From f2d3b9b4a1e199f0740258a73d06bbccf82af307 Mon Sep 17 00:00:00 2001 From: Andrey Kamaev Date: Sat, 9 Jun 2012 15:00:04 +0000 Subject: [PATCH] Warning fixes continued --- cmake/OpenCVCompilerOptions.cmake | 2 +- cmake/OpenCVDetectTBB.cmake | 4 +- cmake/OpenCVUtils.cmake | 2 +- modules/calib3d/src/calibinit.cpp | 89 +- modules/calib3d/src/calibration.cpp | 2 +- modules/calib3d/src/circlesgrid.cpp | 16 +- modules/calib3d/src/epnp.cpp | 166 ++- modules/calib3d/src/p3p.cpp | 638 ++++----- modules/calib3d/src/solvepnp.cpp | 8 +- modules/calib3d/src/stereobm.cpp | 141 +- modules/calib3d/src/stereosgbm.cpp | 336 ++--- modules/calib3d/test/test_cameracalibration.cpp | 1466 ++++++++++---------- .../test/test_cameracalibration_artificial.cpp | 140 +- modules/calib3d/test/test_chesscorners.cpp | 159 ++- modules/calib3d/test/test_compose_rt.cpp | 131 +- modules/calib3d/test/test_homography.cpp | 56 +- modules/calib3d/test/test_solvepnp_ransac.cpp | 17 +- modules/calib3d/test/test_stereomatching.cpp | 8 +- .../contrib/include/opencv2/contrib/contrib.hpp | 306 ++-- .../include/opencv2/contrib/hybridtracker.hpp | 6 +- modules/contrib/src/ba.cpp | 32 +- modules/contrib/src/basicretinafilter.cpp | 16 +- modules/contrib/src/colortracker.cpp | 4 +- modules/contrib/src/facerec.cpp | 60 +- modules/contrib/src/lda.cpp | 208 +-- modules/contrib/src/logpolar_bsm.cpp | 96 +- modules/contrib/src/octree.cpp | 10 +- modules/contrib/src/retina.cpp | 16 +- modules/contrib/src/retinacolor.cpp | 6 +- modules/contrib/src/spinimages.cpp | 352 ++--- modules/contrib/src/stereovar.cpp | 566 ++++---- modules/core/CMakeLists.txt | 6 +- modules/core/include/opencv2/core/gpumat.hpp | 10 +- modules/core/include/opencv2/core/mat.hpp | 358 ++--- .../core/include/opencv2/core/opengl_interop.hpp | 495 +++---- modules/core/include/opencv2/core/operations.hpp | 26 +- modules/core/src/algorithm.cpp | 288 ++-- modules/core/src/convert.cpp | 246 ++-- modules/core/src/copy.cpp | 108 +- modules/core/src/datastructs.cpp | 92 +- modules/core/src/drawing.cpp | 16 +- modules/core/src/gpumat.cpp | 18 +- modules/core/src/matmul.cpp | 58 +- modules/core/src/matop.cpp | 386 +++--- modules/core/src/matrix.cpp | 122 +- modules/core/src/opengl_interop.cpp | 214 +-- modules/core/test/test_ds.cpp | 8 +- modules/core/test/test_mat.cpp | 166 +-- modules/core/test/test_rand.cpp | 72 +- modules/features2d/src/bagofwords.cpp | 4 +- modules/features2d/src/brief.cpp | 6 +- modules/features2d/src/descriptors.cpp | 6 +- modules/features2d/src/draw.cpp | 14 +- modules/features2d/src/features2d_init.cpp | 7 +- modules/features2d/src/matchers.cpp | 24 +- modules/features2d/src/mser.cpp | 4 +- modules/features2d/src/orb.cpp | 32 +- modules/features2d/src/stardetector.cpp | 61 +- modules/flann/include/opencv2/flann/allocator.h | 12 +- .../flann/include/opencv2/flann/dynamic_bitset.h | 10 +- modules/flann/include/opencv2/flann/heap.h | 6 +- .../opencv2/flann/hierarchical_clustering_index.h | 44 +- modules/gpu/CMakeLists.txt | 17 +- modules/highgui/CMakeLists.txt | 2 +- modules/highgui/src/cap_gstreamer.cpp | 24 +- modules/highgui/src/cap_libv4l.cpp | 38 +- modules/highgui/src/grfmt_png.cpp | 16 +- modules/highgui/src/grfmt_tiff.cpp | 24 +- modules/highgui/test/test_drawing.cpp | 16 +- modules/highgui/test/test_video_io.cpp | 16 +- modules/imgproc/src/color.cpp | 956 ++++++------- modules/imgproc/src/filter.cpp | 180 +-- modules/imgproc/src/gcgraph.hpp | 22 +- modules/imgproc/src/histogram.cpp | 408 +++--- modules/imgproc/src/hough.cpp | 28 +- modules/imgproc/src/imgwarp.cpp | 39 +- modules/imgproc/src/moments.cpp | 48 +- modules/imgproc/src/shapedescr.cpp | 84 +- modules/imgproc/src/undistort.cpp | 70 +- modules/imgproc/test/test_contours.cpp | 8 +- modules/imgproc/test/test_convhull.cpp | 66 +- modules/imgproc/test/test_filter.cpp | 28 +- modules/imgproc/test/test_floodfill.cpp | 22 +- modules/imgproc/test/test_histograms.cpp | 98 +- modules/imgproc/test/test_imgwarp.cpp | 168 +-- modules/imgproc/test/test_thresh.cpp | 3 +- modules/legacy/include/opencv2/legacy/legacy.hpp | 48 +- modules/legacy/src/blobtrackanalysistrackdist.cpp | 8 +- modules/legacy/src/blobtrackingcc.cpp | 4 +- modules/legacy/src/kdtree.cpp | 13 +- modules/ml/include/opencv2/ml/ml.hpp | 232 ++-- modules/ml/src/ann_mlp.cpp | 54 +- modules/ml/src/boost.cpp | 56 +- modules/ml/src/ertrees.cpp | 176 +-- modules/ml/src/gbt.cpp | 32 +- modules/ml/src/knearest.cpp | 20 +- modules/ml/src/nbayes.cpp | 22 +- modules/ml/src/rtrees.cpp | 72 +- modules/ml/src/svm.cpp | 31 +- modules/ml/src/tree.cpp | 166 +-- modules/ml/test/test_emknearestkmeans.cpp | 28 +- modules/ml/test/test_gbttest.cpp | 70 +- modules/nonfree/src/sift.cpp | 168 +-- modules/nonfree/src/surf.cpp | 18 +- .../include/opencv2/objdetect/objdetect.hpp | 21 +- modules/objdetect/src/cascadedetect.cpp | 426 +++--- modules/objdetect/src/datamatrix.cpp | 41 +- modules/objdetect/src/hog.cpp | 7 +- modules/objdetect/src/linemod.cpp | 88 +- modules/objdetect/test/test_cascadeandhog.cpp | 124 +- .../include/opencv2/stitching/detail/blenders.hpp | 6 +- .../include/opencv2/stitching/detail/util.hpp | 11 +- .../include/opencv2/stitching/stitcher.hpp | 8 +- modules/stitching/src/matchers.cpp | 8 +- modules/stitching/src/motion_estimators.cpp | 26 +- modules/stitching/src/stitcher.cpp | 18 +- modules/video/src/lkpyramid.cpp | 232 ++-- modules/video/src/optflowgf.cpp | 58 +- .../include/opencv2/videostab/fast_marching.hpp | 2 +- .../include/opencv2/videostab/inpainting.hpp | 6 +- .../include/opencv2/videostab/motion_core.hpp | 6 +- .../opencv2/videostab/motion_stabilizing.hpp | 4 +- .../include/opencv2/videostab/stabilizer.hpp | 2 +- modules/videostab/src/global_motion.cpp | 12 +- modules/videostab/src/inpainting.cpp | 12 +- modules/videostab/src/motion_stabilizing.cpp | 10 +- modules/videostab/src/stabilizer.cpp | 14 +- 127 files changed, 6370 insertions(+), 6349 deletions(-) diff --git a/cmake/OpenCVCompilerOptions.cmake b/cmake/OpenCVCompilerOptions.cmake index d952110..0ce6d68 100644 --- a/cmake/OpenCVCompilerOptions.cmake +++ b/cmake/OpenCVCompilerOptions.cmake @@ -75,7 +75,7 @@ if(CMAKE_COMPILER_IS_GNUCXX) #add_extra_compiler_option(-Wcast-align) #add_extra_compiler_option(-Wstrict-aliasing=2) #add_extra_compiler_option(-Wshadow) - add_extra_compiler_option(-Wno-unnamed-type-template-args) + #add_extra_compiler_option(-Wno-unnamed-type-template-args) # The -Wno-long-long is required in 64bit systems when including sytem headers. if(X86_64) diff --git a/cmake/OpenCVDetectTBB.cmake b/cmake/OpenCVDetectTBB.cmake index 5fc0efb..b15f9f7 100644 --- a/cmake/OpenCVDetectTBB.cmake +++ b/cmake/OpenCVDetectTBB.cmake @@ -10,7 +10,7 @@ elseif(UNIX AND NOT APPLE) if(TBB_FOUND) set(HAVE_TBB 1) if(NOT ${TBB_INCLUDE_DIRS} STREQUAL "") - include_directories(SYSTEM ${TBB_INCLUDE_DIRS}) + ocv_include_directories(${TBB_INCLUDE_DIRS}) endif() link_directories(${TBB_LIBRARY_DIRS}) set(OPENCV_LINKER_LIBS ${OPENCV_LINKER_LIBS} ${TBB_LIBRARIES}) @@ -63,7 +63,7 @@ if(NOT HAVE_TBB) set(HAVE_TBB 1) if(NOT "${TBB_INCLUDE_DIRS}" STREQUAL "") - include_directories(SYSTEM "${TBB_INCLUDE_DIRS}") + ocv_include_directories("${TBB_INCLUDE_DIRS}") endif() endif(TBB_INCLUDE_DIRS) endif(NOT HAVE_TBB) diff --git a/cmake/OpenCVUtils.cmake b/cmake/OpenCVUtils.cmake index fd63354..638ebc8 100644 --- a/cmake/OpenCVUtils.cmake +++ b/cmake/OpenCVUtils.cmake @@ -19,7 +19,7 @@ function(ocv_include_directories) if("${__abs_dir}" MATCHES "^${OpenCV_SOURCE_DIR}" OR "${__abs_dir}" MATCHES "^${OpenCV_BINARY_DIR}") list(APPEND __add_before "${dir}") else() - include_directories(AFTER "${dir}") + include_directories(AFTER SYSTEM "${dir}") endif() endforeach() include_directories(BEFORE ${__add_before}) diff --git a/modules/calib3d/src/calibinit.cpp b/modules/calib3d/src/calibinit.cpp index 4088602..5e6ea1d 100644 --- a/modules/calib3d/src/calibinit.cpp +++ b/modules/calib3d/src/calibinit.cpp @@ -230,7 +230,7 @@ int cvFindChessboardCorners( const void* arr, CvSize pattern_size, int found = 0; CvCBQuad *quads = 0, **quad_group = 0; CvCBCorner *corners = 0, **corner_group = 0; - + try { int k = 0; @@ -252,11 +252,11 @@ int cvFindChessboardCorners( const void* arr, CvSize pattern_size, if( out_corner_count ) *out_corner_count = 0; - + IplImage _img; int check_chessboard_result; - int quad_count = 0, group_idx = 0, i = 0, dilations = 0; - + int quad_count = 0, group_idx = 0, dilations = 0; + img = cvGetMat( img, &stub ); //debug_img = img; @@ -316,8 +316,8 @@ int cvFindChessboardCorners( const void* arr, CvSize pattern_size, for( dilations = min_dilations; dilations <= max_dilations; dilations++ ) { if (found) - break; // already found it - + break; // already found it + cvFree(&quads); cvFree(&corners); @@ -378,7 +378,7 @@ int cvFindChessboardCorners( const void* arr, CvSize pattern_size, cvCopy(dbg_img, dbg1_img); cvNamedWindow("all_quads", 1); // copy corners to temp array - for( i = 0; i < quad_count; i++ ) + for(int i = 0; i < quad_count; i++ ) { for (int k=0; k<4; k++) { @@ -432,7 +432,7 @@ int cvFindChessboardCorners( const void* arr, CvSize pattern_size, cvCopy(dbg_img,dbg2_img); cvNamedWindow("connected_group", 1); // copy corners to temp array - for( i = 0; i < quad_count; i++ ) + for(int i = 0; i < quad_count; i++ ) { if (quads[i].group_idx == group_idx) for (int k=0; k<4; k++) @@ -455,7 +455,7 @@ int cvFindChessboardCorners( const void* arr, CvSize pattern_size, #endif if (count == 0) - continue; // haven't found inner quads + continue; // haven't found inner quads // If count is more than it should be, this will remove those quads @@ -472,7 +472,7 @@ int cvFindChessboardCorners( const void* arr, CvSize pattern_size, float sum_dist = 0; int total = 0; - for( i = 0; i < n; i++ ) + for(int i = 0; i < n; i++ ) { int ni = 0; float avgi = corner_group[i]->meanDist(&ni); @@ -484,7 +484,7 @@ int cvFindChessboardCorners( const void* arr, CvSize pattern_size, if( count > 0 || (out_corner_count && -count > *out_corner_count) ) { // copy corners to output array - for( i = 0; i < n; i++ ) + for(int i = 0; i < n; i++ ) out_corners[i] = corner_group[i]->pt; if( out_corner_count ) @@ -505,19 +505,19 @@ int cvFindChessboardCorners( const void* arr, CvSize pattern_size, if( found ) found = icvCheckBoardMonotony( out_corners, pattern_size ); - // check that none of the found corners is too close to the image boundary + // check that none of the found corners is too close to the image boundary if( found ) - { - const