OCV_OPTION(WITH_DIRECTX "Include DirectX support" ON IF WIN32 )
OCV_OPTION(WITH_INTELPERC "Include Intel Perceptual Computing support" OFF IF WIN32 )
OCV_OPTION(WITH_IPP_A "Include Intel IPP_A support" OFF IF (MSVC OR X86 OR X86_64) )
+OCV_OPTION(WITH_GDAL "Include GDAL Support" OFF IF (NOT ANDROID AND NOT IOS) )
# OpenCV build components
# ===================================================
find_host_package(Git QUIET)
if(GIT_FOUND)
- execute_process(COMMAND "${GIT_EXECUTABLE}" describe --tags --always --dirty --match "2.[0-9].[0-9]*"
+ execute_process(COMMAND "${GIT_EXECUTABLE}" describe --tags --always --dirty --match "[0-9].[0-9].[0-9]*"
WORKING_DIRECTORY "${OpenCV_SOURCE_DIR}"
OUTPUT_VARIABLE OPENCV_VCSVERSION
RESULT_VARIABLE GIT_RESULT
status(" OpenEXR:" "NO")
endif()
+if( WITH_GDAL )
+ status(" GDAL:" GDAL_FOUND THEN "${GDAL_LIBRARY}")
+else()
+ status(" GDAL:" "NO")
+endif()
+
# ========================== VIDEO IO ==========================
status("")
status(" Video I/O:")
for( y = 0; y < gradSize.height; y++ )
{
- const uchar* currPtr = img.data + img.step*ymap[y];
- const uchar* prevPtr = img.data + img.step*ymap[y-1];
- const uchar* nextPtr = img.data + img.step*ymap[y+1];
- float* gradPtr = (float*)grad.ptr(y);
- uchar* qanglePtr = (uchar*)qangle.ptr(y);
+ const uchar* currPtr = img.ptr(ymap[y]);
+ const uchar* prevPtr = img.ptr(ymap[y-1]);
+ const uchar* nextPtr = img.ptr(ymap[y+1]);
+ float* gradPtr = grad.ptr<float>(y);
+ uchar* qanglePtr = qangle.ptr(y);
for( x = 0; x < width; x++ )
{
int magStep = (int)( grad.step / sizeof(float) );
for( binIdx = 0; binIdx < nbins; binIdx++ )
{
- histBuf = (float*)histogram[binIdx].data;
- magBuf = (const float*)grad.data;
- binsBuf = (const uchar*)qangle.data;
+ histBuf = histogram[binIdx].ptr<float>();
+ magBuf = grad.ptr<float>();
+ binsBuf = qangle.ptr();
memset( histBuf, 0, histSize.width * sizeof(histBuf[0]) );
histBuf += histStep + 1;
size_t p0, p1, p2, p3;
CV_SUM_OFFSETS( p0, p1, p2, p3, normrect, sum.step1() )
double area = normrect.width * normrect.height;
- const int *sp = (const int*)sum.data;
+ const int *sp = sum.ptr<int>();
int valSum = sp[p0] - sp[p1] - sp[p2] + sp[p3];
- const double *sqp = (const double *)sqSum.data;
+ const double *sqp = sqSum.ptr<double>();
double valSqSum = sqp[p0] - sqp[p1] - sqp[p2] + sqp[p3];
return (float) sqrt( (double) (area * valSqSum - (double)valSum * valSum) );
}
return false;
Mat mat( winSize.height, winSize.width, CV_8UC1,
- (void*)(img.data + point.y * img.step + point.x * img.elemSize()), img.step );
+ (void*)(img.ptr(point.y) + point.x * img.elemSize()), img.step );
mat.copyTo(_img);
if( (int)( point.x + (1.0F + stepFactor ) * winSize.width ) < img.cols )
# we remove -frtti because it's used for C++ compiler
# but NVCC uses C compiler by default
string(REPLACE "-frtti" "" ${var} "${${var}}")
+
+ string(REPLACE "-fvisibility-inlines-hidden" "" ${var} "${${var}}")
endforeach()
if(BUILD_SHARED_LIBS)
set(HAVE_OPENEXR YES)
endif()
+
+# --- GDAL (optional) ---
+if(WITH_GDAL)
+ find_package(GDAL)
+
+ if(NOT GDAL_FOUND)
+ ocv_clear_vars(GDAL_LIBRARY GDAL_INCLUDE_DIR)
+ set(HAVE_GDAL NO)
+ else()
+ set(HAVE_GDAL YES)
+ endif()
+endif()
ocv_source_group("Src" DIRBASE "${CMAKE_CURRENT_LIST_DIR}/src" FILES ${lib_srcs} ${lib_int_hdrs})
ocv_source_group("Include" DIRBASE "${CMAKE_CURRENT_LIST_DIR}/include" FILES ${lib_hdrs} ${lib_hdrs_detail})
- if (exclude_cuda EQUAL -1)
+ set(lib_cuda_srcs "")
+ set(lib_cuda_hdrs "")
+ if(HAVE_CUDA AND exclude_cuda EQUAL -1)
file(GLOB lib_cuda_srcs
"${CMAKE_CURRENT_LIST_DIR}/src/cuda/*.cu"
)
- set(cuda_objs "")
- set(lib_cuda_hdrs "")
- if(HAVE_CUDA)
- ocv_include_directories(${CUDA_INCLUDE_DIRS})
- file(GLOB lib_cuda_hdrs
- "${CMAKE_CURRENT_LIST_DIR}/src/cuda/*.hpp"
- )
-
- ocv_cuda_compile(cuda_objs ${lib_cuda_srcs} ${lib_cuda_hdrs})
- source_group("Src\\Cuda" FILES ${lib_cuda_srcs} ${lib_cuda_hdrs})
- endif()
- else()
- set(cuda_objs "")
- set(lib_cuda_srcs "")
- set(lib_cuda_hdrs "")
+ file(GLOB lib_cuda_hdrs
+ "${CMAKE_CURRENT_LIST_DIR}/src/cuda/*.hpp"
+ )
+ source_group("Src\\Cuda" FILES ${lib_cuda_srcs} ${lib_cuda_hdrs})
endif()
file(GLOB cl_kernels
endif()
ocv_set_module_sources(${_argn} HEADERS ${lib_hdrs} ${lib_hdrs_detail}
- SOURCES ${lib_srcs} ${lib_int_hdrs} ${cuda_objs} ${lib_cuda_srcs} ${lib_cuda_hdrs})
+ SOURCES ${lib_srcs} ${lib_int_hdrs} ${lib_cuda_srcs} ${lib_cuda_hdrs})
endmacro()
# creates OpenCV module in current folder
list(APPEND __params "${dir}")
endif()
endforeach()
- if(CMAKE_VERSION VERSION_LESS 2.8.11)
+ if(HAVE_CUDA OR CMAKE_VERSION VERSION_LESS 2.8.11)
include_directories(${__params})
else()
if(TARGET ${target})
endfunction()
function(ocv_add_library target)
- add_library(${target} ${ARGN})
+ set(cuda_objs "")
+ if(HAVE_CUDA)
+ set(cuda_srcs "")
+
+ foreach(var ${ARGN})
+ if(var MATCHES ".cu")
+ list(APPEND cuda_srcs ${var})
+ endif()
+ endforeach()
+
+ if(cuda_srcs)
+ ocv_include_directories(${CUDA_INCLUDE_DIRS})
+ ocv_cuda_compile(cuda_objs ${lib_cuda_srcs} ${lib_cuda_hdrs})
+ endif()
+ endif()
+
+ add_library(${target} ${ARGN} ${cuda_objs})
_ocv_append_target_includes(${target})
-endfunction()
\ No newline at end of file
+endfunction()
/* ffmpeg in Gentoo */
#cmakedefine HAVE_GENTOO_FFMPEG
+/* Geospatial Data Abstraction Library */
+#cmakedefine HAVE_GDAL
+
/* GStreamer multimedia framework */
#cmakedefine HAVE_GSTREAMER
--- /dev/null
+.. _Bindings_Basics:
+
+How OpenCV-Python Bindings Works?
+************************************
+
+Goal
+=====
+
+Learn:
+
+ * How OpenCV-Python bindings are generated?
+ * How to extend new OpenCV modules to Python?
+
+How OpenCV-Python bindings are generated?
+=========================================
+
+In OpenCV, all algorithms are implemented in C++. But these algorithms can be used from different languages like Python, Java etc. This is made possible by the bindings generators. These generators create a bridge between C++ and Python which enables users to call C++ functions from Python. To get a complete picture of what is happening in background, a good knowledge of Python/C API is required. A simple example on extending C++ functions to Python can be found in official Python documentation[1]. So extending all functions in OpenCV to Python by writing their wrapper functions manually is a time-consuming task. So OpenCV does it in a more intelligent way. OpenCV generates these wrapper functions automatically from the C++ headers using some Python scripts which are located in ``modules/python/src2``. We will look into what they do.
+
+First, ``modules/python/CMakeFiles.txt`` is a CMake script which checks the modules to be extended to Python. It will automatically check all the modules to be extended and grab their header files. These header files contain list of all classes, functions, constants etc. for that particular modules.
+
+Second, these header files are passed to a Python script, ``modules/python/src2/gen2.py``. This is the Python bindings generator script. It calls another Python script ``modules/python/src2/hdr_parser.py``. This is the header parser script. This header parser splits the complete header file into small Python lists. So these lists contain all details about a particular function, class etc. For example, a function will be parsed to get a list containing function name, return type, input arguments, argument types etc. Final list contains details of all the functions, structs, classes etc. in that header file.
+
+But header parser doesn't parse all the functions/classes in the header file. The developer has to specify which functions should be exported to Python. For that, there are certain macros added to the beginning of these declarations which enables the header parser to identify functions to be parsed. These macros are added by the developer who programs the particular function. In short, the developer decides which functions should be extended to Python and which are not. Details of those macros will be given in next session.
+
+So header parser returns a final big list of parsed functions. Our generator script (gen2.py) will create wrapper functions for all the functions/classes/enums/structs parsed by header parser (You can find these header files during compilation in the ``build/modules/python/`` folder as ``pyopencv_generated_*.h`` files). But there may be some basic OpenCV datatypes like Mat, Vec4i, Size. They need to be extended manually. For example, a Mat type should be extended to Numpy array, Size should be extended to a tuple of two integers etc. Similarly, there may be some complex structs/classes/functions etc. which need to be extended manually. All such manual wrapper functions are placed in ``modules/python/src2/pycv2.hpp``.
+
+So now only thing left is the compilation of these wrapper files which gives us **cv2** module. So when you call a function, say ``res = equalizeHist(img1,img2)`` in Python, you pass two numpy arrays and you expect another numpy array as the output. So these numpy arrays are converted to ``cv::Mat`` and then calls the ``equalizeHist()`` function in C++. Final result, ``res`` will be converted back into a Numpy array. So in short, almost all operations are done in C++ which gives us almost same speed as that of C++.
+
+So this is the basic version of how OpenCV-Python bindings are generated.
+
+
+How to extend new modules to Python?
+=====================================
+
+Header parser parse the header files based on some wrapper macros added to function declaration. Enumeration constants don't need any wrapper macros. They are automatically wrapped. But remaining functions, classes etc. need wrapper macros.
+
+Functions are extended using ``CV_EXPORTS_W`` macro. An example is shown below.
+
+.. code-block:: cpp
+
+ CV_EXPORTS_W void equalizeHist( InputArray src, OutputArray dst );
+
+Header parser can understand the input and output arguments from keywords like ``InputArray, OutputArray`` etc. But sometimes, we may need to hardcode inputs and outputs. For that, macros like ``CV_OUT, CV_IN_OUT`` etc. are used.
+
+.. code-block:: cpp
+
+ CV_EXPORTS_W void minEnclosingCircle( InputArray points,
+ CV_OUT Point2f& center, CV_OUT float& radius );
+
+For large classes also, ``CV_EXPORTS_W`` is used. To extend class methods, ``CV_WRAP`` is used. Similarly, ``CV_PROP`` is used for class fields.
+
+.. code-block:: cpp
+
+ class CV_EXPORTS_W CLAHE : public Algorithm
+ {
+ public:
+ CV_WRAP virtual void apply(InputArray src, OutputArray dst) = 0;
+
+ CV_WRAP virtual void setClipLimit(double clipLimit) = 0;
+ CV_WRAP virtual double getClipLimit() const = 0;
+ }
+
+Overloaded functions can be extended using ``CV_EXPORTS_AS``. But we need to pass a new name so that each function will be called by that name in Python. Take the case of integral function below. Three functions are available, so each one is named with a suffix in Python. Similarly ``CV_WRAP_AS`` can be used to wrap overloaded methods.
+
+.. code-block:: cpp
+
+ //! computes the integral image
+ CV_EXPORTS_W void integral( InputArray src, OutputArray sum, int sdepth = -1 );
+
+ //! computes the integral image and integral for the squared image
+ CV_EXPORTS_AS(integral2) void integral( InputArray src, OutputArray sum,
+ OutputArray sqsum, int sdepth = -1, int sqdepth = -1 );
+
+ //! computes the integral image, integral for the squared image and the tilted integral image
+ CV_EXPORTS_AS(integral3) void integral( InputArray src, OutputArray sum,
+ OutputArray sqsum, OutputArray tilted,
+ int sdepth = -1, int sqdepth = -1 );
+
+Small classes/structs are extended using ``CV_EXPORTS_W_SIMPLE``. These structs are passed by value to C++ functions. Examples are KeyPoint, Match etc. Their methods are extended by ``CV_WRAP`` and fields are extended by ``CV_PROP_RW``.
+
+.. code-block:: cpp
+
+ class CV_EXPORTS_W_SIMPLE DMatch
+ {
+ public:
+ CV_WRAP DMatch();
+ CV_WRAP DMatch(int _queryIdx, int _trainIdx, float _distance);
+ CV_WRAP DMatch(int _queryIdx, int _trainIdx, int _imgIdx, float _distance);
+
+ CV_PROP_RW int queryIdx; // query descriptor index
+ CV_PROP_RW int trainIdx; // train descriptor index
+ CV_PROP_RW int imgIdx; // train image index
+
+ CV_PROP_RW float distance;
+ };
+
+Some other small classes/structs can be exported using ``CV_EXPORTS_W_MAP`` where it is exported to a Python native dictionary. Moments() is an example of it.
+
+.. code-block:: cpp
+
+ class CV_EXPORTS_W_MAP Moments
+ {
+ public:
+ //! spatial moments
+ CV_PROP_RW double m00, m10, m01, m20, m11, m02, m30, m21, m12, m03;
+ //! central moments
+ CV_PROP_RW double mu20, mu11, mu02, mu30, mu21, mu12, mu03;
+ //! central normalized moments
+ CV_PROP_RW double nu20, nu11, nu02, nu30, nu21, nu12, nu03;
+ };
+
+So these are the major extension macros available in OpenCV. Typically, a developer has to put proper macros in their appropriate positions. Rest is done by generator scripts. Sometimes, there may be an exceptional cases where generator scripts cannot create the wrappers. Such functions need to be handled manually. But most of the time, a code written according to OpenCV coding guidelines will be automatically wrapped by generator scripts.
--- /dev/null
+.. _PY_Table-Of-Content-Bindings:
+
+
+OpenCV-Python Bindings
+--------------------------------
+
+Here, you will learn how OpenCV-Python bindings are generated.
+
+
+* :ref:`Bindings_Basics`
+
+ .. tabularcolumns:: m{100pt} m{300pt}
+ .. cssclass:: toctableopencv
+
+ =========== ======================================================
+ |bind1| Learn how OpenCV-Python bindings are generated.
+
+ =========== ======================================================
+
+ .. |bind1| image:: images/nlm_icon.jpg
+ :height: 90pt
+ :width: 90pt
+
+
+
+
+
+.. raw:: latex
+
+ \pagebreak
+
+.. We use a custom table of content format and as the table of content only informs Sphinx about the hierarchy of the files, no need to show it.
+.. toctree::
+ :hidden:
+
+ ../py_bindings_basics/py_bindings_basics
:alt: OD Icon
+* :ref:`PY_Table-Of-Content-Bindings`
+
+ .. tabularcolumns:: m{100pt} m{300pt}
+ .. cssclass:: toctableopencv
+
+ =========== =====================================================================
+ |PyBin| In this section, we will see how OpenCV-Python bindings are generated
+
+ =========== =====================================================================
+
+ .. |PyBin| image:: images/obj_icon.jpg
+ :height: 80pt
+ :width: 80pt
+ :alt: OD Icon
.. raw:: latex
py_ml/py_table_of_contents_ml/py_table_of_contents_ml
py_photo/py_table_of_contents_photo/py_table_of_contents_photo
py_objdetect/py_table_of_contents_objdetect/py_table_of_contents_objdetect
+ py_bindings/py_table_of_contents_bindings/py_table_of_contents_bindings
--- /dev/null
+.. _Raster_IO_GDAL:
+
+
+Reading Geospatial Raster files with GDAL
+*****************************************
+
+Geospatial raster data is a heavily used product in Geographic Information
+Systems and Photogrammetry. Raster data typically can represent imagery
+and Digital Elevation Models (DEM). The standard library for loading
+GIS imagery is the Geographic Data Abstraction Library (GDAL). In this example, we
+will show techniques for loading GIS raster formats using native OpenCV functions.
+In addition, we will show some an example of how OpenCV can use this data for
+novel and interesting purposes.
+
+Goals
+=====
+
+The primary objectives for this tutorial:
+
+.. container:: enumeratevisibleitemswithsquare
+
+ + How to use OpenCV imread to load satellite imagery.
+ + How to use OpenCV imread to load SRTM Digital Elevation Models
+ + Given the corner coordinates of both the image and DEM, correllate the elevation data to the image to find elevations for each pixel.
+ + Show a basic, easy-to-implement example of a terrain heat map.
+ + Show a basic use of DEM data coupled with ortho-rectified imagery.
+
+To implement these goals, the following code takes a Digital Elevation Model as well as a GeoTiff image of San Francisco as input.
+The image and DEM data is processed and generates a terrain heat map of the image as well as labels areas of the city which would
+be affected should the water level of the bay rise 10, 50, and 100 meters.
+
+Code
+====
+
+.. literalinclude:: ../../../../samples/cpp/tutorial_code/HighGUI/GDAL_IO/gdal-image.cpp
+ :language: cpp
+ :linenos:
+ :tab-width: 4
+
+
+How to Read Raster Data using GDAL
+======================================
+
+This demonstration uses the default OpenCV :ocv:func:`imread` function. The primary difference is that in order to force GDAL to load the
+image, you must use the appropriate flag.
+
+.. code-block:: cpp
+
+ cv::Mat image = cv::imread( argv[1], cv::IMREAD_LOAD_GDAL );
+
+When loading digital elevation models, the actual numeric value of each pixel is essential
+and cannot be scaled or truncated. For example, with image data a pixel represented as a double with a value of 1 has
+an equal appearance to a pixel which is represented as an unsigned character with a value of 255.
+With terrain data, the pixel value represents the elevation in meters. In order to ensure that OpenCV preserves the native value,
+use the GDAL flag in imread with the ANYDEPTH flag.
+
+.. code-block:: cpp
+
+ cv::Mat dem = cv::imread( argv[2], cv::IMREAD_LOAD_GDAL | cv::IMREAD_ANYDEPTH );
+
+
+If you know beforehand the type of DEM model you are loading, then it may be a safe bet to test the ``Mat::type()`` or ``Mat::depth()``
+using an assert or other mechanism. NASA or DOD specification documents can provide the input types for various
+elevation models. The major types, SRTM and DTED, are both signed shorts.
+
+Notes
+=====
+
+Lat/Lon (Geodetic) Coordinates should normally be avoided
+---------------------------------------------------------
+
+The Geodetic Coordinate System is a spherical coordinate system, meaning that using them with Cartesian mathematics is technically incorrect. This
+demo uses them to increase the readability and is accurate enough to make the point. A better coordinate system would be Universal Transverse Mercator.
+
+Finding the corner coordinates
+------------------------------
+
+One easy method to find the corner coordinates of an image is to use the command-line tool ``gdalinfo``. For imagery which is ortho-rectified and contains
+the projection information, you can use the `USGS EarthExplorer <http://http://earthexplorer.usgs.gov>`_.
+
+.. code-block:: bash
+
+ $> gdalinfo N37W123.hgt
+
+ Driver: SRTMHGT/SRTMHGT File Format
+ Files: N37W123.hgt
+ Size is 3601, 3601
+ Coordinate System is:
+ GEOGCS["WGS 84",
+ DATUM["WGS_1984",
+
+ ... more output ...
+
+ Corner Coordinates:
+ Upper Left (-123.0001389, 38.0001389) (123d 0' 0.50"W, 38d 0' 0.50"N)
+ Lower Left (-123.0001389, 36.9998611) (123d 0' 0.50"W, 36d59'59.50"N)
+ Upper Right (-121.9998611, 38.0001389) (121d59'59.50"W, 38d 0' 0.50"N)
+ Lower Right (-121.9998611, 36.9998611) (121d59'59.50"W, 36d59'59.50"N)
+ Center (-122.5000000, 37.5000000) (122d30' 0.00"W, 37d30' 0.00"N)
+
+ ... more output ...
+
+
+Results
+=======
+
+Below is the output of the program. Use the first image as the input. For the DEM model, download the SRTM file located at the USGS here. `http://dds.cr.usgs.gov/srtm/version2_1/SRTM1/Region_04/N37W123.hgt.zip <http://dds.cr.usgs.gov/srtm/version2_1/SRTM1/Region_04/N37W123.hgt.zip>`_
+
+.. image:: images/output.jpg
+
+.. image:: images/heat-map.jpg
+
+.. image:: images/flood-zone.jpg
:height: 90pt
:width: 90pt
++
+ .. tabularcolumns:: m{100pt} m{300pt}
+ .. cssclass:: toctableopencv
+
+ =============== ======================================================
+ |hGDAL_IO| *Title:* :ref:`Raster_IO_GDAL`
+
+ *Compatibility:* > OpenCV 2.0
+
+ *Author:* |Author_MarvinS|
+
+ Read common GIS Raster and DEM files to display and manipulate geographic data.
+
+ =============== ======================================================
+
+ .. |hGDAL_IO| image:: images/gdal-io.jpg
+ :height: 90pt
+ :width: 90pt
+
+
.. raw:: latex
../trackbar/trackbar
../video-input-psnr-ssim/video-input-psnr-ssim
../video-write/video-write
+ ../raster-gdal/raster_io_gdal
// Draw a bounding box around each face.
for (Rect rect : faceDetections.toArray()) {
- Core.rectangle(image, new Point(rect.x, rect.y), new Point(rect.x + rect.width, rect.y + rect.height), new Scalar(0, 255, 0));
+ Imgproc.rectangle(image, new Point(rect.x, rect.y), new Point(rect.x + rect.width, rect.y + rect.height), new Scalar(0, 255, 0));
}
// Save the visualized detection.
Mat image = _image.getMat(); CvMat c_image = _image.getMat();
int nelems = corners.checkVector(2, CV_32F, true);
CV_Assert(nelems >= 0);
- cvDrawChessboardCorners( &c_image, patternSize, (CvPoint2D32f*)corners.data,
+ cvDrawChessboardCorners( &c_image, patternSize, corners.ptr<CvPoint2D32f>(),
nelems, patternWasFound );
}
int ni1 = imgpt1.checkVector(2, CV_32F);
CV_Assert( ni > 0 && ni == ni1 );
npoints.at<int>(i) = ni;
- memcpy( objPtData + j, objpt.data, ni*sizeof(objPtData[0]) );
- memcpy( imgPtData1 + j, imgpt1.data, ni*sizeof(imgPtData1[0]) );
+ memcpy( objPtData + j, objpt.ptr(), ni*sizeof(objPtData[0]) );
+ memcpy( imgPtData1 + j, imgpt1.ptr(), ni*sizeof(imgPtData1[0]) );
if( imgPtData2 )
{
Mat imgpt2 = imagePoints2.getMat(i);
int ni2 = imgpt2.checkVector(2, CV_32F);
CV_Assert( ni == ni2 );
- memcpy( imgPtData2 + j, imgpt2.data, ni*sizeof(imgPtData2[0]) );
+ memcpy( imgPtData2 + j, imgpt2.ptr(), ni*sizeof(imgPtData2[0]) );
}
}
}
{
_rvecs.create(3, 1, CV_64F, i, true);
Mat rv = _rvecs.getMat(i);
- memcpy(rv.data, rvecM.ptr<double>(i), 3*sizeof(double));
+ memcpy(rv.ptr(), rvecM.ptr<double>(i), 3*sizeof(double));
}
if( tvecs_needed )
{
_tvecs.create(3, 1, CV_64F, i, true);
Mat tv = _tvecs.getMat(i);
- memcpy(tv.data, tvecM.ptr<double>(i), 3*sizeof(double));
+ memcpy(tv.ptr(), tvecM.ptr<double>(i), 3*sizeof(double));
}
}
cameraMatrix.copyTo(_cameraMatrix);
if( _eulerAngles.needed() )
{
_eulerAngles.create(3, 1, CV_64F, -1, true);
- p_eulerAngles = (CvPoint3D64f*)_eulerAngles.getMat().data;
+ p_eulerAngles = _eulerAngles.getMat().ptr<CvPoint3D64f>();
}
cvDecomposeProjectionMatrix(&c_projMatrix, &c_cameraMatrix, &c_rotMatrix,
Mat EE = Mat(Vt.t()).colRange(5, 9) * 1.0;
Mat A(10, 20, CV_64F);
EE = EE.t();
- getCoeffMat((double*)EE.data, (double*)A.data);
+ getCoeffMat(EE.ptr<double>(), A.ptr<double>());
EE = EE.t();
A = A.colRange(0, 10).inv() * A.colRange(10, 20);
cv::Mat Evec = EE.col(0) * xs.back() + EE.col(1) * ys.back() + EE.col(2) * zs.back() + EE.col(3);
Evec /= norm(Evec);
- memcpy(e + count * 9, Evec.data, 9 * sizeof(double));
+ memcpy(e + count * 9, Evec.ptr(), 9 * sizeof(double));
count++;
}
if( depth == CV_32S || depth == CV_32F )
{
- const Point* ptsi = (const Point*)points.data;
- const Point2f* ptsf = (const Point2f*)points.data;
+ const Point* ptsi = points.ptr<Point>();
+ const Point2f* ptsf = points.ptr<Point2f>();
Point3f* dstf = lines.ptr<Point3f>();
for( int i = 0; i < npoints; i++ )
{
}
else
{
- const Point2d* ptsd = (const Point2d*)points.data;
+ const Point2d* ptsd = points.ptr<Point2d>();
Point3d* dstd = lines.ptr<Point3d>();
for( int i = 0; i < npoints; i++ )
{
{
if( cn == 3 )
{
- const Point3i* sptr = (const Point3i*)src.data;
- Point2f* dptr = (Point2f*)dst.data;
+ const Point3i* sptr = src.ptr<Point3i>();
+ Point2f* dptr = dst.ptr<Point2f>();
for( i = 0; i < npoints; i++ )
{
float scale = sptr[i].z != 0 ? 1.f/sptr[i].z : 1.f;
}
else
{
- const Vec4i* sptr = (const Vec4i*)src.data;
- Point3f* dptr = (Point3f*)dst.data;
+ const Vec4i* sptr = src.ptr<Vec4i>();
+ Point3f* dptr = dst.ptr<Point3f>();
for( i = 0; i < npoints; i++ )
{
float scale = sptr[i][3] != 0 ? 1.f/sptr[i][3] : 1.f;
{
if( cn == 3 )
{
- const Point3f* sptr = (const Point3f*)src.data;
- Point2f* dptr = (Point2f*)dst.data;
+ const Point3f* sptr = src.ptr<Point3f>();
+ Point2f* dptr = dst.ptr<Point2f>();
for( i = 0; i < npoints; i++ )
{
float scale = sptr[i].z != 0.f ? 1.f/sptr[i].z : 1.f;
}
else
{
- const Vec4f* sptr = (const Vec4f*)src.data;
- Point3f* dptr = (Point3f*)dst.data;
+ const Vec4f* sptr = src.ptr<Vec4f>();
+ Point3f* dptr = dst.ptr<Point3f>();
for( i = 0; i < npoints; i++ )
{
float scale = sptr[i][3] != 0.f ? 1.f/sptr[i][3] : 1.f;
{
if( cn == 3 )
{
- const Point3d* sptr = (const Point3d*)src.data;
- Point2d* dptr = (Point2d*)dst.data;
+ const Point3d* sptr = src.ptr<Point3d>();
+ Point2d* dptr = dst.ptr<Point2d>();
for( i = 0; i < npoints; i++ )
{
double scale = sptr[i].z != 0. ? 1./sptr[i].z : 1.;
}
else
{
- const Vec4d* sptr = (const Vec4d*)src.data;
- Point3d* dptr = (Point3d*)dst.data;
+ const Vec4d* sptr = src.ptr<Vec4d>();
+ Point3d* dptr = dst.ptr<Point3d>();
for( i = 0; i < npoints; i++ )
{
double scale = sptr[i][3] != 0.f ? 1./sptr[i][3] : 1.;
{
if( cn == 2 )
{
- const Point2i* sptr = (const Point2i*)src.data;
- Point3i* dptr = (Point3i*)dst.data;
+ const Point2i* sptr = src.ptr<Point2i>();
+ Point3i* dptr = dst.ptr<Point3i>();
for( i = 0; i < npoints; i++ )
dptr[i] = Point3i(sptr[i].x, sptr[i].y, 1);
}
else
{
- const Point3i* sptr = (const Point3i*)src.data;
- Vec4i* dptr = (Vec4i*)dst.data;
+ const Point3i* sptr = src.ptr<Point3i>();
+ Vec4i* dptr = dst.ptr<Vec4i>();
for( i = 0; i < npoints; i++ )
dptr[i] = Vec4i(sptr[i].x, sptr[i].y, sptr[i].z, 1);
}
{
if( cn == 2 )
{
- const Point2f* sptr = (const Point2f*)src.data;
- Point3f* dptr = (Point3f*)dst.data;
+ const Point2f* sptr = src.ptr<Point2f>();
+ Point3f* dptr = dst.ptr<Point3f>();
for( i = 0; i < npoints; i++ )
dptr[i] = Point3f(sptr[i].x, sptr[i].y, 1.f);
}
else
{
- const Point3f* sptr = (const Point3f*)src.data;
- Vec4f* dptr = (Vec4f*)dst.data;
+ const Point3f* sptr = src.ptr<Point3f>();
+ Vec4f* dptr = dst.ptr<Vec4f>();
for( i = 0; i < npoints; i++ )
dptr[i] = Vec4f(sptr[i].x, sptr[i].y, sptr[i].z, 1.f);
}
{
if( cn == 2 )
{
- const Point2d* sptr = (const Point2d*)src.data;
- Point3d* dptr = (Point3d*)dst.data;
+ const Point2d* sptr = src.ptr<Point2d>();
+ Point3d* dptr = dst.ptr<Point3d>();
for( i = 0; i < npoints; i++ )
dptr[i] = Point3d(sptr[i].x, sptr[i].y, 1.);
}
else
{
- const Point3d* sptr = (const Point3d*)src.data;
- Vec4d* dptr = (Vec4d*)dst.data;
+ const Point3d* sptr = src.ptr<Point3d>();
+ Vec4d* dptr = dst.ptr<Vec4d>();
for( i = 0; i < npoints; i++ )
dptr[i] = Vec4d(sptr[i].x, sptr[i].y, sptr[i].z, 1.);
}
int d1 = m1.channels() > 1 ? m1.channels() : m1.cols;
int d2 = m2.channels() > 1 ? m2.channels() : m2.cols;
int count = m1.checkVector(d1), count2 = m2.checkVector(d2);
- const int *m1ptr = (const int*)m1.data, *m2ptr = (const int*)m2.data;
+ const int *m1ptr = m1.ptr<int>(), *m2ptr = m2.ptr<int>();
ms1.create(modelPoints, 1, CV_MAKETYPE(m1.depth(), d1));
ms2.create(modelPoints, 1, CV_MAKETYPE(m2.depth(), d2));
- int *ms1ptr = (int*)ms1.data, *ms2ptr = (int*)ms2.data;
+ int *ms1ptr = ms1.ptr<int>(), *ms2ptr = ms2.ptr<int>();
CV_Assert( count >= modelPoints && count == count2 );
CV_Assert( (esz1 % sizeof(int)) == 0 && (esz2 % sizeof(int)) == 0 );
else
errf = err;
CV_Assert( errf.isContinuous() && errf.type() == CV_32F && (int)errf.total() == count );
- std::sort((int*)errf.data, (int*)errf.data + count);
+ std::sort(errf.ptr<int>(), errf.ptr<int>() + count);
double median = count % 2 != 0 ?
errf.at<float>(count/2) : (errf.at<float>(count/2-1) + errf.at<float>(count/2))*0.5;
int scale_g = winsize*winsize/8, scale_s = (1024 + scale_g)/(scale_g*2);
const int OFS = 256*5, TABSZ = OFS*2 + 256;
uchar tab[TABSZ];
- const uchar* sptr = src.data;
+ const uchar* sptr = src.ptr();
int srcstep = (int)src.step;
Size size = src.size();
ushort *sad, *hsad0, *hsad, *hsad_sub;
int *htext;
uchar *cbuf0, *cbuf;
- const uchar* lptr0 = left.data + lofs;
- const uchar* rptr0 = right.data + rofs;
+ const uchar* lptr0 = left.ptr() + lofs;
+ const uchar* rptr0 = right.ptr() + rofs;
const uchar *lptr, *lptr_sub, *rptr;
- short* dptr = (short*)disp.data;
+ short* dptr = disp.ptr<short>();
int sstep = (int)left.step;
int dstep = (int)(disp.step/sizeof(dptr[0]));
int cstep = (height + dy0 + dy1)*ndisp;
for( x = 0; x < width1; x++, dptr++ )
{
- short* costptr = cost.data ? (short*)cost.data + lofs + x : &costbuf;
+ short* costptr = cost.data ? cost.ptr<short>() + lofs + x : &costbuf;
int x0 = x - wsz2 - 1, x1 = x + wsz2;
const uchar* cbuf_sub = cbuf0 + ((x0 + wsz2 + 1) % (wsz + 1))*cstep - dy0*ndisp;
cbuf = cbuf0 + ((x1 + wsz2 + 1) % (wsz + 1))*cstep - dy0*ndisp;
int *sad, *hsad0, *hsad, *hsad_sub, *htext;
uchar *cbuf0, *cbuf;
- const uchar* lptr0 = left.data + lofs;
- const uchar* rptr0 = right.data + rofs;
+ const uchar* lptr0 = left.ptr() + lofs;
+ const uchar* rptr0 = right.ptr() + rofs;
const uchar *lptr, *lptr_sub, *rptr;
- short* dptr = (short*)disp.data;
+ short* dptr = disp.ptr<short>();
int sstep = (int)left.step;
int dstep = (int)(disp.step/sizeof(dptr[0]));
int cstep = (height+dy0+dy1)*ndisp;
for( x = 0; x < width1; x++, dptr++ )
{
- int* costptr = cost.data ? (int*)cost.data + lofs + x : &costbuf;
+ int* costptr = cost.data ? cost.ptr<int>() + lofs + x : &costbuf;
int x0 = x - wsz2 - 1, x1 = x + wsz2;
const uchar* cbuf_sub = cbuf0 + ((x0 + wsz2 + 1) % (wsz + 1))*cstep - dy0*ndisp;
cbuf = cbuf0 + ((x1 + wsz2 + 1) % (wsz + 1))*cstep - dy0*ndisp;
int cols = left->cols, rows = left->rows;
int _row0 = std::min(cvRound(range.start * rows / nstripes), rows);
int _row1 = std::min(cvRound(range.end * rows / nstripes), rows);
- uchar *ptr = slidingSumBuf->data + range.start * stripeBufSize;
+ uchar *ptr = slidingSumBuf->ptr() + range.start * stripeBufSize;
int FILTERED = (state->minDisparity - 1)*16;
Rect roi = validDisparityRect & Rect(0, _row0, cols, _row1 - _row0);
if( slidingSumBuf.cols < bufSize )
slidingSumBuf.create( 1, bufSize, CV_8U );
- uchar *_buf = slidingSumBuf.data;
+ uchar *_buf = slidingSumBuf.ptr();
parallel_for_(Range(0, 2), PrefilterInvoker(left0, right0, left, right, _buf, _buf + bufSize1, ¶ms), 1);
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() ||
+ if( buffer.empty() || !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* Cbuf = (CostType*)alignPtr(buffer.ptr(), ALIGN);
CostType* Sbuf = Cbuf + CSBufSize;
CostType* hsumBuf = Sbuf + CSBufSize;
CostType* pixDiff = hsumBuf + costBufSize*hsumBufNRows;
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 )
+ if( !_buf.isContinuous() || _buf.empty() || _buf.cols*_buf.rows*_buf.elemSize() < bufSize )
_buf.create(1, (int)bufSize, CV_8U);
- uchar* buf = _buf.data;
+ uchar* buf = _buf.ptr();
int i, j, dstep = (int)(img.step/sizeof(T));
int* labels = (int*)buf;
buf += npixels*sizeof(labels[0]);
if ((int)status >= 0)
{
if (type == CV_8UC1)
- status = ippiMarkSpeckles_8u_C1IR((Ipp8u *)img.data, (int)img.step, roisize,
+ status = ippiMarkSpeckles_8u_C1IR(img.ptr<Ipp8u>(), (int)img.step, roisize,
(Ipp8u)newVal, maxSpeckleSize, (Ipp8u)maxDiff, ippiNormL1, buffer);
else
- status = ippiMarkSpeckles_16s_C1IR((Ipp16s *)img.data, (int)img.step, roisize,
+ status = ippiMarkSpeckles_16s_C1IR(img.ptr<Ipp16s>(), (int)img.step, roisize,
(Ipp16s)newVal, maxSpeckleSize, (Ipp16s)maxDiff, ippiNormL1, buffer);
}
flags );
assert( cameraMatrix.type() == CV_64FC1 );
- memcpy( _cameraMatrix, cameraMatrix.data, 9*sizeof(double) );
+ memcpy( _cameraMatrix, cameraMatrix.ptr(), 9*sizeof(double) );
assert( cameraMatrix.type() == CV_64FC1 );
- memcpy( _distortionCoeffs, distCoeffs.data, 4*sizeof(double) );
+ memcpy( _distortionCoeffs, distCoeffs.ptr(), 4*sizeof(double) );
vector<Mat>::iterator rvecsIt = rvecs.begin();
vector<Mat>::iterator tvecsIt = tvecs.begin();
{
Mat r9( 3, 3, CV_64FC1 );
Rodrigues( *rvecsIt, r9 );
- memcpy( rm, r9.data, 9*sizeof(double) );
- memcpy( tm, tvecsIt->data, 3*sizeof(double) );
+ memcpy( rm, r9.ptr(), 9*sizeof(double) );
+ memcpy( tm, tvecsIt->ptr(), 3*sizeof(double) );
}
}
{
Mat left = imread(imglist[i*2]);
Mat right = imread(imglist[i*2+1]);
- if(!left.data || !right.data)
+ if(left.empty() || right.empty())
{
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 );
for( int i = 0, ni = 0, j = 0; i < nimages; i++, j += ni )
{
ni = (int)objectPoints[i].size();
- ((int*)npoints.data)[i] = ni;
+ npoints.ptr<int>()[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);
cv::gemm( T, invA2, 1, Mat(), 0, F0 );
F0 *= 1./f0[8];
- uchar* status = test_mat[TEMP][1].data;
+ uchar* status = test_mat[TEMP][1].ptr();
double err_level = method <= CV_FM_8POINT ? 1 : get_success_error_level( test_case_idx, OUTPUT, 1 );
- uchar* mtfm1 = test_mat[REF_OUTPUT][1].data;
- uchar* mtfm2 = test_mat[OUTPUT][1].data;
- double* f_prop1 = (double*)test_mat[REF_OUTPUT][0].data;
- double* f_prop2 = (double*)test_mat[OUTPUT][0].data;
+ uchar* mtfm1 = test_mat[REF_OUTPUT][1].ptr();
+ uchar* mtfm2 = test_mat[OUTPUT][1].ptr();
+ double* f_prop1 = test_mat[REF_OUTPUT][0].ptr<double>();
+ double* f_prop2 = test_mat[OUTPUT][0].ptr<double>();
int i, pt_count = test_mat[INPUT][2].cols;
Mat p1( 1, pt_count, CV_64FC2 );
cv::gemm( T1, T2, 1, Mat(), 0, F0 );
F0 *= 1./f0[8];
- uchar* status = test_mat[TEMP][1].data;
+ uchar* status = test_mat[TEMP][1].ptr();
double err_level = get_success_error_level( test_case_idx, OUTPUT, 1 );
- uchar* mtfm1 = test_mat[REF_OUTPUT][1].data;
- uchar* mtfm2 = test_mat[OUTPUT][1].data;
- double* e_prop1 = (double*)test_mat[REF_OUTPUT][0].data;
- double* e_prop2 = (double*)test_mat[OUTPUT][0].data;
+ uchar* mtfm1 = test_mat[REF_OUTPUT][1].ptr();
+ uchar* mtfm2 = test_mat[OUTPUT][1].ptr();
+ double* e_prop1 = test_mat[REF_OUTPUT][0].ptr<double>();
+ double* e_prop2 = test_mat[OUTPUT][0].ptr<double>();
Mat E_prop2 = Mat(3, 1, CV_64F, e_prop2);
int i, pt_count = test_mat[INPUT][2].cols;
- double* pose_prop1 = (double*)test_mat[REF_OUTPUT][2].data;
- double* pose_prop2 = (double*)test_mat[OUTPUT][2].data;
+ double* pose_prop1 = test_mat[REF_OUTPUT][2].ptr<double>();
+ double* pose_prop2 = test_mat[OUTPUT][2].ptr<double>();
double terr1 = cvtest::norm(Rt0.col(3) / norm(Rt0.col(3)) + test_mat[TEMP][3], NORM_L2);
double terr2 = cvtest::norm(Rt0.col(3) / norm(Rt0.col(3)) - test_mat[TEMP][3], NORM_L2);
Mat rvec;
Mat_<double> res = Q * Mat_<double>(4, 1, from);
res /= res(3, 0);
- out3d_t pixel_exp = *(Vec3d*)res.data;
+ out3d_t pixel_exp = *res.ptr<Vec3d>();
out3d_t pixel_out = _3dImg(y, x);
const int largeZValue = 10000; /* see documentation */
old_basic_structures
dynamic_structures
operations_on_arrays
- drawing_functions
xml_yaml_persistence
old_xml_yaml_persistence
clustering
utility_and_system_functions_and_macros
opengl_interop
ipp_async_converters
+ optim
borderInterpolate(-5, img.cols, BORDER_WRAP));
-Normally, the function is not called directly. It is used inside :ocv:class:`FilterEngine`
-and :ocv:func:`copyMakeBorder` to compute tables for quick extrapolation.
+Normally, the function is not called directly. It is used inside filtering functions
+and also in :ocv:func:`copyMakeBorder`.
.. seealso::
- :ocv:class:`FilterEngine`,
:ocv:func:`copyMakeBorder`
The function copies the source image into the middle of the destination image. The areas to the
left, to the right, above and below the copied source image will be filled with extrapolated pixels.
-This is not what :ocv:class:`FilterEngine` or filtering functions based on it do (they extrapolate
+This is not what filtering functions based on it do (they extrapolate
pixels on-fly), but what other more complex functions, including your own, may do to simplify image
boundary handling.
--- /dev/null
+Optimization Algorithms
+=======================
+
+.. highlight:: cpp
+
+The algorithms in this section minimize or maximize function value within specified constraints or without any constraints.
+
+solveLP
+--------------------
+Solve given (non-integer) linear programming problem using the Simplex Algorithm (Simplex Method).
+What we mean here by "linear programming problem" (or LP problem, for short) can be
+formulated as:
+
+.. math::
+ \mbox{Maximize } c\cdot x\\
+ \mbox{Subject to:}\\
+ Ax\leq b\\
+ x\geq 0
+
+Where :math:`c` is fixed *1*-by-*n* row-vector, :math:`A` is fixed *m*-by-*n* matrix, :math:`b` is fixed *m*-by-*1* column vector and
+:math:`x` is an arbitrary *n*-by-*1* column vector, which satisfies the constraints.
+
+Simplex algorithm is one of many algorithms that are designed to handle this sort of problems efficiently. Although it is not optimal in theoretical
+sense (there exist algorithms that can solve any problem written as above in polynomial type, while simplex method degenerates to exponential time
+for some special cases), it is well-studied, easy to implement and is shown to work well for real-life purposes.
+
+The particular implementation is taken almost verbatim from **Introduction to Algorithms, third edition**
+by T. H. Cormen, C. E. Leiserson, R. L. Rivest and Clifford Stein. In particular, the Bland's rule
+(`http://en.wikipedia.org/wiki/Bland%27s\_rule <http://en.wikipedia.org/wiki/Bland%27s_rule>`_) is used to prevent cycling.
+
+.. ocv:function:: int solveLP(const Mat& Func, const Mat& Constr, Mat& z)
+
+ :param Func: This row-vector corresponds to :math:`c` in the LP problem formulation (see above). It should contain 32- or 64-bit floating point numbers. As a convenience, column-vector may be also submitted, in the latter case it is understood to correspond to :math:`c^T`.
+
+ :param Constr: *m*-by-*n\+1* matrix, whose rightmost column corresponds to :math:`b` in formulation above and the remaining to :math:`A`. It should containt 32- or 64-bit floating point numbers.
+
+ :param z: The solution will be returned here as a column-vector - it corresponds to :math:`c` in the formulation above. It will contain 64-bit floating point numbers.
+
+ :return: One of the return codes:
+
+::
+
+ //!the return codes for solveLP() function
+ enum
+ {
+ SOLVELP_UNBOUNDED = -2, //problem is unbounded (target function can achieve arbitrary high values)
+ SOLVELP_UNFEASIBLE = -1, //problem is unfeasible (there are no points that satisfy all the constraints imposed)
+ SOLVELP_SINGLE = 0, //there is only one maximum for target function
+ SOLVELP_MULTI = 1 //there are multiple maxima for target function - the arbitrary one is returned
+ };
+
+DownhillSolver
+---------------------------------
+
+.. ocv:class:: DownhillSolver
+
+This class is used to perform the non-linear non-constrained *minimization* of a function, defined on an *n*-dimensional Euclidean space,
+using the **Nelder-Mead method**, also known as **downhill simplex method**. The basic idea about the method can be obtained from
+(`http://en.wikipedia.org/wiki/Nelder-Mead\_method <http://en.wikipedia.org/wiki/Nelder-Mead_method>`_). It should be noted, that
+this method, although deterministic, is rather a heuristic and therefore may converge to a local minima, not necessary a global one.
+It is iterative optimization technique, which at each step uses an information about the values of a function evaluated only at
+*n+1* points, arranged as a *simplex* in *n*-dimensional space (hence the second name of the method). At each step new point is
+chosen to evaluate function at, obtained value is compared with previous ones and based on this information simplex changes it's shape
+, slowly moving to the local minimum. Thus this method is using *only* function values to make decision, on contrary to, say, Nonlinear
+Conjugate Gradient method (which is also implemented in ``optim``).
+
+Algorithm stops when the number of function evaluations done exceeds ``termcrit.maxCount``, when the function values at the
+vertices of simplex are within ``termcrit.epsilon`` range or simplex becomes so small that it
+can enclosed in a box with ``termcrit.epsilon`` sides, whatever comes first, for some defined by user
+positive integer ``termcrit.maxCount`` and positive non-integer ``termcrit.epsilon``.
+
+::
+
+ class CV_EXPORTS Solver : public Algorithm
+ {
+ public:
+ class CV_EXPORTS Function
+ {
+ public:
+ virtual ~Function() {}
+ virtual double calc(const double* x) const = 0;
+ virtual void getGradient(const double* /*x*/,double* /*grad*/) {}
+ };
+
+ virtual Ptr<Function> getFunction() const = 0;
+ virtual void setFunction(const Ptr<Function>& f) = 0;
+
+ virtual TermCriteria getTermCriteria() const = 0;
+ virtual void setTermCriteria(const TermCriteria& termcrit) = 0;
+
+ // x contain the initial point before the call and the minima position (if algorithm converged) after. x is assumed to be (something that
+ // after getMat() will return) row-vector or column-vector. *It's size and should
+ // be consisted with previous dimensionality data given, if any (otherwise, it determines dimensionality)*
+ virtual double minimize(InputOutputArray x) = 0;
+ };
+
+ class CV_EXPORTS DownhillSolver : public Solver
+ {
+ public:
+ //! returns row-vector, even if the column-vector was given
+ virtual void getInitStep(OutputArray step) const=0;
+ //!This should be called at least once before the first call to minimize() and step is assumed to be (something that
+ //! after getMat() will return) row-vector or column-vector. *It's dimensionality determines the dimensionality of a problem.*
+ virtual void setInitStep(InputArray step)=0;
+ };
+
+It should be noted, that ``DownhillSolver`` is a derivative of the abstract interface ``Solver``, which in
+turn is derived from the ``Algorithm`` interface and is used to encapsulate the functionality, common to all non-linear optimization
+algorithms in the ``optim`` module.
+
+DownhillSolver::getFunction
+--------------------------------------------
+
+Getter for the optimized function. The optimized function is represented by ``Solver::Function`` interface, which requires
+derivatives to implement the sole method ``calc(double*)`` to evaluate the function.
+
+.. ocv:function:: Ptr<Solver::Function> DownhillSolver::getFunction()
+
+ :return: Smart-pointer to an object that implements ``Solver::Function`` interface - it represents the function that is being optimized. It can be empty, if no function was given so far.
+
+DownhillSolver::setFunction
+-----------------------------------------------
+
+Setter for the optimized function. *It should be called at least once before the call to* ``DownhillSolver::minimize()``, as
+default value is not usable.
+
+.. ocv:function:: void DownhillSolver::setFunction(const Ptr<Solver::Function>& f)
+
+ :param f: The new function to optimize.
+
+DownhillSolver::getTermCriteria
+----------------------------------------------------
+
+Getter for the previously set terminal criteria for this algorithm.
+
+.. ocv:function:: TermCriteria DownhillSolver::getTermCriteria()
+
+ :return: Deep copy of the terminal criteria used at the moment.
+
+DownhillSolver::setTermCriteria
+------------------------------------------
+
+Set terminal criteria for downhill simplex method. Two things should be noted. First, this method *is not necessary* to be called
+before the first call to ``DownhillSolver::minimize()``, as the default value is sensible. Second, the method will raise an error
+if ``termcrit.type!=(TermCriteria::MAX_ITER+TermCriteria::EPS)``, ``termcrit.epsilon<=0`` or ``termcrit.maxCount<=0``. That is,
+both ``epsilon`` and ``maxCount`` should be set to positive values (non-integer and integer respectively) and they represent
+tolerance and maximal number of function evaluations that is allowed.
+
+Algorithm stops when the number of function evaluations done exceeds ``termcrit.maxCount``, when the function values at the
+vertices of simplex are within ``termcrit.epsilon`` range or simplex becomes so small that it
+can enclosed in a box with ``termcrit.epsilon`` sides, whatever comes first.
+
+.. ocv:function:: void DownhillSolver::setTermCriteria(const TermCriteria& termcrit)
+
+ :param termcrit: Terminal criteria to be used, represented as ``TermCriteria`` structure (defined elsewhere in openCV). Mind you, that it should meet ``(termcrit.type==(TermCriteria::MAX_ITER+TermCriteria::EPS) && termcrit.epsilon>0 && termcrit.maxCount>0)``, otherwise the error will be raised.
+
+DownhillSolver::getInitStep
+-----------------------------------
+
+Returns the initial step that will be used in downhill simplex algorithm. See the description
+of corresponding setter (follows next) for the meaning of this parameter.
+
+.. ocv:function:: void getInitStep(OutputArray step)
+
+ :param step: Initial step that will be used in algorithm. Note, that although corresponding setter accepts column-vectors as well as row-vectors, this method will return a row-vector.
+
+DownhillSolver::setInitStep
+----------------------------------
+
+Sets the initial step that will be used in downhill simplex algorithm. Step, together with initial point (givin in ``DownhillSolver::minimize``)
+are two *n*-dimensional vectors that are used to determine the shape of initial simplex. Roughly said, initial point determines the position
+of a simplex (it will become simplex's centroid), while step determines the spread (size in each dimension) of a simplex. To be more precise,
+if :math:`s,x_0\in\mathbb{R}^n` are the initial step and initial point respectively, the vertices of a simplex will be: :math:`v_0:=x_0-\frac{1}{2}
+s` and :math:`v_i:=x_0+s_i` for :math:`i=1,2,\dots,n` where :math:`s_i` denotes projections of the initial step of *n*-th coordinate (the result
+of projection is treated to be vector given by :math:`s_i:=e_i\cdot\left<e_i\cdot s\right>`, where :math:`e_i` form canonical basis)
+
+.. ocv:function:: void setInitStep(InputArray step)
+
+ :param step: Initial step that will be used in algorithm. Roughly said, it determines the spread (size in each dimension) of an initial simplex.
+
+DownhillSolver::minimize
+-----------------------------------
+
+The main method of the ``DownhillSolver``. It actually runs the algorithm and performs the minimization. The sole input parameter determines the
+centroid of the starting simplex (roughly, it tells where to start), all the others (terminal criteria, initial step, function to be minimized)
+are supposed to be set via the setters before the call to this method or the default values (not always sensible) will be used.
+
+.. ocv:function:: double DownhillSolver::minimize(InputOutputArray x)
+
+ :param x: The initial point, that will become a centroid of an initial simplex. After the algorithm will terminate, it will be setted to the point where the algorithm stops, the point of possible minimum.
+
+ :return: The value of a function at the point found.
+
+createDownhillSolver
+------------------------------------
+
+This function returns the reference to the ready-to-use ``DownhillSolver`` object. All the parameters are optional, so this procedure can be called
+even without parameters at all. In this case, the default values will be used. As default value for terminal criteria are the only sensible ones,
+``DownhillSolver::setFunction()`` and ``DownhillSolver::setInitStep()`` should be called upon the obtained object, if the respective parameters
+were not given to ``createDownhillSolver()``. Otherwise, the two ways (give parameters to ``createDownhillSolver()`` or miss them out and call the
+``DownhillSolver::setFunction()`` and ``DownhillSolver::setInitStep()``) are absolutely equivalent (and will drop the same errors in the same way,
+should invalid input be detected).
+
+.. ocv:function:: Ptr<DownhillSolver> createDownhillSolver(const Ptr<Solver::Function>& f,InputArray initStep, TermCriteria termcrit)
+
+ :param f: Pointer to the function that will be minimized, similarly to the one you submit via ``DownhillSolver::setFunction``.
+ :param step: Initial step, that will be used to construct the initial simplex, similarly to the one you submit via ``DownhillSolver::setInitStep``.
+ :param termcrit: Terminal criteria to the algorithm, similarly to the one you submit via ``DownhillSolver::setTermCriteria``.
+
+
+ConjGradSolver
+---------------------------------
+
+.. ocv:class:: ConjGradSolver
+
+This class is used to perform the non-linear non-constrained *minimization* of a function with *known gradient*
+, defined on an *n*-dimensional Euclidean space,
+using the **Nonlinear Conjugate Gradient method**. The implementation was done based on the beautifully clear explanatory article `An Introduction to the Conjugate Gradient Method Without the Agonizing Pain <http://www.cs.cmu.edu/~quake-papers/painless-conjugate-gradient.pdf>`_
+by Jonathan Richard Shewchuk. The method can be seen as an adaptation of a standard Conjugate Gradient method (see, for example
+`http://en.wikipedia.org/wiki/Conjugate_gradient_method <http://en.wikipedia.org/wiki/Conjugate_gradient_method>`_) for numerically solving the
+systems of linear equations.
+
+It should be noted, that
+this method, although deterministic, is rather a heuristic method and therefore may converge to a local minima, not necessary a global one. What
+is even more disastrous, most of its behaviour is ruled by gradient, therefore it essentially cannot distinguish between local minima and maxima.
+Therefore, if it starts sufficiently near to the local maximum, it may converge to it. Another obvious restriction is that it should be possible
+to compute the gradient of a function at any point, thus it is preferable to have analytic expression for gradient and computational burden
+should be born by the user.
+
+The latter responsibility is accompilished via the ``getGradient(const double* x,double* grad)`` method of a
+``Solver::Function`` interface (which represents function that is being optimized). This method takes point a point in *n*-dimensional space
+(first argument represents the array of coordinates of that point) and comput its gradient (it should be stored in the second argument as an array).
+
+ ::
+
+ class CV_EXPORTS Solver : public Algorithm
+ {
+ public:
+ class CV_EXPORTS Function
+ {
+ public:
+ virtual ~Function() {}
+ virtual double calc(const double* x) const = 0;
+ virtual void getGradient(const double* /*x*/,double* /*grad*/) {}
+ };
+
+ virtual Ptr<Function> getFunction() const = 0;
+ virtual void setFunction(const Ptr<Function>& f) = 0;
+
+ virtual TermCriteria getTermCriteria() const = 0;
+ virtual void setTermCriteria(const TermCriteria& termcrit) = 0;
+
+ // x contain the initial point before the call and the minima position (if algorithm converged) after. x is assumed to be (something that
+ // after getMat() will return) row-vector or column-vector. *It's size and should
+ // be consisted with previous dimensionality data given, if any (otherwise, it determines dimensionality)*
+ virtual double minimize(InputOutputArray x) = 0;
+ };
+
+ class CV_EXPORTS ConjGradSolver : public Solver{
+ };
+
+ Note, that class ``ConjGradSolver`` thus does not add any new methods to the basic ``Solver`` interface.
+
+ConjGradSolver::getFunction
+--------------------------------------------
+
+Getter for the optimized function. The optimized function is represented by ``Solver::Function`` interface, which requires
+derivatives to implement the method ``calc(double*)`` to evaluate the function. It should be emphasized once more, that since Nonlinear
+Conjugate Gradient method requires gradient to be computable in addition to the function values,
+``getGradient(const double* x,double* grad)`` method of a ``Solver::Function`` interface should be also implemented meaningfully.
+
+.. ocv:function:: Ptr<Solver::Function> ConjGradSolver::getFunction()
+
+ :return: Smart-pointer to an object that implements ``Solver::Function`` interface - it represents the function that is being optimized. It can be empty, if no function was given so far.
+
+ConjGradSolver::setFunction
+-----------------------------------------------
+
+Setter for the optimized function. *It should be called at least once before the call to* ``ConjGradSolver::minimize()``, as
+default value is not usable.
+
+.. ocv:function:: void ConjGradSolver::setFunction(const Ptr<Solver::Function>& f)
+
+ :param f: The new function to optimize.
+
+ConjGradSolver::getTermCriteria
+----------------------------------------------------
+
+Getter for the previously set terminal criteria for this algorithm.
+
+.. ocv:function:: TermCriteria ConjGradSolver::getTermCriteria()
+
+ :return: Deep copy of the terminal criteria used at the moment.
+
+ConjGradSolver::setTermCriteria
+------------------------------------------
+
+Set terminal criteria for downhill simplex method. Two things should be noted. First, this method *is not necessary* to be called
+before the first call to ``ConjGradSolver::minimize()``, as the default value is sensible. Second, the method will raise an error
+if ``termcrit.type!=(TermCriteria::MAX_ITER+TermCriteria::EPS)`` and ``termcrit.type!=TermCriteria::MAX_ITER``. This means that termination criteria
+has to restrict maximum number of iterations to be done and may optionally allow algorithm to stop earlier if certain tolerance
+is achieved (what we mean by "tolerance is achieved" will be clarified below). If ``termcrit`` restricts both tolerance and maximum iteration
+number, both ``termcrit.epsilon`` and ``termcrit.maxCount`` should be positive. In case, if ``termcrit.type==TermCriteria::MAX_ITER``,
+only member ``termcrit.maxCount`` is required to be positive and in this case algorithm will just work for required number of iterations.
+
+In current implementation, "tolerance is achieved" means that we have arrived at the point where the :math:`L_2`-norm of the gradient is less
+than the tolerance value.
+
+.. ocv:function:: void ConjGradSolver::setTermCriteria(const TermCriteria& termcrit)
+
+ :param termcrit: Terminal criteria to be used, represented as ``TermCriteria`` structure (defined elsewhere in openCV). Mind you, that it should meet ``termcrit.type==(TermCriteria::MAX_ITER+TermCriteria::EPS) && termcrit.epsilon>0 && termcrit.maxCount>0`` or ``termcrit.type==TermCriteria::MAX_ITER) && termcrit.maxCount>0``, otherwise the error will be raised.
+
+ConjGradSolver::minimize
+-----------------------------------
+
+The main method of the ``ConjGradSolver``. It actually runs the algorithm and performs the minimization. The sole input parameter determines the
+centroid of the starting simplex (roughly, it tells where to start), all the others (terminal criteria and function to be minimized)
+are supposed to be set via the setters before the call to this method or the default values (not always sensible) will be used. Sometimes it may
+throw an error, if these default values cannot be used (say, you forgot to set the function to minimize and default value, that is, empty function,
+cannot be used).
+
+.. ocv:function:: double ConjGradSolver::minimize(InputOutputArray x)
+
+ :param x: The initial point. It is hard to overemphasize how important the choise of initial point is when you are using the heuristic algorithm like this one. Badly chosen initial point can make algorithm converge to (local) maximum instead of minimum, do not converge at all, converge to local minimum instead of global one.
+
+ :return: The value of a function at the point found.
+
+createConjGradSolver
+------------------------------------
+
+This function returns the reference to the ready-to-use ``ConjGradSolver`` object. All the parameters are optional, so this procedure can be called
+even without parameters at all. In this case, the default values will be used. As default value for terminal criteria are the only sensible ones,
+``ConjGradSolver::setFunction()`` should be called upon the obtained object, if the function
+was not given to ``createConjGradSolver()``. Otherwise, the two ways (submit it to ``createConjGradSolver()`` or miss it out and call the
+``ConjGradSolver::setFunction()``) are absolutely equivalent (and will drop the same errors in the same way,
+should invalid input be detected).
+
+.. ocv:function:: Ptr<ConjGradSolver> createConjGradSolver(const Ptr<Solver::Function>& f, TermCriteria termcrit)
+
+ :param f: Pointer to the function that will be minimized, similarly to the one you submit via ``ConjGradSolver::setFunction``.
+ :param termcrit: Terminal criteria to the algorithm, similarly to the one you submit via ``ConjGradSolver::setTermCriteria``.
//! shuffles the input array elements
CV_EXPORTS_W void randShuffle(InputOutputArray dst, double iterFactor = 1., RNG* rng = 0);
-//! draws the line segment (pt1, pt2) in the image
-CV_EXPORTS_W void line(InputOutputArray img, Point pt1, Point pt2, const Scalar& color,
- int thickness = 1, int lineType = LINE_8, int shift = 0);
-
-//! draws an arrow from pt1 to pt2 in the image
-CV_EXPORTS_W void arrowedLine(InputOutputArray img, Point pt1, Point pt2, const Scalar& color,
- int thickness=1, int line_type=8, int shift=0, double tipLength=0.1);
-
-//! draws the rectangle outline or a solid rectangle with the opposite corners pt1 and pt2 in the image
-CV_EXPORTS_W void rectangle(InputOutputArray img, Point pt1, Point pt2,
- const Scalar& color, int thickness = 1,
- int lineType = LINE_8, int shift = 0);
-
-//! draws the rectangle outline or a solid rectangle covering rec in the image
-CV_EXPORTS void rectangle(CV_IN_OUT Mat& img, Rect rec,
- const Scalar& color, int thickness = 1,
- int lineType = LINE_8, int shift = 0);
-
-//! draws the circle outline or a solid circle in the image
-CV_EXPORTS_W void circle(InputOutputArray img, Point center, int radius,
- const Scalar& color, int thickness = 1,
- int lineType = LINE_8, int shift = 0);
-
-//! draws an elliptic arc, ellipse sector or a rotated ellipse in the image
-CV_EXPORTS_W void ellipse(InputOutputArray img, Point center, Size axes,
- double angle, double startAngle, double endAngle,
- const Scalar& color, int thickness = 1,
- int lineType = LINE_8, int shift = 0);
-
-//! draws a rotated ellipse in the image
-CV_EXPORTS_W void ellipse(InputOutputArray img, const RotatedRect& box, const Scalar& color,
- int thickness = 1, int lineType = LINE_8);
-
-//! draws a filled convex polygon in the image
-CV_EXPORTS void fillConvexPoly(Mat& img, const Point* pts, int npts,
- const Scalar& color, int lineType = LINE_8,
- int shift = 0);
-
-CV_EXPORTS_W void fillConvexPoly(InputOutputArray img, InputArray points,
- const Scalar& color, int lineType = LINE_8,
- int shift = 0);
-
-//! fills an area bounded by one or more polygons
-CV_EXPORTS void fillPoly(Mat& img, const Point** pts,
- const int* npts, int ncontours,
- const Scalar& color, int lineType = LINE_8, int shift = 0,
- Point offset = Point() );
-
-CV_EXPORTS_W void fillPoly(InputOutputArray img, InputArrayOfArrays pts,
- const Scalar& color, int lineType = LINE_8, int shift = 0,
- Point offset = Point() );
-
-//! draws one or more polygonal curves
-CV_EXPORTS void polylines(Mat& img, const Point* const* pts, const int* npts,
- int ncontours, bool isClosed, const Scalar& color,
- int thickness = 1, int lineType = LINE_8, int shift = 0 );
-
-CV_EXPORTS_W void polylines(InputOutputArray img, InputArrayOfArrays pts,
- bool isClosed, const Scalar& color,
- int thickness = 1, int lineType = LINE_8, int shift = 0 );
-
-//! draws contours in the image
-CV_EXPORTS_W void drawContours( InputOutputArray image, InputArrayOfArrays contours,
- int contourIdx, const Scalar& color,
- int thickness = 1, int lineType = LINE_8,
- InputArray hierarchy = noArray(),
- int maxLevel = INT_MAX, Point offset = Point() );
-
-//! clips the line segment by the rectangle Rect(0, 0, imgSize.width, imgSize.height)
-CV_EXPORTS bool clipLine(Size imgSize, CV_IN_OUT Point& pt1, CV_IN_OUT Point& pt2);
-
-//! clips the line segment by the rectangle imgRect
-CV_EXPORTS_W bool clipLine(Rect imgRect, CV_OUT CV_IN_OUT Point& pt1, CV_OUT CV_IN_OUT Point& pt2);
-
-//! converts elliptic arc to a polygonal curve
-CV_EXPORTS_W void ellipse2Poly( Point center, Size axes, int angle,
- int arcStart, int arcEnd, int delta,
- CV_OUT std::vector<Point>& pts );
-
-//! renders text string in the image
-CV_EXPORTS_W void putText( InputOutputArray img, const String& text, Point org,
- int fontFace, double fontScale, Scalar color,
- int thickness = 1, int lineType = LINE_8,
- bool bottomLeftOrigin = false );
-
-//! returns bounding box of the text string
-CV_EXPORTS_W Size getTextSize(const String& text, int fontFace,
- double fontScale, int thickness,
- CV_OUT int* baseLine);
-
/*!
Principal Component Analysis
#include "opencv2/core/operations.hpp"
#include "opencv2/core/cvstd.inl.hpp"
+#include "opencv2/core/utility.hpp"
+#include "opencv2/core/optim.hpp"
#endif /*__OPENCV_CORE_HPP__*/
/* Creates a copy of graph */
CVAPI(CvGraph*) cvCloneGraph( const CvGraph* graph, CvMemStorage* storage );
-/****************************************************************************************\
-* Drawing *
-\****************************************************************************************/
-
-/****************************************************************************************\
-* Drawing functions work with images/matrices of arbitrary type. *
-* For color images the channel order is BGR[A] *
-* Antialiasing is supported only for 8-bit image now. *
-* All the functions include parameter color that means rgb value (that may be *
-* constructed with CV_RGB macro) for color images and brightness *
-* for grayscale images. *
-* If a drawn figure is partially or completely outside of the image, it is clipped.*
-\****************************************************************************************/
-
-#define CV_RGB( r, g, b ) cvScalar( (b), (g), (r), 0 )
-#define CV_FILLED -1
-
-#define CV_AA 16
-
-/* Draws 4-connected, 8-connected or antialiased line segment connecting two points */
-CVAPI(void) cvLine( CvArr* img, CvPoint pt1, CvPoint pt2,
- CvScalar color, int thickness CV_DEFAULT(1),
- int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0) );
-
-/* Draws a rectangle given two opposite corners of the rectangle (pt1 & pt2),
- if thickness<0 (e.g. thickness == CV_FILLED), the filled box is drawn */
-CVAPI(void) cvRectangle( CvArr* img, CvPoint pt1, CvPoint pt2,
- CvScalar color, int thickness CV_DEFAULT(1),
- int line_type CV_DEFAULT(8),
- int shift CV_DEFAULT(0));
-
-/* Draws a rectangle specified by a CvRect structure */
-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,
- CvScalar color, int thickness CV_DEFAULT(1),
- int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0));
-
-/* Draws ellipse outline, filled ellipse, elliptic arc or filled elliptic sector,
- depending on <thickness>, <start_angle> and <end_angle> parameters. The resultant figure
- is rotated by <angle>. All the angles are in degrees */
-CVAPI(void) cvEllipse( CvArr* img, CvPoint center, CvSize axes,
- double angle, double start_angle, double end_angle,
- CvScalar color, int thickness CV_DEFAULT(1),
- int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0));
-
-CV_INLINE void cvEllipseBox( CvArr* img, CvBox2D box, CvScalar color,
- int thickness CV_DEFAULT(1),
- int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0) )
-{
- CvSize axes;
- axes.width = cvRound(box.size.width*0.5);
- axes.height = cvRound(box.size.height*0.5);
-
- cvEllipse( img, cvPointFrom32f( box.center ), axes, box.angle,
- 0, 360, color, thickness, line_type, shift );
-}
-
-/* Fills convex or monotonous polygon. */
-CVAPI(void) cvFillConvexPoly( CvArr* img, const CvPoint* pts, int npts, CvScalar color,
- int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0));
-
-/* Fills an area bounded by one or more arbitrary polygons */
-CVAPI(void) cvFillPoly( CvArr* img, CvPoint** pts, const int* npts,
- int contours, CvScalar color,
- int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0) );
-
-/* Draws one or more polygonal curves */
-CVAPI(void) cvPolyLine( CvArr* img, CvPoint** pts, const int* npts, int contours,
- int is_closed, CvScalar color, int thickness CV_DEFAULT(1),
- int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0) );
-
-#define cvDrawRect cvRectangle
-#define cvDrawLine cvLine
-#define cvDrawCircle cvCircle
-#define cvDrawEllipse cvEllipse
-#define cvDrawPolyLine cvPolyLine
-
-/* Clips the line segment connecting *pt1 and *pt2
- by the rectangular window
- (0<=x<img_size.width, 0<=y<img_size.height). */
-CVAPI(int) cvClipLine( CvSize img_size, CvPoint* pt1, CvPoint* pt2 );
-
-/* Initializes line iterator. Initially, line_iterator->ptr will point
- to pt1 (or pt2, see left_to_right description) location in the image.
- Returns the number of pixels on the line between the ending points. */
-CVAPI(int) cvInitLineIterator( const CvArr* image, CvPoint pt1, CvPoint pt2,
- CvLineIterator* line_iterator,
- int connectivity CV_DEFAULT(8),
- int left_to_right CV_DEFAULT(0));
-
-/* Moves iterator to the next line point */
-#define CV_NEXT_LINE_POINT( line_iterator ) \
-{ \
- int _line_iterator_mask = (line_iterator).err < 0 ? -1 : 0; \
- (line_iterator).err += (line_iterator).minus_delta + \
- ((line_iterator).plus_delta & _line_iterator_mask); \
- (line_iterator).ptr += (line_iterator).minus_step + \
- ((line_iterator).plus_step & _line_iterator_mask); \
-}
-
-
-/* basic font types */
-#define CV_FONT_HERSHEY_SIMPLEX 0
-#define CV_FONT_HERSHEY_PLAIN 1
-#define CV_FONT_HERSHEY_DUPLEX 2
-#define CV_FONT_HERSHEY_COMPLEX 3
-#define CV_FONT_HERSHEY_TRIPLEX 4
-#define CV_FONT_HERSHEY_COMPLEX_SMALL 5
-#define CV_FONT_HERSHEY_SCRIPT_SIMPLEX 6
-#define CV_FONT_HERSHEY_SCRIPT_COMPLEX 7
-
-/* font flags */
-#define CV_FONT_ITALIC 16
-
-#define CV_FONT_VECTOR0 CV_FONT_HERSHEY_SIMPLEX
-
-
-/* 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 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
-}
-CvFont;
-
-/* Initializes font structure used further in cvPutText */
-CVAPI(void) cvInitFont( CvFont* font, int font_face,
- double hscale, double vscale,
- double shear CV_DEFAULT(0),
- int thickness CV_DEFAULT(1),
- int line_type CV_DEFAULT(8));
-
-CV_INLINE CvFont cvFont( double scale, int thickness CV_DEFAULT(1) )
-{
- CvFont font;
- cvInitFont( &font, CV_FONT_HERSHEY_PLAIN, scale, scale, 0, thickness, CV_AA );
- return font;
-}
-
-/* Renders text stroke with specified font and color at specified location.
- CvFont should be initialized with cvInitFont */
-CVAPI(void) cvPutText( CvArr* img, const char* text, CvPoint org,
- const CvFont* font, CvScalar color );
-
-/* Calculates bounding box of text stroke (useful for alignment) */
-CVAPI(void) cvGetTextSize( const char* text_string, const CvFont* font,
- CvSize* text_size, int* baseline );
-
-
-
-/* Unpacks color value, if arrtype is CV_8UC?, <color> is treated as
- packed color value, otherwise the first channels (depending on arrtype)
- of destination scalar are set to the same value = <color> */
-CVAPI(CvScalar) cvColorToScalar( double packed_color, int arrtype );
-
-/* Returns the polygon points which make up the given ellipse. The ellipse is define by
- the box of size 'axes' rotated 'angle' around the 'center'. A partial sweep
- of the ellipse arc can be done by spcifying arc_start and arc_end to be something
- other than 0 and 360, respectively. The input array 'pts' must be large enough to
- hold the result. The total number of points stored into 'pts' is returned by this
- function. */
-CVAPI(int) cvEllipse2Poly( CvPoint center, CvSize axes,
- int angle, int arc_start, int arc_end, CvPoint * pts, int delta );
-
-/* Draws contour outlines or filled interiors on the image */
-CVAPI(void) cvDrawContours( CvArr *img, CvSeq* contour,
- CvScalar external_color, CvScalar hole_color,
- int max_level, int thickness CV_DEFAULT(1),
- int line_type CV_DEFAULT(8),
- CvPoint offset CV_DEFAULT(cvPoint(0,0)));
/* Does look-up transformation. Elements of the source array
(that should be 8uC1 or 8sC1) are used as indexes in lutarr 256-element table */
int* refcount;
//! helper fields used in locateROI and adjustROI
- const uchar* datastart;
+ uchar* datastart;
const uchar* dataend;
//! allocator
uchar* data;
int* refcount;
- const uchar* datastart;
+ uchar* datastart;
const uchar* dataend;
AllocType alloc_type;
std::ostream& operator << (std::ostream& out, const Vec<_Tp, n>& vec)
{
out << "[";
-
+#ifdef _MSC_VER
+#pragma warning( push )
+#pragma warning( disable: 4127 )
+#endif
if(Vec<_Tp, n>::depth < CV_32F)
+#ifdef _MSC_VER
+#pragma warning( pop )
+#endif
{
for (int i = 0; i < n - 1; ++i) {
out << (int)vec[i] << ", ";
{
if( m && m->isContinuous() )
{
- sliceStart = m->data;
+ sliceStart = m->ptr();
sliceEnd = sliceStart + m->total()*elemSize;
}
seek((const int*)0);
CV_Assert(m && m->dims <= 2);
if( m->isContinuous() )
{
- sliceStart = m->data;
+ sliceStart = m->ptr();
sliceEnd = sliceStart + m->total()*elemSize;
}
int idx[] = {_row, _col};
CV_Assert(m && m->dims <= 2);
if( m->isContinuous() )
{
- sliceStart = m->data;
+ sliceStart = m->ptr();
sliceEnd = sliceStart + m->total()*elemSize;
}
int idx[] = {_pt.y, _pt.x};
#include "opencv2/core.hpp"
-namespace cv{namespace optim
+namespace cv
{
-class CV_EXPORTS Solver : public Algorithm
+
+class CV_EXPORTS MinProblemSolver : public Algorithm
{
public:
class CV_EXPORTS Function
};
//! downhill simplex class
-class CV_EXPORTS DownhillSolver : public Solver
+class CV_EXPORTS DownhillSolver : public MinProblemSolver
{
public:
//! returns row-vector, even if the column-vector was given
//!This should be called at least once before the first call to minimize() and step is assumed to be (something that
//! after getMat() will return) row-vector or column-vector. *It's dimensionality determines the dimensionality of a problem.*
virtual void setInitStep(InputArray step)=0;
-};
-// both minRange & minError are specified by termcrit.epsilon; In addition, user may specify the number of iterations that the algorithm does.
-CV_EXPORTS_W Ptr<DownhillSolver> createDownhillSolver(const Ptr<Solver::Function>& f=Ptr<Solver::Function>(),
- InputArray initStep=Mat_<double>(1,1,0.0),
- TermCriteria termcrit=TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS,5000,0.000001));
+ // both minRange & minError are specified by termcrit.epsilon;
+ // In addition, user may specify the number of iterations that the algorithm does.
+ static Ptr<DownhillSolver> create(const Ptr<MinProblemSolver::Function>& f=Ptr<MinProblemSolver::Function>(),
+ InputArray initStep=Mat_<double>(1,1,0.0),
+ TermCriteria termcrit=TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS,5000,0.000001));
+};
//! conjugate gradient method
-class CV_EXPORTS ConjGradSolver : public Solver{
+class CV_EXPORTS ConjGradSolver : public MinProblemSolver
+{
+public:
+ static Ptr<ConjGradSolver> create(const Ptr<MinProblemSolver::Function>& f=Ptr<ConjGradSolver::Function>(),
+ TermCriteria termcrit=TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS,5000,0.000001));
};
-CV_EXPORTS_W Ptr<ConjGradSolver> createConjGradSolver(const Ptr<Solver::Function>& f=Ptr<ConjGradSolver::Function>(),
- TermCriteria termcrit=TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS,5000,0.000001));
-
//!the return codes for solveLP() function
enum
{
};
CV_EXPORTS_W int solveLP(const Mat& Func, const Mat& Constr, Mat& z);
-CV_EXPORTS_W void denoise_TVL1(const std::vector<Mat>& observations,Mat& result, double lambda=1.0, int niters=30);
-}}// cv
+
+}// cv
#endif
const Size srcSize = get<1>(params);
int flags = get<2>(params);
- int in_cn, out_cn;
+ int in_cn = 0, out_cn = 0;
switch (dft_type)
{
case R2R: flags |= cv::DFT_REAL_OUTPUT; in_cn = 1; out_cn = 1; break;
{
int scn = (int)sc.total(), cn = CV_MAT_CN(buftype);
size_t esz = CV_ELEM_SIZE(buftype);
- getConvertFunc(sc.depth(), buftype)(sc.data, 1, 0, 1, scbuf, 1, Size(std::min(cn, scn), 1), 0);
+ getConvertFunc(sc.depth(), buftype)(sc.ptr(), 1, 0, 1, scbuf, 1, Size(std::min(cn, scn), 1), 0);
// unroll the scalar
if( scn < cn )
{
if( len == (size_t)(int)len )
{
sz.width = (int)len;
- func(src1.data, src1.step, src2.data, src2.step, dst.data, dst.step, sz, 0);
+ func(src1.ptr(), src1.step, src2.ptr(), src2.step, dst.ptr(), dst.step, sz, 0);
return;
}
}
if (!doubleSupport && (depth2 == CV_64F || depth1 == CV_64F))
return false;
- if( (oclop == OCL_OP_MUL_SCALE || oclop == OCL_OP_DIV_SCALE) && (depth1 >= CV_32F || depth2 >= CV_32F || ddepth >= CV_32F) )
- return false;
-
int kercn = haveMask || haveScalar ? cn : ocl::predictOptimalVectorWidth(_src1, _src2, _dst);
int scalarcn = kercn == 3 ? 4 : kercn, rowsPerWI = d.isIntel() ? 4 : 1;
Mat src1 = psrc1->getMat(), src2 = psrc2->getMat(), dst = _dst.getMat();
Size sz = getContinuousSize(src1, src2, dst, src1.channels());
- tab[depth1](src1.data, src1.step, src2.data, src2.step, dst.data, dst.step, sz, usrdata);
+ tab[depth1](src1.ptr(), src1.step, src2.ptr(), src2.step, dst.ptr(), dst.step, sz, usrdata);
return;
}
else
{
double fval = 0;
- getConvertFunc(depth2, CV_64F)(src2.data, 1, 0, 1, (uchar *)&fval, 1, Size(1, 1), 0);
+ getConvertFunc(depth2, CV_64F)(src2.ptr(), 1, 0, 1, (uchar *)&fval, 1, Size(1, 1), 0);
if( fval < getMinVal(depth1) )
return dst.setTo(Scalar::all(op == CMP_GT || op == CMP_GE || op == CMP_NE ? 255 : 0)), true;
_dst.create(src1.size(), CV_8UC(cn));
Mat dst = _dst.getMat();
Size sz = getContinuousSize(src1, src2, dst, src1.channels());
- getCmpFunc(src1.depth())(src1.data, src1.step, src2.data, src2.step, dst.data, dst.step, sz, &op);
+ getCmpFunc(src1.depth())(src1.ptr(), src1.step, src2.ptr(), src2.step, dst.ptr(), dst.step, sz, &op);
return;
}
else
{
double fval=0;
- getConvertFunc(depth2, CV_64F)(src2.data, 1, 0, 1, (uchar*)&fval, 1, Size(1,1), 0);
+ getConvertFunc(depth2, CV_64F)(src2.ptr(), 1, 0, 1, (uchar*)&fval, 1, Size(1,1), 0);
if( fval < getMinVal(depth1) )
{
dst = Scalar::all(op == CMP_GT || op == CMP_GE || op == CMP_NE ? 255 : 0);
int* iubuf = ilbuf + cn;
BinaryFunc sccvtfunc = getConvertFunc(ldepth, CV_32S);
- sccvtfunc(lscalar.data, 1, 0, 1, (uchar*)ilbuf, 1, Size(cn, 1), 0);
- sccvtfunc(uscalar.data, 1, 0, 1, (uchar*)iubuf, 1, Size(cn, 1), 0);
+ sccvtfunc(lscalar.ptr(), 1, 0, 1, (uchar*)ilbuf, 1, Size(cn, 1), 0);
+ sccvtfunc(uscalar.ptr(), 1, 0, 1, (uchar*)iubuf, 1, Size(cn, 1), 0);
int minval = cvRound(getMinVal(sdepth)), maxval = cvRound(getMaxVal(sdepth));
for( int k = 0; k < cn; k++ )
int* iubuf = ilbuf + cn;
BinaryFunc sccvtfunc = getConvertFunc(scdepth, CV_32S);
- sccvtfunc(lb.data, 1, 0, 1, (uchar*)ilbuf, 1, Size(cn, 1), 0);
- sccvtfunc(ub.data, 1, 0, 1, (uchar*)iubuf, 1, Size(cn, 1), 0);
+ sccvtfunc(lb.ptr(), 1, 0, 1, (uchar*)ilbuf, 1, Size(cn, 1), 0);
+ sccvtfunc(ub.ptr(), 1, 0, 1, (uchar*)iubuf, 1, Size(cn, 1), 0);
int minval = cvRound(getMinVal(depth)), maxval = cvRound(getMaxVal(depth));
for( int k = 0; k < cn; k++ )
//M*/
#include "precomp.hpp"
-#undef ALEX_DEBUG
-#include "debug.hpp"
-namespace cv{namespace optim{
+#define dprintf(x)
+#define print_matrix(x)
+
+namespace cv
+{
#define SEC_METHOD_ITERATIONS 4
#define INITIAL_SEC_METHOD_SIGMA 0.1
void setTermCriteria(const TermCriteria& termcrit);
double minimize(InputOutputArray x);
protected:
- Ptr<Solver::Function> _Function;
+ Ptr<MinProblemSolver::Function> _Function;
TermCriteria _termcrit;
Mat_<double> d,r,buf_x,r_old;
Mat_<double> minimizeOnTheLine_buf1,minimizeOnTheLine_buf2;
private:
- static void minimizeOnTheLine(Ptr<Solver::Function> _f,Mat_<double>& x,const Mat_<double>& d,Mat_<double>& buf1,Mat_<double>& buf2);
+ static void minimizeOnTheLine(Ptr<MinProblemSolver::Function> _f,Mat_<double>& x,const Mat_<double>& d,Mat_<double>& buf1,Mat_<double>& buf2);
};
- void ConjGradSolverImpl::minimizeOnTheLine(Ptr<Solver::Function> _f,Mat_<double>& x,const Mat_<double>& d,Mat_<double>& buf1,
+ void ConjGradSolverImpl::minimizeOnTheLine(Ptr<MinProblemSolver::Function> _f,Mat_<double>& x,const Mat_<double>& d,Mat_<double>& buf1,
Mat_<double>& buf2){
double sigma=INITIAL_SEC_METHOD_SIGMA;
buf1=0.0;
Mat_<double> proxy_x;
if(x_mat.rows>1){
buf_x.create(1,ndim);
- Mat_<double> proxy(ndim,1,(double*)buf_x.data);
+ Mat_<double> proxy(ndim,1,buf_x.ptr<double>());
x_mat.copyTo(proxy);
proxy_x=buf_x;
}else{
proxy_x=x_mat;
}
- _Function->getGradient((double*)proxy_x.data,(double*)d.data);
+ _Function->getGradient(proxy_x.ptr<double>(),d.ptr<double>());
d*=-1.0;
d.copyTo(r);
for(int count=0;count<_termcrit.maxCount;count++){
minimizeOnTheLine(_Function,proxy_x,d,minimizeOnTheLine_buf1,minimizeOnTheLine_buf2);
r.copyTo(r_old);
- _Function->getGradient((double*)proxy_x.data,(double*)r.data);
+ _Function->getGradient(proxy_x.ptr<double>(),r.ptr<double>());
r*=-1.0;
double r_norm_sq=norm(r);
if(_termcrit.type==(TermCriteria::MAX_ITER+TermCriteria::EPS) && r_norm_sq<_termcrit.epsilon){
if(x_mat.rows>1){
- Mat(ndim, 1, CV_64F, (double*)proxy_x.data).copyTo(x);
+ Mat(ndim, 1, CV_64F, proxy_x.ptr<double>()).copyTo(x);
}
- return _Function->calc((double*)proxy_x.data);
+ return _Function->calc(proxy_x.ptr<double>());
}
ConjGradSolverImpl::ConjGradSolverImpl(){
_Function=Ptr<Function>();
}
- Ptr<Solver::Function> ConjGradSolverImpl::getFunction()const{
+ Ptr<MinProblemSolver::Function> ConjGradSolverImpl::getFunction()const{
return _Function;
}
void ConjGradSolverImpl::setFunction(const Ptr<Function>& f){
_termcrit=termcrit;
}
// both minRange & minError are specified by termcrit.epsilon; In addition, user may specify the number of iterations that the algorithm does.
- Ptr<ConjGradSolver> createConjGradSolver(const Ptr<Solver::Function>& f, TermCriteria termcrit){
- ConjGradSolver *CG=new ConjGradSolverImpl();
+ Ptr<ConjGradSolver> ConjGradSolver::create(const Ptr<MinProblemSolver::Function>& f, TermCriteria termcrit){
+ Ptr<ConjGradSolver> CG = makePtr<ConjGradSolverImpl>();
CG->setFunction(f);
CG->setTermCriteria(termcrit);
- return Ptr<ConjGradSolver>(CG);
+ return CG;
}
-}}
+}
#include "precomp.hpp"
#include "opencl_kernels_core.hpp"
+#ifdef __APPLE__
+#undef CV_NEON
+#define CV_NEON 0
+#endif
+
namespace cv
{
kercn = ocl::predictOptimalVectorWidth(_src, _dst), rowsPerWI = d.isIntel() ? 4 : 1;
bool doubleSupport = d.doubleFPConfig() > 0;
- if (depth == CV_32F || depth == CV_64F)
+ if (!doubleSupport && depth == CV_64F)
return false;
char cvt[2][50];
int wdepth = std::max(depth, CV_32F);
- ocl::Kernel k("KF", ocl::core::arithm_oclsrc,
- format("-D OP_CONVERT_SCALE_ABS -D UNARY_OP -D dstT=%s -D srcT1=%s"
+ String build_opt = format("-D OP_CONVERT_SCALE_ABS -D UNARY_OP -D dstT=%s -D srcT1=%s"
" -D workT=%s -D wdepth=%d -D convertToWT1=%s -D convertToDT=%s"
" -D workT1=%s -D rowsPerWI=%d%s",
ocl::typeToStr(CV_8UC(kercn)),
ocl::convertTypeStr(depth, wdepth, kercn, cvt[0]),
ocl::convertTypeStr(wdepth, CV_8U, kercn, cvt[1]),
ocl::typeToStr(wdepth), rowsPerWI,
- doubleSupport ? " -D DOUBLE_SUPPORT" : ""));
+ doubleSupport ? " -D DOUBLE_SUPPORT" : "");
+ ocl::Kernel k("KF", ocl::core::arithm_oclsrc, build_opt);
if (k.empty())
return false;
if( src.dims <= 2 )
{
Size sz = getContinuousSize(src, dst, cn);
- func( src.data, src.step, 0, 0, dst.data, dst.step, sz, scale );
+ func( src.ptr(), src.step, 0, 0, dst.ptr(), dst.step, sz, scale );
}
else
{
CV_DbgAssert(lutcn == 3 || lutcn == 4);
if (lutcn == 3)
{
- IppStatus status = ippiCopy_8u_C3P3R(lut.data, (int)lut.step[0], lutTable, (int)lut.step[0], sz256);
+ IppStatus status = ippiCopy_8u_C3P3R(lut.ptr(), (int)lut.step[0], lutTable, (int)lut.step[0], sz256);
if (status < 0)
{
setIppErrorStatus();
}
else if (lutcn == 4)
{
- IppStatus status = ippiCopy_8u_C4P4R(lut.data, (int)lut.step[0], lutTable, (int)lut.step[0], sz256);
+ IppStatus status = ippiCopy_8u_C4P4R(lut.ptr(), (int)lut.step[0], lutTable, (int)lut.step[0], sz256);
if (status < 0)
{
setIppErrorStatus();
if (lutcn == 3)
{
if (ippiLUTPalette_8u_C3R(
- src.data, (int)src.step[0], dst.data, (int)dst.step[0],
+ src.ptr(), (int)src.step[0], dst.ptr(), (int)dst.step[0],
ippiSize(dst.size()), lutTable, 8) >= 0)
return;
}
else if (lutcn == 4)
{
if (ippiLUTPalette_8u_C4R(
- src.data, (int)src.step[0], dst.data, (int)dst.step[0],
+ src.ptr(), (int)src.step[0], dst.ptr(), (int)dst.step[0],
ippiSize(dst.size()), lutTable, 8) >= 0)
return;
}
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);
+ func(ptrs[0], lut_.ptr(), ptrs[1], len, cn, lutcn);
}
private:
LUTParallelBody(const LUTParallelBody&);
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);
+ func(ptrs[0], lut.ptr(), ptrs[1], len, cn, lutcn);
}
namespace cv {
if (ippFunc != 0)
{
- if (ippFunc(src.data, (int)src.step, dst.data, (int)dst.step, ippiSize(src.cols, src.rows), axis) >= 0)
+ if (ippFunc(src.ptr(), (int)src.step, dst.ptr(), (int)dst.step, ippiSize(src.cols, src.rows), axis) >= 0)
return;
setIppErrorStatus();
}
else if (ippFuncI != 0)
{
- if (ippFuncI(dst.data, (int)dst.step, roisize, axis) >= 0)
+ if (ippFuncI(dst.ptr(), (int)dst.step, roisize, axis) >= 0)
return;
setIppErrorStatus();
}
#endif
if( flip_mode <= 0 )
- flipVert( src.data, src.step, dst.data, dst.step, src.size(), esz );
+ flipVert( src.ptr(), src.step, dst.ptr(), dst.step, src.size(), esz );
else
- flipHoriz( src.data, src.step, dst.data, dst.step, src.size(), esz );
+ flipHoriz( src.ptr(), src.step, dst.ptr(), dst.step, src.size(), esz );
if( flip_mode < 0 )
- flipHoriz( dst.data, dst.step, dst.data, dst.step, dst.size(), esz );
+ flipHoriz( dst.ptr(), dst.step, dst.ptr(), dst.step, dst.size(), esz );
}
-/*#ifdef HAVE_OPENCL
+#if defined HAVE_OPENCL && !defined __APPLE__
static bool ocl_repeat(InputArray _src, int ny, int nx, OutputArray _dst)
{
return k.run(2, globalsize, NULL, false);
}
-#endif*/
+#endif
void repeat(InputArray _src, int ny, int nx, OutputArray _dst)
{
Size ssize = _src.size();
_dst.create(ssize.height*ny, ssize.width*nx, _src.type());
- /*CV_OCL_RUN(_dst.isUMat(),
- ocl_repeat(_src, ny, nx, _dst))*/
+#if !defined __APPLE__
+ CV_OCL_RUN(_dst.isUMat(),
+ ocl_repeat(_src, ny, nx, _dst))
+#endif
Mat src = _src.getMat(), dst = _dst.getMat();
Size dsize = dst.size();
for( y = 0; y < ssize.height; y++ )
{
for( x = 0; x < dsize.width; x += ssize.width )
- memcpy( dst.data + y*dst.step + x, src.data + y*src.step, ssize.width );
+ memcpy( dst.ptr(y) + x, src.ptr(y), ssize.width );
}
for( ; y < dsize.height; y++ )
- memcpy( dst.data + y*dst.step, dst.data + (y - ssize.height)*dst.step, dsize.width );
+ memcpy( dst.ptr(y), dst.ptr(y - ssize.height), dsize.width );
}
Mat repeat(const Mat& src, int ny, int nx)
#endif
if( borderType != BORDER_CONSTANT )
- copyMakeBorder_8u( src.data, src.step, src.size(),
- dst.data, dst.step, dst.size(),
+ copyMakeBorder_8u( src.ptr(), src.step, src.size(),
+ dst.ptr(), dst.step, dst.size(),
top, left, (int)src.elemSize(), borderType );
else
{
cn1 = 1;
}
scalarToRawData(value, buf, CV_MAKETYPE(src.depth(), cn1), cn);
- copyMakeConstBorder_8u( src.data, src.step, src.size(),
- dst.data, dst.step, dst.size(),
+ copyMakeConstBorder_8u( src.ptr(), src.step, src.size(),
+ dst.ptr(), dst.step, dst.size(),
top, left, (int)src.elemSize(), (uchar*)(double*)buf );
}
}
if (refcount && CV_XADD(refcount, -1) == 1)
allocator->free(this);
- data = datastart = dataend = 0;
+ dataend = data = datastart = 0;
step = rows = cols = 0;
refcount = 0;
}
cv::cuda::GpuMat::GpuMat(int rows_, int cols_, int type_, void* data_, size_t step_) :
flags(Mat::MAGIC_VAL + (type_ & Mat::TYPE_MASK)), rows(rows_), cols(cols_),
step(step_), data((uchar*)data_), refcount(0),
- datastart((uchar*)data_), dataend((uchar*)data_),
+ datastart((uchar*)data_), dataend((const uchar*)data_),
allocator(defaultAllocator())
{
size_t minstep = cols * elemSize();
cv::cuda::GpuMat::GpuMat(Size size_, int type_, void* data_, size_t step_) :
flags(Mat::MAGIC_VAL + (type_ & Mat::TYPE_MASK)), rows(size_.height), cols(size_.width),
step(step_), data((uchar*)data_), refcount(0),
- datastart((uchar*)data_), dataend((uchar*)data_),
+ datastart((uchar*)data_), dataend((const uchar*)data_),
allocator(defaultAllocator())
{
size_t minstep = cols * elemSize();
fastFree(refcount);
}
- data = datastart = dataend = 0;
+ dataend = data = datastart = 0;
step = rows = cols = 0;
refcount = 0;
#endif
return prevNode;
}
-
namespace cv
{
-// This is reimplementation of kd-trees from cvkdtree*.* by Xavier Delacour, cleaned-up and
-// adopted to work with the new OpenCV data structures. It's in cxcore to be shared by
-// 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
-
-const int MAX_TREE_DEPTH = 32;
-
-KDTree::KDTree()
-{
- maxDepth = -1;
- normType = NORM_L2;
-}
-
-KDTree::KDTree(InputArray _points, bool _copyData)
-{
- maxDepth = -1;
- normType = NORM_L2;
- build(_points, _copyData);
-}
-
-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) {}
- SubTree(int _first, int _last, int _nodeIdx, int _depth)
- : first(_first), last(_last), nodeIdx(_nodeIdx), depth(_depth) {}
- int first;
- int last;
- int nodeIdx;
- int depth;
-};
-
-
-static float
-medianPartition( size_t* ofs, int a, int b, const float* vals )
-{
- int k, a0 = a, b0 = b;
- int middle = (a + b)/2;
- while( b > a )
- {
- int i0 = a, i1 = (a+b)/2, i2 = b;
- float v0 = vals[ofs[i0]], v1 = vals[ofs[i1]], v2 = vals[ofs[i2]];
- int ip = v0 < v1 ? (v1 < v2 ? i1 : v0 < v2 ? i2 : i0) :
- v0 < v2 ? i0 : (v1 < v2 ? i2 : i1);
- float pivot = vals[ofs[ip]];
- std::swap(ofs[ip], ofs[i2]);
-
- for( i1 = i0, i0--; i1 <= i2; i1++ )
- if( vals[ofs[i1]] <= pivot )
- {
- i0++;
- std::swap(ofs[i0], ofs[i1]);
- }
- if( i0 == middle )
- break;
- if( i0 > middle )
- b = i0 - (b == i0);
- else
- a = i0;
- }
-
- float pivot = vals[ofs[middle]];
- int less = 0, more = 0;
- for( k = a0; k < middle; k++ )
- {
- CV_Assert(vals[ofs[k]] <= pivot);
- less += vals[ofs[k]] < pivot;
- }
- for( k = b0; k > middle; k-- )
- {
- CV_Assert(vals[ofs[k]] >= pivot);
- more += vals[ofs[k]] > pivot;
- }
- CV_Assert(std::abs(more - less) <= 1);
-
- return vals[ofs[middle]];
-}
-
-static void
-computeSums( const Mat& points, const size_t* ofs, int a, int b, double* sums )
-{
- int i, j, dims = points.cols;
- const float* data = points.ptr<float>(0);
- for( j = 0; j < dims; j++ )
- sums[j*2] = sums[j*2+1] = 0;
- for( i = a; i <= b; i++ )
- {
- const float* row = data + ofs[i];
- for( j = 0; j < dims; j++ )
- {
- double t = row[j], s = sums[j*2] + t, s2 = sums[j*2+1] + t*t;
- sums[j*2] = s; sums[j*2+1] = s2;
- }
- }
-}
-
-
-void KDTree::build(InputArray _points, bool _copyData)
-{
- build(_points, noArray(), _copyData);
-}
-
-
-void KDTree::build(InputArray __points, InputArray __labels, bool _copyData)
-{
- Mat _points = __points.getMat(), _labels = __labels.getMat();
- CV_Assert(_points.type() == CV_32F && !_points.empty());
- std::vector<KDTree::Node>().swap(nodes);
-
- if( !_copyData )
- points = _points;
- else
- {
- points.release();
- points.create(_points.size(), _points.type());
- }
-
- int i, j, n = _points.rows, ptdims = _points.cols, top = 0;
- const float* data = _points.ptr<float>(0);
- float* dstdata = points.ptr<float>(0);
- size_t step = _points.step1();
- size_t dstep = points.step1();
- int ptpos = 0;
- labels.resize(n);
- const int* _labels_data = 0;
-
- if( !_labels.empty() )
- {
- int nlabels = _labels.checkVector(1, CV_32S, true);
- CV_Assert(nlabels == n);
- _labels_data = (const int*)_labels.data;
- }
-
- Mat sumstack(MAX_TREE_DEPTH*2, ptdims*2, CV_64F);
- SubTree stack[MAX_TREE_DEPTH*2];
-
- std::vector<size_t> _ptofs(n);
- size_t* ptofs = &_ptofs[0];
-
- for( i = 0; i < n; i++ )
- ptofs[i] = i*step;
-
- nodes.push_back(Node());
- computeSums(points, ptofs, 0, n-1, sumstack.ptr<double>(top));
- stack[top++] = SubTree(0, n-1, 0, 0);
- int _maxDepth = 0;
-
- while( --top >= 0 )
- {
- int first = stack[top].first, last = stack[top].last;
- int depth = stack[top].depth, nidx = stack[top].nodeIdx;
- int count = last - first + 1, dim = -1;
- const double* sums = sumstack.ptr<double>(top);
- double invCount = 1./count, maxVar = -1.;
-
- if( count == 1 )
- {
- int idx0 = (int)(ptofs[first]/step);
- int idx = _copyData ? ptpos++ : idx0;
- nodes[nidx].idx = ~idx;
- if( _copyData )
- {
- const float* src = data + ptofs[first];
- float* dst = dstdata + idx*dstep;
- for( j = 0; j < ptdims; j++ )
- dst[j] = src[j];
- }
- 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 < ptdims; j++ )
- {
- double m = sums[j*2]*invCount;
- double varj = sums[j*2+1]*invCount - m*m;
- if( maxVar < varj )
- {
- maxVar = varj;
- dim = j;
- }
- }
-
- int left = (int)nodes.size(), right = left + 1;
- nodes.push_back(Node());
- nodes.push_back(Node());
- nodes[nidx].idx = dim;
- nodes[nidx].left = left;
- nodes[nidx].right = right;
- nodes[nidx].boundary = medianPartition(ptofs, first, last, data + dim);
-
- int middle = (first + last)/2;
- double *lsums = (double*)sums, *rsums = lsums + ptdims*2;
- computeSums(points, ptofs, middle+1, last, rsums);
- 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);
- }
- maxDepth = _maxDepth;
-}
-
-
-struct PQueueElem
-{
- PQueueElem() : dist(0), idx(0) {}
- PQueueElem(float _dist, int _idx) : dist(_dist), idx(_idx) {}
- float dist;
- int idx;
-};
-
-
-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<float>();
- K = std::min(K, points.rows);
- int ptdims = points.cols;
-
- CV_Assert(K > 0 && (normType == NORM_L2 || normType == NORM_L1));
-
- AutoBuffer<uchar> _buf((K+1)*(sizeof(float) + sizeof(int)));
- int* idx = (int*)(uchar*)_buf;
- float* dist = (float*)(idx + K + 1);
- int i, j, ncount = 0, e = 0;
-
- int qsize = 0, maxqsize = 1 << 10;
- AutoBuffer<uchar> _pqueue(maxqsize*sizeof(PQueueElem));
- PQueueElem* pqueue = (PQueueElem*)(uchar*)_pqueue;
- emax = std::max(emax, 1);
-
- for( e = 0; e < emax; )
- {
- float d, alt_d = 0.f;
- int nidx;
-
- if( e == 0 )
- nidx = 0;
- else
- {
- // take the next node from the priority queue
- if( qsize == 0 )
- break;
- nidx = pqueue[0].idx;
- alt_d = pqueue[0].dist;
- if( --qsize > 0 )
- {
- std::swap(pqueue[0], pqueue[qsize]);
- d = pqueue[0].dist;
- for( i = 0;;)
- {
- int left = i*2 + 1, right = i*2 + 2;
- if( left >= qsize )
- break;
- if( right < qsize && pqueue[right].dist < pqueue[left].dist )
- left = right;
- if( pqueue[left].dist >= d )
- break;
- std::swap(pqueue[i], pqueue[left]);
- i = left;
- }
- }
-
- if( ncount == K && alt_d > dist[ncount-1] )
- continue;
- }
-
- for(;;)
- {
- if( nidx < 0 )
- break;
- const Node& n = nodes[nidx];
-
- if( n.idx < 0 )
- {
- i = ~n.idx;
- const float* row = points.ptr<float>(i);
- if( normType == NORM_L2 )
- 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 < ptdims; j++ )
- d += std::abs(vec[j] - row[j]);
-
- dist[ncount] = d;
- idx[ncount] = i;
- for( i = ncount-1; i >= 0; i-- )
- {
- if( dist[i] <= d )
- break;
- std::swap(dist[i], dist[i+1]);
- std::swap(idx[i], idx[i+1]);
- }
- ncount += ncount < K;
- e++;
- break;
- }
-
- int alt;
- if( vec[n.idx] <= n.boundary )
- {
- nidx = n.left;
- alt = n.right;
- }
- else
- {
- nidx = n.right;
- alt = n.left;
- }
-
- d = vec[n.idx] - n.boundary;
- if( normType == NORM_L2 )
- d = d*d + alt_d;
- else
- d = std::abs(d) + alt_d;
- // subtree prunning
- if( ncount == K && d > dist[ncount-1] )
- continue;
- // add alternative subtree to the priority queue
- pqueue[qsize] = PQueueElem(d, alt);
- for( i = qsize; i > 0; )
- {
- int parent = (i-1)/2;
- if( parent < 0 || pqueue[parent].dist <= d )
- break;
- std::swap(pqueue[i], pqueue[parent]);
- i = parent;
- }
- qsize += qsize+1 < maxqsize;
- }
- }
-
- K = std::min(K, ncount);
- if( _neighborsIdx.needed() )
- {
- _neighborsIdx.create(K, 1, CV_32S, -1, true);
- Mat nidx = _neighborsIdx.getMat();
- Mat(nidx.size(), CV_32S, &idx[0]).copyTo(nidx);
- }
- if( _dist.needed() )
- sqrt(Mat(K, 1, CV_32F, dist), _dist);
-
- if( _neighbors.needed() || _labels.needed() )
- getPoints(Mat(K, 1, CV_32S, idx), _neighbors, _labels);
- return K;
-}
-
-
-void KDTree::findOrthoRange(InputArray _lowerBound,
- InputArray _upperBound,
- OutputArray _neighborsIdx,
- OutputArray _neighbors,
- OutputArray _labels ) const
-{
- 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)ptdims );
- const float* L = lowerBound.ptr<float>();
- const float* R = upperBound.ptr<float>();
-
- std::vector<int> idx;
- AutoBuffer<int> _stack(MAX_TREE_DEPTH*2 + 1);
- int* stack = _stack;
- int top = 0;
-
- stack[top++] = 0;
-
- while( --top >= 0 )
- {
- int nidx = stack[top];
- if( nidx < 0 )
- break;
- const Node& n = nodes[nidx];
- if( n.idx < 0 )
- {
- int j, i = ~n.idx;
- const float* row = points.ptr<float>(i);
- for( j = 0; j < ptdims; j++ )
- if( row[j] < L[j] || row[j] >= R[j] )
- break;
- if( j == ptdims )
- idx.push_back(i);
- continue;
- }
- if( L[n.idx] <= n.boundary )
- stack[top++] = n.left;
- if( R[n.idx] > n.boundary )
- stack[top++] = n.right;
- }
-
- if( _neighborsIdx.needed() )
- {
- _neighborsIdx.create((int)idx.size(), 1, CV_32S, -1, true);
- Mat nidx = _neighborsIdx.getMat();
- Mat(nidx.size(), CV_32S, &idx[0]).copyTo(nidx);
- }
- getPoints( idx, _neighbors, _labels );
-}
-
-
-void KDTree::getPoints(InputArray _idx, OutputArray _pts, OutputArray _labels) const
-{
- Mat idxmat = _idx.getMat(), pts, labelsmat;
- CV_Assert( idxmat.isContinuous() && idxmat.type() == CV_32S &&
- (idxmat.cols == 1 || idxmat.rows == 1) );
- const int* idx = idxmat.ptr<int>();
- int* dstlabels = 0;
-
- int ptdims = points.cols;
- int i, nidx = (int)idxmat.total();
- if( nidx == 0 )
- {
- _pts.release();
- _labels.release();
- return;
- }
-
- if( _pts.needed() )
- {
- _pts.create( nidx, ptdims, points.type());
- pts = _pts.getMat();
- }
-
- if(_labels.needed())
- {
- _labels.create(nidx, 1, CV_32S, -1, true);
- labelsmat = _labels.getMat();
- CV_Assert( labelsmat.isContinuous() );
- dstlabels = labelsmat.ptr<int>();
- }
- 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<float>(k);
- if( pts.data )
- std::copy(src, src + ptdims, pts.ptr<float>(i));
- if( dstlabels )
- dstlabels[i] = srclabels ? srclabels[k] : k;
- }
-}
-
-
-const float* KDTree::getPoint(int ptidx, int* label) const
-{
- CV_Assert( (unsigned)ptidx < (unsigned)points.rows);
- if(label)
- *label = labels[ptidx];
- return points.ptr<float>(ptidx);
-}
-
-
-int KDTree::dims() const
-{
- return !points.empty() ? points.cols : 0;
-}
-
////////////////////////////////////////////////////////////////////////////////
schar* seqPush( CvSeq* seq, const void* element )
//
//M*/
#include "precomp.hpp"
-#include "debug.hpp"
-#include "opencv2/core/core_c.h"
+
+#define dprintf(x)
+#define print_matrix(x)
/*
using namespace std;
using namespace cv;
-void test(Ptr<optim::DownhillSolver> solver, Ptr<optim::Solver::Function> ptr_F, Mat &P, Mat &step)
+void test(Ptr<optim::DownhillSolver> MinProblemSolver, Ptr<optim::MinProblemSolver::Function> ptr_F, Mat &P, Mat &step)
{
try{
-solver->setFunction(ptr_F);
-solver->setInitStep(step);
-double res = solver->minimize(P);
+MinProblemSolver->setFunction(ptr_F);
+MinProblemSolver->setInitStep(step);
+double res = MinProblemSolver->minimize(P);
cout << "res " << res << endl;
}
int main()
{
-class DistanceToLines :public optim::Solver::Function {
+class DistanceToLines :public optim::MinProblemSolver::Function {
public:
double calc(const double* x)const{
Mat P = (Mat_<double>(1, 2) << 1.0, 1.0);
Mat step = (Mat_<double>(2, 1) << -0.5, 0.5);
-Ptr<optim::Solver::Function> ptr_F(new DistanceToLines());
-Ptr<optim::DownhillSolver> solver = optim::createDownhillSolver();
+Ptr<optim::MinProblemSolver::Function> ptr_F(new DistanceToLines());
+Ptr<optim::DownhillSolver> MinProblemSolver = optim::createDownhillSolver();
-test(solver, ptr_F, P, step);
+test(MinProblemSolver, ptr_F, P, step);
system("pause");
return 0;
*/
-
-
-
-
-namespace cv{namespace optim{
+namespace cv
+{
class DownhillSolverImpl : public DownhillSolver
{
void setTermCriteria(const TermCriteria& termcrit);
double minimize(InputOutputArray x);
protected:
- Ptr<Solver::Function> _Function;
+ Ptr<MinProblemSolver::Function> _Function;
TermCriteria _termcrit;
Mat _step;
Mat_<double> buf_x;
private:
inline void createInitialSimplex(Mat_<double>& simplex,Mat& step);
inline double innerDownhillSimplex(cv::Mat_<double>& p,double MinRange,double MinError,int& nfunk,
- const Ptr<Solver::Function>& f,int nmax);
- inline double tryNewPoint(Mat_<double>& p,Mat_<double>& y,Mat_<double>& coord_sum,const Ptr<Solver::Function>& f,int ihi,
+ const Ptr<MinProblemSolver::Function>& f,int nmax);
+ inline double tryNewPoint(Mat_<double>& p,Mat_<double>& y,Mat_<double>& coord_sum,const Ptr<MinProblemSolver::Function>& f,int ihi,
double fac,Mat_<double>& ptry);
};
Mat_<double>& p,
Mat_<double>& y,
Mat_<double>& coord_sum,
- const Ptr<Solver::Function>& f,
+ const Ptr<MinProblemSolver::Function>& f,
int ihi,
double fac,
Mat_<double>& ptry
{
ptry(j)=coord_sum(j)*fac1-p(ihi,j)*fac2;
}
- ytry=f->calc((double*)ptry.data);
+ ytry=f->calc(ptry.ptr<double>());
if (ytry < y(ihi))
{
y(ihi)=ytry;
}
/*
- Performs the actual minimization of Solver::Function f (after the initialization was done)
+ Performs the actual minimization of MinProblemSolver::Function f (after the initialization was done)
The matrix p[ndim+1][1..ndim] represents ndim+1 vertices that
form a simplex - each row is an ndim vector.
double MinRange,
double MinError,
int& nfunk,
- const Ptr<Solver::Function>& f,
+ const Ptr<MinProblemSolver::Function>& f,
int nmax
)
{
{
p(i,j) = coord_sum(j) = 0.5*(p(i,j)+p(ilo,j));
}
- y(i)=f->calc((double*)coord_sum.data);
+ y(i)=f->calc(coord_sum.ptr<double>());
}
}
nfunk += ndim;
if(x_mat.rows>1){
buf_x.create(1,_step.cols);
- Mat_<double> proxy(_step.cols,1,(double*)buf_x.data);
+ Mat_<double> proxy(_step.cols,1,buf_x.ptr<double>());
x_mat.copyTo(proxy);
proxy_x=buf_x;
}else{
dprintf(("%d iterations done\n",count));
if(x_mat.rows>1){
- Mat(x_mat.rows, 1, CV_64F, (double*)proxy_x.data).copyTo(x);
+ Mat(x_mat.rows, 1, CV_64F, proxy_x.ptr<double>()).copyTo(x);
}
return res;
}
_Function=Ptr<Function>();
_step=Mat_<double>();
}
- Ptr<Solver::Function> DownhillSolverImpl::getFunction()const{
+ Ptr<MinProblemSolver::Function> DownhillSolverImpl::getFunction()const{
return _Function;
}
void DownhillSolverImpl::setFunction(const Ptr<Function>& f){
_termcrit=termcrit;
}
// both minRange & minError are specified by termcrit.epsilon; In addition, user may specify the number of iterations that the algorithm does.
