND); - m_pBlobs[index] = pBlob[0]; + m_pBlobs[idx] = pBlob[0]; m_Blob.x = m_Blob.y = m_Blob.w = m_Blob.h = 0; for(i=0; i= 1200 -#pragma warning( disable: 4701 ) -#endif - CvCalibFilter::CvCalibFilter() { /* etalon data */ @@ -93,24 +89,24 @@ CvCalibFilter::~CvCalibFilter() bool CvCalibFilter::SetEtalon( CvCalibEtalonType type, double* params, - int pointCount, CvPoint2D32f* points ) + int pointCount, CvPoint2D32f* _points ) { int i, arrSize; Stop(); - if (latestPoints != NULL) - { - for( i = 0; i < MAX_CAMERAS; i++ ) - cvFree( latestPoints + i ); - } + if (latestPoints != NULL) + { + for( i = 0; i < MAX_CAMERAS; i++ ) + cvFree( latestPoints + i ); + } if( type == CV_CALIB_ETALON_USER || type != etalonType ) { - if (etalonParams != NULL) - { - cvFree( &etalonParams ); - } + if (etalonParams != NULL) + { + cvFree( &etalonParams ); + } } etalonType = type; @@ -132,7 +128,7 @@ bool CvCalibFilter::SetEtalon( CvCalibEtalonType type, double* params, case CV_CALIB_ETALON_USER: etalonParamCount = 0; - if( !points || pointCount < 4 ) + if( !_points || pointCount < 4 ) { assert(0); return false; @@ -154,10 +150,10 @@ bool CvCalibFilter::SetEtalon( CvCalibEtalonType type, double* params, if( etalonPointCount != pointCount ) { - if (etalonPoints != NULL) - { - cvFree( &etalonPoints ); - } + if (etalonPoints != NULL) + { + cvFree( &etalonPoints ); + } etalonPointCount = pointCount; etalonPoints = (CvPoint2D32f*)cvAlloc( arrSize ); } @@ -184,15 +180,15 @@ bool CvCalibFilter::SetEtalon( CvCalibEtalonType type, double* params, break; case CV_CALIB_ETALON_USER: - if (params != NULL) - { - memcpy( etalonParams, params, arrSize ); - } - if (points != NULL) - { - memcpy( etalonPoints, points, arrSize ); - } - break; + if (params != NULL) + { + memcpy( etalonParams, params, arrSize ); + } + if (_points != NULL) + { + memcpy( etalonPoints, _points, arrSize ); + } + break; default: assert(0); @@ -205,7 +201,7 @@ bool CvCalibFilter::SetEtalon( CvCalibEtalonType type, double* params, CvCalibEtalonType CvCalibFilter::GetEtalon( int* paramCount, const double** params, - int* pointCount, const CvPoint2D32f** points ) const + int* pointCount, const CvPoint2D32f** _points ) const { if( paramCount ) *paramCount = etalonParamCount; @@ -216,8 +212,8 @@ CvCalibFilter::GetEtalon( int* paramCount, const double** params, if( pointCount ) *pointCount = etalonPointCount; - if( points ) - *points = etalonPoints; + if( _points ) + *_points = etalonPoints; return etalonType; } @@ -226,7 +222,7 @@ CvCalibFilter::GetEtalon( int* paramCount, const double** params, void CvCalibFilter::SetCameraCount( int count ) { Stop(); - + if( count != cameraCount ) { for( int i = 0; i < cameraCount; i++ ) @@ -245,7 +241,7 @@ void CvCalibFilter::SetCameraCount( int count ) } } - + bool CvCalibFilter::SetFrames( int frames ) { if( frames < 5 ) @@ -253,7 +249,7 @@ bool CvCalibFilter::SetFrames( int frames ) assert(0); return false; } - + framesTotal = frames; return true; } @@ -304,7 +300,7 @@ void CvCalibFilter::Stop( bool calibrate ) cameraParams[i].imgSize[0] = (float)imgSize.width; cameraParams[i].imgSize[1] = (float)imgSize.height; - + // cameraParams[i].focalLength[0] = cameraParams[i].matrix[0]; // cameraParams[i].focalLength[1] = cameraParams[i].matrix[4]; @@ -315,7 +311,7 @@ void CvCalibFilter::Stop( bool calibrate ) memcpy( cameraParams[i].transVect, transVect, 3 * sizeof(transVect[0])); mat.data.ptr = (uchar*)(cameraParams + i); - + /* check resultant camera parameters: if there are some INF's or NAN's, stop and reset results */ if( !cvCheckArr( &mat, CV_CHECK_RANGE | CV_CHECK_QUIET, -10000, 10000 )) @@ -342,7 +338,7 @@ void CvCalibFilter::Stop( bool calibrate ) { stereo.fundMatr[i] = stereo.fundMatr[i]; } - + } } @@ -499,16 +495,16 @@ bool CvCalibFilter::GetLatestPoints( int idx, CvPoint2D32f** pts, int* count, bool* found ) { int n; - + if( (unsigned)idx >= (unsigned)cameraCount || !pts || !count || !found ) { assert(0); return false; } - + n = latestCounts[idx]; - + *found = n > 0; *count = abs(n); *pts = latestPoints[idx]; @@ -616,7 +612,7 @@ const CvCamera* CvCalibFilter::GetCameraParams( int idx ) const assert(0); return 0; } - + return isCalibrated ? cameraParams + idx : 0; } @@ -630,7 +626,7 @@ const CvStereoCamera* CvCalibFilter::GetStereoParams() const assert(0); return 0; } - + return &stereo; } @@ -640,9 +636,9 @@ bool CvCalibFilter::SetCameraParams( CvCamera* params ) { CvMat mat; int arrSize; - + Stop(); - + if( !params ) { assert(0); @@ -667,7 +663,7 @@ bool CvCalibFilter::SaveCameraParams( const char* filename ) if( isCalibrated ) { int i, j; - + FILE* f = fopen( filename, "w" ); if( !f ) return false; @@ -729,7 +725,7 @@ bool CvCalibFilter::LoadCameraParams( const char* filename ) return false; SetCameraCount( d ); - + for( i = 0; i < cameraCount; i++ ) { for( j = 0; j < (int)(sizeof(cameraParams[i])/sizeof(float)); j++ ) @@ -763,16 +759,16 @@ bool CvCalibFilter::LoadCameraParams( const char* filename ) CV_Assert(values_read == 1); } } - - - - + + + + fclose(f); stereo.warpSize = cvSize( cvRound(cameraParams[0].imgSize[0]), cvRound(cameraParams[0].imgSize[1])); isCalibrated = true; - + return true; } @@ -924,4 +920,4 @@ bool CvCalibFilter::Undistort( CvMat** srcarr, CvMat** dstarr ) return true; } - + diff --git a/modules/legacy/src/calonder.cpp b/modules/legacy/src/calonder.cpp index c933b20..bdd3df8 100644 --- a/modules/legacy/src/calonder.cpp +++ b/modules/legacy/src/calonder.cpp @@ -311,19 +311,19 @@ int RandomizedTree::getIndex(uchar* patch_data) const } void RandomizedTree::train(std::vector const& base_set, - RNG &rng, int depth, int views, size_t reduced_num_dim, + RNG &rng, int _depth, int views, size_t reduced_num_dim, int num_quant_bits) { PatchGenerator make_patch; - train(base_set, rng, make_patch, depth, views, reduced_num_dim, num_quant_bits); + train(base_set, rng, make_patch, _depth, views, reduced_num_dim, num_quant_bits); } void RandomizedTree::train(std::vector const& base_set, RNG &rng, PatchGenerator &make_patch, - int depth, int views, size_t reduced_num_dim, + int _depth, int views, size_t reduced_num_dim, int num_quant_bits) { - init((int)base_set.size(), depth, rng); + init((int)base_set.size(), _depth, rng); Mat patch; @@ -381,10 +381,10 @@ void RandomizedTree::freePosteriors(int which) classes_ = -1; } -void RandomizedTree::init(int num_classes, int depth, RNG &rng) +void RandomizedTree::init(int num_classes, int _depth, RNG &rng) { - depth_ = depth; - num_leaves_ = 1 << depth; // 2**d + depth_ = _depth; + num_leaves_ = 1 << _depth; // 2**d int num_nodes = num_leaves_ - 1; // 2**d - 1 // Initialize probabilities and counts to 0 @@ -631,9 +631,9 @@ void RandomizedTree::savePosteriors(std::string url, bool append) for (int i=0; i //#include "cv.h" //#include "highgui.h" - +#if 0 #include /* Valery Mosyagin */ @@ -53,7 +53,7 @@ /* ===== Function for find corresponding between images ===== */ /* Create feature points on image and return number of them. Array points fills by found points */ -int icvCreateFeaturePoints(IplImage *image, CvMat *points, CvMat *status) +static int icvCreateFeaturePoints(IplImage *image, CvMat *points, CvMat *status) { int foundFeaturePoints = 0; IplImage *grayImage = 0; @@ -175,9 +175,9 @@ int icvCreateFeaturePoints(IplImage *image, CvMat *points, CvMat *status) /* For given points1 (with pntStatus) on image1 finds corresponding points2 on image2 and set pntStatus2 for them */ /* Returns number of corresponding points */ -int icvFindCorrForGivenPoints( IplImage *image1,/* Image 1 */ +static int icvFindCorrForGivenPoints( IplImage *image1,/* Image 1 */ IplImage *image2,/* Image 2 */ - CvMat *points1, + CvMat *points1, CvMat *pntStatus1, CvMat *points2, CvMat *pntStatus2, @@ -203,7 +203,7 @@ int icvFindCorrForGivenPoints( IplImage *image1,/* Image 1 */ /* Test input data for errors */ /* Test for null pointers */ - if( image1 == 0 || image2 == 0 || + if( image1 == 0 || image2 == 0 || points1 == 0 || points2 == 0 || pntStatus1 == 0 || pntStatus2 == 0) { @@ -226,7 +226,7 @@ int icvFindCorrForGivenPoints( IplImage *image1,/* Image 1 */ } /* Test for matrices */ - if( !CV_IS_MAT(points1) || !CV_IS_MAT(points2) || + if( !CV_IS_MAT(points1) || !CV_IS_MAT(points2) || !CV_IS_MAT(pntStatus1) || !CV_IS_MAT(pntStatus2) ) { CV_ERROR( CV_StsUnsupportedFormat, "Input parameters (points and status) must be a matrices" ); @@ -333,11 +333,11 @@ int icvFindCorrForGivenPoints( IplImage *image1,/* Image 1 */ pyrImage1, pyrImage2, cornerPoints1, cornerPoints2, numVisPoints, cvSize(10,10), 3, - status, errors, + status, errors, cvTermCriteria(CV_TERMCRIT_ITER|CV_TERMCRIT_EPS,20,0.03), 0/*CV_LKFLOW_PYR_A_READY*/ ); - + memset(stat2,0,sizeof(uchar)*numPoints); int currVis = 0; @@ -393,7 +393,7 @@ int icvFindCorrForGivenPoints( IplImage *image1,/* Image 1 */ CvMat fundMatr; double fundMatr_dat[9]; fundMatr = cvMat(3,3,CV_64F,fundMatr_dat); - + CV_CALL( pStatus = cvCreateMat(1,totalCorns,CV_32F) ); int num = cvFindFundamentalMat(tmpPoints1,tmpPoints2,&fundMatr,CV_FM_RANSAC,threshold,0.99,pStatus); @@ -435,8 +435,9 @@ int icvFindCorrForGivenPoints( IplImage *image1,/* Image 1 */ return resNumCorrPoints; } + /*-------------------------------------------------------------------------------------*/ -int icvGrowPointsAndStatus(CvMat **oldPoints,CvMat **oldStatus,CvMat *addPoints,CvMat *addStatus,int addCreateNum) +static int icvGrowPointsAndStatus(CvMat **oldPoints,CvMat **oldStatus,CvMat *addPoints,CvMat *addStatus,int addCreateNum) { /* Add to existing points and status arrays new points or just grow */ CvMat *newOldPoint = 0; @@ -445,7 +446,7 @@ int icvGrowPointsAndStatus(CvMat **oldPoints,CvMat **oldStatus,CvMat *addPoints, CV_FUNCNAME( "icvGrowPointsAndStatus" ); __BEGIN__; - + /* Test for errors */ if( oldPoints == 0 || oldStatus == 0 ) { @@ -546,8 +547,9 @@ int icvGrowPointsAndStatus(CvMat **oldPoints,CvMat **oldStatus,CvMat *addPoints, return newTotalNumber; } + /*-------------------------------------------------------------------------------------*/ -int icvRemoveDoublePoins( CvMat *oldPoints,/* Points on prev image */ +static int icvRemoveDoublePoins( CvMat *oldPoints,/* Points on prev image */ CvMat *newPoints,/* New points */ CvMat *oldStatus,/* Status for old points */ CvMat *newStatus, @@ -560,7 +562,7 @@ int icvRemoveDoublePoins( CvMat *oldPoints,/* Points on prev image */ CvSeq* seq = 0; int originalPoints = 0; - + CV_FUNCNAME( "icvRemoveDoublePoins" ); __BEGIN__; @@ -624,7 +626,7 @@ int icvRemoveDoublePoins( CvMat *oldPoints,/* Points on prev image */ { CV_ERROR( CV_StsOutOfRange, "Statuses must have 1 row" ); } - + /* we have points on image and wants add new points */ /* use subdivision for find nearest points */ @@ -731,7 +733,7 @@ int icvRemoveDoublePoins( CvMat *oldPoints,/* Points on prev image */ /* Point is double. Turn it off */ /* Set status */ //newStatus->data.ptr[i] = 0; - + /* No this is a double point */ //originalPoints--; flag = 0; @@ -745,7 +747,7 @@ int icvRemoveDoublePoins( CvMat *oldPoints,/* Points on prev image */ __END__; cvReleaseMemStorage( &storage ); - + return originalPoints; @@ -755,11 +757,11 @@ int icvRemoveDoublePoins( CvMat *oldPoints,/* Points on prev image */ void icvComputeProjectMatrix(CvMat* objPoints,CvMat* projPoints,CvMat* projMatr); /*-------------------------------------------------------------------------------------*/ -void icvComputeProjectMatrixStatus(CvMat *objPoints4D,CvMat *points2,CvMat *status, CvMat *projMatr) +static void icvComputeProjectMatrixStatus(CvMat *objPoints4D,CvMat *points2,CvMat *status, CvMat *projMatr) { /* Compute number of good points */ int num = cvCountNonZero(status); - + /* Create arrays */ CvMat *objPoints = 0; objPoints = cvCreateMat(4,num,CV_64F); @@ -802,7 +804,7 @@ void icvComputeProjectMatrixStatus(CvMat *objPoints4D,CvMat *points2,CvMat *stat currVis++; } - + fprintf(file,"\n"); } @@ -820,17 +822,16 @@ void icvComputeProjectMatrixStatus(CvMat *objPoints4D,CvMat *points2,CvMat *stat /*-------------------------------------------------------------------------------------*/ -/* For given N images +/* For given N images we have corresponding points on N images computed projection matrices reconstructed 4D points - we must to compute - + we must to compute -*/ -void icvAddNewImageToPrevious____( +*/ +static void icvAddNewImageToPrevious____( IplImage *newImage,//Image to add IplImage *oldImage,//Previous image CvMat *oldPoints,// previous 2D points on prev image (some points may be not visible) @@ -868,7 +869,7 @@ void icvAddNewImageToPrevious____( int corrNum; corrNum = icvFindCorrForGivenPoints( oldImage,/* Image 1 */ newImage,/* Image 2 */ - oldPoints, + oldPoints, oldPntStatus, points2, status, @@ -887,10 +888,10 @@ void icvAddNewImageToPrevious____( // icvComputeProjectMatrix(objPoints4D,points2,&projMatr); icvComputeProjectMatrixStatus(objPoints4D,points2,status,&projMatr); cvCopy(&projMatr,newProjMatr); - + /* Create new points and find correspondence */ icvCreateFeaturePoints(newImage, newFPoints2D2,newFPointsStatus); - + /* Good if we test new points before find corr points */ /* Find correspondence for new found points */ @@ -947,7 +948,7 @@ void icvAddNewImageToPrevious____( //CreateGood /*-------------------------------------------------------------------------------------*/ -int icvDeleteSparsInPoints( int numImages, +static int icvDeleteSparsInPoints( int numImages, CvMat **points, CvMat **status, CvMat *wasStatus)/* status of previous configuration */ @@ -979,7 +980,7 @@ int icvDeleteSparsInPoints( int numImages, int numCoord; numCoord = points[0]->rows;// !!! may be number of coordinates is not correct !!! - + int i; int currExistPoint; currExistPoint = 0; @@ -1041,7 +1042,7 @@ int icvDeleteSparsInPoints( int numImages, return comNumber; } -#if 0 + /*-------------------------------------------------------------------------------------*/ void icvGrowPointsArray(CvMat **points) { @@ -1089,7 +1090,7 @@ int AddImageToStruct( IplImage *newImage,//Image to add cvConvert(pntStatus,status); int corrNum = FindCorrForGivenPoints(oldImage,newImage,oldPoints,newPoints,status); - + /* Status has new status of points */ CvMat projMatr; diff --git a/modules/legacy/src/dpstereo.cpp b/modules/legacy/src/dpstereo.cpp index 93e735f..1c6881e 100644 --- a/modules/legacy/src/dpstereo.cpp +++ b/modules/legacy/src/dpstereo.cpp @@ -48,7 +48,7 @@ Stan Birchfield and Carlo Tomasi International Journal of Computer Vision, 35(3): 269-293, December 1999. - + This implementation uses different cost function that results in O(pixPerRow*maxDisparity) complexity of dynamic programming stage versus O(pixPerRow*log(pixPerRow)*maxDisparity) in the above paper. @@ -68,7 +68,7 @@ typedef struct _CvDPCell { uchar step; //local-optimal step - int sum; //current sum + int sum; //current sum }_CvDPCell; typedef struct _CvRightImData @@ -79,17 +79,17 @@ typedef struct _CvRightImData #define CV_IMAX3(a,b,c) ((temp3 = (a) >= (b) ? (a) : (b)),(temp3 >= (c) ? temp3 : (c))) #define CV_IMIN3(a,b,c) ((temp3 = (a) <= (b) ? (a) : (b)),(temp3 <= (c) ? temp3 : (c))) -void icvFindStereoCorrespondenceByBirchfieldDP( uchar* src1, uchar* src2, +static void icvFindStereoCorrespondenceByBirchfieldDP( uchar* src1, uchar* src2, uchar* disparities, CvSize size, int widthStep, - int maxDisparity, - float _param1, float _param2, + int maxDisparity, + float _param1, float _param2, float _param3, float _param4, float _param5 ) { int x, y, i, j, temp3; int d, s; - int dispH = maxDisparity + 3; + int dispH = maxDisparity + 3; uchar *dispdata; int imgW = size.width; int imgH = size.height; @@ -103,22 +103,22 @@ void icvFindStereoCorrespondenceByBirchfieldDP( uchar* src1, uchar* src2, int param5 = cvRound(_param5); #define CELL(d,x) cells[(d)+(x)*dispH] - + uchar* dsi = (uchar*)cvAlloc(sizeof(uchar)*imgW*dispH); uchar* edges = (uchar*)cvAlloc(sizeof(uchar)*imgW*imgH); _CvDPCell* cells = (_CvDPCell*)cvAlloc(sizeof(_CvDPCell)*imgW*MAX(dispH,(imgH+1)/2)); _CvRightImData* rData = (_CvRightImData*)cvAlloc(sizeof(_CvRightImData)*imgW); int* reliabilities = (int*)cells; - - for( y = 0; y < imgH; y++ ) - { + + for( y = 0; y < imgH; y++ ) + { uchar* srcdata1 = src1 + widthStep * y; - uchar* srcdata2 = src2 + widthStep * y; + uchar* srcdata2 = src2 + widthStep * y; //init rData prevval = prev = srcdata2[0]; for( j = 1; j < imgW; j++ ) - { + { curr = srcdata2[j]; val = (uchar)((curr + prev)>>1); rData[j-1].max_val = (uchar)CV_IMAX3( val, prevval, prev ); @@ -130,12 +130,12 @@ void icvFindStereoCorrespondenceByBirchfieldDP( uchar* src1, uchar* src2, // fill dissimularity space image for( i = 1; i <= maxDisparity + 1; i++ ) - { + { dsi += imgW; rData--; for( j = i - 1; j < imgW - 1; j++ ) - { - int t; + { + int t; if( (t = srcdata1[j] - rData[j+1].max_val) >= 0 ) { dsi[j] = (uchar)t; @@ -160,109 +160,109 @@ void icvFindStereoCorrespondenceByBirchfieldDP( uchar* src1, uchar* src2, for( j = 3; j < imgW-4; j++ ) { edges[y*imgW+j] = 0; - - if( ( CV_IMAX3( srcdata1[j-3], srcdata1[j-2], srcdata1[j-1] ) - + + if( ( CV_IMAX3( srcdata1[j-3], srcdata1[j-2], srcdata1[j-1] ) - CV_IMIN3( srcdata1[j-3], srcdata1[j-2], srcdata1[j-1] ) ) >= ICV_BIRCH_DIFF_LUM ) { edges[y*imgW+j] |= 1; } - if( ( CV_IMAX3( srcdata2[j+3], srcdata2[j+2], srcdata2[j+1] ) - + if( ( CV_IMAX3( srcdata2[j+3], srcdata2[j+2], srcdata2[j+1] ) - CV_IMIN3( srcdata2[j+3], srcdata2[j+2], srcdata2[j+1] ) ) >= ICV_BIRCH_DIFF_LUM ) { edges[y*imgW+j] |= 2; - } - } + } + } //find correspondence using dynamical programming //init DP table - for( x = 0; x < imgW; x++ ) + for( x = 0; x < imgW; x++ ) { CELL(0,x).sum = CELL(dispH-1,x).sum = ICV_MAX_DP_SUM_VAL; CELL(0,x).step = CELL(dispH-1,x).step = ICV_DP_STEP_LEFT; } - for( d = 2; d < dispH; d++ ) + for( d = 2; d < dispH; d++ ) { CELL(d,d-2).sum = ICV_MAX_DP_SUM_VAL; CELL(d,d-2).step = ICV_DP_STEP_UP; - } + } CELL(1,0).sum = 0; CELL(1,0).step = ICV_DP_STEP_LEFT; for( x = 1; x < imgW; x++ ) - { - int d = MIN( x + 1, maxDisparity + 1); + { + int dp = MIN( x + 1, maxDisparity + 1); uchar* _edges = edges + y*imgW + x; int e0 = _edges[0] & 1; _CvDPCell* _cell = cells + x*dispH; do { - int s = dsi[d*imgW+x]; + int _s = dsi[dp*imgW+x]; int sum[3]; //check left step - sum[0] = _cell[d-dispH].sum - param2; + sum[0] = _cell[dp-dispH].sum - param2; //check up step - if( _cell[d+1].step != ICV_DP_STEP_DIAG && e0 ) + if( _cell[dp+1].step != ICV_DP_STEP_DIAG && e0 ) { - sum[1] = _cell[d+1].sum + param1; + sum[1] = _cell[dp+1].sum + param1; - if( _cell[d-1-dispH].step != ICV_DP_STEP_UP && (_edges[1-d] & 2) ) + if( _cell[dp-1-dispH].step != ICV_DP_STEP_UP && (_edges[1-dp] & 2) ) { int t; - - sum[2] = _cell[d-1-dispH].sum + param1; + + sum[2] = _cell[dp-1-dispH].sum + param1; t = sum[1] < sum[0]; //choose local-optimal pass if( sum[t] <= sum[2] ) { - _cell[d].step = (uchar)t; - _cell[d].sum = sum[t] + s; + _cell[dp].step = (uchar)t; + _cell[dp].sum = sum[t] + _s; } else - { - _cell[d].step = ICV_DP_STEP_DIAG; - _cell[d].sum = sum[2] + s; + { + _cell[dp].step = ICV_DP_STEP_DIAG; + _cell[dp].sum = sum[2] + _s; } } else { if( sum[0] <= sum[1] ) { - _cell[d].step = ICV_DP_STEP_LEFT; - _cell[d].sum = sum[0] + s; + _cell[dp].step = ICV_DP_STEP_LEFT; + _cell[dp].sum = sum[0] + _s; } else { - _cell[d].step = ICV_DP_STEP_UP; - _cell[d].sum = sum[1] + s; + _cell[dp].step = ICV_DP_STEP_UP; + _cell[dp].sum = sum[1] + _s; } } } - else if( _cell[d-1-dispH].step != ICV_DP_STEP_UP && (_edges[1-d] & 2) ) + else if( _cell[dp-1-dispH].step != ICV_DP_STEP_UP && (_edges[1-dp] & 2) ) { - sum[2] = _cell[d-1-dispH].sum + param1; + sum[2] = _cell[dp-1-dispH].sum + param1; if( sum[0] <= sum[2] ) { - _cell[d].step = ICV_DP_STEP_LEFT; - _cell[d].sum = sum[0] + s; + _cell[dp].step = ICV_DP_STEP_LEFT; + _cell[dp].sum = sum[0] + _s; } else { - _cell[d].step = ICV_DP_STEP_DIAG; - _cell[d].sum = sum[2] + s; + _cell[dp].step = ICV_DP_STEP_DIAG; + _cell[dp].sum = sum[2] + _s; } } else { - _cell[d].step = ICV_DP_STEP_LEFT; - _cell[d].sum = sum[0] + s; + _cell[dp].step = ICV_DP_STEP_LEFT; + _cell[dp].sum = sum[0] + _s; } } - while( --d ); + while( --dp ); }// for x //extract optimal way and fill disparity image @@ -278,25 +278,25 @@ void icvFindStereoCorrespondenceByBirchfieldDP( uchar* src1, uchar* src2, min_val = CELL(i,imgW-1).sum; } } - + //track optimal pass for( x = imgW - 1; x > 0; x-- ) - { + { dispdata[x] = (uchar)(d - 1); while( CELL(d,x).step == ICV_DP_STEP_UP ) d++; if ( CELL(d,x).step == ICV_DP_STEP_DIAG ) { s = x; - while( CELL(d,x).step == ICV_DP_STEP_DIAG ) + while( CELL(d,x).step == ICV_DP_STEP_DIAG ) { - d--; - x--; + d--; + x--; } for( i = x; i < s; i++ ) { dispdata[i] = (uchar)(d-1); - } - } + } + } }//for x }// for y @@ -319,9 +319,9 @@ void icvFindStereoCorrespondenceByBirchfieldDP( uchar* src1, uchar* src2, { for( y = 1; y < imgH - 1; y++ ) { - if( ( CV_IMAX3( src1[(y-1)*widthStep+x], src1[y*widthStep+x], - src1[(y+1)*widthStep+x] ) - - CV_IMIN3( src1[(y-1)*widthStep+x], src1[y*widthStep+x], + if( ( CV_IMAX3( src1[(y-1)*widthStep+x], src1[y*widthStep+x], + src1[(y+1)*widthStep+x] ) - + CV_IMIN3( src1[(y-1)*widthStep+x], src1[y*widthStep+x], src1[(y+1)*widthStep+x] ) ) >= ICV_BIRCH_DIFF_LUM ) { edges[y*imgW+x] |= 4; @@ -332,14 +332,14 @@ void icvFindStereoCorrespondenceByBirchfieldDP( uchar* src1, uchar* src2, } } - //remove along any particular row, every gradient + //remove along any particular row, every gradient //for which two adjacent columns do not agree. for( y = 0; y < imgH; y++ ) { prev = edges[y*imgW]; for( x = 1; x < imgW - 1; x++ ) { - curr = edges[y*imgW+x]; + curr = edges[y*imgW+x]; if( (curr & 4) && ( !( prev & 4 ) || !( edges[y*imgW+x+1] & 4 ) ) ) @@ -360,41 +360,41 @@ void icvFindStereoCorrespondenceByBirchfieldDP( uchar* src1, uchar* src2, ; s = y - i; for( ; i < y; i++ ) - { + { reliabilities[i*imgW+x] = s; - } + } } - } - - //Y - propagate reliable regions + } + + //Y - propagate reliable regions for( x = 0; x < imgW; x++ ) - { + { for( y = 0; y < imgH; y++ ) - { + { d = dest[y*widthStep+x]; if( reliabilities[y*imgW+x] >= param4 && !(edges[y*imgW+x] & 4) && d > 0 )//highly || moderately - { + { disparities[y*widthStep+x] = (uchar)d; //up propagation for( i = y - 1; i >= 0; i-- ) { if( ( edges[i*imgW+x] & 4 ) || - ( dest[i*widthStep+x] < d && + ( dest[i*widthStep+x] < d && reliabilities[i*imgW+x] >= param3 ) || - ( reliabilities[y*imgW+x] < param5 && + ( reliabilities[y*imgW+x] < param5 && dest[i*widthStep+x] - 1 == d ) ) break; - disparities[i*widthStep+x] = (uchar)d; - } - + disparities[i*widthStep+x] = (uchar)d; + } + //down propagation for( i = y + 1; i < imgH; i++ ) { if( ( edges[i*imgW+x] & 4 ) || - ( dest[i*widthStep+x] < d && + ( dest[i*widthStep+x] < d && reliabilities[i*imgW+x] >= param3 ) || - ( reliabilities[y*imgW+x] < param5 && + ( reliabilities[y*imgW+x] < param5 && dest[i*widthStep+x] - 1 == d ) ) break; disparities[i*widthStep+x] = (uchar)d; @@ -417,41 +417,41 @@ void icvFindStereoCorrespondenceByBirchfieldDP( uchar* src1, uchar* src2, for( ; x < imgW && dest[y*widthStep+x] == dest[y*widthStep+x-1]; x++ ); s = x - i; for( ; i < x; i++ ) - { + { reliabilities[y*imgW+i] = s; - } + } } - } - - //X - propagate reliable regions - for( y = 0; y < imgH; y++ ) - { + } + + //X - propagate reliable regions + for( y = 0; y < imgH; y++ ) + { for( x = 0; x < imgW; x++ ) - { + { d = dest[y*widthStep+x]; if( reliabilities[y*imgW+x] >= param4 && !(edges[y*imgW+x] & 1) && d > 0 )//highly || moderately - { + { disparities[y*widthStep+x] = (uchar)d; //up propagation for( i = x - 1; i >= 0; i-- ) { if( (edges[y*imgW+i] & 1) || - ( dest[y*widthStep+i] < d && + ( dest[y*widthStep+i] < d && reliabilities[y*imgW+i] >= param3 ) || - ( reliabilities[y*imgW+x] < param5 && + ( reliabilities[y*imgW+x] < param5 && dest[y*widthStep+i] - 1 == d ) ) break; disparities[y*widthStep+i] = (uchar)d; - } - + } + //down propagation for( i = x + 1; i < imgW; i++ ) { if( (edges[y*imgW+i] & 1) || - ( dest[y*widthStep+i] < d && + ( dest[y*widthStep+i] < d && reliabilities[y*imgW+i] >= param3 ) || - ( reliabilities[y*imgW+x] < param5 && + ( reliabilities[y*imgW+x] < param5 && dest[y*widthStep+i] - 1 == d ) ) break; disparities[y*widthStep+i] = (uchar)d; @@ -466,10 +466,10 @@ void icvFindStereoCorrespondenceByBirchfieldDP( uchar* src1, uchar* src2, } //release resources - cvFree( &dsi ); - cvFree( &edges ); - cvFree( &cells ); - cvFree( &rData ); + cvFree( &dsi ); + cvFree( &edges ); + cvFree( &cells ); + cvFree( &rData ); } @@ -483,7 +483,7 @@ void icvFindStereoCorrespondenceByBirchfieldDP( uchar* src1, uchar* src2, // rightImage - right image of stereo-pair (format 8uC1). // mode -mode of correspondance retrieval (now CV_RETR_DP_BIRCHFIELD only) // dispImage - destination disparity image -// maxDisparity - maximal disparity +// maxDisparity - maximal disparity // param1, param2, param3, param4, param5 - parameters of algorithm // Returns: // Notes: @@ -491,43 +491,43 @@ void icvFindStereoCorrespondenceByBirchfieldDP( uchar* src1, uchar* src2, // All images must have format 8uC1. //F*/ CV_IMPL void -cvFindStereoCorrespondence( +cvFindStereoCorrespondence( const CvArr* leftImage, const CvArr* rightImage, int mode, CvArr* depthImage, - int maxDisparity, - double param1, double param2, double param3, + int maxDisparity, + double param1, double param2, double param3, double param4, double param5 ) -{ +{ CV_FUNCNAME( "cvFindStereoCorrespondence" ); __BEGIN__; - CvMat *src1, *src2; + CvMat *src1, *src2; CvMat *dst; CvMat src1_stub, src2_stub, dst_stub; - int coi; + int coi; CV_CALL( src1 = cvGetMat( leftImage, &src1_stub, &coi )); if( coi ) CV_ERROR( CV_BadCOI, "COI is not supported by the function" ); CV_CALL( src2 = cvGetMat( rightImage, &src2_stub, &coi )); - if( coi ) CV_ERROR( CV_BadCOI, "COI is not supported by the function" ); + if( coi ) CV_ERROR( CV_BadCOI, "COI is not supported by the function" ); CV_CALL( dst = cvGetMat( depthImage, &dst_stub, &coi )); if( coi ) CV_ERROR( CV_BadCOI, "COI is not supported by the function" ); - // check args - if( CV_MAT_TYPE( src1->type ) != CV_8UC1 || - CV_MAT_TYPE( src2->type ) != CV_8UC1 || + // check args + if( CV_MAT_TYPE( src1->type ) != CV_8UC1 || + CV_MAT_TYPE( src2->type ) != CV_8UC1 || CV_MAT_TYPE( dst->type ) != CV_8UC1) CV_ERROR(CV_StsUnsupportedFormat, - "All images must be single-channel and have 8u" ); + "All images must be single-channel and have 8u" ); if( !CV_ARE_SIZES_EQ( src1, src2 ) || !CV_ARE_SIZES_EQ( src1, dst ) ) CV_ERROR( CV_StsUnmatchedSizes, "" ); - + if( maxDisparity <= 0 || maxDisparity >= src1->width || maxDisparity > 255 ) - CV_ERROR(CV_StsOutOfRange, + CV_ERROR(CV_StsOutOfRange, "parameter /maxDisparity/ is out of range"); - + if( mode == CV_DISPARITY_BIRCHFIELD ) { if( param1 == CV_UNDEF_SC_PARAM ) param1 = CV_IDP_BIRCHFIELD_PARAM1; @@ -536,10 +536,10 @@ cvFindStereoCorrespondence( if( param4 == CV_UNDEF_SC_PARAM ) param4 = CV_IDP_BIRCHFIELD_PARAM4; if( param5 == CV_UNDEF_SC_PARAM ) param5 = CV_IDP_BIRCHFIELD_PARAM5; - CV_CALL( icvFindStereoCorrespondenceByBirchfieldDP( src1->data.ptr, - src2->data.ptr, dst->data.ptr, + CV_CALL( icvFindStereoCorrespondenceByBirchfieldDP( src1->data.ptr, + src2->data.ptr, dst->data.ptr, cvGetMatSize( src1 ), src1->step, - maxDisparity, (float)param1, (float)param2, (float)param3, + maxDisparity, (float)param1, (float)param2, (float)param3, (float)param4, (float)param5 ) ); } else @@ -547,7 +547,7 @@ cvFindStereoCorrespondence( CV_ERROR( CV_StsBadArg, "Unsupported mode of function" ); } - __END__; + __END__; } /* End of file. */ diff --git a/modules/legacy/src/eigenobjects.cpp b/modules/legacy/src/eigenobjects.cpp index 1276627..87d5d42 100644 --- a/modules/legacy/src/eigenobjects.cpp +++ b/modules/legacy/src/eigenobjects.cpp @@ -41,7 +41,7 @@ #include "precomp.hpp" -CvStatus CV_STDCALL +static CvStatus icvJacobiEigens_32f(float *A, float *V, float *E, int n, float eps) { int i, j, k, ind; @@ -206,7 +206,7 @@ icvJacobiEigens_32f(float *A, float *V, float *E, int n, float eps) // // Returns: CV_NO_ERR or error code // -// Notes: +// Notes: //F*/ static CvStatus CV_STDCALL icvCalcCovarMatrixEx_8u32fR( int nObjects, void *input, int objStep1, @@ -539,8 +539,7 @@ icvCalcEigenObjects_8u32fR( int nObjects, void* input, int objStep, /* Buffer size determination */ if( ioFlags ) { - int size = icvDefaultBufferSize(); - ioBufSize = MIN( size, n ); + ioBufSize = MIN( icvDefaultBufferSize(), n ); } /* memory allocation (if necesseay) */ @@ -695,13 +694,13 @@ icvCalcEigenObjects_8u32fR( int nObjects, void* input, int objStep, for( igr = 0; igr < ngr; igr++ ) { - int i, io, ie, imin = igr * nio, imax = imin + nio; + int io, ie, imin = igr * nio, imax = imin + nio; if( imax > m1 ) imax = m1; - for( i = 0; i < eigSize.width * (imax - imin); i++ ) - ((float *) buffer)[i] = 0.f; + for(int k = 0; k < eigSize.width * (imax - imin); k++ ) + ((float *) buffer)[k] = 0.f; for( io = 0; io < nObjects; io++ ) { @@ -1313,7 +1312,7 @@ cvCalcEigenObjects( int nObjects, int ioBufSize, void* userData, CvTermCriteria* calcLimit, - IplImage* avg, + IplImage* avg, float* eigVals ) { float *avg_data; @@ -1570,7 +1569,7 @@ cvCalcDecompCoeff( IplImage * obj, IplImage * eigObj, IplImage * avg ) avg_data, avg_step, obj_size ); __END__; - + return coeff; } @@ -1598,9 +1597,9 @@ CV_IMPL void cvEigenDecomposite( IplImage* obj, int nEigObjs, void* eigInput, - int ioFlags, - void* userData, - IplImage* avg, + int ioFlags, + void* userData, + IplImage* avg, float* coeffs ) { float *avg_data; @@ -1716,7 +1715,7 @@ cvEigenProjection( void* eigInput, int nEigObjs, int ioFlags, void* userData, - float* coeffs, + float* coeffs, IplImage* avg, IplImage* proj ) { diff --git a/modules/legacy/src/em.cpp b/modules/legacy/src/em.cpp index 54e8a1b..05e6678 100644 --- a/modules/legacy/src/em.cpp +++ b/modules/legacy/src/em.cpp @@ -158,7 +158,7 @@ bool CvEM::train( const CvMat* _samples, const CvMat* _sample_idx, Mat samples = cvarrToMat(_samples), labels0, labels; if( _labels ) labels0 = labels = cvarrToMat(_labels); - + bool isOk = train(samples, Mat(), _params, _labels ? &labels : 0); CV_Assert( labels0.data == labels.data ); @@ -202,8 +202,8 @@ bool CvEM::train( const Mat& _samples, const Mat& _sample_idx, { CV_Assert(_sample_idx.empty()); Mat prbs, weights, means, logLikelihoods; - std::vector covsHdrs; - init_params(_params, prbs, weights, means, covsHdrs); + std::vector covshdrs; + init_params(_params, prbs, weights, means, covshdrs); emObj = EM(_params.nclusters, _params.cov_mat_type, _params.term_crit); bool isOk = false; @@ -211,14 +211,14 @@ bool CvEM::train( const Mat& _samples, const Mat& _sample_idx, isOk = emObj.train(_samples, logLikelihoods, _labels ? _OutputArray(*_labels) : cv::noArray(), probs); else if( _params.start_step == EM::START_E_STEP ) - isOk = emObj.trainE(_samples, means, covsHdrs, weights, + isOk = emObj.trainE(_samples, means, covshdrs, weights, logLikelihoods, _labels ? _OutputArray(*_labels) : cv::noArray(), probs); else if( _params.start_step == EM::START_M_STEP ) isOk = emObj.trainM(_samples, prbs, logLikelihoods, _labels ? _OutputArray(*_labels) : cv::noArray(), probs); else CV_Error(CV_StsBadArg, "Bad start type of EM algorithm"); - + if(isOk) { logLikelihood = sum(logLikelihoods).val[0]; diff --git a/modules/legacy/src/enteringblobdetection.cpp b/modules/legacy/src/enteringblobdetection.cpp index f39485f..32a83bf 100644 --- a/modules/legacy/src/enteringblobdetection.cpp +++ b/modules/legacy/src/enteringblobdetection.cpp @@ -83,7 +83,7 @@ static int CompareContour(const void* a, const void* b, void* ) return (dx < wt && dy < ht); } -void cvFindBlobsByCCClasters(IplImage* pFG, CvBlobSeq* pBlobs, CvMemStorage* storage) +static void cvFindBlobsByCCClasters(IplImage* pFG, CvBlobSeq* pBlobs, CvMemStorage* storage) { /* Create contours: */ IplImage* pIB = NULL; CvSeq* cnt = NULL; @@ -117,11 +117,11 @@ void cvFindBlobsByCCClasters(IplImage* pFG, CvBlobSeq* pBlobs, CvMemStorage* sto for(cnt_cur=0; cnt_curtotal; ++cnt_cur) { CvRect rect; - CvSeq* cnt; + CvSeq* cont; int k = *(int*)cvGetSeqElem( clasters, cnt_cur ); if(k!=claster_cur) continue; - cnt = *(CvSeq**)cvGetSeqElem( cnt_list, cnt_cur ); - rect = ((CvContour*)cnt)->rect; + cont = *(CvSeq**)cvGetSeqElem( cnt_list, cnt_cur ); + rect = ((CvContour*)cont)->rect; if(rect_res.height<0) { @@ -399,7 +399,7 @@ int CvBlobDetectorSimple::DetectNewBlob(IplImage* /*pImg*/, IplImage* pFGMask, C if(Good) do{ /* For each configuration: */ CvBlob* pBL[EBD_FRAME_NUM]; - int Good = 1; + int good = 1; double Error = 0; CvBlob* pBNew = m_pBlobLists[EBD_FRAME_NUM-1]->GetBlob(pBLIndex[EBD_FRAME_NUM-1]); @@ -408,7 +408,7 @@ int CvBlobDetectorSimple::DetectNewBlob(IplImage* /*pImg*/, IplImage* pFGMask, C Count++; /* Check intersection last blob with existed: */ - if(Good && pOldBlobList) + if(good && pOldBlobList) { /* Check intersection last blob with existed: */ int k; for(k=pOldBlobList->GetBlobNum(); k>0; --k) @@ -416,22 +416,22 @@ int CvBlobDetectorSimple::DetectNewBlob(IplImage* /*pImg*/, IplImage* pFGMask, C CvBlob* pBOld = pOldBlobList->GetBlob(k-1); if((fabs(pBOld->x-pBNew->x) < (CV_BLOB_RX(pBOld)+CV_BLOB_RX(pBNew))) && (fabs(pBOld->y-pBNew->y) < (CV_BLOB_RY(pBOld)+CV_BLOB_RY(pBNew)))) - Good = 0; + good = 0; } } /* Check intersection last blob with existed. */ /* Check distance to image border: */ - if(Good) + if(good) { /* Check distance to image border: */ CvBlob* pB = pBNew; float dx = MIN(pB->x,S.width-pB->x)/CV_BLOB_RX(pB); float dy = MIN(pB->y,S.height-pB->y)/CV_BLOB_RY(pB); - if(dx < 1.1 || dy < 1.1) Good = 0; + if(dx < 1.1 || dy < 1.1) good = 0; } /* Check distance to image border. */ /* Check uniform motion: */ - if(Good) + if(good) { int N = EBD_FRAME_NUM; float sum[2] = {0,0}; @@ -466,13 +466,13 @@ int CvBlobDetectorSimple::DetectNewBlob(IplImage* /*pImg*/, IplImage* pFGMask, C if( Error > S.width*0.01 || fabs(a[0])>S.width*0.1 || fabs(a[1])>S.height*0.1) - Good = 0; + good = 0; } /* Check configuration. */ /* New best trajectory: */ - if(Good && (BestError == -1 || BestError > Error)) + if(good && (BestError == -1 || BestError > Error)) { for(i=0; i 0.00001 ) /* alpha must be > betta */ { @@ -116,7 +116,7 @@ int icvCompute3DPoint( double alpha,double betta, partY = coeffs->Ycoef + coeffs->YcoefA *alpha + coeffs->YcoefB*betta + coeffs->YcoefAB*alphabetta; - + partZ = coeffs->Zcoef + coeffs->ZcoefA *alpha + coeffs->ZcoefB*betta + coeffs->ZcoefAB*alphabetta; @@ -159,12 +159,12 @@ int icvCreateConvertMatrVect( CvMatr64d rotMatr1, icvMulMatrix_64d( convRotMatr, 3,3, transVect2, - 1,3, + 1,3, tmpVect); - + icvSubVector_64d(transVect1,tmpVect,convTransVect,3); - + return CV_NO_ERR; } @@ -182,15 +182,15 @@ int icvConvertPointSystem(CvPoint3D64d M2, icvMulMatrix_64d( rotMatr, 3,3, (double*)&M2, - 1,3, + 1,3, tmpVect); icvAddVector_64d(tmpVect,transVect,(double*)M1,3); - + return CV_NO_ERR; } /*--------------------------------------------------------------------------------------*/ -int icvComputeCoeffForStereoV3( double quad1[4][2], +static int icvComputeCoeffForStereoV3( double quad1[4][2], double quad2[4][2], int numScanlines, CvMatr64d camMatr1, @@ -222,7 +222,7 @@ int icvComputeCoeffForStereoV3( double quad1[4][2], point2.x = (1.0 - alpha) * quad1[1][0] + alpha * quad1[2][0]; point2.y = (1.0 - alpha) * quad1[1][1] + alpha * quad1[2][1]; - + point3.x = (1.0 - alpha) * quad2[0][0] + alpha * quad2[3][0]; point3.y = (1.0 - alpha) * quad2[0][1] + alpha * quad2[3][1]; @@ -243,10 +243,10 @@ int icvComputeCoeffForStereoV3( double quad1[4][2], &startCoeffs[currLine], needSwapCamera); } - return CV_NO_ERR; + return CV_NO_ERR; } /*--------------------------------------------------------------------------------------*/ -int icvComputeCoeffForStereoNew( double quad1[4][2], +static int icvComputeCoeffForStereoNew( double quad1[4][2], double quad2[4][2], int numScanlines, CvMatr32f camMatr1, @@ -260,10 +260,10 @@ int icvComputeCoeffForStereoNew( double quad1[4][2], double camMatr1_64d[9]; double camMatr2_64d[9]; - + double rotMatr1_64d[9]; double transVect1_64d[3]; - + double rotMatr2_64d[9]; double transVect2_64d[3]; @@ -348,21 +348,21 @@ int icvComCoeffForLine( CvPoint2D64d point1, { /* Get direction for all points */ /* Direction for camera 1 */ - + CvPoint3D64f direct1; CvPoint3D64f direct2; CvPoint3D64f camPoint1; - + CvPoint3D64f directS3; CvPoint3D64f directS4; CvPoint3D64f direct3; CvPoint3D64f direct4; CvPoint3D64f camPoint2; - + icvGetDirectionForPoint( point1, camMatr1, &direct1); - + icvGetDirectionForPoint( point2, camMatr1, &direct2); @@ -372,13 +372,13 @@ int icvComCoeffForLine( CvPoint2D64d point1, icvGetDirectionForPoint( point3, camMatr2, &directS3); - + icvGetDirectionForPoint( point4, camMatr2, &directS4); /* Create convertion for camera 2: two direction and camera point */ - + double convRotMatr[9]; double convTransVect[3]; @@ -392,15 +392,15 @@ int icvComCoeffForLine( CvPoint2D64d point1, CvPoint3D64f zeroVect; zeroVect.x = zeroVect.y = zeroVect.z = 0.0; camPoint1.x = camPoint1.y = camPoint1.z = 0.0; - + icvConvertPointSystem(directS3,&direct3,convRotMatr,convTransVect); icvConvertPointSystem(directS4,&direct4,convRotMatr,convTransVect); icvConvertPointSystem(zeroVect,&camPoint2,convRotMatr,convTransVect); CvPoint3D64f pointB; - + int postype = 0; - + /* Changed order */ /* Compute point B: xB,yB,zB */ icvGetCrossLines(camPoint1,direct2, @@ -449,7 +449,7 @@ int icvComCoeffForLine( CvPoint2D64d point1, double gamma; - + double xA,yA,zA; double xB,yB,zB; double xC,yC,zC; @@ -476,7 +476,7 @@ int icvComCoeffForLine( CvPoint2D64d point1, camPoint1, gamma, coeffs); - + return CV_NO_ERR; } @@ -489,7 +489,7 @@ int icvGetDirectionForPoint( CvPoint2D64d point, { /* */ double invMatr[9]; - + /* Invert matrix */ icvInvertMatrix_64d(camMatr,3,invMatr); @@ -504,10 +504,10 @@ int icvGetDirectionForPoint( CvPoint2D64d point, icvMulMatrix_64d( invMatr, 3,3, vect, - 1,3, + 1,3, (double*)direct); - return CV_NO_ERR; + return CV_NO_ERR; } /*--------------------------------------------------------------------------------------*/ @@ -556,7 +556,7 @@ int icvGetCrossLines(CvPoint3D64d point11,CvPoint3D64d point12, double alpha,betta; delta = a11*a22-a12*a21; - + if( fabs(delta) < EPS64D ) { /*return ERROR;*/ @@ -662,7 +662,7 @@ int icvGetAngleLine( CvPoint2D64d startPoint, CvSize imageSize,CvPoint2D64d *poi /* Find four lines */ CvPoint2D64d pa,pb,pc,pd; - + pa.x = 0; pa.y = 0; @@ -674,10 +674,10 @@ int icvGetAngleLine( CvPoint2D64d startPoint, CvSize imageSize,CvPoint2D64d *poi pc.x = 0; pc.y = imageSize.height-1; - + /* We can compute points for angle */ /* Test for place section */ - + if( startPoint.x < 0 ) {/* 1,4,7 */ if( startPoint.y < 0) @@ -782,7 +782,7 @@ void icvGetCoefForPiece( CvPoint2D64d p_start,CvPoint2D64d p_end, /*---------------------------------------------------------------------------------------*/ /* Get common area of rectifying */ -void icvGetCommonArea( CvSize imageSize, +static void icvGetCommonArea( CvSize imageSize, CvPoint3D64d epipole1,CvPoint3D64d epipole2, CvMatr64d fundMatr, CvVect64d coeff11,CvVect64d coeff12, @@ -808,10 +808,10 @@ void icvGetCommonArea( CvSize imageSize, double transFundMatr[3*3]; /* Compute transpose of fundamental matrix */ icvTransposeMatrix_64d( fundMatr, 3, 3, transFundMatr ); - + CvPoint2D64d epipole1_2d; CvPoint2D64d epipole2_2d; - + if( fabs(epipole1.z) < 1e-8 ) {/* epipole1 in infinity */ *result = 0; @@ -853,7 +853,7 @@ void icvGetCommonArea( CvSize imageSize, pointW11[2] = 1.0; icvTransformVector_64d( transFundMatr, /* !!! Modified from not transposed */ - pointW11, + pointW11, corr21, 3,3); @@ -864,7 +864,7 @@ void icvGetCommonArea( CvSize imageSize, corr21[0],corr21[1],corr21[2], &start,&end, &res); - + if( res == 0 ) {/* We have not cross */ /* We must define new angle */ @@ -879,7 +879,7 @@ void icvGetCommonArea( CvSize imageSize, /* corr11 = Fund * p21 */ icvTransformVector_64d( fundMatr, /* !!! Modified */ - pointW21, + pointW21, corr11, 3,3); @@ -889,7 +889,7 @@ void icvGetCommonArea( CvSize imageSize, coeff11[0] = corr11[0]; coeff11[1] = corr11[1]; coeff11[2] = corr11[2]; - + /* Set coefs for line 1 image 2 */ icvGetCoefForPiece( epipole2_2d,point21, &coeff21[0],&coeff21[1],&coeff21[2], @@ -911,12 +911,12 @@ void icvGetCommonArea( CvSize imageSize, *result = 0; return;/* Error */ } - + /* Set coefs for line 1 image 2 */ coeff21[0] = corr21[0]; coeff21[1] = corr21[1]; coeff21[2] = corr21[2]; - + } /* ============= Computation for line 2 ================ */ @@ -928,7 +928,7 @@ void icvGetCommonArea( CvSize imageSize, pointW12[2] = 1.0; icvTransformVector_64d( transFundMatr, - pointW12, + pointW12, corr22, 3,3); @@ -937,7 +937,7 @@ void icvGetCommonArea( CvSize imageSize, corr22[0],corr22[1],corr22[2], &start,&end, &res); - + if( res == 0 ) {/* We have not cross */ /* We must define new angle */ @@ -952,18 +952,18 @@ void icvGetCommonArea( CvSize imageSize, /* corr2 = Fund' * p1 */ icvTransformVector_64d( fundMatr, - pointW22, + pointW22, corr12, 3,3); - + /* We have cross. And it's result cross for down line. Set result coefs */ /* Set coefs for line 2 image 1 */ coeff12[0] = corr12[0]; coeff12[1] = corr12[1]; coeff12[2] = corr12[2]; - + /* Set coefs for line 1 image 2 */ icvGetCoefForPiece( epipole2_2d,point22, &coeff22[0],&coeff22[1],&coeff22[2], @@ -985,12 +985,12 @@ void icvGetCommonArea( CvSize imageSize, *result = 0; return;/* Error */ } - + /* Set coefs for line 1 image 2 */ coeff22[0] = corr22[0]; coeff22[1] = corr22[1]; coeff22[2] = corr22[2]; - + } /* Now we know common area */ @@ -1050,9 +1050,9 @@ void icvGetCrossPieceDirect( CvPoint2D64d p_start,CvPoint2D64d p_end, {/* Have cross */ double det; double detxc,detyc; - + det = a * (p_end.x - p_start.x) + b * (p_end.y - p_start.y); - + if( fabs(det) < EPS64D ) {/* lines are parallel and may be equal or line is point */ if( fabs(a*p_start.x + b*p_start.y + c) < EPS64D ) @@ -1062,7 +1062,7 @@ void icvGetCrossPieceDirect( CvPoint2D64d p_start,CvPoint2D64d p_end, } else { - *result = 2; + *result = 2; } return; } @@ -1131,7 +1131,7 @@ void icvGetCrossPiecePiece( CvPoint2D64d p1_start,CvPoint2D64d p1_end, cross->x = delX / del; cross->y = delY / del; - + *result = 1; return; } @@ -1171,7 +1171,7 @@ void icvGetCrossRectDirect( CvSize imageSize, CvPoint2D64d frameEnd; CvPoint2D64d cross[4]; int haveCross[4]; - + haveCross[0] = 0; haveCross[1] = 0; haveCross[2] = 0; @@ -1182,25 +1182,25 @@ void icvGetCrossRectDirect( CvSize imageSize, frameEnd.x = imageSize.width; frameEnd.y = 0; - icvGetCrossPieceDirect(frameBeg,frameEnd,a,b,c,&cross[0],&haveCross[0]); - + icvGetCrossPieceDirect(frameBeg,frameEnd,a,b,c,&cross[0],&haveCross[0]); + frameBeg.x = imageSize.width; frameBeg.y = 0; frameEnd.x = imageSize.width; frameEnd.y = imageSize.height; - icvGetCrossPieceDirect(frameBeg,frameEnd,a,b,c,&cross[1],&haveCross[1]); + icvGetCrossPieceDirect(frameBeg,frameEnd,a,b,c,&cross[1],&haveCross[1]); frameBeg.x = imageSize.width; frameBeg.y = imageSize.height; frameEnd.x = 0; frameEnd.y = imageSize.height; - icvGetCrossPieceDirect(frameBeg,frameEnd,a,b,c,&cross[2],&haveCross[2]); + icvGetCrossPieceDirect(frameBeg,frameEnd,a,b,c,&cross[2],&haveCross[2]); frameBeg.x = 0; frameBeg.y = imageSize.height; frameEnd.x = 0; frameEnd.y = 0; - icvGetCrossPieceDirect(frameBeg,frameEnd,a,b,c,&cross[3],&haveCross[3]); + icvGetCrossPieceDirect(frameBeg,frameEnd,a,b,c,&cross[3],&haveCross[3]); double maxDist; @@ -1210,7 +1210,7 @@ void icvGetCrossRectDirect( CvSize imageSize, int i,j; maxDist = -1.0; - + double distance; for( i = 0; i < 3; i++ ) @@ -1259,7 +1259,7 @@ void icvProjectPointToImage( CvPoint3D64d point, double tmpVect1[3]; double tmpVect2[3]; - + icvMulMatrix_64d ( rotMatr, 3,3, (double*)&point, @@ -1276,13 +1276,13 @@ void icvProjectPointToImage( CvPoint3D64d point, projPoint->x = tmpVect1[0] / tmpVect1[2]; projPoint->y = tmpVect1[1] / tmpVect1[2]; - + return; } /*---------------------------------------------------------------------------------------*/ /* Get quads for transform images */ -void icvGetQuadsTransform( +void icvGetQuadsTransform( CvSize imageSize, CvMatr64d camMatr1, CvMatr64d rotMatr1, @@ -1338,10 +1338,10 @@ void icvGetQuadsTransform( fundMatr_32f, camMatr1_32f, camMatr2_32f); - + CvPoint3D32f epipole1_32f; CvPoint3D32f epipole2_32f; - + cvComputeEpipolesFromFundMatrix( fundMatr_32f, &epipole1_32f, &epipole2_32f); @@ -1353,7 +1353,7 @@ void icvGetQuadsTransform( epipole2->x = epipole2_32f.x; epipole2->y = epipole2_32f.y; epipole2->z = epipole2_32f.z; - + /* Convert fundamental matrix */ icvCvt_32f_64d(fundMatr_32f,fundMatr,9); } @@ -1466,7 +1466,7 @@ void icvGetQuadsTransform( /* -------------Compute for first image-------------- */ CvPoint2D32f pointb1; CvPoint2D32f pointe1; - + CvPoint2D32f pointb2; CvPoint2D32f pointe2; @@ -1494,11 +1494,11 @@ void icvGetQuadsTransform( double dxOld,dyOld; double dxNew,dyNew; double distOld,distNew; - + dxOld = quad2[1][0] - quad2[0][0]; dyOld = quad2[1][1] - quad2[0][1]; distOld = dxOld*dxOld + dyOld*dyOld; - + dxNew = quad2[1][0] - pointb2.x; dyNew = quad2[1][1] - pointb2.y; distNew = dxNew*dxNew + dyNew*dyNew; @@ -1542,7 +1542,7 @@ void icvGetQuadsTransform( newQuad2[0][1] = quad2[0][1]; newQuad2[3][0] = quad2[3][0]; newQuad2[3][1] = quad2[3][1]; - + newQuad1[0][0] = pointb1.x; newQuad1[0][1] = pointb1.y; newQuad1[3][0] = pointe1.x; @@ -1569,11 +1569,11 @@ void icvGetQuadsTransform( &pointe2); /* Compute distances */ - + dxOld = quad2[0][0] - quad2[1][0]; dyOld = quad2[0][1] - quad2[1][1]; distOld = dxOld*dxOld + dyOld*dyOld; - + dxNew = quad2[0][0] - pointb2.x; dyNew = quad2[0][1] - pointb2.y; distNew = dxNew*dxNew + dyNew*dyNew; @@ -1614,7 +1614,7 @@ void icvGetQuadsTransform( newQuad2[1][1] = quad2[1][1]; newQuad2[2][0] = quad2[2][0]; newQuad2[2][1] = quad2[2][1]; - + newQuad1[1][0] = pointb1.x; newQuad1[1][1] = pointb1.y; newQuad1[2][0] = pointe1.x; @@ -1660,7 +1660,7 @@ void icvGetQuadsTransform( /*---------------------------------------------------------------------------------------*/ -void icvGetQuadsTransformNew( CvSize imageSize, +static void icvGetQuadsTransformNew( CvSize imageSize, CvMatr32f camMatr1, CvMatr32f camMatr2, CvMatr32f rotMatr1, @@ -1732,7 +1732,7 @@ void icvGetQuadsTransformNew( CvSize imageSize, /* Convert fundamental matrix */ icvCvt_64d_32f(fundMatr_64d,fundMatr,9); - + return; } @@ -1771,7 +1771,7 @@ void icvGetQuadsTransformStruct( CvStereoCamera* stereoCamera) /*---------------------------------------------------------------------------------------*/ void icvComputeStereoParamsForCameras(CvStereoCamera* stereoCamera) { - /* For given intrinsic and extrinsic parameters computes rest parameters + /* For given intrinsic and extrinsic parameters computes rest parameters ** such as fundamental matrix. warping coeffs, epipoles, ... */ @@ -1792,14 +1792,14 @@ void icvComputeStereoParamsForCameras(CvStereoCamera* stereoCamera) icvCvt_32f_64d(stereoCamera->camera[0]->transVect,transVect1,3); icvCvt_32f_64d(stereoCamera->camera[1]->transVect,transVect2,3); - + icvCreateConvertMatrVect( rotMatr1, transVect1, rotMatr2, transVect2, convRotMatr, convTransVect); - + /* copy to stereo camera params */ icvCvt_64d_32f(convRotMatr,stereoCamera->rotMatrix,9); icvCvt_64d_32f(convTransVect,stereoCamera->transVector,3); @@ -1837,7 +1837,7 @@ void icvGetCutPiece( CvVect64d areaLineCoef1,CvVect64d areaLineCoef2, /* Find middle line of sector */ double midLine[3]={0,0,0}; - + /* Different way */ CvPoint2D64d pointOnLine1; pointOnLine1.x = pointOnLine1.y = 0; CvPoint2D64d pointOnLine2; pointOnLine2.x = pointOnLine2.y = 0; @@ -1885,7 +1885,7 @@ void icvGetCutPiece( CvVect64d areaLineCoef1,CvVect64d areaLineCoef2, candPoints[numPoints] = cornerPoint; numPoints++; } - + cornerPoint.x = imageSize.width; cornerPoint.y = imageSize.height; icvTestPoint( cornerPoint, areaLineCoef1, areaLineCoef2, epipole, &res); @@ -1919,7 +1919,7 @@ void icvGetCutPiece( CvVect64d areaLineCoef1,CvVect64d areaLineCoef2, areaLineCoef2[0],areaLineCoef2[1],areaLineCoef2[2], &tmpPoints[0], &tmpPoints[1], &res); - + for( i = 0; i < res; i++ ) { candPoints[numPoints++] = tmpPoints[i]; @@ -1941,7 +1941,7 @@ void icvGetCutPiece( CvVect64d areaLineCoef1,CvVect64d areaLineCoef2, double maxDist = 0; double minDist = 10000000; - + for( i = 0; i < numPoints; i++ ) { icvProjectPointToDirect(candPoints[i], midLine, &projPoint); @@ -1960,7 +1960,7 @@ void icvGetCutPiece( CvVect64d areaLineCoef1,CvVect64d areaLineCoef2, } /* We know maximum and minimum points. Now we can compute cut lines */ - + icvGetNormalDirect(midLine,minPoint,cutLine1); icvGetNormalDirect(midLine,maxPoint,cutLine2); @@ -1993,7 +1993,7 @@ void icvGetMiddleAnglePoint( CvPoint2D64d basePoint, CvPoint2D64d point1,CvPoint2D64d point2, CvPoint2D64d* midPoint) {/* !!! May be need to return error */ - + double dist1; double dist2; icvGetPieceLength(basePoint,point1,&dist1); @@ -2020,7 +2020,7 @@ void icvGetNormalDirect(CvVect64d direct,CvPoint2D64d point,CvVect64d normDirect { normDirect[0] = direct[1]; normDirect[1] = - direct[0]; - normDirect[2] = -(normDirect[0]*point.x + normDirect[1]*point.y); + normDirect[2] = -(normDirect[0]*point.x + normDirect[1]*point.y); return; } @@ -2063,7 +2063,7 @@ void icvTestPoint( CvPoint2D64d testPoint, { *result = 0; } - + return; } @@ -2074,7 +2074,7 @@ void icvProjectPointToDirect( CvPoint2D64d point,CvVect64d lineCoeff, { double a = lineCoeff[0]; double b = lineCoeff[1]; - + double det = 1.0 / ( a*a + b*b ); double delta = a*point.y - b*point.x; @@ -2103,7 +2103,7 @@ CV_IMPL IplImage* icvCreateIsometricImage( IplImage* src, IplImage* dst, CvSize src_size ; src_size.width = src->width; src_size.height = src->height; - + CvSize dst_size = src_size; if( dst ) @@ -2127,7 +2127,7 @@ CV_IMPL IplImage* icvCreateIsometricImage( IplImage* src, IplImage* dst, return dst; } -int +static int icvCvt_32f_64d( float *src, double *dst, int size ) { int t; @@ -2147,7 +2147,7 @@ icvCvt_32f_64d( float *src, double *dst, int size ) /*======================================================================================*/ /* Type conversion double -> float */ -int +static int icvCvt_64d_32f( double *src, float *dst, int size ) { int t; @@ -2167,9 +2167,9 @@ icvCvt_64d_32f( double *src, float *dst, int size ) /*----------------------------------------------------------------------------------*/ - +#if 0 /* Find line which cross frame by line(a,b,c) */ -void FindLineForEpiline( CvSize imageSize, +static void FindLineForEpiline( CvSize imageSize, float a,float b,float c, CvPoint2D32f *start,CvPoint2D32f *end, int*) @@ -2191,7 +2191,7 @@ void FindLineForEpiline( CvSize imageSize, frameEnd.x = (float)(imageSize.width); frameEnd.y = 0; haveCross[0] = icvGetCrossLineDirect(frameBeg,frameEnd,a,b,c,&cross[0]); - + frameBeg.x = (float)(imageSize.width); frameBeg.y = 0; frameEnd.x = (float)(imageSize.width); @@ -2203,7 +2203,7 @@ void FindLineForEpiline( CvSize imageSize, frameEnd.x = 0; frameEnd.y = (float)(imageSize.height); haveCross[2] = icvGetCrossLineDirect(frameBeg,frameEnd,a,b,c,&cross[2]); - + frameBeg.x = 0; frameBeg.y = (float)(imageSize.height); frameEnd.x = 0; @@ -2255,13 +2255,12 @@ void FindLineForEpiline( CvSize imageSize, } return; - + } /*----------------------------------------------------------------------------------*/ - -int GetAngleLinee( CvPoint2D32f epipole, CvSize imageSize,CvPoint2D32f point1,CvPoint2D32f point2) +static int GetAngleLinee( CvPoint2D32f epipole, CvSize imageSize,CvPoint2D32f point1,CvPoint2D32f point2) { float width = (float)(imageSize.width); float height = (float)(imageSize.height); @@ -2271,7 +2270,7 @@ int GetAngleLinee( CvPoint2D32f epipole, CvSize imageSize,CvPoint2D32f point1,Cv /* Find four lines */ CvPoint2D32f pa,pb,pc,pd; - + pa.x = 0; pa.y = 0; @@ -2290,7 +2289,7 @@ int GetAngleLinee( CvPoint2D32f epipole, CvSize imageSize,CvPoint2D32f point1,Cv float x,y; x = epipole.x; y = epipole.y; - + if( x < 0 ) {/* 1,4,7 */ if( y < 0) @@ -2344,15 +2343,15 @@ int GetAngleLinee( CvPoint2D32f epipole, CvSize imageSize,CvPoint2D32f point1,Cv return 2; } - + } - + return 0; } /*--------------------------------------------------------------------------------------*/ -void icvComputePerspectiveCoeffs(const CvPoint2D32f srcQuad[4],const CvPoint2D32f dstQuad[4],double coeffs[3][3]) +static void icvComputePerspectiveCoeffs(const CvPoint2D32f srcQuad[4],const CvPoint2D32f dstQuad[4],double coeffs[3][3]) {/* Computes perspective coeffs for transformation from src to dst quad */ @@ -2385,7 +2384,7 @@ void icvComputePerspectiveCoeffs(const CvPoint2D32f srcQuad[4],const CvPoint2D32 double Y = dstQuad[i].y; #endif double* a = A + i*16; - + a[0] = x; a[1] = y; a[2] = 1; @@ -2420,7 +2419,7 @@ void icvComputePerspectiveCoeffs(const CvPoint2D32f srcQuad[4],const CvPoint2D32 CV_CALL( cvPseudoInverse( &matA, &matInvA )); CV_CALL( cvMatMulAdd( &matInvA, &matB, 0, &matX )); } - + coeffs[0][0] = c[0]; coeffs[0][1] = c[1]; coeffs[0][2] = c[2]; @@ -2435,6 +2434,7 @@ void icvComputePerspectiveCoeffs(const CvPoint2D32f srcQuad[4],const CvPoint2D32 return; } +#endif /*--------------------------------------------------------------------------------------*/ @@ -2457,7 +2457,7 @@ CV_IMPL void cvComputePerspectiveMap(const double c[3][3], CvArr* rectMapX, CvAr size = cvGetMatSize(mapx); assert( fabs(c[2][2] - 1.) < FLT_EPSILON ); - + for( i = 0; i < size.height; i++ ) { float* mx = (float*)(mapx->data.ptr + mapx->step*i); @@ -2525,7 +2525,7 @@ CV_IMPL void cvInitPerspectiveTransform( CvSize size, const CvPoint2D32f quad[4] double Y = quad[i].y; #endif double* a = A + i*16; - + a[0] = x; a[1] = y; a[2] = 1; @@ -2560,7 +2560,7 @@ CV_IMPL void cvInitPerspectiveTransform( CvSize size, const CvPoint2D32f quad[4] CV_CALL( cvPseudoInverse( &matA, &matInvA )); CV_CALL( cvMatMulAdd( &matInvA, &matB, 0, &matX )); } - + matrix[0][0] = c[0]; matrix[0][1] = c[1]; matrix[0][2] = c[2]; @@ -2613,7 +2613,7 @@ void icvComputeeInfiniteProject1( CvMatr64d rotMatr, icvMulMatrix_64d( invMatr1, 3,3, p1, - 