int BORDER = 8; - for( k = 0; k < pattern_size.width*pattern_size.height; k++ ) - { - if( out_corners[k].x <= BORDER || out_corners[k].x > img->cols - BORDER || - out_corners[k].y <= BORDER || out_corners[k].y > img->rows - BORDER ) - break; - } - - found = k == pattern_size.width*pattern_size.height; - } + { + const int BORDER = 8; + for( k = 0; k < pattern_size.width*pattern_size.height; k++ ) + { + if( out_corners[k].x <= BORDER || out_corners[k].x > img->cols - BORDER || + out_corners[k].y <= BORDER || out_corners[k].y > img->rows - BORDER ) + break; + } + + found = k == pattern_size.width*pattern_size.height; + } if( found && pattern_size.height % 2 == 0 && pattern_size.width % 2 == 0 ) { @@ -525,8 +525,8 @@ int cvFindChessboardCorners( const void* arr, CvSize pattern_size, double dy0 = out_corners[last_row].y - out_corners[0].y; if( dy0 < 0 ) { - int i, n = pattern_size.width*pattern_size.height; - for( i = 0; i < n/2; i++ ) + int n = pattern_size.width*pattern_size.height; + for(int i = 0; i < n/2; i++ ) { CvPoint2D32f temp; CV_SWAP(out_corners[i], out_corners[n-i-1], temp); @@ -559,7 +559,7 @@ int cvFindChessboardCorners( const void* arr, CvSize pattern_size, cvFree(&corner_group); throw; } - + cvFree(&quads); cvFree(&corners); cvFree(&quad_group); @@ -582,7 +582,7 @@ static int icvCheckBoardMonotony( CvPoint2D32f* corners, CvSize pattern_size ) { int i, j, k; - + for( k = 0; k < 2; k++ ) { for( i = 0; i < (k == 0 ? pattern_size.height : pattern_size.width); i++ ) @@ -627,11 +627,10 @@ icvOrderFoundConnectedQuads( int quad_count, CvCBQuad **quads, { cv::Ptr temp_storage = cvCreateChildMemStorage( storage ); CvSeq* stack = cvCreateSeq( 0, sizeof(*stack), sizeof(void*), temp_storage ); - int i; // first find an interior quad CvCBQuad *start = NULL; - for (i=0; icount == 4) { @@ -682,7 +681,7 @@ icvOrderFoundConnectedQuads( int quad_count, CvCBQuad **quads, case 1: col += 2; break; case 2: - row += 2; break; + row += 2; break; case 3: col -= 2; break; } @@ -700,7 +699,7 @@ icvOrderFoundConnectedQuads( int quad_count, CvCBQuad **quads, } } - for (i=col_min; i<=col_max; i++) + for (int i=col_min; i<=col_max; i++) PRINTF("HIST[%d] = %d\n", i, col_hist[i]); // analyze inner quad structure @@ -763,7 +762,7 @@ icvOrderFoundConnectedQuads( int quad_count, CvCBQuad **quads, // if there is an outer quad missing, fill it in // first order all inner quads int found = 0; - for (i=0; icount == 4) { // ok, look at neighbors @@ -778,7 +777,7 @@ icvOrderFoundConnectedQuads( int quad_count, CvCBQuad **quads, case 1: col += 2; break; case 2: - row += 2; break; + row += 2; break; case 3: col -= 2; break; } @@ -817,7 +816,7 @@ icvOrderFoundConnectedQuads( int quad_count, CvCBQuad **quads, // final trimming of outer quads - if (dcol == w && drow == h) // found correct inner quads + if (dcol == w && drow == h) // found correct inner quads { PRINTF("Inner bounds ok, check outer quads\n"); int rcount = quad_count; @@ -832,7 +831,7 @@ icvOrderFoundConnectedQuads( int quad_count, CvCBQuad **quads, if (quads[i]->neighbors[j] && quads[i]->neighbors[j]->ordered) outer = true; } - if (!outer) // not an outer quad, eliminate + if (!outer) // not an outer quad, eliminate { PRINTF("Removing quad %d\n", i); icvRemoveQuadFromGroup(quads,rcount,quads[i]); @@ -876,7 +875,7 @@ icvAddOuterQuad( CvCBQuad *quad, CvCBQuad **quads, int quad_count, quad->count += 1; q->neighbors[j] = quad; q->group_idx = quad->group_idx; - q->count = 1; // number of neighbors + q->count = 1; // number of neighbors q->ordered = false; q->edge_len = quad->edge_len; @@ -1262,7 +1261,7 @@ icvCheckQuadGroup( CvCBQuad **quad_group, int quad_count, int width = 0, height = 0; int hist[5] = {0,0,0,0,0}; CvCBCorner* first = 0, *first2 = 0, *right, *cur, *below, *c; - + // build dual graph, which vertices are internal quad corners // and two vertices are connected iff they lie on the same quad edge for( i = 0; i < quad_count; i++ ) @@ -1485,7 +1484,7 @@ icvCheckQuadGroup( CvCBQuad **quad_group, int quad_count, result = corner_count; finalize: - + if( result <= 0 ) { corner_count = MIN( corner_count, pattern_size.width*pattern_size.height ); @@ -1697,7 +1696,7 @@ icvGenerateQuads( CvCBQuad **out_quads, CvCBCorner **out_corners, CV_POLY_APPROX_DP, (float)approx_level ); if( dst_contour->total == 4 ) break; - + // we call this again on its own output, because sometimes // cvApproxPoly() does not simplify as much as it should. dst_contour = cvApproxPoly( dst_contour, sizeof(CvContour), temp_storage, @@ -2006,17 +2005,17 @@ bool cv::findCirclesGrid( InputArray _image, Size patternSize, #endif if (isFound) { - switch(parameters.gridType) - { + switch(parameters.gridType) + { case CirclesGridFinderParameters::SYMMETRIC_GRID: boxFinder.getHoles(centers); break; case CirclesGridFinderParameters::ASYMMETRIC_GRID: - boxFinder.getAsymmetricHoles(centers); - break; + boxFinder.getAsymmetricHoles(centers); + break; default: CV_Error(CV_StsBadArg, "Unkown pattern type"); - } + } if (i != 0) { @@ -2027,7 +2026,7 @@ bool cv::findCirclesGrid( InputArray _image, Size patternSize, Mat(centers).copyTo(_centers); return true; } - + boxFinder.getHoles(centers); if (i != attempts - 1) { diff --git a/modules/calib3d/src/calibration.cpp b/modules/calib3d/src/calibration.cpp index 684110a..f211c5d 100644 --- a/modules/calib3d/src/calibration.cpp +++ b/modules/calib3d/src/calibration.cpp @@ -1153,7 +1153,7 @@ CV_IMPL void cvFindExtrinsicCameraParams2( const CvMat* objectPoints, int useExtrinsicGuess ) { const int max_iter = 20; - Ptr matM, _Mxy, _m, _mn, matL, matJ; + Ptr matM, _Mxy, _m, _mn, matL; int i, count; double a[9], ar[9]={1,0,0,0,1,0,0,0,1}, R[9]; diff --git a/modules/calib3d/src/circlesgrid.cpp b/modules/calib3d/src/circlesgrid.cpp index f729919..853e3ad 100644 --- a/modules/calib3d/src/circlesgrid.cpp +++ b/modules/calib3d/src/circlesgrid.cpp @@ -65,14 +65,14 @@ void drawPoints(const vector &points, Mat &outImage, int radius = 2, S } #endif -void CirclesGridClusterFinder::hierarchicalClustering(const vector points, const Size &patternSize, vector &patternPoints) +void CirclesGridClusterFinder::hierarchicalClustering(const vector points, const Size &patternSz, vector &patternPoints) { #ifdef HAVE_TEGRA_OPTIMIZATION - if(tegra::hierarchicalClustering(points, patternSize, patternPoints)) + if(tegra::hierarchicalClustering(points, patternSz, patternPoints)) return; #endif - int i, j, n = (int)points.size(); - size_t pn = static_cast(patternSize.area()); + int j, n = (int)points.size(); + size_t pn = static_cast(patternSz.area()); patternPoints.clear(); if (pn >= points.size()) @@ -84,7 +84,7 @@ void CirclesGridClusterFinder::hierarchicalClustering(const vector poin Mat dists(n, n, CV_32FC1, Scalar(0)); Mat distsMask(dists.size(), CV_8UC1, Scalar(0)); - for(i = 0; i < n; i++) + for(int i = 0; i < n; i++) { for(j = i+1; j < n; j++) { @@ -122,7 +122,7 @@ void CirclesGridClusterFinder::hierarchicalClustering(const vector poin } //the largest cluster can have more than pn points -- we need to filter out such situations - if(clusters[patternClusterIdx].size() != static_cast(patternSize.area())) + if(clusters[patternClusterIdx].size() != static_cast(patternSz.area())) { return; } @@ -505,11 +505,11 @@ void Graph::floydWarshall(cv::Mat &distanceMatrix, int infinity) const { for (Vertices::const_iterator it3 = vertices.begin(); it3 != vertices.end(); it3++) { - int i1 = (int)it1->first, i2 = (int)it2->first, i3 = (int)it3->first; + int i1 = (int)it1->first, i2 = (int)it2->first, i3 = (int)it3->first; int val1 = distanceMatrix.at (i2, i3); int val2; if (distanceMatrix.at (i2, i1) == infinity || - distanceMatrix.at (i1, i3) == infinity) + distanceMatrix.at (i1, i3) == infinity) val2 = val1; else { diff --git a/modules/calib3d/src/epnp.cpp b/modules/calib3d/src/epnp.cpp index 678ccfa..087b898 100644 --- a/modules/calib3d/src/epnp.cpp +++ b/modules/calib3d/src/epnp.cpp @@ -8,26 +8,26 @@ epnp::epnp(const cv::Mat& cameraMatrix, const cv::Mat& opoints, const cv::Mat& i if (cameraMatrix.depth() == CV_32F) init_camera_parameters(cameraMatrix); else - init_camera_parameters(cameraMatrix); + init_camera_parameters(cameraMatrix); number_of_correspondences = std::max(opoints.checkVector(3, CV_32F), opoints.checkVector(3, CV_64F)); pws.resize(3 * number_of_correspondences); - us.resize(2 * number_of_correspondences); - + us.resize(2 * number_of_correspondences); + if (opoints.depth() == ipoints.depth()) { - if (opoints.depth() == CV_32F) - init_points(opoints, ipoints); - else - init_points(opoints, ipoints); + if (opoints.depth() == CV_32F) + init_points(opoints, ipoints); + else + init_points(opoints, ipoints); } else if (opoints.depth() == CV_32F) - init_points(opoints, ipoints); + init_points(opoints, ipoints); else - init_points(opoints, ipoints); + init_points(opoints, ipoints); - alphas.resize(4 * number_of_correspondences); + alphas.resize(4 * number_of_correspondences); pcs.resize(3 * number_of_correspondences); max_nr = 0; @@ -97,15 +97,15 @@ void epnp::compute_barycentric_coordinates(void) for(int j = 0; j < 3; j++) a[1 + j] = - ci[3 * j ] * (pi[0] - cws[0][0]) + - ci[3 * j + 1] * (pi[1] - cws[0][1]) + - ci[3 * j + 2] * (pi[2] - cws[0][2]); + ci[3 * j ] * (pi[0] - cws[0][0]) + + ci[3 * j + 1] * (pi[1] - cws[0][1]) + + ci[3 * j + 2] * (pi[2] - cws[0][2]); a[0] = 1.0f - a[1] - a[2] - a[3]; } } void epnp::fill_M(CvMat * M, - const int row, const double * as, const double u, const double v) + const int row, const double * as, const double u, const double v) { double * M1 = M->data.db + row * 12; double * M2 = M1 + 12; @@ -130,7 +130,7 @@ void epnp::compute_ccs(const double * betas, const double * ut) const double * v = ut + 12 * (11 - i); for(int j = 0; j < 4; j++) for(int k = 0; k < 3; k++) - ccs[j][k] += betas[i] * v[3 * j + k]; + ccs[j][k] += betas[i] * v[3 * j + k]; } } @@ -195,7 +195,7 @@ void epnp::compute_pose(cv::Mat& R, cv::Mat& t) } void epnp::copy_R_and_t(const double R_src[3][3], const double t_src[3], - double R_dst[3][3], double t_dst[3]) + double R_dst[3][3], double t_dst[3]) { for(int i = 0; i < 3; i++) { for(int j = 0; j < 3; j++) @@ -282,7 +282,7 @@ void epnp::solve_for_sign(void) if (pcs[2] < 0.0) { for(int i = 0; i < 4; i++) for(int j = 0; j < 3; j++) - ccs[i][j] = -ccs[i][j]; + ccs[i][j] = -ccs[i][j]; for(int i = 0; i < number_of_correspondences; i++) { pcs[3 * i ] = -pcs[3 * i]; @@ -293,7 +293,7 @@ void epnp::solve_for_sign(void) } double epnp::compute_R_and_t(const