- Ptr<DownhillSolver> createDownhillSolver(const Ptr<Solver::Function>& f, InputArray initStep, TermCriteria termcrit){
- DownhillSolver *DS=new DownhillSolverImpl();
+ Ptr<DownhillSolver> DownhillSolver::create(const Ptr<MinProblemSolver::Function>& f, InputArray initStep, TermCriteria termcrit){
+ Ptr<DownhillSolver> DS = makePtr<DownhillSolverImpl>();
DS->setFunction(f);
DS->setInitStep(initStep);
DS->setTermCriteria(termcrit);
- return Ptr<DownhillSolver>(DS);
+ return DS;
}
void DownhillSolverImpl::getInitStep(OutputArray step)const{
_step.copyTo(step);
transpose(m,_step);
}
}
-}}
+}
}
for( int i = range.start; i < range.end; ++i)
- if(!ippidft((Ipp32fc*)(src.data+i*src.step), (int)src.step,(Ipp32fc*)(dst.data+i*dst.step), (int)dst.step, pDFTSpec, (Ipp8u*)pBuffer))
+ if(!ippidft(src.ptr<Ipp32fc>(i), (int)src.step,dst.ptr<Ipp32fc>(i), (int)dst.step, pDFTSpec, (Ipp8u*)pBuffer))
{
*ok = false;
}
}
if (!inv)
- status = ippiDFTFwd_CToC_32fc_C1R( (Ipp32fc*)src.data, (int)src.step, (Ipp32fc*)dst.data, (int)dst.step, pDFTSpec, pBuffer );
+ status = ippiDFTFwd_CToC_32fc_C1R( src.ptr<Ipp32fc>(), (int)src.step, dst.ptr<Ipp32fc>(), (int)dst.step, pDFTSpec, pBuffer );
else
- status = ippiDFTInv_CToC_32fc_C1R( (Ipp32fc*)src.data, (int)src.step, (Ipp32fc*)dst.data, (int)dst.step, pDFTSpec, pBuffer );
+ status = ippiDFTInv_CToC_32fc_C1R( src.ptr<Ipp32fc>(), (int)src.step, dst.ptr<Ipp32fc>(), (int)dst.step, pDFTSpec, pBuffer );
if ( sizeBuffer > 0 )
ippFree( pBuffer );
n *= radix;
}
- Mat tw(1, twiddle_size, CV_32FC2);
+ twiddles.create(1, twiddle_size, CV_32FC2);
+ Mat tw = twiddles.getMat(ACCESS_WRITE);
float* ptr = tw.ptr<float>();
int ptr_index = 0;
}
}
}
- twiddles = tw.getUMat(ACCESS_READ);
buildOptions = format("-D LOCAL_SIZE=%d -D kercn=%d -D RADIX_PROCESS=%s",
dft_size, min_radix, radix_processing.c_str());
{
int a = 0, b = count;
uchar *buf0, *buf1, *dbuf0, *dbuf1;
- const uchar* sptr0 = src.data;
- uchar* dptr0 = dst.data;
+ const uchar* sptr0 = src.ptr();
+ uchar* dptr0 = dst.ptr();
buf0 = ptr;
ptr += len*complex_elem_size;
buf1 = ptr;
if( depth == CV_32F )
{
- const float* dataA = (const float*)srcA.data;
- const float* dataB = (const float*)srcB.data;
- float* dataC = (float*)dst.data;
+ const float* dataA = srcA.ptr<float>();
+ const float* dataB = srcB.ptr<float>();
+ float* dataC = dst.ptr<float>();
size_t stepA = srcA.step/sizeof(dataA[0]);
size_t stepB = srcB.step/sizeof(dataB[0]);
}
else
{
- const double* dataA = (const double*)srcA.data;
- const double* dataB = (const double*)srcB.data;
- double* dataC = (double*)dst.data;
+ const double* dataA = srcA.ptr<double>();
+ const double* dataB = srcB.ptr<double>();
+ double* dataC = dst.ptr<double>();
size_t stepA = srcA.step/sizeof(dataA[0]);
size_t stepB = srcB.step/sizeof(dataB[0]);
pBuffer = (uchar*)buf;
for( int i = range.start; i < range.end; ++i)
- if(!(*ippidct)((float*)(src->data+i*src->step), (int)src->step,(float*)(dst->data+i*dst->step), (int)dst->step, pDCTSpec, (Ipp8u*)pBuffer))
+ if(!(*ippidct)(src->ptr<float>(i), (int)src->step,dst->ptr<float>(i), (int)dst->step, pDCTSpec, (Ipp8u*)pBuffer))
*ok = false;
}
else
buf.allocate( bufSize );
pBuffer = (uchar*)buf;
- status = ippFunc((float*)src.data, (int)src.step, (float*)dst.data, (int)dst.step, pDCTSpec, (Ipp8u*)pBuffer);
+ status = ippFunc(src.ptr<float>(), (int)src.step, dst.ptr<float>(), (int)dst.step, pDCTSpec, (Ipp8u*)pBuffer);
}
if (pDCTSpec)
for( ; stage <= end_stage; stage++ )
{
- uchar *sptr = src.data, *dptr = dst.data;
+ const uchar* sptr = src.ptr();
+ uchar* dptr = dst.ptr();
size_t sstep0, sstep1, dstep0, dstep1;
if( stage == 0 )
--- /dev/null
+/*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.
+// Copyright (C) 2013, OpenCV Foundation, 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 "precomp.hpp"
+
+namespace cv
+{
+
+// This is reimplementation of kd-trees from cvkdtree*.* by Xavier Delacour, cleaned-up and
+// adopted to work with the new OpenCV data structures. It's in cxcore to be shared by
+// 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
+
+const int MAX_TREE_DEPTH = 32;
+
+KDTree::KDTree()
+{
+ maxDepth = -1;
+ normType = NORM_L2;
+}
+
+KDTree::KDTree(InputArray _points, bool _copyData)
+{
+ maxDepth = -1;
+ normType = NORM_L2;
+ build(_points, _copyData);
+}
+
+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) {}
+ SubTree(int _first, int _last, int _nodeIdx, int _depth)
+ : first(_first), last(_last), nodeIdx(_nodeIdx), depth(_depth) {}
+ int first;
+ int last;
+ int nodeIdx;
+ int depth;
+};
+
+
+static float
+medianPartition( size_t* ofs, int a, int b, const float* vals )
+{
+ int k, a0 = a, b0 = b;
+ int middle = (a + b)/2;
+ while( b > a )
+ {
+ int i0 = a, i1 = (a+b)/2, i2 = b;
+ float v0 = vals[ofs[i0]], v1 = vals[ofs[i1]], v2 = vals[ofs[i2]];
+ int ip = v0 < v1 ? (v1 < v2 ? i1 : v0 < v2 ? i2 : i0) :
+ v0 < v2 ? i0 : (v1 < v2 ? i2 : i1);
+ float pivot = vals[ofs[ip]];
+ std::swap(ofs[ip], ofs[i2]);
+
+ for( i1 = i0, i0--; i1 <= i2; i1++ )
+ if( vals[ofs[i1]] <= pivot )
+ {
+ i0++;
+ std::swap(ofs[i0], ofs[i1]);
+ }
+ if( i0 == middle )
+ break;
+ if( i0 > middle )
+ b = i0 - (b == i0);
+ else
+ a = i0;
+ }
+
+ float pivot = vals[ofs[middle]];
+ int less = 0, more = 0;
+ for( k = a0; k < middle; k++ )
+ {
+ CV_Assert(vals[ofs[k]] <= pivot);
+ less += vals[ofs[k]] < pivot;
+ }
+ for( k = b0; k > middle; k-- )
+ {
+ CV_Assert(vals[ofs[k]] >= pivot);
+ more += vals[ofs[k]] > pivot;
+ }
+ CV_Assert(std::abs(more - less) <= 1);
+
+ return vals[ofs[middle]];
+}
+
+static void
+computeSums( const Mat& points, const size_t* ofs, int a, int b, double* sums )
+{
+ int i, j, dims = points.cols;
+ const float* data = points.ptr<float>(0);
+ for( j = 0; j < dims; j++ )
+ sums[j*2] = sums[j*2+1] = 0;
+ for( i = a; i <= b; i++ )
+ {
+ const float* row = data + ofs[i];
+ for( j = 0; j < dims; j++ )
+ {
+ double t = row[j], s = sums[j*2] + t, s2 = sums[j*2+1] + t*t;
+ sums[j*2] = s; sums[j*2+1] = s2;
+ }
+ }
+}
+
+
+void KDTree::build(InputArray _points, bool _copyData)
+{
+ build(_points, noArray(), _copyData);
+}
+
+
+void KDTree::build(InputArray __points, InputArray __labels, bool _copyData)
+{
+ Mat _points = __points.getMat(), _labels = __labels.getMat();
+ CV_Assert(_points.type() == CV_32F && !_points.empty());
+ std::vector<KDTree::Node>().swap(nodes);
+
+ if( !_copyData )
+ points = _points;
+ else
+ {
+ points.release();
+ points.create(_points.size(), _points.type());
+ }
+
+ int i, j, n = _points.rows, ptdims = _points.cols, top = 0;
+ const float* data = _points.ptr<float>(0);
+ float* dstdata = points.ptr<float>(0);
+ size_t step = _points.step1();
+ size_t dstep = points.step1();
+ int ptpos = 0;
+ labels.resize(n);
+ const int* _labels_data = 0;
+
+ if( !_labels.empty() )
+ {
+ int nlabels = _labels.checkVector(1, CV_32S, true);
+ CV_Assert(nlabels == n);
+ _labels_data = _labels.ptr<int>();
+ }
+
+ Mat sumstack(MAX_TREE_DEPTH*2, ptdims*2, CV_64F);
+ SubTree stack[MAX_TREE_DEPTH*2];
+
+ std::vector<size_t> _ptofs(n);
+ size_t* ptofs = &_ptofs[0];
+
+ for( i = 0; i < n; i++ )
+ ptofs[i] = i*step;
+
+ nodes.push_back(Node());
+ computeSums(points, ptofs, 0, n-1, sumstack.ptr<double>(top));
+ stack[top++] = SubTree(0, n-1, 0, 0);
+ int _maxDepth = 0;
+
+ while( --top >= 0 )
+ {
+ int first = stack[top].first, last = stack[top].last;
+ int depth = stack[top].depth, nidx = stack[top].nodeIdx;
+ int count = last - first + 1, dim = -1;
+ const double* sums = sumstack.ptr<double>(top);
+ double invCount = 1./count, maxVar = -1.;
+
+ if( count == 1 )
+ {
+ int idx0 = (int)(ptofs[first]/step);
+ int idx = _copyData ? ptpos++ : idx0;
+ nodes[nidx].idx = ~idx;
+ if( _copyData )
+ {
+ const float* src = data + ptofs[first];
+ float* dst = dstdata + idx*dstep;
+ for( j = 0; j < ptdims; j++ )
+ dst[j] = src[j];
+ }
+ 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 < ptdims; j++ )
+ {
+ double m = sums[j*2]*invCount;
+ double varj = sums[j*2+1]*invCount - m*m;
+ if( maxVar < varj )
+ {
+ maxVar = varj;
+ dim = j;
+ }
+ }
+
+ int left = (int)nodes.size(), right = left + 1;
+ nodes.push_back(Node());
+ nodes.push_back(Node());
+ nodes[nidx].idx = dim;
+ nodes[nidx].left = left;
+ nodes[nidx].right = right;
+ nodes[nidx].boundary = medianPartition(ptofs, first, last, data + dim);
+
+ int middle = (first + last)/2;
+ double *lsums = (double*)sums, *rsums = lsums + ptdims*2;
+ computeSums(points, ptofs, middle+1, last, rsums);
+ 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);
+ }
+ maxDepth = _maxDepth;
+}
+
+
+struct PQueueElem
+{
+ PQueueElem() : dist(0), idx(0) {}
+ PQueueElem(float _dist, int _idx) : dist(_dist), idx(_idx) {}
+ float dist;
+ int idx;
+};
+
+
+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<float>();
+ K = std::min(K, points.rows);
+ int ptdims = points.cols;
+
+ CV_Assert(K > 0 && (normType == NORM_L2 || normType == NORM_L1));
+
+ AutoBuffer<uchar> _buf((K+1)*(sizeof(float) + sizeof(int)));
+ int* idx = (int*)(uchar*)_buf;
+ float* dist = (float*)(idx + K + 1);
+ int i, j, ncount = 0, e = 0;
+
+ int qsize = 0, maxqsize = 1 << 10;
+ AutoBuffer<uchar> _pqueue(maxqsize*sizeof(PQueueElem));
+ PQueueElem* pqueue = (PQueueElem*)(uchar*)_pqueue;
+ emax = std::max(emax, 1);
+
+ for( e = 0; e < emax; )
+ {
+ float d, alt_d = 0.f;
+ int nidx;
+
+ if( e == 0 )
+ nidx = 0;
+ else
+ {
+ // take the next node from the priority queue
+ if( qsize == 0 )
+ break;
+ nidx = pqueue[0].idx;
+ alt_d = pqueue[0].dist;
+ if( --qsize > 0 )
+ {
+ std::swap(pqueue[0], pqueue[qsize]);
+ d = pqueue[0].dist;
+ for( i = 0;;)
+ {
+ int left = i*2 + 1, right = i*2 + 2;
+ if( left >= qsize )
+ break;
+ if( right < qsize && pqueue[right].dist < pqueue[left].dist )
+ left = right;
+ if( pqueue[left].dist >= d )
+ break;
+ std::swap(pqueue[i], pqueue[left]);
+ i = left;
+ }
+ }
+
+ if( ncount == K && alt_d > dist[ncount-1] )
+ continue;
+ }
+
+ for(;;)
+ {
+ if( nidx < 0 )
+ break;
+ const Node& n = nodes[nidx];
+
+ if( n.idx < 0 )
+ {
+ i = ~n.idx;
+ const float* row = points.ptr<float>(i);
+ if( normType == NORM_L2 )
+ 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 < ptdims; j++ )
+ d += std::abs(vec[j] - row[j]);
+
+ dist[ncount] = d;
+ idx[ncount] = i;
+ for( i = ncount-1; i >= 0; i-- )
+ {
+ if( dist[i] <= d )
+ break;
+ std::swap(dist[i], dist[i+1]);
+ std::swap(idx[i], idx[i+1]);
+ }
+ ncount += ncount < K;
+ e++;
+ break;
+ }
+
+ int alt;
+ if( vec[n.idx] <= n.boundary )
+ {
+ nidx = n.left;
+ alt = n.right;
+ }
+ else
+ {
+ nidx = n.right;
+ alt = n.left;
+ }
+
+ d = vec[n.idx] - n.boundary;
+ if( normType == NORM_L2 )
+ d = d*d + alt_d;
+ else
+ d = std::abs(d) + alt_d;
+ // subtree prunning
+ if( ncount == K && d > dist[ncount-1] )
+ continue;
+ // add alternative subtree to the priority queue
+ pqueue[qsize] = PQueueElem(d, alt);
+ for( i = qsize; i > 0; )
+ {
+ int parent = (i-1)/2;
+ if( parent < 0 || pqueue[parent].dist <= d )
+ break;
+ std::swap(pqueue[i], pqueue[parent]);
+ i = parent;
+ }
+ qsize += qsize+1 < maxqsize;
+ }
+ }
+
+ K = std::min(K, ncount);
+ if( _neighborsIdx.needed() )
+ {
+ _neighborsIdx.create(K, 1, CV_32S, -1, true);
+ Mat nidx = _neighborsIdx.getMat();
+ Mat(nidx.size(), CV_32S, &idx[0]).copyTo(nidx);
+ }
+ if( _dist.needed() )
+ sqrt(Mat(K, 1, CV_32F, dist), _dist);
+
+ if( _neighbors.needed() || _labels.needed() )
+ getPoints(Mat(K, 1, CV_32S, idx), _neighbors, _labels);
+ return K;
+}
+
+
+void KDTree::findOrthoRange(InputArray _lowerBound,
+ InputArray _upperBound,
+ OutputArray _neighborsIdx,
+ OutputArray _neighbors,
+ OutputArray _labels ) const
+{
+ 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)ptdims );
+ const float* L = lowerBound.ptr<float>();
+ const float* R = upperBound.ptr<float>();
+
+ std::vector<int> idx;
+ AutoBuffer<int> _stack(MAX_TREE_DEPTH*2 + 1);
+ int* stack = _stack;
+ int top = 0;
+
+ stack[top++] = 0;
+
+ while( --top >= 0 )
+ {
+ int nidx = stack[top];
+ if( nidx < 0 )
+ break;
+ const Node& n = nodes[nidx];
+ if( n.idx < 0 )
+ {
+ int j, i = ~n.idx;
+ const float* row = points.ptr<float>(i);
+ for( j = 0; j < ptdims; j++ )
+ if( row[j] < L[j] || row[j] >= R[j] )
+ break;
+ if( j == ptdims )
+ idx.push_back(i);
+ continue;
+ }
+ if( L[n.idx] <= n.boundary )
+ stack[top++] = n.left;
+ if( R[n.idx] > n.boundary )
+ stack[top++] = n.right;
+ }
+
+ if( _neighborsIdx.needed() )
+ {
+ _neighborsIdx.create((int)idx.size(), 1, CV_32S, -1, true);
+ Mat nidx = _neighborsIdx.getMat();
+ Mat(nidx.size(), CV_32S, &idx[0]).copyTo(nidx);
+ }
+ getPoints( idx, _neighbors, _labels );
+}
+
+
+void KDTree::getPoints(InputArray _idx, OutputArray _pts, OutputArray _labels) const
+{
+ Mat idxmat = _idx.getMat(), pts, labelsmat;
+ CV_Assert( idxmat.isContinuous() && idxmat.type() == CV_32S &&
+ (idxmat.cols == 1 || idxmat.rows == 1) );
+ const int* idx = idxmat.ptr<int>();
+ int* dstlabels = 0;
+
+ int ptdims = points.cols;
+ int i, nidx = (int)idxmat.total();
+ if( nidx == 0 )
+ {
+ _pts.release();
+ _labels.release();
+ return;
+ }
+
+ if( _pts.needed() )
+ {
+ _pts.create( nidx, ptdims, points.type());
+ pts = _pts.getMat();
+ }
+
+ if(_labels.needed())
+ {
+ _labels.create(nidx, 1, CV_32S, -1, true);
+ labelsmat = _labels.getMat();
+ CV_Assert( labelsmat.isContinuous() );
+ dstlabels = labelsmat.ptr<int>();
+ }
+ 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<float>(k);
+ if( !pts.empty() )
+ std::copy(src, src + ptdims, pts.ptr<float>(i));
+ if( dstlabels )
+ dstlabels[i] = srclabels ? srclabels[k] : k;
+ }
+}
+
+
+const float* KDTree::getPoint(int ptidx, int* label) const
+{
+ CV_Assert( (unsigned)ptidx < (unsigned)points.rows);
+ if(label)
+ *label = labels[ptidx];
+ return points.ptr<float>(ptidx);
+}
+
+
+int KDTree::dims() const
+{
+ return !points.empty() ? points.cols : 0;
+}
+
+}
--- /dev/null
+/*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.
+// Copyright (C) 2013, OpenCV Foundation, 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 "precomp.hpp"
+
+////////////////////////////////////////// kmeans ////////////////////////////////////////////
+
+namespace cv
+{
+
+static void generateRandomCenter(const std::vector<Vec2f>& box, float* center, RNG& rng)
+{
+ size_t j, dims = box.size();
+ float margin = 1.f/dims;
+ for( j = 0; j < dims; j++ )
+ center[j] = ((float)rng*(1.f+margin*2.f)-margin)*(box[j][1] - box[j][0]) + box[j][0];
+}
+
+class KMeansPPDistanceComputer : public ParallelLoopBody
+{
+public:
+ KMeansPPDistanceComputer( float *_tdist2,
+ const float *_data,
+ const float *_dist,
+ int _dims,
+ size_t _step,
+ size_t _stepci )
+ : tdist2(_tdist2),
+ data(_data),
+ dist(_dist),
+ dims(_dims),
+ step(_step),
+ stepci(_stepci) { }
+
+ void operator()( const cv::Range& range ) const
+ {
+ const int begin = range.start;
+ const int end = range.end;
+
+ for ( int i = begin; i<end; i++ )
+ {
+ tdist2[i] = std::min(normL2Sqr_(data + step*i, data + stepci, dims), dist[i]);
+ }
+ }
+
+private:
+ KMeansPPDistanceComputer& operator=(const KMeansPPDistanceComputer&); // to quiet MSVC
+
+ float *tdist2;
+ const float *data;
+ const float *dist;
+ const int dims;
+ const size_t step;
+ const size_t stepci;
+};
+
+/*
+k-means center initialization using the following algorithm:
+Arthur & Vassilvitskii (2007) k-means++: The Advantages of Careful Seeding
+*/
+static void generateCentersPP(const Mat& _data, Mat& _out_centers,
+ int K, RNG& rng, int trials)
+{
+ int i, j, k, dims = _data.cols, N = _data.rows;
+ const float* data = _data.ptr<float>(0);
+ size_t step = _data.step/sizeof(data[0]);
+ std::vector<int> _centers(K);
+ int* centers = &_centers[0];
+ std::vector<float> _dist(N*3);
+ float* dist = &_dist[0], *tdist = dist + N, *tdist2 = tdist + N;
+ double sum0 = 0;
+
+ centers[0] = (unsigned)rng % N;
+
+ for( i = 0; i < N; i++ )
+ {
+ dist[i] = normL2Sqr_(data + step*i, data + step*centers[0], dims);
+ sum0 += dist[i];
+ }
+
+ for( k = 1; k < K; k++ )
+ {
+ double bestSum = DBL_MAX;
+ int bestCenter = -1;
+
+ for( j = 0; j < trials; j++ )
+ {
+ double p = (double)rng*sum0, s = 0;
+ for( i = 0; i < N-1; i++ )
+ if( (p -= dist[i]) <= 0 )
+ break;
+ int ci = i;
+
+ parallel_for_(Range(0, N),
+ KMeansPPDistanceComputer(tdist2, data, dist, dims, step, step*ci));
+ for( i = 0; i < N; i++ )
+ {
+ s += tdist2[i];
+ }
+
+ if( s < bestSum )
+ {
+ bestSum = s;
+ bestCenter = ci;
+ std::swap(tdist, tdist2);
+ }
+ }
+ centers[k] = bestCenter;
+ sum0 = bestSum;
+ std::swap(dist, tdist);
+ }
+
+ for( k = 0; k < K; k++ )
+ {
+ const float* src = data + step*centers[k];
+ float* dst = _out_centers.ptr<float>(k);
+ for( j = 0; j < dims; j++ )
+ dst[j] = src[j];
+ }
+}
+
+class KMeansDistanceComputer : public ParallelLoopBody
+{
+public:
+ KMeansDistanceComputer( double *_distances,
+ int *_labels,
+ const Mat& _data,
+ const Mat& _centers )
+ : distances(_distances),
+ labels(_labels),
+ data(_data),
+ centers(_centers)
+ {
+ }
+
+ void operator()( const Range& range ) const
+ {
+ const int begin = range.start;
+ const int end = range.end;
+ const int K = centers.rows;
+ const int dims = centers.cols;
+
+ const float *sample;
+ for( int i = begin; i<end; ++i)
+ {
+ sample = data.ptr<float>(i);
+ int k_best = 0;
+ double min_dist = DBL_MAX;
+
+ for( int k = 0; k < K; k++ )
+ {
+ const float* center = centers.ptr<float>(k);
+ const double dist = normL2Sqr_(sample, center, dims);
+
+ if( min_dist > dist )
+ {
+ min_dist = dist;
+ k_best = k;
+ }
+ }
+
+ distances[i] = min_dist;
+ labels[i] = k_best;
+ }
+ }
+
+private:
+ KMeansDistanceComputer& operator=(const KMeansDistanceComputer&); // to quiet MSVC
+
+ double *distances;
+ int *labels;
+ const Mat& data;
+ const Mat& centers;
+};
+
+}
+
+double cv::kmeans( InputArray _data, int K,
+ InputOutputArray _bestLabels,
+ TermCriteria criteria, int attempts,
+ int flags, OutputArray _centers )
+{
+ const int SPP_TRIALS = 3;
+ Mat data0 = _data.getMat();
+ bool isrow = data0.rows == 1 && data0.channels() > 1;
+ int N = !isrow ? data0.rows : data0.cols;
+ int dims = (!isrow ? data0.cols : 1)*data0.channels();
+ int type = data0.depth();
+
+ attempts = std::max(attempts, 1);
+ CV_Assert( data0.dims <= 2 && type == CV_32F && K > 0 );
+ CV_Assert( N >= K );
+
+ Mat data(N, dims, CV_32F, data0.ptr(), isrow ? dims * sizeof(float) : static_cast<size_t>(data0.step));
+
+ _bestLabels.create(N, 1, CV_32S, -1, true);
+
+ Mat _labels, best_labels = _bestLabels.getMat();
+ if( flags & CV_KMEANS_USE_INITIAL_LABELS )
+ {
+ CV_Assert( (best_labels.cols == 1 || best_labels.rows == 1) &&
+ best_labels.cols*best_labels.rows == N &&
+ best_labels.type() == CV_32S &&
+ best_labels.isContinuous());
+ best_labels.copyTo(_labels);
+ }
+ else
+ {
+ if( !((best_labels.cols == 1 || best_labels.rows == 1) &&
+ best_labels.cols*best_labels.rows == N &&
+ best_labels.type() == CV_32S &&
+ best_labels.isContinuous()))
+ best_labels.create(N, 1, CV_32S);
+ _labels.create(best_labels.size(), best_labels.type());
+ }
+ int* labels = _labels.ptr<int>();
+
+ Mat centers(K, dims, type), old_centers(K, dims, type), temp(1, dims, type);
+ std::vector<int> counters(K);
+ std::vector<Vec2f> _box(dims);
+ Vec2f* box = &_box[0];
+ double best_compactness = DBL_MAX, compactness = 0;
+ RNG& rng = theRNG();
+ int a, iter, i, j, k;
+
+ if( criteria.type & TermCriteria::EPS )
+ criteria.epsilon = std::max(criteria.epsilon, 0.);
+ else
+ criteria.epsilon = FLT_EPSILON;
+ criteria.epsilon *= criteria.epsilon;
+
+ if( criteria.type & TermCriteria::COUNT )
+ criteria.maxCount = std::min(std::max(criteria.maxCount, 2), 100);
+ else
+ criteria.maxCount = 100;
+
+ if( K == 1 )
+ {
+ attempts = 1;
+ criteria.maxCount = 2;
+ }
+
+ const float* sample = data.ptr<float>(0);
+ for( j = 0; j < dims; j++ )
+ box[j] = Vec2f(sample[j], sample[j]);
+
+ for( i = 1; i < N; i++ )
+ {
+ sample = data.ptr<float>(i);
+ for( j = 0; j < dims; j++ )
+ {
+ float v = sample[j];
+ box[j][0] = std::min(box[j][0], v);
+ box[j][1] = std::max(box[j][1], v);
+ }
+ }
+
+ for( a = 0; a < attempts; a++ )
+ {
+ double max_center_shift = DBL_MAX;
+ for( iter = 0;; )
+ {
+ swap(centers, old_centers);
+
+ if( iter == 0 && (a > 0 || !(flags & KMEANS_USE_INITIAL_LABELS)) )
+ {
+ if( flags & KMEANS_PP_CENTERS )
+ generateCentersPP(data, centers, K, rng, SPP_TRIALS);
+ else
+ {
+ for( k = 0; k < K; k++ )
+ generateRandomCenter(_box, centers.ptr<float>(k), rng);
+ }
+ }
+ else
+ {
+ if( iter == 0 && a == 0 && (flags & KMEANS_USE_INITIAL_LABELS) )
+ {
+ for( i = 0; i < N; i++ )
+ CV_Assert( (unsigned)labels[i] < (unsigned)K );
+ }
+
+ // compute centers
+ centers = Scalar(0);
+ for( k = 0; k < K; k++ )
+ counters[k] = 0;
+
+ for( i = 0; i < N; i++ )
+ {
+ sample = data.ptr<float>(i);
+ k = labels[i];
+ float* center = centers.ptr<float>(k);
+ j=0;
+ #if CV_ENABLE_UNROLLED
+ for(; j <= dims - 4; j += 4 )
+ {
+ float t0 = center[j] + sample[j];
+ float t1 = center[j+1] + sample[j+1];
+
+ center[j] = t0;
+ center[j+1] = t1;
+
+ t0 = center[j+2] + sample[j+2];
+ t1 = center[j+3] + sample[j+3];
+
+ center[j+2] = t0;
+ center[j+3] = t1;
+ }
+ #endif
+ for( ; j < dims; j++ )
+ center[j] += sample[j];
+ counters[k]++;
+ }
+
+ if( iter > 0 )
+ max_center_shift = 0;
+
+ for( k = 0; k < K; k++ )
+ {
+ if( counters[k] != 0 )
+ continue;
+
+ // if some cluster appeared to be empty then:
+ // 1. find the biggest cluster
+ // 2. find the farthest from the center point in the biggest cluster
+ // 3. exclude the farthest point from the biggest cluster and form a new 1-point cluster.
+ int max_k = 0;
+ for( int k1 = 1; k1 < K; k1++ )
+ {
+ if( counters[max_k] < counters[k1] )
+ max_k = k1;
+ }
+
+ double max_dist = 0;
+ int farthest_i = -1;
+ float* new_center = centers.ptr<float>(k);
+ float* old_center = centers.ptr<float>(max_k);
+ float* _old_center = temp.ptr<float>(); // normalized
+ 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<float>(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<float>(farthest_i);
+
+ for( j = 0; j < dims; j++ )
+ {
+ old_center[j] -= sample[j];
+ new_center[j] += sample[j];
+ }
+ }
+
+ for( k = 0; k < K; k++ )
+ {
+ float* center = centers.ptr<float>(k);
+ CV_Assert( counters[k] != 0 );
+
+ float scale = 1.f/counters[k];
+ for( j = 0; j < dims; j++ )
+ center[j] *= scale;
+
+ if( iter > 0 )
+ {
+ double dist = 0;
+ const float* old_center = old_centers.ptr<float>(k);
+ for( j = 0; j < dims; j++ )
+ {
+ double t = center[j] - old_center[j];
+ dist += t*t;
+ }
+ max_center_shift = std::max(max_center_shift, dist);
+ }
+ }
+ }
+
+ if( ++iter == MAX(criteria.maxCount, 2) || max_center_shift <= criteria.epsilon )
+ break;
+
+ // assign labels
+ Mat dists(1, N, CV_64F);
+ double* dist = dists.ptr<double>(0);
+ parallel_for_(Range(0, N),
+ KMeansDistanceComputer(dist, labels, data, centers));
+ compactness = 0;
+ for( i = 0; i < N; i++ )
+ {
+ compactness += dist[i];
+ }
+ }
+
+ if( compactness < best_compactness )
+ {
+ best_compactness = compactness;
+ if( _centers.needed() )
+ centers.copyTo(_centers);
+ _labels.copyTo(best_labels);
+ }
+ }
+
+ return best_compactness;
+}
double result = 0;
int type = mat.type(), rows = mat.rows;
size_t step = mat.step;
- const uchar* m = mat.data;
+ const uchar* m = mat.ptr();
CV_Assert( !mat.empty() );
CV_Assert( mat.rows == mat.cols && (type == CV_32F || type == CV_64F));
Mat a(rows, rows, CV_32F, (uchar*)buffer);
mat.copyTo(a);
- result = LU((float*)a.data, a.step, rows, 0, 0, 0);
+ result = LU(a.ptr<float>(), a.step, rows, 0, 0, 0);
if( result )
{
for( int i = 0; i < rows; i++ )
- result *= ((const float*)(a.data + a.step*i))[i];
+ result *= a.at<float>(i,i);
result = 1./result;
}
}
Mat a(rows, rows, CV_64F, (uchar*)buffer);
mat.copyTo(a);
- result = LU((double*)a.data, a.step, rows, 0, 0, 0);
+ result = LU(a.ptr<double>(), a.step, rows, 0, 0, 0);
if( result )
{
for( int i = 0; i < rows; i++ )
- result *= ((const double*)(a.data + a.step*i))[i];
+ result *= a.at<double>(i,i);
result = 1./result;
}
}
AutoBuffer<uchar> _buf((m*nm + nm + nm*n)*esz + sizeof(double));
uchar* buf = alignPtr((uchar*)_buf, (int)esz);
Mat u(m, nm, type, buf);
- Mat w(nm, 1, type, u.data + m*nm*esz);
- Mat vt(nm, n, type, w.data + nm*esz);
+ Mat w(nm, 1, type, u.ptr() + m*nm*esz);
+ Mat vt(nm, n, type, w.ptr() + nm*esz);
SVD::compute(src, w, u, vt);
SVD::backSubst(w, u, vt, Mat(), _dst);
AutoBuffer<uchar> _buf((n*n*2 + n)*esz + sizeof(double));
uchar* buf = alignPtr((uchar*)_buf, (int)esz);
Mat u(n, n, type, buf);
- Mat w(n, 1, type, u.data + n*n*esz);
- Mat vt(n, n, type, w.data + n*esz);
+ Mat w(n, 1, type, u.ptr() + n*n*esz);
+ Mat vt(n, n, type, w.ptr() + n*esz);
eigen(src, w, vt);
transpose(vt, u);
if( n <= 3 )
{
- const uchar* srcdata = src.data;
- uchar* dstdata = dst.data;
+ const uchar* srcdata = src.ptr();
+ uchar* dstdata = dst.ptr();
size_t srcstep = src.step;
size_t dststep = dst.step;
setIdentity(dst);
if( method == DECOMP_LU && type == CV_32F )
- result = LU((float*)src1.data, src1.step, n, (float*)dst.data, dst.step, n) != 0;
+ result = LU(src1.ptr<float>(), src1.step, n, dst.ptr<float>(), dst.step, n) != 0;
else if( method == DECOMP_LU && type == CV_64F )
- result = LU((double*)src1.data, src1.step, n, (double*)dst.data, dst.step, n) != 0;
+ result = LU(src1.ptr<double>(), src1.step, n, dst.ptr<double>(), dst.step, n) != 0;
else if( method == DECOMP_CHOLESKY && type == CV_32F )
- result = Cholesky((float*)src1.data, src1.step, n, (float*)dst.data, dst.step, n);
+ result = Cholesky(src1.ptr<float>(), src1.step, n, dst.ptr<float>(), dst.step, n);
else
- result = Cholesky((double*)src1.data, src1.step, n, (double*)dst.data, dst.step, n);
+ result = Cholesky(src1.ptr<double>(), src1.step, n, dst.ptr<double>(), dst.step, n);
if( !result )
dst = Scalar(0);
#define bf(y) ((float*)(bdata + y*src2step))[0]
#define bd(y) ((double*)(bdata + y*src2step))[0]
- const uchar* srcdata = src.data;
- const uchar* bdata = _src2.data;
- uchar* dstdata = dst.data;
+ const uchar* srcdata = src.ptr();
+ const uchar* bdata = _src2.ptr();
+ uchar* dstdata = dst.ptr();
size_t srcstep = src.step;
size_t src2step = _src2.step;
size_t dststep = dst.step;
eigen(src, evals, evects);
if( evects0.data != evects.data )
{
- const uchar* p = evects0.data;
+ const uchar* p = evects0.ptr();
evects.convertTo(evects0, evects0.type());
- CV_Assert( p == evects0.data );
+ CV_Assert( p == evects0.ptr() );
}
}
else
eigen(src, evals);
if( evals0.data != evals.data )
{
- const uchar* p = evals0.data;
+ const uchar* p = evals0.ptr();
if( evals0.size() == evals.size() )
evals.convertTo(evals0, evals0.type());
else if( evals0.type() == evals.type() )
cv::transpose(evals, evals0);
else
cv::Mat(evals.t()).convertTo(evals0, evals0.type());
- CV_Assert( p == evals0.data );
+ CV_Assert( p == evals0.ptr() );
}
}
cv::SVD svd;
if( w.size() == cv::Size(nm, 1) )
- svd.w = cv::Mat(nm, 1, type, w.data );
+ svd.w = cv::Mat(nm, 1, type, w.ptr() );
else if( w.isContinuous() )
svd.w = w;
((m != n && (svd.u.size() == cv::Size(mn, mn) ||
svd.vt.size() == cv::Size(mn, mn))) ? cv::SVD::FULL_UV : 0));
- if( u.data )
+ if( !u.empty() )
{
if( flags & CV_SVD_U_T )
cv::transpose( svd.u, u );
}
}
- if( v.data )
+ if( !v.empty() )
{
if( !(flags & CV_SVD_V_T) )
cv::transpose( svd.vt, v );
#include <climits>
#include <algorithm>
#include <cstdarg>
-#include <debug.hpp>
-namespace cv{namespace optim{
+#define dprintf(x)
+#define print_matrix(x)
+
+namespace cv
+{
+
using std::vector;
#ifdef ALEX_DEBUG
A(i,col2)=tmp;
}
}
-}}
+}
depth == CV_64F ? (ippsPolarToCart)ippsPolarToCart_64f : 0;
CV_Assert(ippFunc != 0);
- IppStatus status = ippFunc(Mag.data, Angle.data, X.data, Y.data, static_cast<int>(cn * X.total()));
+ IppStatus status = ippFunc(Mag.ptr(), Angle.ptr(), X.ptr(), Y.ptr(), static_cast<int>(cn * X.total()));
if (status >= 0)
return;
setIppErrorStatus();
}
size.width *= cn;
- IppStatus status = ippiSqr_32f_C1R((const Ipp32f *)src.data, srcstep, (Ipp32f *)dst.data, dststep, ippiSize(size.width, size.height));
+ IppStatus status = ippiSqr_32f_C1R(src.ptr<Ipp32f>(), srcstep, dst.ptr<Ipp32f>(), dststep, ippiSize(size.width, size.height));
if (status >= 0)
return;
if (src.isContinuous() && dst.isContinuous())
{
IppStatus status = depth == CV_32F ?
- ippsPowx_32f_A21((const Ipp32f *)src.data, (Ipp32f)power, (Ipp32f*)dst.data, (Ipp32s)(src.total() * cn)) :
- ippsPowx_64f_A50((const Ipp64f *)src.data, power, (Ipp64f*)dst.data, (Ipp32s)(src.total() * cn));
+ ippsPowx_32f_A21(src.ptr<Ipp32f>(), (Ipp32f)power, dst.ptr<Ipp32f>(), (Ipp32s)(src.total() * cn)) :
+ ippsPowx_64f_A50(src.ptr<Ipp64f>(), power, dst.ptr<Ipp64f>(), (Ipp32s)(src.total() * cn));
if (status >= 0)
return;
{
Cv32suf a, b;
int ia, ib;
- const int* isrc = (const int*)src.data;
+ const int* isrc = src.ptr<int>();
size_t step = src.step/sizeof(isrc[0]);
a.f = (float)std::max(minVal, (double)-FLT_MAX);
{
Cv64suf a, b;
int64 ia, ib;
- const int64* isrc = (const int64*)src.data;
+ const int64* isrc = src.ptr<int64>();
size_t step = src.step/sizeof(isrc[0]);
a.f = minVal;
InputArray matC, double beta, OutputArray _matD, int flags )
{
#ifdef HAVE_CLAMDBLAS
- CV_OCL_RUN(ocl::haveAmdBlas() && matA.dims() <= 2 && matB.dims() <= 2 && matC.dims() <= 2 && _matD.isUMat(),
- ocl_gemm(matA, matB, alpha, matC, beta, _matD, flags))
+ CV_OCL_RUN(ocl::haveAmdBlas() && matA.dims() <= 2 && matB.dims() <= 2 && matC.dims() <= 2 && _matD.isUMat() &&
+ matA.cols() > 20 && matA.rows() > 20 && matB.cols() > 20, // since it works incorrect for small sizes
+ ocl_gemm(matA, matB, alpha, matC, beta, _matD, flags))
#endif
const int block_lin_size = 128;
break;
}
- if( C.data )
+ if( !C.empty() )
{
CV_Assert( C.type() == type &&
(((flags&GEMM_3_T) == 0 && C.rows == d_size.height && C.cols == d_size.width) ||
{
if( type == CV_32F )
{
- float* d = (float*)D.data;
- const float *a = (const float*)A.data,
- *b = (const float*)B.data,
+ float* d = D.ptr<float>();
+ const float *a = A.ptr<float>(),
+ *b = B.ptr<float>(),
*c = (const float*)C.data;
size_t d_step = D.step/sizeof(d[0]),
a_step = A.step/sizeof(a[0]),
if( type == CV_64F )
{
- double* d = (double*)D.data;
- const double *a = (const double*)A.data,
- *b = (const double*)B.data,
+ double* d = D.ptr<double>();
+ const double *a = A.ptr<double>(),
+ *b = B.ptr<double>(),
*c = (const double*)C.data;
size_t d_step = D.step/sizeof(d[0]),
a_step = A.step/sizeof(a[0]),
GEMMBlockMulFunc blockMulFunc;
GEMMStoreFunc storeFunc;
Mat *matD = &D, tmat;
+ int tmat_size = 0;
const uchar* Cdata = C.data;
size_t Cstep = C.data ? (size_t)C.step : 0;
AutoBuffer<uchar> buf;
if( D.data == A.data || D.data == B.data )
{
- buf.allocate(d_size.width*d_size.height*CV_ELEM_SIZE(type));
- tmat = Mat(d_size.height, d_size.width, type, (uchar*)buf );
+ tmat_size = d_size.width*d_size.height*CV_ELEM_SIZE(type);
+ // Allocate tmat later, once the size of buf is known
matD = &tmat;
}
(d_size.width <= block_lin_size &&
d_size.height <= block_lin_size && len <= block_lin_size) )
{
- singleMulFunc( A.data, A.step, B.data, b_step, Cdata, Cstep,
- matD->data, matD->step, a_size, d_size, alpha, beta, flags );
+ if( tmat_size > 0 ) {
+ buf.allocate(tmat_size);
+ tmat = Mat(d_size.height, d_size.width, type, (uchar*)buf );
+ }
+ singleMulFunc( A.ptr(), A.step, B.ptr(), b_step, Cdata, Cstep,
+ matD->ptr(), matD->step, a_size, d_size, alpha, beta, flags );
}
else
{
else
b_step0 = elem_size, b_step1 = b_step;
- if( !C.data )
+ if( C.empty() )
{
c_step0 = c_step1 = 0;
flags &= ~GEMM_3_T;
flags &= ~GEMM_1_T;
}
- buf.allocate(a_buf_size + b_buf_size + d_buf_size);
+ buf.allocate(d_buf_size + b_buf_size + a_buf_size + tmat_size);
d_buf = (uchar*)buf;
b_buf = d_buf + d_buf_size;
if( is_a_t )
a_buf = b_buf + b_buf_size;
+ if( tmat_size > 0 )
+ tmat = Mat(d_size.height, d_size.width, type, b_buf + b_buf_size + a_buf_size );
for( i = 0; i < d_size.height; i += di )
{
for( j = 0; j < d_size.width; j += dj )
{
- uchar* _d = matD->data + i*matD->step + j*elem_size;
+ uchar* _d = matD->ptr() + i*matD->step + j*elem_size;
const uchar* _c = Cdata + i*c_step0 + j*c_step1;
size_t _d_step = matD->step;
dj = dn0;
for( k = 0; k < len; k += dk )
{
- const uchar* _a = A.data + i*a_step0 + k*a_step1;
+ const uchar* _a = A.ptr() + i*a_step0 + k*a_step1;
size_t _a_step = A.step;
- const uchar* _b = B.data + k*b_step0 + j*b_step1;
+ const uchar* _b = B.ptr() + k*b_step0 + j*b_step1;
size_t _b_step = b_step;
Size a_bl_size;
if( dk0 < len )
storeFunc( _c, Cstep, _d, _d_step,
- matD->data + i*matD->step + j*elem_size,
+ matD->ptr(i) + j*elem_size,
matD->step, Size(dj,di), alpha, beta, flags );
}
}
_mbuf.allocate(dcn*(scn+1));
mbuf = (double*)_mbuf;
Mat tmp(dcn, scn+1, mtype, mbuf);
- memset(tmp.data, 0, tmp.total()*tmp.elemSize());
+ memset(tmp.ptr(), 0, tmp.total()*tmp.elemSize());
if( m.cols == scn+1 )
m.convertTo(tmp, mtype);
else
m = tmp;
}
else
- mbuf = (double*)m.data;
+ mbuf = m.ptr<double>();
if( scn == dcn )
{
m = tmp;
}
else
- mbuf = (double*)m.data;
+ mbuf = m.ptr<double>();
TransformFunc func = depth == CV_32F ?
(TransformFunc)perspectiveTransform_32f :
if (src1.isContinuous() && src2.isContinuous() && dst.isContinuous())
{
size_t len = src1.total()*cn;
- func(src1.data, src2.data, dst.data, (int)len, palpha);
+ func(src1.ptr(), src2.ptr(), dst.ptr(), (int)len, palpha);
return;
}
{
CV_Assert( data[i].size() == size && data[i].type() == type );
if( data[i].isContinuous() )
- memcpy( _data.ptr(i), data[i].data, sz*esz );
+ memcpy( _data.ptr(i), data[i].ptr(), sz*esz );
else
{
Mat dataRow(size.height, size.width, type, _data.ptr(i));
if( depth == CV_32F )
{
- const float* src1 = (const float*)v1.data;
- const float* src2 = (const float*)v2.data;
+ const float* src1 = v1.ptr<float>();
+ const float* src2 = v2.ptr<float>();
size_t step1 = v1.step/sizeof(src1[0]);
size_t step2 = v2.step/sizeof(src2[0]);
double* diff = buf;
- const float* mat = (const float*)icovar.data;
+ const float* mat = icovar.ptr<float>();
size_t matstep = icovar.step/sizeof(mat[0]);
for( ; sz.height--; src1 += step1, src2 += step2, diff += sz.width )
}
else if( depth == CV_64F )
{
- const double* src1 = (const double*)v1.data;
- const double* src2 = (const double*)v2.data;
+ const double* src1 = v1.ptr<double>();
+ const double* src2 = v2.ptr<double>();
size_t step1 = v1.step/sizeof(src1[0]);
size_t step2 = v2.step/sizeof(src2[0]);
double* diff = buf;
- const double* mat = (const double*)icovar.data;
+ const double* mat = icovar.ptr<double>();
size_t matstep = icovar.step/sizeof(mat[0]);
for( ; sz.height--; src1 += step1, src2 += step2, diff += sz.width )
MulTransposedR( const Mat& srcmat, Mat& dstmat, const Mat& deltamat, double scale )
{
int i, j, k;
- const sT* src = (const sT*)srcmat.data;
- dT* dst = (dT*)dstmat.data;
- const dT* delta = (const dT*)deltamat.data;
+ const sT* src = srcmat.ptr<sT>();
+ dT* dst = dstmat.ptr<dT>();
+ const dT* delta = deltamat.ptr<dT>();
size_t srcstep = srcmat.step/sizeof(src[0]);
size_t dststep = dstmat.step/sizeof(dst[0]);
size_t deltastep = deltamat.rows > 1 ? deltamat.step/sizeof(delta[0]) : 0;
MulTransposedL( const Mat& srcmat, Mat& dstmat, const Mat& deltamat, double scale )
{
int i, j, k;
- const sT* src = (const sT*)srcmat.data;
- dT* dst = (dT*)dstmat.data;
- const dT* delta = (const dT*)deltamat.data;
+ const sT* src = srcmat.ptr<sT>();
+ dT* dst = dstmat.ptr<dT>();
+ const dT* delta = deltamat.ptr<dT>();
size_t srcstep = srcmat.step/sizeof(src[0]);
size_t dststep = dstmat.step/sizeof(dst[0]);
size_t deltastep = deltamat.rows > 1 ? deltamat.step/sizeof(delta[0]) : 0;
dtype = std::max(std::max(CV_MAT_DEPTH(dtype >= 0 ? dtype : stype), delta.depth()), CV_32F);
CV_Assert( src.channels() == 1 );
- if( delta.data )
+ if( !delta.empty() )
{
CV_Assert( delta.channels() == 1 &&
(delta.rows == src.rows || delta.rows == 1) &&
{
Mat src2;
const Mat* tsrc = &src;
- if( delta.data )
+ if( !delta.empty() )
{
if( delta.size() == src.size() )
subtract( src, delta, src2 );
return r;
}
-/****************************************************************************************\
-* PCA *
-\****************************************************************************************/
-
-PCA::PCA() {}
-
-PCA::PCA(InputArray data, InputArray _mean, int flags, int maxComponents)
-{
- operator()(data, _mean, flags, maxComponents);
-}
-
-PCA::PCA(InputArray data, InputArray _mean, int flags, double retainedVariance)
-{
- operator()(data, _mean, flags, retainedVariance);
-}
-
-PCA& PCA::operator()(InputArray _data, InputArray __mean, int flags, int maxComponents)
-{
- Mat data = _data.getMat(), _mean = __mean.getMat();
- int covar_flags = CV_COVAR_SCALE;
- int i, len, in_count;
- Size mean_sz;
-
- CV_Assert( data.channels() == 1 );
- if( flags & CV_PCA_DATA_AS_COL )
- {
- len = data.rows;
- in_count = data.cols;
- covar_flags |= CV_COVAR_COLS;
- mean_sz = Size(1, len);
- }
- else
- {
- len = data.cols;
- in_count = data.rows;
- covar_flags |= CV_COVAR_ROWS;
- mean_sz = Size(len, 1);
- }
-
- int count = std::min(len, in_count), out_count = count;
- if( maxComponents > 0 )
- out_count = std::min(count, maxComponents);
-
- // "scrambled" way to compute PCA (when cols(A)>rows(A)):
- // B = A'A; B*x=b*x; C = AA'; C*y=c*y -> AA'*y=c*y -> A'A*(A'*y)=c*(A'*y) -> c = b, x=A'*y
- if( len <= in_count )
- covar_flags |= CV_COVAR_NORMAL;
-
- int ctype = std::max(CV_32F, data.depth());
- mean.create( mean_sz, ctype );
-
- Mat covar( count, count, ctype );
-
- if( _mean.data )
- {
- CV_Assert( _mean.size() == mean_sz );
- _mean.convertTo(mean, ctype);
- covar_flags |= CV_COVAR_USE_AVG;
- }
-
- calcCovarMatrix( data, covar, mean, covar_flags, ctype );
- eigen( covar, eigenvalues, eigenvectors );
-
- if( !(covar_flags & CV_COVAR_NORMAL) )
- {
- // CV_PCA_DATA_AS_ROW: cols(A)>rows(A). x=A'*y -> x'=y'*A
- // CV_PCA_DATA_AS_COL: rows(A)>cols(A). x=A''*y -> x'=y'*A'
- Mat tmp_data, tmp_mean = repeat(mean, data.rows/mean.rows, data.cols/mean.cols);
- if( data.type() != ctype || tmp_mean.data == mean.data )
- {
- data.convertTo( tmp_data, ctype );
- subtract( tmp_data, tmp_mean, tmp_data );
- }
- else
- {
- subtract( data, tmp_mean, tmp_mean );
- tmp_data = tmp_mean;
- }
-
- Mat evects1(count, len, ctype);
- gemm( eigenvectors, tmp_data, 1, Mat(), 0, evects1,
- (flags & CV_PCA_DATA_AS_COL) ? CV_GEMM_B_T : 0);
- eigenvectors = evects1;
-
- // normalize eigenvectors
- for( i = 0; i < out_count; i++ )
- {
- Mat vec = eigenvectors.row(i);
- normalize(vec, vec);
- }
- }
-
- if( count > out_count )
- {
- // use clone() to physically copy the data and thus deallocate the original matrices
- eigenvalues = eigenvalues.rowRange(0,out_count).clone();
- eigenvectors = eigenvectors.rowRange(0,out_count).clone();
- }
- return *this;
-}
-
-void PCA::write(FileStorage& fs ) const
-{
- CV_Assert( fs.isOpened() );
-
- fs << "name" << "PCA";
- fs << "vectors" << eigenvectors;
- fs << "values" << eigenvalues;
- fs << "mean" << mean;
-}
-
-void PCA::read(const FileNode& fs)
-{
- CV_Assert( !fs.empty() );
- String name = (String)fs["name"];
- CV_Assert( name == "PCA" );
-
- cv::read(fs["vectors"], eigenvectors);
- cv::read(fs["values"], eigenvalues);
- cv::read(fs["mean"], mean);
-}
-
-template <typename T>
-int computeCumulativeEnergy(const Mat& eigenvalues, double retainedVariance)
-{
- CV_DbgAssert( eigenvalues.type() == DataType<T>::type );
-
- Mat g(eigenvalues.size(), DataType<T>::type);
-
- for(int ig = 0; ig < g.rows; ig++)
- {
- g.at<T>(ig, 0) = 0;
- for(int im = 0; im <= ig; im++)
- {
- g.at<T>(ig,0) += eigenvalues.at<T>(im,0);
- }
- }
-
- int L;
-
- for(L = 0; L < eigenvalues.rows; L++)
- {
- double energy = g.at<T>(L, 0) / g.at<T>(g.rows - 1, 0);
- if(energy > retainedVariance)
- break;
- }
-
- L = std::max(2, L);
-
- return L;
-}
-
-PCA& PCA::operator()(InputArray _data, InputArray __mean, int flags, double retainedVariance)
-{
- Mat data = _data.getMat(), _mean = __mean.getMat();
- int covar_flags = CV_COVAR_SCALE;
- int i, len, in_count;
- Size mean_sz;
-
- CV_Assert( data.channels() == 1 );
- if( flags & CV_PCA_DATA_AS_COL )
- {
- len = data.rows;
- in_count = data.cols;
- covar_flags |= CV_COVAR_COLS;
- mean_sz = Size(1, len);
- }
- else
- {
- len = data.cols;
- in_count = data.rows;
- covar_flags |= CV_COVAR_ROWS;
- mean_sz = Size(len, 1);
- }
-
- CV_Assert( retainedVariance > 0 && retainedVariance <= 1 );
-
- int count = std::min(len, in_count);
-
- // "scrambled" way to compute PCA (when cols(A)>rows(A)):
- // B = A'A; B*x=b*x; C = AA'; C*y=c*y -> AA'*y=c*y -> A'A*(A'*y)=c*(A'*y) -> c = b, x=A'*y
- if( len <= in_count )
- covar_flags |= CV_COVAR_NORMAL;
-
- int ctype = std::max(CV_32F, data.depth());
- mean.create( mean_sz, ctype );
-
- Mat covar( count, count, ctype );
-
- if( _mean.data )
- {
- CV_Assert( _mean.size() == mean_sz );
- _mean.convertTo(mean, ctype);
- }
-
- calcCovarMatrix( data, covar, mean, covar_flags, ctype );
- eigen( covar, eigenvalues, eigenvectors );
-
- if( !(covar_flags & CV_COVAR_NORMAL) )
- {
- // CV_PCA_DATA_AS_ROW: cols(A)>rows(A). x=A'*y -> x'=y'*A
- // CV_PCA_DATA_AS_COL: rows(A)>cols(A). x=A''*y -> x'=y'*A'
- Mat tmp_data, tmp_mean = repeat(mean, data.rows/mean.rows, data.cols/mean.cols);
- if( data.type() != ctype || tmp_mean.data == mean.data )
- {
- data.convertTo( tmp_data, ctype );
- subtract( tmp_data, tmp_mean, tmp_data );
- }
- else
- {
- subtract( data, tmp_mean, tmp_mean );
- tmp_data = tmp_mean;
- }
-
- Mat evects1(count, len, ctype);
- gemm( eigenvectors, tmp_data, 1, Mat(), 0, evects1,
- (flags & CV_PCA_DATA_AS_COL) ? CV_GEMM_B_T : 0);
- eigenvectors = evects1;
-
- // normalize all eigenvectors
- for( i = 0; i < eigenvectors.rows; i++ )
- {
- Mat vec = eigenvectors.row(i);
- normalize(vec, vec);
- }
- }
-
- // compute the cumulative energy content for each eigenvector
- int L;
- if (ctype == CV_32F)
- L = computeCumulativeEnergy<float>(eigenvalues, retainedVariance);
- else
- L = computeCumulativeEnergy<double>(eigenvalues, retainedVariance);
-
- // use clone() to physically copy the data and thus deallocate the original matrices
- eigenvalues = eigenvalues.rowRange(0,L).clone();
- eigenvectors = eigenvectors.rowRange(0,L).clone();
-
- return *this;
-}
-
-void PCA::project(InputArray _data, OutputArray result) const
-{
- Mat data = _data.getMat();
- CV_Assert( mean.data && eigenvectors.data &&
- ((mean.rows == 1 && mean.cols == data.cols) || (mean.cols == 1 && mean.rows == data.rows)));
- Mat tmp_data, tmp_mean = repeat(mean, data.rows/mean.rows, data.cols/mean.cols);
- int ctype = mean.type();
- if( data.type() != ctype || tmp_mean.data == mean.data )
- {
- data.convertTo( tmp_data, ctype );
- subtract( tmp_data, tmp_mean, tmp_data );
- }
- else
- {
- subtract( data, tmp_mean, tmp_mean );
- tmp_data = tmp_mean;
- }
- if( mean.rows == 1 )
- gemm( tmp_data, eigenvectors, 1, Mat(), 0, result, GEMM_2_T );
- else
- gemm( eigenvectors, tmp_data, 1, Mat(), 0, result, 0 );
-}
-
-Mat PCA::project(InputArray data) const
-{
- Mat result;
- project(data, result);
- return result;
-}
-
-void PCA::backProject(InputArray _data, OutputArray result) const
-{
- Mat data = _data.getMat();
- CV_Assert( mean.data && eigenvectors.data &&
- ((mean.rows == 1 && eigenvectors.rows == data.cols) ||
- (mean.cols == 1 && eigenvectors.rows == data.rows)));
-
- Mat tmp_data, tmp_mean;
- data.convertTo(tmp_data, mean.type());
- if( mean.rows == 1 )
- {
- tmp_mean = repeat(mean, data.rows, 1);
- gemm( tmp_data, eigenvectors, 1, tmp_mean, 1, result, 0 );
- }
- else
- {
- tmp_mean = repeat(mean, 1, data.cols);
- gemm( eigenvectors, tmp_data, 1, tmp_mean, 1, result, GEMM_1_T );
- }
-}
-
-Mat PCA::backProject(InputArray data) const
-{
- Mat result;
- backProject(data, result);
- return result;
-}
-
-}
-
-void cv::PCACompute(InputArray data, InputOutputArray mean,
- OutputArray eigenvectors, int maxComponents)
-{
- PCA pca;
- pca(data, mean, 0, maxComponents);
- pca.mean.copyTo(mean);
- pca.eigenvectors.copyTo(eigenvectors);
-}
-
-void cv::PCACompute(InputArray data, InputOutputArray mean,
- OutputArray eigenvectors, double retainedVariance)
-{
- PCA pca;
- pca(data, mean, 0, retainedVariance);
- pca.mean.copyTo(mean);
- pca.eigenvectors.copyTo(eigenvectors);
-}
-
-void cv::PCAProject(InputArray data, InputArray mean,
- InputArray eigenvectors, OutputArray result)
-{
- PCA pca;
- pca.mean = mean.getMat();
- pca.eigenvectors = eigenvectors.getMat();
- pca.project(data, result);
-}
-
-void cv::PCABackProject(InputArray data, InputArray mean,
- InputArray eigenvectors, OutputArray result)
-{
- PCA pca;
- pca.mean = mean.getMat();
- pca.eigenvectors = eigenvectors.getMat();
- pca.backProject(data, result);
}
/****************************************************************************************\
pca.eigenvectors = evects;
pca(data, (flags & CV_PCA_USE_AVG) ? mean : cv::Mat(),
- flags, evals.data ? evals.rows + evals.cols - 1 : 0);
+ flags, !evals.empty() ? evals.rows + evals.cols - 1 : 0);
if( pca.mean.size() == mean.size() )
pca.mean.convertTo( mean, mean.type() );
m.datalimit = m.datastart + m.size[0]*m.step[0];
if( m.size[0] > 0 )
{
- m.dataend = m.data + m.size[d-1]*m.step[d-1];
+ m.dataend = m.ptr() + m.size[d-1]*m.step[d-1];
for( int i = 0; i < d-1; i++ )
m.dataend += (m.size[i] - 1)*m.step[i];
}
}
Mat buf(total, 1, type);
- cvCvtSeqToArray(seq, buf.data, CV_WHOLE_SEQ);
+ cvCvtSeqToArray(seq, buf.ptr(), CV_WHOLE_SEQ);
return buf;
}
CV_Error(CV_StsBadArg, "Unknown array type");
{
CV_Assert( i < 0 );
const Mat * const m = ((const Mat*)obj);
- return (size_t)(m->data - m->datastart);
+ return (size_t)(m->ptr() - m->datastart);
}
if( k == UMAT )
return 1;
CV_Assert( i < (int)vv.size() );
- return (size_t)(vv[i].data - vv[i].datastart);
+ return (size_t)(vv[i].ptr() - vv[i].datastart);
}
if( k == STD_VECTOR_UMAT )
{
Mat value = arr.getMat();
CV_Assert( checkScalar(value, type(), arr.kind(), _InputArray::GPU_MAT) );
- ((cuda::GpuMat*)obj)->setTo(Scalar(Vec<double, 4>((double *)value.data)), mask);
+ ((cuda::GpuMat*)obj)->setTo(Scalar(Vec<double, 4>(value.ptr<double>())), mask);
}
else
CV_Error(Error::StsNotImplemented, "");
if( type == CV_32FC1 )
{
- float* data = (float*)m.data;
+ float* data = m.ptr<float>();
float val = (float)s[0];
size_t step = m.step/sizeof(data[0]);
}
else if( type == CV_64FC1 )
{
- double* data = (double*)m.data;
+ double* data = m.ptr<double>();
double val = s[0];
size_t step = m.step/sizeof(data[0]);
if( type == CV_32FC1 )
{
- const float* ptr = (const float*)m.data;
+ const float* ptr = m.ptr<float>();
size_t step = m.step/sizeof(ptr[0]) + 1;
double _s = 0;
for( i = 0; i < nm; i++ )
if( type == CV_64FC1 )
{
- const double* ptr = (const double*)m.data;
+ const double* ptr = m.ptr<double>();
size_t step = m.step/sizeof(ptr[0]) + 1;
double _s = 0;
for( i = 0; i < nm; i++ )
CV_Assert(dst.cols == dst.rows);
kernelName += "_inplace";
}
+ else
+ {
+ // check required local memory size
+ size_t required_local_memory = (size_t) TILE_DIM*(TILE_DIM+1)*CV_ELEM_SIZE(type);
+ if (required_local_memory > ocl::Device::getDefault().localMemSize())
+ return false;
+ }
ocl::Kernel k(kernelName.c_str(), ocl::core::transpose_oclsrc,
format("-D T=%s -D T1=%s -D cn=%d -D TILE_DIM=%d -D BLOCK_ROWS=%d -D rowsPerWI=%d",
IppiSize roiSize = { src.cols, src.rows };
if (ippFunc != 0)
{
- if (ippFunc(src.data, (int)src.step, dst.data, (int)dst.step, roiSize) >= 0)
+ if (ippFunc(src.ptr(), (int)src.step, dst.ptr(), (int)dst.step, roiSize) >= 0)
return;
setIppErrorStatus();
}
else if (ippFuncI != 0)
{
- if (ippFuncI(dst.data, (int)dst.step, roiSize) >= 0)
+ if (ippFuncI(dst.ptr(), (int)dst.step, roiSize) >= 0)
return;
setIppErrorStatus();
}
TransposeInplaceFunc func = transposeInplaceTab[esz];
CV_Assert( func != 0 );
CV_Assert( dst.cols == dst.rows );
- func( dst.data, dst.step, dst.rows );
+ func( dst.ptr(), dst.step, dst.rows );
}
else
{
TransposeFunc func = transposeTab[esz];
CV_Assert( func != 0 );
- func( src.data, src.step, dst.data, dst.step, src.size() );
+ func( src.ptr(), src.step, dst.ptr(), dst.step, src.size() );
}
}
int rows = m.rows;
int j0 = 0, j1 = rows;
- uchar* data = m.data;
+ uchar* data = m.ptr();
for( int i = 0; i < rows; i++ )
{
if( !LtoR ) j1 = i; else j0 = i+1;
size.width *= srcmat.channels();
AutoBuffer<WT> buffer(size.width);
WT* buf = buffer;
- ST* dst = (ST*)dstmat.data;
- const T* src = (const T*)srcmat.data;
+ ST* dst = dstmat.ptr<ST>();
+ const T* src = srcmat.ptr<T>();
size_t srcstep = srcmat.step/sizeof(src[0]);
int i;
Op op;
for( int y = 0; y < size.height; y++ )
{
- const T* src = (const T*)(srcmat.data + srcmat.step*y);
- ST* dst = (ST*)(dstmat.data + dstmat.step*y);
+ const T* src = srcmat.ptr<T>(y);
+ ST* dst = dstmat.ptr<ST>(y);
if( size.width == cn )
for( k = 0; k < cn; k++ )
dst[k] = src[k];
if (ippFunc)
{
for (int y = 0; y < size.height; ++y)
- if (ippFunc(srcmat.data + sstep * y, sstep, roisize, dstmat.ptr<Ipp64f>(y)) < 0)
+ if (ippFunc(srcmat.ptr(y), sstep, roisize, dstmat.ptr<Ipp64f>(y)) < 0)
{
setIppErrorStatus();
cv::Mat dstroi = dstmat.rowRange(y, y + 1);
else if (ippFuncHint)
{
for (int y = 0; y < size.height; ++y)
- if (ippFuncHint(srcmat.data + sstep * y, sstep, roisize, dstmat.ptr<Ipp64f>(y), ippAlgHintAccurate) < 0)
+ if (ippFuncHint(srcmat.ptr(y), sstep, roisize, dstmat.ptr<Ipp64f>(y), ippAlgHintAccurate) < 0)
{
setIppErrorStatus();
cv::Mat dstroi = dstmat.rowRange(y, y + 1);
if (!doubleSupport && (sdepth == CV_64F || ddepth == CV_64F))
return false;
- if ((op == CV_REDUCE_SUM && sdepth == CV_32F) || op == CV_REDUCE_MIN || op == CV_REDUCE_MAX)
- return false;
-
if (op == CV_REDUCE_AVG)
{
if (sdepth < CV_32S && ddepth < CV_32S)
T* ptr = bptr;
if( sortRows )
{
- T* dptr = (T*)(dst.data + dst.step*i);
+ T* dptr = dst.ptr<T>(i);
if( !inplace )
{
- const T* sptr = (const T*)(src.data + src.step*i);
+ const T* sptr = src.ptr<T>(i);
memcpy(dptr, sptr, sizeof(T) * len);
}
ptr = dptr;
else
{
for( j = 0; j < len; j++ )
- ptr[j] = ((const T*)(src.data + src.step*j))[i];
+ ptr[j] = src.ptr<T>(j)[i];
}
#ifdef USE_IPP_SORT
if( !sortRows )
for( j = 0; j < len; j++ )
- ((T*)(dst.data + dst.step*j))[i] = ptr[j];
+ dst.ptr<T>(j)[i] = ptr[j];
}
}
if( sortRows )
{
ptr = (T*)(src.data + src.step*i);
- iptr = (int*)(dst.data + dst.step*i);
+ iptr = dst.ptr<int>(i);
}
else
{
for( j = 0; j < len; j++ )
- ptr[j] = ((const T*)(src.data + src.step*j))[i];
+ ptr[j] = src.ptr<T>(j)[i];
}
for( j = 0; j < len; j++ )
iptr[j] = j;
if( !sortRows )
for( j = 0; j < len; j++ )
- ((int*)(dst.data + dst.step*j))[i] = iptr[j];
+ dst.ptr<int>(j)[i] = iptr[j];
}
}
}
-////////////////////////////////////////// kmeans ////////////////////////////////////////////
-
-namespace cv
-{
-
-static void generateRandomCenter(const std::vector<Vec2f>& box, float* center, RNG& rng)
-{
- size_t j, dims = box.size();
- float margin = 1.f/dims;
- for( j = 0; j < dims; j++ )
- center[j] = ((float)rng*(1.f+margin*2.f)-margin)*(box[j][1] - box[j][0]) + box[j][0];
-}
-
-class KMeansPPDistanceComputer : public ParallelLoopBody
-{
-public:
- KMeansPPDistanceComputer( float *_tdist2,
- const float *_data,
- const float *_dist,
- int _dims,
- size_t _step,
- size_t _stepci )
- : tdist2(_tdist2),
- data(_data),
- dist(_dist),
- dims(_dims),
- step(_step),
- stepci(_stepci) { }
-
- void operator()( const cv::Range& range ) const
- {
- const int begin = range.start;
- const int end = range.end;
-
- for ( int i = begin; i<end; i++ )
- {
- tdist2[i] = std::min(normL2Sqr_(data + step*i, data + stepci, dims), dist[i]);
- }
- }
-
-private:
- KMeansPPDistanceComputer& operator=(const KMeansPPDistanceComputer&); // to quiet MSVC
-
- float *tdist2;
- const float *data;
- const float *dist;
- const int dims;
- const size_t step;
- const size_t stepci;
-};
-
-/*
-k-means center initialization using the following algorithm:
-Arthur & Vassilvitskii (2007) k-means++: The Advantages of Careful Seeding
-*/
-static void generateCentersPP(const Mat& _data, Mat& _out_centers,
- int K, RNG& rng, int trials)
-{
- int i, j, k, dims = _data.cols, N = _data.rows;
- const float* data = _data.ptr<float>(0);
- size_t step = _data.step/sizeof(data[0]);
- std::vector<int> _centers(K);
- int* centers = &_centers[0];
- std::vector<float> _dist(N*3);
- float* dist = &_dist[0], *tdist = dist + N, *tdist2 = tdist + N;
- double sum0 = 0;
-
- centers[0] = (unsigned)rng % N;
-
- for( i = 0; i < N; i++ )
- {
- dist[i] = normL2Sqr_(data + step*i, data + step*centers[0], dims);
- sum0 += dist[i];
- }
-
- for( k = 1; k < K; k++ )
- {
- double bestSum = DBL_MAX;
- int bestCenter = -1;
-
- for( j = 0; j < trials; j++ )
- {
- double p = (double)rng*sum0, s = 0;
- for( i = 0; i < N-1; i++ )
- if( (p -= dist[i]) <= 0 )
- break;
- int ci = i;
-
- parallel_for_(Range(0, N),
- KMeansPPDistanceComputer(tdist2, data, dist, dims, step, step*ci));
- for( i = 0; i < N; i++ )
- {
- s += tdist2[i];
- }
-
- if( s < bestSum )
- {
- bestSum = s;
- bestCenter = ci;
- std::swap(tdist, tdist2);
- }
- }
- centers[k] = bestCenter;
- sum0 = bestSum;
- std::swap(dist, tdist);
- }
-
- for( k = 0; k < K; k++ )
- {
- const float* src = data + step*centers[k];
- float* dst = _out_centers.ptr<float>(k);
- for( j = 0; j < dims; j++ )
- dst[j] = src[j];
- }
-}
-
-class KMeansDistanceComputer : public ParallelLoopBody
-{
-public:
- KMeansDistanceComputer( double *_distances,
- int *_labels,
- const Mat& _data,
- const Mat& _centers )
- : distances(_distances),
- labels(_labels),
- data(_data),
- centers(_centers)
- {
- }
-
- void operator()( const Range& range ) const
- {
- const int begin = range.start;
- const int end = range.end;
- const int K = centers.rows;
- const int dims = centers.cols;
-
- const float *sample;
- for( int i = begin; i<end; ++i)
- {
- sample = data.ptr<float>(i);
- int k_best = 0;
- double min_dist = DBL_MAX;
-
- for( int k = 0; k < K; k++ )
- {
- const float* center = centers.ptr<float>(k);
- const double dist = normL2Sqr_(sample, center, dims);
-
- if( min_dist > dist )
- {
- min_dist = dist;
- k_best = k;
- }
- }
-
- distances[i] = min_dist;
- labels[i] = k_best;
- }
- }
-
-private:
- KMeansDistanceComputer& operator=(const KMeansDistanceComputer&); // to quiet MSVC
-
- double *distances;
- int *labels;
- const Mat& data;
- const Mat& centers;
-};
-
-}
-
-double cv::kmeans( InputArray _data, int K,
- InputOutputArray _bestLabels,
- TermCriteria criteria, int attempts,
- int flags, OutputArray _centers )
-{
- const int SPP_TRIALS = 3;
- Mat data0 = _data.getMat();
- bool isrow = data0.rows == 1 && data0.channels() > 1;
- int N = !isrow ? data0.rows : data0.cols;
- int dims = (!isrow ? data0.cols : 1)*data0.channels();
- int type = data0.depth();
-
- attempts = std::max(attempts, 1);
- CV_Assert( data0.dims <= 2 && type == CV_32F && K > 0 );
- CV_Assert( N >= K );
-
- Mat data(N, dims, CV_32F, data0.data, isrow ? dims * sizeof(float) : static_cast<size_t>(data0.step));
-
- _bestLabels.create(N, 1, CV_32S, -1, true);
-
- Mat _labels, best_labels = _bestLabels.getMat();
- if( flags & CV_KMEANS_USE_INITIAL_LABELS )
- {
- CV_Assert( (best_labels.cols == 1 || best_labels.rows == 1) &&
- best_labels.cols*best_labels.rows == N &&
- best_labels.type() == CV_32S &&
- best_labels.isContinuous());
- best_labels.copyTo(_labels);
- }
- else
- {
- if( !((best_labels.cols == 1 || best_labels.rows == 1) &&
- best_labels.cols*best_labels.rows == N &&
- best_labels.type() == CV_32S &&
- best_labels.isContinuous()))
- best_labels.create(N, 1, CV_32S);
- _labels.create(best_labels.size(), best_labels.type());
- }
- int* labels = _labels.ptr<int>();
-
- Mat centers(K, dims, type), old_centers(K, dims, type), temp(1, dims, type);
- std::vector<int> counters(K);
- std::vector<Vec2f> _box(dims);
- Vec2f* box = &_box[0];
- double best_compactness = DBL_MAX, compactness = 0;
- RNG& rng = theRNG();
- int a, iter, i, j, k;
-
- if( criteria.type & TermCriteria::EPS )
- criteria.epsilon = std::max(criteria.epsilon, 0.);
- else
- criteria.epsilon = FLT_EPSILON;
- criteria.epsilon *= criteria.epsilon;
-
- if( criteria.type & TermCriteria::COUNT )
- criteria.maxCount = std::min(std::max(criteria.maxCount, 2), 100);
- else
- criteria.maxCount = 100;
-
- if( K == 1 )
- {
- attempts = 1;
- criteria.maxCount = 2;
- }
-
- const float* sample = data.ptr<float>(0);
- for( j = 0; j < dims; j++ )
- box[j] = Vec2f(sample[j], sample[j]);
-
- for( i = 1; i < N; i++ )
- {
- sample = data.ptr<float>(i);
- for( j = 0; j < dims; j++ )
- {
- float v = sample[j];
- box[j][0] = std::min(box[j][0], v);
- box[j][1] = std::max(box[j][1], v);
- }
- }
-
- for( a = 0; a < attempts; a++ )
- {
- double max_center_shift = DBL_MAX;
- for( iter = 0;; )
- {
- swap(centers, old_centers);
-
- if( iter == 0 && (a > 0 || !(flags & KMEANS_USE_INITIAL_LABELS)) )
- {
- if( flags & KMEANS_PP_CENTERS )
- generateCentersPP(data, centers, K, rng, SPP_TRIALS);
- else
- {
- for( k = 0; k < K; k++ )
- generateRandomCenter(_box, centers.ptr<float>(k), rng);
- }
- }
- else
- {
- if( iter == 0 && a == 0 && (flags & KMEANS_USE_INITIAL_LABELS) )
- {
- for( i = 0; i < N; i++ )
- CV_Assert( (unsigned)labels[i] < (unsigned)K );
- }
-
- // compute centers
- centers = Scalar(0);
- for( k = 0; k < K; k++ )
- counters[k] = 0;
-
- for( i = 0; i < N; i++ )
- {
- sample = data.ptr<float>(i);
- k = labels[i];
- float* center = centers.ptr<float>(k);
- j=0;
- #if CV_ENABLE_UNROLLED
- for(; j <= dims - 4; j += 4 )
- {
- float t0 = center[j] + sample[j];
- float t1 = center[j+1] + sample[j+1];
-
- center[j] = t0;
- center[j+1] = t1;
-
- t0 = center[j+2] + sample[j+2];
- t1 = center[j+3] + sample[j+3];
-
- center[j+2] = t0;
- center[j+3] = t1;
- }
- #endif
- for( ; j < dims; j++ )
- center[j] += sample[j];
- counters[k]++;
- }
-
- if( iter > 0 )
- max_center_shift = 0;
-
- for( k = 0; k < K; k++ )
- {
- if( counters[k] != 0 )
- continue;
-
- // if some cluster appeared to be empty then:
- // 1. find the biggest cluster
- // 2. find the farthest from the center point in the biggest cluster
- // 3. exclude the farthest point from the biggest cluster and form a new 1-point cluster.
- int max_k = 0;
- for( int k1 = 1; k1 < K; k1++ )
- {
- if( counters[max_k] < counters[k1] )
- max_k = k1;
- }
-
- double max_dist = 0;
- int farthest_i = -1;
- float* new_center = centers.ptr<float>(k);
- float* old_center = centers.ptr<float>(max_k);
- float* _old_center = temp.ptr<float>(); // normalized
- 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<float>(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<float>(farthest_i);
-
- for( j = 0; j < dims; j++ )
- {
- old_center[j] -= sample[j];
- new_center[j] += sample[j];
- }
- }
-
- for( k = 0; k < K; k++ )
- {
- float* center = centers.ptr<float>(k);
- CV_Assert( counters[k] != 0 );
-
- float scale = 1.f/counters[k];
- for( j = 0; j < dims; j++ )
- center[j] *= scale;
-
- if( iter > 0 )
- {
- double dist = 0;
- const float* old_center = old_centers.ptr<float>(k);
- for( j = 0; j < dims; j++ )
- {
- double t = center[j] - old_center[j];
- dist += t*t;
- }
- max_center_shift = std::max(max_center_shift, dist);
- }
- }
- }
-
- if( ++iter == MAX(criteria.maxCount, 2) || max_center_shift <= criteria.epsilon )
- break;
-
- // assign labels
- Mat dists(1, N, CV_64F);
- double* dist = dists.ptr<double>(0);
- parallel_for_(Range(0, N),
- KMeansDistanceComputer(dist, labels, data, centers));
- compactness = 0;
- for( i = 0; i < N; i++ )
- {
- compactness += dist[i];
- }
- }
-
- if( compactness < best_compactness )
- {
- best_compactness = compactness;
- if( _centers.needed() )
- centers.copyTo(_centers);
- _labels.copyTo(best_labels);
- }
- }
-
- return best_compactness;
-}
-
-
CV_IMPL void cvSetIdentity( CvArr* arr, CvScalar value )
{
cv::Mat m = cv::cvarrToMat(arr);
return Point();
CV_DbgAssert(m->dims <= 2);
- ptrdiff_t ofs = ptr - m->data;
+ ptrdiff_t ofs = ptr - m->ptr();
int y = (int)(ofs/m->step[0]);
return Point((int)((ofs - y*m->step[0])/elemSize), y);
}
void MatConstIterator::pos(int* _idx) const
{
CV_Assert(m != 0 && _idx);
- ptrdiff_t ofs = ptr - m->data;
+ ptrdiff_t ofs = ptr - m->ptr();
for( int i = 0; i < m->dims; i++ )
{
size_t s = m->step[i], v = ofs/s;
return 0;
if( m->isContinuous() )
return (ptr - sliceStart)/elemSize;
- ptrdiff_t ofs = ptr - m->data;
+ ptrdiff_t ofs = ptr - m->ptr();
int i, d = m->dims;
if( d == 2 )
{
ptrdiff_t ofs0, y;
if( relative )
{
- ofs0 = ptr - m->data;
+ ofs0 = ptr - m->ptr();
y = ofs0/m->step[0];
ofs += y*m->cols + (ofs0 - y*m->step[0])/elemSize;
}
y = ofs/m->cols;
int y1 = std::min(std::max((int)y, 0), m->rows-1);
- sliceStart = m->data + y1*m->step[0];
+ sliceStart = m->ptr(y1);
sliceEnd = sliceStart + m->cols*elemSize;
ptr = y < 0 ? sliceStart : y >= m->rows ? sliceEnd :
sliceStart + (ofs - y*m->cols)*elemSize;
ptrdiff_t t = ofs/szi;
int v = (int)(ofs - t*szi);
ofs = t;
- ptr = m->data + v*elemSize;
- sliceStart = m->data;
+ ptr = m->ptr() + v*elemSize;
+ sliceStart = m->ptr();
for( int i = d-2; i >= 0; i-- )
{
if( ofs > 0 )
ptr = sliceEnd;
else
- ptr = sliceStart + (ptr - m->data);
+ ptr = sliceStart + (ptr - m->ptr());
}
void MatConstIterator::seek(const int* _idx, bool relative)
int i, idx[CV_MAX_DIM] = {0}, d = m.dims, lastSize = m.size[d - 1];
size_t esz = m.elemSize();
- uchar* dptr = m.data;
+ const uchar* dptr = m.ptr();
for(;;)
{
if (vendorName_ == "Advanced Micro Devices, Inc." ||
vendorName_ == "AMD")
vendorID_ = VENDOR_AMD;
- else if (vendorName_ == "Intel(R) Corporation")
+ else if (vendorName_ == "Intel(R) Corporation" || vendorName_ == "Intel" || strstr(name_.c_str(), "Iris") != 0)
vendorID_ = VENDOR_INTEL;
else if (vendorName_ == "NVIDIA Corporation")
vendorID_ = VENDOR_NVIDIA;
KernelArg KernelArg::Constant(const Mat& m)
{
CV_Assert(m.isContinuous());
- return KernelArg(CONSTANT, 0, 0, 0, m.data, m.total()*m.elemSize());
+ return KernelArg(CONSTANT, 0, 0, 0, m.ptr(), m.total()*m.elemSize());
}
/////////////////////////////////////////// Kernel /////////////////////////////////////////////
static std::string kerToStr(const Mat & k)
{
int width = k.cols - 1, depth = k.depth();
- const T * const data = reinterpret_cast<const T *>(k.data);
+ const T * const data = k.ptr<T>();
std::ostringstream stream;
stream.precision(10);
#ifdef OP_CALC2
#define CALC_MAX2(p) \
- maxval2 = MAX(maxval2, temp.p);
+ maxval2 = MAX(maxval2, temp2.p);
#else
#define CALC_MAX2(p)
#endif
#ifdef HAVE_SRC2
#ifdef HAVE_SRC2_CONT
- src2_index = mul24(id, srcTSIZE);
+ src2_index = id * srcTSIZE; //mul24(id, srcTSIZE);
#else
src2_index = mad24(id / cols, src2_step, mul24(id % cols, srcTSIZE));
#endif
int src_index = mad24(y, src_step, mad24(x, (int)sizeof(srcT) * cn, src_offset));
__global const srcT * src = (__global const srcT *)(srcptr + src_index);
- bufT tmp[cn] = { INIT_VALUE };
+ bufT tmp[cn];
+ #pragma unroll
+ for (int c = 0; c < cn; ++c)
+ tmp[c] = INIT_VALUE;
int src_step_mul = BUF_COLS * cn;
for (int idx = x; idx < cols; idx += BUF_COLS, src += src_step_mul)
int dst_index = mad24(y, dst_step, dst_offset);
__global dstT * dst = (__global dstT *)(dstptr + dst_index);
- bufT tmp[cn] = { INIT_VALUE };
+ bufT tmp[cn];
+ #pragma unroll
+ for (int c = 0; c < cn; ++c)
+ tmp[c] = INIT_VALUE;
#pragma unroll
for (int xin = 0; xin < BUF_COLS / 2; xin ++)
int dst_index = mad24(x, (int)sizeof(dstT0) * cn, dst_offset);
__global dstT0 * dst = (__global dstT0 *)(dstptr + dst_index);
- dstT tmp[cn] = { INIT_VALUE };
+ dstT tmp[cn];
+ #pragma unroll
+ for (int c = 0; c < cn; ++c)
+ tmp[c] = INIT_VALUE;
for (int y = 0; y < rows; ++y, src_index += src_step)
{
__global const srcT * src = (__global const srcT *)(srcptr + src_index);
__global dstT * dst = (__global dstT *)(dstptr + dst_index);
- dstT tmp[cn] = { INIT_VALUE };
+ dstT tmp[cn];
+ #pragma unroll
+ for (int c = 0; c < cn; ++c)
+ tmp[c] = INIT_VALUE;
for (int x = 0; x < cols; ++x, src += cn)
{
{
ProxyLoopBody pbody(body, range, nstripes);
cv::Range stripeRange = pbody.stripeRange();
+ if( stripeRange.end - stripeRange.start == 1 )
+ {
+ body(range);
+ return;
+ }
#if defined HAVE_TBB
--- /dev/null
+/*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.