1,3, + 1,3, P1); double invR[9]; @@ -2624,7 +2624,7 @@ void icvComputeeInfiniteProject1( CvMatr64d rotMatr, icvMulMatrix_64d( invR, 3,3, P1, - 1,3, + 1,3, P2); /* Now we can project this point to image 2 */ @@ -2633,7 +2633,7 @@ void icvComputeeInfiniteProject1( CvMatr64d rotMatr, icvMulMatrix_64d( camMatr2, 3,3, P2, - 1,3, + 1,3, projP); point2->x = (float)(projP[0] / projP[2]); @@ -2661,7 +2661,7 @@ void icvComputeeInfiniteProject2( CvMatr64d rotMatr, icvMulMatrix_64d( invMatr2, 3,3, p2, - 1,3, + 1,3, P2); /* Change system 1 to system 2 */ @@ -2670,7 +2670,7 @@ void icvComputeeInfiniteProject2( CvMatr64d rotMatr, icvMulMatrix_64d( rotMatr, 3,3, P2, - 1,3, + 1,3, P1); /* Now we can project this point to image 2 */ @@ -2679,7 +2679,7 @@ void icvComputeeInfiniteProject2( CvMatr64d rotMatr, icvMulMatrix_64d( camMatr1, 3,3, P1, - 1,3, + 1,3, projP); point1->x = (float)(projP[0] / projP[2]); @@ -2690,7 +2690,7 @@ void icvComputeeInfiniteProject2( CvMatr64d rotMatr, /* Select best R and t for given cameras, points, ... */ /* For both cameras */ -int icvSelectBestRt( int numImages, +static int icvSelectBestRt( int numImages, int* numPoints, CvPoint2D32f* imagePoints1, CvPoint2D32f* imagePoints2, @@ -2713,7 +2713,7 @@ int icvSelectBestRt( int numImages, /* Need to convert input data 32 -> 64 */ CvPoint3D64d* objectPoints_64d; - + double* rotMatrs1_64d; double* rotMatrs2_64d; @@ -2729,14 +2729,13 @@ int icvSelectBestRt( int numImages, /* allocate memory for 64d data */ int totalNum = 0; - int i; - for( i = 0; i < numImages; i++ ) + for(int i = 0; i < numImages; i++ ) { totalNum += numPoints[i]; } objectPoints_64d = (CvPoint3D64d*)calloc(totalNum,sizeof(CvPoint3D64d)); - + rotMatrs1_64d = (double*)calloc(numImages,sizeof(double)*9); rotMatrs2_64d = (double*)calloc(numImages,sizeof(double)*9); @@ -2744,7 +2743,7 @@ int icvSelectBestRt( int numImages, transVects2_64d = (double*)calloc(numImages,sizeof(double)*3); /* Convert input data to 64d */ - + icvCvt_32f_64d((float*)objectPoints, (double*)objectPoints_64d, totalNum*3); icvCvt_32f_64d(rotMatrs1, rotMatrs1_64d, numImages*9); @@ -2774,14 +2773,14 @@ int icvSelectBestRt( int numImages, int currRt; for( currRt = 0; currRt < numImages; currRt++ ) { - int begPoint = 0; + int begPoint = 0; for(currImagePair = 0; currImagePair < numImages; currImagePair++ ) { /* For current R,t R,t compute relative position of cameras */ double convRotMatr[9]; double convTransVect[3]; - + icvCreateConvertMatrVect( rotMatrs1_64d + currRt*9, transVects1_64d + currRt*3, rotMatrs2_64d + currRt*9, @@ -2828,15 +2827,14 @@ int icvSelectBestRt( int numImages, points2 = (CvPoint3D64d*)calloc(numberPnt,sizeof(CvPoint3D64d)); /* Transform object points to first camera position */ - int i; - for( i = 0; i < numberPnt; i++ ) + for(int i = 0; i < numberPnt; i++ ) { /* Create second camera point */ CvPoint3D64d tmpPoint; tmpPoint.x = (double)(objectPoints[i].x); tmpPoint.y = (double)(objectPoints[i].y); tmpPoint.z = (double)(objectPoints[i].z); - + icvConvertPointSystem( tmpPoint, points2+i, rotMatrs2_64d + currImagePair*9, @@ -2859,10 +2857,8 @@ int icvSelectBestRt( int numImages, dy = tmpPoint2.y - points1[i].y; dz = tmpPoint2.z - points1[i].z; err = sqrt(dx*dx + dy*dy + dz*dz);*/ - - } - + #if 0 cvProjectPointsSimple( numPoints[currImagePair], objectPoints_64d + begPoint, @@ -2901,7 +2897,7 @@ int icvSelectBestRt( int numImages, cameraMatrix2_64d, nodist, projImagePoints2); - + } #endif @@ -2929,7 +2925,7 @@ int icvSelectBestRt( int numImages, double err; for( currPoint = 0; currPoint < numberPnt; currPoint++ ) { - double len1,len2; + double len1,len2; double dx1,dy1; dx1 = imagePoints1[begPoint+currPoint].x - projImagePoints1[currPoint].x; dy1 = imagePoints1[begPoint+currPoint].y - projImagePoints1[currPoint].y; @@ -3030,12 +3026,12 @@ int icvConvertWarpCoordinates(double coeffs[3][3], int direction) { double x,y; - double det; + double det; if( direction == CV_WARP_TO_CAMERA ) {/* convert from camera image to warped image coordinates */ x = warpPoint->x; y = warpPoint->y; - + det = (coeffs[2][0] * x + coeffs[2][1] * y + coeffs[2][2]); if( fabs(det) > 1e-8 ) { @@ -3058,7 +3054,7 @@ int icvConvertWarpCoordinates(double coeffs[3][3], return CV_OK; } } - + return CV_BADFACTOR_ERR; } @@ -3094,8 +3090,7 @@ int icvComputeRestStereoParams(CvStereoCamera *stereoparams) corns[3].x = 0; corns[3].y = (float)(stereoparams->camera[0]->imgSize[1]-1); - int i; - for( i = 0; i < 4; i++ ) + for(int i = 0; i < 4; i++ ) { /* For first camera */ icvConvertWarpCoordinates( stereoparams->coeffs[0], @@ -3233,8 +3228,9 @@ int icvStereoCalibration( int numImages, return CV_NO_ERR; } +#if 0 /* Find line from epipole */ -void FindLine(CvPoint2D32f epipole,CvSize imageSize,CvPoint2D32f point,CvPoint2D32f *start,CvPoint2D32f *end) +static void FindLine(CvPoint2D32f epipole,CvSize imageSize,CvPoint2D32f point,CvPoint2D32f *start,CvPoint2D32f *end) { CvPoint2D32f frameBeg; CvPoint2D32f frameEnd; @@ -3252,7 +3248,7 @@ void FindLine(CvPoint2D32f epipole,CvSize imageSize,CvPoint2D32f point,CvPoint2D frameEnd.x = (float)(imageSize.width); frameEnd.y = 0; haveCross[0] = icvGetCrossPieceVector(frameBeg,frameEnd,epipole,point,&cross[0]); - + frameBeg.x = (float)(imageSize.width); frameBeg.y = 0; frameEnd.x = (float)(imageSize.width); @@ -3264,7 +3260,7 @@ void FindLine(CvPoint2D32f epipole,CvSize imageSize,CvPoint2D32f point,CvPoint2D frameEnd.x = 0; frameEnd.y = (float)(imageSize.height); haveCross[2] = icvGetCrossPieceVector(frameBeg,frameEnd,epipole,point,&cross[2]); - + frameBeg.x = 0; frameBeg.y = (float)(imageSize.height); frameEnd.x = 0; @@ -3277,7 +3273,7 @@ void FindLine(CvPoint2D32f epipole,CvSize imageSize,CvPoint2D32f point,CvPoint2D int maxN = -1; int minN = -1; - + for( n = 0; n < 4; n++ ) { if( haveCross[n] > 0 ) @@ -3315,9 +3311,8 @@ void FindLine(CvPoint2D32f epipole,CvSize imageSize,CvPoint2D32f point,CvPoint2D return; } - /* Find line which cross frame by line(a,b,c) */ -void FindLineForEpiline(CvSize imageSize,float a,float b,float c,CvPoint2D32f *start,CvPoint2D32f *end) +static void FindLineForEpiline(CvSize imageSize,float a,float b,float c,CvPoint2D32f *start,CvPoint2D32f *end) { CvPoint2D32f frameBeg; CvPoint2D32f frameEnd; @@ -3335,7 +3330,7 @@ void FindLineForEpiline(CvSize imageSize,float a,float b,float c,CvPoint2D32f *s frameEnd.x = (float)(imageSize.width); frameEnd.y = 0; haveCross[0] = icvGetCrossLineDirect(frameBeg,frameEnd,a,b,c,&cross[0]); - + frameBeg.x = (float)(imageSize.width); frameBeg.y = 0; frameEnd.x = (float)(imageSize.width); @@ -3347,7 +3342,7 @@ void FindLineForEpiline(CvSize imageSize,float a,float b,float c,CvPoint2D32f *s frameEnd.x = 0; frameEnd.y = (float)(imageSize.height); haveCross[2] = icvGetCrossLineDirect(frameBeg,frameEnd,a,b,c,&cross[2]); - + frameBeg.x = 0; frameBeg.y = (float)(imageSize.height); frameEnd.x = 0; @@ -3399,11 +3394,11 @@ void FindLineForEpiline(CvSize imageSize,float a,float b,float c,CvPoint2D32f *s } return; - + } /* Cross lines */ -int GetCrossLines(CvPoint2D32f p1_start,CvPoint2D32f p1_end,CvPoint2D32f p2_start,CvPoint2D32f p2_end,CvPoint2D32f *cross) +static int GetCrossLines(CvPoint2D32f p1_start,CvPoint2D32f p1_end,CvPoint2D32f p2_start,CvPoint2D32f p2_end,CvPoint2D32f *cross) { double ex1,ey1,ex2,ey2; double px1,py1,px2,py2; @@ -3448,7 +3443,7 @@ int GetCrossLines(CvPoint2D32f p1_start,CvPoint2D32f p1_end,CvPoint2D32f p2_star cross->y = (float)(-delY / del); return 1; } - +#endif int icvGetCrossPieceVector(CvPoint2D32f p1_start,CvPoint2D32f p1_end,CvPoint2D32f v2_start,CvPoint2D32f v2_end,CvPoint2D32f *cross) { @@ -3527,11 +3522,12 @@ int icvGetCrossLineDirect(CvPoint2D32f p1,CvPoint2D32f p2,float a,float b,float cross->x = (float)X; cross->y = (float)Y; - + return 1; } -int cvComputeEpipoles( CvMatr32f camMatr1, CvMatr32f camMatr2, +#if 0 +static int cvComputeEpipoles( CvMatr32f camMatr1, CvMatr32f camMatr2, CvMatr32f rotMatr1, CvMatr32f rotMatr2, CvVect32f transVect1,CvVect32f transVect2, CvVect32f epipole1, @@ -3571,7 +3567,7 @@ int cvComputeEpipoles( CvMatr32f camMatr1, CvMatr32f camMatr2, cvmMul( &ccamMatr1, &crotMatr1, &cmatrP1); cvmInvert( &cmatrP1,&cinvP1 ); cvmMul( &ccamMatr1, &ctransVect1, &cvectp1 ); - + /* Compute second */ cvmMul( &ccamMatr2, &crotMatr2, &cmatrP2 ); cvmInvert( &cmatrP2,&cinvP2 ); @@ -3610,7 +3606,7 @@ int cvComputeEpipoles( CvMatr32f camMatr1, CvMatr32f camMatr2, return CV_NO_ERR; }/* cvComputeEpipoles */ - +#endif /* Compute epipoles for fundamental matrix */ int cvComputeEpipolesFromFundMatrix(CvMatr32f fundMatr, @@ -3632,7 +3628,7 @@ int cvComputeEpipolesFromFundMatrix(CvMatr32f fundMatr, epipole1->x = matrU->data.fl[6]; epipole1->y = matrU->data.fl[7]; epipole1->z = matrU->data.fl[8]; - + /* Get last row from V' and compute epipole2 */ epipole2->x = matrV->data.fl[6]; epipole2->y = matrV->data.fl[7]; @@ -3640,7 +3636,7 @@ int cvComputeEpipolesFromFundMatrix(CvMatr32f fundMatr, cvReleaseMat(&matrW); cvReleaseMat(&matrU); - cvReleaseMat(&matrV); + cvReleaseMat(&matrV); return CV_OK; } @@ -3660,7 +3656,7 @@ int cvConvertEssential2Fundamental( CvMatr32f essMatr, CvMat* invCM1T = cvCreateMat(3,3,CV_MAT32F); cvTranspose(&cameraMatr1C,tmpMatr); - cvInvert(tmpMatr,invCM1T); + cvInvert(tmpMatr,invCM1T); cvmMul(invCM1T,&essMatrC,tmpMatr); cvInvert(&cameraMatr2C,invCM2); cvmMul(tmpMatr,invCM2,&fundMatrC); @@ -3673,7 +3669,7 @@ int cvConvertEssential2Fundamental( CvMatr32f essMatr, cvReleaseMat(&invCM2); cvReleaseMat(&tmpMatr); cvReleaseMat(&invCM1T); - + return CV_OK; } @@ -3689,11 +3685,11 @@ int cvComputeEssentialMatrix( CvMatr32f rotMatr, transMatr[0] = 0; transMatr[1] = - transVect[2]; transMatr[2] = transVect[1]; - + transMatr[3] = transVect[2]; transMatr[4] = 0; transMatr[5] = - transVect[0]; - + transMatr[6] = - transVect[1]; transMatr[7] = transVect[0]; transMatr[8] = 0; diff --git a/modules/legacy/src/extendededges.cpp b/modules/legacy/src/extendededges.cpp index 2671ddb..8ade446 100644 --- a/modules/legacy/src/extendededges.cpp +++ b/modules/legacy/src/extendededges.cpp @@ -41,14 +41,15 @@ #include "precomp.hpp" -#ifdef WIN32 /* make sure it builds under Linux whenever it is included into Makefile.am or not. */ +#if 0 +//#ifdef WIN32 /* make sure it builds under Linux whenever it is included into Makefile.am or not. */ //void icvCutContour( CvSeq* current, IplImage* image ); CvSeq* icvCutContourRaster( CvSeq* current, CvMemStorage* storage, IplImage* image ); //create lists of segments of all contours from image -CvSeq* cvExtractSingleEdges( IplImage* image, //bw image - it's content will be destroyed by cvFindContours +CvSeq* cvExtractSingleEdges( IplImage* image, //bw image - it's content will be destroyed by cvFindContours CvMemStorage* storage ) { CvMemStorage* tmp_storage = cvCreateChildMemStorage( storage ); @@ -57,29 +58,29 @@ CvSeq* cvExtractSingleEdges( IplImage* image, //bw image - it's content will be cvZero( image ); //iterate through contours - //iterate through tree + //iterate through tree CvSeq* current = contours; int number = 0; int level = 1; CvSeq* output = 0; - CvSeq* tail_seq = 0; + CvSeq* tail_seq = 0; //actually this loop can iterates through tree, //but still we use CV_RETR_LIST it is not useful while( current ) { - number++; - + number++; + //get vertical list of segments for one contour CvSeq* new_seq = icvCutContourRaster( current, storage, image ); //add this vertical list to horisontal list if( new_seq ) { - if( tail_seq ) - { - tail_seq->h_next = new_seq; + if( tail_seq ) + { + tail_seq->h_next = new_seq; new_seq->h_prev = tail_seq; tail_seq = new_seq; } @@ -90,13 +91,13 @@ CvSeq* cvExtractSingleEdges( IplImage* image, //bw image - it's content will be } //iteration through tree - if( current->v_next ) - { + if( current->v_next ) + { //goto child current = current->v_next; level++; } - else + else { //go parent while( !current->h_next ) @@ -105,7 +106,7 @@ CvSeq* cvExtractSingleEdges( IplImage* image, //bw image - it's content will be level--; if( !level ) break; } - + if( current ) //go brother current = current->h_next; } @@ -114,25 +115,25 @@ CvSeq* cvExtractSingleEdges( IplImage* image, //bw image - it's content will be //free temporary memstorage with initial contours cvReleaseMemStorage( &tmp_storage ); - return output; + return output; } //makes vertical list of segments for 1 contour CvSeq* icvCutContourRaster( CvSeq* current, CvMemStorage* storage, IplImage* image /*tmp image*/) { //iplSet(image, 0 ); // this can cause double edges if two contours have common edge - // for example if object is circle with 1 pixel width - // to remove such problem - remove this iplSet + // for example if object is circle with 1 pixel width + // to remove such problem - remove this iplSet //approx contour by single edges CvSeqReader reader; CvSeqWriter writer; - + int writing = 0; cvStartReadSeq( current, &reader, 0 ); //below line just to avoid warning cvStartWriteSeq( current->flags, sizeof(CvContour), sizeof(CvPoint), storage, &writer ); - + CvSeq* output = 0; CvSeq* tail = 0; @@ -147,7 +148,7 @@ CvSeq* icvCutContourRaster( CvSeq* current, CvMemStorage* storage, IplImage* ima //mark point ((uchar*)image->imageData)[image->widthStep * cur.y + cur.x]++; assert( ((uchar*)image->imageData)[image->widthStep * cur.y + cur.x] != 255 ); - + } //second pass - create separate edges @@ -161,22 +162,22 @@ CvSeq* icvCutContourRaster( CvSeq* current, CvMemStorage* storage, IplImage* ima uchar flag = image->imageData[image->widthStep * cur.y + cur.x]; if( flag != 255 && flag < 3) // { - if(!writing) + if(!writing) { cvStartWriteSeq( current->flags, sizeof(CvContour), sizeof(CvPoint), storage, &writer ); - writing = 1 ; + writing = 1 ; } //mark point if( flag < 3 ) ((uchar*)image->imageData)[image->widthStep * cur.y + cur.x] = 255; //add it to another seq CV_WRITE_SEQ_ELEM( cur, writer ); - + } else { //exclude this point from contour - if( writing ) + if( writing ) { CvSeq* newseq = cvEndWriteSeq( &writer ); writing = 0; @@ -191,7 +192,7 @@ CvSeq* icvCutContourRaster( CvSeq* current, CvMemStorage* storage, IplImage* ima { output = tail = newseq; } - } + } } } @@ -211,7 +212,7 @@ CvSeq* icvCutContourRaster( CvSeq* current, CvMemStorage* storage, IplImage* ima { output = tail = newseq; } - } + } return output; @@ -224,12 +225,12 @@ CvSeq* icvCutContourRaster( CvSeq* current, CvMemStorage* storage, IplImage* ima //approx contour by single edges CvSeqReader reader; CvSeqReader rev_reader; - + cvStartReadSeq( current, &reader, 0 ); int64* cur_pt = (int64*)reader.ptr; int64* prev_pt = (int64*)reader.prev_elem; - + //search for point a in aba position for( int i = 0; i < current->total; i++ ) { @@ -240,7 +241,7 @@ CvSeq* icvCutContourRaster( CvSeq* current, CvMemStorage* storage, IplImage* ima { //return to prev pos CV_PREV_SEQ_ELEM( sizeof(int64), reader ); - + //this point is end of edge //start going both directions and collect edge @@ -248,7 +249,7 @@ CvSeq* icvCutContourRaster( CvSeq* current, CvMemStorage* storage, IplImage* ima int pos = cvGetSeqReaderPos( &reader ); cvSetSeqReaderPos( &rev_reader, pos ); - + //walk in both directions while(1); @@ -259,10 +260,10 @@ CvSeq* icvCutContourRaster( CvSeq* current, CvMemStorage* storage, IplImage* ima } } - + */ #endif /* WIN32 */ - + diff --git a/modules/legacy/src/facedetection.cpp b/modules/legacy/src/facedetection.cpp index 3f95559..4487fda 100644 --- a/modules/legacy/src/facedetection.cpp +++ b/modules/legacy/src/facedetection.cpp @@ -67,7 +67,7 @@ FaceDetection::FaceDetection() m_iNumLayers = 16; assert(m_iNumLayers <= MAX_LAYERS); m_pFaceList = new FaceDetectionList(); - + m_bBoosting = false; @@ -87,7 +87,7 @@ FaceDetection::~FaceDetection() if (m_mstgRects) cvReleaseMemStorage(&m_mstgRects); - + }// ~FaceDetection() @@ -111,7 +111,7 @@ void FaceDetection::FindContours(IplImage* imgGray) m_mstgRects = cvCreateMemStorage(); if (NULL == m_mstgRects) return; - m_seqRects = cvCreateSeq(0, sizeof(CvSeq), sizeof(CvContourRect), m_mstgRects); + m_seqRects = cvCreateSeq(0, sizeof(CvSeq), sizeof(CvContourRect), m_mstgRects); if (NULL == m_seqRects) return; // find contours @@ -148,23 +148,23 @@ void FaceDetection::ThresholdingParam(IplImage *imgGray, int iNumLayers, int &iM buffImg += imgGray->widthStep; } // params - + for (i = 0; i <= GIST_NUM; i ++) { if (gistImg[i] >= GIST_MIN) break; } - + iMinLevel = i * GIST_STEP; - + for (i = GIST_NUM; i >= 0; i --) { if (gistImg[i] >= GIST_MIN) break; } - + iMaxLevel = i * GIST_STEP; - + int dLevels = iMaxLevel - iMinLevel; if (dLevels <= 0) { @@ -191,12 +191,12 @@ void FaceDetection::ThresholdingParam(IplImage *imgGray, int iNumLayers, int &iM void FaceDetection::CreateResults(CvSeq * lpSeq) { - + Face * tmp; - + double Max = 0; double CurStat = 0; - + FaceData tmpData; if (m_bBoosting) { @@ -218,12 +218,12 @@ void FaceDetection::CreateResults(CvSeq * lpSeq) if (CurStat > Max) Max = CurStat; } - + while ( (tmp = m_pFaceList->GetData()) != 0 ) { tmp->CreateFace(&tmpData); CurStat = tmp->GetWeight(); - + if (CurStat == Max) { CvFace tmpFace; @@ -232,7 +232,7 @@ void FaceDetection::CreateResults(CvSeq * lpSeq) tmpFace.RightEyeRect = tmpData.RightEyeRect; cvSeqPush(lpSeq,&tmpFace); - + } } } @@ -265,7 +265,7 @@ void FaceDetection::AddContours2Rect(CvSeq *seq, int color, int iLayer) cvSeqPush(m_seqRects, &cr); for (CvSeq* internal = external->v_next; internal; internal = internal->h_next) { - cr.r = cvContourBoundingRect(internal, 0); + cr.r = cvContourBoundingRect(internal, 0); cr.pCenter.x = cr.r.x + cr.r.width / 2; cr.pCenter.y = cr.r.y + cr.r.height / 2; cr.iNumber = iLayer; @@ -294,8 +294,8 @@ void FaceDetection::FindFace(IplImage *img) if (m_bBoosting) PostBoostingFindCandidats(img); else - FindCandidats(); - + FindCandidats(); + }// void FaceDetection::FindFace(IplImage *img) @@ -306,7 +306,7 @@ void FaceDetection::FindCandidats() RFace * lpFace1 = 0; bool bInvalidRect1 = false; CvRect * lpRect1 = NULL; - + try { for (int i = 0; i < m_seqRects->total; i++) @@ -320,38 +320,38 @@ void FaceDetection::FindCandidats() 3*(double)rect.width/(double)4, (double)rect.width/(double)2, (double)rect.width/(double)2); - + lpFace1 = new RFace(lpFaceTemplate1); - + for (int j = 0; j < m_seqRects->total; j++) { - CvContourRect* pRect = (CvContourRect*)cvGetSeqElem(m_seqRects, j); - + CvContourRect* prect = (CvContourRect*)cvGetSeqElem(m_seqRects, j); + if ( !bInvalidRect1 ) { lpRect1 = NULL; lpRect1 = new CvRect(); - *lpRect1 = pRect->r; + *lpRect1 = prect->r; }else { delete lpRect1; lpRect1 = new CvRect(); - *lpRect1 = pRect->r; + *lpRect1 = prect->r; } - - + + if ( lpFace1->isFeature(lpRect1) ) - { + { bFound1 = true; bInvalidRect1 = false; }else bInvalidRect1 = true; - + } - + if (bFound1) { m_pFaceList->AddElem(lpFace1); @@ -363,10 +363,10 @@ void FaceDetection::FindCandidats() lpFace1 = NULL; } - + delete lpFaceTemplate1; } - + } } catch(...) @@ -381,10 +381,10 @@ void FaceDetection::FindCandidats() void FaceDetection::PostBoostingFindCandidats(IplImage * FaceImage) { BoostingFaceTemplate * lpFaceTemplate1 = 0; - RFace * lpFace1 = 0; + RFace * lpFace1 = 0; bool bInvalidRect1 = false; CvRect * lpRect1 = NULL; - + try { if ( ( !FaceImage->roi ) ) @@ -392,13 +392,13 @@ void FaceDetection::PostBoostingFindCandidats(IplImage * FaceImage) else lpFaceTemplate1 = new BoostingFaceTemplate(3,cvRect(FaceImage->roi->xOffset,FaceImage->roi->yOffset, FaceImage->roi->width,FaceImage->roi->height)); - + lpFace1 = new RFace(lpFaceTemplate1); for (int i = 0; i < m_seqRects->total; i++) { CvContourRect* pRect = (CvContourRect*)cvGetSeqElem(m_seqRects, i); - + if ( !bInvalidRect1 ) { lpRect1 = NULL; @@ -410,21 +410,21 @@ void FaceDetection::PostBoostingFindCandidats(IplImage * FaceImage) lpRect1 = new CvRect(); *lpRect1 = pRect->r; } - - + + if ( lpFace1->isFeature(lpRect1) ) - { + { //bFound1 = true; bInvalidRect1 = false; }else bInvalidRect1 = true; - + } - + m_pFaceList->AddElem(lpFace1); lpFace1 = NULL; - + delete lpFaceTemplate1; } catch(...) diff --git a/modules/legacy/src/hmm.cpp b/modules/legacy/src/hmm.cpp index 4baf808..d1af336 100644 --- a/modules/legacy/src/hmm.cpp +++ b/modules/legacy/src/hmm.cpp @@ -49,12 +49,12 @@ #define _CV_CAUSAL 2 #define _CV_LAST_STATE 1 -#define _CV_BEST_STATE 2 +#define _CV_BEST_STATE 2 //*F/////////////////////////////////////////////////////////////////////////////////////// // Name: _cvCreateObsInfo -// Purpose: The function allocates memory for CvImgObsInfo structure +// Purpose: The function allocates memory for CvImgObsInfo structure // and its inner stuff // Context: // Parameters: obs_info - addres of pointer to CvImgObsInfo structure @@ -64,27 +64,27 @@ // // Returns: error status // -// Notes: -//F*/ -static CvStatus CV_STDCALL icvCreateObsInfo( CvImgObsInfo** obs_info, +// Notes: +//F*/ +static CvStatus CV_STDCALL icvCreateObsInfo( CvImgObsInfo** obs_info, CvSize num_obs, int obs_size ) { int total = num_obs.height * num_obs.width; - + CvImgObsInfo* obs = (CvImgObsInfo*)cvAlloc( sizeof( CvImgObsInfo) ); - + obs->obs_x = num_obs.width; obs->obs_y = num_obs.height; obs->obs = (float*)cvAlloc( total * obs_size * sizeof(float) ); obs->state = (int*)cvAlloc( 2 * total * sizeof(int) ); - obs->mix = (int*)cvAlloc( total * sizeof(int) ); - + obs->mix = (int*)cvAlloc( total * sizeof(int) ); + obs->obs_size = obs_size; obs_info[0] = obs; - + return CV_NO_ERR; } @@ -94,23 +94,23 @@ static CvStatus CV_STDCALL icvReleaseObsInfo( CvImgObsInfo** p_obs_info ) cvFree( &(obs_info->obs) ); cvFree( &(obs_info->mix) ); - cvFree( &(obs_info->state) ); + cvFree( &(obs_info->state) ); cvFree( &(obs_info) ); p_obs_info[0] = NULL; return CV_NO_ERR; -} +} + - //*F/////////////////////////////////////////////////////////////////////////////////////// // Name: icvCreate2DHMM -// Purpose: The function allocates memory for 2-dimensional embedded HMM model +// Purpose: The function allocates memory for 2-dimensional embedded HMM model // and its inner stuff // Context: // Parameters: hmm - addres of pointer to CvEHMM structure // state_number - array of hmm sizes (size of array == state_number[0]+1 ) -// num_mix - number of gaussian mixtures in low-level HMM states +// num_mix - number of gaussian mixtures in low-level HMM states // size of array is defined by previous array values // obs_size - length of observation vectors // @@ -118,7 +118,7 @@ static CvStatus CV_STDCALL icvReleaseObsInfo( CvImgObsInfo** p_obs_info ) // // Notes: state_number[0] - number of states in external HMM. // state_number[i] - number of states in embedded HMM -// +// // example for face recognition: state_number = { 5 3 6 6 6 3 }, // length of num_mix array = 3+6+6+6+3 = 24// // @@ -142,11 +142,11 @@ static CvStatus CV_STDCALL icvCreate2DHMM( CvEHMM** this_hmm, /* allocate memory for all hmms (from all levels) */ hmm = (CvEHMM*)cvAlloc( (state_number[0] + 1) * sizeof(CvEHMM) ); - + /* set number of superstates */ hmm[0].num_states = state_number[0]; hmm[0].level = 1; - + /* allocate memory for all states */ all_states = (CvEHMMState *)cvAlloc( real_states * sizeof( CvEHMMState ) ); @@ -160,51 +160,51 @@ static CvStatus CV_STDCALL icvCreate2DHMM( CvEHMM** this_hmm, for( i = 0; i < real_states; i++ ) { total_mix += num_mix[i]; - } + } /* allocate memory for states stuff */ - pointers = (float*)cvAlloc( total_mix * (2/*for mu invvar */ * obs_size + + pointers = (float*)cvAlloc( total_mix * (2/*for mu invvar */ * obs_size + 2/*for weight and log_var_val*/ ) * sizeof( float) ); - + /* organize memory */ for( i = 0; i < real_states; i++ ) { - all_states[i].mu = pointers; pointers += num_mix[i] * obs_size; + all_states[i].mu = pointers; pointers += num_mix[i] * obs_size; all_states[i].inv_var = pointers; pointers += num_mix[i] * obs_size; all_states[i].log_var_val = pointers; pointers += num_mix[i]; all_states[i].weight = pointers; pointers += num_mix[i]; - } - + } + /* set pointer to embedded hmm array */ hmm->u.ehmm = hmm + 1; - + for( i = 0; i < hmm[0].num_states; i++ ) { hmm[i+1].u.state = all_states; all_states += state_number[i+1]; hmm[i+1].num_states = state_number[i+1]; - } - + } + for( i = 0; i <= state_number[0]; i++ ) { hmm[i].transP = icvCreateMatrix_32f( hmm[i].num_states, hmm[i].num_states ); hmm[i].obsProb = NULL; hmm[i].level = i ? 0 : 1; } - + /* if all ok - return pointer */ *this_hmm = hmm; return CV_NO_ERR; -} +} static CvStatus CV_STDCALL icvRelease2DHMM( CvEHMM** phmm ) { - CvEHMM* hmm = phmm[0]; + CvEHMM* hmm = phmm[0]; int i; for( i = 0; i < hmm[0].num_states + 1; i++ ) { icvDeleteMatrix( hmm[i].transP ); - } + } if (hmm->obsProb != NULL) { @@ -222,7 +222,7 @@ static CvStatus CV_STDCALL icvRelease2DHMM( CvEHMM** phmm ) phmm[0] = NULL; return CV_NO_ERR; -} +} /* distance between 2 vectors */ static float icvSquareDistance( CvVect32f v1, CvVect32f v2, int len ) @@ -251,7 +251,7 @@ static float icvSquareDistance( CvVect32f v1, CvVect32f v2, int len ) } return (float)(dist0 + dist1); -} +} /*can be used in CHMM & DHMM */ static CvStatus CV_STDCALL @@ -268,27 +268,27 @@ icvUniformImgSegm( CvImgObsInfo* obs_info, CvEHMM* hmm ) if ( !obs_info || !hmm ) return CV_NULLPTR_ERR; first_state = hmm->u.ehmm->u.state; - + for (i = 0; i < obs_info->obs_y; i++) { //bad line (division ) int superstate = (int)((i * hmm->num_states)*inv_y);/* /obs_info->obs_y; */ - + int index = (int)(hmm->u.ehmm[superstate].u.state - first_state); for (j = 0; j < obs_info->obs_x; j++, counter++) { int state = (int)((j * hmm->u.ehmm[superstate].num_states)* inv_x); /* / obs_info->obs_x; */ - + obs_info->state[2 * counter] = superstate; obs_info->state[2 * counter + 1] = state + index; } - } + } #else //this is not ready yet int i,j,k,m; - CvEHMMState* first_state = hmm->u.ehmm->u.state; + CvEHMMState* first_state = hmm->u.ehmm->u.state; /* check bad arguments */ if ( hmm->num_states > obs_info->obs_y ) return CV_BADSIZE_ERR; @@ -296,7 +296,7 @@ icvUniformImgSegm( CvImgObsInfo* obs_info, CvEHMM* hmm ) //compute vertical subdivision float row_per_state = (float)obs_info->obs_y / hmm->num_states; float col_per_state[1024]; /* maximum 1024 superstates */ - + //for every horizontal band compute subdivision for( i = 0; i < hmm->num_states; i++ ) { @@ -338,24 +338,24 @@ icvUniformImgSegm( CvImgObsInfo* obs_info, CvEHMM* hmm ) obs_info->state[row * obs_info->obs_x + 2 * k] = i; obs_info->state[row * obs_info->obs_x + 2 * k + 1] = j + index; } - col = es_bound[j]; + col = es_bound[j]; } //copy the same to other rows of superstate for( m = row; m < ss_bound[i]; m++ ) { - memcpy( &(obs_info->state[m * obs_info->obs_x * 2]), + memcpy( &(obs_info->state[m * obs_info->obs_x * 2]), &(obs_info->state[row * obs_info->obs_x * 2]), obs_info->obs_x * 2 * sizeof(int) ); } - row = ss_bound[i]; - } + row = ss_bound[i]; + } #endif return CV_NO_ERR; } - + /*F/////////////////////////////////////////////////////////////////////////////////////// // Name: InitMixSegm @@ -364,59 +364,59 @@ icvUniformImgSegm( CvImgObsInfo* obs_info, CvEHMM* hmm ) // Context: used with the Viterbi training of the embedded HMM // Function uses K-Means algorithm for clustering // -// Parameters: obs_info_array - array of pointers to image observations +// Parameters: obs_info_array - array of pointers to image observations // num_img - length of above array -// hmm - pointer to HMM structure -// +// hmm - pointer to HMM structure +// // Returns: error status // -// Notes: +// Notes: //F*/ static CvStatus CV_STDCALL icvInitMixSegm( CvImgObsInfo** obs_info_array, int num_img, CvEHMM* hmm ) -{ - int k, i, j; +{ + int k, i, j; int* num_samples; /* number of observations in every state */ int* counter; /* array of counters for every state */ - + int** a_class; /* for every state - characteristic array */ - + CvVect32f** samples; /* for every state - pointer to observation vectors */ - int*** samples_mix; /* for every state - array of pointers to vectors mixtures */ - + int*** samples_mix; /* for every state - array of pointers to vectors mixtures */ + CvTermCriteria criteria = cvTermCriteria( CV_TERMCRIT_EPS|CV_TERMCRIT_ITER, 1000, /* iter */ 0.01f ); /* eps */ - + int total = 0; - - CvEHMMState* first_state = hmm->u.ehmm->u.state; - + + CvEHMMState* first_state = hmm->u.ehmm->u.state; + for( i = 0 ; i < hmm->num_states; i++ ) { total += hmm->u.ehmm[i].num_states; - } - + } + /* for every state integer is allocated - number of vectors in state */ num_samples = (int*)cvAlloc( total * sizeof(int) ); - + /* integer counter is allocated for every state */ counter = (int*)cvAlloc( total * sizeof(int) ); - - samples = (CvVect32f**)cvAlloc( total * sizeof(CvVect32f*) ); - samples_mix = (int***)cvAlloc( total * sizeof(int**) ); - + + samples = (CvVect32f**)cvAlloc( total * sizeof(CvVect32f*) ); + samples_mix = (int***)cvAlloc( total * sizeof(int**) ); + /* clear */ memset( num_samples, 0 , total*sizeof(int) ); memset( counter, 0 , total*sizeof(int) ); - - + + /* for every state the number of vectors which belong to it is computed (smth. like histogram) */ for (k = 0; k < num_img; k++) - { + { CvImgObsInfo* obs = obs_info_array[k]; int count = 0; - + for (i = 0; i < obs->obs_y; i++) { for (j = 0; j < obs->obs_x; j++, count++) @@ -425,21 +425,21 @@ icvInitMixSegm( CvImgObsInfo** obs_info_array, int num_img, CvEHMM* hmm ) num_samples[state] += 1; } } - } - + } + /* for every state int* is allocated */ a_class = (int**)cvAlloc( total*sizeof(int*) ); - + for (i = 0; i < total; i++) { a_class[i] = (int*)cvAlloc( num_samples[i] * sizeof(int) ); samples[i] = (CvVect32f*)cvAlloc( num_samples[i] * sizeof(CvVect32f) ); samples_mix[i] = (int**)cvAlloc( num_samples[i] * sizeof(int*) ); } - + /* for every state vectors which belong to state are gathered */ for (k = 0; k < num_img; k++) - { + { CvImgObsInfo* obs = obs_info_array[k]; int num_obs = ( obs->obs_x ) * ( obs->obs_y ); float* vector = obs->obs; @@ -447,35 +447,35 @@ icvInitMixSegm( CvImgObsInfo** obs_info_array, int num_img, CvEHMM* hmm ) for (i = 0; i < num_obs; i++, vector+=obs->obs_size ) { int state = obs->state[2*i+1]; - + samples[state][counter[state]] = vector; samples_mix[state][counter[state]] = &(obs->mix[i]); - counter[state]++; + counter[state]++; } - } - + } + /* clear counters */ memset( counter, 0, total*sizeof(int) ); - + /* do the actual clustering using the K Means algorithm */ for (i = 0; i < total; i++) { if ( first_state[i].num_mix == 1) - { + { for (k = 0; k < num_samples[i]; k++) - { + { /* all vectors belong to one mixture */ a_class[i][k] = 0; } - } + } else if( num_samples[i] ) { /* clusterize vectors */ - cvKMeans( first_state[i].num_mix, samples[i], num_samples[i], + cvKMeans( first_state[i].num_mix, samples[i], num_samples[i], obs_info_array[0]->obs_size, criteria, a_class[i] ); - } + } } - + /* for every vector number of mixture is assigned */ for( i = 0; i < total; i++ ) { @@ -484,7 +484,7 @@ icvInitMixSegm( CvImgObsInfo** obs_info_array, int num_img, CvEHMM* hmm ) samples_mix[i][j][0] = a_class[i][j]; } } - + for (i = 0; i < total; i++) { cvFree( &(a_class[i]) ); @@ -496,28 +496,28 @@ icvInitMixSegm( CvImgObsInfo** obs_info_array, int num_img, CvEHMM* hmm ) cvFree( &samples ); cvFree( &samples_mix ); cvFree( &counter ); - cvFree( &num_samples ); - + cvFree( &num_samples ); + return CV_NO_ERR; } /*F/////////////////////////////////////////////////////////////////////////////////////// // Name: ComputeUniModeGauss -// Purpose: The function computes the Gaussian pdf for a sample vector +// Purpose: The function computes the Gaussian pdf for a sample vector // Context: // Parameters: obsVeq - pointer to the sample vector // mu - pointer to the mean vector of the Gaussian pdf // var - pointer to the variance vector of the Gaussian pdf // VecSize - the size of sample vector -// -// Returns: the pdf of the sample vector given the specified Gaussian // -// Notes: +// Returns: the pdf of the sample vector given the specified Gaussian +// +// Notes: //F*/ -/*static float icvComputeUniModeGauss(CvVect32f vect, CvVect32f mu, - CvVect32f inv_var, float log_var_val, int vect_size) +/*static float icvComputeUniModeGauss(CvVect32f vect, CvVect32f mu, + CvVect32f inv_var, float log_var_val, int vect_size) { - int n; + int n; double tmp; double prob; @@ -529,42 +529,42 @@ icvInitMixSegm( CvImgObsInfo** obs_info_array, int num_img, CvEHMM* hmm ) prob = prob - tmp * tmp; } //prob *= 0.5f; - + return (float)prob; -}*/ +}*/ /*F/////////////////////////////////////////////////////////////////////////////////////// // Name: ComputeGaussMixture -// Purpose: The function computes the mixture Gaussian pdf of a sample vector. +// Purpose: The function computes the mixture Gaussian pdf of a sample vector. // Context: // Parameters: obsVeq - pointer to the sample vector // mu - two-dimensional pointer to the mean vector of the Gaussian pdf; -// the first dimension is indexed over the number of mixtures and +// the first dimension is indexed over the number of mixtures and // the second dimension is indexed along the size of the mean vector // var - two-dimensional pointer to the variance vector of the Gaussian pdf; -// the first dimension is indexed over the number of mixtures and +// the first dimension is indexed over the number of mixtures and // the second dimension is indexed along the size of the variance vector // VecSize - the size of sample vector // weight - pointer to the wights of the Gaussian mixture // NumMix - the number of Gaussian mixtures -// -// Returns: the pdf of the sample vector given the specified Gaussian mixture. // -// Notes: +// Returns: the pdf of the sample vector given the specified Gaussian mixture. +// +// Notes: //F*/ /* Calculate probability of observation at state in logarithmic scale*/ /*static float -icvComputeGaussMixture( CvVect32f vect, float* mu, - float* inv_var, float* log_var_val, +icvComputeGaussMixture( CvVect32f vect, float* mu, + float* inv_var, float* log_var_val, int vect_size, float* weight, int num_mix ) -{ +{ double prob, l_prob; - - prob = 0.0f; + + prob = 0.0f; if (num_mix == 1) { - return icvComputeUniModeGauss( vect, mu, inv_var, log_var_val[0], vect_size); + return icvComputeUniModeGauss( vect, mu, inv_var, log_var_val[0], vect_size); } else { @@ -572,30 +572,30 @@ icvComputeGaussMixture( CvVect32f vect, float* mu, for (m = 0; m < num_mix; m++) { if ( weight[m] > 0.0) - { - l_prob = icvComputeUniModeGauss(vect, mu + m*vect_size, + { + l_prob = icvComputeUniModeGauss(vect, mu + m*vect_size, inv_var + m * vect_size, - log_var_val[m], - vect_size); + log_var_val[m], + vect_size); prob = prob + weight[m]*exp((double)l_prob); } - } - prob = log(prob); - } - return (float)prob; -}*/ + } + prob = log(prob); + } + return (float)prob; +}*/ /*F/////////////////////////////////////////////////////////////////////////////////////// // Name: EstimateObsProb -// Purpose: The function computes the probability of every observation in every state +// Purpose: The function computes the probability of every observation in every state // Context: // Parameters: obs_info - observations // hmm - hmm -// Returns: error status +// Returns: error status // -// Notes: +// Notes: //F*/ static CvStatus CV_STDCALL icvEstimateObsProb( CvImgObsInfo* obs_info, CvEHMM* hmm ) { @@ -604,7 +604,7 @@ static CvStatus CV_STDCALL icvEstimateObsProb( CvImgObsInfo* obs_info, CvEHMM* h /* check if matrix exist and check current size if not sufficient - realloc */ - int status = 0; /* 1 - not allocated, 2 - allocated but small size, + int status = 0; /* 1 - not allocated, 2 - allocated but small size, 3 - size is enough, but distribution is bad, 0 - all ok */ for( j = 0; j < hmm->num_states; j++ ) @@ -612,7 +612,7 @@ static CvStatus CV_STDCALL icvEstimateObsProb( CvImgObsInfo* obs_info, CvEHMM* h total_states += hmm->u.ehmm[j].num_states; } - if ( hmm->obsProb == NULL ) + if ( hmm->obsProb == NULL ) { /* allocare memory */ int need_size = ( obs_info->obs_x * obs_info->obs_y * total_states * sizeof(float) + @@ -624,10 +624,10 @@ static CvStatus CV_STDCALL icvEstimateObsProb( CvImgObsInfo* obs_info, CvEHMM* h buffer[2] = obs_info->obs_x; hmm->obsProb = (float**) (buffer + 3); status = 3; - + } else - { + { /* check current size */ int* total= (int*)(((int*)(hmm->obsProb)) - 3); int need_size = ( obs_info->obs_x * obs_info->obs_y * total_states * sizeof(float) + @@ -635,7 +635,7 @@ static CvStatus CV_STDCALL icvEstimateObsProb( CvImgObsInfo* obs_info, CvEHMM* h assert( sizeof(float*) == sizeof(int) ); - if ( need_size > (*total) ) + if ( need_size > (*total) ) { int* buffer = ((int*)(hmm->obsProb)) - 3; cvFree( &buffer); @@ -645,22 +645,22 @@ static CvStatus CV_STDCALL icvEstimateObsProb( CvImgObsInfo* obs_info, CvEHMM* h buffer[2] = obs_info->obs_x; hmm->obsProb = (float**)(buffer + 3); - + status = 3; - } + } } if (!status) { int* obsx = ((int*)(hmm->obsProb)) - 1; int* obsy = ((int*)(hmm->obsProb)) - 2; - + assert( (*obsx > 0) && (*obsy > 0) ); /* is good distribution? */ - if ( (obs_info->obs_x > (*obsx) ) || (obs_info->obs_y > (*obsy) ) ) - status = 3; + if ( (obs_info->obs_x > (*obsx) ) || (obs_info->obs_y > (*obsy) ) ) + status = 3; } - + /* if bad status - do reallocation actions */ assert( (status == 0) || (status == 3) ); @@ -672,7 +672,7 @@ static CvStatus CV_STDCALL icvEstimateObsProb( CvImgObsInfo* obs_info, CvEHMM* h /* distribute pointers of ehmm->obsProb */ for( i = 0; i < hmm->num_states; i++ ) { - hmm->u.ehmm[i].obsProb = tmp; + hmm->u.ehmm[i].obsProb = tmp; tmp += obs_info->obs_y; } @@ -682,16 +682,16 @@ static CvStatus CV_STDCALL icvEstimateObsProb( CvImgObsInfo* obs_info, CvEHMM* h for( i = 0; i < hmm->num_states; i++ ) { CvEHMM* ehmm = &( hmm->u.ehmm[i] ); - + for( j = 0; j < obs_info->obs_y; j++ ) { ehmm->obsProb[j] = tmpf; tmpf += ehmm->num_states * obs_info->obs_x; - } + } } - }/* end of pointer distribution */ + }/* end of pointer distribution */ -#if 1 +#if 1 { #define MAX_BUF_SIZE 1200 float local_log_mix_prob[MAX_BUF_SIZE]; @@ -701,7 +701,7 @@ static CvStatus CV_STDCALL icvEstimateObsProb( CvImgObsInfo* obs_info, CvEHMM* h float* log_mix_prob = local_log_mix_prob; double* mix_prob = local_mix_prob; - + int max_size = 0; int obs_x = obs_info->obs_x; @@ -722,7 +722,7 @@ static CvStatus CV_STDCALL icvEstimateObsProb( CvImgObsInfo* obs_info, CvEHMM* h } max_size *= obs_x * vect_size; - + /* allocate buffer */ if( max_size > MAX_BUF_SIZE ) { @@ -734,13 +734,13 @@ static CvStatus CV_STDCALL icvEstimateObsProb( CvImgObsInfo* obs_info, CvEHMM* h memset( log_mix_prob, 0, max_size*sizeof(float)); /*****************computing probabilities***********************/ - + /* loop through external states */ for( i = 0; i < hmm->num_states; i++ ) { CvEHMM* ehmm = &(hmm->u.ehmm[i]); CvEHMMState* state = ehmm->u.state; - + int max_mix = 0; int n_states = ehmm->num_states; @@ -755,13 +755,13 @@ static CvStatus CV_STDCALL icvEstimateObsProb( CvImgObsInfo* obs_info, CvEHMM* h for( j = 0; j < obs_info->obs_y; j++ ) { int m, n; - + float* obs = obs_info->obs + j * obs_x * vect_size; float* log_mp = max_mix > 1 ? log_mix_prob : ehmm->obsProb[j]; double* mp = mix_prob; - + /* several passes are done below */ - + /* 1. calculate logarithms of probabilities for each mixture */ /* loop through mixtures */ @@ -845,21 +845,21 @@ static CvStatus CV_STDCALL icvEstimateObsProb( CvImgObsInfo* obs_info, CvEHMM* h for( j = 0; j < obs_info->obs_y; j++ ) { int k,m; - + int obs_index = j * obs_info->obs_x; float* B = ehmm->obsProb[j]; - + /* cycles through obs and states */ for( k = 0; k < obs_info->obs_x; k++ ) { CvVect32f vect = (obs_info->obs) + (obs_index + k) * vect_size; - + float* matr_line = B + k * ehmm->num_states; for( m = 0; m < ehmm->num_states; m++ ) { - matr_line[m] = icvComputeGaussMixture( vect, state[m].mu, state[m].inv_var, + matr_line[m] = icvComputeGaussMixture( vect, state[m].mu, state[m].inv_var, state[m].log_var_val, vect_size, state[m].weight, state[m].num_mix ); } @@ -872,16 +872,16 @@ static CvStatus CV_STDCALL icvEstimateObsProb( CvImgObsInfo* obs_info, CvEHMM* h /*F/////////////////////////////////////////////////////////////////////////////////////// // Name: EstimateTransProb -// Purpose: The function calculates the state and super state transition probabilities -// of the model given the images, +// Purpose: The function calculates the state and super state transition probabilities +// of the model given the images, // the state segmentation and the input parameters // Context: -// Parameters: obs_info_array - array of pointers to image observations +// Parameters: obs_info_array - array of pointers to image observations // num_img - length of above array -// hmm - pointer to HMM structure +// hmm - pointer to HMM structure // Returns: void // -// Notes: +// Notes: //F*/ static CvStatus CV_STDCALL icvEstimateTransProb( CvImgObsInfo** obs_info_array, int num_img, CvEHMM* hmm ) @@ -890,47 +890,47 @@ icvEstimateTransProb( CvImgObsInfo** obs_info_array, int num_img, CvEHMM* hmm ) CvEHMMState* first_state = hmm->u.ehmm->u.state; /* as a counter we will use transP matrix */ - + /* initialization */ - + /* clear transP */ icvSetZero_32f( hmm->transP, hmm->num_states, hmm->num_states ); for (i = 0; i < hmm->num_states; i++ ) { icvSetZero_32f( hmm->u.ehmm[i].transP , hmm->u.ehmm[i].num_states, hmm->u.ehmm[i].num_states ); } - + /* compute the counters */ for (i = 0; i < num_img; i++) { int counter = 0; CvImgObsInfo* info = obs_info_array[i]; - + for (j = 0; j < info->obs_y; j++) { for (k = 0; k < info->obs_x; k++, counter++) { /* compute how many transitions from state to state - occured both in horizontal and vertical direction */ + occured both in horizontal and vertical direction */ int superstate, state; int nextsuperstate, nextstate; int begin_ind; superstate = info->state[2 * counter]; begin_ind = (int)(hmm->u.ehmm[superstate].u.state - first_state); - state = info->state[ 2 * counter + 1] - begin_ind; - + state = info->state[ 2 * counter + 1] - begin_ind; + if (j < info->obs_y - 1) { int transP_size = hmm->num_states; - + nextsuperstate = info->state[ 2*(counter + info->obs_x) ]; hmm->transP[superstate * transP_size + nextsuperstate] += 1; } - + if (k < info->obs_x - 1) - { + { int transP_size = hmm->u.ehmm[superstate].num_states; nextstate = info->state[2*(counter+1) + 1] - begin_ind; @@ -951,15 +951,15 @@ icvEstimateTransProb( CvImgObsInfo** obs_info_array, int num_img, CvEHMM* hmm ) //assert( total ); inv_total = total ? 1.f/total : 0; - + for( j = 0; j < hmm->num_states; j++) - { - hmm->transP[i * hmm->num_states + j] = - hmm->transP[i * hmm->num_states + j] ? + { + hmm->transP[i * hmm->num_states + j] = + hmm->transP[i * hmm->num_states + j] ? (float)log( hmm->transP[i * hmm->num_states + j] * inv_total ) : -BIG_FLT; } } - + /* estimate other matrices */ for( k = 0; k < hmm->num_states; k++ ) { @@ -975,18 +975,18 @@ icvEstimateTransProb( CvImgObsInfo** obs_info_array, int num_img, CvEHMM* hmm ) } //assert( total ); inv_total = total ? 1.f/total : 0; - + for( j = 0; j < ehmm->num_states; j++) - { - ehmm->transP[i * ehmm->num_states + j] = + { + ehmm->transP[i * ehmm->num_states + j] = (ehmm->transP[i * ehmm->num_states + j]) ? (float)log( ehmm->transP[i * ehmm->num_states + j] * inv_total) : -BIG_FLT ; } } } return CV_NO_ERR; -} - +} + /*F/////////////////////////////////////////////////////////////////////////////////////// // Name: MixSegmL2 @@ -994,24 +994,24 @@ icvEstimateTransProb( CvImgObsInfo** obs_info_array, int num_img, CvEHMM* hmm ) // embedded HMM // Context: used with the Viterbi training of the embedded HMM // -// Parameters: +// Parameters: // obs_info_array // num_img // hmm // Returns: void // -// Notes: +// Notes: //F*/ static CvStatus CV_STDCALL icvMixSegmL2( CvImgObsInfo** obs_info_array, int num_img, CvEHMM* hmm ) { int k, i, j, m; - + CvEHMMState* state = hmm->u.ehmm[0].u.state; - - + + for (k = 0; k < num_img; k++) - { + { int counter = 0; CvImgObsInfo* info = obs_info_array[k]; @@ -1021,11 +1021,11 @@ icvMixSegmL2( CvImgObsInfo** obs_info_array, int num_img, CvEHMM* hmm ) { int e_state = info->state[2 * counter + 1]; float min_dist; - - min_dist = icvSquareDistance((info->obs) + (counter * info->obs_size), + + min_dist = icvSquareDistance((info->obs) + (counter * info->obs_size), state[e_state].mu, info->obs_size); - info->mix[counter] = 0; - + info->mix[counter] = 0; + for (m = 1; m < state[e_state].num_mix; m++) { float dist=icvSquareDistance( (info->obs) + (counter * info->obs_size), @@ -1034,7 +1034,7 @@ icvMixSegmL2( CvImgObsInfo** obs_info_array, int num_img, CvEHMM* hmm ) if (dist < min_dist) { min_dist = dist; - /* assign mixture with smallest distance */ + /* assign mixture with smallest distance */ info->mix[counter] = m; } } @@ -1042,18 +1042,18 @@ icvMixSegmL2( CvImgObsInfo** obs_info_array, int num_img, CvEHMM* hmm ) } } return CV_NO_ERR; -} +} /* CvStatus icvMixSegmProb(CvImgObsInfo* obs_info, int num_img, CvEHMM* hmm ) { int k, i, j, m; - + CvEHMMState* state = hmm->ehmm[0].state_info; - - + + for (k = 0; k < num_img; k++) - { + { int counter = 0; CvImgObsInfo* info = obs_info + k; @@ -1063,32 +1063,32 @@ CvStatus icvMixSegmProb(CvImgObsInfo* obs_info, int num_img, CvEHMM* hmm ) { int e_state = info->in_state[counter]; float max_prob; - - max_prob = icvComputeUniModeGauss( info->obs[counter], state[e_state].mu[0], - state[e_state].inv_var[0], + + max_prob = icvComputeUniModeGauss( info->obs[counter], state[e_state].mu[0], + state[e_state].inv_var[0], state[e_state].log_var[0], info->obs_size ); - info->mix[counter] = 0; - + info->mix[counter] = 0; + for (m = 1; m < state[e_state].num_mix; m++) { float prob=icvComputeUniModeGauss(info->obs[counter], state[e_state].mu[m], - state[e_state].inv_var[m], + state[e_state].inv_var[m], state[e_state].log_var[m], info->obs_size); if (prob > max_prob) { max_prob = prob; - // assign mixture with greatest probability. + // assign mixture with greatest probability. info->mix[counter] = m; } } } } - } + } return CV_NO_ERR; -} +} */ static CvStatus CV_STDCALL icvViterbiSegmentation( int num_states, int /*num_obs*/, CvMatr32f transP, @@ -1096,45 +1096,45 @@ icvViterbiSegmentation( int num_states, int /*num_obs*/, CvMatr32f transP, int** q, int min_num_obs, int max_num_obs, float* prob ) { - // memory allocation + // memory allocation int i, j, last_obs; int m_HMMType = _CV_ERGODIC; /* _CV_CAUSAL or _CV_ERGODIC */ - + int m_ProbType = prob_type; /* _CV_LAST_STATE or _CV_BEST_STATE */ - + int m_minNumObs = min_num_obs; /*??*/ int m_maxNumObs = max_num_obs; /*??