double * ut, const double * betas, - double R[3][3], double t[3]) + double R[3][3], double t[3]) { compute_ccs(betas, ut); compute_pcs(); @@ -322,13 +322,13 @@ double epnp::reprojection_error(const double R[3][3], const double t[3]) } return sum2 / number_of_correspondences; -} +} // betas10 = [B11 B12 B22 B13 B23 B33 B14 B24 B34 B44] // betas_approx_1 = [B11 B12 B13 B14] void epnp::find_betas_approx_1(const CvMat * L_6x10, const CvMat * Rho, - double * betas) + double * betas) { double l_6x4[6 * 4], b4[4]; CvMat L_6x4 = cvMat(6, 4, CV_64F, l_6x4); @@ -360,7 +360,7 @@ void epnp::find_betas_approx_1(const CvMat * L_6x10, const CvMat * Rho, // betas_approx_2 = [B11 B12 B22 ] void epnp::find_betas_approx_2(const CvMat * L_6x10, const CvMat * Rho, - double * betas) + double * betas) { double l_6x3[6 * 3], b3[3]; CvMat L_6x3 = cvMat(6, 3, CV_64F, l_6x3); @@ -392,7 +392,7 @@ void epnp::find_betas_approx_2(const CvMat * L_6x10, const CvMat * Rho, // betas_approx_3 = [B11 B12 B22 B13 B23 ] void epnp::find_betas_approx_3(const CvMat * L_6x10, const CvMat * Rho, - double * betas) + double * betas) { double l_6x5[6 * 5], b5[5]; CvMat L_6x5 = cvMat(6, 5, CV_64F, l_6x5); @@ -440,8 +440,8 @@ void epnp::compute_L_6x10(const double * ut, double * l_6x10) b++; if (b > 3) { - a++; - b = a + 1; + a++; + b = a + 1; } } } @@ -473,7 +473,7 @@ void epnp::compute_rho(double * rho) } void epnp::compute_A_and_b_gauss_newton(const double * l_6x10, const double * rho, - const double betas[4], CvMat * A, CvMat * b) + const double betas[4], CvMat * A, CvMat * b) { for(int i = 0; i < 6; i++) { const double * rowL = l_6x10 + i * 10; @@ -485,23 +485,22 @@ void epnp::compute_A_and_b_gauss_newton(const double * l_6x10, const double * rh rowA[3] = rowL[6] * betas[0] + rowL[7] * betas[1] + rowL[8] * betas[2] + 2 * rowL[9] * betas[3]; cvmSet(b, i, 0, rho[i] - - ( - rowL[0] * betas[0] * betas[0] + - rowL[1] * betas[0] * betas[1] + - rowL[2] * betas[1] * betas[1] + - rowL[3] * betas[0] * betas[2] + - rowL[4] * betas[1] * betas[2] + - rowL[5] * betas[2] * betas[2] + - rowL[6] * betas[0] * betas[3] + - rowL[7] * betas[1] * betas[3] + - rowL[8] * betas[2] * betas[3] + - rowL[9] * betas[3] * betas[3] - )); + ( + rowL[0] * betas[0] * betas[0] + + rowL[1] * betas[0] * betas[1] + + rowL[2] * betas[1] * betas[1] + + rowL[3] * betas[0] * betas[2] + + rowL[4] * betas[1] * betas[2] + + rowL[5] * betas[2] * betas[2] + + rowL[6] * betas[0] * betas[3] + + rowL[7] * betas[1] * betas[3] + + rowL[8] * betas[2] * betas[3] + + rowL[9] * betas[3] * betas[3] + )); } } -void epnp::gauss_newton(const CvMat * L_6x10, const CvMat * Rho, - double betas[4]) +void epnp::gauss_newton(const CvMat * L_6x10, const CvMat * Rho, double betas[4]) { const int iterations_number = 5; @@ -510,12 +509,13 @@ void epnp::gauss_newton(const CvMat * L_6x10, const CvMat * Rho, CvMat B = cvMat(6, 1, CV_64F, b); CvMat X = cvMat(4, 1, CV_64F, x); - for(int k = 0; k < iterations_number; k++) { + for(int k = 0; k < iterations_number; k++) + { compute_A_and_b_gauss_newton(L_6x10->data.db, Rho->data.db, - betas, &A, &B); + betas, &A, &B); qr_solve(&A, &B, &X); for(int i = 0; i < 4; i++) - betas[i] += x[i]; + betas[i] += x[i]; } } @@ -524,53 +524,64 @@ void epnp::qr_solve(CvMat * A, CvMat * b, CvMat * X) const int nr = A->rows; const int nc = A->cols; - if (max_nr != 0 && max_nr < nr) { + if (max_nr != 0 && max_nr < nr) + { delete [] A1; delete [] A2; } - if (max_nr < nr) { + if (max_nr < nr) + { max_nr = nr; A1 = new double[nr]; A2 = new double[nr]; } double * pA = A->data.db, * ppAkk = pA; - for(int k = 0; k < nc; k++) { - double * ppAik = ppAkk, eta = fabs(*ppAik); - for(int i = k + 1; i < nr; i++) { - double elt = fabs(*ppAik); + for(int k = 0; k < nc; k++) + { + double * ppAik1 = ppAkk, eta = fabs(*ppAik1); + for(int i = k + 1; i < nr; i++) + { + double elt = fabs(*ppAik1); if (eta < elt) eta = elt; - ppAik += nc; + ppAik1 += nc; } - if (eta == 0) { + if (eta == 0) + { A1[k] = A2[k] = 0.0; //cerr << "God damnit, A is singular, this shouldn't happen." << endl; return; - } else { - double * ppAik = ppAkk, sum = 0.0, inv_eta = 1. / eta; - for(int i = k; i < nr; i++) { - *ppAik *= inv_eta; - sum += *ppAik * *ppAik; - ppAik += nc; + } + else + { + double * ppAik2 = ppAkk, sum2 = 0.0, inv_eta = 1. / eta; + for(int i = k; i < nr; i++) + { + *ppAik2 *= inv_eta; + sum2 += *ppAik2 * *ppAik2; + ppAik2 += nc; } - double sigma = sqrt(sum); + double sigma = sqrt(sum2); if (*ppAkk < 0) - sigma = -sigma; + sigma = -sigma; *ppAkk += sigma; A1[k] = sigma * *ppAkk; A2[k] = -eta * sigma; - for(int j = k + 1; j < nc; j++) { - double * ppAik = ppAkk, sum = 0; - for(int i = k; i < nr; i++) { - sum += *ppAik * ppAik[j - k]; - ppAik += nc; - } - double tau = sum / A1[k]; - ppAik = ppAkk; - for(int i = k; i < nr; i++) { - ppAik[j - k] -= tau * *ppAik; - ppAik += nc; - } + for(int j = k + 1; j < nc; j++) + { + double * ppAik = ppAkk, sum = 0; + for(int i = k; i < nr; i++) + { + sum += *ppAik * ppAik[j - k]; + ppAik += nc; + } + double tau = sum / A1[k]; + ppAik = ppAkk; + for(int i = k; i < nr; i++) + { + ppAik[j - k] -= tau * *ppAik; + ppAik += nc; + } } } ppAkk += nc + 1; @@ -578,15 +589,18 @@ void epnp::qr_solve(CvMat * A, CvMat * b, CvMat * X) // b <- Qt b double * ppAjj = pA, * pb = b->data.db; - for(int j = 0; j < nc; j++) { + for(int j = 0; j < nc; j++) + { double * ppAij = ppAjj, tau = 0; - for(int i = j; i < nr; i++) { + for(int i = j; i < nr; i++) + { tau += *ppAij * pb[i]; ppAij += nc; } tau /= A1[j]; ppAij = ppAjj; - for(int i = j; i < nr; i++) { + for(int i = j; i < nr; i++) + { pb[i] -= tau * *ppAij; ppAij += nc; } @@ -596,10 +610,12 @@ void epnp::qr_solve(CvMat * A, CvMat * b, CvMat * X) // X = R-1 b double * pX = X->data.db; pX[nc - 1] = pb[nc - 1] / A2[nc - 1]; - for(int i = nc - 2; i >= 0; i--) { + for(int i = nc - 2; i >= 0; i--) + { double * ppAij = pA + i * nc + (i + 1), sum = 0; - for(int j = i + 1; j < nc; j++) { + for(int j = i + 1; j < nc; j++) + { sum += *ppAij * pX[j]; ppAij++; } diff --git a/modules/calib3d/src/p3p.cpp b/modules/calib3d/src/p3p.cpp index 300f230..a02da3e 100644 --- a/modules/calib3d/src/p3p.cpp +++ b/modules/calib3d/src/p3p.cpp @@ -9,151 +9,151 @@ using namespace std; void p3p::init_inverse_parameters() { - inv_fx = 1. / fx; - inv_fy = 1. / fy; - cx_fx = cx / fx; - cy_fy = cy / fy; + inv_fx = 1. / fx; + inv_fy = 1. / fy; + cx_fx = cx / fx; + cy_fy = cy / fy; } p3p::p3p(cv::Mat cameraMatrix) { - if (cameraMatrix.depth() == CV_32F) - init_camera_parameters(cameraMatrix); - else - init_camera_parameters(cameraMatrix); - init_inverse_parameters(); + if (cameraMatrix.depth() == CV_32F) + init_camera_parameters(cameraMatrix); + else + init_camera_parameters(cameraMatrix); + init_inverse_parameters(); } p3p::p3p(double _fx, double _fy, double _cx, double _cy) { - fx = _fx; - fy = _fy; - cx = _cx; - cy = _cy; - init_inverse_parameters(); + fx = _fx; + fy = _fy; + cx = _cx; + cy = _cy; + init_inverse_parameters(); } bool p3p::solve(cv::Mat& R, cv::Mat& tvec, const cv::Mat& opoints, const cv::Mat& ipoints) { - double rotation_matrix[3][3], translation[3]; - std::vector points; - if (opoints.depth() == ipoints.depth()) - { - if (opoints.depth() == CV_32F) - extract_points(opoints, ipoints, points); - else - extract_points(opoints, ipoints, points); - } - else if (opoints.depth() == CV_32F) - extract_points(opoints, ipoints, points); - else - extract_points(opoints, ipoints, points); - - bool result = solve(rotation_matrix, translation, points[0], points[1], points[2], points[3], points[4], points[5], - points[6], points[7], points[8], points[9], points[10], points[11], points[12], points[13], points[14], - points[15], points[16], points[17], points[18], points[19]); - cv::Mat(3, 1, CV_64F, translation).copyTo(tvec); + double rotation_matrix[3][3], translation[3]; + std::vector points; + if (opoints.depth() == ipoints.depth()) + { + if (opoints.depth() == CV_32F) + extract_points(opoints, ipoints, points); + else + extract_points(opoints, ipoints, points); + } + else if (opoints.depth() == CV_32F) + extract_points(opoints, ipoints, points); + else + extract_points(opoints, ipoints, points); + + bool result = solve(rotation_matrix, translation, points[0], points[1], points[2], points[3], points[4], points[5], + points[6], points[7], points[8], points[9], points[10], points[11], points[12], points[13], points[14], + points[15], points[16], points[17], points[18], points[19]); + cv::Mat(3, 1, CV_64F, translation).copyTo(tvec); cv::Mat(3, 3, CV_64F, rotation_matrix).copyTo(R); - return result; + return result; } bool p3p::solve(double R[3][3], double t[3], - double mu0, double mv0, double X0, double Y0, double Z0, - double mu1, double mv1, double X1, double Y1, double Z1, - double mu2, double mv2, double X2, double Y2, double Z2, - double mu3, double mv3, double X3, double Y3, double Z3) + double mu0, double mv0, double X0, double Y0, double Z0, + double mu1, double mv1, double X1, double Y1, double Z1, + double mu2, double mv2, double X2, double Y2, double Z2, + double mu3, double mv3, double X3, double Y3, double Z3) { - double Rs[4][3][3], ts[4][3]; - - int n = solve(Rs, ts, mu0, mv0, X0, Y0, Z0, mu1, mv1, X1, Y1, Z1, mu2, mv2, X2, Y2, Z2); - - if (n == 0) - return false; - - int ns = 0; - double min_reproj = 0; - for(int i = 0; i < n; i++) { - double X3p = Rs[i][0][0] * X3 + Rs[i][0][1] * Y3 + Rs[i][0][2] * Z3 + ts[i][0]; - double Y3p = Rs[i][1][0] * X3 + Rs[i][1][1] * Y3 + Rs[i][1][2] * Z3 + ts[i][1]; - double Z3p = Rs[i][2][0] * X3 + Rs[i][2][1] * Y3 + Rs[i][2][2] * Z3 + ts[i][2]; - double mu3p = cx + fx * X3p / Z3p; - double mv3p = cy + fy * Y3p / Z3p; - double reproj = (mu3p - mu3) * (mu3p - mu3) + (mv3p - mv3) * (mv3p - mv3); - if (i == 0 || min_reproj > reproj) { - ns = i; - min_reproj = reproj; - } - } - - for(int i = 0; i < 3; i++) { - for(int j = 0; j < 3; j++) - R[i][j] = Rs[ns][i][j]; - t[i] = ts[ns][i]; - } - - return true; + double Rs[4][3][3], ts[4][3]; + + int n = solve(Rs, ts, mu0, mv0, X0, Y0, Z0, mu1, mv1, X1, Y1, Z1, mu2, mv2, X2, Y2, Z2); + + if (n == 0) + return false; + + int ns = 0; + double min_reproj = 0; + for(int i = 0; i < n; i++) { + double X3p = Rs[i][0][0] * X3 + Rs[i][0][1] * Y3 + Rs[i][0][2] * Z3 + ts[i][0]; + double Y3p = Rs[i][1][0] * X3 + Rs[i][1][1] * Y3 + Rs[i][1][2] * Z3 + ts[i][1]; + double Z3p = Rs[i][2][0] * X3 + Rs[i][2][1] * Y3 + Rs[i][2][2] * Z3 + ts[i][2]; + double mu3p = cx + fx * X3p / Z3p; + double mv3p = cy + fy * Y3p / Z3p; + double reproj = (mu3p - mu3) * (mu3p - mu3) + (mv3p - mv3) * (mv3p - mv3); + if (i == 0 || min_reproj > reproj) { + ns = i; + min_reproj = reproj; + } + } + + for(int i = 0; i < 3; i++) { + for(int j = 0; j < 3; j++) + R[i][j] = Rs[ns][i][j]; + t[i] = ts[ns][i]; + } + + return true; } int p3p::solve(double R[4][3][3], double t[4][3], - double mu0, double mv0, double X0, double Y0, double Z0, - double mu1, double mv1, double X1, double Y1, double Z1, - double mu2, double mv2, double X2, double Y2, double Z2) + double mu0, double mv0, double X0, double Y0, double Z0, + double mu1, double mv1, double X1, double Y1, double Z1, + double mu2, double mv2, double X2, double Y2, double Z2) { - double mk0, mk1, mk2; - double norm; + double mk0, mk1, mk2; + double norm; - mu0 = inv_fx * mu0 - cx_fx; - mv0 = inv_fy * mv0 - cy_fy; - norm = sqrt(mu0 * mu0 + mv0 * mv0 + 1); - mk0 = 1. / norm; mu0 *= mk0; mv0 *= mk0; + mu0 = inv_fx * mu0 - cx_fx; + mv0 = inv_fy * mv0 - cy_fy; + norm = sqrt(mu0 * mu0 + mv0 * mv0 + 1); + mk0 = 1. / norm; mu0 *= mk0; mv0 *= mk0; - mu1 = inv_fx * mu1 - cx_fx; - mv1 = inv_fy * mv1 - cy_fy; - norm = sqrt(mu1 * mu1 + mv1 * mv1 + 1); - mk1 = 1. / norm; mu1 *= mk1; mv1 *= mk1; + mu1 = inv_fx * mu1 - cx_fx; + mv1 = inv_fy * mv1 - cy_fy; + norm = sqrt(mu1 * mu1 + mv1 * mv1 + 1); + mk1 = 1. / norm; mu1 *= mk1; mv1 *= mk1; - mu2 = inv_fx * mu2 - cx_fx; - mv2 = inv_fy * mv2 - cy_fy; - norm = sqrt(mu2 * mu2 + mv2 * mv2 + 1); - mk2 = 1. / norm; mu2 *= mk2; mv2 *= mk2; + mu2 = inv_fx * mu2 - cx_fx; + mv2 = inv_fy * mv2 - cy_fy; + norm = sqrt(mu2 * mu2 + mv2 * mv2 + 1); + mk2 = 1. / norm; mu2 *= mk2; mv2 *= mk2; - double distances[3]; - distances[0] = sqrt( (X1 - X2) * (X1 - X2) + (Y1 - Y2) * (Y1 - Y2) + (Z1 - Z2) * (Z1 - Z2) ); - distances[1] = sqrt( (X0 - X2) * (X0 - X2) + (Y0 - Y2) * (Y0 - Y2) + (Z0 - Z2) * (Z0 - Z2) ); - distances[2] = sqrt( (X0 - X1) * (X0 - X1) + (Y0 - Y1) * (Y0 - Y1) + (Z0 - Z1) * (Z0 - Z1) ); + double distances[3]; + distances[0] = sqrt( (X1 - X2) * (X1 - X2) + (Y1 - Y2) * (Y1 - Y2) + (Z1 - Z2) * (Z1 - Z2) ); + distances[1] = sqrt( (X0 - X2) * (X0 - X2) + (Y0 - Y2) * (Y0 - Y2) + (Z0 - Z2) * (Z0 - Z2) ); + distances[2] = sqrt( (X0 - X1) * (X0 - X1) + (Y0 - Y1) * (Y0 - Y1) + (Z0 - Z1) * (Z0 - Z1) ); - // Calculate angles - double cosines[3]; - cosines[0] = mu1 * mu2 + mv1 * mv2 + mk1 * mk2; - cosines[1] = mu0 * mu2 + mv0 * mv2 + mk0 * mk2; - cosines[2] = mu0 * mu1 + mv0 * mv1 + mk0 * mk1; + // Calculate angles + double cosines[3]; + cosines[0] = mu1 * mu2 + mv1 * mv2 + mk1 * mk2; + cosines[1] = mu0 * mu2 + mv0 * mv2 + mk0 * mk2; + cosines[2] = mu0 * mu1 + mv0 * mv1 + mk0 * mk1; - double lengths[4][3]; - int n = solve_for_lengths(lengths, distances, cosines); + double lengths[4][3]; + int n = solve_for_lengths(lengths, distances, cosines); - int nb_solutions = 0; - for(int i = 0; i < n; i++) { - double M_orig[3][3]; + int nb_solutions = 0; + for(int i = 0; i < n; i++) { + double M_orig[3][3]; - M_orig[0][0] = lengths[i][0] * mu0; - M_orig[0][1] = lengths[i][0] * mv0; - M_orig[0][2] = lengths[i][0] * mk0; + M_orig[0][0] = lengths[i][0] * mu0; + M_orig[0][1] = lengths[i][0] * mv0; + M_orig[0][2] = lengths[i][0] * mk0; - M_orig[1][0] = lengths[i][1] * mu1; - M_orig[1][1] = lengths[i][1] * mv1; - M_orig[1][2] = lengths[i][1] * mk1; + M_orig[1][0] = lengths[i][1] * mu1; + M_orig[1][1] = lengths[i][1] * mv1; + M_orig[1][2] = lengths[i][1] * mk1; - M_orig[2][0] = lengths[i][2] * mu2; - M_orig[2][1] = lengths[i][2] * mv2; - M_orig[2][2] = lengths[i][2] * mk2; + M_orig[2][0] = lengths[i][2] * mu2; + M_orig[2][1] = lengths[i][2] * mv2; + M_orig[2][2] = lengths[i][2] * mk2; - if (!align(M_orig, X0, Y0, Z0, X1, Y1, Z1, X2, Y2, Z2, R[nb_solutions], t[nb_solutions])) - continue; + if (!align(M_orig, X0, Y0, Z0, X1, Y1, Z1, X2, Y2, Z2, R[nb_solutions], t[nb_solutions])) + continue; - nb_solutions++; - } + nb_solutions++; + } - return nb_solutions; + return nb_solutions; } /// Given 3D distances between three points and cosines of 3 angles at the apex, calculates @@ -170,247 +170,247 @@ int p3p::solve(double R[4][3][3], double t[4][3], int p3p::solve_for_lengths(double lengths[4][3], double distances[3], double cosines[3]) { - double p = cosines[0] * 2; - double q = cosines[1] * 2; - double r = cosines[2] * 2; + double p = cosines[0] * 2; + double q = cosines[1] * 2; + double r = cosines[2] * 2; - double inv_d22 = 1. / (distances[2] * distances[2]); - double a = inv_d22 * (distances[0] * distances[0]); - double b = inv_d22 * (distances[1] * distances[1]); + double inv_d22 = 1. / (distances[2] * distances[2]); + double a = inv_d22 * (distances[0] * distances[0]); + double b = inv_d22 * (distances[1] * distances[1]); - double a2 = a * a, b2 = b * b, p2 = p * p, q2 = q * q, r2 = r * r; - double pr = p * r, pqr = q * pr; + double a2 = a * a, b2 = b * b, p2 = p * p, q2 = q * q, r2 = r * r; + double pr = p * r, pqr = q * pr; - // Check reality condition (the four points should not be coplanar) - if (p2 + q2 + r2 - pqr - 1 == 0) - return 0; + // Check reality condition (the four points should not be coplanar) + if (p2 + q2 + r2 - pqr - 1 == 0) + return 0; - double ab = a * b, a_2 = 2*a; + double ab = a * b, a_2 = 2*a; - double A = -2 * b + b2 + a2 + 1 + ab*(2 - r2) - a_2; + double A = -2 * b + b2 + a2 + 1 + ab*(2 - r2) - a_2; - // Check reality condition - if (A == 0) return 0; + // Check reality condition + if (A == 0) return 0; - double a_4 = 4*a; + double a_4 = 4*a; - double B = q*(-2*(ab + a2 + 1 - b) + r2*ab + a_4) + pr*(b - b2 + ab); - double C = q2 + b2*(r2 + p2 - 2) - b*(p2 + pqr) - ab*(r2 + pqr) + (a2 - a_2)*(2 + q2) + 2; - double D = pr*(ab-b2+b) + q*((p2-2)*b + 2 * (ab - a2) + a_4 - 2); - double E = 1 + 2*(b - a - ab) + b2 - b*p2 + a2; + double B = q*(-2*(ab + a2 + 1 - b) + r2*ab + a_4) + pr*(b - b2 + ab); + double C = q2 + b2*(r2 + p2 - 2) - b*(p2 + pqr) - ab*(r2 + pqr) + (a2 - a_2)*(2 + q2) + 2; + double D = pr*(ab-b2+b) + q*((p2-2)*b + 2 * (ab - a2) + a_4 - 2); + double E = 1 + 2*(b - a - ab) + b2 - b*p2 + a2; - double temp = (p2*(a-1+b) + r2*(a-1-b) + pqr - a*pqr); - double b0 = b * temp * temp; - // Check reality condition - if (b0 == 0) - return 0; + double temp = (p2*(a-1+b) + r2*(a-1-b) + pqr - a*pqr); + double b0 = b * temp * temp; + // Check reality condition + if (b0 == 0) + return 0; - double real_roots[4]; - int n = solve_deg4(A, B, C, D, E, real_roots[0], real_roots[1], real_roots[2], real_roots[3]); + double real_roots[4]; + int n = solve_deg4(A, B, C, D, E, real_roots[0], real_roots[1], real_roots[2], real_roots[3]); - if (n == 0) - return 0; + if (n == 0) + return 0; - int nb_solutions = 0; - double r3 = r2*r, pr2 = p*r2, r3q = r3 * q; - double inv_b0 = 1. / b0; + int nb_solutions = 0; + double r3 = r2*r, pr2 = p*r2, r3q = r3 * q; + double inv_b0 = 1. / b0; - // For each solution of x - for(int i = 0; i < n; i++) { - double x = real_roots[i]; + // For each solution of x + for(int i = 0; i < n; i++) { + double x = real_roots[i]; - // Check reality condition - if (x <= 0) - continue; + // Check reality condition + if (x <= 0) + continue; - double x2 = x*x; + double x2 = x*x; - double b1 = - ((1-a-b)*x2 + (q*a-q)*x + 1 - a + b) * - (((r3*(a2 + ab*(2 - r2) - a_2 + b2 - 2*b + 1)) * x + + double b1 = + ((1-a-b)*x2 + (q*a-q)*x + 1 - a + b) * + (((r3*(a2 + ab*(2 - r2) - a_2 + b2 - 2*b + 1)) * x + - (r3q*(2*(b-a2) + a_4 + ab*(r2 - 2) - 2) + pr2*(1 + a2 + 2*(ab-a-b) + r2*(b - b2) + b2))) * x2 + + (r3q*(2*(b-a2) + a_4 + ab*(r2 - 2) - 2) + pr2*(1 + a2 + 2*(ab-a-b) + r2*(b - b2) + b2))) * x2 + - (r3*(q2*(1-2*a+a2) + r2*(b2-ab) - a_4 + 2*(a2 - b2) + 2) + r*p2*(b2 + 2*(ab - b - a) + 1 + a2) + pr2*q*(a_4 + 2*(b - ab - a2) - 2 - r2*b)) * x + + (r3*(q2*(1-2*a+a2) + r2*(b2-ab) - a_4 + 2*(a2 - b2) + 2) + r*p2*(b2 + 2*(ab - b - a) + 1 + a2) + pr2*q*(a_4 + 2*(b - ab - a2) - 2 - r2*b)) * x + - 2*r3q*(a_2 - b - a2 + ab - 1) + pr2*(q2 - a_4 + 2*(a2 - b2) + r2*b + q2*(a2 - a_2) + 2) + - p2*(p*(2*(ab - a - b) + a2 + b2 + 1) + 2*q*r*(b + a_2 - a2 - ab - 1))); + 2*r3q*(a_2 - b - a2 + ab - 1) + pr2*(q2 - a_4 + 2*(a2 - b2) + r2*b + q2*(a2 - a_2) + 2) + + p2*(p*(2*(ab - a - b) + a2 + b2 + 1) + 2*q*r*(b + a_2 - a2 - ab - 1))); - // Check reality condition - if (b1 <= 0) - continue; + // Check reality condition + if (b1 <= 0) + continue; - double y = inv_b0 * b1; - double v = x2 + y*y - x*y*r; + double y = inv_b0 * b1; + double v = x2 + y*y - x*y*r; - if (v <= 0) - continue; + if (v <= 0) + continue; - double Z = distances[2] / sqrt(v); - double X = x * Z; - double Y = y * Z; + double Z = distances[2] / sqrt(v); + double X = x * Z; + double Y = y * Z; - lengths[nb_solutions][0] = X; - lengths[nb_solutions][1] = Y; - lengths[nb_solutions][2] = Z; + lengths[nb_solutions][0] = X; + lengths[nb_solutions][1] = Y; + lengths[nb_solutions][2] = Z; - nb_solutions++; - } + nb_solutions++; + } - return nb_solutions; + return nb_solutions; } bool p3p::align(double M_end[3][3], - double X0, double Y0, double Z0, - double X1, double Y1, double Z1, - double X2, double Y2, double Z2, - double R[3][3], double T[3]) + double X0, double Y0, double Z0, + double X1, double Y1, double Z1, + double X2, double Y2, double Z2, + double R[3][3], double T[3]) { - // Centroids: - double C_start[3], C_end[3]; - for(int i = 0; i < 3; i++) C_end[i] = (M_end[0][i] + M_end[1][i] + M_end[2][i]) / 3; - C_start[0] = (X0 + X1 + X2) / 3; - C_start[1] = (Y0 + Y1 + Y2) / 3; - C_start[2] = (Z0 + Z1 + Z2) / 3; - - // Covariance matrix s: - double s[3 * 3]; - for(int j = 0; j < 3; j++) { - s[0 * 3 + j] = (X0 * M_end[0][j] + X1 * M_end[1][j] + X2 * M_end[2][j]) / 3 - C_end[j] * C_start[0]; - s[1 * 3 + j] = (Y0 * M_end[0][j] + Y1 * M_end[1][j] + Y2 * M_end[2][j]) / 3 - C_end[j] * C_start[1]; - s[2 * 3 + j] = (Z0 * M_end[0][j] + Z1 * M_end[1][j] + Z2 * M_end[2][j]) / 3 - C_end[j] * C_start[2]; - } - - double Qs[16], evs[4], U[16]; - - Qs[0 * 4 + 0] = s[0 * 3 + 0] + s[1 * 3 + 1] + s[2 * 3 + 2]; - Qs[1 * 4 + 1] = s[0 * 3 + 0] - s[1 * 3 + 1] - s[2 * 3 + 2]; - Qs[2 * 4 + 2] = s[1 * 3 + 1] - s[2 * 3 + 2] - s[0 * 3 + 0]; - Qs[3 * 4 + 3] = s[2 * 3 + 2] - s[0 * 3 + 0] - s[1 * 3 + 1]; - - Qs[1 * 4 + 0] = Qs[0 * 4 + 1] = s[1 * 3 + 2] - s[2 * 3 + 1]; - Qs[2 * 4 + 0] = Qs[0 * 4 + 2] = s[2 * 3 + 0] - s[0 * 3 + 2]; - Qs[3 * 4 + 0] = Qs[0 * 4 + 3] = s[0 * 3 + 1] - s[1 * 3 + 0]; - Qs[2 * 4 + 1] = Qs[1 * 4 + 2] = s[1 * 3 + 0] + s[0 * 3 + 1]; - Qs[3 * 4 + 1] = Qs[1 * 4 + 3] = s[2 * 3 + 0] + s[0 * 3 + 2]; - Qs[3 * 4 + 2] = Qs[2 * 4 + 3] = s[2 * 3 + 1] + s[1 * 3 + 2]; - - jacobi_4x4(Qs, evs, U); - - // Looking for the largest eigen value: - int i_ev = 0; - double ev_max = evs[i_ev]; - for(int i = 1; i < 4; i++) - if (evs[i] > ev_max) - ev_max = evs[i_ev = i]; - - // Quaternion: - double q[4]; - for(int i = 0; i < 4; i++) - q[i] = U[i * 4 + i_ev]; - - double q02 = q[0] * q[0], q12 = q[1] * q[1], q22 = q[2] * q[2], q32 = q[3] * q[3]; - double q0_1 = q[0] * q[1], q0_2 = q[0] * q[2], q0_3 = q[0] * q[3]; - double q1_2 = q[1] * q[2], q1_3 = q[1] * q[3]; - double q2_3 = q[2] * q[3]; - - R[0][0] = q02 + q12 - q22 - q32; - R[0][1] = 2. * (q1_2 - q0_3); - R[0][2] = 2. * (q1_3 + q0_2); - - R[1][0] = 2. * (q1_2 + q0_3); - R[1][1] = q02 + q22 - q12 - q32; - R[1][2] = 2. * (q2_3 - q0_1); - - R[2][0] = 2. * (q1_3 - q0_2); - R[2][1] = 2. * (q2_3 + q0_1); - R[2][2] = q02 + q32 - q12 - q22; - - for(int i = 0; i < 3; i++) - T[i] = C_end[i] - (R[i][0] * C_start[0] + R[i][1] * C_start[1] + R[i][2] * C_start[2]); - - return true; + // Centroids: + double C_start[3], C_end[3]; + for(int i = 0; i < 3; i++) C_end[i] = (M_end[0][i] + M_end[1][i] + M_end[2][i]) / 3; + C_start[0] = (X0 + X1 + X2) / 3; + C_start[1] = (Y0 + Y1 + Y2) / 3; + C_start[2] = (Z0 + Z1 + Z2) / 3; + + // Covariance matrix s: + double s[3 * 3]; + for(int j = 0; j < 3; j++) { + s[0 * 3 + j] = (X0 * M_end[0][j] + X1 * M_end[1][j] + X2 * M_end[2][j]) / 3 - C_end[j] * C_start[0]; + s[1 * 3 + j] = (Y0 * M_end[0][j] + Y1 * M_end[1][j] + Y2 * M_end[2][j]) / 3 - C_end[j] * C_start[1]; + s[2 * 3 + j] = (Z0 * M_end[0][j] + Z1 * M_end[1][j] + Z2 * M_end[2][j]) / 3 - C_end[j] * C_start[2]; + } + + double Qs[16], evs[4], U[16]; + + Qs[0 * 4 + 0] = s[0 * 3 + 0] + s[1 * 3 + 1] + s[2 * 3 + 2]; + Qs[1 * 4 + 1] = s[0 * 3 + 0] - s[1 * 3 + 1] - s[2 * 3 + 2]; + Qs[2 * 4 + 2] = s[1 * 3 + 1] - s[2 * 3 + 2] - s[0 * 3 + 0]; + Qs[3 * 4 + 3] = s[2 * 3 + 2] - s[0 * 3 + 0] - s[1 * 3 + 1]; + + Qs[1 * 4 + 0] = Qs[0 * 4 + 1] = s[1 * 3 + 2] - s[2 * 3 + 1]; + Qs[2 * 4 + 0] = Qs[0 * 4 + 2] = s[2 * 3 + 0] - s[0 * 3 + 2]; + Qs[3 * 4 + 0] = Qs[0 * 4 + 3] = s[0 * 3 + 1] - s[1 * 3 + 0]; + Qs[2 * 4 + 1] = Qs[1 * 4 + 2] = s[1 * 3 + 0] + s[0 * 3 + 1]; + Qs[3 * 4 + 1] = Qs[1 * 4 + 3] = s[2 * 3 + 0] + s[0 * 3 + 2]; + Qs[3 * 4 + 2] = Qs[2 * 4 + 3] = s[2 * 3 + 1] + s[1 * 3 + 2]; + + jacobi_4x4(Qs, evs, U); + + // Looking for the largest eigen value: + int i_ev = 0; + double ev_max = evs[i_ev]; + for(int i = 1; i < 4; i++) + if (evs[i] > ev_max) + ev_max = evs[i_ev = i]; + + // Quaternion: + double q[4]; + for(int i = 0; i < 4; i++) + q[i] = U[i * 4 + i_ev]; + + double q02 = q[0] * q[0], q12 = q[1] * q[1], q22 = q[2] * q[2], q32 = q[3] * q[3]; + double q0_1 = q[0] * q[1], q0_2 = q[0] * q[2], q0_3 = q[0] * q[3]; + double q1_2 = q[1] * q[2], q1_3 = q[1] * q[3]; + double q2_3 = q[2] * q[3]; + + R[0][0] = q02 + q12 - q22 - q32; + R[0][1] = 2. * (q1_2 - q0_3); + R[0][2] = 2. * (q1_3 + q0_2); + + R[1][0] = 2. * (q1_2 + q0_3); + R[1][1] = q02 + q22 - q12 - q32; + R[1][2] = 2. * (q2_3 - q0_1); + + R[2][0] = 2. * (q1_3 - q0_2); + R[2][1] = 2. * (q2_3 + q0_1); + R[2][2] = q02 + q32 - q12 - q22; + + for(int i = 0; i < 3; i++) + T[i] = C_end[i] - (R[i][0] * C_start[0] + R[i][1] * C_start[1] + R[i][2] * C_start[2]); + + return true; } bool p3p::jacobi_4x4(double * A, double * D, double * U) { - double B[4], Z[4]; - double Id[16] = {1., 0., 0., 0., - 0., 1., 0., 0., - 0., 0., 1., 0., - 0., 0., 0., 1.}; - - memcpy(U, Id, 16 * sizeof(double)); - - B[0] = A[0]; B[1] = A[5]; B[2] = A[10]; B[3] = A[15]; - memcpy(D, B, 4 * sizeof(double)); - memset(Z, 0, 4 * sizeof(double)); - - for(int iter = 0; iter < 50; iter++) { - double sum = fabs(A[1]) + fabs(A[2]) + fabs(A[3]) + fabs(A[6]) + fabs(A[7]) + fabs(A[11]); - - if (sum == 0.0) - return true; - - double tresh = (iter < 3) ? 0.2 * sum / 16. : 0.0; - for(int i = 0; i < 3; i++) { - double * pAij = A + 5 * i + 1; - for(int j = i + 1 ; j < 4; j++) { - double Aij = *pAij; - double eps_machine = 100.0 * fabs(Aij); - - if ( iter > 3 && fabs(D[i]) + eps_machine == fabs(D[i]) && fabs(D[j]) + eps_machine == fabs(D[j]) ) - *pAij = 0.0; - else if (fabs(Aij) > tresh) { - double h = D[j] - D[i], t; - if (fabs(h) + eps_machine == fabs(h)) - t = Aij / h; - else { - double theta = 0.5 * h / Aij; - t = 1.0 / (fabs(theta) + sqrt(1.0 + theta * theta)); - if (theta < 0.0) t = -t; - } - - h = t * Aij; - Z[i] -= h; - Z[j] += h; - D[i] -= h; - D[j] += h; - *pAij = 0.0; - - double c = 1.0 / sqrt(1 + t * t); - double s = t * c; - double tau = s / (1.0 + c); - for(int k = 0; k <= i - 1; k++) { - double g = A[k * 4 + i], h = A[k * 4 + j]; - A[k * 4 + i] = g - s * (h + g * tau); - A[k * 4 + j] = h + s * (g - h * tau); - } - for(int k = i + 1; k <= j - 1; k++) { - double g = A[i * 4 + k], h = A[k * 4 + j]; - A[i * 4 + k] = g - s * (h + g * tau); - A[k * 4 + j] = h + s * (g - h * tau); - } - for(int k = j + 1; k < 4; k++) { - double g = A[i * 4 + k], h = A[j * 4 + k]; - A[i * 4 + k] = g - s * (h + g * tau); - A[j * 4 + k] = h + s * (g - h * tau); - } - for(int k = 0; k < 4; k++) { - double g = U[k * 4 + i], h = U[k * 4 + j]; - U[k * 4 + i] = g - s * (h + g * tau); - U[k * 4 + j] = h + s * (g - h * tau); - } - } - pAij++; - } - } - - for(int i = 0; i < 4; i++) B[i] += Z[i]; - memcpy(D, B, 4 * sizeof(double)); - memset(Z, 0, 4 * sizeof(double)); - } - - return false; + double B[4], Z[4]; + double Id[16] = {1., 0., 0., 0., + 0., 1., 0., 0., + 0., 0., 1., 0., + 0., 0., 0., 1.}; + + memcpy(U, Id, 16 * sizeof(double)); + + B[0] = A[0]; B[1] = A[5]; B[2] = A[10]; B[3] = A[15]; + memcpy(D, B, 4 * sizeof(double)); + memset(Z, 0, 4 * sizeof(double)); + + for(int iter = 0; iter < 50; iter++) { + double sum = fabs(A[1]) + fabs(A[2]) + fabs(A[3]) + fabs(A[6]) + fabs(A[7]) + fabs(A[11]); + + if (sum == 0.0) + return true; + + double tresh = (iter < 3) ? 0.2 * sum / 16. : 0.0; + for(int i = 0; i < 3; i++) { + double * pAij = A + 5 * i + 1; + for(int j = i + 1 ; j < 4; j++) { + double Aij = *pAij; + double eps_machine = 100.0 * fabs(Aij); + + if ( iter > 3 && fabs(D[i]) + eps_machine == fabs(D[i]) && fabs(D[j]) + eps_machine == fabs(D[j]) ) + *pAij = 0.0; + else if (fabs(Aij) > tresh) { + double hh = D[j] - D[i], t; + if (fabs(hh) + eps_machine == fabs(hh)) + t = Aij / hh; + else { + double theta = 0.5 * hh / Aij; + t = 1.0 / (fabs(theta) + sqrt(1.0 + theta * theta)); + if (theta < 0.0) t = -t; + } + + hh = t * Aij; + Z[i] -= hh; + Z[j] += hh; + D[i] -= hh; + D[j] += hh; + *pAij = 0.0; + + double c = 1.0 / sqrt(1 + t * t); + double s = t * c; + double tau = s / (1.0 + c); + for(int k = 0; k <= i - 1; k++) { + double g = A[k * 4 + i], h = A[k * 4 + j]; + A[k * 4 + i] = g - s * (h + g * tau); + A[k * 4 + j] = h + s * (g - h * tau); + } + for(int k = i + 1; k <= j - 1; k++) { + double g = A[i * 4 + k], h = A[k * 4 + j]; + A[i * 4 + k] = g - s * (h + g * tau); + A[k * 4 + j] = h + s * (g - h * tau); + } + for(int k = j + 1; k < 4; k++) { + double g = A[i * 4 + k], h = A[j * 4 + k]; + A[i * 4 + k] = g - s * (h + g * tau); + A[j * 4 + k] = h + s * (g - h * tau); + } + for(int k = 0; k < 4; k++) { + double g = U[k * 4 + i], h = U[k * 4 + j]; + U[k * 4 + i] = g - s * (h + g * tau); + U[k * 4 + j] = h + s * (g - h * tau); + } + } + pAij++; + } + } + + for(int i = 0; i < 4; i++) B[i] += Z[i]; + memcpy(D, B, 4 * sizeof(double)); + memset(Z, 0, 4 * sizeof(double)); + } + + return false; } diff --git a/modules/calib3d/src/solvepnp.cpp b/modules/calib3d/src/solvepnp.cpp index 4465339..125d7a9 100644 --- a/modules/calib3d/src/solvepnp.cpp +++ b/modules/calib3d/src/solvepnp.cpp @@ -253,10 +253,10 @@ namespace cv } } } - PnPSolver(const Mat& objectPoints, const Mat& imagePoints, const Parameters& parameters, - Mat& rvec, Mat& tvec, vector& inliers): - objectPoints(objectPoints), imagePoints(imagePoints), parameters(parameters), - rvec(rvec), tvec(tvec), inliers(inliers) + PnPSolver(const Mat& _objectPoints, const Mat& _imagePoints, const Parameters& _parameters, + Mat& _rvec, Mat& _tvec, vector& _inliers): + objectPoints(_objectPoints), imagePoints(_imagePoints), parameters(_parameters), + rvec(_rvec), tvec(_tvec), inliers(_inliers) { rvec.copyTo(initRvec); tvec.copyTo(initTvec); diff --git a/modules/calib3d/src/stereobm.cpp b/modules/calib3d/src/stereobm.cpp index 5a0b6b7..b69fa23 100644 --- a/modules/calib3d/src/stereobm.cpp +++ b/modules/calib3d/src/stereobm.cpp @@ -155,7 +155,7 @@ static void prefilterNorm( const Mat& src, Mat& dst, int winsize, int ftzero, uc val = ((curr[x]*4 + curr[x-1] + curr[x+1] + prev[x] + next[x])*scale_g - sum*scale_s) >> 10; dptr[x] = tab[val + OFS]; } - + sum += vsum[x+wsz2] - vsum[x-wsz2-1]; val = ((curr[x]*5 + curr[x-1] + prev[x] + next[x])*scale_g - sum*scale_s) >> 10; dptr[x] = tab[val + OFS]; @@ -170,15 +170,15 @@ prefilterXSobel( const Mat& src, Mat& dst, int ftzero ) const int OFS = 256*4, TABSZ = OFS*2 + 256; uchar tab[TABSZ]; Size size = src.size(); - + for( x = 0; x < TABSZ; x++ ) tab[x] = (uchar)(x - OFS < -ftzero ? 