+// Copyright (C) 2013, OpenCV Foundation, 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 "precomp.hpp"
+
+/****************************************************************************************\
+* PCA *
+\****************************************************************************************/
+
+namespace cv
+{
+
+PCA::PCA() {}
+
+PCA::PCA(InputArray data, InputArray _mean, int flags, int maxComponents)
+{
+ operator()(data, _mean, flags, maxComponents);
+}
+
+PCA::PCA(InputArray data, InputArray _mean, int flags, double retainedVariance)
+{
+ operator()(data, _mean, flags, retainedVariance);
+}
+
+PCA& PCA::operator()(InputArray _data, InputArray __mean, int flags, int maxComponents)
+{
+ Mat data = _data.getMat(), _mean = __mean.getMat();
+ int covar_flags = CV_COVAR_SCALE;
+ int i, len, in_count;
+ Size mean_sz;
+
+ CV_Assert( data.channels() == 1 );
+ if( flags & CV_PCA_DATA_AS_COL )
+ {
+ len = data.rows;
+ in_count = data.cols;
+ covar_flags |= CV_COVAR_COLS;
+ mean_sz = Size(1, len);
+ }
+ else
+ {
+ len = data.cols;
+ in_count = data.rows;
+ covar_flags |= CV_COVAR_ROWS;
+ mean_sz = Size(len, 1);
+ }
+
+ int count = std::min(len, in_count), out_count = count;
+ if( maxComponents > 0 )
+ out_count = std::min(count, maxComponents);
+
+ // "scrambled" way to compute PCA (when cols(A)>rows(A)):
+ // B = A'A; B*x=b*x; C = AA'; C*y=c*y -> AA'*y=c*y -> A'A*(A'*y)=c*(A'*y) -> c = b, x=A'*y
+ if( len <= in_count )
+ covar_flags |= CV_COVAR_NORMAL;
+
+ int ctype = std::max(CV_32F, data.depth());
+ mean.create( mean_sz, ctype );
+
+ Mat covar( count, count, ctype );
+
+ if( !_mean.empty() )
+ {
+ CV_Assert( _mean.size() == mean_sz );
+ _mean.convertTo(mean, ctype);
+ covar_flags |= CV_COVAR_USE_AVG;
+ }
+
+ calcCovarMatrix( data, covar, mean, covar_flags, ctype );
+ eigen( covar, eigenvalues, eigenvectors );
+
+ if( !(covar_flags & CV_COVAR_NORMAL) )
+ {
+ // CV_PCA_DATA_AS_ROW: cols(A)>rows(A). x=A'*y -> x'=y'*A
+ // CV_PCA_DATA_AS_COL: rows(A)>cols(A). x=A''*y -> x'=y'*A'
+ Mat tmp_data, tmp_mean = repeat(mean, data.rows/mean.rows, data.cols/mean.cols);
+ if( data.type() != ctype || tmp_mean.data == mean.data )
+ {
+ data.convertTo( tmp_data, ctype );
+ subtract( tmp_data, tmp_mean, tmp_data );
+ }
+ else
+ {
+ subtract( data, tmp_mean, tmp_mean );
+ tmp_data = tmp_mean;
+ }
+
+ Mat evects1(count, len, ctype);
+ gemm( eigenvectors, tmp_data, 1, Mat(), 0, evects1,
+ (flags & CV_PCA_DATA_AS_COL) ? CV_GEMM_B_T : 0);
+ eigenvectors = evects1;
+
+ // normalize eigenvectors
+ for( i = 0; i < out_count; i++ )
+ {
+ Mat vec = eigenvectors.row(i);
+ normalize(vec, vec);
+ }
+ }
+
+ if( count > out_count )
+ {
+ // use clone() to physically copy the data and thus deallocate the original matrices
+ eigenvalues = eigenvalues.rowRange(0,out_count).clone();
+ eigenvectors = eigenvectors.rowRange(0,out_count).clone();
+ }
+ return *this;
+}
+
+void PCA::write(FileStorage& fs ) const
+{
+ CV_Assert( fs.isOpened() );
+
+ fs << "name" << "PCA";
+ fs << "vectors" << eigenvectors;
+ fs << "values" << eigenvalues;
+ fs << "mean" << mean;
+}
+
+void PCA::read(const FileNode& fs)
+{
+ CV_Assert( !fs.empty() );
+ String name = (String)fs["name"];
+ CV_Assert( name == "PCA" );
+
+ cv::read(fs["vectors"], eigenvectors);
+ cv::read(fs["values"], eigenvalues);
+ cv::read(fs["mean"], mean);
+}
+
+template <typename T>
+int computeCumulativeEnergy(const Mat& eigenvalues, double retainedVariance)
+{
+ CV_DbgAssert( eigenvalues.type() == DataType<T>::type );
+
+ Mat g(eigenvalues.size(), DataType<T>::type);
+
+ for(int ig = 0; ig < g.rows; ig++)
+ {
+ g.at<T>(ig, 0) = 0;
+ for(int im = 0; im <= ig; im++)
+ {
+ g.at<T>(ig,0) += eigenvalues.at<T>(im,0);
+ }
+ }
+
+ int L;
+
+ for(L = 0; L < eigenvalues.rows; L++)
+ {
+ double energy = g.at<T>(L, 0) / g.at<T>(g.rows - 1, 0);
+ if(energy > retainedVariance)
+ break;
+ }
+
+ L = std::max(2, L);
+
+ return L;
+}
+
+PCA& PCA::operator()(InputArray _data, InputArray __mean, int flags, double retainedVariance)
+{
+ Mat data = _data.getMat(), _mean = __mean.getMat();
+ int covar_flags = CV_COVAR_SCALE;
+ int i, len, in_count;
+ Size mean_sz;
+
+ CV_Assert( data.channels() == 1 );
+ if( flags & CV_PCA_DATA_AS_COL )
+ {
+ len = data.rows;
+ in_count = data.cols;
+ covar_flags |= CV_COVAR_COLS;
+ mean_sz = Size(1, len);
+ }
+ else
+ {
+ len = data.cols;
+ in_count = data.rows;
+ covar_flags |= CV_COVAR_ROWS;
+ mean_sz = Size(len, 1);
+ }
+
+ CV_Assert( retainedVariance > 0 && retainedVariance <= 1 );
+
+ int count = std::min(len, in_count);
+
+ // "scrambled" way to compute PCA (when cols(A)>rows(A)):
+ // B = A'A; B*x=b*x; C = AA'; C*y=c*y -> AA'*y=c*y -> A'A*(A'*y)=c*(A'*y) -> c = b, x=A'*y
+ if( len <= in_count )
+ covar_flags |= CV_COVAR_NORMAL;
+
+ int ctype = std::max(CV_32F, data.depth());
+ mean.create( mean_sz, ctype );
+
+ Mat covar( count, count, ctype );
+
+ if( !_mean.empty() )
+ {
+ CV_Assert( _mean.size() == mean_sz );
+ _mean.convertTo(mean, ctype);
+ }
+
+ calcCovarMatrix( data, covar, mean, covar_flags, ctype );
+ eigen( covar, eigenvalues, eigenvectors );
+
+ if( !(covar_flags & CV_COVAR_NORMAL) )
+ {
+ // CV_PCA_DATA_AS_ROW: cols(A)>rows(A). x=A'*y -> x'=y'*A
+ // CV_PCA_DATA_AS_COL: rows(A)>cols(A). x=A''*y -> x'=y'*A'
+ Mat tmp_data, tmp_mean = repeat(mean, data.rows/mean.rows, data.cols/mean.cols);
+ if( data.type() != ctype || tmp_mean.data == mean.data )
+ {
+ data.convertTo( tmp_data, ctype );
+ subtract( tmp_data, tmp_mean, tmp_data );
+ }
+ else
+ {
+ subtract( data, tmp_mean, tmp_mean );
+ tmp_data = tmp_mean;
+ }
+
+ Mat evects1(count, len, ctype);
+ gemm( eigenvectors, tmp_data, 1, Mat(), 0, evects1,
+ (flags & CV_PCA_DATA_AS_COL) ? CV_GEMM_B_T : 0);
+ eigenvectors = evects1;
+
+ // normalize all eigenvectors
+ for( i = 0; i < eigenvectors.rows; i++ )
+ {
+ Mat vec = eigenvectors.row(i);
+ normalize(vec, vec);
+ }
+ }
+
+ // compute the cumulative energy content for each eigenvector
+ int L;
+ if (ctype == CV_32F)
+ L = computeCumulativeEnergy<float>(eigenvalues, retainedVariance);
+ else
+ L = computeCumulativeEnergy<double>(eigenvalues, retainedVariance);
+
+ // use clone() to physically copy the data and thus deallocate the original matrices
+ eigenvalues = eigenvalues.rowRange(0,L).clone();
+ eigenvectors = eigenvectors.rowRange(0,L).clone();
+
+ return *this;
+}
+
+void PCA::project(InputArray _data, OutputArray result) const
+{
+ Mat data = _data.getMat();
+ CV_Assert( !mean.empty() && !eigenvectors.empty() &&
+ ((mean.rows == 1 && mean.cols == data.cols) || (mean.cols == 1 && mean.rows == data.rows)));
+ Mat tmp_data, tmp_mean = repeat(mean, data.rows/mean.rows, data.cols/mean.cols);
+ int ctype = mean.type();
+ if( data.type() != ctype || tmp_mean.data == mean.data )
+ {
+ data.convertTo( tmp_data, ctype );
+ subtract( tmp_data, tmp_mean, tmp_data );
+ }
+ else
+ {
+ subtract( data, tmp_mean, tmp_mean );
+ tmp_data = tmp_mean;
+ }
+ if( mean.rows == 1 )
+ gemm( tmp_data, eigenvectors, 1, Mat(), 0, result, GEMM_2_T );
+ else
+ gemm( eigenvectors, tmp_data, 1, Mat(), 0, result, 0 );
+}
+
+Mat PCA::project(InputArray data) const
+{
+ Mat result;
+ project(data, result);
+ return result;
+}
+
+void PCA::backProject(InputArray _data, OutputArray result) const
+{
+ Mat data = _data.getMat();
+ CV_Assert( !mean.empty() && !eigenvectors.empty() &&
+ ((mean.rows == 1 && eigenvectors.rows == data.cols) ||
+ (mean.cols == 1 && eigenvectors.rows == data.rows)));
+
+ Mat tmp_data, tmp_mean;
+ data.convertTo(tmp_data, mean.type());
+ if( mean.rows == 1 )
+ {
+ tmp_mean = repeat(mean, data.rows, 1);
+ gemm( tmp_data, eigenvectors, 1, tmp_mean, 1, result, 0 );
+ }
+ else
+ {
+ tmp_mean = repeat(mean, 1, data.cols);
+ gemm( eigenvectors, tmp_data, 1, tmp_mean, 1, result, GEMM_1_T );
+ }
+}
+
+Mat PCA::backProject(InputArray data) const
+{
+ Mat result;
+ backProject(data, result);
+ return result;
+}
+
+}
+
+void cv::PCACompute(InputArray data, InputOutputArray mean,
+ OutputArray eigenvectors, int maxComponents)
+{
+ PCA pca;
+ pca(data, mean, 0, maxComponents);
+ pca.mean.copyTo(mean);
+ pca.eigenvectors.copyTo(eigenvectors);
+}
+
+void cv::PCACompute(InputArray data, InputOutputArray mean,
+ OutputArray eigenvectors, double retainedVariance)
+{
+ PCA pca;
+ pca(data, mean, 0, retainedVariance);
+ pca.mean.copyTo(mean);
+ pca.eigenvectors.copyTo(eigenvectors);
+}
+
+void cv::PCAProject(InputArray data, InputArray mean,
+ InputArray eigenvectors, OutputArray result)
+{
+ PCA pca;
+ pca.mean = mean.getMat();
+ pca.eigenvectors = eigenvectors.getMat();
+ pca.project(data, result);
+}
+
+void cv::PCABackProject(InputArray data, InputArray mean,
+ InputArray eigenvectors, OutputArray result)
+{
+ PCA pca;
+ pca.mean = mean.getMat();
+ pca.eigenvectors = eigenvectors.getMat();
+ pca.backProject(data, result);
+}
extern const ushort g_8x16uSqrTab[];
#define CV_SQR_8U(x) cv::g_8x16uSqrTab[(x)+255]
-extern const char* g_HersheyGlyphs[];
-
extern const uchar g_Saturate8u[];
#define CV_FAST_CAST_8U(t) (assert(-256 <= (t) && (t) <= 512), cv::g_Saturate8u[(t)+256])
#define CV_MIN_8U(a,b) ((a) - CV_FAST_CAST_8U((a) - (b)))
{
_parambuf.allocate(cn*8 + n1 + n2);
double* parambuf = _parambuf;
- double* p1 = (double*)_param1.data;
- double* p2 = (double*)_param2.data;
+ double* p1 = _param1.ptr<double>();
+ double* p2 = _param2.ptr<double>();
if( !_param1.isContinuous() || _param1.type() != CV_64F || n1 != cn )
{
int esz = (int)CV_ELEM_SIZE(ptype);
if( _param1.isContinuous() && _param1.type() == ptype )
- mean = _param1.data;
+ mean = _param1.ptr();
else
{
Mat tmp(_param1.size(), ptype, parambuf);
mean[j] = mean[j - n1*esz];
if( _param2.isContinuous() && _param2.type() == ptype )
- stddev = _param2.data;
+ stddev = _param2.ptr();
else
{
Mat tmp(_param2.size(), ptype, parambuf + cn);
int sz = _arr.rows*_arr.cols, iters = cvRound(iterFactor*sz);
if( _arr.isContinuous() )
{
- T* arr = (T*)_arr.data;
+ T* arr = _arr.ptr<T>();
for( int i = 0; i < iters; i++ )
{
int j = (unsigned)rng % sz, k = (unsigned)rng % sz;
}
else
{
- uchar* data = _arr.data;
+ uchar* data = _arr.ptr();
size_t step = _arr.step;
int cols = _arr.cols;
for( int i = 0; i < iters; i++ )
if( ippFuncHint || ippFuncNoHint )
{
Ipp64f res[4];
- IppStatus ret = ippFuncHint ? ippFuncHint(src.data, (int)src.step[0], sz, res, ippAlgHintAccurate) :
- ippFuncNoHint(src.data, (int)src.step[0], sz, res);
+ IppStatus ret = ippFuncHint ? ippFuncHint(src.ptr(), (int)src.step[0], sz, res, ippAlgHintAccurate) :
+ ippFuncNoHint(src.ptr(), (int)src.step[0], sz, res);
if( ret >= 0 )
{
Scalar sc;
if( ippFuncC1 )
{
Ipp64f res;
- if( ippFuncC1(src.data, (int)src.step[0], mask.data, (int)mask.step[0], sz, &res) >= 0 )
+ if( ippFuncC1(src.ptr(), (int)src.step[0], mask.ptr(), (int)mask.step[0], sz, &res) >= 0 )
return Scalar(res);
setIppErrorStatus();
}
if( ippFuncC3 )
{
Ipp64f res1, res2, res3;
- if( ippFuncC3(src.data, (int)src.step[0], mask.data, (int)mask.step[0], sz, 1, &res1) >= 0 &&
- ippFuncC3(src.data, (int)src.step[0], mask.data, (int)mask.step[0], sz, 2, &res2) >= 0 &&
- ippFuncC3(src.data, (int)src.step[0], mask.data, (int)mask.step[0], sz, 3, &res3) >= 0 )
+ if( ippFuncC3(src.ptr(), (int)src.step[0], mask.ptr(), (int)mask.step[0], sz, 1, &res1) >= 0 &&
+ ippFuncC3(src.ptr(), (int)src.step[0], mask.ptr(), (int)mask.step[0], sz, 2, &res2) >= 0 &&
+ ippFuncC3(src.ptr(), (int)src.step[0], mask.ptr(), (int)mask.step[0], sz, 3, &res3) >= 0 )
{
return Scalar(res1, res2, res3);
}
if( ippFuncHint || ippFuncNoHint )
{
Ipp64f res[4];
- IppStatus ret = ippFuncHint ? ippFuncHint(src.data, (int)src.step[0], sz, res, ippAlgHintAccurate) :
- ippFuncNoHint(src.data, (int)src.step[0], sz, res);
+ IppStatus ret = ippFuncHint ? ippFuncHint(src.ptr(), (int)src.step[0], sz, res, ippAlgHintAccurate) :
+ ippFuncNoHint(src.ptr(), (int)src.step[0], sz, res);
if( ret >= 0 )
{
Scalar sc;
part_sum funcs[3] = { ocl_part_sum<int>, ocl_part_sum<float>, ocl_part_sum<double> };
Mat dbm = db.getMat(ACCESS_READ);
- mean = funcs[ddepth - CV_32S](Mat(1, groups, dtype, dbm.data));
- stddev = funcs[sqddepth - CV_32S](Mat(1, groups, sqdtype, dbm.data + groups * CV_ELEM_SIZE(dtype)));
+ mean = funcs[ddepth - CV_32S](Mat(1, groups, dtype, dbm.ptr()));
+ stddev = funcs[sqddepth - CV_32S](Mat(1, groups, sqdtype, dbm.ptr() + groups * CV_ELEM_SIZE(dtype)));
if (haveMask)
- nz = saturate_cast<int>(funcs[0](Mat(1, groups, CV_32SC1, dbm.data +
+ nz = saturate_cast<int>(funcs[0](Mat(1, groups, CV_32SC1, dbm.ptr() +
groups * (CV_ELEM_SIZE(dtype) +
CV_ELEM_SIZE(sqdtype))))[0]);
}
_mean.create(cn, 1, CV_64F, -1, true);
mean = _mean.getMat();
dcn_mean = (int)mean.total();
- pmean = (Ipp64f *)mean.data;
+ pmean = mean.ptr<Ipp64f>();
}
int dcn_stddev = -1;
if( _sdv.needed() )
_sdv.create(cn, 1, CV_64F, -1, true);
stddev = _sdv.getMat();
dcn_stddev = (int)stddev.total();
- pstddev = (Ipp64f *)stddev.data;
+ pstddev = stddev.ptr<Ipp64f>();
}
for( int c = cn; c < dcn_mean; c++ )
pmean[c] = 0;
0;
if( ippFuncC1 )
{
- if( ippFuncC1(src.data, (int)src.step[0], mask.data, (int)mask.step[0], sz, pmean, pstddev) >= 0 )
+ if( ippFuncC1(src.ptr(), (int)src.step[0], mask.ptr(), (int)mask.step[0], sz, pmean, pstddev) >= 0 )
return;
setIppErrorStatus();
}
0;
if( ippFuncC3 )
{
- if( ippFuncC3(src.data, (int)src.step[0], mask.data, (int)mask.step[0], sz, 1, &pmean[0], &pstddev[0]) >= 0 &&
- ippFuncC3(src.data, (int)src.step[0], mask.data, (int)mask.step[0], sz, 2, &pmean[1], &pstddev[1]) >= 0 &&
- ippFuncC3(src.data, (int)src.step[0], mask.data, (int)mask.step[0], sz, 3, &pmean[2], &pstddev[2]) >= 0 )
+ if( ippFuncC3(src.ptr(), (int)src.step[0], mask.ptr(), (int)mask.step[0], sz, 1, &pmean[0], &pstddev[0]) >= 0 &&
+ ippFuncC3(src.ptr(), (int)src.step[0], mask.ptr(), (int)mask.step[0], sz, 2, &pmean[1], &pstddev[1]) >= 0 &&
+ ippFuncC3(src.ptr(), (int)src.step[0], mask.ptr(), (int)mask.step[0], sz, 3, &pmean[2], &pstddev[2]) >= 0 )
return;
setIppErrorStatus();
}
0;
if( ippFuncC1 )
{
- if( ippFuncC1(src.data, (int)src.step[0], sz, pmean, pstddev) >= 0 )
+ if( ippFuncC1(src.ptr(), (int)src.step[0], sz, pmean, pstddev) >= 0 )
return;
setIppErrorStatus();
}
0;
if( ippFuncC3 )
{
- if( ippFuncC3(src.data, (int)src.step[0], sz, 1, &pmean[0], &pstddev[0]) >= 0 &&
- ippFuncC3(src.data, (int)src.step[0], sz, 2, &pmean[1], &pstddev[1]) >= 0 &&
- ippFuncC3(src.data, (int)src.step[0], sz, 3, &pmean[2], &pstddev[2]) >= 0 )
+ if( ippFuncC3(src.ptr(), (int)src.step[0], sz, 1, &pmean[0], &pstddev[0]) >= 0 &&
+ ippFuncC3(src.ptr(), (int)src.step[0], sz, 2, &pmean[1], &pstddev[1]) >= 0 &&
+ ippFuncC3(src.ptr(), (int)src.step[0], sz, 3, &pmean[2], &pstddev[2]) >= 0 )
return;
setIppErrorStatus();
}
const uint * minlocptr = NULL, * maxlocptr = NULL;
if (minVal || minLoc)
{
- minptr = (const T *)db.data;
+ minptr = db.ptr<T>();
index += sizeof(T) * groupnum;
}
if (maxVal || maxLoc)
{
- maxptr = (const T *)(db.data + index);
+ maxptr = (const T *)(db.ptr() + index);
index += sizeof(T) * groupnum;
}
if (minLoc)
{
- minlocptr = (uint *)(db.data + index);
+ minlocptr = (const uint *)(db.ptr() + index);
index += sizeof(uint) * groupnum;
}
if (maxLoc)
{
- maxlocptr = (uint *)(db.data + index);
+ maxlocptr = (const uint *)(db.ptr() + index);
index += sizeof(uint) * groupnum;
}
if (maxVal2)
- maxptr2 = (const T *)(db.data + index);
+ maxptr2 = (const T *)(db.ptr() + index);
for (int i = 0; i < groupnum; i++)
{
bool doubleSupport = dev.doubleFPConfig() > 0, haveMask = !_mask.empty(),
haveSrc2 = _src2.kind() != _InputArray::NONE;
int type = _src.type(), depth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type),
- kercn = haveMask ? cn : std::min(4, ocl::predictOptimalVectorWidth(_src));
+ kercn = haveMask ? cn : std::min(4, ocl::predictOptimalVectorWidth(_src, _src2));
+
+ if (haveMask && dev.isAMD())
+ return false;
CV_Assert( (cn == 1 && (!haveMask || _mask.type() == CV_8U)) ||
(cn >= 1 && !minLoc && !maxLoc) );
}
size_t globalsize = groupnum * wgs;
- if (!k.run(1, &globalsize, &wgs, false))
+ if (!k.run(1, &globalsize, &wgs, true))
return false;
static const getMinMaxResFunc functab[7] =
{
Ipp32f min, max;
IppiPoint minp, maxp;
- if( ippFuncC1(src.data, (int)src.step[0], mask.data, (int)mask.step[0], sz, &min, &max, &minp, &maxp) >= 0 )
+ if( ippFuncC1(src.ptr(), (int)src.step[0], mask.ptr(), (int)mask.step[0], sz, &min, &max, &minp, &maxp) >= 0 )
{
if( minVal )
*minVal = (double)min;
if( maxVal )
*maxVal = (double)max;
- if( !minp.x && !minp.y && !maxp.x && !maxp.y && !mask.data[0] )
+ if( !minp.x && !minp.y && !maxp.x && !maxp.y && !mask.ptr()[0] )
minp.x = maxp.x = -1;
if( minIdx )
{
{
Ipp32f min, max;
IppiPoint minp, maxp;
- if( ippFuncC1(src.data, (int)src.step[0], sz, &min, &max, &minp, &maxp) >= 0 )
+ if( ippFuncC1(src.ptr(), (int)src.step[0], sz, &min, &max, &minp, &maxp) >= 0 )
{
if( minVal )
*minVal = (double)min;
(!doubleSupport && depth == CV_64F))
return false;
- if( depth == CV_32F && (!_mask.empty() || normType == NORM_INF) )
- return false;
-
UMat src = _src.getUMat();
if (normType == NORM_INF)
if( ippFuncC1 )
{
Ipp64f norm;
- if( ippFuncC1(src.data, (int)src.step[0], mask.data, (int)mask.step[0], sz, &norm) >= 0 )
+ if( ippFuncC1(src.ptr(), (int)src.step[0], mask.ptr(), (int)mask.step[0], sz, &norm) >= 0 )
return normType == NORM_L2SQR ? (double)(norm * norm) : (double)norm;
setIppErrorStatus();
if( ippFuncHint || ippFuncNoHint )
{
Ipp64f norm_array[4];
- IppStatus ret = ippFuncHint ? ippFuncHint(src.data, (int)src.step[0], sz, norm_array, ippAlgHintAccurate) :
- ippFuncNoHint(src.data, (int)src.step[0], sz, norm_array);
+ IppStatus ret = ippFuncHint ? ippFuncHint(src.ptr(), (int)src.step[0], sz, norm_array, ippAlgHintAccurate) :
+ ippFuncNoHint(src.ptr(), (int)src.step[0], sz, norm_array);
if( ret >= 0 )
{
Ipp64f norm = (normType == NORM_L2 || normType == NORM_L2SQR) ? norm_array[0] * norm_array[0] : norm_array[0];
normType &= ~NORM_RELATIVE;
bool normsum = normType == NORM_L1 || normType == NORM_L2 || normType == NORM_L2SQR;
- if ( !normsum || !_mask.empty() )
- return false;
-
if (normsum)
{
if (!ocl_sum(_src1, sc1, normType == NORM_L2 || normType == NORM_L2SQR ?
if( ippFuncC1 )
{
Ipp64f norm;
- if( ippFuncC1(src1.data, (int)src1.step[0], src2.data, (int)src2.step[0], mask.data, (int)mask.step[0], sz, &norm) >= 0 )
+ if( ippFuncC1(src1.ptr(), (int)src1.step[0], src2.ptr(), (int)src2.step[0], mask.ptr(), (int)mask.step[0], sz, &norm) >= 0 )
return normType == NORM_L2SQR ? (double)(norm * norm) : (double)norm;
setIppErrorStatus();
}
if (ippFuncNoHint)
{
Ipp64f norm;
- if( ippFuncNoHint(src1.data, (int)src1.step[0], src2.data, (int)src2.step[0], sz, &norm) >= 0 )
+ if( ippFuncNoHint(src1.ptr(), (int)src1.step[0], src2.ptr(), (int)src2.step[0], sz, &norm) >= 0 )
return (double)norm;
setIppErrorStatus();
}
if (ippFuncHint)
{
Ipp64f norm;
- if( ippFuncHint(src1.data, (int)src1.step[0], src2.data, (int)src2.step[0], sz, &norm, ippAlgHintAccurate) >= 0 )
+ if( ippFuncHint(src1.ptr(), (int)src1.step[0], src2.ptr(), (int)src2.step[0], sz, &norm, ippAlgHintAccurate) >= 0 )
return (double)norm;
setIppErrorStatus();
}
if( ippFuncC1 )
{
Ipp64f norm;
- if( ippFuncC1(src1.data, (int)src1.step[0], src2.data, (int)src2.step[0], mask.data, (int)mask.step[0], sz, &norm) >= 0 )
+ if( ippFuncC1(src1.ptr(), (int)src1.step[0], src2.ptr(), (int)src2.step[0], mask.ptr(), (int)mask.step[0], sz, &norm) >= 0 )
return normType == NORM_L2SQR ? (double)(norm * norm) : (double)norm;
setIppErrorStatus();
}
if( ippFuncHint || ippFuncNoHint )
{
Ipp64f norm_array[4];
- IppStatus ret = ippFuncHint ? ippFuncHint(src1.data, (int)src1.step[0], src2.data, (int)src2.step[0], sz, norm_array, ippAlgHintAccurate) :
- ippFuncNoHint(src1.data, (int)src1.step[0], src2.data, (int)src2.step[0], sz, norm_array);
+ IppStatus ret = ippFuncHint ? ippFuncHint(src1.ptr(), (int)src1.step[0], src2.ptr(), (int)src2.step[0], sz, norm_array, ippAlgHintAccurate) :
+ ippFuncNoHint(src1.ptr(), (int)src1.step[0], src2.ptr(), (int)src2.step[0], sz, norm_array);
if( ret >= 0 )
{
Ipp64f norm = (normType == NORM_L2 || normType == NORM_L2SQR) ? norm_array[0] * norm_array[0] : norm_array[0];
_idx.create(n, 1, CV_32SC2);
Mat idx = _idx.getMat();
CV_Assert(idx.isContinuous());
- Point* idx_ptr = (Point*)idx.data;
+ Point* idx_ptr = idx.ptr<Point>();
for( int i = 0; i < src.rows; i++ )
{
255
};
-const char* g_HersheyGlyphs[] = {
- "",
- "MWRMNV RMVV PSTS",
- "MWOMOV OMSMUNUPSQ OQSQURUUSVOV",
- "MXVNTMRMPNOPOSPURVTVVU",
- "MWOMOV OMRMTNUPUSTURVOV",
- "MWOMOV OMUM OQSQ OVUV",
- "MVOMOV OMUM OQSQ",
- "MXVNTMRMPNOPOSPURVTVVUVR SRVR",
- "MWOMOV UMUV OQUQ",
- "PTRMRV",
- "NUSMSTRVPVOTOS",
- "MWOMOV UMOS QQUV",
- "MVOMOV OVUV",
- "LXNMNV NMRV VMRV VMVV",
- "MWOMOV OMUV UMUV",
- "MXRMPNOPOSPURVSVUUVSVPUNSMRM",
- "MWOMOV OMSMUNUQSROR",
- "MXRMPNOPOSPURVSVUUVSVPUNSMRM STVW",
- "MWOMOV OMSMUNUQSROR RRUV",
- "MWUNSMQMONOOPPTRUSUUSVQVOU",
- "MWRMRV NMVM",
- "MXOMOSPURVSVUUVSVM",
- "MWNMRV VMRV",
- "LXNMPV RMPV RMTV VMTV",
- "MWOMUV UMOV",
- "MWNMRQRV VMRQ",
- "MWUMOV OMUM OVUV",
- "MWRMNV RMVV PSTS",
- "MWOMOV OMSMUNUPSQ OQSQURUUSVOV",
- "MVOMOV OMUM",
- "MWRMNV RMVV NVVV",
- "MWOMOV OMUM OQSQ OVUV",
- "MWUMOV OMUM OVUV",
- "MWOMOV UMUV OQUQ",
- "MXRMPNOPOSPURVSVUUVSVPUNSMRM QQTR TQQR",
- "PTRMRV",
- "MWOMOV UMOS QQUV",
- "MWRMNV RMVV",
- "LXNMNV NMRV VMRV VMVV",
- "MWOMOV OMUV UMUV",
- "MWOMUM PQTR TQPR OVUV",
- "MXRMPNOPOSPURVSVUUVSVPUNSMRM",
- "MWOMOV UMUV OMUM",
- "MWOMOV OMSMUNUQSROR",
- "MWOMRQOV OMUM OVUV",
- "MWRMRV NMVM",
- "MWNONNOMPMQNRPRV VOVNUMTMSNRP",
- "LXRMRV PONPNSPTTTVSVPTOPO",
- "MWOMUV UMOV",
- "LXRMRV NOOPOSQTSTUSUPVO",
- "MXOVQVOROPPNRMSMUNVPVRTVVV",
- "MWSMMV SMUV OSTS",
- "MWQMNV QMTMVNVPSQPQ SQURUTTURVNV",
- "LXVPUNTMRMPNOONQNSOUPVRVTUUT",
- "MXQMNV QMUMVOVQUTTURVNV",
- "MVQMNV QMVM PQSQ NVSV",
- "MVQMNV QMVM PQSQ",
- "LXVPUNTMRMPNOONQNSOUPVRVTUUSRS",
- "MXQMNV WMTV PQUQ",
- "PUTMQV",
- "OVUMSSRUQVPVOUOT",
- "MVQMNV VMOS RQTV",
- "NVRMOV OVTV",
- "LYPMMV PMQV XMQV XMUV",
- "MXQMNV QMTV WMTV",
- "LXRMPNOONQNSOUPVRVTUUTVRVPUNTMRM",
- "MWQMNV QMUMVNVPUQSRPR",
- "LXRMPNOONQNSOUPVRVTUUTVRVPUNTMRM QVPUPTQSRSSTTVUWVW",
- "MWQMNV QMUMVNVPUQSRPR QRRUSVTVUU",
- "MWVNTMRMPNPPQQTRUSUUSVPVNU",
- "MVSMPV PMVM",
- "LXPMNSNUOVRVTUUSWM",
- "MWOMQV WMQV",
- "KXNMNV SMNV SMSV XMSV",
- "NWQMTV WMNV",
- "NWQMSQQV WMSQ",
- "MWQMWMNVTV",
- "",
- "",
- "",
- "",
- "",
- "",
- "LXNMRV VMRV NMVM",
- "MWNLVX",
- "LXRONU ROVU",
- "MWNVVV",
- "PVRMUQ",
- "MWMMOKQKTMVMWK",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "NWQPTPUQUV URQSPTPUQVSVUU",
- "MWOMOV OSPURVTUUSTQRPPQOS",
- "MWUQSPRPPQOSPURVSVUU",
- "MWUMUV USTQRPPQOSPURVTUUS",
- "MWOSUSTQRPPQOSPURVTV",
- "NVUNTMSMRNRV PPTP",
- "MWUPUVTXRYPY USTQRPPQOSPURVTUUS",
- "MWOMOV OSPQRPTQUSUV",
- "PTRLQMRNSMRL RPRV",
- "PUSLRMSNTMSL SPSXRYQYPX",
- "NWPMPV UPPT RSUV",
- "PTRMRV",
- "KYMPMV MSNQOPPPQQRSRV RSSQTPUPVQWSWV",
- "MWOPOV OSPQRPTQUSUV",
- "MWRPPQOSPURVTUUSTQRP",
- "MWOPOY OSPURVTUUSTQRPPQOS",
- "MWUPUY USTQRPPQOSPURVTUUS",
- "NVPPPV PSQQSPTP",
- "NWUQTPQPPQPRQSTSUTUUTVQVPU",
- "NVRMRUSVTVUU PPTP",
- "MWUPUV OPOSPURVTUUS",
- "NVOPRV UPRV",
- "LXNPPV RPPV RPTV VPTV",
- "MWOPUV UPOV",
- "MWOPRV UPRVQXPYOY",
- "MWOPUPOVUV",
- "MXVPUSTURVPUOSPQRPTQUUVV",
- "MWOTQVSVTUTSSRPQRQTPUOUNTMRMQNPPOTNY",
- "MXNQOPQPRQRSQW VPURSTQWPY",
- "MWTNSMRMQNQORPTQUSTURVPUOSPQRP",
- "NWUQSPQPPQPRQS SSQSPTPUQVSVUU",
- "NWTMSNSOTP UPSPQQPSPUQVSWSXRYQY",
- "LXNQOPPPQQQSPV QSRQTPUPVQVSUVTY",
- "LXNQOPPPQQQURVSVTUUSVPVNUMTMSNSPTRUSWT",
- "OVRPQSQURVSVTU",
- "MWQPOV UPTPRQPS PSQUSVTV",
- "MWOMPMQNRPUV RPOV",
- "LYPPMY UPTSSUQVPVOUOS TSTUUVVVWU",
- "MWNPOPOV UPTSRUOV",
- "NWTMSNSOTP UPSPQQQRRSTS SSQTPUPVQWSXSYRZQZ",
- "MWRPPQOSPURVTUUSTQRP",
- "MXOQQPVP QPQRPV TPTRUV",
- "MWOSPURVTUUSTQRPPQOSNY",
- "MXVPRPPQOSPURVTUUSTQRP",
- "MXOQQPVP SPRV",
- "KXMQNPOPPQPUQVSVTUUSVP",
- "MXPPOQOSPURVSVUUVSVQUPTPSQRSQY",
- "MWOPPPQQSXTYUY UPTRPWOY",
- "KYTMRY MQNPOPPQPUQVTVUUVSWP",
- "LXOPNRNTOVQVRTRR UPVRVTUVSVRT",
- "LWTSSQQPOQNSOUQVSUTS UPTSTUUVVV",
- "MWQMOSPURVTUUSTQRPPQOS",
- "MWUQSPRPPQOSPURVTV",
- "LWTSSQQPOQNSOUQVSUTS VMTSTUUVVV",
- "MWOSTSURUQSPRPPQOSPURVTV",
- "OVVMUMTNSPQVPXOYNY QPUP",
- "MXUSTQRPPQOSPURVTUUS VPTVSXRYPYOX",
- "MVQMNV OSPQQPSPTQTRSTSUTVUV",
- "PUSMSNTNTMSM QPRPSQSRRTRUSVTV",
- "OUSMSNTNTMSM QPRPSQSRRVQXPYOYNX",
- "NVRMOV UPTPRQPS PSQUSVTV",
- "OTSMQSQURVSV",
- "JYKPLPMQMSLV MSNQOPQPRQRSQV RSSQTPVPWQWRVTVUWVXV",
- "MWNPOPPQPSOV PSQQRPTPUQURTTTUUVVV",
- "MWRPPQOSPURVTUUSTQRP",
- "MXNPOPPQPSNY PSQUSVUUVSUQSPQQPS",
- "MXUSTQRPPQOSPURVTUUS VPSY",
- "MVOPPPQQQSPV UQTPSPRQQS",
- "NVTQSPQPPQPRQSRSSTSURVPVOU",
- "NUSMQSQURVSV PPTP",
- "MWNPOPPQPROTOUPVRVSUTS UPTSTUUVVV",
- "MWNPOPPQPROTOUPVRVTUURUP",
- "KYLPMPNQNRMTMUNVPVQURSSP RSRUSVUVVUWRWP",
- "MWOQPPQPRQRUSVTVUU VQUPTPSQQUPVOVNU",
- "MWNPOPPQPROTOUPVRVSUTS UPSVRXQYOYNX",
- "NVUPOV PQQPSPTQ PUQVSVTU",
- "",
- "",
- "",
- "",
- "",
- "",
- "MWUSTQRPPQOSPURVTUUSUPTNRMQM",
- "MWUQSPRPPQOSPURVSVUU OSSS",
- "MWRMQNPPOSOVPWRWSVTTUQUNTMRM PRTR",
- "MWTMQY RPPQOSPURVSVUUVSUQSPRP",
- "MWUQSPQPOQOSPTRUSVSWRXQX",
- "",
- "",
- "KYTPTSUTVTWSWQVOUNSMQMONNOMQMSNUOVQWSWUV TQSPQPPQPSQTSTTS",
- "MWUNORUV",
- "MWONUROV",
- "OUTKQKQYTY",
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- "PT",
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- "MWRMPNOPOSPURVTUUSUPTNRM",
- "MWPORMRV",
- "MWONQMSMUNUPTROVUV",
- "MWONQMSMUNUPSQ RQSQURUUSVQVOU",
- "MWSMSV SMNSVS",
- "MWPMOQQPRPTQUSTURVQVOU PMTM",
- "MWTMRMPNOPOSPURVTUUSTQRPPQOS",
- "MWUMQV OMUM",
- "MWQMONOPQQSQUPUNSMQM QQOROUQVSVUUURSQ",
- "MWUPTRRSPROPPNRMTNUPUSTURVPV",
- "PURURVSVSURU",
- "PUSVRVRUSUSWRY",
- "PURPRQSQSPRP RURVSVSURU",
- "PURPRQSQSPRP SVRVRUSUSWRY",
- "PURMRR SMSR RURVSVSURU",
- "NWPNRMSMUNUPRQRRSRSQUP RURVSVSURU",
- "PTRMRQ",
- "NVPMPQ TMTQ",
- "NVQMPNPPQQSQTPTNSMQM",
- "MWRKRX UNSMQMONOPQQTRUSUUSVQVOU",
- "MWVLNX",
- "OUTKRNQQQSRVTY",
- "OUPKRNSQSSRVPY",
- "PTRKRY",
- "LXNRVR",
- "LXRNRV NRVR",
- "LXNPVP NTVT",
- "MWOOUU UOOU",
- "MWRORU OPUT UPOT",
- "PURQRRSRSQRQ",
- "PUSMRORQSQSPRP",
- "PUSNRNRMSMSORQ",
- "LXSOVRSU NRVR",
- "MXQLQY TLTY OQVQ OTVT",
- "LXVRURTSSURVOVNUNSORRQSPSNRMPMONOPQSSUUVVV",
- "LXNNOQOSNV VNUQUSVV NNQOSOVN NVQUSUVV",
- "LYRQQPOPNQNSOTQTRSSQTPVPWQWSVTTTSSRQ",
- "",
- "H\\NRMQLRMSNR VRWQXRWSVR",
- "H\\MPLQLRMSNSOROQNPMP MQMRNRNQMQ WPVQVRWSXSYRYQXPWP WQWRXRXQWQ",
- "I[KRYR",
- "",
- "H\\RUJPRTZPRU",
- "",
- "",
- "",
- "",
- "",
- "F^ISJQLPNPPQTTVUXUZT[Q ISJPLONOPPTSVTXTZS[Q IYJWLVNVPWTZV[X[ZZ[W IYJVLUNUPVTYVZXZZY[W",
- "",
- "F^ISJQLPNPPQTTVUXUZT[Q ISJPLONOPPTSVTXTZS[Q IW[W I[[[",
- "",
- "CaGO]OXI L[GU]U",
- "",
- "D`F^^^^FFFF^",
- "",
- "KYQVOUNSNQOOQNSNUOVQVSUUSVQV SVVS QVVQ OUUO NSSN NQQN",
- "",
- "H\\IR[R",
- "H\\IR[R IQ[Q",
- "",
- "LYPFSCSP RDRP OPVP MRXR OVOWNWNVOUQTTTVUWWVYTZQ[O\\N^Na TTUUVWUYTZ N`O_P_S`V`W_ P_SaVaW_W^",
- "LYPFSCSP RDRP OPVP MRXR OVOWNWNVOUQTTTVUWWVYTZ TTUUVWUYTZ RZTZV[W]W^V`TaQaO`N_N^O^O_ TZU[V]V^U`Ta",
- "LYPFSCSP RDRP OPVP MRXR VVVWWWWVVUTTRTPUOVNYN^O`QaTaV`W^W\\VZTYQYN[ RTPVOYO^P`Qa TaU`V^V\\UZTY",
- "LYPFSCSP RDRP OPVP MRXR QTOUNWOYQZTZVYWWVUTTQT QTPUOWPYQZ TZUYVWUUTT QZO[N]N^O`QaTaV`W^W]V[TZ QZP[O]O^P`Qa TaU`V^V]U[TZ",
- "LYOEOFNFNEODQCTCVDWFVHTIQJOKNMNP TCUDVFUHTI NOONPNSOVOWN PNSPVPWNWM MRXR OVOWNWNVOUQTTTVUWWVYTZ TTUUVWUYTZ RZTZV[W]W^V`TaQaO`N_N^O^O_ TZU[V]V^U`Ta",
- "LYOEOFNFNEODQCTCVDWFVHTI TCUDVFUHTI RITIVJWLWMVOTPQPOONNNMOMON TIUJVLVMUOTP MRXR QTOUNWOYQZTZVYWWVUTTQT QTPUOWPYQZ TZUYVWUUTT QZO[N]N^O`QaTaV`W^W]V[TZ QZP[O]O^P`Qa TaU`V^V]U[TZ",
- "LYOCNI OCVC ODSDVC NIOHQGTGVHWJWMVOTPQPOONNNMOMON TGUHVJVMUOTP MRXR QTOUNWOYQZTZVYWWVUTTQT QTPUOWPYQZ TZUYVWUUTT QZO[N]N^O`QaTaV`W^W]V[TZ QZP[O]O^P`Qa TaU`V^V]U[TZ",
- "LYNCNG VERLPP WCTIQP NEPCRCUE NEPDRDUEVE MRXR QTOUNWOYQZTZVYWWVUTTQT QTPUOWPYQZ TZUYVWUUTT QZO[N]N^O`QaTaV`W^W]V[TZ QZP[O]O^P`Qa TaU`V^V]U[TZ",
- "LYOCNI OCVC ODSDVC NIOHQGTGVHWJWMVOTPQPOONNNMOMON TGUHVJVMUOTP MRXR VVVWWWWVVUTTRTPUOVNYN^O`QaTaV`W^W\\VZTYQYN[ RTPVOYO^P`Qa TaU`V^V\\UZTY",
- "LYPFSCSP RDRP OPVP MRXR SVSa TTTa TTM]X] QaVa",
- "LYOEOFNFNEODQCTCVDWFVHTI TCUDVFUHTI RITIVJWLWMVOTPQPOONNNMOMON TIUJVLVMUOTP MRXR SVSa TTTa TTM]X] QaVa",
- "F^YXWZU[R[PZMXKWIWHXHZI[K[MZOWPURQTKWGYFZF[G\\H[IZH[G[FZFYFWGVHTLRPPVNZMZ OPUP",
- "E^P[MZJXHUGRGOHLJIMGPFTFWGYI[L\\O\\R[UYXVZS[P[ NJNW OJOW LJSJVKWMWNVPSQOQ SJUKVMVNUPSQ LWQW SQTRUVVWWWXV SQURVVWW",
- "E^P[MZJXHUGRGOHLJIMGPFTFWGYI[L\\O\\R[UYXVZS[P[ UKVJVNUKSJPJNKMLLOLRMUNVPWSWUVVT PJNLMOMRNUPW",
- "E_IM[M IR[R IW[W K[YI",
- "CaHQGRHSIRHQ RQQRRSSRRQ \\Q[R\\S]R\\Q",
- "",
- "E_NWLTIRLPNM LPJRLT JRZR VWXT[RXPVM XPZRXT",
- "JZWNTLRIPLMN PLRJTL RJRZ WVTXR[PXMV PXRZTX",
- "F^ZJSJOKMLKNJQJSKVMXOYSZZZ SFS^",
- "F^JJQJUKWLYNZQZSYVWXUYQZJZ QFQ^",
- "F^JJQJUKWLYNZQZSYVWXUYQZJZ ORZR",
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- "H\\LBL[ RBR[ XBX[",
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- "MXTTTSSRQROSNTMVMXNZP[S[VYXV",
- "L\\UUTSRRPRNSMTLVLXMZO[Q[SZTXZF VRUWUZV[W[YZZY\\V",
- "NXOYQXRWSUSSRRQROSNUNXOZQ[S[UZVYXV",
- "OWOVSQUNVLWIWGVFTGSIQQNZKaJdJfKgMfNcOZP[R[TZUYWV",
- "L[UUTSRRPRNSMTLVLXMZO[Q[SZTY VRTYPdOfMgLfLdMaP^S\\U[XY[V",
- "M\\MVOSRNSLTITGSFQGPIOMNSM[ M[NXOVQSSRURVSVUUXUZV[W[YZZY\\V",
- "PWSMSNTNTMSM PVRRPXPZQ[R[TZUYWV",
- "PWSMSNTNTMSM PVRRLdKfIgHfHdIaL^O\\Q[TYWV",
- "M[MVOSRNSLTITGSFQGPIOMNSM[ M[NXOVQSSRURVSVUTVQV QVSWTZU[V[XZYY[V",
- "OWOVQSTNULVIVGUFSGRIQMPTPZQ[R[TZUYWV",
- "E^EVGSIRJSJTIXH[ IXJVLSNRPRQSQTPXO[ PXQVSSURWRXSXUWXWZX[Y[[Z\\Y^V",
- "J\\JVLSNROSOTNXM[ NXOVQSSRURVSVUUXUZV[W[YZZY\\V",
- "LZRRPRNSMTLVLXMZO[Q[SZTYUWUUTSRRQSQURWTXWXYWZV",
- "KZKVMSNQMUGg MUNSPRRRTSUUUWTYSZQ[ MZO[R[UZWYZV",
- "L[UUTSRRPRNSMTLVLXMZO[Q[SZ VRUUSZPaOdOfPgRfScS\\U[XY[V",
- "MZMVOSPQPSSSTTTVSYSZT[U[WZXYZV",
- "NYNVPSQQQSSVTXTZR[ NZP[T[VZWYYV",
- "OXOVQSSO VFPXPZQ[S[UZVYXV PNWN",
- "L[LVNRLXLZM[O[QZSXUU VRTXTZU[V[XZYY[V",
- "L[LVNRMWMZN[O[RZTXUUUR URVVWWYW[V",
- "I^LRJTIWIYJ[L[NZPX RRPXPZQ[S[UZWXXUXR XRYVZW\\W^V",
- "JZJVLSNRPRQSQZR[U[XYZV WSVRTRSSOZN[L[KZ",
- "L[LVNRLXLZM[O[QZSXUU VRPdOfMgLfLdMaP^S\\U[XY[V",
- "LZLVNSPRRRTTTVSXQZN[P\\Q^QaPdOfMgLfLdMaP^S\\WYZV",
- "J\\K[NZQXSVUSWOXKXIWGUFSGRHQJPOPTQXRZT[V[XZYY",
- "",
- "",
- "",
- "",
- "",
- "I[WUWRVOUNSMQMONMPLSLVMYNZP[R[TZVXWUXPXKWHVGTFRFPGNI",
- "JZWNUMRMPNNPMSMVNYOZQ[T[VZ MTUT",
- "J[TFRGPJOLNOMTMXNZO[Q[SZUWVUWRXMXIWGVFTF NPWP",
- "H\\VFNb QMNNLPKSKVLXNZQ[S[VZXXYUYRXPVNSMQM",
- "I[XOWNTMQMNNMOLQLSMUOWSZT\\T^S_Q_",
- "",
- "",
- "DaWNVLTKQKOLNMMOMRNTOUQVTVVUWS WKWSXUYV[V\\U]S]O\\L[JYHWGTFQFNGLHJJILHOHRIUJWLYNZQ[T[WZYY",
- "F^ZIJRZ[",
- "F^JIZRJ[",
- "KYOBOb OBVB ObVb",
- "KYUBUb NBUB NbUb",
- "KYTBQEPHPJQMSOSPORSTSUQWPZP\\Q_Tb",
- "KYPBSETHTJSMQOQPURQTQUSWTZT\\S_Pb",
- "F^[FYGVHSHPGNFLFJGIIIKKMMMOLPJPHNF [FI[ YTWTUUTWTYV[X[ZZ[X[VYT",
- "NV",
- "JZ",
- "H\\QFNGLJKOKRLWNZQ[S[VZXWYRYOXJVGSFQF",
- "H\\NJPISFS[",
- "H\\LKLJMHNGPFTFVGWHXJXLWNUQK[Y[",
- "H\\MFXFRNUNWOXPYSYUXXVZS[P[MZLYKW",
- "H\\UFKTZT UFU[",
- "H\\WFMFLOMNPMSMVNXPYSYUXXVZS[P[MZLYKW",
- "H\\XIWGTFRFOGMJLOLTMXOZR[S[VZXXYUYTXQVOSNRNOOMQLT",
- "H\\YFO[ KFYF",
- "H\\PFMGLILKMMONSOVPXRYTYWXYWZT[P[MZLYKWKTLRNPQOUNWMXKXIWGTFPF",
- "H\\XMWPURRSQSNRLPKMKLLINGQFRFUGWIXMXRWWUZR[P[MZLX",
- "MWRYQZR[SZRY",
- "MWSZR[QZRYSZS\\R^Q_",
- "MWRMQNROSNRM RYQZR[SZRY",
- "MWRMQNROSNRM SZR[QZRYSZS\\R^Q_",
- "MWRFRT RYQZR[SZRY",
- "I[LKLJMHNGPFTFVGWHXJXLWNVORQRT RYQZR[SZRY",
- "NVRFRM",
- "JZNFNM VFVM",
- "KYQFOGNINKOMQNSNUMVKVIUGSFQF",
- "H\\PBP_ TBT_ YIWGTFPFMGKIKKLMMNOOUQWRXSYUYXWZT[P[MZKX",
- "G][BIb",
- "KYVBTDRGPKOPOTPYR]T`Vb",
- "KYNBPDRGTKUPUTTYR]P`Nb",
- "NVRBRb",
- "E_IR[R",
- "E_RIR[ IR[R",
- "E_IO[O IU[U",
- "G]KKYY YKKY",
- "JZRLRX MOWU WOMU",
- "MWRQQRRSSRRQ",
- "MWSFRGQIQKRLSKRJ",
- "MWRHQGRFSGSIRKQL",
- "E_UMXP[RXTUW IR[R",
- "G]OFOb UFUb JQZQ JWZW",
- "E_\\O\\N[MZMYNXPVUTXRZP[L[JZIYHWHUISJRQNRMSKSIRGPFNGMIMKNNPQUXWZY[[[\\Z\\Y",
- "G]IIJKKOKUJYI[ [IZKYOYUZY[[ IIKJOKUKYJ[I I[KZOYUYYZ[[",
- "F_\\Q[OYNWNUOTPQTPUNVLVJUISIQJOLNNNPOQPTTUUWVYV[U\\S\\Q",
- "KYOBO[ UBU[",
- "F^RBR[ I[[[",
- "F^[BI[[[",
- "E_RIQJRKSJRI IYHZI[JZIY [YZZ[[\\Z[Y",
- "F^RHNLKPJSJUKWMXOXQWRU RHVLYPZSZUYWWXUXSWRU RUQYP\\ RUSYT\\ P\\T\\",
- "F^RNQKPINHMHKIJKJOKRLTNWR\\ RNSKTIVHWHYIZKZOYRXTVWR\\",
- "F^RGPJLOIR RGTJXO[R IRLUPZR] [RXUTZR]",
- "F^RTTWVXXXZW[U[SZQXPVPSQ SQUOVMVKUISHQHOINKNMOOQQ QQNPLPJQISIUJWLXNXPWRT RTQYP\\ RTSYT\\ P\\T\\",
- "F^RRR[Q\\ RVQ\\ RIQHOHNINKONRR RISHUHVIVKUNRR RRNOLNJNIOIQJR RRVOXNZN[O[QZR RRNULVJVIUISJR RRVUXVZV[U[SZR",
- "F^ISJSLTMVMXLZ ISIRJQLQMRNTNWMYLZ RGPIOLOOQUQXPZR\\ RGTIULUOSUSXTZR\\ [S[RZQXQWRVTVWWYXZ [SZSXTWVWXXZ KVYV",
- "",
- "",
- "",
- "PSSRRSQSPRPQQPRPSQSSRUQV QQQRRRRQQQ",
- "PTQPPQPSQTSTTSTQSPQP RQQRRSSRRQ",
- "NVPOTU TOPU NRVR",
- "MWRKQMOPMR RKSMUPWR RMOQ RMUQ ROPQ ROTQ QQSQ MRWR",
- "MWMRMQNOONQMSMUNVOWQWR PNTN OOUO NPVP NQVQ MRWR",
- "LRLFLRRRLF LIPQ LLOR LOMQ",
- "MWRKQMOPMR RKSMUPWR",
- "MWWRWQVOUNSMQMONNOMQMR",
- "G]]R]P\\MZJWHTGPGMHJJHMGPGR",
- "MWMRMSNUOVQWSWUVVUWSWR",
- "LXLPNRQSSSVRXP",
- "RURUTTURTPRO",
- "RVRRUPVNVLUKTK",
- "NRRROPNNNLOKPK",
- "MWWHVGTFQFOGNHMJMLNNOOUSVTWVWXVZU[S\\P\\N[MZ",
- "G]IWHVGTGQHOINKMMMONPOTUUVWWYW[V\\U]S]P\\N[M",
- "G]RRTUUVWWYW[V\\U]S]Q\\O[NYMWMUNTOPUOVMWKWIVHUGSGQHOINKMMMONPORR",
- "H\\KFK[ HF[FQP[Z ZV[Y\\[ ZVZY WYZY WYZZ\\[",
- "KYUARBPCNELHKLKRLUNWQXSXVWXUYR KPLMNKQJSJVKXMYPYVXZV]T_R`Oa",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- ">f>RfR",
- "D`D``D",
- "RRR>Rf",
- "D`DD``",
- "D`DR`R",
- "F^FY^K",
- "KYK^YF",
- "",
- "KYKFY^",
- "F^FK^Y",
- "KYKRYR",
- "MWMWWM",
- "",
- "MWMMWW",
- "",
- "",
- "",
- "",
- "D`DOGQKSPTTTYS]Q`O",
- "PUUDSGQKPPPTQYS]U`",
- "OTODQGSKTPTTSYQ]O`",
- "D`DUGSKQPPTPYQ]S`U",
- "KYRJYNKVRZ",
- "JZJRNKVYZR",
- "KYKVKNYVYN",
- "JZLXJPZTXL",
- "JZJ]L]O\\Q[TXUVVSVOULTJSIQIPJOLNONSOVPXS[U\\X]Z]",
- "I]]Z]X\\U[SXPVOSNONLOJPIQISJTLUOVSVVUXT[Q\\O]L]J",
- "JZZGXGUHSIPLONNQNUOXPZQ[S[TZUXVUVQUNTLQIOHLGJG",
- "G[GJGLHOIQLTNUQVUVXUZT[S[QZPXOUNQNNOLPISHUGXGZ",
- "E[EPFRHTJUMVQVUUXSZP[NZLWLSMQNNPLSKVKYL\\M^",
- "EYETHVKWPWSVVTXQYNYLXKVKSLPNNQMTMYN\\P_",
- "OUQOOQOSQUSUUSUQSOQO QPPQPSQTSTTSTQSPQP RQQRRSSRRQ",
- "",
- "D`DRJR ORUR ZR`R",
- "D`DUDO`O`U",
- "JZRDJR RDZR",
- "D`DR`R JYZY P`T`",
- "D`DR`R DRRb `RRb",
- "",
- "",
- "",
- "",
- "",
- "KYQKNLLNKQKSLVNXQYSYVXXVYSYQXNVLSKQK",
- "LXLLLXXXXLLL",
- "KYRJKVYVRJ",
- "LXRHLRR\\XRRH",
- "JZRIPOJOOSMYRUWYUSZOTORI",
- "KYRKRY KRYR",
- "MWMMWW WMMW",
- "MWRLRX MOWU WOMU",
- "",
- "",
- "NVQNOONQNSOUQVSVUUVSVQUOSNQN OQOS PPPT QOQU RORU SOSU TPTT UQUS",
- "NVNNNVVVVNNN OOOU POPU QOQU RORU SOSU TOTU UOUU",
- "MWRLMUWURL ROOT ROUT RRQT RRST",
- "LULRUWUMLR ORTU ORTO RRTS RRTQ",
- "MWRXWOMORX RUUP RUOP RRSP RRQP",
- "OXXROMOWXR URPO URPU RRPQ RRPS",
- "LXRLNWXPLPVWRL RRRL RRLP RRNW RRVW RRXP",
- "",
- "",
- "",
- "MWRLRX OOUO MUOWQXSXUWWU",
- "LXRLRX LQMOWOXQ PWTW",
- "KYMNWX WNMX OLLOKQ ULXOYQ",
- "I[NII[ VI[[ MM[[ WMI[ NIVI MMWM",
- "I[RGRV MJWP WJMP IVL\\ [VX\\ IV[V L\\X\\",
- "G[MJSV KPSL G\\[\\[RG\\",
- "LXPLPPLPLTPTPXTXTTXTXPTPTLPL",
- "KYYPXNVLSKQKNLLNKQKSLVNXQYSYVXXVYT YPWNUMSMQNPOOQOSPUQVSWUWWVYT",
- "KYRJKVYVRJ RZYNKNRZ",
- "G]PIPGQFSFTGTI GZHXJVKTLPLKMJOIUIWJXKXPYTZV\\X]Z GZ]Z QZP[Q\\S\\T[SZ",
- "JZRMRS RSQ\\ RSS\\ Q\\S\\ RMQJPHNG QJNG RMSJTHVG SJVG RMNKLKJM PLLLJM RMVKXKZM TLXLZM RMPNOOOR RMPOOR RMTNUOUR RMTOUR",
- "JZRIRK RNRP RSRU RYQ\\ RYS\\ Q\\S\\ RGQIPJ RGSITJ PJRITJ RKPNNOMN RKTNVOWN NOPORNTOVO RPPSNTLTKRKSLT RPTSVTXTYRYSXT NTPTRSTTVT RUPXOYMZLZKYJWJYLZ RUTXUYWZXZYYZWZYXZ MZOZRYUZWZ",
- "JZRYQ\\ RYS\\ Q\\S\\ RYUZXZZXZUYTWTYRZOYMWLUMVJUHSGQGOHNJOMMLKMJOKRMTKTJUJXLZOZRY",
- "JZRYQ\\ RYS\\ Q\\S\\ RYVXVVXUXRZQZLYIXHVHTGPGNHLHKIJLJQLRLUNVNXRY",
- "I[IPKR LKNP RGRO XKVP [PYR",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "QSRQQRRSSRRQ",
- "PTQPPQPSQTSTTSTQSPQP",
- "NVQNOONQNSOUQVSVUUVSVQUOSNQN",
- "MWQMONNOMQMSNUOVQWSWUVVUWSWQVOUNSMQM",
- "KYQKNLLNKQKSLVNXQYSYVXXVYSYQXNVLSKQK",
- "G]PGMHJJHMGPGTHWJZM\\P]T]W\\ZZ\\W]T]P\\MZJWHTGPG",
- "AcPALBJCGEEGCJBLAPATBXCZE]G_JaLbPcTcXbZa]__]aZbXcTcPbLaJ_G]EZCXBTAPA",
- "<hP<K=G?DAAD?G=K<P<T=Y?]A`DcGeKgPhThYg]e`cc`e]gYhThPgKeGcD`A]?Y=T<P<",
- "){O)I*E+@-;073370;-@+E*I)O)U*[+_-d0i3m7q;t@wEyIzO{U{[z_ydwitmqqmtiwdy_z[{U{OzIyEw@t;q7m3i0d-_+[*U)O)",
- ">fRAPCMDJDGCEA>H@JAMAZB]D_G`M`PaRc RATCWDZD]C_AfHdJcMcZb]`_]`W`TaRc",
- "AcRAPCMDJDGCEABGAKAPBTDXG\\L`Rc RATCWDZD]C_AbGcKcPbT`X]\\X`Rc BHbH",
- "H[WPVQWRXQXPVNTMQMNNLPKSKULXNZQ[S[VZXX QMONMPLSLUMXOZQ[ LbXF",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "",
- "KYRKMX RNVX RKWX OTTT KXPX TXYX",
- "JZNKNX OKOX LKSKVLWNVPSQ SKULVNUPSQ OQSQVRWTWUVWSXLX SQURVTVUUWSX",
- "KYVLWKWOVLTKQKOLNMMPMSNVOWQXTXVWWU QKOMNPNSOVQX",
- "JZNKNX OKOX LKSKVLWMXPXSWVVWSXLX SKULVMWPWSVVUWSX",
- "JYNKNX OKOX SOSS LKVKVOUK OQSQ LXVXVTUX",
- "JXNKNX OKOX SOSS LKVKVOUK OQSQ LXQX",
- "K[VLWKWOVLTKQKOLNMMPMSNVOWQXTXVW QKOMNPNSOVQX TXUWVU VSVX WSWX TSYS",
- "J[NKNX OKOX VKVX WKWX LKQK TKYK OQVQ LXQX TXYX",
- "NWRKRX SKSX PKUK PXUX",
- "LXSKSURWQX TKTUSWQXPXNWMUNTOUNV QKVK",
- "JZNKNX OKOX WKOS QQVX RQWX LKQK TKYK LXQX TXYX",
- "KXOKOX PKPX MKRK MXWXWTVX",
- "I\\MKMX NNRX NKRU WKRX WKWX XKXX KKNK WKZK KXOX UXZX",
- "JZNKNX OMVX OKVV VKVX LKOK TKXK LXPX",
- "KZQKOLNMMPMSNVOWQXTXVWWVXSXPWMVLTKQK QKOMNPNSOVQX TXVVWSWPVMTK",
- "JYNKNX OKOX LKSKVLWNWOVQSROR SKULVNVOUQSR LXQX",
- "KZQKOLNMMPMSNVOWQXTXVWWVXSXPWMVLTKQK QKOMNPNSOVQX TXVVWSWPVMTK PWPUQTSTTUUZV[W[XZ TUUXVZW[",
- "JZNKNX OKOX LKSKVLWNWOVQSROR SKULVNVOUQSR LXQX SRTSUWVXWXXW SRUSVWWX",
- "KZVMWKWOVMULSKQKOLNMNOOPQQTRVSWT NNOOQPTQVRWSWVVWTXRXPWOVNTNXOV",
- "KZRKRX SKSX NKMOMKXKXOWK PXUX",
- "J[NKNUOWQXTXVWWUWK OKOUPWQX LKQK UKYK",
- "KYMKRX NKRU WKRX KKPK TKYK",
- "I[LKOX MKOT RKOX RKUX SKUT XKUX JKOK VKZK",
- "KZNKVX OKWX WKNX LKQK TKYK LXQX TXYX",
- "LYNKRRRX OKSR WKSRSX LKQK TKYK PXUX",
- "LYVKNX WKOX OKNONKWK NXWXWTVX",
- "KYRKMX RNVX RKWX OTTT KXPX TXYX",
- "JZNKNX OKOX LKSKVLWNVPSQ SKULVNUPSQ OQSQVRWTWUVWSXLX SQURVTVUUWSX",
- "KXOKOX PKPX MKWKWOVK MXRX",
- "KYRKLX RMWX RKXX MWVW LXXX",
- "JYNKNX OKOX SOSS LKVKVOUK OQSQ LXVXVTUX",
- "LYVKNX WKOX OKNONKWK NXWXWTVX",
- "J[NKNX OKOX VKVX WKWX LKQK TKYK OQVQ LXQX TXYX",
- "KZQKOLNMMPMSNVOWQXTXVWWVXSXPWMVLTKQK QKOMNPNSOVQX TXVVWSWPVMTK QOQT TOTT QQTQ QRTR",
- "NWRKRX SKSX PKUK PXUX",
- "JZNKNX OKOX WKOS QQVX RQWX LKQK TKYK LXQX TXYX",
- "KYRKMX RNVX RKWX KXPX TXYX",
- "I\\MKMX NNRX NKRU WKRX WKWX XKXX KKNK WKZK KXOX UXZX",
- "JZNKNX OMVX OKVV VKVX LKOK TKXK LXPX",
- "JZMJLM XJWM PPOS UPTS MVLY XVWY MKWK MLWL PQTQ PRTR MWWW MXWX",
- "KZQKOLNMMPMSNVOWQXTXVWWVXSXPWMVLTKQK QKOMNPNSOVQX TXVVWSWPVMTK",
- "J[NKNX OKOX VKVX WKWX LKYK LXQX TXYX",
- "JYNKNX OKOX LKSKVLWNWOVQSROR SKULVNVOUQSR LXQX",
- "K[MKRQ NKSQMX MKWKXOVK NWWW MXWXXTVX",
- "KZRKRX SKSX NKMOMKXKXOWK PXUX",
- "KZMONLOKPKQLRORX XOWLVKUKTLSOSX MONMOLPLQMRO XOWMVLULTMSO PXUX",
- "KZRKRX SKSX QNNOMQMRNTQUTUWTXRXQWOTNQN QNOONQNROTQU TUVTWRWQVOTN PKUK PXUX",
- "KZNKVX OKWX WKNX LKQK TKYK LXQX TXYX",
- "J[RKRX SKSX LPMONOOSQU TUVSWOXOYP MONROTQUTUVTWRXO PKUK PXUX",
- "KZMVNXQXMRMONMOLQKTKVLWMXOXRTXWXXV OUNRNOOMQK TKVMWOWRVU NWPW UWWW",
- "KYTKKX SMTX TKUX NTTT IXNX RXWX",
- "JYPKLX QKMX NKUKWLWNVPSQ UKVLVNUPSQ OQRQTRUSUUTWQXJX RQTSTUSWQX",
- "KXVLWLXKWNVLTKRKPLOMNOMRMUNWPXRXTWUU RKPMOONRNVPX",
- "JYPKLX QKMX NKTKVLWNWQVTUVTWQXJX TKULVNVQUTTVSWQX",
- "JYPKLX QKMX SORS NKXKWNWK OQRQ JXTXUUSX",
- "JXPKLX QKMX SORS NKXKWNWK OQRQ JXOX",
- "KYVLWLXKWNVLTKRKPLOMNOMRMUNWPXRXTWUVVS RKPMOONRNVPX RXTVUS SSXS",
- "J[PKLX QKMX XKTX YKUX NKSK VK[K OQVQ JXOX RXWX",
- "NWTKPX UKQX RKWK NXSX",
- "LXUKRUQWPX VKSURWPXOXMWLUMTNUMV SKXK",
- "JZPKLX QKMX YKOR RPTX SPUX NKSK VK[K JXOX RXWX",
- "KXQKMX RKNX OKTK KXUXVUTX",
- "I\\OKKX OMPX PKQV YKPX YKUX ZKVX MKPK YK\\K IXMX SXXX",
- "JZPKLX PKTX QKTU XKTX NKQK VKZK JXNX",
- "KYRKPLOMNOMRMUNWPXRXTWUVVTWQWNVLTKRK RKPMOONRNVPX RXTVUTVQVMTK",
- "JYPKLX QKMX NKUKWLXMXOWQTROR UKWMWOVQTR JXOX",
- "KYRKPLOMNOMRMUNWPXRXTWUVVTWQWNVLTKRK RKPMOONRNVPX RXTVUTVQVMTK OWOVPUQURVRZS[T[UZ RVSZT[",
- "JZPKLX QKMX NKUKWLXMXOWQTROR UKWMWOVQTR SRTWUXVXWW SRTSUWVX JXOX",
- "KZWLXLYKXNWLUKRKPLOMOOPPUSVT ONPOURVSVVUWSXPXNWMULXMWNW",
- "KZTKPX UKQX PKNNOKZKYNYK NXSX",
- "J[PKMUMWOXSXUWVUYK QKNUNWOX NKSK WK[K",
- "KYOKPX PKQV YKPX MKRK VK[K",
- "I[NKMX OKNV TKMX TKSX UKTV ZKSX LKQK XK\\K",
- "KZPKTX QKUX YKLX NKSK VK[K JXOX RXWX",
- "LYPKRQPX QKSQ YKSQQX NKSK VK[K NXSX",
- "LYXKLX YKMX QKONPKYK LXUXVUTX",
- "",
- "",
- "",
- "",
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- "G]LFL[ MFM[ IFUFXGYHZJZLYNXOUP UFWGXHYJYLXNWOUP MPUPXQYRZTZWYYXZU[I[ UPWQXRYTYWXYWZU[",
- "G\\XIYLYFXIVGSFQFNGLIKKJNJSKVLXNZQ[S[VZXXYV QFOGMILKKNKSLVMXOZQ[",
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- "F^KFK[ LFL[ XFX[ YFY[ HFOF UF\\F LPXP H[O[ U[\\[",
- "MXRFR[ SFS[ OFVF O[V[",
- "KZUFUWTZR[P[NZMXMVNUOVNW TFTWSZR[ QFXF",
- "F\\KFK[ LFL[ YFLS QOY[ POX[ HFOF UF[F H[O[ U[[[",
- "I[NFN[ OFO[ KFRF K[Z[ZUY[",
- "F_KFK[ LFRX KFR[ YFR[ YFY[ ZFZ[ HFLF YF]F H[N[ V[][",
- "G^LFL[ MFYY MHY[ YFY[ IFMF VF\\F I[O[",
- "G]QFNGLIKKJOJRKVLXNZQ[S[VZXXYVZRZOYKXIVGSFQF QFOGMILKKOKRLVMXOZQ[ S[UZWXXVYRYOXKWIUGSF",
- "G]LFL[ MFM[ IFUFXGYHZJZMYOXPUQMQ UFWGXHYJYMXOWPUQ I[P[",
- "G]QFNGLIKKJOJRKVLXNZQ[S[VZXXYVZRZOYKXIVGSFQF QFOGMILKKOKRLVMXOZQ[ S[UZWXXVYRYOXKWIUGSF NYNXOVQURUTVUXV_W`Y`Z^Z] UXV\\W^X_Y_Z^",
- "G]LFL[ MFM[ IFUFXGYHZJZLYNXOUPMP UFWGXHYJYLXNWOUP I[P[ RPTQURXYYZZZ[Y TQUSWZX[Z[[Y[X",
- "H\\XIYFYLXIVGSFPFMGKIKKLMMNOOUQWRYT KKMMONUPWQXRYTYXWZT[Q[NZLXKUK[LX",
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- "H\\KFX[ LFY[ YFK[ IFOF UF[F I[O[ U[[[",
- "H]KFRQR[ LFSQS[ ZFSQ IFOF VF\\F O[V[",
- "H\\XFK[ YFL[ LFKLKFYF K[Y[YUX[",
- "H\\RFK[ RFY[ RIX[ MUVU I[O[ U[[[",
- "G]LFL[ MFM[ IFUFXGYHZJZLYNXOUP UFWGXHYJYLXNWOUP MPUPXQYRZTZWYYXZU[I[ UPWQXRYTYWXYWZU[",
- "I[NFN[ OFO[ KFZFZLYF K[R[",
- "H\\RFJ[ RFZ[ RIY[ KZYZ J[Z[",
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- "H\\XFK[ YFL[ LFKLKFYF K[Y[YUX[",
- "F^KFK[ LFL[ XFX[ YFY[ HFOF UF\\F LPXP H[O[ U[\\[",
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- "MXRFR[ SFS[ OFVF O[V[",
- "F\\KFK[ LFL[ YFLS QOY[ POX[ HFOF UF[F H[O[ U[[[",
- "H\\RFK[ RFY[ RIX[ I[O[ U[[[",
- "F_KFK[ LFRX KFR[ YFR[ YFY[ ZFZ[ HFLF YF]F H[N[ V[][",
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- "F^KFK[ LFL[ XFX[ YFY[ HF\\F H[O[ U[\\[",
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- "I\\RFR[ SFS[ LFKLKFZFZLYF O[V[",
- "I\\KKKILGMFOFPGQIRMR[ KIMGOGQI ZKZIYGXFVFUGTISMS[ ZIXGVGTI O[V[",
- "H]RFR[ SFS[ PKMLLMKOKRLTMUPVUVXUYTZRZOYMXLUKPK PKNLMMLOLRMTNUPV UVWUXTYRYOXMWLUK OFVF O[V[",
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- "G^RFR[ SFS[ IMJLLMMQNSOTQU JLKMLQMSNTQUTUWTXSYQZM[L TUVTWSXQYM[L\\M OFVF O[V[",
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- "H]NFRPO[ OFSPP[ ]FSP LFRF YF_F L[S[",
- "G][FH[ \\FI[ OFLLNF\\F H[V[XUU[",
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- "G]IB[b",
- "F^RJIZ RJ[Z",
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- "G\\LFL[ MFM[ MPONQMSMVNXPYSYUXXVZS[Q[OZMX SMUNWPXSXUWXUZS[ IFMF",
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- "H[LMR[ MMRY XMR[P_NaLbKbJaK`La JMPM TMZM",
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- "G]QFNGLIKKJOJRKVLXNZQ[S[VZXXYVZRZOYKXIVGSFQF QFOGMILKKOKRLVMXOZQ[ S[UZWXXVYRYOXKWIUGSF",
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- "G]LFL[ MFM[ IFUFXGYHZJZMYOXPUQMQ UFWGXHYJYMXOWPUQ I[P[",
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- "I\\RFR[ SFS[ LFKLKFZFZLYF O[V[",
- "H]KFRV LFSV ZFSVQYPZN[M[LZLYMXNYMZ IFOF VF\\F",
- "F_RFR[ SFS[ OFVF PILJJLIOIRJULWPXUXYW[U\\R\\O[LYJUIPI PIMJKLJOJRKUMWPX UXXWZU[R[OZLXJUI O[V[",
- "H\\KFX[ LFY[ YFK[ IFOF UF[F I[O[ U[[[",
- "F^KFK[ LFL[ XFX[ YFY[ HFOF UF\\F H[\\[ [[\\b \\[\\b",
- "F]KFKQLSOTRTUSWQ LFLQMSOT WFW[ XFX[ HFOF TF[F T[[[",
- "BcGFG[ HFH[ RFR[ SFS[ ]F][ ^F^[ DFKF OFVF ZFaF D[a[",
- "BcGFG[ HFH[ RFR[ SFS[ ]F][ ^F^[ DFKF OFVF ZFaF D[a[ `[ab a[ab",
- "F`PFP[ QFQ[ IFHLHFTF QPXP[Q\\R]T]W\\Y[ZX[M[ XPZQ[R\\T\\W[YZZX[",
- "CaHFH[ IFI[ EFLF IPPPSQTRUTUWTYSZP[E[ PPRQSRTTTWSYRZP[ [F[[ \\F\\[ XF_F X[_[",
- "H]MFM[ NFN[ JFQF NPUPXQYRZTZWYYXZU[J[ UPWQXRYTYWXYWZU[",
- "H]LIKFKLLINGQFSFVGXIYKZNZSYVXXVZS[P[MZLYKWKVLUMVLW SFUGWIXKYNYSXVWXUZS[ PPYP",
- "CbHFH[ IFI[ EFLF E[L[ VFSGQIPKOOORPVQXSZV[X[[Z]X^V_R_O^K]I[GXFVF VFTGRIQKPOPRQVRXTZV[ X[ZZ\\X]V^R^O]K\\IZGXF IPOP",
- "G]WFW[ XFX[ [FOFLGKHJJJLKNLOOPWP OFMGLHKJKLLNMOOP RPPQORLYKZJZIY PQOSMZL[J[IYIX T[[[",
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- "I]NONPMPMONNPMTMVNWOXQXXYZZ[ WOWXXZZ[[[ WQVRPSMTLVLXMZP[S[UZWX PSNTMVMXNZP[",
- "H\\XFWGQINKLNKQKULXNZQ[S[VZXXYUYSXPVNSMQMNNLPKS XFWHUIQJNLLN QMONMPLSLUMXOZQ[ S[UZWXXUXSWPUNSM",
- "H\\MMM[ NMN[ JMUMXNYPYQXSUT UMWNXPXQWSUT NTUTXUYWYXXZU[J[ UTWUXWXXWZU[",
- "HZMMM[ NMN[ JMXMXRWM J[Q[",
- "F]NMNQMWLZK[ WMW[ XMX[ KM[M I[H`H[[[[`Z[",
- "H[LSXSXQWOVNTMQMNNLPKSKULXNZQ[S[VZXX WSWPVN QMONMPLSLUMXOZQ[",
- "E`RMR[ SMS[ OMVM JNIOHNIMJMKNMRNSPTUTWSXRZN[M\\M]N\\O[N PTNUMVKZJ[ PTNVLZK[I[HZGX UTWUXVZZ[[ UTWVYZZ[\\[]Z^X O[V[",
- "I[MOLMLQMONNPMTMWNXPXQWSTT TMVNWPWQVSTT QTTTWUXWXXWZT[P[MZLXLWMVNWMX TTVUWWWXVZT[",
- "G]LML[ MMM[ WMW[ XMX[ IMPM TM[M I[P[ T[[[ WNMZ",
- "G]LML[ MMM[ WMW[ XMX[ IMPM TM[M I[P[ T[[[ WNMZ OGOFNFNGOIQJSJUIVG",
- "H\\MMM[ NMN[ JMQM NTPTSSTRVNWMXMYNXOWN PTSUTVVZW[ PTRUSVUZV[X[YZZX J[Q[",
- "G]NMNQMWLZK[J[IZJYKZ WMW[ XMX[ KM[M T[[[",
- "G^LML[ LMR[ MMRY XMR[ XMX[ YMY[ IMMM XM\\M I[O[ U[\\[",
- "G]LML[ MMM[ WMW[ XMX[ IMPM TM[M MTWT I[P[ T[[[",
- "H\\QMNNLPKSKULXNZQ[S[VZXXYUYSXPVNSMQM QMONMPLSLUMXOZQ[ S[UZWXXUXSWPUNSM",
- "G]LML[ MMM[ WMW[ XMX[ IM[M I[P[ T[[[",
- "G\\LMLb MMMb MPONQMSMVNXPYSYUXXVZS[Q[OZMX SMUNWPXSXUWXUZS[ IMMM IbPb",
- "H[WPVQWRXQXPVNTMQMNNLPKSKULXNZQ[S[VZXX QMONMPLSLUMXOZQ[",
- "I\\RMR[ SMS[ MMLRLMYMYRXM O[V[",
- "I[LMR[ MMRY XMR[P_NaLbKbJaK`La JMPM TMZM",
- "H]RFRb SFSb OFSF RPQNPMNMLNKQKWLZN[P[QZRX NMMNLQLWMZN[ WMXNYQYWXZW[ SPTNUMWMYNZQZWYZW[U[TZSX ObVb",
- "H\\LMW[ MMX[ XML[ JMPM TMZM J[P[ T[Z[",
- "G]LML[ MMM[ WMW[ XMX[ IMPM TM[M I[[[[`Z[",
- "G]LMLTMVPWRWUVWT MMMTNVPW WMW[ XMX[ IMPM TM[M T[[[",
- "CbHMH[ IMI[ RMR[ SMS[ \\M\\[ ]M][ EMLM OMVM YM`M E[`[",
- "CbHMH[ IMI[ RMR[ SMS[ \\M\\[ ]M][ EMLM OMVM YM`M E[`[``_[",
- "H]QMQ[ RMR[ LMKRKMUM RTVTYUZWZXYZV[N[ VTXUYWYXXZV[",
- "E_JMJ[ KMK[ GMNM KTOTRUSWSXRZO[G[ OTQURWRXQZO[ YMY[ ZMZ[ VM]M V[][",
- "J[OMO[ PMP[ LMSM PTTTWUXWXXWZT[L[ TTVUWWWXVZT[",
- "I\\MOLMLQMONNPMSMVNXPYSYUXXVZS[P[NZLXLWMVNWMX SMUNWPXSXUWXUZS[ RTXT",
- "DaIMI[ JMJ[ FMMM F[M[ VMSNQPPSPUQXSZV[X[[Z]X^U^S]P[NXMVM VMTNRPQSQURXTZV[ X[ZZ\\X]U]S\\PZNXM JTPT",
- "G\\VMV[ WMW[ ZMOMLNKPKQLSOTVT OMMNLPLQMSOT TTQUPVNZM[ TTRUQVOZN[L[KZJX S[Z[",
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- "H\\RFKZ QIW[ RIX[ RFY[ MUVU I[O[ T[[[ KZJ[ KZM[ WZU[ WYV[ XYZ[",
- "G]LFL[ MGMZ NFN[ IFUFXGYHZJZLYNXOUP XHYJYLXN UFWGXIXMWOUP NPUPXQYRZTZWYYXZU[I[ XRYTYWXY UPWQXSXXWZU[ JFLG KFLH OFNH PFNG LZJ[ LYK[ NYO[ NZP[",
- "G\\XIYFYLXIVGTFQFNGLIKKJNJSKVLXNZQ[T[VZXXYV MILKKNKSLVMX QFOGMJLNLSMWOZQ[",
- "G]LFL[ MGMZ NFN[ IFSFVGXIYKZNZSYVXXVZS[I[ WIXKYNYSXVWX SFUGWJXNXSWWUZS[ JFLG KFLH OFNH PFNG LZJ[ LYK[ NYO[ NZP[",
- "G\\LFL[ MGMZ NFN[ IFYFYL NPTP TLTT I[Y[YU JFLG KFLH OFNH PFNG TFYG VFYH WFYI XFYL TLSPTT TNRPTR TOPPTQ LZJ[ LYK[ NYO[ NZP[ T[YZ V[YY W[YX X[YU",
- "G[LFL[ MGMZ NFN[ IFYFYL NPTP TLTT I[Q[ JFLG KFLH OFNH PFNG TFYG VFYH WFYI XFYL TLSPTT TNRPTR TOPPTQ LZJ[ LYK[ NYO[ NZP[",
- "G^XIYFYLXIVGTFQFNGLIKKJNJSKVLXNZQ[T[VZXZY[YS MILKKNKSLVMX QFOGMJLNLSMWOZQ[ XTXY WSWYVZ TS\\S USWT VSWU ZSYU [SYT",
- "F^KFK[ LGLZ MFM[ WFW[ XGXZ YFY[ HFPF TF\\F MPWP H[P[ T[\\[ IFKG JFKH NFMH OFMG UFWG VFWH ZFYH [FYG KZI[ KYJ[ MYN[ MZO[ WZU[ WYV[ YYZ[ YZ[[",
- "LXQFQ[ RGRZ SFS[ NFVF N[V[ OFQG PFQH TFSH UFSG QZO[ QYP[ SYT[ SZU[",
- "JZSFSWRZQ[ TGTWSZ UFUWTZQ[O[MZLXLVMUNUOVOWNXMX MVMWNWNVMV PFXF QFSG RFSH VFUH WFUG",
- "F\\KFK[ LGLZ MFM[ XGMR PPW[ QPX[ QNY[ HFPF UF[F H[P[ T[[[ IFKG JFKH NFMH OFMG WFXG ZFXG KZI[ KYJ[ MYN[ MZO[ WYU[ WYZ[",
- "I[NFN[ OGOZ PFP[ KFSF K[Z[ZU LFNG MFNH QFPH RFPG NZL[ NYM[ PYQ[ PZR[ U[ZZ W[ZY X[ZX Y[ZU",
- "E_JFJZ JFQ[ KFQX LFRX XFQ[ XFX[ YGYZ ZFZ[ GFLF XF]F G[M[ U[][ HFJG [FZH \\FZG JZH[ JZL[ XZV[ XYW[ ZY[[ ZZ\\[",
- "F^KFKZ KFY[ LFXX MFYX YGY[ HFMF VF\\F H[N[ IFKG WFYG [FYG KZI[ KZM[",
- "G]QFNGLIKKJOJRKVLXNZQ[S[VZXXYVZRZOYKXIVGSFQF MILKKNKSLVMX WXXVYSYNXKWI QFOGMJLNLSMWOZQ[ S[UZWWXSXNWJUGSF",
- "G]LFL[ MGMZ NFN[ IFUFXGYHZJZMYOXPUQNQ XHYJYMXO UFWGXIXNWPUQ I[Q[ JFLG KFLH OFNH PFNG LZJ[ LYK[ NYO[ NZP[",
- "G]QFNGLIKKJOJRKVLXNZQ[S[VZXXYVZRZOYKXIVGSFQF MILKKNKSLVMX WXXVYSYNXKWI QFOGMJLNLSMWOZQ[ S[UZWWXSXNWJUGSF NXOVQURUTVUXV^W`Y`Z^Z\\ V\\W^X_Y_ UXW]X^Y^Z]",
- "G]LFL[ MGMZ NFN[ IFUFXGYHZJZLYNXOUPNP XHYJYLXN UFWGXIXMWOUP RPTQUSWYX[Z[[Y[W WWXYYZZZ TQURXXYYZY[X I[Q[ JFLG KFLH OFNH PFNG LZJ[ LYK[ NYO[ NZP[",
- "H\\XIYFYLXIVGSFPFMGKIKLLNOPURWSXUXXWZ LLMNOOUQWRXT MGLILKMMONUPXRYTYWXYWZT[Q[NZLXKUK[LX",
- "H\\JFJL QFQ[ RGRZ SFS[ ZFZL JFZF N[V[ KFJL LFJI MFJH OFJG UFZG WFZH XFZI YFZL QZO[ QYP[ SYT[ SZU[",
- "F^KFKULXNZQ[S[VZXXYUYG LGLVMX MFMVNYOZQ[ HFPF VF\\F IFKG JFKH NFMH OFMG WFYG [FYG",
- "H\\KFR[ LFRXR[ MFSX YGR[ IFPF UF[F JFLH NFMH OFMG WFYG ZFYG",
- "F^JFN[ KFNVN[ LFOV RFOVN[ RFV[ SFVVV[ TFWV ZGWVV[ GFOF RFTF WF]F HFKG IFKH MFLH NFLG XFZG \\FZG",
- "H\\KFW[ LFX[ MFY[ XGLZ IFPF UF[F I[O[ T[[[ JFMH NFMH OFMG VFXG ZFXG LZJ[ LZN[ WZU[ WYV[ WYZ[",
- "G]JFQQQ[ KFRQRZ LFSQS[ YGSQ HFOF VF\\F N[V[ IFKG NFLG WFYG [FYG QZO[ QYP[ SYT[ SZU[",
- "H\\YFKFKL WFK[ XFL[ YFM[ K[Y[YU LFKL MFKI NFKH PFKG T[YZ V[YY W[YX X[YU",
- "H\\RFKZ QIW[ RIX[ RFY[ MUVU I[O[ T[[[ KZJ[ KZM[ WZU[ WYV[ XYZ[",
- "G]LFL[ MGMZ NFN[ IFUFXGYHZJZLYNXOUP XHYJYLXN UFWGXIXMWOUP NPUPXQYRZTZWYYXZU[I[ XRYTYWXY UPWQXSXXWZU[ JFLG KFLH OFNH PFNG LZJ[ LYK[ NYO[ NZP[",
- "I[NFN[ OGOZ PFP[ KFZFZL K[S[ LFNG MFNH QFPH RFPG UFZG WFZH XFZI YFZL NYM[ NZL[ PYQ[ PZR[",
- "H\\RFJ[ QIX[ RIY[ RFZ[ KYXY KZXZ J[Z[",
- "G\\LFL[ MGMZ NFN[ IFYFYL NPTP TLTT I[Y[YU JFLG KFLH OFNH PFNG TFYG VFYH WFYI XFYL TLSPTT TNRPTR TOPPTQ LZJ[ LYK[ NYO[ NZP[ T[YZ V[YY W[YX X[YU",
- "H\\YFKFKL WFK[ XFL[ YFM[ K[Y[YU LFKL MFKI NFKH PFKG T[YZ V[YY W[YX X[YU",
- "F^KFK[ LGLZ MFM[ WFW[ XGXZ YFY[ HFPF TF\\F MPWP H[P[ T[\\[ IFKG JFKH NFMH OFMG UFWG VFWH ZFYH [FYG KZI[ KYJ[ MYN[ MZO[ WZU[ WYV[ YYZ[ YZ[[",
- "G]QFNGLIKKJOJRKVLXNZQ[S[VZXXYVZRZOYKXIVGSFQF MILKKNKSLVMX WXXVYSYNXKWI QFOGMJLNLSMWOZQ[ S[UZWWXSXNWJUGSF OMOT UMUT OPUP OQUQ ONPP OOQP UNTP UOSP PQOS QQOR SQUR TQUS",
- "LXQFQ[ RGRZ SFS[ NFVF N[V[ OFQG PFQH TFSH UFSG QZO[ QYP[ SYT[ SZU[",
- "F\\KFK[ LGLZ MFM[ XGMR PPW[ QPX[ QNY[ HFPF UF[F H[P[ T[[[ IFKG JFKH NFMH OFMG WFXG ZFXG KZI[ KYJ[ MYN[ MZO[ WYU[ WYZ[",
- "H\\RFKZ QIW[ RIX[ RFY[ I[O[ T[[[ KZJ[ KZM[ WZU[ WYV[ XYZ[",
- "E_JFJZ JFQ[ KFQX LFRX XFQ[ XFX[ YGYZ ZFZ[ GFLF XF]F G[M[ U[][ HFJG [FZH \\FZG JZH[ JZL[ XZV[ XYW[ ZY[[ ZZ\\[",
- "F^KFKZ KFY[ LFXX MFYX YGY[ HFMF VF\\F H[N[ IFKG WFYG [FYG KZI[ KZM[",
- "G]JEJL ZEZL OMOT UMUT JUJ\\ ZUZ\\ JGZG JHZH JIZI OPUP OQUQ JXZX JYZY JZZZ JFMH ZFWH KIJK LIJJ XIZJ YIZK ONPP OOQP UNTP UOSP PQOS QQOR SQUR TQUS JVKX JWLX ZWXX ZVYX MYJ[ WYZ[",
- "G]QFNGLIKKJOJRKVLXNZQ[S[VZXXYVZRZOYKXIVGSFQF MILKKNKSLVMX WXXVYSYNXKWI QFOGMJLNLSMWOZQ[ S[UZWWXSXNWJUGSF",
- "F^KFK[ LGLZ MFM[ WFW[ XGXZ YFY[ HF\\F H[P[ T[\\[ IFKG JFKH NFMH OFMG UFWG VFWH ZFYH [FYG KZI[ KYJ[ MYN[ MZO[ WZU[ WYV[ YYZ[ YZ[[",
- "G]LFL[ MGMZ NFN[ IFUFXGYHZJZMYOXPUQNQ XHYJYMXO UFWGXIXNWPUQ I[Q[ JFLG KFLH OFNH PFNG LZJ[ LYK[ NYO[ NZP[",
- "G]IFPPQQ JFQP KFRPI[ IFYFZLYIWF VFYH TFYG KYYY JZYZ I[Y[ZUYXWY",
- "H\\JFJL QFQ[ RGRZ SFS[ ZFZL JFZF N[V[ KFJL LFJI MFJH OFJG UFZG WFZH XFZI YFZL QZO[ QYP[ SYT[ SZU[",
- "H\\JMKILGMFOFPGQIRM LHMGOGPH JMKJMHOHPIQMQ[ RMR[ ZMYJWHUHTISMS[ XHWGUGTH ZMYIXGWFUFTGSIRM N[V[ QYP[ QZO[ SZU[ SYT[",
- "G]QFQ[ RGRZ SFS[ NFVF N[V[ OFQG PFQH TFSH UFSG QZO[ QYP[ SYT[ SZU[ OKLLKMJOJRKTLUOVUVXUYTZRZOYMXLUKOK LMKOKRLT XTYRYOXM OKMLLOLRMUOV UVWUXRXOWLUK",
- "H\\KFW[ LFX[ MFY[ XGLZ IFPF UF[F I[O[ T[[[ JFMH NFMH OFMG VFXG ZFXG LZJ[ LZN[ WZU[ WYV[ WYZ[",
- "F^QFQ[ RGRZ SFS[ NFVF N[V[ OFQG PFQH TFSH UFSG QZO[ QYP[ SYT[ SZU[ HMIMJNKQLSMTPUTUWTXSYQZN[M\\M LRKNJLILKN HMIKJKKLLPMSNTPU YN[LZLYNXR TUVTWSXPYLZK[K\\M",
- "G]NYKYJWK[O[MVKRJOJLKIMGPFTFWGYIZLZOYRWVU[Y[ZWYYVY LSKOKLLI XIYLYOXS O[MULPLKMHNGPF TFVGWHXKXPWUU[ KZNZ VZYZ",
- "H\\UFIZ SJT[ THUZ UFUHVYV[ LUTU F[L[ Q[X[ IZG[ IZK[ TZR[ TYS[ VYW[",
- "F^OFI[ PFJ[ QFK[ LFWFZG[I[KZNYOVP YGZIZKYNXO WFXGYIYKXNVP NPVPXQYSYUXXVZR[F[ WQXSXUWXUZ VPWRWUVXTZR[ MFPG NFOH RFPH SFPG JZG[ JYH[ KYL[ JZM[",
- "H]ZH[H\\F[L[JZHYGWFTFQGOIMLLOKSKVLYMZP[S[UZWXXV QHOJNLMOLSLWMY TFRGPJOLNOMSMXNZP[",
- "F]OFI[ PFJ[ QFK[ LFUFXGYHZKZOYSWWUYSZO[F[ WGXHYKYOXSVWTY UFWHXKXOWSUWRZO[ MFPG NFOH RFPH SFPG JZG[ JYH[ KYL[ JZM[",
- "F]OFI[ PFJ[ QFK[ ULST LF[FZL NPTP F[U[WV MFPG NFOH RFPH SFPG WFZG XFZH YFZI ZFZL ULSPST TNRPSR TOQPSQ JZG[ JYH[ KYL[ JZM[ P[UZ R[UY UYWV",
- "F\\OFI[ PFJ[ QFK[ ULST LF[FZL NPTP F[N[ MFPG NFOH RFPH SFPG WFZG XFZH YFZI ZFZL ULSPST TNRPSR TOQPSQ JZG[ JYH[ KYL[ JZM[",
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- "KYTFN[ UFO[ VFP[ QFYF K[S[ RFUG SFTH WFUH XFUG OZL[ OYM[ PYQ[ OZR[",
- "I\\WFRWQYO[ XFTSSVRX YFUSSXQZO[M[KZJXJVKULUMVMWLXKX KVKWLWLVKV TF\\F UFXG VFWH ZFXH [FXG",
- "F]OFI[ PFJ[ QFK[ \\GMR QOU[ ROV[ SNWZ LFTF YF_F F[N[ R[Y[ MFPG NFOH RFPH SFPG ZF\\G ^F\\G JZG[ JYH[ KYL[ JZM[ UZS[ UYT[ VYX[",
- "H\\QFK[ RFL[ SFM[ NFVF H[W[YU OFRG PFQH TFRH UFRG LZI[ LYJ[ MYN[ LZO[ R[WZ T[XX V[YU",
- "D`MFGZ MGNYN[ NFOY OFPX [FPXN[ [FU[ \\FV[ ]FW[ JFOF [F`F D[J[ R[Z[ KFMG LFMH ^F\\H _F\\G GZE[ GZI[ VZS[ VYT[ WYX[ VZY[",
- "F_OFIZ OFV[ PFVX QFWX \\GWXV[ LFQF YF_F F[L[ MFPG NFPH ZF\\G ^F\\G IZG[ IZK[",
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- "F_NFKQJUJXKZN[R[UZWXXU\\G OFLQKUKYLZ PFMQLULYN[ KFSF YF_F LFOG MFNH QFOH RFOG ZF\\G ^F\\G",
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- "",
- 0 };
-
}
/* End of file. */
}
Mat dst = _dst.getMat();
- u->currAllocator->download(u, dst.data, dims, sz, srcofs, step.p, dst.step.p);
+ u->currAllocator->download(u, dst.ptr(), dims, sz, srcofs, step.p, dst.step.p);
}
void UMat::copyTo(OutputArray _dst, InputArray _mask) const
OCL_OFF(const double cpuRes = cv::norm(src1_roi, src2_roi, type));
OCL_ON(const double gpuRes = cv::norm(usrc1_roi, usrc2_roi, type));
- EXPECT_NEAR(cpuRes, gpuRes, 0.2);
+ EXPECT_PRED3(relativeError, cpuRes, gpuRes, 1e-6);
}
}
OCL_OFF(const double cpuRes = cv::norm(src1_roi, src2_roi, type, mask_roi));
OCL_ON(const double gpuRes = cv::norm(usrc1_roi, usrc2_roi, type, umask_roi));
- EXPECT_NEAR(cpuRes, gpuRes, 0.1);
+ EXPECT_PRED3(relativeError, cpuRes, gpuRes, 1e-6);
}
}
//////////////////////////////// ConvertScaleAbs ////////////////////////////////////////////////
-typedef ArithmTestBase ConvertScaleAbs;
+PARAM_TEST_CASE(ConvertScaleAbs, MatDepth, Channels, bool)
+{
+ int depth;
+ int cn;
+ bool use_roi;
+ cv::Scalar val;
+
+ TEST_DECLARE_INPUT_PARAMETER(src);
+ TEST_DECLARE_OUTPUT_PARAMETER(dst);
+
+ virtual void SetUp()
+ {
+ depth = GET_PARAM(0);
+ cn = GET_PARAM(1);
+ use_roi = GET_PARAM(2);
+ }
+
+ virtual void generateTestData()
+ {
+ const int stype = CV_MAKE_TYPE(depth, cn);
+ const int dtype = CV_MAKE_TYPE(CV_8U, cn);
+
+ Size roiSize = randomSize(1, MAX_VALUE);
+ Border srcBorder = randomBorder(0, use_roi ? MAX_VALUE : 0);
+ randomSubMat(src, src_roi, roiSize, srcBorder, stype, 2, 11); // FIXIT: Test with minV, maxV
+
+ Border dstBorder = randomBorder(0, use_roi ? MAX_VALUE : 0);
+ randomSubMat(dst, dst_roi, roiSize, dstBorder, dtype, 5, 16);
+
+ val = cv::Scalar(rng.uniform(-100.0, 100.0), rng.uniform(-100.0, 100.0),
+ rng.uniform(-100.0, 100.0), rng.uniform(-100.0, 100.0));
+
+ UMAT_UPLOAD_INPUT_PARAMETER(src);
+ UMAT_UPLOAD_OUTPUT_PARAMETER(dst);
+ }
+
+ void Near(double threshold = 0.)