*/ - + int m_numStates = num_states; - + float* m_pi = (float*)cvAlloc( num_states* sizeof(float) ); CvMatr32f m_a = transP; - // offset brobability matrix to starting observation + // offset brobability matrix to starting observation CvMatr32f m_b = B + start_obs * num_states; //so m_xl will not be used more - //m_xl = start_obs; + //m_xl = start_obs; - /* if (muDur != NULL){ + /* if (muDur != NULL){ m_d = new int[m_numStates]; m_l = new double[m_numStates]; for (i = 0; i < m_numStates; i++){ - m_l[i] = muDur[i]; + m_l[i] = muDur[i]; + } } - } else{ m_d = NULL; m_l = NULL; } */ - + CvMatr32f m_Gamma = icvCreateMatrix_32f( num_states, m_maxNumObs ); int* m_csi = (int*)cvAlloc( num_states * m_maxNumObs * sizeof(int) ); - + //stores maximal result for every ending observation */ CvVect32f m_MaxGamma = prob; - + // assert( m_xl + max_num_obs <= num_obs ); @@ -1151,31 +1151,31 @@ icvViterbiSegmentation( int num_states, int /*num_obs*/, CvMatr32f transP, m_pi[i] = -BIG_FLT; } m_pi[0] = 0.0f; - + for (i = 0; i < num_states; i++) { m_Gamma[0 * num_states + i] = m_pi[i] + m_b[0 * num_states + i]; - m_csi[0 * num_states + i] = 0; + m_csi[0 * num_states + i] = 0; } - + /******************************************************************/ /* Viterbi recursion */ - + if ( m_HMMType == _CV_CAUSAL ) //causal model { - int t,j; - + int t; + for (t = 1 ; t < m_maxNumObs; t++) { // evaluate self-to-self transition for state 0 m_Gamma[t * num_states + 0] = m_Gamma[(t-1) * num_states + 0] + m_a[0]; m_csi[t * num_states + 0] = 0; - + for (j = 1; j < num_states; j++) - { + { float self = m_Gamma[ (t-1) * num_states + j] + m_a[ j * num_states + j]; float prev = m_Gamma[ (t-1) * num_states +(j-1)] + m_a[ (j-1) * num_states + j]; - + if ( prev > self ) { m_csi[t * num_states + j] = j-1; @@ -1186,34 +1186,33 @@ icvViterbiSegmentation( int num_states, int /*num_obs*/, CvMatr32f transP, m_csi[t * num_states + j] = j; m_Gamma[t * num_states + j] = self; } - - m_Gamma[t * num_states + j] = m_Gamma[t * num_states + j] + m_b[t * num_states + j]; - } + + m_Gamma[t * num_states + j] = m_Gamma[t * num_states + j] + m_b[t * num_states + j]; + } } } - else if ( m_HMMType == _CV_ERGODIC ) //ergodic model - { + else if ( m_HMMType == _CV_ERGODIC ) //ergodic model + { int t; for (t = 1 ; t < m_maxNumObs; t++) - { + { for (j = 0; j < num_states; j++) - { - int i; + { m_Gamma[ t*num_states + j] = m_Gamma[(t-1) * num_states + 0] + m_a[0*num_states+j]; m_csi[t *num_states + j] = 0; - + for (i = 1; i < num_states; i++) { - float currGamma = m_Gamma[(t-1) *num_states + i] + m_a[i *num_states + j]; + float currGamma = m_Gamma[(t-1) *num_states + i] + m_a[i *num_states + j]; if (currGamma > m_Gamma[t *num_states + j]) - { + { m_Gamma[t * num_states + j] = currGamma; m_csi[t * num_states + j] = i; } - } + } m_Gamma[t *num_states + j] = m_Gamma[t *num_states + j] + m_b[t * num_states + j]; - } - } + } + } } for( last_obs = m_minNumObs-1, i = 0; last_obs < m_maxNumObs; last_obs++, i++ ) @@ -1222,7 +1221,7 @@ icvViterbiSegmentation( int num_states, int /*num_obs*/, CvMatr32f transP, /******************************************************************/ /* Viterbi termination */ - + if ( m_ProbType == _CV_LAST_STATE ) { m_MaxGamma[i] = m_Gamma[last_obs * num_states + num_states - 1]; @@ -1231,48 +1230,48 @@ icvViterbiSegmentation( int num_states, int /*num_obs*/, CvMatr32f transP, else if( m_ProbType == _CV_BEST_STATE ) { int k; - q[i][last_obs] = 0; - m_MaxGamma[i] = m_Gamma[last_obs * num_states + 0]; - + q[i][last_obs] = 0; + m_MaxGamma[i] = m_Gamma[last_obs * num_states + 0]; + for(k = 1; k < num_states; k++) - { + { if ( m_Gamma[last_obs * num_states + k] > m_MaxGamma[i] ) { m_MaxGamma[i] = m_Gamma[last_obs * num_states + k]; q[i][last_obs] = k; - } + } } - } - + } + /******************************************************************/ /* Viterbi backtracking */ for (t = last_obs-1; t >= 0; t--) { - q[i][t] = m_csi[(t+1) * num_states + q[i][t+1] ]; - } - } - + q[i][t] = m_csi[(t+1) * num_states + q[i][t+1] ]; + } + } + /* memory free */ cvFree( &m_pi ); cvFree( &m_csi ); - icvDeleteMatrix( m_Gamma ); - + icvDeleteMatrix( m_Gamma ); + return CV_NO_ERR; -} +} /*F/////////////////////////////////////////////////////////////////////////////////////// // Name: icvEViterbi // Purpose: The function calculates the embedded Viterbi algorithm -// for 1 image +// for 1 image // Context: -// Parameters: +// Parameters: // obs_info - observations // hmm - HMM -// -// Returns: the Embedded Viterbi probability (float) +// +// Returns: the Embedded Viterbi probability (float) // and do state segmentation of observations // -// Notes: +// Notes: //F*/ static float CV_STDCALL icvEViterbi( CvImgObsInfo* obs_info, CvEHMM* hmm ) { @@ -1282,70 +1281,70 @@ static float CV_STDCALL icvEViterbi( CvImgObsInfo* obs_info, CvEHMM* hmm ) float inv_obs_x = 1.f / obs_info->obs_x; CvEHMMState* first_state = hmm->u.ehmm->u.state; - + /* memory allocation for superB */ CvMatr32f superB = icvCreateMatrix_32f(hmm->num_states, obs_info->obs_y ); - + /* memory allocation for q */ int*** q = (int***)cvAlloc( hmm->num_states * sizeof(int**) ); int* super_q = (int*)cvAlloc( obs_info->obs_y * sizeof(int) ); - + for (i = 0; i < hmm->num_states; i++) { q[i] = (int**)cvAlloc( obs_info->obs_y * sizeof(int*) ); - + for (j = 0; j < obs_info->obs_y ; j++) { q[i][j] = (int*)cvAlloc( obs_info->obs_x * sizeof(int) ); } - } - + } + /* start Viterbi segmentation */ for (i = 0; i < hmm->num_states; i++) { CvEHMM* ehmm = &(hmm->u.ehmm[i]); - + for (j = 0; j < obs_info->obs_y; j++) { float max_gamma; - + /* 1D HMM Viterbi segmentation */ - icvViterbiSegmentation( ehmm->num_states, obs_info->obs_x, - ehmm->transP, ehmm->obsProb[j], 0, + icvViterbiSegmentation( ehmm->num_states, obs_info->obs_x, + ehmm->transP, ehmm->obsProb[j], 0, _CV_LAST_STATE, &q[i][j], obs_info->obs_x, obs_info->obs_x, &max_gamma); - + superB[j * hmm->num_states + i] = max_gamma * inv_obs_x; } } - + /* perform global Viterbi segmentation (i.e. process higher-level HMM) */ - - icvViterbiSegmentation( hmm->num_states, obs_info->obs_y, - hmm->transP, superB, 0, + + icvViterbiSegmentation( hmm->num_states, obs_info->obs_y, + hmm->transP, superB, 0, _CV_LAST_STATE, &super_q, obs_info->obs_y, obs_info->obs_y, &log_likelihood ); - - log_likelihood /= obs_info->obs_y ; - - + + log_likelihood /= obs_info->obs_y ; + + counter = 0; /* assign new state to observation vectors */ for (i = 0; i < obs_info->obs_y; i++) - { + { for (j = 0; j < obs_info->obs_x; j++, counter++) { int superstate = super_q[i]; int state = (int)(hmm->u.ehmm[superstate].u.state - first_state); - + obs_info->state[2 * counter] = superstate; obs_info->state[2 * counter + 1] = state + q[superstate][i][j]; } } - + /* memory deallocation for superB */ icvDeleteMatrix( superB ); - + /*memory deallocation for q */ for (i = 0; i < hmm->num_states; i++) { @@ -1355,12 +1354,12 @@ static float CV_STDCALL icvEViterbi( CvImgObsInfo* obs_info, CvEHMM* hmm ) } cvFree( &q[i] ); } - + cvFree( &q ); cvFree( &super_q ); - + return log_likelihood; -} +} static CvStatus CV_STDCALL icvEstimateHMMStateParams( CvImgObsInfo** obs_info_array, int num_img, CvEHMM* hmm ) @@ -1373,7 +1372,7 @@ icvEstimateHMMStateParams( CvImgObsInfo** obs_info_array, int num_img, CvEHMM* h float start_log_var_val = LN2PI * vect_len; CvVect32f tmp_vect = icvCreateVector_32f( vect_len ); - + CvEHMMState* first_state = hmm->u.ehmm[0].u.state; assert( sizeof(float) == sizeof(int) ); @@ -1390,9 +1389,9 @@ icvEstimateHMMStateParams( CvImgObsInfo** obs_info_array, int num_img, CvEHMM* h for (m = 0; m < first_state[i].num_mix; m++) { ((int*)(first_state[i].weight))[m] = 0; - } + } } - + /* maybe gamma must be computed in mixsegm process ?? */ /* compute gamma */ @@ -1406,23 +1405,23 @@ icvEstimateHMMStateParams( CvImgObsInfo** obs_info_array, int num_img, CvEHMM* h int state, mixture; state = info->state[2*i + 1]; mixture = info->mix[i]; - /* computes gamma - number of observations corresponding - to every mixture of every state */ - ((int*)(first_state[state].weight))[mixture] += 1; + /* computes gamma - number of observations corresponding + to every mixture of every state */ + ((int*)(first_state[state].weight))[mixture] += 1; } - } + } /***************Mean and Var***********************/ /* compute means and variances of every item */ /* initially variance placed to inv_var */ /* zero mean and variance */ for (i = 0; i < total; i++) { - memset( (void*)first_state[i].mu, 0, first_state[i].num_mix * vect_len * + memset( (void*)first_state[i].mu, 0, first_state[i].num_mix * vect_len * sizeof(float) ); - memset( (void*)first_state[i].inv_var, 0, first_state[i].num_mix * vect_len * + memset( (void*)first_state[i].inv_var, 0, first_state[i].num_mix * vect_len * sizeof(float) ); } - + /* compute sums */ for (i = 0; i < num_img; i++) { @@ -1432,42 +1431,41 @@ icvEstimateHMMStateParams( CvImgObsInfo** obs_info_array, int num_img, CvEHMM* h float* vector = info->obs; for (j = 0; j < total_obs; j++, vector+=vect_len ) - { + { int state = info->state[2 * j + 1]; - int mixture = info->mix[j]; - + int mixture = info->mix[j]; + CvVect32f mean = first_state[state].mu + mixture * vect_len; CvVect32f mean2 = first_state[state].inv_var + mixture * vect_len; - + icvAddVector_32f( mean, vector, mean, vect_len ); for( k = 0; k < vect_len; k++ ) mean2[k] += vector[k]*vector[k]; - } + } } - + /*compute the means and variances */ /* assume gamma already computed */ for (i = 0; i < total; i++) - { + { CvEHMMState* state = &(first_state[i]); for (m = 0; m < state->num_mix; m++) { - int k; CvVect32f mu = state->mu + m * vect_len; - CvVect32f invar = state->inv_var + m * vect_len; - + CvVect32f invar = state->inv_var + m * vect_len; + if ( ((int*)state->weight)[m] > 1) { float inv_gamma = 1.f/((int*)(state->weight))[m]; - + icvScaleVector_32f( mu, mu, vect_len, inv_gamma); icvScaleVector_32f( invar, invar, vect_len, inv_gamma); } icvMulVectors_32f(mu, mu, tmp_vect, vect_len); - icvSubVector_32f( invar, tmp_vect, invar, vect_len); - + icvSubVector_32f( invar, tmp_vect, invar, vect_len); + /* low bound of variance - 100 (Ara's experimental result) */ for( k = 0; k < vect_len; k++ ) { @@ -1479,7 +1477,7 @@ icvEstimateHMMStateParams( CvImgObsInfo** obs_info_array, int num_img, CvEHMM* h for( k = 0; k < vect_len; k++ ) { state->log_var_val[m] += (float)log( invar[k] ); - } + } /* SMOLI 27.10.2000 */ state->log_var_val[m] *= 0.5; @@ -1490,32 +1488,32 @@ icvEstimateHMMStateParams( CvImgObsInfo** obs_info_array, int num_img, CvEHMM* h cvbInvSqrt( invar, invar, vect_len ); } } - + /***************Weights***********************/ /* normilize gammas - i.e. compute mixture weights */ - + //compute weights for (i = 0; i < total; i++) - { + { int gamma_total = 0; float norm; for (m = 0; m < first_state[i].num_mix; m++) { - gamma_total += ((int*)(first_state[i].weight))[m]; + gamma_total += ((int*)(first_state[i].weight))[m]; } norm = gamma_total ? (1.f/(float)gamma_total) : 0.f; - + for (m = 0; m < first_state[i].num_mix; m++) { first_state[i].weight[m] = ((int*)(first_state[i].weight))[m] * norm; - } - } + } + } icvDeleteVector( tmp_vect); - return CV_NO_ERR; -} + return CV_NO_ERR; +} /* CvStatus icvLightingCorrection8uC1R( uchar* img, CvSize roi, int src_step ) @@ -1523,28 +1521,28 @@ CvStatus icvLightingCorrection8uC1R( uchar* img, CvSize roi, int src_step ) int i, j; int width = roi.width; int height = roi.height; - + float x1, x2, y1, y2; int f[3] = {0, 0, 0}; float a[3] = {0, 0, 0}; - + float h1; float h2; - + float c1,c2; - + float min = FLT_MAX; float max = -FLT_MAX; float correction; - + float* float_img = icvAlloc( width * height * sizeof(float) ); - + x1 = width * (width + 1) / 2.0f; // Sum (1, ... , width) x2 = width * (width + 1 ) * (2 * width + 1) / 6.0f; // Sum (1^2, ... , width^2) y1 = height * (height + 1)/2.0f; // Sum (1, ... , width) y2 = height * (height + 1 ) * (2 * height + 1) / 6.0f; // Sum (1^2, ... , width^2) - - + + // extract grayvalues for (i = 0; i < height; i++) { @@ -1555,38 +1553,38 @@ CvStatus icvLightingCorrection8uC1R( uchar* img, CvSize roi, int src_step ) f[0] = f[0] + img[i*src_step + j]; } } - + h1 = (float)f[0] * (float)x1 / (float)width; h2 = (float)f[0] * (float)y1 / (float)height; - + a[2] = ((float)f[2] - h1) / (float)(x2*height - x1*x1*height/(float)width); a[1] = ((float)f[1] - h2) / (float)(y2*width - y1*y1*width/(float)height); - a[0] = (float)f[0]/(float)(width*height) - (float)y1*a[1]/(float)height - + a[0] = (float)f[0]/(float)(width*height) - (float)y1*a[1]/(float)height - (float)x1*a[2]/(float)width; - - for (i = 0; i < height; i++) - { + + for (i = 0; i < height; i++) + { for (j = 0; j < width; j++) { - + correction = a[0] + a[1]*(float)i + a[2]*(float)j; - + float_img[i*width + j] = img[i*src_step + j] - correction; - + if (float_img[i*width + j] < min) min = float_img[i*width+j]; if (float_img[i*width + j] > max) max = float_img[i*width+j]; } } - + //rescaling to the range 0:255 c2 = 0; if (max == min) c2 = 255.0f; else c2 = 255.0f/(float)(max - min); - + c1 = (-(float)min)*c2; - + for (i = 0; i < height; i++) { for (j = 0; j < width; j++) @@ -1601,17 +1599,17 @@ CvStatus icvLightingCorrection8uC1R( uchar* img, CvSize roi, int src_step ) cvFree( &float_img ); return CV_NO_ERR; } - -CvStatus icvLightingCorrection( icvImage* img ) + +CvStatus icvLightingCorrection( icvImage* img ) { CvSize roi; if ( img->type != IPL_DEPTH_8U || img->channels != 1 ) return CV_BADFACTOR_ERR; roi = _cvSize( img->roi.width, img->roi.height ); - - return _cvLightingCorrection8uC1R( img->data + img->roi.y * img->step + img->roi.x, + + return _cvLightingCorrection8uC1R( img->data + img->roi.y * img->step + img->roi.x, roi, img->step ); } diff --git a/modules/legacy/src/image.cpp b/modules/legacy/src/image.cpp index eb1ab46..729cd6c 100644 --- a/modules/legacy/src/image.cpp +++ b/modules/legacy/src/image.cpp @@ -182,13 +182,13 @@ void CvImage::show( const char* window_name ) /////////////////////////////// CvMatrix implementation ////////////////////////////////// -CvMatrix::CvMatrix( int rows, int cols, int type, CvMemStorage* storage, bool alloc_data ) +CvMatrix::CvMatrix( int _rows, int _cols, int _type, CvMemStorage* storage, bool alloc_data ) { if( storage ) { matrix = (CvMat*)cvMemStorageAlloc( storage, sizeof(*matrix) ); - cvInitMatHeader( matrix, rows, cols, type, alloc_data ? - cvMemStorageAlloc( storage, rows*cols*CV_ELEM_SIZE(type) ) : 0 ); + cvInitMatHeader( matrix, _rows, _cols, _type, alloc_data ? + cvMemStorageAlloc( storage, _rows*_cols*CV_ELEM_SIZE(_type) ) : 0 ); } else matrix = 0; diff --git a/modules/legacy/src/kdtree.cpp b/modules/legacy/src/kdtree.cpp index 2cfed90..12a4acd 100644 --- a/modules/legacy/src/kdtree.cpp +++ b/modules/legacy/src/kdtree.cpp @@ -48,10 +48,6 @@ #include "_kdtree.hpp" #include "_featuretree.h" -#if _MSC_VER >= 1400 -#pragma warning(disable:4996) // suppress "function call with parameters may be unsafe" in std::copy -#endif - class CvKDTreeWrap : public CvFeatureTree { template struct deref { @@ -93,18 +89,19 @@ class CvKDTreeWrap : public CvFeatureTree { assert(results->cols == k); assert(dist->cols == k); - for (int j = 0; j < d->rows; ++j) { - const typename __treetype::scalar_type* dj = - (const typename __treetype::scalar_type*) dptr; + for (int j = 0; j < d->rows; ++j) + { + const typename __treetype::scalar_type* dj = (const typename __treetype::scalar_type*) dptr; int* resultsj = (int*) resultsptr; double* distj = (double*) distptr; tr->find_nn_bbf(dj, k, emax, nn); assert((int)nn.size() <= k); - for (unsigned int j = 0; j < nn.size(); ++j) { - *resultsj++ = *nn[j].p; - *distj++ = nn[j].dist; + for (unsigned int i = 0; i < nn.size(); ++i) + { + *resultsj++ = *nn[i].p; + *distj++ = nn[i].dist; } std::fill(resultsj, resultsj + k - nn.size(), -1); std::fill(distj, distj + k - nn.size(), 0); @@ -117,16 +114,16 @@ class CvKDTreeWrap : public CvFeatureTree { template int find_ortho_range(CvMat* bounds_min, CvMat* bounds_max, - CvMat* results) { + CvMat* results) { int rn = results->rows * results->cols; std::vector inbounds; dispatch_cvtype(mat, ((__treetype*)data)-> - find_ortho_range((typename __treetype::scalar_type*)bounds_min->data.ptr, - (typename __treetype::scalar_type*)bounds_max->data.ptr, - inbounds)); + find_ortho_range((typename __treetype::scalar_type*)bounds_min->data.ptr, + (typename __treetype::scalar_type*)bounds_max->data.ptr, + inbounds)); std::copy(inbounds.begin(), - inbounds.begin() + std::min((int)inbounds.size(), rn), - (int*) results->data.ptr); + inbounds.begin() + std::min((int)inbounds.size(), rn), + (int*) results->data.ptr); return (int)inbounds.size(); } @@ -135,7 +132,7 @@ class CvKDTreeWrap : public CvFeatureTree { public: CvKDTreeWrap(CvMat* _mat) : mat(_mat) { // * a flag parameter should tell us whether - // * (a) user ensures *mat outlives *this and is unchanged, + // * (a) user ensures *mat outlives *this and is unchanged, // * (b) we take reference and user ensures mat is unchanged, // * (c) we copy data, (d) we own and release data. @@ -144,8 +141,8 @@ public: tmp[j] = j; dispatch_cvtype(mat, data = new tree_type - (&tmp[0], &tmp[0] + tmp.size(), mat->cols, - tree_type::deref_type(mat))); + (&tmp[0], &tmp[0] + tmp.size(), mat->cols, + tree_type::deref_type(mat))); } ~CvKDTreeWrap() { dispatch_cvtype(mat, delete (tree_type*) data); @@ -185,15 +182,15 @@ public: assert(CV_MAT_TYPE(results->type) == CV_32SC1); dispatch_cvtype(mat, find_nn - (desc, k, emax, results, dist)); + (desc, k, emax, results, dist)); } int FindOrthoRange(CvMat* bounds_min, CvMat* bounds_max, - CvMat* results) { + CvMat* results) { bool free_bounds = false; int count = -1; if (bounds_min->cols * bounds_min->rows != dims() || - bounds_max->cols * bounds_max->rows != dims()) + bounds_max->cols * bounds_max->rows != dims()) CV_Error(CV_StsUnmatchedSizes, "bounds_{min,max} must 1 x dims or dims x 1"); if (CV_MAT_TYPE(bounds_min->type) != CV_MAT_TYPE(bounds_max->type)) CV_Error(CV_StsUnmatchedFormats, "bounds_{min,max} must have same type"); @@ -218,7 +215,7 @@ public: assert(bounds_max->rows * bounds_max->cols == dims()); dispatch_cvtype(mat, count = find_ortho_range - (bounds_min, bounds_max,results)); + (bounds_min, bounds_max,results)); if (free_bounds) { cvReleaseMat(&bounds_min); diff --git a/modules/legacy/src/lee.cpp b/modules/legacy/src/lee.cpp index 518ec77..9a20fa5 100644 --- a/modules/legacy/src/lee.cpp +++ b/modules/legacy/src/lee.cpp @@ -1247,7 +1247,7 @@ int _cvSolveEqu1th(T c1, T c0, T* X); vertices_number: in, number of vertices in polygon Return : --------------------------------------------------------------------------*/ -void _cvSetSeqBlockSize(CvVoronoiDiagramInt* pVoronoiDiagramInt,int vertices_number) +static void _cvSetSeqBlockSize(CvVoronoiDiagramInt* pVoronoiDiagramInt,int vertices_number) { int N = 2*vertices_number; cvSetSeqBlockSize(pVoronoiDiagramInt->SiteSeq,N*pVoronoiDiagramInt->SiteSeq->elem_size); diff --git a/modules/legacy/src/levmar.cpp b/modules/legacy/src/levmar.cpp index 976f6f4..aa2514e 100644 --- a/modules/legacy/src/levmar.cpp +++ b/modules/legacy/src/levmar.cpp @@ -50,6 +50,7 @@ typedef void (*pointer_LMJac)( const CvMat* src, CvMat* dst ); typedef void (*pointer_LMFunc)( const CvMat* src, CvMat* dst ); +#if 0 /* Optimization using Levenberg-Marquardt */ void cvLevenbergMarquardtOptimization(pointer_LMJac JacobianFunction, pointer_LMFunc function, @@ -75,7 +76,7 @@ void cvLevenbergMarquardtOptimization(pointer_LMJac JacobianFunction, CvMat *matrJtJN = 0; CvMat *matrJt = 0; CvMat *vectB = 0; - + CV_FUNCNAME( "cvLevenbegrMarquardtOptimization" ); __BEGIN__; @@ -104,7 +105,7 @@ void cvLevenbergMarquardtOptimization(pointer_LMJac JacobianFunction, { CV_ERROR( CV_StsUnmatchedSizes, "Number of colomn of vector X0 must be 1" ); } - + if( observRes->cols != 1 ) { CV_ERROR( CV_StsUnmatchedSizes, "Number of colomn of vector observed rusult must be 1" ); @@ -157,8 +158,8 @@ void cvLevenbergMarquardtOptimization(pointer_LMJac JacobianFunction, /* Print result of function to file */ /* Compute error */ - cvSub(observRes,resFunc,error); - + cvSub(observRes,resFunc,error); + //valError = error_function(observRes,resFunc); /* Need to use new version of computing error (norm) */ valError = cvNorm(observRes,resFunc); @@ -169,7 +170,7 @@ void cvLevenbergMarquardtOptimization(pointer_LMJac JacobianFunction, /* Define optimal delta for J'*J*delta=J'*error */ /* compute J'J */ cvMulTransposed(Jac,matrJtJ,1); - + cvCopy(matrJtJ,matrJtJN); /* compute J'*error */ @@ -244,6 +245,7 @@ void cvLevenbergMarquardtOptimization(pointer_LMJac JacobianFunction, return; } +#endif /*------------------------------------------------------------------------------*/ #if 0 diff --git a/modules/legacy/src/levmarprojbandle.cpp b/modules/legacy/src/levmarprojbandle.cpp index 1581896..162ea52 100644 --- a/modules/legacy/src/levmarprojbandle.cpp +++ b/modules/legacy/src/levmarprojbandle.cpp @@ -65,9 +65,13 @@ void icvReconstructPoints4DStatus(CvMat** projPoints, CvMat **projMatrs, CvMat** */ #define TRACK_BUNDLE_FILE "d:\\test\\bundle.txt" +void cvOptimizeLevenbergMarquardtBundle( CvMat** projMatrs, CvMat** observProjPoints, + CvMat** pointsPres, int numImages, + CvMat** resultProjMatrs, CvMat* resultPoints4D,int maxIter,double epsilon ); + /* ============== Bundle adjustment optimization ================= */ -void icvComputeDerivateProj(CvMat *points4D,CvMat *projMatr, CvMat *status, CvMat *derivProj) +static void icvComputeDerivateProj(CvMat *points4D,CvMat *projMatr, CvMat *status, CvMat *derivProj) { /* Compute derivate for given projection matrix points and status of points */ @@ -131,13 +135,11 @@ void icvComputeDerivateProj(CvMat *points4D,CvMat *projMatr, CvMat *status, CvMa } /* ----- End test ----- */ - int i; - /* Allocate memory for derivates */ double p[12]; /* Copy projection matrix */ - for( i = 0; i < 12; i++ ) + for(int i = 0; i < 12; i++ ) { p[i] = cvmGet(projMatr,i/4,i%4); } @@ -164,7 +166,6 @@ void icvComputeDerivateProj(CvMat *points4D,CvMat *projMatr, CvMat *status, CvMa piX[1] = X[0]*p[4] + X[1]*p[5] + X[2]*p[6] + X[3]*p[7]; piX[2] = X[0]*p[8] + X[1]*p[9] + X[2]*p[10] + X[3]*p[11]; - int i; /* fill derivate by point */ double tmp3 = 1/(piX[2]*piX[2]); @@ -173,7 +174,7 @@ void icvComputeDerivateProj(CvMat *points4D,CvMat *projMatr, CvMat *status, CvMa double tmp2 = -piX[1]*tmp3; /* fill derivate by projection matrix */ - for( i = 0; i < 4; i++ ) + for(int i = 0; i < 4; i++ ) { /* derivate for x */ cvmSet(derivProj,currVisPoint*2,i,X[i]/piX[2]);//x' p1i @@ -201,7 +202,7 @@ void icvComputeDerivateProj(CvMat *points4D,CvMat *projMatr, CvMat *status, CvMa } /*======================================================================================*/ -void icvComputeDerivateProjAll(CvMat *points4D, CvMat **projMatrs, CvMat **pointPres, int numImages,CvMat **projDerives) +static void icvComputeDerivateProjAll(CvMat *points4D, CvMat **projMatrs, CvMat **pointPres, int numImages,CvMat **projDerives) { CV_FUNCNAME( "icvComputeDerivateProjAll" ); __BEGIN__; @@ -228,7 +229,7 @@ void icvComputeDerivateProjAll(CvMat *points4D, CvMat **projMatrs, CvMat **point } /*======================================================================================*/ -void icvComputeDerivatePoints(CvMat *points4D,CvMat *projMatr, CvMat *presPoints, CvMat *derivPoint) +static void icvComputeDerivatePoints(CvMat *points4D,CvMat *projMatr, CvMat *presPoints, CvMat *derivPoint) { CV_FUNCNAME( "icvComputeDerivatePoints" ); @@ -267,7 +268,7 @@ void icvComputeDerivatePoints(CvMat *points4D,CvMat *projMatr, CvMat *presPoints { CV_ERROR( CV_StsOutOfRange, "Size of projection matrix (projMatr) must be 3x4" ); } - + if( !CV_IS_MAT(presPoints) ) { CV_ERROR( CV_StsUnsupportedFormat, "Status must be a matrix 1xN" ); @@ -282,13 +283,12 @@ void icvComputeDerivatePoints(CvMat *points4D,CvMat *projMatr, CvMat *presPoints { CV_ERROR( CV_StsUnsupportedFormat, "derivPoint must be a matrix 2 x 4VisNum" ); } - /* ----- End test ----- */ - + /* ----- End test ----- */ + /* Compute derivates by points */ - + double p[12]; - int i; - for( i = 0; i < 12; i++ ) + for(int i = 0; i < 12; i++ ) { p[i] = cvmGet(projMatr,i/4,i%4); } @@ -311,16 +311,14 @@ void icvComputeDerivatePoints(CvMat *points4D,CvMat *projMatr, CvMat *presPoints piX[0] = X[0]*p[0] + X[1]*p[1] + X[2]*p[2] + X[3]*p[3]; piX[1] = X[0]*p[4] + X[1]*p[5] + X[2]*p[6] + X[3]*p[7]; piX[2] = X[0]*p[8] + X[1]*p[9] + X[2]*p[10] + X[3]*p[11]; - - int i,j; double tmp3 = 1/(piX[2]*piX[2]); - - for( j = 0; j < 2; j++ )//for x and y + + for(int j = 0; j < 2; j++ )//for x and y { - for( i = 0; i < 4; i++ )// for X,Y,Z,W + for(int i = 0; i < 4; i++ )// for X,Y,Z,W { - cvmSet( derivPoint, + cvmSet( derivPoint, j, currVisPoint*4+i, (p[j*4+i]*piX[2]-p[8+i]*piX[j]) * tmp3 ); } @@ -337,8 +335,9 @@ void icvComputeDerivatePoints(CvMat *points4D,CvMat *projMatr, CvMat *presPoints __END__; return; } + /*======================================================================================*/ -void icvComputeDerivatePointsAll(CvMat *points4D, CvMat **projMatrs, CvMat **pointPres, int numImages,CvMat **pointDerives) +static void icvComputeDerivatePointsAll(CvMat *points4D, CvMat **projMatrs, CvMat **pointPres, int numImages,CvMat **pointDerives) { CV_FUNCNAME( "icvComputeDerivatePointsAll" ); __BEGIN__; @@ -364,7 +363,7 @@ void icvComputeDerivatePointsAll(CvMat *points4D, CvMat **projMatrs, CvMat **poi return; } /*======================================================================================*/ -void icvComputeMatrixVAll(int numImages,CvMat **pointDeriv,CvMat **presPoints, CvMat **matrV) +static void icvComputeMatrixVAll(int numImages,CvMat **pointDeriv,CvMat **presPoints, CvMat **matrV) { int *shifts = 0; @@ -404,10 +403,10 @@ void icvComputeMatrixVAll(int numImages,CvMat **pointDeriv,CvMat **presPoints, C { if( cvmGet(presPoints[currImage],0,currPoint) > 0 ) { - sum += cvmGet(pointDeriv[currImage],0,shifts[currImage]*4+i) * + sum += cvmGet(pointDeriv[currImage],0,shifts[currImage]*4+i) * cvmGet(pointDeriv[currImage],0,shifts[currImage]*4+j); - sum += cvmGet(pointDeriv[currImage],1,shifts[currImage]*4+i) * + sum += cvmGet(pointDeriv[currImage],1,shifts[currImage]*4+i) * cvmGet(pointDeriv[currImage],1,shifts[currImage]*4+j); } } @@ -429,11 +428,11 @@ void icvComputeMatrixVAll(int numImages,CvMat **pointDeriv,CvMat **presPoints, C __END__; cvFree( &shifts); - + return; } /*======================================================================================*/ -void icvComputeMatrixUAll(int numImages,CvMat **projDeriv,CvMat** matrU) +static void icvComputeMatrixUAll(int numImages,CvMat **projDeriv,CvMat** matrU) { CV_FUNCNAME( "icvComputeMatrixVAll" ); __BEGIN__; @@ -460,7 +459,7 @@ void icvComputeMatrixUAll(int numImages,CvMat **projDeriv,CvMat** matrU) return; } /*======================================================================================*/ -void icvComputeMatrixW(int numImages, CvMat **projDeriv, CvMat **pointDeriv, CvMat **presPoints, CvMat *matrW) +static void icvComputeMatrixW(int numImages, CvMat **projDeriv, CvMat **pointDeriv, CvMat **presPoints, CvMat *matrW) { CV_FUNCNAME( "icvComputeMatrixW" ); __BEGIN__; @@ -509,10 +508,10 @@ void icvComputeMatrixW(int numImages, CvMat **projDeriv, CvMat **pointDeriv, CvM for( int currCol = 0; currCol < 4; currCol++ ) { double sum; - sum = cvmGet(projDeriv[currImage],currVis*2+0,currLine) * + sum = cvmGet(projDeriv[currImage],currVis*2+0,currLine) * cvmGet(pointDeriv[currImage],0,currVis*4+currCol); - sum += cvmGet(projDeriv[currImage],currVis*2+1,currLine) * + sum += cvmGet(projDeriv[currImage],currVis*2+1,currLine) * cvmGet(pointDeriv[currImage],1,currVis*4+currCol); cvmSet(matrW,currImage*12+currLine,currPoint*4+currCol,sum); @@ -529,7 +528,7 @@ void icvComputeMatrixW(int numImages, CvMat **projDeriv, CvMat **pointDeriv, CvM } } } - + #ifdef TRACK_BUNDLE { FILE *file; @@ -560,9 +559,10 @@ void icvComputeMatrixW(int numImages, CvMat **projDeriv, CvMat **pointDeriv, CvM __END__; return; } + /*======================================================================================*/ /* Compute jacobian mult projection matrices error */ -void icvComputeJacErrorProj(int numImages,CvMat **projDeriv,CvMat **projErrors,CvMat *jacProjErr ) +static void icvComputeJacErrorProj(int numImages,CvMat **projDeriv,CvMat **projErrors,CvMat *jacProjErr ) { CV_FUNCNAME( "icvComputeJacErrorProj" ); __BEGIN__; @@ -596,7 +596,7 @@ void icvComputeJacErrorProj(int numImages,CvMat **projDeriv,CvMat **projErrors,C double sum = 0; for( int i = 0; i < num; i++ ) { - sum += cvmGet(projDeriv[currImage],i,currCol) * + sum += cvmGet(projDeriv[currImage],i,currCol) * cvmGet(projErrors[currImage],i%2,i/2); } cvmSet(jacProjErr,currImage*12+currCol,0,sum); @@ -627,9 +627,10 @@ void icvComputeJacErrorProj(int numImages,CvMat **projDeriv,CvMat **projErrors,C __END__; return; } + /*======================================================================================*/ /* Compute jacobian mult points error */ -void icvComputeJacErrorPoint(int numImages,CvMat **pointDeriv,CvMat **projErrors, CvMat **presPoints,CvMat *jacPointErr ) +static void icvComputeJacErrorPoint(int numImages,CvMat **pointDeriv,CvMat **projErrors, CvMat **presPoints,CvMat *jacPointErr ) { int *shifts = 0; @@ -734,6 +735,7 @@ void icvComputeJacErrorPoint(int numImages,CvMat **pointDeriv,CvMat **projErrors } /*======================================================================================*/ + /* Reconstruct 4D points using status */ void icvReconstructPoints4DStatus(CvMat** projPoints, CvMat **projMatrs, CvMat** presPoints, CvMat *points4D,int numImages,CvMat **projError) @@ -797,7 +799,7 @@ void icvReconstructPoints4DStatus(CvMat** projPoints, CvMat **projMatrs, CvMat** numVisProj++; } } - + if( numVisProj < 2 ) { /* This point can't be reconstructed */ @@ -821,7 +823,7 @@ void icvReconstructPoints4DStatus(CvMat** projPoints, CvMat **projMatrs, CvMat** y = cvmGet(projPoints[currImage],1,currPoint); for( int k = 0; k < 4; k++ ) { - matrA_dat[currVisProj*12 + k] = + matrA_dat[currVisProj*12 + k] = x * cvmGet(projMatrs[currImage],2,k) - cvmGet(projMatrs[currImage],0,k); matrA_dat[currVisProj*12+4 + k] = @@ -854,27 +856,26 @@ void icvReconstructPoints4DStatus(CvMat** projPoints, CvMat **projMatrs, CvMat** CvMat point3D; double point3D_dat[3]; point3D = cvMat(3,1,CV_64F,point3D_dat); - - int currPoint; + int numVis = 0; double totalError = 0; - for( currPoint = 0; currPoint < numPoints; currPoint++ ) + for(int curPoint = 0; curPoint < numPoints; curPoint++ ) { - if( cvmGet(presPoints[currImage],0,currPoint) > 0) + if( cvmGet(presPoints[currImage],0,curPoint) > 0) { double dx,dy; - cvGetCol(points4D,&point4D,currPoint); + cvGetCol(points4D,&point4D,curPoint); cvmMul(projMatrs[currImage],&point4D,&point3D); double w = point3D_dat[2]; double x = point3D_dat[0] / w; double y = point3D_dat[1] / w; - dx = cvmGet(projPoints[currImage],0,currPoint) - x; - dy = cvmGet(projPoints[currImage],1,currPoint) - y; + dx = cvmGet(projPoints[currImage],0,curPoint) - x; + dy = cvmGet(projPoints[currImage],1,curPoint) - y; if( projError ) { - cvmSet(projError[currImage],0,currPoint,dx); - cvmSet(projError[currImage],1,currPoint,dy); + cvmSet(projError[currImage],0,curPoint,dx); + cvmSet(projError[currImage],1,curPoint,dy); } totalError += sqrt(dx*dx+dy*dy); numVis++; @@ -897,7 +898,7 @@ void icvReconstructPoints4DStatus(CvMat** projPoints, CvMat **projMatrs, CvMat** /*======================================================================================*/ -void icvProjPointsStatusFunc( int numImages, CvMat *points4D, CvMat **projMatrs, CvMat **pointsPres, CvMat **projPoints) +static void icvProjPointsStatusFunc( int numImages, CvMat *points4D, CvMat **projMatrs, CvMat **pointsPres, CvMat **projPoints) { CV_FUNCNAME( "icvProjPointsStatusFunc" ); __BEGIN__; @@ -943,7 +944,7 @@ void icvProjPointsStatusFunc( int numImages, CvMat *points4D, CvMat **projMatrs, fclose(file); } #endif - + int currImage; for( currImage = 0; currImage < numImages; currImage++ ) { @@ -969,7 +970,7 @@ void icvProjPointsStatusFunc( int numImages, CvMat *points4D, CvMat **projMatrs, fclose(file); } #endif - + cvmMul(projMatrs[currImage],&point4D,&point3D); double w = point3D_dat[2]; cvmSet(projPoints[currImage],0,currVisPoint,point3D_dat[0]/w); @@ -998,11 +999,11 @@ void icvProjPointsStatusFunc( int numImages, CvMat *points4D, CvMat **projMatrs, } /*======================================================================================*/ -void icvFreeMatrixArray(CvMat ***matrArray,int numMatr) +static void icvFreeMatrixArray(CvMat ***matrArray,int numMatr) { /* Free each matrix */ int currMatr; - + if( *matrArray != 0 ) {/* Need delete */ for( currMatr = 0; currMatr < numMatr; currMatr++ ) @@ -1015,7 +1016,7 @@ void icvFreeMatrixArray(CvMat ***matrArray,int numMatr) } /*======================================================================================*/ -void *icvClearAlloc(int size) +static void *icvClearAlloc(int size) { void *ptr = 0; @@ -1047,6 +1048,7 @@ int icvDeleteSparsInPoints( int numImages, } #endif + /*======================================================================================*/ /* !!! may be useful to return norm of error */ /* !!! may be does not work correct with not all visible 4D points */ @@ -1054,15 +1056,15 @@ void cvOptimizeLevenbergMarquardtBundle( CvMat** projMatrs, CvMat** observProjPo CvMat** pointsPres, int numImages, CvMat** resultProjMatrs, CvMat* resultPoints4D,int maxIter,double epsilon ) { - + CvMat *vectorX_points4D = 0; - CvMat **vectorX_projMatrs = 0; + CvMat **vectorX_projMatrs = 0; CvMat *newVectorX_points4D = 0; CvMat **newVectorX_projMatrs = 0; CvMat *changeVectorX_points4D = 0; - CvMat *changeVectorX_projMatrs = 0; + CvMat *changeVectorX_projMatrs = 0; CvMat **observVisPoints = 0; CvMat **projVisPoints = 0; @@ -1097,17 +1099,17 @@ void cvOptimizeLevenbergMarquardtBundle( CvMat** projMatrs, CvMat** observProjPo { CV_ERROR( CV_StsOutOfRange, "Number of images must be more than zero" ); } - + if( maxIter < 1 || maxIter > 2000 ) { CV_ERROR( CV_StsOutOfRange, "Maximum number of iteration must be in [1..1000]" ); } - + if( epsilon < 0 ) { CV_ERROR( CV_StsOutOfRange, "Epsilon parameter must be >= 0" ); } - + if( !CV_IS_MAT(resultPoints4D) ) { CV_ERROR( CV_StsUnsupportedFormat, "resultPoints4D must be a matrix 4 x NumPnt" ); @@ -1138,10 +1140,8 @@ void cvOptimizeLevenbergMarquardtBundle( CvMat** projMatrs, CvMat** observProjPo CV_CALL( changeVectorX_points4D = cvCreateMat(4,numPoints,CV_64F)); CV_CALL( changeVectorX_projMatrs = cvCreateMat(3,4,CV_64F)); - int currImage; - /* ----- Test input params ----- */ - for( currImage = 0; currImage < numImages; currImage++ ) + for(int currImage = 0; currImage < numImages; currImage++ ) { /* Test size of input initial and result projection matrices */ if( !CV_IS_MAT(projMatrs[currImage]) ) @@ -1185,7 +1185,7 @@ void cvOptimizeLevenbergMarquardtBundle( CvMat** projMatrs, CvMat** observProjPo /* ----- End test ----- */ /* Copy projection matrices to vectorX0 */ - for( currImage = 0; currImage < numImages; currImage++ ) + for(int currImage = 0; currImage < numImages; currImage++ ) { CV_CALL( vectorX_projMatrs[currImage] = cvCreateMat(3,4,CV_64F)); CV_CALL( newVectorX_projMatrs[currImage] = cvCreateMat(3,4,CV_64F)); @@ -1221,7 +1221,7 @@ void cvOptimizeLevenbergMarquardtBundle( CvMat** projMatrs, CvMat** observProjPo CV_CALL( workMatrsInvVi[i] = cvCreateMat(4,4,CV_64F) ); } - for( currImage = 0; currImage < numImages; currImage++ ) + for(int currImage = 0; currImage < numImages; currImage++ ) { CV_CALL( matrsUk[currImage] = cvCreateMat(12,12,CV_64F) ); CV_CALL( workMatrsUk[currImage] = cvCreateMat(12,12,CV_64F) ); @@ -1290,7 +1290,7 @@ void cvOptimizeLevenbergMarquardtBundle( CvMat** projMatrs, CvMat** observProjPo /* Compute error with observed value and computed projection */ double prevError; prevError = 0; - for( currImage = 0; currImage < numImages; currImage++ ) + for(int currImage = 0; currImage < numImages; currImage++ ) { cvSub(observVisPoints[currImage],projVisPoints[currImage],errorProjPoints[currImage]); double currNorm = cvNorm(errorProjPoints[currImage]); @@ -1316,8 +1316,7 @@ void cvOptimizeLevenbergMarquardtBundle( CvMat** projMatrs, CvMat** observProjPo fprintf(file,"projection errors\n"); /* Print all proejction errors */ - int currImage; - for( currImage = 0; currImage < numImages; currImage++) + for(int currImage = 0; currImage < numImages; currImage++) { fprintf(file,"\nImage=%d\n",currImage); int numPn = errorProjPoints[currImage]->cols; @@ -1355,7 +1354,7 @@ void cvOptimizeLevenbergMarquardtBundle( CvMat** projMatrs, CvMat** observProjPo double norm = cvNorm(vectorX_projMatrs[i]); fprintf(file," test 6.01 prev normProj=%lf\n",norm); } - + fclose(file); } #endif @@ -1384,7 +1383,7 @@ void cvOptimizeLevenbergMarquardtBundle( CvMat** projMatrs, CvMat** observProjPo double norm = cvNorm(matrsUk[i]); fprintf(file," test 6.01 prev matrsUk=%lf\n",norm); } - + for( i = 0; i < numPoints; i++ ) { double norm = cvNorm(matrsVi[i]); @@ -1410,7 +1409,7 @@ void cvOptimizeLevenbergMarquardtBundle( CvMat** projMatrs, CvMat** observProjPo } #endif /* Copy matrices Uk to work matrices Uk */ - for( currImage = 0; currImage < numImages; currImage++ ) + for(int currImage = 0; currImage < numImages; currImage++ ) { cvCopy(matrsUk[currImage],workMatrsUk[currImage]); } @@ -1427,7 +1426,7 @@ void cvOptimizeLevenbergMarquardtBundle( CvMat** projMatrs, CvMat** observProjPo double norm = cvNorm(matrsUk[i]); fprintf(file," test 6.01 post1 matrsUk=%lf\n",norm); } - + for( i = 0; i < numPoints; i++ ) { double norm = cvNorm(matrsVi[i]); @@ -1450,7 +1449,7 @@ void cvOptimizeLevenbergMarquardtBundle( CvMat** projMatrs, CvMat** observProjPo { cvCopy(matrsVi[currV],workMatrVi); - for( int i = 0; i < 4; i++ ) + for( i = 0; i < 4; i++ ) { cvmSet(workMatrVi,i,i,cvmGet(matrsVi[currV],i,i)*(1+alpha) ); } @@ -1459,7 +1458,7 @@ void cvOptimizeLevenbergMarquardtBundle( CvMat** projMatrs, CvMat** observProjPo } /* Add alpha to matrUk and make matrix workMatrsUk */ - for( currImage = 0; currImage< numImages; currImage++ ) + for(int currImage = 0; currImage< numImages; currImage++ ) { for( i = 0; i < 12; i++ ) @@ -1476,7 +1475,7 @@ void cvOptimizeLevenbergMarquardtBundle( CvMat** projMatrs, CvMat** observProjPo int currRowV; for( currRowV = 0; currRowV < 4; currRowV++ ) { - for( currImage = 0; currImage < numImages; currImage++ ) + for(int currImage = 0; currImage < numImages; currImage++ ) { for( int currCol = 0; currCol < 12; currCol++ )/* For each column of transposed matrix W */ { @@ -1497,7 +1496,7 @@ void cvOptimizeLevenbergMarquardtBundle( CvMat** projMatrs, CvMat** observProjPo cvmMul(matrW,matrTmpSys1,matrSysDeltaP); /* need to compute U-matrTmpSys2. But we compute matTmpSys2-U */ - for( currImage = 0; currImage < numImages; currImage++ ) + for(int currImage = 0; currImage < numImages; currImage++ ) { CvMat subMatr; cvGetSubRect(matrSysDeltaP,&subMatr,cvRect(currImage*12,currImage*12,12,12)); @@ -1527,8 +1526,7 @@ void cvOptimizeLevenbergMarquardtBundle( CvMat** projMatrs, CvMat** observProjPo FILE* file; file = fopen( TRACK_BUNDLE_FILE_DELTAP ,"w"); - int currImage; - for( currImage = 0; currImage < numImages; currImage++ ) + for(int currImage = 0; currImage < numImages; currImage++ ) { fprintf(file,"\nImage=%d\n",currImage); int i; @@ -1567,7 +1565,7 @@ void cvOptimizeLevenbergMarquardtBundle( CvMat** projMatrs, CvMat** observProjPo /* We know delta and compute new value of vector X: nextVectX = vectX + deltas */ /* Compute new P */ - for( currImage = 0; currImage < numImages; currImage++ ) + for(int currImage = 0; currImage < numImages; currImage++ ) { for( i = 0; i < 3; i++ ) { @@ -1595,7 +1593,7 @@ void cvOptimizeLevenbergMarquardtBundle( CvMat** projMatrs, CvMat** observProjPo icvProjPointsStatusFunc(numImages, newVectorX_points4D, newVectorX_projMatrs, pointsPres, projVisPoints); /* Compute error with observed value and computed projection */ double newError = 0; - for( currImage = 0; currImage < numImages; currImage++ ) + for(int currImage = 0; currImage < numImages; currImage++ ) { cvSub(observVisPoints[currImage],projVisPoints[currImage],errorProjPoints[currImage]); double currNorm = cvNorm(errorProjPoints[currImage]); @@ -1612,7 +1610,7 @@ void cvOptimizeLevenbergMarquardtBundle( CvMat** projMatrs, CvMat** observProjPo newError += currNorm * currNorm; } newError = sqrt(newError); - + currIter++; @@ -1634,8 +1632,7 @@ void cvOptimizeLevenbergMarquardtBundle( CvMat** projMatrs, CvMat** observProjPo /* Print all projection errors */ #if 0 fprintf(file,"projection errors\n"); - int currImage; - for( currImage = 0; currImage < numImages; currImage++) + for(int currImage = 0; currImage < numImages; currImage++) { fprintf(file,"\nImage=%d\n",currImage); int numPn = errorProjPoints[currImage]->cols; @@ -1667,7 +1664,7 @@ void cvOptimizeLevenbergMarquardtBundle( CvMat** projMatrs, CvMat** observProjPo double currNorm1 = 0; double currNorm2 = 0; /* compute norm for projection matrices */ - for( currImage = 0; currImage < numImages; currImage++ ) + for(int currImage = 0; currImage < numImages; currImage++ ) { currNorm1 = cvNorm(newVectorX_projMatrs[currImage],vectorX_projMatrs[currImage]); currNorm2 = cvNorm(newVectorX_projMatrs[currImage]); @@ -1704,7 +1701,7 @@ void cvOptimizeLevenbergMarquardtBundle( CvMat** projMatrs, CvMat** observProjPo } alpha /= 10; - for( currImage = 0; currImage < numImages; currImage++ ) + for(int currImage = 0; currImage < numImages; currImage++ ) { cvCopy(newVectorX_projMatrs[currImage],vectorX_projMatrs[currImage]); } @@ -1732,11 +1729,11 @@ void cvOptimizeLevenbergMarquardtBundle( CvMat** projMatrs, CvMat** observProjPo } while( change > epsilon && currIter < maxIter ); - + /*--------------------------------------------*/ /* Optimization complete copy computed params */ /* Copy projection matrices */ - for( currImage = 0; currImage < numImages; currImage++ ) + for(int currImage = 0; currImage < numImages; currImage++ ) { cvCopy(newVectorX_projMatrs[currImage],resultProjMatrs[currImage]); } diff --git a/modules/legacy/src/levmartrif.cpp b/modules/legacy/src/levmartrif.cpp index e8a7ad5..e86b52f 100644 --- a/modules/legacy/src/levmartrif.cpp +++ b/modules/legacy/src/levmartrif.cpp @@ -46,6 +46,8 @@ /* Valery Mosyagin */ +#if 0 + typedef void (*pointer_LMJac)( const CvMat* src, CvMat* dst ); typedef void (*pointer_LMFunc)( const CvMat* src, CvMat* dst ); @@ -61,7 +63,7 @@ void icvReconstructPointsFor3View( CvMat* projMatr1,CvMat* projMatr2,CvMat* proj /* Jacobian computation for trifocal case */ -void icvJacobianFunction_ProjTrifocal(const CvMat *vectX,CvMat *Jacobian) +static void icvJacobianFunction_ProjTrifocal(const CvMat *vectX,CvMat *Jacobian) { CV_FUNCNAME( "icvJacobianFunction_ProjTrifocal" ); __BEGIN__; @@ -101,7 +103,7 @@ void icvJacobianFunction_ProjTrifocal(const CvMat *vectX,CvMat *Jacobian) /* Fill Jacobian matrix */ int currProjPoint; int currMatr; - + cvZero(Jacobian); for( currMatr = 0; currMatr < 3; currMatr++ ) { @@ -137,7 +139,7 @@ void icvJacobianFunction_ProjTrifocal(const CvMat *vectX,CvMat *Jacobian) { for( i = 0; i < 4; i++ )// for X,Y,Z,W { - cvmSet( Jacobian, + cvmSet( Jacobian, currMatr*numPoints*2+currProjPoint*2+j, 36+currProjPoint*4+i, (p[j*4+i]*piX[2]-p[8+i]*piX[j]) * tmp3 ); } @@ -161,7 +163,7 @@ void icvJacobianFunction_ProjTrifocal(const CvMat *vectX,CvMat *Jacobian) return; } -void icvFunc_ProjTrifocal(const CvMat *vectX, CvMat *resFunc) +static void icvFunc_ProjTrifocal(const CvMat *vectX, CvMat *resFunc) { /* Computes function in a given point */ /* Computers project points using 3 projection matrices and points 3D */ @@ -264,7 +266,7 @@ void icvFunc_ProjTrifocal(const CvMat *vectX, CvMat *resFunc) /*----------------------------------------------------------------------------------------*/ -void icvOptimizeProjectionTrifocal(CvMat **projMatrs,CvMat **projPoints, +static void icvOptimizeProjectionTrifocal(CvMat **projMatrs,CvMat **projPoints, CvMat **resultProjMatrs, CvMat *resultPoints4D) { @@ -312,7 +314,7 @@ void icvOptimizeProjectionTrifocal(CvMat **projMatrs,CvMat **projPoints, { CV_ERROR( CV_StsNullPtr, "Some of projPoints is a NULL pointer" ); } - + if( resultProjMatrs[i] == 0 ) { CV_ERROR( CV_StsNullPtr, "Some of resultProjMatrs is a NULL pointer" ); @@ -402,7 +404,7 @@ void icvOptimizeProjectionTrifocal(CvMat **projMatrs,CvMat **projPoints, cvmSet(vectorX0,36 + currPoint*4 + 3,0,cvmGet(points4D,3,currPoint)); } - + /* Allocate memory for result */ cvLevenbergMarquardtOptimization( icvJacobianFunction_ProjTrifocal, icvFunc_ProjTrifocal, vectorX0,observRes,optimX,100,1e-6); @@ -441,7 +443,7 @@ void icvOptimizeProjectionTrifocal(CvMat **projMatrs,CvMat **projPoints, /*------------------------------------------------------------------------------*/ /* Create good points using status information */ -void icvCreateGoodPoints(CvMat *points,CvMat **goodPoints, CvMat *status) +static void icvCreateGoodPoints(CvMat *points,CvMat **goodPoints, CvMat *status) { *goodPoints = 0; @@ -493,3 +495,4 @@ void icvCreateGoodPoints(CvMat *points,CvMat **goodPoints, CvMat *status) return; } +#endif diff --git a/modules/legacy/src/morphcontours.cpp b/modules/legacy/src/morphcontours.cpp index fbc5696..851b00b 100644 --- a/modules/legacy/src/morphcontours.cpp +++ b/modules/legacy/src/morphcontours.cpp @@ -87,7 +87,7 @@ double _cvStretchingWork(CvPoint2D32f* P1, L1 = sqrt( (double)P1->x*P1->x + P1->y*P1->y); L2 = sqrt( (double)P2->x*P2->x + P2->y*P2->y); - + L_min = MIN(L1, L2); dL = fabs( L1 - L2 ); @@ -96,15 +96,15 @@ double _cvStretchingWork(CvPoint2D32f* P1, //////////////////////////////////////////////////////////////////////////////////// +CvPoint2D32f Q( CvPoint2D32f q0, CvPoint2D32f q1, CvPoint2D32f q2, double t ); +double angle( CvPoint2D32f A, CvPoint2D32f B ); + double _cvBendingWork( CvPoint2D32f* B0, CvPoint2D32f* F0, CvPoint2D32f* B1, CvPoint2D32f* F1/*, CvPoint* K*/) { - CvPoint2D32f Q( CvPoint2D32f q0, CvPoint2D32f q1, CvPoint2D32f q2, double t ); - double angle( CvPoint2D32f A, CvPoint2D32f B ); - CvPoint2D32f Q0, Q1, Q2; CvPoint2D32f Q1_nm = { 0, 0 }, Q2_nm = { 0, 0 }; double d0, d1, d2, des, t_zero; @@ -140,7 +140,7 @@ double _cvBendingWork( CvPoint2D32f* B0, d_angle = d_angle - CV_PI*0.5; d_angle = fabs(d_angle); - + K->x = -K->x; K->y = -K->y; B1->x = -B1->x; @@ -427,7 +427,7 @@ void _cvWorkSouthEast(int i, int j, _CvWork** W, CvPoint2D32f* edges1, CvPoint2D small_edge.y = NULL_EDGE*edges1[i-2].y; w1 = W[i-1][j-1].w_east + _cvBendingWork(&edges1[i-2], - &edges1[i-1], + &edges1[i-1], /*&null_edge*/&small_edge, &edges2[j-1]/*, &edges2[j-2]*/); @@ -442,7 +442,7 @@ void _cvWorkSouthEast(int i, int j, _CvWork** W, CvPoint2D32f* edges1, CvPoint2D small_edge.y = NULL_EDGE*edges2[j-2].y; w3 = W[i-1][j-1].w_south + _cvBendingWork( /*&null_edge*/&small_edge, - &edges1[i-1], + &edges1[i-1], &edges2[j-2], &edges2[j-1]/*, &edges1[i-2]*/); @@ -511,6 +511,7 @@ void _cvWorkSouth(int i, int j, _CvWork** W, CvPoint2D32f* edges1, CvPoint2D32f* } } + //=================================================== CvPoint2D32f Q(CvPoint2D32f q0,CvPoint2D32f q1,CvPoint2D32f q2,double t) { @@ -519,14 +520,14 @@ CvPoint2D32f Q(CvPoint2D32f q0,CvPoint2D32f q1,CvPoint2D32f q2,double t) q.x = (float)(q0.x*(1-t)*(1-t) + 2*q1.x*t*(1-t) + q2.x*t*t); q.y = (float)(q0.y*(1-t)*(1-t) + 2*q1.y*t*(1-t) + q2.y*t*t); - return q; + return q; } double angle(CvPoint2D32f A, CvPoint2D32f B) { return acos( (A.x*B.x + A.y*B.y)/sqrt( (double)(A.x*A.x + A.y*A.y)*(B.x*B.x + B.y*B.y) ) ); } - +#if 0 /***************************************************************************************\ * * This function compute intermediate polygon between contour1 and contour2 @@ -536,14 +537,14 @@ double angle(CvPoint2D32f A, CvPoint2D32f B) * param = [0,1]; 0 correspondence to contour1, 1 - contour2 * \***************************************************************************************/ -CvSeq* icvBlendContours(CvSeq* contour1, +static CvSeq* icvBlendContours(CvSeq* contour1, CvSeq* contour2, CvSeq* corr, double param, CvMemStorage* storage) { int j; - + CvSeqWriter writer01; CvSeqReader reader01; @@ -558,7 +559,7 @@ CvSeq* icvBlendContours(CvSeq* contour1, int corr_point; // Create output sequence. - CvSeq* output = cvCreateSeq(0, + CvSeq* output = cvCreateSeq(0, sizeof(CvSeq), sizeof(CvPoint), storage ); @@ -570,7 +571,7 @@ CvSeq* icvBlendContours(CvSeq* contour1, point1 = (CvPoint* )malloc( Ni*sizeof(CvPoint) ); point2 = (CvPoint* )malloc( Nj*sizeof(CvPoint) ); - // Initialize arrays of point + // Initialize arrays of point cvCvtSeqToArray( contour1, point1, CV_WHOLE_SEQ ); cvCvtSeqToArray( contour2, point2, CV_WHOLE_SEQ ); @@ -583,7 +584,7 @@ CvSeq* icvBlendContours(CvSeq* contour1, i = Ni-1; //correspondence to points of contour1 for( ; corr; corr = corr->h_next ) - { + { //Initializes process of sequential reading from sequence cvStartReadSeq( corr, &reader01, 0 ); @@ -595,7 +596,7 @@ CvSeq* icvBlendContours(CvSeq* contour1, // Compute point of intermediate polygon. point_output.x = cvRound(point1[i].x + param*( point2[corr_point].x - point1[i].x )); point_output.y = cvRound(point1[i].y + param*( point2[corr_point].y - point1[i].y )); - + // Write element to sequence. CV_WRITE_SEQ_ELEM( point_output, writer01 ); } @@ -603,7 +604,7 @@ CvSeq* icvBlendContours(CvSeq* contour1, } // Updates sequence header. cvFlushSeqWriter( &writer01 ); - + return output; } @@ -621,9 +622,9 @@ CvSeq* icvBlendContours(CvSeq* contour1, **************************************************************************************************/ -void icvCalcContoursCorrespondence(CvSeq* contour1, - CvSeq* contour2, - CvSeq** corr, +static void icvCalcContoursCorrespondence(CvSeq* contour1, + CvSeq* contour2, + CvSeq** corr, CvMemStorage* storage) { int i,j; // counter of cycles @@ -660,7 +661,7 @@ void icvCalcContoursCorrespondence(CvSeq* contour1, edges1 = (CvPoint2D32f* )malloc( (Ni-1)*sizeof(CvPoint2D32f) ); edges2 = (CvPoint2D32f* )malloc( (Nj-1)*sizeof(CvPoint2D32f) ); - // Initialize arrays of point + // Initialize arrays of point cvCvtSeqToArray( contour1, point1, CV_WHOLE_SEQ ); cvCvtSeqToArray( contour2, point2, CV_WHOLE_SEQ ); @@ -679,7 +680,7 @@ void icvCalcContoursCorrespondence(CvSeq* contour1, edges2[i].y = (float)( point2[i+1].y - point2[i].y ); }; - // Find infinity constant + // Find infinity constant //inf=1; ///////////// @@ -716,11 +717,11 @@ void icvCalcContoursCorrespondence(CvSeq* contour1, { j=0;///////// W[i][j].w_east = W[i-1][j].w_east; - W[i][j].w_east = W[i][j].w_east /*+ + W[i][j].w_east = W[i][j].w_east /*+ _cvBendingWork( &edges1[i-2], &edges1[i-1], &null_edge, &null_edge, NULL )*/; W[i][j].w_east = W[i][j].w_east + _cvStretchingWork( &edges2[i-1], &null_edge ); W[i][j].path_e = PATH_TO_E; - + j=1;////////// W[i][j].w_south = inf; @@ -732,18 +733,18 @@ void icvCalcContoursCorrespondence(CvSeq* contour1, small_edge.x = NULL_EDGE*edges1[i-2].x; small_edge.y = NULL_EDGE*edges1[i-2].y; - W[i][j].w_southeast = W[i][j].w_southeast + + W[i][j].w_southeast = W[i][j].w_southeast + _cvBendingWork( &edges1[i-2], &edges1[i-1], /*&null_edge*/&small_edge, &edges2[j-1]/*, &edges2[Nj-2]*/); W[i][j].path_se = PATH_TO_E; } for(j=2; jh_next = cvCreateSeq( 0, - sizeof(CvSeq), + corr01->h_next = cvCreateSeq( 0, + sizeof(CvSeq), sizeof(int), storage ); corr01 = corr01->h_next; cvStartAppendToSeq( corr01, &writer ); break; - + case PATH_TO_SE: path = W[i][j].path_se; j--; i--; cvFlushSeqWriter( &writer ); - corr01->h_next = cvCreateSeq( 0, - sizeof(CvSeq), + corr01->h_next = cvCreateSeq( 0, + sizeof(CvSeq), sizeof(int), storage ); corr01 = corr01->h_next; @@ -852,4 +853,4 @@ void icvCalcContoursCorrespondence(CvSeq* contour1, free(edges1); free(edges2); } - +#endif diff --git a/modules/legacy/src/oneway.cpp b/modules/legacy/src/oneway.cpp index 737cecf..3e5b445 100644 --- a/modules/legacy/src/oneway.cpp +++ b/modules/legacy/src/oneway.cpp @@ -12,7 +12,7 @@ #include namespace cv{ - + inline int round(float value) { if(value > 0) @@ -24,15 +24,15 @@ namespace cv{ return int(value - 0.5f); } } - + inline CvRect resize_rect(CvRect rect, float alpha) { return cvRect(rect.x + round((float)(0.5*(1 - alpha)*rect.width)), rect.y + round((float)(0.5*(1 - alpha)*rect.height)), round(rect.width*alpha), round(rect.height*alpha)); } - + CvMat* ConvertImageToMatrix(IplImage* patch); - + class CvCameraPose { public: @@ -41,104 +41,104 @@ namespace cv{ m_rotation = cvCreateMat(1, 3, CV_32FC1); m_translation = cvCreateMat(1, 3, CV_32FC1); }; - + ~CvCameraPose() { cvReleaseMat(&m_rotation); cvReleaseMat(&m_translation); }; - + void SetPose(CvMat* rotation, CvMat* translation) { cvCopy(rotation, m_rotation); cvCopy(translation, m_translation); }; - + CvMat* GetRotation() {return m_rotation;}; CvMat* GetTranslation() {return m_translation;}; - + protected: CvMat* m_rotation; CvMat* m_translation; }; - + // AffineTransformPatch: generates an affine transformed image patch. // - src: source image (roi is supported) // - dst: output image. ROI of dst image should be 2 times smaller than ROI of src. // - pose: parameters of an affine transformation void AffineTransformPatch(IplImage* src, IplImage* dst, CvAffinePose pose); - + // GenerateAffineTransformFromPose: generates an affine transformation matrix from CvAffinePose instance // - size: the size of image patch // - pose: affine transformation // - transform: 2x3 transformation matrix void GenerateAffineTransformFromPose(CvSize size, CvAffinePose pose, CvMat* transform); - + // Generates a random affine pose CvAffinePose GenRandomAffinePose(); - - + + const static int num_mean_components = 500; const static float noise_intensity = 0.15f; - - + + static inline CvPoint rect_center(CvRect rect) { return cvPoint(rect.x + rect.width/2, rect.y + rect.height/2); } - - void homography_transform(IplImage* frontal, IplImage* result, CvMat* homography) - { - cvWarpPerspective(frontal, result, homography); - } - - CvAffinePose perturbate_pose(CvAffinePose pose, float noise) + + // static void homography_transform(IplImage* frontal, IplImage* result, CvMat* homography) + // { + // cvWarpPerspective(frontal, result, homography); + // } + + static CvAffinePose perturbate_pose(CvAffinePose pose, float noise) { // perturbate the matrix float noise_mult_factor = 1 + (0.5f - float(rand())/RAND_MAX)*noise; float noise_add_factor = noise_mult_factor - 1; - + CvAffinePose pose_pert = pose; pose_pert.phi += noise_add_factor; pose_pert.theta += noise_mult_factor; pose_pert.lambda1 *= noise_mult_factor; pose_pert.lambda2 *= noise_mult_factor; - + return pose_pert; } - - void generate_mean_patch(IplImage* frontal, IplImage* result, CvAffinePose pose, int pose_count, float noise) + + static void generate_mean_patch(IplImage* frontal, IplImage* result, CvAffinePose pose, int pose_count, float noise) { IplImage* sum = cvCreateImage(cvSize(result->width, result->height), IPL_DEPTH_32F, 1); IplImage* workspace = cvCloneImage(result); IplImage* workspace_float = cvCloneImage(sum); - + cvSetZero(sum); for(int i = 0; i < pose_count; i++) { CvAffinePose pose_pert = perturbate_pose(pose, noise); - + AffineTransformPatch(frontal, workspace, pose_pert); cvConvertScale(workspace, workspace_float); cvAdd(sum, workspace_float, sum); } - + cvConvertScale(sum, result, 1.0f/pose_count); - + cvReleaseImage(&workspace); cvReleaseImage(&sum); cvReleaseImage(&workspace_float); } - - void generate_mean_patch_fast(IplImage* /*frontal*/, IplImage* /*result*/, CvAffinePose /*pose*/, - CvMat* /*pca_hr_avg*/, CvMat* /*pca_hr_eigenvectors*/, const OneWayDescriptor* /*pca_descriptors*/) - { - /*for(int i = 0; i < pca_hr_eigenvectors->cols; i++) - { - - }*/ - } - + + // static void generate_mean_patch_fast(IplImage* /*frontal*/, IplImage* /*result*/, CvAffinePose /*pose*/, + // CvMat* /*pca_hr_avg*/, CvMat* /*pca_hr_eigenvectors*/, const OneWayDescriptor* /*pca_descriptors*/) + // { + // /*for(int i = 0; i < pca_hr_eigenvectors->cols; i++) + // { + + // }*/ + // } + void readPCAFeatures(const char *filename, CvMat** avg, CvMat** eigenvectors, const char *postfix = ""); void readPCAFeatures(const FileNode &fn, CvMat** avg, CvMat** eigenvectors, const char* postfix = ""); void savePCAFeatures(FileStorage &fs, const char* postfix, CvMat* avg, CvMat* eigenvectors); @@ -147,35 +147,35 @@ namespace cv{ void loadPCAFeatures(const char* path, const char* images_list, vector& patches, CvSize patch_size); void generatePCAFeatures(const char* path, const char* img_filename, FileStorage& fs, const char* postfix, CvSize patch_size, CvMat** avg, CvMat** eigenvectors); - + void eigenvector2image(CvMat* eigenvector, IplImage* img); void FindOneWayDescriptor(int desc_count, const OneWayDescriptor* descriptors, IplImage* patch, int& desc_idx, int& pose_idx, float& distance, CvMat* avg = 0, CvMat* eigenvalues = 0); - + void FindOneWayDescriptor(int desc_count, const OneWayDescriptor* descriptors, IplImage* patch, int n, std::vector& desc_idxs, std::vector& pose_idxs, std::vector& distances, CvMat* avg = 0, CvMat* eigenvalues = 0); - + void FindOneWayDescriptor(cv::flann::Index* m_pca_descriptors_tree, CvSize patch_size, int m_pca_dim_low, int m_pose_count, IplImage* patch, int& desc_idx, int& pose_idx, float& distance, CvMat* avg = 0, CvMat* eigenvalues = 0); - + void FindOneWayDescriptorEx(int desc_count, const OneWayDescriptor* descriptors, IplImage* patch, float scale_min, float scale_max, float scale_step, int& desc_idx, int& pose_idx, float& distance, float& scale, CvMat* avg, CvMat* eigenvectors); - + void FindOneWayDescriptorEx(int desc_count, const OneWayDescriptor* descriptors, IplImage* patch, float scale_min, float scale_max, float scale_step, int n, std::vector& desc_idxs, std::vector& pose_idxs, std::vector& distances, std::vector& scales, CvMat* avg, CvMat* eigenvectors); - + void FindOneWayDescriptorEx(cv::flann::Index* m_pca_descriptors_tree, CvSize patch_size, int m_pca_dim_low, int m_pose_count, IplImage* patch, float scale_min, float scale_max, float scale_step, int& desc_idx, int& pose_idx, float& distance, float& scale, CvMat* avg, CvMat* eigenvectors); - + inline CvRect fit_rect_roi_fixedsize(CvRect rect, CvRect roi) { CvRect fit = rect; @@ -185,13 +185,13 @@ namespace cv{ fit.y = MIN(fit.y, roi.y + roi.height - fit.height - 1); return(fit); } - + inline CvRect fit_rect_fixedsize(CvRect rect, IplImage* img) { CvRect roi = cvGetImageROI(img); return fit_rect_roi_fixedsize(rect, roi); } - + OneWayDescriptor::OneWayDescriptor() { m_pose_count = 0; @@ -204,7 +204,7 @@ namespace cv{ m_pca_dim_low = 100; m_pca_dim_high = 100; } - + OneWayDescriptor::~OneWayDescriptor() { if(m_pose_count) @@ -218,50 +218,50 @@ namespace cv{ cvReleaseImage(&m_train_patch); delete []m_samples; delete []m_pca_coeffs; - + if(!m_transforms) { delete []m_affine_poses; } } } - + void OneWayDescriptor::Allocate(int pose_count, CvSize size, int nChannels) { m_pose_count = pose_count; m_samples = new IplImage* [m_pose_count]; m_pca_coeffs = new CvMat* [m_pose_count]; m_patch_size = cvSize(size.width/2, size.height/2); - + if(!m_transforms) { m_affine_poses = new CvAffinePose[m_pose_count]; } - + int length = m_pca_dim_low;//roi.width*roi.height; for(int i = 0; i < m_pose_count; i++) { m_samples[i] = cvCreateImage(cvSize(size.width/2, size.height/2), IPL_DEPTH_32F, nChannels); m_pca_coeffs[i] = cvCreateMat(1, length, CV_32FC1); } - + m_input_patch = cvCreateImage(GetPatchSize(), IPL_DEPTH_8U, 1); m_train_patch = cvCreateImage(GetInputPatchSize(), IPL_DEPTH_8U, 1); } - - void cvmSet2DPoint(CvMat* matrix, int row, int col, CvPoint2D32f point) - { - cvmSet(matrix, row, col, point.x); - cvmSet(matrix, row, col + 1, point.y); - } - - void cvmSet3DPoint(CvMat* matrix, int row, int col, CvPoint3D32f point) - { - cvmSet(matrix, row, col, point.x); - cvmSet(matrix, row, col + 1, point.y); - cvmSet(matrix, row, col + 2, point.z); - } - + + // static void cvmSet2DPoint(CvMat* matrix, int row, int col, CvPoint2D32f point) + // { + // cvmSet(matrix, row, col, point.x); + // cvmSet(matrix, row, col + 1, point.y); + // } + + // static void cvmSet3DPoint(CvMat* matrix, int row, int col, CvPoint3D32f point) + // { + // cvmSet(matrix, row, col, point.x); + // cvmSet(matrix, row, col + 1, point.y); + // cvmSet(matrix, row, col + 2, point.z); + // } + CvAffinePose GenRandomAffinePose() { const float scale_min = 0.8f; @@ -271,10 +271,10 @@ namespace cv{ pose.phi = float(rand())/RAND_MAX*360; pose.lambda1 = scale_min + float(rand())/RAND_MAX*(scale_max - scale_min); pose.lambda2 = scale_min + float(rand())/RAND_MAX*(scale_max - scale_min); - + return pose; } - + void GenerateAffineTransformFromPose(CvSize size, CvAffinePose pose, CvMat* transform) { CvMat* temp = cvCreateMat(3, 3, CV_32FC1); @@ -282,13 +282,13 @@ namespace cv{ cvmSet(temp, 2, 0, 0.0f); cvmSet(temp, 2, 1, 0.0f); cvmSet(temp, 2, 2, 1.0f); - + CvMat rotation; cvGetSubRect(temp, &rotation, cvRect(0, 0, 3, 2)); - + cv2DRotationMatrix(cvPoint2D32f(size.width/2, size.height/2), pose.phi, 1.0, &rotation); cvCopy(temp, final); - + cvmSet(temp, 0, 0, pose.lambda1); cvmSet(temp, 0, 1, 0.0f); cvmSet(temp, 1, 0, 0.0f); @@ -296,53 +296,53 @@ namespace cv{ cvmSet(temp, 0, 2, size.width/2*(1 - pose.lambda1)); cvmSet(temp, 1, 2, size.height/2*(1 - pose.lambda2)); cvMatMul(temp, final, final); - + cv2DRotationMatrix(cvPoint2D32f(size.width/2, size.height/2), pose.theta - pose.phi, 1.0, &rotation); cvMatMul(temp, final, final); - + cvGetSubRect(final, &rotation, cvRect(0, 0, 3, 2)); cvCopy(&rotation, transform); - + cvReleaseMat(&temp); cvReleaseMat(&final); } - + void AffineTransformPatch(IplImage* src, IplImage* dst, CvAffinePose pose) { CvRect src_large_roi = cvGetImageROI(src); - + IplImage* temp = cvCreateImage(cvSize(src_large_roi.width, src_large_roi.height), IPL_DEPTH_32F, src->nChannels); cvSetZero(temp); IplImage* temp2 = cvCloneImage(temp); CvMat* rotation_phi = cvCreateMat(2, 3, CV_32FC1); - + CvSize new_size = cvSize(cvRound(temp->width*pose.lambda1), cvRound(temp->height*pose.lambda2)); IplImage* temp3 = cvCreateImage(new_size, IPL_DEPTH_32F, src->nChannels); - + cvConvertScale(src, temp); cvResetImageROI(temp); - - + + cv2DRotationMatrix(cvPoint2D32f(temp->width/2, temp->height/2), pose.phi, 1.0, rotation_phi); cvWarpAffine(temp, temp2, rotation_phi); - + cvSetZero(temp); - + cvResize(temp2, temp3); - + cv2DRotationMatrix(cvPoint2D32f(temp3->width/2, temp3->height/2), pose.theta - pose.phi, 1.0, rotation_phi); cvWarpAffine(temp3, temp, rotation_phi); - + cvSetImageROI(temp, cvRect(temp->width/2 - src_large_roi.width/4, temp->height/2 - src_large_roi.height/4, src_large_roi.width/2, src_large_roi.height/2)); cvConvertScale(temp, dst); cvReleaseMat(&rotation_phi); - + cvReleaseImage(&temp3); cvReleaseImage(&temp2); cvReleaseImage(&temp); } - + void OneWayDescriptor::GenerateSamples(int pose_count, IplImage* frontal, int norm) { /* if(m_transforms) @@ -361,7 +361,7 @@ namespace cv{ } //AffineTransformPatch(frontal, patch_8u, m_affine_poses[i]); generate_mean_patch(frontal, patch_8u, m_affine_poses[i], num_mean_components, noise_intensity); - + double scale = 1.0f; if(norm) { @@ -369,7 +369,7 @@ namespace cv{ scale = 1/sum; } cvConvertScale(patch_8u, m_samples[i], scale); - + #if 0 double maxval; cvMinMaxLoc(m_samples[i], 0, &maxval); @@ -382,7 +382,7 @@ namespace cv{ } cvReleaseImage(&patch_8u); } - + void OneWayDescriptor::GenerateSamplesFast(IplImage* frontal, CvMat* pca_hr_avg, CvMat* pca_hr_eigenvectors, OneWayDescriptor* pca_descriptors) { @@ -392,12 +392,12 @@ namespace cv{ cvResize(frontal, m_train_patch); frontal = m_train_patch; } - + CvMat* pca_coeffs = cvCreateMat(1, pca_hr_eigenvectors->cols, CV_32FC1); double maxval; cvMinMaxLoc(frontal, 0, &maxval); CvMat* frontal_data = ConvertImageToMatrix(frontal); - + double sum = cvSum(frontal_data).val[0]; cvConvertScale(frontal_data, frontal_data, 1.0f/sum); cvProjectPCA(frontal_data, pca_hr_avg, pca_hr_eigenvectors, pca_coeffs); @@ -409,7 +409,7 @@ namespace cv{ double coeff = cvmGet(pca_coeffs, 0, j); IplImage* patch = pca_descriptors[j + 1].GetPatch(i); cvAddWeighted(m_samples[i], 1.0, patch, coeff, 0, m_samples[i]); - + #if 0 printf("coeff%d = %f\n", j, coeff); IplImage* test = cvCreateImage(cvSize(12, 12), IPL_DEPTH_8U, 1); @@ -421,11 +421,11 @@ namespace cv{ cvWaitKey(0); #endif } - + cvAdd(pca_descriptors[0].GetPatch(i), m_samples[i], m_samples[i]); - double sum = cvSum(m_samples[i]).val[0]; - cvConvertScale(m_samples[i], m_samples[i], 1.0/sum); - + double sm = cvSum(m_samples[i]).val[0]; + cvConvertScale(m_samples[i], m_samples[i], 1.0/sm); + #if 0 IplImage* test = cvCreateImage(cvSize(12, 12), IPL_DEPTH_8U, 1); /* IplImage* temp1 = cvCreateImage(cvSize(12, 12), IPL_DEPTH_32F, 1); @@ -436,7 +436,7 @@ namespace cv{ cvConvertScale(temp1, test, 255.0/maxval);*/ cvMinMaxLoc(m_samples[i], 0, &maxval); cvConvertScale(m_samples[i], test, 255.0/maxval); - + cvNamedWindow("1", 1); cvShowImage("1", frontal); cvNamedWindow("2", 1); @@ -444,33 +444,33 @@ namespace cv{ cvWaitKey(0); #endif } - + cvReleaseMat(&pca_coeffs); cvReleaseMat(&frontal_data); } - + void OneWayDescriptor::SetTransforms(CvAffinePose* poses, CvMat** transforms) { if(m_affine_poses) { delete []m_affine_poses; } - + m_affine_poses = poses; m_transforms = transforms; } - + void OneWayDescriptor::Initialize(int pose_count, IplImage* frontal, const char* feature_name, int norm) { m_feature_name = std::string(feature_name); CvRect roi = cvGetImageROI(frontal); m_center = rect_center(roi); - + Allocate(pose_count, cvSize(roi.width, roi.height), frontal->nChannels); - + GenerateSamples(pose_count, frontal, norm); } - + void OneWayDescriptor::InitializeFast(int pose_count, IplImage* frontal, const char* feature_name, CvMat* pca_hr_avg, CvMat* pca_hr_eigenvectors, OneWayDescriptor* pca_descriptors) { @@ -482,12 +482,12 @@ namespace cv{ m_feature_name = std::string(feature_name); CvRect roi = cvGetImageROI(frontal); m_center = rect_center(roi); - + Allocate(pose_count, cvSize(roi.width, roi.height), frontal->nChannels); - + GenerateSamplesFast(frontal, pca_hr_avg, pca_hr_eigenvectors, pca_descriptors); } - + void OneWayDescriptor::InitializePCACoeffs(CvMat* avg, CvMat* eigenvectors) { for(int i = 0; i < m_pose_count; i++) @@ -495,7 +495,7 @@ namespace cv{ ProjectPCASample(m_samples[i], avg, eigenvectors, m_pca_coeffs[i]); } } - + void OneWayDescriptor::ProjectPCASample(IplImage* patch, CvMat* avg, CvMat* eigenvectors, CvMat* pca_coeffs) const { CvMat* patch_mat = ConvertImageToMatrix(patch); @@ -506,11 +506,11 @@ namespace cv{ CvMat temp1; cvGetSubRect(temp, &temp1, cvRect(0, 0, pca_coeffs->cols, 1)); cvCopy(&temp1, pca_coeffs); - + cvReleaseMat(&temp); cvReleaseMat(&patch_mat); } - + void OneWayDescriptor::EstimatePosePCA(CvArr* patch, int& pose_idx, float& distance, CvMat* avg, CvMat* eigenvectors) const { if(avg == 0) @@ -522,7 +522,7 @@ namespace cv{ } else { - + } return; } @@ -537,9 +537,9 @@ namespace cv{ roi = cvGetImageROI((IplImage*)patch); } } - + CvMat* pca_coeffs = cvCreateMat(1, m_pca_dim_low, CV_32FC1); - + if (CV_IS_MAT(patch)) { cvCopy((CvMat*)patch, pca_coeffs); @@ -552,41 +552,41 @@ namespace cv{ ProjectPCASample(patch_32f, avg, eigenvectors, pca_coeffs); cvReleaseImage(&patch_32f); } - - + + distance = 1e10; pose_idx = -1; - + for(int i = 0; i < m_pose_count; i++) { double dist = cvNorm(m_pca_coeffs[i], pca_coeffs); - // float dist = 0; - // float data1, data2; - // //CvMat* pose_pca_coeffs = m_pca_coeffs[i]; - // for (int x=0; x < pca_coeffs->width; x++) - // for (int y =0 ; y < pca_coeffs->height; y++) - // { - // data1 = ((float*)(pca_coeffs->data.ptr + pca_coeffs->step*x))[y]; - // data2 = ((float*)(m_pca_coeffs[i]->data.ptr + m_pca_coeffs[i]->step*x))[y]; - // dist+=(data1-data2)*(data1-data2); - // } + // float dist = 0; + // float data1, data2; + // //CvMat* pose_pca_coeffs = m_pca_coeffs[i]; + // for (int x=0; x < pca_coeffs->width; x++) + // for (int y =0 ; y < pca_coeffs->height; y++) + // { + // data1 = ((float*)(pca_coeffs->data.ptr + pca_coeffs->step*x))[y]; + // data2 = ((float*)(m_pca_coeffs[i]->data.ptr + m_pca_coeffs[i]->step*x))[y]; + // dist+=(data1-data2)*(data1-data2); + // } ////#if 1 - // for (int j = 0; j < m_pca_dim_low; j++) - // { - // dist += (pose_pca_coeffs->data.fl[j]- pca_coeffs->data.fl[j])*(pose_pca_coeffs->data.fl[j]- pca_coeffs->data.fl[j]); - // } + // for (int j = 0; j < m_pca_dim_low; j++) + // { + // dist += (pose_pca_coeffs->data.fl[j]- pca_coeffs->data.fl[j])*(pose_pca_coeffs->data.fl[j]- pca_coeffs->data.fl[j]); + // } //#else - // for (int j = 0; j <= m_pca_dim_low - 4; j += 4) - // { - // dist += (pose_pca_coeffs->data.fl[j]- pca_coeffs->data.fl[j])* - // (pose_pca_coeffs->data.fl[j]- pca_coeffs->data.fl[j]); - // dist += (pose_pca_coeffs->data.fl[j+1]- pca_coeffs->data.fl[j+1])* - // (pose_pca_coeffs->data.fl[j+1]- pca_coeffs->data.fl[j+1]); - // dist += (pose_pca_coeffs->data.fl[j+2]- pca_coeffs->data.fl[j+2])* - // (pose_pca_coeffs->data.fl[j+2]- pca_coeffs->data.fl[j+2]); - // dist += (pose_pca_coeffs->data.fl[j+3]- pca_coeffs->data.fl[j+3])* - // (pose_pca_coeffs->data.fl[j+3]- pca_coeffs->data.fl[j+3]); - // } + // for (int j = 0; j <= m_pca_dim_low - 4; j += 4) + // { + // dist += (pose_pca_coeffs->data.fl[j]- pca_coeffs->data.fl[j])* + // (pose_pca_coeffs->data.fl[j]- pca_coeffs->data.fl[j]); + // dist += (pose_pca_coeffs->data.fl[j+1]- pca_coeffs->data.fl[j+1])* + // (pose_pca_coeffs->data.fl[j+1]- pca_coeffs->data.fl[j+1]); + // dist += (pose_pca_coeffs->data.fl[j+2]- pca_coeffs->data.fl[j+2])* + // (pose_pca_coeffs->data.fl[j+2]- pca_coeffs->data.fl[j+2]); + // dist += (pose_pca_coeffs->data.fl[j+3]- pca_coeffs->data.fl[j+3])* + // (pose_pca_coeffs->data.fl[j+3]- pca_coeffs->data.fl[j+3]); + // } //#endif if(dist < distance) { @@ -594,20 +594,20 @@ namespace cv{ pose_idx = i; } } - + cvReleaseMat(&pca_coeffs); } - + void OneWayDescriptor::EstimatePose(IplImage* patch, int& pose_idx, float& distance) const { distance = 1e10; pose_idx = -1; - + CvRect roi = cvGetImageROI(patch); IplImage* patch_32f = cvCreateImage(cvSize(roi.width, roi.height), IPL_DEPTH_32F, patch->nChannels); double sum = cvSum(patch).val[0]; cvConvertScale(patch, patch_32f, 1/sum); - + for(int i = 0; i < m_pose_count; i++) { if(m_samples[i]->width != patch_32f->width || m_samples[i]->height != patch_32f->height) @@ -617,21 +617,21 @@ namespace cv{ double dist = cvNorm(m_samples[i], patch_32f); //float dist = 0.0f; //float i1,i2; - + //for (int y = 0; yheight; y++) - // for (int x = 0; x< patch_32f->width; x++) - // { - // i1 = ((float*)(m_samples[i]->imageData + m_samples[i]->widthStep*y))[x]; - // i2 = ((float*)(patch_32f->imageData + patch_32f->widthStep*y))[x]; - // dist+= (i1-i2)*(i1-i2); - // } - + // for (int x = 0; x< patch_32f->width; x++) + // { + // i1 = ((float*)(m_samples[i]->imageData + m_samples[i]->widthStep*y))[x]; + // i2 = ((float*)(patch_32f->imageData + patch_32f->widthStep*y))[x]; + // dist+= (i1-i2)*(i1-i2); + // } + if(dist < distance) { distance = (float)dist; pose_idx = i; } - + #if 0 IplImage* img1 = cvCreateImage(cvSize(roi.width, roi.height), IPL_DEPTH_8U, 1); IplImage* img2 = cvCreateImage(cvSize(roi.width, roi.height), IPL_DEPTH_8U, 1); @@ -640,7 +640,7 @@ namespace cv{ cvConvertScale(m_samples[i], img1, 255.0/maxval); cvMinMaxLoc(patch_32f, 0, &maxval); cvConvertScale(patch_32f, img2, 255.0/maxval); - + cvNamedWindow("1", 1); cvShowImage("1", img1); cvNamedWindow("2", 1); @@ -649,10 +649,10 @@ namespace cv{ cvWaitKey(0); #endif } - + cvReleaseImage(&patch_32f); } - + void OneWayDescriptor::Save(const char* path) { for(int i = 0; i < m_pose_count; i++) @@ -660,21 +660,21 @@ namespace cv{ char buf[1024]; sprintf(buf, "%s/patch_%04d.jpg", path, i); IplImage* patch = cvCreateImage(cvSize(m_samples[i]->width, m_samples[i]->height), IPL_DEPTH_8U, m_samples[i]->nChannels); - + double maxval; cvMinMaxLoc(m_samples[i], 0, &maxval); cvConvertScale(m_samples[i], patch, 255/maxval); - + cvSaveImage(buf, patch); - + cvReleaseImage(&patch); } } - + void OneWayDescriptor::Write(CvFileStorage* fs, const char* name) { CvMat* mat = cvCreateMat(m_pose_count, m_samples[0]->width*m_samples[0]->height, CV_32FC1); - + // prepare data to write as a single matrix for(int i = 0; i < m_pose_count; i++) { @@ -687,12 +687,12 @@ namespace cv{ } } } - + cvWrite(fs, name, mat); - + cvReleaseMat(&mat); } - + int OneWayDescriptor::ReadByName(const FileNode &parent, const char* name) { CvMat* mat = reinterpret_cast (parent[name].readObj ()); @@ -700,8 +700,8 @@ namespace cv{ { return 0; } - - + + for(int i = 0; i < m_pose_count; i++) { for(int y = 0; y < m_samples[i]->height; y++) @@ -713,7 +713,7 @@ namespace cv{ } } } - + cvReleaseMat(&mat); return 1; } @@ -722,17 +722,17 @@ namespace cv{ { return ReadByName (FileNode (fs, parent), name); } - + IplImage* OneWayDescriptor::GetPatch(int index) { return m_samples[index]; } - + CvAffinePose OneWayDescriptor::GetPose(int index) const { return m_affine_poses[index]; } - + void FindOneWayDescriptor(int desc_count, const OneWayDescriptor* descriptors, IplImage* patch, int& desc_idx, int& pose_idx, float& distance, CvMat* avg, CvMat* eigenvectors) { @@ -751,7 +751,7 @@ namespace cv{ IplImage* test_img = cvCreateImage(cvSize(patch_width,patch_height), IPL_DEPTH_8U, 1); if(_roi.width != patch_width|| _roi.height != patch_height) { - + cvResize(patch, test_img); _roi = cvGetImageROI(test_img); } @@ -762,7 +762,7 @@ namespace cv{ IplImage* patch_32f = cvCreateImage(cvSize(_roi.width, _roi.height), IPL_DEPTH_32F, 1); double sum = cvSum(test_img).val[0]; cvConvertScale(test_img, patch_32f, 1.0f/sum); - + //ProjectPCASample(patch_32f, avg, eigenvectors, pca_coeffs); //Projecting PCA CvMat* patch_mat = ConvertImageToMatrix(patch_32f); @@ -774,20 +774,20 @@ namespace cv{ cvReleaseMat(&temp); cvReleaseMat(&patch_mat); //End of projecting - + cvReleaseImage(&patch_32f); cvReleaseImage(&test_img); } - + //-------- - - - + + + for(int i = 0; i < desc_count; i++) { int _pose_idx = -1; float _distance = 0; - + #if 0 descriptors[i].EstimatePose(patch, _pose_idx, _distance); #else @@ -800,7 +800,7 @@ namespace cv{ descriptors[i].EstimatePosePCA(pca_coeffs, _pose_idx, _distance, avg, eigenvectors); } #endif - + if(_distance < distance) { desc_idx = i; @@ -810,9 +810,9 @@ namespace cv{ } cvReleaseMat(&pca_coeffs); } - + #if defined(_KDTREE) - + void FindOneWayDescriptor(cv::flann::Index* m_pca_descriptors_tree, CvSize patch_size, int m_pca_dim_low, int m_pose_count, IplImage* patch, int& desc_idx, int& pose_idx, float& distance, CvMat* avg, CvMat* eigenvectors) { @@ -826,77 +826,77 @@ namespace cv{ int patch_height = patch_size.height; //if (avg) //{ - CvRect _roi = cvGetImageROI((IplImage*)patch); - IplImage* test_img = cvCreateImage(cvSize(patch_width,patch_height), IPL_DEPTH_8U, 1); - if(_roi.width != patch_width|| _roi.height != patch_height) - { - - cvResize(patch, test_img); - _roi = cvGetImageROI(test_img); - } - else - { - cvCopy(patch,test_img); - } - IplImage* patch_32f = cvCreateImage(cvSize(_roi.width, _roi.height), IPL_DEPTH_32F, 1); - float sum = cvSum(test_img).val[0]; - cvConvertScale(test_img, patch_32f, 1.0f/sum); - - //ProjectPCASample(patch_32f, avg, eigenvectors, pca_coeffs); - //Projecting PCA - CvMat* patch_mat = ConvertImageToMatrix(patch_32f); - CvMat* temp = cvCreateMat(1, eigenvectors->cols, CV_32FC1); - cvProjectPCA(patch_mat, avg, eigenvectors, temp); - CvMat temp1; - cvGetSubRect(temp, &temp1, cvRect(0, 0, pca_coeffs->cols, 1)); - cvCopy(&temp1, pca_coeffs); - cvReleaseMat(&temp); - cvReleaseMat(&patch_mat); - //End of projecting - - cvReleaseImage(&patch_32f); - cvReleaseImage(&test_img); - // } - + CvRect _roi = cvGetImageROI((IplImage*)patch); + IplImage* test_img = cvCreateImage(cvSize(patch_width,patch_height), IPL_DEPTH_8U, 1); + if(_roi.width != patch_width|| _roi.height != patch_height) + { + + cvResize(patch, test_img); + _roi = cvGetImageROI(test_img); + } + else + { + cvCopy(patch,test_img); + } + IplImage* patch_32f = cvCreateImage(cvSize(_roi.width, _roi.height), IPL_DEPTH_32F, 1); + float sum = cvSum(test_img).val[0]; + cvConvertScale(test_img, patch_32f, 1.0f/sum); + + //ProjectPCASample(patch_32f, avg, eigenvectors, pca_coeffs); + //Projecting PCA + CvMat* patch_mat = ConvertImageToMatrix(patch_32f); + CvMat* temp = cvCreateMat(1, eigenvectors->cols, CV_32FC1); + cvProjectPCA(patch_mat, avg, eigenvectors, temp); + CvMat temp1; + cvGetSubRect(temp, &temp1, cvRect(0, 0, pca_coeffs->cols, 1)); + cvCopy(&temp1, pca_coeffs); + cvReleaseMat(&temp); + cvReleaseMat(&patch_mat); + //End of projecting + + cvReleaseImage(&patch_32f); + cvReleaseImage(&test_img); + // } + //-------- - - //float* target = new float[m_pca_dim_low]; - //::cvflann::KNNResultSet res(1,pca_coeffs->data.fl,m_pca_dim_low); - //::cvflann::SearchParams params; - //params.checks = -1; - - //int maxDepth = 1000000; - //int neighbors_count = 1; - //int* neighborsIdx = new int[neighbors_count]; - //float* distances = new float[neighbors_count]; - //if (m_pca_descriptors_tree->findNearest(pca_coeffs->data.fl,neighbors_count,maxDepth,neighborsIdx,0,distances) > 0) - //{ - // desc_idx = neighborsIdx[0] / m_pose_count; - // pose_idx = neighborsIdx[0] % m_pose_count; - // distance = distances[0]; - //} - //delete[] neighborsIdx; - //delete[] distances; - - cv::Mat m_object(1, m_pca_dim_low, CV_32F); - cv::Mat m_indices(1, 1, CV_32S); - cv::Mat m_dists(1, 1, CV_32F); - - float* object_ptr = m_object.ptr(0); - for (int i=0;idata.fl[i]; - } - - m_pca_descriptors_tree->knnSearch(m_object, m_indices, m_dists, 1, cv::flann::SearchParams(-1) ); - - desc_idx = ((int*)(m_indices.ptr(0)))[0] / m_pose_count; - pose_idx = ((int*)(m_indices.ptr(0)))[0] % m_pose_count; - distance = ((float*)(m_dists.ptr(0)))[0]; - - // delete[] target; - - + + //float* target = new float[m_pca_dim_low]; + //::cvflann::KNNResultSet res(1,pca_coeffs->data.fl,m_pca_dim_low); + //::cvflann::SearchParams params; + //params.checks = -1; + + //int maxDepth = 1000000; + //int neighbors_count = 1; + //int* neighborsIdx = new int[neighbors_count]; + //float* distances = new float[neighbors_count]; + //if (m_pca_descriptors_tree->findNearest(pca_coeffs->data.fl,neighbors_count,maxDepth,neighborsIdx,0,distances) > 0) + //{ + // desc_idx = neighborsIdx[0] / m_pose_count; + // pose_idx = neighborsIdx[0] % m_pose_count; + // distance = distances[0]; + //} + //delete[] neighborsIdx; + //delete[] distances; + + cv::Mat m_object(1, m_pca_dim_low, CV_32F); + cv::Mat m_indices(1, 1, CV_32S); + cv::Mat m_dists(1, 1, CV_32F); + + float* object_ptr = m_object.ptr(0); + for (int i=0;idata.fl[i]; + } + + m_pca_descriptors_tree->knnSearch(m_object, m_indices, m_dists, 1, cv::flann::SearchParams(-1) ); + + desc_idx = ((int*)(m_indices.ptr(0)))[0] / m_pose_count; + pose_idx = ((int*)(m_indices.ptr(0)))[0] % m_pose_count; + distance = ((float*)(m_dists.ptr(0)))[0]; + + // delete[] target; + + // for(int i = 0; i < desc_count; i++) // { // int _pose_idx = -1; @@ -905,14 +905,14 @@ namespace cv{ //#if 0 // descriptors[i].EstimatePose(patch, _pose_idx, _distance); //#else - // if (!avg) - // { - // descriptors[i].EstimatePosePCA(patch, _pose_idx, _distance, avg, eigenvectors); - // } - // else - // { - // descriptors[i].EstimatePosePCA(pca_coeffs, _pose_idx, _distance, avg, eigenvectors); - // } + // if (!avg) + // { + // descriptors[i].EstimatePosePCA(patch, _pose_idx, _distance, avg, eigenvectors); + // } + // else + // { + // descriptors[i].EstimatePosePCA(pca_coeffs, _pose_idx, _distance, avg, eigenvectors); + // } //#endif // // if(_distance < distance) @@ -948,7 +948,7 @@ namespace cv{ IplImage* test_img = cvCreateImage(cvSize(patch_width,patch_height), IPL_DEPTH_8U, 1); if(_roi.width != patch_width|| _roi.height != patch_height) { - + cvResize(patch, test_img); _roi = cvGetImageROI(test_img); } @@ -959,7 +959,7 @@ namespace cv{ IplImage* patch_32f = cvCreateImage(cvSize(_roi.width, _roi.height), IPL_DEPTH_32F, 1); double sum = cvSum(test_img).val[0]; cvConvertScale(test_img, patch_32f, 1.0f/sum); - + //ProjectPCASample(patch_32f, avg, eigenvectors, pca_coeffs); //Projecting PCA CvMat* patch_mat = ConvertImageToMatrix(patch_32f); @@ -971,19 +971,19 @@ namespace cv{ cvReleaseMat(&temp); cvReleaseMat(&patch_mat); //End of projecting - + cvReleaseImage(&patch_32f); cvReleaseImage(&test_img); } //-------- - - - + + + for(int i = 0; i < desc_count; i++) { int _pose_idx = -1; float _distance = 0; - + #if 0 descriptors[i].EstimatePose(patch, _pose_idx, _distance); #else @@ -996,7 +996,7 @@ namespace cv{ descriptors[i].EstimatePosePCA(pca_coeffs, _pose_idx, _distance, avg, eigenvectors); } #endif - + for (int j=0;j 244 && roi.y < 200) { @@ -1049,7 +1049,7 @@ namespace cv{ cvWaitKey(0); } #endif - + FindOneWayDescriptor(desc_count, descriptors, input_patch, _desc_idx, _pose_idx, _distance, avg, eigenvectors); if(_distance < distance) { @@ -1059,13 +1059,13 @@ namespace cv{ scale = cur_scale; } } - - + + cvSetImageROI((IplImage*)patch, roi); cvReleaseImage(&input_patch); - + } - + void FindOneWayDescriptorEx(int desc_count, const OneWayDescriptor* descriptors, IplImage* patch, float scale_min, float scale_max, float scale_step, int n, std::vector& desc_idxs, std::vector& pose_idxs, @@ -1075,10 +1075,10 @@ namespace cv{ CvSize patch_size = descriptors[0].GetPatchSize(); IplImage* input_patch; CvRect roi; - + input_patch= cvCreateImage(patch_size, IPL_DEPTH_8U, 1); roi = cvGetImageROI((IplImage*)patch); - + // float min_distance = 1e10; std::vector _desc_idxs; _desc_idxs.resize(n); @@ -1086,22 +1086,22 @@ namespace cv{ _pose_idxs.resize(n); std::vector _distances; _distances.resize(n); - - + + for (int i=0;idepth == 32) { for(int y = 0; y < roi.height; y++) @@ -1212,10 +1212,10 @@ namespace cv{ printf("Image depth %d is not supported\n", patch->depth); return 0; } - + return mat; } - + OneWayDescriptorBase::OneWayDescriptorBase(CvSize patch_size, int pose_count, const char* train_path, const char* pca_config, const char* pca_hr_config, const char* pca_desc_config, int pyr_levels, @@ -1226,21 +1226,21 @@ namespace cv{ m_pca_descriptors_matrix = 0; m_pca_descriptors_tree = 0; #endif - // m_pca_descriptors_matrix = 0; + // m_pca_descriptors_matrix = 0; m_patch_size = patch_size; m_pose_count = pose_count; m_pyr_levels = pyr_levels; m_poses = 0; m_transforms = 0; - + m_pca_avg = 0; m_pca_eigenvectors = 0; m_pca_hr_avg = 0; m_pca_hr_eigenvectors = 0; m_pca_descriptors = 0; - + m_descriptors = 0; - + if(train_path == 0 || strlen(train_path) == 0) { // skip pca loading @@ -1255,9 +1255,9 @@ namespace cv{ sprintf(pca_hr_config_filename, "%s/%s", train_path, pca_hr_config); readPCAFeatures(pca_hr_config_filename, &m_pca_hr_avg, &m_pca_hr_eigenvectors); } - + m_pca_descriptors = new OneWayDescriptor[m_pca_dim_high + 1]; - + #if !defined(_GH_REGIONS) if(pca_desc_config && strlen(pca_desc_config) > 0) // if(0) @@ -1277,7 +1277,7 @@ namespace cv{ } #endif //_GH_REGIONS // SavePCADescriptors("./pca_descriptors.yml"); - + } OneWayDescriptorBase::OneWayDescriptorBase(CvSize patch_size, int pose_count, const string &pca_filename, @@ -1346,8 +1346,8 @@ namespace cv{ scale_min = fn["minScale"]; scale_max = fn["maxScale"]; scale_step = fn["stepScale"]; - - LoadPCAall (fn); + + LoadPCAall (fn); } void OneWayDescriptorBase::LoadPCAall (const FileNode &fn) @@ -1364,21 +1364,21 @@ namespace cv{ { cvReleaseMat(&m_pca_avg); cvReleaseMat(&m_pca_eigenvectors); - + if(m_pca_hr_eigenvectors) { delete[] m_pca_descriptors; cvReleaseMat(&m_pca_hr_avg); cvReleaseMat(&m_pca_hr_eigenvectors); } - - + + if(m_descriptors) delete []m_descriptors; if(m_poses) delete []m_poses; - + if (m_transforms) { for(int i = 0; i < m_pose_count; i++) @@ -1398,7 +1398,7 @@ namespace cv{ } #endif } - + void OneWayDescriptorBase::clear(){ if (m_descriptors) { @@ -1428,7 +1428,7 @@ namespace cv{ m_poses[i] = GenRandomAffinePose(); } } - + void OneWayDescriptorBase::InitializeTransformsFromPoses() { m_transforms = new CvMat*[m_pose_count]; @@ -1438,19 +1438,19 @@ namespace cv{ GenerateAffineTransformFromPose(cvSize(m_patch_size.width*2, m_patch_size.height*2), m_poses[i], m_transforms[i]); } } - + void OneWayDescriptorBase::InitializePoseTransforms() { InitializePoses(); InitializeTransformsFromPoses(); } - + void OneWayDescriptorBase::InitializeDescriptor(int desc_idx, IplImage* train_image, const KeyPoint& keypoint, const char* feature_label) { - + // TBD add support for octave != 0 CvPoint center = keypoint.pt; - + CvRect roi = cvRect(center.x - m_patch_size.width/2, center.y - m_patch_size.height/2, m_patch_size.width, m_patch_size.height); cvResetImageROI(train_image); roi = fit_rect_fixedsize(roi, train_image); @@ -1459,17 +1459,17 @@ namespace cv{ { return; } - + InitializeDescriptor(desc_idx, train_image, feature_label); cvResetImageROI(train_image); } - + void OneWayDescriptorBase::InitializeDescriptor(int desc_idx, IplImage* train_image, const char* feature_label) { m_descriptors[desc_idx].SetPCADimHigh(m_pca_dim_high); m_descriptors[desc_idx].SetPCADimLow(m_pca_dim_low); m_descriptors[desc_idx].SetTransforms(m_poses, m_transforms); - + if(!m_pca_hr_eigenvectors) { m_descriptors[desc_idx].Initialize(m_pose_count, train_image, feature_label); @@ -1479,24 +1479,24 @@ namespace cv{ m_descriptors[desc_idx].InitializeFast(m_pose_count, train_image, feature_label, m_pca_hr_avg, m_pca_hr_eigenvectors, m_pca_descriptors); } - + if(m_pca_avg) { m_descriptors[desc_idx].InitializePCACoeffs(m_pca_avg, m_pca_eigenvectors); } } - + void OneWayDescriptorBase::FindDescriptor(IplImage* src, cv::Point2f pt, int& desc_idx, int& pose_idx, float& distance) const { CvRect roi = cvRect(cvRound(pt.x - m_patch_size.width/4), cvRound(pt.y - m_patch_size.height/4), m_patch_size.width/2, m_patch_size.height/2); cvSetImageROI(src, roi); - + FindDescriptor(src, desc_idx, pose_idx, distance); - cvResetImageROI(src); + cvResetImageROI(src); } - + void OneWayDescriptorBase::FindDescriptor(IplImage* patch, int& desc_idx, int& pose_idx, float& distance, float* _scale, float* scale_ranges) const { #if 0 @@ -1505,15 +1505,15 @@ namespace cv{ float min = scale_min; float max = scale_max; float step = scale_step; - + if (scale_ranges) { min = scale_ranges[0]; max = scale_ranges[1]; } - + float scale = 1.0f; - + #if !defined(_KDTREE) cv::FindOneWayDescriptorEx(m_train_feature_count, m_descriptors, patch, min, max, step, desc_idx, pose_idx, distance, scale, @@ -1523,50 +1523,50 @@ namespace cv{ min, max, step, desc_idx, pose_idx, distance, scale, m_pca_avg, m_pca_eigenvectors); #endif - + if (_scale) *_scale = scale; - + #endif } - + void OneWayDescriptorBase::FindDescriptor(IplImage* patch, int n, std::vector& desc_idxs, std::vector& pose_idxs, std::vector& distances, std::vector& _scales, float* scale_ranges) const { float min = scale_min; float max = scale_max; float step = scale_step; - + if (scale_ranges) { min = scale_ranges[0]; max = scale_ranges[1]; } - + distances.resize(n); _scales.resize(n); desc_idxs.resize(n); pose_idxs.resize(n); /*float scales = 1.0f;*/ - + cv::FindOneWayDescriptorEx(m_train_feature_count, m_descriptors, patch, min, max, step ,n, desc_idxs, pose_idxs, distances, _scales, m_pca_avg, m_pca_eigenvectors); - + } - + void OneWayDescriptorBase::SetPCAHigh(CvMat* avg, CvMat* eigenvectors) { m_pca_hr_avg = cvCloneMat(avg); m_pca_hr_eigenvectors = cvCloneMat(eigenvectors); } - + void OneWayDescriptorBase::SetPCALow(CvMat* avg, CvMat* eigenvectors) { m_pca_avg = cvCloneMat(avg); m_pca_eigenvectors = cvCloneMat(eigenvectors); } - + void OneWayDescriptorBase::AllocatePCADescriptors() { m_pca_descriptors = new OneWayDescriptor[m_pca_dim_high + 1]; @@ -1576,7 +1576,7 @@ namespace cv{ m_pca_descriptors[i].SetPCADimLow(m_pca_dim_low); } } - + void OneWayDescriptorBase::CreatePCADescriptors() { if(m_pca_descriptors == 0) @@ -1584,27 +1584,27 @@ namespace cv{ AllocatePCADescriptors(); } IplImage* frontal = cvCreateImage(m_patch_size, IPL_DEPTH_32F, 1); - + eigenvector2image(m_pca_hr_avg, frontal); m_pca_descriptors[0].SetTransforms(m_poses, m_transforms); m_pca_descriptors[0].Initialize(m_pose_count, frontal, "", 0); - + for(int j = 0; j < m_pca_dim_high; j++) { CvMat eigenvector; cvGetSubRect(m_pca_hr_eigenvectors, &eigenvector, cvRect(0, j, m_pca_hr_eigenvectors->cols, 1)); eigenvector2image(&eigenvector, frontal); - + m_pca_descriptors[j + 1].SetTransforms(m_poses, m_transforms); m_pca_descriptors[j + 1].Initialize(m_pose_count, frontal, "", 0); - + printf("Created descriptor for PCA component %d\n", j); } - + cvReleaseImage(&frontal); } - - + + int OneWayDescriptorBase::LoadPCADescriptors(const char* filename) { FileStorage fs = FileStorage (filename, FileStorage::READ); @@ -1618,7 +1618,7 @@ namespace cv{ printf("Successfully read %d pca components\n", m_pca_dim_high); fs.release (); - + return 1; } @@ -1671,7 +1671,6 @@ namespace cv{ if (! m_pca_descriptors[i].ReadByName(fn, buf)) { - char buf[1024]; sprintf(buf, "descriptor for pca component %d", i); m_pca_descriptors[i].ReadByName(fn, buf); } @@ -1726,7 +1725,7 @@ namespace cv{ cvReleaseMat(&eigenvalues); } - void extractPatches (IplImage *img, vector& patches, CvSize patch_size) + static void extractPatches (IplImage *img, vector& patches, CvSize patch_size) { vector features; Ptr surf_extractor = FeatureDetector::create("SURF"); @@ -1891,13 +1890,13 @@ namespace cv{ { CvMemStorage* storage = cvCreateMemStorage(); CvFileStorage* fs = cvOpenFileStorage(filename, storage, CV_STORAGE_WRITE); - + SavePCADescriptors (fs); - + cvReleaseMemStorage(&storage); cvReleaseFileStorage(&fs); } - + void OneWayDescriptorBase::SavePCADescriptors(CvFileStorage *fs) const { cvWriteInt(fs, "pca_components_number", m_pca_dim_high); @@ -1939,32 +1938,32 @@ namespace cv{ m_descriptors[i].SetPCADimLow(m_pca_dim_low); } } - + void OneWayDescriptorBase::InitializeDescriptors(IplImage* train_image, const vector& features, const char* feature_label, int desc_start_idx) { for(int i = 0; i < (int)features.size(); i++) { InitializeDescriptor(desc_start_idx + i, train_image, features[i], feature_label); - + } cvResetImageROI(train_image); - + #if defined(_KDTREE) ConvertDescriptorsArrayToTree(); #endif } - + void OneWayDescriptorBase::CreateDescriptorsFromImage(IplImage* src, const std::vector& features) { m_train_feature_count = (int)features.size(); - + m_descriptors = new OneWayDescriptor[m_train_feature_count]; - + InitializeDescriptors(src, features); - + } - + #if defined(_KDTREE) void OneWayDescriptorBase::ConvertDescriptorsArrayToTree() { @@ -1972,18 +1971,18 @@ namespace cv{ if (n<1) return; int pca_dim_low = this->GetDescriptor(0)->GetPCADimLow(); - + //if (!m_pca_descriptors_matrix) - // m_pca_descriptors_matrix = new ::cvflann::Matrix(n*m_pose_count,pca_dim_low); + // m_pca_descriptors_matrix = new ::cvflann::Matrix(n*m_pose_count,pca_dim_low); //else //{ - // if ((m_pca_descriptors_matrix->cols != pca_dim_low)&&(m_pca_descriptors_matrix->rows != n*m_pose_count)) - // { - // delete m_pca_descriptors_matrix; - // m_pca_descriptors_matrix = new ::cvflann::Matrix(n*m_pose_count,pca_dim_low); - // } + // if ((m_pca_descriptors_matrix->cols != pca_dim_low)&&(m_pca_descriptors_matrix->rows != n*m_pose_count)) + // { + // delete m_pca_descriptors_matrix; + // m_pca_descriptors_matrix = new ::cvflann::Matrix(n*m_pose_count,pca_dim_low); + // } //} - + m_pca_descriptors_matrix = cvCreateMat(n*m_pose_count,pca_dim_low,CV_32FC1); for (int i=0;ibuildIndex(); } #endif - + void OneWayDescriptorObject::Allocate(int train_feature_count, int object_feature_count) { OneWayDescriptorBase::Allocate(train_feature_count); m_object_feature_count = object_feature_count; - + m_part_id = new int[m_object_feature_count]; } - - + + void OneWayDescriptorObject::InitializeObjectDescriptors(IplImage* train_image, const vector& features, const char* feature_label, int desc_start_idx, float scale, int is_background) { InitializeDescriptors(train_image, features, feature_label, desc_start_idx); - + for(int i = 0; i < (int)features.size(); i++) { CvPoint center = features[i].pt; - + if(!is_background) { // remember descriptor part id @@ -2034,12 +2033,12 @@ namespace cv{ } cvResetImageROI(train_image); } - + int OneWayDescriptorObject::IsDescriptorObject(int desc_idx) const { return desc_idx < m_object_feature_count ? 1 : 0; } - + int OneWayDescriptorObject::MatchPointToPart(CvPoint pt) const { int idx = -1; @@ -2052,23 +2051,23 @@ namespace cv{ break; } } - + return idx; } - + int OneWayDescriptorObject::GetDescriptorPart(int desc_idx) const { // return MatchPointToPart(GetDescriptor(desc_idx)->GetCenter()); return desc_idx < m_object_feature_count ? m_part_id[desc_idx] : -1; } - + OneWayDescriptorObject::OneWayDescriptorObject(CvSize patch_size, int pose_count, const char* train_path, const char* pca_config, const char* pca_hr_config, const char* pca_desc_config, int pyr_levels) : OneWayDescriptorBase(patch_size, pose_count, train_path, pca_config, pca_hr_config, pca_desc_config, pyr_levels) { m_part_id = 0; } - + OneWayDescriptorObject::OneWayDescriptorObject(CvSize patch_size, int pose_count, const string &pca_filename, const string &train_path, const string &images_list, float _scale_min, float _scale_max, float _scale_step, int pyr_levels) : OneWayDescriptorBase(patch_size, pose_count, pca_filename, train_path, images_list, _scale_min, _scale_max, _scale_step, pyr_levels) @@ -2081,7 +2080,7 @@ namespace cv{ if (m_part_id) delete []m_part_id; } - + vector OneWayDescriptorObject::_GetLabeledFeatures() const { vector features; @@ -2089,10 +2088,10 @@ namespace cv{ { features.push_back(m_train_features[i]); } - + return features; } - + void eigenvector2image(CvMat* eigenvector, IplImage* img) { CvRect roi = cvGetImageROI(img); @@ -2150,11 +2149,11 @@ namespace cv{ cvReleaseMat(&_eigenvectors); } } - + /****************************************************************************************\ * OneWayDescriptorMatcher * \****************************************************************************************/ - + OneWayDescriptorMatcher::Params::Params( int _poseCount, Size _patchSize, string _pcaFilename, string _trainPath, string _trainImagesList, float _minScale, float _maxScale, float _stepScale ) : @@ -2162,45 +2161,45 @@ namespace cv{ trainPath(_trainPath), trainImagesList(_trainImagesList), minScale(_minScale), maxScale(_maxScale), stepScale(_stepScale) {} - - + + OneWayDescriptorMatcher::OneWayDescriptorMatcher( const Params& _params) { initialize(_params); } - + OneWayDescriptorMatcher::~OneWayDescriptorMatcher() {} - + void OneWayDescriptorMatcher::initialize( const Params& _params, const Ptr& _base ) { clear(); - + if( _base.empty() ) base = _base; - + params = _params; } - + void OneWayDescriptorMatcher::clear() { GenericDescriptorMatcher::clear(); - + prevTrainCount = 0; if( !base.empty() ) base->clear(); } - + void OneWayDescriptorMatcher::train() { if( base.empty() || prevTrainCount < (int)trainPointCollection.keypointCount() ) { base = new OneWayDescriptorObject( params.patchSize, params.poseCount, params.pcaFilename, params.trainPath, params.trainImagesList, params.minScale, params.maxScale, params.stepScale ); - + base->Allocate( (int)trainPointCollection.keypointCount() ); prevTrainCount = (int)trainPointCollection.keypointCount(); - + const vector >& points = trainPointCollection.getKeypoints(); int count = 0; for( size_t i = 0; i < points.size(); i++ ) @@ -2209,26 +2208,26 @@ namespace cv{ for( size_t j = 0; j < points[i].size(); j++ ) base->InitializeDescriptor( count++, &_image, points[i][j], "" ); } - + #if defined(_KDTREE) base->ConvertDescriptorsArrayToTree(); #endif } } - + bool OneWayDescriptorMatcher::isMaskSupported() { return false; } - + void OneWayDescriptorMatcher::knnMatchImpl( const Mat& queryImage, vector& queryKeypoints, vector >& matches, int knn, const vector& /*masks*/, bool /*compactResult*/ ) { train(); - + CV_Assert( knn == 1 ); // knn > 1 unsupported because of bug in OneWayDescriptorBase for this case - + matches.resize( queryKeypoints.size() ); IplImage _qimage = queryImage; for( size_t i = 0; i < queryKeypoints.size(); i++ ) @@ -2239,13 +2238,13 @@ namespace cv{ matches[i].push_back( DMatch((int)i, descIdx, distance) ); } } - + void OneWayDescriptorMatcher::radiusMatchImpl( const Mat& queryImage, vector& queryKeypoints, vector >& matches, float maxDistance, const vector& /*masks*/, bool /*compactResult*/ ) { train(); - + matches.resize( queryKeypoints.size() ); IplImage _qimage = queryImage; for( size_t i = 0; i < queryKeypoints.size(); i++ ) @@ -2257,33 +2256,33 @@ namespace cv{ matches[i].push_back( DMatch((int)i, descIdx, distance) ); } } - + void OneWayDescriptorMatcher::read( const FileNode &fn ) { base = new OneWayDescriptorObject( params.patchSize, params.poseCount, string (), string (), string (), params.minScale, params.maxScale, params.stepScale ); base->Read (fn); } - + void OneWayDescriptorMatcher::write( FileStorage& fs ) const { base->Write (fs); } - + bool OneWayDescriptorMatcher::empty() const { return base.empty() || base->empty(); } - + Ptr OneWayDescriptorMatcher::clone( bool emptyTrainData ) const { OneWayDescriptorMatcher* matcher = new OneWayDescriptorMatcher( params ); - + if( !emptyTrainData ) { CV_Error( CV_StsNotImplemented, "deep clone functionality is not implemented, because " "OneWayDescriptorBase has not copy constructor or clone method "); - + //matcher->base; matcher->params = params; matcher->prevTrainCount = prevTrainCount; diff --git a/modules/legacy/src/precomp.hpp b/modules/legacy/src/precomp.hpp index 7c1993b..48b9e4a 100644 --- a/modules/legacy/src/precomp.hpp +++ b/modules/legacy/src/precomp.hpp @@ -41,11 +41,7 @@ #ifndef __OPENCV_PRECOMP_H__ #define __OPENCV_PRECOMP_H__ -#if _MSC_VER >= 1200 -#pragma warning( disable: 4251 4710 4711 4514 4996 ) -#endif - -#ifdef HAVE_CVCONFIG_H +#ifdef HAVE_CVCONFIG_H #include "cvconfig.h" #endif diff --git a/modules/legacy/src/stereogc.cpp b/modules/legacy/src/stereogc.cpp index 6ea3635..42466a0 100644 --- a/modules/legacy/src/stereogc.cpp +++ b/modules/legacy/src/stereogc.cpp @@ -143,7 +143,7 @@ static int64 icvGCMaxFlow( GCVtx* vtx, int nvtx, GCEdge* edges, GCVtx**& _orphan int norphans = 0, maxOrphans = _maxOrphans; GCVtx** orphans = _orphans; stub.next = nilNode; - + // initialize the active queue and the graph vertices for( i = 0; i < nvtx; i++ ) { @@ -170,7 +170,7 @@ static int64 icvGCMaxFlow( GCVtx* vtx, int nvtx, GCEdge* edges, GCVtx**& _orphan GCVtx* v, *u; int e0 = -1, ei = 0, ej = 0, min_weight, weight; uchar vt; - + // grow S & T search trees, find an edge connecting them while( first != nilNode ) { @@ -262,7 +262,7 @@ static int64 icvGCMaxFlow( GCVtx* vtx, int nvtx, GCEdge* edges, GCVtx**& _orphan v->parent = ORPHAN; } } - + v->weight = (short)(v->weight + min_weight*(1-k*2)); if( v->weight == 0 ) { @@ -277,12 +277,12 @@ static int64 icvGCMaxFlow( GCVtx* vtx, int nvtx, GCEdge* edges, GCVtx**& _orphan curr_ts++; while( norphans > 0 ) { - GCVtx* v = orphans[--norphans]; + GCVtx* v1 = orphans[--norphans]; int d, min_dist = INT_MAX; e0 = 0; - vt = v->t; + vt = v1->t; - for( ei = v->first; ei != 0; ei = edges[ei].next ) + for( ei = v1->first; ei != 0; ei = edges[ei].next ) { if( edges[ei^(vt^1)].weight == 0 ) continue; @@ -329,16 +329,16 @@ static int64 icvGCMaxFlow( GCVtx* vtx, int nvtx, GCEdge* edges, GCVtx**& _orphan } } - if( (v->parent = e0) > 0 ) + if( (v1->parent = e0) > 0 ) { - v->ts = curr_ts; - v->dist = min_dist; + v1->ts = curr_ts; + v1->dist = min_dist; continue; } /* no parent is found */ - v->ts = 0; - for( ei = v->first; ei != 0; ei = edges[ei].next ) + v1->ts = 0; + for( ei = v1->first; ei != 0; ei = edges[ei].next ) { u = edges[ei].dst; ej = u->parent; @@ -349,7 +349,7 @@ static int64 icvGCMaxFlow( GCVtx* vtx, int nvtx, GCEdge* edges, GCVtx**& _orphan u->next = nilNode; last = last->next = u; } - if( ej > 0 && edges[ej].dst == v ) + if( ej > 0 && edges[ej].dst == v1 ) { if( norphans >= maxOrphans ) maxOrphans = icvGCResizeOrphansBuf( orphans, norphans ); @@ -387,7 +387,7 @@ CvStereoGCState* cvCreateStereoGCState( int numberOfDisparities, int maxIters ) void cvReleaseStereoGCState( CvStereoGCState** _state ) { CvStereoGCState* state; - + if( !_state && !*_state ) return; @@ -438,7 +438,7 @@ static void icvInitGraySubpix( const CvMat* left, const CvMat* right, CvMat* left3, CvMat* right3 ) { int k, x, y, rows = left->rows, cols = left->cols; - + for( k = 0; k < 2; k++ ) { const CvMat* src = k == 0 ? left : right; @@ -452,11 +452,11 @@ static void icvInitGraySubpix( const CvMat* left, const CvMat* right, const uchar* sptr_next = y < rows-1 ? sptr + sstep : sptr; uchar* dptr = dst->data.ptr + dst->step*y; int v_prev = sptr[0]; - + for( x = 0; x < cols; x++, dptr += 3 ) { int v = sptr[x], v1, minv = v, maxv = v; - + v1 = (v + v_prev)/2; minv = MIN(minv, v1); maxv = MAX(maxv, v1); v1 = (v + sptr_prev[x])/2; @@ -492,7 +492,7 @@ icvComputeK( CvStereoGCState* state ) { const uchar* lptr = state->left->data.ptr + state->left->step*y; const uchar* rptr = state->right->data.ptr + state->right->step*y; - + for( x = 0; x < cols; x++ ) { for( d = maxd-1, i = 0; d >= mind; d-- ) @@ -701,7 +701,7 @@ static int64 icvAlphaExpand( int64 Eprev, int alpha, CvStereoGCState* state, CvS GCVtx** pright = pright0 + pstep*y; const uchar* lr[] = { left, right }; const short* dlr[] = { dleft, dright }; - GCVtx** plr[] = { pleft, pright }; + GCVtx** plr[] = { pleft, pright }; for( k = 0; k < 2; k++ ) { @@ -820,12 +820,12 @@ static int64 icvAlphaExpand( int64 Eprev, int alpha, CvStereoGCState* state, CvS GCVtx** pright = pright0 + pstep*y; for( x = 0; x < cols; x++ ) { - GCVtx* var = pleft[x]; - if( var && var->parent && var->t ) - dleft[x] = (short)alpha; + GCVtx* var2 = pleft[x]; + if( var2 && var2->parent && var2->t ) + dleft[x] = (short)alpha; - var = pright[x]; - if( var && var->parent && var->t ) + var2 = pright[x]; + if( var2 && var2->parent && var2->t ) dright[x] = (short)-alpha; } } @@ -903,7 +903,7 @@ CV_IMPL void cvFindStereoCorrespondenceGC( const CvArr* _left, const CvArr* _rig icvInitStereoConstTabs(); icvInitGraySubpix( left, right, state->left, state->right ); - + std::vector disp(state->numberOfDisparities); CvMat _disp = cvMat( 1, (int)disp.size(), CV_32S, &disp[0] ); cvRange( &_disp, state->minDisparity, state->minDisparity + state->numberOfDisparities ); diff --git a/modules/legacy/src/testseq.cpp b/modules/legacy/src/testseq.cpp index 1b15efe..37e19f7 100644 --- a/modules/legacy/src/testseq.cpp +++ b/modules/legacy/src/testseq.cpp @@ -65,7 +65,7 @@ typedef struct CvTSTrans float angle; } CvTSTrans; -void SET_TRANS_0(CvTSTrans *pT) +static void SET_TRANS_0(CvTSTrans *pT) { memset(pT,0,sizeof(CvTSTrans)); pT->C = 1; @@ -500,15 +500,15 @@ static CvTestSeqElem* icvTestSeqReadElemOne(CvTestSeq_* pTS, CvFileStorage* fs, int y0=0, y1=pFG->height-1; for(y0=0; y0height; ++y0) { - CvMat m; - CvScalar s = cvSum(cvGetRow(pFG, &m, y0)); + CvMat tmp; + CvScalar s = cvSum(cvGetRow(pFG, &tmp, y0)); if(s.val[0] > 255*7) break; } for(y1=pFG->height-1; y1>0; --y1) { - CvMat m; - CvScalar s = cvSum(cvGetRow(pFG, &m, y1)); + CvMat tmp; + CvScalar s = cvSum(cvGetRow(pFG, &tmp, y1)); if(s.val[0] > 255*7) break; } @@ -573,8 +573,8 @@ static CvTestSeqElem* icvTestSeqReadElemOne(CvTestSeq_* pTS, CvFileStorage* fs, p->FrameNum = cvReadIntByName( fs, node, "FrameNum", p->FrameNum ); p->FrameNum = cvReadIntByName( fs, node, "Dur", p->FrameNum ); { - int LastFrame = cvReadIntByName( fs, node, "LastFrame", p->FrameBegin+p->FrameNum-1 ); - p->FrameNum = MIN(p->FrameNum,LastFrame - p->FrameBegin+1); + int lastFrame = cvReadIntByName( fs, node, "LastFrame", p->FrameBegin+p->FrameNum-1 ); + p->FrameNum = MIN(p->FrameNum,lastFrame - p->FrameBegin+1); } icvTestSeqAllocTrans(p); @@ -621,8 +621,8 @@ static CvTestSeqElem* icvTestSeqReadElemOne(CvTestSeq_* pTS, CvFileStorage* fs, if(pTransSeq&&KeyFrameNum>1) { - int i0,i1,i; - for(i=0; ii0); - for(i=i0+1; iTransNum; ++i) { @@ -683,15 +681,15 @@ static CvTestSeqElem* icvTestSeqReadElemOne(CvTestSeq_* pTS, CvFileStorage* fs, double v0; double v1; - CvFileNode* pTN = (CvFileNode*)cvGetSeqElem(pTransSeq,0); - v0 = cvReadRealByName(fs, pTN,name,defv); + CvFileNode* pTN1 = (CvFileNode*)cvGetSeqElem(pTransSeq,0); + v0 = cvReadRealByName(fs, pTN1,name,defv); for(i1=1,i0=0; i1cols != 4 || projMatrs[i]->rows != 3 ) { @@ -867,7 +869,7 @@ int icvComputeProjectMatricesNPoints( CvMat* points1,CvMat* points2,CvMat* poin } } - for( i = 0; i < 3; i++ ) + for(int i = 0; i < 3; i++ ) { if( points[i]->rows != 2) { @@ -948,10 +950,9 @@ int icvComputeProjectMatricesNPoints( CvMat* points1,CvMat* points2,CvMat* poin icvProject4DPoints(recPoints4D,&proj6[2],tmpProjPoints[2]); /* Compute distances and number of good points (inliers) */ - int i; int currImage; numGoodPoints = 0; - for( i = 0; i < numPoints; i++ ) + for(int i = 0; i < numPoints; i++ ) { double dist=-1; dist = 0; @@ -1048,7 +1049,7 @@ int icvComputeProjectMatricesNPoints( CvMat* points1,CvMat* points2,CvMat* poin CvMat *optStatus; optStatus = cvCreateMat(1,numPoints,CV_64F); int testNumber = 0; - for( i=0;iwidth, imgGray->height), 8, 1); - imgThresh = cvCreateImage(cvSize(imgGray->width, imgGray->height), 8, 1); + imgray = cvCreateImage(cvSize(imgray->width, imgray->height), 8, 1); + imgThresh = cvCreateImage(cvSize(imgray->width, imgray->height), 8, 1); mstgContours = cvCreateMemStorage(); - if ((NULL == imgGray) || - (NULL == imgThresh) || + if ((NULL == imgray) || + (NULL == imgThresh) || (NULL == mstgContours)) return FALSE; return TRUE; @@ -162,11 +162,11 @@ struct CvFaceTracker ReallocImage(&imgThresh, sz, 1); ptRotate = face[MOUTH].ptCenter; float m[6]; - CvMat mat = cvMat( 2, 3, CV_32FC1, m ); + CvMat mat = cvMat( 2, 3, CV_32FC1, m ); if (NULL == imgGray || NULL == imgThresh) return FALSE; - + /*m[0] = (float)cos(-dbRotateAngle*CV_PI/180.); m[1] = (float)sin(-dbRotateAngle*CV_PI/180.); m[2] = (float)ptRotate.x; @@ -175,7 +175,7 @@ struct CvFaceTracker m[5] = (float)ptRotate.y;*/ cv2DRotationMatrix( cvPointTo32f(ptRotate), -dbRotateAngle, 1., &mat ); cvWarpAffine( img, imgGray, &mat ); - + if (NULL == mstgContours) mstgContours = cvCreateMemStorage(); else @@ -225,7 +225,7 @@ protected: void Energy(); }; //class CvFaceElement -int CV_CDECL CompareEnergy(const void* el1, const void* el2, void*) +inline int CV_CDECL CompareEnergy(const void* el1, const void* el2, void*) { return ((CvTrackingRect*)el1)->iEnergy - ((CvTrackingRect*)el2)->iEnergy; }// int CV_CDECL CompareEnergy(const void* el1, const void* el2, void*) @@ -322,7 +322,7 @@ void CvFaceElement::FindContours(IplImage* img, IplImage* thresh, int nLayers, i } for (CvSeq* internal = external->v_next; internal; internal = internal->h_next) { - cr.r = cvContourBoundingRect(internal); + cr.r = cvContourBoundingRect(internal); Move(cr.r, roi.x, roi.y); if (RectInRect(cr.r, m_rROI) && cr.r.width > dMinSize && cr.r.height > dMinSize) { @@ -353,7 +353,7 @@ void CvFaceElement::MergeRects(int d) for (j = i + 1; j < nRects; j++) { CvTrackingRect* pRect2 = (CvTrackingRect*)(reader2.ptr); - if (abs(pRect1->ptCenter.y - pRect2->ptCenter.y) < d && + if (abs(pRect1->ptCenter.y - pRect2->ptCenter.y) < d && abs(pRect1->r.height - pRect2->r.height) < d) { CvTrackingRect rNew; @@ -432,7 +432,7 @@ cvInitFaceTracker(CvFaceTracker* pFaceTracker, const IplImage* imgGray, CvRect* (NULL == pRects) || (nRects < NUM_FACE_ELEMENTS)) return NULL; - + //int new_face = FALSE; CvFaceTracker* pFace = pFaceTracker; if (NULL == pFace) @@ -468,7 +468,7 @@ cvTrackFace(CvFaceTracker* pFaceTracker, IplImage* imgGray, CvRect* pRects, int pFaceTracker->InitNextImage(imgGray); *ptRotate = pFaceTracker->ptRotate; *dbAngleRotate = pFaceTracker->dbRotateAngle; - + int nElements = 16; double dx = pFaceTracker->face[LEYE].ptCenter.x - pFaceTracker->face[REYE].ptCenter.x; double dy = pFaceTracker->face[LEYE].ptCenter.y - pFaceTracker->face[REYE].ptCenter.y; @@ -476,9 +476,9 @@ cvTrackFace(CvFaceTracker* pFaceTracker, IplImage* imgGray, CvRect* pRects, int int d = cvRound(0.25 * d_eyes); int dMinSize = d; int nRestarts = 0; - + int elem; - + CvFaceElement big_face[NUM_FACE_ELEMENTS]; START: // init @@ -533,7 +533,7 @@ START: } if (2 == elements) find2 = TRUE; - else + else restart = TRUE; } } @@ -563,13 +563,13 @@ RESTART: pFaceTracker->iTrackingFaceType = noel; found = TRUE; } - else + else { restart = TRUE; goto RESTART; } } - + if (found) { // angle by mouth & eyes @@ -613,7 +613,7 @@ void ThresholdingParam(IplImage *imgGray, int iNumLayers, int &iMinLevel, int &i { assert(imgGray != NULL); assert(imgGray->nChannels == 1); - int i, j; + int i, j; // create histogram int histImg[256] = {0}; uchar* buffImg = (uchar*)imgGray->imageData; @@ -760,7 +760,7 @@ int ChoiceTrackingFace2(CvFaceTracker* pTF, const int nElements, const CvFaceEle double prev_d02 = sqrt((double)prev_v02.x*prev_v02.x + prev_v02.y*prev_v02.y); double new_d01 = sqrt((double)new_v01.x*new_v01.x + new_v01.y*new_v01.y); double scale = templ_d01 / new_d01; - double new_d02 = templ_d02 / scale; + double new_d02 = templ_d02 / scale; double sin_a = double(prev_v01.x * prev_v02.y - prev_v01.y * prev_v02.x) / (prev_d01 * prev_d02); double cos_a = cos(asin(sin_a)); double x = double(new_v01.x) * cos_a - double(new_v01.y) * sin_a; @@ -806,12 +806,12 @@ inline int GetEnergy(CvTrackingRect** ppNew, const CvTrackingRect* pPrev, CvPoin double h_mouth = double(ppNew[MOUTH]->r.height) * scale; energy += int(512.0 * (e_prev + 16.0 * e_templ)) + - 4 * pow2(ppNew[LEYE]->r.width - ppNew[REYE]->r.width) + - 4 * pow2(ppNew[LEYE]->r.height - ppNew[REYE]->r.height) + - 4 * (int)pow(w_eye - double(rTempl[LEYE].width + rTempl[REYE].width) / 2.0, 2) + - 2 * (int)pow(h_eye - double(rTempl[LEYE].height + rTempl[REYE].height) / 2.0, 2) + - 1 * (int)pow(w_mouth - double(rTempl[MOUTH].width), 2) + - 1 * (int)pow(h_mouth - double(rTempl[MOUTH].height), 2) + + 4 * pow2(ppNew[LEYE]->r.width - ppNew[REYE]->r.width) + + 4 * pow2(ppNew[LEYE]->r.height - ppNew[REYE]->r.height) + + 4 * (int)pow(w_eye - double(rTempl[LEYE].width + rTempl[REYE].width) / 2.0, 2) + + 2 * (int)pow(h_eye - double(rTempl[LEYE].height + rTempl[REYE].height) / 2.0, 2) + + 1 * (int)pow(w_mouth - double(rTempl[MOUTH].width), 2) + + 1 * (int)pow(h_mouth - double(rTempl[MOUTH].height), 2) + 0; return energy; } @@ -832,20 +832,20 @@ inline int GetEnergy2(CvTrackingRect** ppNew, const CvTrackingRect* pPrev, CvPoi double h0 = (double)ppNew[element[0]]->r.height * scale_templ; double w1 = (double)ppNew[element[1]]->r.width * scale_templ; double h1 = (double)ppNew[element[1]]->r.height * scale_templ; - + int energy = ppNew[element[0]]->iEnergy + ppNew[element[1]]->iEnergy + - - 2 * (ppNew[element[0]]->nRectsInThis - ppNew[element[1]]->nRectsInThis) + + - 2 * (ppNew[element[0]]->nRectsInThis - ppNew[element[1]]->nRectsInThis) + (int)pow(w0 - (double)rTempl[element[0]].width, 2) + (int)pow(h0 - (double)rTempl[element[0]].height, 2) + (int)pow(w1 - (double)rTempl[element[1]].width, 2) + (int)pow(h1 - (double)rTempl[element[1]].height, 2) + (int)pow(new_d - prev_d, 2) + 0; - + return energy; } -inline double CalculateTransformationLMS3( CvPoint* pTemplPoints, +inline double CalculateTransformationLMS3( CvPoint* pTemplPoints, CvPoint* pSrcPoints, double* pdbAverageScale, double* pdbAverageRotate, @@ -866,41 +866,41 @@ inline double CalculateTransformationLMS3( CvPoint* pTemplPoints, double dbYt = double(pTemplPoints[0].y + pTemplPoints[1].y + pTemplPoints[2].y ) / 3.0; double dbXs = double(pSrcPoints[0].x + pSrcPoints[1].x + pSrcPoints[2].x) / 3.0; double dbYs = double(pSrcPoints[0].y + pSrcPoints[1].y + pSrcPoints[2].y) / 3.0; - + double dbXtXt = double(pow2(pTemplPoints[0].x) + pow2(pTemplPoints[1].x) + pow2(pTemplPoints[2].x)) / 3.0; double dbYtYt = double(pow2(pTemplPoints[0].y) + pow2(pTemplPoints[1].y) + pow2(pTemplPoints[2].y)) / 3.0; - + double dbXsXs = double(pow2(pSrcPoints[0].x) + pow2(pSrcPoints[1].x) + pow2(pSrcPoints[2].x)) / 3.0; double dbYsYs = double(pow2(pSrcPoints[0].y) + pow2(pSrcPoints[1].y) + pow2(pSrcPoints[2].y)) / 3.0; - - double dbXtXs = double(pTemplPoints[0].x * pSrcPoints[0].x + - pTemplPoints[1].x * pSrcPoints[1].x + + + double dbXtXs = double(pTemplPoints[0].x * pSrcPoints[0].x + + pTemplPoints[1].x * pSrcPoints[1].x + pTemplPoints[2].x * pSrcPoints[2].x) / 3.0; - double dbYtYs = double(pTemplPoints[0].y * pSrcPoints[0].y + - pTemplPoints[1].y * pSrcPoints[1].y + + double dbYtYs = double(pTemplPoints[0].y * pSrcPoints[0].y + + pTemplPoints[1].y * pSrcPoints[1].y + pTemplPoints[2].y * pSrcPoints[2].y) / 3.0; - - double dbXtYs = double(pTemplPoints[0].x * pSrcPoints[0].y + - pTemplPoints[1].x * pSrcPoints[1].y + + + double dbXtYs = double(pTemplPoints[0].x * pSrcPoints[0].y + + pTemplPoints[1].x * pSrcPoints[1].y + pTemplPoints[2].x * pSrcPoints[2].y) / 3.0; - double dbYtXs = double(pTemplPoints[0].y * pSrcPoints[0].x + - pTemplPoints[1].y * pSrcPoints[1].x + + double dbYtXs = double(pTemplPoints[0].y * pSrcPoints[0].x + + pTemplPoints[1].y * pSrcPoints[1].x + pTemplPoints[2].y * pSrcPoints[2].x ) / 3.0; - + dbXtXt -= dbXt * dbXt; dbYtYt -= dbYt * dbYt; - + dbXsXs -= dbXs * dbXs; dbYsYs -= dbYs * dbYs; - + dbXtXs -= dbXt * dbXs; dbYtYs -= dbYt * dbYs; - + dbXtYs -= dbXt * dbYs; dbYtXs -= dbYt * dbXs; - + dbAverageRotate = atan2( dbXtYs - dbYtXs, dbXtXs + dbYtYs ); - + double cosR = cos(dbAverageRotate); double sinR = sin(dbAverageRotate); double del = dbXsXs + dbYsYs; @@ -909,15 +909,15 @@ inline double CalculateTransformationLMS3( CvPoint* pTemplPoints, dbAverageScale = (double(dbXtXs + dbYtYs) * cosR + double(dbXtYs - dbYtXs) * sinR) / del; dbLMS = dbXtXt + dbYtYt - ((double)pow(dbXtXs + dbYtYs,2) + (double)pow(dbXtYs - dbYtXs,2)) / del; } - + dbAverageShiftX = double(dbXt) - dbAverageScale * (double(dbXs) * cosR + double(dbYs) * sinR); dbAverageShiftY = double(dbYt) - dbAverageScale * (double(dbYs) * cosR - double(dbXs) * sinR); - + if( pdbAverageScale != NULL ) *pdbAverageScale = dbAverageScale; if( pdbAverageRotate != NULL ) *pdbAverageRotate = dbAverageRotate; if( pdbAverageShiftX != NULL ) *pdbAverageShiftX = dbAverageShiftX; if( pdbAverageShiftY != NULL ) *pdbAverageShiftY = dbAverageShiftY; - + assert(dbLMS >= 0); return dbLMS; } @@ -933,39 +933,39 @@ inline double CalculateTransformationLMS3_0( CvPoint* pTemplPoints, CvPoint* pSr double dbYt = double(pTemplPoints[0].y + pTemplPoints[1].y + pTemplPoints[2].y ) / 3.0; double dbXs = double(pSrcPoints[0].x + pSrcPoints[1].x + pSrcPoints[2].x) / 3.0; double dbYs = double(pSrcPoints[0].y + pSrcPoints[1].y + pSrcPoints[2].y) / 3.0; - + double dbXtXt = double(pow2(pTemplPoints[0].x) + pow2(pTemplPoints[1].x) + pow2(pTemplPoints[2].x)) / 3.0; double dbYtYt = double(pow2(pTemplPoints[0].y) + pow2(pTemplPoints[1].y) + pow2(pTemplPoints[2].y)) / 3.0; - + double dbXsXs = double(pow2(pSrcPoints[0].x) + pow2(pSrcPoints[1].x) + pow2(pSrcPoints[2].x)) / 3.0; double dbYsYs = double(pow2(pSrcPoints[0].y) + pow2(pSrcPoints[1].y) + pow2(pSrcPoints[2].y)) / 3.0; - - double dbXtXs = double(pTemplPoints[0].x * pSrcPoints[0].x + - pTemplPoints[1].x * pSrcPoints[1].x + + + double dbXtXs = double(pTemplPoints[0].x * pSrcPoints[0].x + + pTemplPoints[1].x * pSrcPoints[1].x + pTemplPoints[2].x * pSrcPoints[2].x) / 3.0; - double dbYtYs = double(pTemplPoints[0].y * pSrcPoints[0].y + - pTemplPoints[1].y * pSrcPoints[1].y + + double dbYtYs = double(pTemplPoints[0].y * pSrcPoints[0].y + + pTemplPoints[1].y * pSrcPoints[1].y + pTemplPoints[2].y * pSrcPoints[2].y) / 3.0; - - double dbXtYs = double(pTemplPoints[0].x * pSrcPoints[0].y + - pTemplPoints[1].x * pSrcPoints[1].y + + + double dbXtYs = double(pTemplPoints[0].x * pSrcPoints[0].y + + pTemplPoints[1].x * pSrcPoints[1].y + pTemplPoints[2].x * pSrcPoints[2].y) / 3.0; - double dbYtXs = double(pTemplPoints[0].y * pSrcPoints[0].x + - pTemplPoints[1].y * pSrcPoints[1].x + + double dbYtXs = double(pTemplPoints[0].y * pSrcPoints[0].x + + pTemplPoints[1].y * pSrcPoints[1].x + pTemplPoints[2].y * pSrcPoints[2].x ) / 3.0; - + dbXtXt -= dbXt * dbXt; dbYtYt -= dbYt * dbYt; - + dbXsXs -= dbXs * dbXs; dbYsYs -= dbYs * dbYs; - + dbXtXs -= dbXt * dbXs; dbYtYs -= dbYt * dbYs; - + dbXtYs -= dbXt * dbYs; dbYtXs -= dbYt * dbXs; - + double del = dbXsXs + dbYsYs; if( del != 0 ) dbLMS = dbXtXt + dbYtYt - ((double)pow(dbXtXs + dbYtYs,2) + (double)pow(dbXtYs - dbYtXs,2)) / del; diff --git a/modules/legacy/test/test_precomp.hpp b/modules/legacy/test/test_precomp.hpp index 06b45a7..54bfe2b 100644 --- a/modules/legacy/test/test_precomp.hpp +++ b/modules/legacy/test/test_precomp.hpp @@ -1,3 +1,7 @@ +#ifdef __GNUC__ +# pragma GCC diagnostic ignored "-Wmissing-declarations" +#endif + #ifndef __OPENCV_TEST_PRECOMP_HPP__ #define __OPENCV_TEST_PRECOMP_HPP__ diff --git a/modules/legacy/test/test_stereomatching.cpp b/modules/legacy/test/test_stereomatching.cpp index c3d37d5..34b6abd 100644 --- a/modules/legacy/test/test_stereomatching.cpp +++ b/modules/legacy/test/test_stereomatching.cpp @@ -593,11 +593,11 @@ int CV_StereoMatchingTest::readDatasetsParams( FileStorage& fs ) assert(fn.isSeq()); for( int i = 0; i < (int)fn.size(); i+=3 ) { - string name = fn[i]; + string nm = fn[i]; DatasetParams params; string sf = fn[i+1]; params.dispScaleFactor = atoi(sf.c_str()); string uv = fn[i+2]; params.dispUnknVal = atoi(uv.c_str()); - datasetsParams[name] = params; + datasetsParams[nm] = params; } return cvtest::TS::OK; } diff --git a/modules/ml/include/opencv2/ml/ml.hpp b/modules/ml/include/opencv2/ml/ml.hpp index 9267e84..2a434b1 100644 --- a/modules/ml/include/opencv2/ml/ml.hpp +++ b/modules/ml/include/opencv2/ml/ml.hpp @@ -170,12 +170,7 @@ struct CV_EXPORTS_W_MAP CvParamGrid min_val = max_val = step = 0; } - CvParamGrid( double min_val, double max_val, double log_step ) - { - this->min_val = min_val; - this->max_val = max_val; - step = log_step; - } + CvParamGrid( double min_val, double max_val, double log_step ); //CvParamGrid( int param_id ); bool check() const; @@ -184,6 +179,13 @@ struct CV_EXPORTS_W_MAP CvParamGrid CV_PROP_RW double step; }; +inline CvParamGrid::CvParamGrid( double _min_val, double _max_val, double _log_step ) +{ + min_val = _min_val; + max_val = _max_val; + step = _log_step; +} + class CV_EXPORTS_W CvNormalBayesClassifier : public CvStatModel { public: @@ -192,10 +194,10 @@ public: CvNormalBayesClassifier( const CvMat* trainData, const CvMat* responses, const CvMat* varIdx=0, const CvMat* sampleIdx=0 ); - + virtual bool train( const CvMat* trainData, const CvMat* responses, const CvMat* varIdx = 0, const CvMat* sampleIdx=0, bool update=false ); - + virtual float predict( const CvMat* samples, CV_OUT CvMat* results=0 ) const; CV_WRAP virtual void clear(); @@ -207,7 +209,7 @@ public: bool update=false ); CV_WRAP virtual float predict( const cv::Mat& samples, CV_OUT cv::Mat* results=0 ) const; #endif - + virtual void write( CvFileStorage* storage, const char* name ) const; virtual void read( CvFileStorage* storage, CvFileNode* node ); @@ -243,31 +245,31 @@ public: virtual bool train( const CvMat* trainData, const CvMat* responses, const CvMat* sampleIdx=0, bool is_regression=false, int maxK=32, bool updateBase=false ); - + virtual float find_nearest( const CvMat* samples, int k, CV_OUT CvMat* results=0, const float** neighbors=0, CV_OUT CvMat* neighborResponses=0, CV_OUT CvMat* dist=0 ) const; - + #ifndef SWIG CV_WRAP CvKNearest( const cv::Mat& trainData, const cv::Mat& responses, const cv::Mat& sampleIdx=cv::Mat(), bool isRegression=false, int max_k=32 ); - + CV_WRAP virtual bool train( const cv::Mat& trainData, const cv::Mat& responses, const cv::Mat& sampleIdx=cv::Mat(), bool isRegression=false, - int maxK=32, bool updateBase=false ); - + int maxK=32, bool updateBase=false ); + virtual float find_nearest( const cv::Mat& samples, int k, cv::Mat* results=0, const float** neighbors=0, cv::Mat* neighborResponses=0, cv::Mat* dist=0 ) const; CV_WRAP virtual float find_nearest( const cv::Mat& samples, int k, CV_OUT cv::Mat& results, CV_OUT cv::Mat& neighborResponses, CV_OUT cv::Mat& dists) const; #endif - + virtual void clear(); int get_max_k() const; int get_var_count() const; int get_sample_count() const; bool is_regression() const; - + virtual float write_results( int k, int k1, int start, int end, const float* neighbor_responses, const float* dist, CvMat* _results, CvMat* _neighbor_responses, CvMat* _dist, Cv32suf* sort_buf ) const; @@ -473,7 +475,7 @@ public: virtual bool train( const CvMat* trainData, const CvMat* responses, const CvMat* varIdx=0, const CvMat* sampleIdx=0, CvSVMParams params=CvSVMParams() ); - + virtual bool train_auto( const CvMat* trainData, const CvMat* responses, const CvMat* varIdx, const CvMat* sampleIdx, CvSVMParams params, int kfold = 10, @@ -487,16 +489,16 @@ public: virtual float predict( const CvMat* sample, bool returnDFVal=false ) const; virtual float predict( const CvMat* samples, CvMat* results ) const; - + #ifndef SWIG CV_WRAP CvSVM( const cv::Mat& trainData, const cv::Mat& responses, const cv::Mat& varIdx=cv::Mat(), const cv::Mat& sampleIdx=cv::Mat(), CvSVMParams params=CvSVMParams() ); - + CV_WRAP virtual bool train( const cv::Mat& trainData, const cv::Mat& responses, const cv::Mat& varIdx=cv::Mat(), const cv::Mat& sampleIdx=cv::Mat(), CvSVMParams params=CvSVMParams() ); - + CV_WRAP virtual bool train_auto( const cv::Mat& trainData, const cv::Mat& responses, const cv::Mat& varIdx, const cv::Mat& sampleIdx, CvSVMParams params, int k_fold = 10, @@ -509,7 +511,7 @@ public: bool balanced=false); CV_WRAP virtual float predict( const cv::Mat& sample, bool returnDFVal=false ) const; #endif - + CV_WRAP virtual int get_support_vector_count() const; virtual const float* get_support_vector(int i) const; virtual CvSVMParams get_params() const { return params; }; @@ -564,14 +566,14 @@ public: // Default parameters enum {DEFAULT_NCLUSTERS=5, DEFAULT_MAX_ITERS=100}; - + // The initial step enum {START_E_STEP=1, START_M_STEP=2, START_AUTO_STEP=0}; CV_WRAP EM(int nclusters=EM::DEFAULT_NCLUSTERS, int covMatType=EM::COV_MAT_DIAGONAL, const TermCriteria& termCrit=TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, EM::DEFAULT_MAX_ITERS, FLT_EPSILON)); - + virtual ~EM(); CV_WRAP virtual void clear(); @@ -579,7 +581,7 @@ public: OutputArray logLikelihoods=noArray(), OutputArray labels=noArray(), OutputArray probs=noArray()); - + CV_WRAP virtual bool trainE(InputArray samples, InputArray means0, InputArray covs0=noArray(), @@ -587,13 +589,13 @@ public: OutputArray logLikelihoods=noArray(), OutputArray labels=noArray(), OutputArray probs=noArray()); - + CV_WRAP virtual bool trainM(InputArray samples, InputArray probs0, OutputArray logLikelihoods=noArray(), OutputArray labels=noArray(), OutputArray probs=noArray()); - + CV_WRAP Vec2d predict(InputArray sample, OutputArray probs=noArray()) const; @@ -603,7 +605,7 @@ public: virtual void read(const FileNode& fn); protected: - + virtual void setTrainData(int startStep, const Mat& samples, const Mat* probs0, const Mat* means0, @@ -802,7 +804,7 @@ struct CV_EXPORTS CvDTreeTrainData int buf_count, buf_size; bool shared; int is_buf_16u; - + CvMat* cat_count; CvMat* cat_ofs; CvMat* cat_map; @@ -871,12 +873,12 @@ public: const cv::Mat& sampleIdx=cv::Mat(), const cv::Mat& varType=cv::Mat(), const cv::Mat& missingDataMask=cv::Mat(), CvDTreeParams params=CvDTreeParams() ); - + CV_WRAP virtual CvDTreeNode* predict( const cv::Mat& sample, const cv::Mat& missingDataMask=cv::Mat(), bool preprocessedInput=false ) const; CV_WRAP virtual cv::Mat getVarImportance(); #endif - + virtual const CvMat* get_var_importance(); CV_WRAP virtual void clear(); @@ -900,13 +902,13 @@ protected: virtual void try_split_node( CvDTreeNode* n ); virtual void split_node_data( CvDTreeNode* n ); virtual CvDTreeSplit* find_best_split( CvDTreeNode* n ); - virtual CvDTreeSplit* find_split_ord_class( CvDTreeNode* n, int vi, + virtual CvDTreeSplit* find_split_ord_class( CvDTreeNode* n, int vi, float init_quality = 0, CvDTreeSplit* _split = 0, uchar* ext_buf = 0 ); virtual CvDTreeSplit* find_split_cat_class( CvDTreeNode* n, int vi, float init_quality = 0, CvDTreeSplit* _split = 0, uchar* ext_buf = 0 ); - virtual CvDTreeSplit* find_split_ord_reg( CvDTreeNode* n, int vi, + virtual CvDTreeSplit* find_split_ord_reg( CvDTreeNode* n, int vi, float init_quality = 0, CvDTreeSplit* _split = 0, uchar* ext_buf = 0 ); - virtual CvDTreeSplit* find_split_cat_reg( CvDTreeNode* n, int vi, + virtual CvDTreeSplit* find_split_cat_reg( CvDTreeNode* n, int vi, float init_quality = 0, CvDTreeSplit* _split = 0, uchar* ext_buf = 0 ); virtual CvDTreeSplit* find_surrogate_split_ord( CvDTreeNode* n, int vi, uchar* ext_buf = 0 ); virtual CvDTreeSplit* find_surrogate_split_cat( CvDTreeNode* n, int vi, uchar* ext_buf = 0 ); @@ -1003,7 +1005,7 @@ public: const CvMat* sampleIdx=0, const CvMat* varType=0, const CvMat* missingDataMask=0, CvRTParams params=CvRTParams() ); - + virtual bool train( CvMLData* data, CvRTParams params=CvRTParams() ); virtual float predict( const CvMat* sample, const CvMat* missing = 0 ) const; virtual float predict_prob( const CvMat* sample, const CvMat* missing = 0 ) const; @@ -1018,16 +1020,16 @@ public: CV_WRAP virtual float predict_prob( const cv::Mat& sample, const cv::Mat& missing = cv::Mat() ) const; CV_WRAP virtual cv::Mat getVarImportance(); #endif - + CV_WRAP virtual void clear(); virtual const CvMat* get_var_importance(); virtual float get_proximity( const CvMat* sample1, const CvMat* sample2, const CvMat* missing1 = 0, const CvMat* missing2 = 0 ) const; - + virtual float calc_error( CvMLData* data, int type , std::vector* resp = 0 ); // type in {CV_TRAIN_ERROR, CV_TEST_ERROR} - virtual float get_train_error(); + virtual float get_train_error(); virtual void read( CvFileStorage* fs, CvFileNode* node ); virtual void write( CvFileStorage* fs, const char* name ) const; @@ -1083,13 +1085,13 @@ class CV_EXPORTS CvForestERTree : public CvForestTree { protected: virtual double calc_node_dir( CvDTreeNode* node ); - virtual CvDTreeSplit* find_split_ord_class( CvDTreeNode* n, int vi, + virtual CvDTreeSplit* find_split_ord_class( CvDTreeNode* n, int vi, float init_quality = 0, CvDTreeSplit* _split = 0, uchar* ext_buf = 0 ); virtual CvDTreeSplit* find_split_cat_class( CvDTreeNode* n, int vi, float init_quality = 0, CvDTreeSplit* _split = 0, uchar* ext_buf = 0 ); - virtual CvDTreeSplit* find_split_ord_reg( CvDTreeNode* n, int vi, + virtual CvDTreeSplit* find_split_ord_reg( CvDTreeNode* n, int vi, float init_quality = 0, CvDTreeSplit* _split = 0, uchar* ext_buf = 0 ); - virtual CvDTreeSplit* find_split_cat_reg( CvDTreeNode* n, int vi, + virtual CvDTreeSplit* find_split_cat_reg( CvDTreeNode* n, int vi, float init_quality = 0, CvDTreeSplit* _split = 0, uchar* ext_buf = 0 ); virtual void split_node_data( CvDTreeNode* n ); }; @@ -1169,13 +1171,13 @@ protected: virtual void try_split_node( CvDTreeNode* n ); virtual CvDTreeSplit* find_surrogate_split_ord( CvDTreeNode* n, int vi, uchar* ext_buf = 0 ); virtual CvDTreeSplit* find_surrogate_split_cat( CvDTreeNode* n, int vi, uchar* ext_buf = 0 ); - virtual CvDTreeSplit* find_split_ord_class( CvDTreeNode* n, int vi, + virtual CvDTreeSplit* find_split_ord_class( CvDTreeNode* n, int vi, float init_quality = 0, CvDTreeSplit* _split = 0, uchar* ext_buf = 0 ); virtual CvDTreeSplit* find_split_cat_class( CvDTreeNode* n, int vi, float init_quality = 0, CvDTreeSplit* _split = 0, uchar* ext_buf = 0 ); - virtual CvDTreeSplit* find_split_ord_reg( CvDTreeNode* n, int vi, + virtual CvDTreeSplit* find_split_ord_reg( CvDTreeNode* n, int vi, float init_quality = 0, CvDTreeSplit* _split = 0, uchar* ext_buf = 0 ); - virtual CvDTreeSplit* find_split_cat_reg( CvDTreeNode* n, int vi, + virtual CvDTreeSplit* find_split_cat_reg( CvDTreeNode* n, int vi, float init_quality = 0, CvDTreeSplit* _split = 0, uchar* ext_buf = 0 ); virtual void calc_node_value( CvDTreeNode* n ); virtual double calc_node_dir( CvDTreeNode* n ); @@ -1201,14 +1203,14 @@ public: const CvMat* sampleIdx=0, const CvMat* varType=0, const CvMat* missingDataMask=0, CvBoostParams params=CvBoostParams() ); - + virtual bool train( const CvMat* trainData, int tflag, const CvMat* responses, const CvMat* varIdx=0, const CvMat* sampleIdx=0, const CvMat* varType=0, const CvMat* missingDataMask=0, CvBoostParams params=CvBoostParams(), bool update=false ); - + virtual bool train( CvMLData* data, CvBoostParams params=CvBoostParams(), bool update=false ); @@ -1223,19 +1225,19 @@ public: const cv::Mat& sampleIdx=cv::Mat(), const cv::Mat& varType=cv::Mat(), const cv::Mat& missingDataMask=cv::Mat(), CvBoostParams params=CvBoostParams() ); - + CV_WRAP virtual bool train( const cv::Mat& trainData, int tflag, const cv::Mat& responses, const cv::Mat& varIdx=cv::Mat(), const cv::Mat& sampleIdx=cv::Mat(), const cv::Mat& varType=cv::Mat(), const cv::Mat& missingDataMask=cv::Mat(), CvBoostParams params=CvBoostParams(), bool update=false ); - + CV_WRAP virtual float predict( const cv::Mat& sample, const cv::Mat& missing=cv::Mat(), const cv::Range& slice=cv::Range::all(), bool rawMode=false, bool returnSum=false ) const; #endif - + virtual float calc_error( CvMLData* _data, int type , std::vector *resp = 0 ); // type in {CV_TRAIN_ERROR, CV_TEST_ERROR} CV_WRAP virtual void prune( CvSlice slice ); @@ -1315,7 +1317,7 @@ struct CV_EXPORTS_W_MAP CvGBTreesParams : public CvDTreeParams // DataType: CLASS CvGBTrees // Gradient Boosting Trees (GBT) algorithm implementation. -// +// // data - training dataset // params - parameters of the CvGBTrees // weak - array[0..(class_count-1)] of CvSeq @@ -1347,7 +1349,7 @@ struct CV_EXPORTS_W_MAP CvGBTreesParams : public CvDTreeParams // missing - mask of the missing values in the training set. This // matrix has the same size as train_data. 1 - missing // value, 0 - not a missing value. -// class_labels - output class labels map. +// class_labels - output class labels map. // rng - random number generator. Used for spliting the // training set. // class_count - count of output classes. @@ -1368,15 +1370,15 @@ public: /* // DataType: ENUM // Loss functions implemented in CvGBTrees. - // + // // SQUARED_LOSS // problem: regression // loss = (x - x')^2 - // + // // ABSOLUTE_LOSS // problem: regression // loss = abs(x - x') - // + // // HUBER_LOSS // problem: regression // loss = delta*( abs(x - x') - delta/2), if abs(x - x') > delta @@ -1386,18 +1388,18 @@ public: // // DEVIANCE_LOSS // problem: classification - // - */ + // + */ enum {SQUARED_LOSS=0, ABSOLUTE_LOSS, HUBER_LOSS=3, DEVIANCE_LOSS}; - - + + /* // Default constructor. Creates a model only (without training). // Should be followed by one form of the train(...) function. // // API // CvGBTrees(); - + // INPUT // OUTPUT // RESULT @@ -1415,7 +1417,7 @@ public: const CvMat* sampleIdx=0, const CvMat* varType=0, const CvMat* missingDataMask=0, CvGBTreesParams params=CvGBTreesParams() ); - + // INPUT // trainData - a set of input feature vectors. // size of matrix is @@ -1448,13 +1450,13 @@ public: const CvMat* missingDataMask=0, CvGBTreesParams params=CvGBTreesParams() ); - + /* // Destructor. */ virtual ~CvGBTrees(); - - + + /* // Gradient tree boosting model training // @@ -1465,7 +1467,7 @@ public: const CvMat* missingDataMask=0, CvGBTreesParams params=CvGBTreesParams(), bool update=false ); - + // INPUT // trainData - a set of input feature vectors. // size of matrix is @@ -1500,8 +1502,8 @@ public: const CvMat* missingDataMask=0, CvGBTreesParams params=CvGBTreesParams(), bool update=false ); - - + + /* // Gradient tree boosting model training // @@ -1509,7 +1511,7 @@ public: // virtual bool train( CvMLData* data, CvGBTreesParams params=CvGBTreesParams(), bool update=false ) {return false;}; - + // INPUT // data - training set. // params - parameters of GTB algorithm. @@ -1522,7 +1524,7 @@ public: CvGBTreesParams params=CvGBTreesParams(), bool update=false ); - + /* // Response value prediction // @@ -1530,7 +1532,7 @@ public: // virtual float predict_serial( const CvMat* sample, const CvMat* missing=0, CvMat* weak_responses=0, CvSlice slice = CV_WHOLE_SEQ, int k=-1 ) const; - + // INPUT // sample - input sample of the same type as in the training set. // missing - missing values mask. missing=0 if there are no @@ -1541,7 +1543,7 @@ public: // slice = CV_WHOLE_SEQ when all trees are used. // k - number of ensemble used. // k is in {-1,0,1,..,}. - // in the case of classification problem + // in the case of classification problem // ensembles are built. // If k = -1 ordinary prediction is the result, // otherwise function gives the prediction of the @@ -1553,7 +1555,7 @@ public: virtual float predict_serial( const CvMat* sample, const CvMat* missing=0, CvMat* weakResponses=0, CvSlice slice = CV_WHOLE_SEQ, int k=-1 ) const; - + /* // Response value prediction. // Parallel version (in the case of TBB existence) @@ -1562,7 +1564,7 @@ public: // virtual float predict( const CvMat* sample, const CvMat* missing=0, CvMat* weak_responses=0, CvSlice slice = CV_WHOLE_SEQ, int k=-1 ) const; - + // INPUT // sample - input sample of the same type as in the training set. // missing - missing values mask. missing=0 if there are no @@ -1573,7 +1575,7 @@ public: // slice = CV_WHOLE_SEQ when all trees are used. // k - number of ensemble used. // k is in {-1,0,1,..,}. - // in the case of classification problem + // in the case of classification problem // ensembles are built. // If k = -1 ordinary prediction is the result, // otherwise function gives the prediction of the @@ -1581,7 +1583,7 @@ public: // OUTPUT // RESULT // Predicted value. - */ + */ virtual float predict( const CvMat* sample, const CvMat* missing=0, CvMat* weakResponses=0, CvSlice slice = CV_WHOLE_SEQ, int k=-1 ) const; @@ -1591,7 +1593,7 @@ public: // // API // virtual void clear(); - + // INPUT // OUTPUT // delete data, weak, orig_response, sum_response, @@ -1622,7 +1624,7 @@ public: std::vector *resp = 0 ); /* - // + // // Write parameters of the gtb model and data. Write learned model. // // API @@ -1638,7 +1640,7 @@ public: /* - // + // // Read parameters of the gtb model and data. Read learned model. // // API @@ -1652,14 +1654,14 @@ public: */ virtual void read( CvFileStorage* fs, CvFileNode* node ); - + // new-style C++ interface CV_WRAP CvGBTrees( const cv::Mat& trainData, int tflag, const cv::Mat& responses, const cv::Mat& varIdx=cv::Mat(), const cv::Mat& sampleIdx=cv::Mat(), const cv::Mat& varType=cv::Mat(), const cv::Mat& missingDataMask=cv::Mat(), CvGBTreesParams params=CvGBTreesParams() ); - + CV_WRAP virtual bool train( const cv::Mat& trainData, int tflag, const cv::Mat& responses, const cv::Mat& varIdx=cv::Mat(), const cv::Mat& sampleIdx=cv::Mat(), const cv::Mat& varType=cv::Mat(), @@ -1670,7 +1672,7 @@ public: CV_WRAP virtual float predict( const cv::Mat& sample, const cv::Mat& missing=cv::Mat(), const cv::Range& slice = cv::Range::all(), int k=-1 ) const; - + protected: /* @@ -1678,7 +1680,7 @@ protected: // // API // virtual void find_gradient( const int k = 0); - + // INPUT // k - used for classification problem, determining current // tree ensemble. @@ -1690,9 +1692,9 @@ protected: */ virtual void find_gradient( const int k = 0); - + /* - // + // // Change values in tree leaves according to the used loss function. // // API @@ -1711,7 +1713,7 @@ protected: /* - // + // // Find optimal constant prediction value according to the used loss // function. // The goal is to find a constant which gives the minimal summary loss @@ -1728,9 +1730,9 @@ protected: */ virtual float find_optimal_value( const CvMat* _Idx ); - + /* - // + // // Randomly split the whole training set in two parts according // to params.portion. // @@ -1747,7 +1749,7 @@ protected: /* - // + // // Internal recursive function giving an array of subtree tree leaves. // // API @@ -1761,10 +1763,10 @@ protected: // RESULT */ void leaves_get( CvDTreeNode** leaves, int& count, CvDTreeNode* node ); - - + + /* - // + // // Get leaves of the tree. // // API @@ -1779,9 +1781,9 @@ protected: */ CvDTreeNode** GetLeaves( const CvDTree* dtree, int& len ); - + /* - // + // // Is it a regression or a classification. // // API @@ -1797,7 +1799,7 @@ protected: /* - // + // // Write parameters of the gtb model. // // API @@ -1812,7 +1814,7 @@ protected: /* - // + // // Read parameters of the gtb model and data. // // API @@ -1829,9 +1831,9 @@ protected: // RESULT */ virtual void read_params( CvFileStorage* fs, CvFileNode* fnode ); - int get_len(const CvMat* mat) const; + int get_len(const CvMat* mat) const; + - CvDTreeTrainData* data; CvGBTreesParams params; @@ -1894,30 +1896,30 @@ public: virtual void create( const CvMat* layerSizes, int activateFunc=CvANN_MLP::SIGMOID_SYM, double fparam1=0, double fparam2=0 ); - + virtual int train( const CvMat* inputs, const CvMat* outputs, const CvMat* sampleWeights, const CvMat* sampleIdx=0, CvANN_MLP_TrainParams params = CvANN_MLP_TrainParams(), int flags=0 ); virtual float predict( const CvMat* inputs, CV_OUT CvMat* outputs ) const; - + #ifndef SWIG CV_WRAP CvANN_MLP( const cv::Mat& layerSizes, int activateFunc=CvANN_MLP::SIGMOID_SYM, double fparam1=0, double fparam2=0 ); - + CV_WRAP virtual void create( const cv::Mat& layerSizes, int activateFunc=CvANN_MLP::SIGMOID_SYM, - double fparam1=0, double fparam2=0 ); - + double fparam1=0, double fparam2=0 ); + CV_WRAP virtual int train( const cv::Mat& inputs, const cv::Mat& outputs, const cv::Mat& sampleWeights, const cv::Mat& sampleIdx=cv::Mat(), CvANN_MLP_TrainParams params = CvANN_MLP_TrainParams(), - int flags=0 ); - + int flags=0 ); + CV_WRAP virtual float predict( const cv::Mat& inputs, CV_OUT cv::Mat& outputs ) const; #endif - + CV_WRAP virtual void clear(); // possible activation functions @@ -2031,7 +2033,7 @@ public: virtual ~CvMLData(); // returns: - // 0 - OK + // 0 - OK // -1 - file can not be opened or is not correct int read_csv( const char* filename ); @@ -2047,7 +2049,7 @@ public: const CvMat* get_train_sample_idx() const; const CvMat* get_test_sample_idx() const; void mix_train_and_test_idx(); - + const CvMat* get_var_idx(); void chahge_var_idx( int vi, bool state ); // misspelled (saved for back compitability), // use change_var_idx @@ -2061,14 +2063,14 @@ public: void set_var_types( const char* str ); // str examples: // "ord[0-17],cat[18]", "ord[0,2,4,10-12], cat[1,3,5-9,13,14]", // "cat", "ord" (all vars are categorical/ordered) - void change_var_type( int var_idx, int type); // type in { CV_VAR_ORDERED, CV_VAR_CATEGORICAL } - + void change_var_type( int var_idx, int type); // type in { CV_VAR_ORDERED, CV_VAR_CATEGORICAL } + void set_delimiter( char ch ); char get_delimiter() const; void set_miss_ch( char ch ); char get_miss_ch() const; - + const std::map& get_class_labels_map() const; protected: @@ -2076,7 +2078,7 @@ protected: void str_to_flt_elem( const char* token, float& flt_elem, int& type); void free_train_test_idx(); - + char delimiter; char miss_ch; //char flt_separator; @@ -2094,7 +2096,7 @@ protected: int train_sample_count; bool mix; - + int total_class_count; std::map class_map; @@ -2108,7 +2110,7 @@ protected: namespace cv { - + typedef CvStatModel StatModel; typedef CvParamGrid ParamGrid; typedef CvNormalBayesClassifier NormalBayesClassifier; @@ -2137,7 +2139,7 @@ typedef CvGBTrees GradientBoostingTrees; template<> CV_EXPORTS void Ptr::delete_obj(); CV_EXPORTS bool initModule_ml(void); - + } #endif // __cplusplus diff --git a/modules/ml/src/ann_mlp.cpp b/modules/ml/src/ann_mlp.cpp index 0b9db71..438872a 100644 --- a/modules/ml/src/ann_mlp.cpp +++ b/modules/ml/src/ann_mlp.cpp @@ -504,7 +504,7 @@ void CvANN_MLP::calc_activ_func_deriv( CvMat* _xf, CvMat* _df, n *= cols; xf -= n; df -= n; - + for( i = 0; i < n; i++ ) df[i] *= xf[i]; } @@ -517,7 +517,7 @@ void CvANN_MLP::calc_activ_func_deriv( CvMat* _xf, CvMat* _df, xf[j] = (xf[j] + bias[j])*scale; df[j] = -fabs(xf[j]); } - + cvExp( _df, _df ); n *= cols; @@ -1023,9 +1023,9 @@ int CvANN_MLP::train_backprop( CvVectors x0, CvVectors u, const double* sw ) } struct rprop_loop { - rprop_loop(const CvANN_MLP* _point, double**& _weights, int& _count, int& _ivcount, CvVectors* _x0, + rprop_loop(const CvANN_MLP* _point, double**& _weights, int& _count, int& _ivcount, CvVectors* _x0, int& _l_count, CvMat*& _layer_sizes, int& _ovcount, int& _max_count, - CvVectors* _u, const double*& _sw, double& _inv_count, CvMat*& _dEdw, int& _dcount0, double* _E, int _buf_sz) + CvVectors* _u, const double*& _sw, double& _inv_count, CvMat*& _dEdw, int& _dcount0, double* _E, int _buf_sz) { point = _point; weights = _weights; @@ -1044,7 +1044,7 @@ struct rprop_loop { E = _E; buf_sz = _buf_sz; } - + const CvANN_MLP* point; double** weights; int count; @@ -1062,14 +1062,14 @@ struct rprop_loop { double* E; int buf_sz; - + void operator()( const cv::BlockedRange& range ) const { double* buf_ptr; double** x = 0; - double **df = 0; + double **df = 0; int total = 0; - + for(int i = 0; i < l_count; i++ ) total += layer_sizes->data.i[i]; CvMat* buf; @@ -1087,7 +1087,7 @@ struct rprop_loop { for(int si = range.begin(); si < range.end(); si++ ) { if (si % dcount0 != 0) continue; - int n1, n2, j, k; + int n1, n2, k; double* w; CvMat _w, _dEdw, hdr1, hdr2, ghdr1, ghdr2, _df; CvMat *x1, *x2, *grad1, *grad2, *temp; @@ -1100,23 +1100,23 @@ struct rprop_loop { // grab and preprocess input data if( x0->type == CV_32F ) - { + { for(int i = 0; i < dcount; i++ ) { const float* x0data = x0->data.fl[si+i]; double* xdata = x[0]+i*ivcount; - for( j = 0; j < ivcount; j++ ) + for(int j = 0; j < ivcount; j++ ) xdata[j] = x0data[j]*w[j*2] + w[j*2+1]; } - } + } else for(int i = 0; i < dcount; i++ ) { const double* x0data = x0->data.db[si+i]; double* xdata = x[0]+i*ivcount; - for( j = 0; j < ivcount; j++ ) + for(int j = 0; j < ivcount; j++ ) xdata[j] = x0data[j]*w[j*2] + w[j*2+1]; - } + } cvInitMatHeader( x1, dcount, ivcount, CV_64F, x[0] ); // forward pass, compute y[i]=w*x[i-1], x[i]=f(y[i]), df[i]=f'(y[i]) @@ -1144,7 +1144,7 @@ struct rprop_loop { double* gdata = grad1->data.db + i*ovcount; double sweight = sw ? sw[si+i] : inv_count, E1 = 0; - for( j = 0; j < ovcount; j++ ) + for(int j = 0; j < ovcount; j++ ) { double t = udata[j]*w[j*2] + w[j*2+1] - xdata[j]; gdata[j] = t*sweight; @@ -1168,7 +1168,7 @@ struct rprop_loop { } *E += sweight*E1; } - + // backward pass, update dEdw #ifdef HAVE_TBB static tbb::spin_mutex mutex; @@ -1191,10 +1191,10 @@ struct rprop_loop { { double* dst = _dEdw.data.db + n1*n2; const double* src = grad1->data.db + k*n2; - for( j = 0; j < n2; j++ ) + for(int j = 0; j < n2; j++ ) dst[j] += src[j]; } - + if (i > 1) cvInitMatHeader( &_w, n1, n2, CV_64F, weights[i] ); #ifdef HAVE_TBB @@ -1215,7 +1215,7 @@ struct rprop_loop { int CvANN_MLP::train_rprop( CvVectors x0, CvVectors u, const double* sw ) { - const int max_buf_sz = 1 << 16; + const int max_buf_size = 1 << 16; CvMat* dw = 0; CvMat* dEdw = 0; CvMat* prev_dEdw_sign = 0; @@ -1256,7 +1256,7 @@ int CvANN_MLP::train_rprop( CvVectors x0, CvVectors u, const double* sw ) cvZero( prev_dEdw_sign ); inv_count = 1./count; - dcount0 = max_buf_sz/(2*total); + dcount0 = max_buf_size/(2*total); dcount0 = MAX( dcount0, 1 ); dcount0 = MIN( dcount0, count ); buf_sz = dcount0*(total + max_count)*2; @@ -1297,8 +1297,8 @@ int CvANN_MLP::train_rprop( CvVectors x0, CvVectors u, const double* sw ) double E = 0; // first, iterate through all the samples and compute dEdw - cv::parallel_for(cv::BlockedRange(0, count), - rprop_loop(this, weights, count, ivcount, &x0, l_count, layer_sizes, + cv::parallel_for(cv::BlockedRange(0, count), + rprop_loop(this, weights, count, ivcount, &x0, l_count, layer_sizes, ovcount, max_count, &u, sw, inv_count, dEdw, dcount0, &E, buf_sz) ); @@ -1600,8 +1600,8 @@ CvANN_MLP::CvANN_MLP( const Mat& _layer_sizes, int _activ_func, void CvANN_MLP::create( const Mat& _layer_sizes, int _activ_func, double _f_param1, double _f_param2 ) { - CvMat layer_sizes = _layer_sizes; - create( &layer_sizes, _activ_func, _f_param1, _f_param2 ); + CvMat cvlayer_sizes = _layer_sizes; + create( &cvlayer_sizes, _activ_func, _f_param1, _f_param2 ); } int CvANN_MLP::train( const Mat& _inputs, const Mat& _outputs, @@ -1610,7 +1610,7 @@ int CvANN_MLP::train( const Mat& _inputs, const Mat& _outputs, { CvMat inputs = _inputs, outputs = _outputs, sweights = _sample_weights, sidx = _sample_idx; return train(&inputs, &outputs, sweights.data.ptr ? &sweights : 0, - sidx.data.ptr ? &sidx : 0, _params, flags); + sidx.data.ptr ? &sidx : 0, _params, flags); } float CvANN_MLP::predict( const Mat& _inputs, Mat& _outputs ) const @@ -1618,8 +1618,8 @@ float CvANN_MLP::predict( const Mat& _inputs, Mat& _outputs ) const CV_Assert(layer_sizes != 0); _outputs.create(_inputs.rows, layer_sizes->data.i[layer_sizes->cols-1], _inputs.type()); CvMat inputs = _inputs, outputs = _outputs; - - return predict(&inputs, &outputs); + + return predict(&inputs, &outputs); } /* End of file. */ diff --git a/modules/ml/src/boost.cpp b/modules/ml/src/boost.cpp index ff7120c..b31a83f 100644 --- a/modules/ml/src/boost.cpp +++ b/modules/ml/src/boost.cpp @@ -129,7 +129,7 @@ CvBoostTree::train( CvDTreeTrainData*, const CvMat* ) void -CvBoostTree::scale( double scale ) +CvBoostTree::scale( double _scale ) { CvDTreeNode* node = root; @@ -139,7 +139,7 @@ CvBoostTree::scale( double scale ) CvDTreeNode* parent; for(;;) { - node->value *= scale; + node->value *= _scale; if( !node->left ) break; node = node->left; @@ -501,7 +501,7 @@ CvBoostTree::find_split_ord_reg( CvDTreeNode* node, int vi, float init_quality, int i, best_i = -1; double L = 0, R = weights[n]; double best_val = init_quality, lsum = 0, rsum = node->value*R; - + // compensate for missing values for( i = n1; i < n; i++ ) { @@ -590,7 +590,7 @@ CvBoostTree::find_split_cat_reg( CvDTreeNode* node, int vi, float init_quality, { R += counts[i]; rsum += sum[i]; - sum[i] = fabs(counts[i]) > DBL_EPSILON ? sum[i]/counts[i] : 0; + sum[i] = fabs(counts[i]) > DBL_EPSILON ? sum[i]/counts[i] : 0; sum_ptr[i] = sum + i; } @@ -1030,7 +1030,7 @@ CvBoost::train( const CvMat* _train_data, int _tflag, __BEGIN__; int i; - + set_params( _params ); cvReleaseMat( &active_vars ); @@ -1057,7 +1057,7 @@ CvBoost::train( const CvMat* _train_data, int _tflag, if ( (_params.boost_type == LOGIT) || (_params.boost_type == GENTLE) ) data->do_responses_copy(); - + update_weights( 0 ); for( i = 0; i < params.weak_count; i++ ) @@ -1088,7 +1088,7 @@ CvBoost::train( const CvMat* _train_data, int _tflag, } bool CvBoost::train( CvMLData* _data, - CvBoostParams params, + CvBoostParams _params, bool update ) { bool result = false; @@ -1105,7 +1105,7 @@ bool CvBoost::train( CvMLData* _data, const CvMat* var_idx = _data->get_var_idx(); CV_CALL( result = train( values, CV_ROW_SAMPLE, response, var_idx, - train_sidx, var_types, missing, params, update ) ); + train_sidx, var_types, missing, _params, update ) ); __END__; @@ -1258,7 +1258,7 @@ CvBoost::update_weights( CvBoostTree* tree ) // invert the subsample mask cvXorS( subsample_mask, cvScalar(1.), subsample_mask ); data->get_vectors( subsample_mask, values, missing, 0 ); - + _sample = cvMat( 1, data->var_count, CV_32F ); _mask = cvMat( 1, data->var_count, CV_8U ); @@ -1458,17 +1458,17 @@ CvBoost::trim_weights() } -const CvMat* +const CvMat* CvBoost::get_active_vars( bool absolute_idx ) { CvMat* mask = 0; CvMat* inv_map = 0; CvMat* result = 0; - + CV_FUNCNAME( "CvBoost::get_active_vars" ); __BEGIN__; - + if( !weak ) CV_ERROR( CV_StsError, "The boosted tree ensemble has not been trained yet" ); @@ -1478,7 +1478,7 @@ CvBoost::get_active_vars( bool absolute_idx ) int i, j, nactive_vars; CvBoostTree* wtree; const CvDTreeNode* node; - + assert(!active_vars && !active_vars_abs); mask = cvCreateMat( 1, data->var_count, CV_8U ); inv_map = cvCreateMat( 1, data->var_count, CV_32S ); @@ -1518,7 +1518,7 @@ CvBoost::get_active_vars( bool absolute_idx ) } nactive_vars = cvCountNonZero(mask); - + //if ( nactive_vars > 0 ) { active_vars = cvCreateMat( 1, nactive_vars, CV_32S ); @@ -1538,7 +1538,7 @@ CvBoost::get_active_vars( bool absolute_idx ) j++; } } - + // second pass: now compute the condensed indices cvStartReadSeq( weak, &reader ); @@ -1638,7 +1638,7 @@ CvBoost::predict( const CvMat* _sample, const CvMat* _missing, "floating-point vector of the same number of components as the length of input slice" ); wstep = CV_IS_MAT_CONT(weak_responses->type) ? 1 : weak_responses->step/sizeof(float); } - + int var_count = active_vars->cols; const int* vtype = data->var_type->data.i; const int* cmap = data->cat_map->data.i; @@ -1738,7 +1738,7 @@ CvBoost::predict( const CvMat* _sample, const CvMat* _missing, CvBoostTree* wtree; const CvDTreeNode* node; CV_READ_SEQ_ELEM( wtree, reader ); - + node = wtree->get_root(); while( node->left ) { @@ -1757,14 +1757,14 @@ CvBoost::predict( const CvMat* _sample, const CvMat* _missing, { const int* avars = active_vars->data.i; const uchar* m = _missing ? _missing->data.ptr : 0; - + // full-featured version for( i = 0; i < weak_count; i++ ) { CvBoostTree* wtree; const CvDTreeNode* node; CV_READ_SEQ_ELEM( wtree, reader ); - + node = wtree->get_root(); while( node->left ) { @@ -1841,9 +1841,9 @@ float CvBoost::calc_error( CvMLData* _data, int type, std::vector *resp ) { CvMat sample, miss; int si = sidx ? sidx[i] : i; - cvGetRow( values, &sample, si ); - if( missing ) - cvGetRow( missing, &miss, si ); + cvGetRow( values, &sample, si ); + if( missing ) + cvGetRow( missing, &miss, si ); float r = (float)predict( &sample, missing ? &miss : 0 ); if( pred_resp ) pred_resp[i] = r; @@ -1859,15 +1859,15 @@ float CvBoost::calc_error( CvMLData* _data, int type, std::vector *resp ) CvMat sample, miss; int si = sidx ? sidx[i] : i; cvGetRow( values, &sample, si ); - if( missing ) - cvGetRow( missing, &miss, si ); + if( missing ) + cvGetRow( missing, &miss, si ); float r = (float)predict( &sample, missing ? &miss : 0 ); if( pred_resp ) pred_resp[i] = r; float d = r - response->data.fl[si*r_step]; err += d*d; } - err = sample_count ? err / (float)sample_count : -FLT_MAX; + err = sample_count ? err / (float)sample_count : -FLT_MAX; } return err; } @@ -2097,10 +2097,10 @@ CvBoost::CvBoost( const Mat& _train_data, int _tflag, default_model_name = "my_boost_tree"; active_vars = active_vars_abs = orig_response = sum_response = weak_eval = subsample_mask = weights = subtree_weights = 0; - + train( _train_data, _tflag, _responses, _var_idx, _sample_idx, _var_type, _missing_mask, _params ); -} +} bool @@ -2130,7 +2130,7 @@ CvBoost::predict( const Mat& _sample, const Mat& _missing, weak_count = weak->total; slice.start_index = 0; } - + if( !(weak_responses->data && weak_responses->type() == CV_32FC1 && (weak_responses->cols == 1 || weak_responses->rows == 1) && weak_responses->cols + weak_responses->rows - 1 == weak_count) ) diff --git a/modules/ml/src/ertrees.cpp b/modules/ml/src/ertrees.cpp index 0460527..8a3828d 100644 --- a/modules/ml/src/ertrees.cpp +++ b/modules/ml/src/ertrees.cpp @@ -71,7 +71,7 @@ void CvERTreeTrainData::set_data( const CvMat* _train_data, int _tflag, CV_FUNCNAME( "CvERTreeTrainData::set_data" ); __BEGIN__; - + int sample_all = 0, r_type, cv_n; int total_c_count = 0; int tree_block_size, temp_block_size, max_split_size, nv_size, cv_size = 0; @@ -79,10 +79,10 @@ void CvERTreeTrainData::set_data( const CvMat* _train_data, int _tflag, int vi, i, size; char err[100]; const int *sidx = 0, *vidx = 0; - + if ( _params.use_surrogates ) CV_ERROR(CV_StsBadArg, "CvERTrees do not support surrogate splits"); - + if( _update_data && data_root ) { CV_ERROR(CV_StsBadArg, "CvERTrees do not support data update"); @@ -143,17 +143,17 @@ void CvERTreeTrainData::set_data( const CvMat* _train_data, int _tflag, CV_ERROR( CV_StsBadArg, "The array of _responses must be an integer or " "floating-point vector containing as many elements as " "the total number of samples in the training data matrix" ); - + is_buf_16u = false; - if ( sample_count < 65536 ) - is_buf_16u = true; - + if ( sample_count < 65536 ) + is_buf_16u = true; + r_type = CV_VAR_CATEGORICAL; if( _var_type ) CV_CALL( var_type0 = cvPreprocessVarType( _var_type, var_idx, var_count, &r_type )); CV_CALL( var_type = cvCreateMat( 1, var_count+2, CV_32SC1 )); - + cat_var_count = 0; ord_var_count = -1; @@ -182,7 +182,7 @@ void CvERTreeTrainData::set_data( const CvMat* _train_data, int _tflag, buf_size = (work_var_count + 1)*sample_count; shared = _shared; buf_count = shared ? 2 : 1; - + if ( is_buf_16u ) { CV_CALL( buf = cvCreateMat( buf_count, buf_size, CV_16UC1 )); @@ -192,13 +192,13 @@ void CvERTreeTrainData::set_data( const CvMat* _train_data, int _tflag, { CV_CALL( buf = cvCreateMat( buf_count, buf_size, CV_32SC1 )); CV_CALL( int_ptr = (int**)cvAlloc( sample_count*sizeof(int_ptr[0]) )); - } + } size = is_classifier ? cat_var_count+1 : cat_var_count; size = !size ? 1 : size; CV_CALL( cat_count = cvCreateMat( 1, size, CV_32SC1 )); CV_CALL( cat_ofs = cvCreateMat( 1, size, CV_32SC1 )); - + size = is_classifier ? (cat_var_count + 1)*params.max_categories : cat_var_count*params.max_categories; size = !size ? 1 : size; CV_CALL( cat_map = cvCreateMat( 1, size, CV_32SC1 )); @@ -283,12 +283,12 @@ void CvERTreeTrainData::set_data( const CvMat* _train_data, int _tflag, { int c_count, prev_label; int* c_map; - + if (is_buf_16u) udst = (unsigned short*)(buf->data.s + ci*sample_count); else idst = buf->data.i + ci*sample_count; - + // copy data for( i = 0; i < sample_count; i++ ) { @@ -322,7 +322,7 @@ void CvERTreeTrainData::set_data( const CvMat* _train_data, int _tflag, _idst[i] = val; pair16u32s_ptr[i].u = udst + i; pair16u32s_ptr[i].i = _idst + i; - } + } else { idst[i] = val; @@ -397,7 +397,7 @@ void CvERTreeTrainData::set_data( const CvMat* _train_data, int _tflag, // replace labels for missing values with -1 for( ; i < sample_count; i++ ) *int_ptr[i] = -1; - } + } } else if( ci < 0 ) // process ordered variable { @@ -442,15 +442,15 @@ void CvERTreeTrainData::set_data( const CvMat* _train_data, int _tflag, if( cv_n ) { - unsigned short* udst = 0; - int* idst = 0; + unsigned short* usdst = 0; + int* idst2 = 0; if (is_buf_16u) { - udst = (unsigned short*)(buf->data.s + (get_work_var_count()-1)*sample_count); + usdst = (unsigned short*)(buf->data.s + (get_work_var_count()-1)*sample_count); for( i = vi = 0; i < sample_count; i++ ) { - udst[i] = (unsigned short)vi++; + usdst[i] = (unsigned short)vi++; vi &= vi < cv_n ? -1 : 0; } @@ -459,15 +459,15 @@ void CvERTreeTrainData::set_data( const CvMat* _train_data, int _tflag, int a = (*rng)(sample_count); int b = (*rng)(sample_count); unsigned short unsh = (unsigned short)vi; - CV_SWAP( udst[a], udst[b], unsh ); + CV_SWAP( usdst[a], usdst[b], unsh ); } } else { - idst = buf->data.i + (get_work_var_count()-1)*sample_count; + idst2 = buf->data.i + (get_work_var_count()-1)*sample_count; for( i = vi = 0; i < sample_count; i++ ) { - idst[i] = vi++; + idst2[i] = vi++; vi &= vi < cv_n ? -1 : 0; } @@ -475,12 +475,12 @@ void CvERTreeTrainData::set_data( const CvMat* _train_data, int _tflag, { int a = (*rng)(sample_count); int b = (*rng)(sample_count); - CV_SWAP( idst[a], idst[b], vi ); + CV_SWAP( idst2[a], idst2[b], vi ); } } } - if ( cat_map ) + if ( cat_map ) cat_map->cols = MAX( total_c_count, 1 ); max_split_size = cvAlign(sizeof(CvDTreeSplit) + @@ -532,7 +532,7 @@ void CvERTreeTrainData::get_ord_var_data( CvDTreeNode* n, int vi, float* ord_val const float** ord_values, const int** missing, int* sample_indices_buf ) { int vidx = var_idx ? var_idx->data.i[vi] : vi; - int node_sample_count = n->sample_count; + int node_sample_count = n->sample_count; // may use missing_buf as buffer for sample indices! const int* sample_indices = get_sample_indices(n, sample_indices_buf ? sample_indices_buf : missing_buf); @@ -580,7 +580,7 @@ const int* CvERTreeTrainData::get_cat_var_data( CvDTreeNode* n, int vi, int* cat if( !is_buf_16u ) cat_values = buf->data.i + n->buf_idx*buf->cols + ci*sample_count + n->offset; else { - const unsigned short* short_values = (const unsigned short*)(buf->data.s + n->buf_idx*buf->cols + + const unsigned short* short_values = (const unsigned short*)(buf->data.s + n->buf_idx*buf->cols + ci*sample_count + n->offset); for( int i = 0; i < n->sample_count; i++ ) cat_values_buf[i] = short_values[i]; @@ -591,7 +591,7 @@ const int* CvERTreeTrainData::get_cat_var_data( CvDTreeNode* n, int vi, int* cat void CvERTreeTrainData::get_vectors( const CvMat* _subsample_idx, float* values, uchar* missing, - float* responses, bool get_class_idx ) + float* _responses, bool get_class_idx ) { CvMat* subsample_idx = 0; CvMat* subsample_co = 0; @@ -664,7 +664,7 @@ void CvERTreeTrainData::get_vectors( const CvMat* _subsample_idx, } // copy responses - if( responses ) + if( _responses ) { if( is_classifier ) { @@ -675,10 +675,10 @@ void CvERTreeTrainData::get_vectors( const CvMat* _subsample_idx, int idx = sidx ? sidx[i] : i; int val = get_class_idx ? src[idx] : cat_map->data.i[cat_ofs->data.i[cat_var_count]+src[idx]]; - responses[i] = (float)val; + _responses[i] = (float)val; } } - else + else { float* _values_buf = (float*)(uchar*)inn_buf; int* sample_idx_buf = (int*)(_values_buf + sample_count); @@ -686,7 +686,7 @@ void CvERTreeTrainData::get_vectors( const CvMat* _subsample_idx, for( i = 0; i < count; i++ ) { int idx = sidx ? sidx[i] : i; - responses[i] = _values[idx]; + _responses[i] = _values[idx]; } } } @@ -700,7 +700,7 @@ void CvERTreeTrainData::get_vectors( const CvMat* _subsample_idx, CvDTreeNode* CvERTreeTrainData::subsample_data( const CvMat* _subsample_idx ) { CvDTreeNode* root = 0; - + CV_FUNCNAME( "CvERTreeTrainData::subsample_data" ); __BEGIN__; @@ -853,7 +853,7 @@ CvDTreeSplit* CvForestERTree::find_split_ord_class( CvDTreeNode* node, int vi, f const float epsilon = FLT_EPSILON*2; const float split_delta = (1 + FLT_EPSILON) * FLT_EPSILON; - int n = node->sample_count, i; + int n = node->sample_count; int m = data->get_num_classes(); cv::AutoBuffer inn_buf; @@ -882,8 +882,8 @@ CvDTreeSplit* CvForestERTree::find_split_ord_class( CvDTreeNode* node, int vi, f for (; smpi < n; smpi++) { float ptemp = values[smpi]; - int m = missing[smpi]; - if (m) continue; + int ms = missing[smpi]; + if (ms) continue; if ( ptemp < pmin) pmin = ptemp; if ( ptemp > pmax) @@ -898,7 +898,7 @@ CvDTreeSplit* CvForestERTree::find_split_ord_class( CvDTreeNode* node, int vi, f if (split_val - pmin <= FLT_EPSILON) split_val = pmin + split_delta; if (pmax - split_val <= FLT_EPSILON) - split_val = pmax - split_delta; + split_val = pmax - split_delta; // calculate Gini index if ( !priors ) @@ -906,9 +906,9 @@ CvDTreeSplit* CvForestERTree::find_split_ord_class( CvDTreeNode* node, int vi, f cv::AutoBuffer lrc(m*2); int *lc = lrc, *rc = lc + m; int L = 0, R = 0; - + // init arrays of class instance counters on both sides of the split - for( i = 0; i < m; i++ ) + for(int i = 0; i < m; i++ ) { lc[i] = 0; rc[i] = 0; @@ -917,8 +917,8 @@ CvDTreeSplit* CvForestERTree::find_split_ord_class( CvDTreeNode* node, int vi, f { int r = responses[si]; float val = values[si]; - int m = missing[si]; - if (m) continue; + int ms = missing[si]; + if (ms) continue; if ( val < split_val ) { lc[r]++; @@ -942,9 +942,9 @@ CvDTreeSplit* CvForestERTree::find_split_ord_class( CvDTreeNode* node, int vi, f cv::AutoBuffer lrc(m*2); double *lc = lrc, *rc = lc + m; double L = 0, R = 0; - + // init arrays of class instance counters on both sides of the split - for( i = 0; i < m; i++ ) + for(int i = 0; i < m; i++ ) { lc[i] = 0; rc[i] = 0; @@ -953,9 +953,9 @@ CvDTreeSplit* CvForestERTree::find_split_ord_class( CvDTreeNode* node, int vi, f { int r = responses[si]; float val = values[si]; - int m = missing[si]; + int ms = missing[si]; double p = priors[r]; - if (m) continue; + if (ms) continue; if ( val < split_val ) { lc[r] += p; @@ -974,7 +974,7 @@ CvDTreeSplit* CvForestERTree::find_split_ord_class( CvDTreeNode* node, int vi, f } best_val = (lbest_val*R + rbest_val*L) / (L*R); } - + } CvDTreeSplit* split = 0; @@ -995,7 +995,7 @@ CvDTreeSplit* CvForestERTree::find_split_cat_class( CvDTreeNode* node, int vi, f { int ci = data->get_var_type(vi); int n = node->sample_count; - int cm = data->get_num_classes(); + int cm = data->get_num_classes(); int vm = data->cat_count->data.i[ci]; double best_val = init_quality; CvDTreeSplit *split = 0; @@ -1009,8 +1009,8 @@ CvDTreeSplit* CvForestERTree::find_split_cat_class( CvDTreeNode* node, int vi, f const int* labels = data->get_cat_var_data( node, vi, ext_buf ); const int* responses = data->get_class_labels( node, ext_buf + n ); - - const double* priors = data->have_priors ? data->priors_mult->data.db : 0; + + const double* priors = data->have_priors ? data->priors_mult->data.db : 0; // create random class mask cv::AutoBuffer valid_cidx(vm); @@ -1078,7 +1078,7 @@ CvDTreeSplit* CvForestERTree::find_split_cat_class( CvDTreeNode* node, int vi, f if (var_class_mask->data.ptr[mask_class_idx]) { lc[r]++; - L++; + L++; split->subset[var_class_idx >> 5] |= 1 << (var_class_idx & 31); } else @@ -1091,7 +1091,7 @@ CvDTreeSplit* CvForestERTree::find_split_cat_class( CvDTreeNode* node, int vi, f { lbest_val += lc[i]*lc[i]; rbest_val += rc[i]*rc[i]; - } + } best_val = (lbest_val*R + rbest_val*L) / ((double)(L*R)); } else @@ -1113,11 +1113,11 @@ CvDTreeSplit* CvForestERTree::find_split_cat_class( CvDTreeNode* node, int vi, f continue; double p = priors[si]; int mask_class_idx = valid_cidx[var_class_idx]; - + if (var_class_mask->data.ptr[mask_class_idx]) { lc[r]+=(int)p; - L+=p; + L+=p; split->subset[var_class_idx >> 5] |= 1 << (var_class_idx & 31); } else @@ -1136,8 +1136,8 @@ CvDTreeSplit* CvForestERTree::find_split_cat_class( CvDTreeNode* node, int vi, f split->quality = (float)best_val; cvReleaseMat(&var_class_mask); - } - } + } + } return split; } @@ -1193,7 +1193,7 @@ CvDTreeSplit* CvForestERTree::find_split_ord_reg( CvDTreeNode* node, int vi, flo if (split_val - pmin <= FLT_EPSILON) split_val = pmin + split_delta; if (pmax - split_val <= FLT_EPSILON) - split_val = pmax - split_delta; + split_val = pmax - split_delta; for (int si = 0; si < n; si++) { @@ -1209,7 +1209,7 @@ CvDTreeSplit* CvForestERTree::find_split_ord_reg( CvDTreeNode* node, int vi, flo else { rsum += r; - R++; + R++; } } best_val = (lsum*lsum*R + rsum*rsum*L)/((double)L*R); @@ -1306,7 +1306,7 @@ CvDTreeSplit* CvForestERTree::find_split_cat_reg( CvDTreeNode* node, int vi, flo if (var_class_mask->data.ptr[mask_class_idx]) { lsum += r; - L++; + L++; split->subset[var_class_idx >> 5] |= 1 << (var_class_idx & 31); } else @@ -1320,8 +1320,8 @@ CvDTreeSplit* CvForestERTree::find_split_cat_reg( CvDTreeNode* node, int vi, flo split->quality = (float)best_val; cvReleaseMat(&var_class_mask); - } - } + } + } return split; } @@ -1358,7 +1358,7 @@ void CvForestERTree::split_node_data( CvDTreeNode* node ) { int ci = data->get_var_type(vi); if (ci >= 0) continue; - + int n1 = node->get_num_valid(vi), nr1 = 0; float* values_buf = (float*)(uchar*)inn_buf; int* missing_buf = (int*)(values_buf + n); @@ -1369,7 +1369,7 @@ void CvForestERTree::split_node_data( CvDTreeNode* node ) for( i = 0; i < n; i++ ) nr1 += ((!missing[i]) & dir[i]); left->set_num_valid(vi, n1 - nr1); - right->set_num_valid(vi, nr1); + right->set_num_valid(vi, nr1); } // split categorical vars, responses and cv_labels using new_idx relocation table for( vi = 0; vi < data->get_work_var_count() + data->ord_var_count; vi++ ) @@ -1385,11 +1385,11 @@ void CvForestERTree::split_node_data( CvDTreeNode* node ) if (data->is_buf_16u) { - unsigned short *ldst = (unsigned short *)(buf->data.s + left->buf_idx*buf->cols + + unsigned short *ldst = (unsigned short *)(buf->data.s + left->buf_idx*buf->cols + ci*scount + left->offset); - unsigned short *rdst = (unsigned short *)(buf->data.s + right->buf_idx*buf->cols + + unsigned short *rdst = (unsigned short *)(buf->data.s + right->buf_idx*buf->cols + ci*scount + right->offset); - + for( i = 0; i < n; i++ ) { int d = dir[i]; @@ -1415,11 +1415,11 @@ void CvForestERTree::split_node_data( CvDTreeNode* node ) } else { - int *ldst = buf->data.i + left->buf_idx*buf->cols + + int *ldst = buf->data.i + left->buf_idx*buf->cols + ci*scount + left->offset; - int *rdst = buf->data.i + right->buf_idx*buf->cols + + int *rdst = buf->data.i + right->buf_idx*buf->cols + ci*scount + right->offset; - + for( i = 0; i < n; i++ ) { int d = dir[i]; @@ -1435,7 +1435,7 @@ void CvForestERTree::split_node_data( CvDTreeNode* node ) *ldst = idx; ldst++; } - + } if( vi < data->var_count ) @@ -1443,7 +1443,7 @@ void CvForestERTree::split_node_data( CvDTreeNode* node ) left->set_num_valid(vi, n1 - nr1); right->set_num_valid(vi, nr1); } - } + } } // split sample indices @@ -1457,14 +1457,14 @@ void CvForestERTree::split_node_data( CvDTreeNode* node ) temp_buf[i] = sample_idx_src[i]; int pos = data->get_work_var_count(); - + if (data->is_buf_16u) { - unsigned short* ldst = (unsigned short*)(buf->data.s + left->buf_idx*buf->cols + + unsigned short* ldst = (unsigned short*)(buf->data.s + left->buf_idx*buf->cols + pos*scount + left->offset); - unsigned short* rdst = (unsigned short*)(buf->data.s + right->buf_idx*buf->cols + + unsigned short* rdst = (unsigned short*)(buf->data.s + right->buf_idx*buf->cols + pos*scount + right->offset); - + for (i = 0; i < n; i++) { int d = dir[i]; @@ -1483,9 +1483,9 @@ void CvForestERTree::split_node_data( CvDTreeNode* node ) } else { - int* ldst = buf->data.i + left->buf_idx*buf->cols + + int* ldst = buf->data.i + left->buf_idx*buf->cols + pos*scount + left->offset; - int* rdst = buf->data.i + right->buf_idx*buf->cols + + int* rdst = buf->data.i + right->buf_idx*buf->cols + pos*scount + right->offset; for (i = 0; i < n; i++) { @@ -1504,9 +1504,9 @@ void CvForestERTree::split_node_data( CvDTreeNode* node ) } } } - + // deallocate the parent node data that is not needed anymore - data->free_node_data(node); + data->free_node_data(node); } CvERTrees::CvERTrees() @@ -1576,10 +1576,10 @@ bool CvERTrees::train( const CvMat* _train_data, int _tflag, __END__ return result; - + } -bool CvERTrees::train( CvMLData* data, CvRTParams params) +bool CvERTrees::train( CvMLData* _data, CvRTParams params) { bool result = false; @@ -1587,7 +1587,7 @@ bool CvERTrees::train( CvMLData* data, CvRTParams params) __BEGIN__; - CV_CALL( result = CvRTrees::train( data, params) ); + CV_CALL( result = CvRTrees::train( _data, params) ); __END__; @@ -1609,7 +1609,7 @@ bool CvERTrees::grow_forest( const CvTermCriteria term_crit ) const int dims = data->var_count; float maximal_response = 0; - CvMat* oob_sample_votes = 0; + CvMat* oob_sample_votes = 0; CvMat* oob_responses = 0; float* oob_samples_perm_ptr= 0; @@ -1625,7 +1625,7 @@ bool CvERTrees::grow_forest( const CvTermCriteria term_crit ) // initialize these variable to avoid warning C4701 CvMat oob_predictions_sum = cvMat( 1, 1, CV_32FC1 ); CvMat oob_num_of_predictions = cvMat( 1, 1, CV_32FC1 ); - + nsamples = data->sample_count; nclasses = data->get_num_classes(); @@ -1647,11 +1647,11 @@ bool CvERTrees::grow_forest( const CvTermCriteria term_crit ) cvGetRow( oob_responses, &oob_predictions_sum, 0 ); cvGetRow( oob_responses, &oob_num_of_predictions, 1 ); } - + CV_CALL(oob_samples_perm_ptr = (float*)cvAlloc( sizeof(float)*nsamples*dims )); CV_CALL(samples_ptr = (float*)cvAlloc( sizeof(float)*nsamples*dims )); CV_CALL(missing_ptr = (uchar*)cvAlloc( sizeof(uchar)*nsamples*dims )); - CV_CALL(true_resp_ptr = (float*)cvAlloc( sizeof(float)*nsamples )); + CV_CALL(true_resp_ptr = (float*)cvAlloc( sizeof(float)*nsamples )); CV_CALL(data->get_vectors( 0, samples_ptr, missing_ptr, true_resp_ptr )); { @@ -1661,7 +1661,7 @@ bool CvERTrees::grow_forest( const CvTermCriteria term_crit ) maximal_response = (float)MAX( MAX( fabs(minval), fabs(maxval) ), 0 ); } } - + trees = (CvForestTree**)cvAlloc( sizeof(trees[0])*max_ntrees ); memset( trees, 0, sizeof(trees[0])*max_ntrees ); @@ -1692,7 +1692,7 @@ bool CvERTrees::grow_forest( const CvTermCriteria term_crit ) sample.data.fl += dims, missing.data.ptr += dims ) { CvDTreeNode* predicted_node = 0; - + // predict oob samples if( !predicted_node ) CV_CALL(predicted_node = tree->predict(&sample, &missing, true)); @@ -1796,12 +1796,12 @@ bool CvERTrees::grow_forest( const CvTermCriteria term_crit ) } result = true; - + cvFree( &oob_samples_perm_ptr ); cvFree( &samples_ptr ); cvFree( &missing_ptr ); cvFree( &true_resp_ptr ); - + cvReleaseMat( &sample_idx_for_tree ); cvReleaseMat( &oob_sample_votes ); diff --git a/modules/ml/src/gbt.cpp b/modules/ml/src/gbt.cpp index 002f240..7b08d4c 100644 --- a/modules/ml/src/gbt.cpp +++ b/modules/ml/src/gbt.cpp @@ -12,7 +12,7 @@ using namespace std; static CV_IMPLEMENT_QSORT_EX( icvSortFloat, float, CV_CMP_FLOAT, float) //=========================================================================== -string ToString(int i) +static string ToString(int i) { stringstream tmp; tmp << i; @@ -25,7 +25,7 @@ string ToString(int i) //----------------------------- CvGBTreesParams ----------------------------- //=========================================================================== -CvGBTreesParams::CvGBTreesParams() +CvGBTreesParams::CvGBTreesParams() : CvDTreeParams( 3, 10, 0, false, 10, 0, false, false, 0 ) { weak_count = 200; @@ -36,8 +36,8 @@ CvGBTreesParams::CvGBTreesParams() //=========================================================================== -CvGBTreesParams::CvGBTreesParams( int _loss_function_type, int _weak_count, - float _shrinkage, float _subsample_portion, +CvGBTreesParams::CvGBTreesParams( int _loss_function_type, int _weak_count, + float _shrinkage, float _subsample_portion, int _max_depth, bool _use_surrogates ) : CvDTreeParams( 3, 10, 0, false, 10, 0, false, false, 0 ) { @@ -64,7 +64,7 @@ CvGBTrees::CvGBTrees() class_labels = 0; class_count = 1; delta = 0.0f; - + clear(); } @@ -88,10 +88,10 @@ void CvGBTrees::clear() //data->shared = false; for (int i=0; istorage) ); delete[] weak; } - if (data) + if (data) { data->shared = false; delete data; @@ -165,14 +165,14 @@ bool CvGBTrees::problem_type() const //=========================================================================== -bool -CvGBTrees::train( CvMLData* data, CvGBTreesParams params, bool update ) +bool +CvGBTrees::train( CvMLData* _data, CvGBTreesParams _params, bool update ) { bool result; - result = train ( data->get_values(), CV_ROW_SAMPLE, - data->get_responses(), data->get_var_idx(), - data->get_train_sample_idx(), data->get_var_types(), - data->get_missing(), params, update); + result = train ( _data->get_values(), CV_ROW_SAMPLE, + _data->get_responses(), _data->get_var_idx(), + _data->get_train_sample_idx(), _data->get_var_types(), + _data->get_missing(), _params, update); //update is not supported return result; } @@ -218,14 +218,14 @@ CvGBTrees::train( const CvMat* _train_data, int _tflag, } orig_response = cvCreateMat( 1, n, CV_32F ); - int step = (_responses->cols > _responses->rows) ? 