0 : x - OFS > ftzero ? ftzero*2 : x - OFS + ftzero); uchar val0 = tab[0 + OFS]; - + #if CV_SSE2 volatile bool useSIMD = checkHardwareSupport(CV_CPU_SSE2); #endif - + for( y = 0; y < size.height-1; y += 2 ) { const uchar* srow1 = src.ptr(y); @@ -187,10 +187,10 @@ prefilterXSobel( const Mat& src, Mat& dst, int ftzero ) const uchar* srow3 = y < size.height-2 ? srow1 + src.step*2 : srow1; uchar* dptr0 = dst.ptr(y); uchar* dptr1 = dptr0 + dst.step; - + dptr0[0] = dptr0[size.width-1] = dptr1[0] = dptr1[size.width-1] = val0; x = 1; - + #if CV_SSE2 if( useSIMD ) { @@ -205,26 +205,26 @@ prefilterXSobel( const Mat& src, Mat& dst, int ftzero ) d0 = _mm_sub_epi16(d0, c0); d1 = _mm_sub_epi16(d1, c1); - + __m128i c2 = _mm_unpacklo_epi8(_mm_loadl_epi64((__m128i*)(srow2 + x - 1)), z); __m128i c3 = _mm_unpacklo_epi8(_mm_loadl_epi64((__m128i*)(srow2 + x - 1)), z); __m128i d2 = _mm_unpacklo_epi8(_mm_loadl_epi64((__m128i*)(srow2 + x + 1)), z); __m128i d3 = _mm_unpacklo_epi8(_mm_loadl_epi64((__m128i*)(srow2 + x + 1)), z); - + d2 = _mm_sub_epi16(d2, c2); d3 = _mm_sub_epi16(d3, c3); - + __m128i v0 = _mm_add_epi16(d0, _mm_add_epi16(d2, _mm_add_epi16(d1, d1))); __m128i v1 = _mm_add_epi16(d1, _mm_add_epi16(d3, _mm_add_epi16(d2, d2))); v0 = _mm_packus_epi16(_mm_add_epi16(v0, ftz), _mm_add_epi16(v1, ftz)); v0 = _mm_min_epu8(v0, ftz2); - + _mm_storel_epi64((__m128i*)(dptr0 + x), v0); _mm_storel_epi64((__m128i*)(dptr1 + x), _mm_unpackhi_epi64(v0, v0)); } } #endif - + for( ; x < size.width-1; x++ ) { int d0 = srow0[x+1] - srow0[x-1], d1 = srow1[x+1] - srow1[x-1], @@ -235,7 +235,7 @@ prefilterXSobel( const Mat& src, Mat& dst, int ftzero ) dptr1[x] = (uchar)v1; } } - + for( ; y < size.height; y++ ) { uchar* dptr = dst.ptr(y); @@ -336,7 +336,7 @@ static void findStereoCorrespondenceBM_SSE2( const Mat& left, const Mat& right, short* costptr = cost.data ? (short*)cost.data + lofs + x : &costbuf; int x0 = x - wsz2 - 1, x1 = x + wsz2; const uchar* cbuf_sub = cbuf0 + ((x0 + wsz2 + 1) % (wsz + 1))*cstep - dy0*ndisp; - uchar* cbuf = cbuf0 + ((x1 + wsz2 + 1) % (wsz + 1))*cstep - dy0*ndisp; + cbuf = cbuf0 + ((x1 + wsz2 + 1) % (wsz + 1))*cstep - dy0*ndisp; hsad = hsad0 - dy0*ndisp; lptr_sub = lptr0 + MIN(MAX(x0, -lofs), width-1-lofs) - dy0*sstep; lptr = lptr0 + MIN(MAX(x1, -lofs), width-1-lofs) - dy0*sstep; @@ -374,7 +374,7 @@ static void findStereoCorrespondenceBM_SSE2( const Mat& left, const Mat& right, // initialize sums for( d = 0; d < ndisp; d++ ) sad[d] = (ushort)(hsad0[d-ndisp*dy0]*(wsz2 + 2 - dy0)); - + hsad = hsad0 + (1 - dy0)*ndisp; for( y = 1 - dy0; y < wsz2; y++, hsad += ndisp ) for( d = 0; d < ndisp; d += 16 ) @@ -405,28 +405,28 @@ static void findStereoCorrespondenceBM_SSE2( const Mat& left, const Mat& right, { __m128i u0 = _mm_load_si128((__m128i*)(hsad_sub + d)); __m128i u1 = _mm_load_si128((__m128i*)(hsad + d)); - + __m128i v0 = _mm_load_si128((__m128i*)(hsad_sub + d + 8)); __m128i v1 = _mm_load_si128((__m128i*)(hsad + d + 8)); - + __m128i usad8 = _mm_load_si128((__m128i*)(sad + d)); __m128i vsad8 = _mm_load_si128((__m128i*)(sad + d + 8)); - + u1 = _mm_sub_epi16(u1, u0); v1 = _mm_sub_epi16(v1, v0); usad8 = _mm_add_epi16(usad8, u1); vsad8 = _mm_add_epi16(vsad8, v1); - + mask = _mm_cmpgt_epi16(minsad8, usad8); minsad8 = _mm_min_epi16(minsad8, usad8); mind8 = _mm_max_epi16(mind8, _mm_and_si128(mask, d8)); - + _mm_store_si128((__m128i*)(sad + d), usad8); _mm_store_si128((__m128i*)(sad + d + 8), vsad8); - + mask = _mm_cmpgt_epi16(minsad8, vsad8); minsad8 = _mm_min_epi16(minsad8, vsad8); - + d8 = _mm_add_epi16(d8, dd_8); mind8 = _mm_max_epi16(mind8, _mm_and_si128(mask, d8)); d8 = _mm_add_epi16(d8, dd_8); @@ -438,32 +438,33 @@ static void findStereoCorrespondenceBM_SSE2( const Mat& left, const Mat& right, dptr[y*dstep] = FILTERED; continue; } - + __m128i minsad82 = _mm_unpackhi_epi64(minsad8, minsad8); __m128i mind82 = _mm_unpackhi_epi64(mind8, mind8); mask = _mm_cmpgt_epi16(minsad8, minsad82); mind8 = _mm_xor_si128(mind8,_mm_and_si128(_mm_xor_si128(mind82,mind8),mask)); minsad8 = _mm_min_epi16(minsad8, minsad82); - + minsad82 = _mm_shufflelo_epi16(minsad8, _MM_SHUFFLE(3,2,3,2)); mind82 = _mm_shufflelo_epi16(mind8, _MM_SHUFFLE(3,2,3,2)); mask = _mm_cmpgt_epi16(minsad8, minsad82); mind8 = _mm_xor_si128(mind8,_mm_and_si128(_mm_xor_si128(mind82,mind8),mask)); minsad8 = _mm_min_epi16(minsad8, minsad82); - + minsad82 = _mm_shufflelo_epi16(minsad8, 1); mind82 = _mm_shufflelo_epi16(mind8, 1); mask = _mm_cmpgt_epi16(minsad8, minsad82); mind8 = _mm_xor_si128(mind8,_mm_and_si128(_mm_xor_si128(mind82,mind8),mask)); mind = (short)_mm_cvtsi128_si32(mind8); minsad = sad[mind]; - + if( uniquenessRatio > 0 ) { int thresh = minsad + ((minsad * uniquenessRatio) >> 8); __m128i thresh8 = _mm_set1_epi16((short)(thresh + 1)); __m128i d1 = _mm_set1_epi16((short)(mind-1)), d2 = _mm_set1_epi16((short)(mind+1)); - __m128i dd_16 = _mm_add_epi16(dd_8, dd_8), d8 = _mm_sub_epi16(d0_8, dd_16); + __m128i dd_16 = _mm_add_epi16(dd_8, dd_8); + d8 = _mm_sub_epi16(d0_8, dd_16); for( d = 0; d < ndisp; d += 16 ) { @@ -492,7 +493,8 @@ static void findStereoCorrespondenceBM_SSE2( const Mat& left, const Mat& right, if( 0 < mind && mind < ndisp - 1 ) { - int p = sad[mind+1], n = sad[mind-1], d = p + n - 2*sad[mind] + std::abs(p - n); + int p = sad[mind+1], n = sad[mind-1]; + d = p + n - 2*sad[mind] + std::abs(p - n); dptr[y*dstep] = (short)(((ndisp - mind - 1 + mindisp)*256 + (d != 0 ? (p-n)*256/d : 0) + 15) >> 4); } else @@ -567,7 +569,7 @@ findStereoCorrespondenceBM( const Mat& left, const Mat& right, htext[y] += tab[lval]; } } - + // initialize the left and right borders of the disparity map for( y = 0; y < height; y++ ) { @@ -583,7 +585,7 @@ findStereoCorrespondenceBM( const Mat& left, const Mat& right, int* costptr = cost.data ? (int*)cost.data + lofs + x : &costbuf; int x0 = x - wsz2 - 1, x1 = x + wsz2; const uchar* cbuf_sub = cbuf0 + ((x0 + wsz2 + 1) % (wsz + 1))*cstep - dy0*ndisp; - uchar* cbuf = cbuf0 + ((x1 + wsz2 + 1) % (wsz + 1))*cstep - dy0*ndisp; + cbuf = cbuf0 + ((x1 + wsz2 + 1) % (wsz + 1))*cstep - dy0*ndisp; hsad = hsad0 - dy0*ndisp; lptr_sub = lptr0 + MIN(MAX(x0, -lofs), width-1-lofs) - dy0*sstep; lptr = lptr0 + MIN(MAX(x1, -lofs), width-1-lofs) - dy0*sstep; @@ -611,7 +613,7 @@ findStereoCorrespondenceBM( const Mat& left, const Mat& right, // initialize sums for( d = 0; d < ndisp; d++ ) sad[d] = (int)(hsad0[d-ndisp*dy0]*(wsz2 + 2 - dy0)); - + hsad = hsad0 + (1 - dy0)*ndisp; for( y = 1 - dy0; y < wsz2; y++, hsad += ndisp ) for( d = 0; d < ndisp; d++ ) @@ -662,7 +664,8 @@ findStereoCorrespondenceBM( const Mat& left, const Mat& right, { sad[-1] = sad[1]; sad[ndisp] = sad[ndisp-2]; - int p = sad[mind+1], n = sad[mind-1], d = p + n - 2*sad[mind] + std::abs(p - n); + int p = sad[mind+1], n = sad[mind-1]; + d = p + n - 2*sad[mind] + std::abs(p - n); dptr[y*dstep] = (short)(((ndisp - mind - 1 + mindisp)*256 + (d != 0 ? (p-n)*256/d : 0) + 15) >> 4); costptr[y*coststep] = sad[mind]; } @@ -681,16 +684,16 @@ struct PrefilterInvoker state = _state; } - void operator()( int ind ) const + void operator()( int ind ) const { if( state->preFilterType == CV_STEREO_BM_NORMALIZED_RESPONSE ) prefilterNorm( *imgs0[ind], *imgs[ind], state->preFilterSize, state->preFilterCap, buf[ind] ); else - prefilterXSobel( *imgs0[ind], *imgs[ind], state->preFilterCap ); + prefilterXSobel( *imgs0[ind], *imgs[ind], state->preFilterCap ); } - + const Mat* imgs0[2]; - Mat* imgs[2]; + Mat* imgs[2]; uchar* buf[2]; CvStereoBMState *state; }; @@ -709,21 +712,21 @@ struct FindStereoCorrespInvoker useShorts = _useShorts; validDisparityRect = _validDisparityRect; } - - void operator()( const BlockedRange& range ) const + + void operator()( const BlockedRange& range ) const { int cols = left->cols, rows = left->rows; int _row0 = min(cvRound(range.begin() * rows / nstripes), rows); int _row1 = min(cvRound(range.end() * rows / nstripes), rows); uchar *ptr = state->slidingSumBuf->data.ptr + range.begin() * stripeBufSize; int FILTERED = (state->minDisparity - 1)*16; - + Rect roi = validDisparityRect & Rect(0, _row0, cols, _row1 - _row0); if( roi.height == 0 ) return; int row0 = roi.y; int row1 = roi.y + roi.height; - + Mat part; if( row0 > _row0 ) { @@ -735,22 +738,22 @@ struct FindStereoCorrespInvoker part = disp->rowRange(row1, _row1); part = Scalar::all(FILTERED); } - + Mat left_i = left->rowRange(row0, row1); Mat right_i = right->rowRange(row0, row1); Mat disp_i = disp->rowRange(row0, row1); Mat cost_i = state->disp12MaxDiff >= 0 ? Mat(state->cost).rowRange(row0, row1) : Mat(); - -#if CV_SSE2 + +#if CV_SSE2 if( useShorts ) findStereoCorrespondenceBM_SSE2( left_i, right_i, disp_i, cost_i, *state, ptr, row0, rows - row1 ); else -#endif +#endif findStereoCorrespondenceBM( left_i, right_i, disp_i, cost_i, *state, ptr, row0, rows - row1 ); - + if( state->disp12MaxDiff >= 0 ) validateDisparity( disp_i, cost_i, state->minDisparity, state->numberOfDisparities, state->disp12MaxDiff ); - + if( roi.x > 0 ) { part = disp_i.colRange(0, roi.x); @@ -767,7 +770,7 @@ protected: const Mat *left, *right; Mat* disp; CvStereoBMState *state; - + int nstripes; int stripeBufSize; bool useShorts; @@ -775,7 +778,7 @@ protected: }; static void findStereoCorrespondenceBM( const Mat& left0, const Mat& right0, Mat& disp0, CvStereoBMState* state) -{ +{ if (left0.size() != right0.size() || disp0.size() != left0.size()) CV_Error( CV_StsUnmatchedSizes, "All the images must have the same size" ); @@ -783,7 +786,7 @@ static void findStereoCorrespondenceBM( const Mat& left0, const Mat& right0, Mat CV_Error( CV_StsUnsupportedFormat, "Both input images must have CV_8UC1" ); if (disp0.type() != CV_16SC1 && disp0.type() != CV_32FC1) - CV_Error( CV_StsUnsupportedFormat, "Disparity image must have CV_16SC1 or CV_32FC1 format" ); + CV_Error( CV_StsUnsupportedFormat, "Disparity image must have CV_16SC1 or CV_32FC1 format" ); if( !state ) CV_Error( CV_StsNullPtr, "Stereo BM state is NULL." ); @@ -809,7 +812,7 @@ static void findStereoCorrespondenceBM( const Mat& left0, const Mat& right0, Mat if( state->uniquenessRatio < 0 ) CV_Error( CV_StsOutOfRange, "uniqueness ratio must be non-negative" ); - + if( !state->preFilteredImg0 || state->preFilteredImg0->cols * state->preFilteredImg0->rows < left0.cols * left0.rows ) { cvReleaseMat( &state->preFilteredImg0 ); @@ -822,7 +825,7 @@ static void findStereoCorrespondenceBM( const Mat& left0, const Mat& right0, Mat } Mat left(left0.size(), CV_8U, state->preFilteredImg0->data.ptr); Mat right(right0.size(), CV_8U, state->preFilteredImg1->data.ptr); - + int mindisp = state->minDisparity; int ndisp = state->numberOfDisparities; @@ -832,15 +835,15 @@ static void findStereoCorrespondenceBM( const Mat& left0, const Mat& right0, Mat int rofs = -min(ndisp - 1 + mindisp, 0); int width1 = width - rofs - ndisp + 1; int FILTERED = (state->minDisparity - 1) << DISPARITY_SHIFT; - + if( lofs >= width || rofs >= width || width1 < 1 ) { - disp0 = Scalar::all( FILTERED * ( disp0.type() < CV_32F ? 1 : 1./(1 << DISPARITY_SHIFT) ) ); + disp0 = Scalar::all( FILTERED * ( disp0.type() < CV_32F ? 1 : 1./(1 << DISPARITY_SHIFT) ) ); return; } Mat disp = disp0; - + if( disp0.type() == CV_32F) { if( !state->disp || state->disp->rows != disp0.rows || state->disp->cols != disp0.cols ) @@ -850,8 +853,8 @@ static void findStereoCorrespondenceBM( const Mat& left0, const Mat& right0, Mat } disp = cv::cvarrToMat(state->disp); } - - int wsz = state->SADWindowSize; + + int wsz = state->SADWindowSize; int bufSize0 = (int)((ndisp + 2)*sizeof(int)); bufSize0 += (int)((height+wsz+2)*ndisp*sizeof(int)); bufSize0 += (int)((height + wsz + 2)*sizeof(int)); @@ -861,16 +864,16 @@ static void findStereoCorrespondenceBM( const Mat& left0, const Mat& right0, Mat int bufSize2 = 0; if( state->speckleRange >= 0 && state->speckleWindowSize > 0 ) bufSize2 = width*height*(sizeof(cv::Point_) + sizeof(int) + sizeof(uchar)); - + #if CV_SSE2 bool useShorts = state->preFilterCap <= 31 && state->SADWindowSize <= 21 && checkHardwareSupport(CV_CPU_SSE2); #else const bool useShorts = false; #endif - -#ifdef HAVE_TBB + +#ifdef HAVE_TBB const double SAD_overhead_coeff = 10.0; - double N0 = 8000000 / (useShorts ? 