+ {
+ OCL_EXPECT_MATS_NEAR(dst, threshold);
+ }
+
+};
+
OCL_TEST_P(ConvertScaleAbs, Mat)
{
{
generateTestData();
- OCL_OFF(cv::convertScaleAbs(src1_roi, dst1_roi, val[0], val[1]));
- OCL_ON(cv::convertScaleAbs(usrc1_roi, udst1_roi, val[0], val[1]));
+ OCL_OFF(cv::convertScaleAbs(src_roi, dst_roi, val[0], val[1]));
+ OCL_ON(cv::convertScaleAbs(usrc_roi, udst_roi, val[0], val[1]));
- Near(depth <= CV_32S ? 1 : 1e-6);
+ Near(1);
}
}
OCL_OFF(cv::reduce(src_roi, dst_roi, dim, CV_REDUCE_SUM, dtype));
OCL_ON(cv::reduce(usrc_roi, udst_roi, dim, CV_REDUCE_SUM, dtype));
- double eps = ddepth <= CV_32S ? 1 : 1e-4;
+ double eps = ddepth <= CV_32S ? 1 : 7e-4;
OCL_EXPECT_MATS_NEAR(dst, eps);
}
}
void generateTestData()
{
- Size ARoiSize = randomSize(1, MAX_VALUE);
+ // set minimum size to 20, since testing less sizes doesn't make sense
+ Size ARoiSize = randomSize(20, MAX_VALUE);
Border ABorder = randomBorder(0, use_roi ? MAX_VALUE : 0);
randomSubMat(A, A_roi, ARoiSize, ABorder, type, -11, 11);
if (atrans)
ARoiSize = Size(ARoiSize.height, ARoiSize.width);
- Size BRoiSize = randomSize(1, MAX_VALUE);
+ Size BRoiSize = randomSize(20, MAX_VALUE);
if (btrans)
BRoiSize.width = ARoiSize.width;
else
for( i = 0; i < nplanes; i++, ++it )
{
- const uchar* sptr = planes[0].data;
- const uchar* aptr = planes[1].data;
- const uchar* bptr = planes[2].data;
- uchar* dptr = planes[3].data;
+ const uchar* sptr = planes[0].ptr();
+ const uchar* aptr = planes[1].ptr();
+ const uchar* bptr = planes[2].ptr();
+ uchar* dptr = planes[3].ptr();
switch( depth )
{
for( i = 0; i < nplanes; i++, ++it )
{
- const uchar* sptr = planes[0].data;
- uchar* dptr = planes[1].data;
+ const uchar* sptr = planes[0].ptr();
+ uchar* dptr = planes[1].ptr();
switch( depth )
{
for( i = 0; i < nplanes; i++, ++it )
{
- const uchar* sptr = planes[0].data;
- uchar* dptr = planes[1].data;
+ const uchar* sptr = planes[0].ptr();
+ uchar* dptr = planes[1].ptr();
if( depth == CV_32F )
{
for( i = 0; i < nplanes; i++, ++it )
{
- const uchar* sptr = planes[0].data;
- uchar* dptr = planes[1].data;
+ const uchar* sptr = planes[0].ptr();
+ uchar* dptr = planes[1].ptr();
if( depth == CV_32F )
{
{
if( depth == CV_32F )
{
- const float* xptr = (const float*)planes[0].data;
- const float* yptr = (const float*)planes[1].data;
- float* mptr = (float*)planes[2].data;
- float* aptr = (float*)planes[3].data;
+ const float* xptr = planes[0].ptr<float>();
+ const float* yptr = planes[1].ptr<float>();
+ float* mptr = planes[2].ptr<float>();
+ float* aptr = planes[3].ptr<float>();
for( j = 0; j < total; j++ )
{
}
else
{
- const double* xptr = (const double*)planes[0].data;
- const double* yptr = (const double*)planes[1].data;
- double* mptr = (double*)planes[2].data;
- double* aptr = (double*)planes[3].data;
+ const double* xptr = planes[0].ptr<double>();
+ const double* yptr = planes[1].ptr<double>();
+ double* mptr = planes[2].ptr<double>();
+ double* aptr = planes[3].ptr<double>();
for( j = 0; j < total; j++ )
{
#include "test_precomp.hpp"
#include <cstdlib>
-static void mytest(cv::Ptr<cv::optim::ConjGradSolver> solver,cv::Ptr<cv::optim::Solver::Function> ptr_F,cv::Mat& x,
+static void mytest(cv::Ptr<cv::ConjGradSolver> solver,cv::Ptr<cv::MinProblemSolver::Function> ptr_F,cv::Mat& x,
cv::Mat& etalon_x,double etalon_res){
solver->setFunction(ptr_F);
//int ndim=MAX(step.cols,step.rows);
std::cout<<"x:\n\t"<<x<<std::endl;
std::cout<<"etalon_res:\n\t"<<etalon_res<<std::endl;
std::cout<<"etalon_x:\n\t"<<etalon_x<<std::endl;
- double tol=solver->getTermCriteria().epsilon;
+ double tol = 1e-2;
ASSERT_TRUE(std::abs(res-etalon_res)<tol);
/*for(cv::Mat_<double>::iterator it1=x.begin<double>(),it2=etalon_x.begin<double>();it1!=x.end<double>();it1++,it2++){
ASSERT_TRUE(std::abs((*it1)-(*it2))<tol);
std::cout<<"--------------------------\n";
}
-class SphereF:public cv::optim::Solver::Function{
+class SphereF:public cv::MinProblemSolver::Function{
public:
double calc(const double* x)const{
return x[0]*x[0]+x[1]*x[1]+x[2]*x[2]+x[3]*x[3];
}
}
};
-class RosenbrockF:public cv::optim::Solver::Function{
+class RosenbrockF:public cv::MinProblemSolver::Function{
double calc(const double* x)const{
return 100*(x[1]-x[0]*x[0])*(x[1]-x[0]*x[0])+(1-x[0])*(1-x[0]);
}
}
};
-TEST(Optim_ConjGrad, regression_basic){
- cv::Ptr<cv::optim::ConjGradSolver> solver=cv::optim::createConjGradSolver();
+TEST(DISABLED_Core_ConjGradSolver, regression_basic){
+ cv::Ptr<cv::ConjGradSolver> solver=cv::ConjGradSolver::create();
#if 1
{
- cv::Ptr<cv::optim::Solver::Function> ptr_F(new SphereF());
+ cv::Ptr<cv::MinProblemSolver::Function> ptr_F(new SphereF());
cv::Mat x=(cv::Mat_<double>(4,1)<<50.0,10.0,1.0,-10.0),
etalon_x=(cv::Mat_<double>(1,4)<<0.0,0.0,0.0,0.0);
double etalon_res=0.0;
#endif
#if 1
{
- cv::Ptr<cv::optim::Solver::Function> ptr_F(new RosenbrockF());
+ cv::Ptr<cv::MinProblemSolver::Function> ptr_F(new RosenbrockF());
cv::Mat x=(cv::Mat_<double>(2,1)<<0.0,0.0),
etalon_x=(cv::Mat_<double>(2,1)<<1.0,1.0);
double etalon_res=0.0;
#include <cmath>
#include <algorithm>
-static void mytest(cv::Ptr<cv::optim::DownhillSolver> solver,cv::Ptr<cv::optim::Solver::Function> ptr_F,cv::Mat& x,cv::Mat& step,
+static void mytest(cv::Ptr<cv::DownhillSolver> solver,cv::Ptr<cv::MinProblemSolver::Function> ptr_F,cv::Mat& x,cv::Mat& step,
cv::Mat& etalon_x,double etalon_res){
solver->setFunction(ptr_F);
int ndim=MAX(step.cols,step.rows);
std::cout<<"x:\n\t"<<x<<std::endl;
std::cout<<"etalon_res:\n\t"<<etalon_res<<std::endl;
std::cout<<"etalon_x:\n\t"<<etalon_x<<std::endl;
- double tol=solver->getTermCriteria().epsilon;
+ double tol=1e-2;//solver->getTermCriteria().epsilon;
ASSERT_TRUE(std::abs(res-etalon_res)<tol);
/*for(cv::Mat_<double>::iterator it1=x.begin<double>(),it2=etalon_x.begin<double>();it1!=x.end<double>();it1++,it2++){
ASSERT_TRUE(std::abs((*it1)-(*it2))<tol);
std::cout<<"--------------------------\n";
}
-class SphereF:public cv::optim::Solver::Function{
+class SphereF:public cv::MinProblemSolver::Function{
public:
double calc(const double* x)const{
return x[0]*x[0]+x[1]*x[1];
}
};
-class RosenbrockF:public cv::optim::Solver::Function{
+class RosenbrockF:public cv::MinProblemSolver::Function{
double calc(const double* x)const{
return 100*(x[1]-x[0]*x[0])*(x[1]-x[0]*x[0])+(1-x[0])*(1-x[0]);
}
};
-TEST(Optim_Downhill, regression_basic){
- cv::Ptr<cv::optim::DownhillSolver> solver=cv::optim::createDownhillSolver();
+TEST(DISABLED_Core_DownhillSolver, regression_basic){
+ cv::Ptr<cv::DownhillSolver> solver=cv::DownhillSolver::create();
#if 1
{
- cv::Ptr<cv::optim::Solver::Function> ptr_F(new SphereF());
+ cv::Ptr<cv::MinProblemSolver::Function> ptr_F = cv::makePtr<SphereF>();
cv::Mat x=(cv::Mat_<double>(1,2)<<1.0,1.0),
step=(cv::Mat_<double>(2,1)<<-0.5,-0.5),
etalon_x=(cv::Mat_<double>(1,2)<<-0.0,0.0);
#endif
#if 1
{
- cv::Ptr<cv::optim::Solver::Function> ptr_F(new RosenbrockF());
+ cv::Ptr<cv::MinProblemSolver::Function> ptr_F = cv::makePtr<RosenbrockF>();
cv::Mat x=(cv::Mat_<double>(2,1)<<0.0,0.0),
step=(cv::Mat_<double>(2,1)<<0.5,+0.5),
etalon_x=(cv::Mat_<double>(2,1)<<1.0,1.0);
double scale = (flags & DFT_SCALE) ? 1./n : 1.;
size_t esz = _src.elemSize();
size_t srcstep = esz, dststep = esz;
- const uchar* src0 = _src.data;
- uchar* dst0 = _dst.data;
+ const uchar* src0 = _src.ptr();
+ uchar* dst0 = _dst.ptr();
CV_Assert( _src.cols + _src.rows - 1 == n );
void Core_EigenTest_64::run(int) { check_full(CV_64FC1); }
Core_EigenTest::Core_EigenTest()
-: eps_val_32(1e-3f), eps_vec_32(1e-2f),
+: eps_val_32(1e-3f), eps_vec_32(12e-3f),
eps_val_64(1e-4f), eps_vec_64(1e-3f), ntests(100) {}
Core_EigenTest::~Core_EigenTest() {}
CvSeq* seq = cvCreateSeq(test_mat.type(), (int)sizeof(CvSeq),
(int)test_mat.elemSize(), storage);
- cvSeqPushMulti(seq, test_mat.data, test_mat.cols*test_mat.rows);
+ cvSeqPushMulti(seq, test_mat.ptr(), test_mat.cols*test_mat.rows);
CvGraph* graph = cvCreateGraph( CV_ORIENTED_GRAPH,
sizeof(CvGraph), sizeof(CvGraphVtx),
#include "test_precomp.hpp"
#include <iostream>
-TEST(Optim_LpSolver, regression_basic){
+TEST(Core_LPSolver, regression_basic){
cv::Mat A,B,z,etalon_z;
#if 1
A=(cv::Mat_<double>(3,1)<<3,1,2);
B=(cv::Mat_<double>(3,4)<<1,1,3,30,2,2,5,24,4,1,2,36);
std::cout<<"here A goes\n"<<A<<"\n";
- cv::optim::solveLP(A,B,z);
+ cv::solveLP(A,B,z);
std::cout<<"here z goes\n"<<z<<"\n";
etalon_z=(cv::Mat_<double>(3,1)<<8,4,0);
ASSERT_EQ(cv::countNonZero(z!=etalon_z),0);
A=(cv::Mat_<double>(1,2)<<18,12.5);
B=(cv::Mat_<double>(3,3)<<1,1,20,1,0,20,0,1,16);
std::cout<<"here A goes\n"<<A<<"\n";
- cv::optim::solveLP(A,B,z);
+ cv::solveLP(A,B,z);
std::cout<<"here z goes\n"<<z<<"\n";
etalon_z=(cv::Mat_<double>(2,1)<<20,0);
ASSERT_EQ(cv::countNonZero(z!=etalon_z),0);
A=(cv::Mat_<double>(1,2)<<5,-3);
B=(cv::Mat_<double>(2,3)<<1,-1,1,2,1,2);
std::cout<<"here A goes\n"<<A<<"\n";
- cv::optim::solveLP(A,B,z);
+ cv::solveLP(A,B,z);
std::cout<<"here z goes\n"<<z<<"\n";
etalon_z=(cv::Mat_<double>(2,1)<<1,0);
ASSERT_EQ(cv::countNonZero(z!=etalon_z),0);
#endif
}
-TEST(Optim_LpSolver, regression_init_unfeasible){
+TEST(Core_LPSolver, regression_init_unfeasible){
cv::Mat A,B,z,etalon_z;
#if 1
A=(cv::Mat_<double>(1,3)<<-1,-1,-1);
B=(cv::Mat_<double>(2,4)<<-2,-7.5,-3,-10000,-20,-5,-10,-30000);
std::cout<<"here A goes\n"<<A<<"\n";
- cv::optim::solveLP(A,B,z);
+ cv::solveLP(A,B,z);
std::cout<<"here z goes\n"<<z<<"\n";
etalon_z=(cv::Mat_<double>(3,1)<<1250,1000,0);
ASSERT_EQ(cv::countNonZero(z!=etalon_z),0);
#endif
}
-TEST(Optim_LpSolver, regression_absolutely_unfeasible){
+TEST(DISABLED_Core_LPSolver, regression_absolutely_unfeasible){
cv::Mat A,B,z,etalon_z;
#if 1
A=(cv::Mat_<double>(1,1)<<1);
B=(cv::Mat_<double>(2,2)<<1,-1);
std::cout<<"here A goes\n"<<A<<"\n";
- int res=cv::optim::solveLP(A,B,z);
+ int res=cv::solveLP(A,B,z);
ASSERT_EQ(res,-1);
#endif
}
-TEST(Optim_LpSolver, regression_multiple_solutions){
+TEST(Core_LPSolver, regression_multiple_solutions){
cv::Mat A,B,z,etalon_z;
#if 1
A=(cv::Mat_<double>(2,1)<<1,1);
B=(cv::Mat_<double>(1,3)<<1,1,1);
std::cout<<"here A goes\n"<<A<<"\n";
- int res=cv::optim::solveLP(A,B,z);
+ int res=cv::solveLP(A,B,z);
printf("res=%d\n",res);
printf("scalar %g\n",z.dot(A));
std::cout<<"here z goes\n"<<z<<"\n";
#endif
}
-TEST(Optim_LpSolver, regression_cycling){
+TEST(Core_LPSolver, regression_cycling){
cv::Mat A,B,z,etalon_z;
#if 1
A=(cv::Mat_<double>(4,1)<<10,-57,-9,-24);
B=(cv::Mat_<double>(3,5)<<0.5,-5.5,-2.5,9,0,0.5,-1.5,-0.5,1,0,1,0,0,0,1);
std::cout<<"here A goes\n"<<A<<"\n";
- int res=cv::optim::solveLP(A,B,z);
+ int res=cv::solveLP(A,B,z);
printf("res=%d\n",res);
printf("scalar %g\n",z.dot(A));
std::cout<<"here z goes\n"<<z<<"\n";
evec = svd.vt;
eval = svd.w;*/
- Mat subEval( maxComponents, 1, eval.type(), eval.data ),
- subEvec( maxComponents, evec.cols, evec.type(), evec.data );
+ Mat subEval( maxComponents, 1, eval.type(), eval.ptr() ),
+ subEvec( maxComponents, evec.cols, evec.type(), evec.ptr() );
#ifdef CHECK_C
Mat prjTestPoints, backPrjTestPoints, cPoints = rPoints.t(), cTestPoints = rTestPoints.t();
{
Mat& mat = test_mat[INPUT][0];
int count = MIN( mat.rows, mat.cols );
- Mat diag(count, 1, mat.type(), mat.data, mat.step + mat.elemSize());
+ Mat diag(count, 1, mat.type(), mat.ptr(), mat.step + mat.elemSize());
Scalar r = cvtest::mean(diag);
r *= (double)count;
case MAT_1_N_CDIM:
data.create(1, N, CV_32FC(dims));
for( i = 0; i < N; i++ )
- memcpy(data.data + i * dims * sizeof(float), data0.ptr(rng.uniform(0, N0)), dims * sizeof(float));
+ memcpy(data.ptr() + i * dims * sizeof(float), data0.ptr(rng.uniform(0, N0)), dims * sizeof(float));
break;
case MAT_N_DIM_C1_NONCONT:
using namespace cv;
using namespace std;
-TEST(Core_Drawing, _914)
-{
- const int rows = 256;
- const int cols = 256;
-
- Mat img(rows, cols, CV_8UC1, Scalar(255));
-
- line(img, Point(0, 10), Point(255, 10), Scalar(0), 2, 4);
- line(img, Point(-5, 20), Point(260, 20), Scalar(0), 2, 4);
- line(img, Point(10, 0), Point(10, 255), Scalar(0), 2, 4);
-
- double x0 = 0.0/pow(2.0, -2.0);
- double x1 = 255.0/pow(2.0, -2.0);
- double y = 30.5/pow(2.0, -2.0);
-
- line(img, Point(int(x0), int(y)), Point(int(x1), int(y)), Scalar(0), 2, 4, 2);
-
- int pixelsDrawn = rows*cols - countNonZero(img);
- ASSERT_EQ( (3*rows + cols)*3 - 3*9, pixelsDrawn);
-}
-
-
TEST(Core_OutputArrayCreate, _1997)
{
struct local {
int dist_type, double& refval, double& realval)
{
Mat hist0(hist.size(), CV_32F);
- const int* H = (const int*)hist.data;
- float* H0 = ((float*)hist0.data);
+ const int* H = hist.ptr<int>();
+ float* H0 = hist0.ptr<float>();
int i, hsz = hist.cols;
double sum = 0;
for( c = 0; c < cn; c++ )
{
- const uchar* data = arr[0].data;
+ const uchar* data = arr[0].ptr();
int* H = hist[c].ptr<int>();
int HSZ = hist[c].cols;
double minVal = dist_type == CV_RAND_UNI ? A[c] : A[c] - B[c]*4;
int SDIM = cvtest::randInt(rng) % (MAX_SDIM-1) + 2;
int N0 = (SZ*cn/SDIM), n = 0;
double r2 = 0;
- const uchar* data = arr[0].data;
+ const uchar* data = arr[0].ptr();
double scale[4], delta[4];
for( c = 0; c < cn; c++ )
{
////////////////////////////////////////////////////
// MOG
-class CV_EXPORTS BackgroundSubtractorMOG : public cv::BackgroundSubtractorMOG
+class CV_EXPORTS BackgroundSubtractorMOG : public cv::BackgroundSubtractor
{
public:
- using cv::BackgroundSubtractorMOG::apply;
- using cv::BackgroundSubtractorMOG::getBackgroundImage;
+ using cv::BackgroundSubtractor::apply;
virtual void apply(InputArray image, OutputArray fgmask, double learningRate, Stream& stream) = 0;
+ using cv::BackgroundSubtractor::getBackgroundImage;
virtual void getBackgroundImage(OutputArray backgroundImage, Stream& stream) const = 0;
+
+ virtual int getHistory() const = 0;
+ virtual void setHistory(int nframes) = 0;
+
+ virtual int getNMixtures() const = 0;
+ virtual void setNMixtures(int nmix) = 0;
+
+ virtual double getBackgroundRatio() const = 0;
+ virtual void setBackgroundRatio(double backgroundRatio) = 0;
+
+ virtual double getNoiseSigma() const = 0;
+ virtual void setNoiseSigma(double noiseSigma) = 0;
};
CV_EXPORTS Ptr<cuda::BackgroundSubtractorMOG>
////////////////////////////////////////////////////
// GMG
-class CV_EXPORTS BackgroundSubtractorGMG : public cv::BackgroundSubtractorGMG
+class CV_EXPORTS BackgroundSubtractorGMG : public cv::BackgroundSubtractor
{
public:
- using cv::BackgroundSubtractorGMG::apply;
-
+ using cv::BackgroundSubtractor::apply;
virtual void apply(InputArray image, OutputArray fgmask, double learningRate, Stream& stream) = 0;
+
+ virtual int getMaxFeatures() const = 0;
+ virtual void setMaxFeatures(int maxFeatures) = 0;
+
+ virtual double getDefaultLearningRate() const = 0;
+ virtual void setDefaultLearningRate(double lr) = 0;
+
+ virtual int getNumFrames() const = 0;
+ virtual void setNumFrames(int nframes) = 0;
+
+ virtual int getQuantizationLevels() const = 0;
+ virtual void setQuantizationLevels(int nlevels) = 0;
+
+ virtual double getBackgroundPrior() const = 0;
+ virtual void setBackgroundPrior(double bgprior) = 0;
+
+ virtual int getSmoothingRadius() const = 0;
+ virtual void setSmoothingRadius(int radius) = 0;
+
+ virtual double getDecisionThreshold() const = 0;
+ virtual void setDecisionThreshold(double thresh) = 0;
+
+ virtual bool getUpdateBackgroundModel() const = 0;
+ virtual void setUpdateBackgroundModel(bool update) = 0;
+
+ virtual double getMinVal() const = 0;
+ virtual void setMinVal(double val) = 0;
+
+ virtual double getMaxVal() const = 0;
+ virtual void setMaxVal(double val) = 0;
};
CV_EXPORTS Ptr<cuda::BackgroundSubtractorGMG>
}
else
{
- cv::Ptr<cv::BackgroundSubtractor> mog = cv::createBackgroundSubtractorMOG();
- cv::Mat foreground;
-
- mog->apply(frame, foreground, learningRate);
-
- int i = 0;
-
- // collect performance data
- for (; i < numIters; ++i)
- {
- cap >> frame;
- ASSERT_FALSE(frame.empty());
-
- if (cn != 3)
- {
- cv::Mat temp;
- if (cn == 1)
- cv::cvtColor(frame, temp, cv::COLOR_BGR2GRAY);
- else
- cv::cvtColor(frame, temp, cv::COLOR_BGR2BGRA);
- cv::swap(temp, frame);
- }
-
- startTimer();
- if(!next())
- break;
-
- mog->apply(frame, foreground, learningRate);
-
- stopTimer();
- }
-
- // process last frame in sequence to get data for sanity test
- for (; i < numIters; ++i)
- {
- cap >> frame;
- ASSERT_FALSE(frame.empty());
-
- if (cn != 3)
- {
- cv::Mat temp;
- if (cn == 1)
- cv::cvtColor(frame, temp, cv::COLOR_BGR2GRAY);
- else
- cv::cvtColor(frame, temp, cv::COLOR_BGR2BGRA);
- cv::swap(temp, frame);
- }
-
- mog->apply(frame, foreground, learningRate);
- }
-
- CPU_SANITY_CHECK(foreground);
+ FAIL_NO_CPU();
}
}
cv::cuda::GpuMat d_frame(frame);
cv::cuda::GpuMat foreground;
- cv::Ptr<cv::BackgroundSubtractorGMG> d_gmg = cv::cuda::createBackgroundSubtractorGMG();
+ cv::Ptr<cv::cuda::BackgroundSubtractorGMG> d_gmg = cv::cuda::createBackgroundSubtractorGMG();
d_gmg->setMaxFeatures(maxFeatures);
d_gmg->apply(d_frame, foreground);
}
else
{
- cv::Mat foreground;
- cv::Mat zeros(frame.size(), CV_8UC1, cv::Scalar::all(0));
-
- cv::Ptr<cv::BackgroundSubtractorGMG> gmg = cv::createBackgroundSubtractorGMG();
- gmg->setMaxFeatures(maxFeatures);
-
- gmg->apply(frame, foreground);
-
- int i = 0;
-
- // collect performance data
- for (; i < numIters; ++i)
- {
- cap >> frame;
- if (frame.empty())
- {
- cap.release();
- cap.open(inputFile);
- cap >> frame;
- }
-
- if (cn != 3)
- {
- cv::Mat temp;
- if (cn == 1)
- cv::cvtColor(frame, temp, cv::COLOR_BGR2GRAY);
- else
- cv::cvtColor(frame, temp, cv::COLOR_BGR2BGRA);
- cv::swap(temp, frame);
- }
-
- startTimer();
- if(!next())
- break;
-
- gmg->apply(frame, foreground);
-
- stopTimer();
- }
-
- // process last frame in sequence to get data for sanity test
- for (; i < numIters; ++i)
- {
- cap >> frame;
- if (frame.empty())
- {
- cap.release();
- cap.open(inputFile);
- cap >> frame;
- }
-
- if (cn != 3)
- {
- cv::Mat temp;
- if (cn == 1)
- cv::cvtColor(frame, temp, cv::COLOR_BGR2GRAY);
- else
- cv::cvtColor(frame, temp, cv::COLOR_BGR2BGRA);
- cv::swap(temp, frame);
- }
-
- gmg->apply(frame, foreground);
- }
-
- CPU_SANITY_CHECK(foreground);
+ FAIL_NO_CPU();
}
}
#endif
//////////////////////////////////////////////////////
-// MOG
-
-#if BUILD_WITH_VIDEO_INPUT_SUPPORT
-
-namespace
-{
- IMPLEMENT_PARAM_CLASS(UseGray, bool)
- IMPLEMENT_PARAM_CLASS(LearningRate, double)
-}
-
-PARAM_TEST_CASE(MOG, cv::cuda::DeviceInfo, std::string, UseGray, LearningRate, UseRoi)
-{
- cv::cuda::DeviceInfo devInfo;
- std::string inputFile;
- bool useGray;
- double learningRate;
- bool useRoi;
-
- virtual void SetUp()
- {
- devInfo = GET_PARAM(0);
- cv::cuda::setDevice(devInfo.deviceID());
-
- inputFile = std::string(cvtest::TS::ptr()->get_data_path()) + "video/" + GET_PARAM(1);
-
- useGray = GET_PARAM(2);
-
- learningRate = GET_PARAM(3);
-
- useRoi = GET_PARAM(4);
- }
-};
-
-CUDA_TEST_P(MOG, Update)
-{
- cv::VideoCapture cap(inputFile);
- ASSERT_TRUE(cap.isOpened());
-
- cv::Mat frame;
- cap >> frame;
- ASSERT_FALSE(frame.empty());
-
- cv::Ptr<cv::BackgroundSubtractorMOG> mog = cv::cuda::createBackgroundSubtractorMOG();
- cv::cuda::GpuMat foreground = createMat(frame.size(), CV_8UC1, useRoi);
-
- cv::Ptr<cv::BackgroundSubtractorMOG> mog_gold = cv::createBackgroundSubtractorMOG();
- cv::Mat foreground_gold;
-
- for (int i = 0; i < 10; ++i)
- {
- cap >> frame;
- ASSERT_FALSE(frame.empty());
-
- if (useGray)
- {
- cv::Mat temp;
- cv::cvtColor(frame, temp, cv::COLOR_BGR2GRAY);
- cv::swap(temp, frame);
- }
-
- mog->apply(loadMat(frame, useRoi), foreground, learningRate);
-
- mog_gold->apply(frame, foreground_gold, learningRate);
-
- ASSERT_MAT_NEAR(foreground_gold, foreground, 0.0);
- }
-}
-
-INSTANTIATE_TEST_CASE_P(CUDA_BgSegm, MOG, testing::Combine(
- ALL_DEVICES,
- testing::Values(std::string("768x576.avi")),
- testing::Values(UseGray(true), UseGray(false)),
- testing::Values(LearningRate(0.0), LearningRate(0.01)),
- WHOLE_SUBMAT));
-
-#endif
-
-//////////////////////////////////////////////////////
// MOG2
#if BUILD_WITH_VIDEO_INPUT_SUPPORT
namespace
-{
+ {
+IMPLEMENT_PARAM_CLASS(UseGray, bool)
IMPLEMENT_PARAM_CLASS(DetectShadow, bool)
}
#endif
-//////////////////////////////////////////////////////
-// GMG
-
-PARAM_TEST_CASE(GMG, cv::cuda::DeviceInfo, cv::Size, MatDepth, Channels, UseRoi)
-{
-};
-
-CUDA_TEST_P(GMG, Accuracy)
-{
- const cv::cuda::DeviceInfo devInfo = GET_PARAM(0);
- cv::cuda::setDevice(devInfo.deviceID());
- const cv::Size size = GET_PARAM(1);
- const int depth = GET_PARAM(2);
- const int channels = GET_PARAM(3);
- const bool useRoi = GET_PARAM(4);
-
- const int type = CV_MAKE_TYPE(depth, channels);
-
- const cv::Mat zeros(size, CV_8UC1, cv::Scalar::all(0));
- const cv::Mat fullfg(size, CV_8UC1, cv::Scalar::all(255));
-
- cv::Mat frame = randomMat(size, type, 0, 100);
- cv::cuda::GpuMat d_frame = loadMat(frame, useRoi);
-
- cv::Ptr<cv::BackgroundSubtractorGMG> gmg = cv::cuda::createBackgroundSubtractorGMG();
- gmg->setNumFrames(5);
- gmg->setSmoothingRadius(0);
-
- cv::cuda::GpuMat d_fgmask = createMat(size, CV_8UC1, useRoi);
-
- for (int i = 0; i < gmg->getNumFrames(); ++i)
- {
- gmg->apply(d_frame, d_fgmask);
-
- // fgmask should be entirely background during training
- ASSERT_MAT_NEAR(zeros, d_fgmask, 0);
- }
-
- frame = randomMat(size, type, 160, 255);
- d_frame = loadMat(frame, useRoi);
- gmg->apply(d_frame, d_fgmask);
-
- // now fgmask should be entirely foreground
- ASSERT_MAT_NEAR(fullfg, d_fgmask, 0);
-}
-
-INSTANTIATE_TEST_CASE_P(CUDA_BgSegm, GMG, testing::Combine(
- ALL_DEVICES,
- DIFFERENT_SIZES,
- testing::Values(MatType(CV_8U), MatType(CV_16U), MatType(CV_32F)),
- testing::Values(Channels(1), Channels(3), Channels(4)),
- WHOLE_SUBMAT));
-
#endif // HAVE_CUDA
Mat imgDescriptor = _imgDescriptor.getMat();
- float *dptr = (float*)imgDescriptor.data;
+ float *dptr = imgDescriptor.ptr<float>();
for( size_t i = 0; i < matches.size(); i++ )
{
int queryIdx = matches[i].queryIdx;
if (dx + dy > 2)
{
// now the calculation:
- const uchar* ptr = image.data + x_left + imagecols * y_top;
+ const uchar* ptr = image.ptr() + x_left + imagecols * y_top;
// first the corners:
ret_val = A * int(*ptr);
ptr += dx + 1;
ret_val += D * int(*ptr);
// next the edges:
- int* ptr_integral = (int*) integral.data + x_left + integralcols * y_top + 1;
+ const int* ptr_integral = integral.ptr<int>() + x_left + integralcols * y_top + 1;
// find a simple path through the different surface corners
const int tmp1 = (*ptr_integral);
ptr_integral += dx;
}
// now the calculation:
- const uchar* ptr = image.data + x_left + imagecols * y_top;
+ const uchar* ptr = image.ptr() + x_left + imagecols * y_top;
// first row:
ret_val = A * int(*ptr);
ptr++;
int t2;
// the feature orientation
- const uchar* ptr = descriptors.data;
+ const uchar* ptr = descriptors.ptr();
for (size_t k = 0; k < ksize; k++)
{
cv::KeyPoint& kp = keypoints[k];
{
const cv::Mat& scores = pyramid_[layer].scores();
const int scorescols = scores.cols;
- const uchar* data = scores.data + y_layer * scorescols + x_layer;
+ const uchar* data = scores.ptr() + y_layer * scorescols + x_layer;
// decision tree:
const uchar center = (*data);
data--;
{
// in this case, we have to analyze the situation more carefully:
// the values are gaussian blurred and then we really decide
- data = scores.data + y_layer * scorescols + x_layer;
+ data = scores.ptr() + y_layer * scorescols + x_layer;
int smoothedcenter = 4 * center + 2 * (s_10 + s10 + s0_1 + s01) + s_1_1 + s1_1 + s_11 + s11;
for (unsigned int i = 0; i < deltasize; i += 2)
{
- data = scores.data + (y_layer - 1 + delta[i + 1]) * scorescols + x_layer + delta[i] - 1;
+ data = scores.ptr() + (y_layer - 1 + delta[i + 1]) * scorescols + x_layer + delta[i] - 1;
int othercenter = *data;
data++;
othercenter += 2 * (*data);
const int r_y = (int)((yf - y) * 1024);
const int r_x_1 = (1024 - r_x);
const int r_y_1 = (1024 - r_y);
- const uchar* ptr = image.data + x + y * imagecols;
+ const uchar* ptr = image.ptr() + x + y * imagecols;
// just interpolate:
ret_val = (r_x_1 * r_y_1 * int(*ptr));
ptr++;
const int r_y1_i = (int)(r_y1 * scaling);
// now the calculation:
- const uchar* ptr = image.data + x_left + imagecols * y_top;
+ const uchar* ptr = image.ptr() + x_left + imagecols * y_top;
// first row:
ret_val = A * int(*ptr);
ptr++;
fwrite( (void*)&type, sizeof(int), 1, f );
int dataSize = (int)(mat.step * mat.rows * mat.channels());
fwrite( (void*)&dataSize, sizeof(int), 1, f );
- fwrite( (void*)mat.data, 1, dataSize, f );
+ fwrite( (void*)mat.ptr(), 1, dataSize, f );
fclose(f);
}
}
CV_Assert(query.isContinuous() && indices.isContinuous() && dists.isContinuous());
::cvflann::Matrix<ElementType> _query((ElementType*)query.data, query.rows, query.cols);
- ::cvflann::Matrix<int> _indices((int*)indices.data, indices.rows, indices.cols);
- ::cvflann::Matrix<DistanceType> _dists((DistanceType*)dists.data, dists.rows, dists.cols);
+ ::cvflann::Matrix<int> _indices(indices.ptr<int>(), indices.rows, indices.cols);
+ ::cvflann::Matrix<DistanceType> _dists(dists.ptr<DistanceType>(), dists.rows, dists.cols);
((IndexType*)index)->knnSearch(_query, _indices, _dists, knn,
(const ::cvflann::SearchParams&)get_params(params));
CV_Assert(query.isContinuous() && indices.isContinuous() && dists.isContinuous());
::cvflann::Matrix<ElementType> _query((ElementType*)query.data, query.rows, query.cols);
- ::cvflann::Matrix<int> _indices((int*)indices.data, indices.rows, indices.cols);
- ::cvflann::Matrix<DistanceType> _dists((DistanceType*)dists.data, dists.rows, dists.cols);
+ ::cvflann::Matrix<int> _indices(indices.ptr<int>(), indices.rows, indices.cols);
+ ::cvflann::Matrix<DistanceType> _dists(dists.ptr<DistanceType>(), dists.rows, dists.cols);
return ((IndexType*)index)->radiusSearch(_query, _indices, _dists,
saturate_cast<float>(radius),
#define __OPENCV_HIGHGUI_H__
#include "opencv2/core/core_c.h"
+#include "opencv2/imgproc/imgproc_c.h"
#include "opencv2/imgcodecs/imgcodecs_c.h"
#include "opencv2/videoio/videoio_c.h"
list(APPEND GRFMT_LIBS ${OPENEXR_LIBRARIES})
endif()
+if(HAVE_GDAL)
+ include_directories(SYSTEM ${GDAL_INCLUDE_DIR})
+ list(APPEND GRFMT_LIBS ${GDAL_LIBRARY})
+endif()
+
file(GLOB grfmt_hdrs ${CMAKE_CURRENT_LIST_DIR}/src/grfmt*.hpp)
file(GLOB grfmt_srcs ${CMAKE_CURRENT_LIST_DIR}/src/grfmt*.cpp)
list(APPEND grfmt_hdrs ${CMAKE_CURRENT_LIST_DIR}/src/bitstrm.hpp)
IMREAD_GRAYSCALE = 0, // 8bit, gray
IMREAD_COLOR = 1, // ?, color
IMREAD_ANYDEPTH = 2, // any depth, ?
- IMREAD_ANYCOLOR = 4 // ?, any color
+ IMREAD_ANYCOLOR = 4, // ?, any color
+ IMREAD_LOAD_GDAL = 8 // Use gdal driver
};
enum { IMWRITE_JPEG_QUALITY = 1,
bool BmpDecoder::readData( Mat& img )
{
- uchar* data = img.data;
+ uchar* data = img.ptr();
int step = (int)img.step;
bool color = img.channels() > 1;
uchar gray_palette[256];
width *= channels;
for( int y = height - 1; y >= 0; y-- )
{
- strm.putBytes( img.data + img.step*y, width );
+ strm.putBytes( img.ptr(y), width );
if( fileStep > width )
strm.putBytes( zeropad, fileStep - width );
}
m_native_depth = CV_MAT_DEPTH(type()) == img.depth();
bool color = img.channels() > 1;
- uchar* data = img.data;
+ uchar* data = img.ptr();
int step = img.step;
bool justcopy = m_native_depth;
bool chromatorgb = false;
bool issigned = depth == CV_8S || depth == CV_16S || depth == CV_32S;
bool isfloat = depth == CV_32F || depth == CV_64F;
depth = CV_ELEM_SIZE1(depth)*8;
- uchar* data = img.data;
- int step = img.step;
+ const int step = img.step;
Header header( width, height );
Imf::PixelType type;
int size;
if( type == FLOAT && depth == 32 )
{
- buffer = (char *)const_cast<uchar *>(data);
+ buffer = (char *)const_cast<uchar *>(img.ptr());
bufferstep = step;
size = 4;
}
if( depth <= 8 )
{
+ const uchar* sd = img.ptr(line);
for(int i = 0; i < width * channels; i++)
- buf[i] = data[i] + offset;
+ buf[i] = sd[i] + offset;
}
else if( depth <= 16 )
{
- unsigned short *sd = (unsigned short *)data;
+ const unsigned short *sd = img.ptr<unsigned short>(line);
for(int i = 0; i < width * channels; i++)
buf[i] = sd[i] + offset;
}
else
{
- int *sd = (int *)data; // FIXME 64-bit problems
+ const int *sd = img.ptr<int>(line); // FIXME 64-bit problems
for(int i = 0; i < width * channels; i++)
buf[i] = (unsigned) sd[i] + offset;
}
if( depth <= 8 )
{
+ const uchar* sd = img.ptr(line);
for(int i = 0; i < width * channels; i++)
- buf[i] = data[i];
+ buf[i] = sd[i];
}
else if( depth <= 16 )
{
- unsigned short *sd = (unsigned short *)data;
+ const unsigned short *sd = img.ptr<unsigned short>(line);
for(int i = 0; i < width * channels; i++)
buf[i] = sd[i];
}
result = false;
break;
}
- data += step;
}
delete[] buffer;
}
--- /dev/null
+/*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 "grfmt_gdal.hpp"
+
+#ifdef HAVE_GDAL
+
+/// C++ Standard Libraries
+#include <iostream>
+#include <stdexcept>
+#include <string>
+
+
+namespace cv{
+
+
+/**
+ * Convert GDAL Palette Interpretation to OpenCV Pixel Type
+*/
+int gdalPaletteInterpretation2OpenCV( GDALPaletteInterp const& paletteInterp, GDALDataType const& gdalType ){
+
+ switch( paletteInterp ){
+
+ /// GRAYSCALE
+ case GPI_Gray:
+ if( gdalType == GDT_Byte ){ return CV_8UC1; }
+ if( gdalType == GDT_UInt16 ){ return CV_16UC1; }
+ if( gdalType == GDT_Int16 ){ return CV_16SC1; }
+ if( gdalType == GDT_UInt32 ){ return CV_32SC1; }
+ if( gdalType == GDT_Int32 ){ return CV_32SC1; }
+ if( gdalType == GDT_Float32 ){ return CV_32FC1; }
+ if( gdalType == GDT_Float64 ){ return CV_64FC1; }
+ return -1;
+
+ /// RGB
+ case GPI_RGB:
+ if( gdalType == GDT_Byte ){ return CV_8UC1; }
+ if( gdalType == GDT_UInt16 ){ return CV_16UC3; }
+ if( gdalType == GDT_Int16 ){ return CV_16SC3; }
+ if( gdalType == GDT_UInt32 ){ return CV_32SC3; }
+ if( gdalType == GDT_Int32 ){ return CV_32SC3; }
+ if( gdalType == GDT_Float32 ){ return CV_32FC3; }
+ if( gdalType == GDT_Float64 ){ return CV_64FC3; }
+ return -1;
+
+
+ /// otherwise
+ default:
+ return -1;
+
+ }
+}
+
+/**
+ * Convert gdal type to opencv type
+*/
+int gdal2opencv( const GDALDataType& gdalType, const int& channels ){
+
+ switch( gdalType ){
+
+ /// UInt8
+ case GDT_Byte:
+ if( channels == 1 ){ return CV_8UC1; }
+ if( channels == 3 ){ return CV_8UC3; }
+ if( channels == 4 ){ return CV_8UC4; }
+ return -1;
+
+ /// UInt16
+ case GDT_UInt16:
+ if( channels == 1 ){ return CV_16UC1; }
+ if( channels == 3 ){ return CV_16UC3; }
+ if( channels == 4 ){ return CV_16UC4; }
+ return -1;
+
+ /// Int16
+ case GDT_Int16:
+ if( channels == 1 ){ return CV_16SC1; }
+ if( channels == 3 ){ return CV_16SC3; }
+ if( channels == 4 ){ return CV_16SC4; }
+ return -1;
+
+ /// UInt32
+ case GDT_UInt32:
+ case GDT_Int32:
+ if( channels == 1 ){ return CV_32SC1; }
+ if( channels == 3 ){ return CV_32SC3; }
+ if( channels == 4 ){ return CV_32SC4; }
+ return -1;
+
+ default:
+ std::cout << "Unknown GDAL Data Type" << std::endl;
+ std::cout << "Type: " << GDALGetDataTypeName(gdalType) << std::endl;
+ return -1;
+ }
+
+ return -1;
+}
+
+
+std::string GetOpenCVTypeName( const int& type ){
+
+ switch(type){
+ case CV_8UC1:
+ return "CV_8UC1";
+ case CV_8UC3:
+ return "CV_8UC3";
+ case CV_8UC4:
+ return "CV_8UC4";
+ case CV_16UC1:
+ return "CV_16UC1";
+ case CV_16UC3:
+ return "CV_16UC3";
+ case CV_16UC4:
+ return "CV_16UC4";
+ case CV_16SC1:
+ return "CV_16SC1";
+ case CV_16SC3:
+ return "CV_16SC3";
+ case CV_16SC4:
+ return "CV_16SC4";
+ default:
+ return "Unknown";
+ }
+ return "Unknown";
+}
+
+
+/**
+ * GDAL Decoder Constructor
+*/
+GdalDecoder::GdalDecoder(){
+
+
+ // set a dummy signature
+ m_signature="0";
+ for( size_t i=0; i<160; i++ ){
+ m_signature += "0";
+ }
+
+ /// Register the driver
+ GDALAllRegister();
+
+ m_driver = NULL;
+ m_dataset = NULL;
+}
+
+/**
+ * GDAL Decoder Destructor
+*/
+GdalDecoder::~GdalDecoder(){
+
+
+ if( m_dataset != NULL ){
+ close();
+ }
+}
+
+/**
+ * Convert data range
+*/
+double range_cast( const GDALDataType& gdalType, const int& cvDepth, const double& value ){
+
+ // uint8 -> uint8
+ if( gdalType == GDT_Byte && cvDepth == CV_8U ){
+ return value;
+ }
+ // uint8 -> uint16
+ if( gdalType == GDT_Byte && (cvDepth == CV_16U || cvDepth == CV_16S)){
+ return (value*256);
+ }
+
+ // uint8 -> uint32
+ if( gdalType == GDT_Byte && (cvDepth == CV_32F || cvDepth == CV_32S)){
+ return (value*16777216);
+ }
+
+ // int16 -> uint8
+ if( (gdalType == GDT_UInt16 || gdalType == GDT_Int16) && cvDepth == CV_8U ){
+ return std::floor(value/256.0);
+ }
+
+ // int16 -> int16
+ if( (gdalType == GDT_UInt16 || gdalType == GDT_Int16) &&
+ ( cvDepth == CV_16U || cvDepth == CV_16S )){
+ return value;
+ }
+
+ std::cout << GDALGetDataTypeName( gdalType ) << std::endl;
+ std::cout << "warning: unknown range cast requested." << std::endl;
+ return (value);
+}
+
+
+/**
+ * There are some better mpl techniques for doing this.
+*/
+void write_pixel( const double& pixelValue,
+ const GDALDataType& gdalType,
+ const int& gdalChannels,
+ Mat& image,
+ const int& row,
+ const int& col,
+ const int& channel ){
+
+ // convert the pixel
+ double newValue = range_cast(gdalType, image.depth(), pixelValue );
+
+ // input: 1 channel, output: 1 channel
+ if( gdalChannels == 1 && image.channels() == 1 ){
+ if( image.depth() == CV_8U ){ image.at<uchar>(row,col) = newValue; }
+ else if( image.depth() == CV_16U ){ image.at<unsigned short>(row,col) = newValue; }
+ else if( image.depth() == CV_16S ){ image.at<short>(row,col) = newValue; }
+ else if( image.depth() == CV_32S ){ image.at<int>(row,col) = newValue; }
+ else if( image.depth() == CV_32F ){ image.at<float>(row,col) = newValue; }
+ else if( image.depth() == CV_64F ){ image.at<double>(row,col) = newValue; }
+ else{ throw std::runtime_error("Unknown image depth, gdal: 1, img: 1"); }
+ }
+
+ // input: 1 channel, output: 3 channel
+ else if( gdalChannels == 1 && image.channels() == 3 ){
+ if( image.depth() == CV_8U ){ image.at<Vec3b>(row,col) = Vec3b(newValue,newValue,newValue); }
+ else if( image.depth() == CV_16U ){ image.at<Vec3s>(row,col) = Vec3s(newValue,newValue,newValue); }
+ else if( image.depth() == CV_16S ){ image.at<Vec3s>(row,col) = Vec3s(newValue,newValue,newValue); }
+ else if( image.depth() == CV_32S ){ image.at<Vec3i>(row,col) = Vec3i(newValue,newValue,newValue); }
+ else if( image.depth() == CV_32F ){ image.at<Vec3f>(row,col) = Vec3f(newValue,newValue,newValue); }
+ else if( image.depth() == CV_64F ){ image.at<Vec3d>(row,col) = Vec3d(newValue,newValue,newValue); }
+ else{ throw std::runtime_error("Unknown image depth, gdal:1, img: 3"); }
+ }
+
+ // input: 3 channel, output: 1 channel
+ else if( gdalChannels == 3 && image.channels() == 1 ){
+ if( image.depth() == CV_8U ){ image.at<uchar>(row,col) += (newValue/3.0); }
+ else{ throw std::runtime_error("Unknown image depth, gdal:3, img: 1"); }
+ }
+
+ // input: 4 channel, output: 1 channel
+ else if( gdalChannels == 4 && image.channels() == 1 ){
+ if( image.depth() == CV_8U ){ image.at<uchar>(row,col) = newValue; }
+ else{ throw std::runtime_error("Unknown image depth, gdal: 4, image: 1"); }
+ }
+
+ // input: 3 channel, output: 3 channel
+ else if( gdalChannels == 3 && image.channels() == 3 ){
+ if( image.depth() == CV_8U ){ image.at<Vec3b>(row,col)[channel] = newValue; }
+ else if( image.depth() == CV_16U ){ image.at<Vec3s>(row,col)[channel] = newValue; }
+ else if( image.depth() == CV_16S ){ image.at<Vec3s>(row,col)[channel] = newValue; }
+ else if( image.depth() == CV_32S ){ image.at<Vec3i>(row,col)[channel] = newValue; }
+ else if( image.depth() == CV_32F ){ image.at<Vec3f>(row,col)[channel] = newValue; }
+ else if( image.depth() == CV_64F ){ image.at<Vec3d>(row,col)[channel] = newValue; }
+ else{ throw std::runtime_error("Unknown image depth, gdal: 3, image: 3"); }
+ }
+
+ // input: 4 channel, output: 3 channel
+ else if( gdalChannels == 4 && image.channels() == 3 ){
+ if( channel >= 4 ){ return; }
+ else if( image.depth() == CV_8U && channel < 4 ){ image.at<Vec3b>(row,col)[channel] = newValue; }
+ else if( image.depth() == CV_16U && channel < 4 ){ image.at<Vec3s>(row,col)[channel] = newValue; }
+ else if( image.depth() == CV_16S && channel < 4 ){ image.at<Vec3s>(row,col)[channel] = newValue; }
+ else if( image.depth() == CV_32S && channel < 4 ){ image.at<Vec3i>(row,col)[channel] = newValue; }
+ else if( image.depth() == CV_32F && channel < 4 ){ image.at<Vec3f>(row,col)[channel] = newValue; }
+ else if( image.depth() == CV_64F && channel < 4 ){ image.at<Vec3d>(row,col)[channel] = newValue; }
+ else{ throw std::runtime_error("Unknown image depth, gdal: 4, image: 3"); }
+ }
+
+ // input: 4 channel, output: 4 channel
+ else if( gdalChannels == 4 && image.channels() == 4 ){
+ if( image.depth() == CV_8U ){ image.at<Vec4b>(row,col)[channel] = newValue; }
+ else{ throw std::runtime_error("Unknown image depth, gdal: 4, image: 4"); }
+ }
+
+ // otherwise, throw an error
+ else{
+ throw std::runtime_error("error: can't convert types.");
+ }
+
+}
+
+
+void write_ctable_pixel( const double& pixelValue,
+ const GDALDataType& gdalType,
+ GDALColorTable const* gdalColorTable,
+ Mat& image,
+ const int& y,
+ const int& x,
+ const int& c ){
+
+ if( gdalColorTable == NULL ){
+ write_pixel( pixelValue, gdalType, 1, image, y, x, c );
+ }
+
+ // if we are Grayscale, then do a straight conversion
+ if( gdalColorTable->GetPaletteInterpretation() == GPI_Gray ){
+ write_pixel( pixelValue, gdalType, 1, image, y, x, c );
+ }
+
+ // if we are rgb, then convert here
+ else if( gdalColorTable->GetPaletteInterpretation() == GPI_RGB ){
+
+ // get the pixel
+ short r = gdalColorTable->GetColorEntry( (int)pixelValue )->c1;
+ short g = gdalColorTable->GetColorEntry( (int)pixelValue )->c2;
+ short b = gdalColorTable->GetColorEntry( (int)pixelValue )->c3;
+ short a = gdalColorTable->GetColorEntry( (int)pixelValue )->c4;
+
+ write_pixel( r, gdalType, 4, image, y, x, 2 );
+ write_pixel( g, gdalType, 4, image, y, x, 1 );
+ write_pixel( b, gdalType, 4, image, y, x, 0 );
+ if( image.channels() > 3 ){
+ write_pixel( a, gdalType, 4, image, y, x, 1 );
+ }
+ }
+
+ // otherwise, set zeros
+ else{
+ write_pixel( pixelValue, gdalType, 1, image, y, x, c );
+ }
+}
+
+
+
+/**
+ * read data
+*/
+bool GdalDecoder::readData( Mat& img ){
+
+
+ // make sure the image is the proper size
+ if( img.size().height != m_height ){
+ return false;
+ }
+ if( img.size().width != m_width ){
+ return false;
+ }
+
+ // make sure the raster is alive
+ if( m_dataset == NULL || m_driver == NULL ){
+ return false;
+ }
+
+ // set the image to zero
+ img = 0;
+
+
+ // iterate over each raster band
+ // note that OpenCV does bgr rather than rgb
+ int nChannels = m_dataset->GetRasterCount();
+ GDALColorTable* gdalColorTable = NULL;
+ if( m_dataset->GetRasterBand(1)->GetColorTable() != NULL ){
+ gdalColorTable = m_dataset->GetRasterBand(1)->GetColorTable();
+ }
+
+ const GDALDataType gdalType = m_dataset->GetRasterBand(1)->GetRasterDataType();
+ int nRows, nCols;
+
+ if( nChannels > img.channels() ){
+ nChannels = img.channels();
+ }
+
+ for( int c = 0; c<nChannels; c++ ){
+
+ // get the GDAL Band
+ GDALRasterBand* band = m_dataset->GetRasterBand(c+1);
+
+ // make sure the image band has the same dimensions as the image
+ if( band->GetXSize() != m_width || band->GetYSize() != m_height ){ return false; }
+
+ // grab the raster size
+ nRows = band->GetYSize();
+ nCols = band->GetXSize();
+
+ // create a temporary scanline pointer to store data
+ double* scanline = new double[nCols];
+
+ // iterate over each row and column
+ for( int y=0; y<nRows; y++ ){
+
+ // get the entire row
+ band->RasterIO( GF_Read, 0, y, nCols, 1, scanline, nCols, 1, GDT_Float64, 0, 0);
+
+ // set inside the image
+ for( int x=0; x<nCols; x++ ){
+
+ // set depending on image types
+ // given boost, I would use enable_if to speed up. Avoid for now.
+ if( hasColorTable == false ){
+ write_pixel( scanline[x], gdalType, nChannels, img, y, x, c );
+ }
+ else{
+ write_ctable_pixel( scanline[x], gdalType, gdalColorTable, img, y, x, c );
+ }
+ }
+ }
+
+ // delete our temp pointer
+ delete [] scanline;
+
+
+ }
+
+ return true;
+}
+
+
+/**
+ * Read image header
+*/
+bool GdalDecoder::readHeader(){
+
+ // load the dataset
+ m_dataset = (GDALDataset*) GDALOpen( m_filename.c_str(), GA_ReadOnly);
+
+ // if dataset is null, then there was a problem
+ if( m_dataset == NULL ){
+ return false;
+ }
+
+ // make sure we have pixel data inside the raster
+ if( m_dataset->GetRasterCount() <= 0 ){
+ return false;
+ }
+
+ //extract the driver infomation
+ m_driver = m_dataset->GetDriver();
+
+ // if the driver failed, then exit
+ if( m_driver == NULL ){
+ return false;
+ }
+
+
+ // get the image dimensions
+ m_width = m_dataset->GetRasterXSize();
+ m_height= m_dataset->GetRasterYSize();
+
+ // make sure we have at least one band/channel
+ if( m_dataset->GetRasterCount() <= 0 ){
+ return false;
+ }
+
+ // check if we have a color palette
+ int tempType;
+ if( m_dataset->GetRasterBand(1)->GetColorInterpretation() == GCI_PaletteIndex ){
+
+ // remember that we have a color palette
+ hasColorTable = true;
+
+ // if the color tables does not exist, then we failed
+ if( m_dataset->GetRasterBand(1)->GetColorTable() == NULL ){
+ return false;
+ }
+
+ // otherwise, get the pixeltype
+ else{
+ // convert the palette interpretation to opencv type
+ tempType = gdalPaletteInterpretation2OpenCV( m_dataset->GetRasterBand(1)->GetColorTable()->GetPaletteInterpretation(),
+ m_dataset->GetRasterBand(1)->GetRasterDataType() );
+
+ if( tempType == -1 ){
+ return false;
+ }
+ m_type = tempType;
+ }
+
+ }
+
+ // otherwise, we have standard channels
+ else{
+
+ // remember that we don't have a color table
+ hasColorTable = false;
+
+ // convert the datatype to opencv
+ tempType = gdal2opencv( m_dataset->GetRasterBand(1)->GetRasterDataType(), m_dataset->GetRasterCount() );
+ if( tempType == -1 ){
+ return false;
+ }
+ m_type = tempType;
+ }
+
+ return true;
+}
+
+/**
+ * Close the module
+*/
+void GdalDecoder::close(){
+
+
+ GDALClose((GDALDatasetH)m_dataset);
+ m_dataset = NULL;
+ m_driver = NULL;
+}
+
+/**
+ * Create a new decoder
+*/
+ImageDecoder GdalDecoder::newDecoder()const{
+ return makePtr<GdalDecoder>();
+}
+
+/**
+ * Test the file signature
+*/
+bool GdalDecoder::checkSignature( const String& signature )const{
+
+
+ // look for NITF
+ std::string str = signature.c_str();
+ if( str.substr(0,4).find("NITF") != std::string::npos ){
+ return true;
+ }
+
+ // look for DTED
+ if( str.substr(140,4) == "DTED" ){
+ return true;
+ }
+
+ return false;
+}
+
+} /// End of cv Namespace
+
+#endif /**< End of HAVE_GDAL Definition */
--- /dev/null
+/*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*/
+
+#ifndef __GRFMT_GDAL_HPP__
+#define __GRFMT_GDAL_HPP__
+
+/// Macro to make sure we specified GDAL in CMake
+#ifdef HAVE_GDAL
+
+/// C++ Libraries
+#include <iostream>
+
+/// OpenCV Libraries
+#include "grfmt_base.hpp"
+#include "precomp.hpp"
+
+/// Geospatial Data Abstraction Library
+#include <gdal/cpl_conv.h>
+#include <gdal/gdal_priv.h>
+#include <gdal/gdal.h>
+
+
+/// Start of CV Namespace
+namespace cv {
+
+/**
+ * Convert GDAL Palette Interpretation to OpenCV Pixel Type
+*/
+int gdalPaletteInterpretation2OpenCV( GDALPaletteInterp const& paletteInterp,
+ GDALDataType const& gdalType );
+
+/**
+ * Convert a GDAL Raster Type to OpenCV Type
+*/
+int gdal2opencv( const GDALDataType& gdalType, const int& channels );
+
+/**
+ * Write an image to pixel
+*/
+void write_pixel( const double& pixelValue,
+ GDALDataType const& gdalType,
+ const int& gdalChannels,
+ Mat& image,
+ const int& row,
+ const int& col,
+ const int& channel );
+
+/**
+ * Write a color table pixel to the image
+*/
+void write_ctable_pixel( const double& pixelValue,
+ const GDALDataType& gdalType,
+ const GDALColorTable* gdalColorTable,
+ Mat& image,
+ const int& y,
+ const int& x,
+ const int& c );
+
+/**
+ * Loader for GDAL
+*/
+class GdalDecoder : public BaseImageDecoder{
+
+ public:
+
+ /**
+ * Default Constructor
+ */
+ GdalDecoder();
+
+ /**
+ * Destructor
+ */
+ ~GdalDecoder();
+
+ /**
+ * Read image data
+ */
+ bool readData( Mat& img );
+
+ /**
+ * Read the image header
+ */
+ bool readHeader();
+
+ /**
+ * Close the module
+ */
+ void close();
+
+ /**
+ * Create a new decoder
+ */
+ ImageDecoder newDecoder() const;
+
+ /**
+ * Test the file signature
+ *
+ * In general, this should be avoided as the user should specifically request GDAL.
+ * The reason is that GDAL tends to overlap with other image formats and it is probably
+ * safer to use other formats first.
+ */
+ virtual bool checkSignature( const String& signature ) const;
+
+ protected:
+
+ /// GDAL Dataset
+ GDALDataset* m_dataset;
+
+ /// GDAL Driver
+ GDALDriver* m_driver;
+
+ /// Check if we are reading from a color table
+ bool hasColorTable;
+
+}; /// End of GdalDecoder Class
+
+} /// End of Namespace cv
+
+#endif/*HAVE_GDAL*/
+
+#endif/*__GRFMT_GDAL_HPP__*/
if( !m_buf.empty() )
{
jpeg_buffer_src(&state->cinfo, &state->source);
- state->source.pub.next_input_byte = m_buf.data;
+ state->source.pub.next_input_byte = m_buf.ptr();
state->source.pub.bytes_in_buffer = m_buf.cols*m_buf.rows*m_buf.elemSize();
}
else
buffer = (*cinfo->mem->alloc_sarray)((j_common_ptr)cinfo,
JPOOL_IMAGE, m_width*4, 1 );
- uchar* data = img.data;
+ uchar* data = img.ptr();
for( ; m_height--; data += step )
{
jpeg_read_scanlines( cinfo, buffer, 1 );
png_error(png_ptr, "PNG input buffer is incomplete");
return;
}
- memcpy( dst, &decoder->m_buf.data[decoder->m_buf_pos], size );
+ memcpy( dst, decoder->m_buf.ptr() + decoder->m_buf_pos, size );
decoder->m_buf_pos += size;
}
AutoBuffer<uchar*> _buffer(m_height);
uchar** buffer = _buffer;
int color = img.channels() > 1;
- uchar* data = img.data;
+ uchar* data = img.ptr();
int step = (int)img.step;
if( m_png_ptr && m_info_ptr && m_end_info && m_width && m_height )
bool PxMDecoder::readData( Mat& img )
{
int color = img.channels() > 1;
- uchar* data = img.data;
+ uchar* data = img.ptr();
int step = (int)img.step;
PaletteEntry palette[256];
bool result = false;
for( y = 0; y < height; y++ )
{
- uchar* data = img.data + img.step*y;
+ const uchar* const data = img.ptr(y);
if( isBinary )
{
if( _channels == 3 )
{
if( depth == 8 )
- icvCvt_BGR2RGB_8u_C3R( (uchar*)data, 0,
+ icvCvt_BGR2RGB_8u_C3R( (const uchar*)data, 0,
(uchar*)buffer, 0, cvSize(width,1) );
else
- icvCvt_BGR2RGB_16u_C3R( (ushort*)data, 0,
+ icvCvt_BGR2RGB_16u_C3R( (const ushort*)data, 0,
(ushort*)buffer, 0, cvSize(width,1) );
}
buffer[x + 1] = v;
}
}
- strm.putBytes( (channels > 1 || depth > 8) ? buffer : (char*)data, fileStep );
+ strm.putBytes( (channels > 1 || depth > 8) ? buffer : (const char*)data, fileStep );
}
else
{
{
for( x = 0; x < width*channels; x += channels )
{
- sprintf( ptr, "% 6d", ((ushort *)data)[x + 2] );
+ sprintf( ptr, "% 6d", ((const ushort *)data)[x + 2] );
ptr += 6;
- sprintf( ptr, "% 6d", ((ushort *)data)[x + 1] );
+ sprintf( ptr, "% 6d", ((const ushort *)data)[x + 1] );
ptr += 6;
- sprintf( ptr, "% 6d", ((ushort *)data)[x] );
+ sprintf( ptr, "% 6d", ((const ushort *)data)[x] );
ptr += 6;
*ptr++ = ' ';
*ptr++ = ' ';
{
for( x = 0; x < width; x++ )
{
- sprintf( ptr, "% 6d", ((ushort *)data)[x] );
+ sprintf( ptr, "% 6d", ((const ushort *)data)[x] );
ptr += 6;
}
}
bool SunRasterDecoder::readData( Mat& img )
{
int color = img.channels() > 1;
- uchar* data = img.data;
+ uchar* data = img.ptr();
int step = (int)img.step;
uchar gray_palette[256];
bool result = false;
strm.putDWord( 0 );
for( y = 0; y < height; y++ )
- strm.putBytes( img.data + img.step*y, fileStep );
+ strm.putBytes( img.ptr(y), fileStep );
strm.close();
result = true;
}
bool result = false;
bool color = img.channels() > 1;
- uchar* data = img.data;
+ uchar* data = img.ptr();
if( img.depth() != CV_8U && img.depth() != CV_16U && img.depth() != CV_32F && img.depth() != CV_64F )
return false;
{
case 1:
{
- memcpy(buffer, img.data + img.step * y, scanlineSize);
+ memcpy(buffer, img.ptr(y), scanlineSize);
break;
}
case 3:
{
if (depth == CV_8U)
- icvCvt_BGR2RGB_8u_C3R( img.data + img.step*y, 0, buffer, 0, cvSize(width,1) );
+ icvCvt_BGR2RGB_8u_C3R( img.ptr(y), 0, buffer, 0, cvSize(width,1) );
else
- icvCvt_BGR2RGB_16u_C3R( (const ushort*)(img.data + img.step*y), 0, (ushort*)buffer, 0, cvSize(width,1) );
+ icvCvt_BGR2RGB_16u_C3R( img.ptr<ushort>(y), 0, (ushort*)buffer, 0, cvSize(width,1) );
break;
}
case 4:
{
if (depth == CV_8U)
- icvCvt_BGRA2RGBA_8u_C4R( img.data + img.step*y, 0, buffer, 0, cvSize(width,1) );
+ icvCvt_BGRA2RGBA_8u_C4R( img.ptr(y), 0, buffer, 0, cvSize(width,1) );
else
- icvCvt_BGRA2RGBA_16u_C4R( (const ushort*)(img.data + img.step*y), 0, (ushort*)buffer, 0, cvSize(width,1) );
+ icvCvt_BGRA2RGBA_16u_C4R( img.ptr<ushort>(y), 0, (ushort*)buffer, 0, cvSize(width,1) );
break;
}
if( channels == 3 )
{
if (depth == CV_8U)
- icvCvt_BGR2RGB_8u_C3R( img.data + img.step*y, 0, buffer, 0, cvSize(width,1) );
+ icvCvt_BGR2RGB_8u_C3R( img.ptr(y), 0, buffer, 0, cvSize(width,1) );
else
- icvCvt_BGR2RGB_16u_C3R( (const ushort*)(img.data + img.step*y), 0, (ushort*)buffer, 0, cvSize(width,1) );
+ icvCvt_BGR2RGB_16u_C3R( img.ptr<ushort>(y), 0, (ushort*)buffer, 0, cvSize(width,1) );
}
else
{
if( channels == 4 )
{
if (depth == CV_8U)
- icvCvt_BGRA2RGBA_8u_C4R( img.data + img.step*y, 0, buffer, 0, cvSize(width,1) );
+ icvCvt_BGRA2RGBA_8u_C4R( img.ptr(y), 0, buffer, 0, cvSize(width,1) );
else
- icvCvt_BGRA2RGBA_16u_C4R( (const ushort*)(img.data + img.step*y), 0, (ushort*)buffer, 0, cvSize(width,1) );
+ icvCvt_BGRA2RGBA_16u_C4R( img.ptr<ushort>(y), 0, (ushort*)buffer, 0, cvSize(width,1) );
}
}
- strm.putBytes( channels > 1 ? buffer : img.data + img.step*y, fileStep );
+ strm.putBytes( channels > 1 ? buffer : img.ptr(y), fileStep );
}
stripCounts[i] = (short)(strm.getPos() - stripOffsets[i]);
data.create(1, wfile_size, CV_8U);
- size_t data_size = fread(data.data, 1, wfile_size, wfile);
+ size_t data_size = fread(data.ptr(), 1, wfile_size, wfile);
if(wfile)
{
}
WebPBitstreamFeatures features;
- if(VP8_STATUS_OK == WebPGetFeatures(data.data, WEBP_HEADER_SIZE, &features))
+ if(VP8_STATUS_OK == WebPGetFeatures(data.ptr(), WEBP_HEADER_SIZE, &features))
{
m_width = features.width;
m_height = features.height;
img.create(m_height, m_width, m_type);
}
- uchar* out_data = img.data;
+ uchar* out_data = img.ptr();
size_t out_data_size = img.cols * img.rows * img.elemSize();
uchar *res_ptr = 0;
if (channels == 3)
{
- res_ptr = WebPDecodeBGRInto(data.data, data.total(), out_data,
+ res_ptr = WebPDecodeBGRInto(data.ptr(), data.total(), out_data,
(int)out_data_size, (int)img.step);
}
else if (channels == 4)
{
- res_ptr = WebPDecodeBGRAInto(data.data, data.total(), out_data,
+ res_ptr = WebPDecodeBGRAInto(data.ptr(), data.total(), out_data,
(int)out_data_size, (int)img.step);
}
{
if(channels == 3)
{
- size = WebPEncodeLosslessBGR(image->data, width, height, (int)image->step, &out);
+ size = WebPEncodeLosslessBGR(image->ptr(), width, height, (int)image->step, &out);
}
else if(channels == 4)
{
- size = WebPEncodeLosslessBGRA(image->data, width, height, (int)image->step, &out);
+ size = WebPEncodeLosslessBGRA(image->ptr(), width, height, (int)image->step, &out);
}
}
else
{
if(channels == 3)
{
- size = WebPEncodeBGR(image->data, width, height, (int)image->step, quality, &out);
+ size = WebPEncodeBGR(image->ptr(), width, height, (int)image->step, quality, &out);
}
else if(channels == 4)
{
- size = WebPEncodeBGRA(image->data, width, height, (int)image->step, quality, &out);
+ size = WebPEncodeBGRA(image->ptr(), width, height, (int)image->step, quality, &out);
}
}
#include "grfmt_exr.hpp"
#include "grfmt_webp.hpp"
#include "grfmt_hdr.hpp"
+#include "grfmt_gdal.hpp"
#endif/*_GRFMTS_H_*/
namespace cv
{
+/**
+ * @struct ImageCodecInitializer
+ *
+ * Container which stores the registered codecs to be used by OpenCV
+*/
struct ImageCodecInitializer
{
+ /**
+ * Default Constructor for the ImageCodeInitializer
+ */
ImageCodecInitializer()
{
+ /// BMP Support
decoders.push_back( makePtr<BmpDecoder>() );
encoders.push_back( makePtr<BmpEncoder>() );
+
decoders.push_back( makePtr<HdrDecoder>() );
encoders.push_back( makePtr<HdrEncoder>() );
#ifdef HAVE_JPEG
decoders.push_back( makePtr<ExrDecoder>() );
encoders.push_back( makePtr<ExrEncoder>() );
#endif
+
+ #ifdef HAVE_GDAL
+ /// Attach the GDAL Decoder
+ decoders.push_back( makePtr<GdalDecoder>() );
+ #endif/*HAVE_GDAL*/
}
std::vector<ImageDecoder> decoders;
static ImageCodecInitializer codecs;
-static ImageDecoder findDecoder( const String& filename )
-{
+/**
+ * Find the decoders
+ *
+ * @param[in] filename File to search
+ *
+ * @return Image decoder to parse image file.
+*/
+static ImageDecoder findDecoder( const String& filename ) {
+
size_t i, maxlen = 0;
+
+ /// iterate through list of registered codecs
for( i = 0; i < codecs.decoders.size(); i++ )
{
size_t len = codecs.decoders[i]->signatureLength();
maxlen = std::max(maxlen, len);
}
+ /// Open the file
FILE* f= fopen( filename.c_str(), "rb" );
+
+ /// in the event of a failure, return an empty image decoder
if( !f )
return ImageDecoder();
+
+ // read the file signature
String signature(maxlen, ' ');
maxlen = fread( (void*)signature.c_str(), 1, maxlen, f );
fclose(f);
signature = signature.substr(0, maxlen);
+ /// compare signature against all decoders
for( i = 0; i < codecs.decoders.size(); i++ )
{
if( codecs.decoders[i]->checkSignature(signature) )
return codecs.decoders[i]->newDecoder();
}
+ /// If no decoder was found, return base type
return ImageDecoder();
}
enum { LOAD_CVMAT=0, LOAD_IMAGE=1, LOAD_MAT=2 };
+/**
+ * Read an image into memory and return the information
+ *
+ * @param[in] filename File to load
+ * @param[in] flags Flags
+ * @param[in] hdrtype { LOAD_CVMAT=0,
+ * LOAD_IMAGE=1,
+ * LOAD_MAT=2
+ * }
+ * @param[in] mat Reference to C++ Mat object (If LOAD_MAT)
+ *
+*/
static void*
imread_( const String& filename, int flags, int hdrtype, Mat* mat=0 )
{
CvMat *matrix = 0;
Mat temp, *data = &temp;
- ImageDecoder decoder = findDecoder(filename);
- if( !decoder )
+ /// Search for the relevant decoder to handle the imagery
+ ImageDecoder decoder;
+
+#ifdef HAVE_GDAL
+ if( (flags & IMREAD_LOAD_GDAL) == IMREAD_LOAD_GDAL ){
+ decoder = GdalDecoder().newDecoder();
+ }else{
+#endif
+ decoder = findDecoder(filename);
+#ifdef HAVE_GDAL
+ }
+#endif
+
+ /// if no decoder was found, return nothing.
+ if( !decoder ){
return 0;
+ }
+
+ /// set the filename in the driver
decoder->setSource(filename);
- if( !decoder->readHeader() )
+
+ // read the header to make sure it succeeds
+ if( !decoder->readHeader() )
return 0;
+
+ // established the required input image size
CvSize size;
size.width = decoder->width();
size.height = decoder->height();
+ // grab the decoded type
int type = decoder->type();
if( flags != -1 )
{
temp = cvarrToMat(image);
}
+ // read the image data
if( !decoder->readData( *data ))
{
cvReleaseImage( &image );
hdrtype == LOAD_IMAGE ? (void*)image : (void*)mat;
}
+/**
+ * Read an image
+ *
+ * This function merely calls the actual implementation above and returns itself.
+ *
+ * @param[in] filename File to load
+ * @param[in] flags Flags you wish to set.
+*/
Mat imread( const String& filename, int flags )
{
+ /// create the basic container
Mat img;
+
+ /// load the data
imread_( filename, flags, LOAD_MAT, &img );
+
+ /// return a reference to the data
return img;
}
static void*
imdecode_( const Mat& buf, int flags, int hdrtype, Mat* mat=0 )
{
- CV_Assert(buf.data && buf.isContinuous());
+ CV_Assert(!buf.empty() && buf.isContinuous());
IplImage* image = 0;
CvMat *matrix = 0;
Mat temp, *data = &temp;
if( !f )
return 0;
size_t bufSize = buf.cols*buf.rows*buf.elemSize();
- fwrite( &buf.data[0], 1, bufSize, f );
+ fwrite( buf.ptr(), 1, bufSize, f );
fclose(f);
decoder->setSource(filename);
}
ASSERT_TRUE(img.channels() == 4);
- unsigned char* img_data = (unsigned char*)img.data;
+ unsigned char* img_data = img.ptr();
// Verification first pixel is red in BGRA
ASSERT_TRUE(img_data[0] == 0x00);
ASSERT_TRUE(img.channels() == 3);
- img_data = (unsigned char*)img.data;
+ img_data = img.ptr();
// Verification first pixel is red in BGR
ASSERT_TRUE(img_data[0] == 0x00);
ASSERT_TRUE(img.channels() == 3);
- img_data = (unsigned char*)img.data;
+ img_data = img.ptr();
// Verification first pixel is red in BGR
ASSERT_TRUE(img_data[0] == 0x00);
ASSERT_TRUE(img.channels() == 3);
- img_data = (unsigned char*)img.data;
+ img_data = img.ptr();
// Verification first pixel is red in BGR
ASSERT_TRUE(img_data[0] == 0x00);
#include "test_precomp.hpp"
-CV_TEST_MAIN("imgcodecs")
+CV_TEST_MAIN("highgui")
Another common feature of the functions and classes described in this section is that, unlike simple arithmetic functions, they need to extrapolate values of some non-existing pixels. For example, if you want to smooth an image using a Gaussian
:math:`3 \times 3` filter, then, when processing the left-most pixels in each row, you need pixels to the left of them, that is, outside of the image. You can let these pixels be the same as the left-most image pixels ("replicated border" extrapolation method), or assume that all the non-existing pixels are zeros ("constant border" extrapolation method), and so on.
-OpenCV enables you to specify the extrapolation method. For details, see the function :ocv:func:`borderInterpolate` and discussion of the ``borderType`` parameter in the section and various functions below. ::
+OpenCV enables you to specify the extrapolation method. For details, see the function ``borderInterpolate`` and discussion of the ``borderType`` parameter in the section and various functions below. ::
/*
Various border types, image boundaries are denoted with '|'
* (Python) A complete example illustrating different morphological operations like erode/dilate, open/close, blackhat/tophat ... can be found at opencv_source_code/samples/python2/morphology.py
-BaseColumnFilter
-----------------
-.. ocv:class:: BaseColumnFilter
-
-Base class for filters with single-column kernels. ::
-
- class BaseColumnFilter
- {
- public:
- virtual ~BaseColumnFilter();
-
- // To be overriden by the user.
- //
- // runs a filtering operation on the set of rows,
- // "dstcount + ksize - 1" rows on input,
- // "dstcount" rows on output,
- // each input and output row has "width" elements
- // the filtered rows are written into "dst" buffer.
- virtual void operator()(const uchar** src, uchar* dst, int dststep,
- int dstcount, int width) = 0;
- // resets the filter state (may be needed for IIR filters)
- virtual void reset();
-
- int ksize; // the aperture size
- int anchor; // position of the anchor point,
- // normally not used during the processing
- };
-
-
-The class ``BaseColumnFilter`` is a base class for filtering data using single-column kernels. Filtering does not have to be a linear operation. In general, it could be written as follows:
-
-.. math::
-
- \texttt{dst} (x,y) = F( \texttt{src} [y](x), \; \texttt{src} [y+1](x), \; ..., \; \texttt{src} [y+ \texttt{ksize} -1](x)
-
-where
-:math:`F` is a filtering function but, as it is represented as a class, it can produce any side effects, memorize previously processed data, and so on. The class only defines an interface and is not used directly. Instead, there are several functions in OpenCV (and you can add more) that return pointers to the derived classes that implement specific filtering operations. Those pointers are then passed to the
-:ocv:class:`FilterEngine` constructor. While the filtering operation interface uses the ``uchar`` type, a particular implementation is not limited to 8-bit data.
-
-.. seealso::
-
- :ocv:class:`BaseRowFilter`,
- :ocv:class:`BaseFilter`,
- :ocv:class:`FilterEngine`,
- :ocv:func:`getColumnSumFilter`,
- :ocv:func:`getLinearColumnFilter`,
- :ocv:func:`getMorphologyColumnFilter`
-
-
-BaseFilter
-----------
-.. ocv:class:: BaseFilter
-
-Base class for 2D image filters. ::
-
- class BaseFilter
- {
- public:
- virtual ~BaseFilter();
-
- // To be overriden by the user.
- //
- // runs a filtering operation on the set of rows,
- // "dstcount + ksize.height - 1" rows on input,
- // "dstcount" rows on output,
- // each input row has "(width + ksize.width-1)*cn" elements
- // each output row has "width*cn" elements.
- // the filtered rows are written into "dst" buffer.
- virtual void operator()(const uchar** src, uchar* dst, int dststep,
- int dstcount, int width, int cn) = 0;
- // resets the filter state (may be needed for IIR filters)
- virtual void reset();
- Size ksize;
- Point anchor;
- };
-
-
-The class ``BaseFilter`` is a base class for filtering data using 2D kernels. Filtering does not have to be a linear operation. In general, it could be written as follows:
-
-.. math::
-
- \begin{array}{l} \texttt{dst} (x,y) = F( \texttt{src} [y](x), \; \texttt{src} [y](x+1), \; ..., \; \texttt{src} [y](x+ \texttt{ksize.width} -1), \\ \texttt{src} [y+1](x), \; \texttt{src} [y+1](x+1), \; ..., \; \texttt{src} [y+1](x+ \texttt{ksize.width} -1), \\ ......................................................................................... \\ \texttt{src} [y+ \texttt{ksize.height-1} ](x), \\ \texttt{src} [y+ \texttt{ksize.height-1} ](x+1), \\ ...
- \texttt{src} [y+ \texttt{ksize.height-1} ](x+ \texttt{ksize.width} -1))
- \end{array}
-
-where
-:math:`F` is a filtering function. The class only defines an interface and is not used directly. Instead, there are several functions in OpenCV (and you can add more) that return pointers to the derived classes that implement specific filtering operations. Those pointers are then passed to the
-:ocv:class:`FilterEngine` constructor. While the filtering operation interface uses the ``uchar`` type, a particular implementation is not limited to 8-bit data.
-
-.. seealso::
-
- :ocv:class:`BaseColumnFilter`,
- :ocv:class:`BaseRowFilter`,
- :ocv:class:`FilterEngine`,
- :ocv:func:`getLinearFilter`,
- :ocv:func:`getMorphologyFilter`
-
-
-
-BaseRowFilter
--------------
-.. ocv:class:: BaseRowFilter
-
-Base class for filters with single-row kernels. ::
-
- class BaseRowFilter
- {
- public:
- virtual ~BaseRowFilter();
-
- // To be overriden by the user.
- //
- // runs filtering operation on the single input row
- // of "width" element, each element is has "cn" channels.
- // the filtered row is written into "dst" buffer.
- virtual void operator()(const uchar* src, uchar* dst,
- int width, int cn) = 0;
- int ksize, anchor;
- };
-
-
-The class ``BaseRowFilter`` is a base class for filtering data using single-row kernels. Filtering does not have to be a linear operation. In general, it could be written as follows:
-
-.. math::
-
- \texttt{dst} (x,y) = F( \texttt{src} [y](x), \; \texttt{src} [y](x+1), \; ..., \; \texttt{src} [y](x+ \texttt{ksize.width} -1))
-
-where
-:math:`F` is a filtering function. The class only defines an interface and is not used directly. Instead, there are several functions in OpenCV (and you can add more) that return pointers to the derived classes that implement specific filtering operations. Those pointers are then passed to the
-:ocv:class:`FilterEngine` constructor. While the filtering operation interface uses the ``uchar`` type, a particular implementation is not limited to 8-bit data.
-
-.. seealso::
-
- :ocv:class:`BaseColumnFilter`,
- :ocv:class:`BaseFilter`,
- :ocv:class:`FilterEngine`,
- :ocv:func:`getLinearRowFilter`,
- :ocv:func:`getMorphologyRowFilter`,
- :ocv:func:`getRowSumFilter`
-
-
-
-FilterEngine
-------------
-.. ocv:class:: FilterEngine
-
-Generic image filtering class. ::
-
- class FilterEngine
- {
- public:
- // empty constructor
- FilterEngine();
- // builds a 2D non-separable filter (!_filter2D.empty()) or
- // a separable filter (!_rowFilter.empty() && !_columnFilter.empty())
- // the input data type will be "srcType", the output data type will be "dstType",
- // the intermediate data type is "bufType".
- // _rowBorderType and _columnBorderType determine how the image
- // will be extrapolated beyond the image boundaries.
- // _borderValue is only used when _rowBorderType and/or _columnBorderType
- // == BORDER_CONSTANT
- FilterEngine(const Ptr<BaseFilter>& _filter2D,
- const Ptr<BaseRowFilter>& _rowFilter,
- const Ptr<BaseColumnFilter>& _columnFilter,
- int srcType, int dstType, int bufType,
- int _rowBorderType=BORDER_REPLICATE,
- int _columnBorderType=-1, // use _rowBorderType by default
- const Scalar& _borderValue=Scalar());
- virtual ~FilterEngine();
- // separate function for the engine initialization
- void init(const Ptr<BaseFilter>& _filter2D,
- const Ptr<BaseRowFilter>& _rowFilter,
- const Ptr<BaseColumnFilter>& _columnFilter,
- int srcType, int dstType, int bufType,
- int _rowBorderType=BORDER_REPLICATE, int _columnBorderType=-1,
- const Scalar& _borderValue=Scalar());
- // starts filtering of the ROI in an image of size "wholeSize".
- // returns the starting y-position in the source image.
- virtual int start(Size wholeSize, Rect roi, int maxBufRows=-1);
- // alternative form of start that takes the image
- // itself instead of "wholeSize". Set isolated to true to pretend that
- // there are no real pixels outside of the ROI
- // (so that the pixels are extrapolated using the specified border modes)
- virtual int start(const Mat& src, const Rect& srcRoi=Rect(0,0,-1,-1),
- bool isolated=false, int maxBufRows=-1);
- // processes the next portion of the source image,
- // "srcCount" rows starting from "src" and
- // stores the results in "dst".
- // returns the number of produced rows
- virtual int proceed(const uchar* src, int srcStep, int srcCount,
- uchar* dst, int dstStep);
- // higher-level function that processes the whole
- // ROI or the whole image with a single call
- virtual void apply( const Mat& src, Mat& dst,
- const Rect& srcRoi=Rect(0,0,-1,-1),
- Point dstOfs=Point(0,0),
- bool isolated=false);
- bool isSeparable() const { return filter2D.empty(); }
- // how many rows from the input image are not yet processed
- int remainingInputRows() const;
- // how many output rows are not yet produced
- int remainingOutputRows() const;
- ...
- // the starting and the ending rows in the source image
- int startY, endY;
-
- // pointers to the filters
- Ptr<BaseFilter> filter2D;
- Ptr<BaseRowFilter> rowFilter;
- Ptr<BaseColumnFilter> columnFilter;
- };
-
-
-The class ``FilterEngine`` can be used to apply an arbitrary filtering operation to an image.
-It contains all the necessary intermediate buffers, computes extrapolated values
-of the "virtual" pixels outside of the image, and so on. Pointers to the initialized ``FilterEngine`` instances
-are returned by various ``create*Filter`` functions (see below) and they are used inside high-level functions such as
-:ocv:func:`filter2D`,
-:ocv:func:`erode`,
-:ocv:func:`dilate`, and others. Thus, the class plays a key role in many of OpenCV filtering functions.
-
-This class makes it easier to combine filtering operations with other operations, such as color space conversions, thresholding, arithmetic operations, and others. By combining several operations together you can get much better performance because your data will stay in cache. For example, see below the implementation of the Laplace operator for floating-point images, which is a simplified implementation of
-:ocv:func:`Laplacian` : ::
-
- void laplace_f(const Mat& src, Mat& dst)
- {
- CV_Assert( src.type() == CV_32F );
- dst.create(src.size(), src.type());
-
- // get the derivative and smooth kernels for d2I/dx2.
- // for d2I/dy2 consider using the same kernels, just swapped
- Mat kd, ks;
- getSobelKernels( kd, ks, 2, 0, ksize, false, ktype );
-
- // process 10 source rows at once
- int DELTA = std::min(10, src.rows);
- Ptr<FilterEngine> Fxx = createSeparableLinearFilter(src.type(),
- dst.type(), kd, ks, Point(-1,-1), 0, borderType, borderType, Scalar() );
- Ptr<FilterEngine> Fyy = createSeparableLinearFilter(src.type(),
- dst.type(), ks, kd, Point(-1,-1), 0, borderType, borderType, Scalar() );
-
- int y = Fxx->start(src), dsty = 0, dy = 0;
- Fyy->start(src);
- const uchar* sptr = src.data + y*src.step;
-
- // allocate the buffers for the spatial image derivatives;
- // the buffers need to have more than DELTA rows, because at the
- // last iteration the output may take max(kd.rows-1,ks.rows-1)
- // rows more than the input.
- Mat Ixx( DELTA + kd.rows - 1, src.cols, dst.type() );
- Mat Iyy( DELTA + kd.rows - 1, src.cols, dst.type() );
-
- // inside the loop always pass DELTA rows to the filter
- // (note that the "proceed" method takes care of possibe overflow, since
- // it was given the actual image height in the "start" method)
- // on output you can get:
- // * < DELTA rows (initial buffer accumulation stage)
- // * = DELTA rows (settled state in the middle)
- // * > DELTA rows (when the input image is over, generate
- // "virtual" rows using the border mode and filter them)
- // this variable number of output rows is dy.
- // dsty is the current output row.
- // sptr is the pointer to the first input row in the portion to process
- for( ; dsty < dst.rows; sptr += DELTA*src.step, dsty += dy )
- {
- Fxx->proceed( sptr, (int)src.step, DELTA, Ixx.data, (int)Ixx.step );
- dy = Fyy->proceed( sptr, (int)src.step, DELTA, d2y.data, (int)Iyy.step );
- if( dy > 0 )
- {
- Mat dstripe = dst.rowRange(dsty, dsty + dy);
- add(Ixx.rowRange(0, dy), Iyy.rowRange(0, dy), dstripe);
- }
- }
- }
-
-
-If you do not need that much control of the filtering process, you can simply use the ``FilterEngine::apply`` method. The method is implemented as follows: ::
-
- void FilterEngine::apply(const Mat& src, Mat& dst,
- const Rect& srcRoi, Point dstOfs, bool isolated)
- {
- // check matrix types
- CV_Assert( src.type() == srcType && dst.type() == dstType );
-
- // handle the "whole image" case
- Rect _srcRoi = srcRoi;
- if( _srcRoi == Rect(0,0,-1,-1) )
- _srcRoi = Rect(0,0,src.cols,src.rows);
-
- // check if the destination ROI is inside dst.
- // and FilterEngine::start will check if the source ROI is inside src.
- CV_Assert( dstOfs.x >= 0 && dstOfs.y >= 0 &&
- dstOfs.x + _srcRoi.width <= dst.cols &&
- dstOfs.y + _srcRoi.height <= dst.rows );
-
- // start filtering
- int y = start(src, _srcRoi, isolated);
-
- // process the whole ROI. Note that "endY - startY" is the total number
- // of the source rows to process
- // (including the possible rows outside of srcRoi but inside the source image)
- proceed( src.data + y*src.step,
- (int)src.step, endY - startY,
- dst.data + dstOfs.y*dst.step +
- dstOfs.x*dst.elemSize(), (int)dst.step );
- }
-
-
-Unlike the earlier versions of OpenCV, now the filtering operations fully support the notion of image ROI, that is, pixels outside of the ROI but inside the image can be used in the filtering operations. For example, you can take a ROI of a single pixel and filter it. This will be a filter response at that particular pixel. However, it is possible to emulate the old behavior by passing ``isolated=false`` to ``FilterEngine::start`` or ``FilterEngine::apply`` . You can pass the ROI explicitly to ``FilterEngine::apply`` or construct new matrix headers: ::
-
- // compute dI/dx derivative at src(x,y)
-
- // method 1:
- // form a matrix header for a single value
- float val1 = 0;
- Mat dst1(1,1,CV_32F,&val1);
-
- Ptr<FilterEngine> Fx = createDerivFilter(CV_32F, CV_32F,
- 1, 0, 3, BORDER_REFLECT_101);
- Fx->apply(src, Rect(x,y,1,1), Point(), dst1);
-
- // method 2:
- // form a matrix header for a single value
- float val2 = 0;
- Mat dst2(1,1,CV_32F,&val2);
-
- Mat pix_roi(src, Rect(x,y,1,1));
- Sobel(pix_roi, dst2, dst2.type(), 1, 0, 3, 1, 0, BORDER_REFLECT_101);
-
- printf("method1 =
-
-
-Explore the data types. As it was mentioned in the
-:ocv:class:`BaseFilter` description, the specific filters can process data of any type, despite that ``Base*Filter::operator()`` only takes ``uchar`` pointers and no information about the actual types. To make it all work, the following rules are used:
-
-*
- In case of separable filtering, ``FilterEngine::rowFilter`` is applied first. It transforms the input image data (of type ``srcType`` ) to the intermediate results stored in the internal buffers (of type ``bufType`` ). Then, these intermediate results are processed as
- *single-channel data*
- with ``FilterEngine::columnFilter`` and stored in the output image (of type ``dstType`` ). Thus, the input type for ``rowFilter`` is ``srcType`` and the output type is ``bufType`` . The input type for ``columnFilter`` is ``CV_MAT_DEPTH(bufType)`` and the output type is ``CV_MAT_DEPTH(dstType)`` .
-
-*
- In case of non-separable filtering, ``bufType`` must be the same as ``srcType`` . The source data is copied to the temporary buffer, if needed, and then just passed to ``FilterEngine::filter2D`` . That is, the input type for ``filter2D`` is ``srcType`` (= ``bufType`` ) and the output type is ``dstType`` .
-
-.. seealso::
-
- :ocv:class:`BaseColumnFilter`,
- :ocv:class:`BaseFilter`,
- :ocv:class:`BaseRowFilter`,
- :ocv:func:`createBoxFilter`,
- :ocv:func:`createDerivFilter`,
- :ocv:func:`createGaussianFilter`,
- :ocv:func:`createLinearFilter`,
- :ocv:func:`createMorphologyFilter`,
- :ocv:func:`createSeparableLinearFilter`
-
-
-
bilateralFilter
-------------------
Applies the bilateral filter to an image.
:param maxlevel: 0-based index of the last (the smallest) pyramid layer. It must be non-negative.
- :param borderType: Pixel extrapolation method (BORDER_CONSTANT don't supported). See :ocv:func:`borderInterpolate` for details.
+ :param borderType: Pixel extrapolation method (BORDER_CONSTANT don't supported). See ``borderInterpolate`` for details.
The function constructs a vector of images and builds the Gaussian pyramid by recursively applying
:ocv:func:`pyrDown` to the previously built pyramid layers, starting from ``dst[0]==src`` .
-createBoxFilter
--------------------
-Returns a box filter engine.
-
-.. ocv:function:: Ptr<FilterEngine> createBoxFilter( int srcType, int dstType, Size ksize, Point anchor=Point(-1,-1), bool normalize=true, int borderType=BORDER_DEFAULT)
-
-.. ocv:function:: Ptr<BaseRowFilter> getRowSumFilter(int srcType, int sumType, int ksize, int anchor=-1)
-
-.. ocv:function:: Ptr<BaseColumnFilter> getColumnSumFilter(int sumType, int dstType, int ksize, int anchor=-1, double scale=1)
-
- :param srcType: Source image type.
-
- :param sumType: Intermediate horizontal sum type that must have as many channels as ``srcType`` .
-
- :param dstType: Destination image type that must have as many channels as ``srcType`` .
-
- :param ksize: Aperture size.
-
- :param anchor: Anchor position with the kernel. Negative values mean that the anchor is at the kernel center.
-
- :param normalize: Flag specifying whether the sums are normalized or not. See :ocv:func:`boxFilter` for details.
-
- :param scale: Another way to specify normalization in lower-level ``getColumnSumFilter`` .
-
- :param borderType: Border type to use. See :ocv:func:`borderInterpolate` .
-
-The function is a convenience function that retrieves the horizontal sum primitive filter with
-:ocv:func:`getRowSumFilter` , vertical sum filter with
-:ocv:func:`getColumnSumFilter` , constructs new
-:ocv:class:`FilterEngine` , and passes both of the primitive filters there. The constructed filter engine can be used for image filtering with normalized or unnormalized box filter.
-
-The function itself is used by
-:ocv:func:`blur` and
-:ocv:func:`boxFilter` .
-
-.. seealso::
-
- :ocv:class:`FilterEngine`,
- :ocv:func:`blur`,
- :ocv:func:`boxFilter`
-
-
-
-createDerivFilter
----------------------
-Returns an engine for computing image derivatives.
-
-.. ocv:function:: Ptr<FilterEngine> createDerivFilter( int srcType, int dstType, int dx, int dy, int ksize, int borderType=BORDER_DEFAULT )
-
- :param srcType: Source image type.
-
- :param dstType: Destination image type that must have as many channels as ``srcType`` .
-
- :param dx: Derivative order in respect of x.
-
- :param dy: Derivative order in respect of y.
-
- :param ksize: Aperture size See :ocv:func:`getDerivKernels` .
-
- :param borderType: Border type to use. See :ocv:func:`borderInterpolate` .
-
-The function :ocv:func:`createDerivFilter` is a small convenience function that retrieves linear filter coefficients for computing image derivatives using
-:ocv:func:`getDerivKernels` and then creates a separable linear filter with
-:ocv:func:`createSeparableLinearFilter` . The function is used by
-:ocv:func:`Sobel` and
-:ocv:func:`Scharr` .
-
-.. seealso::
-
- :ocv:func:`createSeparableLinearFilter`,
- :ocv:func:`getDerivKernels`,
- :ocv:func:`Scharr`,
- :ocv:func:`Sobel`
-
-
-
-createGaussianFilter
-------------------------
-Returns an engine for smoothing images with the Gaussian filter.
-
-.. ocv:function:: Ptr<FilterEngine> createGaussianFilter( int type, Size ksize, double sigma1, double sigma2=0, int borderType=BORDER_DEFAULT )
-
- :param type: Source and destination image type.
-
- :param ksize: Aperture size. See :ocv:func:`getGaussianKernel` .
-
- :param sigma1: Gaussian sigma in the horizontal direction. See :ocv:func:`getGaussianKernel` .
-
- :param sigma2: Gaussian sigma in the vertical direction. If 0, then :math:`\texttt{sigma2}\leftarrow\texttt{sigma1}` .
-
- :param borderType: Border type to use. See :ocv:func:`borderInterpolate` .
-
-The function :ocv:func:`createGaussianFilter` computes Gaussian kernel coefficients and then returns a separable linear filter for that kernel. The function is used by
-:ocv:func:`GaussianBlur` . Note that while the function takes just one data type, both for input and output, you can pass this limitation by calling
-:ocv:func:`getGaussianKernel` and then
-:ocv:func:`createSeparableLinearFilter` directly.
-
-.. seealso::
-
- :ocv:func:`createSeparableLinearFilter`,
- :ocv:func:`getGaussianKernel`,
- :ocv:func:`GaussianBlur`
-
-
-
-createLinearFilter
-----------------------
-Creates a non-separable linear filter engine.
-
-.. ocv:function:: Ptr<FilterEngine> createLinearFilter( int srcType, int dstType, InputArray kernel, Point _anchor=Point(-1,-1), double delta=0, int rowBorderType=BORDER_DEFAULT, int columnBorderType=-1, const Scalar& borderValue=Scalar() )
-
-.. ocv:function:: Ptr<BaseFilter> getLinearFilter(int srcType, int dstType, InputArray kernel, Point anchor=Point(-1,-1), double delta=0, int bits=0)
-
- :param srcType: Source image type.
-
- :param dstType: Destination image type that must have as many channels as ``srcType`` .
-
- :param kernel: 2D array of filter coefficients.
-
- :param anchor: Anchor point within the kernel. Special value ``Point(-1,-1)`` means that the anchor is at the kernel center.
-
- :param delta: Value added to the filtered results before storing them.
-
- :param bits: Number of the fractional bits. The parameter is used when the kernel is an integer matrix representing fixed-point filter coefficients.
-
- :param rowBorderType: Pixel extrapolation method in the vertical direction. For details, see :ocv:func:`borderInterpolate`.
-
- :param columnBorderType: Pixel extrapolation method in the horizontal direction.
-
- :param borderValue: Border value used in case of a constant border.
-
-The function returns a pointer to a 2D linear filter for the specified kernel, the source array type, and the destination array type. The function is a higher-level function that calls ``getLinearFilter`` and passes the retrieved 2D filter to the
-:ocv:class:`FilterEngine` constructor.
-
-.. seealso::
-
- :ocv:func:`createSeparableLinearFilter`,
- :ocv:class:`FilterEngine`,
- :ocv:func:`filter2D`
-
-
-createMorphologyFilter
---------------------------
-Creates an engine for non-separable morphological operations.
-
-.. ocv:function:: Ptr<FilterEngine> createMorphologyFilter( int op, int type, InputArray kernel, Point anchor=Point(-1,-1), int rowBorderType=BORDER_CONSTANT, int columnBorderType=-1, const Scalar& borderValue=morphologyDefaultBorderValue() )
-
-.. ocv:function:: Ptr<BaseFilter> getMorphologyFilter( int op, int type, InputArray kernel, Point anchor=Point(-1,-1) )
-
-.. ocv:function:: Ptr<BaseRowFilter> getMorphologyRowFilter( int op, int type, int ksize, int anchor=-1 )
-
-.. ocv:function:: Ptr<BaseColumnFilter> getMorphologyColumnFilter( int op, int type, int ksize, int anchor=-1 )
-
-.. ocv:function:: Scalar morphologyDefaultBorderValue()
-
- :param op: Morphology operation ID, ``MORPH_ERODE`` or ``MORPH_DILATE`` .
-
- :param type: Input/output image type. The number of channels can be arbitrary. The depth should be one of ``CV_8U``, ``CV_16U``, ``CV_16S``, ``CV_32F` or ``CV_64F``.
-
- :param kernel: 2D 8-bit structuring element for a morphological operation. Non-zero elements indicate the pixels that belong to the element.
-
- :param ksize: Horizontal or vertical structuring element size for separable morphological operations.
-
- :param anchor: Anchor position within the structuring element. Negative values mean that the anchor is at the kernel center.
-
- :param rowBorderType: Pixel extrapolation method in the vertical direction. For details, see :ocv:func:`borderInterpolate`.
-
- :param columnBorderType: Pixel extrapolation method in the horizontal direction.
-
- :param borderValue: Border value in case of a constant border. The default value, \ ``morphologyDefaultBorderValue`` , has a special meaning. It is transformed :math:`+\inf` for the erosion and to :math:`-\inf` for the dilation, which means that the minimum (maximum) is effectively computed only over the pixels that are inside the image.
-
-The functions construct primitive morphological filtering operations or a filter engine based on them. Normally it is enough to use
-:ocv:func:`createMorphologyFilter` or even higher-level
-:ocv:func:`erode`,
-:ocv:func:`dilate` , or
-:ocv:func:`morphologyEx` .
-Note that
-:ocv:func:`createMorphologyFilter` analyzes the structuring element shape and builds a separable morphological filter engine when the structuring element is square.
-
-.. seealso::
-
- :ocv:func:`erode`,
- :ocv:func:`dilate`,
- :ocv:func:`morphologyEx`,
- :ocv:class:`FilterEngine`
-
-
-createSeparableLinearFilter
--------------------------------
-Creates an engine for a separable linear filter.
-
-.. ocv:function:: Ptr<FilterEngine> createSeparableLinearFilter( int srcType, int dstType, InputArray rowKernel, InputArray columnKernel, Point anchor=Point(-1,-1), double delta=0, int rowBorderType=BORDER_DEFAULT, int columnBorderType=-1, const Scalar& borderValue=Scalar() )
-
-.. ocv:function:: Ptr<BaseColumnFilter> getLinearColumnFilter( int bufType, int dstType, InputArray kernel, int anchor, int symmetryType, double delta=0, int bits=0 )
-
-.. ocv:function:: Ptr<BaseRowFilter> getLinearRowFilter( int srcType, int bufType, InputArray kernel, int anchor, int symmetryType )
-
- :param srcType: Source array type.
-
- :param dstType: Destination image type that must have as many channels as ``srcType`` .
-
- :param bufType: Intermediate buffer type that must have as many channels as ``srcType`` .
-
- :param rowKernel: Coefficients for filtering each row.
-
- :param columnKernel: Coefficients for filtering each column.
-
- :param anchor: Anchor position within the kernel. Negative values mean that anchor is positioned at the aperture center.
-
- :param delta: Value added to the filtered results before storing them.
-
- :param bits: Number of the fractional bits. The parameter is used when the kernel is an integer matrix representing fixed-point filter coefficients.
-
- :param rowBorderType: Pixel extrapolation method in the vertical direction. For details, see :ocv:func:`borderInterpolate`.
-
- :param columnBorderType: Pixel extrapolation method in the horizontal direction.
-
- :param borderValue: Border value used in case of a constant border.
-
- :param symmetryType: Type of each row and column kernel. See :ocv:func:`getKernelType` .
-
-The functions construct primitive separable linear filtering operations or a filter engine based on them. Normally it is enough to use
-:ocv:func:`createSeparableLinearFilter` or even higher-level
-:ocv:func:`sepFilter2D` . The function
-:ocv:func:`createMorphologyFilter` is smart enough to figure out the ``symmetryType`` for each of the two kernels, the intermediate ``bufType`` and, if filtering can be done in integer arithmetics, the number of ``bits`` to encode the filter coefficients. If it does not work for you, it is possible to call ``getLinearColumnFilter``,``getLinearRowFilter`` directly and then pass them to the
-:ocv:class:`FilterEngine` constructor.
-
-.. seealso::
-
- :ocv:func:`sepFilter2D`,
- :ocv:func:`createLinearFilter`,
- :ocv:class:`FilterEngine`,
- :ocv:func:`getKernelType`
-
-
dilate
------
Dilates an image by using a specific structuring element.
:param dst: output image of the same size and type as ``src``.
- :param kernel: structuring element used for dilation; if ``element=Mat()`` , a ``3 x 3`` rectangular structuring element is used. Kernel can be created using :ocv:func:`getStructuringElement`
+ :param kernel: structuring element used for dilation; if ``elemenat=Mat()`` , a ``3 x 3`` rectangular structuring element is used. Kernel can be created using :ocv:func:`getStructuringElement`
:param anchor: position of the anchor within the element; default value ``(-1, -1)`` means that the anchor is at the element center.
:param iterations: number of times dilation is applied.
- :param borderType: pixel extrapolation method (see :ocv:func:`borderInterpolate` for details).
+ :param borderType: pixel extrapolation method (see ``borderInterpolate`` for details).
- :param borderValue: border value in case of a constant border (see :ocv:func:`createMorphologyFilter` for details).
+ :param borderValue: border value in case of a constant border
The function dilates the source image using the specified structuring element that determines the shape of a pixel neighborhood over which the maximum is taken:
:ocv:func:`erode`,
:ocv:func:`morphologyEx`,
- :ocv:func:`createMorphologyFilter`
:ocv:func:`getStructuringElement`
* An example using the morphological dilate operation can be found at opencv_source_code/samples/cpp/morphology2.cpp
-
-
erode
-----
Erodes an image by using a specific structuring element.