1 : _responses->step / CV_ELEM_SIZE(_responses->type); + int step = (_responses->cols > _responses->rows) ? 1 : _responses->step / CV_ELEM_SIZE(_responses->type); switch (CV_MAT_TYPE(_responses->type)) { case CV_32FC1: - { - for (int i=0; idata.fl[i] = _responses->data.fl[i*step]; - }; break; + }; break; case CV_32SC1: { for (int i=0; idata.i[0] = int(orig_response->data.fl[0]); int j = 1; @@ -274,14 +274,14 @@ CvGBTrees::train( const CvMat* _train_data, int _tflag, if (_sample_idx) { int sample_idx_len = get_len(_sample_idx); - + switch (CV_MAT_TYPE(_sample_idx->type)) { case CV_32SC1: { sample_idx = cvCreateMat( 1, sample_idx_len, CV_32S ); for (int i=0; idata.i[i] = _sample_idx->data.i[i]; + sample_idx->data.i[i] = _sample_idx->data.i[i]; } break; case CV_8S: case CV_8U: @@ -294,7 +294,7 @@ CvGBTrees::train( const CvMat* _train_data, int _tflag, for (int i=0; idata.ptr[i] )) sample_idx->data.i[active_samples_count++] = i; - + } break; default: CV_Error(CV_StsUnmatchedFormats, "_sample_idx should be a 32sC1, 8sC1 or 8uC1 vector."); } @@ -335,14 +335,14 @@ CvGBTrees::train( const CvMat* _train_data, int _tflag, storage = cvCreateMemStorage(); weak[i] = cvCreateSeq( 0, sizeof(CvSeq), sizeof(CvDTree*), storage ); storage = 0; - } + } // subsample params and data rng = &cv::theRNG(); - int samples_count = get_len(sample_idx); + int samples_count = get_len(sample_idx); - params.subsample_portion = params.subsample_portion <= FLT_EPSILON || + params.subsample_portion = params.subsample_portion <= FLT_EPSILON || 1 - params.subsample_portion <= FLT_EPSILON ? 1 : params.subsample_portion; int train_sample_count = cvFloor(params.subsample_portion * samples_count); @@ -358,12 +358,12 @@ CvGBTrees::train( const CvMat* _train_data, int _tflag, *subsample_test = cvMat( 1, test_sample_count, CV_32SC1, idx_data + train_sample_count ); } - + // training procedure for ( int i=0; i < params.weak_count; ++i ) { - do_subsample(); + do_subsample(); for ( int k=0; k < class_count; ++k ) { find_gradient(k); @@ -387,21 +387,21 @@ CvGBTrees::train( const CvMat* _train_data, int _tflag, cvGetRow( data->train_data, &x, idx); else cvGetCol( data->train_data, &x, idx); - + if (missing) { if (_tflag == CV_ROW_SAMPLE) cvGetRow( missing, &x_miss, idx); else cvGetCol( missing, &x_miss, idx); - + res = (float)tree->predict(&x, &x_miss)->value; } else { res = (float)tree->predict(&x)->value; } - sum_response_tmp->data.fl[idx + k*n] = + sum_response_tmp->data.fl[idx + k*n] = sum_response->data.fl[idx + k*n] + params.shrinkage * res; } @@ -421,13 +421,13 @@ CvGBTrees::train( const CvMat* _train_data, int _tflag, cvReleaseMat(&new_responses); data->free_train_data(); - return true; + return true; } // CvGBTrees::train(...) //=========================================================================== -float Sign(float x) +inline float Sign(float x) { if (x<0.0f) return -1.0f; else if (x>0.0f) return 1.0f; @@ -484,7 +484,7 @@ void CvGBTrees::find_gradient(const int k) residuals[i] = fabs(resp_data[idx] - current_data[idx]); } icvSortFloat(residuals, n, 0.0f); - + delta = residuals[int(ceil(n*alpha))]; for (int i=0; icols > sample_idx->rows) ? 1 : sample_idx->step/CV_ELEM_SIZE(sample_idx->type); int idx = *(sample_data + subsample_data[i]*s_step); - + for (int j=0; jdata.i[orig_label]+1)) - (float)(exp_fk / exp_sfi); - */ - int ensemble_label = 0; - while (class_labels->data.i[ensemble_label] - orig_label) - ensemble_label++; - + */ + int ensemble_label = 0; + while (class_labels->data.i[ensemble_label] - orig_label) + ensemble_label++; + grad_data[idx] = (float)(!(k-ensemble_label)) - (float)(exp_fk / exp_sfi); } @@ -550,19 +550,19 @@ void CvGBTrees::change_values(CvDTree* tree, const int _k) for (int i=0; itflag == CV_ROW_SAMPLE) + int idx = *(sample_data + subsample_data[i]*s_step); + if (data->tflag == CV_ROW_SAMPLE) cvGetRow( data->train_data, &x, idx); else cvGetCol( data->train_data, &x, idx); - + if (missing) { if (data->tflag == CV_ROW_SAMPLE) cvGetRow( missing, &miss_x, idx); else cvGetCol( missing, &miss_x, idx); - + predictions[i] = tree->predict(&x, &miss_x); } else @@ -585,7 +585,7 @@ void CvGBTrees::change_values(CvDTree* tree, const int _k) if (!samples_in_leaf) // It should not be done anyways! but... { leaves[i]->value = 0.0; - continue; + continue; } CvMat* leaf_idx = cvCreateMat(1, samples_in_leaf, CV_32S); @@ -606,12 +606,12 @@ void CvGBTrees::change_values(CvDTree* tree, const int _k) int len = sum_response_tmp->cols; for (int j=0; jdata.fl[idx + _k*len] = sum_response->data.fl[idx + _k*len] + params.shrinkage * value; } - leaf_idx_data = 0; + leaf_idx_data = 0; cvReleaseMat(&leaf_idx); } @@ -634,13 +634,13 @@ void CvGBTrees::change_values(CvDTree* tree, const int _k) /* void CvGBTrees::change_values(CvDTree* tree, const int _k) { - + CvDTreeNode** leaves; int leaves_count = 0; - int offset = _k*sum_response_tmp->cols; - CvMat leaf_idx; - leaf_idx.rows = 1; - + int offset = _k*sum_response_tmp->cols; + CvMat leaf_idx; + leaf_idx.rows = 1; + leaves = GetLeaves( tree, leaves_count); for (int i=0; iget_sample_indices(leaves[i], leaf_idx_data); //CvMat* leaf_idx = new CvMat(); //cvInitMatHeader(leaf_idx, n, 1, CV_32S, leaf_idx_data); - leaf_idx.cols = n; - leaf_idx.data.i = leaf_idx_data; + leaf_idx.cols = n; + leaf_idx.data.i = leaf_idx_data; float value = find_optimal_value(&leaf_idx); leaves[i]->value = value; - float val = params.shrinkage * value; + float val = params.shrinkage * value; + - for (int j=0; jtype) != CV_32F) + if (CV_MAT_TYPE(weak_responses->type) != CV_32F) return 0.0f; if ((k >= 0) && (krows != 1)) return 0.0f; @@ -839,7 +839,7 @@ float CvGBTrees::predict_serial( const CvMat* _sample, const CvMat* _missing, if (weak_responses->cols != weak_count) return 0.0f; } - + float* sum = new float[class_count]; memset(sum, 0, class_count*sizeof(float)); @@ -847,7 +847,7 @@ float CvGBTrees::predict_serial( const CvMat* _sample, const CvMat* _missing, { if ((weak[i]) && (weak_count)) { - cvStartReadSeq( weak[i], &reader ); + cvStartReadSeq( weak[i], &reader ); cvSetSeqReaderPos( &reader, slice.start_index ); for (int j=0; jdata.i[i] == class_label+1) orig_class_label = i; - */ - int orig_class_label = class_labels->data.i[class_label]; + */ + int orig_class_label = class_labels->data.i[class_label]; return float(orig_class_label); } @@ -903,69 +903,71 @@ float CvGBTrees::predict_serial( const CvMat* _sample, const CvMat* _missing, class Tree_predictor { private: - pCvSeq* weak; - float* sum; - const int k; - const CvMat* sample; - const CvMat* missing; + pCvSeq* weak; + float* sum; + const int k; + const CvMat* sample; + const CvMat* missing; const float shrinkage; - + #ifdef HAVE_TBB static tbb::spin_mutex SumMutex; #endif public: - Tree_predictor() : weak(0), sum(0), k(0), sample(0), missing(0), shrinkage(1.0f) {} - Tree_predictor(pCvSeq* _weak, const int _k, const float _shrinkage, - const CvMat* _sample, const CvMat* _missing, float* _sum ) : - weak(_weak), sum(_sum), k(_k), sample(_sample), + Tree_predictor() : weak(0), sum(0), k(0), sample(0), missing(0), shrinkage(1.0f) {} + Tree_predictor(pCvSeq* _weak, const int _k, const float _shrinkage, + const CvMat* _sample, const CvMat* _missing, float* _sum ) : + weak(_weak), sum(_sum), k(_k), sample(_sample), missing(_missing), shrinkage(_shrinkage) - {} - + {} + Tree_predictor( const Tree_predictor& p, cv::Split ) : - weak(p.weak), sum(p.sum), k(p.k), sample(p.sample), + weak(p.weak), sum(p.sum), k(p.k), sample(p.sample), missing(p.missing), shrinkage(p.shrinkage) - {} + {} + + Tree_predictor& operator=( const Tree_predictor& ) + { return *this; } - Tree_predictor& operator=( const Tree_predictor& ) - { return *this; } - virtual void operator()(const cv::BlockedRange& range) const - { + { #ifdef HAVE_TBB tbb::spin_mutex::scoped_lock lock; #endif CvSeqReader reader; - int begin = range.begin(); - int end = range.end(); - - int weak_count = end - begin; - CvDTree* tree; - - for (int i=0; ipredict(sample, missing)->value); - } - } + int begin = range.begin(); + int end = range.end(); + + int weak_count = end - begin; + CvDTree* tree; + + for (int i=0; ipredict(sample, missing)->value); + } + } #ifdef HAVE_TBB lock.acquire(SumMutex); - sum[i] += tmp_sum; + sum[i] += tmp_sum; lock.release(); #else sum[i] += tmp_sum; #endif - } - } // Tree_predictor::operator() - + } + } // Tree_predictor::operator() + + virtual ~Tree_predictor() {} + }; // class Tree_predictor @@ -976,28 +978,28 @@ tbb::spin_mutex Tree_predictor::SumMutex; float CvGBTrees::predict( const CvMat* _sample, const CvMat* _missing, - CvMat* /*weak_responses*/, CvSlice slice, int k) const + CvMat* /*weak_responses*/, CvSlice slice, int k) const { float result = 0.0f; - if (!weak) return 0.0f; + if (!weak) return 0.0f; float* sum = new float[class_count]; for (int i=0; ipredict_serial(&x,&miss,0,slice); } } - } // Sample_predictor::operator() + } // Sample_predictor::operator() + + virtual ~Sample_predictor() {} }; // class Sample_predictor // type in {CV_TRAIN_ERROR, CV_TEST_ERROR} -float +float CvGBTrees::calc_error( CvMLData* _data, int type, std::vector *resp ) { float err = 0.0f; - const CvMat* sample_idx = (type == CV_TRAIN_ERROR) ? + const CvMat* _sample_idx = (type == CV_TRAIN_ERROR) ? _data->get_train_sample_idx() : _data->get_test_sample_idx(); const CvMat* response = _data->get_responses(); - - int n = sample_idx ? get_len(sample_idx) : 0; + + int n = _sample_idx ? get_len(_sample_idx) : 0; n = (type == CV_TRAIN_ERROR && n == 0) ? _data->get_values()->rows : n; - + if (!n) return -FLT_MAX; - - float* pred_resp = 0; + + float* pred_resp = 0; if (resp) { resp->resize(n); @@ -1311,18 +1315,18 @@ CvGBTrees::calc_error( CvMLData* _data, int type, std::vector *resp ) pred_resp = new float[n]; Sample_predictor predictor = Sample_predictor(this, pred_resp, _data->get_values(), - _data->get_missing(), sample_idx); - + _data->get_missing(), _sample_idx); + //#ifdef HAVE_TBB // tbb::parallel_for(cv::BlockedRange(0,n), predictor, tbb::auto_partitioner()); //#else cv::parallel_for(cv::BlockedRange(0,n), predictor); //#endif - - int* sidx = sample_idx ? sample_idx->data.i : 0; + + int* sidx = _sample_idx ? _sample_idx->data.i : 0; int r_step = CV_IS_MAT_CONT(response->type) ? 1 : response->step / CV_ELEM_SIZE(response->type); - + if ( !problem_type() ) { @@ -1342,9 +1346,9 @@ CvGBTrees::calc_error( CvMLData* _data, int type, std::vector *resp ) float d = pred_resp[i] - response->data.fl[si*r_step]; err += d*d; } - err = err / (float)n; + err = err / (float)n; } - + return err; } @@ -1353,7 +1357,7 @@ CvGBTrees::CvGBTrees( const cv::Mat& trainData, int tflag, const cv::Mat& responses, const cv::Mat& varIdx, const cv::Mat& sampleIdx, const cv::Mat& varType, const cv::Mat& missingDataMask, - CvGBTreesParams params ) + CvGBTreesParams _params ) { data = 0; weak = 0; @@ -1364,32 +1368,32 @@ CvGBTrees::CvGBTrees( const cv::Mat& trainData, int tflag, class_labels = 0; class_count = 1; delta = 0.0f; - + clear(); - - train(trainData, tflag, responses, varIdx, sampleIdx, varType, missingDataMask, params, false); + + train(trainData, tflag, responses, varIdx, sampleIdx, varType, missingDataMask, _params, false); } bool CvGBTrees::train( const cv::Mat& trainData, int tflag, const cv::Mat& responses, const cv::Mat& varIdx, const cv::Mat& sampleIdx, const cv::Mat& varType, const cv::Mat& missingDataMask, - CvGBTreesParams params, + CvGBTreesParams _params, bool update ) { CvMat _trainData = trainData, _responses = responses; CvMat _varIdx = varIdx, _sampleIdx = sampleIdx, _varType = varType; CvMat _missingDataMask = missingDataMask; - + return train( &_trainData, tflag, &_responses, varIdx.empty() ? 0 : &_varIdx, sampleIdx.empty() ? 0 : &_sampleIdx, varType.empty() ? 0 : &_varType, - missingDataMask.empty() ? 0 : &_missingDataMask, params, update); + missingDataMask.empty() ? 0 : &_missingDataMask, _params, update); } -float CvGBTrees::predict( const cv::Mat& sample, const cv::Mat& missing, +float CvGBTrees::predict( const cv::Mat& sample, const cv::Mat& _missing, const cv::Range& slice, int k ) const { - CvMat _sample = sample, _missing = missing; - return predict(&_sample, missing.empty() ? 0 : &_missing, 0, + CvMat _sample = sample, miss = _missing; + return predict(&_sample, _missing.empty() ? 0 : &miss, 0, slice==cv::Range::all() ? CV_WHOLE_SEQ : cvSlice(slice.start, slice.end), k); } diff --git a/modules/ml/src/inner_functions.cpp b/modules/ml/src/inner_functions.cpp index 0059597..f0e085d 100644 --- a/modules/ml/src/inner_functions.cpp +++ b/modules/ml/src/inner_functions.cpp @@ -122,7 +122,7 @@ void CvStatModel::read( CvFileStorage*, CvFileNode* ) /* Calculates upper triangular matrix S, where A is a symmetrical matrix A=S'*S */ -CV_IMPL void cvChol( CvMat* A, CvMat* S ) +static void cvChol( CvMat* A, CvMat* S ) { int dim = A->rows; @@ -182,7 +182,7 @@ CV_IMPL void cvRandMVNormal( CvMat* mean, CvMat* cov, CvMat* sample, CvRNG* rng /* Generates of points from a discrete variate xi, where Pr{xi = k} == probs[k], 0 < k < len - 1. */ -CV_IMPL void cvRandSeries( float probs[], int len, int sample[], int amount ) +static void cvRandSeries( float probs[], int len, int sample[], int amount ) { CvMat* univals = cvCreateMat(1, amount, CV_32FC1); float* knots = (float*)cvAlloc( len * sizeof(float) ); @@ -321,48 +321,48 @@ CvMat* icvGenerateRandomClusterCenters ( int seed, const CvMat* data, #define ICV_RAND_MAX 4294967296 // == 2^32 -CV_IMPL void cvRandRoundUni (CvMat* center, - float radius_small, - float radius_large, - CvMat* desired_matrix, - CvRNG* rng_state_ptr) -{ - float rad, norm, coefficient; - int dim, size, i, j; - CvMat *cov, sample; - CvRNG rng_local; - - CV_FUNCNAME("cvRandRoundUni"); - __BEGIN__ - - rng_local = *rng_state_ptr; - - CV_ASSERT ((radius_small >= 0) && - (radius_large > 0) && - (radius_small <= radius_large)); - CV_ASSERT (center && desired_matrix && rng_state_ptr); - CV_ASSERT (center->rows == 1); - CV_ASSERT (center->cols == desired_matrix->cols); - - dim = desired_matrix->cols; - size = desired_matrix->rows; - cov = cvCreateMat (dim, dim, CV_32FC1); - cvSetIdentity (cov); - cvRandMVNormal (center, cov, desired_matrix, &rng_local); - - for (i = 0; i < size; i++) - { - rad = (float)(cvRandReal(&rng_local)*(radius_large - radius_small) + radius_small); - cvGetRow (desired_matrix, &sample, i); - norm = (float) cvNorm (&sample, 0, CV_L2); - coefficient = rad / norm; - for (j = 0; j < dim; j++) - CV_MAT_ELEM (sample, float, 0, j) *= coefficient; - } - - __END__ - -} +// static void cvRandRoundUni (CvMat* center, +// float radius_small, +// float radius_large, +// CvMat* desired_matrix, +// CvRNG* rng_state_ptr) +// { +// float rad, norm, coefficient; +// int dim, size, i, j; +// CvMat *cov, sample; +// CvRNG rng_local; + +// CV_FUNCNAME("cvRandRoundUni"); +// __BEGIN__ + +// rng_local = *rng_state_ptr; + +// CV_ASSERT ((radius_small >= 0) && +// (radius_large > 0) && +// (radius_small <= radius_large)); +// CV_ASSERT (center && desired_matrix && rng_state_ptr); +// CV_ASSERT (center->rows == 1); +// CV_ASSERT (center->cols == desired_matrix->cols); + +// dim = desired_matrix->cols; +// size = desired_matrix->rows; +// cov = cvCreateMat (dim, dim, CV_32FC1); +// cvSetIdentity (cov); +// cvRandMVNormal (center, cov, desired_matrix, &rng_local); + +// for (i = 0; i < size; i++) +// { +// rad = (float)(cvRandReal(&rng_local)*(radius_large - radius_small) + radius_small); +// cvGetRow (desired_matrix, &sample, i); +// norm = (float) cvNorm (&sample, 0, CV_L2); +// coefficient = rad / norm; +// for (j = 0; j < dim; j++) +// CV_MAT_ELEM (sample, float, 0, j) *= coefficient; +// } + +// __END__ + +// } // By S. Dilman - end - @@ -1769,7 +1769,7 @@ void cvCombineResponseMaps (CvMat* _responses, } -int icvGetNumberOfCluster( double* prob_vector, int num_of_clusters, float r, +static int icvGetNumberOfCluster( double* prob_vector, int num_of_clusters, float r, float outlier_thresh, int normalize_probs ) { int max_prob_loc = 0; diff --git a/modules/ml/src/knearest.cpp b/modules/ml/src/knearest.cpp index e073d1e..fa6b15e 100644 --- a/modules/ml/src/knearest.cpp +++ b/modules/ml/src/knearest.cpp @@ -141,7 +141,7 @@ bool CvKNearest::train( const CvMat* _train_data, const CvMat* _responses, ok = true; __END__; - + if( responses && responses->data.ptr != _responses->data.ptr ) cvReleaseMat(&responses); @@ -318,7 +318,7 @@ struct P1 { result = _result; buf_sz = _buf_sz; } - + const CvKNearest* pointer; int k; const CvMat* _samples; @@ -329,7 +329,7 @@ struct P1 { CvMat* _dist; float* result; int buf_sz; - + void operator()( const cv::BlockedRange& range ) const { cv::AutoBuffer buf(buf_sz); @@ -429,7 +429,7 @@ bool CvKNearest::train( const Mat& _train_data, const Mat& _responses, int _max_k, bool _update_base ) { CvMat tdata = _train_data, responses = _responses, sidx = _sample_idx; - + return train(&tdata, &responses, sidx.data.ptr ? &sidx : 0, _is_regression, _max_k, _update_base ); } @@ -439,7 +439,7 @@ float CvKNearest::find_nearest( const Mat& _samples, int k, Mat* _results, Mat* _dist ) const { CvMat s = _samples, results, *presults = 0, nresponses, *pnresponses = 0, dist, *pdist = 0; - + if( _results ) { if(!(_results->data && (_results->type() == CV_32F || @@ -449,7 +449,7 @@ float CvKNearest::find_nearest( const Mat& _samples, int k, Mat* _results, _results->create(_samples.rows, 1, CV_32F); presults = &(results = *_results); } - + if( _neighbor_responses ) { if(!(_neighbor_responses->data && _neighbor_responses->type() == CV_32F && @@ -457,7 +457,7 @@ float CvKNearest::find_nearest( const Mat& _samples, int k, Mat* _results, _neighbor_responses->create(_samples.rows, k, CV_32F); pnresponses = &(nresponses = *_neighbor_responses); } - + if( _dist ) { if(!(_dist->data && _dist->type() == CV_32F && @@ -465,15 +465,15 @@ float CvKNearest::find_nearest( const Mat& _samples, int k, Mat* _results, _dist->create(_samples.rows, k, CV_32F); pdist = &(dist = *_dist); } - + return find_nearest(&s, k, presults, _neighbors, pnresponses, pdist ); } -float CvKNearest::find_nearest( const cv::Mat& samples, int k, CV_OUT cv::Mat& results, +float CvKNearest::find_nearest( const cv::Mat& _samples, int k, CV_OUT cv::Mat& results, CV_OUT cv::Mat& neighborResponses, CV_OUT cv::Mat& dists) const { - return find_nearest(samples, k, &results, 0, &neighborResponses, &dists); + return find_nearest(_samples, k, &results, 0, &neighborResponses, &dists); } /* End of file */ diff --git a/modules/ml/src/nbayes.cpp b/modules/ml/src/nbayes.cpp index 594007b..15146d6 100644 --- a/modules/ml/src/nbayes.cpp +++ b/modules/ml/src/nbayes.cpp @@ -241,13 +241,13 @@ bool CvNormalBayesClassifier::train( const CvMat* _train_data, const CvMat* _res double* cov_data = cov->data.db + i*_var_count; double s1val = sum1[i]; double avg1 = avg_data[i]; - int count = count_data[i]; + int _count = count_data[i]; for( j = 0; j <= i; j++ ) { double avg2 = avg2_data[j]; - double cov_val = prod_data[j] - avg1 * sum2[j] - avg2 * s1val + avg1 * avg2 * count; - cov_val = (count > 1) ? cov_val / (count - 1) : cov_val; + double cov_val = prod_data[j] - avg1 * sum2[j] - avg2 * s1val + avg1 * avg2 * _count; + cov_val = (_count > 1) ? cov_val / (_count - 1) : cov_val; cov_data[j] = cov_val; } } @@ -294,7 +294,7 @@ struct predict_body { value = _value; var_count1 = _var_count1; } - + CvMat* c; CvMat** cov_rotate_mats; CvMat** inv_eigen_values; @@ -306,15 +306,15 @@ struct predict_body { CvMat* results; float* value; int var_count1; - + void operator()( const cv::BlockedRange& range ) const { int cls = -1; - int rtype = 0, rstep = 0; + int rtype = 0, rstep = 0; int nclasses = cls_labels->cols; int _var_count = avg[0]->cols; - + if (results) { rtype = CV_MAT_TYPE(results->type); @@ -323,7 +323,7 @@ struct predict_body { // allocate memory and initializing headers for calculating cv::AutoBuffer buffer(nclasses + var_count1); CvMat diff = cvMat( 1, var_count1, CV_64FC1, &buffer[0] ); - + for(int k = range.begin(); k < range.end(); k += 1 ) { int ival; @@ -592,7 +592,7 @@ CvNormalBayesClassifier::CvNormalBayesClassifier( const Mat& _train_data, const cov_rotate_mats = 0; c = 0; default_model_name = "my_nb"; - + CvMat tdata = _train_data, responses = _responses, vidx = _var_idx, sidx = _sample_idx; train(&tdata, &responses, vidx.data.ptr ? &vidx : 0, sidx.data.ptr ? &sidx : 0); @@ -609,7 +609,7 @@ bool CvNormalBayesClassifier::train( const Mat& _train_data, const Mat& _respons float CvNormalBayesClassifier::predict( const Mat& _samples, Mat* _results ) const { CvMat samples = _samples, results, *presults = 0; - + if( _results ) { if( !(_results->data && _results->type() == CV_32F && @@ -618,7 +618,7 @@ float CvNormalBayesClassifier::predict( const Mat& _samples, Mat* _results ) con _results->create(_samples.rows, 1, CV_32F); presults = &(results = *_results); } - + return predict(&samples, presults); } diff --git a/modules/ml/src/precomp.hpp b/modules/ml/src/precomp.hpp index 285abea..63002a8 100644 --- a/modules/ml/src/precomp.hpp +++ b/modules/ml/src/precomp.hpp @@ -41,11 +41,7 @@ #ifndef __OPENCV_PRECOMP_H__ #define __OPENCV_PRECOMP_H__ -#if _MSC_VER >= 1200 -#pragma warning( disable: 4251 4514 4710 4711 4710 ) -#endif - -#ifdef HAVE_CVCONFIG_H +#ifdef HAVE_CVCONFIG_H #include "cvconfig.h" #endif @@ -298,10 +294,10 @@ cvPrepareTrainData( const char* /*funcname*/, CvMat** out_sample_idx=0 ); void -cvSortSamplesByClasses( const float** samples, const CvMat* classes, +cvSortSamplesByClasses( const float** samples, const CvMat* classes, int* class_ranges, const uchar** mask CV_DEFAULT(0) ); -void +void cvCombineResponseMaps (CvMat* _responses, const CvMat* old_response_map, CvMat* new_response_map, @@ -329,7 +325,7 @@ CvFileNode* icvFileNodeGetNext(CvFileNode* n, const char* name); void cvCheckTrainData( const CvMat* train_data, int tflag, - const CvMat* missing_mask, + const CvMat* missing_mask, int* var_all, int* sample_all ); CvMat* cvPreprocessIndexArray( const CvMat* idx_arr, int data_arr_size, bool check_for_duplicates=false ); @@ -365,7 +361,7 @@ namespace cv CvDTree* tree; CvDTreeNode* node; }; - + struct ForestTreeBestSplitFinder : DTreeBestSplitFinder { ForestTreeBestSplitFinder() : DTreeBestSplitFinder() {} diff --git a/modules/ml/src/rtrees.cpp b/modules/ml/src/rtrees.cpp index 81576c3..d88611b 100644 --- a/modules/ml/src/rtrees.cpp +++ b/modules/ml/src/rtrees.cpp @@ -307,14 +307,14 @@ bool CvRTrees::train( const CvMat* _train_data, int _tflag, return grow_forest( params.term_crit ); } -bool CvRTrees::train( CvMLData* data, CvRTParams params ) +bool CvRTrees::train( CvMLData* _data, CvRTParams params ) { - const CvMat* values = data->get_values(); - const CvMat* response = data->get_responses(); - const CvMat* missing = data->get_missing(); - const CvMat* var_types = data->get_var_types(); - const CvMat* train_sidx = data->get_train_sample_idx(); - const CvMat* var_idx = data->get_var_idx(); + const CvMat* values = _data->get_values(); + const CvMat* response = _data->get_responses(); + const CvMat* missing = _data->get_missing(); + const CvMat* var_types = _data->get_var_types(); + const CvMat* train_sidx = _data->get_train_sample_idx(); + const CvMat* var_idx = _data->get_var_idx(); return train( values, CV_ROW_SAMPLE, response, var_idx, train_sidx, var_types, missing, params ); @@ -331,7 +331,7 @@ bool CvRTrees::grow_forest( const CvTermCriteria term_crit ) const int dims = data->var_count; float maximal_response = 0; - CvMat* oob_sample_votes = 0; + CvMat* oob_sample_votes = 0; CvMat* oob_responses = 0; float* oob_samples_perm_ptr= 0; @@ -347,7 +347,7 @@ bool CvRTrees::grow_forest( const CvTermCriteria term_crit ) // initialize these variable to avoid warning C4701 CvMat oob_predictions_sum = cvMat( 1, 1, CV_32FC1 ); CvMat oob_num_of_predictions = cvMat( 1, 1, CV_32FC1 ); - + nsamples = data->sample_count; nclasses = data->get_num_classes(); @@ -369,14 +369,14 @@ bool CvRTrees::grow_forest( const CvTermCriteria term_crit ) cvGetRow( oob_responses, &oob_predictions_sum, 0 ); cvGetRow( oob_responses, &oob_num_of_predictions, 1 ); } - + oob_samples_perm_ptr = (float*)cvAlloc( sizeof(float)*nsamples*dims ); samples_ptr = (float*)cvAlloc( sizeof(float)*nsamples*dims ); missing_ptr = (uchar*)cvAlloc( sizeof(uchar)*nsamples*dims ); - true_resp_ptr = (float*)cvAlloc( sizeof(float)*nsamples ); + true_resp_ptr = (float*)cvAlloc( sizeof(float)*nsamples ); data->get_vectors( 0, samples_ptr, missing_ptr, true_resp_ptr ); - + double minval, maxval; CvMat responses = cvMat(1, nsamples, CV_32FC1, true_resp_ptr); cvMinMaxLoc( &responses, &minval, &maxval ); @@ -536,7 +536,7 @@ bool CvRTrees::grow_forest( const CvTermCriteria term_crit ) cvFree( &samples_ptr ); cvFree( &missing_ptr ); cvFree( &true_resp_ptr ); - + cvReleaseMat( &sample_idx_mask_for_tree ); cvReleaseMat( &sample_idx_for_tree ); @@ -592,9 +592,9 @@ float CvRTrees::calc_error( CvMLData* _data, int type , std::vector *resp { CvMat sample, miss; int si = sidx ? sidx[i] : i; - cvGetRow( values, &sample, si ); - if( missing ) - cvGetRow( missing, &miss, si ); + cvGetRow( values, &sample, si ); + if( missing ) + cvGetRow( missing, &miss, si ); float r = (float)predict( &sample, missing ? &miss : 0 ); if( pred_resp ) pred_resp[i] = r; @@ -610,15 +610,15 @@ float CvRTrees::calc_error( CvMLData* _data, int type , std::vector *resp CvMat sample, miss; int si = sidx ? sidx[i] : i; cvGetRow( values, &sample, si ); - if( missing ) - cvGetRow( missing, &miss, si ); + if( missing ) + cvGetRow( missing, &miss, si ); float r = (float)predict( &sample, missing ? &miss : 0 ); if( pred_resp ) pred_resp[i] = r; float d = r - response->data.fl[si*r_step]; err += d*d; } - err = sample_count ? err / (float)sample_count : -FLT_MAX; + err = sample_count ? err / (float)sample_count : -FLT_MAX; } return err; } @@ -635,12 +635,12 @@ float CvRTrees::get_train_error() float *responses_ptr = (float*)cvAlloc( sizeof(float)*sample_count ); data->get_vectors( 0, values_ptr, missing_ptr, responses_ptr); - + if (data->is_classifier) { int err_count = 0; float *vp = values_ptr; - uchar *mp = missing_ptr; + uchar *mp = missing_ptr; for (int si = 0; si < sample_count; si++, vp += var_count, mp += var_count) { CvMat sample = cvMat( 1, var_count, CV_32FC1, vp ); @@ -653,10 +653,10 @@ float CvRTrees::get_train_error() } else CV_Error( CV_StsBadArg, "This method is not supported for regression problems" ); - + cvFree( &values_ptr ); cvFree( &missing_ptr ); - cvFree( &responses_ptr ); + cvFree( &responses_ptr ); return err; } @@ -701,7 +701,7 @@ float CvRTrees::predict( const CvMat* sample, const CvMat* missing ) const float CvRTrees::predict_prob( const CvMat* sample, const CvMat* missing) const { - if( nclasses == 2 ) //classification + if( nclasses == 2 ) //classification { cv::AutoBuffer _votes(nclasses); int* votes = _votes; @@ -711,15 +711,15 @@ float CvRTrees::predict_prob( const CvMat* sample, const CvMat* missing) const CvDTreeNode* predicted_node = trees[k]->predict( sample, missing ); int class_idx = predicted_node->class_idx; CV_Assert( 0 <= class_idx && class_idx < nclasses ); - + ++votes[class_idx]; } - - return float(votes[1])/ntrees; + + return float(votes[1])/ntrees; } else // regression - CV_Error(CV_StsBadArg, "This function works for binary classification problems only..."); - + CV_Error(CV_StsBadArg, "This function works for binary classification problems only..."); + return -1; } @@ -809,15 +809,15 @@ void CvRTrees::read( CvFileStorage* fs, CvFileNode* fnode ) { // initialize active variables mask CvMat submask1; - cvGetCols( active_var_mask, &submask1, 0, nactive_vars ); + cvGetCols( active_var_mask, &submask1, 0, nactive_vars ); cvSet( &submask1, cvScalar(1) ); - if( nactive_vars < var_count ) - { - CvMat submask2; - cvGetCols( active_var_mask, &submask2, nactive_vars, var_count ); - cvZero( &submask2 ); - } + if( nactive_vars < var_count ) + { + CvMat submask2; + cvGetCols( active_var_mask, &submask2, nactive_vars, var_count ); + cvZero( &submask2 ); + } } } diff --git a/modules/ml/src/svm.cpp b/modules/ml/src/svm.cpp index c16cce5..d2bc88f 100644 --- a/modules/ml/src/svm.cpp +++ b/modules/ml/src/svm.cpp @@ -88,10 +88,6 @@ using namespace cv; #include #include -#if _MSC_VER >= 1200 -#pragma warning( disable: 4514 ) /* unreferenced inline functions */ -#endif - #if 1 typedef float Qfloat; #define QFLOAT_TYPE CV_32F @@ -1065,10 +1061,10 @@ bool CvSVMSolver::solve_eps_svr( int _sample_count, int _var_count, const float* CvSVMKernel* _kernel, double* _alpha, CvSVMSolutionInfo& _si ) { int i; - double p = _kernel->params->p, C = _kernel->params->C; + double p = _kernel->params->p, kernel_param_c = _kernel->params->C; if( !create( _sample_count, _var_count, _samples, 0, - _sample_count*2, 0, C, C, _storage, _kernel, &CvSVMSolver::get_row_svr, + _sample_count*2, 0, kernel_param_c, kernel_param_c, _storage, _kernel, &CvSVMSolver::get_row_svr, &CvSVMSolver::select_working_set, &CvSVMSolver::calc_rho )) return false; @@ -1101,7 +1097,7 @@ bool CvSVMSolver::solve_nu_svr( int _sample_count, int _var_count, const float** CvSVMKernel* _kernel, double* _alpha, CvSVMSolutionInfo& _si ) { int i; - double C = _kernel->params->C, sum; + double kernel_param_c = _kernel->params->C, sum; if( !create( _sample_count, _var_count, _samples, 0, _sample_count*2, 0, 1., 1., _storage, _kernel, &CvSVMSolver::get_row_svr, @@ -1110,11 +1106,11 @@ bool CvSVMSolver::solve_nu_svr( int _sample_count, int _var_count, const float** y = (schar*)cvMemStorageAlloc( storage, sample_count*2*sizeof(y[0]) ); alpha = (double*)cvMemStorageAlloc( storage, alpha_count*sizeof(alpha[0]) ); - sum = C * _kernel->params->nu * sample_count * 0.5; + sum = kernel_param_c * _kernel->params->nu * sample_count * 0.5; for( i = 0; i < sample_count; i++ ) { - alpha[i] = alpha[i + sample_count] = MIN(sum, C); + alpha[i] = alpha[i + sample_count] = MIN(sum, kernel_param_c); sum -= alpha[i]; b[i] = -_y[i]; @@ -1593,7 +1589,7 @@ bool CvSVM::train( const CvMat* _train_data, const CvMat* _responses, return ok; } -struct indexedratio +struct indexedratio { double val; int ind; @@ -1628,12 +1624,11 @@ bool CvSVM::train_auto( const CvMat* _train_data, const CvMat* _responses, int svm_type, sample_count, var_count, sample_size; int block_size = 1 << 16; double* alpha; - int i, k; RNG* rng = &theRNG(); // all steps are logarithmic and must be > 1 double degree_step = 10, g_step = 10, coef_step = 10, C_step = 10, nu_step = 10, p_step = 10; - double gamma = 0, C = 0, degree = 0, coef = 0, p = 0, nu = 0; + double gamma = 0, curr_c = 0, degree = 0, coef = 0, p = 0, nu = 0; double best_degree = 0, best_gamma = 0, best_coef = 0, best_C = 0, best_nu = 0, best_p = 0; float min_error = FLT_MAX, error; @@ -1760,7 +1755,7 @@ bool CvSVM::train_auto( const CvMat* _train_data, const CvMat* _responses, cvZero( responses_local ); // randomly permute samples and responses - for( i = 0; i < sample_count; i++ ) + for(int i = 0; i < sample_count; i++ ) { int i1 = (*rng)(sample_count); int i2 = (*rng)(sample_count); @@ -1774,25 +1769,25 @@ bool CvSVM::train_auto( const CvMat* _train_data, const CvMat* _responses, else CV_SWAP( responses->data.i[i1], responses->data.i[i2], y ); } - + if (!is_regression && class_labels->cols==2 && balanced) { // count class samples int num_0=0,num_1=0; - for (i=0; idata.i[i]==class_labels->data.i[0]) ++num_0; else ++num_1; } - + int label_smallest_class; int label_biggest_class; if (num_0 < num_1) { label_biggest_class = class_labels->data.i[1]; - label_smallest_class = class_labels->data.i[0]; + label_smallest_class = class_labels->data.i[0]; } else { @@ -1875,10 +1870,10 @@ bool CvSVM::train_auto( const CvMat* _train_data, const CvMat* _responses, } int* cls_lbls = class_labels ? class_labels->data.i : 0; - C = C_grid.min_val; + curr_c = C_grid.min_val; do { - params.C = C; + params.C = curr_c; gamma = gamma_grid.min_val; do { @@ -1906,7 +1901,7 @@ bool CvSVM::train_auto( const CvMat* _train_data, const CvMat* _responses, int train_size = trainset_size; error = 0; - for( k = 0; k < k_fold; k++ ) + for(int k = 0; k < k_fold; k++ ) { memcpy( samples_local, samples, sizeof(samples[0])*test_size*k ); memcpy( samples_local + test_size*k, test_samples_ptr + test_size, @@ -1930,7 +1925,7 @@ bool CvSVM::train_auto( const CvMat* _train_data, const CvMat* _responses, EXIT; // Compute test set error on samples - for( i = 0; i < test_size; i++, true_resp += resp_elem_size, test_samples_ptr++ ) + for(int i = 0; i < test_size; i++, true_resp += resp_elem_size, test_samples_ptr++ ) { float resp = predict( *test_samples_ptr, var_count ); error += is_regression ? powf( resp - *(float*)true_resp, 2 ) @@ -1943,7 +1938,7 @@ bool CvSVM::train_auto( const CvMat* _train_data, const CvMat* _responses, best_degree = degree; best_gamma = gamma; best_coef = coef; - best_C = C; + best_C = curr_c; best_nu = nu; best_p = p; } @@ -1962,9 +1957,9 @@ bool CvSVM::train_auto( const CvMat* _train_data, const CvMat* _responses, gamma *= gamma_grid.step; } while( gamma < gamma_grid.max_val ); - C *= C_grid.step; + curr_c *= C_grid.step; } - while( C < C_grid.max_val ); + while( curr_c < C_grid.max_val ); } min_error /= (float) sample_count; @@ -2001,7 +1996,7 @@ float CvSVM::predict( const float* row_sample, int row_len, bool returnDFVal ) c int var_count = get_var_count(); assert( row_len == var_count ); - (void)row_len; + (void)row_len; int class_count = class_labels ? class_labels->cols : params.svm_type == ONE_CLASS ? 1 : 0; @@ -2072,7 +2067,7 @@ float CvSVM::predict( const CvMat* sample, bool returnDFVal ) const __BEGIN__; int class_count; - + if( !kernel ) CV_ERROR( CV_StsBadArg, "The SVM should be trained first" ); @@ -2082,7 +2077,7 @@ float CvSVM::predict( const CvMat* sample, bool returnDFVal ) const CV_CALL( cvPreparePredictData( sample, var_all, var_idx, class_count, 0, &row_sample )); result = predict( row_sample, get_var_count(), returnDFVal ); - + __END__; if( sample && (!CV_IS_MAT(sample) || sample->data.fl != row_sample) ) @@ -2099,12 +2094,12 @@ struct predict_body_svm { samples = _samples; results = _results; } - + const CvSVM* pointer; float* result; const CvMat* samples; CvMat* results; - + void operator()( const cv::BlockedRange& range ) const { for(int i = range.begin(); i < range.end(); i++ ) @@ -2116,15 +2111,15 @@ struct predict_body_svm { results->data.fl[i] = (float)r; if (i == 0) *result = (float)r; - } + } } }; float CvSVM::predict(const CvMat* samples, CV_OUT CvMat* results) const { float result = 0; - cv::parallel_for(cv::BlockedRange(0, samples->rows), - predict_body_svm(this, &result, samples, results) + cv::parallel_for(cv::BlockedRange(0, samples->rows), + predict_body_svm(this, &result, samples, results) ); return result; } @@ -2141,7 +2136,7 @@ CvSVM::CvSVM( const Mat& _train_data, const Mat& _responses, kernel = 0; solver = 0; default_model_name = "my_svm"; - + train( _train_data, _responses, _var_idx, _sample_idx, _params ); } @@ -2166,7 +2161,7 @@ bool CvSVM::train_auto( const Mat& _train_data, const Mat& _responses, float CvSVM::predict( const Mat& _sample, bool returnDFVal ) const { - CvMat sample = _sample; + CvMat sample = _sample; return predict(&sample, returnDFVal); } @@ -2648,11 +2643,11 @@ cvTrainSVM_CrossValidation( const CvMat* train_data, int tflag, __BEGIN__; double degree_step = 7, - g_step = 15, - coef_step = 14, - C_step = 20, - nu_step = 5, - p_step = 7; // all steps must be > 1 + g_step = 15, + coef_step = 14, + C_step = 20, + nu_step = 5, + p_step = 7; // all steps must be > 1 double degree_begin = 0.01, degree_end = 2; double g_begin = 1e-5, g_end = 0.5; double coef_begin = 0.1, coef_end = 300; @@ -2662,12 +2657,12 @@ cvTrainSVM_CrossValidation( const CvMat* train_data, int tflag, double rate = 0, gamma = 0, C = 0, degree = 0, coef = 0, p = 0, nu = 0; - double best_rate = 0; + double best_rate = 0; double best_degree = degree_begin; double best_gamma = g_begin; double best_coef = coef_begin; - double best_C = C_begin; - double best_nu = nu_begin; + double best_C = C_begin; + double best_nu = nu_begin; double best_p = p_begin; CvSVMModelParams svm_params, *psvm_params; diff --git a/modules/ml/src/testset.cpp b/modules/ml/src/testset.cpp index e4f3249..5edb3b4 100644 --- a/modules/ml/src/testset.cpp +++ b/modules/ml/src/testset.cpp @@ -46,7 +46,7 @@ typedef struct CvDI int i; } CvDI; -int CV_CDECL +static int CV_CDECL icvCmpDI( const void* a, const void* b, void* ) { const CvDI* e1 = (const CvDI*) a; @@ -65,7 +65,7 @@ cvCreateTestSet( int type, CvMat** samples, CvMat* mean = NULL; CvMat* cov = NULL; CvMemStorage* storage = NULL; - + CV_FUNCNAME( "cvCreateTestSet" ); __BEGIN__; @@ -125,7 +125,7 @@ cvCreateTestSet( int type, CvMat** samples, CV_WRITE_SEQ_ELEM( elem, writer ); } CV_CALL( seq = cvEndWriteSeq( &writer ) ); - + /* sort the sequence in a distance ascending order */ CV_CALL( cvSeqSort( seq, icvCmpDI, NULL ) ); @@ -137,7 +137,7 @@ cvCreateTestSet( int type, CvMat** samples, { int last_idx; double max_dst; - + last_idx = num_samples * (cur_class + 1) / num_classes - 1; CV_CALL( max_dst = (*((CvDI*) cvGetSeqElem( seq, last_idx ))).d ); max_dst = MAX( max_dst, elem.d ); diff --git a/modules/ml/src/tree.cpp b/modules/ml/src/tree.cpp index e69f888..936f552 100644 --- a/modules/ml/src/tree.cpp +++ b/modules/ml/src/tree.cpp @@ -156,7 +156,7 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag, int vi, i, size; char err[100]; const int *sidx = 0, *vidx = 0; - + if( _update_data && data_root ) { data = new CvDTreeTrainData( _train_data, _tflag, _responses, _var_idx, @@ -224,7 +224,7 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag, sample_count = sample_all; var_count = var_all; - + if( _sample_idx ) { CV_CALL( sample_indices = cvPreprocessIndexArray( _sample_idx, sample_all )); @@ -239,10 +239,10 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag, var_count = var_idx->rows + var_idx->cols - 1; } - is_buf_16u = false; - if ( sample_count < 65536 ) - is_buf_16u = true; - + is_buf_16u = false; + if ( sample_count < 65536 ) + is_buf_16u = true; + if( !CV_IS_MAT(_responses) || (CV_MAT_TYPE(_responses->type) != CV_32SC1 && CV_MAT_TYPE(_responses->type) != CV_32FC1) || @@ -251,13 +251,13 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag, CV_ERROR( CV_StsBadArg, "The array of _responses must be an integer or " "floating-point vector containing as many elements as " "the total number of samples in the training data matrix" ); - + r_type = CV_VAR_CATEGORICAL; if( _var_type ) CV_CALL( var_type0 = cvPreprocessVarType( _var_type, var_idx, var_count, &r_type )); CV_CALL( var_type = cvCreateMat( 1, var_count+2, CV_32SC1 )); - + cat_var_count = 0; ord_var_count = -1; @@ -284,11 +284,11 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag, work_var_count = var_count + (is_classifier ? 1 : 0) // for responses class_labels + (have_labels ? 1 : 0); // for cv_labels - + buf_size = (work_var_count + 1 /*for sample_indices*/) * sample_count; shared = _shared; buf_count = shared ? 2 : 1; - + if ( is_buf_16u ) { CV_CALL( buf = cvCreateMat( buf_count, buf_size, CV_16UC1 )); @@ -298,13 +298,13 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag, { CV_CALL( buf = cvCreateMat( buf_count, buf_size, CV_32SC1 )); CV_CALL( int_ptr = (int**)cvAlloc( sample_count*sizeof(int_ptr[0]) )); - } + } size = is_classifier ? (cat_var_count+1) : cat_var_count; size = !size ? 1 : size; CV_CALL( cat_count = cvCreateMat( 1, size, CV_32SC1 )); CV_CALL( cat_ofs = cvCreateMat( 1, size, CV_32SC1 )); - + size = is_classifier ? (cat_var_count + 1)*params.max_categories : cat_var_count*params.max_categories; size = !size ? 1 : size; CV_CALL( cat_map = cvCreateMat( 1, size, CV_32SC1 )); @@ -389,12 +389,12 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag, { int c_count, prev_label; int* c_map; - + if (is_buf_16u) udst = (unsigned short*)(buf->data.s + vi*sample_count); else idst = buf->data.i + vi*sample_count; - + // copy data for( i = 0; i < sample_count; i++ ) { @@ -428,7 +428,7 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag, _idst[i] = val; pair16u32s_ptr[i].u = udst + i; pair16u32s_ptr[i].i = _idst + i; - } + } else { idst[i] = val; @@ -502,7 +502,7 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag, // replace labels for missing values with -1 for( ; i < sample_count; i++ ) *int_ptr[i] = -1; - } + } } else if( ci < 0 ) // process ordered variable { @@ -536,14 +536,14 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag, else idst[i] = i; _fdst[i] = val; - + } if (is_buf_16u) icvSortUShAux( udst, sample_count, _fdst); else icvSortIntAux( idst, sample_count, _fdst ); } - + if( vi < var_count ) data_root->set_num_valid(vi, num_valid); } @@ -564,15 +564,15 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag, if( cv_n ) { - unsigned short* udst = 0; - int* idst = 0; + unsigned short* usdst = 0; + int* idst2 = 0; if (is_buf_16u) { - udst = (unsigned short*)(buf->data.s + (get_work_var_count()-1)*sample_count); + usdst = (unsigned short*)(buf->data.s + (get_work_var_count()-1)*sample_count); for( i = vi = 0; i < sample_count; i++ ) { - udst[i] = (unsigned short)vi++; + usdst[i] = (unsigned short)vi++; vi &= vi < cv_n ? -1 : 0; } @@ -581,15 +581,15 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag, int a = (*rng)(sample_count); int b = (*rng)(sample_count); unsigned short unsh = (unsigned short)vi; - CV_SWAP( udst[a], udst[b], unsh ); + CV_SWAP( usdst[a], usdst[b], unsh ); } } else { - idst = buf->data.i + (get_work_var_count()-1)*sample_count; + idst2 = buf->data.i + (get_work_var_count()-1)*sample_count; for( i = vi = 0; i < sample_count; i++ ) { - idst[i] = vi++; + idst2[i] = vi++; vi &= vi < cv_n ? -1 : 0; } @@ -597,12 +597,12 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag, { int a = (*rng)(sample_count); int b = (*rng)(sample_count); - CV_SWAP( idst[a], idst[b], vi ); + CV_SWAP( idst2[a], idst2[b], vi ); } } } - if ( cat_map ) + if ( cat_map ) cat_map->cols = MAX( total_c_count, 1 ); max_split_size = cvAlign(sizeof(CvDTreeSplit) + @@ -751,7 +751,7 @@ CvDTreeNode* CvDTreeTrainData::subsample_data( const CvMat* _subsample_idx ) if (is_buf_16u) { - unsigned short* udst = (unsigned short*)(buf->data.s + root->buf_idx*buf->cols + + unsigned short* udst = (unsigned short*)(buf->data.s + root->buf_idx*buf->cols + vi*sample_count + root->offset); for( i = 0; i < count; i++ ) { @@ -762,7 +762,7 @@ CvDTreeNode* CvDTreeTrainData::subsample_data( const CvMat* _subsample_idx ) } else { - int* idst = buf->data.i + root->buf_idx*buf->cols + + int* idst = buf->data.i + root->buf_idx*buf->cols + vi*sample_count + root->offset; for( i = 0; i < count; i++ ) { @@ -788,7 +788,7 @@ CvDTreeNode* CvDTreeTrainData::subsample_data( const CvMat* _subsample_idx ) if (is_buf_16u) { - unsigned short* udst_idx = (unsigned short*)(buf->data.s + root->buf_idx*buf->cols + + unsigned short* udst_idx = (unsigned short*)(buf->data.s + root->buf_idx*buf->cols + vi*sample_count + data_root->offset); for( i = 0; i < num_valid; i++ ) { @@ -812,7 +812,7 @@ CvDTreeNode* CvDTreeTrainData::subsample_data( const CvMat* _subsample_idx ) } else { - int* idst_idx = buf->data.i + root->buf_idx*buf->cols + + int* idst_idx = buf->data.i + root->buf_idx*buf->cols + vi*sample_count + root->offset; for( i = 0; i < num_valid; i++ ) { @@ -840,14 +840,14 @@ CvDTreeNode* CvDTreeTrainData::subsample_data( const CvMat* _subsample_idx ) const int* sample_idx_src = get_sample_indices(data_root, (int*)(uchar*)inn_buf); if (is_buf_16u) { - unsigned short* sample_idx_dst = (unsigned short*)(buf->data.s + root->buf_idx*buf->cols + + unsigned short* sample_idx_dst = (unsigned short*)(buf->data.s + root->buf_idx*buf->cols + workVarCount*sample_count + root->offset); for (i = 0; i < count; i++) sample_idx_dst[i] = (unsigned short)sample_idx_src[sidx[i]]; } else { - int* sample_idx_dst = buf->data.i + root->buf_idx*buf->cols + + int* sample_idx_dst = buf->data.i + root->buf_idx*buf->cols + workVarCount*sample_count + root->offset; for (i = 0; i < count; i++) sample_idx_dst[i] = sample_idx_src[sidx[i]]; @@ -865,7 +865,7 @@ CvDTreeNode* CvDTreeTrainData::subsample_data( const CvMat* _subsample_idx ) void CvDTreeTrainData::get_vectors( const CvMat* _subsample_idx, float* values, uchar* missing, - float* responses, bool get_class_idx ) + float* _responses, bool get_class_idx ) { CvMat* subsample_idx = 0; CvMat* subsample_co = 0; @@ -962,7 +962,7 @@ void CvDTreeTrainData::get_vectors( const CvMat* _subsample_idx, } // copy responses - if( responses ) + if( _responses ) { if( is_classifier ) { @@ -972,7 +972,7 @@ void CvDTreeTrainData::get_vectors( const CvMat* _subsample_idx, int idx = sidx ? sidx[i] : i; int val = get_class_idx ? src[idx] : cat_map->data.i[cat_ofs->data.i[cat_var_count]+src[idx]]; - responses[i] = (float)val; + _responses[i] = (float)val; } } else @@ -983,7 +983,7 @@ void CvDTreeTrainData::get_vectors( const CvMat* _subsample_idx, for( i = 0; i < count; i++ ) { int idx = sidx ? sidx[i] : i; - responses[i] = _values[idx]; + _responses[i] = _values[idx]; } } } @@ -1122,7 +1122,7 @@ void CvDTreeTrainData::clear() cvReleaseMat( &cat_map ); cvReleaseMat( &priors ); cvReleaseMat( &priors_mult ); - + node_heap = split_heap = 0; sample_count = var_all = var_count = max_c_count = ord_var_count = cat_var_count = 0; @@ -1130,7 +1130,7 @@ void CvDTreeTrainData::clear() buf_count = buf_size = 0; shared = false; - + data_root = 0; rng = &cv::theRNG(); @@ -1152,7 +1152,7 @@ void CvDTreeTrainData::get_ord_var_data( CvDTreeNode* n, int vi, float* ord_valu const float** ord_values, const int** sorted_indices, int* sample_indices_buf ) { int vidx = var_idx ? var_idx->data.i[vi] : vi; - int node_sample_count = n->sample_count; + int node_sample_count = n->sample_count; int td_step = train_data->step/CV_ELEM_SIZE(train_data->type); const int* sample_indices = get_sample_indices(n, sample_indices_buf); @@ -1161,16 +1161,16 @@ void CvDTreeTrainData::get_ord_var_data( CvDTreeNode* n, int vi, float* ord_valu *sorted_indices = buf->data.i + n->buf_idx*buf->cols + vi*sample_count + n->offset; else { - const unsigned short* short_indices = (const unsigned short*)(buf->data.s + n->buf_idx*buf->cols + + const unsigned short* short_indices = (const unsigned short*)(buf->data.s + n->buf_idx*buf->cols + vi*sample_count + n->offset ); for( int i = 0; i < node_sample_count; i++ ) sorted_indices_buf[i] = short_indices[i]; *sorted_indices = sorted_indices_buf; } - + if( tflag == CV_ROW_SAMPLE ) { - for( int i = 0; i < node_sample_count && + for( int i = 0; i < node_sample_count && ((((*sorted_indices)[i] >= 0) && !is_buf_16u) || (((*sorted_indices)[i] != 65535) && is_buf_16u)); i++ ) { int idx = (*sorted_indices)[i]; @@ -1179,14 +1179,14 @@ void CvDTreeTrainData::get_ord_var_data( CvDTreeNode* n, int vi, float* ord_valu } } else - for( int i = 0; i < node_sample_count && + for( int i = 0; i < node_sample_count && ((((*sorted_indices)[i] >= 0) && !is_buf_16u) || (((*sorted_indices)[i] != 65535) && is_buf_16u)); i++ ) { int idx = (*sorted_indices)[i]; idx = sample_indices[idx]; ord_values_buf[i] = *(train_data->data.fl + vidx* td_step + idx); } - + *ord_values = ord_values_buf; } @@ -1205,17 +1205,17 @@ const int* CvDTreeTrainData::get_sample_indices( CvDTreeNode* n, int* indices_bu const float* CvDTreeTrainData::get_ord_responses( CvDTreeNode* n, float* values_buf, int*sample_indices_buf ) { - int sample_count = n->sample_count; + int _sample_count = n->sample_count; int r_step = CV_IS_MAT_CONT(responses->type) ? 