1 : 4); // approx tbb's min number instructions reasonable for one thread + double N0 = 8000000 / (useShorts ? 1 : 4); // approx tbb's min number instructions reasonable for one thread double maxStripeSize = min(max(N0 / (width * ndisp), (wsz-1) * SAD_overhead_coeff), (double)height); int nstripes = cvCeil(height / maxStripeSize); #else @@ -878,27 +881,27 @@ static void findStereoCorrespondenceBM( const Mat& left0, const Mat& right0, Mat #endif int bufSize = max(bufSize0 * nstripes, max(bufSize1 * 2, bufSize2)); - + if( !state->slidingSumBuf || state->slidingSumBuf->cols < bufSize ) { cvReleaseMat( &state->slidingSumBuf ); state->slidingSumBuf = cvCreateMat( 1, bufSize, CV_8U ); } - + uchar *_buf = state->slidingSumBuf->data.ptr; int idx[] = {0,1}; parallel_do(idx, idx+2, PrefilterInvoker(left0, right0, left, right, _buf, _buf + bufSize1, state)); - + Rect validDisparityRect(0, 0, width, height), R1 = state->roi1, R2 = state->roi2; validDisparityRect = getValidDisparityROI(R1.area() > 0 ? Rect(0, 0, width, height) : validDisparityRect, R2.area() > 0 ? Rect(0, 0, width, height) : validDisparityRect, state->minDisparity, state->numberOfDisparities, - state->SADWindowSize); - + state->SADWindowSize); + parallel_for(BlockedRange(0, nstripes), FindStereoCorrespInvoker(left, right, disp, state, nstripes, bufSize0, useShorts, validDisparityRect)); - + if( state->speckleRange >= 0 && state->speckleWindowSize > 0 ) { Mat buf(state->slidingSumBuf); @@ -906,7 +909,7 @@ static void findStereoCorrespondenceBM( const Mat& left0, const Mat& right0, Mat } if (disp0.data != disp.data) - disp.convertTo(disp0, disp0.type(), 1./(1 << DISPARITY_SHIFT), 0); + disp.convertTo(disp0, disp0.type(), 1./(1 << DISPARITY_SHIFT), 0); } StereoBM::StereoBM() @@ -928,13 +931,13 @@ void StereoBM::operator()( InputArray _left, InputArray _right, CV_Assert( disptype == CV_16S || disptype == CV_32F ); _disparity.create(left.size(), disptype); Mat disparity = _disparity.getMat(); - + findStereoCorrespondenceBM(left, right, disparity, state); } template<> void Ptr::delete_obj() { cvReleaseStereoBMState(&obj); } - + } CV_IMPL void cvFindStereoCorrespondenceBM( const CvArr* leftarr, const CvArr* rightarr, @@ -942,7 +945,7 @@ CV_IMPL void cvFindStereoCorrespondenceBM( const CvArr* leftarr, const CvArr* ri { cv::Mat left = cv::cvarrToMat(leftarr), right = cv::cvarrToMat(rightarr), - disp = cv::cvarrToMat(disparr); + disp = cv::cvarrToMat(disparr); cv::findStereoCorrespondenceBM(left, right, disp, state); } diff --git a/modules/calib3d/src/stereosgbm.cpp b/modules/calib3d/src/stereosgbm.cpp index c794da0..53fedef 100644 --- a/modules/calib3d/src/stereosgbm.cpp +++ b/modules/calib3d/src/stereosgbm.cpp @@ -44,17 +44,17 @@ This is a variation of "Stereo Processing by Semiglobal Matching and Mutual Information" by Heiko Hirschmuller. - + We match blocks rather than individual pixels, thus the algorithm is called SGBM (Semi-global block matching) - */ + */ #include "precomp.hpp" #include namespace cv { - + typedef uchar PixType; typedef short CostType; typedef short DispType; @@ -105,7 +105,7 @@ StereoSGBM::~StereoSGBM() row1[x] and row2[x-d]. The subpixel algorithm from "Depth Discontinuities by Pixel-to-Pixel Stereo" by Stan Birchfield and C. Tomasi is used, hence the suffix BT. - + the temporary buffer should contain width2*2 elements */ static void calcPixelCostBT( const Mat& img1, const Mat& img2, int y, @@ -119,25 +119,25 @@ static void calcPixelCostBT( const Mat& img1, const Mat& img2, int y, int D = maxD - minD, width1 = maxX1 - minX1, width2 = maxX2 - minX2; const PixType *row1 = img1.ptr(y), *row2 = img2.ptr(y); PixType *prow1 = buffer + width2*2, *prow2 = prow1 + width*cn*2; - + tab += tabOfs; - + for( c = 0; c < cn*2; c++ ) { - prow1[width*c] = prow1[width*c + width-1] = + prow1[width*c] = prow1[width*c + width-1] = prow2[width*c] = prow2[width*c + width-1] = tab[0]; } - + int n1 = y > 0 ? -(int)img1.step : 0, s1 = y < img1.rows-1 ? (int)img1.step : 0; int n2 = y > 0 ? -(int)img2.step : 0, s2 = y < img2.rows-1 ? (int)img2.step : 0; - + if( cn == 1 ) { for( x = 1; x < width-1; x++ ) { prow1[x] = tab[(row1[x+1] - row1[x-1])*2 + row1[x+n1+1] - row1[x+n1-1] + row1[x+s1+1] - row1[x+s1-1]]; prow2[width-1-x] = tab[(row2[x+1] - row2[x-1])*2 + row2[x+n2+1] - row2[x+n2-1] + row2[x+s2+1] - row2[x+s2-1]]; - + prow1[x+width] = row1[x]; prow2[width-1-x+width] = row2[x]; } @@ -149,35 +149,35 @@ static void calcPixelCostBT( const Mat& img1, const Mat& img2, int y, prow1[x] = tab[(row1[x*3+3] - row1[x*3-3])*2 + row1[x*3+n1+3] - row1[x*3+n1-3] + row1[x*3+s1+3] - row1[x*3+s1-3]]; prow1[x+width] = tab[(row1[x*3+4] - row1[x*3-2])*2 + row1[x*3+n1+4] - row1[x*3+n1-2] + row1[x*3+s1+4] - row1[x*3+s1-2]]; prow1[x+width*2] = tab[(row1[x*3+5] - row1[x*3-1])*2 + row1[x*3+n1+5] - row1[x*3+n1-1] + row1[x*3+s1+5] - row1[x*3+s1-1]]; - + prow2[width-1-x] = tab[(row2[x*3+3] - row2[x*3-3])*2 + row2[x*3+n2+3] - row2[x*3+n2-3] + row2[x*3+s2+3] - row2[x*3+s2-3]]; prow2[width-1-x+width] = tab[(row2[x*3+4] - row2[x*3-2])*2 + row2[x*3+n2+4] - row2[x*3+n2-2] + row2[x*3+s2+4] - row2[x*3+s2-2]]; prow2[width-1-x+width*2] = tab[(row2[x*3+5] - row2[x*3-1])*2 + row2[x*3+n2+5] - row2[x*3+n2-1] + row2[x*3+s2+5] - row2[x*3+s2-1]]; - + prow1[x+width*3] = row1[x*3]; prow1[x+width*4] = row1[x*3+1]; prow1[x+width*5] = row1[x*3+2]; - + prow2[width-1-x+width*3] = row2[x*3]; prow2[width-1-x+width*4] = row2[x*3+1]; prow2[width-1-x+width*5] = row2[x*3+2]; } } - + memset( cost, 0, width1*D*sizeof(cost[0]) ); - + buffer -= minX2; cost -= minX1*D + minD; // simplify the cost indices inside the loop - -#if CV_SSE2 + +#if CV_SSE2 volatile bool useSIMD = checkHardwareSupport(CV_CPU_SSE2); #endif - -#if 1 + +#if 1 for( c = 0; c < cn*2; c++, prow1 += width, prow2 += width ) { int diff_scale = c < cn ? 0 : 2; - + // precompute // v0 = min(row2[x-1/2], row2[x], row2[x+1/2]) and // v1 = max(row2[x-1/2], row2[x], row2[x+1/2]) and @@ -191,7 +191,7 @@ static void calcPixelCostBT( const Mat& img1, const Mat& img2, int y, buffer[x] = (PixType)v0; buffer[x + width2] = (PixType)v1; } - + for( x = minX1; x < maxX1; x++ ) { int u = prow1[x]; @@ -199,14 +199,14 @@ static void calcPixelCostBT( const Mat& img1, const Mat& img2, int y, int ur = x < width-1 ? (u + prow1[x+1])/2 : u; int u0 = min(ul, ur); u0 = min(u0, u); int u1 = max(ul, ur); u1 = max(u1, u); - + #if CV_SSE2 if( useSIMD ) { __m128i _u = _mm_set1_epi8((char)u), _u0 = _mm_set1_epi8((char)u0); __m128i _u1 = _mm_set1_epi8((char)u1), z = _mm_setzero_si128(); __m128i ds = _mm_cvtsi32_si128(diff_scale); - + for( int d = minD; d < maxD; d += 16 ) { __m128i _v = _mm_loadu_si128((const __m128i*)(prow2 + width-x-1 + d)); @@ -215,10 +215,10 @@ static void calcPixelCostBT( const Mat& img1, const Mat& img2, int y, __m128i c0 = _mm_max_epu8(_mm_subs_epu8(_u, _v1), _mm_subs_epu8(_v0, _u)); __m128i c1 = _mm_max_epu8(_mm_subs_epu8(_v, _u1), _mm_subs_epu8(_u0, _v)); __m128i diff = _mm_min_epu8(c0, c1); - + c0 = _mm_load_si128((__m128i*)(cost + x*D + d)); c1 = _mm_load_si128((__m128i*)(cost + x*D + d + 8)); - + _mm_store_si128((__m128i*)(cost + x*D + d), _mm_adds_epi16(c0, _mm_srl_epi16(_mm_unpacklo_epi8(diff,z), ds))); _mm_store_si128((__m128i*)(cost + x*D + d + 8), _mm_adds_epi16(c1, _mm_srl_epi16(_mm_unpackhi_epi8(diff,z), ds))); } @@ -233,7 +233,7 @@ static void calcPixelCostBT( const Mat& img1, const Mat& img2, int y, int v1 = buffer[width-x-1 + d + width2]; int c0 = max(0, u - v1); c0 = max(c0, v0 - u); int c1 = max(0, v - u1); c1 = max(c1, u0 - v); - + cost[x*D + d] = (CostType)(cost[x*D+d] + (min(c0, c1) >> diff_scale)); } } @@ -249,14 +249,14 @@ static void calcPixelCostBT( const Mat& img1, const Mat& img2, int y, if( useSIMD ) { __m128i _u = _mm_set1_epi8(u), z = _mm_setzero_si128(); - + for( int d = minD; d < maxD; d += 16 ) { __m128i _v = _mm_loadu_si128((const __m128i*)(prow2 + width-1-x + d)); __m128i diff = _mm_adds_epu8(_mm_subs_epu8(_u,_v), _mm_subs_epu8(_v,_u)); __m128i c0 = _mm_load_si128((__m128i*)(cost + x*D + d)); __m128i c1 = _mm_load_si128((__m128i*)(cost + x*D + d + 8)); - + _mm_store_si128((__m128i*)(cost + x*D + d), _mm_adds_epi16(c0, _mm_unpacklo_epi8(diff,z))); _mm_store_si128((__m128i*)(cost + x*D + d + 8), _mm_adds_epi16(c1, _mm_unpackhi_epi8(diff,z))); } @@ -282,22 +282,22 @@ static void calcPixelCostBT( const Mat& img1, const Mat& img2, int y, minD <= d < maxD. disp2full is the reverse disparity map, that is: disp2full(x+roi.x,y+roi.y)=d means that img2(x+roi.x, y+roi.y) ~ img1(x+roi.x+d, y+roi.y) - + note that disp1buf will have the same size as the roi and disp2full will have the same size as img1 (or img2). On exit disp2buf is not the final disparity, it is an intermediate result that becomes final after all the tiles are processed. - + the disparity in disp1buf is written with sub-pixel accuracy (4 fractional bits, see CvStereoSGBM::DISP_SCALE), using quadratic interpolation, while the disparity in disp2buf is written as is, without interpolation. - + disp2cost also has the same size as img1 (or img2). It contains the minimum current cost, used to find the best disparity, corresponding to the minimal cost. - */ + */ static void computeDisparitySGBM( const Mat& img1, const Mat& img2, - Mat& disp1, const StereoSGBM& params, + Mat& disp1, const StereoSGBM& params, Mat& buffer ) { #if CV_SSE2 @@ -312,15 +312,15 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2, 6, 0, 1, 0, 2, 0, 1, 0, 3, 0, 1, 0, 2, 0, 1, 0, 4, 0, 1, 0, 2, 0, 1, 0, 3, 0, 1, 0, 2, 0, 1, 0, 5, 0, 1, 0, 2, 0, 1, 0, 3, 0, 1, 0, 2, 0, 1, 0, 4, 0, 1, 0, 2, 0, 1, 0, 3, 0, 1, 0, 2, 0, 1, 0 }; - + volatile bool useSIMD = checkHardwareSupport(CV_CPU_SSE2); -#endif - +#endif + const int ALIGN = 16; const int DISP_SHIFT = StereoSGBM::DISP_SHIFT; const int DISP_SCALE = StereoSGBM::DISP_SCALE; const CostType MAX_COST = SHRT_MAX; - + int minD = params.minDisparity, maxD = minD + params.numberOfDisparities; Size SADWindowSize; SADWindowSize.width = SADWindowSize.height = params.SADWindowSize > 0 ? params.SADWindowSize : 5; @@ -336,28 +336,28 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2, int npasses = params.fullDP ? 