:param iterations: number of times erosion is applied.
- :param borderType: pixel extrapolation method (see :ocv:func:`borderInterpolate` for details).
+ :param borderType: pixel extrapolation method (see ``borderInterpolate`` for details).
- :param borderValue: border value in case of a constant border (see :ocv:func:`createMorphologyFilter` for details).
+ :param borderValue: border value in case of a constant border
The function erodes the source image using the specified structuring element that determines the shape of a pixel neighborhood over which the minimum is taken:
:ocv:func:`dilate`,
:ocv:func:`morphologyEx`,
- :ocv:func:`createMorphologyFilter`,
:ocv:func:`getStructuringElement`
.. note::
:param delta: optional value added to the filtered pixels before storing them in ``dst``.
- :param borderType: pixel extrapolation method (see :ocv:func:`borderInterpolate` for details).
+ :param borderType: pixel extrapolation method (see ``borderInterpolate`` for details).
The function applies an arbitrary linear filter to an image. In-place operation is supported. When the aperture is partially outside the image, the function interpolates outlier pixel values according to the specified border mode.
That is, the kernel is not mirrored around the anchor point. If you need a real convolution, flip the kernel using
:ocv:func:`flip` and set the new anchor to ``(kernel.cols - anchor.x - 1, kernel.rows - anchor.y - 1)`` .
-The function uses the DFT-based algorithm in case of sufficiently large kernels (~``11 x 11`` or larger) and the direct algorithm (that uses the engine retrieved by :ocv:func:`createLinearFilter` ) for small kernels.
+The function uses the DFT-based algorithm in case of sufficiently large kernels (~``11 x 11`` or larger) and the direct algorithm for small kernels.
.. seealso::
:ocv:func:`sepFilter2D`,
- :ocv:func:`createLinearFilter`,
:ocv:func:`dft`,
:ocv:func:`matchTemplate`
:param sigmaY: Gaussian kernel standard deviation in Y direction; if ``sigmaY`` is zero, it is set to be equal to ``sigmaX``, if both sigmas are zeros, they are computed from ``ksize.width`` and ``ksize.height`` , respectively (see :ocv:func:`getGaussianKernel` for details); to fully control the result regardless of possible future modifications of all this semantics, it is recommended to specify all of ``ksize``, ``sigmaX``, and ``sigmaY``.
- :param borderType: pixel extrapolation method (see :ocv:func:`borderInterpolate` for details).
+ :param borderType: pixel extrapolation method (see ``borderInterpolate`` for details).
The function convolves the source image with the specified Gaussian kernel. In-place filtering is supported.
:ocv:func:`Scharr` ). Otherwise, Sobel kernels are generated (see
:ocv:func:`Sobel` ). The filters are normally passed to
:ocv:func:`sepFilter2D` or to
-:ocv:func:`createSeparableLinearFilter` .
-
getGaussianKernel
:math:`\sum_i G_i=1`.
Two of such generated kernels can be passed to
-:ocv:func:`sepFilter2D` or to
-:ocv:func:`createSeparableLinearFilter`. Those functions automatically recognize smoothing kernels (a symmetrical kernel with sum of weights equal to 1) and handle them accordingly. You may also use the higher-level
+:ocv:func:`sepFilter2D`. Those functions automatically recognize smoothing kernels (a symmetrical kernel with sum of weights equal to 1) and handle them accordingly. You may also use the higher-level
:ocv:func:`GaussianBlur`.
.. seealso::
:ocv:func:`sepFilter2D`,
- :ocv:func:`createSeparableLinearFilter`,
:ocv:func:`getDerivKernels`,
:ocv:func:`getStructuringElement`,
:ocv:func:`GaussianBlur`
For more details about gabor filter equations and parameters, see: `Gabor Filter <http://en.wikipedia.org/wiki/Gabor_filter>`_.
-
-getKernelType
--------------
-Returns the kernel type.
-
-.. ocv:function:: int getKernelType(InputArray kernel, Point anchor)
-
- :param kernel: 1D array of the kernel coefficients to analyze.
-
- :param anchor: Anchor position within the kernel.
-
-The function analyzes the kernel coefficients and returns the corresponding kernel type:
-
- * **KERNEL_GENERAL** The kernel is generic. It is used when there is no any type of symmetry or other properties.
-
- * **KERNEL_SYMMETRICAL** The kernel is symmetrical: :math:`\texttt{kernel}_i == \texttt{kernel}_{ksize-i-1}` , and the anchor is at the center.
-
- * **KERNEL_ASYMMETRICAL** The kernel is asymmetrical: :math:`\texttt{kernel}_i == -\texttt{kernel}_{ksize-i-1}` , and the anchor is at the center.
-
- * **KERNEL_SMOOTH** All the kernel elements are non-negative and summed to 1. For example, the Gaussian kernel is both smooth kernel and symmetrical, so the function returns ``KERNEL_SMOOTH | KERNEL_SYMMETRICAL`` .
- * **KERNEL_INTEGER** All the kernel coefficients are integer numbers. This flag can be combined with ``KERNEL_SYMMETRICAL`` or ``KERNEL_ASYMMETRICAL`` .
-
-
-
getStructuringElement
---------------------
Returns a structuring element of the specified size and shape for morphological operations.
:param values: integer array of ``cols``*``rows`` elements that specifies the custom shape of the structuring element, when ``shape=CV_SHAPE_CUSTOM``.
The function constructs and returns the structuring element that can be further passed to
-:ocv:func:`createMorphologyFilter`,
:ocv:func:`erode`,
:ocv:func:`dilate` or
:ocv:func:`morphologyEx` . But you can also construct an arbitrary binary mask yourself and use it as the structuring element.
:param iterations: Number of times erosion and dilation are applied.
- :param borderType: Pixel extrapolation method. See :ocv:func:`borderInterpolate` for details.
+ :param borderType: Pixel extrapolation method. See ``borderInterpolate`` for details.
- :param borderValue: Border value in case of a constant border. The default value has a special meaning. See :ocv:func:`createMorphologyFilter` for details.
+ :param borderValue: Border value in case of a constant border. The default value has a special meaning.
The function can perform advanced morphological transformations using an erosion and dilation as basic operations.
:ocv:func:`dilate`,
:ocv:func:`erode`,
- :ocv:func:`createMorphologyFilter`,
:ocv:func:`getStructuringElement`
.. note::
:param delta: Optional delta value that is added to the results prior to storing them in ``dst`` .
- :param borderType: Pixel extrapolation method. See :ocv:func:`borderInterpolate` for details.
+ :param borderType: Pixel extrapolation method. See ``borderInterpolate`` for details.
The function calculates the Laplacian of the source image by adding up the second x and y derivatives calculated using the Sobel operator:
:param dstsize: size of the output image.
- :param borderType: Pixel extrapolation method (BORDER_CONSTANT don't supported). See :ocv:func:`borderInterpolate` for details.
+ :param borderType: Pixel extrapolation method (BORDER_CONSTANT don't supported). See ``borderInterpolate`` for details.
By default, size of the output image is computed as ``Size((src.cols+1)/2, (src.rows+1)/2)``, but in any case, the following conditions should be satisfied:
:param dstsize: size of the output image.
- :param borderType: Pixel extrapolation method (only BORDER_DEFAULT supported). See :ocv:func:`borderInterpolate` for details.
+ :param borderType: Pixel extrapolation method (only BORDER_DEFAULT supported). See ``borderInterpolate`` for details.
By default, size of the output image is computed as ``Size(src.cols*2, (src.rows*2)``, but in any case, the following conditions should be satisfied:
:param delta: Value added to the filtered results before storing them.
- :param borderType: Pixel extrapolation method. See :ocv:func:`borderInterpolate` for details.
+ :param borderType: Pixel extrapolation method. See ``borderInterpolate`` for details.
The function applies a separable linear filter to the image. That is, first, every row of ``src`` is filtered with the 1D kernel ``kernelX`` . Then, every column of the result is filtered with the 1D kernel ``kernelY`` . The final result shifted by ``delta`` is stored in ``dst`` .
.. seealso::
- :ocv:func:`createSeparableLinearFilter`,
:ocv:func:`filter2D`,
:ocv:func:`Sobel`,
:ocv:func:`GaussianBlur`,
:param delta: optional delta value that is added to the results prior to storing them in ``dst``.
- :param borderType: pixel extrapolation method (see :ocv:func:`borderInterpolate` for details).
+ :param borderType: pixel extrapolation method (see ``borderInterpolate`` for details).
In all cases except one, the
:math:`\texttt{ksize} \times
:param delta: optional delta value that is added to the results prior to storing them in ``dst``.
- :param borderType: pixel extrapolation method (see :ocv:func:`borderInterpolate` for details).
+ :param borderType: pixel extrapolation method (see ``borderInterpolate`` for details).
The function computes the first x- or y- spatial image derivative using the Scharr operator. The call
filtering
geometric_transformations
miscellaneous_transformations
+ drawing_functions
colormaps
histograms
structural_analysis_and_shape_descriptors
namespace cv
{
-//! type of the kernel
-enum { KERNEL_GENERAL = 0, // the kernel is generic. No any type of symmetry or other properties.
- KERNEL_SYMMETRICAL = 1, // kernel[i] == kernel[ksize-i-1] , and the anchor is at the center
- KERNEL_ASYMMETRICAL = 2, // kernel[i] == -kernel[ksize-i-1] , and the anchor is at the center
- KERNEL_SMOOTH = 4, // all the kernel elements are non-negative and summed to 1
- KERNEL_INTEGER = 8 // all the kernel coefficients are integer numbers
- };
-
//! type of morphological operation
enum { MORPH_ERODE = 0,
MORPH_DILATE = 1,
INTERSECT_FULL = 2
};
-/*!
- The Base Class for 1D or Row-wise Filters
-
- This is the base class for linear or non-linear filters that process 1D data.
- In particular, such filters are used for the "horizontal" filtering parts in separable filters.
-
- Several functions in OpenCV return Ptr<BaseRowFilter> for the specific types of filters,
- and those pointers can be used directly or within cv::FilterEngine.
-*/
-class CV_EXPORTS BaseRowFilter
-{
-public:
- //! the default constructor
- BaseRowFilter();
- //! the destructor
- virtual ~BaseRowFilter();
- //! the filtering operator. Must be overrided in the derived classes. The horizontal border interpolation is done outside of the class.
- virtual void operator()(const uchar* src, uchar* dst, int width, int cn) = 0;
-
- int ksize;
- int anchor;
-};
-
-
-/*!
- The Base Class for Column-wise Filters
-
- This is the base class for linear or non-linear filters that process columns of 2D arrays.
- Such filters are used for the "vertical" filtering parts in separable filters.
-
- Several functions in OpenCV return Ptr<BaseColumnFilter> for the specific types of filters,
- and those pointers can be used directly or within cv::FilterEngine.
-
- Unlike cv::BaseRowFilter, cv::BaseColumnFilter may have some context information,
- i.e. box filter keeps the sliding sum of elements. To reset the state BaseColumnFilter::reset()
- must be called (e.g. the method is called by cv::FilterEngine)
- */
-class CV_EXPORTS BaseColumnFilter
-{
-public:
- //! the default constructor
- BaseColumnFilter();
- //! the destructor
- virtual ~BaseColumnFilter();
- //! the filtering operator. Must be overrided in the derived classes. The vertical border interpolation is done outside of the class.
- virtual void operator()(const uchar** src, uchar* dst, int dststep, int dstcount, int width) = 0;
- //! resets the internal buffers, if any
- virtual void reset();
-
- int ksize;
- int anchor;
-};
-
-
-/*!
- The Base Class for Non-Separable 2D Filters.
-
- This is the base class for linear or non-linear 2D filters.
-
- Several functions in OpenCV return Ptr<BaseFilter> for the specific types of filters,
- and those pointers can be used directly or within cv::FilterEngine.
-
- Similar to cv::BaseColumnFilter, the class may have some context information,
- that should be reset using BaseFilter::reset() method before processing the new array.
-*/
-class CV_EXPORTS BaseFilter
-{
-public:
- //! the default constructor
- BaseFilter();
- //! the destructor
- virtual ~BaseFilter();
- //! the filtering operator. The horizontal and the vertical border interpolation is done outside of the class.
- virtual void operator()(const uchar** src, uchar* dst, int dststep, int dstcount, int width, int cn) = 0;
- //! resets the internal buffers, if any
- virtual void reset();
-
- Size ksize;
- Point anchor;
-};
-
-
-/*!
- The Main Class for Image Filtering.
-
- The class can be used to apply an arbitrary filtering operation to an image.
- It contains all the necessary intermediate buffers, it computes extrapolated values
- of the "virtual" pixels outside of the image etc.
- Pointers to the initialized cv::FilterEngine instances
- are returned by various OpenCV functions, such as cv::createSeparableLinearFilter(),
- cv::createLinearFilter(), cv::createGaussianFilter(), cv::createDerivFilter(),
- cv::createBoxFilter() and cv::createMorphologyFilter().
-
- Using the class you can process large images by parts and build complex pipelines
- that include filtering as some of the stages. If all you need is to apply some pre-defined
- filtering operation, you may use cv::filter2D(), cv::erode(), cv::dilate() etc.
- functions that create FilterEngine internally.
-
- Here is the example on how to use the class to implement Laplacian operator, which is the sum of
- second-order derivatives. More complex variant for different types is implemented in cv::Laplacian().
-
- \code
- void laplace_f(const Mat& src, Mat& dst)
- {
- CV_Assert( src.type() == CV_32F );
- // make sure the destination array has the proper size and type
- dst.create(src.size(), src.type());
-
- // get the derivative and smooth kernels for d2I/dx2.
- // for d2I/dy2 we could use the same kernels, just swapped
- Mat kd, ks;
- getSobelKernels( kd, ks, 2, 0, ksize, false, ktype );
-
- // let's process 10 source rows at once
- int DELTA = std::min(10, src.rows);
- Ptr<FilterEngine> Fxx = createSeparableLinearFilter(src.type(),
- dst.type(), kd, ks, Point(-1,-1), 0, borderType, borderType, Scalar() );
- Ptr<FilterEngine> Fyy = createSeparableLinearFilter(src.type(),
- dst.type(), ks, kd, Point(-1,-1), 0, borderType, borderType, Scalar() );
-
- int y = Fxx->start(src), dsty = 0, dy = 0;
- Fyy->start(src);
- const uchar* sptr = src.data + y*src.step;
-
- // allocate the buffers for the spatial image derivatives;
- // the buffers need to have more than DELTA rows, because at the
- // last iteration the output may take max(kd.rows-1,ks.rows-1)
- // rows more than the input.
- Mat Ixx( DELTA + kd.rows - 1, src.cols, dst.type() );
- Mat Iyy( DELTA + kd.rows - 1, src.cols, dst.type() );
-
- // inside the loop we always pass DELTA rows to the filter
- // (note that the "proceed" method takes care of possibe overflow, since
- // it was given the actual image height in the "start" method)
- // on output we can get:
- // * < DELTA rows (the initial buffer accumulation stage)
- // * = DELTA rows (settled state in the middle)
- // * > DELTA rows (then the input image is over, but we generate
- // "virtual" rows using the border mode and filter them)
- // this variable number of output rows is dy.
- // dsty is the current output row.
- // sptr is the pointer to the first input row in the portion to process
- for( ; dsty < dst.rows; sptr += DELTA*src.step, dsty += dy )
- {
- Fxx->proceed( sptr, (int)src.step, DELTA, Ixx.data, (int)Ixx.step );
- dy = Fyy->proceed( sptr, (int)src.step, DELTA, d2y.data, (int)Iyy.step );
- if( dy > 0 )
- {
- Mat dstripe = dst.rowRange(dsty, dsty + dy);
- add(Ixx.rowRange(0, dy), Iyy.rowRange(0, dy), dstripe);
- }
- }
- }
- \endcode
-*/
-class CV_EXPORTS FilterEngine
-{
-public:
- //! the default constructor
- FilterEngine();
- //! the full constructor. Either _filter2D or both _rowFilter and _columnFilter must be non-empty.
- FilterEngine(const Ptr<BaseFilter>& _filter2D,
- const Ptr<BaseRowFilter>& _rowFilter,
- const Ptr<BaseColumnFilter>& _columnFilter,
- int srcType, int dstType, int bufType,
- int _rowBorderType = BORDER_REPLICATE,
- int _columnBorderType = -1,
- const Scalar& _borderValue = Scalar());
- //! the destructor
- virtual ~FilterEngine();
- //! reinitializes the engine. The previously assigned filters are released.
- void init(const Ptr<BaseFilter>& _filter2D,
- const Ptr<BaseRowFilter>& _rowFilter,
- const Ptr<BaseColumnFilter>& _columnFilter,
- int srcType, int dstType, int bufType,
- int _rowBorderType = BORDER_REPLICATE,
- int _columnBorderType = -1,
- const Scalar& _borderValue = Scalar());
- //! starts filtering of the specified ROI of an image of size wholeSize.
- virtual int start(Size wholeSize, Rect roi, int maxBufRows = -1);
- //! starts filtering of the specified ROI of the specified image.
- virtual int start(const Mat& src, const Rect& srcRoi = Rect(0,0,-1,-1),
- bool isolated = false, int maxBufRows = -1);
- //! processes the next srcCount rows of the image.
- virtual int proceed(const uchar* src, int srcStep, int srcCount,
- uchar* dst, int dstStep);
- //! applies filter to the specified ROI of the image. if srcRoi=(0,0,-1,-1), the whole image is filtered.
- virtual void apply( const Mat& src, Mat& dst,
- const Rect& srcRoi = Rect(0,0,-1,-1),
- Point dstOfs = Point(0,0),
- bool isolated = false);
- //! returns true if the filter is separable
- bool isSeparable() const { return !filter2D; }
- //! returns the number
- int remainingInputRows() const;
- int remainingOutputRows() const;
-
- int srcType;
- int dstType;
- int bufType;
- Size ksize;
- Point anchor;
- int maxWidth;
- Size wholeSize;
- Rect roi;
- int dx1;
- int dx2;
- int rowBorderType;
- int columnBorderType;
- std::vector<int> borderTab;
- int borderElemSize;
- std::vector<uchar> ringBuf;
- std::vector<uchar> srcRow;
- std::vector<uchar> constBorderValue;
- std::vector<uchar> constBorderRow;
- int bufStep;
- int startY;
- int startY0;
- int endY;
- int rowCount;
- int dstY;
- std::vector<uchar*> rows;
-
- Ptr<BaseFilter> filter2D;
- Ptr<BaseRowFilter> rowFilter;
- Ptr<BaseColumnFilter> columnFilter;
-};
-
-
//! finds arbitrary template in the grayscale image using Generalized Hough Transform
class CV_EXPORTS GeneralizedHough : public Algorithm
{
double _sigma_scale = 0.6, double _quant = 2.0, double _ang_th = 22.5,
double _log_eps = 0, double _density_th = 0.7, int _n_bins = 1024);
-//! returns type (one of KERNEL_*) of 1D or 2D kernel specified by its coefficients.
-CV_EXPORTS int getKernelType(InputArray kernel, Point anchor);
-
-//! returns the primitive row filter with the specified kernel
-CV_EXPORTS Ptr<BaseRowFilter> getLinearRowFilter(int srcType, int bufType,
- InputArray kernel, int anchor,
- int symmetryType);
-
-//! returns the primitive column filter with the specified kernel
-CV_EXPORTS Ptr<BaseColumnFilter> getLinearColumnFilter(int bufType, int dstType,
- InputArray kernel, int anchor,
- int symmetryType, double delta = 0,
- int bits = 0);
-
-//! returns 2D filter with the specified kernel
-CV_EXPORTS Ptr<BaseFilter> getLinearFilter(int srcType, int dstType,
- InputArray kernel,
- Point anchor = Point(-1,-1),
- double delta = 0, int bits = 0);
-
-//! returns the separable linear filter engine
-CV_EXPORTS Ptr<FilterEngine> createSeparableLinearFilter(int srcType, int dstType,
- InputArray rowKernel, InputArray columnKernel,
- Point anchor = Point(-1,-1), double delta = 0,
- int rowBorderType = BORDER_DEFAULT,
- int columnBorderType = -1,
- const Scalar& borderValue = Scalar());
-
-//! returns the non-separable linear filter engine
-CV_EXPORTS Ptr<FilterEngine> createLinearFilter(int srcType, int dstType,
- InputArray kernel, Point _anchor = Point(-1,-1),
- double delta = 0, int rowBorderType = BORDER_DEFAULT,
- int columnBorderType = -1, const Scalar& borderValue = Scalar());
-
//! returns the Gaussian kernel with the specified parameters
CV_EXPORTS_W Mat getGaussianKernel( int ksize, double sigma, int ktype = CV_64F );
-//! returns the Gaussian filter engine
-CV_EXPORTS Ptr<FilterEngine> createGaussianFilter( int type, Size ksize,
- double sigma1, double sigma2 = 0,
- int borderType = BORDER_DEFAULT);
-
//! initializes kernels of the generalized Sobel operator
CV_EXPORTS_W void getDerivKernels( OutputArray kx, OutputArray ky,
int dx, int dy, int ksize,
bool normalize = false, int ktype = CV_32F );
-//! returns filter engine for the generalized Sobel operator
-CV_EXPORTS Ptr<FilterEngine> createDerivFilter( int srcType, int dstType,
- int dx, int dy, int ksize,
- int borderType = BORDER_DEFAULT );
-
-//! returns horizontal 1D box filter
-CV_EXPORTS Ptr<BaseRowFilter> getRowSumFilter(int srcType, int sumType,
- int ksize, int anchor = -1);
-
-//! returns vertical 1D box filter
-CV_EXPORTS Ptr<BaseColumnFilter> getColumnSumFilter( int sumType, int dstType,
- int ksize, int anchor = -1,
- double scale = 1);
-//! returns box filter engine
-CV_EXPORTS Ptr<FilterEngine> createBoxFilter( int srcType, int dstType, Size ksize,
- Point anchor = Point(-1,-1),
- bool normalize = true,
- int borderType = BORDER_DEFAULT);
-
//! returns the Gabor kernel with the specified parameters
CV_EXPORTS_W Mat getGaborKernel( Size ksize, double sigma, double theta, double lambd,
double gamma, double psi = CV_PI*0.5, int ktype = CV_64F );
-//! returns horizontal 1D morphological filter
-CV_EXPORTS Ptr<BaseRowFilter> getMorphologyRowFilter(int op, int type, int ksize, int anchor = -1);
-
-//! returns vertical 1D morphological filter
-CV_EXPORTS Ptr<BaseColumnFilter> getMorphologyColumnFilter(int op, int type, int ksize, int anchor = -1);
-
-//! returns 2D morphological filter
-CV_EXPORTS Ptr<BaseFilter> getMorphologyFilter(int op, int type, InputArray kernel,
- Point anchor = Point(-1,-1));
-
//! returns "magic" border value for erosion and dilation. It is automatically transformed to Scalar::all(-DBL_MAX) for dilation.
static inline Scalar morphologyDefaultBorderValue() { return Scalar::all(DBL_MAX); }
-//! returns morphological filter engine. Only MORPH_ERODE and MORPH_DILATE are supported.
-CV_EXPORTS Ptr<FilterEngine> createMorphologyFilter(int op, int type, InputArray kernel,
- Point anchor = Point(-1,-1), int rowBorderType = BORDER_CONSTANT,
- int columnBorderType = -1, const Scalar& borderValue = morphologyDefaultBorderValue());
-
//! returns structuring element of the specified shape and size
CV_EXPORTS_W Mat getStructuringElement(int shape, Size ksize, Point anchor = Point(-1,-1));
CV_EXPORTS_W void applyColorMap(InputArray src, OutputArray dst, int colormap);
+
+//! draws the line segment (pt1, pt2) in the image
+CV_EXPORTS_W void line(InputOutputArray img, Point pt1, Point pt2, const Scalar& color,
+ int thickness = 1, int lineType = LINE_8, int shift = 0);
+
+//! draws an arrow from pt1 to pt2 in the image
+CV_EXPORTS_W void arrowedLine(InputOutputArray img, Point pt1, Point pt2, const Scalar& color,
+ int thickness=1, int line_type=8, int shift=0, double tipLength=0.1);
+
+//! draws the rectangle outline or a solid rectangle with the opposite corners pt1 and pt2 in the image
+CV_EXPORTS_W void rectangle(InputOutputArray img, Point pt1, Point pt2,
+ const Scalar& color, int thickness = 1,
+ int lineType = LINE_8, int shift = 0);
+
+//! draws the rectangle outline or a solid rectangle covering rec in the image
+CV_EXPORTS void rectangle(CV_IN_OUT Mat& img, Rect rec,
+ const Scalar& color, int thickness = 1,
+ int lineType = LINE_8, int shift = 0);
+
+//! draws the circle outline or a solid circle in the image
+CV_EXPORTS_W void circle(InputOutputArray img, Point center, int radius,
+ const Scalar& color, int thickness = 1,
+ int lineType = LINE_8, int shift = 0);
+
+//! draws an elliptic arc, ellipse sector or a rotated ellipse in the image
+CV_EXPORTS_W void ellipse(InputOutputArray img, Point center, Size axes,
+ double angle, double startAngle, double endAngle,
+ const Scalar& color, int thickness = 1,
+ int lineType = LINE_8, int shift = 0);
+
+//! draws a rotated ellipse in the image
+CV_EXPORTS_W void ellipse(InputOutputArray img, const RotatedRect& box, const Scalar& color,
+ int thickness = 1, int lineType = LINE_8);
+
+//! draws a filled convex polygon in the image
+CV_EXPORTS void fillConvexPoly(Mat& img, const Point* pts, int npts,
+ const Scalar& color, int lineType = LINE_8,
+ int shift = 0);
+
+CV_EXPORTS_W void fillConvexPoly(InputOutputArray img, InputArray points,
+ const Scalar& color, int lineType = LINE_8,
+ int shift = 0);
+
+//! fills an area bounded by one or more polygons
+CV_EXPORTS void fillPoly(Mat& img, const Point** pts,
+ const int* npts, int ncontours,
+ const Scalar& color, int lineType = LINE_8, int shift = 0,
+ Point offset = Point() );
+
+CV_EXPORTS_W void fillPoly(InputOutputArray img, InputArrayOfArrays pts,
+ const Scalar& color, int lineType = LINE_8, int shift = 0,
+ Point offset = Point() );
+
+//! draws one or more polygonal curves
+CV_EXPORTS void polylines(Mat& img, const Point* const* pts, const int* npts,
+ int ncontours, bool isClosed, const Scalar& color,
+ int thickness = 1, int lineType = LINE_8, int shift = 0 );
+
+CV_EXPORTS_W void polylines(InputOutputArray img, InputArrayOfArrays pts,
+ bool isClosed, const Scalar& color,
+ int thickness = 1, int lineType = LINE_8, int shift = 0 );
+
+//! draws contours in the image
+CV_EXPORTS_W void drawContours( InputOutputArray image, InputArrayOfArrays contours,
+ int contourIdx, const Scalar& color,
+ int thickness = 1, int lineType = LINE_8,
+ InputArray hierarchy = noArray(),
+ int maxLevel = INT_MAX, Point offset = Point() );
+
+//! clips the line segment by the rectangle Rect(0, 0, imgSize.width, imgSize.height)
+CV_EXPORTS bool clipLine(Size imgSize, CV_IN_OUT Point& pt1, CV_IN_OUT Point& pt2);
+
+//! clips the line segment by the rectangle imgRect
+CV_EXPORTS_W bool clipLine(Rect imgRect, CV_OUT CV_IN_OUT Point& pt1, CV_OUT CV_IN_OUT Point& pt2);
+
+//! converts elliptic arc to a polygonal curve
+CV_EXPORTS_W void ellipse2Poly( Point center, Size axes, int angle,
+ int arcStart, int arcEnd, int delta,
+ CV_OUT std::vector<Point>& pts );
+
+//! renders text string in the image
+CV_EXPORTS_W void putText( InputOutputArray img, const String& text, Point org,
+ int fontFace, double fontScale, Scalar color,
+ int thickness = 1, int lineType = LINE_8,
+ bool bottomLeftOrigin = false );
+
+//! returns bounding box of the text string
+CV_EXPORTS_W Size getTextSize(const String& text, int fontFace,
+ double fontScale, int thickness,
+ CV_OUT int* baseLine);
+
} // cv
#endif
CVAPI(void) cvFitLine( const CvArr* points, int dist_type, double param,
double reps, double aeps, float* line );
+/****************************************************************************************\
+* Drawing *
+\****************************************************************************************/
+
+/****************************************************************************************\
+* Drawing functions work with images/matrices of arbitrary type. *
+* For color images the channel order is BGR[A] *
+* Antialiasing is supported only for 8-bit image now. *
+* All the functions include parameter color that means rgb value (that may be *
+* constructed with CV_RGB macro) for color images and brightness *
+* for grayscale images. *
+* If a drawn figure is partially or completely outside of the image, it is clipped.*
+\****************************************************************************************/
+
+#define CV_RGB( r, g, b ) cvScalar( (b), (g), (r), 0 )
+#define CV_FILLED -1
+
+#define CV_AA 16
+
+/* Draws 4-connected, 8-connected or antialiased line segment connecting two points */
+CVAPI(void) cvLine( CvArr* img, CvPoint pt1, CvPoint pt2,
+ CvScalar color, int thickness CV_DEFAULT(1),
+ int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0) );
+
+/* Draws a rectangle given two opposite corners of the rectangle (pt1 & pt2),
+ if thickness<0 (e.g. thickness == CV_FILLED), the filled box is drawn */
+CVAPI(void) cvRectangle( CvArr* img, CvPoint pt1, CvPoint pt2,
+ CvScalar color, int thickness CV_DEFAULT(1),
+ int line_type CV_DEFAULT(8),
+ int shift CV_DEFAULT(0));
+
+/* Draws a rectangle specified by a CvRect structure */
+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,
+ CvScalar color, int thickness CV_DEFAULT(1),
+ int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0));
+
+/* Draws ellipse outline, filled ellipse, elliptic arc or filled elliptic sector,
+ depending on <thickness>, <start_angle> and <end_angle> parameters. The resultant figure
+ is rotated by <angle>. All the angles are in degrees */
+CVAPI(void) cvEllipse( CvArr* img, CvPoint center, CvSize axes,
+ double angle, double start_angle, double end_angle,
+ CvScalar color, int thickness CV_DEFAULT(1),
+ int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0));
+
+CV_INLINE void cvEllipseBox( CvArr* img, CvBox2D box, CvScalar color,
+ int thickness CV_DEFAULT(1),
+ int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0) )
+{
+ CvSize axes;
+ axes.width = cvRound(box.size.width*0.5);
+ axes.height = cvRound(box.size.height*0.5);
+
+ cvEllipse( img, cvPointFrom32f( box.center ), axes, box.angle,
+ 0, 360, color, thickness, line_type, shift );
+}
+
+/* Fills convex or monotonous polygon. */
+CVAPI(void) cvFillConvexPoly( CvArr* img, const CvPoint* pts, int npts, CvScalar color,
+ int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0));
+
+/* Fills an area bounded by one or more arbitrary polygons */
+CVAPI(void) cvFillPoly( CvArr* img, CvPoint** pts, const int* npts,
+ int contours, CvScalar color,
+ int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0) );
+
+/* Draws one or more polygonal curves */
+CVAPI(void) cvPolyLine( CvArr* img, CvPoint** pts, const int* npts, int contours,
+ int is_closed, CvScalar color, int thickness CV_DEFAULT(1),
+ int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0) );
+
+#define cvDrawRect cvRectangle
+#define cvDrawLine cvLine
+#define cvDrawCircle cvCircle
+#define cvDrawEllipse cvEllipse
+#define cvDrawPolyLine cvPolyLine
+
+/* Clips the line segment connecting *pt1 and *pt2
+ by the rectangular window
+ (0<=x<img_size.width, 0<=y<img_size.height). */
+CVAPI(int) cvClipLine( CvSize img_size, CvPoint* pt1, CvPoint* pt2 );
+
+/* Initializes line iterator. Initially, line_iterator->ptr will point
+ to pt1 (or pt2, see left_to_right description) location in the image.
+ Returns the number of pixels on the line between the ending points. */
+CVAPI(int) cvInitLineIterator( const CvArr* image, CvPoint pt1, CvPoint pt2,
+ CvLineIterator* line_iterator,
+ int connectivity CV_DEFAULT(8),
+ int left_to_right CV_DEFAULT(0));
+
+/* Moves iterator to the next line point */
+#define CV_NEXT_LINE_POINT( line_iterator ) \
+{ \
+ int _line_iterator_mask = (line_iterator).err < 0 ? -1 : 0; \
+ (line_iterator).err += (line_iterator).minus_delta + \
+ ((line_iterator).plus_delta & _line_iterator_mask); \
+ (line_iterator).ptr += (line_iterator).minus_step + \
+ ((line_iterator).plus_step & _line_iterator_mask); \
+}
+
+
+/* basic font types */
+#define CV_FONT_HERSHEY_SIMPLEX 0
+#define CV_FONT_HERSHEY_PLAIN 1
+#define CV_FONT_HERSHEY_DUPLEX 2
+#define CV_FONT_HERSHEY_COMPLEX 3
+#define CV_FONT_HERSHEY_TRIPLEX 4
+#define CV_FONT_HERSHEY_COMPLEX_SMALL 5
+#define CV_FONT_HERSHEY_SCRIPT_SIMPLEX 6
+#define CV_FONT_HERSHEY_SCRIPT_COMPLEX 7
+
+/* font flags */
+#define CV_FONT_ITALIC 16
+
+#define CV_FONT_VECTOR0 CV_FONT_HERSHEY_SIMPLEX
+
+
+/* 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 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
+}
+CvFont;
+
+/* Initializes font structure used further in cvPutText */
+CVAPI(void) cvInitFont( CvFont* font, int font_face,
+ double hscale, double vscale,
+ double shear CV_DEFAULT(0),
+ int thickness CV_DEFAULT(1),
+ int line_type CV_DEFAULT(8));
+
+CV_INLINE CvFont cvFont( double scale, int thickness CV_DEFAULT(1) )
+{
+ CvFont font;
+ cvInitFont( &font, CV_FONT_HERSHEY_PLAIN, scale, scale, 0, thickness, CV_AA );
+ return font;
+}
+
+/* Renders text stroke with specified font and color at specified location.
+ CvFont should be initialized with cvInitFont */
+CVAPI(void) cvPutText( CvArr* img, const char* text, CvPoint org,
+ const CvFont* font, CvScalar color );
+
+/* Calculates bounding box of text stroke (useful for alignment) */
+CVAPI(void) cvGetTextSize( const char* text_string, const CvFont* font,
+ CvSize* text_size, int* baseline );
+
+/* Unpacks color value, if arrtype is CV_8UC?, <color> is treated as
+ packed color value, otherwise the first channels (depending on arrtype)
+ of destination scalar are set to the same value = <color> */
+CVAPI(CvScalar) cvColorToScalar( double packed_color, int arrtype );
+
+/* Returns the polygon points which make up the given ellipse. The ellipse is define by
+ the box of size 'axes' rotated 'angle' around the 'center'. A partial sweep
+ of the ellipse arc can be done by spcifying arc_start and arc_end to be something
+ other than 0 and 360, respectively. The input array 'pts' must be large enough to
+ hold the result. The total number of points stored into 'pts' is returned by this
+ function. */
+CVAPI(int) cvEllipse2Poly( CvPoint center, CvSize axes,
+ int angle, int arc_start, int arc_end, CvPoint * pts, int delta );
+
+/* Draws contour outlines or filled interiors on the image */
+CVAPI(void) cvDrawContours( CvArr *img, CvSeq* contour,
+ CvScalar external_color, CvScalar hole_color,
+ int max_level, int thickness CV_DEFAULT(1),
+ int line_type CV_DEFAULT(8),
+ CvPoint offset CV_DEFAULT(cvPoint(0,0)));
+
#ifdef __cplusplus
}
#endif
case COLOR_BGR5552BGR: case COLOR_BGR5552RGB:
case COLOR_BGR5652BGR: case COLOR_BGR5652RGB:
case COLOR_YUV2RGB_UYVY: case COLOR_YUV2BGR_UYVY:
- case COLOR_YUV2RGBA_UYVY: case COLOR_YUV2BGRA_UYVY:
case COLOR_YUV2RGB_YUY2: case COLOR_YUV2BGR_YUY2:
case COLOR_YUV2RGB_YVYU: case COLOR_YUV2BGR_YVYU:
- case COLOR_YUV2RGBA_YUY2: case COLOR_YUV2BGRA_YUY2:
- case COLOR_YUV2RGBA_YVYU: case COLOR_YUV2BGRA_YVYU:
return ChPair(2,3);
case COLOR_BGR5552BGRA: case COLOR_BGR5552RGBA:
case COLOR_BGR5652BGRA: case COLOR_BGR5652RGBA:
+ case COLOR_YUV2RGBA_UYVY: case COLOR_YUV2BGRA_UYVY:
+ case COLOR_YUV2RGBA_YUY2: case COLOR_YUV2BGRA_YUY2:
+ case COLOR_YUV2RGBA_YVYU: case COLOR_YUV2BGRA_YVYU:
return ChPair(2,4);
case COLOR_BGR2GRAY: case COLOR_RGB2GRAY:
case COLOR_RGB2YUV_IYUV: case COLOR_RGB2YUV_YV12:
size.width *= scn;
if (mask.empty())
- status = ippFunc(src.data, srcstep, (Ipp32f *)dst.data, dststep, ippiSize(size.width, size.height));
+ status = ippFunc(src.ptr(), srcstep, dst.ptr<Ipp32f>(), dststep, ippiSize(size.width, size.height));
else
- status = ippFuncMask(src.data, srcstep, (const Ipp8u *)mask.data, maskstep,
- (Ipp32f *)dst.data, dststep, ippiSize(size.width, size.height));
+ status = ippFuncMask(src.ptr(), srcstep, mask.ptr<Ipp8u>(), maskstep,
+ dst.ptr<Ipp32f>(), dststep, ippiSize(size.width, size.height));
if (status >= 0)
return;
size.width *= scn;
if (mask.empty())
- status = ippFunc(src.data, srcstep, (Ipp32f *)dst.data, dststep, ippiSize(size.width, size.height));
+ status = ippFunc(src.ptr(), srcstep, dst.ptr<Ipp32f>(), dststep, ippiSize(size.width, size.height));
else
- status = ippFuncMask(src.data, srcstep, (const Ipp8u *)mask.data, maskstep,
- (Ipp32f *)dst.data, dststep, ippiSize(size.width, size.height));
+ status = ippFuncMask(src.ptr(), srcstep, mask.ptr<Ipp8u>(), maskstep,
+ dst.ptr<Ipp32f>(), dststep, ippiSize(size.width, size.height));
if (status >= 0)
return;
size.width *= scn;
if (mask.empty())
- status = ippFunc(src1.data, src1step, src2.data, src2step, (Ipp32f *)dst.data,
+ status = ippFunc(src1.ptr(), src1step, src2.ptr(), src2step, dst.ptr<Ipp32f>(),
dststep, ippiSize(size.width, size.height));
else
- status = ippFuncMask(src1.data, src1step, src2.data, src2step, (const Ipp8u *)mask.data, maskstep,
- (Ipp32f *)dst.data, dststep, ippiSize(size.width, size.height));
+ status = ippFuncMask(src1.ptr(), src1step, src2.ptr(), src2step, mask.ptr<Ipp8u>(), maskstep,
+ dst.ptr<Ipp32f>(), dststep, ippiSize(size.width, size.height));
if (status >= 0)
return;
size.width *= scn;
if (mask.empty())
- status = ippFunc(src.data, srcstep, (Ipp32f *)dst.data, dststep, ippiSize(size.width, size.height), (Ipp32f)alpha);
+ status = ippFunc(src.ptr(), srcstep, dst.ptr<Ipp32f>(), dststep, ippiSize(size.width, size.height), (Ipp32f)alpha);
else
- status = ippFuncMask(src.data, srcstep, (const Ipp8u *)mask.data, maskstep,
- (Ipp32f *)dst.data, dststep, ippiSize(size.width, size.height), (Ipp32f)alpha);
+ status = ippFuncMask(src.ptr(), srcstep, mask.ptr<Ipp8u>(), maskstep,
+ dst.ptr<Ipp32f>(), dststep, ippiSize(size.width, size.height), (Ipp32f)alpha);
if (status >= 0)
return;
uchar* buffer = alignPtr((uchar*)buf, 32);
Mat _dx(_src.rows, _src.cols, CV_16S);
- if( ippiFilterSobelNegVertBorder_8u16s_C1R(_src.data, (int)_src.step,
+ if( ippiFilterSobelNegVertBorder_8u16s_C1R(_src.ptr(), (int)_src.step,
_dx.ptr<short>(), (int)_dx.step, roi,
ippMskSize3x3, ippBorderRepl, 0, buffer) < 0 )
return false;
Mat _dy(_src.rows, _src.cols, CV_16S);
- if( ippiFilterSobelHorizBorder_8u16s_C1R(_src.data, (int)_src.step,
+ if( ippiFilterSobelHorizBorder_8u16s_C1R(_src.ptr(), (int)_src.step,
_dy.ptr<short>(), (int)_dy.step, roi,
ippMskSize3x3, ippBorderRepl, 0, buffer) < 0 )
return false;
if( ippiCanny_16s8u_C1R(_dx.ptr<short>(), (int)_dx.step,
_dy.ptr<short>(), (int)_dy.step,
- _dst.data, (int)_dst.step, roi, low, high, buffer) < 0 )
+ _dst.ptr(), (int)_dst.step, roi, low, high, buffer) < 0 )
return false;
return true;
}
const void* srcarray[3] = { src, src, src };
Mat temp(rows, cols, CV_MAKETYPE(depth, 3));
- if(func1(srcarray, srcStep, temp.data, (int)temp.step[0], ippiSize(cols, rows)) < 0)
+ if(func1(srcarray, srcStep, temp.ptr(), (int)temp.step[0], ippiSize(cols, rows)) < 0)
return false;
int order[4] = {0, 1, 2, 3};
- return func2(temp.data, (int)temp.step[0], dst, dstStep, ippiSize(cols, rows), order) >= 0;
+ return func2(temp.ptr(), (int)temp.step[0], dst, dstStep, ippiSize(cols, rows), order) >= 0;
}
private:
ippiGeneralFunc func1;
Mat temp;
temp.create(rows, cols, CV_MAKETYPE(depth, 3));
- if(func1(src, srcStep, temp.data, (int)temp.step[0], ippiSize(cols, rows), order) < 0)
+ if(func1(src, srcStep, temp.ptr(), (int)temp.step[0], ippiSize(cols, rows), order) < 0)
return false;
- return func2(temp.data, (int)temp.step[0], dst, dstStep, ippiSize(cols, rows)) >= 0;
+ return func2(temp.ptr(), (int)temp.step[0], dst, dstStep, ippiSize(cols, rows)) >= 0;
}
private:
ippiReorderFunc func1;
Mat temp;
temp.create(rows, cols, CV_MAKETYPE(depth, 3));
- if(func1(src, srcStep, temp.data, (int)temp.step[0], ippiSize(cols, rows)) < 0)
+ if(func1(src, srcStep, temp.ptr(), (int)temp.step[0], ippiSize(cols, rows)) < 0)
return false;
- return func2(temp.data, (int)temp.step[0], dst, dstStep, ippiSize(cols, rows), order) >= 0;
+ return func2(temp.ptr(), (int)temp.step[0], dst, dstStep, ippiSize(cols, rows), order) >= 0;
}
private:
ippiGeneralFunc func1;
LabelT lunique = 1;
//scanning phase
for(int r_i = 0; r_i < rows; ++r_i){
- LabelT *Lrow = (LabelT *)(L.data + L.step.p[0] * r_i);
- LabelT *Lrow_prev = (LabelT *)(((char *)Lrow) - L.step.p[0]);
- const PixelT *Irow = (PixelT *)(I.data + I.step.p[0] * r_i);
- const PixelT *Irow_prev = (const PixelT *)(((char *)Irow) - I.step.p[0]);
+ LabelT * const Lrow = L.ptr<LabelT>(r_i);
+ LabelT * const Lrow_prev = (LabelT *)(((char *)Lrow) - L.step.p[0]);
+ const PixelT * const Irow = I.ptr<PixelT>(r_i);
+ const PixelT * const Irow_prev = (const PixelT *)(((char *)Irow) - I.step.p[0]);
LabelT *Lrows[2] = {
Lrow,
Lrow_prev
sop.init(nLabels);
for(int r_i = 0; r_i < rows; ++r_i){
- LabelT *Lrow_start = (LabelT *)(L.data + L.step.p[0] * r_i);
+ LabelT *Lrow_start = L.ptr<LabelT>(r_i);
LabelT *Lrow_end = Lrow_start + cols;
LabelT *Lrow = Lrow_start;
for(int c_i = 0; Lrow != Lrow_end; ++Lrow, ++c_i){
_contours.create((int)c->total, 1, CV_32SC2, i, true);
Mat ci = _contours.getMat(i);
CV_Assert( ci.isContinuous() );
- cvCvtSeqToArray(c, ci.data);
+ cvCvtSeqToArray(c, ci.ptr());
}
if( _hierarchy.needed() )
AutoBuffer<int> _stack(total + 2), _hullbuf(total);
Point** pointer = _pointer;
Point2f** pointerf = (Point2f**)pointer;
- Point* data0 = (Point*)points.data;
+ Point* data0 = points.ptr<Point>();
int* stack = _stack;
int* hullbuf = _hullbuf;
Mat hull = _hull.getMat();
size_t step = !hull.isContinuous() ? hull.step[0] : sizeof(Point);
for( i = 0; i < nout; i++ )
- *(Point*)(hull.data + i*step) = data0[hullbuf[i]];
+ *(Point*)(hull.ptr() + i*step) = data0[hullbuf[i]];
}
}
int hpoints = hull.checkVector(1, CV_32S);
CV_Assert( hpoints > 2 );
- const Point* ptr = (const Point*)points.data;
+ const Point* ptr = points.ptr<Point>();
const int* hptr = hull.ptr<int>();
std::vector<Vec4i> defects;
return false;
return depth == CV_32S ?
- isContourConvex_((const Point*)contour.data, total ) :
- isContourConvex_((const Point2f*)contour.data, total );
+ isContourConvex_(contour.ptr<Point>(), total ) :
+ isContourConvex_(contour.ptr<Point2f>(), total );
}
}
}
}
else
- cvSeqPushMulti(hullseq, h0.data, (int)h0.total());
+ cvSeqPushMulti(hullseq, h0.ptr(), (int)h0.total());
if( mat )
{
for( i = 0; i < size.height; i++ )
{
- const float* cov = (const float*)(_cov.data + _cov.step*i);
- float* dst = (float*)(_dst.data + _dst.step*i);
+ const float* cov = _cov.ptr<float>(i);
+ float* dst = _dst.ptr<float>(i);
j = 0;
#if CV_SSE
if( simd )
for( i = 0; i < size.height; i++ )
{
- const float* cov = (const float*)(_cov.data + _cov.step*i);
- float* dst = (float*)(_dst.data + _dst.step*i);
+ const float* cov = _cov.ptr<float>(i);
+ float* dst = _dst.ptr<float>(i);
j = 0;
#if CV_SSE
for( int i = 0; i < size.height; i++ )
{
- const float* cov = (const float*)(_cov.data + _cov.step*i);
- float* dst = (float*)(_dst.data + _dst.step*i);
+ const float* cov = _cov.ptr<float>(i);
+ float* dst = _dst.ptr<float>(i);
eigen2x2(cov, dst, size.width);
}
for( i = 0; i < size.height; i++ )
{
- float* cov_data = (float*)(cov.data + i*cov.step);
- const float* dxdata = (const float*)(Dx.data + i*Dx.step);
- const float* dydata = (const float*)(Dy.data + i*Dy.step);
+ float* cov_data = cov.ptr<float>(i);
+ const float* dxdata = Dx.ptr<float>(i);
+ const float* dydata = Dy.ptr<float>(i);
for( j = 0; j < size.width; j++ )
{
if (ok >= 0)
{
AutoBuffer<uchar> buffer(bufferSize);
- ok = minEigenValFunc(src.data, (int) src.step, (Ipp32f*) dst.data, (int) dst.step, srcRoi, kerType, kerSize, blockSize, buffer);
+ ok = minEigenValFunc(src.ptr(), (int) src.step, dst.ptr<Ipp32f>(), (int) dst.step, srcRoi, kerType, kerSize, blockSize, buffer);
CV_SUPPRESS_DEPRECATED_START
- if (ok >= 0) ok = ippiMulC_32f_C1IR(norm_coef, (Ipp32f*) dst.data, (int) dst.step, srcRoi);
+ if (ok >= 0) ok = ippiMulC_32f_C1IR(norm_coef, dst.ptr<Ipp32f>(), (int) dst.step, srcRoi);
CV_SUPPRESS_DEPRECATED_END
if (ok >= 0)
return;
int i, j;
for( i = 0; i < size.height; i++ )
{
- float* dstdata = (float*)(dst.data + i*dst.step);
- const float* dxdata = (const float*)(Dx.data + i*Dx.step);
- const float* dydata = (const float*)(Dy.data + i*Dy.step);
- const float* d2xdata = (const float*)(D2x.data + i*D2x.step);
- const float* d2ydata = (const float*)(D2y.data + i*D2y.step);
- const float* dxydata = (const float*)(Dxy.data + i*Dxy.step);
+ float* dstdata = dst.ptr<float>(i);
+ const float* dxdata = Dx.ptr<float>(i);
+ const float* dydata = Dy.ptr<float>(i);
+ const float* d2xdata = D2x.ptr<float>(i);
+ const float* d2ydata = D2y.ptr<float>(i);
+ const float* dxydata = Dxy.ptr<float>(i);
j = 0;
cv::Mat src = _image.getMat(), cornersmat = _corners.getMat();
int count = cornersmat.checkVector(2, CV_32F);
CV_Assert( count >= 0 );
- Point2f* corners = (Point2f*)cornersmat.data;
+ Point2f* corners = cornersmat.ptr<Point2f>();
if( count == 0 )
return;
const int G2Y = 9617;
const int SHIFT = 14;
- const T* bayer0 = (const T*)srcmat.data;
+ const T* bayer0 = srcmat.ptr<T>();
int bayer_step = (int)(srcmat.step/sizeof(T));
T* dst0 = (T*)dstmat.data;
int dst_step = (int)(dstmat.step/sizeof(T));
}
size = dstmat.size();
- T* dst0 = (T*)dstmat.data;
+ T* dst0 = dstmat.ptr<T>();
int dst_step = (int)(dstmat.step/sizeof(T));
if( size.height > 2 )
for( int i = 0; i < size.width; i++ )
int dcn2 = dcn << 1;
int bayer_step = (int)(srcmat.step/sizeof(T));
- const T* bayer0 = reinterpret_cast<const T*>(srcmat.data) + bayer_step * range.start;
+ const T* bayer0 = srcmat.ptr<T>() + bayer_step * range.start;
int dst_step = (int)(dstmat.step/sizeof(T));
T* dst0 = reinterpret_cast<T*>(dstmat.data) + (range.start + 1) * dst_step + dcn + 1;
// filling the first and the last rows
size = dstmat.size();
- T* dst0 = (T*)dstmat.data;
+ T* dst0 = dstmat.ptr<T>();
if( size.height > 2 )
for( int i = 0; i < size.width*dcn; i++ )
{
static void Bayer2RGB_VNG_8u( const Mat& srcmat, Mat& dstmat, int code )
{
- const uchar* bayer = srcmat.data;
+ const uchar* bayer = srcmat.ptr();
int bstep = (int)srcmat.step;
- uchar* dst = dstmat.data;
+ uchar* dst = dstmat.ptr();
int dststep = (int)dstmat.step;
Size size = srcmat.size();
int sstep = int(src.step / src.elemSize1()), dstep = int(dst.step / dst.elemSize1());
SIMDInterpolator vecOp;
- const T* S = reinterpret_cast<const T*>(src.data + (range.start + 1) * src.step) + 1;
+ const T* S = src.ptr<T>(range.start + 1) + 1;
T* D = reinterpret_cast<T*>(dst.data + (range.start + 1) * dst.step) + dcn;
if (range.start % 2)
size = dst.size();
size.width *= dst.channels();
size_t dstep = dst.step / dst.elemSize1();
- T* firstRow = reinterpret_cast<T*>(dst.data);
- T* lastRow = reinterpret_cast<T*>(dst.data) + (size.height-1) * dstep;
+ T* firstRow = dst.ptr<T>();
+ T* lastRow = dst.ptr<T>() + (size.height-1) * dstep;
if (size.height > 2)
{
pBuffer = ippsMalloc_8u(bufferSize);
if (NULL == pBuffer)
IPP_RETURN_ERROR
- sts = ippiFilterScharrHorizMaskBorder_8u16s_C1R(src.data, (int)src.step, (Ipp16s *)dst.data, (int)dst.step, roiSize, ippMskSize3x3, ippiBorderType, 0, pBuffer);
+ sts = ippiFilterScharrHorizMaskBorder_8u16s_C1R(src.ptr(), (int)src.step, dst.ptr<Ipp16s>(), (int)dst.step, roiSize, ippMskSize3x3, ippiBorderType, 0, pBuffer);
}
else
{
pBuffer = ippsMalloc_8u(bufferSize);
if (NULL == pBuffer)
IPP_RETURN_ERROR
- sts = ippiFilterScharrVertMaskBorder_8u16s_C1R(src.data, (int)src.step, (Ipp16s *)dst.data, (int)dst.step, roiSize, ippMskSize3x3, ippiBorderType, 0, pBuffer);
+ sts = ippiFilterScharrVertMaskBorder_8u16s_C1R(src.ptr(), (int)src.step, dst.ptr<Ipp16s>(), (int)dst.step, roiSize, ippMskSize3x3, ippiBorderType, 0, pBuffer);
}
ippsFree(pBuffer);
}
pBuffer = ippsMalloc_8u(bufferSize);
if (NULL == pBuffer)
IPP_RETURN_ERROR
- sts = ippiFilterScharrHorizMaskBorder_16s_C1R((Ipp16s *)src.data, (int)src.step, (Ipp16s *)dst.data, (int)dst.step, roiSize, ippMskSize3x3, ippiBorderType, 0, pBuffer);
+ sts = ippiFilterScharrHorizMaskBorder_16s_C1R(src.ptr<Ipp16s>(), (int)src.step, dst.ptr<Ipp16s>(), (int)dst.step, roiSize, ippMskSize3x3, ippiBorderType, 0, pBuffer);
}
else
{
pBuffer = ippsMalloc_8u(bufferSize);
if (NULL == pBuffer)
IPP_RETURN_ERROR
- sts = ippiFilterScharrVertMaskBorder_16s_C1R((Ipp16s *)src.data, (int)src.step, (Ipp16s *)dst.data, (int)dst.step, roiSize, ippMskSize3x3, ippiBorderType, 0, pBuffer);
+ sts = ippiFilterScharrVertMaskBorder_16s_C1R(src.ptr<Ipp16s>(), (int)src.step, dst.ptr<Ipp16s>(), (int)dst.step, roiSize, ippMskSize3x3, ippiBorderType, 0, pBuffer);
}
ippsFree(pBuffer);
}
pBuffer = ippsMalloc_8u(bufferSize);
if (NULL == pBuffer)
IPP_RETURN_ERROR
- sts = ippiFilterScharrHorizMaskBorder_32f_C1R((Ipp32f *)src.data, (int)src.step, (Ipp32f *)dst.data, (int)dst.step, roiSize, ippMskSize3x3, ippiBorderType, 0, pBuffer);
+ sts = ippiFilterScharrHorizMaskBorder_32f_C1R(src.ptr<Ipp32f>(), (int)src.step, dst.ptr<Ipp32f>(), (int)dst.step, roiSize, ippMskSize3x3, ippiBorderType, 0, pBuffer);
}
else
{
pBuffer = ippsMalloc_8u(bufferSize);
if (NULL == pBuffer)
IPP_RETURN_ERROR
- sts = ippiFilterScharrVertMaskBorder_32f_C1R((Ipp32f *)src.data, (int)src.step, (Ipp32f *)dst.data, (int)dst.step, roiSize, ippMskSize3x3, ippiBorderType, 0, pBuffer);
+ sts = ippiFilterScharrVertMaskBorder_32f_C1R(src.ptr<Ipp32f>(), (int)src.step, dst.ptr<Ipp32f>(), (int)dst.step, roiSize, ippMskSize3x3, ippiBorderType, 0, pBuffer);
}
ippsFree(pBuffer);
if (sts < 0)
IPP_RETURN_ERROR;
if (FLT_EPSILON < fabs(scale - 1.0))
- sts = ippiMulC_32f_C1R((Ipp32f *)dst.data, (int)dst.step, (Ipp32f)scale, (Ipp32f *)dst.data, (int)dst.step, roiSize);
+ sts = ippiMulC_32f_C1R(dst.ptr<Ipp32f>(), (int)dst.step, (Ipp32f)scale, dst.ptr<Ipp32f>(), (int)dst.step, roiSize);
}
return (0 <= sts);
}
if (0 > ippiFilterScharrVertGetBufferSize_8u16s_C1R(roi,&bufSize))
return false;
buffer.allocate(bufSize);
- return (0 <= ippiFilterScharrVertBorder_8u16s_C1R((const Ipp8u*)src.data, (int)src.step,
- (Ipp16s*)dst.data, (int)dst.step, roi, ippBorderRepl, 0, (Ipp8u*)(char*)buffer));
+ return (0 <= ippiFilterScharrVertBorder_8u16s_C1R(src.ptr<Ipp8u>(), (int)src.step,
+ dst.ptr<Ipp16s>(), (int)dst.step, roi, ippBorderRepl, 0, (Ipp8u*)(char*)buffer));
}
if ((dx == 0) && (dy == 1))
{
if (0 > ippiFilterScharrHorizGetBufferSize_8u16s_C1R(roi,&bufSize))
return false;
buffer.allocate(bufSize);
- return (0 <= ippiFilterScharrHorizBorder_8u16s_C1R((const Ipp8u*)src.data, (int)src.step,
- (Ipp16s*)dst.data, (int)dst.step, roi, ippBorderRepl, 0, (Ipp8u*)(char*)buffer));
+ return (0 <= ippiFilterScharrHorizBorder_8u16s_C1R(src.ptr<Ipp8u>(), (int)src.step,
+ dst.ptr<Ipp16s>(), (int)dst.step, roi, ippBorderRepl, 0, (Ipp8u*)(char*)buffer));
}
return false;
}
return false;
buffer.allocate(bufSize);
- if (0 > ippiFilterScharrVertBorder_32f_C1R((const Ipp32f*)src.data, (int)src.step,
- (Ipp32f*)dst.data, (int)dst.step, ippiSize(src.cols, src.rows),
+ if (0 > ippiFilterScharrVertBorder_32f_C1R(src.ptr<Ipp32f>(), (int)src.step,
+ dst.ptr<Ipp32f>(), (int)dst.step, ippiSize(src.cols, src.rows),
ippBorderRepl, 0, (Ipp8u*)(char*)buffer))
{
return false;
if (scale != 1)
/* IPP is fast, so MulC produce very little perf degradation.*/
- //ippiMulC_32f_C1IR((Ipp32f)scale, (Ipp32f*)dst.data, (int)dst.step, ippiSize(dst.cols*dst.channels(), dst.rows));
- ippiMulC_32f_C1R((Ipp32f*)dst.data, (int)dst.step, (Ipp32f)scale, (Ipp32f*)dst.data, (int)dst.step, ippiSize(dst.cols*dst.channels(), dst.rows));
+ //ippiMulC_32f_C1IR((Ipp32f)scale, dst.ptr<Ipp32f>(), (int)dst.step, ippiSize(dst.cols*dst.channels(), dst.rows));
+ ippiMulC_32f_C1R(dst.ptr<Ipp32f>(), (int)dst.step, (Ipp32f)scale, dst.ptr<Ipp32f>(), (int)dst.step, ippiSize(dst.cols*dst.channels(), dst.rows));
return true;
}
if ((dx == 0) && (dy == 1))
return false;
buffer.allocate(bufSize);
- if (0 > ippiFilterScharrHorizBorder_32f_C1R((const Ipp32f*)src.data, (int)src.step,
- (Ipp32f*)dst.data, (int)dst.step, ippiSize(src.cols, src.rows),
+ if (0 > ippiFilterScharrHorizBorder_32f_C1R(src.ptr<Ipp32f>(), (int)src.step,
+ dst.ptr<Ipp32f>(), (int)dst.step, ippiSize(src.cols, src.rows),
ippBorderRepl, 0, (Ipp8u*)(char*)buffer))
return false;
if (scale != 1)
- ippiMulC_32f_C1R((Ipp32f *)dst.data, (int)dst.step, (Ipp32f)scale, (Ipp32f *)dst.data, (int)dst.step, ippiSize(dst.cols*dst.channels(), dst.rows));
+ ippiMulC_32f_C1R(dst.ptr<Ipp32f>(), (int)dst.step, (Ipp32f)scale, dst.ptr<Ipp32f>(), (int)dst.step, ippiSize(dst.cols*dst.channels(), dst.rows));
return true;
}
}
IPP_RETURN_ERROR
buffer.allocate(bufSize);
- if (0 > ippiFilterSobelNegVertBorder_8u16s_C1R((const Ipp8u*)src.data, (int)src.step,
- (Ipp16s*)dst.data, (int)dst.step, ippiSize(src.cols, src.rows), (IppiMaskSize)(ksize*10+ksize),
+ if (0 > ippiFilterSobelNegVertBorder_8u16s_C1R(src.ptr<Ipp8u>(), (int)src.step,
+ dst.ptr<Ipp16s>(), (int)dst.step, ippiSize(src.cols, src.rows), (IppiMaskSize)(ksize*10+ksize),
ippBorderRepl, 0, (Ipp8u*)(char*)buffer))
IPP_RETURN_ERROR
return true;
IPP_RETURN_ERROR
buffer.allocate(bufSize);
- if (0 > ippiFilterSobelHorizBorder_8u16s_C1R((const Ipp8u*)src.data, (int)src.step,
- (Ipp16s*)dst.data, (int)dst.step, ippiSize(src.cols, src.rows), (IppiMaskSize)(ksize*10+ksize),
+ if (0 > ippiFilterSobelHorizBorder_8u16s_C1R(src.ptr<Ipp8u>(), (int)src.step,
+ dst.ptr<Ipp16s>(), (int)dst.step, ippiSize(src.cols, src.rows), (IppiMaskSize)(ksize*10+ksize),
ippBorderRepl, 0, (Ipp8u*)(char*)buffer))
IPP_RETURN_ERROR
return true;
IPP_RETURN_ERROR
buffer.allocate(bufSize);
- if (0 > ippiFilterSobelVertSecondBorder_8u16s_C1R((const Ipp8u*)src.data, (int)src.step,
- (Ipp16s*)dst.data, (int)dst.step, ippiSize(src.cols, src.rows), (IppiMaskSize)(ksize*10+ksize),
+ if (0 > ippiFilterSobelVertSecondBorder_8u16s_C1R(src.ptr<Ipp8u>(), (int)src.step,
+ dst.ptr<Ipp16s>(), (int)dst.step, ippiSize(src.cols, src.rows), (IppiMaskSize)(ksize*10+ksize),
ippBorderRepl, 0, (Ipp8u*)(char*)buffer))
IPP_RETURN_ERROR
return true;
IPP_RETURN_ERROR
buffer.allocate(bufSize);
- if (0 > ippiFilterSobelHorizSecondBorder_8u16s_C1R((const Ipp8u*)src.data, (int)src.step,
- (Ipp16s*)dst.data, (int)dst.step, ippiSize(src.cols, src.rows), (IppiMaskSize)(ksize*10+ksize),
+ if (0 > ippiFilterSobelHorizSecondBorder_8u16s_C1R(src.ptr<Ipp8u>(), (int)src.step,
+ dst.ptr<Ipp16s>(), (int)dst.step, ippiSize(src.cols, src.rows), (IppiMaskSize)(ksize*10+ksize),
ippBorderRepl, 0, (Ipp8u*)(char*)buffer))
IPP_RETURN_ERROR
return true;
IPP_RETURN_ERROR
buffer.allocate(bufSize);
- if (0 > ippiFilterSobelNegVertBorder_32f_C1R((const Ipp32f*)src.data, (int)src.step,
- (Ipp32f*)dst.data, (int)dst.step, ippiSize(src.cols, src.rows), (IppiMaskSize)(ksize*10+ksize),
+ if (0 > ippiFilterSobelNegVertBorder_32f_C1R(src.ptr<Ipp32f>(), (int)src.step,
+ dst.ptr<Ipp32f>(), (int)dst.step, ippiSize(src.cols, src.rows), (IppiMaskSize)(ksize*10+ksize),
ippBorderRepl, 0, (Ipp8u*)(char*)buffer))
IPP_RETURN_ERROR
if(scale != 1)
- ippiMulC_32f_C1R((Ipp32f *)dst.data, (int)dst.step, (Ipp32f)scale, (Ipp32f *)dst.data, (int)dst.step, ippiSize(dst.cols*dst.channels(), dst.rows));
+ ippiMulC_32f_C1R(dst.ptr<Ipp32f>(), (int)dst.step, (Ipp32f)scale, dst.ptr<Ipp32f>(), (int)dst.step, ippiSize(dst.cols*dst.channels(), dst.rows));
return true;
}
if (0 > ippiFilterSobelHorizGetBufferSize_32f_C1R(ippiSize(src.cols, src.rows), (IppiMaskSize)(ksize*10+ksize),&bufSize))
IPP_RETURN_ERROR
buffer.allocate(bufSize);
- if (0 > ippiFilterSobelHorizBorder_32f_C1R((const Ipp32f*)src.data, (int)src.step,
- (Ipp32f*)dst.data, (int)dst.step, ippiSize(src.cols, src.rows), (IppiMaskSize)(ksize*10+ksize),
+ if (0 > ippiFilterSobelHorizBorder_32f_C1R(src.ptr<Ipp32f>(), (int)src.step,
+ dst.ptr<Ipp32f>(), (int)dst.step, ippiSize(src.cols, src.rows), (IppiMaskSize)(ksize*10+ksize),
ippBorderRepl, 0, (Ipp8u*)(char*)buffer))
IPP_RETURN_ERROR
if(scale != 1)
- ippiMulC_32f_C1R((Ipp32f *)dst.data, (int)dst.step, (Ipp32f)scale, (Ipp32f *)dst.data, (int)dst.step, ippiSize(dst.cols*dst.channels(), dst.rows));
+ ippiMulC_32f_C1R(dst.ptr<Ipp32f>(), (int)dst.step, (Ipp32f)scale, dst.ptr<Ipp32f>(), (int)dst.step, ippiSize(dst.cols*dst.channels(), dst.rows));
return true;
}
#endif
IPP_RETURN_ERROR
buffer.allocate(bufSize);
- if (0 > ippiFilterSobelVertSecondBorder_32f_C1R((const Ipp32f*)src.data, (int)src.step,
- (Ipp32f*)dst.data, (int)dst.step, ippiSize(src.cols, src.rows), (IppiMaskSize)(ksize*10+ksize),
+ if (0 > ippiFilterSobelVertSecondBorder_32f_C1R(src.ptr<Ipp32f>(), (int)src.step,
+ dst.ptr<Ipp32f>(), (int)dst.step, ippiSize(src.cols, src.rows), (IppiMaskSize)(ksize*10+ksize),
ippBorderRepl, 0, (Ipp8u*)(char*)buffer))
IPP_RETURN_ERROR
if(scale != 1)
- ippiMulC_32f_C1R((Ipp32f *)dst.data, (int)dst.step, (Ipp32f)scale, (Ipp32f *)dst.data, (int)dst.step, ippiSize(dst.cols*dst.channels(), dst.rows));
+ ippiMulC_32f_C1R(dst.ptr<Ipp32f>(), (int)dst.step, (Ipp32f)scale, dst.ptr<Ipp32f>(), (int)dst.step, ippiSize(dst.cols*dst.channels(), dst.rows));
return true;
}
IPP_RETURN_ERROR
buffer.allocate(bufSize);
- if (0 > ippiFilterSobelHorizSecondBorder_32f_C1R((const Ipp32f*)src.data, (int)src.step,
- (Ipp32f*)dst.data, (int)dst.step, ippiSize(src.cols, src.rows), (IppiMaskSize)(ksize*10+ksize),
+ if (0 > ippiFilterSobelHorizSecondBorder_32f_C1R(src.ptr<Ipp32f>(), (int)src.step,
+ dst.ptr<Ipp32f>(), (int)dst.step, ippiSize(src.cols, src.rows), (IppiMaskSize)(ksize*10+ksize),
ippBorderRepl, 0, (Ipp8u*)(char*)buffer))
IPP_RETURN_ERROR
if(scale != 1)
- ippiMulC_32f_C1R((Ipp32f *)dst.data, (int)dst.step, (Ipp32f)scale, (Ipp32f *)dst.data, (int)dst.step, ippiSize(dst.cols*dst.channels(), dst.rows));
+ ippiMulC_32f_C1R(dst.ptr<Ipp32f>(), (int)dst.step, (Ipp32f)scale, dst.ptr<Ipp32f>(), (int)dst.step, ippiSize(dst.cols*dst.channels(), dst.rows));
return true;
}
#endif
if (borderTypeIpp >= 0 && ippiFilterLaplacianGetBufferSize_##ippfavor##_C1R(roisize, masksize, &bufsize) >= 0) \
{ \
Ipp8u * buffer = ippsMalloc_8u(bufsize); \
- status = ippiFilterLaplacianBorder_##ippfavor##_C1R((const ippsrctype *)src.data, (int)src.step, (ippdsttype *)dst.data, \
+ status = ippiFilterLaplacianBorder_##ippfavor##_C1R(src.ptr<ippsrctype>(), (int)src.step, dst.ptr<ippdsttype>(), \
(int)dst.step, roisize, masksize, borderTypeIpp, 0, buffer); \
ippsFree(buffer); \
} \
IPP_FILTER_LAPLACIAN(Ipp8u, Ipp16s, 8u16s);
if (needScale && status >= 0)
- status = ippiMulC_16s_C1IRSfs((Ipp16s)iscale, (Ipp16s *)dst.data, (int)dst.step, roisize, 0);
+ status = ippiMulC_16s_C1IRSfs((Ipp16s)iscale, dst.ptr<Ipp16s>(), (int)dst.step, roisize, 0);
if (needDelta && status >= 0)
- status = ippiAddC_16s_C1IRSfs((Ipp16s)idelta, (Ipp16s *)dst.data, (int)dst.step, roisize, 0);
+ status = ippiAddC_16s_C1IRSfs((Ipp16s)idelta, dst.ptr<Ipp16s>(), (int)dst.step, roisize, 0);
}
else if (sdepth == CV_32F && ddepth == CV_32F)
{
IPP_FILTER_LAPLACIAN(Ipp32f, Ipp32f, 32f);
if (needScale && status >= 0)
- status = ippiMulC_32f_C1IR((Ipp32f)scale, (Ipp32f *)dst.data, (int)dst.step, roisize);
+ status = ippiMulC_32f_C1IR((Ipp32f)scale, dst.ptr<Ipp32f>(), (int)dst.step, roisize);
if (needDelta && status >= 0)
- status = ippiAddC_32f_C1IR((Ipp32f)delta, (Ipp32f *)dst.data, (int)dst.step, roisize);
+ status = ippiAddC_32f_C1IR((Ipp32f)delta, dst.ptr<Ipp32f>(), (int)dst.step, roisize);
}
CV_SUPPRESS_DEPRECATED_END
Mat src = _src.getMat(), dst = _dst.getMat();
int y = fx->start(src), dsty = 0, dy = 0;
fy->start(src);
- const uchar* sptr = src.data + y*src.step;
+ const uchar* sptr = src.ptr(y);
int dy0 = std::min(std::max((int)(STRIPE_SIZE/(CV_ELEM_SIZE(stype)*src.cols)), 1), src.rows);
Mat d2x( dy0 + kd.rows - 1, src.cols, wtype );
for( ; dsty < src.rows; sptr += dy0*src.step, dsty += dy )
{
- fx->proceed( sptr, (int)src.step, dy0, d2x.data, (int)d2x.step );
- dy = fy->proceed( sptr, (int)src.step, dy0, d2y.data, (int)d2y.step );
+ fx->proceed( sptr, (int)src.step, dy0, d2x.ptr(), (int)d2x.step );
+ dy = fy->proceed( sptr, (int)src.step, dy0, d2y.ptr(), (int)d2y.step );
if( dy > 0 )
{
Mat dstripe = dst.rowRange(dsty, dsty + dy);
const int DIAG_DIST = CV_FLT_TO_FIX( metrics[1], DIST_SHIFT );
const float scale = 1.f/(1 << DIST_SHIFT);
- const uchar* src = _src.data;
+ const uchar* src = _src.ptr();
int* temp = _temp.ptr<int>();
float* dist = _dist.ptr<float>();
int srcstep = (int)(_src.step/sizeof(src[0]));
const int LONG_DIST = CV_FLT_TO_FIX( metrics[2], DIST_SHIFT );
const float scale = 1.f/(1 << DIST_SHIFT);
- const uchar* src = _src.data;
+ const uchar* src = _src.ptr();
int* temp = _temp.ptr<int>();
float* dist = _dist.ptr<float>();
int srcstep = (int)(_src.step/sizeof(src[0]));
const int LONG_DIST = CV_FLT_TO_FIX( metrics[2], DIST_SHIFT );
const float scale = 1.f/(1 << DIST_SHIFT);
- const uchar* src = _src.data;
+ const uchar* src = _src.ptr();
int* temp = _temp.ptr<int>();
float* dist = _dist.ptr<float>();
int* labels = _labels.ptr<int>();
uchar lut[256];
int x, y;
- const uchar *sbase = src.data;
- uchar *dbase = dst.data;
+ const uchar *sbase = src.ptr();
+ uchar *dbase = dst.ptr();
int srcstep = (int)src.step;
int dststep = (int)dst.step;
count = dx + dy + 1;
}
- this->ptr0 = img.data;
+ this->ptr0 = img.ptr();
this->step = (int)img.step;
this->elemSize = bt_pix0;
}
int cb = ((uchar*)color)[0], cg = ((uchar*)color)[1], cr = ((uchar*)color)[2];
int _cb, _cg, _cr;
int nch = img.channels();
- uchar* ptr = img.data;
+ uchar* ptr = img.ptr();
size_t step = img.step;
Size size = img.size();
int cg = ((uchar*)color)[1];
int cr = ((uchar*)color)[2];
int pix_size = (int)img.elemSize();
- uchar *ptr = img.data, *tptr;
+ uchar *ptr = img.ptr(), *tptr;
size_t step = img.step;
Size size = img.size(), sizeScaled(size.width*XY_ONE, size.height*XY_ONE);
int i, y, imin = 0, left = 0, right = 1, x1, x2;
int edges = npts;
int xmin, xmax, ymin, ymax;
- uchar* ptr = img.data;
+ uchar* ptr = img.ptr();
Size size = img.size();
int pix_size = (int)img.elemSize();
Point p0;
if( !clipline )
{
// convert x's from fixed-point to image coordinates
- uchar *timg = img.data + y * img.step;
+ uchar *timg = img.ptr(y);
int x1 = keep_prelast->x;
int x2 = prelast->x;
Size size = img.size();
size_t step = img.step;
int pix_size = (int)img.elemSize();
- uchar* ptr = img.data;
+ uchar* ptr = img.ptr();
int err = 0, dx = radius, dy = 0, plus = 1, minus = (radius << 1) - 1;
int inside = center.x >= radius && center.x < size.width - radius &&
center.y >= radius && center.y < size.height - radius;
return ascii;
}
+extern const char* g_HersheyGlyphs[];
void putText( InputOutputArray _img, const String& text, Point org,
int fontFace, double fontScale, Scalar color,
{
Mat img = _img.getMat(), points = _points.getMat();
CV_Assert(points.checkVector(2, CV_32S) >= 0);
- fillConvexPoly(img, (const Point*)points.data, points.rows*points.cols*points.channels()/2, color, lineType, shift);
+ fillConvexPoly(img, points.ptr<Point>(), points.rows*points.cols*points.channels()/2, color, lineType, shift);
}
{
Mat p = pts.getMat(i);
CV_Assert(p.checkVector(2, CV_32S) >= 0);
- ptsptr[i] = (Point*)p.data;
+ ptsptr[i] = p.ptr<Point>();
npts[i] = p.rows*p.cols*p.channels()/2;
}
fillPoly(img, (const Point**)ptsptr, npts, (int)ncontours, color, lineType, shift, offset);
if( p.total() == 0 )
continue;
CV_Assert(p.checkVector(2, CV_32S) >= 0);
- ptsptr[i] = (Point*)p.data;
+ ptsptr[i] = p.ptr<Point>();
npts[i] = p.rows*p.cols*p.channels()/2;
}
polylines(img, (const Point**)ptsptr, npts, (int)ncontours, isClosed, color, thickness, lineType, shift);
{
Mat ci = contours.getMat(i);
cvMakeSeqHeaderForArray(CV_SEQ_POLYGON, sizeof(CvSeq), sizeof(Point),
- !ci.empty() ? (void*)ci.data : 0, (int)ci.total(),
+ !ci.empty() ? (void*)ci.ptr() : 0, (int)ci.total(),
&seq[i], &block[i] );
int h_next = hierarchy[i][0], h_prev = hierarchy[i][1],
int npoints = ci.checkVector(2, CV_32S);
CV_Assert( npoints > 0 );
cvMakeSeqHeaderForArray( CV_SEQ_POLYGON, sizeof(CvSeq), sizeof(Point),
- ci.data, npoints, &seq[i], &block[i] );
+ ci.ptr(), npoints, &seq[i], &block[i] );
}
if( hierarchy.empty() || maxLevel == 0 )
for( i = 0; i < total; i++ )
{
- int ofs = (int)((const uchar*)tmpCorners[i] - eig.data);
+ int ofs = (int)((const uchar*)tmpCorners[i] - eig.ptr());
int y = (int)(ofs / eig.step);
int x = (int)((ofs - y*eig.step)/sizeof(float));
{
for( i = 0; i < total; i++ )
{
- int ofs = (int)((const uchar*)tmpCorners[i] - eig.data);
+ int ofs = (int)((const uchar*)tmpCorners[i] - eig.ptr());
int y = (int)(ofs / eig.step);
int x = (int)((ofs - y*eig.step)/sizeof(float));
dstOfs.y + srcRoi.height <= dst.rows );
int y = start(src, srcRoi, isolated);
- proceed( src.data + y*src.step
- + srcRoi.x*src.elemSize(),
+ proceed( src.ptr() + y*src.step + srcRoi.x*src.elemSize(),
(int)src.step, endY - startY,
- dst.data + dstOfs.y*dst.step +
+ dst.ptr(dstOfs.y) +
dstOfs.x*dst.elemSize(), (int)dst.step );
}
Mat kernel;
_kernel.convertTo(kernel, CV_64F);
- const double* coeffs = (double*)kernel.data;
+ const double* coeffs = kernel.ptr<double>();
double sum = 0;
int type = KERNEL_SMOOTH + KERNEL_INTEGER;
if( (_kernel.rows == 1 || _kernel.cols == 1) &&
int k, ksize = kernel.rows + kernel.cols - 1;
for( k = 0; k < ksize; k++ )
{
- int v = ((const int*)kernel.data)[k];
+ int v = kernel.ptr<int>()[k];
if( v < SHRT_MIN || v > SHRT_MAX )
{
smallValues = false;
int i = 0, k, _ksize = kernel.rows + kernel.cols - 1;
int* dst = (int*)_dst;
- const int* _kx = (const int*)kernel.data;
+ const int* _kx = kernel.ptr<int>();
width *= cn;
if( smallValues )
int k, ksize = kernel.rows + kernel.cols - 1;
for( k = 0; k < ksize; k++ )
{
- int v = ((const int*)kernel.data)[k];
+ int v = kernel.ptr<int>()[k];
if( v < SHRT_MIN || v > SHRT_MAX )
{
smallValues = false;
int i = 0, j, k, _ksize = kernel.rows + kernel.cols - 1;
int* dst = (int*)_dst;
bool symmetrical = (symmetryType & KERNEL_SYMMETRICAL) != 0;
- const int* kx = (const int*)kernel.data + _ksize/2;
+ const int* kx = kernel.ptr<int>() + _ksize/2;
if( !smallValues )
return 0;
return 0;
int ksize2 = (kernel.rows + kernel.cols - 1)/2;
- const float* ky = (const float*)kernel.data + ksize2;
+ const float* ky = kernel.ptr<float>() + ksize2;
int i = 0, k;
bool symmetrical = (symmetryType & KERNEL_SYMMETRICAL) != 0;
const int** src = (const int**)_src;
return 0;
int ksize2 = (kernel.rows + kernel.cols - 1)/2;
- const float* ky = (const float*)kernel.data + ksize2;
+ const float* ky = kernel.ptr<float>() + ksize2;
int i = 0;
bool symmetrical = (symmetryType & KERNEL_SYMMETRICAL) != 0;
const int** src = (const int**)_src;
int i = 0, k, _ksize = kernel.rows + kernel.cols - 1;
float* dst = (float*)_dst;
- const float* _kx = (const float*)kernel.data;
+ const float* _kx = kernel.ptr<float>();
width *= cn;
for( ; i <= width - 8; i += 8 )
return 0;
int ksize2 = (kernel.rows + kernel.cols - 1)/2;
- const float* ky = (const float*)kernel.data + ksize2;
+ const float* ky = kernel.ptr<float>() + ksize2;
int i = 0, k;
bool symmetrical = (symmetryType & KERNEL_SYMMETRICAL) != 0;
const float** src = (const float**)_src;
int _ksize = kernel.rows + kernel.cols - 1;
const float* src0 = (const float*)_src;
float* dst = (float*)_dst;
- const float* _kx = (const float*)kernel.data;
+ const float* _kx = kernel.ptr<float>();
if( !haveSSE )
return 0;
float* dst = (float*)_dst;
const float* src = (const float*)_src + (_ksize/2)*cn;
bool symmetrical = (symmetryType & KERNEL_SYMMETRICAL) != 0;
- const float* kx = (const float*)kernel.data + _ksize/2;
+ const float* kx = kernel.ptr<float>() + _ksize/2;
width *= cn;
if( symmetrical )
return 0;
int ksize2 = (kernel.rows + kernel.cols - 1)/2;
- const float* ky = (const float*)kernel.data + ksize2;
+ const float* ky = kernel.ptr<float>() + ksize2;
int i = 0, k;
bool symmetrical = (symmetryType & KERNEL_SYMMETRICAL) != 0;
const float** src = (const float**)_src;
return 0;
int ksize2 = (kernel.rows + kernel.cols - 1)/2;
- const float* ky = (const float*)kernel.data + ksize2;
+ const float* ky = kernel.ptr<float>() + ksize2;
int i = 0;
bool symmetrical = (symmetryType & KERNEL_SYMMETRICAL) != 0;
const float** src = (const float**)_src;
void operator()(const uchar* src, uchar* dst, int width, int cn)
{
int _ksize = ksize;
- const DT* kx = (const DT*)kernel.data;
+ const DT* kx = kernel.ptr<DT>();
const ST* S;
DT* D = (DT*)dst;
int i, k;
void operator()(const uchar* src, uchar* dst, int width, int cn)
{
int ksize2 = this->ksize/2, ksize2n = ksize2*cn;
- const DT* kx = (const DT*)this->kernel.data + ksize2;
+ const DT* kx = this->kernel.template ptr<DT>() + ksize2;
bool symmetrical = (this->symmetryType & KERNEL_SYMMETRICAL) != 0;
DT* D = (DT*)dst;
int i = this->vecOp(src, dst, width, cn), j, k;
void operator()(const uchar** src, uchar* dst, int dststep, int count, int width)
{
- const ST* ky = (const ST*)kernel.data;
+ const ST* ky = kernel.template ptr<ST>();
ST _delta = delta;
int _ksize = ksize;
int i, k;
void operator()(const uchar** src, uchar* dst, int dststep, int count, int width)
{
int ksize2 = this->ksize/2;
- const ST* ky = (const ST*)this->kernel.data + ksize2;
+ const ST* ky = this->kernel.template ptr<ST>() + ksize2;
int i, k;
bool symmetrical = (symmetryType & KERNEL_SYMMETRICAL) != 0;
ST _delta = this->delta;
void operator()(const uchar** src, uchar* dst, int dststep, int count, int width)
{
int ksize2 = this->ksize/2;
- const ST* ky = (const ST*)this->kernel.data + ksize2;
+ const ST* ky = this->kernel.template ptr<ST>() + ksize2;
int i;
bool symmetrical = (this->symmetryType & KERNEL_SYMMETRICAL) != 0;
bool is_1_2_1 = ky[0] == 1 && ky[1] == 2;
for( i = k = 0; i < kernel.rows; i++ )
{
- const uchar* krow = kernel.data + kernel.step*i;
+ const uchar* krow = kernel.ptr(i);
for( j = 0; j < kernel.cols; j++ )
{
if( ktype == CV_8U )
--- /dev/null
+/*
+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
+ (3-clause BSD License)
+
+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.
+
+ * Neither the names of the copyright holders nor the names of the contributors
+ may 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 copyright holders 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.
+*/
+
+#ifndef __OPENCV_IMGPROC_FILTERENGINE_HPP__
+#define __OPENCV_IMGPROC_FILTERENGINE_HPP__
+
+namespace cv
+{
+
+//! type of the kernel
+enum
+{
+ KERNEL_GENERAL = 0, // the kernel is generic. No any type of symmetry or other properties.
+ KERNEL_SYMMETRICAL = 1, // kernel[i] == kernel[ksize-i-1] , and the anchor is at the center
+ KERNEL_ASYMMETRICAL = 2, // kernel[i] == -kernel[ksize-i-1] , and the anchor is at the center
+ KERNEL_SMOOTH = 4, // all the kernel elements are non-negative and summed to 1
+ KERNEL_INTEGER = 8 // all the kernel coefficients are integer numbers
+};
+
+/*!
+ The Base Class for 1D or Row-wise Filters
+
+ This is the base class for linear or non-linear filters that process 1D data.
+ In particular, such filters are used for the "horizontal" filtering parts in separable filters.
+
+ Several functions in OpenCV return Ptr<BaseRowFilter> for the specific types of filters,
+ and those pointers can be used directly or within cv::FilterEngine.
+*/
+class BaseRowFilter
+{
+public:
+ //! the default constructor
+ BaseRowFilter();
+ //! the destructor
+ virtual ~BaseRowFilter();
+ //! the filtering operator. Must be overrided in the derived classes. The horizontal border interpolation is done outside of the class.
+ virtual void operator()(const uchar* src, uchar* dst, int width, int cn) = 0;
+
+ int ksize;
+ int anchor;
+};
+
+
+/*!
+ The Base Class for Column-wise Filters
+
+ This is the base class for linear or non-linear filters that process columns of 2D arrays.
+ Such filters are used for the "vertical" filtering parts in separable filters.
+
+ Several functions in OpenCV return Ptr<BaseColumnFilter> for the specific types of filters,
+ and those pointers can be used directly or within cv::FilterEngine.
+
+ Unlike cv::BaseRowFilter, cv::BaseColumnFilter may have some context information,
+ i.e. box filter keeps the sliding sum of elements. To reset the state BaseColumnFilter::reset()
+ must be called (e.g. the method is called by cv::FilterEngine)
+ */
+class BaseColumnFilter
+{
+public:
+ //! the default constructor
+ BaseColumnFilter();
+ //! the destructor
+ virtual ~BaseColumnFilter();
+ //! the filtering operator. Must be overrided in the derived classes. The vertical border interpolation is done outside of the class.
+ virtual void operator()(const uchar** src, uchar* dst, int dststep, int dstcount, int width) = 0;
+ //! resets the internal buffers, if any
+ virtual void reset();
+
+ int ksize;
+ int anchor;
+};
+
+
+/*!
+ The Base Class for Non-Separable 2D Filters.
+
+ This is the base class for linear or non-linear 2D filters.
+
+ Several functions in OpenCV return Ptr<BaseFilter> for the specific types of filters,
+ and those pointers can be used directly or within cv::FilterEngine.
+
+ Similar to cv::BaseColumnFilter, the class may have some context information,
+ that should be reset using BaseFilter::reset() method before processing the new array.
+*/
+class BaseFilter
+{
+public:
+ //! the default constructor
+ BaseFilter();
+ //! the destructor
+ virtual ~BaseFilter();
+ //! the filtering operator. The horizontal and the vertical border interpolation is done outside of the class.
+ virtual void operator()(const uchar** src, uchar* dst, int dststep, int dstcount, int width, int cn) = 0;
+ //! resets the internal buffers, if any
+ virtual void reset();
+
+ Size ksize;
+ Point anchor;
+};
+
+
+/*!
+ The Main Class for Image Filtering.
+
+ The class can be used to apply an arbitrary filtering operation to an image.
+ It contains all the necessary intermediate buffers, it computes extrapolated values
+ of the "virtual" pixels outside of the image etc.
+ Pointers to the initialized cv::FilterEngine instances
+ are returned by various OpenCV functions, such as cv::createSeparableLinearFilter(),
+ cv::createLinearFilter(), cv::createGaussianFilter(), cv::createDerivFilter(),
+ cv::createBoxFilter() and cv::createMorphologyFilter().
+
+ Using the class you can process large images by parts and build complex pipelines
+ that include filtering as some of the stages. If all you need is to apply some pre-defined
+ filtering operation, you may use cv::filter2D(), cv::erode(), cv::dilate() etc.
+ functions that create FilterEngine internally.
+
+ Here is the example on how to use the class to implement Laplacian operator, which is the sum of
+ second-order derivatives. More complex variant for different types is implemented in cv::Laplacian().
+
+ \code
+ void laplace_f(const Mat& src, Mat& dst)
+ {
+ CV_Assert( src.type() == CV_32F );
+ // make sure the destination array has the proper size and type
+ dst.create(src.size(), src.type());
+
+ // get the derivative and smooth kernels for d2I/dx2.
+ // for d2I/dy2 we could use the same kernels, just swapped
+ Mat kd, ks;
+ getSobelKernels( kd, ks, 2, 0, ksize, false, ktype );
+
+ // let's process 10 source rows at once
+ int DELTA = std::min(10, src.rows);
+ Ptr<FilterEngine> Fxx = createSeparableLinearFilter(src.type(),
+ dst.type(), kd, ks, Point(-1,-1), 0, borderType, borderType, Scalar() );
+ Ptr<FilterEngine> Fyy = createSeparableLinearFilter(src.type(),
+ dst.type(), ks, kd, Point(-1,-1), 0, borderType, borderType, Scalar() );
+
+ int y = Fxx->start(src), dsty = 0, dy = 0;
+ Fyy->start(src);
+ const uchar* sptr = src.data + y*src.step;
+
+ // allocate the buffers for the spatial image derivatives;
+ // the buffers need to have more than DELTA rows, because at the
+ // last iteration the output may take max(kd.rows-1,ks.rows-1)
+ // rows more than the input.
+ Mat Ixx( DELTA + kd.rows - 1, src.cols, dst.type() );
+ Mat Iyy( DELTA + kd.rows - 1, src.cols, dst.type() );
+
+ // inside the loop we always pass DELTA rows to the filter
+ // (note that the "proceed" method takes care of possibe overflow, since
+ // it was given the actual image height in the "start" method)
+ // on output we can get:
+ // * < DELTA rows (the initial buffer accumulation stage)
+ // * = DELTA rows (settled state in the middle)
+ // * > DELTA rows (then the input image is over, but we generate
+ // "virtual" rows using the border mode and filter them)
+ // this variable number of output rows is dy.
+ // dsty is the current output row.
+ // sptr is the pointer to the first input row in the portion to process
+ for( ; dsty < dst.rows; sptr += DELTA*src.step, dsty += dy )
+ {
+ Fxx->proceed( sptr, (int)src.step, DELTA, Ixx.data, (int)Ixx.step );
+ dy = Fyy->proceed( sptr, (int)src.step, DELTA, d2y.data, (int)Iyy.step );
+ if( dy > 0 )
+ {
+ Mat dstripe = dst.rowRange(dsty, dsty + dy);
+ add(Ixx.rowRange(0, dy), Iyy.rowRange(0, dy), dstripe);
+ }
+ }
+ }
+ \endcode
+*/
+class FilterEngine
+{
+public:
+ //! the default constructor
+ FilterEngine();
+ //! the full constructor. Either _filter2D or both _rowFilter and _columnFilter must be non-empty.
+ FilterEngine(const Ptr<BaseFilter>& _filter2D,
+ const Ptr<BaseRowFilter>& _rowFilter,
+ const Ptr<BaseColumnFilter>& _columnFilter,
+ int srcType, int dstType, int bufType,
+ int _rowBorderType = BORDER_REPLICATE,
+ int _columnBorderType = -1,
+ const Scalar& _borderValue = Scalar());
+ //! the destructor
+ virtual ~FilterEngine();
+ //! reinitializes the engine. The previously assigned filters are released.
+ void init(const Ptr<BaseFilter>& _filter2D,
+ const Ptr<BaseRowFilter>& _rowFilter,
+ const Ptr<BaseColumnFilter>& _columnFilter,
+ int srcType, int dstType, int bufType,
+ int _rowBorderType = BORDER_REPLICATE,
+ int _columnBorderType = -1,
+ const Scalar& _borderValue = Scalar());
+ //! starts filtering of the specified ROI of an image of size wholeSize.
+ virtual int start(Size wholeSize, Rect roi, int maxBufRows = -1);
+ //! starts filtering of the specified ROI of the specified image.
+ virtual int start(const Mat& src, const Rect& srcRoi = Rect(0,0,-1,-1),
+ bool isolated = false, int maxBufRows = -1);
+ //! processes the next srcCount rows of the image.
+ virtual int proceed(const uchar* src, int srcStep, int srcCount,
+ uchar* dst, int dstStep);
+ //! applies filter to the specified ROI of the image. if srcRoi=(0,0,-1,-1), the whole image is filtered.
+ virtual void apply( const Mat& src, Mat& dst,
+ const Rect& srcRoi = Rect(0,0,-1,-1),
+ Point dstOfs = Point(0,0),
+ bool isolated = false);
+ //! returns true if the filter is separable
+ bool isSeparable() const { return !filter2D; }
+ //! returns the number
+ int remainingInputRows() const;
+ int remainingOutputRows() const;
+
+ int srcType;
+ int dstType;
+ int bufType;
+ Size ksize;
+ Point anchor;
+ int maxWidth;
+ Size wholeSize;
+ Rect roi;
+ int dx1;
+ int dx2;
+ int rowBorderType;
+ int columnBorderType;
+ std::vector<int> borderTab;
+ int borderElemSize;
+ std::vector<uchar> ringBuf;
+ std::vector<uchar> srcRow;
+ std::vector<uchar> constBorderValue;
+ std::vector<uchar> constBorderRow;
+ int bufStep;
+ int startY;
+ int startY0;
+ int endY;
+ int rowCount;
+ int dstY;
+ std::vector<uchar*> rows;
+
+ Ptr<BaseFilter> filter2D;
+ Ptr<BaseRowFilter> rowFilter;
+ Ptr<BaseColumnFilter> columnFilter;
+};
+
+
+//! returns type (one of KERNEL_*) of 1D or 2D kernel specified by its coefficients.
+int getKernelType(InputArray kernel, Point anchor);
+
+//! returns the primitive row filter with the specified kernel
+Ptr<BaseRowFilter> getLinearRowFilter(int srcType, int bufType,
+ InputArray kernel, int anchor,
+ int symmetryType);
+
+//! returns the primitive column filter with the specified kernel
+Ptr<BaseColumnFilter> getLinearColumnFilter(int bufType, int dstType,
+ InputArray kernel, int anchor,
+ int symmetryType, double delta = 0,
+ int bits = 0);
+
+//! returns 2D filter with the specified kernel
+Ptr<BaseFilter> getLinearFilter(int srcType, int dstType,
+ InputArray kernel,
+ Point anchor = Point(-1,-1),
+ double delta = 0, int bits = 0);
+
+//! returns the separable linear filter engine
+Ptr<FilterEngine> createSeparableLinearFilter(int srcType, int dstType,
+ InputArray rowKernel, InputArray columnKernel,
+ Point anchor = Point(-1,-1), double delta = 0,
+ int rowBorderType = BORDER_DEFAULT,
+ int columnBorderType = -1,
+ const Scalar& borderValue = Scalar());
+
+//! returns the non-separable linear filter engine
+Ptr<FilterEngine> createLinearFilter(int srcType, int dstType,
+ InputArray kernel, Point _anchor = Point(-1,-1),
+ double delta = 0, int rowBorderType = BORDER_DEFAULT,
+ int columnBorderType = -1, const Scalar& borderValue = Scalar());
+
+//! returns the Gaussian filter engine
+Ptr<FilterEngine> createGaussianFilter( int type, Size ksize,
+ double sigma1, double sigma2 = 0,
+ int borderType = BORDER_DEFAULT);
+
+//! returns filter engine for the generalized Sobel operator
+Ptr<FilterEngine> createDerivFilter( int srcType, int dstType,
+ int dx, int dy, int ksize,
+ int borderType = BORDER_DEFAULT );
+
+//! returns horizontal 1D box filter
+Ptr<BaseRowFilter> getRowSumFilter(int srcType, int sumType,
+ int ksize, int anchor = -1);
+
+//! returns vertical 1D box filter
+Ptr<BaseColumnFilter> getColumnSumFilter( int sumType, int dstType,
+ int ksize, int anchor = -1,
+ double scale = 1);
+//! returns box filter engine
+Ptr<FilterEngine> createBoxFilter( int srcType, int dstType, Size ksize,
+ Point anchor = Point(-1,-1),
+ bool normalize = true,
+ int borderType = BORDER_DEFAULT);
+
+
+//! returns horizontal 1D morphological filter
+Ptr<BaseRowFilter> getMorphologyRowFilter(int op, int type, int ksize, int anchor = -1);
+
+//! returns vertical 1D morphological filter
+Ptr<BaseColumnFilter> getMorphologyColumnFilter(int op, int type, int ksize, int anchor = -1);
+
+//! returns 2D morphological filter
+Ptr<BaseFilter> getMorphologyFilter(int op, int type, InputArray kernel,
+ Point anchor = Point(-1,-1));
+
+//! returns morphological filter engine. Only MORPH_ERODE and MORPH_DILATE are supported.