1 : responses->step/CV_ELEM_SIZE(responses->type); const int* indices = get_sample_indices(n, sample_indices_buf); - for( int i = 0; i < sample_count && + for( int i = 0; i < _sample_count && (((indices[i] >= 0) && !is_buf_16u) || ((indices[i] != 65535) && is_buf_16u)); i++ ) { int idx = indices[i]; values_buf[i] = *(responses->data.fl + idx * r_step); } - + return values_buf; } @@ -1235,7 +1235,7 @@ const int* CvDTreeTrainData::get_cat_var_data( CvDTreeNode* n, int vi, int* cat_ cat_values = buf->data.i + n->buf_idx*buf->cols + vi*sample_count + n->offset; else { - const unsigned short* short_values = (const unsigned short*)(buf->data.s + n->buf_idx*buf->cols + + const unsigned short* short_values = (const unsigned short*)(buf->data.s + n->buf_idx*buf->cols + vi*sample_count + n->offset); for( int i = 0; i < n->sample_count; i++ ) cat_values_buf[i] = short_values[i]; @@ -1562,7 +1562,7 @@ bool CvDTree::train( const Mat& _train_data, int _tflag, const Mat& _missing_mask, CvDTreeParams _params ) { CvMat tdata = _train_data, responses = _responses, vidx=_var_idx, - sidx=_sample_idx, vtype=_var_type, mmask=_missing_mask; + sidx=_sample_idx, vtype=_var_type, mmask=_missing_mask; return train(&tdata, _tflag, &responses, vidx.data.ptr ? &vidx : 0, sidx.data.ptr ? &sidx : 0, vtype.data.ptr ? &vtype : 0, mmask.data.ptr ? &mmask : 0, _params); } @@ -1794,7 +1794,7 @@ double CvDTree::calc_node_dir( CvDTreeNode* node ) const float* val = 0; const int* sorted = 0; data->get_ord_var_data( node, vi, val_buf, sorted_buf, &val, &sorted, sample_idx_buf); - + assert( 0 <= split_point && split_point < n1-1 ); if( !data->have_priors ) @@ -1848,7 +1848,7 @@ template<> CV_EXPORTS void Ptr::delete_obj() { fastFree(obj); } - + DTreeBestSplitFinder::DTreeBestSplitFinder( CvDTree* _tree, CvDTreeNode* _node) { tree = _tree; @@ -2310,7 +2310,7 @@ CvDTreeSplit* CvDTree::find_split_cat_class( CvDTreeNode* node, int vi, float in } CvDTreeSplit* split = 0; - if( best_subset >= 0 ) + if( best_subset >= 0 ) { split = _split ? _split : data->new_split_cat( 0, -1.0f ); split->var_idx = vi; @@ -2933,7 +2933,7 @@ void CvDTree::complete_node_dir( CvDTreeNode* node ) { int idx = labels[i]; if( !dir[i] && ( ((idx >= 0)&&(!data->is_buf_16u)) || ((idx != 65535)&&(data->is_buf_16u)) )) - + { int d = CV_DTREE_CAT_DIR(idx,subset); dir[i] = (char)((d ^ inversed_mask) - inversed_mask); @@ -3049,7 +3049,7 @@ void CvDTree::split_node_data( CvDTreeNode* node ) { unsigned short *ldst, *rdst, *ldst0, *rdst0; //unsigned short tl, tr; - ldst0 = ldst = (unsigned short*)(buf->data.s + left->buf_idx*buf->cols + + ldst0 = ldst = (unsigned short*)(buf->data.s + left->buf_idx*buf->cols + vi*scount + left->offset); rdst0 = rdst = (unsigned short*)(ldst + nl); @@ -3095,9 +3095,9 @@ void CvDTree::split_node_data( CvDTreeNode* node ) else { int *ldst0, *ldst, *rdst0, *rdst; - ldst0 = ldst = buf->data.i + left->buf_idx*buf->cols + + ldst0 = ldst = buf->data.i + left->buf_idx*buf->cols + vi*scount + left->offset; - rdst0 = rdst = buf->data.i + right->buf_idx*buf->cols + + rdst0 = rdst = buf->data.i + right->buf_idx*buf->cols + vi*scount + right->offset; // split sorted @@ -3146,7 +3146,7 @@ void CvDTree::split_node_data( CvDTreeNode* node ) { int ci = data->get_var_type(vi); int n1 = node->get_num_valid(vi), nr1 = 0; - + if( ci < 0 || (vi < data->var_count && !split_input_data) ) continue; @@ -3158,11 +3158,11 @@ void CvDTree::split_node_data( CvDTreeNode* node ) if (data->is_buf_16u) { - unsigned short *ldst = (unsigned short *)(buf->data.s + left->buf_idx*buf->cols + + unsigned short *ldst = (unsigned short *)(buf->data.s + left->buf_idx*buf->cols + vi*scount + left->offset); - unsigned short *rdst = (unsigned short *)(buf->data.s + right->buf_idx*buf->cols + + unsigned short *rdst = (unsigned short *)(buf->data.s + right->buf_idx*buf->cols + vi*scount + right->offset); - + for( i = 0; i < n; i++ ) { int d = dir[i]; @@ -3188,11 +3188,11 @@ void CvDTree::split_node_data( CvDTreeNode* node ) } else { - int *ldst = buf->data.i + left->buf_idx*buf->cols + + int *ldst = buf->data.i + left->buf_idx*buf->cols + vi*scount + left->offset; - int *rdst = buf->data.i + right->buf_idx*buf->cols + + int *rdst = buf->data.i + right->buf_idx*buf->cols + vi*scount + right->offset; - + for( i = 0; i < n; i++ ) { int d = dir[i]; @@ -3208,7 +3208,7 @@ void CvDTree::split_node_data( CvDTreeNode* node ) *ldst = idx; ldst++; } - + } if( vi < data->var_count ) @@ -3216,7 +3216,7 @@ void CvDTree::split_node_data( CvDTreeNode* node ) left->set_num_valid(vi, n1 - nr1); right->set_num_valid(vi, nr1); } - } + } } @@ -3230,9 +3230,9 @@ void CvDTree::split_node_data( CvDTreeNode* node ) int pos = data->get_work_var_count(); if (data->is_buf_16u) { - unsigned short* ldst = (unsigned short*)(buf->data.s + left->buf_idx*buf->cols + + unsigned short* ldst = (unsigned short*)(buf->data.s + left->buf_idx*buf->cols + pos*scount + left->offset); - unsigned short* rdst = (unsigned short*)(buf->data.s + right->buf_idx*buf->cols + + unsigned short* rdst = (unsigned short*)(buf->data.s + right->buf_idx*buf->cols + pos*scount + right->offset); for (i = 0; i < n; i++) { @@ -3252,9 +3252,9 @@ void CvDTree::split_node_data( CvDTreeNode* node ) } else { - int* ldst = buf->data.i + left->buf_idx*buf->cols + + int* ldst = buf->data.i + left->buf_idx*buf->cols + pos*scount + left->offset; - int* rdst = buf->data.i + right->buf_idx*buf->cols + + int* rdst = buf->data.i + right->buf_idx*buf->cols + pos*scount + right->offset; for (i = 0; i < n; i++) { @@ -3272,9 +3272,9 @@ void CvDTree::split_node_data( CvDTreeNode* node ) } } } - + // deallocate the parent node data that is not needed anymore - data->free_node_data(node); + data->free_node_data(node); } float CvDTree::calc_error( CvMLData* _data, int type, vector *resp ) @@ -3304,9 +3304,9 @@ float CvDTree::calc_error( CvMLData* _data, int type, vector *resp ) { CvMat sample, miss; int si = sidx ? sidx[i] : i; - cvGetRow( values, &sample, si ); - if( missing ) - cvGetRow( missing, &miss, si ); + cvGetRow( values, &sample, si ); + if( missing ) + cvGetRow( missing, &miss, si ); float r = (float)predict( &sample, missing ? &miss : 0 )->value; if( pred_resp ) pred_resp[i] = r; @@ -3321,16 +3321,16 @@ float CvDTree::calc_error( CvMLData* _data, int type, vector *resp ) { CvMat sample, miss; int si = sidx ? sidx[i] : i; - cvGetRow( values, &sample, si ); - if( missing ) - cvGetRow( missing, &miss, si ); + cvGetRow( values, &sample, si ); + if( missing ) + cvGetRow( missing, &miss, si ); float r = (float)predict( &sample, missing ? &miss : 0 )->value; if( pred_resp ) pred_resp[i] = r; float d = r - response->data.fl[si*r_step]; err += d*d; } - err = sample_count ? err / (float)sample_count : -FLT_MAX; + err = sample_count ? err / (float)sample_count : -FLT_MAX; } return err; } @@ -3527,7 +3527,7 @@ int CvDTree::cut_tree( int T, int fold, double min_alpha ) } -void CvDTree::free_prune_data(bool cut_tree) +void CvDTree::free_prune_data(bool _cut_tree) { CvDTreeNode* node = root; @@ -3548,7 +3548,7 @@ void CvDTree::free_prune_data(bool cut_tree) for( parent = node->parent; parent && parent->right == node; node = parent, parent = parent->parent ) { - if( cut_tree && parent->Tn <= pruned_tree_idx ) + if( _cut_tree && parent->Tn <= pruned_tree_idx ) { data->free_node( parent->left ); data->free_node( parent->right ); @@ -3650,12 +3650,12 @@ CvDTreeNode* CvDTree::predict( const CvMat* _sample, { int a = c = cofs[ci]; int b = (ci+1 >= data->cat_ofs->cols) ? data->cat_map->cols : cofs[ci+1]; - + int ival = cvRound(val); if( ival != val ) CV_Error( CV_StsBadArg, "one of input categorical variable is not an integer" ); - + int sh = 0; while( a < b ) { diff --git a/modules/ml/test/test_emknearestkmeans.cpp b/modules/ml/test/test_emknearestkmeans.cpp index 3dedb3a..a6a6f08 100644 --- a/modules/ml/test/test_emknearestkmeans.cpp +++ b/modules/ml/test/test_emknearestkmeans.cpp @@ -80,7 +80,7 @@ void generateData( Mat& data, Mat& labels, const vector& sizes, const Mat& CV_Assert( _means.rows == (int)sizes.size() && covs.size() == sizes.size() ); CV_Assert( !data.empty() && data.rows == total ); CV_Assert( data.type() == dataType ); - + labels.create( data.rows, 1, labelType ); randn( data, Scalar::all(-1.0), Scalar::all(1.0) ); @@ -99,7 +99,7 @@ void generateData( Mat& data, Mat& labels, const vector& sizes, const Mat& for( int i = bi; i < ei; i++, p++ ) { Mat r = data.row(i); - r = r * (*cit) + *mit; + r = r * (*cit) + *mit; if( labelType == CV_32FC1 ) labels.at(p, 0) = (float)l; else if( labelType == CV_32SC1 ) @@ -224,14 +224,14 @@ void CV_KMeansTest::run( int /*start_from*/ ) const int iters = 100; int sizesArr[] = { 5000, 7000, 8000 }; int pointsCount = sizesArr[0]+ sizesArr[1] + sizesArr[2]; - + Mat data( pointsCount, 2, CV_32FC1 ), labels; vector sizes( sizesArr, sizesArr + sizeof(sizesArr) / sizeof(sizesArr[0]) ); Mat means; vector covs; defaultDistribs( means, covs ); generateData( data, labels, sizes, means, covs, CV_32FC1, CV_32SC1 ); - + int code = cvtest::TS::OK; float err; Mat bestLabels; @@ -327,24 +327,24 @@ void CV_KNearestTest::run( int /*start_from*/ ) class EM_Params { public: - EM_Params(int nclusters=10, int covMatType=EM::COV_MAT_DIAGONAL, int startStep=EM::START_AUTO_STEP, - const cv::TermCriteria& termCrit=cv::TermCriteria(cv::TermCriteria::COUNT+cv::TermCriteria::EPS, 100, FLT_EPSILON), - const cv::Mat* probs=0, const cv::Mat* weights=0, - const cv::Mat* means=0, const std::vector* covs=0) - : nclusters(nclusters), covMatType(covMatType), startStep(startStep), - probs(probs), weights(weights), means(means), covs(covs), termCrit(termCrit) + EM_Params(int _nclusters=10, int _covMatType=EM::COV_MAT_DIAGONAL, int _startStep=EM::START_AUTO_STEP, + const cv::TermCriteria& _termCrit=cv::TermCriteria(cv::TermCriteria::COUNT+cv::TermCriteria::EPS, 100, FLT_EPSILON), + const cv::Mat* _probs=0, const cv::Mat* _weights=0, + const cv::Mat* _means=0, const std::vector* _covs=0) + : nclusters(_nclusters), covMatType(_covMatType), startStep(_startStep), + probs(_probs), weights(_weights), means(_means), covs(_covs), termCrit(_termCrit) {} - + int nclusters; int covMatType; int startStep; - + // all 4 following matrices should have type CV_32FC1 const cv::Mat* probs; const cv::Mat* weights; const cv::Mat* means; const std::vector* covs; - + cv::TermCriteria termCrit; }; @@ -497,7 +497,7 @@ void CV_EMTest::run( int /*start_from*/ ) int currCode = runCase(caseIndex++, params, trainData, trainLabels, testData, testLabels, sizes); code = currCode == cvtest::TS::OK ? code : currCode; } - + ts->set_failed_test_info( code ); } diff --git a/modules/ml/test/test_gbttest.cpp b/modules/ml/test/test_gbttest.cpp index f9ecc4f..1e6d0fb 100644 --- a/modules/ml/test/test_gbttest.cpp +++ b/modules/ml/test/test_gbttest.cpp @@ -12,8 +12,8 @@ class CV_GBTreesTest : public cvtest::BaseTest { public: CV_GBTreesTest(); - ~CV_GBTreesTest(); - + ~CV_GBTreesTest(); + protected: void run(int); @@ -21,21 +21,21 @@ protected: int TestSaveLoad(); int checkPredictError(int test_num); - int checkLoadSave(); - + int checkLoadSave(); + string model_file_name1; string model_file_name2; string* datasets; string data_path; - + CvMLData* data; CvGBTrees* gtb; - + vector test_resps1; vector test_resps2; - int64 initSeed; + int64 initSeed; }; @@ -47,7 +47,7 @@ int _get_len(const CvMat* mat) CV_GBTreesTest::CV_GBTreesTest() { - int64 seeds[] = { CV_BIG_INT(0x00009fff4f9c8d52), + int64 seeds[] = { CV_BIG_INT(0x00009fff4f9c8d52), CV_BIG_INT(0x0000a17166072c7c), CV_BIG_INT(0x0201b32115cd1f9a), CV_BIG_INT(0x0513cb37abcd1234), @@ -55,7 +55,7 @@ CV_GBTreesTest::CV_GBTreesTest() }; int seedCount = sizeof(seeds)/sizeof(seeds[0]); - cv::RNG& rng = cv::theRNG(); + cv::RNG& rng = cv::theRNG(); initSeed = rng.state; rng.state = seeds[rng(seedCount)]; @@ -69,14 +69,14 @@ CV_GBTreesTest::~CV_GBTreesTest() if (data) delete data; delete[] datasets; - cv::theRNG().state = initSeed; + cv::theRNG().state = initSeed; } int CV_GBTreesTest::TestTrainPredict(int test_num) { int code = cvtest::TS::OK; - + int weak_count = 200; float shrinkage = 0.1f; float subsample_portion = 0.5f; @@ -89,7 +89,7 @@ int CV_GBTreesTest::TestTrainPredict(int test_num) case (2) : loss_function_type = CvGBTrees::ABSOLUTE_LOSS; break; case (3) : loss_function_type = CvGBTrees::HUBER_LOSS; break; case (0) : loss_function_type = CvGBTrees::DEVIANCE_LOSS; break; - default : + default : { ts->printf( cvtest::TS::LOG, "Bad test_num value in CV_GBTreesTest::TestTrainPredict(..) function." ); return cvtest::TS::FAIL_BAD_ARG_CHECK; @@ -101,7 +101,7 @@ int CV_GBTreesTest::TestTrainPredict(int test_num) { data = new CvMLData(); data->set_delimiter(','); - + if (data->read_csv(datasets[dataset_num].c_str())) { ts->printf( cvtest::TS::LOG, "File reading error." ); @@ -124,25 +124,25 @@ int CV_GBTreesTest::TestTrainPredict(int test_num) CvTrainTestSplit spl( train_sample_count ); data->set_train_test_split( &spl ); } - - data->mix_train_and_test_idx(); - - + + data->mix_train_and_test_idx(); + + if (gtb) delete gtb; gtb = new CvGBTrees(); bool tmp_code = true; tmp_code = gtb->train(data, CvGBTreesParams(loss_function_type, weak_count, shrinkage, subsample_portion, max_depth, use_surrogates)); - + if (!tmp_code) { ts->printf( cvtest::TS::LOG, "Model training was failed."); return cvtest::TS::FAIL_INVALID_OUTPUT; } - + code = checkPredictError(test_num); - + return code; } @@ -152,14 +152,14 @@ int CV_GBTreesTest::checkPredictError(int test_num) { if (!gtb) return cvtest::TS::FAIL_GENERIC; - + //float mean[] = {5.430247f, 13.5654f, 12.6569f, 13.1661f}; //float sigma[] = {0.4162694f, 3.21161f, 3.43297f, 3.00624f}; - float mean[] = {5.80226f, 12.68689f, 13.49095f, 13.19628f}; + float mean[] = {5.80226f, 12.68689f, 13.49095f, 13.19628f}; float sigma[] = {0.4764534f, 3.166919f, 3.022405f, 2.868722f}; - + float current_error = gtb->calc_error(data, CV_TEST_ERROR); - + if ( abs( current_error - mean[test_num]) > 6*sigma[test_num] ) { ts->printf( cvtest::TS::LOG, "Test error is out of range:\n" @@ -177,7 +177,7 @@ int CV_GBTreesTest::TestSaveLoad() { if (!gtb) return cvtest::TS::FAIL_GENERIC; - + model_file_name1 = cv::tempfile(); model_file_name2 = cv::tempfile(); @@ -186,9 +186,9 @@ int CV_GBTreesTest::TestSaveLoad() gtb->load(model_file_name1.c_str()); gtb->calc_error(data, CV_TEST_ERROR, &test_resps2); gtb->save(model_file_name2.c_str()); - + return checkLoadSave(); - + } @@ -200,7 +200,7 @@ int CV_GBTreesTest::checkLoadSave() // 1. compare files ifstream f1( model_file_name1.c_str() ), f2( model_file_name2.c_str() ); string s1, s2; - int lineIdx = 0; + int lineIdx = 0; CV_Assert( f1.is_open() && f2.is_open() ); for( ; !f1.eof() && !f2.eof(); lineIdx++ ) { @@ -244,23 +244,23 @@ int CV_GBTreesTest::checkLoadSave() void CV_GBTreesTest::run(int) { - string data_path = string(ts->get_data_path()); + string dataPath = string(ts->get_data_path()); datasets = new string[2]; - datasets[0] = data_path + string("spambase.data"); /*string("dataset_classification.csv");*/ - datasets[1] = data_path + string("housing_.data"); /*string("dataset_regression.csv");*/ + datasets[0] = dataPath + string("spambase.data"); /*string("dataset_classification.csv");*/ + datasets[1] = dataPath + string("housing_.data"); /*string("dataset_regression.csv");*/ int code = cvtest::TS::OK; for (int i = 0; i < 4; i++) { - + int temp_code = TestTrainPredict(i); if (temp_code != cvtest::TS::OK) { code = temp_code; break; } - + else if (i==0) { temp_code = TestSaveLoad(); @@ -269,13 +269,13 @@ void CV_GBTreesTest::run(int) delete data; data = 0; } - + delete gtb; gtb = 0; } delete data; data = 0; - + ts->set_failed_test_info( code ); } diff --git a/modules/ml/test/test_precomp.hpp b/modules/ml/test/test_precomp.hpp index a939d1c..407ad9d 100644 --- a/modules/ml/test/test_precomp.hpp +++ b/modules/ml/test/test_precomp.hpp @@ -1,3 +1,7 @@ +#ifdef __GNUC__ +# pragma GCC diagnostic ignored "-Wmissing-declarations" +#endif + #ifndef __OPENCV_TEST_PRECOMP_HPP__ #define __OPENCV_TEST_PRECOMP_HPP__ diff --git a/modules/nonfree/perf/perf_precomp.hpp b/modules/nonfree/perf/perf_precomp.hpp index d86769c..66eea25 100644 --- a/modules/nonfree/perf/perf_precomp.hpp +++ b/modules/nonfree/perf/perf_precomp.hpp @@ -1,3 +1,7 @@ +#ifdef __GNUC__ +# pragma GCC diagnostic ignored "-Wmissing-declarations" +#endif + #ifndef __OPENCV_PERF_PRECOMP_HPP__ #define __OPENCV_PERF_PRECOMP_HPP__ @@ -5,7 +9,7 @@ #include "opencv2/nonfree/nonfree.hpp" #include "opencv2/highgui/highgui.hpp" -#if GTEST_CREATE_SHARED_LIBRARY +#ifdef GTEST_CREATE_SHARED_LIBRARY #error no modules except ts should have GTEST_CREATE_SHARED_LIBRARY defined #endif diff --git a/modules/nonfree/src/precomp.hpp b/modules/nonfree/src/precomp.hpp index 9d7c4f0..1730b8b 100644 --- a/modules/nonfree/src/precomp.hpp +++ b/modules/nonfree/src/precomp.hpp @@ -43,11 +43,7 @@ #ifndef __OPENCV_PRECOMP_H__ #define __OPENCV_PRECOMP_H__ -#if _MSC_VER >= 1200 -#pragma warning( disable: 4251 4512 4710 4711 4514 4996 ) -#endif - -#ifdef HAVE_CVCONFIG_H +#ifdef HAVE_CVCONFIG_H #include "cvconfig.h" #endif diff --git a/modules/nonfree/src/sift.cpp b/modules/nonfree/src/sift.cpp index 7edba13..0bb7848 100644 --- a/modules/nonfree/src/sift.cpp +++ b/modules/nonfree/src/sift.cpp @@ -43,7 +43,7 @@ /**********************************************************************************************\ Implementation of SIFT is based on the code from http://blogs.oregonstate.edu/hess/code/sift/ Below is the original copyright. - + // Copyright (c) 2006-2010, Rob Hess // All rights reserved. @@ -101,7 +101,7 @@ // NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS // SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. \**********************************************************************************************/ - + #include "precomp.hpp" #include #include @@ -161,10 +161,10 @@ static const float SIFT_DESCR_MAG_THR = 0.2f; // factor used to convert floating-point descriptor to unsigned char static const float SIFT_INT_DESCR_FCTR = 512.f; - + static const int SIFT_FIXPT_SCALE = 48; - - + + static Mat createInitialImage( const Mat& img, bool doubleImageSize, float sigma ) { Mat gray, gray_fpt; @@ -173,9 +173,9 @@ static Mat createInitialImage( const Mat& img, bool doubleImageSize, float sigma else img.copyTo(gray); gray.convertTo(gray_fpt, CV_16S, SIFT_FIXPT_SCALE, 0); - + float sig_diff; - + if( doubleImageSize ) { sig_diff = sqrtf( std::max(sigma * sigma - SIFT_INIT_SIGMA * SIFT_INIT_SIGMA * 4, 0.01f) ); @@ -191,13 +191,13 @@ static Mat createInitialImage( const Mat& img, bool doubleImageSize, float sigma return gray_fpt; } } - - + + void SIFT::buildGaussianPyramid( const Mat& base, vector& pyr, int nOctaves ) const { vector sig(nOctaveLayers + 3); pyr.resize(nOctaves*(nOctaveLayers + 3)); - + // precompute Gaussian sigmas using the following formula: // \sigma_{total}^2 = \sigma_{i}^2 + \sigma_{i-1}^2 sig[0] = sigma; @@ -208,7 +208,7 @@ void SIFT::buildGaussianPyramid( const Mat& base, vector& pyr, int nOctaves double sig_total = sig_prev*k; sig[i] = std::sqrt(sig_total*sig_total - sig_prev*sig_prev); } - + for( int o = 0; o < nOctaves; o++ ) { for( int i = 0; i < nOctaveLayers + 3; i++ ) @@ -237,7 +237,7 @@ void SIFT::buildDoGPyramid( const vector& gpyr, vector& dogpyr ) const { int nOctaves = (int)gpyr.size()/(nOctaveLayers + 3); dogpyr.resize( nOctaves*(nOctaveLayers + 2) ); - + for( int o = 0; o < nOctaves; o++ ) { for( int i = 0; i < nOctaveLayers + 2; i++ ) @@ -250,21 +250,21 @@ void SIFT::buildDoGPyramid( const vector& gpyr, vector& dogpyr ) const } } - + // Computes a gradient orientation histogram at a specified pixel static float calcOrientationHist( const Mat& img, Point pt, int radius, float sigma, float* hist, int n ) { int i, j, k, len = (radius*2+1)*(radius*2+1); - + float expf_scale = -1.f/(2.f * sigma * sigma); AutoBuffer buf(len*4 + n+4); float *X = buf, *Y = X + len, *Mag = X, *Ori = Y + len, *W = Ori + len; float* temphist = W + len + 2; - + for( i = 0; i < n; i++ ) temphist[i] = 0.f; - + for( i = -radius, k = 0; i <= radius; i++ ) { int y = pt.y + i; @@ -275,22 +275,22 @@ static float calcOrientationHist( const Mat& img, Point pt, int radius, int x = pt.x + j; if( x <= 0 || x >= img.cols - 1 ) continue; - + float dx = (float)(img.at(y, x+1) - img.at(y, x-1)); float dy = (float)(img.at(y-1, x) - img.at(y+1, x)); - + X[k] = dx; Y[k] = dy; W[k] = (i*i + j*j)*expf_scale; k++; } } - + len = k; - + // compute gradient values, orientations and the weights over the pixel neighborhood exp(W, W, len); fastAtan2(Y, X, Ori, len, true); magnitude(X, Y, Mag, len); - + for( k = 0; k < len; k++ ) { int bin = cvRound((n/360.f)*Ori[k]); @@ -300,7 +300,7 @@ static float calcOrientationHist( const Mat& img, Point pt, int radius, bin += n; temphist[bin] += W[k]*Mag[k]; } - + // smooth the histogram temphist[-1] = temphist[n-1]; temphist[-2] = temphist[n-2]; @@ -312,19 +312,19 @@ static float calcOrientationHist( const Mat& img, Point pt, int radius, (temphist[i-1] + temphist[i+1])*(4.f/16.f) + temphist[i]*(6.f/16.f); } - + float maxval = hist[0]; for( i = 1; i < n; i++ ) maxval = std::max(maxval, hist[i]); - + return maxval; } - + // // Interpolates a scale-space extremum's location and scale to subpixel // accuracy to form an image feature. Rejects features with low contrast. -// Based on Section 4 of Lowe's paper. +// Based on Section 4 of Lowe's paper. static bool adjustLocalExtrema( const vector& dog_pyr, KeyPoint& kpt, int octv, int& layer, int& r, int& c, int nOctaveLayers, float contrastThreshold, float edgeThreshold, float sigma ) @@ -333,21 +333,21 @@ static bool adjustLocalExtrema( const vector& dog_pyr, KeyPoint& kpt, int o const float deriv_scale = img_scale*0.5f; const float second_deriv_scale = img_scale; const float cross_deriv_scale = img_scale*0.25f; - + float xi=0, xr=0, xc=0, contr; int i = 0; - + for( ; i < SIFT_MAX_INTERP_STEPS; i++ ) { int idx = octv*(nOctaveLayers+2) + layer; const Mat& img = dog_pyr[idx]; const Mat& prev = dog_pyr[idx-1]; const Mat& next = dog_pyr[idx+1]; - + Vec3f dD((img.at(r, c+1) - img.at(r, c-1))*deriv_scale, (img.at(r+1, c) - img.at(r-1, c))*deriv_scale, (next.at(r, c) - prev.at(r, c))*deriv_scale); - + float v2 = (float)img.at(r, c)*2; float dxx = (img.at(r, c+1) + img.at(r, c-1) - v2)*second_deriv_scale; float dyy = (img.at(r+1, c) + img.at(r-1, c) - v2)*second_deriv_scale; @@ -358,34 +358,34 @@ static bool adjustLocalExtrema( const vector& dog_pyr, KeyPoint& kpt, int o prev.at(r, c+1) + prev.at(r, c-1))*cross_deriv_scale; float dys = (next.at(r+1, c) - next.at(r-1, c) - prev.at(r+1, c) + prev.at(r-1, c))*cross_deriv_scale; - + Matx33f H(dxx, dxy, dxs, dxy, dyy, dys, dxs, dys, dss); - + Vec3f X = H.solve(dD, DECOMP_LU); - + xi = -X[2]; xr = -X[1]; xc = -X[0]; - + if( std::abs( xi ) < 0.5f && std::abs( xr ) < 0.5f && std::abs( xc ) < 0.5f ) break; - + c += cvRound( xc ); r += cvRound( xr ); layer += cvRound( xi ); - + if( layer < 1 || layer > nOctaveLayers || c < SIFT_IMG_BORDER || c >= img.cols - SIFT_IMG_BORDER || r < SIFT_IMG_BORDER || r >= img.rows - SIFT_IMG_BORDER ) return false; } - + /* ensure convergence of interpolation */ if( i >= SIFT_MAX_INTERP_STEPS ) return false; - + { int idx = octv*(nOctaveLayers+2) + layer; const Mat& img = dog_pyr[idx]; @@ -395,11 +395,11 @@ static bool adjustLocalExtrema( const vector& dog_pyr, KeyPoint& kpt, int o (img.at(r+1, c) - img.at(r-1, c))*deriv_scale, (next.at(r, c) - prev.at(r, c))*deriv_scale); float t = dD.dot(Matx31f(xc, xr, xi)); - + contr = img.at(r, c)*img_scale + t * 0.5f; if( std::abs( contr ) * nOctaveLayers < contrastThreshold ) return false; - + /* principal curvatures are computed using the trace and det of Hessian */ float v2 = img.at(r, c)*2.f; float dxx = (img.at(r, c+1) + img.at(r, c-1) - v2)*second_deriv_scale; @@ -408,20 +408,20 @@ static bool adjustLocalExtrema( const vector& dog_pyr, KeyPoint& kpt, int o img.at(r-1, c+1) + img.at(r-1, c-1)) * cross_deriv_scale; float tr = dxx + dyy; float det = dxx * dyy - dxy * dxy; - + if( det <= 0 || tr*tr*edgeThreshold >= (edgeThreshold + 1)*(edgeThreshold + 1)*det ) return false; } - + kpt.pt.x = (c + xc) * (1 << octv); kpt.pt.y = (r + xr) * (1 << octv); kpt.octave = octv + (layer << 8) + (cvRound((xi + 0.5)*255) << 16); kpt.size = sigma*powf(2.f, (layer + xi) / nOctaveLayers)*(1 << octv)*2; - + return true; } - - + + // // Detects features at extrema in DoG scale space. Bad features are discarded // based on contrast and ratio of principal curvatures. @@ -433,9 +433,9 @@ void SIFT::findScaleSpaceExtrema( const vector& gauss_pyr, const vector& gauss_pyr, const vector(r); const short* prevptr = prev.ptr(r); const short* nextptr = next.ptr(r); - + for( int c = SIFT_IMG_BORDER; c < cols-SIFT_IMG_BORDER; c++) { int val = currptr[c]; - + // find local extrema with pixel accuracy if( std::abs(val) > threshold && ((val > 0 && val >= currptr[c-1] && val >= currptr[c+1] && @@ -492,11 +492,11 @@ void SIFT::findScaleSpaceExtrema( const vector& gauss_pyr, const vector 0 ? j - 1 : n - 1; - int r = j < n-1 ? j + 1 : 0; - - if( hist[j] > hist[l] && hist[j] > hist[r] && hist[j] >= mag_thr ) + int r2 = j < n-1 ? j + 1 : 0; + + if( hist[j] > hist[l] && hist[j] > hist[r2] && hist[j] >= mag_thr ) { - float bin = j + 0.5f * (hist[l]-hist[r]) / (hist[l] - 2*hist[j] + hist[r]); + float bin = j + 0.5f * (hist[l]-hist[r2]) / (hist[l] - 2*hist[j] + hist[r2]); bin = bin < 0 ? n + bin : bin >= n ? bin - n : bin; kpt.angle = (float)((360.f/n) * bin); keypoints.push_back(kpt); @@ -506,9 +506,9 @@ void SIFT::findScaleSpaceExtrema( const vector& gauss_pyr, const vector buf(len*6 + histlen); float *X = buf, *Y = X + len, *Mag = Y, *Ori = Mag + len, *W = Ori + len; float *RBin = W + len, *CBin = RBin + len, *hist = CBin + len; - + for( i = 0; i < d+2; i++ ) { for( j = 0; j < d+2; j++ ) for( k = 0; k < n+2; k++ ) hist[(i*(d+2) + j)*(n+2) + k] = 0.; } - + for( i = -radius, k = 0; i <= radius; i++ ) for( j = -radius; j <= radius; j++ ) { @@ -549,7 +549,7 @@ static void calcSIFTDescriptor( const Mat& img, Point2f ptf, float ori, float sc float rbin = r_rot + d/2 - 0.5f; float cbin = c_rot + d/2 - 0.5f; int r = pt.y + i, c = pt.x + j; - + if( rbin > -1 && rbin < d && cbin > -1 && cbin < d && r > 0 && r < rows - 1 && c > 0 && c < cols - 1 ) { @@ -560,30 +560,30 @@ static void calcSIFTDescriptor( const Mat& img, Point2f ptf, float ori, float sc k++; } } - + len = k; fastAtan2(Y, X, Ori, len, true); magnitude(X, Y, Mag, len); exp(W, W, len); - + for( k = 0; k < len; k++ ) { float rbin = RBin[k], cbin = CBin[k]; float obin = (Ori[k] - ori)*bins_per_rad; float mag = Mag[k]*W[k]; - + int r0 = cvFloor( rbin ); int c0 = cvFloor( cbin ); int o0 = cvFloor( obin ); rbin -= r0; cbin -= c0; obin -= o0; - + if( o0 < 0 ) o0 += n; if( o0 >= n ) o0 -= n; - + // histogram update using tri-linear interpolation float v_r1 = mag*rbin, v_r0 = mag - v_r1; float v_rc11 = v_r1*cbin, v_rc10 = v_r1 - v_rc11; @@ -592,7 +592,7 @@ static void calcSIFTDescriptor( const Mat& img, Point2f ptf, float ori, float sc float v_rco101 = v_rc10*obin, v_rco100 = v_rc10 - v_rco101; float v_rco011 = v_rc01*obin, v_rco010 = v_rc01 - v_rco011; float v_rco001 = v_rc00*obin, v_rco000 = v_rc00 - v_rco001; - + int idx = ((r0+1)*(d+2) + c0+1)*(n+2) + o0; hist[idx] += v_rco000; hist[idx+1] += v_rco001; @@ -603,7 +603,7 @@ static void calcSIFTDescriptor( const Mat& img, Point2f ptf, float ori, float sc hist[idx+(d+3)*(n+2)] += v_rco110; hist[idx+(d+3)*(n+2)+1] += v_rco111; } - + // finalize histogram, since the orientation histograms are circular for( i = 0; i < d; i++ ) for( j = 0; j < d; j++ ) @@ -635,12 +635,12 @@ static void calcSIFTDescriptor( const Mat& img, Point2f ptf, float ori, float sc dst[k] = saturate_cast(dst[k]*nrm2); } } - + static void calcDescriptors(const vector& gpyr, const vector& keypoints, Mat& descriptors, int nOctaveLayers ) { int d = SIFT_DESCR_WIDTH, n = SIFT_DESCR_HIST_BINS; - + for( size_t i = 0; i < keypoints.size(); i++ ) { KeyPoint kpt = keypoints[i]; @@ -649,7 +649,7 @@ static void calcDescriptors(const vector& gpyr, const vector& key float size=kpt.size*scale; Point2f ptf(kpt.pt.x*scale, kpt.pt.y*scale); const Mat& img = gpyr[octv*(nOctaveLayers + 3) + layer]; - + calcSIFTDescriptor(img, ptf, kpt.angle, size*0.5f, d, n, descriptors.ptr((int)i)); } } @@ -687,34 +687,34 @@ void SIFT::operator()(InputArray _image, InputArray _mask, bool useProvidedKeypoints) const { Mat image = _image.getMat(), mask = _mask.getMat(); - + if( image.empty() || image.depth() != CV_8U ) CV_Error( CV_StsBadArg, "image is empty or has incorrect depth (!=CV_8U)" ); - + if( !mask.empty() && mask.type() != CV_8UC1 ) CV_Error( CV_StsBadArg, "mask has incorrect type (!=CV_8UC1)" ); - + Mat base = createInitialImage(image, false, (float)sigma); vector gpyr, dogpyr; int nOctaves = cvRound(log( (double)std::min( base.cols, base.rows ) ) / log(2.) - 2); - + //double t, tf = getTickFrequency(); //t = (double)getTickCount(); buildGaussianPyramid(base, gpyr, nOctaves); buildDoGPyramid(gpyr, dogpyr); - + //t = (double)getTickCount() - t; //printf("pyramid construction time: %g\n", t*1000./tf); - + if( !useProvidedKeypoints ) { //t = (double)getTickCount(); findScaleSpaceExtrema(gpyr, dogpyr, keypoints); KeyPointsFilter::removeDuplicated( keypoints ); - + if( !mask.empty() ) KeyPointsFilter::runByPixelsMask( keypoints, mask ); - + if( nfeatures > 0 ) KeyPointsFilter::retainBest(keypoints, nfeatures); //t = (double)getTickCount() - t; @@ -725,14 +725,14 @@ void SIFT::operator()(InputArray _image, InputArray _mask, // filter keypoints by mask //KeyPointsFilter::runByPixelsMask( keypoints, mask ); } - + if( _descriptors.needed() ) { //t = (double)getTickCount(); int dsize = descriptorSize(); _descriptors.create((int)keypoints.size(), dsize, CV_32F); Mat descriptors = _descriptors.getMat(); - + calcDescriptors(gpyr, keypoints, descriptors, nOctaveLayers); //t = (double)getTickCount() - t; //printf("descriptor extraction time: %g\n", t*1000./tf); @@ -742,8 +742,8 @@ void SIFT::operator()(InputArray _image, InputArray _mask, void SIFT::detectImpl( const Mat& image, vector& keypoints, const Mat& mask) const { (*this)(image, mask, keypoints, noArray()); -} - +} + void SIFT::computeImpl( const Mat& image, vector& keypoints, Mat& descriptors) const { (*this)(image, Mat(), keypoints, descriptors, true); diff --git a/modules/nonfree/src/surf.cpp b/modules/nonfree/src/surf.cpp index 76939f8..2690b35 100644 --- a/modules/nonfree/src/surf.cpp +++ b/modules/nonfree/src/surf.cpp @@ -17,16 +17,16 @@ * Redistribution and use in source and binary forms, with or * without modification, are permitted provided that the following * conditions are met: - * Redistributions of source code must retain the above - * copyright notice, this list of conditions and the following - * disclaimer. - * Redistributions in binary form must reproduce the above - * copyright notice, this list of conditions and the following - * disclaimer in the documentation and/or other materials - * provided with the distribution. - * The name of Contributor may not be used to endorse or - * promote products derived from this software without - * specific prior written permission. + * Redistributions of source code must retain the above + * copyright notice, this list of conditions and the following + * disclaimer. + * Redistributions in binary form must reproduce the above + * copyright notice, this list of conditions and the following + * disclaimer in the documentation and/or other materials + * provided with the distribution. + * The name of Contributor may not be used to endorse or + * promote products derived from this software without + * specific prior written permission. * * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND * CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, @@ -43,7 +43,7 @@ * OF SUCH DAMAGE. */ -/* +/* The following changes have been made, comparing to the original contribution: 1. A lot of small optimizations, less memory allocations, got rid of global buffers 2. Reversed order of cvGetQuadrangleSubPix and cvResize calls; probably less accurate, but much faster @@ -79,8 +79,8 @@ octave. The extraction of the patch of pixels surrounding a keypoint used to build a descriptor has been simplified. -KeyPoint descriptor normalisation has been changed from normalising each 4x4 -cell (resulting in a descriptor of magnitude 16) to normalising the entire +KeyPoint descriptor normalisation has been changed from normalising each 4x4 +cell (resulting in a descriptor of magnitude 16) to normalising the entire descriptor to magnitude 1. The default number of octaves has been increased from 3 to 4 to match the @@ -88,20 +88,20 @@ original SURF binary default. The increase in computation time is minimal since the higher octaves are sampled sparsely. The default number of layers per octave has been reduced from 3 to 2, to prevent -redundant calculation of similar sizes in consecutive octaves. This decreases -computation time. The number of features extracted may be less, however the +redundant calculation of similar sizes in consecutive octaves. This decreases +computation time. The number of features extracted may be less, however the additional features were mostly redundant. The radius of the circle of gradient samples used to assign an orientation has -been increased from 4 to 6 to match the description in the SURF paper. This is +been increased from 4 to 6 to match the description in the SURF paper. This is now defined by ORI_RADIUS, and could be made into a parameter. The size of the sliding window used in orientation assignment has been reduced from 120 to 60 degrees to match the description in the SURF paper. This is now defined by ORI_WIN, and could be made into a parameter. -Other options like HAAR_SIZE0, HAAR_SIZE_INC, SAMPLE_STEP0, ORI_SEARCH_INC, -ORI_SIGMA and DESC_SIGMA have been separated from the code and documented. +Other options like HAAR_SIZE0, HAAR_SIZE_INC, SAMPLE_STEP0, ORI_SEARCH_INC, +ORI_SIGMA and DESC_SIGMA have been separated from the code and documented. These could also be made into parameters. Modifications by Ian Mahon @@ -124,12 +124,14 @@ static const int SURF_HAAR_SIZE0 = 9; // This ensures that when looking for the neighbours of a sample, the layers // above and below are aligned correctly. static const int SURF_HAAR_SIZE_INC = 6; - - + + struct SurfHF { int p0, p1, p2, p3; float w; + + SurfHF(): p0(0), p1(0), p2(0), p3(0), w(0) {} }; inline float calcHaarPattern( const int* origin, const SurfHF* f, int n ) @@ -208,10 +210,10 @@ static void calcLayerDetAndTrace( const Mat& sum, int size, int sampleStep, * Maxima location interpolation as described in "Invariant Features from * Interest Point Groups" by Matthew Brown and David Lowe. This is performed by * fitting a 3D quadratic to a set of neighbouring samples. - * - * The gradient vector and Hessian matrix at the initial keypoint location are + * + * The gradient vector and Hessian matrix at the initial keypoint location are * approximated using central differences. The linear system Ax = b is then - * solved, where A is the Hessian, b is the negative gradient, and x is the + * solved, where A is the Hessian, b is the negative gradient, and x is the * offset of the interpolated maxima coordinates from the initial estimate. * This is equivalent to an iteration of Netwon's optimisation algorithm. * @@ -234,18 +236,18 @@ interpolateKeypoint( float N9[3][9], int dx, int dy, int ds, KeyPoint& kpt ) N9[1][3]-2*N9[1][4]+N9[1][5], // 2nd deriv x, x (N9[1][8]-N9[1][6]-N9[1][2]+N9[1][0])/4, // 2nd deriv x, y (N9[2][5]-N9[2][3]-N9[0][5]+N9[0][3])/4, // 2nd deriv x, s - (N9[1][8]-N9[1][6]-N9[1][2]+N9[1][0])/4, // 2nd deriv x, y - N9[1][1]-2*N9[1][4]+N9[1][7], // 2nd deriv y, y - (N9[2][7]-N9[2][1]-N9[0][7]+N9[0][1])/4, // 2nd deriv y, s + (N9[1][8]-N9[1][6]-N9[1][2]+N9[1][0])/4, // 2nd deriv x, y + N9[1][1]-2*N9[1][4]+N9[1][7], // 2nd deriv y, y + (N9[2][7]-N9[2][1]-N9[0][7]+N9[0][1])/4, // 2nd deriv y, s (N9[2][5]-N9[2][3]-N9[0][5]+N9[0][3])/4, // 2nd deriv x, s (N9[2][7]-N9[2][1]-N9[0][7]+N9[0][1])/4, // 2nd deriv y, s N9[0][4]-2*N9[1][4]+N9[2][4]); // 2nd deriv s, s Vec3f x = A.solve(b, DECOMP_LU); - + bool ok = (x[0] != 0 || x[1] != 0 || x[2] != 0) && std::abs(x[0]) <= 1 && std::abs(x[1]) <= 1 && std::abs(x[2]) <= 1; - + if( ok ) { kpt.pt.x += x[0]*dx; @@ -425,7 +427,7 @@ struct SURFFindInvoker { int layer = (*middleIndices)[i]; int octave = i / nOctaveLayers; - findMaximaInLayer( *sum, *mask_sum, *dets, *traces, *sizes, + findMaximaInLayer( *sum, *mask_sum, *dets, *traces, *sizes, *keypoints, octave, layer, hessianThreshold, (*sampleSteps)[layer] ); } @@ -459,7 +461,7 @@ struct KeypointGreater } }; - + static void fastHessianDetector( const Mat& sum, const Mat& mask_sum, vector& keypoints, int nOctaves, int nOctaveLayers, float hessianThreshold ) { @@ -479,7 +481,7 @@ static void fastHessianDetector( const Mat& sum, const Mat& mask_sum, vector winbuf = cvCreateMat( 1, imaxSize*imaxSize, CV_8U ); for( k = k1; k < k2; k++ ) { - int i, j, kk, x, y, nangle; + int i, j, kk, nangle; float* vec; SurfHF dx_t[NX], dy_t[NY]; KeyPoint& kp = (*keypoints)[k]; @@ -601,7 +603,7 @@ struct SURFInvoker float s = size*1.2f/9.0f; /* To find the dominant orientation, the gradients in x and y are sampled in a circle of radius 6s using wavelets of size 4s. - We ensure the gradient wavelet size is even to ensure the + We ensure the gradient wavelet size is even to ensure the wavelet pattern is balanced and symmetric around its center */ int grad_wav_size = 2*cvRound( 2*s ); if( sum->rows < grad_wav_size || sum->cols < grad_wav_size ) @@ -620,8 +622,8 @@ struct SURFInvoker resizeHaarPattern( dy_s, dy_t, NY, 4, grad_wav_size, sum->cols ); for( kk = 0, nangle = 0; kk < nOriSamples; kk++ ) { - x = cvRound( center.x + apt[kk].x*s - (float)(grad_wav_size-1)/2 ); - y = cvRound( center.y + apt[kk].y*s - (float)(grad_wav_size-1)/2 ); + int x = cvRound( center.x + apt[kk].x*s - (float)(grad_wav_size-1)/2 ); + int y = cvRound( center.y + apt[kk].y*s - (float)(grad_wav_size-1)/2 ); if( y < 0 || y >= sum->rows - grad_wav_size || x < 0 || x >= sum->cols - grad_wav_size ) continue; @@ -670,7 +672,7 @@ struct SURFInvoker kp.angle = descriptor_dir; if( !descriptors || !descriptors->data ) continue; - + /* Extract a window of pixels around the keypoint of size 20s */ int win_size = (int)((PATCH_SZ+1)*s); CV_Assert( winbuf->cols >= win_size*win_size ); @@ -678,13 +680,13 @@ struct SURFInvoker if( !upright ) { - descriptor_dir *= (float)(CV_PI/180); + descriptor_dir *= (float)(CV_PI/180); float sin_dir = std::sin(descriptor_dir); float cos_dir = std::cos(descriptor_dir); /* Subpixel interpolation version (slower). Subpixel not required since the pixels will all get averaged when we scale down to 20 pixels */ - /* + /* float w[] = { cos_dir, sin_dir, center.x, -sin_dir, cos_dir , center.y }; CvMat W = cvMat(2, 3, CV_32F, w); @@ -711,12 +713,12 @@ struct SURFInvoker else { // extract rect - slightly optimized version of the code above - // TODO: find faster code, as this is simply an extract rect operation, + // TODO: find faster code, as this is simply an extract rect operation, // e.g. by using cvGetSubRect, problem is the border processing // descriptor_dir == 90 grad // sin_dir == 1 // cos_dir == 0 - + float win_offset = -(float)(win_size-1)/2; int start_x = cvRound(center.x + win_offset); int start_y = cvRound(center.y - win_offset); @@ -727,13 +729,13 @@ struct SURFInvoker int pixel_y = start_y; for( j = 0; j < win_size; j++, pixel_y-- ) { - x = MAX( pixel_x, 0 ); - y = MAX( pixel_y, 0 ); + int x = MAX( pixel_x, 0 ); + int y = MAX( pixel_y, 0 ); x = MIN( x, img->cols-1 ); y = MIN( y, img->rows-1 ); WIN[i*win_size + j] = img->at(y, x); } - } + } } // Scale the window to size PATCH_SZ so each pixel's size is s. This // makes calculating the gradients with wavelets of size 2s easy @@ -761,9 +763,9 @@ struct SURFInvoker for( i = 0; i < 4; i++ ) for( j = 0; j < 4; j++ ) { - for( y = i*5; y < i*5+5; y++ ) + for(int y = i*5; y < i*5+5; y++ ) { - for( x = j*5; x < j*5+5; x++ ) + for(int x = j*5; x < j*5+5; x++ ) { float tx = DX[y][x], ty = DY[y][x]; if( ty >= 0 ) @@ -795,9 +797,9 @@ struct SURFInvoker for( i = 0; i < 4; i++ ) for( j = 0; j < 4; j++ ) { - for( y = i*5; y < i*5+5; y++ ) + for(int y = i*5; y < i*5+5; y++ ) { - for( x = j*5; x < j*5+5; x++ ) + for(int x = j*5; x < j*5+5; x++ ) { float tx = DX[y][x], ty = DY[y][x]; vec[0] += tx; vec[1] += ty; @@ -860,7 +862,7 @@ void SURF::operator()(InputArray imgarg, InputArray maskarg, { (*this)(imgarg, maskarg, keypoints, noArray(), false); } - + void SURF::operator()(InputArray _img, InputArray _mask, CV_OUT vector& keypoints, OutputArray _descriptors, @@ -868,18 +870,18 @@ void SURF::operator()(InputArray _img, InputArray _mask, { Mat img = _img.getMat(), mask = _mask.getMat(), mask1, sum, msum; bool doDescriptors = _descriptors.needed(); - + CV_Assert(!img.empty() && img.depth() == CV_8U); if( img.channels() > 1 ) cvtColor(img, img, COLOR_BGR2GRAY); - + CV_Assert(mask.empty() || (mask.type() == CV_8U && mask.size() == img.size())); CV_Assert(hessianThreshold >= 0); CV_Assert(nOctaves > 0); CV_Assert(nOctaveLayers > 0); - + integral(img, sum, CV_32S); - + // Compute keypoints only if we are not asked for evaluating the descriptors are some given locations: if( !useProvidedKeypoints ) { @@ -890,7 +892,7 @@ void SURF::operator()(InputArray _img, InputArray _mask, } fastHessianDetector( sum, msum, keypoints, nOctaves, nOctaveLayers, (float)hessianThreshold ); } - + int i, j, N = (int)keypoints.size(); if( N > 0 ) { @@ -898,7 +900,7 @@ void SURF::operator()(InputArray _img, InputArray _mask, bool _1d = false; int dcols = extended ? 128 : 64; size_t dsize = dcols*sizeof(float); - + if( doDescriptors ) { _1d = _descriptors.kind() == _InputArray::STD_VECTOR && _descriptors.type() == CV_32F; @@ -913,11 +915,11 @@ void SURF::operator()(InputArray _img, InputArray _mask, descriptors = _descriptors.getMat(); } } - + // we call SURFInvoker in any case, even if we do not need descriptors, // since it computes orientation of each feature. parallel_for(BlockedRange(0, N), SURFInvoker(img, sum, keypoints, descriptors, extended, upright) ); - + // remove keypoints that were marked for deletion for( i = j = 0; i < N; i++ ) { @@ -951,7 +953,7 @@ void SURF::operator()(InputArray _img, InputArray _mask, void SURF::detectImpl( const Mat& image, vector& keypoints, const Mat& mask) const { (*this)(image, mask, keypoints, noArray(), false); -} +} void SURF::computeImpl( const Mat& image, vector& keypoints, Mat& descriptors) const { diff --git a/modules/nonfree/test/test_precomp.hpp b/modules/nonfree/test/test_precomp.hpp index 01a3978..ac4ce4b 100644 --- a/modules/nonfree/test/test_precomp.hpp +++ b/modules/nonfree/test/test_precomp.hpp @@ -1,3 +1,7 @@ +#ifdef __GNUC__ +# pragma GCC diagnostic ignored "-Wmissing-declarations" +#endif + #ifndef __OPENCV_TEST_PRECOMP_HPP__ #define __OPENCV_TEST_PRECOMP_HPP__ diff --git a/modules/objdetect/include/opencv2/objdetect/objdetect.hpp b/modules/objdetect/include/opencv2/objdetect/objdetect.hpp index 7568986..a754238 100644 --- a/modules/objdetect/include/opencv2/objdetect/objdetect.hpp +++ b/modules/objdetect/include/opencv2/objdetect/objdetect.hpp @@ -636,12 +636,14 @@ struct CV_EXPORTS Feature int label; ///< Quantization Feature() : x(0), y(0), label(0) {} - Feature(int x, int y, int label) : x(x), y(y), label(label) {} + Feature(int x, int y, int label); void read(const FileNode& fn); void write(FileStorage& fs) const; }; +inline Feature::Feature(int _x, int _y, int _label) : x(_x), y(_y), label(_label) {} + struct CV_EXPORTS Template { int width; @@ -688,10 +690,7 @@ protected: /// Candidate feature with a score struct Candidate { - Candidate(int x, int y, int label, float score) - : f(x, y, label), score(score) - { - } + Candidate(int x, int y, int label, float score); /// Sort candidates with high score to the front bool operator<(const Candidate& rhs) const @@ -716,6 +715,8 @@ protected: size_t num_features, float distance); }; +inline QuantizedPyramid::Candidate::Candidate(int x, int y, int label, float _score) : f(x, y, label), score(_score) {} + /** * \brief Interface for modalities that plug into the LINE template matching representation. * @@ -853,10 +854,7 @@ struct CV_EXPORTS Match { } - Match(int x, int y, float similarity, const std::string& class_id, int template_id) - : x(x), y(y), similarity(similarity), class_id(class_id), template_id(template_id) - { - } + Match(int x, int y, float similarity, const std::string& class_id, int template_id); /// Sort matches with high similarity to the front bool operator<(const Match& rhs) const @@ -880,6 +878,11 @@ struct CV_EXPORTS Match int template_id; }; +inline Match::Match(int _x, int _y, float _similarity, const std::string& _class_id, int _template_id) + : x(_x), y(_y), similarity(_similarity), class_id(_class_id), template_id(_template_id) + { + } + /** * \brief Object detector using the LINE template matching algorithm with any set of * modalities. diff --git a/modules/objdetect/perf/perf_precomp.hpp b/modules/objdetect/perf/perf_precomp.hpp index 397f127..2682e1c 100644 --- a/modules/objdetect/perf/perf_precomp.hpp +++ b/modules/objdetect/perf/perf_precomp.hpp @@ -1,3 +1,7 @@ +#ifdef __GNUC__ +# pragma GCC diagnostic ignored "-Wmissing-declarations" +#endif + #ifndef __OPENCV_PERF_PRECOMP_HPP__ #define __OPENCV_PERF_PRECOMP_HPP__ @@ -5,7 +9,7 @@ #include "opencv2/objdetect/objdetect.hpp" #include "opencv2/highgui/highgui.hpp" -#if GTEST_CREATE_SHARED_LIBRARY +#ifdef GTEST_CREATE_SHARED_LIBRARY #error no modules except ts should have GTEST_CREATE_SHARED_LIBRARY defined #endif diff --git a/modules/objdetect/src/cascadedetect.cpp b/modules/objdetect/src/cascadedetect.cpp index 7cb9708..91b8ba5 100644 --- a/modules/objdetect/src/cascadedetect.cpp +++ b/modules/objdetect/src/cascadedetect.cpp @@ -46,12 +46,12 @@ namespace cv { - + // class for grouping object candidates, detected by Cascade Classifier, HOG etc. // instance of the class is to be passed to cv::partition (see cxoperations.hpp) class CV_EXPORTS SimilarRects { -public: +public: SimilarRects(double _eps) : eps(_eps) {} inline bool operator()(const Rect& r1, const Rect& r2) const { @@ -62,8 +62,8 @@ public: std::abs(r1.y + r1.height - r2.y - r2.height) <= delta; } double eps; -}; - +}; + void groupRectangles(vector& rectList, int groupThreshold, double eps, vector* weights, vector* levelWeights) { @@ -78,13 +78,13 @@ void groupRectangles(vector& rectList, int groupThreshold, double eps, vec } return; } - + vector labels; int nclasses = partition(rectList, labels, SimilarRects(eps)); - + vector rrects(nclasses); vector rweights(nclasses, 0); - vector rejectLevels(nclasses, 0); + vector rejectLevels(nclasses, 0); vector rejectWeights(nclasses, DBL_MIN); int i, j, nlabels = (int)labels.size(); for( i = 0; i < nlabels; i++ ) @@ -97,10 +97,10 @@ void groupRectangles(vector& rectList, int groupThreshold, double eps, vec rweights[cls]++; } if ( levelWeights && weights && !weights->empty() && !levelWeights->empty() ) - { - for( i = 0; i < nlabels; i++ ) - { - int cls = labels[i]; + { + for( i = 0; i < nlabels; i++ ) + { + int cls = labels[i]; if( (*weights)[i] > rejectLevels[cls] ) { rejectLevels[cls] = (*weights)[i]; @@ -108,9 +108,9 @@ void groupRectangles(vector& rectList, int groupThreshold, double eps, vec } else if( ( (*weights)[i] == rejectLevels[cls] ) && ( (*levelWeights)[i] > rejectWeights[cls] ) ) rejectWeights[cls] = (*levelWeights)[i]; - } - } - + } + } + for( i = 0; i < nclasses; i++ ) { Rect r = rrects[i]; @@ -120,32 +120,32 @@ void groupRectangles(vector& rectList, int groupThreshold, double eps, vec saturate_cast(r.width*s), saturate_cast(r.height*s)); } - + rectList.clear(); if( weights ) weights->clear(); - if( levelWeights ) - levelWeights->clear(); - + if( levelWeights ) + levelWeights->clear(); + for( i = 0; i < nclasses; i++ ) { Rect r1 = rrects[i]; int n1 = levelWeights ? rejectLevels[i] : rweights[i]; - double w1 = rejectWeights[i]; + double w1 = rejectWeights[i]; if( n1 <= groupThreshold ) continue; // filter out small face rectangles inside large rectangles for( j = 0; j < nclasses; j++ ) { int n2 = rweights[j]; - + if( j == i || n2 <= groupThreshold ) continue; Rect r2 = rrects[j]; - + int dx = saturate_cast( r2.width * eps ); int dy = saturate_cast( r2.height * eps ); - + if( i != j && r1.x >= r2.x - dx && r1.y >= r2.y - dy && @@ -154,14 +154,14 @@ void groupRectangles(vector& rectList, int groupThreshold, double eps, vec (n2 > std::max(3, n1) || n1 < 3) ) break; } - + if( j == nclasses ) { rectList.push_back(r1); if( weights ) weights->push_back(n1); - if( levelWeights ) - levelWeights->push_back(w1); + if( levelWeights ) + levelWeights->push_back(w1); } } } @@ -169,158 +169,158 @@ void groupRectangles(vector& rectList, int groupThreshold, double eps, vec class MeanshiftGrouping { public: - MeanshiftGrouping(const Point3d& densKer, const vector& posV, - const vector& wV, double, int maxIter = 20) + MeanshiftGrouping(const Point3d& densKer, const vector& posV, + const vector& wV, double, int maxIter = 20) { - densityKernel = densKer; + densityKernel = densKer; weightsV = wV; positionsV = posV; positionsCount = (int)posV.size(); - meanshiftV.resize(positionsCount); + meanshiftV.resize(positionsCount); distanceV.resize(positionsCount); - iterMax = maxIter; - - for (unsigned i = 0; i& modesV, vector& resWeightsV, const double eps) + + void getModes(vector& modesV, vector& resWeightsV, const double eps) { - for (size_t i=0; i positionsV; - vector weightsV; + vector positionsV; + vector weightsV; - Point3d densityKernel; - int positionsCount; + Point3d densityKernel; + int positionsCount; - vector meanshiftV; - vector distanceV; - int iterMax; - double modeEps; + vector meanshiftV; + vector distanceV; + int iterMax; + double modeEps; - Point3d getNewValue(const Point3d& inPt) const + Point3d getNewValue(const Point3d& inPt) const { - Point3d resPoint(.0); - Point3d ratPoint(.0); - for (size_t i=0; i& rectList, double detectThreshold, vector* foundWeights, - vector& scales, Size winDetSize) +static void groupRectangles_meanshift(vector& rectList, double detectThreshold, vector* foundWeights, + vector& scales, Size winDetSize) { int detectionCount = (int)rectList.size(); vector hits(detectionCount), resultHits; vector hitWeights(detectionCount), resultWeights; Point2d hitCenter; - for (int i=0; i < detectionCount; i++) + for (int i=0; i < detectionCount; i++) { hitWeights[i] = (*foundWeights)[i]; hitCenter = (rectList[i].tl() + rectList[i].br())*(0.5); //center of rectangles @@ -338,17 +338,17 @@ static void groupRectangles_meanshift(vector& rectList, double detectThres msGrouping.getModes(resultHits, resultWeights, 1); - for (unsigned i=0; i < resultHits.size(); ++i) + for (unsigned i=0; i < resultHits.size(); ++i) { double scale = exp(resultHits[i].z); hitCenter.x = resultHits[i].x; hitCenter.y = resultHits[i].y; Size s( int(winDetSize.width * scale), int(winDetSize.height * scale) ); - Rect resultRect( int(hitCenter.x-s.width/2), int(hitCenter.y-s.height/2), - int(s.width), int(s.height) ); + Rect resultRect( int(hitCenter.x-s.width/2), int(hitCenter.y-s.height/2), + int(s.width), int(s.height) ); - if (resultWeights[i] > detectThreshold) + if (resultWeights[i] > detectThreshold) { rectList.push_back(resultRect); foundWeights->push_back(resultWeights[i]); @@ -371,13 +371,13 @@ void groupRectangles(vector& rectList, vector& rejectLevels, vector& rectList, vector& foundWeights, - vector& foundScales, double detectThreshold, Size winDetSize) +void groupRectangles_meanshift(vector& rectList, vector& foundWeights, + vector& foundScales, double detectThreshold, Size winDetSize) { - groupRectangles_meanshift(rectList, detectThreshold, &foundWeights, foundScales, winDetSize); + groupRectangles_meanshift(rectList, detectThreshold, &foundWeights, foundScales, winDetSize); } - + FeatureEvaluator::~FeatureEvaluator() {} bool FeatureEvaluator::read(const FileNode&) {return true;} @@ -394,21 +394,21 @@ bool HaarEvaluator::Feature :: read( const FileNode& node ) { FileNode rnode = node[CC_RECTS]; FileNodeIterator it = rnode.begin(), it_end = rnode.end(); - + int ri; for( ri = 0; ri < RECT_NUM; ri++ ) { rect[ri].r = Rect(); rect[ri].weight = 0.f; } - + for(ri = 0; it != it_end; ++it, ri++) { FileNodeIterator it2 = (*it).begin(); it2 >> rect[ri].r.x >> rect[ri].r.y >> rect[ri].r.width >> rect[ri].r.height >> rect[ri].weight; } - + tilted = (int)node[CC_TILTED] != 0; return true; } @@ -427,7 +427,7 @@ bool HaarEvaluator::read(const FileNode& node) featuresPtr = &(*features)[0]; FileNodeIterator it = node.begin(), it_end = node.end(); hasTiltedFeatures = false; - + for(int i = 0; it != it_end; ++it, i++) { if(!featuresPtr[i].read(*it)) @@ -437,7 +437,7 @@ bool HaarEvaluator::read(const FileNode& node) } return true; } - + Ptr HaarEvaluator::clone() const { HaarEvaluator* ret = new HaarEvaluator; @@ -451,7 +451,7 @@ Ptr HaarEvaluator::clone() const memcpy( ret->p, p, 4*sizeof(p[0]) ); memcpy( ret->pq, pq, 4*sizeof(pq[0]) ); ret->offset = offset; - ret->varianceNormFactor = varianceNormFactor; + ret->varianceNormFactor = varianceNormFactor; return ret; } @@ -460,10 +460,10 @@ bool HaarEvaluator::setImage( const Mat &image, Size _origWinSize ) int rn = image.rows+1, cn = image.cols+1; origWinSize = _origWinSize; normrect = Rect(1, 1, origWinSize.width-2, origWinSize.height-2); - + if (image.cols < origWinSize.width || image.rows < origWinSize.height) return false; - + if( sum0.rows < rn || sum0.cols < cn ) { sum0.create(rn, cn, CV_32S); @@ -485,10 +485,10 @@ bool HaarEvaluator::setImage( const Mat &image, Size _origWinSize ) const double* sqdata = (const double*)sqsum.data; size_t sumStep = sum.step/sizeof(sdata[0]); size_t sqsumStep = sqsum.step/sizeof(sqdata[0]); - + CV_SUM_PTRS( p[0], p[1], p[2], p[3], sdata, normrect, sumStep ); CV_SUM_PTRS( pq[0], pq[1], pq[2], pq[3], sqdata, normrect, sqsumStep ); - + size_t fi, nfeatures = features->size(); for( fi = 0; fi < nfeatures; fi++ ) @@ -568,19 +568,19 @@ bool LBPEvaluator::setImage( const Mat& image, Size _origWinSize ) if( image.cols < origWinSize.width || image.rows < origWinSize.height ) return false; - + if( sum0.rows < rn || sum0.cols < cn ) sum0.create(rn, cn, CV_32S); sum = Mat(rn, cn, CV_32S, sum0.data); integral(image, sum); - + size_t fi, nfeatures = features->size(); - + for( fi = 0; fi < nfeatures; fi++ ) featuresPtr[fi].updatePtrs( sum ); return true; } - + bool LBPEvaluator::setWindow( Point pt ) { if( pt.x < 0 || pt.y < 0 || @@ -589,7 +589,7 @@ bool LBPEvaluator::setWindow( Point pt ) return false; offset = pt.y * ((int)sum.step/sizeof(int)) + pt.x; return true; -} +} //---------------------------------------------- HOGEvaluator --------------------------------------- bool HOGEvaluator::Feature :: read( const FileNode& node ) @@ -638,7 +638,7 @@ Ptr HOGEvaluator::clone() const ret->featuresPtr = &(*ret->features)[0]; ret->offset = offset; ret->hist = hist; - ret->normSum = normSum; + ret->normSum = normSum; return ret; } @@ -756,7 +756,7 @@ void HOGEvaluator::integralHistogram(const Mat &img, vector &histogram, Mat memset( histBuf, 0, histSize.width * sizeof(histBuf[0]) ); histBuf += histStep + 1; for( y = 0; y < qangle.rows; y++ ) - { + { histBuf[-1] = 0.f; float strSum = 0.f; for( x = 0; x < qangle.cols; x++ ) @@ -775,7 +775,7 @@ void HOGEvaluator::integralHistogram(const Mat &img, vector &histogram, Mat Ptr FeatureEvaluator::create( int featureType ) { return featureType == HAAR ? Ptr(new HaarEvaluator) : - featureType == LBP ? Ptr(new LBPEvaluator) : + featureType == LBP ? Ptr(new LBPEvaluator) : featureType == HOG ? Ptr(new HOGEvaluator) : Ptr(); } @@ -787,13 +787,13 @@ CascadeClassifier::CascadeClassifier() } CascadeClassifier::CascadeClassifier(const string& filename) -{ - load(filename); +{ + load(filename); } CascadeClassifier::~CascadeClassifier() { -} +} bool CascadeClassifier::empty() const { @@ -805,57 +805,57 @@ bool CascadeClassifier::load(const string& filename) oldCascade.release(); data = Data(); featureEvaluator.release(); - + FileStorage fs(filename, FileStorage::READ); if( !fs.isOpened() ) return false; - + if( read(fs.getFirstTopLevelNode()) ) return true; - + fs.release(); - + oldCascade = Ptr((CvHaarClassifierCascade*)cvLoad(filename.c_str(), 0, 0, 0)); return !oldCascade.empty(); } - -int CascadeClassifier::runAt( Ptr& featureEvaluator, Point pt, double& weight ) + +int CascadeClassifier::runAt( Ptr& evaluator, Point pt, double& weight ) { CV_Assert( oldCascade.empty() ); - + assert( data.featureType == FeatureEvaluator::HAAR || data.featureType == FeatureEvaluator::LBP || data.featureType == FeatureEvaluator::HOG ); - if( !featureEvaluator->setWindow(pt) ) + if( !evaluator->setWindow(pt) ) return -1; if( data.isStumpBased ) { if( data.featureType == FeatureEvaluator::HAAR ) - return predictOrderedStump( *this, featureEvaluator, weight ); + return predictOrderedStump( *this, evaluator, weight ); else if( data.featureType == FeatureEvaluator::LBP ) - return predictCategoricalStump( *this, featureEvaluator, weight ); + return predictCategoricalStump( *this, evaluator, weight ); else if( data.featureType == FeatureEvaluator::HOG ) - return predictOrderedStump( *this, featureEvaluator, weight ); + return predictOrderedStump( *this, evaluator, weight ); else return -2; } else { if( data.featureType == FeatureEvaluator::HAAR ) - return predictOrdered( *this, featureEvaluator, weight ); + return predictOrdered( *this, evaluator, weight ); else if( data.featureType == FeatureEvaluator::LBP ) - return predictCategorical( *this, featureEvaluator, weight ); + return predictCategorical( *this, evaluator, weight ); else if( data.featureType == FeatureEvaluator::HOG ) - return predictOrdered( *this, featureEvaluator, weight ); + return predictOrdered( *this, evaluator, weight ); else return -2; } } - -bool CascadeClassifier::setImage( Ptr& featureEvaluator, const Mat& image ) + +bool CascadeClassifier::setImage( Ptr& evaluator, const Mat& image ) { - return empty() ? false : featureEvaluator->setImage(image, data.origWinSize); + return empty() ? false : evaluator->setImage(image, data.origWinSize); } void CascadeClassifier::setMaskGenerator(Ptr _maskGenerator) @@ -878,7 +878,7 @@ void CascadeClassifier::setFaceDetectionMaskGenerator() struct CascadeClassifierInvoker { - CascadeClassifierInvoker( CascadeClassifier& _cc, Size _sz1, int _stripSize, int _yStep, double _factor, + CascadeClassifierInvoker( CascadeClassifier& _cc, Size _sz1, int _stripSize, int _yStep, double _factor, ConcurrentRectVector& _vec, vector& _levels, vector& _weights, bool outputLevels, const Mat& _mask) { classifier = &_cc; @@ -891,7 +891,7 @@ struct CascadeClassifierInvoker levelWeights = outputLevels ? &_weights : 0; mask=_mask; } - + void operator()(const BlockedRange& range) const { Ptr evaluator = classifier->featureEvaluator->clone(); @@ -916,11 +916,11 @@ struct CascadeClassifierInvoker result = -(int)classifier->data.stages.size(); if( classifier->data.stages.size() + result < 4 ) { - rectangles->push_back(Rect(cvRound(x*scalingFactor), cvRound(y*scalingFactor), winSize.width, winSize.height)); + rectangles->push_back(Rect(cvRound(x*scalingFactor), cvRound(y*scalingFactor), winSize.width, winSize.height)); rejectLevels->push_back(-result); levelWeights->push_back(gypWeight); } - } + } else if( result > 0 ) rectangles->push_back(Rect(cvRound(x*scalingFactor), cvRound(y*scalingFactor), winSize.width, winSize.height)); @@ -929,7 +929,7 @@ struct CascadeClassifierInvoker } } } - + CascadeClassifier* classifier; ConcurrentRectVector* rectangles; Size processingRectSize; @@ -939,7 +939,7 @@ struct CascadeClassifierInvoker vector *levelWeights; Mat mask; }; - + struct getRect { Rect operator ()(const CvAvgComp& e) const { return e.rect; } }; bool CascadeClassifier::detectSingleScale( const Mat& image, int stripCount, Size processingRectSize, @@ -995,17 +995,17 @@ bool CascadeClassifier::setImage(const Mat& image) return featureEvaluator->setImage(image, data.origWinSize); } -void CascadeClassifier::detectMultiScale( const Mat& image, vector& objects, +void CascadeClassifier::detectMultiScale( const Mat& image, vector& objects, vector& rejectLevels, vector& levelWeights, double scaleFactor, int minNeighbors, - int flags, Size minObjectSize, Size maxObjectSize, + int flags, Size minObjectSize, Size maxObjectSize, bool outputRejectLevels ) { const double GROUP_EPS = 0.2; - + CV_Assert( scaleFactor > 1 && image.depth() == CV_8U ); - + if( empty() ) return; @@ -1031,7 +1031,7 @@ void CascadeClassifier::detectMultiScale( const Mat& image, vector& object if( maxObjectSize.height == 0 || maxObjectSize.width == 0 ) maxObjectSize = image.size(); - + Mat grayImage = image; if( grayImage.channels() > 1 ) { @@ -1039,7 +1039,7 @@ void CascadeClassifier::detectMultiScale( const Mat& image, vector& object cvtColor(grayImage, temp, CV_BGR2GRAY); grayImage = temp; } - + Mat imageBuffer(image.rows + 1, image.cols + 1, CV_8U); vector candidates; @@ -1050,14 +1050,14 @@ void CascadeClassifier::detectMultiScale( const Mat& image, vector& object Size windowSize( cvRound(originalWindowSize.width*factor), cvRound(originalWindowSize.height*factor) ); Size scaledImageSize( cvRound( grayImage.cols/factor ), cvRound( grayImage.rows/factor ) ); Size processingRectSize( scaledImageSize.width - originalWindowSize.width + 1, scaledImageSize.height - originalWindowSize.height + 1 ); - + if( processingRectSize.width <= 0 || processingRectSize.height <= 0 ) break; if( windowSize.width > maxObjectSize.width || windowSize.height > maxObjectSize.height ) break; if( windowSize.width < minObjectSize.width || windowSize.height < minObjectSize.height ) continue; - + Mat scaledImage( scaledImageSize, CV_8U, imageBuffer.data ); resize( grayImage, scaledImage, scaledImageSize, 0, 0, CV_INTER_LINEAR ); @@ -1083,12 +1083,12 @@ void CascadeClassifier::detectMultiScale( const Mat& image, vector& object stripSize = processingRectSize.height; #endif - if( !detectSingleScale( scaledImage, stripCount, processingRectSize, stripSize, yStep, factor, candidates, + if( !detectSingleScale( scaledImage, stripCount, processingRectSize, stripSize, yStep, factor, candidates, rejectLevels, levelWeights, outputRejectLevels ) ) break; } - + objects.resize(candidates.size()); std::copy(candidates.begin(), candidates.end(), objects.begin()); @@ -1108,14 +1108,14 @@ void CascadeClassifier::detectMultiScale( const Mat& image, vector& object { vector fakeLevels; vector fakeWeights; - detectMultiScale( image, objects, fakeLevels, fakeWeights, scaleFactor, + detectMultiScale( image, objects, fakeLevels, fakeWeights, scaleFactor, minNeighbors, flags, minObjectSize, maxObjectSize, false ); -} +} bool CascadeClassifier::Data::read(const FileNode &root) { static const float THRESHOLD_EPS = 1e-5f; - + // load stage params string stageTypeStr = (string)root[CC_STAGE_TYPE]; if( stageTypeStr == CC_BOOST ) @@ -1232,11 +1232,11 @@ bool CascadeClassifier::read(const FileNode& root) FileNode fn = root[CC_FEATURES]; if( fn.empty() ) return false; - + return featureEvaluator->read(fn); } - + template<> void Ptr::delete_obj() -{ cvReleaseHaarClassifierCascade(&obj); } +{ cvReleaseHaarClassifierCascade(&obj); } } // namespace cv diff --git a/modules/objdetect/src/datamatrix.cpp b/modules/objdetect/src/datamatrix.cpp index a4258bd..39f3616 100644 --- a/modules/objdetect/src/datamatrix.cpp +++ b/modules/objdetect/src/datamatrix.cpp @@ -256,14 +256,14 @@ static int decode(Sampler &sa, code &cc) { uchar binary[8] = {0,0,0,0,0,0,0,0}; uchar b = 0; - int i, sum; + int sum; sum = 0; - for (i = 0; i < 64; i++) + for (int i = 0; i < 64; i++) sum += sa.getpixel(1 + (i & 7), 1 + (i >> 3)); uchar mean = (uchar)(sum / 64); - for (i = 0; i < 64; i++) { + for (int i = 0; i < 64; i++) { b = (b << 1) + (sa.getpixel(pickup[i].x, pickup[i].y) <= mean); if ((i & 7) == 7) { binary[i >> 3] = b; @@ -275,12 +275,11 @@ static int decode(Sampler &sa, code &cc) uchar c[5] = {0,0,0,0,0}; { - int i, j; uchar a[5] = {228, 48, 15, 111, 62}; int k = 5; - for (i = 0; i < 3; i++) { + for (int i = 0; i < 3; i++) { uchar t = binary[i] ^ c[4]; - for (j = k - 1; j != -1; j--) { + for (int j = k - 1; j != -1; j--) { if (t == 0) c[j] = 0; else @@ -390,12 +389,12 @@ deque cvFindDataMatrix(CvMat *im) deque candidates; { int x, y; - int r = cxy->rows; - int c = cxy->cols; - for (y = 0; y < r; y++) { + int rows = cxy->rows; + int cols = cxy->cols; + for (y = 0; y < rows; y++) { const short *cd = (const short*)cvPtr2D(cxy, y, 0); const short *ccd = (const short*)cvPtr2D(ccxy, y, 0); - for (x = 0; x < c; x += 4, cd += 8, ccd += 8) { + for (x = 0; x < cols; x += 4, cd += 8, ccd += 8) { __m128i v = _mm_loadu_si128((const __m128i*)cd); __m128 cyxyxA = _mm_cvtepi32_ps(_mm_srai_epi32(_mm_unpacklo_epi16(v, v), 16)); __m128 cyxyxB = _mm_cvtepi32_ps(_mm_srai_epi32(_mm_unpackhi_epi16(v, v), 16)); @@ -496,7 +495,7 @@ endo: ; // end search for this o namespace cv { - + void findDataMatrix(InputArray _image, vector& codes, OutputArray _corners, @@ -507,23 +506,23 @@ void findDataMatrix(InputArray _image, deque rc = cvFindDataMatrix(&m); int i, n = (int)rc.size(); Mat corners; - + if( _corners.needed() ) { _corners.create(n, 4, CV_32SC2); corners = _corners.getMat(); } - + if( _dmtx.needed() ) _dmtx.create(n, 1, CV_8U); - + codes.resize(n); - + for( i = 0; i < n; i++ ) { CvDataMatrixCode& rc_i = rc[i]; codes[i] = string(rc_i.msg); - + if( corners.data ) { const Point* srcpt = (Point*)rc_i.corners->data.ptr; @@ -532,7 +531,7 @@ void findDataMatrix(InputArray _image, dstpt[k] = srcpt[k]; } cvReleaseMat(&rc_i.corners); - + if( _dmtx.needed() ) { _dmtx.create(rc_i.original->rows, rc_i.original->cols, rc_i.original->type, i); @@ -550,20 +549,20 @@ void drawDataMatrixCodes(InputOutputArray _image, Mat image = _image.getMat(); Mat corners = _corners.getMat(); int i, n = corners.rows; - + if( n > 0 ) { CV_Assert( corners.depth() == CV_32S && corners.cols*corners.channels() == 8 && n == (int)codes.size() ); } - + for( i = 0; i < n; i++ ) { Scalar c(0, 255, 0); Scalar c2(255, 0,0); const Point* pt = (const Point*)corners.ptr(i); - + for( int k = 0; k < 4; k++ ) line(image, pt[k], pt[(k+1)%4], c); //int baseline = 0; @@ -571,5 +570,5 @@ void drawDataMatrixCodes(InputOutputArray _image, putText(image, codes[i], pt[0], CV_FONT_HERSHEY_SIMPLEX, 0.8, c2, 1, CV_AA, false); } } - + } diff --git a/modules/objdetect/src/distancetransform.cpp b/modules/objdetect/src/distancetransform.cpp index dd8c22c..1e3555e 100644 --- a/modules/objdetect/src/distancetransform.cpp +++ b/modules/objdetect/src/distancetransform.cpp @@ -9,8 +9,8 @@ // // // API -// int GetPointOfIntersection(const float *f, - const float a, const float b, +// int GetPointOfIntersection(const float *f, + const float a, const float b, int q1, int q2, float *point); // INPUT // f - function on the regular grid @@ -23,15 +23,15 @@ // RESULT // Error status */ -int GetPointOfIntersection(const float *f, - const float a, const float b, +int GetPointOfIntersection(const float *f, + const float a, const float b, int q1, int q2, float *point) { if (q1 == q2) { return DISTANCE_TRANSFORM_EQUAL_POINTS; - } /* if (q1 == q2) */ - (*point) = ( (f[q2] - a * q2 + b *q2 * q2) - + } /* if (q1 == q2) */ + (*point) = ( (f[q2] - a * q2 + b *q2 * q2) - (f[q1] - a * q1 + b * q1 * q1) ) / (2 * b * (q2 - q1)); return DISTANCE_TRANSFORM_OK; } @@ -43,9 +43,9 @@ int GetPointOfIntersection(const float *f, // // API // int DistanceTransformOneDimensionalProblem(const float *f, const int n, - const float a, const float b, + const float a, const float b, float *distanceTransform, - int *points); + int *points); // INPUT // f - function on the regular grid // n - grid dimension @@ -58,7 +58,7 @@ int GetPointOfIntersection(const float *f, // Error status */ int DistanceTransformOneDimensionalProblem(const float *f, const int n, - const float a, const float b, + const float a, const float b, float *distanceTransform, int *points) { @@ -73,7 +73,7 @@ int DistanceTransformOneDimensionalProblem(const float *f, const int n, // Allocation memory (must be free in this function) v = (int *)malloc (sizeof(int) * n); z = (float *)malloc (sizeof(float) * (n + 1)); - + v[0] = 0; z[0] = (float)F_MIN; // left border of envelope z[1] = (float)F_MAX; // right border of envelope @@ -89,7 +89,7 @@ int DistanceTransformOneDimensionalProblem(const float *f, const int n, } /* if (tmp != DISTANCE_TRANSFORM_OK) */ if (pointIntersection <= z[k]) { - // Envelope doesn't contain current parabola + // Envelope doesn't contain current parabola do { k--; @@ -144,7 +144,7 @@ int DistanceTransformOneDimensionalProblem(const float *f, const int n, // INPUT // k - index of the previous cycle element // n - number of matrix rows -// q - parameter that equal +// q - parameter that equal (number_of_rows * number_of_columns - 1) // OUTPUT // None @@ -196,7 +196,7 @@ void TransposeCycleElements(float *a, int *cycle, int cycle_len) // RESULT // Error status */ -void TransposeCycleElements_int(int *a, int *cycle, int cycle_len) +static void TransposeCycleElements_int(int *a, int *cycle, int cycle_len) { int i; int buf; @@ -229,7 +229,7 @@ void Transpose(float *a, int n, int m) int max_cycle_len; max_cycle_len = n * m; - + // Allocation memory (must be free in this function) cycle = (int *)malloc(sizeof(int) * max_cycle_len); @@ -240,12 +240,12 @@ void Transpose(float *a, int n, int m) k = GetNextCycleElement(i, n, q); cycle[cycle_len] = i; cycle_len++; - + while (k > i) - { - cycle[cycle_len] = k; + { + cycle[cycle_len] = k; cycle_len++; - k = GetNextCycleElement(k, n, q); + k = GetNextCycleElement(k, n, q); } if (k == i) { @@ -272,14 +272,14 @@ void Transpose(float *a, int n, int m) // RESULT // None */ -void Transpose_int(int *a, int n, int m) +static void Transpose_int(int *a, int n, int m) { int *cycle; int i, k, q, cycle_len; int max_cycle_len; max_cycle_len = n * m; - + // Allocation memory (must be free in this function) cycle = (int *)malloc(sizeof(int) * max_cycle_len); @@ -290,12 +290,12 @@ void Transpose_int(int *a, int n, int m) k = GetNextCycleElement(i, n, q); cycle[cycle_len] = i; cycle_len++; - + while (k > i) - { - cycle[cycle_len] = k; + { + cycle[cycle_len] = k; cycle_len++; - k = GetNextCycleElement(k, n, q); + k = GetNextCycleElement(k, n, q); } if (k == i) { @@ -311,21 +311,21 @@ void Transpose_int(int *a, int n, int m) /* // Decision of two dimensional problem generalized distance transform // on the regular grid at all points -// min{d2(y' - y) + d4(y' - y)(y' - y) + +// min{d2(y' - y) + d4(y' - y)(y' - y) + min(d1(x' - x) + d3(x' - x)(x' - x) + f(x',y'))} (on x', y') // // API -// int DistanceTransformTwoDimensionalProblem(const float *f, +// int DistanceTransformTwoDimensionalProblem(const float *f, const int n, const int m, - const float coeff[4], + const float coeff[4], float *distanceTransform, - int *pointsX, int *pointsY); + int *pointsX, int *pointsY); // INPUT // f - function on the regular grid // n - number of rows // m - number of columns // coeff - coefficients of optimizable function - coeff[0] = d1, coeff[1] = d2, + coeff[0] = d1, coeff[1] = d2, coeff[2] = d3, coeff[3] = d4 // OUTPUT // distanceTransform - values of generalized distance transform @@ -334,9 +334,9 @@ void Transpose_int(int *a, int n, int m) // RESULT // Error status */ -int DistanceTransformTwoDimensionalProblem(const float *f, +int DistanceTransformTwoDimensionalProblem(const float *f, const int n, const int m, - const float coeff[4], + const float coeff[4], float *distanceTransform, int *pointsX, int *pointsY) { @@ -349,10 +349,10 @@ int DistanceTransformTwoDimensionalProblem(const float *f, for (i = 0; i < n; i++) { resOneDimProblem = DistanceTransformOneDimensionalProblem( - f + i * m, m, - coeff[0], coeff[2], - &internalDistTrans[i * m], - &internalPointsX[i * m]); + f + i * m, m, + coeff[0], coeff[2], + &internalDistTrans[i * m], + &internalPointsX[i * m]); if (resOneDimProblem != DISTANCE_TRANSFORM_OK) return DISTANCE_TRANSFORM_ERROR; } @@ -360,9 +360,9 @@ int DistanceTransformTwoDimensionalProblem(const float *f, for (j = 0; j < m; j++) { resOneDimProblem = DistanceTransformOneDimensionalProblem( - &internalDistTrans[j * n], n, - coeff[1], coeff[3], - distanceTransform + j * n, + &internalDistTrans[j * n], n, + coeff[1], coeff[3], + distanceTransform + j * n, pointsY + j * n); if (resOneDimProblem != DISTANCE_TRANSFORM_OK) return DISTANCE_TRANSFORM_ERROR; diff --git a/modules/objdetect/src/featurepyramid.cpp b/modules/objdetect/src/featurepyramid.cpp index 22bee33..fb7806a 100644 --- a/modules/objdetect/src/featurepyramid.cpp +++ b/modules/objdetect/src/featurepyramid.cpp @@ -30,12 +30,12 @@ int getFeatureMaps(const IplImage* image, const int k, CvLSVMFeatureMap **map) int height, width, numChannels; int i, j, kk, c, ii, jj, d; float * datadx, * datady; - + //íîìåð êàíàëà â öèêëå - int ch; + int ch; //ïåðåìåííûå âû÷èñëåíèÿ ìàãíèòóäû float magnitude, x, y, tx, ty; - + IplImage * dx, * dy; int *nearest; float *w, a_x, b_x; @@ -51,7 +51,7 @@ int getFeatureMaps(const IplImage* image, const int k, CvLSVMFeatureMap **map) // ÷åòíûå èííåêñû íå êîíòðàñòíîå èçîáðàæåíèå // íå ÷åòíûå èííåêñû êîíòðàñòíîå èçîáðàæåíèå int * alfa; - + // âåêòîðû ãðàíèö ñåêòîðîâ float boundary_x[NUM_SECTOR + 1]; float boundary_y[NUM_SECTOR + 1]; @@ -63,9 +63,9 @@ int getFeatureMaps(const IplImage* image, const int k, CvLSVMFeatureMap **map) numChannels = image->nChannels; - dx = cvCreateImage(cvSize(image->width, image->height), + dx = cvCreateImage(cvSize(image->width, image->height), IPL_DEPTH_32F, 3); - dy = cvCreateImage(cvSize(image->width, image->height), + dy = cvCreateImage(cvSize(image->width, image->height), IPL_DEPTH_32F, 3); sizeX = width / k; @@ -77,7 +77,7 @@ int getFeatureMaps(const IplImage* image, const int k, CvLSVMFeatureMap **map) cvFilter2D(image, dx, &kernel_dx, cvPoint(-1, 0)); cvFilter2D(image, dy, &kernel_dy, cvPoint(0, -1)); - + float arg_vector; for(i = 0; i <= NUM_SECTOR; i++) { @@ -113,20 +113,20 @@ int getFeatureMaps(const IplImage* image, const int k, CvLSVMFeatureMap **map) y = ty; } }/*for(ch = 1; ch < numChannels; ch++)*/ - + max = boundary_x[0] * x + boundary_y[0] * y; maxi = 0; - for (kk = 0; kk < NUM_SECTOR; kk++) + for (kk = 0; kk < NUM_SECTOR; kk++) { dotProd = boundary_x[kk] * x + boundary_y[kk] * y; - if (dotProd > max) + if (dotProd > max) { max = dotProd; maxi = kk; } - else + else { - if (-dotProd > max) + if (-dotProd > max) { max = -dotProd; maxi = kk + NUM_SECTOR; @@ -134,14 +134,14 @@ int getFeatureMaps(const IplImage* image, const int k, CvLSVMFeatureMap **map) } } alfa[j * width * 2 + i * 2 ] = maxi % NUM_SECTOR; - alfa[j * width * 2 + i * 2 + 1] = maxi; + alfa[j * width * 2 + i * 2 + 1] = maxi; }/*for(i = 0; i < width; i++)*/ }/*for(j = 0; j < height; j++)*/ //ïîäñ÷åò âåñîâ è ñìåùåíèé nearest = (int *)malloc(sizeof(int ) * k); w = (float*)malloc(sizeof(float) * (k * 2)); - + for(i = 0; i < k / 2; i++) { nearest[i] = -1; @@ -155,15 +155,15 @@ int getFeatureMaps(const IplImage* image, const int k, CvLSVMFeatureMap **map) { b_x = k / 2 + j + 0.5f; a_x = k / 2 - j - 0.5f; - w[j * 2 ] = 1.0f/a_x * ((a_x * b_x) / ( a_x + b_x)); - w[j * 2 + 1] = 1.0f/b_x * ((a_x * b_x) / ( a_x + b_x)); + w[j * 2 ] = 1.0f/a_x * ((a_x * b_x) / ( a_x + b_x)); + w[j * 2 + 1] = 1.0f/b_x * ((a_x * b_x) / ( a_x + b_x)); }/*for(j = 0; j < k / 2; j++)*/ for(j = k / 2; j < k; j++) { a_x = j - k / 2 + 0.5f; b_x =-j + k / 2 - 0.5f + k; - w[j * 2 ] = 1.0f/a_x * ((a_x * b_x) / ( a_x + b_x)); - w[j * 2 + 1] = 1.0f/b_x * ((a_x * b_x) / ( a_x + b_x)); + w[j * 2 ] = 1.0f/a_x * ((a_x * b_x) / ( a_x + b_x)); + w[j * 2 + 1] = 1.0f/b_x * ((a_x * b_x) / ( a_x + b_x)); }/*for(j = k / 2; j < k; j++)*/ @@ -176,40 +176,40 @@ int getFeatureMaps(const IplImage* image, const int k, CvLSVMFeatureMap **map) { for(jj = 0; jj < k; jj++) { - if ((i * k + ii > 0) && - (i * k + ii < height - 1) && - (j * k + jj > 0) && + if ((i * k + ii > 0) && + (i * k + ii < height - 1) && + (j * k + jj > 0) && (j * k + jj < width - 1)) { d = (k * i + ii) * width + (j * k + jj); - (*map)->map[ i * stringSize + j * (*map)->numFeatures + alfa[d * 2 ]] += + (*map)->map[ i * stringSize + j * (*map)->numFeatures + alfa[d * 2 ]] += r[d] * w[ii * 2] * w[jj * 2]; - (*map)->map[ i * stringSize + j * (*map)->numFeatures + alfa[d * 2 + 1] + NUM_SECTOR] += + (*map)->map[ i * stringSize + j * (*map)->numFeatures + alfa[d * 2 + 1] + NUM_SECTOR] += r[d] * w[ii * 2] * w[jj * 2]; - if ((i + nearest[ii] >= 0) && + if ((i + nearest[ii] >= 0) && (i + nearest[ii] <= sizeY - 1)) { - (*map)->map[(i + nearest[ii]) * stringSize + j * (*map)->numFeatures + alfa[d * 2 ] ] += + (*map)->map[(i + nearest[ii]) * stringSize + j * (*map)->numFeatures + alfa[d * 2 ] ] += r[d] * w[ii * 2 + 1] * w[jj * 2 ]; - (*map)->map[(i + nearest[ii]) * stringSize + j * (*map)->numFeatures + alfa[d * 2 + 1] + NUM_SECTOR] += + (*map)->map[(i + nearest[ii]) * stringSize + j * (*map)->numFeatures + alfa[d * 2 + 1] + NUM_SECTOR] += r[d] * w[ii * 2 + 1] * w[jj * 2 ]; } - if ((j + nearest[jj] >= 0) && + if ((j + nearest[jj] >= 0) && (j + nearest[jj] <= sizeX - 1)) { - (*map)->map[i * stringSize + (j + nearest[jj]) * (*map)->numFeatures + alfa[d * 2 ] ] += + (*map)->map[i * stringSize + (j + nearest[jj]) * (*map)->numFeatures + alfa[d * 2 ] ] += r[d] * w[ii * 2] * w[jj * 2 + 1]; - (*map)->map[i * stringSize + (j + nearest[jj]) * (*map)->numFeatures + alfa[d * 2 + 1] + NUM_SECTOR] += + (*map)->map[i * stringSize + (j + nearest[jj]) * (*map)->numFeatures + alfa[d * 2 + 1] + NUM_SECTOR] += r[d] * w[ii * 2] * w[jj * 2 + 1]; } - if ((i + nearest[ii] >= 0) && - (i + nearest[ii] <= sizeY - 1) && - (j + nearest[jj] >= 0) && + if ((i + nearest[ii] >= 0) && + (i + nearest[ii] <= sizeY - 1) && + (j + nearest[jj] >= 0) && (j + nearest[jj] <= sizeX - 1)) { - (*map)->map[(i + nearest[ii]) * stringSize + (j + nearest[jj]) * (*map)->numFeatures + alfa[d * 2 ] ] += + (*map)->map[(i + nearest[ii]) * stringSize + (j + nearest[jj]) * (*map)->numFeatures + alfa[d * 2 ] ] += r[d] * w[ii * 2 + 1] * w[jj * 2 + 1]; - (*map)->map[(i + nearest[ii]) * stringSize + (j + nearest[jj]) * (*map)->numFeatures + alfa[d * 2 + 1] + NUM_SECTOR] += + (*map)->map[(i + nearest[ii]) * stringSize + (j + nearest[jj]) * (*map)->numFeatures + alfa[d * 2 + 1] + NUM_SECTOR] += r[d] * w[ii * 2 + 1] * w[jj * 2 + 1]; } } @@ -217,14 +217,14 @@ int getFeatureMaps(const IplImage* image, const int k, CvLSVMFeatureMap **map) }/*for(ii = 0; ii < k; ii++)*/ }/*for(j = 1; j < sizeX - 1; j++)*/ }/*for(i = 1; i < sizeY - 1; i++)*/ - + cvReleaseImage(&dx); cvReleaseImage(&dy); free(w); free(nearest); - + free(r); free(alfa); @@ -232,7 +232,7 @@ int getFeatureMaps(const IplImage* image, const int k, CvLSVMFeatureMap **map) } /* -// Feature map Normalization and Truncation +// Feature map Normalization and Truncation // // API // int normalizeAndTruncate(featureMap *map, const float alfa); @@ -270,7 +270,7 @@ int normalizeAndTruncate(CvLSVMFeatureMap *map, const float alfa) }/*for(j = 0; j < p; j++)*/ partOfNorm[i] = valOfNorm; }/*for(i = 0; i < sizeX * sizeY; i++)*/ - + sizeX -= 2; sizeY -= 2; @@ -369,13 +369,13 @@ int normalizeAndTruncate(CvLSVMFeatureMap *map, const float alfa) // Error status */ int PCAFeatureMaps(CvLSVMFeatureMap *map) -{ +{ int i,j, ii, jj, k; int sizeX, sizeY, p, pp, xp, yp, pos1, pos2; float * newData; float val; float nx, ny; - + sizeX = map->sizeX; sizeY = map->sizeY; p = map->numFeatures; @@ -424,7 +424,7 @@ int PCAFeatureMaps(CvLSVMFeatureMap *map) }/*for(jj = 0; jj < xp; jj++)*/ newData[pos2 + k] = val * nx; k++; - } /*for(ii = 0; ii < yp; ii++)*/ + } /*for(ii = 0; ii < yp; ii++)*/ }/*for(j = 0; j < sizeX; j++)*/ }/*for(i = 0; i < sizeY; i++)*/ //swop data @@ -439,22 +439,22 @@ int PCAFeatureMaps(CvLSVMFeatureMap *map) } -int getPathOfFeaturePyramid(IplImage * image, +static int getPathOfFeaturePyramid(IplImage * image, float step, int numStep, int startIndex, int sideLength, CvLSVMFeaturePyramid **maps) { CvLSVMFeatureMap *map; IplImage *scaleTmp; float scale; - int i, err; - + int i; + for(i = 0; i < numStep; i++) { scale = 1.0f / powf(step, (float)i); scaleTmp = resize_opencv (image, scale); - err = getFeatureMaps(scaleTmp, sideLength, &map); - err = normalizeAndTruncate(map, VAL_OF_TRUNCATE); - err = PCAFeatureMaps(map); + getFeatureMaps(scaleTmp, sideLength, &map); + normalizeAndTruncate(map, VAL_OF_TRUNCATE); + PCAFeatureMaps(map); (*maps)->pyramid[startIndex + i] = map; cvReleaseImage(&scaleTmp); }/*for(i = 0; i < numStep; i++)*/ @@ -462,13 +462,13 @@ int getPathOfFeaturePyramid(IplImage * image, } /* -// Getting feature pyramid +// Getting feature pyramid // // API -// int getFeaturePyramid(IplImage * image, const filterObject **all_F, +// int getFeaturePyramid(IplImage * image, const filterObject **all_F, const int n_f, - const int lambda, const int k, - const int startX, const int startY, + const int lambda, const int k, + const int startX, const int startY, const int W, const int H, featurePyramid **maps); // INPUT // image - image @@ -484,7 +484,7 @@ int getFeaturePyramid(IplImage * image, CvLSVMFeaturePyramid **maps) int numStep; int maxNumCells; int W, H; - + if(image->depth == IPL_DEPTH_32F) { imgResize = image; @@ -493,9 +493,9 @@ int getFeaturePyramid(IplImage * image, CvLSVMFeaturePyramid **maps) { imgResize = cvCreateImage(cvSize(image->width , image->height) , IPL_DEPTH_32F , 3); - cvConvert(image, imgResize); + cvConvert(image, imgResize); } - + W = imgResize->width; H = imgResize->height; @@ -506,14 +506,14 @@ int getFeaturePyramid(IplImage * image, CvLSVMFeaturePyramid **maps) maxNumCells = H / SIDE_LENGTH; } numStep = (int)(logf((float) maxNumCells / (5.0f)) / logf( step )) + 1; - + allocFeaturePyramidObject(maps, numStep + LAMBDA); - getPathOfFeaturePyramid(imgResize, step , LAMBDA, 0, + getPathOfFeaturePyramid(imgResize, step , LAMBDA, 0, SIDE_LENGTH / 2, maps); - getPathOfFeaturePyramid(imgResize, step, numStep, LAMBDA, + getPathOfFeaturePyramid(imgResize, step, numStep, LAMBDA, SIDE_LENGTH , maps); - + if(image->depth != IPL_DEPTH_32F) { cvReleaseImage(&imgResize); diff --git a/modules/objdetect/src/fft.cpp b/modules/objdetect/src/fft.cpp index f60121f..b4164f5 100644 --- a/modules/objdetect/src/fft.cpp +++ b/modules/objdetect/src/fft.cpp @@ -1,14 +1,14 @@ #include "precomp.hpp" #include "_lsvm_fft.h" -int getEntireRes(int number, int divisor, int *entire, int *res) -{ - *entire = number / divisor; - *res = number % divisor; - return FFT_OK; -} +// static int getEntireRes(int number, int divisor, int *entire, int *res) +// { +// *entire = number / divisor; +// *res = number % divisor; +// return FFT_OK; +// } -int getMultipliers(int n, int *n1, int *n2) +static int getMultipliers(int n, int *n1, int *n2) { int multiplier, i; if (n == 1) @@ -36,13 +36,13 @@ int getMultipliers(int n, int *n1, int *n2) // 1-dimensional FFT // // API -// int fft(float *x_in, float *x_out, int n, int shift); +// int fft(float *x_in, float *x_out, int n, int shift); // INPUT // x_in - input signal // n - number of elements for searching Fourier image // shift - shift between input elements // OUTPUT -// x_out - output signal (contains 2n elements in order +// x_out - output signal (contains 2n elements in order Re(x_in[0]), Im(x_in[0]), Re(x_in[1]), Im(x_in[1]) and etc.) // RESULT // Error status @@ -107,8 +107,8 @@ int fft(float *x_in, float *x_out, int n, int shift) // API // int fftInverse(float *x_in, float *x_out, int n, int shift); // INPUT -// x_in - Fourier image of 1d input signal(contains 2n elements - in order Re(x_in[0]), Im(x_in[0]), +// x_in - Fourier image of 1d input signal(contains 2n elements + in order Re(x_in[0]), Im(x_in[0]), Re(x_in[1]), Im(x_in[1]) and etc.) // n - number of elements for searching counter FFT image // shift - shift between input elements @@ -180,7 +180,7 @@ int fftInverse(float *x_in, float *x_out, int n, int shift) // numColls - number of collumns // OUTPUT // x_out - output signal (contains (2 * numRows * numColls) elements - in order Re(x_in[0][0]), Im(x_in[0][0]), + in order Re(x_in[0][0]), Im(x_in[0][0]), Re(x_in[0][1]), Im(x_in[0][1]) and etc.) // RESULT // Error status @@ -193,14 +193,14 @@ int fft2d(float *x_in, float *x_out, int numRows, int numColls) x_outTmp = (float *)malloc(sizeof(float) * (2 * size)); for (i = 0; i < numRows; i++) { - fft(x_in + i * 2 * numColls, + fft(x_in + i * 2 * numColls, x_outTmp + i * 2 * numColls, numColls, 2); } for (i = 0; i < numColls; i++) { - fft(x_outTmp + 2 * i, - x_out + 2 * i, + fft(x_outTmp + 2 * i, + x_out + 2 * i, numRows, 2 * numColls); } free(x_outTmp); @@ -213,8 +213,8 @@ int fft2d(float *x_in, float *x_out, int numRows, int numColls) // API // int fftInverse2d(float *x_in, float *x_out, int numRows, int numColls); // INPUT -// x_in - Fourier image of matrix (contains (2 * numRows * numColls) - elements in order Re(x_in[0][0]), Im(x_in[0][0]), +// x_in - Fourier image of matrix (contains (2 * numRows * numColls) + elements in order Re(x_in[0][0]), Im(x_in[0][0]), Re(x_in[0][1]), Im(x_in[0][1]) and etc.) // numRows - number of rows // numColls - number of collumns @@ -237,8 +237,8 @@ int fftInverse2d(float *x_in, float *x_out, int numRows, int numColls) } for (i = 0; i < numColls; i++) { - fftInverse(x_outTmp + 2 * i, - x_out + 2 * i, + fftInverse(x_outTmp + 2 * i, + x_out + 2 * i, numRows, 2 * numColls); } free(x_outTmp); diff --git a/modules/objdetect/src/haar.cpp b/modules/objdetect/src/haar.cpp index f6c7d61..06e89e6 100644 --- a/modules/objdetect/src/haar.cpp +++ b/modules/objdetect/src/haar.cpp @@ -653,7 +653,7 @@ double icvEvalHidHaarClassifier( CvHidHaarClassifier* classifier, } -CV_IMPL int +static int cvRunHaarClassifierCascadeSum( const CvHaarClassifierCascade* _cascade, CvPoint pt, double& stage_sum, int start_stage ) { @@ -759,7 +759,7 @@ cvRunHaarClassifierCascadeSum( const CvHaarClassifierCascade* _cascade, sum += calc_sum(node->feature.rect[1],p_offset) * node->feature.rect[1].weight; if( node->feature.rect[2].p0 ) sum += calc_sum(node->feature.rect[2],p_offset) * node->feature.rect[2].weight; - + stage_sum += classifier->alpha[sum >= t]; #else // ayasin - NHM perf optim. Avoid use of costly flaky jcc @@ -771,7 +771,7 @@ cvRunHaarClassifierCascadeSum( const CvHaarClassifierCascade* _cascade, if( node->feature.rect[2].p0 ) _sum += calc_sum(node->feature.rect[2],p_offset) * node->feature.rect[2].weight; __m128d sum = _mm_set_sd(_sum); - + t = _mm_cmpgt_sd(t, sum); stage_sum = _mm_add_sd(stage_sum, _mm_blendv_pd(b, a, t)); #endif @@ -823,7 +823,7 @@ struct HaarDetectObjects_ScaleImage_Invoker HaarDetectObjects_ScaleImage_Invoker( const CvHaarClassifierCascade* _cascade, int _stripSize, double _factor, const Mat& _sum1, const Mat& _sqsum1, Mat* _norm1, - Mat* _mask1, Rect _equRect, ConcurrentRectVector& _vec, + Mat* _mask1, Rect _equRect, ConcurrentRectVector& _vec, std::vector& _levels, std::vector& _weights, bool _outputLevels ) { @@ -839,19 +839,19 @@ struct HaarDetectObjects_ScaleImage_Invoker rejectLevels = _outputLevels ? &_levels : 0; levelWeights = _outputLevels ? &_weights : 0; } - + void operator()( const BlockedRange& range ) const { Size winSize0 = cascade->orig_window_size; Size winSize(cvRound(winSize0.width*factor), cvRound(winSize0.height*factor)); int y1 = range.begin()*stripSize, y2 = min(range.end()*stripSize, sum1.rows - 1 - winSize0.height); - + if (y2 <= y1 || sum1.cols <= 1 + winSize0.width) return; - + Size ssz(sum1.cols - 1 - winSize0.width, y2 - y1); int x, y, ystep = factor > 2 ? 1 : 2; - + #ifdef HAVE_IPP if( cascade->hid_cascade->ipp_stages ) { @@ -860,7 +860,7 @@ struct HaarDetectObjects_ScaleImage_Invoker sqsum1.ptr(y1), sqsum1.step, norm1->ptr(y1), norm1->step, ippiSize(ssz.width, ssz.height), iequRect ); - + int positive = (ssz.width/ystep)*((ssz.height + ystep-1)/ystep); if( ystep == 1 ) @@ -870,12 +870,12 @@ struct HaarDetectObjects_ScaleImage_Invoker { uchar* mask1row = mask1->ptr(y); memset( mask1row, 0, ssz.width ); - + if( y % ystep == 0 ) for( x = 0; x < ssz.width; x += ystep ) mask1row[x] = (uchar)1; } - + for( int j = 0; j < cascade->count; j++ ) { if( ippiApplyHaarClassifier_32f_C1R( @@ -889,7 +889,7 @@ struct HaarDetectObjects_ScaleImage_Invoker if( positive <= 0 ) break; } - + if( positive > 0 ) for( y = y1; y < y2; y += ystep ) { @@ -929,11 +929,11 @@ struct HaarDetectObjects_ScaleImage_Invoker { if( result > 0 ) vec->push_back(Rect(cvRound(x*factor), cvRound(y*factor), - winSize.width, winSize.height)); + winSize.width, winSize.height)); } } } - + const CvHaarClassifierCascade* cascade; int stripSize; double factor; @@ -943,7 +943,7 @@ struct HaarDetectObjects_ScaleImage_Invoker std::vector* rejectLevels; std::vector* levelWeights; }; - + struct HaarDetectObjects_ScaleCascade_Invoker { @@ -960,7 +960,7 @@ struct HaarDetectObjects_ScaleCascade_Invoker p = _p; pq = _pq; vec = &_vec; } - + void operator()( const BlockedRange& range ) const { int iy, startY = range.begin(), endY = range.end(); @@ -968,14 +968,14 @@ struct HaarDetectObjects_ScaleCascade_Invoker const int *pq0 = pq[0], *pq1 = pq[1], *pq2 = pq[2], *pq3 = pq[3]; bool doCannyPruning = p0 != 0; int sstep = (int)(sumstep/sizeof(p0[0])); - + for( iy = startY; iy < endY; iy++ ) { int ix, y = cvRound(iy*ystep), ixstep = 1; for( ix = xrange.start; ix < xrange.end; ix += ixstep ) { int x = cvRound(ix*ystep); // it should really be ystep, not ixstep - + if( doCannyPruning ) { int offset = y*sstep + x; @@ -987,7 +987,7 @@ struct HaarDetectObjects_ScaleCascade_Invoker continue; } } - + int result = cvRunHaarClassifierCascade( cascade, cvPoint(x, y), 0 ); if( result > 0 ) vec->push_back(Rect(x, y, winsize.width, winsize.height)); @@ -995,7 +995,7 @@ struct HaarDetectObjects_ScaleCascade_Invoker } } } - + const CvHaarClassifierCascade* cascade; double ystep; size_t sumstep; @@ -1005,16 +1005,16 @@ struct HaarDetectObjects_ScaleCascade_Invoker const int** pq; ConcurrentRectVector* vec; }; - - + + } - + CvSeq* -cvHaarDetectObjectsForROC( const CvArr* _img, +cvHaarDetectObjectsForROC( const CvArr* _img, CvHaarClassifierCascade* cascade, CvMemStorage* storage, std::vector& rejectLevels, std::vector& levelWeights, - double scaleFactor, int minNeighbors, int flags, + double scaleFactor, int minNeighbors, int flags, CvSize minSize, CvSize maxSize, bool outputRejectLevels ) { const double GROUP_EPS = 0.2; @@ -1044,13 +1044,13 @@ cvHaarDetectObjectsForROC( const CvArr* _img, if( CV_MAT_DEPTH(img->type) != CV_8U ) CV_Error( CV_StsUnsupportedFormat, "Only 8-bit images are supported" ); - + if( scaleFactor <= 1 ) CV_Error( CV_StsOutOfRange, "scale factor must be > 1" ); if( findBiggestObject ) flags &= ~CV_HAAR_SCALE_IMAGE; - + if( maxSize.height == 0 || maxSize.width == 0 ) { maxSize.height = img->rows; @@ -1132,7 +1132,7 @@ cvHaarDetectObjectsForROC( const CvArr* _img, #else const int stripCount = 1; #endif - + #ifdef HAVE_IPP if( use_ipp ) { @@ -1141,8 +1141,8 @@ cvHaarDetectObjectsForROC( const CvArr* _img, } else #endif - cvSetImagesForHaarClassifierCascade( cascade, &sum1, &sqsum1, _tilted, 1. ); - + cvSetImagesForHaarClassifierCascade( cascade, &sum1, &sqsum1, _tilted, 1. ); + cv::Mat _norm1(&norm1), _mask1(&mask1); cv::parallel_for(cv::BlockedRange(0, stripCount), cv::HaarDetectObjects_ScaleImage_Invoker(cascade, @@ -1242,22 +1242,22 @@ cvHaarDetectObjectsForROC( const CvArr* _img, { rectList.resize(allCandidates.size()); std::copy(allCandidates.begin(), allCandidates.end(), rectList.begin()); - + groupRectangles(rectList, std::max(minNeighbors, 1), GROUP_EPS); - + if( !rectList.empty() ) { size_t i, sz = rectList.size(); cv::Rect maxRect; - + for( i = 0; i < sz; i++ ) { if( rectList[i].area() > maxRect.area() ) maxRect = rectList[i]; } - + allCandidates.push_back(maxRect); - + scanROI = maxRect; int dx = cvRound(maxRect.width*GROUP_EPS); int dy = cvRound(maxRect.height*GROUP_EPS); @@ -1265,7 +1265,7 @@ cvHaarDetectObjectsForROC( const CvArr* _img, scanROI.y = std::max(scanROI.y - dy, 0); scanROI.width = std::min(scanROI.width + dx*2, img->cols-1-scanROI.x); scanROI.height = std::min(scanROI.height + dy*2, img->rows-1-scanROI.y); - + double minScale = roughSearch ? 0.6 : 0.4; minSize.width = cvRound(maxRect.width*minScale); minSize.height = cvRound(maxRect.height*minScale); @@ -1277,7 +1277,7 @@ cvHaarDetectObjectsForROC( const CvArr* _img, rectList.resize(allCandidates.size()); if(!allCandidates.empty()) std::copy(allCandidates.begin(), allCandidates.end(), rectList.begin()); - + if( minNeighbors != 0 || findBiggestObject ) { if( outputRejectLevels ) @@ -1291,11 +1291,11 @@ cvHaarDetectObjectsForROC( const CvArr* _img, } else rweights.resize(rectList.size(),0); - + if( findBiggestObject && rectList.size() ) { CvAvgComp result_comp = {{0,0,0,0},0}; - + for( size_t i = 0; i < rectList.size(); i++ ) { cv::Rect r = rectList[i]; @@ -1322,14 +1322,14 @@ cvHaarDetectObjectsForROC( const CvArr* _img, } CV_IMPL CvSeq* -cvHaarDetectObjects( const CvArr* _img, +cvHaarDetectObjects( const CvArr* _img, CvHaarClassifierCascade* cascade, CvMemStorage* storage, double scaleFactor, int minNeighbors, int flags, CvSize minSize, CvSize maxSize ) { std::vector fakeLevels; std::vector fakeWeights; - return cvHaarDetectObjectsForROC( _img, cascade, storage, fakeLevels, fakeWeights, + return cvHaarDetectObjectsForROC( _img, cascade, storage, fakeLevels, fakeWeights, scaleFactor, minNeighbors, flags, minSize, maxSize, false ); } @@ -2091,7 +2091,7 @@ namespace cv HaarClassifierCascade::HaarClassifierCascade() {} HaarClassifierCascade::HaarClassifierCascade(const String& filename) { load(filename); } - + bool HaarClassifierCascade::load(const String& filename) { cascade = Ptr((CvHaarClassifierCascade*)cvLoad(filename.c_str(), 0, 0, 0)); diff --git a/modules/objdetect/src/hog.cpp b/modules/objdetect/src/hog.cpp index 053b988..d96386d 100644 --- a/modules/objdetect/src/hog.cpp +++ b/modules/objdetect/src/hog.cpp @@ -456,7 +456,6 @@ void HOGCache::init(const HOGDescriptor* _descriptor, Size blockSize = descriptor->blockSize; Size blockStride = descriptor->blockStride; Size cellSize = descriptor->cellSize; - Size winSize = descriptor->winSize; int i, j, nbins = descriptor->nbins; int rawBlockSize = blockSize.width*blockSize.height; @@ -471,10 +470,10 @@ void HOGCache::init(const HOGDescriptor* _descriptor, (winSize.height/cacheStride.height)+1); blockCache.create(cacheSize.height, cacheSize.width*blockHistogramSize); blockCacheFlags.create(cacheSize); - size_t i, cacheRows = blockCache.rows; + size_t cacheRows = blockCache.rows; ymaxCached.resize(cacheRows); - for( i = 0; i < cacheRows; i++ ) - ymaxCached[i] = -1; + for(size_t ii = 0; ii < cacheRows; ii++ ) + ymaxCached[ii] = -1; } Mat_ weights(blockSize); diff --git a/modules/objdetect/src/latentsvm.cpp b/modules/objdetect/src/latentsvm.cpp index d61755a..89fd048 100644 --- a/modules/objdetect/src/latentsvm.cpp +++ b/modules/objdetect/src/latentsvm.cpp @@ -3,11 +3,11 @@ #include "_lsvm_matching.h" /* -// Transformation filter displacement from the block space +// Transformation filter displacement from the block space // to the space of pixels at the initial image // // API -// int convertPoints(int countLevel, CvPoint *points, int *levels, +// int convertPoints(int countLevel, CvPoint *points, int *levels, CvPoint **partsDisplacement, int kPoints, int n); // INPUT // countLevel - the number of levels in the feature pyramid @@ -25,10 +25,10 @@ // RESULT // Error status */ -int convertPoints(int /*countLevel*/, int lambda, +int convertPoints(int /*countLevel*/, int lambda, int initialImageLevel, - CvPoint *points, int *levels, - CvPoint **partsDisplacement, int kPoints, int n, + CvPoint *points, int *levels, + CvPoint **partsDisplacement, int kPoints, int n, int maxXBorder, int maxYBorder) { @@ -37,7 +37,7 @@ int convertPoints(int /*countLevel*/, int lambda, step = powf( 2.0f, 1.0f / ((float)lambda) ); computeBorderSize(maxXBorder, maxYBorder, &bx, &by); - + for (i = 0; i < kPoints; i++) { // scaling factor for root filter @@ -48,10 +48,10 @@ int convertPoints(int /*countLevel*/, int lambda, // scaling factor for part filters scale = SIDE_LENGTH * powf(step, (float)(levels[i] - lambda - initialImageLevel)); for (j = 0; j < n; j++) - { - partsDisplacement[i][j].x = (int)((partsDisplacement[i][j].x - + { + partsDisplacement[i][j].x = (int)((partsDisplacement[i][j].x - 2 * bx + 1) * scale); - partsDisplacement[i][j].y = (int)((partsDisplacement[i][j].y - + partsDisplacement[i][j].y = (int)((partsDisplacement[i][j].y - 2 * by + 1) * scale); } } @@ -62,7 +62,7 @@ int convertPoints(int /*countLevel*/, int lambda, // Elimination boxes that are outside the image boudaries // // API -// int clippingBoxes(int width, int height, +// int clippingBoxes(int width, int height, CvPoint *points, int kPoints); // INPUT // width - image wediht @@ -72,12 +72,12 @@ int convertPoints(int /*countLevel*/, int lambda, // kPoints - points number // OUTPUT // points - updated points (if coordinates less than zero then - set zero coordinate, if coordinates more than image + set zero coordinate, if coordinates more than image size then set coordinates equal image size) // RESULT // Error status */ -int clippingBoxes(int width, int height, +int clippingBoxes(int width, int height, CvPoint *points, int kPoints) { int i; @@ -111,7 +111,7 @@ int clippingBoxes(int width, int height, int maxXBorder, int maxYBorder); // INPUT -// image - initial image +// image - initial image // maxXBorder - the largest root filter size (X-direction) // maxYBorder - the largest root filter size (Y-direction) // OUTPUT @@ -149,54 +149,54 @@ CvLSVMFeaturePyramid* createFeaturePyramidWithBorder(IplImage *image, // Computation of the root filter displacement and values of score function // // API -// int searchObject(const featurePyramid *H, const filterObject **all_F, int n, - float b, +// int searchObject(const featurePyramid *H, const filterObject **all_F, int n, + float b, int maxXBorder, - int maxYBorder, + int maxYBorder, CvPoint **points, int **levels, int *kPoints, float *score, CvPoint ***partsDisplacement); // INPUT // image - initial image for searhing object -// all_F - the set of filters (the first element is root filter, +// all_F - the set of filters (the first element is root filter, other elements - part filters) // n - the number of part filters // b - linear term of the score function // maxXBorder - the largest root filter size (X-direction) // maxYBorder - the largest root filter size (Y-direction) // OUTPUT -// points - positions (x, y) of the upper-left corner +// points - positions (x, y) of the upper-left corner of root filter frame // levels - levels that correspond to each position // kPoints - number of positions // score - value of the score function -// partsDisplacement - part filters displacement for each position +// partsDisplacement - part filters displacement for each position of the root filter // RESULT // Error status */ -int searchObject(const CvLSVMFeaturePyramid *H, const CvLSVMFilterObject **all_F, - int n, float b, +int searchObject(const CvLSVMFeaturePyramid *H, const CvLSVMFilterObject **all_F, + int n, float b, int maxXBorder, - int maxYBorder, + int maxYBorder, CvPoint **points, int **levels, int *kPoints, float *score, CvPoint ***partsDisplacement) { int opResult; // Matching - opResult = maxFunctionalScore(all_F, n, H, b, maxXBorder, maxYBorder, - score, points, levels, + opResult = maxFunctionalScore(all_F, n, H, b, maxXBorder, maxYBorder, + score, points, levels, kPoints, partsDisplacement); if (opResult != LATENT_SVM_OK) { return LATENT_SVM_SEARCH_OBJECT_FAILED; } - - // Transformation filter displacement from the block space + + // Transformation filter displacement from the block space // to the space of pixels at the initial image // that settles at the level number LAMBDA - convertPoints(H->numLevels, LAMBDA, LAMBDA, (*points), - (*levels), (*partsDisplacement), (*kPoints), n, + convertPoints(H->numLevels, LAMBDA, LAMBDA, (*points), + (*levels), (*partsDisplacement), (*kPoints), n, maxXBorder, maxYBorder); return LATENT_SVM_OK; @@ -206,7 +206,7 @@ int searchObject(const CvLSVMFeaturePyramid *H, const CvLSVMFilterObject **all_F // Computation right bottom corners coordinates of bounding boxes // // API -// int estimateBoxes(CvPoint *points, int *levels, int kPoints, +// int estimateBoxes(CvPoint *points, int *levels, int kPoints, int sizeX, int sizeY, CvPoint **oppositePoints); // INPUT // points - left top corners coordinates of bounding boxes @@ -217,7 +217,7 @@ int searchObject(const CvLSVMFeaturePyramid *H, const CvLSVMFilterObject **all_F // RESULT // Error status */ -int estimateBoxes(CvPoint *points, int *levels, int kPoints, +static int estimateBoxes(CvPoint *points, int *levels, int kPoints, int sizeX, int sizeY, CvPoint **oppositePoints) { int i; @@ -237,16 +237,16 @@ int estimateBoxes(CvPoint *points, int *levels, int kPoints, // Computation of the root filter displacement and values of score function // // API -// int searchObjectThreshold(const featurePyramid *H, +// int searchObjectThreshold(const featurePyramid *H, const filterObject **all_F, int n, - float b, - int maxXBorder, int maxYBorder, + float b, + int maxXBorder, int maxYBorder, float scoreThreshold, - CvPoint **points, int **levels, int *kPoints, + CvPoint **points, int **levels, int *kPoints, float **score, CvPoint ***partsDisplacement); // INPUT // H - feature pyramid -// all_F - the set of filters (the first element is root filter, +// all_F - the set of filters (the first element is root filter, other elements - part filters) // n - the number of part filters // b - linear term of the score function @@ -254,22 +254,22 @@ int estimateBoxes(CvPoint *points, int *levels, int kPoints, // maxYBorder - the largest root filter size (Y-direction) // scoreThreshold - score threshold // OUTPUT -// points - positions (x, y) of the upper-left corner +// points - positions (x, y) of the upper-left corner of root filter frame // levels - levels that correspond to each position // kPoints - number of positions // score - values of the score function -// partsDisplacement - part filters displacement for each position +// partsDisplacement - part filters displacement for each position of the root filter // RESULT // Error status */ -int searchObjectThreshold(const CvLSVMFeaturePyramid *H, +int searchObjectThreshold(const CvLSVMFeaturePyramid *H, const CvLSVMFilterObject **all_F, int n, - float b, - int maxXBorder, int maxYBorder, + float b, + int maxXBorder, int maxYBorder, float scoreThreshold, - CvPoint **points, int **levels, int *kPoints, + CvPoint **points, int **levels, int *kPoints, float **score, CvPoint ***partsDisplacement, int numThreads) { @@ -284,28 +284,28 @@ int searchObjectThreshold(const CvLSVMFeaturePyramid *H, return opResult; } opResult = tbbThresholdFunctionalScore(all_F, n, H, b, maxXBorder, maxYBorder, - scoreThreshold, numThreads, score, - points, levels, kPoints, + scoreThreshold, numThreads, score, + points, levels, kPoints, partsDisplacement); #else - opResult = thresholdFunctionalScore(all_F, n, H, b, - maxXBorder, maxYBorder, - scoreThreshold, - score, points, levels, + opResult = thresholdFunctionalScore(all_F, n, H, b, + maxXBorder, maxYBorder, + scoreThreshold, + score, points, levels, kPoints, partsDisplacement); - (void)numThreads; + (void)numThreads; #endif if (opResult != LATENT_SVM_OK) { return LATENT_SVM_SEARCH_OBJECT_FAILED; - } - - // Transformation filter displacement from the block space + } + + // Transformation filter displacement from the block space // to the space of pixels at the initial image // that settles at the level number LAMBDA - convertPoints(H->numLevels, LAMBDA, LAMBDA, (*points), - (*levels), (*partsDisplacement), (*kPoints), n, + convertPoints(H->numLevels, LAMBDA, LAMBDA, (*points), + (*levels), (*partsDisplacement), (*kPoints), n, maxXBorder, maxYBorder); return LATENT_SVM_OK; @@ -350,9 +350,9 @@ int getOppositePoint(CvPoint point, // // API // int showRootFilterBoxes(const IplImage *image, - const filterObject *filter, + const filterObject *filter, CvPoint *points, int *levels, int kPoints, - CvScalar color, int thickness, + CvScalar color, int thickness, int line_type, int shift); // INPUT // image - initial image @@ -370,22 +370,22 @@ int getOppositePoint(CvPoint point, // Error status */ int showRootFilterBoxes(IplImage *image, - const CvLSVMFilterObject *filter, + const CvLSVMFilterObject *filter, CvPoint *points, int *levels, int kPoints, - CvScalar color, int thickness, + CvScalar color, int thickness, int line_type, int shift) -{ +{ int i; float step; CvPoint oppositePoint; step = powf( 2.0f, 1.0f / ((float)LAMBDA)); - + for (i = 0; i < kPoints; i++) { // Drawing rectangle for filter - getOppositePoint(points[i], filter->sizeX, filter->sizeY, + getOppositePoint(points[i], filter->sizeX, filter->sizeY, step, levels[i] - LAMBDA, &oppositePoint); - cvRectangle(image, points[i], oppositePoint, + cvRectangle(image, points[i], oppositePoint, color, thickness, line_type, shift); } #ifdef HAVE_OPENCV_HIGHGUI @@ -399,9 +399,9 @@ int showRootFilterBoxes(IplImage *image, // // API // int showPartFilterBoxes(const IplImage *image, - const filterObject *filter, + const filterObject *filter, CvPoint *points, int *levels, int kPoints, - CvScalar color, int thickness, + CvScalar color, int thickness, int line_type, int shift); // INPUT // image - initial image @@ -421,9 +421,9 @@ int showRootFilterBoxes(IplImage *image, */ int showPartFilterBoxes(IplImage *image, const CvLSVMFilterObject **filters, - int n, CvPoint **partsDisplacement, + int n, CvPoint **partsDisplacement, int *levels, int kPoints, - CvScalar color, int thickness, + CvScalar color, int thickness, int line_type, int shift) { int i, j; @@ -437,10 +437,10 @@ int showPartFilterBoxes(IplImage *image, for (j = 0; j < n; j++) { // Drawing rectangles for part filters - getOppositePoint(partsDisplacement[i][j], - filters[j + 1]->sizeX, filters[j + 1]->sizeY, + getOppositePoint(partsDisplacement[i][j], + filters[j + 1]->sizeX, filters[j + 1]->sizeY, step, levels[i] - 2 * LAMBDA, &oppositePoint); - cvRectangle(image, partsDisplacement[i][j], oppositePoint, + cvRectangle(image, partsDisplacement[i][j], oppositePoint, color, thickness, line_type, shift); } } @@ -454,8 +454,8 @@ int showPartFilterBoxes(IplImage *image, // Drawing boxes // // API -// int showBoxes(const IplImage *img, - const CvPoint *points, const CvPoint *oppositePoints, int kPoints, +// int showBoxes(const IplImage *img, + const CvPoint *points, const CvPoint *oppositePoints, int kPoints, CvScalar color, int thickness, int line_type, int shift); // INPUT // img - initial image @@ -470,14 +470,14 @@ int showPartFilterBoxes(IplImage *image, // RESULT // Error status */ -int showBoxes(IplImage *img, - const CvPoint *points, const CvPoint *oppositePoints, int kPoints, +int showBoxes(IplImage *img, + const CvPoint *points, const CvPoint *oppositePoints, int kPoints, CvScalar color, int thickness, int line_type, int shift) { int i; for (i = 0; i < kPoints; i++) { - cvRectangle(img, points[i], oppositePoints[i], + cvRectangle(img, points[i], oppositePoints[i], color, thickness, line_type, shift); } #ifdef HAVE_OPENCV_HIGHGUI @@ -491,10 +491,10 @@ int showBoxes(IplImage *img, // // API // int getMaxFilterDims(const filterObject **filters, int kComponents, - const int *kPartFilters, + const int *kPartFilters, unsigned int *maxXBorder, unsigned int *maxYBorder); // INPUT -// filters - a set of filters (at first root filter, then part filters +// filters - a set of filters (at first root filter, then part filters and etc. for all components) // kComponents - number of components // kPartFilters - number of part filters for each component @@ -505,10 +505,10 @@ int showBoxes(IplImage *img, // Error status */ int getMaxFilterDims(const CvLSVMFilterObject **filters, int kComponents, - const int *kPartFilters, + const int *kPartFilters, unsigned int *maxXBorder, unsigned int *maxYBorder) { - int i, componentIndex; + int i, componentIndex; *maxXBorder = filters[0]->sizeX; *maxYBorder = filters[0]->sizeY; componentIndex = kPartFilters[0] + 1; @@ -532,7 +532,7 @@ int getMaxFilterDims(const CvLSVMFilterObject **filters, int kComponents, // // API // int searchObjectThresholdSomeComponents(const featurePyramid *H, - const filterObject **filters, + const filterObject **filters, int kComponents, const int *kPartFilters, const float *b, float scoreThreshold, CvPoint **points, CvPoint **oppPoints, @@ -553,20 +553,20 @@ int getMaxFilterDims(const CvLSVMFilterObject **filters, int kComponents, // Error status */ int searchObjectThresholdSomeComponents(const CvLSVMFeaturePyramid *H, - const CvLSVMFilterObject **filters, + const CvLSVMFilterObject **filters, int kComponents, const int *kPartFilters, const float *b, float scoreThreshold, CvPoint **points, CvPoint **oppPoints, float **score, int *kPoints, int numThreads) { - int error = 0; + //int error = 0; int i, j, s, f, componentIndex; unsigned int maxXBorder, maxYBorder; CvPoint **pointsArr, **oppPointsArr, ***partsDisplacementArr; float **scoreArr; int *kPointsArr, **levelsArr; - + // Allocation memory pointsArr = (CvPoint **)malloc(sizeof(CvPoint *) * kComponents); oppPointsArr = (CvPoint **)malloc(sizeof(CvPoint *) * kComponents); @@ -574,18 +574,18 @@ int searchObjectThresholdSomeComponents(const CvLSVMFeaturePyramid *H, kPointsArr = (int *)malloc(sizeof(int) * kComponents); levelsArr = (int **)malloc(sizeof(int *) * kComponents); partsDisplacementArr = (CvPoint ***)malloc(sizeof(CvPoint **) * kComponents); - + // Getting maximum filter dimensions - error = getMaxFilterDims(filters, kComponents, kPartFilters, &maxXBorder, &maxYBorder); + /*error = */getMaxFilterDims(filters, kComponents, kPartFilters, &maxXBorder, &maxYBorder); componentIndex = 0; *kPoints = 0; // For each component perform searching for (i = 0; i < kComponents; i++) { #ifdef HAVE_TBB - error = searchObjectThreshold(H, &(filters[componentIndex]), kPartFilters[i], + int error = searchObjectThreshold(H, &(filters[componentIndex]), kPartFilters[i], b[i], maxXBorder, maxYBorder, scoreThreshold, - &(pointsArr[i]), &(levelsArr[i]), &(kPointsArr[i]), + &(pointsArr[i]), &(levelsArr[i]), &(kPointsArr[i]), &(scoreArr[i]), &(partsDisplacementArr[i]), numThreads); if (error != LATENT_SVM_OK) { @@ -599,17 +599,17 @@ int searchObjectThresholdSomeComponents(const CvLSVMFeaturePyramid *H, return LATENT_SVM_SEARCH_OBJECT_FAILED; } #else - (void)numThreads; + (void)numThreads; searchObjectThreshold(H, &(filters[componentIndex]), kPartFilters[i], - b[i], maxXBorder, maxYBorder, scoreThreshold, - &(pointsArr[i]), &(levelsArr[i]), &(kPointsArr[i]), + b[i], maxXBorder, maxYBorder, scoreThreshold, + &(pointsArr[i]), &(levelsArr[i]), &(kPointsArr[i]), &(scoreArr[i]), &(partsDisplacementArr[i])); #endif - estimateBoxes(pointsArr[i], levelsArr[i], kPointsArr[i], - filters[componentIndex]->sizeX, filters[componentIndex]->sizeY, &(oppPointsArr[i])); + estimateBoxes(pointsArr[i], levelsArr[i], kPointsArr[i], + filters[componentIndex]->sizeX, filters[componentIndex]->sizeY, &(oppPointsArr[i])); componentIndex += (kPartFilters[i] + 1); *kPoints += kPointsArr[i]; - } + } *points = (CvPoint *)malloc(sizeof(CvPoint) * (*kPoints)); *oppPoints = (CvPoint *)malloc(sizeof(CvPoint) * (*kPoints)); diff --git a/modules/objdetect/src/latentsvmdetector.cpp b/modules/objdetect/src/latentsvmdetector.cpp index cf20702..dd417fa 100644 --- a/modules/objdetect/src/latentsvmdetector.cpp +++ b/modules/objdetect/src/latentsvmdetector.cpp @@ -192,7 +192,7 @@ size_t LatentSvmDetector::getClassCount() const return classNames.size(); } -string extractModelName( const string& filename ) +static string extractModelName( const string& filename ) { size_t startPos = filename.rfind('/'); if( startPos == string::npos ) diff --git a/modules/objdetect/src/linemod.cpp b/modules/objdetect/src/linemod.cpp index 4635cc0..3eff82a 100644 --- a/modules/objdetect/src/linemod.cpp +++ b/modules/objdetect/src/linemod.cpp @@ -91,7 +91,7 @@ void Feature::write(FileStorage& fs) const * * \return The bounding box of all the templates in original image coordinates. */ -Rect cropTemplates(std::vector