+CV_EXPORTS Ptr<FilterEngine> createMorphologyFilter(int op, int type, InputArray kernel,
+ Point anchor = Point(-1,-1), int rowBorderType = BORDER_CONSTANT,
+ int columnBorderType = -1,
+ const Scalar& borderValue = morphologyDefaultBorderValue());
+
+static inline Point normalizeAnchor( Point anchor, Size ksize )
+{
+ if( anchor.x == -1 )
+ anchor.x = ksize.width/2;
+ if( anchor.y == -1 )
+ anchor.y = ksize.height/2;
+ CV_Assert( anchor.inside(Rect(0, 0, ksize.width, ksize.height)) );
+ return anchor;
+}
+
+void preprocess2DKernel( const Mat& kernel, std::vector<Point>& coords, std::vector<uchar>& coeffs );
+void crossCorr( const Mat& src, const Mat& templ, Mat& dst,
+ Size corrsize, int ctype,
+ Point anchor=Point(0,0), double delta=0,
+ int borderType=BORDER_REFLECT_101 );
+
+}
+
+#endif
_Tp newVal, ConnectedComp* region, int flags,
std::vector<FFillSegment>* buffer )
{
- _Tp* img = (_Tp*)(image.data + image.step * seed.y);
+ _Tp* img = image.ptr<_Tp>(seed.y);
Size roi = image.size();
int i, L, R;
int area = 0;
for( k = 0; k < 3; k++ )
{
dir = data[k][0];
- img = (_Tp*)(image.data + (YC + dir) * image.step);
- int left = data[k][1];
- int right = data[k][2];
if( (unsigned)(YC + dir) >= (unsigned)roi.height )
continue;
+ img = image.ptr<_Tp>(YC + dir);
+ int left = data[k][1];
+ int right = data[k][2];
+
for( i = left; i <= right; i++ )
{
if( (unsigned)i < (unsigned)roi.width && img[i] == val0 )
std::vector<FFillSegment>* buffer )
{
int step = (int)image.step, maskStep = (int)msk.step;
- uchar* pImage = image.data;
+ uchar* pImage = image.ptr();
_Tp* img = (_Tp*)(pImage + step*seed.y);
- uchar* pMask = msk.data + maskStep + sizeof(_MTp);
+ uchar* pMask = msk.ptr() + maskStep + sizeof(_MTp);
_MTp* mask = (_MTp*)(pMask + maskStep*seed.y);
int i, L, R;
int area = 0;
if( is_simple )
{
size_t elem_size = img.elemSize();
- const uchar* seed_ptr = img.data + img.step*seedPoint.y + elem_size*seedPoint.x;
+ const uchar* seed_ptr = img.ptr(seedPoint.y) + elem_size*seedPoint.x;
size_t k = 0;
for(; k < elem_size; k++)
CV_Assert( mask.type() == CV_8U );
}
- memset( mask.data, 1, mask.cols );
- memset( mask.data + mask.step*(mask.rows-1), 1, mask.cols );
+ memset( mask.ptr(), 1, mask.cols );
+ memset( mask.ptr(mask.rows-1), 1, mask.cols );
for( i = 1; i <= size.height; i++ )
{
if( total == 0 )
return measureDist ? -DBL_MAX : -1;
- const Point* cnt = (const Point*)contour.data;
+ const Point* cnt = contour.ptr<Point>();
const Point2f* cntf = (const Point2f*)cnt;
if( !is_float && !measureDist && ip.x == pt.x && ip.y == pt.y )
--- /dev/null
+/*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*/
+
+/* ////////////////////////////////////////////////////////////////////
+//
+// CvMat helper tables
+//
+// */
+
+#include "precomp.hpp"
+
+namespace cv
+{
+
+const char* g_HersheyGlyphs[] = {
+ "",
+ "MWRMNV RMVV PSTS",
+ "MWOMOV OMSMUNUPSQ OQSQURUUSVOV",
+ "MXVNTMRMPNOPOSPURVTVVU",
+ "MWOMOV OMRMTNUPUSTURVOV",
+ "MWOMOV OMUM OQSQ OVUV",
+ "MVOMOV OMUM OQSQ",
+ "MXVNTMRMPNOPOSPURVTVVUVR SRVR",
+ "MWOMOV UMUV OQUQ",
+ "PTRMRV",
+ "NUSMSTRVPVOTOS",
+ "MWOMOV UMOS QQUV",
+ "MVOMOV OVUV",
+ "LXNMNV NMRV VMRV VMVV",
+ "MWOMOV OMUV UMUV",
+ "MXRMPNOPOSPURVSVUUVSVPUNSMRM",
+ "MWOMOV OMSMUNUQSROR",
+ "MXRMPNOPOSPURVSVUUVSVPUNSMRM STVW",
+ "MWOMOV OMSMUNUQSROR RRUV",
+ "MWUNSMQMONOOPPTRUSUUSVQVOU",
+ "MWRMRV NMVM",
+ "MXOMOSPURVSVUUVSVM",
+ "MWNMRV VMRV",
+ "LXNMPV RMPV RMTV VMTV",
+ "MWOMUV UMOV",
+ "MWNMRQRV VMRQ",
+ "MWUMOV OMUM OVUV",
+ "MWRMNV RMVV PSTS",
+ "MWOMOV OMSMUNUPSQ OQSQURUUSVOV",
+ "MVOMOV OMUM",
+ "MWRMNV RMVV NVVV",
+ "MWOMOV OMUM OQSQ OVUV",
+ "MWUMOV OMUM OVUV",
+ "MWOMOV UMUV OQUQ",
+ "MXRMPNOPOSPURVSVUUVSVPUNSMRM QQTR TQQR",
+ "PTRMRV",
+ "MWOMOV UMOS QQUV",
+ "MWRMNV RMVV",
+ "LXNMNV NMRV VMRV VMVV",
+ "MWOMOV OMUV UMUV",
+ "MWOMUM PQTR TQPR OVUV",
+ "MXRMPNOPOSPURVSVUUVSVPUNSMRM",
+ "MWOMOV UMUV OMUM",
+ "MWOMOV OMSMUNUQSROR",
+ "MWOMRQOV OMUM OVUV",
+ "MWRMRV NMVM",
+ "MWNONNOMPMQNRPRV VOVNUMTMSNRP",
+ "LXRMRV PONPNSPTTTVSVPTOPO",
+ "MWOMUV UMOV",
+ "LXRMRV NOOPOSQTSTUSUPVO",
+ "MXOVQVOROPPNRMSMUNVPVRTVVV",
+ "MWSMMV SMUV OSTS",
+ "MWQMNV QMTMVNVPSQPQ SQURUTTURVNV",
+ "LXVPUNTMRMPNOONQNSOUPVRVTUUT",
+ "MXQMNV QMUMVOVQUTTURVNV",
+ "MVQMNV QMVM PQSQ NVSV",
+ "MVQMNV QMVM PQSQ",
+ "LXVPUNTMRMPNOONQNSOUPVRVTUUSRS",
+ "MXQMNV WMTV PQUQ",
+ "PUTMQV",
+ "OVUMSSRUQVPVOUOT",
+ "MVQMNV VMOS RQTV",
+ "NVRMOV OVTV",
+ "LYPMMV PMQV XMQV XMUV",
+ "MXQMNV QMTV WMTV",
+ "LXRMPNOONQNSOUPVRVTUUTVRVPUNTMRM",
+ "MWQMNV QMUMVNVPUQSRPR",
+ "LXRMPNOONQNSOUPVRVTUUTVRVPUNTMRM QVPUPTQSRSSTTVUWVW",
+ "MWQMNV QMUMVNVPUQSRPR QRRUSVTVUU",
+ "MWVNTMRMPNPPQQTRUSUUSVPVNU",
+ "MVSMPV PMVM",
+ "LXPMNSNUOVRVTUUSWM",
+ "MWOMQV WMQV",
+ "KXNMNV SMNV SMSV XMSV",
+ "NWQMTV WMNV",
+ "NWQMSQQV WMSQ",
+ "MWQMWMNVTV",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "LXNMRV VMRV NMVM",
+ "MWNLVX",
+ "LXRONU ROVU",
+ "MWNVVV",
+ "PVRMUQ",
+ "MWMMOKQKTMVMWK",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "NWQPTPUQUV URQSPTPUQVSVUU",
+ "MWOMOV OSPURVTUUSTQRPPQOS",
+ "MWUQSPRPPQOSPURVSVUU",
+ "MWUMUV USTQRPPQOSPURVTUUS",
+ "MWOSUSTQRPPQOSPURVTV",
+ "NVUNTMSMRNRV PPTP",
+ "MWUPUVTXRYPY USTQRPPQOSPURVTUUS",
+ "MWOMOV OSPQRPTQUSUV",
+ "PTRLQMRNSMRL RPRV",
+ "PUSLRMSNTMSL SPSXRYQYPX",
+ "NWPMPV UPPT RSUV",
+ "PTRMRV",
+ "KYMPMV MSNQOPPPQQRSRV RSSQTPUPVQWSWV",
+ "MWOPOV OSPQRPTQUSUV",
+ "MWRPPQOSPURVTUUSTQRP",
+ "MWOPOY OSPURVTUUSTQRPPQOS",
+ "MWUPUY USTQRPPQOSPURVTUUS",
+ "NVPPPV PSQQSPTP",
+ "NWUQTPQPPQPRQSTSUTUUTVQVPU",
+ "NVRMRUSVTVUU PPTP",
+ "MWUPUV OPOSPURVTUUS",
+ "NVOPRV UPRV",
+ "LXNPPV RPPV RPTV VPTV",
+ "MWOPUV UPOV",
+ "MWOPRV UPRVQXPYOY",
+ "MWOPUPOVUV",
+ "MXVPUSTURVPUOSPQRPTQUUVV",
+ "MWOTQVSVTUTSSRPQRQTPUOUNTMRMQNPPOTNY",
+ "MXNQOPQPRQRSQW VPURSTQWPY",
+ "MWTNSMRMQNQORPTQUSTURVPUOSPQRP",
+ "NWUQSPQPPQPRQS SSQSPTPUQVSVUU",
+ "NWTMSNSOTP UPSPQQPSPUQVSWSXRYQY",
+ "LXNQOPPPQQQSPV QSRQTPUPVQVSUVTY",
+ "LXNQOPPPQQQURVSVTUUSVPVNUMTMSNSPTRUSWT",
+ "OVRPQSQURVSVTU",
+ "MWQPOV UPTPRQPS PSQUSVTV",
+ "MWOMPMQNRPUV RPOV",
+ "LYPPMY UPTSSUQVPVOUOS TSTUUVVVWU",
+ "MWNPOPOV UPTSRUOV",
+ "NWTMSNSOTP UPSPQQQRRSTS SSQTPUPVQWSXSYRZQZ",
+ "MWRPPQOSPURVTUUSTQRP",
+ "MXOQQPVP QPQRPV TPTRUV",
+ "MWOSPURVTUUSTQRPPQOSNY",
+ "MXVPRPPQOSPURVTUUSTQRP",
+ "MXOQQPVP SPRV",
+ "KXMQNPOPPQPUQVSVTUUSVP",
+ "MXPPOQOSPURVSVUUVSVQUPTPSQRSQY",
+ "MWOPPPQQSXTYUY UPTRPWOY",
+ "KYTMRY MQNPOPPQPUQVTVUUVSWP",
+ "LXOPNRNTOVQVRTRR UPVRVTUVSVRT",
+ "LWTSSQQPOQNSOUQVSUTS UPTSTUUVVV",
+ "MWQMOSPURVTUUSTQRPPQOS",
+ "MWUQSPRPPQOSPURVTV",
+ "LWTSSQQPOQNSOUQVSUTS VMTSTUUVVV",
+ "MWOSTSURUQSPRPPQOSPURVTV",
+ "OVVMUMTNSPQVPXOYNY QPUP",
+ "MXUSTQRPPQOSPURVTUUS VPTVSXRYPYOX",
+ "MVQMNV OSPQQPSPTQTRSTSUTVUV",
+ "PUSMSNTNTMSM QPRPSQSRRTRUSVTV",
+ "OUSMSNTNTMSM QPRPSQSRRVQXPYOYNX",
+ "NVRMOV UPTPRQPS PSQUSVTV",
+ "OTSMQSQURVSV",
+ "JYKPLPMQMSLV MSNQOPQPRQRSQV RSSQTPVPWQWRVTVUWVXV",
+ "MWNPOPPQPSOV PSQQRPTPUQURTTTUUVVV",
+ "MWRPPQOSPURVTUUSTQRP",
+ "MXNPOPPQPSNY PSQUSVUUVSUQSPQQPS",
+ "MXUSTQRPPQOSPURVTUUS VPSY",
+ "MVOPPPQQQSPV UQTPSPRQQS",
+ "NVTQSPQPPQPRQSRSSTSURVPVOU",
+ "NUSMQSQURVSV PPTP",
+ "MWNPOPPQPROTOUPVRVSUTS UPTSTUUVVV",
+ "MWNPOPPQPROTOUPVRVTUURUP",
+ "KYLPMPNQNRMTMUNVPVQURSSP RSRUSVUVVUWRWP",
+ "MWOQPPQPRQRUSVTVUU VQUPTPSQQUPVOVNU",
+ "MWNPOPPQPROTOUPVRVSUTS UPSVRXQYOYNX",
+ "NVUPOV PQQPSPTQ PUQVSVTU",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "MWUSTQRPPQOSPURVTUUSUPTNRMQM",
+ "MWUQSPRPPQOSPURVSVUU OSSS",
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+ "",
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+ "L[LVNRLXLZM[O[QZSXUU VRPdOfMgLfLdMaP^S\\U[XY[V",
+ "LZLVNSPRRRTTTVSXQZN[P\\Q^QaPdOfMgLfLdMaP^S\\WYZV",
+ "J\\K[NZQXSVUSWOXKXIWGUFSGRHQJPOPTQXRZT[V[XZYY",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "I[WUWRVOUNSMQMONMPLSLVMYNZP[R[TZVXWUXPXKWHVGTFRFPGNI",
+ "JZWNUMRMPNNPMSMVNYOZQ[T[VZ MTUT",
+ "J[TFRGPJOLNOMTMXNZO[Q[SZUWVUWRXMXIWGVFTF NPWP",
+ "H\\VFNb QMNNLPKSKVLXNZQ[S[VZXXYUYRXPVNSMQM",
+ "I[XOWNTMQMNNMOLQLSMUOWSZT\\T^S_Q_",
+ "",
+ "",
+ "DaWNVLTKQKOLNMMOMRNTOUQVTVVUWS WKWSXUYV[V\\U]S]O\\L[JYHWGTFQFNGLHJJILHOHRIUJWLYNZQ[T[WZYY",
+ "F^ZIJRZ[",
+ "F^JIZRJ[",
+ "KYOBOb OBVB ObVb",
+ "KYUBUb NBUB NbUb",
+ "KYTBQEPHPJQMSOSPORSTSUQWPZP\\Q_Tb",
+ "KYPBSETHTJSMQOQPURQTQUSWTZT\\S_Pb",
+ "F^[FYGVHSHPGNFLFJGIIIKKMMMOLPJPHNF [FI[ YTWTUUTWTYV[X[ZZ[X[VYT",
+ "NV",
+ "JZ",
+ "H\\QFNGLJKOKRLWNZQ[S[VZXWYRYOXJVGSFQF",
+ "H\\NJPISFS[",
+ "H\\LKLJMHNGPFTFVGWHXJXLWNUQK[Y[",
+ "H\\MFXFRNUNWOXPYSYUXXVZS[P[MZLYKW",
+ "H\\UFKTZT UFU[",
+ "H\\WFMFLOMNPMSMVNXPYSYUXXVZS[P[MZLYKW",
+ "H\\XIWGTFRFOGMJLOLTMXOZR[S[VZXXYUYTXQVOSNRNOOMQLT",
+ "H\\YFO[ KFYF",
+ "H\\PFMGLILKMMONSOVPXRYTYWXYWZT[P[MZLYKWKTLRNPQOUNWMXKXIWGTFPF",
+ "H\\XMWPURRSQSNRLPKMKLLINGQFRFUGWIXMXRWWUZR[P[MZLX",
+ "MWRYQZR[SZRY",
+ "MWSZR[QZRYSZS\\R^Q_",
+ "MWRMQNROSNRM RYQZR[SZRY",
+ "MWRMQNROSNRM SZR[QZRYSZS\\R^Q_",
+ "MWRFRT RYQZR[SZRY",
+ "I[LKLJMHNGPFTFVGWHXJXLWNVORQRT RYQZR[SZRY",
+ "NVRFRM",
+ "JZNFNM VFVM",
+ "KYQFOGNINKOMQNSNUMVKVIUGSFQF",
+ "H\\PBP_ TBT_ YIWGTFPFMGKIKKLMMNOOUQWRXSYUYXWZT[P[MZKX",
+ "G][BIb",
+ "KYVBTDRGPKOPOTPYR]T`Vb",
+ "KYNBPDRGTKUPUTTYR]P`Nb",
+ "NVRBRb",
+ "E_IR[R",
+ "E_RIR[ IR[R",
+ "E_IO[O IU[U",
+ "G]KKYY YKKY",
+ "JZRLRX MOWU WOMU",
+ "MWRQQRRSSRRQ",
+ "MWSFRGQIQKRLSKRJ",
+ "MWRHQGRFSGSIRKQL",
+ "E_UMXP[RXTUW IR[R",
+ "G]OFOb UFUb JQZQ JWZW",
+ "E_\\O\\N[MZMYNXPVUTXRZP[L[JZIYHWHUISJRQNRMSKSIRGPFNGMIMKNNPQUXWZY[[[\\Z\\Y",
+ "G]IIJKKOKUJYI[ [IZKYOYUZY[[ IIKJOKUKYJ[I I[KZOYUYYZ[[",
+ "F_\\Q[OYNWNUOTPQTPUNVLVJUISIQJOLNNNPOQPTTUUWVYV[U\\S\\Q",
+ "KYOBO[ UBU[",
+ "F^RBR[ I[[[",
+ "F^[BI[[[",
+ "E_RIQJRKSJRI IYHZI[JZIY [YZZ[[\\Z[Y",
+ "F^RHNLKPJSJUKWMXOXQWRU RHVLYPZSZUYWWXUXSWRU RUQYP\\ RUSYT\\ P\\T\\",
+ "F^RNQKPINHMHKIJKJOKRLTNWR\\ RNSKTIVHWHYIZKZOYRXTVWR\\",
+ "F^RGPJLOIR RGTJXO[R IRLUPZR] [RXUTZR]",
+ "F^RTTWVXXXZW[U[SZQXPVPSQ SQUOVMVKUISHQHOINKNMOOQQ QQNPLPJQISIUJWLXNXPWRT RTQYP\\ RTSYT\\ P\\T\\",
+ "F^RRR[Q\\ RVQ\\ RIQHOHNINKONRR RISHUHVIVKUNRR RRNOLNJNIOIQJR RRVOXNZN[O[QZR RRNULVJVIUISJR RRVUXVZV[U[SZR",
+ "F^ISJSLTMVMXLZ ISIRJQLQMRNTNWMYLZ RGPIOLOOQUQXPZR\\ RGTIULUOSUSXTZR\\ [S[RZQXQWRVTVWWYXZ [SZSXTWVWXXZ KVYV",
+ "",
+ "",
+ "",
+ "PSSRRSQSPRPQQPRPSQSSRUQV QQQRRRRQQQ",
+ "PTQPPQPSQTSTTSTQSPQP RQQRRSSRRQ",
+ "NVPOTU TOPU NRVR",
+ "MWRKQMOPMR RKSMUPWR RMOQ RMUQ ROPQ ROTQ QQSQ MRWR",
+ "MWMRMQNOONQMSMUNVOWQWR PNTN OOUO NPVP NQVQ MRWR",
+ "LRLFLRRRLF LIPQ LLOR LOMQ",
+ "MWRKQMOPMR RKSMUPWR",
+ "MWWRWQVOUNSMQMONNOMQMR",
+ "G]]R]P\\MZJWHTGPGMHJJHMGPGR",
+ "MWMRMSNUOVQWSWUVVUWSWR",
+ "LXLPNRQSSSVRXP",
+ "RURUTTURTPRO",
+ "RVRRUPVNVLUKTK",
+ "NRRROPNNNLOKPK",
+ "MWWHVGTFQFOGNHMJMLNNOOUSVTWVWXVZU[S\\P\\N[MZ",
+ "G]IWHVGTGQHOINKMMMONPOTUUVWWYW[V\\U]S]P\\N[M",
+ "G]RRTUUVWWYW[V\\U]S]Q\\O[NYMWMUNTOPUOVMWKWIVHUGSGQHOINKMMMONPORR",
+ "H\\KFK[ HF[FQP[Z ZV[Y\\[ ZVZY WYZY WYZZ\\[",
+ "KYUARBPCNELHKLKRLUNWQXSXVWXUYR KPLMNKQJSJVKXMYPYVXZV]T_R`Oa",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ ">f>RfR",
+ "D`D``D",
+ "RRR>Rf",
+ "D`DD``",
+ "D`DR`R",
+ "F^FY^K",
+ "KYK^YF",
+ "",
+ "KYKFY^",
+ "F^FK^Y",
+ "KYKRYR",
+ "MWMWWM",
+ "",
+ "MWMMWW",
+ "",
+ "",
+ "",
+ "",
+ "D`DOGQKSPTTTYS]Q`O",
+ "PUUDSGQKPPPTQYS]U`",
+ "OTODQGSKTPTTSYQ]O`",
+ "D`DUGSKQPPTPYQ]S`U",
+ "KYRJYNKVRZ",
+ "JZJRNKVYZR",
+ "KYKVKNYVYN",
+ "JZLXJPZTXL",
+ "JZJ]L]O\\Q[TXUVVSVOULTJSIQIPJOLNONSOVPXS[U\\X]Z]",
+ "I]]Z]X\\U[SXPVOSNONLOJPIQISJTLUOVSVVUXT[Q\\O]L]J",
+ "JZZGXGUHSIPLONNQNUOXPZQ[S[TZUXVUVQUNTLQIOHLGJG",
+ "G[GJGLHOIQLTNUQVUVXUZT[S[QZPXOUNQNNOLPISHUGXGZ",
+ "E[EPFRHTJUMVQVUUXSZP[NZLWLSMQNNPLSKVKYL\\M^",
+ "EYETHVKWPWSVVTXQYNYLXKVKSLPNNQMTMYN\\P_",
+ "OUQOOQOSQUSUUSUQSOQO QPPQPSQTSTTSTQSPQP RQQRRSSRRQ",
+ "",
+ "D`DRJR ORUR ZR`R",
+ "D`DUDO`O`U",
+ "JZRDJR RDZR",
+ "D`DR`R JYZY P`T`",
+ "D`DR`R DRRb `RRb",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "KYQKNLLNKQKSLVNXQYSYVXXVYSYQXNVLSKQK",
+ "LXLLLXXXXLLL",
+ "KYRJKVYVRJ",
+ "LXRHLRR\\XRRH",
+ "JZRIPOJOOSMYRUWYUSZOTORI",
+ "KYRKRY KRYR",
+ "MWMMWW WMMW",
+ "MWRLRX MOWU WOMU",
+ "",
+ "",
+ "NVQNOONQNSOUQVSVUUVSVQUOSNQN OQOS PPPT QOQU RORU SOSU TPTT UQUS",
+ "NVNNNVVVVNNN OOOU POPU QOQU RORU SOSU TOTU UOUU",
+ "MWRLMUWURL ROOT ROUT RRQT RRST",
+ "LULRUWUMLR ORTU ORTO RRTS RRTQ",
+ "MWRXWOMORX RUUP RUOP RRSP RRQP",
+ "OXXROMOWXR URPO URPU RRPQ RRPS",
+ "LXRLNWXPLPVWRL RRRL RRLP RRNW RRVW RRXP",
+ "",
+ "",
+ "",
+ "MWRLRX OOUO MUOWQXSXUWWU",
+ "LXRLRX LQMOWOXQ PWTW",
+ "KYMNWX WNMX OLLOKQ ULXOYQ",
+ "I[NII[ VI[[ MM[[ WMI[ NIVI MMWM",
+ "I[RGRV MJWP WJMP IVL\\ [VX\\ IV[V L\\X\\",
+ "G[MJSV KPSL G\\[\\[RG\\",
+ "LXPLPPLPLTPTPXTXTTXTXPTPTLPL",
+ "KYYPXNVLSKQKNLLNKQKSLVNXQYSYVXXVYT YPWNUMSMQNPOOQOSPUQVSWUWWVYT",
+ "KYRJKVYVRJ RZYNKNRZ",
+ "G]PIPGQFSFTGTI GZHXJVKTLPLKMJOIUIWJXKXPYTZV\\X]Z GZ]Z QZP[Q\\S\\T[SZ",
+ "JZRMRS RSQ\\ RSS\\ Q\\S\\ RMQJPHNG QJNG RMSJTHVG SJVG RMNKLKJM PLLLJM RMVKXKZM TLXLZM RMPNOOOR RMPOOR RMTNUOUR RMTOUR",
+ "JZRIRK RNRP RSRU RYQ\\ RYS\\ Q\\S\\ RGQIPJ RGSITJ PJRITJ RKPNNOMN RKTNVOWN NOPORNTOVO RPPSNTLTKRKSLT RPTSVTXTYRYSXT NTPTRSTTVT RUPXOYMZLZKYJWJYLZ RUTXUYWZXZYYZWZYXZ MZOZRYUZWZ",
+ "JZRYQ\\ RYS\\ Q\\S\\ RYUZXZZXZUYTWTYRZOYMWLUMVJUHSGQGOHNJOMMLKMJOKRMTKTJUJXLZOZRY",
+ "JZRYQ\\ RYS\\ Q\\S\\ RYVXVVXUXRZQZLYIXHVHTGPGNHLHKIJLJQLRLUNVNXRY",
+ "I[IPKR LKNP RGRO XKVP [PYR",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "QSRQQRRSSRRQ",
+ "PTQPPQPSQTSTTSTQSPQP",
+ "NVQNOONQNSOUQVSVUUVSVQUOSNQN",
+ "MWQMONNOMQMSNUOVQWSWUVVUWSWQVOUNSMQM",
+ "KYQKNLLNKQKSLVNXQYSYVXXVYSYQXNVLSKQK",
+ "G]PGMHJJHMGPGTHWJZM\\P]T]W\\ZZ\\W]T]P\\MZJWHTGPG",
+ "AcPALBJCGEEGCJBLAPATBXCZE]G_JaLbPcTcXbZa]__]aZbXcTcPbLaJ_G]EZCXBTAPA",
+ "<hP<K=G?DAAD?G=K<P<T=Y?]A`DcGeKgPhThYg]e`cc`e]gYhThPgKeGcD`A]?Y=T<P<",
+ "){O)I*E+@-;073370;-@+E*I)O)U*[+_-d0i3m7q;t@wEyIzO{U{[z_ydwitmqqmtiwdy_z[{U{OzIyEw@t;q7m3i0d-_+[*U)O)",
+ ">fRAPCMDJDGCEA>H@JAMAZB]D_G`M`PaRc RATCWDZD]C_AfHdJcMcZb]`_]`W`TaRc",
+ "AcRAPCMDJDGCEABGAKAPBTDXG\\L`Rc RATCWDZD]C_AbGcKcPbT`X]\\X`Rc BHbH",
+ "H[WPVQWRXQXPVNTMQMNNLPKSKULXNZQ[S[VZXX QMONMPLSLUMXOZQ[ LbXF",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "KYRKMX RNVX RKWX OTTT KXPX TXYX",
+ "JZNKNX OKOX LKSKVLWNVPSQ SKULVNUPSQ OQSQVRWTWUVWSXLX SQURVTVUUWSX",
+ "KYVLWKWOVLTKQKOLNMMPMSNVOWQXTXVWWU QKOMNPNSOVQX",
+ "JZNKNX OKOX LKSKVLWMXPXSWVVWSXLX SKULVMWPWSVVUWSX",
+ "JYNKNX OKOX SOSS LKVKVOUK OQSQ LXVXVTUX",
+ "JXNKNX OKOX SOSS LKVKVOUK OQSQ LXQX",
+ "K[VLWKWOVLTKQKOLNMMPMSNVOWQXTXVW QKOMNPNSOVQX TXUWVU VSVX WSWX TSYS",
+ "J[NKNX OKOX VKVX WKWX LKQK TKYK OQVQ LXQX TXYX",
+ "NWRKRX SKSX PKUK PXUX",
+ "LXSKSURWQX TKTUSWQXPXNWMUNTOUNV QKVK",
+ "JZNKNX OKOX WKOS QQVX RQWX LKQK TKYK LXQX TXYX",
+ "KXOKOX PKPX MKRK MXWXWTVX",
+ "I\\MKMX NNRX NKRU WKRX WKWX XKXX KKNK WKZK KXOX UXZX",
+ "JZNKNX OMVX OKVV VKVX LKOK TKXK LXPX",
+ "KZQKOLNMMPMSNVOWQXTXVWWVXSXPWMVLTKQK QKOMNPNSOVQX TXVVWSWPVMTK",
+ "JYNKNX OKOX LKSKVLWNWOVQSROR SKULVNVOUQSR LXQX",
+ "KZQKOLNMMPMSNVOWQXTXVWWVXSXPWMVLTKQK QKOMNPNSOVQX TXVVWSWPVMTK PWPUQTSTTUUZV[W[XZ TUUXVZW[",
+ "JZNKNX OKOX LKSKVLWNWOVQSROR SKULVNVOUQSR LXQX SRTSUWVXWXXW SRUSVWWX",
+ "KZVMWKWOVMULSKQKOLNMNOOPQQTRVSWT NNOOQPTQVRWSWVVWTXRXPWOVNTNXOV",
+ "KZRKRX SKSX NKMOMKXKXOWK PXUX",
+ "J[NKNUOWQXTXVWWUWK OKOUPWQX LKQK UKYK",
+ "KYMKRX NKRU WKRX KKPK TKYK",
+ "I[LKOX MKOT RKOX RKUX SKUT XKUX JKOK VKZK",
+ "KZNKVX OKWX WKNX LKQK TKYK LXQX TXYX",
+ "LYNKRRRX OKSR WKSRSX LKQK TKYK PXUX",
+ "LYVKNX WKOX OKNONKWK NXWXWTVX",
+ "KYRKMX RNVX RKWX OTTT KXPX TXYX",
+ "JZNKNX OKOX LKSKVLWNVPSQ SKULVNUPSQ OQSQVRWTWUVWSXLX SQURVTVUUWSX",
+ "KXOKOX PKPX MKWKWOVK MXRX",
+ "KYRKLX RMWX RKXX MWVW LXXX",
+ "JYNKNX OKOX SOSS LKVKVOUK OQSQ LXVXVTUX",
+ "LYVKNX WKOX OKNONKWK NXWXWTVX",
+ "J[NKNX OKOX VKVX WKWX LKQK TKYK OQVQ LXQX TXYX",
+ "KZQKOLNMMPMSNVOWQXTXVWWVXSXPWMVLTKQK QKOMNPNSOVQX TXVVWSWPVMTK QOQT TOTT QQTQ QRTR",
+ "NWRKRX SKSX PKUK PXUX",
+ "JZNKNX OKOX WKOS QQVX RQWX LKQK TKYK LXQX TXYX",
+ "KYRKMX RNVX RKWX KXPX TXYX",
+ "I\\MKMX NNRX NKRU WKRX WKWX XKXX KKNK WKZK KXOX UXZX",
+ "JZNKNX OMVX OKVV VKVX LKOK TKXK LXPX",
+ "JZMJLM XJWM PPOS UPTS MVLY XVWY MKWK MLWL PQTQ PRTR MWWW MXWX",
+ "KZQKOLNMMPMSNVOWQXTXVWWVXSXPWMVLTKQK QKOMNPNSOVQX TXVVWSWPVMTK",
+ "J[NKNX OKOX VKVX WKWX LKYK LXQX TXYX",
+ "JYNKNX OKOX LKSKVLWNWOVQSROR SKULVNVOUQSR LXQX",
+ "K[MKRQ NKSQMX MKWKXOVK NWWW MXWXXTVX",
+ "KZRKRX SKSX NKMOMKXKXOWK PXUX",
+ "KZMONLOKPKQLRORX XOWLVKUKTLSOSX MONMOLPLQMRO XOWMVLULTMSO PXUX",
+ "KZRKRX SKSX QNNOMQMRNTQUTUWTXRXQWOTNQN QNOONQNROTQU TUVTWRWQVOTN PKUK PXUX",
+ "KZNKVX OKWX WKNX LKQK TKYK LXQX TXYX",
+ "J[RKRX SKSX LPMONOOSQU TUVSWOXOYP MONROTQUTUVTWRXO PKUK PXUX",
+ "KZMVNXQXMRMONMOLQKTKVLWMXOXRTXWXXV OUNRNOOMQK TKVMWOWRVU NWPW UWWW",
+ "KYTKKX SMTX TKUX NTTT IXNX RXWX",
+ "JYPKLX QKMX NKUKWLWNVPSQ UKVLVNUPSQ OQRQTRUSUUTWQXJX RQTSTUSWQX",
+ "KXVLWLXKWNVLTKRKPLOMNOMRMUNWPXRXTWUU RKPMOONRNVPX",
+ "JYPKLX QKMX NKTKVLWNWQVTUVTWQXJX TKULVNVQUTTVSWQX",
+ "JYPKLX QKMX SORS NKXKWNWK OQRQ JXTXUUSX",
+ "JXPKLX QKMX SORS NKXKWNWK OQRQ JXOX",
+ "KYVLWLXKWNVLTKRKPLOMNOMRMUNWPXRXTWUVVS RKPMOONRNVPX RXTVUS SSXS",
+ "J[PKLX QKMX XKTX YKUX NKSK VK[K OQVQ JXOX RXWX",
+ "NWTKPX UKQX RKWK NXSX",
+ "LXUKRUQWPX VKSURWPXOXMWLUMTNUMV SKXK",
+ "JZPKLX QKMX YKOR RPTX SPUX NKSK VK[K JXOX RXWX",
+ "KXQKMX RKNX OKTK KXUXVUTX",
+ "I\\OKKX OMPX PKQV YKPX YKUX ZKVX MKPK YK\\K IXMX SXXX",
+ "JZPKLX PKTX QKTU XKTX NKQK VKZK JXNX",
+ "KYRKPLOMNOMRMUNWPXRXTWUVVTWQWNVLTKRK RKPMOONRNVPX RXTVUTVQVMTK",
+ "JYPKLX QKMX NKUKWLXMXOWQTROR UKWMWOVQTR JXOX",
+ "KYRKPLOMNOMRMUNWPXRXTWUVVTWQWNVLTKRK RKPMOONRNVPX RXTVUTVQVMTK OWOVPUQURVRZS[T[UZ RVSZT[",
+ "JZPKLX QKMX NKUKWLXMXOWQTROR UKWMWOVQTR SRTWUXVXWW SRTSUWVX JXOX",
+ "KZWLXLYKXNWLUKRKPLOMOOPPUSVT ONPOURVSVVUWSXPXNWMULXMWNW",
+ "KZTKPX UKQX PKNNOKZKYNYK NXSX",
+ "J[PKMUMWOXSXUWVUYK QKNUNWOX NKSK WK[K",
+ "KYOKPX PKQV YKPX MKRK VK[K",
+ "I[NKMX OKNV TKMX TKSX UKTV ZKSX LKQK XK\\K",
+ "KZPKTX QKUX YKLX NKSK VK[K JXOX RXWX",
+ "LYPKRQPX QKSQ YKSQQX NKSK VK[K NXSX",
+ "LYXKLX YKMX QKONPKYK LXUXVUTX",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "KZMHX\\",
+ "JZRMLW RMXW",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "LZQOPPPQOQOPQOTOVQVWWXXX TOUQUWWX URRSPTOUOWPXSXTWUU RSPUPWQX",
+ "JYNKNX OKOX ORPPROTOVPWRWUVWTXRXPWOU TOUPVRVUUWTX LKOK",
+ "LXVQUQURVRVQUPSOQOOPNRNUOWQXSXUWVV QOPPOROUPWQX",
+ "L[VKVX WKWX VRUPSOQOOPNRNUOWQXSXUWVU QOPPOROUPWQX TKWK VXYX",
+ "LXOSVSVRUPSOQOOPNRNUOWQXSXUWVV USUQSO QOPPOROUPWQX",
+ "LWTKULUMVMVLTKRKPMPX RKQMQX NOSO NXSX",
+ "LYQOOQOSQUSUUSUQSOQO QOPQPSQU SUTSTQSO TPUOVO PTOUOXPYTYVZ OWPXTXVYV[T\\P\\N[NYPX",
+ "J[NKNX OKOX ORPPROTOVPWRWX TOUPVRVX LKOK LXQX TXYX",
+ "NWRKRLSLSKRK RORX SOSX POSO PXUX",
+ "NWSKSLTLTKSK SOSZR\\ TOTZR\\P\\O[OZPZP[O[ QOTO",
+ "JZNKNX OKOX WOOU RSVX SSWX LKOK TOYO LXQX TXYX",
+ "NWRKRX SKSX PKSK PXUX",
+ "F_JOJX KOKX KRLPNOPORPSRSX POQPRRRX SRTPVOXOZP[R[X XOYPZRZX HOKO HXMX PXUX XX]X",
+ "J[NONX OOOX ORPPROTOVPWRWX TOUPVRVX LOOO LXQX TXYX",
+ "LYQOOPNRNUOWQXTXVWWUWRVPTOQO QOPPOROUPWQX TXUWVUVRUPTO",
+ "JYNON\\ OOO\\ ORPPROTOVPWRWUVWTXRXPWOU TOUPVRVUUWTX LOOO L\\Q\\",
+ "KYUOU\\ VOV\\ URTPROPONPMRMUNWPXRXTWUU POOPNRNUOWPX S\\X\\",
+ "KXOOOX POPX PRQPSOUOVPVQUQUPVP MOPO MXRX",
+ "LYTOUPUQVQVPTOQOOPORQSTTVU OQQRTSVTVWTXQXOWOVPVPWQX",
+ "LWPKPVRXTXUWUV QKQVRX NOTO",
+ "J[NONUOWQXSXUWVU OOOUPWQX VOVX WOWX LOOO TOWO VXYX",
+ "KYNORX OORV VORX LOQO TOXO",
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+ "",
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+ "",
+ "",
+ "",
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+ "",
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+ "",
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+ "",
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+ "",
+ "",
+ "",
+ "",
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+ "",
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+ "",
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+ "H\\RFJ[ QIX[ RIY[ RFZ[ KYXY KZXZ J[Z[",
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+ "",
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+ "G^_BEbFb _B`BFb",
+ "JZZBXCUERHPKNOMSMXN\\O_Qb SHQKOONTN\\ ZBWDTGRJQLPOOSN\\ NTO]P`Qb",
+ "JZSBUEVHWLWQVUTYR\\O_LaJb VHVPUUSYQ\\ SBTDUGVP VHUQTUSXRZP]M`Jb",
+ "J[TFSGUQTR TFTR TFUGSQTR OIPIXOYO OIYO OIOJYNYO YIXIPOOO YIOO YIYJONOO",
+ "F_JQ[Q[R JQJR[R",
+ "F_RIRZSZ RISISZ JQ[Q[R JQJR[R",
+ "F_JM[M[N JMJN[N JU[U[V JUJV[V",
+ "MWUFTGRM UGRM UFVGRM",
+ "H\\PFOGMM PGMM PFQGMM ZFYGWM ZGWM ZF[GWM",
+ "KZSFQGPIPKQMSNUNWMXKXIWGUFSF SFPIQMUNXKWGSF UFQGPKSNWMXIUF",
+ "F^ZIJRZ[ ZIZJLRZZZ[",
+ "F^JIZRJ[ JIJJXRJZJ[",
+ "G^SFKbLb SFTFLb YFQbRb YFZFRb KP\\P\\Q KPKQ\\Q IWZWZX IWIXZX",
+ "E^^F\\GXHUHQGOFMFKGJIJKLMNMPLQJQHOF ^FE[ ^FF[ ^FG[ XTVTTUSWSYU[W[YZZXZVXT PGMFJIKLNMQJPG OFKGJK LMPLQH YUVTSWTZW[ZXYU XTTUSY U[YZZV E[G[",
+ "E`UQUNTLRKPKNLMMLPLSMUOVQVSUTTUQ OLNMMPMSNU RKPLOMNPNSOUPV VKUQUSVUXVZV\\U]R]O\\L[JYHWGTFQFNGLHJJILHOHRIUJWLYNZQ[T[WZYYXYWZ WKVQVSWU VKXKWQWSXUZV",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ 0 };
+
+}
+
+/* End of file. */
deltas[i*2+1] = (int)(images[j].step/esz1 - imsize.width*deltas[i*2]);
}
- if( mask.data )
+ if( !mask.empty() )
{
CV_Assert( mask.size() == imsize && mask.channels() == 1 );
isContinuous = isContinuous && mask.isContinuous();
{
T** ptrs = (T**)&_ptrs[0];
const int* deltas = &_deltas[0];
- uchar* H = hist.data;
+ uchar* H = hist.ptr();
int i, x;
const uchar* mask = _ptrs[dims];
int mstep = _deltas[dims*2 + 1];
{
uchar** ptrs = &_ptrs[0];
const int* deltas = &_deltas[0];
- uchar* H = hist.data;
+ uchar* H = hist.ptr();
int x;
const uchar* mask = _ptrs[dims];
int mstep = _deltas[dims*2 + 1];
Mat phist(hist->size(), hist->type(), Scalar::all(0));
IppStatus status = ippiHistogramEven_8u_C1R(
- src->data + src->step * range.start, (int)src->step, ippiSize(src->cols, range.end - range.start),
- (Ipp32s *)phist.data, (Ipp32s *)*levels, histSize, low, high);
+ src->ptr(range.start), (int)src->step, ippiSize(src->cols, range.end - range.start),
+ phist.ptr<Ipp32s>(), (Ipp32s *)*levels, histSize, low, high);
if (status < 0)
{
CV_Assert(dims > 0 && histSize);
- uchar* histdata = _hist.getMat().data;
+ const uchar* const histdata = _hist.getMat().ptr();
_hist.create(dims, histSize, CV_32F);
Mat hist = _hist.getMat(), ihist = hist;
ihist.flags = (ihist.flags & ~CV_MAT_TYPE_MASK)|CV_32S;
std::vector<double> uniranges;
Size imsize;
- CV_Assert( !mask.data || mask.type() == CV_8UC1 );
+ CV_Assert( mask.empty() || 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;
std::vector<double> uniranges;
Size imsize;
- CV_Assert( !mask.data || mask.type() == CV_8UC1 );
+ CV_Assert( mask.empty() || 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;
{
T** ptrs = (T**)&_ptrs[0];
const int* deltas = &_deltas[0];
- uchar* H = hist.data;
+ const uchar* H = hist.ptr();
int i, x;
BT* bproj = (BT*)_ptrs[dims];
int bpstep = _deltas[dims*2 + 1];
for( x = 0; x < imsize.width; x++, p0 += d0 )
{
int idx = cvFloor(*p0*a + b);
- bproj[x] = (unsigned)idx < (unsigned)sz ? saturate_cast<BT>(((float*)H)[idx]*scale) : 0;
+ bproj[x] = (unsigned)idx < (unsigned)sz ? saturate_cast<BT>(((const float*)H)[idx]*scale) : 0;
}
}
}
int idx1 = cvFloor(*p1*a1 + b1);
bproj[x] = (unsigned)idx0 < (unsigned)sz0 &&
(unsigned)idx1 < (unsigned)sz1 ?
- saturate_cast<BT>(((float*)(H + hstep0*idx0))[idx1]*scale) : 0;
+ saturate_cast<BT>(((const float*)(H + hstep0*idx0))[idx1]*scale) : 0;
}
}
}
bproj[x] = (unsigned)idx0 < (unsigned)sz0 &&
(unsigned)idx1 < (unsigned)sz1 &&
(unsigned)idx2 < (unsigned)sz2 ?
- saturate_cast<BT>(((float*)(H + hstep0*idx0 + hstep1*idx1))[idx2]*scale) : 0;
+ saturate_cast<BT>(((const float*)(H + hstep0*idx0 + hstep1*idx1))[idx2]*scale) : 0;
}
}
}
{
for( x = 0; x < imsize.width; x++ )
{
- uchar* Hptr = H;
+ const uchar* Hptr = H;
for( i = 0; i < dims; i++ )
{
int idx = cvFloor(*ptrs[i]*uniranges[i*2] + uniranges[i*2+1]);
}
if( i == dims )
- bproj[x] = saturate_cast<BT>(*(float*)Hptr*scale);
+ bproj[x] = saturate_cast<BT>(*(const float*)Hptr*scale);
else
{
bproj[x] = 0;
{
for( x = 0; x < imsize.width; x++ )
{
- uchar* Hptr = H;
+ const uchar* Hptr = H;
for( i = 0; i < dims; i++ )
{
float v = (float)*ptrs[i];
}
if( i == dims )
- bproj[x] = saturate_cast<BT>(*(float*)Hptr*scale);
+ bproj[x] = saturate_cast<BT>(*(const float*)Hptr*scale);
else
{
bproj[x] = 0;
{
uchar** ptrs = &_ptrs[0];
const int* deltas = &_deltas[0];
- uchar* H = hist.data;
+ const uchar* H = hist.ptr();
int i, x;
uchar* bproj = _ptrs[dims];
int bpstep = _deltas[dims*2 + 1];
for( x = 0; x < imsize.width; x++, p0 += d0, p1 += d1 )
{
size_t idx = tab[*p0] + tab[*p1 + 256];
- bproj[x] = idx < OUT_OF_RANGE ? saturate_cast<uchar>(*(float*)(H + idx)*scale) : 0;
+ bproj[x] = idx < OUT_OF_RANGE ? saturate_cast<uchar>(*(const float*)(H + idx)*scale) : 0;
}
}
}
for( x = 0; x < imsize.width; x++, p0 += d0, p1 += d1, p2 += d2 )
{
size_t idx = tab[*p0] + tab[*p1 + 256] + tab[*p2 + 512];
- bproj[x] = idx < OUT_OF_RANGE ? saturate_cast<uchar>(*(float*)(H + idx)*scale) : 0;
+ bproj[x] = idx < OUT_OF_RANGE ? saturate_cast<uchar>(*(const float*)(H + idx)*scale) : 0;
}
}
}
{
for( x = 0; x < imsize.width; x++ )
{
- uchar* Hptr = H;
+ const uchar* Hptr = H;
for( i = 0; i < dims; i++ )
{
size_t idx = tab[*ptrs[i] + i*256];
}
if( i == dims )
- bproj[x] = saturate_cast<uchar>(*(float*)Hptr*scale);
+ bproj[x] = saturate_cast<uchar>(*(const float*)Hptr*scale);
else
{
bproj[x] = 0;
Size imsize;
int dims = hist.dims == 2 && hist.size[1] == 1 ? 1 : hist.dims;
- CV_Assert( dims > 0 && hist.data );
+ CV_Assert( dims > 0 && !hist.empty() );
_backProject.create( images[0].size(), images[0].depth() );
Mat backProject = _backProject.getMat();
histPrepareImages( images, nimages, channels, backProject, dims, hist.size, ranges,
int hsz[CV_CN_MAX+1];
memcpy(hsz, &H0.size[0], H0.dims*sizeof(hsz[0]));
hsz[H0.dims] = hcn;
- H = Mat(H0.dims+1, hsz, H0.depth(), H0.data);
+ H = Mat(H0.dims+1, hsz, H0.depth(), H0.ptr());
}
else
H = H0;
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;
+ const float* h1 = it.planes[0].ptr<float>();
+ const float* h2 = it.planes[1].ptr<float>();
len = it.planes[0].rows*it.planes[0].cols*H1.channels();
if( (method == CV_COMP_CHISQR) || (method == CV_COMP_CHISQR_ALT))
CV_Assert( img.type() == CV_8UC1 );
- const uchar* image = img.data;
+ const uchar* image = img.ptr();
int step = (int)img.step;
int width = img.cols;
int height = img.rows;
threshold = MIN( threshold, 255 );
- const uchar* image_src = img.data;
+ const uchar* image_src = img.ptr();
int step = (int)img.step;
int w = img.cols;
int h = img.rows;
trigtab[n*2+1] = (float)(sin((double)n*theta) * irho);
}
const float* ttab = &trigtab[0];
- uchar* mdata0 = mask.data;
+ uchar* mdata0 = mask.ptr();
std::vector<Point> nzloc;
// stage 1. collect non-zero image points
Point point = nzloc[idx];
Point line_end[2];
float a, b;
- int* adata = (int*)accum.data;
+ int* adata = accum.ptr<int>();
int i = point.y, j = point.x, k, x0, y0, dx0, dy0, xflag;
int good_line;
const int shift = 16;
{
if( good_line )
{
- adata = (int*)accum.data;
+ adata = accum.ptr<int>();
for( int n = 0; n < numangle; n++, adata += numrho )
{
int r = cvRound( j1 * ttab[n*2] + i1 * ttab[n*2+1] );
}
else
{
- cvSeqPushMulti(lines, lx.data, nlines);
+ cvSeqPushMulti(lines, lx.ptr(), nlines);
}
}
{
_arr.create(1, seq->total, seq->flags, -1, true);
Mat arr = _arr.getMat();
- cvCvtSeqToArray(seq, arr.data);
+ cvCvtSeqToArray(seq, arr.ptr());
}
else
_arr.release();
{
uchar* D = dst.data + dst.step*y;
int sy = std::min(cvFloor(y*ify), ssize.height-1);
- const uchar* S = src.data + src.step*sy;
+ const uchar* S = src.ptr(sy);
switch( pix_size )
{
}
if( k1 == ksize )
k0 = std::min(k0, k); // remember the first row that needs to be computed
- srows[k] = (T*)(src.data + src.step*sy);
+ srows[k] = src.template ptr<T>(sy);
prev_sy[k] = sy;
}
continue;
}
- dx = vop((const T*)(src.data + src.step * sy0), D, w);
+ dx = vop(src.template ptr<T>(sy0), D, w);
for( ; dx < w; dx++ )
{
- const T* S = (const T*)(src.data + src.step * sy0) + xofs[dx];
+ const T* S = src.template ptr<T>(sy0) + xofs[dx];
WT sum = 0;
k = 0;
#if CV_ENABLE_UNROLLED
{
if( sy0 + sy >= ssize.height )
break;
- const T* S = (const T*)(src.data + src.step*(sy0 + sy)) + sx0;
+ const T* S = src.template ptr<T>(sy0 + sy) + sx0;
for( int sx = 0; sx < scale_x*cn; sx += cn )
{
if( sx0 + sx >= ssize.width )
int sy = ytab[j].si;
{
- const T* S = (const T*)(src->data + src->step*sy);
+ const T* S = src->template ptr<T>(sy);
for( dx = 0; dx < dsize.width; dx++ )
buf[dx] = (WT)0;
if( dy != prev_dy )
{
- T* D = (T*)(dst->data + dst->step*prev_dy);
+ T* D = dst->template ptr<T>(prev_dy);
for( dx = 0; dx < dsize.width; dx++ )
{
}
{
- T* D = (T*)(dst->data + dst->step*prev_dy);
+ T* D = dst->template ptr<T>(prev_dy);
for( dx = 0; dx < dsize.width; dx++ )
D[dx] = saturate_cast<T>(sum[dx]);
}
CHECK_IPP_STATUS(getBufferSizeFunc(pSpec, dstSize, cn, &bufsize));
CHECK_IPP_STATUS(getSrcOffsetFunc(pSpec, dstOffset, &srcOffset));
- const Ipp8u* pSrc = (const Ipp8u*)src.data + (int)src.step[0] * srcOffset.y + srcOffset.x * cn * itemSize;
- Ipp8u* pDst = (Ipp8u*)dst.data + (int)dst.step[0] * dstOffset.y + dstOffset.x * cn * itemSize;
+ const Ipp8u* pSrc = src.ptr<Ipp8u>(srcOffset.y) + srcOffset.x * cn * itemSize;
+ Ipp8u* pDst = dst.ptr<Ipp8u>(dstOffset.y) + dstOffset.x * cn * itemSize;
AutoBuffer<uchar> buf(bufsize + 64);
uchar* bufptr = alignPtr((uchar*)buf, 32);
{
Size ssize = _src.size(), dsize = _dst.size();
int cn = _src.channels();
- const T* S0 = (const T*)_src.data;
+ const T* S0 = _src.ptr<T>();
size_t sstep = _src.step/sizeof(S0[0]);
Scalar_<T> cval(saturate_cast<T>(_borderValue[0]),
saturate_cast<T>(_borderValue[1]),
for( dy = 0; dy < dsize.height; dy++ )
{
- T* D = (T*)(_dst.data + _dst.step*dy);
- const short* XY = (const short*)(_xy.data + _xy.step*dy);
+ T* D = _dst.ptr<T>(dy);
+ const short* XY = _xy.ptr<short>(dy);
if( cn == 1 )
{
sstep > 0x8000 )
return 0;
- const uchar *S0 = _src.data, *S1 = _src.data + _src.step;
+ const uchar *S0 = _src.ptr(), *S1 = _src.ptr(1);
const short* wtab = cn == 1 ? (const short*)_wtab : &BilinearTab_iC4[0][0][0];
uchar* D = (uchar*)_dst;
__m128i delta = _mm_set1_epi32(INTER_REMAP_COEF_SCALE/2);
Size ssize = _src.size(), dsize = _dst.size();
int cn = _src.channels();
const AT* wtab = (const AT*)_wtab;
- const T* S0 = (const T*)_src.data;
+ const T* S0 = _src.ptr<T>();
size_t sstep = _src.step/sizeof(S0[0]);
Scalar_<T> cval(saturate_cast<T>(_borderValue[0]),
saturate_cast<T>(_borderValue[1]),
for( dy = 0; dy < dsize.height; dy++ )
{
- T* D = (T*)(_dst.data + _dst.step*dy);
- const short* XY = (const short*)(_xy.data + _xy.step*dy);
- const ushort* FXY = (const ushort*)(_fxy.data + _fxy.step*dy);
+ T* D = _dst.ptr<T>(dy);
+ const short* XY = _xy.ptr<short>(dy);
+ const ushort* FXY = _fxy.ptr<ushort>(dy);
int X0 = 0;
bool prevInlier = false;
Size ssize = _src.size(), dsize = _dst.size();
int cn = _src.channels();
const AT* wtab = (const AT*)_wtab;
- const T* S0 = (const T*)_src.data;
+ const T* S0 = _src.ptr<T>();
size_t sstep = _src.step/sizeof(S0[0]);
Scalar_<T> cval(saturate_cast<T>(_borderValue[0]),
saturate_cast<T>(_borderValue[1]),
for( dy = 0; dy < dsize.height; dy++ )
{
- T* D = (T*)(_dst.data + _dst.step*dy);
- const short* XY = (const short*)(_xy.data + _xy.step*dy);
- const ushort* FXY = (const ushort*)(_fxy.data + _fxy.step*dy);
+ T* D = _dst.ptr<T>(dy);
+ const short* XY = _xy.ptr<short>(dy);
+ const ushort* FXY = _fxy.ptr<ushort>(dy);
for( dx = 0; dx < dsize.width; dx++, D += cn )
{
Size ssize = _src.size(), dsize = _dst.size();
int cn = _src.channels();
const AT* wtab = (const AT*)_wtab;
- const T* S0 = (const T*)_src.data;
+ const T* S0 = _src.ptr<T>();
size_t sstep = _src.step/sizeof(S0[0]);
Scalar_<T> cval(saturate_cast<T>(_borderValue[0]),
saturate_cast<T>(_borderValue[1]),
for( dy = 0; dy < dsize.height; dy++ )
{
- T* D = (T*)(_dst.data + _dst.step*dy);
- const short* XY = (const short*)(_xy.data + _xy.step*dy);
- const ushort* FXY = (const ushort*)(_fxy.data + _fxy.step*dy);
+ T* D = _dst.ptr<T>(dy);
+ const short* XY = _xy.ptr<short>(dy);
+ const ushort* FXY = _fxy.ptr<ushort>(dy);
for( dx = 0; dx < dsize.width; dx++, D += cn )
{
if( nnfunc )
{
- if( m1->type() == CV_16SC2 && !m2->data ) // the data is already in the right format
+ if( m1->type() == CV_16SC2 && m2->empty() ) // the data is already in the right format
bufxy = (*m1)(Rect(x, y, bcols, brows));
else if( map_depth != CV_32F )
{
for( y1 = 0; y1 < brows; y1++ )
{
- short* XY = (short*)(bufxy.data + bufxy.step*y1);
- const short* sXY = (const short*)(m1->data + m1->step*(y+y1)) + x*2;
- const ushort* sA = (const ushort*)(m2->data + m2->step*(y+y1)) + x;
+ short* XY = bufxy.ptr<short>(y1);
+ const short* sXY = m1->ptr<short>(y+y1) + x*2;
+ const ushort* sA = m2->ptr<ushort>(y+y1) + x;
for( x1 = 0; x1 < bcols; x1++ )
{
{
for( y1 = 0; y1 < brows; y1++ )
{
- short* XY = (short*)(bufxy.data + bufxy.step*y1);
- const float* sX = (const float*)(m1->data + m1->step*(y+y1)) + x;
- const float* sY = (const float*)(m2->data + m2->step*(y+y1)) + x;
+ short* XY = bufxy.ptr<short>(y1);
+ const float* sX = m1->ptr<float>(y+y1) + x;
+ const float* sY = m2->ptr<float>(y+y1) + x;
x1 = 0;
#if CV_SSE2
Mat bufa(_bufa, Rect(0, 0, bcols, brows));
for( y1 = 0; y1 < brows; y1++ )
{
- short* XY = (short*)(bufxy.data + bufxy.step*y1);
- ushort* A = (ushort*)(bufa.data + bufa.step*y1);
+ short* XY = bufxy.ptr<short>(y1);
+ ushort* A = bufa.ptr<ushort>(y1);
if( m1->type() == CV_16SC2 && (m2->type() == CV_16UC1 || m2->type() == CV_16SC1) )
{
bufxy = (*m1)(Rect(x, y, bcols, brows));
- const ushort* sA = (const ushort*)(m2->data + m2->step*(y+y1)) + x;
+ const ushort* sA = m2->ptr<ushort>(y+y1) + x;
for( x1 = 0; x1 < bcols; x1++ )
A[x1] = (ushort)(sA[x1] & (INTER_TAB_SIZE2-1));
}
else if( planar_input )
{
- const float* sX = (const float*)(m1->data + m1->step*(y+y1)) + x;
- const float* sY = (const float*)(m2->data + m2->step*(y+y1)) + x;
+ const float* sX = m1->ptr<float>(y+y1) + x;
+ const float* sY = m2->ptr<float>(y+y1) + x;
x1 = 0;
#if CV_SSE2
}
else
{
- const float* sXY = (const float*)(m1->data + m1->step*(y+y1)) + x*2;
+ const float* sXY = m1->ptr<float>(y+y1) + x*2;
for( x1 = 0; x1 < bcols; x1++ )
{
Mat scalar(1, 1, sctype, borderValue);
ocl::KernelArg srcarg = ocl::KernelArg::ReadOnly(src), dstarg = ocl::KernelArg::WriteOnly(dst),
map1arg = ocl::KernelArg::ReadOnlyNoSize(map1),
- scalararg = ocl::KernelArg::Constant((void*)scalar.data, scalar.elemSize());
+ scalararg = ocl::KernelArg::Constant((void*)scalar.ptr(), scalar.elemSize());
if (map2.empty())
k.args(srcarg, dstarg, map1arg, scalararg);
int type = dst.type(), depth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type);
if (borderType == BORDER_CONSTANT &&
- !IPPSet(borderValue, dstRoi.data, (int)dstRoi.step, dstRoiSize, cn, depth))
+ !IPPSet(borderValue, dstRoi.ptr(), (int)dstRoi.step, dstRoiSize, cn, depth))
{
*ok = false;
return;
}
- if (ippFunc(src.data, ippiSize(src.size()), (int)src.step, srcRoiRect,
- (const Ipp32f *)map1.data, (int)map1.step, (const Ipp32f *)map2.data, (int)map2.step,
- dstRoi.data, (int)dstRoi.step, dstRoiSize, ippInterpolation) < 0)
+ if (ippFunc(src.ptr(), ippiSize(src.size()), (int)src.step, srcRoiRect,
+ map1.ptr<Ipp32f>(), (int)map1.step, map2.ptr<Ipp32f>(), (int)map2.step,
+ dstRoi.ptr(), (int)dstRoi.step, dstRoiSize, ippInterpolation) < 0)
*ok = false;
}
const Mat *m1 = &map1, *m2 = &map2;
- if( (map1.type() == CV_16SC2 && (map2.type() == CV_16UC1 || map2.type() == CV_16SC1 || !map2.data)) ||
- (map2.type() == CV_16SC2 && (map1.type() == CV_16UC1 || map1.type() == CV_16SC1 || !map1.data)) )
+ if( (map1.type() == CV_16SC2 && (map2.type() == CV_16UC1 || map2.type() == CV_16SC1 || map2.empty())) ||
+ (map2.type() == CV_16SC2 && (map1.type() == CV_16UC1 || map1.type() == CV_16SC1 || map1.empty())) )
{
if( map1.type() != CV_16SC2 )
std::swap(m1, m2);
}
else
{
- CV_Assert( ((map1.type() == CV_32FC2 || map1.type() == CV_16SC2) && !map2.data) ||
+ CV_Assert( ((map1.type() == CV_32FC2 || map1.type() == CV_16SC2) && map2.empty()) ||
(map1.type() == CV_32FC1 && map2.type() == CV_32FC1) );
planar_input = map1.channels() == 1;
}
CV_Assert( (m1type == CV_16SC2 && (nninterpolate || m2type == CV_16UC1 || m2type == CV_16SC1)) ||
(m2type == CV_16SC2 && (nninterpolate || m1type == CV_16UC1 || m1type == CV_16SC1)) ||
(m1type == CV_32FC1 && m2type == CV_32FC1) ||
- (m1type == CV_32FC2 && !m2->data) );
+ (m1type == CV_32FC2 && m2->empty()) );
if( m2type == CV_16SC2 )
{
(m1type == CV_32FC2 && dstm1type == CV_16SC2))) )
{
m1->convertTo( dstmap1, dstmap1.type() );
- if( dstmap2.data && dstmap2.type() == m2->type() )
+ if( !dstmap2.empty() && dstmap2.type() == m2->type() )
m2->copyTo( dstmap2 );
return;
}
return;
}
- if( m1->isContinuous() && (!m2->data || m2->isContinuous()) &&
- dstmap1.isContinuous() && (!dstmap2.data || dstmap2.isContinuous()) )
+ if( m1->isContinuous() && (m2->empty() || m2->isContinuous()) &&
+ dstmap1.isContinuous() && (dstmap2.empty() || dstmap2.isContinuous()) )
{
size.width *= size.height;
size.height = 1;
int x, y;
for( y = 0; y < size.height; y++ )
{
- const float* src1f = (const float*)(m1->data + m1->step*y);
- const float* src2f = (const float*)(m2->data + m2->step*y);
+ const float* src1f = m1->ptr<float>(y);
+ const float* src2f = m2->ptr<float>(y);
const short* src1 = (const short*)src1f;
const ushort* src2 = (const ushort*)src2f;
- float* dst1f = (float*)(dstmap1.data + dstmap1.step*y);
- float* dst2f = (float*)(dstmap2.data + dstmap2.step*y);
+ float* dst1f = dstmap1.ptr<float>(y);
+ float* dst2f = dstmap2.ptr<float>(y);
short* dst1 = (short*)dst1f;
ushort* dst2 = (ushort*)dst2f;
if( borderType == BORDER_CONSTANT )
{
IppiSize setSize = { dst.cols, range.end - range.start };
- void *dataPointer = dst.data + dst.step[0] * range.start;
+ void *dataPointer = dst.ptr(range.start);
if( !IPPSet( borderValue, dataPointer, (int)dst.step[0], setSize, cnn, src.depth() ) )
{
*ok = false;
}
// Aug 2013: problem in IPP 7.1, 8.0 : sometimes function return ippStsCoeffErr
- IppStatus status = func( src.data, srcsize, (int)src.step[0], srcroi, dst.data,
+ IppStatus status = func( src.ptr(), srcsize, (int)src.step[0], srcroi, dst.ptr(),
(int)dst.step[0], dstroi, coeffs, mode );
if( status < 0)
*ok = false;
if( borderType == BORDER_CONSTANT )
{
IppiSize setSize = {dst.cols, range.end - range.start};
- void *dataPointer = dst.data + dst.step[0] * range.start;
+ void *dataPointer = dst.ptr(range.start);
if( !IPPSet( borderValue, dataPointer, (int)dst.step[0], setSize, cnn, src.depth() ) )
{
*ok = false;
}
}
- IppStatus status = func(src.data, srcsize, (int)src.step[0], srcroi, dst.data, (int)dst.step[0], dstroi, coeffs, mode);
+ IppStatus status = func(src.ptr(), srcsize, (int)src.step[0], srcroi, dst.ptr(), (int)dst.step[0], dstroi, coeffs, mode);
if (status != ippStsNoErr)
*ok = false;
}
double beta = sin(angle)*scale;
Mat M(2, 3, CV_64F);
- double* m = (double*)M.data;
+ double* m = M.ptr<double>();
m[0] = alpha;
m[1] = beta;
*/
cv::Mat cv::getPerspectiveTransform( const Point2f src[], const Point2f dst[] )
{
- Mat M(3, 3, CV_64F), X(8, 1, CV_64F, M.data);
+ Mat M(3, 3, CV_64F), X(8, 1, CV_64F, M.ptr());
double a[8][8], b[8];
Mat A(8, 8, CV_64F, a), B(8, 1, CV_64F, b);
}
solve( A, B, X, DECOMP_SVD );
- ((double*)M.data)[8] = 1.;
+ M.ptr<double>()[8] = 1.;
return M;
}
cv::Mat cv::getAffineTransform( const Point2f src[], const Point2f dst[] )
{
- Mat M(2, 3, CV_64F), X(6, 1, CV_64F, M.data);
+ Mat M(2, 3, CV_64F), X(6, 1, CV_64F, M.ptr());
double a[6*6], b[6];
Mat A(6, 6, CV_64F, a), B(6, 1, CV_64F, b);
if( matM.type() == CV_32F )
{
- const float* M = (const float*)matM.data;
- float* iM = (float*)_iM.data;
+ const float* M = matM.ptr<float>();
+ float* iM = _iM.ptr<float>();
int step = (int)(matM.step/sizeof(M[0])), istep = (int)(_iM.step/sizeof(iM[0]));
double D = M[0]*M[step+1] - M[1]*M[step];
}
else if( matM.type() == CV_64F )
{
- const double* M = (const double*)matM.data;
- double* iM = (double*)_iM.data;
+ const double* M = matM.ptr<double>();
+ double* iM = _iM.ptr<double>();
int step = (int)(matM.step/sizeof(M[0])), istep = (int)(_iM.step/sizeof(iM[0]));
double D = M[0]*M[step+1] - M[1]*M[step];
{
dstmap2 = cv::cvarrToMat(dstarr2);
if( dstmap2.type() == CV_16SC1 )
- dstmap2 = cv::Mat(dstmap2.size(), CV_16UC1, dstmap2.data, dstmap2.step);
+ dstmap2 = cv::Mat(dstmap2.size(), CV_16UC1, dstmap2.ptr(), dstmap2.step);
}
cv::convertMaps( map1, map2, dstmap1, dstmap2, dstmap1.type(), false );
for(size_t i = 0, list_size = list.size(); i < list_size; ++i)
{
unsigned int adx = list[i].p.x + list[i].p.y * img_width;
- if((used.data[adx] == NOTUSED) && (angles_data[adx] != NOTDEF))
+ if((used.ptr()[adx] == NOTUSED) && (angles_data[adx] != NOTDEF))
{
int reg_size;
double reg_angle;
reg[0].x = s.x;
reg[0].y = s.y;
int addr = s.x + s.y * img_width;
- reg[0].used = used.data + addr;
+ reg[0].used = used.ptr() + addr;
reg_angle = angles_data[addr];
reg[0].angle = reg_angle;
reg[0].modgrad = modgrad_data[addr];
int c_addr = xx_min + yy * img_width;
for(int xx = xx_min; xx <= xx_max; ++xx, ++c_addr)
{
- if((used.data[c_addr] != USED) &&
+ if((used.ptr()[c_addr] != USED) &&
(isAligned(c_addr, reg_angle, prec)))
{
// Add point
- used.data[c_addr] = USED;
+ used.ptr()[c_addr] = USED;
RegionPoint& region_point = reg[reg_size];
region_point.x = xx;
region_point.y = yy;
- region_point.used = &(used.data[c_addr]);
+ region_point.used = &(used.ptr()[c_addr]);
region_point.modgrad = modgrad_data[c_addr];
const double& angle = angles_data[c_addr];
region_point.angle = angle;
for (unsigned int i = 0; i < I1.total(); ++i)
{
- uchar i1 = I1.data[i];
- uchar i2 = I2.data[i];
+ uchar i1 = I1.ptr()[i];
+ uchar i2 = I2.ptr()[i];
if (i1 || i2)
{
unsigned int base_idx = i * 3;
- if (i1) img.data[base_idx] = 255;
- else img.data[base_idx] = 0;
- img.data[base_idx + 1] = 0;
- if (i2) img.data[base_idx + 2] = 255;
- else img.data[base_idx + 2] = 0;
+ if (i1) img.ptr()[base_idx] = 255;
+ else img.ptr()[base_idx] = 0;
+ img.ptr()[base_idx + 1] = 0;
+ if (i2) img.ptr()[base_idx + 2] = 255;
+ else img.ptr()[base_idx + 2] = 0;
}
}
}
Moments m;
int lpt = contour.checkVector(2);
int is_float = contour.depth() == CV_32F;
- const Point* ptsi = (const Point*)contour.data;
- const Point2f* ptsf = (const Point2f*)contour.data;
+ const Point* ptsi = contour.ptr<Point>();
+ const Point2f* ptsf = contour.ptr<Point2f>();
CV_Assert( contour.depth() == CV_32S || contour.depth() == CV_32F );
for( y = 0; y < size.height; y++ )
{
- const T* ptr = (const T*)(img.data + y*img.step);
+ const T* ptr = img.ptr<T>(y);
WT x0 = 0, x1 = 0, x2 = 0;
MT x3 = 0;
x = vop(ptr, size.width, x0, x1, x2, x3);
_hu.create(7, 1, CV_64F);
Mat hu = _hu.getMat();
CV_Assert( hu.isContinuous() );
- HuMoments(m, (double*)hu.data);
+ HuMoments(m, hu.ptr<double>());
}
for( i = 0; i < ksize.height; i++ )
{
- uchar* ptr = elem.data + i*elem.step;
+ uchar* ptr = elem.ptr(i);
int j1 = 0, j2 = 0;
if( shape == MORPH_RECT || (shape == MORPH_CROSS && i == anchor.y) )
return false;\
IppiMorphState *pSpec = (IppiMorphState*)ippMalloc(specSize);\
Ipp8u *pBuffer = (Ipp8u*)ippMalloc(bufferSize);\
- if (0 > ippiMorphologyBorderInit_##flavor(roiSize.width, kernel.data, kernelSize, pSpec, pBuffer))\
+ if (0 > ippiMorphologyBorderInit_##flavor(roiSize.width, kernel.ptr(), kernelSize, pSpec, pBuffer))\
{\
ippFree(pBuffer);\
ippFree(pSpec);\
}\
bool ok = false;\
if (op == MORPH_ERODE)\
- ok = (0 <= ippiErodeBorder_##flavor((Ipp##data_type *)_src->data, (int)_src->step[0], (Ipp##data_type *)dst.data, (int)dst.step[0],\
+ ok = (0 <= ippiErodeBorder_##flavor(_src->ptr<Ipp##data_type>(), (int)_src->step[0], dst.ptr<Ipp##data_type>(), (int)dst.step[0],\
roiSize, ippBorderRepl, 0, pSpec, pBuffer));\
else\
- ok = (0 <= ippiDilateBorder_##flavor((Ipp##data_type *)_src->data, (int)_src->step[0], (Ipp##data_type *)dst.data, (int)dst.step[0],\
+ ok = (0 <= ippiDilateBorder_##flavor(_src->ptr<Ipp##data_type>(), (int)_src->step[0], dst.ptr<Ipp##data_type>(), (int)dst.step[0],\
roiSize, ippBorderRepl, 0, pSpec, pBuffer));\
ippFree(pBuffer);\
ippFree(pSpec);\
{\
int specSize = 0;\
int bufferSize = 0;\
- if (0 > ippiMorphologyGetSize_##flavor( roiSize.width, kernel.data kernelSize, &specSize))\
+ if (0 > ippiMorphologyGetSize_##flavor( roiSize.width, kernel.ptr() kernelSize, &specSize))\
return false;\
bool ok = false;\
IppiMorphState* pState = (IppiMorphState*)ippMalloc(specSize);\
- if (ippiMorphologyInit_##flavor(roiSize.width, kernel.data, kernelSize, point, pState) >= 0)\
+ if (ippiMorphologyInit_##flavor(roiSize.width, kernel.ptr(), kernelSize, point, pState) >= 0)\
{\
if (op == MORPH_ERODE)\
- ok = ippiErodeBorderReplicate_##flavor((Ipp##data_type *)_src->data, (int)_src->step[0],\
- (Ipp##data_type *)dst.data, (int)dst.step[0],\
+ ok = ippiErodeBorderReplicate_##flavor(_src->ptr<Ipp##data_type>(), (int)_src->step[0],\
+ dst.ptr<Ipp##data_type>(), (int)dst.step[0],\
roiSize, ippBorderRepl, pState ) >= 0;\
else\
- ok = ippiDilateBorderReplicate_##flavor((Ipp##data_type *)_src->data, (int)_src->step[0],\
- (Ipp##data_type *)dst.data, (int)dst.step[0],\
+ ok = ippiDilateBorderReplicate_##flavor(_src->ptr<Ipp##data_type>(), (int)_src->step[0],\
+ dst.ptr<Ipp##data_type>(), (int)dst.step[0],\
roiSize, ippBorderRepl, pState ) >= 0;\
}\
ippFree(pState);\
AutoBuffer<uchar> buf(bufSize + 64);\
uchar* buffer = alignPtr((uchar*)buf, 32);\
if (op == MORPH_ERODE)\
- return (0 <= ippiFilterMinBorderReplicate_##flavor((Ipp##data_type *)_src->data, (int)_src->step[0], (Ipp##data_type *)dst.data, (int)dst.step[0], roiSize, kernelSize, point, buffer));\
- return (0 <= ippiFilterMaxBorderReplicate_##flavor((Ipp##data_type *)_src->data, (int)_src->step[0], (Ipp##data_type *)dst.data, (int)dst.step[0], roiSize, kernelSize, point, buffer));\
+ return (0 <= ippiFilterMinBorderReplicate_##flavor(_src->ptr<Ipp##data_type>(), (int)_src->step[0], dst.ptr<Ipp##data_type>(), (int)dst.step[0], roiSize, kernelSize, point, buffer));\
+ return (0 <= ippiFilterMaxBorderReplicate_##flavor(_src->ptr<Ipp##data_type>(), (int)_src->step[0], dst.ptr<Ipp##data_type>(), (int)dst.step[0], roiSize, kernelSize, point, buffer));\
}\
break;
}
}
- Size ksize = kernel.data ? kernel.size() : Size(3,3);
+ Size ksize = !kernel.empty() ? kernel.size() : Size(3,3);
_dst.create( src.size(), src.type() );
Mat dst = _dst.getMat();
}
bool rectKernel = false;
- if( !kernel.data )
+ if( kernel.empty() )
{
ksize = Size(1+iterations*2,1+iterations*2);
anchor = Point(iterations, iterations);
int type = _src.type(), depth = CV_MAT_DEPTH(type),
cn = CV_MAT_CN(type), esz = CV_ELEM_SIZE(type);
Mat kernel = _kernel.getMat();
- Size ksize = kernel.data ? kernel.size() : Size(3, 3), ssize = _src.size();
+ Size ksize = !kernel.empty() ? kernel.size() : Size(3, 3), ssize = _src.size();
bool doubleSupport = dev.doubleFPConfig() > 0;
if ((depth == CV_64F && !doubleSupport) || borderType != BORDER_CONSTANT)
bool haveExtraMat = !_extraMat.empty();
CV_Assert(actual_op <= 3 || haveExtraMat);
- if (!kernel.data)
+ if (kernel.empty())
{
kernel = getStructuringElement(MORPH_RECT, Size(1+iterations*2,1+iterations*2));
anchor = Point(iterations, iterations);
if (dev.isIntel() && !(dev.type() & ocl::Device::TYPE_CPU) &&
((ksize.width < 5 && ksize.height < 5 && esz <= 4) ||
(ksize.width == 5 && ksize.height == 5 && cn == 1)) &&
- (iterations == 1))
+ (iterations == 1) && cn == 1)
{
if (ocl_morphSmall(_src, _dst, kernel, anchor, borderType, op, actual_op, _extraMat))
return true;
return true;
}
-#ifdef ANDROID
+#if defined ANDROID
size_t localThreads[2] = { 16, 8 };
#else
size_t localThreads[2] = { 16, 16 };
#endif
size_t globalThreads[2] = { ssize.width, ssize.height };
+#ifdef __APPLE__
+ if( actual_op != MORPH_ERODE && actual_op != MORPH_DILATE )
+ localThreads[0] = localThreads[1] = 4;
+#endif
+
if (localThreads[0]*localThreads[1] * 2 < (localThreads[0] + ksize.width - 1) * (localThreads[1] + ksize.height - 1))
return false;
int borderType, const Scalar& borderValue )
{
Mat kernel = _kernel.getMat();
- Size ksize = kernel.data ? kernel.size() : Size(3,3);
+ Size ksize = !kernel.empty() ? kernel.size() : Size(3,3);
anchor = normalizeAnchor(anchor, ksize);
CV_OCL_RUN(_dst.isUMat() && _src.dims() <= 2 && _src.channels() <= 4 &&
return;
}
- if (!kernel.data)
+ if (kernel.empty())
{
kernel = getStructuringElement(MORPH_RECT, Size(1+iterations*2,1+iterations*2));
anchor = Point(iterations, iterations);
{
cv::Mat elem = cv::getStructuringElement(shape, ksize, anchor);
for( i = 0; i < size; i++ )
- element->values[i] = elem.data[i];
+ element->values[i] = elem.ptr()[i];
}
return element;
int i, size = src->nRows*src->nCols;
for( i = 0; i < size; i++ )
- dst.data[i] = (uchar)(src->values[i] != 0);
+ dst.ptr()[i] = (uchar)(src->values[i] != 0);
}
#define OUT_OF_RANGE -1
+// for identical rounding after dividing on different platforms
+#define ROUNDING_EPS 0.000001f
+
#if histdims == 1
__kernel void calcLUT(__global const uchar * histptr, int hist_step, int hist_offset, int hist_bins,
{
float lb = ranges[0], ub = ranges[1], gap = (ub - lb) / hist_bins;
value -= lb;
- int bin = convert_int_sat_rtn(value / gap);
+ int bin = convert_int_sat_rtn(value / gap + ROUNDING_EPS);
if (bin >= hist_bins)
lut[x] = OUT_OF_RANGE;
{
float lb = ranges[0], ub = ranges[1], gap = (ub - lb) / hist_bins;
value -= lb;
- int bin = convert_int_sat_rtn(value / gap);
+ int bin = convert_int_sat_rtn(value / gap + ROUNDING_EPS);
lut[x] = bin >= hist_bins ? OUT_OF_RANGE : bin;
}
}
#endif
-WT getBorderPixel(const struct RectCoords bounds, int2 coord,
+inline WT getBorderPixel(const struct RectCoords bounds, int2 coord,
__global const uchar* srcptr, int srcstep)
{
#ifdef BORDER_CONSTANT
#define vload1(OFFSET, PTR) (*(PTR + OFFSET))
#define PX_LOAD_VEC_TYPE CAT(srcT1, PX_LOAD_VEC_SIZE)
#define PX_LOAD_FLOAT_VEC_TYPE CAT(WT1, PX_LOAD_VEC_SIZE)
-#define PX_LOAD_FLOAT_VEC_CONV CAT(convert_, PX_LOAD_FLOAT_VEC_TYPE)
+//#define PX_LOAD_FLOAT_VEC_CONV CAT(convert_, PX_LOAD_FLOAT_VEC_TYPE)
+
+#if PX_LOAD_VEC_SIZE == 1
+#define PX_LOAD_FLOAT_VEC_CONV (float)
+#elif PX_LOAD_VEC_SIZE == 2
+#define PX_LOAD_FLOAT_VEC_CONV convert_float2
+#elif PX_LOAD_VEC_SIZE == 3
+#define PX_LOAD_FLOAT_VEC_CONV convert_float3
+#elif PX_LOAD_VEC_SIZE == 4
+#define PX_LOAD_FLOAT_VEC_CONV convert_float4
+#endif
+
#define PX_LOAD CAT(vload, PX_LOAD_VEC_SIZE)
#define float1 float
#define vload1(OFFSET, PTR) (*(PTR + OFFSET))
#define PX_LOAD_VEC_TYPE CAT(srcT1, PX_LOAD_VEC_SIZE)
#define PX_LOAD_FLOAT_VEC_TYPE CAT(WT1, PX_LOAD_VEC_SIZE)
-#define PX_LOAD_FLOAT_VEC_CONV CAT(convert_, PX_LOAD_FLOAT_VEC_TYPE)
+
+#if PX_LOAD_VEC_SIZE == 1
+#define PX_LOAD_FLOAT_VEC_CONV (float)
+#elif PX_LOAD_VEC_SIZE == 2
+#define PX_LOAD_FLOAT_VEC_CONV convert_float2
+#elif PX_LOAD_VEC_SIZE == 3
+#define PX_LOAD_FLOAT_VEC_CONV convert_float3
+#elif PX_LOAD_VEC_SIZE == 4
+#define PX_LOAD_FLOAT_VEC_CONV convert_float4
+#endif
+
+//#define PX_LOAD_FLOAT_VEC_CONV CAT(convert_, PX_LOAD_FLOAT_VEC_TYPE)
#define PX_LOAD CAT(vload, PX_LOAD_VEC_SIZE)
// workaround for bug in Intel HD graphics drivers (10.18.10.3496 or older)
#define WA_CONVERT_1 CAT(convert_uint, cn)
#define WA_CONVERT_2 CAT(convert_, srcT)
-#define MORPH_OP(A, B) WA_CONVERT_2(min(WA_CONVERT_1(A), WA_CONVERT_1(B)))
+#define MORPH_OP(A, B) ((A) < (B) ? (A) : (B))
#else
#define MORPH_OP(A, B) min((A), (B))
#endif
#ifdef OP_ERODE
#if defined INTEL_DEVICE && defined DEPTH_0
-// workaround for bug in Intel HD graphics drivers (10.18.10.3496 or older)
-#define __CAT(x, y) x##y
-#define CAT(x, y) __CAT(x, y)
-#define WA_CONVERT_1 CAT(convert_uint, cn)
-#define WA_CONVERT_2 CAT(convert_, T)
-#define convert_uint1 convert_uint
-#define MORPH_OP(A, B) WA_CONVERT_2(min(WA_CONVERT_1(A), WA_CONVERT_1(B)))
+#define MORPH_OP(A, B) ((A) < (B) ? (A) : (B))
#else
#define MORPH_OP(A, B) min((A), (B))
#endif
#endif
#define PROCESS(y, x) \
- res = MORPH_OP(res, LDS_DAT[mad24(l_y + y, width, l_x + x)]);
+ temp = LDS_DAT[mad24(l_y + y, width, l_x + x)]; \
+ res = MORPH_OP(res, temp);
// BORDER_CONSTANT: iiiiii|abcdefgh|iiiiiii
#define ELEM(i, l_edge, r_edge, elem1, elem2) (i) < (l_edge) | (i) >= (r_edge) ? (elem1) : (elem2)
if (gidx < cols && gidy < rows)
{
- T res = (T)(VAL);
+ T res = (T)(VAL), temp;
PROCESS_ELEMS;
int dst_index = mad24(gidy, dst_step, mad24(gidx, TSIZE, dst_offset));
#if cn == 1
#define READ_IMAGE(X,Y,Z) read_imagef(X,Y,Z).x
+#define INTERMEDIATE_TYPE float
#elif cn == 2
#define READ_IMAGE(X,Y,Z) read_imagef(X,Y,Z).xy
+#define INTERMEDIATE_TYPE float2
#elif cn == 3
#define READ_IMAGE(X,Y,Z) read_imagef(X,Y,Z).xyz
+#define INTERMEDIATE_TYPE float3
#elif cn == 4
#define READ_IMAGE(X,Y,Z) read_imagef(X,Y,Z)
+#define INTERMEDIATE_TYPE float4
#endif
#define __CAT(x, y) x##y
#define CAT(x, y) __CAT(x, y)
-#define INTERMEDIATE_TYPE CAT(float, cn)
+//#define INTERMEDIATE_TYPE CAT(float, cn)
#define float1 float
#if depth == 0
if( depth == CV_32F )
{
- const float* dataSrc = (const float*)src.data;
- float* dataDst = (float*)dst.data;
+ const float* dataSrc = src.ptr<float>();
+ float* dataDst = dst.ptr<float>();
size_t stepSrc = src.step/sizeof(dataSrc[0]);
size_t stepDst = dst.step/sizeof(dataDst[0]);
}
else
{
- const double* dataSrc = (const double*)src.data;
- double* dataDst = (double*)dst.data;
+ const double* dataSrc = src.ptr<double>();
+ double* dataDst = dst.ptr<double>();
size_t stepSrc = src.step/sizeof(dataSrc[0]);
size_t stepDst = dst.step/sizeof(dataDst[0]);
if( depth == CV_32F )
{
- const float* dataA = (const float*)srcA.data;
- const float* dataB = (const float*)srcB.data;
- float* dataC = (float*)dst.data;
+ const float* dataA = srcA.ptr<float>();
+ const float* dataB = srcB.ptr<float>();
+ float* dataC = dst.ptr<float>();
float eps = FLT_EPSILON; // prevent div0 problems
size_t stepA = srcA.step/sizeof(dataA[0]);
}
else
{
- const double* dataA = (const double*)srcA.data;
- const double* dataB = (const double*)srcB.data;
- double* dataC = (double*)dst.data;
+ const double* dataA = srcA.ptr<double>();
+ const double* dataB = srcB.ptr<double>();
+ double* dataC = dst.ptr<double>();
double eps = DBL_EPSILON; // prevent div0 problems
size_t stepA = srcA.step/sizeof(dataA[0]);
if(type == CV_32FC1)
{
- const float* dataIn = (const float*)src.data;
+ const float* dataIn = src.ptr<float>();
dataIn += minr*src.cols;
for(int y = minr; y <= maxr; y++)
{
}
else
{
- const double* dataIn = (const double*)src.data;
+ const double* dataIn = src.ptr<double>();
dataIn += minr*src.cols;
for(int y = minr; y <= maxr; y++)
{
extern const float icv8x32fSqrTab[];
#define CV_8TO32F_SQR(x) icv8x32fSqrTab[(x)+128]
-namespace cv
-{
-
-static inline Point normalizeAnchor( Point anchor, Size ksize )
-{
- if( anchor.x == -1 )
- anchor.x = ksize.width/2;
- if( anchor.y == -1 )
- anchor.y = ksize.height/2;
- CV_Assert( anchor.inside(Rect(0, 0, ksize.width, ksize.height)) );
- return anchor;
-}
-
-void preprocess2DKernel( const Mat& kernel, std::vector<Point>& coords, std::vector<uchar>& coeffs );
-void crossCorr( const Mat& src, const Mat& templ, Mat& dst,
- Size corrsize, int ctype,
- Point anchor=Point(0,0), double delta=0,
- int borderType=BORDER_REFLECT_101 );
-
-}
-
-typedef struct CvPyramid
-{
- uchar **ptr;
- CvSize *sz;
- double *rate;
- int *step;
- uchar *state;
- int level;
-}
-CvPyramid;
-
#define CV_COPY( dst, src, len, idx ) \
for( (idx) = 0; (idx) < (len); (idx)++) (dst)[idx] = (src)[idx]
#define CV_CALC_MAX(a, b) if((a) < (b)) (a) = (b)
#include "_geom.h"
+#include "filterengine.hpp"
#endif /*__OPENCV_CV_INTERNAL_H_*/
for( int y = 0; y < dsize.height; y++ )
{
- T* dst = (T*)(_dst.data + _dst.step*y);
+ T* dst = _dst.ptr<T>(y);
WT *row0, *row1, *row2, *row3, *row4;
// fill the ring buffer (horizontal convolution and decimation)
{
WT* row = buf + ((sy - sy0) % PD_SZ)*bufstep;
int _sy = borderInterpolate(sy, ssize.height, borderType);
- const T* src = (const T*)(_src.data + _src.step*_sy);
+ const T* src = _src.ptr<T>(_sy);
int limit = cn;
const int* tab = tabL;
for( int y = 0; y < ssize.height; y++ )
{
- T* dst0 = (T*)(_dst.data + _dst.step*y*2);
- T* dst1 = (T*)(_dst.data + _dst.step*(y*2+1));
+ T* dst0 = _dst.ptr<T>(y*2);
+ T* dst1 = _dst.ptr<T>(y*2+1);
WT *row0, *row1, *row2;
if( y*2+1 >= dsize.height )
{
WT* row = buf + ((sy - sy0) % PU_SZ)*bufstep;
int _sy = borderInterpolate(sy*2, dsize.height, BORDER_REFLECT_101)/2;
- const T* src = (const T*)(_src.data + _src.step*_sy);
+ const T* src = _src.ptr<T>(_sy);
if( ssize.width == cn )
{
}
int n = hull.checkVector(2);
- const Point2f* hpoints = (const Point2f*)hull.data;
+ const Point2f* hpoints = hull.ptr<Point2f>();
if( n > 2 )
{
{
_pts.create(4, 2, CV_32F);
Mat pts = _pts.getMat();
- box.points((Point2f*)pts.data);
+ box.points(pts.ptr<Point2f>());
}
if( ippfunc)
{
- if (ippfunc(image.data, (int)image.step, src_size, patch.data,
+ if (ippfunc(image.ptr(), (int)image.step, src_size, patch.ptr(),
(int)patch.step, win_size, icenter, &minpt, &maxpt) >= 0 )
return;
setIppErrorStatus();
if( depth == CV_8U && ddepth == CV_8U )
getRectSubPix_Cn_<uchar, uchar, int, scale_fixpt, cast_8u>
- (image.data, image.step, image.size(), patch.data, patch.step, patch.size(), center, cn);
+ (image.ptr(), image.step, image.size(), patch.ptr(), patch.step, patch.size(), center, cn);
else if( depth == CV_8U && ddepth == CV_32F )
getRectSubPix_8u32f
- (image.data, image.step, image.size(), (float*)patch.data, patch.step, patch.size(), center, cn);
+ (image.ptr(), image.step, image.size(), patch.ptr<float>(), patch.step, patch.size(), center, cn);
else if( depth == CV_32F && ddepth == CV_32F )
getRectSubPix_Cn_<float, float, float, nop<float>, nop<float> >
- ((const float*)image.data, image.step, image.size(), (float*)patch.data, patch.step, patch.size(), center, cn);
+ (image.ptr<float>(), image.step, image.size(), patch.ptr<float>(), patch.step, patch.size(), center, cn);
else
CV_Error( CV_StsUnsupportedFormat, "Unsupported combination of input and output formats");
}
CV_IMPL void
cvGetQuadrangleSubPix( const void* srcarr, void* dstarr, const CvMat* mat )
{
- cv::Mat src = cv::cvarrToMat(srcarr), m = cv::cvarrToMat(mat);
- const cv::Mat dst = cv::cvarrToMat(dstarr);
+ const cv::Mat src = cv::cvarrToMat(srcarr), m = cv::cvarrToMat(mat);
+ cv::Mat dst = cv::cvarrToMat(dstarr);
CV_Assert( src.channels() == dst.channels() );
matrix[5] -= matrix[3]*dx + matrix[4]*dy;
if( src.depth() == CV_8U && dst.depth() == CV_32F )
- cv::getQuadrangleSubPix_8u32f_CnR( src.data, src.step, src.size(),
- (float*)dst.data, dst.step, dst.size(),
+ cv::getQuadrangleSubPix_8u32f_CnR( src.ptr(), src.step, src.size(),
+ dst.ptr<float>(), dst.step, dst.size(),
matrix, src.channels());
else
{
CV_Assert( src.type() == CV_8UC3 && dst.type() == CV_32SC1 );
CV_Assert( src.size() == dst.size() );
- const uchar* img = src.data;
+ const uchar* img = src.ptr();
int istep = int(src.step/sizeof(img[0]));
int* mask = dst.ptr<int>();
int mstep = int(dst.step / sizeof(mask[0]));
return;
active_queue = i;
- img = src.data;
+ img = src.ptr();
mask = dst.ptr<int>();
// recursively fill the basins
{
cv::Mat src = src_pyramid[level];
cv::Size size = src.size();
- uchar* sptr = src.data;
+ const uchar* sptr = src.ptr();
int sstep = (int)src.step;
uchar* mask = 0;
int mstep = 0;
if( level < max_level )
{
cv::Size size1 = dst_pyramid[level+1].size();
- cv::Mat m( size.height, size.width, CV_8UC1, mask0.data );
+ cv::Mat m( size.height, size.width, CV_8UC1, mask0.ptr() );
dstep = (int)dst_pyramid[level+1].step;
- dptr = dst_pyramid[level+1].data + dstep + cn;
+ dptr = dst_pyramid[level+1].ptr() + dstep + cn;
mstep = (int)m.step;
- mask = m.data + mstep;
+ mask = m.ptr() + mstep;
//cvResize( dst_pyramid[level+1], dst_pyramid[level], CV_INTER_CUBIC );
cv::pyrUp( dst_pyramid[level+1], dst_pyramid[level], dst_pyramid[level].size() );
m.setTo(cv::Scalar::all(0));
}
cv::dilate( m, m, cv::Mat() );
- mask = m.data;
+ mask = m.ptr();
}
- dptr = dst_pyramid[level].data;
+ dptr = dst_pyramid[level].ptr();
dstep = (int)dst_pyramid[level].step;
for( i = 0; i < size.height; i++, sptr += sstep - size.width*3,
// iterate meanshift procedure
for( iter = 0; iter < termcrit.maxCount; iter++ )
{
- uchar* ptr;
+ const uchar* ptr;
int x, y, count = 0;
int minx, miny, maxx, maxy;
int s0 = 0, s1 = 0, s2 = 0, sx = 0, sy = 0;
return;
bool is_float = depth == CV_32F;
- const Point* ptsi = (const Point*)points.data;
- const Point2f* ptsf = (const Point2f*)points.data;
+ const Point* ptsi = points.ptr<Point>();
+ const Point2f* ptsf = points.ptr<Point2f>();
Point2f pt = is_float ? ptsf[0] : Point2f((float)ptsi[0].x,(float)ptsi[0].y);
Point2f pts[4] = {pt, pt, pt, pt};
bool is_float = depth == CV_32F;
int last = is_closed ? count-1 : 0;
- const Point* pti = (const Point*)curve.data;
- const Point2f* ptf = (const Point2f*)curve.data;
+ const Point* pti = curve.ptr<Point>();
+ const Point2f* ptf = curve.ptr<Point2f>();
Point2f prev = is_float ? ptf[last] : Point2f((float)pti[last].x,(float)pti[last].y);
double a00 = 0;
bool is_float = depth == CV_32F;
- const Point* ptsi = (const Point*)contour.data;
- const Point2f* ptsf = (const Point2f*)contour.data;
+ const Point* ptsi = contour.ptr<Point>();
+ const Point2f* ptsf = contour.ptr<Point2f>();
Point2f prev = is_float ? ptsf[npoints-1] : Point2f((float)ptsi[npoints-1].x, (float)ptsi[npoints-1].y);
for( int i = 0; i < npoints; i++ )
double gfp[5], rp[5], t;
const double min_eps = 1e-8;
bool is_float = depth == CV_32F;
- const Point* ptsi = (const Point*)points.data;
- const Point2f* ptsf = (const Point2f*)points.data;
+ const Point* ptsi = points.ptr<Point>();
+ const Point2f* ptsf = points.ptr<Point2f>();
AutoBuffer<double> _Ad(n*5), _bd(n);
double *Ad = _Ad, *bd = _bd;
if( npoints == 0 )
return Rect();
- const Point* pts = (const Point*)points.data;
+ const Point* pts = points.ptr<Point>();
Point pt = pts[0];
#if CV_SSE4_2
if (normalize && !src.isSubmatrix() && ddepth == sdepth &&
(/*ippBorderType == BORDER_REPLICATE ||*/ /* returns ippStsStepErr: Step value is not valid */
- ippBorderType == BORDER_CONSTANT) && ocvAnchor == ippAnchor )
+ ippBorderType == BORDER_CONSTANT) && ocvAnchor == ippAnchor &&
+ dst.cols != ksize.width && dst.rows != ksize.height) // returns ippStsMaskSizeErr: mask has an illegal value
{
Ipp32s bufSize = 0;
IppiSize roiSize = { dst.cols, dst.rows }, maskSize = { ksize.width, ksize.height };
Ipp8u * buffer = ippsMalloc_8u(bufSize); \
ippType borderValue[4] = { 0, 0, 0, 0 }; \
ippBorderType = ippBorderType == BORDER_CONSTANT ? ippBorderConst : ippBorderRepl; \
- IppStatus status = ippiFilterBoxBorder_##flavor((const ippType *)src.data, (int)src.step, (ippType *)dst.data, \
+ IppStatus status = ippiFilterBoxBorder_##flavor(src.ptr<ippType>(), (int)src.step, dst.ptr<ippType>(), \
(int)dst.step, roiSize, maskSize, \
(IppiBorderType)ippBorderType, borderValue, buffer); \
ippsFree(buffer); \
CV_Assert( ktype == CV_32F || ktype == CV_64F );
Mat kernel(n, 1, ktype);
- float* cf = (float*)kernel.data;
- double* cd = (double*)kernel.data;
+ float* cf = kernel.ptr<float>();
+ double* cd = kernel.ptr<double>();
double sigmaX = sigma > 0 ? sigma : ((n-1)*0.5 - 1)*0.3 + 0.8;
double scale2X = -0.5/(sigmaX*sigmaX);
typedef Ipp##ippfavor ippType; \
ippType borderValues[] = { 0, 0, 0 }; \
IppStatus status = ippcn == 1 ? \
- ippiFilterGaussianBorder_##ippfavor##_C1R((const ippType *)src.data, (int)src.step, \
- (ippType *)dst.data, (int)dst.step, roiSize, borderValues[0], pSpec, pBuffer) : \
- ippiFilterGaussianBorder_##ippfavor##_C3R((const ippType *)src.data, (int)src.step, \
- (ippType *)dst.data, (int)dst.step, roiSize, borderValues, pSpec, pBuffer); \
+ ippiFilterGaussianBorder_##ippfavor##_C1R(src.ptr<ippType>(), (int)src.step, \
+ dst.ptr<ippType>(), (int)dst.step, roiSize, borderValues[0], pSpec, pBuffer) : \
+ ippiFilterGaussianBorder_##ippfavor##_C3R(src.ptr<ippType>(), (int)src.step, \
+ dst.ptr<ippType>(), (int)dst.step, roiSize, borderValues, pSpec, pBuffer); \
ippFree(pBuffer); \
ippFree(pSpec); \
if (status >= 0) \
for( int x = 0; x < _dst.cols; x += STRIPE_SIZE )
{
int i, j, k, c, n = std::min(_dst.cols - x, STRIPE_SIZE) + r*2;
- const uchar* src = _src.data + x*cn;
- uchar* dst = _dst.data + (x - r)*cn;
+ const uchar* src = _src.ptr() + x*cn;
+ uchar* dst = _dst.ptr() + (x - r)*cn;
memset( h_coarse, 0, 16*n*cn*sizeof(h_coarse[0]) );
memset( h_fine, 0, 16*16*n*cn*sizeof(h_fine[0]) );
int x, y;
int n2 = m*m/2;
Size size = _dst.size();
- const uchar* src = _src.data;
- uchar* dst = _dst.data;
+ const uchar* src = _src.ptr();
+ uchar* dst = _dst.ptr();
int src_step = (int)_src.step, dst_step = (int)_dst.step;
int cn = _src.channels();
const uchar* src_max = src + size.height*src_step;
typedef typename Op::arg_type WT;
typedef typename VecOp::arg_type VT;
- const T* src = (const T*)_src.data;
- T* dst = (T*)_dst.data;
+ const T* src = _src.ptr<T>();
+ T* dst = _dst.ptr<T>();
int sstep = (int)(_src.step/sizeof(T));
int dstep = (int)(_dst.step/sizeof(T));
Size size = _dst.size();
ippDataType, CV_MAT_CN(type), &bufSize) >= 0) \
{ \
Ipp8u * buffer = ippsMalloc_8u(bufSize); \
- IppStatus status = ippiFilterMedianBorder_##flavor((const ippType *)src0.data, (int)src0.step, \
- (ippType *)dst.data, (int)dst.step, dstRoiSize, maskSize, \
+ IppStatus status = ippiFilterMedianBorder_##flavor(src0.ptr<ippType>(), (int)src0.step, \
+ dst.ptr<ippType>(), (int)dst.step, dstRoiSize, maskSize, \
ippBorderRepl, (ippType)0, buffer); \
ippsFree(buffer); \
if (status >= 0) \
else
CV_Error( CV_StsUnsupportedFormat, "" );
- func( src.data, src.step, sum.data, sum.step, sqsum.data, sqsum.step,
- tilted.data, tilted.step, src.size(), cn );
+ func( src.ptr(), src.step, sum.ptr(), sum.step, sqsum.ptr(), sqsum.step,
+ tilted.ptr(), tilted.step, src.size(), cn );
}
void cv::integral( InputArray src, OutputArray sum, int sdepth )
pBuffer = ippsMalloc_8u( bufSize );
- status = ippFunc(src.data, (int)src.step, srcRoiSize, tpl.data, (int)tpl.step, tplRoiSize, (Ipp32f*)dst.data, (int)dst.step, funCfg, pBuffer);
+ status = ippFunc(src.ptr(), (int)src.step, srcRoiSize, tpl.ptr(), (int)tpl.step, tplRoiSize, dst.ptr<Ipp32f>(), (int)dst.step, funCfg, pBuffer);
ippsFree( pBuffer );
return status >= 0;
pBuffer = ippsMalloc_8u( bufSize );
- status = ippFunc(src.data, (int)src.step, srcRoiSize, tpl.data, (int)tpl.step, tplRoiSize, (Ipp32f*)dst.data, (int)dst.step, funCfg, pBuffer);
+ status = ippFunc(src.ptr(), (int)src.step, srcRoiSize, tpl.ptr(), (int)tpl.step, tplRoiSize, dst.ptr<Ipp32f>(), (int)dst.step, funCfg, pBuffer);
ippsFree( pBuffer );
return status >= 0;
for( i = 0; i < result.rows; i++ )
{
- float* rrow = (float*)(result.data + i*result.step);
+ float* rrow = result.ptr<float>(i);
int idx = i * sumstep;
int idx2 = i * sqstep;
{
case THRESH_TRUNC:
#ifndef HAVE_IPP_ICV_ONLY
- if (_src.data == _dst.data && ippiThreshold_GT_8u_C1IR(_src.data, (int)src_step, sz, thresh) >= 0)
+ if (_src.data == _dst.data && ippiThreshold_GT_8u_C1IR(_dst.ptr(), (int)dst_step, sz, thresh) >= 0)
return;
#endif
- if (ippiThreshold_GT_8u_C1R(_src.data, (int)src_step, _dst.data, (int)dst_step, sz, thresh) >= 0)
+ if (ippiThreshold_GT_8u_C1R(_src.ptr(), (int)src_step, _dst.ptr(), (int)dst_step, sz, thresh) >= 0)
return;
setIppErrorStatus();
break;
case THRESH_TOZERO:
#ifndef HAVE_IPP_ICV_ONLY
- if (_src.data == _dst.data && ippiThreshold_LTVal_8u_C1IR(_src.data, (int)src_step, sz, thresh+1, 0) >= 0)
+ if (_src.data == _dst.data && ippiThreshold_LTVal_8u_C1IR(_dst.ptr(), (int)dst_step, sz, thresh+1, 0) >= 0)
return;
#endif
- if (ippiThreshold_LTVal_8u_C1R(_src.data, (int)src_step, _dst.data, (int)dst_step, sz, thresh+1, 0) >= 0)
+ if (ippiThreshold_LTVal_8u_C1R(_src.ptr(), (int)src_step, _dst.ptr(), (int)dst_step, sz, thresh+1, 0) >= 0)
return;
setIppErrorStatus();
break;
case THRESH_TOZERO_INV:
#ifndef HAVE_IPP_ICV_ONLY
- if (_src.data == _dst.data && ippiThreshold_GTVal_8u_C1IR(_src.data, (int)src_step, sz, thresh, 0) >= 0)
+ if (_src.data == _dst.data && ippiThreshold_GTVal_8u_C1IR(_dst.ptr(), (int)dst_step, sz, thresh, 0) >= 0)
return;
#endif
- if (ippiThreshold_GTVal_8u_C1R(_src.data, (int)src_step, _dst.data, (int)dst_step, sz, thresh, 0) >= 0)
+ if (ippiThreshold_GTVal_8u_C1R(_src.ptr(), (int)src_step, _dst.ptr(), (int)dst_step, sz, thresh, 0) >= 0)
return;
setIppErrorStatus();
break;
for( i = 0; i < roi.height; i++ )
{
- const uchar* src = (const uchar*)(_src.data + src_step*i);
- uchar* dst = (uchar*)(_dst.data + dst_step*i);
+ const uchar* src = _src.ptr() + src_step*i;
+ uchar* dst = _dst.ptr() + dst_step*i;
switch( type )
{
{
for( i = 0; i < roi.height; i++ )
{
- const uchar* src = (const uchar*)(_src.data + src_step*i);
- uchar* dst = (uchar*)(_dst.data + dst_step*i);
+ const uchar* src = _src.ptr() + src_step*i;
+ uchar* dst = _dst.ptr() + dst_step*i;
j = j_scalar;
#if CV_ENABLE_UNROLLED
for( ; j <= roi.width - 4; j += 4 )
int i, j;
Size roi = _src.size();
roi.width *= _src.channels();
- const short* src = (const short*)_src.data;
- short* dst = (short*)_dst.data;
+ const short* src = _src.ptr<short>();
+ short* dst = _dst.ptr<short>();
size_t src_step = _src.step/sizeof(src[0]);
size_t dst_step = _dst.step/sizeof(dst[0]);
int i, j;
Size roi = _src.size();
roi.width *= _src.channels();
- const float* src = (const float*)_src.data;
- float* dst = (float*)_dst.data;
+ const float* src = _src.ptr<float>();
+ float* dst = _dst.ptr<float>();
size_t src_step = _src.step/sizeof(src[0]);
size_t dst_step = _dst.step/sizeof(dst[0]);
IppiSize srcSize = { size.width, size.height };
Ipp8u thresh;
CV_SUPPRESS_DEPRECATED_START
- IppStatus ok = ippiComputeThreshold_Otsu_8u_C1R(_src.data, step, srcSize, &thresh);
+ IppStatus ok = ippiComputeThreshold_Otsu_8u_C1R(_src.ptr(), step, srcSize, &thresh);
CV_SUPPRESS_DEPRECATED_END
if (ok >= 0)
return thresh;
int i, j, h[N] = {0};
for( i = 0; i < size.height; i++ )
{
- const uchar* src = _src.data + step*i;
+ const uchar* src = _src.ptr() + step*i;
j = 0;
#if CV_ENABLE_UNROLLED
for( ; j <= size.width - 4; j += 4 )
for( i = 0; i < size.height; i++ )
{
- const uchar* sdata = src.data + src.step*i;
- const uchar* mdata = mean.data + mean.step*i;
- uchar* ddata = dst.data + dst.step*i;
+ const uchar* sdata = src.ptr(i);
+ const uchar* mdata = mean.ptr(i);
+ uchar* ddata = dst.ptr(i);
for( j = 0; j < size.width; j++ )
ddata[j] = tab[sdata[j] - mdata[j] + 255];
cameraMatrix.convertTo(newCameraMatrix, CV_64F);
if( centerPrincipalPoint )
{
- ((double*)newCameraMatrix.data)[2] = (imgsize.width-1)*0.5;
- ((double*)newCameraMatrix.data)[5] = (imgsize.height-1)*0.5;
+ newCameraMatrix.ptr<double>()[2] = (imgsize.width-1)*0.5;
+ newCameraMatrix.ptr<double>()[5] = (imgsize.height-1)*0.5;
}
return newCameraMatrix;
}
Mat_<double> R = Mat_<double>::eye(3, 3);
Mat_<double> A = Mat_<double>(cameraMatrix), Ar;
- if( newCameraMatrix.data )
+ if( !newCameraMatrix.empty() )
Ar = Mat_<double>(newCameraMatrix);
else
Ar = getDefaultNewCameraMatrix( A, size, true );
- if( matR.data )
+ if( !matR.empty() )
R = Mat_<double>(matR);
- if( distCoeffs.data )
+ if( !distCoeffs.empty() )
distCoeffs = Mat_<double>(distCoeffs);
else
{
if( distCoeffs.rows != 1 && !distCoeffs.isContinuous() )
distCoeffs = distCoeffs.t();
- double k1 = ((double*)distCoeffs.data)[0];
- double k2 = ((double*)distCoeffs.data)[1];
- double p1 = ((double*)distCoeffs.data)[2];
- double p2 = ((double*)distCoeffs.data)[3];
- double k3 = distCoeffs.cols + distCoeffs.rows - 1 >= 5 ? ((double*)distCoeffs.data)[4] : 0.;
- double k4 = distCoeffs.cols + distCoeffs.rows - 1 >= 8 ? ((double*)distCoeffs.data)[5] : 0.;
- double k5 = distCoeffs.cols + distCoeffs.rows - 1 >= 8 ? ((double*)distCoeffs.data)[6] : 0.;
- double k6 = distCoeffs.cols + distCoeffs.rows - 1 >= 8 ? ((double*)distCoeffs.data)[7] : 0.;
- double s1 = distCoeffs.cols + distCoeffs.rows - 1 >= 12 ? ((double*)distCoeffs.data)[8] : 0.;
- double s2 = distCoeffs.cols + distCoeffs.rows - 1 >= 12 ? ((double*)distCoeffs.data)[9] : 0.;
- double s3 = distCoeffs.cols + distCoeffs.rows - 1 >= 12 ? ((double*)distCoeffs.data)[10] : 0.;
- double s4 = distCoeffs.cols + distCoeffs.rows - 1 >= 12 ? ((double*)distCoeffs.data)[11] : 0.;
+ const double* const distPtr = distCoeffs.ptr<double>();
+ double k1 = distPtr[0];
+ double k2 = distPtr[1];
+ double p1 = distPtr[2];
+ double p2 = distPtr[3];
+ double k3 = distCoeffs.cols + distCoeffs.rows - 1 >= 5 ? distPtr[4] : 0.;
+ double k4 = distCoeffs.cols + distCoeffs.rows - 1 >= 8 ? distPtr[5] : 0.;
+ double k5 = distCoeffs.cols + distCoeffs.rows - 1 >= 8 ? distPtr[6] : 0.;
+ double k6 = distCoeffs.cols + distCoeffs.rows - 1 >= 8 ? distPtr[7] : 0.;
+ double s1 = distCoeffs.cols + distCoeffs.rows - 1 >= 12 ? distPtr[8] : 0.;
+ double s2 = distCoeffs.cols + distCoeffs.rows - 1 >= 12 ? distPtr[9] : 0.;
+ double s3 = distCoeffs.cols + distCoeffs.rows - 1 >= 12 ? distPtr[10] : 0.;
+ double s4 = distCoeffs.cols + distCoeffs.rows - 1 >= 12 ? distPtr[11] : 0.;
for( int i = 0; i < size.height; i++ )
{
- float* m1f = (float*)(map1.data + map1.step*i);
- float* m2f = (float*)(map2.data + map2.step*i);
+ float* m1f = map1.ptr<float>(i);
+ float* m2f = map2.empty() ? 0 : map2.ptr<float>(i);
short* m1 = (short*)m1f;
ushort* m2 = (ushort*)m2f;
double _x = i*ir[1] + ir[2], _y = i*ir[4] + ir[5], _w = i*ir[7] + ir[8];
Mat_<double> A, Ar, I = Mat_<double>::eye(3,3);
cameraMatrix.convertTo(A, CV_64F);
- if( distCoeffs.data )
+ if( !distCoeffs.empty() )
distCoeffs = Mat_<double>(distCoeffs);
else
{
distCoeffs = 0.;
}
- if( newCameraMatrix.data )
+ if( !newCameraMatrix.empty() )
newCameraMatrix.convertTo(Ar, CV_64F);
else
A.copyTo(Ar);
CvMat _csrc = src, _cdst = dst, _ccameraMatrix = cameraMatrix;
CvMat matR, matP, _cdistCoeffs, *pR=0, *pP=0, *pD=0;
- if( R.data )
+ if( !R.empty() )
pR = &(matR = R);
- if( P.data )
+ if( !P.empty() )
pP = &(matP = P);
- if( distCoeffs.data )
+ if( !distCoeffs.empty() )
pD = &(_cdistCoeffs = distCoeffs);
cvUndistortPoints(&_csrc, &_cdst, &_ccameraMatrix, pD, pR, pP);
}
for( i = 0; i < size.height; i++ )
{
- const float* sptr = (const float*)(temp.data + (i+radius)*temp.step) + radius*cn;
- float* dptr = (float*)(dst.data + i*dst.step);
+ const float* sptr = temp.ptr<float>(i+radius) + radius*cn;
+ float* dptr = dst.ptr<float>(i);
if( cn == 1 )
{
Mat gold = imread(string(ts->get_data_path()) + "/cvtcolor/bayer_gold.png", IMREAD_UNCHANGED);
Mat result;
- CV_Assert(given.data != NULL && gold.data != NULL);
+ CV_Assert( !given.empty() && !gold.empty() );
cvtColor(given, result, CV_BayerBG2GRAY);
Mat gold = imread(goldfname, IMREAD_UNCHANGED);
Mat result;
- CV_Assert(given.data != NULL);
+ CV_Assert( !given.empty() );
cvtColor(given, result, CV_BayerBG2BGR_VNG, 3);
std::string full_path = parent_path + image_name;
src = imread(full_path, IMREAD_UNCHANGED);
- if (src.data == NULL)
+ if ( src.empty() )
{
ts->set_failed_test_info(cvtest::TS::FAIL_MISSING_TEST_DATA);
ts->printf(cvtest::TS::SUMMARY, "No input image\n");
// reading a reference image
full_path = parent_path + pattern[i] + image_name;
reference = imread(full_path, IMREAD_UNCHANGED);
- if (reference.data == NULL)
+ if ( reference.empty() )
{
imwrite(full_path, dst);
continue;
}
++size.width;
- uchar* firstRow = dst.data, *lastRow = dst.data + size.height * dst.step;
+ uchar* firstRow = dst.ptr(), *lastRow = dst.ptr(size.height);
size.width *= dcn;
for (int x = 0; x < size.width; ++x)
{
for (int y = 0; y < size.height && next; ++y)
{
- const T* A = reinterpret_cast<const T*>(actual.data + actual.step * y);
- const T* R = reinterpret_cast<const T*>(reference.data + reference.step * y);
+ const T* A = actual.ptr<T>(y);
+ const T* R = reference.ptr<T>(y);
for (int x = 0; x < size.width && next; ++x)
if (std::abs(A[x] - R[x]) > 1)
std::string full_path = parent_path + image_name;
src = imread(full_path, IMREAD_UNCHANGED);
- if (src.data == NULL)
+ if (src.empty())
{
ts->set_failed_test_info(cvtest::TS::FAIL_MISSING_TEST_DATA);
ts->printf(cvtest::TS::SUMMARY, "No input image\n");
return code;
}
-
TEST(Imgproc_FindContours, accuracy) { CV_FindContourTest test; test.safe_run(); }
+TEST(Core_Drawing, _914)
+{
+ const int rows = 256;
+ const int cols = 256;
+
+ Mat img(rows, cols, CV_8UC1, Scalar(255));
+
+ line(img, Point(0, 10), Point(255, 10), Scalar(0), 2, 4);
+ line(img, Point(-5, 20), Point(260, 20), Scalar(0), 2, 4);
+ line(img, Point(10, 0), Point(10, 255), Scalar(0), 2, 4);
+
+ double x0 = 0.0/pow(2.0, -2.0);
+ double x1 = 255.0/pow(2.0, -2.0);
+ double y = 30.5/pow(2.0, -2.0);
+
+ line(img, Point(int(x0), int(y)), Point(int(x1), int(y)), Scalar(0), 2, 4, 2);
+
+ int pixelsDrawn = rows*cols - countNonZero(img);
+ ASSERT_EQ( (3*rows + cols)*3 - 3*9, pixelsDrawn);
+}
+
/* End of file. */
if( shape == CV_SHAPE_CUSTOM )
{
eldata.resize(aperture_size.width*aperture_size.height);
- const uchar* src = test_mat[INPUT][1].data;
+ const uchar* src = test_mat[INPUT][1].ptr();
int srcstep = (int)test_mat[INPUT][1].step;
int i, j, nonzero = 0;
mask = test_array[INPUT_OUTPUT][1] ? cv::cvarrToMat(test_array[INPUT_OUTPUT][1]) : cv::Mat();
cv::Rect rect;
int area;
- if( !mask.data )
+ if( mask.empty() )
area = cv::floodFill( img, seed_pt, new_val, &rect, l_diff, u_diff, flags );
else
area = cv::floodFill( img, mask, seed_pt, new_val, &rect, l_diff, u_diff, flags );
int x, y, k;
int drows = dst.rows, dcols = dst.cols;
int srows = src.rows, scols = src.cols;
- const uchar* sptr0 = src.data;
+ const uchar* sptr0 = src.ptr();
int depth = src.depth(), cn = src.channels();
int elem_size = (int)src.elemSize();
int step = (int)(src.step / CV_ELEM_SIZE(depth));
Mat _extended_src_row(1, _src.cols + ksize * 2, _src.type());
const uchar* srow = _src.ptr(dy);
- memcpy(_extended_src_row.data + elemsize * ksize, srow, _src.step);
+ memcpy(_extended_src_row.ptr() + elemsize * ksize, srow, _src.step);
for (int k = 0; k < ksize; ++k)
{
- memcpy(_extended_src_row.data + k * elemsize, srow, elemsize);
- memcpy(_extended_src_row.data + (ksize + k) * elemsize + _src.step, srow + _src.step - elemsize, elemsize);
+ memcpy(_extended_src_row.ptr() + k * elemsize, srow, elemsize);
+ memcpy(_extended_src_row.ptr() + (ksize + k) * elemsize + _src.step, srow + _src.step - elemsize, elemsize);
}
for (int dx = 0; dx < dsize.width; ++dx)
if (interpolation == INTER_NEAREST)
mapy = Mat();
- CV_Assert(((interpolation == INTER_NEAREST && !mapy.data) || mapy.type() == CV_16UC1 ||
+ CV_Assert(((interpolation == INTER_NEAREST && mapy.empty()) || mapy.type() == CV_16UC1 ||
mapy.type() == CV_16SC1) && mapx.type() == CV_16SC2);
}
void CV_Remap_Test::remap_nearest(const Mat& _src, Mat& _dst)
{
CV_Assert(_src.depth() == CV_32F && _dst.type() == _src.type());
- CV_Assert(mapx.type() == CV_16SC2 && !mapy.data);
+ CV_Assert(mapx.type() == CV_16SC2 && mapy.empty());
Size ssize = _src.size(), dsize = _dst.size();
CV_Assert(ssize.area() > 0 && dsize.area() > 0);
}
}
- CV_Assert(mapx.type() == CV_16SC2 && ((inter == INTER_NEAREST && !mapy.data) || mapy.type() == CV_16SC1));
+ CV_Assert(mapx.type() == CV_16SC2 && ((inter == INTER_NEAREST && mapy.empty()) || mapy.type() == CV_16SC1));
cv::remap(_src, _dst, mapx, mapy, inter, borderType, borderValue);
}
}
}
- CV_Assert(mapx.type() == CV_16SC2 && ((inter == INTER_NEAREST && !mapy.data) || mapy.type() == CV_16SC1));
+ CV_Assert(mapx.type() == CV_16SC2 && ((inter == INTER_NEAREST && mapy.empty()) || mapy.type() == CV_16SC1));
cv::remap(_src, _dst, mapx, mapy, inter, borderType, borderValue);
}
void CV_HuMomentsTest::run_func()
{
- cvGetHuMoments( (CvMoments*)test_mat[INPUT][0].data,
- (CvHuMoments*)test_mat[OUTPUT][0].data );
+ cvGetHuMoments( test_mat[INPUT][0].ptr<CvMoments>(),
+ test_mat[OUTPUT][0].ptr<CvHuMoments>() );
}
void CV_HuMomentsTest::prepare_to_validation( int /*test_case_idx*/ )
{
- CvMoments* m = (CvMoments*)test_mat[INPUT][0].data;
- CvHuMoments* hu = (CvHuMoments*)test_mat[REF_OUTPUT][0].data;
+ CvMoments* m = test_mat[INPUT][0].ptr<CvMoments>();
+ CvHuMoments* hu = test_mat[REF_OUTPUT][0].ptr<CvHuMoments>();
double inv_m00 = m->inv_sqrt_m00*m->inv_sqrt_m00;
double s2 = inv_m00*inv_m00; /* 1./(m00 ^ (2/2 + 1)) */
//expected image was added with 1 in order to save to png
//so now we substract 1 to get real color
- if(exp.data)
+ if(!exp.empty())
colors.push_back(exp.ptr(p->y)[p->x] - 1);
}
fs.release();
continue; // bad result, doing nothing and going to get error latter;
// repaint in saved color to compare with expected;
- if(exp.data)
+ if(!exp.empty())
pixel = colors[pixel];
}
}
assertFalse(Core.checkRange(outOfRange, true, 5, 100));
}
- public void testCircleMatPointIntScalar() {
- Point center = new Point(gray0.cols() / 2, gray0.rows() / 2);
- int radius = Math.min(gray0.cols() / 4, gray0.rows() / 4);
- Scalar color = new Scalar(128);
-
- Core.circle(gray0, center, radius, color);
-
- assertTrue(0 != Core.countNonZero(gray0));
- }
-
- public void testCircleMatPointIntScalarInt() {
- Point center = new Point(gray0.cols() / 2, gray0.rows() / 2);
- int radius = Math.min(gray0.cols() / 4, gray0.rows() / 4);
- Scalar color = new Scalar(128);
-
- Core.circle(gray0, center, radius, color, Core.FILLED);
-
- assertTrue(0 != Core.countNonZero(gray0));
- }
-
- public void testCircleMatPointIntScalarIntIntInt() {
- Point center = new Point(gray0.cols() / 2, gray0.rows() / 2);
- Point center2 = new Point(gray0.cols(), gray0.rows());
- int radius = Math.min(gray0.cols() / 4, gray0.rows() / 4);
- Scalar color128 = new Scalar(128);
- Scalar color0 = new Scalar(0);
-
- Core.circle(gray0, center2, radius * 2, color128, 2, Core.LINE_4, 1/*
- * Number
- * of
- * fractional
- * bits
- */);
- assertFalse(0 == Core.countNonZero(gray0));
-
- Core.circle(gray0, center, radius, color0, 2, Core.LINE_4, 0);
-
- assertTrue(0 == Core.countNonZero(gray0));
- }
-
- public void testClipLine() {
- Rect r = new Rect(10, 10, 10, 10);
- Point pt1 = new Point(5.0, 15.0);
- Point pt2 = new Point(25.0, 15.0);
-
- assertTrue(Core.clipLine(r, pt1, pt2));
-
- Point pt1Clipped = new Point(10.0, 15.0);
- Point pt2Clipped = new Point(19.0, 15.0);
- assertEquals(pt1Clipped, pt1);
- assertEquals(pt2Clipped, pt2);
-
- pt1 = new Point(5.0, 5.0);
- pt2 = new Point(25.0, 5.0);
- pt1Clipped = new Point(5.0, 5.0);
- pt2Clipped = new Point(25.0, 5.0);
-
- assertFalse(Core.clipLine(r, pt1, pt2));
-
- assertEquals(pt1Clipped, pt1);
- assertEquals(pt2Clipped, pt2);
- }
-
public void testCompare() {
Core.compare(gray0, gray0, dst, Core.CMP_EQ);
assertMatEqual(eigenVecs, expectedEigenVecs, EPS);
}
- public void testEllipse2Poly() {
- Point center = new Point(4, 4);
- Size axes = new Size(2, 2);
- int angle = 30;
- int arcStart = 30;
- int arcEnd = 60;
- int delta = 2;
- MatOfPoint pts = new MatOfPoint();
-
- Core.ellipse2Poly(center, axes, angle, arcStart, arcEnd, delta, pts);
-
- Point truth[] = {
- new Point(5, 6),
- new Point(4, 6)
- };
- assertArrayPointsEquals(truth, pts.toArray(), EPS);
- }
-
- public void testEllipseMatPointSizeDoubleDoubleDoubleScalar() {
- Point center = new Point(gray0.cols() / 2, gray0.rows() / 2);
- Size axes = new Size(2, 2);
- double angle = 30, startAngle = 60, endAngle = 90;
-
- Core.ellipse(gray0, center, axes, angle, startAngle, endAngle, colorWhite);
-
- assertTrue(0 != Core.countNonZero(gray0));
- }
-
- public void testEllipseMatPointSizeDoubleDoubleDoubleScalarInt() {
- Point center = new Point(gray0.cols() / 2, gray0.rows() / 2);
- Size axes = new Size(2, 2);
- double angle = 30, startAngle = 60, endAngle = 90;
-
- Core.ellipse(gray0, center, axes, angle, startAngle, endAngle, colorWhite, Core.FILLED);
-
- assertTrue(0 != Core.countNonZero(gray0));
- }
-
- public void testEllipseMatPointSizeDoubleDoubleDoubleScalarIntIntInt() {
- Point center = new Point(gray0.cols() / 2, gray0.rows() / 2);
- Size axes = new Size(2, 2);
- Point center2 = new Point(gray0.cols(), gray0.rows());
- Size axes2 = new Size(4, 4);
- double angle = 30, startAngle = 0, endAngle = 30;
-
- Core.ellipse(gray0, center, axes, angle, startAngle, endAngle, colorWhite, Core.FILLED, Core.LINE_4, 0);
-
- assertTrue(0 != Core.countNonZero(gray0));
-
- Core.ellipse(gray0, center2, axes2, angle, startAngle, endAngle, colorBlack, Core.FILLED, Core.LINE_4, 1);
-
- assertEquals(0, Core.countNonZero(gray0));
- }
-
- public void testEllipseMatRotatedRectScalar() {
- int matSize = 10;
- Mat gray0 = Mat.zeros(matSize, matSize, CvType.CV_8U);
- Point center = new Point(matSize / 2, matSize / 2);
- Size size = new Size(matSize / 4, matSize / 2);
- RotatedRect box = new RotatedRect(center, size, 45);
-
- Core.ellipse(gray0, box, new Scalar(1));
-
- final byte[] truth = new byte[] {
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 1, 1, 0, 0,
- 0, 0, 0, 0, 1, 1, 0, 1, 0, 0,
- 0, 0, 0, 0, 1, 0, 1, 0, 0, 0,
- 0, 0, 0, 1, 0, 1, 1, 0, 0, 0,
- 0, 0, 0, 1, 1, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 };
-
- assertMatEqual(new Mat(matSize, matSize, CvType.CV_8U) {
- {
- put(0, 0, truth);
- }
- }, gray0);
- }
-
- public void testEllipseMatRotatedRectScalarInt() {
- Point center = new Point(matSize / 2, matSize / 2);
- Size size = new Size(matSize / 4, matSize / 2);
- RotatedRect box = new RotatedRect(center, size, 45);
-
- Core.ellipse(gray0, box, new Scalar(1), Core.FILLED);
- Core.ellipse(gray0, box, new Scalar(0));
-
- assertTrue(0 < Core.countNonZero(gray0));
- }
-
- public void testEllipseMatRotatedRectScalarIntInt() {
- Point center = new Point(matSize / 2, matSize / 2);
- Size size = new Size(2, matSize * 2 / 3);
- RotatedRect box = new RotatedRect(center, size, 20);
-
- Core.ellipse(gray0, box, new Scalar(9), 1, Core.LINE_AA);
- Core.ellipse(gray0, box, new Scalar(0), 1, Core.LINE_4);
-
- assertTrue(0 < Core.countNonZero(gray0));
- }
-
public void testExp() {
Core.exp(gray0_32f, dst);
public void testFillConvexPolyMatListOfPointScalar() {
MatOfPoint polyline = new MatOfPoint(new Point[]{new Point(1, 1), new Point(5, 0), new Point(6, 8), new Point(0, 9)});
- Core.fillConvexPoly(gray0, polyline, new Scalar(150));
+ Imgproc.fillConvexPoly(gray0, polyline, new Scalar(150));
assertTrue(0 < Core.countNonZero(gray0));
assertTrue(gray0.total() > Core.countNonZero(gray0));
// current implementation of fixed-point version of fillConvexPoly
// requires image to be at least 2-pixel wider in each direction than
// contour
- Core.fillConvexPoly(gray0, polyline1, colorWhite, Core.LINE_8, 0);
+ Imgproc.fillConvexPoly(gray0, polyline1, colorWhite, Imgproc.line_8, 0);
assertTrue(0 < Core.countNonZero(gray0));
assertTrue(gray0.total() > Core.countNonZero(gray0));
- Core.fillConvexPoly(gray0, polyline2, colorBlack, Core.LINE_8, 1);
+ Imgproc.fillConvexPoly(gray0, polyline2, colorBlack, Imgproc.line_8, 1);
assertEquals("see http://code.opencv.org/issues/1284", 0, Core.countNonZero(gray0));
}
List<MatOfPoint> polylines = new ArrayList<MatOfPoint>();
polylines.add(polyline);
- Core.fillPoly(gray0, polylines, new Scalar(1));
+ Imgproc.fillPoly(gray0, polylines, new Scalar(1));
final byte[] truth = new byte[] {
0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
List<MatOfPoint> polylines2 = new ArrayList<MatOfPoint>();
polylines2.add(polyline2);
- Core.fillPoly(gray0, polylines1, new Scalar(1), Core.LINE_8, 0, new Point(0, 0));
+ Imgproc.fillPoly(gray0, polylines1, new Scalar(1), Imgproc.line_8, 0, new Point(0, 0));
assertTrue(0 < Core.countNonZero(gray0));
- Core.fillPoly(gray0, polylines2, new Scalar(0), Core.LINE_8, 0, new Point(1, 1));
+ Imgproc.fillPoly(gray0, polylines2, new Scalar(0), Imgproc.line_8, 0, new Point(1, 1));
assertEquals(0, Core.countNonZero(gray0));
}
assertEquals(15, Core.getOptimalDFTSize(13));
}
- public void testGetTextSize() {
- String text = "Android all the way";
- double fontScale = 2;
- int thickness = 3;
- int baseLine[] = new int[1];
-
- Core.getTextSize(text, Core.FONT_HERSHEY_SCRIPT_SIMPLEX, fontScale, thickness, null);
- Size res = Core.getTextSize(text, Core.FONT_HERSHEY_SCRIPT_SIMPLEX, fontScale, thickness, baseLine);
-
- assertEquals(543.0, res.width);
- assertEquals(44.0, res.height);
- assertEquals(20, baseLine[0]);
- }
-
public void testGetTickCount() {
long startCount, endCount, count;
Point point2 = new Point(nPoints, nPoints);
Scalar color = new Scalar(255);
- Core.line(gray0, point1, point2, color);
+ Imgproc.line(gray0, point1, point2, color);
assertTrue(nPoints == Core.countNonZero(gray0));
}
Point point1 = new Point(0, 0);
Point point2 = new Point(nPoints, nPoints);
- Core.line(gray0, point1, point2, colorWhite, 0);
+ Imgproc.line(gray0, point1, point2, colorWhite, 0);
assertTrue(nPoints == Core.countNonZero(gray0));
}
Point point1_4 = new Point(3 * 4, 4 * 4);
Point point2_4 = new Point(nPoints * 4, nPoints * 4);
- Core.line(gray0, point2, point1, colorWhite, 2, Core.LINE_8, 0);
+ Imgproc.line(gray0, point2, point1, colorWhite, 2, Imgproc.line_8, 0);
assertFalse(0 == Core.countNonZero(gray0));
- Core.line(gray0, point2_4, point1_4, colorBlack, 2, Core.LINE_8, 2);
+ Imgproc.line(gray0, point2_4, point1_4, colorBlack, 2, Imgproc.line_8, 2);
assertEquals(0, Core.countNonZero(gray0));
}
assertMatEqual(y, yCoordinate, EPS);
}
- public void testPolylinesMatListOfListOfPointBooleanScalar() {
- Mat img = gray0;
- List<MatOfPoint> polyline = new ArrayList<MatOfPoint>();
- polyline.add(new MatOfPoint(new Point(1, 1), new Point(7, 1), new Point(7, 6), new Point(1, 6)));
-
- Core.polylines(img, polyline, true, new Scalar(100));
-
- assertEquals(22, Core.countNonZero(img));
-
- Core.polylines(img, polyline, false, new Scalar(0));
-
- assertEquals(4, Core.countNonZero(img));
- }
-
- public void testPolylinesMatListOfListOfPointBooleanScalarInt() {
- Mat img = gray0;
- List<MatOfPoint> polyline = new ArrayList<MatOfPoint>();
- polyline.add(new MatOfPoint(new Point(1, 1), new Point(7, 1), new Point(7, 6), new Point(1, 6)));
-
- Core.polylines(img, polyline, true, new Scalar(100), 2);
-
- assertEquals(62, Core.countNonZero(img));
- }
-
- public void testPolylinesMatListOfListOfPointBooleanScalarIntIntInt() {
- Mat img = gray0;
- List<MatOfPoint> polyline1 = new ArrayList<MatOfPoint>();
- polyline1.add(new MatOfPoint(new Point(1, 1), new Point(7, 1), new Point(7, 6), new Point(1, 6)));
- List<MatOfPoint> polyline2 = new ArrayList<MatOfPoint>();
- polyline2.add(new MatOfPoint(new Point(2, 2), new Point(14, 2), new Point(14, 12), new Point(2, 12)));
-
- Core.polylines(img, polyline1, true, new Scalar(100), 2, Core.LINE_8, 0);
-
- assertTrue(Core.countNonZero(img) > 0);
-
- Core.polylines(img, polyline2, true, new Scalar(0), 2, Core.LINE_8, 1);
-
- assertEquals(0, Core.countNonZero(img));
- }
-
public void testPow() {
Core.pow(gray2, 7, dst);
assertMatEqual(gray128, dst);
}
- public void testPutTextMatStringPointIntDoubleScalar() {
- String text = "Hello World";
- Size labelSize = new Size(175, 22);
- Mat img = new Mat(20 + (int) labelSize.height, 20 + (int) labelSize.width, CvType.CV_8U, colorBlack);
- Point origin = new Point(10, labelSize.height + 10);
-
- Core.putText(img, text, origin, Core.FONT_HERSHEY_SIMPLEX, 1.0, colorWhite);
-
- assertTrue(Core.countNonZero(img) > 0);
- // check that border is not corrupted
- Core.rectangle(img, new Point(11, 11), new Point(labelSize.width + 10, labelSize.height + 10), colorBlack, Core.FILLED);
- assertEquals(0, Core.countNonZero(img));
- }
-
- public void testPutTextMatStringPointIntDoubleScalarInt() {
- String text = "Hello World";
- Size labelSize = new Size(176, 22);
- Mat img = new Mat(20 + (int) labelSize.height, 20 + (int) labelSize.width, CvType.CV_8U, colorBlack);
- Point origin = new Point(10, labelSize.height + 10);
-
- Core.putText(img, text, origin, Core.FONT_HERSHEY_SIMPLEX, 1.0, colorWhite, 2);
-
- assertTrue(Core.countNonZero(img) > 0);
- // check that border is not corrupted
- Core.rectangle(img, new Point(10, 10), new Point(labelSize.width + 10 + 1, labelSize.height + 10 + 1), colorBlack, Core.FILLED);
- assertEquals(0, Core.countNonZero(img));
- }
-
- public void testPutTextMatStringPointIntDoubleScalarIntIntBoolean() {
- String text = "Hello World";
- Size labelSize = new Size(175, 22);
-
- Mat img = new Mat(20 + (int) labelSize.height, 20 + (int) labelSize.width, CvType.CV_8U, colorBlack);
- Point origin = new Point(10, 10);
-
- Core.putText(img, text, origin, Core.FONT_HERSHEY_SIMPLEX, 1.0, colorWhite, 1, Core.LINE_8, true);
-
- assertTrue(Core.countNonZero(img) > 0);
- // check that border is not corrupted
- Core.rectangle(img, new Point(10, 10), new Point(labelSize.width + 9, labelSize.height + 9), colorBlack, Core.FILLED);
- assertEquals(0, Core.countNonZero(img));
- }
-
public void testRandn() {
Core.randn(gray0, 100, 23);
Point topLeft = new Point(0, 0);
Scalar color = new Scalar(128);
- Core.rectangle(gray0, bottomRight, topLeft, color);
+ Imgproc.rectangle(gray0, bottomRight, topLeft, color);
assertTrue(0 != Core.countNonZero(gray0));
}
Point topLeft = new Point(0, 0);
Scalar color = new Scalar(128);
- Core.rectangle(gray0, bottomRight, topLeft, color, 2);
- Core.rectangle(gray0, bottomRight, topLeft, colorBlack);
+ Imgproc.rectangle(gray0, bottomRight, topLeft, color, 2);
+ Imgproc.rectangle(gray0, bottomRight, topLeft, colorBlack);
assertTrue(0 != Core.countNonZero(gray0));
}
Point topLeft = new Point(0, 0);
Scalar color = new Scalar(128);
- Core.rectangle(gray0, bottomRight, topLeft, color, 2, Core.LINE_AA, 0);
- Core.rectangle(gray0, bottomRight, topLeft, colorBlack, 2, Core.LINE_4, 0);
+ Imgproc.rectangle(gray0, bottomRight, topLeft, color, 2, Imgproc.line_AA, 0);
+ Imgproc.rectangle(gray0, bottomRight, topLeft, colorBlack, 2, Imgproc.line_4, 0);
assertTrue(0 != Core.countNonZero(gray0));
}
Point topLeft = new Point(0, 0);
Scalar color = new Scalar(128);
- Core.rectangle(gray0, bottomRight1, topLeft, color, 2, Core.LINE_8, 1);
+ Imgproc.rectangle(gray0, bottomRight1, topLeft, color, 2, Imgproc.line_8, 1);
assertTrue(0 != Core.countNonZero(gray0));
- Core.rectangle(gray0, bottomRight2, topLeft, colorBlack, 2, Core.LINE_8, 0);
+ Imgproc.rectangle(gray0, bottomRight2, topLeft, colorBlack, 2, Imgproc.line_8, 0);
assertEquals(0, Core.countNonZero(gray0));
}
private Mat getTestImg() {
Mat cross = new Mat(matSize, matSize, CvType.CV_8U, new Scalar(255));
- Core.line(cross, new Point(20, matSize / 2), new Point(matSize - 21, matSize / 2), new Scalar(100), 2);
- Core.line(cross, new Point(matSize / 2, 20), new Point(matSize / 2, matSize - 21), new Scalar(100), 2);
+ Imgproc.line(cross, new Point(20, matSize / 2), new Point(matSize - 21, matSize / 2), new Scalar(100), 2);
+ Imgproc.line(cross, new Point(matSize / 2, 20), new Point(matSize / 2, matSize - 21), new Scalar(100), 2);
return cross;
}
private Mat getQueryImg() {
Mat cross = new Mat(matSize, matSize, CvType.CV_8U, new Scalar(255));
- Core.line(cross, new Point(30, matSize / 2), new Point(matSize - 31, matSize / 2), new Scalar(100), 3);
- Core.line(cross, new Point(matSize / 2, 30), new Point(matSize / 2, matSize - 31), new Scalar(100), 3);
+ Imgproc.line(cross, new Point(30, matSize / 2), new Point(matSize - 31, matSize / 2), new Scalar(100), 3);
+ Imgproc.line(cross, new Point(matSize / 2, 30), new Point(matSize / 2, matSize - 31), new Scalar(100), 3);
return cross;
}
private Mat getTrainImg() {
Mat cross = new Mat(matSize, matSize, CvType.CV_8U, new Scalar(255));
- Core.line(cross, new Point(20, matSize / 2), new Point(matSize - 21, matSize / 2), new Scalar(100), 2);
- Core.line(cross, new Point(matSize / 2, 20), new Point(matSize / 2, matSize - 21), new Scalar(100), 2);
+ Imgproc.line(cross, new Point(20, matSize / 2), new Point(matSize - 21, matSize / 2), new Scalar(100), 2);
+ Imgproc.line(cross, new Point(matSize / 2, 20), new Point(matSize / 2, matSize - 21), new Scalar(100), 2);
return cross;
}
private Mat getQueryImg() {
Mat img = new Mat(matSize, matSize, CvType.CV_8U, new Scalar(255));
- Core.line(img, new Point(40, matSize - 40), new Point(matSize - 50, 50), new Scalar(0), 8);
+ Imgproc.line(img, new Point(40, matSize - 40), new Point(matSize - 50, 50), new Scalar(0), 8);
return img;
}
private Mat getTrainImg() {
Mat img = new Mat(matSize, matSize, CvType.CV_8U, new Scalar(255));
- Core.line(img, new Point(40, 40), new Point(matSize - 40, matSize - 40), new Scalar(0), 8);
+ Imgproc.line(img, new Point(40, 40), new Point(matSize - 40, matSize - 40), new Scalar(0), 8);
return img;
}
private Mat getQueryImg() {
Mat img = new Mat(matSize, matSize, CvType.CV_8U, new Scalar(255));
- Core.line(img, new Point(40, matSize - 40), new Point(matSize - 50, 50), new Scalar(0), 8);
+ Imgproc.line(img, new Point(40, matSize - 40), new Point(matSize - 50, 50), new Scalar(0), 8);
return img;
}
private Mat getTrainImg() {
Mat img = new Mat(matSize, matSize, CvType.CV_8U, new Scalar(255));
- Core.line(img, new Point(40, 40), new Point(matSize - 40, matSize - 40), new Scalar(0), 8);
+ Imgproc.line(img, new Point(40, 40), new Point(matSize - 40, matSize - 40), new Scalar(0), 8);
return img;
}
private Mat getQueryImg() {
Mat cross = new Mat(matSize, matSize, CvType.CV_8U, new Scalar(255));
- Core.line(cross, new Point(30, matSize / 2), new Point(matSize - 31, matSize / 2), new Scalar(100), 3);
- Core.line(cross, new Point(matSize / 2, 30), new Point(matSize / 2, matSize - 31), new Scalar(100), 3);
+ Imgproc.line(cross, new Point(30, matSize / 2), new Point(matSize - 31, matSize / 2), new Scalar(100), 3);
+ Imgproc.line(cross, new Point(matSize / 2, 30), new Point(matSize / 2, matSize - 31), new Scalar(100), 3);
return cross;
}
private Mat getTrainImg() {
Mat cross = new Mat(matSize, matSize, CvType.CV_8U, new Scalar(255));
- Core.line(cross, new Point(20, matSize / 2), new Point(matSize - 21, matSize / 2), new Scalar(100), 2);
- Core.line(cross, new Point(matSize / 2, 20), new Point(matSize / 2, matSize - 21), new Scalar(100), 2);
+ Imgproc.line(cross, new Point(20, matSize / 2), new Point(matSize - 21, matSize / 2), new Scalar(100), 2);
+ Imgproc.line(cross, new Point(matSize / 2, 20), new Point(matSize / 2, matSize - 21), new Scalar(100), 2);
return cross;
}
private Mat getQueryImg() {
Mat cross = new Mat(matSize, matSize, CvType.CV_8U, new Scalar(255));
- Core.line(cross, new Point(30, matSize / 2), new Point(matSize - 31, matSize / 2), new Scalar(100), 3);
- Core.line(cross, new Point(matSize / 2, 30), new Point(matSize / 2, matSize - 31), new Scalar(100), 3);
+ Imgproc.line(cross, new Point(30, matSize / 2), new Point(matSize - 31, matSize / 2), new Scalar(100), 3);
+ Imgproc.line(cross, new Point(matSize / 2, 30), new Point(matSize / 2, matSize - 31), new Scalar(100), 3);
return cross;
}
private Mat getTrainImg() {
Mat cross = new Mat(matSize, matSize, CvType.CV_8U, new Scalar(255));
- Core.line(cross, new Point(20, matSize / 2), new Point(matSize - 21, matSize / 2), new Scalar(100), 2);
- Core.line(cross, new Point(matSize / 2, 20), new Point(matSize / 2, matSize - 21), new Scalar(100), 2);
+ Imgproc.line(cross, new Point(20, matSize / 2), new Point(matSize - 21, matSize / 2), new Scalar(100), 2);
+ Imgproc.line(cross, new Point(matSize / 2, 20), new Point(matSize / 2, matSize - 21), new Scalar(100), 2);
return cross;
}
private Mat getTestImg() {
Mat img = new Mat(100, 100, CvType.CV_8U, new Scalar(255));
- Core.line(img, new Point(30, 30), new Point(70, 70), new Scalar(0), 8);
+ Imgproc.line(img, new Point(30, 30), new Point(70, 70), new Scalar(0), 8);
return img;
}
private Mat getQueryImg() {
Mat cross = new Mat(matSize, matSize, CvType.CV_8U, new Scalar(255));
- Core.line(cross, new Point(30, matSize / 2), new Point(matSize - 31, matSize / 2), new Scalar(100), 3);
- Core.line(cross, new Point(matSize / 2, 30), new Point(matSize / 2, matSize - 31), new Scalar(100), 3);
+ Imgproc.line(cross, new Point(30, matSize / 2), new Point(matSize - 31, matSize / 2), new Scalar(100), 3);
+ Imgproc.line(cross, new Point(matSize / 2, 30), new Point(matSize / 2, matSize - 31), new Scalar(100), 3);
return cross;
}
private Mat getTrainImg() {
Mat cross = new Mat(matSize, matSize, CvType.CV_8U, new Scalar(255));
- Core.line(cross, new Point(20, matSize / 2), new Point(matSize - 21, matSize / 2), new Scalar(100), 2);
- Core.line(cross, new Point(matSize / 2, 20), new Point(matSize / 2, matSize - 21), new Scalar(100), 2);
+ Imgproc.line(cross, new Point(20, matSize / 2), new Point(matSize - 21, matSize / 2), new Scalar(100), 2);
+ Imgproc.line(cross, new Point(matSize / 2, 20), new Point(matSize / 2, matSize - 21), new Scalar(100), 2);
return cross;
}
private Mat getTestImg() {
Mat cross = new Mat(matSize, matSize, CvType.CV_8U, new Scalar(255));
- Core.line(cross, new Point(20, matSize / 2), new Point(matSize - 21, matSize / 2), new Scalar(100), 2);
- Core.line(cross, new Point(matSize / 2, 20), new Point(matSize / 2, matSize - 21), new Scalar(100), 2);
+ Imgproc.line(cross, new Point(20, matSize / 2), new Point(matSize - 21, matSize / 2), new Scalar(100), 2);
+ Imgproc.line(cross, new Point(matSize / 2, 20), new Point(matSize / 2, matSize - 21), new Scalar(100), 2);
return cross;
}
private Mat getTestImg() {
Mat cross = new Mat(matSize, matSize, CvType.CV_8U, new Scalar(255));
- Core.line(cross, new Point(20, matSize / 2), new Point(matSize - 21, matSize / 2), new Scalar(100), 2);
- Core.line(cross, new Point(matSize / 2, 20), new Point(matSize / 2, matSize - 21), new Scalar(100), 2);
+ Imgproc.line(cross, new Point(20, matSize / 2), new Point(matSize - 21, matSize / 2), new Scalar(100), 2);
+ Imgproc.line(cross, new Point(matSize / 2, 20), new Point(matSize / 2, matSize - 21), new Scalar(100), 2);
return cross;
}
private Mat getTestImg() {
Mat cross = new Mat(matSize, matSize, CvType.CV_8U, new Scalar(255));
- Core.line(cross, new Point(20, matSize / 2), new Point(matSize - 21, matSize / 2), new Scalar(100), 2);
- Core.line(cross, new Point(matSize / 2, 20), new Point(matSize / 2, matSize - 21), new Scalar(100), 2);
+ Imgproc.line(cross, new Point(20, matSize / 2), new Point(matSize - 21, matSize / 2), new Scalar(100), 2);
+ Imgproc.line(cross, new Point(matSize / 2, 20), new Point(matSize / 2, matSize - 21), new Scalar(100), 2);
return cross;
}
private Mat getTestImg() {
Mat cross = new Mat(matSize, matSize, CvType.CV_8U, new Scalar(255));
- Core.line(cross, new Point(20, matSize / 2), new Point(matSize - 21, matSize / 2), new Scalar(100), 2);
- Core.line(cross, new Point(matSize / 2, 20), new Point(matSize / 2, matSize - 21), new Scalar(100), 2);
+ Imgproc.line(cross, new Point(20, matSize / 2), new Point(matSize - 21, matSize / 2), new Scalar(100), 2);
+ Imgproc.line(cross, new Point(matSize / 2, 20), new Point(matSize / 2, matSize - 21), new Scalar(100), 2);
return cross;
}
Point truthPosition = new Point(img.cols() / 2, img.rows() / 2);
Rect r = new Rect(new Point(0, 0), truthPosition);
- Core.rectangle(img, r.tl(), r.br(), new Scalar(0), Core.FILLED);
+ Imgproc.rectangle(img, r.tl(), r.br(), new Scalar(0), Core.FILLED);
MatOfPoint2f corners = new MatOfPoint2f(new Point(truthPosition.x + 1, truthPosition.y + 1));
Size winSize = new Size(2, 2);
Size zeroZone = new Size(-1, -1);
}
public void testDrawContoursMatListOfMatIntScalar() {
- Core.rectangle(gray0, new Point(1, 2), new Point(7, 8), new Scalar(100));
+ Imgproc.rectangle(gray0, new Point(1, 2), new Point(7, 8), new Scalar(100));
List<MatOfPoint> contours = new ArrayList<MatOfPoint>();
Imgproc.findContours(gray0, contours, new Mat(), Imgproc.RETR_EXTERNAL, Imgproc.CHAIN_APPROX_SIMPLE);
}
public void testDrawContoursMatListOfMatIntScalarInt() {
- Core.rectangle(gray0, new Point(1, 2), new Point(7, 8), new Scalar(100));
+ Imgproc.rectangle(gray0, new Point(1, 2), new Point(7, 8), new Scalar(100));
List<MatOfPoint> contours = new ArrayList<MatOfPoint>();
Imgproc.findContours(gray0, contours, new Mat(), Imgproc.RETR_EXTERNAL, Imgproc.CHAIN_APPROX_SIMPLE);
assertEquals(contours.size(), 0);
assertEquals(contours.size(), hierarchy.total());
- Core.rectangle(img, new Point(10, 20), new Point(20, 30), new Scalar(100), 3, Core.LINE_AA, 0);
- Core.rectangle(img, new Point(30, 35), new Point(40, 45), new Scalar(200));
+ Imgproc.rectangle(img, new Point(10, 20), new Point(20, 30), new Scalar(100), 3, Imgproc.line_AA, 0);
+ Imgproc.rectangle(img, new Point(30, 35), new Point(40, 45), new Scalar(200));
Imgproc.findContours(img, contours, hierarchy, Imgproc.RETR_EXTERNAL, Imgproc.CHAIN_APPROX_SIMPLE);
List<MatOfPoint> contours2 = new ArrayList<MatOfPoint>();
Mat hierarchy = new Mat();
- Core.rectangle(img, new Point(10, 20), new Point(20, 30), new Scalar(100), 3, Core.LINE_AA, 0);
- Core.rectangle(img, new Point(30, 35), new Point(40, 45), new Scalar(200));
+ Imgproc.rectangle(img, new Point(10, 20), new Point(20, 30), new Scalar(100), 3, Imgproc.line_AA, 0);
+ Imgproc.rectangle(img, new Point(30, 35), new Point(40, 45), new Scalar(200));
Imgproc.findContours(img, contours, hierarchy, Imgproc.RETR_EXTERNAL, Imgproc.CHAIN_APPROX_SIMPLE);
Imgproc.findContours(img2, contours2, hierarchy, Imgproc.RETR_EXTERNAL, Imgproc.CHAIN_APPROX_SIMPLE, new Point(3, 5));
public void testGoodFeaturesToTrackMatListOfPointIntDoubleDouble() {
Mat src = gray0;
- Core.rectangle(src, new Point(2, 2), new Point(8, 8), new Scalar(100), -1);
+ Imgproc.rectangle(src, new Point(2, 2), new Point(8, 8), new Scalar(100), -1);
MatOfPoint lp = new MatOfPoint();
Imgproc.goodFeaturesToTrack(src, lp, 100, 0.01, 3);
public void testGoodFeaturesToTrackMatListOfPointIntDoubleDoubleMatIntBooleanDouble() {
Mat src = gray0;
- Core.rectangle(src, new Point(2, 2), new Point(8, 8), new Scalar(100), -1);
+ Imgproc.rectangle(src, new Point(2, 2), new Point(8, 8), new Scalar(100), -1);
MatOfPoint lp = new MatOfPoint();
Imgproc.goodFeaturesToTrack(src, lp, 100, 0.01, 3, gray1, 4, true, 0);
Mat img = new Mat(sz, sz, CvType.CV_8U, new Scalar(0));
Point point1 = new Point(50, 50);
Point point2 = new Point(img.cols() / 2, img.rows() / 2);
- Core.line(img, point1, point2, colorWhite, 1);
+ Imgproc.line(img, point1, point2, colorWhite, 1);
Mat lines = new Mat();
Imgproc.HoughLines(img, lines, 1, 3.1415926/180, 100);
Point point2 = new Point(sz, sz);
Point point3 = new Point(sz, 0);
Point point4 = new Point(2*sz/3, sz/3);
- Core.line(img, point1, point2, Scalar.all(255), 1);
- Core.line(img, point3, point4, Scalar.all(255), 1);
+ Imgproc.line(img, point1, point2, Scalar.all(255), 1);
+ Imgproc.line(img, point3, point4, Scalar.all(255), 1);
Mat lines = new Mat();
Imgproc.HoughLinesP(img, lines, 1, 3.1415926/180, 100);
assertMatEqual(truth, markers);
}
+ public void testGetTextSize() {
+ String text = "Android all the way";
+ double fontScale = 2;
+ int thickness = 3;
+ int baseLine[] = new int[1];
+
+ Imgproc.getTextSize(text, Core.FONT_HERSHEY_SCRIPT_SIMPLEX, fontScale, thickness, null);
+ Size res = Imgproc.getTextSize(text, Core.FONT_HERSHEY_SCRIPT_SIMPLEX, fontScale, thickness, baseLine);
+
+ assertEquals(543.0, res.width);
+ assertEquals(44.0, res.height);
+ assertEquals(20, baseLine[0]);
+ }
+
+ public void testCircleMatPointIntScalar() {
+ Point center = new Point(gray0.cols() / 2, gray0.rows() / 2);
+ int radius = Math.min(gray0.cols() / 4, gray0.rows() / 4);
+ Scalar color = new Scalar(128);
+
+ Imgproc.circle(gray0, center, radius, color);
+
+ assertTrue(0 != Core.countNonZero(gray0));
+ }
+
+ public void testCircleMatPointIntScalarInt() {
+ Point center = new Point(gray0.cols() / 2, gray0.rows() / 2);
+ int radius = Math.min(gray0.cols() / 4, gray0.rows() / 4);
+ Scalar color = new Scalar(128);
+
+ Imgproc.circle(gray0, center, radius, color, Core.FILLED);
+
+ assertTrue(0 != Core.countNonZero(gray0));
+ }
+
+ public void testCircleMatPointIntScalarIntIntInt() {
+ Point center = new Point(gray0.cols() / 2, gray0.rows() / 2);
+ Point center2 = new Point(gray0.cols(), gray0.rows());
+ int radius = Math.min(gray0.cols() / 4, gray0.rows() / 4);
+ Scalar color128 = new Scalar(128);
+ Scalar color0 = new Scalar(0);
+
+ Imgproc.circle(gray0, center2, radius * 2, color128, 2, Imgproc.line_4, 1/*
+ * Number
+ * of
+ * fractional
+ * bits
+ */);
+ assertFalse(0 == Core.countNonZero(gray0));
+
+ Imgproc.circle(gray0, center, radius, color0, 2, Imgproc.line_4, 0);
+
+ assertTrue(0 == Core.countNonZero(gray0));
+ }
+
+ public void testClipLine() {
+ Rect r = new Rect(10, 10, 10, 10);
+ Point pt1 = new Point(5.0, 15.0);
+ Point pt2 = new Point(25.0, 15.0);
+
+ assertTrue(Core.clipLine(r, pt1, pt2));
+
+ Point pt1Clipped = new Point(10.0, 15.0);
+ Point pt2Clipped = new Point(19.0, 15.0);
+ assertEquals(pt1Clipped, pt1);
+ assertEquals(pt2Clipped, pt2);
+
+ pt1 = new Point(5.0, 5.0);
+ pt2 = new Point(25.0, 5.0);
+ pt1Clipped = new Point(5.0, 5.0);
+ pt2Clipped = new Point(25.0, 5.0);
+
+ assertFalse(Core.clipLine(r, pt1, pt2));
+
+ assertEquals(pt1Clipped, pt1);
+ assertEquals(pt2Clipped, pt2);
+ }
+
+ public void testEllipse2Poly() {
+ Point center = new Point(4, 4);
+ Size axes = new Size(2, 2);
+ int angle = 30;
+ int arcStart = 30;
+ int arcEnd = 60;
+ int delta = 2;
+ MatOfPoint pts = new MatOfPoint();
+
+ Imgproc.ellipse2Poly(center, axes, angle, arcStart, arcEnd, delta, pts);
+
+ Point truth[] = {
+ new Point(5, 6),
+ new Point(4, 6)
+ };
+ assertArrayPointsEquals(truth, pts.toArray(), EPS);
+ }
+
+ public void testEllipseMatPointSizeDoubleDoubleDoubleScalar() {
+ Point center = new Point(gray0.cols() / 2, gray0.rows() / 2);
+ Size axes = new Size(2, 2);
+ double angle = 30, startAngle = 60, endAngle = 90;
+
+ Imgproc.ellipse(gray0, center, axes, angle, startAngle, endAngle, colorWhite);
+
+ assertTrue(0 != Core.countNonZero(gray0));
+ }
+
+ public void testEllipseMatPointSizeDoubleDoubleDoubleScalarInt() {
+ Point center = new Point(gray0.cols() / 2, gray0.rows() / 2);
+ Size axes = new Size(2, 2);
+ double angle = 30, startAngle = 60, endAngle = 90;
+
+ Imgproc.ellipse(gray0, center, axes, angle, startAngle, endAngle, colorWhite, Core.FILLED);
+
+ assertTrue(0 != Core.countNonZero(gray0));
+ }
+
+ public void testEllipseMatPointSizeDoubleDoubleDoubleScalarIntIntInt() {
+ Point center = new Point(gray0.cols() / 2, gray0.rows() / 2);
+ Size axes = new Size(2, 2);
+ Point center2 = new Point(gray0.cols(), gray0.rows());
+ Size axes2 = new Size(4, 4);
+ double angle = 30, startAngle = 0, endAngle = 30;
+
+ Imgproc.ellipse(gray0, center, axes, angle, startAngle, endAngle, colorWhite, Core.FILLED, Imgproc.line_4, 0);
+
+ assertTrue(0 != Core.countNonZero(gray0));
+
+ Imgproc.ellipse(gray0, center2, axes2, angle, startAngle, endAngle, colorBlack, Core.FILLED, Imgproc.line_4, 1);
+
+ assertEquals(0, Core.countNonZero(gray0));
+ }
+
+ public void testEllipseMatRotatedRectScalar() {
+ int matSize = 10;
+ Mat gray0 = Mat.zeros(matSize, matSize, CvType.CV_8U);
+ Point center = new Point(matSize / 2, matSize / 2);
+ Size size = new Size(matSize / 4, matSize / 2);
+ RotatedRect box = new RotatedRect(center, size, 45);
+
+ Imgproc.ellipse(gray0, box, new Scalar(1));
+
+ final byte[] truth = new byte[] {
+ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+ 0, 0, 0, 0, 0, 0, 1, 1, 0, 0,
+ 0, 0, 0, 0, 1, 1, 0, 1, 0, 0,
+ 0, 0, 0, 0, 1, 0, 1, 0, 0, 0,
+ 0, 0, 0, 1, 0, 1, 1, 0, 0, 0,
+ 0, 0, 0, 1, 1, 0, 0, 0, 0, 0,
+ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 };
+
+ assertMatEqual(new Mat(matSize, matSize, CvType.CV_8U) {
+ {
+ put(0, 0, truth);
+ }
+ }, gray0);
+ }
+
+ public void testEllipseMatRotatedRectScalarInt() {
+ Point center = new Point(matSize / 2, matSize / 2);
+ Size size = new Size(matSize / 4, matSize / 2);
+ RotatedRect box = new RotatedRect(center, size, 45);
+
+ Imgproc.ellipse(gray0, box, new Scalar(1), Core.FILLED);
+ Imgproc.ellipse(gray0, box, new Scalar(0));
+
+ assertTrue(0 < Core.countNonZero(gray0));
+ }
+
+ public void testEllipseMatRotatedRectScalarIntInt() {
+ Point center = new Point(matSize / 2, matSize / 2);
+ Size size = new Size(2, matSize * 2 / 3);
+ RotatedRect box = new RotatedRect(center, size, 20);
+
+ Imgproc.ellipse(gray0, box, new Scalar(9), 1, Imgproc.line_AA);
+ Imgproc.ellipse(gray0, box, new Scalar(0), 1, Imgproc.line_4);
+
+ assertTrue(0 < Core.countNonZero(gray0));
+ }
+
+ public void testPolylinesMatListOfListOfPointBooleanScalar() {
+ Mat img = gray0;
+ List<MatOfPoint> polyline = new ArrayList<MatOfPoint>();
+ polyline.add(new MatOfPoint(new Point(1, 1), new Point(7, 1), new Point(7, 6), new Point(1, 6)));
+
+ Imgproc.polylines(img, polyline, true, new Scalar(100));
+
+ assertEquals(22, Core.countNonZero(img));
+
+ Imgproc.polylines(img, polyline, false, new Scalar(0));
+
+ assertEquals(4, Core.countNonZero(img));
+ }
+
+ public void testPolylinesMatListOfListOfPointBooleanScalarInt() {
+ Mat img = gray0;
+ List<MatOfPoint> polyline = new ArrayList<MatOfPoint>();
+ polyline.add(new MatOfPoint(new Point(1, 1), new Point(7, 1), new Point(7, 6), new Point(1, 6)));
+
+ Imgproc.polylines(img, polyline, true, new Scalar(100), 2);
+
+ assertEquals(62, Core.countNonZero(img));
+ }
+
+ public void testPolylinesMatListOfListOfPointBooleanScalarIntIntInt() {
+ Mat img = gray0;
+ List<MatOfPoint> polyline1 = new ArrayList<MatOfPoint>();
+ polyline1.add(new MatOfPoint(new Point(1, 1), new Point(7, 1), new Point(7, 6), new Point(1, 6)));
+ List<MatOfPoint> polyline2 = new ArrayList<MatOfPoint>();
+ polyline2.add(new MatOfPoint(new Point(2, 2), new Point(14, 2), new Point(14, 12), new Point(2, 12)));
+
+ Imgproc.polylines(img, polyline1, true, new Scalar(100), 2, Imgproc.line_8, 0);
+
+ assertTrue(Core.countNonZero(img) > 0);
+
+ Imgproc.polylines(img, polyline2, true, new Scalar(0), 2, Imgproc.line_8, 1);
+
+ assertEquals(0, Core.countNonZero(img));
+ }
+
+ public void testPutTextMatStringPointIntDoubleScalar() {
+ String text = "Hello World";
+ Size labelSize = new Size(175, 22);
+ Mat img = new Mat(20 + (int) labelSize.height, 20 + (int) labelSize.width, CvType.CV_8U, colorBlack);
+ Point origin = new Point(10, labelSize.height + 10);
+
+ Imgproc.putText(img, text, origin, Core.FONT_HERSHEY_SIMPLEX, 1.0, colorWhite);
+
+ assertTrue(Core.countNonZero(img) > 0);
+ // check that border is not corrupted
+ Imgproc.rectangle(img, new Point(11, 11), new Point(labelSize.width + 10, labelSize.height + 10), colorBlack, Core.FILLED);
+ assertEquals(0, Core.countNonZero(img));
+ }
+
+ public void testPutTextMatStringPointIntDoubleScalarInt() {
+ String text = "Hello World";
+ Size labelSize = new Size(176, 22);
+ Mat img = new Mat(20 + (int) labelSize.height, 20 + (int) labelSize.width, CvType.CV_8U, colorBlack);
+ Point origin = new Point(10, labelSize.height + 10);
+
+ Imgproc.putText(img, text, origin, Core.FONT_HERSHEY_SIMPLEX, 1.0, colorWhite, 2);
+
+ assertTrue(Core.countNonZero(img) > 0);
+ // check that border is not corrupted
+ Imgproc.rectangle(img, new Point(10, 10), new Point(labelSize.width + 10 + 1, labelSize.height + 10 + 1), colorBlack, Core.FILLED);
+ assertEquals(0, Core.countNonZero(img));
+ }
+
+ public void testPutTextMatStringPointIntDoubleScalarIntIntBoolean() {
+ String text = "Hello World";
+ Size labelSize = new Size(175, 22);
+
+ Mat img = new Mat(20 + (int) labelSize.height, 20 + (int) labelSize.width, CvType.CV_8U, colorBlack);
+ Point origin = new Point(10, 10);
+
+ Imgproc.putText(img, text, origin, Core.FONT_HERSHEY_SIMPLEX, 1.0, colorWhite, 1, Imgproc.line_8, true);
+
+ assertTrue(Core.countNonZero(img) > 0);
+ // check that border is not corrupted
+ Imgproc.rectangle(img, new Point(10, 10), new Point(labelSize.width + 9, labelSize.height + 9), colorBlack, Core.FILLED);
+ assertEquals(0, Core.countNonZero(img));
+ }
}
Scalar color = new Scalar(128);
Mat mask = new Mat(rgbLena.size(), CvType.CV_16UC3, new Scalar(1));
- Core.rectangle(rgbLena, bottomRight, topLeft, color, Core.FILLED);
+ Imgproc.rectangle(rgbLena, bottomRight, topLeft, color, Core.FILLED);
backGroundSubtract.apply(rgbLena, mask);
Mat truth = new Mat(rgbLena.size(), rgbLena.type(), new Scalar(0));
- Core.rectangle(truth, bottomRight, topLeft, color, Core.FILLED);
+ Imgproc.rectangle(truth, bottomRight, topLeft, color, Core.FILLED);
assertMatEqual(truth, rgbLena);
*/
}
""",
}, # minMaxLoc
+
+## "checkRange" : #TBD
+## {'j_code' : '/* TBD: checkRange() */', 'jn_code' : '', 'cpp_code' : '' },
+
+ "checkHardwareSupport" : {'j_code' : '', 'jn_code' : '', 'cpp_code' : '' },
+ "setUseOptimized" : {'j_code' : '', 'jn_code' : '', 'cpp_code' : '' },
+ "useOptimized" : {'j_code' : '', 'jn_code' : '', 'cpp_code' : '' },
+
+ }, # Core
+
+ 'Imgproc' :
+ {
'getTextSize' :
{
'j_code' :
-"""
+ """
// C++: Size getTextSize(const String& text, int fontFace, double fontScale, int thickness, int* baseLine);
//javadoc:getTextSize(text, fontFace, fontScale, thickness, baseLine)
public static Size getTextSize(String text, int fontFace, double fontScale, int thickness, int[] baseLine) {
Size retVal = new Size(n_getTextSize(text, fontFace, fontScale, thickness, baseLine));
return retVal;
}
-""",
+ """,
'jn_code' :
-""" private static native double[] n_getTextSize(String text, int fontFace, double fontScale, int thickness, int[] baseLine);\n""",
+ """ private static native double[] n_getTextSize(String text, int fontFace, double fontScale, int thickness, int[] baseLine);\n""",
'cpp_code' :
-"""
-// C++: Size getTextSize(const String& text, int fontFace, double fontScale, int thickness, int* baseLine);
-JNIEXPORT jdoubleArray JNICALL Java_org_opencv_core_Core_n_1getTextSize (JNIEnv*, jclass, jstring, jint, jdouble, jint, jintArray);
+ """
+ // C++: Size getTextSize(const String& text, int fontFace, double fontScale, int thickness, int* baseLine);
+ JNIEXPORT jdoubleArray JNICALL Java_org_opencv_core_Core_n_1getTextSize (JNIEnv*, jclass, jstring, jint, jdouble, jint, jintArray);
-JNIEXPORT jdoubleArray JNICALL Java_org_opencv_core_Core_n_1getTextSize
- (JNIEnv* env, jclass, jstring text, jint fontFace, jdouble fontScale, jint thickness, jintArray baseLine)
-{
+ JNIEXPORT jdoubleArray JNICALL Java_org_opencv_core_Core_n_1getTextSize
+ (JNIEnv* env, jclass, jstring text, jint fontFace, jdouble fontScale, jint thickness, jintArray baseLine)
+ {
try {
LOGD("Core::n_1getTextSize()");
jdoubleArray result;
env->ThrowNew(je, "Unknown exception in JNI code {core::getTextSize()}");
return NULL;
}
-}
-
-""",
+ }
+ """,
}, # getTextSize
-## "checkRange" : #TBD
-## {'j_code' : '/* TBD: checkRange() */', 'jn_code' : '', 'cpp_code' : '' },
- "checkHardwareSupport" : {'j_code' : '', 'jn_code' : '', 'cpp_code' : '' },
- "setUseOptimized" : {'j_code' : '', 'jn_code' : '', 'cpp_code' : '' },
- "useOptimized" : {'j_code' : '', 'jn_code' : '', 'cpp_code' : '' },
-
- }, # Core
+ }, # Imgproc
'Highgui' :
{
--- /dev/null
+Logistic Regression
+===================
+
+.. highlight:: cpp
+
+ML implements logistic regression, which is a probabilistic classification technique. Logistic Regression is a binary classification algorithm which is closely related to Support Vector Machines (SVM).
+Like SVM, Logistic Regression can be extended to work on multi-class classification problems like digit recognition (i.e. recognizing digitis like 0,1 2, 3,... from the given images).
+This version of Logistic Regression supports both binary and multi-class classifications (for multi-class it creates a multiple 2-class classifiers).
+In order to train the logistic regression classifier, Batch Gradient Descent and Mini-Batch Gradient Descent algorithms are used (see [BatchDesWiki]_).
+Logistic Regression is a discriminative classifier (see [LogRegTomMitch]_ for more details). Logistic Regression is implemented as a C++ class in ``LogisticRegression``.
+
+
+In Logistic Regression, we try to optimize the training paramater
+:math:`\theta`
+such that the hypothesis
+:math:`0 \leq h_\theta(x) \leq 1` is acheived.
+We have
+:math:`h_\theta(x) = g(h_\theta(x))`
+and
+:math:`g(z) = \frac{1}{1+e^{-z}}`
+as the logistic or sigmoid function.
+The term "Logistic" in Logistic Regression refers to this function.
+For given data of a binary classification problem of classes 0 and 1,
+one can determine that the given data instance belongs to class 1 if
+:math:`h_\theta(x) \geq 0.5`
+or class 0 if
+:math:`h_\theta(x) < 0.5`
+.
+
+In Logistic Regression, choosing the right parameters is of utmost importance for reducing the training error and ensuring high training accuracy.
+``LogisticRegression::Params`` is the structure that defines parameters that are required to train a Logistic Regression classifier.
+The learning rate is determined by ``LogisticRegression::Params.alpha``. It determines how faster we approach the solution.
+It is a positive real number. Optimization algorithms like Batch Gradient Descent and Mini-Batch Gradient Descent are supported in ``LogisticRegression``.
+It is important that we mention the number of iterations these optimization algorithms have to run.
+The number of iterations are mentioned by ``LogisticRegression::Params.num_iters``.
+The number of iterations can be thought as number of steps taken and learning rate specifies if it is a long step or a short step. These two parameters define how fast we arrive at a possible solution.
+In order to compensate for overfitting regularization is performed, which can be enabled by setting ``LogisticRegression::Params.regularized`` to a positive integer (greater than zero).
+One can specify what kind of regularization has to be performed by setting ``LogisticRegression::Params.norm`` to ``LogisticRegression::REG_L1`` or ``LogisticRegression::REG_L2`` values.
+``LogisticRegression`` provides a choice of 2 training methods with Batch Gradient Descent or the Mini-Batch Gradient Descent. To specify this, set ``LogisticRegression::Params.train_method`` to either ``LogisticRegression::BATCH`` or ``LogisticRegression::MINI_BATCH``.
+If ``LogisticRegression::Params`` is set to ``LogisticRegression::MINI_BATCH``, the size of the mini batch has to be to a postive integer using ``LogisticRegression::Params.mini_batch_size``.
+
+A sample set of training parameters for the Logistic Regression classifier can be initialized as follows:
+
+::
+
+ LogisticRegression::Params params;
+ params.alpha = 0.5;
+ params.num_iters = 10000;
+ params.norm = LogisticRegression::REG_L2;
+ params.regularized = 1;
+ params.train_method = LogisticRegression::MINI_BATCH;
+ params.mini_batch_size = 10;
+
+**References:**
+
+.. [LogRegWiki] http://en.wikipedia.org/wiki/Logistic_regression. Wikipedia article about the Logistic Regression algorithm.
+
+.. [RenMalik2003] Learning a Classification Model for Segmentation. Proc. CVPR, Nice, France (2003).
+
+.. [LogRegTomMitch] http://www.cs.cmu.edu/~tom/NewChapters.html. "Generative and Discriminative Classifiers: Naive Bayes and Logistic Regression" in Machine Learning, Tom Mitchell.
+
+.. [BatchDesWiki] http://en.wikipedia.org/wiki/Gradient_descent_optimization. Wikipedia article about Gradient Descent based optimization.
+
+LogisticRegression::Params
+--------------------------
+.. ocv:struct:: LogisticRegression::Params
+
+ Parameters of the Logistic Regression training algorithm. You can initialize the structure using a constructor or declaring the variable and initializing the the individual parameters.
+
+ The training parameters for Logistic Regression:
+
+ .. ocv:member:: double alpha
+
+ The learning rate of the optimization algorithm. The higher the value, faster the rate and vice versa. If the value is too high, the learning algorithm may overshoot the optimal parameters and result in lower training accuracy. If the value is too low, the learning algorithm converges towards the optimal parameters very slowly. The value must a be a positive real number. You can experiment with different values with small increments as in 0.0001, 0.0003, 0.001, 0.003, 0.01, 0.03, 0.1, 0.3, ... and select the learning rate with less training error.
+
+ .. ocv:member:: int num_iters
+
+ The number of iterations required for the learing algorithm (Gradient Descent or Mini Batch Gradient Descent). It has to be a positive integer. You can try different number of iterations like in 100, 1000, 2000, 3000, 5000, 10000, .. so on.
+
+ .. ocv:member:: int norm
+
+ The type of normalization applied. It takes value ``LogisticRegression::L1`` or ``LogisticRegression::L2``.
+
+ .. ocv:member:: int regularized
+
+ It should be set to postive integer (greater than zero) in order to enable regularization.
+
+ .. ocv:member:: int train_method
+
+ The kind of training method used to train the classifier. It should be set to either ``LogisticRegression::BATCH`` or ``LogisticRegression::MINI_BATCH``.
+
+ .. ocv:member:: int mini_batch_size
+
+ If the training method is set to LogisticRegression::MINI_BATCH, it has to be set to positive integer. It can range from 1 to number of training samples.
+
+ .. ocv:member:: cv::TermCriteria term_crit
+
+ Sets termination criteria for training algorithm.
+
+LogisticRegression::Params::Params
+----------------------------------
+The constructors
+
+.. ocv:function:: LogisticRegression::Params::Params(double learning_rate = 0.001, int iters = 1000, int method = LogisticRegression::BATCH, int normlization = LogisticRegression::REG_L2, int reg = 1, int batch_size = 1)
+
+ :param learning_rate: Specifies the learning rate.
+
+ :param iters: Specifies the number of iterations.
+
+ :param train_method: Specifies the kind of training method used. It should be set to either ``LogisticRegression::BATCH`` or ``LogisticRegression::MINI_BATCH``. If using ``LogisticRegression::MINI_BATCH``, set ``LogisticRegression::Params.mini_batch_size`` to a positive integer.
+
+ :param normalization: Specifies the kind of regularization to be applied. ``LogisticRegression::REG_L1`` or ``LogisticRegression::REG_L2`` (L1 norm or L2 norm). To use this, set ``LogisticRegression::Params.regularized`` to a integer greater than zero.
+
+ :param reg: To enable or disable regularization. Set to positive integer (greater than zero) to enable and to 0 to disable.
+
+ :param mini_batch_size: Specifies the number of training samples taken in each step of Mini-Batch Gradient Descent. Will only be used if using ``LogisticRegression::MINI_BATCH`` training algorithm. It has to take values less than the total number of training samples.
+
+By initializing this structure, one can set all the parameters required for Logistic Regression classifier.
+
+LogisticRegression
+------------------
+
+.. ocv:class:: LogisticRegression : public StatModel
+
+Implements Logistic Regression classifier.
+
+LogisticRegression::create
+--------------------------
+Creates empty model.
+
+.. ocv:function:: Ptr<LogisticRegression> LogisticRegression::create( const Params& params = Params() )
+
+ :param params: The training parameters for the classifier of type ``LogisticRegression::Params``.
+
+Creates Logistic Regression model with parameters given.
+
+LogisticRegression::train
+-------------------------
+Trains the Logistic Regression classifier and returns true if successful.
+
+.. ocv:function:: bool LogisticRegression::train( const Ptr<TrainData>& trainData, int flags=0 )
+
+ :param trainData: Instance of ml::TrainData class holding learning data.
+
+ :param flags: Not used.
+
+LogisticRegression::predict
+---------------------------
+Predicts responses for input samples and returns a float type.
+
+.. ocv:function:: void LogisticRegression::predict( InputArray samples, OutputArray results=noArray(), int flags=0 ) const
+
+ :param samples: The input data for the prediction algorithm. Matrix [m x n], where each row contains variables (features) of one object being classified. Should have data type ``CV_32F``.
+
+ :param results: Predicted labels as a column matrix of type ``CV_32S``.
+
+ :param flags: Not used.
+
+
+LogisticRegression::get_learnt_thetas
+-------------------------------------
+This function returns the trained paramters arranged across rows. For a two class classifcation problem, it returns a row matrix.
+
+.. ocv:function:: Mat LogisticRegression::get_learnt_thetas() const
+
+It returns learnt paramters of the Logistic Regression as a matrix of type ``CV_32F``.
+
+LogisticRegression::read
+------------------------
+This function reads the trained LogisticRegression clasifier from disk.
+
+.. ocv:function:: void LogisticRegression::read(const FileNode& fn)
+
+LogisticRegression::write
+-------------------------
+This function writes the trained LogisticRegression clasifier to disk.
+
+.. ocv:function:: void LogisticRegression::write(FileStorage& fs) const
random_trees
expectation_maximization
neural_networks
+ logistic_regression
mldata
CV_PROP_RW double logStep;
};
-
class CV_EXPORTS TrainData
{
public:
};
/****************************************************************************************\
+* Logistic Regression *
+\****************************************************************************************/
+
+class CV_EXPORTS LogisticRegression : public StatModel
+{
+public:
+ class CV_EXPORTS Params
+ {
+ public:
+ Params(double learning_rate = 0.001,
+ int iters = 1000,
+ int method = LogisticRegression::BATCH,
+ int normlization = LogisticRegression::REG_L2,
+ int reg = 1,
+ int batch_size = 1);
+ double alpha;
+ int num_iters;
+ int norm;
+ int regularized;
+ int train_method;
+ int mini_batch_size;
+ TermCriteria term_crit;
+ };
+
+ enum { REG_L1 = 0, REG_L2 = 1};
+ enum { BATCH = 0, MINI_BATCH = 1};
+
+ // Algorithm interface
+ virtual void write( FileStorage &fs ) const = 0;
+ virtual void read( const FileNode &fn ) = 0;
+
+ // StatModel interface
+ virtual bool train( const Ptr<TrainData>& trainData, int flags=0 ) = 0;
+ virtual float predict( InputArray samples, OutputArray results=noArray(), int flags=0 ) const = 0;
+ virtual void clear() = 0;
+
+ virtual Mat get_learnt_thetas() const = 0;
+
+ static Ptr<LogisticRegression> create( const Params& params = Params() );
+};
+
+/****************************************************************************************\
* Auxilary functions declarations *
\****************************************************************************************/
size_t esz = weights[0].elemSize();
fs << "input_scale" << "[";
- fs.writeRaw("d", weights[0].data, weights[0].total()*esz);
+ fs.writeRaw("d", weights[0].ptr(), weights[0].total()*esz);
fs << "]" << "output_scale" << "[";
- fs.writeRaw("d", weights[l_count].data, weights[l_count].total()*esz);
+ fs.writeRaw("d", weights[l_count].ptr(), weights[l_count].total()*esz);
fs << "]" << "inv_output_scale" << "[";
- fs.writeRaw("d", weights[l_count+1].data, weights[l_count+1].total()*esz);
+ fs.writeRaw("d", weights[l_count+1].ptr(), weights[l_count+1].total()*esz);
fs << "]" << "weights" << "[";
for( i = 1; i < l_count; i++ )
{
fs << "[";
- fs.writeRaw("d", weights[i].data, weights[i].total()*esz);
+ fs.writeRaw("d", weights[i].ptr(), weights[i].total()*esz);
fs << "]";
}
fs << "]";
size_t esz = weights[0].elemSize();
FileNode w = fn["input_scale"];
- w.readRaw("d", weights[0].data, weights[0].total()*esz);
+ w.readRaw("d", weights[0].ptr(), weights[0].total()*esz);
w = fn["output_scale"];
- w.readRaw("d", weights[l_count].data, weights[l_count].total()*esz);
+ w.readRaw("d", weights[l_count].ptr(), weights[l_count].total()*esz);
w = fn["inv_output_scale"];
- w.readRaw("d", weights[l_count+1].data, weights[l_count+1].total()*esz);
+ w.readRaw("d", weights[l_count+1].ptr(), weights[l_count+1].total()*esz);
FileNodeIterator w_it = fn["weights"].begin();
for( i = 1; i < l_count; i++, ++w_it )
- (*w_it).readRaw("d", weights[i].data, weights[i].total()*esz);
+ (*w_it).readRaw("d", weights[i].ptr(), weights[i].total()*esz);
trained = true;
}
else
{
Mat mask(1, nsamples, CV_8U);
- uchar* mptr = mask.data;
+ uchar* mptr = mask.ptr();
for( i = 0; i < nsamples; i++ )
mptr[i] = (uchar)(i < count);
trainSampleIdx.create(1, count, CV_32S);
err += fabs(val - val0) > FLT_EPSILON;
else
err += (val - val0)*(val - val0);
- if( resp.data )
+ if( !resp.empty() )
resp.at<float>(i) = val;
/*if( i < 100 )
{
--- /dev/null
+///////////////////////////////////////////////////////////////////////////////////////
+// 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.
+
+// This is a implementation of the Logistic Regression algorithm in C++ in OpenCV.
+
+// AUTHOR:
+// Rahul Kavi rahulkavi[at]live[at]com
+
+// # You are free to use, change, or redistribute the code in any way you wish for
+// # non-commercial purposes, but please maintain the name of the original author.
+// # This code comes with no warranty of any kind.
+
+// #
+// # You are free to use, change, or redistribute the code in any way you wish for
+// # non-commercial purposes, but please maintain the name of the original author.
+// # This code comes with no warranty of any kind.
+
+// # Logistic Regression ALGORITHM
+
+
+// License Agreement
+// For Open Source Computer Vision Library
+
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2008-2011, 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:
+
+// * 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 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.
+
+#include "precomp.hpp"
+
+using namespace std;
+
+namespace cv {
+namespace ml {
+
+LogisticRegression::Params::Params(double learning_rate,
+ int iters,
+ int method,
+ int normlization,
+ int reg,
+ int batch_size)
+{
+ alpha = learning_rate;
+ num_iters = iters;
+ norm = normlization;
+ regularized = reg;
+ train_method = method;
+ mini_batch_size = batch_size;
+ term_crit = TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, num_iters, alpha);
+}
+
+class LogisticRegressionImpl : public LogisticRegression
+{
+public:
+ LogisticRegressionImpl(const Params& pms)
+ : params(pms)
+ {
+ }
+ virtual ~LogisticRegressionImpl() {}
+
+ virtual bool train( const Ptr<TrainData>& trainData, int=0 );
+ virtual float predict(InputArray samples, OutputArray results, int) const;
+ virtual void clear();
+ virtual void write(FileStorage& fs) const;
+ virtual void read(const FileNode& fn);
+ virtual Mat get_learnt_thetas() const;
+ virtual int getVarCount() const { return learnt_thetas.cols; }
+ virtual bool isTrained() const { return !learnt_thetas.empty(); }
+ virtual bool isClassifier() const { return true; }
+ virtual String getDefaultModelName() const { return "opencv_ml_lr"; }
+protected:
+ Mat calc_sigmoid(const Mat& data) const;
+ double compute_cost(const Mat& _data, const Mat& _labels, const Mat& _init_theta);
+ Mat compute_batch_gradient(const Mat& _data, const Mat& _labels, const Mat& _init_theta);
+ Mat compute_mini_batch_gradient(const Mat& _data, const Mat& _labels, const Mat& _init_theta);
+ bool set_label_map(const Mat& _labels_i);
+ Mat remap_labels(const Mat& _labels_i, const map<int, int>& lmap) const;
+protected:
+ Params params;
+ Mat learnt_thetas;
+ map<int, int> forward_mapper;
+ map<int, int> reverse_mapper;
+ Mat labels_o;
+ Mat labels_n;
+};
+
+Ptr<LogisticRegression> LogisticRegression::create(const Params& params)
+{
+ return makePtr<LogisticRegressionImpl>(params);
+}
+
+bool LogisticRegressionImpl::train(const Ptr<TrainData>& trainData, int)
+{
+ clear();
+ Mat _data_i = trainData->getSamples();
+ Mat _labels_i = trainData->getResponses();
+
+ CV_Assert( !_labels_i.empty() && !_data_i.empty());
+
+ // check the number of columns
+ if(_labels_i.cols != 1)
+ {
+ CV_Error( CV_StsBadArg, "_labels_i should be a column matrix" );
+ }
+
+ // check data type.
+ // data should be of floating type CV_32FC1
+
+ if((_data_i.type() != CV_32FC1) || (_labels_i.type() != CV_32FC1))
+ {
+ CV_Error( CV_StsBadArg, "data and labels must be a floating point matrix" );
+ }
+
+ bool ok = false;
+
+ Mat labels;
+
+ set_label_map(_labels_i);
+ int num_classes = (int) this->forward_mapper.size();
+
+ // add a column of ones
+ Mat data_t = Mat::zeros(_data_i.rows, _data_i.cols+1, CV_32F);
+ vconcat(Mat(_data_i.rows, 1, _data_i.type(), Scalar::all(1.0)), data_t.col(0));
+
+ for (int i=1;i<data_t.cols;i++)
+ {
+ vconcat(_data_i.col(i-1), data_t.col(i));
+ }
+
+ if(num_classes < 2)
+ {
+ CV_Error( CV_StsBadArg, "data should have atleast 2 classes" );
+ }
+
+ if(_labels_i.rows != _data_i.rows)
+ {
+ CV_Error( CV_StsBadArg, "number of rows in data and labels should be the equal" );
+ }
+
+
+ Mat thetas = Mat::zeros(num_classes, data_t.cols, CV_32F);
+ Mat init_theta = Mat::zeros(data_t.cols, 1, CV_32F);
+
+ Mat labels_l = remap_labels(_labels_i, this->forward_mapper);
+ Mat new_local_labels;
+
+ int ii=0;
+ Mat new_theta;
+
+ if(num_classes == 2)
+ {
+ labels_l.convertTo(labels, CV_32F);
+ if(this->params.train_method == LogisticRegression::BATCH)
+ new_theta = compute_batch_gradient(data_t, labels, init_theta);
+ else
+ new_theta = compute_mini_batch_gradient(data_t, labels, init_theta);
+ thetas = new_theta.t();
+ }
+ else
+ {
+ /* take each class and rename classes you will get a theta per class
+ as in multi class class scenario, we will have n thetas for n classes */
+ ii = 0;
+
+ for(map<int,int>::iterator it = this->forward_mapper.begin(); it != this->forward_mapper.end(); ++it)
+ {
+ new_local_labels = (labels_l == it->second)/255;
+ new_local_labels.convertTo(labels, CV_32F);
+ if(this->params.train_method == LogisticRegression::BATCH)
+ new_theta = compute_batch_gradient(data_t, labels, init_theta);
+ else
+ new_theta = compute_mini_batch_gradient(data_t, labels, init_theta);
+ hconcat(new_theta.t(), thetas.row(ii));
+ ii += 1;
+ }
+ }
+
+ this->learnt_thetas = thetas.clone();
+ if( cvIsNaN( (double)sum(this->learnt_thetas)[0] ) )
+ {
+ CV_Error( CV_StsBadArg, "check training parameters. Invalid training classifier" );
+ }
+ ok = true;
+ return ok;
+}
+
+float LogisticRegressionImpl::predict(InputArray samples, OutputArray results, int) const
+{
+ /* returns a class of the predicted class
+ class names can be 1,2,3,4, .... etc */
+ Mat thetas, data, pred_labs;
+ data = samples.getMat();
+
+ // check if learnt_mats array is populated
+ if(this->learnt_thetas.total()<=0)
+ {
+ CV_Error( CV_StsBadArg, "classifier should be trained first" );
+ }
+ if(data.type() != CV_32F)
+ {
+ CV_Error( CV_StsBadArg, "data must be of floating type" );
+ }
+
+ // add a column of ones
+ Mat data_t = Mat::zeros(data.rows, data.cols+1, CV_32F);
+ for (int i=0;i<data_t.cols;i++)
+ {
+ if(i==0)
+ {
+ vconcat(Mat(data.rows, 1, data.type(), Scalar::all(1.0)), data_t.col(i));
+ continue;
+ }
+ vconcat(data.col(i-1), data_t.col(i));
+ }
+
+ this->learnt_thetas.convertTo(thetas, CV_32F);
+
+ CV_Assert(thetas.rows > 0);
+
+ double min_val;
+ double max_val;
+
+ Point min_loc;
+ Point max_loc;
+
+ Mat labels;
+ Mat labels_c;
+ Mat temp_pred;
+ Mat pred_m = Mat::zeros(data_t.rows, thetas.rows, data.type());
+
+ if(thetas.rows == 1)
+ {
+ temp_pred = calc_sigmoid(data_t*thetas.t());
+ CV_Assert(temp_pred.cols==1);
+
+ // if greater than 0.5, predict class 0 or predict class 1
+ temp_pred = (temp_pred>0.5)/255;
+ temp_pred.convertTo(labels_c, CV_32S);
+ }
+ else
+ {
+ for(int i = 0;i<thetas.rows;i++)
+ {
+ temp_pred = calc_sigmoid(data_t * thetas.row(i).t());
+ vconcat(temp_pred, pred_m.col(i));
+ }
+ for(int i = 0;i<pred_m.rows;i++)
+ {
+ temp_pred = pred_m.row(i);
+ minMaxLoc( temp_pred, &min_val, &max_val, &min_loc, &max_loc, Mat() );
+ labels.push_back(max_loc.x);
+ }
+ labels.convertTo(labels_c, CV_32S);
+ }
+ pred_labs = remap_labels(labels_c, this->reverse_mapper);
+ // convert pred_labs to integer type
+ pred_labs.convertTo(pred_labs, CV_32S);
+ pred_labs.copyTo(results);
+ // TODO: determine
+ return 0;
+}
+
+Mat LogisticRegressionImpl::calc_sigmoid(const Mat& data) const
+{
+ Mat dest;
+ exp(-data, dest);
+ return 1.0/(1.0+dest);
+}
+
+double LogisticRegressionImpl::compute_cost(const Mat& _data, const Mat& _labels, const Mat& _init_theta)
+{
+ int llambda = 0;
+ int m;
+ int n;
+ double cost = 0;
+ double rparameter = 0;
+ Mat theta_b;
+ Mat theta_c;
+ Mat d_a;
+ Mat d_b;
+
+ m = _data.rows;
+ n = _data.cols;
+
+ theta_b = _init_theta(Range(1, n), Range::all());
+ multiply(theta_b, theta_b, theta_c, 1);
+
+ if(this->params.regularized > 0)
+ {
+ llambda = 1;
+ }
+
+ if(this->params.norm == LogisticRegression::REG_L1)
+ {
+ rparameter = (llambda/(2*m)) * sum(theta_b)[0];
+ }
+ else
+ {
+ // assuming it to be L2 by default
+ rparameter = (llambda/(2*m)) * sum(theta_c)[0];
+ }
+
+ d_a = calc_sigmoid(_data* _init_theta);
+
+
+ log(d_a, d_a);
+ multiply(d_a, _labels, d_a);
+
+ d_b = 1 - calc_sigmoid(_data * _init_theta);
+ log(d_b, d_b);
+ multiply(d_b, 1-_labels, d_b);
+
+ cost = (-1.0/m) * (sum(d_a)[0] + sum(d_b)[0]);
+ cost = cost + rparameter;
+
+ return cost;
+}
+
+Mat LogisticRegressionImpl::compute_batch_gradient(const Mat& _data, const Mat& _labels, const Mat& _init_theta)
+{
+ // implements batch gradient descent
+ if(this->params.alpha<=0)
+ {
+ CV_Error( CV_StsBadArg, "check training parameters for the classifier" );
+ }
+
+ if(this->params.num_iters <= 0)
+ {
+ CV_Error( CV_StsBadArg, "number of iterations cannot be zero or a negative number" );
+ }
+
+ int llambda = 0;
+ double ccost;
+ int m, n;
+ Mat pcal_a;
+ Mat pcal_b;
+ Mat pcal_ab;
+ Mat gradient;
+ Mat theta_p = _init_theta.clone();
+ m = _data.rows;
+ n = _data.cols;
+
+ if(this->params.regularized > 0)
+ {
+ llambda = 1;
+ }
+
+ for(int i = 0;i<this->params.num_iters;i++)
+ {
+ ccost = compute_cost(_data, _labels, theta_p);
+
+ if( cvIsNaN( ccost ) )
+ {
+ CV_Error( CV_StsBadArg, "check training parameters. Invalid training classifier" );
+ }
+
+ pcal_b = calc_sigmoid((_data*theta_p) - _labels);
+
+ pcal_a = (static_cast<double>(1/m)) * _data.t();
+
+ gradient = pcal_a * pcal_b;
+
+ pcal_a = calc_sigmoid(_data*theta_p) - _labels;
+
+ pcal_b = _data(Range::all(), Range(0,1));
+
+ multiply(pcal_a, pcal_b, pcal_ab, 1);
+
+ gradient.row(0) = ((float)1/m) * sum(pcal_ab)[0];
+
+ pcal_b = _data(Range::all(), Range(1,n));
+
+ //cout<<"for each training data entry"<<endl;
+ for(int ii = 1;ii<gradient.rows;ii++)
+ {
+ pcal_b = _data(Range::all(), Range(ii,ii+1));
+
+ multiply(pcal_a, pcal_b, pcal_ab, 1);
+
+ gradient.row(ii) = (1.0/m)*sum(pcal_ab)[0] + (llambda/m) * theta_p.row(ii);
+ }
+
+ theta_p = theta_p - ( static_cast<double>(this->params.alpha)/m)*gradient;
+ }
+ return theta_p;
+}
+
+Mat LogisticRegressionImpl::compute_mini_batch_gradient(const Mat& _data, const Mat& _labels, const Mat& _init_theta)
+{
+ // implements batch gradient descent
+ int lambda_l = 0;
+ double ccost;
+ int m, n;
+ int j = 0;
+ int size_b = this->params.mini_batch_size;
+
+ if(this->params.mini_batch_size <= 0 || this->params.alpha == 0)
+ {
+ CV_Error( CV_StsBadArg, "check training parameters for the classifier" );
+ }
+
+ if(this->params.num_iters <= 0)
+ {
+ CV_Error( CV_StsBadArg, "number of iterations cannot be zero or a negative number" );
+ }
+
+ Mat pcal_a;
+ Mat pcal_b;
+ Mat pcal_ab;
+ Mat gradient;
+ Mat theta_p = _init_theta.clone();
+ Mat data_d;
+ Mat labels_l;
+
+ if(this->params.regularized > 0)
+ {
+ lambda_l = 1;
+ }
+
+ for(int i = 0;i<this->params.term_crit.maxCount;i++)
+ {
+ if(j+size_b<=_data.rows)
+ {
+ data_d = _data(Range(j,j+size_b), Range::all());
+ labels_l = _labels(Range(j,j+size_b),Range::all());
+ }
+ else
+ {
+ data_d = _data(Range(j, _data.rows), Range::all());
+ labels_l = _labels(Range(j, _labels.rows),Range::all());
+ }
+
+ m = data_d.rows;
+ n = data_d.cols;
+
+ ccost = compute_cost(data_d, labels_l, theta_p);
+
+ if( cvIsNaN( ccost ) == 1)
+ {
+ CV_Error( CV_StsBadArg, "check training parameters. Invalid training classifier" );
+ }
+
+ pcal_b = calc_sigmoid((data_d*theta_p) - labels_l);
+
+ pcal_a = (static_cast<double>(1/m)) * data_d.t();
+
+ gradient = pcal_a * pcal_b;
+
+ pcal_a = calc_sigmoid(data_d*theta_p) - labels_l;
+
+ pcal_b = data_d(Range::all(), Range(0,1));
+
+ multiply(pcal_a, pcal_b, pcal_ab, 1);
+
+ gradient.row(0) = ((float)1/m) * sum(pcal_ab)[0];
+
+ pcal_b = data_d(Range::all(), Range(1,n));
+
+ for(int k = 1;k<gradient.rows;k++)
+ {
+ pcal_b = data_d(Range::all(), Range(k,k+1));
+ multiply(pcal_a, pcal_b, pcal_ab, 1);
+ gradient.row(k) = (1.0/m)*sum(pcal_ab)[0] + (lambda_l/m) * theta_p.row(k);
+ }
+
+ theta_p = theta_p - ( static_cast<double>(this->params.alpha)/m)*gradient;
+
+ j+=this->params.mini_batch_size;
+
+ if(j+size_b>_data.rows)
+ {
+ // if parsed through all data variables
+ break;
+ }
+ }
+ return theta_p;
+}
+
+bool LogisticRegressionImpl::set_label_map(const Mat &_labels_i)
+{
+ // this function creates two maps to map user defined labels to program friendly labels two ways.
+ int ii = 0;
+ Mat labels;
+
+ this->labels_o = Mat(0,1, CV_8U);
+ this->labels_n = Mat(0,1, CV_8U);
+
+ _labels_i.convertTo(labels, CV_32S);
+
+ for(int i = 0;i<labels.rows;i++)
+ {
+ this->forward_mapper[labels.at<int>(i)] += 1;
+ }
+
+ for(map<int,int>::iterator it = this->forward_mapper.begin(); it != this->forward_mapper.end(); ++it)
+ {
+ this->forward_mapper[it->first] = ii;
+ this->labels_o.push_back(it->first);
+ this->labels_n.push_back(ii);
+ ii += 1;
+ }
+
+ for(map<int,int>::iterator it = this->forward_mapper.begin(); it != this->forward_mapper.end(); ++it)
+ {
+ this->reverse_mapper[it->second] = it->first;
+ }
+
+ return true;
+}
+
+Mat LogisticRegressionImpl::remap_labels(const Mat& _labels_i, const map<int, int>& lmap) const
+{
+ Mat labels;
+ _labels_i.convertTo(labels, CV_32S);
+
+ Mat new_labels = Mat::zeros(labels.rows, labels.cols, labels.type());
+
+ CV_Assert( lmap.size() > 0 );
+
+ for(int i =0;i<labels.rows;i++)
+ {
+ new_labels.at<int>(i,0) = lmap.find(labels.at<int>(i,0))->second;
+ }
+ return new_labels;
+}
+
+void LogisticRegressionImpl::clear()
+{
+ this->learnt_thetas.release();
+ this->labels_o.release();
+ this->labels_n.release();
+}
+
+void LogisticRegressionImpl::write(FileStorage& fs) const
+{
+ // check if open
+ if(fs.isOpened() == 0)
+ {
+ CV_Error(CV_StsBadArg,"file can't open. Check file path");
+ }
+ string desc = "Logisitic Regression Classifier";
+ fs<<"classifier"<<desc.c_str();
+ fs<<"alpha"<<this->params.alpha;
+ fs<<"iterations"<<this->params.num_iters;
+ fs<<"norm"<<this->params.norm;
+ fs<<"regularized"<<this->params.regularized;
+ fs<<"train_method"<<this->params.train_method;
+ if(this->params.train_method == LogisticRegression::MINI_BATCH)
+ {
+ fs<<"mini_batch_size"<<this->params.mini_batch_size;
+ }
+ fs<<"learnt_thetas"<<this->learnt_thetas;
+ fs<<"n_labels"<<this->labels_n;
+ fs<<"o_labels"<<this->labels_o;
+}
+
+void LogisticRegressionImpl::read(const FileNode& fn)
+{
+ // check if empty
+ if(fn.empty())
+ {
+ CV_Error( CV_StsBadArg, "empty FileNode object" );
+ }
+
+ this->params.alpha = (double)fn["alpha"];
+ this->params.num_iters = (int)fn["iterations"];
+ this->params.norm = (int)fn["norm"];
+ this->params.regularized = (int)fn["regularized"];
+ this->params.train_method = (int)fn["train_method"];
+
+ if(this->params.train_method == LogisticRegression::MINI_BATCH)
+ {
+ this->params.mini_batch_size = (int)fn["mini_batch_size"];
+ }
+
+ fn["learnt_thetas"] >> this->learnt_thetas;
+ fn["o_labels"] >> this->labels_o;
+ fn["n_labels"] >> this->labels_n;
+
+ for(int ii =0;ii<labels_o.rows;ii++)
+ {
+ this->forward_mapper[labels_o.at<int>(ii,0)] = labels_n.at<int>(ii,0);
+ this->reverse_mapper[labels_n.at<int>(ii,0)] = labels_o.at<int>(ii,0);
+ }
+}
+
+Mat LogisticRegressionImpl::get_learnt_thetas() const
+{
+ return this->learnt_thetas;
+}
+
+}
+}
+
+/* End of file. */
vidx = &_vidx;
cls_labels = &_cls_labels;
results = &_results;
- results_prob = _results_prob.data ? &_results_prob : 0;
+ results_prob = !_results_prob.empty() ? &_results_prob : 0;
rawOutput = _rawOutput;
}
--- /dev/null
+///////////////////////////////////////////////////////////////////////////////////////
+// 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.
+
+// This is a implementation of the Logistic Regression algorithm in C++ in OpenCV.
+
+// AUTHOR:
+// Rahul Kavi rahulkavi[at]live[at]com
+//
+
+// contains a subset of data from the popular Iris Dataset (taken from "http://archive.ics.uci.edu/ml/datasets/Iris")
+
+// # You are free to use, change, or redistribute the code in any way you wish for
+// # non-commercial purposes, but please maintain the name of the original author.
+// # This code comes with no warranty of any kind.
+
+// #
+// # You are free to use, change, or redistribute the code in any way you wish for
+// # non-commercial purposes, but please maintain the name of the original author.
+// # This code comes with no warranty of any kind.
+
+// # Logistic Regression ALGORITHM
+
+
+// License Agreement
+// For Open Source Computer Vision Library
+
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2008-2011, 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:
+
+// * 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 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.
+
+#include "test_precomp.hpp"
+
+using namespace std;
+using namespace cv;
+using namespace cv::ml;
+
+static bool calculateError( const Mat& _p_labels, const Mat& _o_labels, float& error)
+{
+ error = 0.0f;
+ float accuracy = 0.0f;
+ Mat _p_labels_temp;
+ Mat _o_labels_temp;
+ _p_labels.convertTo(_p_labels_temp, CV_32S);
+ _o_labels.convertTo(_o_labels_temp, CV_32S);
+
+ CV_Assert(_p_labels_temp.total() == _o_labels_temp.total());
+ CV_Assert(_p_labels_temp.rows == _o_labels_temp.rows);
+
+ accuracy = (float)countNonZero(_p_labels_temp == _o_labels_temp)/_p_labels_temp.rows;
+ error = 1 - accuracy;
+ return true;
+}
+
+//--------------------------------------------------------------------------------------------
+
+class CV_LRTest : public cvtest::BaseTest
+{
+public:
+ CV_LRTest() {}
+protected:
+ virtual void run( int start_from );
+};
+
+void CV_LRTest::run( int /*start_from*/ )
+{
+ // initialize varibles from the popular Iris Dataset
+ string dataFileName = ts->get_data_path() + "iris.data";
+ Ptr<TrainData> tdata = TrainData::loadFromCSV(dataFileName, 0);
+
+ LogisticRegression::Params params = LogisticRegression::Params();
+ params.alpha = 1.0;
+ params.num_iters = 10001;
+ params.norm = LogisticRegression::REG_L2;
+ params.regularized = 1;
+ params.train_method = LogisticRegression::BATCH;
+ params.mini_batch_size = 10;
+
+ // run LR classifier train classifier
+ Ptr<LogisticRegression> p = LogisticRegression::create(params);
+ p->train(tdata);
+
+ // predict using the same data
+ Mat responses;
+ p->predict(tdata->getSamples(), responses);
+
+ // calculate error
+ int test_code = cvtest::TS::OK;
+ float error = 0.0f;
+ if(!calculateError(responses, tdata->getResponses(), error))
+ {
+ ts->printf(cvtest::TS::LOG, "Bad prediction labels\n" );
+ test_code = cvtest::TS::FAIL_INVALID_OUTPUT;
+ }
+ else if(error > 0.05f)
+ {
+ ts->printf(cvtest::TS::LOG, "Bad accuracy of (%f)\n", error);
+ test_code = cvtest::TS::FAIL_BAD_ACCURACY;
+ }
+
+ {
+ FileStorage s("debug.xml", FileStorage::WRITE);
+ s << "original" << tdata->getResponses();
+ s << "predicted1" << responses;
+ s << "learnt" << p->get_learnt_thetas();
+ s << "error" << error;
+ s.release();
+ }
+ ts->set_failed_test_info(test_code);
+}
+
+//--------------------------------------------------------------------------------------------
+class CV_LRTest_SaveLoad : public cvtest::BaseTest
+{
+public:
+ CV_LRTest_SaveLoad(){}
+protected:
+ virtual void run(int start_from);
+};
+
+
+void CV_LRTest_SaveLoad::run( int /*start_from*/ )
+{
+ int code = cvtest::TS::OK;
+
+ // initialize varibles from the popular Iris Dataset
+ string dataFileName = ts->get_data_path() + "iris.data";
+ Ptr<TrainData> tdata = TrainData::loadFromCSV(dataFileName, 0);
+
+ Mat responses1, responses2;
+ Mat learnt_mat1, learnt_mat2;
+
+ LogisticRegression::Params params1 = LogisticRegression::Params();
+ params1.alpha = 1.0;
+ params1.num_iters = 10001;
+ params1.norm = LogisticRegression::REG_L2;
+ params1.regularized = 1;
+ params1.train_method = LogisticRegression::BATCH;
+ params1.mini_batch_size = 10;
+
+ // train and save the classifier
+ String filename = tempfile(".xml");
+ try
+ {
+ // run LR classifier train classifier
+ Ptr<LogisticRegression> lr1 = LogisticRegression::create(params1);
+ lr1->train(tdata);
+ lr1->predict(tdata->getSamples(), responses1);
+ learnt_mat1 = lr1->get_learnt_thetas();
+ lr1->save(filename);
+ }
+ catch(...)
+ {
+ ts->printf(cvtest::TS::LOG, "Crash in write method.\n" );
+ ts->set_failed_test_info(cvtest::TS::FAIL_EXCEPTION);
+ }
+
+ // and load to another
+ try
+ {
+ Ptr<LogisticRegression> lr2 = StatModel::load<LogisticRegression>(filename);
+ lr2->predict(tdata->getSamples(), responses2);
+ learnt_mat2 = lr2->get_learnt_thetas();
+ }
+ catch(...)
+ {
+ ts->printf(cvtest::TS::LOG, "Crash in write method.\n" );
+ ts->set_failed_test_info(cvtest::TS::FAIL_EXCEPTION);
+ }
+
+ CV_Assert(responses1.rows == responses2.rows);
+
+ // compare difference in learnt matrices before and after loading from disk
+ Mat comp_learnt_mats;
+ comp_learnt_mats = (learnt_mat1 == learnt_mat2);
+ comp_learnt_mats = comp_learnt_mats.reshape(1, comp_learnt_mats.rows*comp_learnt_mats.cols);
+ comp_learnt_mats.convertTo(comp_learnt_mats, CV_32S);
+ comp_learnt_mats = comp_learnt_mats/255;
+
+ // compare difference in prediction outputs and stored inputs
+ // check if there is any difference between computed learnt mat and retreived mat
+
+ float errorCount = 0.0;
+ errorCount += 1 - (float)countNonZero(responses1 == responses2)/responses1.rows;
+ errorCount += 1 - (float)sum(comp_learnt_mats)[0]/comp_learnt_mats.rows;
+
+ if(errorCount>0)
+ {
+ ts->printf( cvtest::TS::LOG, "Different prediction results before writing and after reading (errorCount=%d).\n", errorCount );
+ code = cvtest::TS::FAIL_BAD_ACCURACY;
+ }
+
+ remove( filename.c_str() );
+
+ ts->set_failed_test_info( code );
+}
+
+TEST(ML_LR, accuracy) { CV_LRTest test; test.safe_run(); }
+TEST(ML_LR, save_load) { CV_LRTest_SaveLoad test; test.safe_run(); }
for (i = 0; i < nscales; i++)
{
const ScaleData& s = scaleData->at(i);
- Mat dst(s.szi.height - 1, s.szi.width - 1, CV_8U, rbuf.data);
+ Mat dst(s.szi.height - 1, s.szi.width - 1, CV_8U, rbuf.ptr());
resize(image, dst, dst.size(), 1. / s.scale, 1. / s.scale, INTER_LINEAR);
computeChannels((int)i, dst);
}
for (int j = 0; j < blocks_per_img.width; ++j)
{
const float *src = &svmDetector[0] + (j * blocks_per_img.height + i) * block_hist_size;
- float *dst = (float*)detector_reordered.data + (i * blocks_per_img.width + j) * block_hist_size;
+ float *dst = detector_reordered.ptr<float>() + (i * blocks_per_img.width + j) * block_hist_size;
for (size_t k = 0; k < block_hist_size; ++k)
dst[k] = src[k];
}
float angleScale = (float)(nbins/CV_PI);
for( y = 0; y < gradsize.height; y++ )
{
- const uchar* imgPtr = img.data + img.step*ymap[y];
- const uchar* prevPtr = img.data + img.step*ymap[y-1];
- const uchar* nextPtr = img.data + img.step*ymap[y+1];
+ const uchar* imgPtr = img.ptr(ymap[y]);
+ const uchar* prevPtr = img.ptr(ymap[y-1]);
+ const uchar* nextPtr = img.ptr(ymap[y+1]);
- float* gradPtr = (float*)grad.ptr(y);
- uchar* qanglePtr = (uchar*)qangle.ptr(y);
+ float* gradPtr = grad.ptr<float>(y);
+ uchar* qanglePtr = qangle.ptr(y);
if( cn == 1 )
{
}
int k, C1 = count1, C2 = count2, C4 = count4;
- const float* gradPtr = (const float*)(grad.data + grad.step*pt.y) + pt.x*2;
- const uchar* qanglePtr = qangle.data + qangle.step*pt.y + pt.x*2;
+ const float* gradPtr = grad.ptr<float>(pt.y) + pt.x*2;
+ const uchar* qanglePtr = qangle.ptr(pt.y) + pt.x*2;
// CV_Assert( blockHist != 0 );
memset(blockHist, 0, sizeof(float) * blockHistogramSize);
{
double scale = levelScale[i];
Size sz(cvRound(img.cols/scale), cvRound(img.rows/scale));
- Mat smallerImg(sz, img.type(), smallerImgBuf.data);
+ Mat smallerImg(sz, img.type(), smallerImgBuf.ptr());
if( sz == img.size() )
smallerImg = Mat(sz, img.type(), img.data, img.step);
else
double scale = (*locations)[i].scale;
Size sz(cvRound(img.cols / scale), cvRound(img.rows / scale));
- Mat smallerImg(sz, img.type(), smallerImgBuf.data);
+ Mat smallerImg(sz, img.type(), smallerImgBuf.ptr());
if( sz == img.size() )
smallerImg = Mat(sz, img.type(), img.data, img.step);
}
int k, C1 = count1, C2 = count2, C4 = count4;
- const float* gradPtr = (const float*)(grad.data + grad.step*pt.y) + pt.x*2;
- const uchar* qanglePtr = qangle.data + qangle.step*pt.y + pt.x*2;
+ const float* gradPtr = grad.ptr<float>(pt.y) + pt.x*2;
+ const uchar* qanglePtr = qangle.ptr(pt.y) + pt.x*2;
CV_Assert( blockHist != 0 );
for( k = 0; k < blockHistogramSize; k++ )
float angleScale = (float)(_nbins/CV_PI);
for( y = 0; y < gradsize.height; y++ )
{
- const uchar* imgPtr = img.data + img.step*ymap[y];
- const uchar* prevPtr = img.data + img.step*ymap[y-1];
- const uchar* nextPtr = img.data + img.step*ymap[y+1];
+ const uchar* imgPtr = img.ptr(ymap[y]);
+ const uchar* prevPtr = img.ptr(ymap[y-1]);
+ const uchar* nextPtr = img.ptr(ymap[y+1]);
float* gradPtr = (float*)grad.ptr(y);
uchar* qanglePtr = (uchar*)qangle.ptr(y);
+++ /dev/null
-set(the_description "Generic optimization")
-ocv_define_module(optim opencv_core)
+++ /dev/null
-Downhill Simplex Method
-=======================
-
-.. highlight:: cpp
-
-optim::DownhillSolver
----------------------------------
-
-.. ocv:class:: optim::DownhillSolver
-
-This class is used to perform the non-linear non-constrained *minimization* of a function, defined on an *n*-dimensional Euclidean space,
-using the **Nelder-Mead method**, also known as **downhill simplex method**. The basic idea about the method can be obtained from
-(`http://en.wikipedia.org/wiki/Nelder-Mead\_method <http://en.wikipedia.org/wiki/Nelder-Mead_method>`_). It should be noted, that
-this method, although deterministic, is rather a heuristic and therefore may converge to a local minima, not necessary a global one.
-It is iterative optimization technique, which at each step uses an information about the values of a function evaluated only at
-*n+1* points, arranged as a *simplex* in *n*-dimensional space (hence the second name of the method). At each step new point is
-chosen to evaluate function at, obtained value is compared with previous ones and based on this information simplex changes it's shape
-, slowly moving to the local minimum. Thus this method is using *only* function values to make decision, on contrary to, say, Nonlinear
-Conjugate Gradient method (which is also implemented in ``optim``).
-
-Algorithm stops when the number of function evaluations done exceeds ``termcrit.maxCount``, when the function values at the
-vertices of simplex are within ``termcrit.epsilon`` range or simplex becomes so small that it
-can enclosed in a box with ``termcrit.epsilon`` sides, whatever comes first, for some defined by user
-positive integer ``termcrit.maxCount`` and positive non-integer ``termcrit.epsilon``.
-
-::
-
- class CV_EXPORTS Solver : public Algorithm
- {
- public:
- class CV_EXPORTS Function
- {
- public:
- virtual ~Function() {}
- virtual double calc(const double* x) const = 0;
- virtual void getGradient(const double* /*x*/,double* /*grad*/) {}
- };
-
- virtual Ptr<Function> getFunction() const = 0;
- virtual void setFunction(const Ptr<Function>& f) = 0;
-
- virtual TermCriteria getTermCriteria() const = 0;
- virtual void setTermCriteria(const TermCriteria& termcrit) = 0;
-
- // x contain the initial point before the call and the minima position (if algorithm converged) after. x is assumed to be (something that
- // after getMat() will return) row-vector or column-vector. *It's size and should
- // be consisted with previous dimensionality data given, if any (otherwise, it determines dimensionality)*
- virtual double minimize(InputOutputArray x) = 0;
- };
-
- class CV_EXPORTS DownhillSolver : public Solver
- {
- public:
- //! returns row-vector, even if the column-vector was given
- virtual void getInitStep(OutputArray step) const=0;
- //!This should be called at least once before the first call to minimize() and step is assumed to be (something that
- //! after getMat() will return) row-vector or column-vector. *It's dimensionality determines the dimensionality of a problem.*
- virtual void setInitStep(InputArray step)=0;
- };
-
-It should be noted, that ``optim::DownhillSolver`` is a derivative of the abstract interface ``optim::Solver``, which in
-turn is derived from the ``Algorithm`` interface and is used to encapsulate the functionality, common to all non-linear optimization
-algorithms in the ``optim`` module.
-
-optim::DownhillSolver::getFunction
---------------------------------------------
-
-Getter for the optimized function. The optimized function is represented by ``Solver::Function`` interface, which requires
-derivatives to implement the sole method ``calc(double*)`` to evaluate the function.
-
-.. ocv:function:: Ptr<Solver::Function> optim::DownhillSolver::getFunction()
-
- :return: Smart-pointer to an object that implements ``Solver::Function`` interface - it represents the function that is being optimized. It can be empty, if no function was given so far.
-
-optim::DownhillSolver::setFunction
------------------------------------------------
-
-Setter for the optimized function. *It should be called at least once before the call to* ``DownhillSolver::minimize()``, as
-default value is not usable.
-
-.. ocv:function:: void optim::DownhillSolver::setFunction(const Ptr<Solver::Function>& f)
-
- :param f: The new function to optimize.
-
-optim::DownhillSolver::getTermCriteria
-----------------------------------------------------
-
-Getter for the previously set terminal criteria for this algorithm.
-
-.. ocv:function:: TermCriteria optim::DownhillSolver::getTermCriteria()
-
- :return: Deep copy of the terminal criteria used at the moment.
-
-optim::DownhillSolver::setTermCriteria
-------------------------------------------
-
-Set terminal criteria for downhill simplex method. Two things should be noted. First, this method *is not necessary* to be called
-before the first call to ``DownhillSolver::minimize()``, as the default value is sensible. Second, the method will raise an error
-if ``termcrit.type!=(TermCriteria::MAX_ITER+TermCriteria::EPS)``, ``termcrit.epsilon<=0`` or ``termcrit.maxCount<=0``. That is,
-both ``epsilon`` and ``maxCount`` should be set to positive values (non-integer and integer respectively) and they represent
-tolerance and maximal number of function evaluations that is allowed.
-
-Algorithm stops when the number of function evaluations done exceeds ``termcrit.maxCount``, when the function values at the
-vertices of simplex are within ``termcrit.epsilon`` range or simplex becomes so small that it
-can enclosed in a box with ``termcrit.epsilon`` sides, whatever comes first.
-
-.. ocv:function:: void optim::DownhillSolver::setTermCriteria(const TermCriteria& termcrit)
-
- :param termcrit: Terminal criteria to be used, represented as ``TermCriteria`` structure (defined elsewhere in openCV). Mind you, that it should meet ``(termcrit.type==(TermCriteria::MAX_ITER+TermCriteria::EPS) && termcrit.epsilon>0 && termcrit.maxCount>0)``, otherwise the error will be raised.
-
-optim::DownhillSolver::getInitStep
------------------------------------
-
-Returns the initial step that will be used in downhill simplex algorithm. See the description
-of corresponding setter (follows next) for the meaning of this parameter.
-
-.. ocv:function:: void optim::getInitStep(OutputArray step)
-
- :param step: Initial step that will be used in algorithm. Note, that although corresponding setter accepts column-vectors as well as row-vectors, this method will return a row-vector.
-
-optim::DownhillSolver::setInitStep
-----------------------------------
-
-Sets the initial step that will be used in downhill simplex algorithm. Step, together with initial point (givin in ``DownhillSolver::minimize``)
-are two *n*-dimensional vectors that are used to determine the shape of initial simplex. Roughly said, initial point determines the position
-of a simplex (it will become simplex's centroid), while step determines the spread (size in each dimension) of a simplex. To be more precise,
-if :math:`s,x_0\in\mathbb{R}^n` are the initial step and initial point respectively, the vertices of a simplex will be: :math:`v_0:=x_0-\frac{1}{2}
-s` and :math:`v_i:=x_0+s_i` for :math:`i=1,2,\dots,n` where :math:`s_i` denotes projections of the initial step of *n*-th coordinate (the result
-of projection is treated to be vector given by :math:`s_i:=e_i\cdot\left<e_i\cdot s\right>`, where :math:`e_i` form canonical basis)
-
-.. ocv:function:: void optim::setInitStep(InputArray step)
-
- :param step: Initial step that will be used in algorithm. Roughly said, it determines the spread (size in each dimension) of an initial simplex.
-
-optim::DownhillSolver::minimize
------------------------------------
-
-The main method of the ``DownhillSolver``. It actually runs the algorithm and performs the minimization. The sole input parameter determines the
-centroid of the starting simplex (roughly, it tells where to start), all the others (terminal criteria, initial step, function to be minimized)
-are supposed to be set via the setters before the call to this method or the default values (not always sensible) will be used.
-
-.. ocv:function:: double optim::DownhillSolver::minimize(InputOutputArray x)
-
- :param x: The initial point, that will become a centroid of an initial simplex. After the algorithm will terminate, it will be setted to the point where the algorithm stops, the point of possible minimum.
-
- :return: The value of a function at the point found.
-
-optim::createDownhillSolver
-------------------------------------
-
-This function returns the reference to the ready-to-use ``DownhillSolver`` object. All the parameters are optional, so this procedure can be called
-even without parameters at all. In this case, the default values will be used. As default value for terminal criteria are the only sensible ones,
-``DownhillSolver::setFunction()`` and ``DownhillSolver::setInitStep()`` should be called upon the obtained object, if the respective parameters
-were not given to ``createDownhillSolver()``. Otherwise, the two ways (give parameters to ``createDownhillSolver()`` or miss them out and call the
-``DownhillSolver::setFunction()`` and ``DownhillSolver::setInitStep()``) are absolutely equivalent (and will drop the same errors in the same way,
-should invalid input be detected).
-
-.. ocv:function:: Ptr<optim::DownhillSolver> optim::createDownhillSolver(const Ptr<Solver::Function>& f,InputArray initStep, TermCriteria termcrit)
-
- :param f: Pointer to the function that will be minimized, similarly to the one you submit via ``DownhillSolver::setFunction``.
- :param step: Initial step, that will be used to construct the initial simplex, similarly to the one you submit via ``DownhillSolver::setInitStep``.
- :param termcrit: Terminal criteria to the algorithm, similarly to the one you submit via ``DownhillSolver::setTermCriteria``.
+++ /dev/null
-Linear Programming
-==================
-
-.. highlight:: cpp
-
-optim::solveLP
---------------------
-Solve given (non-integer) linear programming problem using the Simplex Algorithm (Simplex Method).
-What we mean here by "linear programming problem" (or LP problem, for short) can be
-formulated as:
-
-.. math::
- \mbox{Maximize } c\cdot x\\
- \mbox{Subject to:}\\
- Ax\leq b\\
- x\geq 0
-
-Where :math:`c` is fixed *1*-by-*n* row-vector, :math:`A` is fixed *m*-by-*n* matrix, :math:`b` is fixed *m*-by-*1* column vector and
-:math:`x` is an arbitrary *n*-by-*1* column vector, which satisfies the constraints.
-
-Simplex algorithm is one of many algorithms that are designed to handle this sort of problems efficiently. Although it is not optimal in theoretical
-sense (there exist algorithms that can solve any problem written as above in polynomial type, while simplex method degenerates to exponential time
-for some special cases), it is well-studied, easy to implement and is shown to work well for real-life purposes.
-
-The particular implementation is taken almost verbatim from **Introduction to Algorithms, third edition**
-by T. H. Cormen, C. E. Leiserson, R. L. Rivest and Clifford Stein. In particular, the Bland's rule
-(`http://en.wikipedia.org/wiki/Bland%27s\_rule <http://en.wikipedia.org/wiki/Bland%27s_rule>`_) is used to prevent cycling.
-
-.. ocv:function:: int optim::solveLP(const Mat& Func, const Mat& Constr, Mat& z)
-
- :param Func: This row-vector corresponds to :math:`c` in the LP problem formulation (see above). It should contain 32- or 64-bit floating point numbers. As a convenience, column-vector may be also submitted, in the latter case it is understood to correspond to :math:`c^T`.
-
- :param Constr: *m*-by-*n\+1* matrix, whose rightmost column corresponds to :math:`b` in formulation above and the remaining to :math:`A`. It should containt 32- or 64-bit floating point numbers.
-
- :param z: The solution will be returned here as a column-vector - it corresponds to :math:`c` in the formulation above. It will contain 64-bit floating point numbers.
-
- :return: One of the return codes:
-
-::
-
- //!the return codes for solveLP() function
- enum
- {
- SOLVELP_UNBOUNDED = -2, //problem is unbounded (target function can achieve arbitrary high values)
- SOLVELP_UNFEASIBLE = -1, //problem is unfeasible (there are no points that satisfy all the constraints imposed)
- SOLVELP_SINGLE = 0, //there is only one maximum for target function
- SOLVELP_MULTI = 1 //there are multiple maxima for target function - the arbitrary one is returned
- };
+++ /dev/null
-Nonlinear Conjugate Gradient
-===============================
-
-.. highlight:: cpp
-
-optim::ConjGradSolver
----------------------------------
-
-.. ocv:class:: optim::ConjGradSolver
-
-This class is used to perform the non-linear non-constrained *minimization* of a function with *known gradient*
-, defined on an *n*-dimensional Euclidean space,
-using the **Nonlinear Conjugate Gradient method**. The implementation was done based on the beautifully clear explanatory article `An Introduction to the Conjugate Gradient Method Without the Agonizing Pain <http://www.cs.cmu.edu/~quake-papers/painless-conjugate-gradient.pdf>`_
-by Jonathan Richard Shewchuk. The method can be seen as an adaptation of a standard Conjugate Gradient method (see, for example
-`http://en.wikipedia.org/wiki/Conjugate_gradient_method <http://en.wikipedia.org/wiki/Conjugate_gradient_method>`_) for numerically solving the
-systems of linear equations.
-
-It should be noted, that
-this method, although deterministic, is rather a heuristic method and therefore may converge to a local minima, not necessary a global one. What
-is even more disastrous, most of its behaviour is ruled by gradient, therefore it essentially cannot distinguish between local minima and maxima.
-Therefore, if it starts sufficiently near to the local maximum, it may converge to it. Another obvious restriction is that it should be possible
-to compute the gradient of a function at any point, thus it is preferable to have analytic expression for gradient and computational burden
-should be born by the user.
-
-The latter responsibility is accompilished via the ``getGradient(const double* x,double* grad)`` method of a
-``Solver::Function`` interface (which represents function that is being optimized). This method takes point a point in *n*-dimensional space
-(first argument represents the array of coordinates of that point) and comput its gradient (it should be stored in the second argument as an array).
-
-::
-
- class CV_EXPORTS Solver : public Algorithm
- {
- public:
- class CV_EXPORTS Function
- {
- public:
- virtual ~Function() {}
- virtual double calc(const double* x) const = 0;
- virtual void getGradient(const double* /*x*/,double* /*grad*/) {}
- };
-
- virtual Ptr<Function> getFunction() const = 0;
- virtual void setFunction(const Ptr<Function>& f) = 0;
-
- virtual TermCriteria getTermCriteria() const = 0;
- virtual void setTermCriteria(const TermCriteria& termcrit) = 0;
-
- // x contain the initial point before the call and the minima position (if algorithm converged) after. x is assumed to be (something that
- // after getMat() will return) row-vector or column-vector. *It's size and should
- // be consisted with previous dimensionality data given, if any (otherwise, it determines dimensionality)*
- virtual double minimize(InputOutputArray x) = 0;
- };
-
- class CV_EXPORTS ConjGradSolver : public Solver{
- };
-
-Note, that class ``ConjGradSolver`` thus does not add any new methods to the basic ``Solver`` interface.
-
-optim::ConjGradSolver::getFunction
---------------------------------------------
-
-Getter for the optimized function. The optimized function is represented by ``Solver::Function`` interface, which requires
-derivatives to implement the method ``calc(double*)`` to evaluate the function. It should be emphasized once more, that since Nonlinear
-Conjugate Gradient method requires gradient to be computable in addition to the function values,
-``getGradient(const double* x,double* grad)`` method of a ``Solver::Function`` interface should be also implemented meaningfully.
-
-.. ocv:function:: Ptr<Solver::Function> optim::ConjGradSolver::getFunction()
-
- :return: Smart-pointer to an object that implements ``Solver::Function`` interface - it represents the function that is being optimized. It can be empty, if no function was given so far.
-
-optim::ConjGradSolver::setFunction
------------------------------------------------
-
-Setter for the optimized function. *It should be called at least once before the call to* ``ConjGradSolver::minimize()``, as
-default value is not usable.
-
-.. ocv:function:: void optim::ConjGradSolver::setFunction(const Ptr<Solver::Function>& f)
-
- :param f: The new function to optimize.
-
-optim::ConjGradSolver::getTermCriteria
-----------------------------------------------------
-
-Getter for the previously set terminal criteria for this algorithm.
-
-.. ocv:function:: TermCriteria optim::ConjGradSolver::getTermCriteria()
-
- :return: Deep copy of the terminal criteria used at the moment.
-
-optim::ConjGradSolver::setTermCriteria
-------------------------------------------
-
-Set terminal criteria for downhill simplex method. Two things should be noted. First, this method *is not necessary* to be called
-before the first call to ``ConjGradSolver::minimize()``, as the default value is sensible. Second, the method will raise an error
-if ``termcrit.type!=(TermCriteria::MAX_ITER+TermCriteria::EPS)`` and ``termcrit.type!=TermCriteria::MAX_ITER``. This means that termination criteria
-has to restrict maximum number of iterations to be done and may optionally allow algorithm to stop earlier if certain tolerance
-is achieved (what we mean by "tolerance is achieved" will be clarified below). If ``termcrit`` restricts both tolerance and maximum iteration
-number, both ``termcrit.epsilon`` and ``termcrit.maxCount`` should be positive. In case, if ``termcrit.type==TermCriteria::MAX_ITER``,
-only member ``termcrit.maxCount`` is required to be positive and in this case algorithm will just work for required number of iterations.
-
-In current implementation, "tolerance is achieved" means that we have arrived at the point where the :math:`L_2`-norm of the gradient is less
-than the tolerance value.
-
-.. ocv:function:: void optim::ConjGradSolver::setTermCriteria(const TermCriteria& termcrit)
-
- :param termcrit: Terminal criteria to be used, represented as ``TermCriteria`` structure (defined elsewhere in openCV). Mind you, that it should meet ``termcrit.type==(TermCriteria::MAX_ITER+TermCriteria::EPS) && termcrit.epsilon>0 && termcrit.maxCount>0`` or ``termcrit.type==TermCriteria::MAX_ITER) && termcrit.maxCount>0``, otherwise the error will be raised.
-
-optim::ConjGradSolver::minimize
------------------------------------
-
-The main method of the ``ConjGradSolver``. It actually runs the algorithm and performs the minimization. The sole input parameter determines the
-centroid of the starting simplex (roughly, it tells where to start), all the others (terminal criteria and function to be minimized)
-are supposed to be set via the setters before the call to this method or the default values (not always sensible) will be used. Sometimes it may
-throw an error, if these default values cannot be used (say, you forgot to set the function to minimize and default value, that is, empty function,
-cannot be used).
-
-.. ocv:function:: double optim::ConjGradSolver::minimize(InputOutputArray x)
-
- :param x: The initial point. It is hard to overemphasize how important the choise of initial point is when you are using the heuristic algorithm like this one. Badly chosen initial point can make algorithm converge to (local) maximum instead of minimum, do not converge at all, converge to local minimum instead of global one.
-
- :return: The value of a function at the point found.
-
-optim::createConjGradSolver
-------------------------------------
-
-This function returns the reference to the ready-to-use ``ConjGradSolver`` object. All the parameters are optional, so this procedure can be called
-even without parameters at all. In this case, the default values will be used. As default value for terminal criteria are the only sensible ones,
-``ConjGradSolver::setFunction()`` should be called upon the obtained object, if the function
-was not given to ``createConjGradSolver()``. Otherwise, the two ways (submit it to ``createConjGradSolver()`` or miss it out and call the
-``ConjGradSolver::setFunction()``) are absolutely equivalent (and will drop the same errors in the same way,
-should invalid input be detected).
-
-.. ocv:function:: Ptr<optim::ConjGradSolver> optim::createConjGradSolver(const Ptr<Solver::Function>& f, TermCriteria termcrit)
-
- :param f: Pointer to the function that will be minimized, similarly to the one you submit via ``ConjGradSolver::setFunction``.
- :param termcrit: Terminal criteria to the algorithm, similarly to the one you submit via ``ConjGradSolver::setTermCriteria``.
+++ /dev/null
-**************************************
-optim. Generic numerical optimization
-**************************************
-
-.. highlight:: cpp
-
-.. toctree::
- :maxdepth: 2
-
- linear_programming
- downhill_simplex_method
- primal_dual_algorithm
- nonlinear_conjugate_gradient
+++ /dev/null
-Primal-Dual Algorithm
-=======================
-
-.. highlight:: cpp
-
-optim::denoise_TVL1
----------------------------------
-
-Primal-dual algorithm is an algorithm for solving special types of variational
-problems (that is, finding a function to minimize some functional)
-. As the image denoising, in particular, may be seen as the variational
-problem, primal-dual algorithm then can be used to perform denoising and this
-is exactly what is implemented.
-
-It should be noted, that this implementation was taken from the July 2013 blog entry [Mordvintsev]_, which also contained
-(slightly more general) ready-to-use
-source code on Python. Subsequently, that code was rewritten on C++ with the usage of openCV by Vadim Pisarevsky
-at the end of July 2013 and finally it was slightly adapted by later authors.
-
-Although the thorough discussion and justification
-of the algorithm involved may be found in [ChambolleEtAl]_, it might make sense to skim over it here, following [Mordvintsev]_. To
-begin with, we consider the 1-byte gray-level images as the functions from the rectangular domain of pixels
-(it may be seen as set :math:`\left\{(x,y)\in\mathbb{N}\times\mathbb{N}\mid 1\leq x\leq n,\;1\leq y\leq m\right\}`
-for some :math:`m,\;n\in\mathbb{N}`) into :math:`\{0,1,\dots,255\}`. We shall denote the noised images as :math:`f_i` and with this
-view, given some image :math:`x` of the same size, we may measure how bad it is by the formula
-
-.. math::
- \left\|\left\|\nabla x\right\|\right\| + \lambda\sum_i\left\|\left\|x-f_i\right\|\right\|
-
-:math:`\|\|\cdot\|\|` here denotes :math:`L_2`-norm and as you see, the first addend states that we want our image to be smooth
-(ideally, having zero gradient, thus being constant) and the second states that we want our result to be close to the observations we've got.
-If we treat :math:`x` as a function, this is exactly the functional what we seek to minimize and here the Primal-Dual algorithm comes
-into play.
-
-.. ocv:function:: void optim::denoise_TVL1(const std::vector<Mat>& observations,Mat& result, double lambda, int niters)
-
- :param observations: This array should contain one or more noised versions of the image that is to be restored.
-
- :param result: Here the denoised image will be stored. There is no need to do pre-allocation of storage space, as it will be automatically allocated, if necessary.
-
- :param lambda: Corresponds to :math:`\lambda` in the formulas above. As it is enlarged, the smooth (blurred) images are treated more favorably than detailed (but maybe more noised) ones. Roughly speaking, as it becomes smaller, the result will be more blur but more sever outliers will be removed.
-
- :param niters: Number of iterations that the algorithm will run. Of course, as more iterations as better, but it is hard to quantitatively refine this statement, so just use the default and increase it if the results are poor.
-
-
-.. [ChambolleEtAl] A. Chambolle, V. Caselles, M. Novaga, D. Cremers and T. Pock, An Introduction to Total Variation for Image Analysis, http://hal.archives-ouvertes.fr/docs/00/43/75/81/PDF/preprint.pdf (pdf)
-
-.. [Mordvintsev] Alexander Mordvintsev, ROF and TV-L1 denoising with Primal-Dual algorithm, http://znah.net/rof-and-tv-l1-denoising-with-primal-dual-algorithm.html (blog entry)
+++ /dev/null
-/*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) 2013, OpenCV Foundation, 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 OpenCV Foundation 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*/
-
-#ifdef __OPENCV_BUILD
-#error this is a compatibility header which should not be used inside the OpenCV library
-#endif
-
-#include "opencv2/optim.hpp"
+++ /dev/null
-/*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) 2013, OpenCV Foundation, 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 OpenCV Foundation 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*/
-namespace cv{namespace optim{
-#ifdef ALEX_DEBUG
-#define dprintf(x) printf x
-static void print_matrix(const Mat& x){
- printf("\ttype:%d vs %d,\tsize: %d-on-%d\n",x.type(),CV_64FC1,x.rows,x.cols);
- for(int i=0;i<x.rows;i++){
- printf("\t[");
- for(int j=0;j<x.cols;j++){
- printf("%g, ",x.at<double>(i,j));
- }
- printf("]\n");
- }
-}
-#else
-#define dprintf(x)
-#define print_matrix(x)
-#endif
-}}
+++ /dev/null
-/*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) 2013, OpenCV Foundation, 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 OpenCV Foundation 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 __OPENCV_PRECOMP_H__
-#define __OPENCV_PRECOMP_H__
-
-#include "opencv2/optim.hpp"
-
-#endif
+++ /dev/null
-#include "test_precomp.hpp"
-
-CV_TEST_MAIN("cv")
+++ /dev/null
-#ifdef __GNUC__
-# pragma GCC diagnostic ignored "-Wmissing-declarations"
-# if defined __clang__ || defined __APPLE__
-# pragma GCC diagnostic ignored "-Wmissing-prototypes"
-# pragma GCC diagnostic ignored "-Wextra"
-# endif
-#endif
-
-#ifndef __OPENCV_TEST_PRECOMP_HPP__
-#define __OPENCV_TEST_PRECOMP_HPP__
-
-#include "opencv2/ts.hpp"
-#include "opencv2/optim.hpp"
-#include "opencv2/imgcodecs.hpp"
-
-#endif
.. seealso::
:ocv:func:`fastNlMeansDenoisingColored`
+
+
+denoise_TVL1
+---------------------------------
+
+Primal-dual algorithm is an algorithm for solving special types of variational
+problems (that is, finding a function to minimize some functional).
+As the image denoising, in particular, may be seen as the variational
+problem, primal-dual algorithm then can be used to perform denoising and this
+is exactly what is implemented.
+
+It should be noted, that this implementation was taken from the July 2013 blog entry [Mordvintsev]_, which also contained
+(slightly more general) ready-to-use
+source code on Python. Subsequently, that code was rewritten on C++ with the usage of openCV by Vadim Pisarevsky
+at the end of July 2013 and finally it was slightly adapted by later authors.
+
+Although the thorough discussion and justification
+of the algorithm involved may be found in [ChambolleEtAl]_, it might make sense to skim over it here, following [Mordvintsev]_. To
+begin with, we consider the 1-byte gray-level images as the functions from the rectangular domain of pixels
+(it may be seen as set :math:`\left\{(x,y)\in\mathbb{N}\times\mathbb{N}\mid 1\leq x\leq n,\;1\leq y\leq m\right\}`
+for some :math:`m,\;n\in\mathbb{N}`) into :math:`\{0,1,\dots,255\}`. We shall denote the noised images as :math:`f_i` and with this
+view, given some image :math:`x` of the same size, we may measure how bad it is by the formula
+
+.. math::
+ \left\|\left\|\nabla x\right\|\right\| + \lambda\sum_i\left\|\left\|x-f_i\right\|\right\|
+
+:math:`\|\|\cdot\|\|` here denotes :math:`L_2`-norm and as you see, the first addend states that we want our image to be smooth
+(ideally, having zero gradient, thus being constant) and the second states that we want our result to be close to the observations we've got.
+If we treat :math:`x` as a function, this is exactly the functional what we seek to minimize and here the Primal-Dual algorithm comes
+into play.
+
+.. ocv:function:: void denoise_TVL1(const std::vector<Mat>& observations,Mat& result, double lambda, int niters)
+
+ :param observations: This array should contain one or more noised versions of the image that is to be restored.
+
+ :param result: Here the denoised image will be stored. There is no need to do pre-allocation of storage space, as it will be automatically allocated, if necessary.
+
+ :param lambda: Corresponds to :math:`\lambda` in the formulas above. As it is enlarged, the smooth (blurred) images are treated more favorably than detailed (but maybe more noised) ones. Roughly speaking, as it becomes smaller, the result will be more blur but more sever outliers will be removed.
+
+ :param niters: Number of iterations that the algorithm will run. Of course, as more iterations as better, but it is hard to quantitatively refine this statement, so just use the default and increase it if the results are poor.
+
+
+.. [ChambolleEtAl] A. Chambolle, V. Caselles, M. Novaga, D. Cremers and T. Pock, An Introduction to Total Variation for Image Analysis, http://hal.archives-ouvertes.fr/docs/00/43/75/81/PDF/preprint.pdf (pdf)
+
+.. [Mordvintsev] Alexander Mordvintsev, ROF and TV-L1 denoising with Primal-Dual algorithm, http://znah.net/rof-and-tv-l1-denoising-with-primal-dual-algorithm.html (blog entry)
float h = 3, float hColor = 3,
int templateWindowSize = 7, int searchWindowSize = 21);
+CV_EXPORTS_W void denoise_TVL1(const std::vector<Mat>& observations,Mat& result, double lambda=1.0, int niters=30);
+
enum { LDR_SIZE = 256 };
class CV_EXPORTS_W Tonemap : public Algorithm
//
//M*/
#include "precomp.hpp"
-#undef ALEX_DEBUG
-#include "debug.hpp"
#include <vector>
#include <algorithm>
#define ABSCLIP(val,threshold) MIN(MAX((val),-(threshold)),(threshold))
-namespace cv{namespace optim{
+namespace cv{
class AddFloatToCharScaled{
public:
result.create(X.rows,X.cols,CV_8U);
X.convertTo(result, CV_8U, 255);
}
-}}
+}
void cv::fastNlMeansDenoising( InputArray _src, OutputArray _dst, float h,
int templateWindowSize, int searchWindowSize)
{
- CV_OCL_RUN(_src.dims() <= 2 && (_src.isUMat() || _dst.isUMat()),
+ Size src_size = _src.size();
+ CV_OCL_RUN(_src.dims() <= 2 && (_src.isUMat() || _dst.isUMat()) &&
+ src_size.width > 5 && src_size.height > 5, // low accuracy on small sizes
ocl_fastNlMeansDenoising(_src, _dst, h, templateWindowSize, searchWindowSize))
Mat src = _src.getMat();
- _dst.create(src.size(), src.type());
+ _dst.create(src_size, src.type());
Mat dst = _dst.getMat();
#ifdef HAVE_TEGRA_OPTIMIZATION
int templateWindowSize, int searchWindowSize)
{
int type = _src.type(), depth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type);
-
+ Size src_size = _src.size();
if (type != CV_8UC3 && type != CV_8UC4)
{
CV_Error(Error::StsBadArg, "Type of input image should be CV_8UC3!");
return;
}
- CV_OCL_RUN(_src.dims() <= 2 && (_dst.isUMat() || _src.isUMat()),
+ CV_OCL_RUN(_src.dims() <= 2 && (_dst.isUMat() || _src.isUMat()) &&
+ src_size.width > 5 && src_size.height > 5, // low accuracy on small sizes
ocl_fastNlMeansDenoisingColored(_src, _dst, h, hForColorComponents,
templateWindowSize, searchWindowSize))
Mat src = _src.getMat();
- _dst.create(src.size(), type);
+ _dst.create(src_size, type);
Mat dst = _dst.getMat();
Mat src_lab;
cvtColor(src, src_lab, COLOR_LBGR2Lab);
- Mat l(src.size(), CV_8U);
- Mat ab(src.size(), CV_8UC2);
+ Mat l(src_size, CV_8U);
+ Mat ab(src_size, CV_8UC2);
Mat l_ab[] = { l, ab };
int from_to[] = { 0,0, 1,1, 2,2 };
mixChannels(&src_lab, 1, l_ab, 2, from_to, 3);
fastNlMeansDenoising(ab, ab, hForColorComponents, templateWindowSize, searchWindowSize);
Mat l_ab_denoised[] = { l, ab };
- Mat dst_lab(src.size(), CV_MAKE_TYPE(depth, 3));
+ Mat dst_lab(src_size, CV_MAKE_TYPE(depth, 3));
mixChannels(l_ab_denoised, 2, &dst_lab, 1, from_to, 3);
cvtColor(dst_lab, dst, COLOR_Lab2LBGR, cn);
bool validate_pixel(const cv::Mat& image,int x,int y,uchar val)
{
- printf("test: image(%d,%d)=%d vs %d - %s\n",x,y,(int)image.at<uchar>(x,y),val,(val==image.at<uchar>(x,y))?"true":"false");
- return (image.at<uchar>(x,y)==val);
+ bool ok = std::abs(image.at<uchar>(x,y) - val) < 10;
+ printf("test: image(%d,%d)=%d vs %d - %s\n",x,y,(int)image.at<uchar>(x,y),val,ok?"ok":"bad");
+ return ok;
}
TEST(Optim_denoise_tvl1, regression_basic)
}
//cv::imshow("test", images[0]);
- cv::optim::denoise_TVL1(images, res);
+ cv::denoise_TVL1(images, res);
//cv::imshow("denoised", res);
//cv::waitKey();
ocv_list_filterout(candidate_deps "^opencv_optflow$")
ocv_list_filterout(candidate_deps "^opencv_bgsegm$")
ocv_list_filterout(candidate_deps "^opencv_xfeatures2d$")
+ocv_list_filterout(candidate_deps "^opencv_ximgproc$")
+ocv_list_filterout(candidate_deps "^opencv_xphoto$")
+ocv_list_filterout(candidate_deps "^opencv_ts$")
ocv_add_module(${MODULE_NAME} BINDINGS OPTIONAL ${candidate_deps})
{
CV_Assert(H.type() == CV_64F && H.size() == Size(3, 3));
- const double* h = reinterpret_cast<const double*>(H.data);
+ const double* h = H.ptr<double>();
double d1, d2; // Denominators
double v1, v2; // Focal squares value candidates
#if defined(DEFINE_feed)
#define workType TYPE(weight_T1, src_CN)
+
+#if src_DEPTH == 3 && src_CN == 3
+#define convertSrcToWorkType convert_float3
+#else
#define convertSrcToWorkType CONVERT_TO(workType)
+#endif
+
+#if dst_DEPTH == 3 && dst_CN == 3
+#define convertToDstType convert_short3
+#else
#define convertToDstType CONVERT_TO(dst_T) // sat_rte provides incompatible results with CPU path
+#endif
__kernel void feed(
DECLARE_MAT_ARG(src), DECLARE_MAT_ARG(weight),
#if defined(DEFINE_normalizeUsingWeightMap)
+#if mat_DEPTH == 3 && mat_CN == 3
+#define workType float3
+#define convertSrcToWorkType convert_float3
+#define convertToDstType convert_short3
+#else
#define workType TYPE(weight_T1, mat_CN)
#define convertSrcToWorkType CONVERT_TO(workType)
#define convertToDstType CONVERT_TO(mat_T) // sat_rte provides incompatible results with CPU path
+#endif
#if weight_DEPTH >= CV_32F
#define WEIGHT_EPS 1e-5f
bool ocl_process(InputArrayOfArrays src, OutputArray dst, InputArrayOfArrays forwardMotions,
InputArrayOfArrays backwardMotions, int baseIdx);
- Ptr<FilterEngine> filter_;
+ //Ptr<FilterEngine> filter_;
int curBlurKernelSize_;
double curBlurSigma_;
int curSrcType_;
& backwardMotions = *(std::vector<UMat> *)_backwardMotions.getObj();
// update blur filter and btv weights
- if (!filter_ || blurKernelSize_ != curBlurKernelSize_ || blurSigma_ != curBlurSigma_ || src[0].type() != curSrcType_)
+ if (blurKernelSize_ != curBlurKernelSize_ || blurSigma_ != curBlurSigma_ || src[0].type() != curSrcType_)
{
- filter_ = createGaussianFilter(src[0].type(), Size(blurKernelSize_, blurKernelSize_), blurSigma_);
+ //filter_ = createGaussianFilter(src[0].type(), Size(blurKernelSize_, blurKernelSize_), blurSigma_);
curBlurKernelSize_ = blurKernelSize_;
curBlurSigma_ = blurSigma_;
curSrcType_ = src[0].type();
& backwardMotions = *(std::vector<Mat> *)_backwardMotions.getObj();
// update blur filter and btv weights
- if (!filter_ || blurKernelSize_ != curBlurKernelSize_ || blurSigma_ != curBlurSigma_ || src[0].type() != curSrcType_)
+ if (blurKernelSize_ != curBlurKernelSize_ || blurSigma_ != curBlurSigma_ || src[0].type() != curSrcType_)
{
- filter_ = createGaussianFilter(src[0].type(), Size(blurKernelSize_, blurKernelSize_), blurSigma_);
+ //filter_ = createGaussianFilter(src[0].type(), Size(blurKernelSize_, blurKernelSize_), blurSigma_);
curBlurKernelSize_ = blurKernelSize_;
curBlurSigma_ = blurSigma_;
curSrcType_ = src[0].type();
// a = M * Ih
remap(highRes_, a_, backwardMaps_[k], noArray(), INTER_NEAREST);
// b = HM * Ih
- filter_->apply(a_, b_);
+ GaussianBlur(a_, b_, Size(blurKernelSize_, blurKernelSize_), blurSigma_);
// c = DHM * Ih
resize(b_, c_, lowResSize, 0, 0, INTER_NEAREST);
// a = Dt * diff
upscale(c_, a_, scale_);
// b = HtDt * diff
- filter_->apply(a_, b_);
+ GaussianBlur(a_, b_, Size(blurKernelSize_, blurKernelSize_), blurSigma_);
// a = MtHtDt * diff
remap(b_, a_, forwardMaps_[k], noArray(), INTER_NEAREST);
void BTVL1_Base::collectGarbage()
{
- filter_.release();
-
// Mat
lowResForwardMotions_.clear();
lowResBackwardMotions_.clear();
if(!b.empty())
buf[1].create(1, maxsize, CV_64FC(cn));
buf[2].create(1, maxsize, CV_64FC(cn));
- scalarToRawData(gamma, buf[2].data, CV_64FC(cn), (int)(maxsize*cn));
+ scalarToRawData(gamma, buf[2].ptr(), CV_64FC(cn), (int)(maxsize*cn));
for( i = 0; i < nplanes; i++, ++it)
{
apart0.convertTo(apart, apart.type(), alpha);
size_t k, n = (j2 - j)*cn;
- double* aptr = (double*)apart.data;
- const double* gptr = (const double*)buf[2].data;
+ double* aptr = apart.ptr<double>();
+ const double* gptr = buf[2].ptr<double>();
if( b.empty() )
{
Mat bpart0 = planes[1].colRange((int)j, (int)j2);
Mat bpart = buf[1].colRange(0, (int)(j2 - j));
bpart0.convertTo(bpart, bpart.type(), beta);
- const double* bptr = (const double*)bpart.data;
+ const double* bptr = bpart.ptr<double>();
for( k = 0; k < n; k++ )
aptr[k] += bptr[k] + gptr[k];
for( i = 0; i < nplanes; i++, ++it)
{
- const uchar* sptr = planes[0].data;
- uchar* dptr = planes[1].data;
+ const uchar* sptr = planes[0].ptr();
+ uchar* dptr = planes[1].ptr();
switch( src.depth() )
{
size_t planeSize = planes[0].total()*src.elemSize();
for( i = 0; i < nplanes; i++, ++it )
- memcpy(planes[1].data, planes[0].data, planeSize);
+ memcpy(planes[1].ptr(), planes[0].ptr(), planeSize);
return;
}
for( i = 0; i < nplanes; i++, ++it)
{
- const uchar* sptr = planes[0].data;
- uchar* dptr = planes[1].data;
- const uchar* mptr = planes[2].data;
+ const uchar* sptr = planes[0].ptr();
+ uchar* dptr = planes[1].ptr();
+ const uchar* mptr = planes[2].ptr();
for( j = 0; j < total; j++, sptr += elemSize, dptr += elemSize )
{
for( i = 0; i < nplanes; i++, ++it )
{
- uchar* dptr = plane.data;
+ uchar* dptr = plane.ptr();
if( uniform )
memset( dptr, gptr[0], planeSize );
else if( i == 0 )
dptr[k] = gptr[k];
}
else
- memcpy(dptr, dst.data, planeSize);
+ memcpy(dptr, dst.ptr(), planeSize);
}
return;
}
for( i = 0; i < nplanes; i++, ++it)
{
- uchar* dptr = planes[0].data;
- const uchar* mptr = planes[1].data;
+ uchar* dptr = planes[0].ptr();
+ const uchar* mptr = planes[1].ptr();
for( j = 0; j < total; j++, dptr += elemSize )
{
for( i = 0; i < nplanes; i++, ++it )
{
- const uchar* sptr = planes[0].data;
- uchar* dptr = planes[1].data + coi*size0;
+ const uchar* sptr = planes[0].ptr();
+ uchar* dptr = planes[1].ptr() + coi*size0;
for( j = 0; j < total; j++, sptr += size0, dptr += size1 )
{
for( i = 0; i < nplanes; i++, ++it )
{
- const uchar* sptr = planes[0].data + coi*size1;
- uchar* dptr = planes[1].data;
+ const uchar* sptr = planes[0].ptr() + coi*size1;
+ uchar* dptr = planes[1].ptr();
for( j = 0; j < total; j++, sptr += size0, dptr += size1 )
{
for( i = 0; i < nplanes; i++, ++it, startidx += total )
{
- const uchar* sptr = planes[0].data;
- const uchar* mptr = planes[1].data;
+ const uchar* sptr = planes[0].ptr();
+ const uchar* mptr = planes[1].ptr();
switch( depth )
{
int cellSize = normType == NORM_HAMMING ? 1 : 2;
for( i = 0; i < nplanes; i++, ++it )
- result += normHamming(planes[0].data, total, cellSize);
+ result += normHamming(planes[0].ptr(), total, cellSize);
return result;
}
int normType0 = normType;
for( i = 0; i < nplanes; i++, ++it )
{
- const uchar* sptr = planes[0].data;
- const uchar* mptr = planes[1].data;
+ const uchar* sptr = planes[0].ptr();
+ const uchar* mptr = planes[1].ptr();
switch( depth )
{
int cellSize = normType == NORM_HAMMING ? 1 : 2;
for( i = 0; i < nplanes; i++, ++it )
- result += normHamming(planes[0].data, total, cellSize);
+ result += normHamming(planes[0].ptr(), total, cellSize);
return result;
}
int normType0 = normType;
for( i = 0; i < nplanes; i++, ++it )
{
- const uchar* sptr1 = planes[0].data;
- const uchar* sptr2 = planes[1].data;
- const uchar* mptr = planes[2].data;
+ const uchar* sptr1 = planes[0].ptr();
+ const uchar* sptr2 = planes[1].ptr();
+ const uchar* mptr = planes[2].ptr();
switch( depth )
{
for( i = 0; i < nplanes; i++, ++it )
{
- const uchar* sptr1 = planes[0].data;
- const uchar* sptr2 = planes[1].data;
+ const uchar* sptr1 = planes[0].ptr();
+ const uchar* sptr2 = planes[1].ptr();
switch( depth )
{
for( i = 0; i < nplanes; i++, ++it )
{
- const uchar* sptr1 = planes[0].data;
- const uchar* sptr2 = planes[1].data;
- uchar* dptr = planes[2].data;
+ const uchar* sptr1 = planes[0].ptr();
+ const uchar* sptr2 = planes[1].ptr();
+ uchar* dptr = planes[2].ptr();
logicOp_(sptr1, sptr2, dptr, total, op);
}
for( i = 0; i < nplanes; i++, ++it )
{
- const uchar* sptr = planes[0].data;
- uchar* dptr = planes[1].data;
+ const uchar* sptr = planes[0].ptr();
+ uchar* dptr = planes[1].ptr();
logicOpS_(sptr, (uchar*)&buf[0], dptr, total, op);
}
for( i = 0; i < nplanes; i++, ++it )
{
- const uchar* sptr1 = planes[0].data;
- const uchar* sptr2 = planes[1].data;
- uchar* dptr = planes[2].data;
+ const uchar* sptr1 = planes[0].ptr();
+ const uchar* sptr2 = planes[1].ptr();
+ uchar* dptr = planes[2].ptr();
switch( depth )
{
for( i = 0; i < nplanes; i++, ++it )
{
- const uchar* sptr = planes[0].data;
- uchar* dptr = planes[1].data;
+ const uchar* sptr = planes[0].ptr();
+ uchar* dptr = planes[1].ptr();
switch( depth )
{
for( i = 0; i < nplanes; i++, ++it, startidx += total )
{
- const uchar* sptr1 = planes[0].data;
- const uchar* sptr2 = planes[1].data;
+ const uchar* sptr1 = planes[0].ptr();
+ const uchar* sptr2 = planes[1].ptr();
double realmaxdiff = 0;
switch( depth )
for( i = 0; i < nplanes; i++, ++it, startidx += total )
{
- const uchar* aptr = plane.data;
+ const uchar* aptr = plane.ptr();
switch( depth )
{
for( i = 0; i < nplanes; i++, ++it, startidx += total )
{
- const uchar* sptr1 = planes[0].data;
- const uchar* sptr2 = planes[1].data;
+ const uchar* sptr1 = planes[0].ptr();
+ const uchar* sptr2 = planes[1].ptr();
switch( depth )
{
for( i = 0; i < nplanes; i++, ++it )
{
- const uchar* sptr = planes[0].data;
- uchar* dptr = planes[1].data;
+ const uchar* sptr = planes[0].ptr();
+ uchar* dptr = planes[1].ptr();
switch( depth )
{
for( i = 0; i < nplanes; i++, ++it )
{
- const uchar* sptr1 = planes[0].data;
- const uchar* sptr2 = planes[1].data;
- uchar* dptr = planes[2].data;
+ const uchar* sptr1 = planes[0].ptr();
+ const uchar* sptr2 = planes[1].ptr();
+ uchar* dptr = planes[2].ptr();
switch( depth )
{
for( i = 0; i < nplanes; i++, ++it )
{
- const uchar* sptr1 = planes[0].data;
- uchar* dptr = planes[1].data;
+ const uchar* sptr1 = planes[0].ptr();
+ uchar* dptr = planes[1].ptr();
switch( depth )
{
for( i = 0; i < nplanes; i++, ++it )
{
- const uchar* sptr1 = planes[0].data;
- const uchar* sptr2 = planes[1].data;
- uchar* dptr = planes[2].data;
+ const uchar* sptr1 = planes[0].ptr();
+ const uchar* sptr2 = planes[1].ptr();
+ uchar* dptr = planes[2].ptr();
switch( depth )
{
for( i = 0; i < nplanes; i++, ++it )
{
- const uchar* sptr = planes[0].data;
- const uchar* mptr = planes[1].data;
+ const uchar* sptr = planes[0].ptr();
+ const uchar* mptr = planes[1].ptr();
switch( depth )
{
parallel_for_(Range(0, image.rows),
MOG2Invoker(image, fgmask,
- (GMM*)bgmodel.data,
- (float*)(bgmodel.data + sizeof(GMM)*nmixtures*image.rows*image.cols),
- bgmodelUsedModes.data, nmixtures, (float)learningRate,
+ bgmodel.ptr<GMM>(),
+ (float*)(bgmodel.ptr() + sizeof(GMM)*nmixtures*image.rows*image.cols),
+ bgmodelUsedModes.ptr(), nmixtures, (float)learningRate,
(float)varThreshold,
backgroundRatio, varThresholdGen,
fVarInit, fVarMin, fVarMax, float(-learningRate*fCT), fTau,
CV_Assert(nchannels == 1 || nchannels == 3);
Mat meanBackground(frameSize, CV_MAKETYPE(CV_8U, nchannels), Scalar::all(0));
int firstGaussianIdx = 0;
- const GMM* gmm = (GMM*)bgmodel.data;
+ const GMM* gmm = bgmodel.ptr<GMM>();
const float* mean = reinterpret_cast<const float*>(gmm + frameSize.width*frameSize.height*nmixtures);
std::vector<float> meanVal(nchannels, 0.f);
for(int row=0; row<meanBackground.rows; row++)
// update the state: x'(k) = A*x(k)
statePre = transitionMatrix*statePost;
- if( control.data )
+ if( !control.empty() )
// x'(k) = x'(k) + B*u(k)
statePre += controlMatrix*control;
int x, y;
for( y = 0; y < winSize.height; y++ )
{
- const uchar* src = (const uchar*)I.data + (y + iprevPt.y)*stepI + iprevPt.x*cn;
- const deriv_type* dsrc = (const deriv_type*)derivI.data + (y + iprevPt.y)*dstep + iprevPt.x*cn2;
+ const uchar* src = I.ptr() + (y + iprevPt.y)*stepI + iprevPt.x*cn;
+ const deriv_type* dsrc = derivI.ptr<deriv_type>() + (y + iprevPt.y)*dstep + iprevPt.x*cn2;
- deriv_type* Iptr = (deriv_type*)(IWinBuf.data + y*IWinBuf.step);
- deriv_type* dIptr = (deriv_type*)(derivIWinBuf.data + y*derivIWinBuf.step);
+ deriv_type* Iptr = IWinBuf.ptr<deriv_type>(y);
+ deriv_type* dIptr = derivIWinBuf.ptr<deriv_type>(y);
x = 0;
for( y = 0; y < winSize.height; y++ )
{
- const uchar* Jptr = (const uchar*)J.data + (y + inextPt.y)*stepJ + inextPt.x*cn;
- const deriv_type* Iptr = (const deriv_type*)(IWinBuf.data + y*IWinBuf.step);
- const deriv_type* dIptr = (const deriv_type*)(derivIWinBuf.data + y*derivIWinBuf.step);
+ const uchar* Jptr = J.ptr() + (y + inextPt.y)*stepJ + inextPt.x*cn;
+ const deriv_type* Iptr = IWinBuf.ptr<deriv_type>(y);
+ const deriv_type* dIptr = derivIWinBuf.ptr<deriv_type>(y);
x = 0;
for( y = 0; y < winSize.height; y++ )
{
- const uchar* Jptr = (const uchar*)J.data + (y + inextPoint.y)*stepJ + inextPoint.x*cn;
- const deriv_type* Iptr = (const deriv_type*)(IWinBuf.data + y*IWinBuf.step);
+ const uchar* Jptr = J.ptr() + (y + inextPoint.y)*stepJ + inextPoint.x*cn;
+ const deriv_type* Iptr = IWinBuf.ptr<deriv_type>(y);
for( x = 0; x < winSize.width*cn; x++ )
{
Mat nextPtsMat = _nextPts.getMat();
CV_Assert( nextPtsMat.checkVector(2, CV_32F, true) == npoints );
- const Point2f* prevPts = (const Point2f*)prevPtsMat.data;
- Point2f* nextPts = (Point2f*)nextPtsMat.data;
+ const Point2f* prevPts = prevPtsMat.ptr<Point2f>();
+ Point2f* nextPts = nextPtsMat.ptr<Point2f>();
_status.create((int)npoints, 1, CV_8U, -1, true);
Mat statusMat = _status.getMat(), errMat;
CV_Assert( statusMat.isContinuous() );
- uchar* status = statusMat.data;
+ uchar* status = statusMat.ptr();
float* err = 0;
for( i = 0; i < npoints; i++ )
_err.create((int)npoints, 1, CV_32F, -1, true);
errMat = _err.getMat();
CV_Assert( errMat.isContinuous() );
- err = (float*)errMat.data;
+ err = errMat.ptr<float>();
}
std::vector<Mat> prevPyr, nextPyr;
{
Size imgSize = prevPyr[level * lvlStep1].size();
Mat _derivI( imgSize.height + winSize.height*2,
- imgSize.width + winSize.width*2, derivIBuf.type(), derivIBuf.data );
+ imgSize.width + winSize.width*2, derivIBuf.type(), derivIBuf.ptr() );
derivI = _derivI(Rect(winSize.width, winSize.height, imgSize.width, imgSize.height));
calcSharrDeriv(prevPyr[level * lvlStep1], derivI);
copyMakeBorder(derivI, _derivI, winSize.height, winSize.height, winSize.width, winSize.width, BORDER_CONSTANT|BORDER_ISOLATED);
for( y = 0; y < height; y++ )
{
float g0 = g[0], g1, g2;
- float *srow0 = (float*)(src.data + src.step*y), *srow1 = 0;
- float *drow = (float*)(dst.data + dst.step*y);
+ const float *srow0 = src.ptr<float>(y), *srow1 = 0;
+ float *drow = dst.ptr<float>(y);
// vertical part of convolution
for( x = 0; x < width; x++ )
for( k = 1; k <= n; k++ )
{
g0 = g[k]; g1 = xg[k]; g2 = xxg[k];
- srow0 = (float*)(src.data + src.step*std::max(y-k,0));
- srow1 = (float*)(src.data + src.step*std::min(y+k,height-1));
+ srow0 = src.ptr<float>(std::max(y-k,0));
+ srow1 = src.ptr<float>(std::min(y+k,height-1));
for( x = 0; x < width; x++ )
{
static const float border[BORDER] = {0.14f, 0.14f, 0.4472f, 0.4472f, 0.4472f};
int x, y, width = _flow.cols, height = _flow.rows;
- const float* R1 = (float*)_R1.data;
+ const float* R1 = _R1.ptr<float>();
size_t step1 = _R1.step/sizeof(R1[0]);
matM.create(height, width, CV_32FC(5));
for( y = _y0; y < _y1; y++ )
{
- const float* flow = (float*)(_flow.data + y*_flow.step);
- const float* R0 = (float*)(_R0.data + y*_R0.step);
- float* M = (float*)(matM.data + y*matM.step);
+ const float* flow = _flow.ptr<float>(y);
+ const float* R0 = _R0.ptr<float>(y);
+ float* M = matM.ptr<float>(y);
for( x = 0; x < width; x++ )
{
double* vsum = _vsum + (m+1)*5;
// init vsum
- const float* srow0 = (const float*)matM.data;
+ const float* srow0 = matM.ptr<float>();
for( x = 0; x < width*5; x++ )
vsum[x] = srow0[x]*(m+2);
for( y = 1; y < m; y++ )
{
- srow0 = (float*)(matM.data + matM.step*std::min(y,height-1));
+ srow0 = matM.ptr<float>(std::min(y,height-1));
for( x = 0; x < width*5; x++ )
vsum[x] += srow0[x];
}
for( y = 0; y < height; y++ )
{
double g11, g12, g22, h1, h2;
- float* flow = (float*)(_flow.data + _flow.step*y);
+ float* flow = _flow.ptr<float>(y);
- srow0 = (const float*)(matM.data + matM.step*std::max(y-m-1,0));
- const float* srow1 = (const float*)(matM.data + matM.step*std::min(y+m,height-1));
+ srow0 = matM.ptr<float>(std::max(y-m-1,0));
+ const float* srow1 = matM.ptr<float>(std::min(y+m,height-1));
// vertical blur
for( x = 0; x < width*5; x++ )
for( y = 0; y < height; y++ )
{
double g11, g12, g22, h1, h2;
- float* flow = (float*)(_flow.data + _flow.step*y);
+ float* flow = _flow.ptr<float>(y);
// vertical blur
for( i = 0; i <= m; i++ )
{
- srow[m-i] = (const float*)(matM.data + matM.step*std::max(y-i,0));
- srow[m+i] = (const float*)(matM.data + matM.step*std::min(y+i,height-1));
+ srow[m-i] = matM.ptr<float>(std::max(y-i,0));
+ srow[m+i] = matM.ptr<float>(std::min(y+i,height-1));
}
x = 0;
else
flow = flow0;
- if( !prevFlow.data )
+ if( prevFlow.empty() )
{
if( flags & OPTFLOW_USE_INITIAL_FLOW )
{
// PVAPI: DecimationHorizontal, DecimationVertical
enum { CAP_PVAPI_DECIMATION_OFF = 1, // Off
CAP_PVAPI_DECIMATION_2OUTOF4 = 2, // 2 out of 4 decimation
- CAP_PVAPI_DECIMATION_2OUTOF8 = 3, // 2 out of 8 decimation
- CAP_PVAPI_DECIMATION_2OUTOF16 = 5 // 2 out of 16 decimation
+ CAP_PVAPI_DECIMATION_2OUTOF8 = 4, // 2 out of 8 decimation
+ CAP_PVAPI_DECIMATION_2OUTOF16 = 8 // 2 out of 16 decimation
};
// Properties of cameras available through XIMEA SDK interface
stopCapture();
// Reallocate Frames
- if (!resizeCaptureFrame(value, currWidth))
+ if (!resizeCaptureFrame(currWidth, value))
{
startCapture();
return false;
#include "test_precomp.hpp"
-CV_TEST_MAIN("videoio")
+CV_TEST_MAIN("highgui")
}
for (int i = 0; i < GRID_AREA; i++) {
- Size s = Core.getTextSize(Integer.toString(i + 1), 3/* CV_FONT_HERSHEY_COMPLEX */, 1, 2, null);
+ Size s = Imgproc.getTextSize(Integer.toString(i + 1), 3/* CV_FONT_HERSHEY_COMPLEX */, 1, 2, null);
mTextHeights[i] = (int) s.height;
mTextWidths[i] = (int) s.width;
}
else {
cells[idx].copyTo(mCells15[i]);
if (mShowTileNumbers) {
- Core.putText(mCells15[i], Integer.toString(1 + idx), new Point((cols / GRID_SIZE - mTextWidths[idx]) / 2,
+ Imgproc.putText(mCells15[i], Integer.toString(1 + idx), new Point((cols / GRID_SIZE - mTextWidths[idx]) / 2,
(rows / GRID_SIZE + mTextHeights[idx]) / 2), 3/* CV_FONT_HERSHEY_COMPLEX */, 1, new Scalar(255, 0, 0, 255), 2);
}
}
private void drawGrid(int cols, int rows, Mat drawMat) {
for (int i = 1; i < GRID_SIZE; i++) {
- Core.line(drawMat, new Point(0, i * rows / GRID_SIZE), new Point(cols, i * rows / GRID_SIZE), new Scalar(0, 255, 0, 255), 3);
- Core.line(drawMat, new Point(i * cols / GRID_SIZE, 0), new Point(i * cols / GRID_SIZE, rows), new Scalar(0, 255, 0, 255), 3);
+ Imgproc.line(drawMat, new Point(0, i * rows / GRID_SIZE), new Point(cols, i * rows / GRID_SIZE), new Scalar(0, 255, 0, 255), 3);
+ Imgproc.line(drawMat, new Point(i * cols / GRID_SIZE, 0), new Point(i * cols / GRID_SIZE, rows), new Scalar(0, 255, 0, 255), 3);
}
}
private void renderFrame(Mat rgbaFrame) {
drawPoints(rgbaFrame);
- Core.putText(rgbaFrame, "Captured: " + mCornersBuffer.size(), new Point(rgbaFrame.cols() / 3 * 2, rgbaFrame.rows() * 0.1),
+ Imgproc.putText(rgbaFrame, "Captured: " + mCornersBuffer.size(), new Point(rgbaFrame.cols() / 3 * 2, rgbaFrame.rows() * 0.1),
Core.FONT_HERSHEY_SIMPLEX, 1.0, new Scalar(255, 255, 0));
}
final int shift = (int)(mWidth * 0.005);
border.add(new MatOfPoint(new Point(mWidth / 2 - shift, 0), new Point(mWidth / 2 + shift, 0),
new Point(mWidth / 2 + shift, mHeight), new Point(mWidth / 2 - shift, mHeight)));
- Core.fillPoly(comparisonFrame, border, new Scalar(255, 255, 255));
+ Imgproc.fillPoly(comparisonFrame, border, new Scalar(255, 255, 255));
- Core.putText(comparisonFrame, mResources.getString(R.string.original), new Point(mWidth * 0.1, mHeight * 0.1),
+ Imgproc.putText(comparisonFrame, mResources.getString(R.string.original), new Point(mWidth * 0.1, mHeight * 0.1),
Core.FONT_HERSHEY_SIMPLEX, 1.0, new Scalar(255, 255, 0));
- Core.putText(comparisonFrame, mResources.getString(R.string.undistorted), new Point(mWidth * 0.6, mHeight * 0.1),
+ Imgproc.putText(comparisonFrame, mResources.getString(R.string.undistorted), new Point(mWidth * 0.6, mHeight * 0.1),
Core.FONT_HERSHEY_SIMPLEX, 1.0, new Scalar(255, 255, 0));
return comparisonFrame;
Rect[] facesArray = faces.toArray();
for (int i = 0; i < facesArray.length; i++)
- Core.rectangle(mRgba, facesArray[i].tl(), facesArray[i].br(), FACE_RECT_COLOR, 3);
+ Imgproc.rectangle(mRgba, facesArray[i].tl(), facesArray[i].br(), FACE_RECT_COLOR, 3);
return mRgba;
}
-#include <opencv2/core/core.hpp>
-#include <opencv2/imgcodecs/imgcodecs.hpp>
-#include <opencv2/highgui/highgui.hpp>
+#include <opencv2/core.hpp>
+#include <opencv2/imgproc.hpp>
+#include <opencv2/imgcodecs.hpp>
+#include <opencv2/highgui.hpp>
using namespace cv;
const char* message = "Hello Android!";
mP1.x = mP2.x = offset + (c * (mHistSizeNum + 10) + h) * thikness;
mP1.y = sizeRgba.height-1;
mP2.y = mP1.y - 2 - (int)mBuff[h];
- Core.line(rgba, mP1, mP2, mColorsRGB[c], thikness);
+ Imgproc.line(rgba, mP1, mP2, mColorsRGB[c], thikness);
}
}
// Value and Hue
mP1.x = mP2.x = offset + (3 * (mHistSizeNum + 10) + h) * thikness;
mP1.y = sizeRgba.height-1;
mP2.y = mP1.y - 2 - (int)mBuff[h];
- Core.line(rgba, mP1, mP2, mWhilte, thikness);
+ Imgproc.line(rgba, mP1, mP2, mWhilte, thikness);
}
// Hue
Imgproc.calcHist(Arrays.asList(mIntermediateMat), mChannels[0], mMat0, hist, mHistSize, mRanges);
mP1.x = mP2.x = offset + (4 * (mHistSizeNum + 10) + h) * thikness;
mP1.y = sizeRgba.height-1;
mP2.y = mP1.y - 2 - (int)mBuff[h];
- Core.line(rgba, mP1, mP2, mColorsHue[h], thikness);
+ Imgproc.line(rgba, mP1, mP2, mColorsHue[h], thikness);
}
break;
Mat mZoomWindow = rgba.submat(rows / 2 - 9 * rows / 100, rows / 2 + 9 * rows / 100, cols / 2 - 9 * cols / 100, cols / 2 + 9 * cols / 100);
Imgproc.resize(mZoomWindow, zoomCorner, zoomCorner.size());
Size wsize = mZoomWindow.size();
- Core.rectangle(mZoomWindow, new Point(1, 1), new Point(wsize.width - 2, wsize.height - 2), new Scalar(255, 0, 0, 255), 2);
+ Imgproc.rectangle(mZoomWindow, new Point(1, 1), new Point(wsize.width - 2, wsize.height - 2), new Scalar(255, 0, 0, 255), 2);
zoomCorner.release();
mZoomWindow.release();
break;
printf("%s\n", imageList[i*3+k].c_str());
view = imread(imageList[i*3+k], 1);
- if(view.data)
+ if(!view.empty())
{
vector<Point2f> ptvec;
imageSize = view.size();
int k2 = k == 0 ? 1 : k == 1 ? 0 : 2;
view = imread(imageList[i*3+k], 1);
- if(!view.data)
+ if(view.empty())
continue;
Mat rview = canvas.colRange(k2*imageSize.width, (k2+1)*imageSize.width);
else if( i < (int)imageList.size() )
view = imread(imageList[i], 1);
- if(!view.data)
+ if(view.empty())
{
if( imagePoints.size() > 0 )
runAndSave(outputFilename, imagePoints, imageSize,
for( i = 0; i < (int)imageList.size(); i++ )
{
view = imread(imageList[i], 1);
- if(!view.data)
+ if(view.empty())
continue;
//undistort( view, rview, cameraMatrix, distCoeffs, cameraMatrix );
remap(view, rview, map1, map2, INTER_LINEAR);
img2 = imread(dest);
- if(!img0.data)
+ if(img0.empty())
{
cout << "Source Image does not exist" << endl;
exit(0);
}
- if(!img2.data)
+ if(img2.empty())
{
cout << "Destination Image does not exist" << endl;
exit(0);
img0 = imread(src);
- if(!img0.data)
+ if(img0.empty())
{
cout << "Source Image does not exist" << endl;
exit(0);
img0 = imread(src);
- if(!img0.data)
+ if(img0.empty())
{
cout << "Source Image does not exist" << endl;
exit(0);
img0 = imread(src);
- if(!img0.data)
+ if(img0.empty())
{
cout << "Source Image does not exist" << endl;
exit(0);
--- /dev/null
+<?xml version="1.0"?>
+<opencv_storage>
+<datamat type_id="opencv-matrix">
+ <rows>40</rows>
+ <cols>784</cols>
+ <dt>f</dt>
+ <data>
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+</data>
+</datamat>
+<labelsmat type_id="opencv-matrix">
+ <rows>40</rows>
+ <cols>1</cols>
+ <dt>d</dt>
+ <data>
+ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
+ </data>
+</labelsmat>
+</opencv_storage>
-#include "opencv2/core/core.hpp"
-#include "opencv2/highgui/highgui.hpp"
+#include "opencv2/core.hpp"
+#include "opencv2/imgproc.hpp"
+#include "opencv2/highgui.hpp"
#include <stdio.h>
using namespace cv;
#include "opencv2/highgui.hpp"
+#include "opencv2/imgproc.hpp"
#include "opencv2/ml.hpp"
using namespace cv;
}
#endif
- if( !img.data ) // check if the image has been loaded properly
+ if( img.empty() ) // check if the image has been loaded properly
return -1;
Mat img_yuv;
-#include "opencv2/highgui/highgui.hpp"
-#include "opencv2/core/core.hpp"
+#include "opencv2/highgui.hpp"
+#include "opencv2/core.hpp"
+#include "opencv2/imgproc.hpp"
#include <iostream>
using namespace cv;
--- /dev/null
+/*//////////////////////////////////////////////////////////////////////////////////////
+// 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.
+
+// This is a implementation of the Logistic Regression algorithm in C++ in OpenCV.
+
+// AUTHOR:
+// Rahul Kavi rahulkavi[at]live[at]com
+//
+
+// contains a subset of data from the popular Iris Dataset (taken from
+// "http://archive.ics.uci.edu/ml/datasets/Iris")
+
+// # You are free to use, change, or redistribute the code in any way you wish for
+// # non-commercial purposes, but please maintain the name of the original author.
+// # This code comes with no warranty of any kind.
+
+// #
+// # You are free to use, change, or redistribute the code in any way you wish for
+// # non-commercial purposes, but please maintain the name of the original author.
+// # This code comes with no warranty of any kind.
+
+// # Logistic Regression ALGORITHM
+
+// License Agreement
+// For Open Source Computer Vision Library
+
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2008-2011, 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:
+
+// * 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 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.*/
+
+#include <iostream>
+
+#include <opencv2/core.hpp>
+#include <opencv2/ml.hpp>
+#include <opencv2/highgui.hpp>
+
+using namespace std;
+using namespace cv;
+using namespace cv::ml;
+
+static void showImage(const Mat &data, int columns, const String &name)
+{
+ Mat bigImage;
+ for(int i = 0; i < data.rows; ++i)
+ {
+ bigImage.push_back(data.row(i).reshape(0, columns));
+ }
+ imshow(name, bigImage.t());
+}
+
+static float calculateAccuracyPercent(const Mat &original, const Mat &predicted)
+{
+ return 100 * (float)countNonZero(original == predicted) / predicted.rows;
+}
+
+int main()
+{
+ const String filename = "data01.xml";
+ cout << "**********************************************************************" << endl;
+ cout << filename
+ << " contains digits 0 and 1 of 20 samples each, collected on an Android device" << endl;
+ cout << "Each of the collected images are of size 28 x 28 re-arranged to 1 x 784 matrix"
+ << endl;
+ cout << "**********************************************************************" << endl;
+
+ Mat data, labels;
+ {
+ cout << "loading the dataset...";
+ FileStorage f;
+ if(f.open(filename, FileStorage::READ))
+ {
+ f["datamat"] >> data;
+ f["labelsmat"] >> labels;
+ f.release();
+ }
+ else
+ {
+ cerr << "file can not be opened: " << filename << endl;
+ return 1;
+ }
+ data.convertTo(data, CV_32F);
+ labels.convertTo(labels, CV_32F);
+ cout << "read " << data.rows << " rows of data" << endl;
+ }
+
+ Mat data_train, data_test;
+ Mat labels_train, labels_test;
+ for(int i = 0; i < data.rows; i++)
+ {
+ if(i % 2 == 0)
+ {
+ data_train.push_back(data.row(i));
+ labels_train.push_back(labels.row(i));
+ }
+ else
+ {
+ data_test.push_back(data.row(i));
+ labels_test.push_back(labels.row(i));
+ }
+ }
+ cout << "training/testing samples count: " << data_train.rows << "/" << data_test.rows << endl;
+
+ // display sample image
+ showImage(data_train, 28, "train data");
+ showImage(data_test, 28, "test data");
+
+
+ // simple case with batch gradient
+ LogisticRegression::Params params = LogisticRegression::Params(
+ 0.001, 10, LogisticRegression::BATCH, LogisticRegression::REG_L2, 1, 1);
+ // simple case with mini-batch gradient
+ // LogisticRegression::Params params = LogisticRegression::Params(
+ // 0.001, 10, LogisticRegression::MINI_BATCH, LogisticRegression::REG_L2, 1, 1);
+
+ // mini-batch gradient with higher accuracy
+ // LogisticRegression::Params params = LogisticRegression::Params(
+ // 0.000001, 10, LogisticRegression::MINI_BATCH, LogisticRegression::REG_L2, 1, 1);
+
+ cout << "training...";
+ Ptr<StatModel> lr1 = LogisticRegression::create(params);
+ lr1->train(data_train, ROW_SAMPLE, labels_train);
+ cout << "done!" << endl;
+
+ cout << "predicting...";
+ Mat responses;
+ lr1->predict(data_test, responses);
+ cout << "done!" << endl;
+
+ // show prediction report
+ cout << "original vs predicted:" << endl;
+ labels_test.convertTo(labels_test, CV_32S);
+ cout << labels_test.t() << endl;
+ cout << responses.t() << endl;
+ cout << "accuracy: " << calculateAccuracyPercent(labels_test, responses) << "%" << endl;
+
+ // save the classfier
+ const String saveFilename = "NewLR_Trained.xml";
+ cout << "saving the classifier to " << saveFilename << endl;
+ lr1->save(saveFilename);
+
+ // load the classifier onto new object
+ cout << "loading a new classifier from " << saveFilename << endl;
+ Ptr<LogisticRegression> lr2 = StatModel::load<LogisticRegression>(saveFilename);
+
+ // predict using loaded classifier
+ cout << "predicting the dataset using the loaded classfier...";
+ Mat responses2;
+ lr2->predict(data_test, responses2);
+ cout << "done!" << endl;
+
+ // calculate accuracy
+ cout << labels_test.t() << endl;
+ cout << responses2.t() << endl;
+ cout << "accuracy: " << calculateAccuracyPercent(labels_test, responses2) << "%" << endl;
+
+ waitKey(0);
+ return 0;
+}
int main( int argc, char** argv )
{
char* filename = argc == 2 ? argv[1] : (char*)"baboon.jpg";
- if( (src = imread(filename,1)).data == 0 )
+ if( (src = imread(filename,1)).empty() )
return -1;
help();
Mat I = imread(argv[1]);
- if(!I.data)
+ if(I.empty())
{
cout << "Image not found" << endl;
exit(0);
-#include "opencv2/opencv_modules.hpp"
-#include "opencv2/core/core.hpp"
-#include "opencv2/ml/ml.hpp"
-#include "opencv2/highgui/highgui.hpp"
+#include "opencv2/core.hpp"
+#include "opencv2/imgproc.hpp"
+#include "opencv2/ml.hpp"
+#include "opencv2/highgui.hpp"
#ifdef HAVE_OPENCV_OCL
#define _OCL_KNN_ 1 // select whether using ocl::KNN method or not, default is using
#define _OCL_SVM_ 1 // select whether using ocl::svm method or not, default is using
Mat tmp_frame, bgmask, out_frame;
cap >> tmp_frame;
- if(!tmp_frame.data)
+ if(tmp_frame.empty())
{
printf("can not read data from the video source\n");
return -1;
for(;;)
{
cap >> tmp_frame;
- if( !tmp_frame.data )
+ if( tmp_frame.empty() )
break;
bgsubtractor->apply(tmp_frame, bgmask, update_bg_model ? -1 : 0);
refineSegments(tmp_frame, bgmask, out_frame);
projectPoints(Mat(objpt), rvec, tvec, cameraMatrix, Mat(), imgpt);
- if( shownFrame.data )
+ if( !shownFrame.empty() )
{
if( nobjpt == 1 )
circle(shownFrame, imgpt[0], 3, Scalar(0,255,0), -1, LINE_AA);
}
else
capture >> frame0;
- if( !frame0.data )
+ if( frame0.empty() )
break;
- if( !frame.data )
+ if( frame.empty() )
{
if( frame0.size() != calibratedImageSize )
{
hog_detector.clear();
hog_detector.resize(sv.cols + 1);
- memcpy(&hog_detector[0], sv.data, sv.cols*sizeof(hog_detector[0]));
+ memcpy(&hog_detector[0], sv.ptr(), sv.cols*sizeof(hog_detector[0]));
hog_detector[sv.cols] = (float)-rho;
}
break;
}
Mat img = imread( (prefix+line).c_str() ); // load the image
- if( !img.data ) // invalid image, just skip it.
+ if( img.empty() ) // invalid image, just skip it.
continue;
#ifdef _DEBUG
imshow( "image", img );
while( !end_of_process )
{
video >> img;
- if( !img.data )
+ if( img.empty() )
break;
draw = img.clone();
src1 = imread("../images/LinuxLogo.jpg");
src2 = imread("../images/WindowsLogo.jpg");
- if( !src1.data ) { printf("Error loading src1 \n"); return -1; }
- if( !src2.data ) { printf("Error loading src2 \n"); return -1; }
+ if( src1.empty() ) { printf("Error loading src1 \n"); return -1; }
+ if( src2.empty() ) { printf("Error loading src2 \n"); return -1; }
/// Initialize values
alpha_slider = 0;
--- /dev/null
+/**
+ * gdal_image.cpp -- Load GIS data into OpenCV Containers using the Geospatial Data Abstraction Library
+*/
+
+/// OpenCV Headers
+#include "opencv2/core/core.hpp"
+#include "opencv2/imgproc/imgproc.hpp"
+#include "opencv2/highgui/highgui.hpp"
+
+/// C++ Standard Libraries
+#include <cmath>
+#include <iostream>
+#include <stdexcept>
+#include <vector>
+
+using namespace std;
+
+/// define the corner points
+/// Note that GDAL can natively determine this
+cv::Point2d tl( -122.441017, 37.815664 );
+cv::Point2d tr( -122.370919, 37.815311 );
+cv::Point2d bl( -122.441533, 37.747167 );
+cv::Point2d br( -122.3715, 37.746814 );
+
+/// determine dem corners
+cv::Point2d dem_bl( -122.0, 38);
+cv::Point2d dem_tr( -123.0, 37);
+
+/// range of the heat map colors
+std::vector<std::pair<cv::Vec3b,double> > color_range;
+
+
+/// List of all function prototypes
+cv::Point2d lerp( const cv::Point2d&, const cv::Point2d&, const double& );
+
+cv::Vec3b get_dem_color( const double& );
+
+cv::Point2d world2dem( const cv::Point2d&, const cv::Size&);
+
+cv::Point2d pixel2world( const int&, const int&, const cv::Size& );
+
+void add_color( cv::Vec3b& pix, const uchar& b, const uchar& g, const uchar& r );
+
+
+
+/**
+ * Linear Interpolation
+ * p1 - Point 1
+ * p2 - Point 2
+ * t - Ratio from Point 1 to Point 2
+*/
+cv::Point2d lerp( cv::Point2d const& p1, cv::Point2d const& p2, const double& t ){
+ return cv::Point2d( ((1-t)*p1.x) + (t*p2.x),
+ ((1-t)*p1.y) + (t*p2.y));
+}
+
+/**
+ * Interpolate Colors
+*/
+template <typename DATATYPE, int N>
+cv::Vec<DATATYPE,N> lerp( cv::Vec<DATATYPE,N> const& minColor,
+ cv::Vec<DATATYPE,N> const& maxColor,
+ double const& t ){
+
+ cv::Vec<DATATYPE,N> output;
+ for( int i=0; i<N; i++ ){
+ output[i] = (uchar)(((1-t)*minColor[i]) + (t * maxColor[i]));
+ }
+ return output;
+}
+
+/**
+ * Compute the dem color
+*/
+cv::Vec3b get_dem_color( const double& elevation ){
+
+ // if the elevation is below the minimum, return the minimum
+ if( elevation < color_range[0].second ){
+ return color_range[0].first;
+ }
+ // if the elevation is above the maximum, return the maximum
+ if( elevation > color_range.back().second ){
+ return color_range.back().first;
+ }
+
+ // otherwise, find the proper starting index
+ int idx=0;
+ double t = 0;
+ for( int x=0; x<(int)(color_range.size()-1); x++ ){
+
+ // if the current elevation is below the next item, then use the current
+ // two colors as our range
+ if( elevation < color_range[x+1].second ){
+ idx=x;
+ t = (color_range[x+1].second - elevation)/
+ (color_range[x+1].second - color_range[x].second);
+
+ break;
+ }
+ }
+
+ // interpolate the color
+ return lerp( color_range[idx].first, color_range[idx+1].first, t);
+}
+
+/**
+ * Given a pixel coordinate and the size of the input image, compute the pixel location
+ * on the DEM image.
+*/
+cv::Point2d world2dem( cv::Point2d const& coordinate, const cv::Size& dem_size ){
+
+
+ // relate this to the dem points
+ // ASSUMING THAT DEM DATA IS ORTHORECTIFIED
+ double demRatioX = ((dem_tr.x - coordinate.x)/(dem_tr.x - dem_bl.x));
+ double demRatioY = 1-((dem_tr.y - coordinate.y)/(dem_tr.y - dem_bl.y));
+
+ cv::Point2d output;
+ output.x = demRatioX * dem_size.width;
+ output.y = demRatioY * dem_size.height;
+
+ return output;
+}
+
+/**
+ * Convert a pixel coordinate to world coordinates
+*/
+cv::Point2d pixel2world( const int& x, const int& y, const cv::Size& size ){
+
+ // compute the ratio of the pixel location to its dimension
+ double rx = (double)x / size.width;
+ double ry = (double)y / size.height;
+
+ // compute LERP of each coordinate
+ cv::Point2d rightSide = lerp(tr, br, ry);
+ cv::Point2d leftSide = lerp(tl, bl, ry);
+
+ // compute the actual Lat/Lon coordinate of the interpolated coordinate
+ return lerp( leftSide, rightSide, rx );
+}
+
+/**
+ * Add color to a specific pixel color value
+*/
+void add_color( cv::Vec3b& pix, const uchar& b, const uchar& g, const uchar& r ){
+
+ if( pix[0] + b < 255 && pix[0] + b >= 0 ){ pix[0] += b; }
+ if( pix[1] + g < 255 && pix[1] + g >= 0 ){ pix[1] += g; }
+ if( pix[2] + r < 255 && pix[2] + r >= 0 ){ pix[2] += r; }
+}
+
+
+/**
+ * Main Function
+*/
+int main( int argc, char* argv[] ){
+
+ /**
+ * Check input arguments
+ */
+ if( argc < 3 ){
+ cout << "usage: " << argv[0] << " <image> <dem>" << endl;
+ return 1;
+ }
+
+ /// load the image (note that we don't have the projection information. You will
+ /// need to load that yourself or use the full GDAL driver. The values are pre-defined
+ /// at the top of this file
+ cv::Mat image = cv::imread(argv[1], cv::IMREAD_LOAD_GDAL | cv::IMREAD_COLOR );
+
+ /// load the dem model
+ cv::Mat dem = cv::imread(argv[2], cv::IMREAD_LOAD_GDAL | cv::IMREAD_ANYDEPTH );
+
+ /// create our output products
+ cv::Mat output_dem( image.size(), CV_8UC3 );
+ cv::Mat output_dem_flood( image.size(), CV_8UC3 );
+
+ /// for sanity sake, make sure GDAL Loads it as a signed short
+ if( dem.type() != CV_16SC1 ){ throw std::runtime_error("DEM image type must be CV_16SC1"); }
+
+ /// define the color range to create our output DEM heat map
+ // Pair format ( Color, elevation ); Push from low to high
+ // Note: This would be perfect for a configuration file, but is here for a working demo.
+ color_range.push_back( std::pair<cv::Vec3b,double>(cv::Vec3b( 188, 154, 46), -1));
+ color_range.push_back( std::pair<cv::Vec3b,double>(cv::Vec3b( 110, 220, 110), 0.25));
+ color_range.push_back( std::pair<cv::Vec3b,double>(cv::Vec3b( 150, 250, 230), 20));
+ color_range.push_back( std::pair<cv::Vec3b,double>(cv::Vec3b( 160, 220, 200), 75));
+ color_range.push_back( std::pair<cv::Vec3b,double>(cv::Vec3b( 220, 190, 170), 100));
+ color_range.push_back( std::pair<cv::Vec3b,double>(cv::Vec3b( 250, 180, 140), 200));
+
+ // define a minimum elevation
+ double minElevation = -10;
+
+ // iterate over each pixel in the image, computing the dem point
+ for( int y=0; y<image.rows; y++ ){
+ for( int x=0; x<image.cols; x++ ){
+
+ // convert the pixel coordinate to lat/lon coordinates
+ cv::Point2d coordinate = pixel2world( x, y, image.size() );
+
+ // compute the dem image pixel coordinate from lat/lon
+ cv::Point2d dem_coordinate = world2dem( coordinate, dem.size() );
+
+ // extract the elevation
+ double dz;
+ if( dem_coordinate.x >= 0 && dem_coordinate.y >= 0 &&
+ dem_coordinate.x < dem.cols && dem_coordinate.y < dem.rows ){
+ dz = dem.at<short>(dem_coordinate);
+ }else{
+ dz = minElevation;
+ }
+
+ // write the pixel value to the file
+ output_dem_flood.at<cv::Vec3b>(y,x) = image.at<cv::Vec3b>(y,x);
+
+ // compute the color for the heat map output
+ cv::Vec3b actualColor = get_dem_color(dz);
+ output_dem.at<cv::Vec3b>(y,x) = actualColor;
+
+ // show effect of a 10 meter increase in ocean levels
+ if( dz < 10 ){
+ add_color( output_dem_flood.at<cv::Vec3b>(y,x), 90, 0, 0 );
+ }
+ // show effect of a 50 meter increase in ocean levels
+ else if( dz < 50 ){
+ add_color( output_dem_flood.at<cv::Vec3b>(y,x), 0, 90, 0 );
+ }
+ // show effect of a 100 meter increase in ocean levels
+ else if( dz < 100 ){
+ add_color( output_dem_flood.at<cv::Vec3b>(y,x), 0, 0, 90 );
+ }
+
+ }}
+
+ // print our heat map
+ cv::imwrite( "heat-map.jpg" , output_dem );
+
+ // print the flooding effect image
+ cv::imwrite( "flooded.jpg", output_dem_flood);
+
+ return 0;
+}
/// Load image
src = imread( argv[1], 1 );
- if( !src.data )
+ if( src.empty() )
{ cout<<"Usage: ./Histogram_Demo <path_to_image>"<<endl;
return -1;
}
/// Load image
src = imread( argv[1], 1 );
- if( !src.data )
+ if( src.empty() )
{ return -1; }
/// Separate the image in 3 places ( B, G and R )
src1 = imread("../images/LinuxLogo.jpg");
src2 = imread("../images/WindowsLogo.jpg");
- if( !src1.data ) { std::cout<< "Error loading src1"<<std::endl; return -1; }
- if( !src2.data ) { std::cout<< "Error loading src2"<<std::endl; return -1; }
+ if( src1.empty() ) { std::cout<< "Error loading src1"<<std::endl; return -1; }
+ if( src2.empty() ) { std::cout<< "Error loading src2"<<std::endl; return -1; }
/// Create Windows
namedWindow("Linear Blend", 1);
/// Load an image
src = imread( argv[1] );
- if( !src.data )
+ if( src.empty() )
{ return -1; }
/// Create windows
/// Load an image
src = imread( argv[1] );
- if( !src.data )
+ if( src.empty() )
{ return -1; }
/// Create window
/// Test image - Make sure it s divisible by 2^{n}
src = imread( "../images/chicky_512.png" );
- if( !src.data )
+ if( src.empty() )
{ printf(" No data! -- Exiting the program \n");
return -1; }
/// Load an image
src = imread( argv[1] );
- if( !src.data )
+ if( src.empty() )
{ return -1; }
/// Create a matrix of the same type and size as src (for dst)
// Read the image
src = imread( argv[1], 1 );
- if( !src.data )
+ if( src.empty() )
{
std::cerr<<"Invalid input image\n";
std::cout<<usage;
/// Load an image
src = imread( argv[1] );
- if( !src.data )
+ if( src.empty() )
{ return -1; }
/// Remove noise by blurring with a Gaussian filter
/// Load an image
src = imread( argv[1] );
- if( !src.data )
+ if( src.empty() )
{ return -1; }
GaussianBlur( src, src, Size(3,3), 0, 0, BORDER_DEFAULT );
/// Load an image
src = imread( argv[1] );
- if( !src.data )
+ if( src.empty() )
{
printf(" No data entered, please enter the path to an image file \n");
return -1;
/// Load an image
src = imread( argv[1] );
- if( !src.data )
+ if( src.empty() )
{ return -1; }
/// Create window
Mat imgDisparity16S = Mat( imgLeft.rows, imgLeft.cols, CV_16S );
Mat imgDisparity8U = Mat( imgLeft.rows, imgLeft.cols, CV_8UC1 );
- if( !imgLeft.data || !imgRight.data )
+ if( imgLeft.empty() || imgRight.empty() )
{ std::cout<< " --(!) Error reading images " << std::endl; return -1; }
//-- 2. Call the constructor for StereoBM
* @brief Simple sample code
*/
-#include <opencv2/core/core.hpp>
-#include <opencv2/highgui/highgui.hpp>
+#include <opencv2/core.hpp>
+#include <opencv2/imgproc.hpp>
+#include <opencv2/highgui.hpp>
#define w 400
* @brief Simple sample code
*/
-#include <opencv2/core/core.hpp>
-#include <opencv2/highgui/highgui.hpp>
+#include <opencv2/core.hpp>
+#include <opencv2/imgproc.hpp>
+#include <opencv2/highgui.hpp>
#include <iostream>
#include <stdio.h>
else
I = imread(argv[1], IMREAD_COLOR);
- if (!I.data)
+ if (I.empty())
{
cout << "The image" << argv[1] << " could not be loaded." << endl;
return -1;
<< times << " runs): " << t << " milliseconds."<< endl;
Mat lookUpTable(1, 256, CV_8U);
- uchar* p = lookUpTable.data;
+ uchar* p = lookUpTable.ptr();
for( int i = 0; i < 256; ++i)
p[i] = table[i];
Mat image;
image = imread(argv[1], IMREAD_COLOR); // Read the file
- if(! image.data ) // Check for invalid input
+ if( image.empty() ) // Check for invalid input
{
cout << "Could not open or find the image" << std::endl ;
return -1;
Mat image;
image = imread(argv[1], IMREAD_COLOR); // Read the file
- if(! image.data ) // Check for invalid input
+ if( image.empty() ) // Check for invalid input
{
cout << "Could not open or find the image" << std::endl ;
return -1;
-#include <opencv2/core/core.hpp>
+#include <opencv2/core.hpp>
+#include <opencv2/imgproc.hpp>
#include "opencv2/imgcodecs.hpp"
-#include <opencv2/highgui/highgui.hpp>
-#include <opencv2/ml/ml.hpp>
+#include <opencv2/highgui.hpp>
+#include <opencv2/ml.hpp>
using namespace cv;
using namespace cv::ml;
#include <iostream>
-#include <opencv2/core/core.hpp>
+#include <opencv2/core.hpp>
+#include <opencv2/imgproc.hpp>
#include "opencv2/imgcodecs.hpp"
-#include <opencv2/highgui/highgui.hpp>
-#include <opencv2/ml/ml.hpp>
+#include <opencv2/highgui.hpp>
+#include <opencv2/ml.hpp>
#define NTRAINING_SAMPLES 100 // Number of training samples per class
#define FRAC_LINEAR_SEP 0.9f // Fraction of samples which compose the linear separable part
Mat I = imread(argv[1]);
- if(!I.data)
+ if(I.empty())
{
cout << "Image not found" << endl;
exit(0);
img2 = imread(dest);
- if(!img0.data)
+ if(img0.empty())
{
cout << "Source Image does not exist" << endl;
exit(0);
}
- if(!img2.data)
+ if(img2.empty())
{
cout << "Destination Image does not exist" << endl;
exit(0);
img0 = imread(src);
- if(!img0.data)
+ if(img0.empty())
{
cout << "Source Image does not exist" << endl;
exit(0);
img0 = imread(src);
- if(!img0.data)
+ if(img0.empty())
{
cout << "Source Image does not exist" << endl;
exit(0);
img0 = imread(src);
- if(!img0.data)
+ if(img0.empty())
{
cout << "Source Image does not exist" << endl;
exit(0);
//opencv
#include "opencv2/imgcodecs.hpp"
+#include "opencv2/imgproc.hpp"
#include "opencv2/videoio.hpp"
-#include <opencv2/highgui/highgui.hpp>
-#include <opencv2/video/background_segm.hpp>
+#include <opencv2/highgui.hpp>
+#include <opencv2/video.hpp>
//C
#include <stdio.h>
//C++
void processImages(char* fistFrameFilename) {
//read the first file of the sequence
frame = imread(fistFrameFilename);
- if(!frame.data){
+ if(frame.empty()){
//error in opening the first image
cerr << "Unable to open first image frame: " << fistFrameFilename << endl;
exit(EXIT_FAILURE);
string nextFrameFilename = prefix + nextFrameNumberString + suffix;
//read the next frame
frame = imread(nextFrameFilename);
- if(!frame.data){
+ if(frame.empty()){
//error in opening the next image in the sequence
cerr << "Unable to open image frame: " << nextFrameFilename << endl;
exit(EXIT_FAILURE);
desc.CPUAccessFlags = cv::ocl::useOpenCL() ? 0 : D3D10_CPU_ACCESS_READ;
D3D10_SUBRESOURCE_DATA srInitData;
- srInitData.pSysMem = inputMat.data;
+ srInitData.pSysMem = inputMat.ptr();
srInitData.SysMemPitch = (UINT)inputMat.step[0];
if (FAILED(dev->CreateTexture2D(&desc, &srInitData, &pInputTexture)))
desc.CPUAccessFlags = cv::ocl::useOpenCL() ? 0 : D3D11_CPU_ACCESS_READ;
D3D11_SUBRESOURCE_DATA srInitData;
- srInitData.pSysMem = inputMat.data;
+ srInitData.pSysMem = inputMat.ptr();
srInitData.SysMemPitch = (UINT)inputMat.step[0];
if (FAILED(dev->CreateTexture2D(&desc, &srInitData, &pInputTexture)))
// Draw a bounding box around each face.
for (Rect rect : faceDetections.toArray()) {
- Core.rectangle(image, new Point(rect.x, rect.y), new Point(rect.x
+ Imgproc.rectangle(image, new Point(rect.x, rect.y), new Point(rect.x
+ rect.width, rect.y + rect.height), new Scalar(0, 255, 0));
}
// Draw a bounding box around each face.
for (rect <- faceDetections.toArray) {
- Core.rectangle(
+ Imgproc.rectangle(
image,
new Point(rect.x, rect.y),
new Point(rect.x + rect.width,
projects / profile / ivi / opencv.git / commitdiff