2 : 1; const int TAB_OFS = 256*4, TAB_SIZE = 256 + TAB_OFS*2; PixType clipTab[TAB_SIZE]; - + for( k = 0; k < TAB_SIZE; k++ ) clipTab[k] = (PixType)(min(max(k - TAB_OFS, -ftzero), ftzero) + ftzero); - + if( minX1 >= maxX1 ) { disp1 = Scalar::all(INVALID_DISP_SCALED); return; } - + CV_Assert( D % 16 == 0 ); - + // NR - the number of directions. the loop on x below that computes Lr assumes that NR == 8. // if you change NR, please, modify the loop as well. int D2 = D+16, NRD2 = NR2*D2; - + // the number of L_r(.,.) and min_k L_r(.,.) lines in the buffer: // for 8-way dynamic programming we need the current row and // the previous row, i.e. 2 rows in total const int NLR = 2; const int LrBorder = NLR - 1; - + // for each possible stereo match (img1(x,y) <=> img2(x-d,y)) // we keep pixel difference cost (C) and the summary cost over NR directions (S). // we also keep all the partial costs for the previous line L_r(x,d) and also min_k L_r(x, k) @@ -370,29 +370,29 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2, CSBufSize*2*sizeof(CostType) + // C, S width*16*img1.channels()*sizeof(PixType) + // temp buffer for computing per-pixel cost width*(sizeof(CostType) + sizeof(DispType)) + 1024; // disp2cost + disp2 - + if( !buffer.data || !buffer.isContinuous() || buffer.cols*buffer.rows*buffer.elemSize() < totalBufSize ) buffer.create(1, (int)totalBufSize, CV_8U); - + // summary cost over different (nDirs) directions CostType* Cbuf = (CostType*)alignPtr(buffer.data, ALIGN); CostType* Sbuf = Cbuf + CSBufSize; CostType* hsumBuf = Sbuf + CSBufSize; CostType* pixDiff = hsumBuf + costBufSize*hsumBufNRows; - + CostType* disp2cost = pixDiff + costBufSize + (LrSize + minLrSize)*NLR; DispType* disp2ptr = (DispType*)(disp2cost + width); PixType* tempBuf = (PixType*)(disp2ptr + width); - + // add P2 to every C(x,y). it saves a few operations in the inner loops for( k = 0; k < width1*D; k++ ) Cbuf[k] = (CostType)P2; - + for( int pass = 1; pass <= npasses; pass++ ) { int x1, y1, x2, y2, dx, dy; - + if( pass == 1 ) { y1 = 0; y2 = height; dy = 1; @@ -403,9 +403,9 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2, y1 = height-1; y2 = -1; dy = -1; x1 = width1-1; x2 = -1; dx = -1; } - + CostType *Lr[NLR]={0}, *minLr[NLR]={0}; - + for( k = 0; k < NLR; k++ ) { // shift Lr[k] and minLr[k] pointers, because we allocated them with the borders, @@ -418,26 +418,26 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2, minLr[k] = pixDiff + costBufSize + LrSize*NLR + minLrSize*k + NR2*2; memset( minLr[k] - LrBorder*NR2, 0, minLrSize*sizeof(CostType) ); } - + for( int y = y1; y != y2; y += dy ) { int x, d; DispType* disp1ptr = disp1.ptr(y); CostType* C = Cbuf + (!params.fullDP ? 0 : y*costBufSize); CostType* S = Sbuf + (!params.fullDP ? 0 : y*costBufSize); - + if( pass == 1 ) // compute C on the first pass, and reuse it on the second pass, if any. { int dy1 = y == 0 ? 0 : y + SH2, dy2 = y == 0 ? SH2 : dy1; - + for( k = dy1; k <= dy2; k++ ) { CostType* hsumAdd = hsumBuf + (min(k, height-1) % hsumBufNRows)*costBufSize; - + if( k < height ) { calcPixelCostBT( img1, img2, k, minD, maxD, pixDiff, tempBuf, clipTab, TAB_OFS, ftzero ); - + memset(hsumAdd, 0, D*sizeof(CostType)); for( x = 0; x <= SW2*D; x += D ) { @@ -445,17 +445,17 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2, for( d = 0; d < D; d++ ) hsumAdd[d] = (CostType)(hsumAdd[d] + pixDiff[x + d]*scale); } - + if( y > 0 ) { const CostType* hsumSub = hsumBuf + (max(y - SH2 - 1, 0) % hsumBufNRows)*costBufSize; const CostType* Cprev = !params.fullDP || y == 0 ? C : C - costBufSize; - + for( x = D; x < width1*D; x += D ) { const CostType* pixAdd = pixDiff + min(x + SW2*D, (width1-1)*D); const CostType* pixSub = pixDiff + max(x - (SW2+1)*D, 0); - + #if CV_SSE2 if( useSIMD ) { @@ -490,13 +490,13 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2, { const CostType* pixAdd = pixDiff + min(x + SW2*D, (width1-1)*D); const CostType* pixSub = pixDiff + max(x - (SW2+1)*D, 0); - + for( d = 0; d < D; d++ ) hsumAdd[x + d] = (CostType)(hsumAdd[x - D + d] + pixAdd[d] - pixSub[d]); } } } - + if( y == 0 ) { int scale = k == 0 ? SH2 + 1 : 1; @@ -504,18 +504,18 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2, C[x] = (CostType)(C[x] + hsumAdd[x]*scale); } } - + // also, clear the S buffer for( k = 0; k < width1*D; k++ ) S[k] = 0; } - + // clear the left and the right borders memset( Lr[0] - NRD2*LrBorder - 8, 0, NRD2*LrBorder*sizeof(CostType) ); memset( Lr[0] + width1*NRD2 - 8, 0, NRD2*LrBorder*sizeof(CostType) ); memset( minLr[0] - NR2*LrBorder, 0, NR2*LrBorder*sizeof(CostType) ); memset( minLr[0] + width1*NR2, 0, NR2*LrBorder*sizeof(CostType) ); - + /* [formula 13 in the paper] compute L_r(p, d) = C(p, d) + @@ -537,87 +537,87 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2, for( x = x1; x != x2; x += dx ) { int xm = x*NR2, xd = xm*D2; - + int delta0 = minLr[0][xm - dx*NR2] + P2, delta1 = minLr[1][xm - NR2 + 1] + P2; int delta2 = minLr[1][xm + 2] + P2, delta3 = minLr[1][xm + NR2 + 3] + P2; - + CostType* Lr_p0 = Lr[0] + xd - dx*NRD2; CostType* Lr_p1 = Lr[1] + xd - NRD2 + D2; CostType* Lr_p2 = Lr[1] + xd + D2*2; CostType* Lr_p3 = Lr[1] + xd + NRD2 + D2*3; - + Lr_p0[-1] = Lr_p0[D] = Lr_p1[-1] = Lr_p1[D] = Lr_p2[-1] = Lr_p2[D] = Lr_p3[-1] = Lr_p3[D] = MAX_COST; - + CostType* Lr_p = Lr[0] + xd; const CostType* Cp = C + x*D; CostType* Sp = S + x*D; - + #if CV_SSE2 if( useSIMD ) { __m128i _P1 = _mm_set1_epi16((short)P1); - + __m128i _delta0 = _mm_set1_epi16((short)delta0); __m128i _delta1 = _mm_set1_epi16((short)delta1); __m128i _delta2 = _mm_set1_epi16((short)delta2); __m128i _delta3 = _mm_set1_epi16((short)delta3); __m128i _minL0 = _mm_set1_epi16((short)MAX_COST); - + for( d = 0; d < D; d += 8 ) { __m128i Cpd = _mm_load_si128((const __m128i*)(Cp + d)); __m128i L0, L1, L2, L3; - + L0 = _mm_load_si128((const __m128i*)(Lr_p0 + d)); L1 = _mm_load_si128((const __m128i*)(Lr_p1 + d)); L2 = _mm_load_si128((const __m128i*)(Lr_p2 + d)); L3 = _mm_load_si128((const __m128i*)(Lr_p3 + d)); - + L0 = _mm_min_epi16(L0, _mm_adds_epi16(_mm_loadu_si128((const __m128i*)(Lr_p0 + d - 1)), _P1)); L0 = _mm_min_epi16(L0, _mm_adds_epi16(_mm_loadu_si128((const __m128i*)(Lr_p0 + d + 1)), _P1)); - + L1 = _mm_min_epi16(L1, _mm_adds_epi16(_mm_loadu_si128((const __m128i*)(Lr_p1 + d - 1)), _P1)); L1 = _mm_min_epi16(L1, _mm_adds_epi16(_mm_loadu_si128((const __m128i*)(Lr_p1 + d + 1)), _P1)); - + L2 = _mm_min_epi16(L2, _mm_adds_epi16(_mm_loadu_si128((const __m128i*)(Lr_p2 + d - 1)), _P1)); L2 = _mm_min_epi16(L2, _mm_adds_epi16(_mm_loadu_si128((const __m128i*)(Lr_p2 + d + 1)), _P1)); - + L3 = _mm_min_epi16(L3, _mm_adds_epi16(_mm_loadu_si128((const __m128i*)(Lr_p3 + d - 1)), _P1)); L3 = _mm_min_epi16(L3, _mm_adds_epi16(_mm_loadu_si128((const __m128i*)(Lr_p3 + d + 1)), _P1)); - + L0 = _mm_min_epi16(L0, _delta0); L0 = _mm_adds_epi16(_mm_subs_epi16(L0, _delta0), Cpd); - + L1 = _mm_min_epi16(L1, _delta1); L1 = _mm_adds_epi16(_mm_subs_epi16(L1, _delta1), Cpd); - + L2 = _mm_min_epi16(L2, _delta2); L2 = _mm_adds_epi16(_mm_subs_epi16(L2, _delta2), Cpd); - + L3 = _mm_min_epi16(L3, _delta3); L3 = _mm_adds_epi16(_mm_subs_epi16(L3, _delta3), Cpd); - + _mm_store_si128( (__m128i*)(Lr_p + d), L0); _mm_store_si128( (__m128i*)(Lr_p + d + D2), L1); _mm_store_si128( (__m128i*)(Lr_p + d + D2*2), L2); _mm_store_si128( (__m128i*)(Lr_p + d + D2*3), L3); - + __m128i t0 = _mm_min_epi16(_mm_unpacklo_epi16(L0, L2), _mm_unpackhi_epi16(L0, L2)); __m128i t1 = _mm_min_epi16(_mm_unpacklo_epi16(L1, L3), _mm_unpackhi_epi16(L1, L3)); t0 = _mm_min_epi16(_mm_unpacklo_epi16(t0, t1), _mm_unpackhi_epi16(t0, t1)); _minL0 = _mm_min_epi16(_minL0, t0); - + __m128i Sval = _mm_load_si128((const __m128i*)(Sp + d)); - + L0 = _mm_adds_epi16(L0, L1); L2 = _mm_adds_epi16(L2, L3); Sval = _mm_adds_epi16(Sval, L0); Sval = _mm_adds_epi16(Sval, L2); - + _mm_store_si128((__m128i*)(Sp + d), Sval); } - + _minL0 = _mm_min_epi16(_minL0, _mm_srli_si128(_minL0, 8)); _mm_storel_epi64((__m128i*)&minLr[0][xm], _minL0); } @@ -625,28 +625,28 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2, #endif { int minL0 = MAX_COST, minL1 = MAX_COST, minL2 = MAX_COST, minL3 = MAX_COST; - + for( d = 0; d < D; d++ ) { int Cpd = Cp[d], L0, L1, L2, L3; - + L0 = Cpd + min((int)Lr_p0[d], min(Lr_p0[d-1] + P1, min(Lr_p0[d+1] + P1, delta0))) - delta0; - L1 = Cpd + min((int)Lr_p1[d], min(Lr_p1[d-1] + P1, min(Lr_p1[d+1] + P1, delta1))) - delta1; + L1 = Cpd + min((int)Lr_p1[d], min(Lr_p1[d-1] + P1, min(Lr_p1[d+1] + P1, delta1))) - delta1; L2 = Cpd + min((int)Lr_p2[d], min(Lr_p2[d-1] + P1, min(Lr_p2[d+1] + P1, delta2))) - delta2; L3 = Cpd + min((int)Lr_p3[d], min(Lr_p3[d-1] + P1, min(Lr_p3[d+1] + P1, delta3))) - delta3; - + Lr_p[d] = (CostType)L0; minL0 = min(minL0, L0); - + Lr_p[d + D2] = (CostType)L1; minL1 = min(minL1, L1); - + Lr_p[d + D2*2] = (CostType)L2; minL2 = min(minL2, L2); - + Lr_p[d + D2*3] = (CostType)L3; minL3 = min(minL3, L3); - + Sp[d] = saturate_cast(Sp[d] + L0 + L1 + L2 + L3); } minLr[0][xm] = (CostType)minL0; @@ -655,7 +655,7 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2, minLr[0][xm+3] = (CostType)minL3; } } - + if( pass == npasses ) { for( x = 0; x < width; x++ ) @@ -663,73 +663,73 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2, disp1ptr[x] = disp2ptr[x] = (DispType)INVALID_DISP_SCALED; disp2cost[x] = MAX_COST; } - + for( x = width1 - 1; x >= 0; x-- ) { CostType* Sp = S + x*D; int minS = MAX_COST, bestDisp = -1; - + if( npasses == 1 ) { int xm = x*NR2, xd = xm*D2; - + int minL0 = MAX_COST; int delta0 = minLr[0][xm + NR2] + P2; CostType* Lr_p0 = Lr[0] + xd + NRD2; Lr_p0[-1] = Lr_p0[D] = MAX_COST; CostType* Lr_p = Lr[0] + xd; - + const CostType* Cp = C + x*D; - + #if CV_SSE2 if( useSIMD ) { __m128i _P1 = _mm_set1_epi16((short)P1); __m128i _delta0 = _mm_set1_epi16((short)delta0); - + __m128i _minL0 = _mm_set1_epi16((short)minL0); __m128i _minS = _mm_set1_epi16(MAX_COST), _bestDisp = _mm_set1_epi16(-1); __m128i _d8 = _mm_setr_epi16(0, 1, 2, 3, 4, 5, 6, 7), _8 = _mm_set1_epi16(8); - + for( d = 0; d < D; d += 8 ) { __m128i Cpd = _mm_load_si128((const __m128i*)(Cp + d)), L0; - + L0 = _mm_load_si128((const __m128i*)(Lr_p0 + d)); L0 = _mm_min_epi16(L0, _mm_adds_epi16(_mm_loadu_si128((const __m128i*)(Lr_p0 + d - 1)), _P1)); L0 = _mm_min_epi16(L0, _mm_adds_epi16(_mm_loadu_si128((const __m128i*)(Lr_p0 + d + 1)), _P1)); L0 = _mm_min_epi16(L0, _delta0); L0 = _mm_adds_epi16(_mm_subs_epi16(L0, _delta0), Cpd); - + _mm_store_si128((__m128i*)(Lr_p + d), L0); _minL0 = _mm_min_epi16(_minL0, L0); L0 = _mm_adds_epi16(L0, *(__m128i*)(Sp + d)); _mm_store_si128((__m128i*)(Sp + d), L0); - + __m128i mask = _mm_cmpgt_epi16(_minS, L0); _minS = _mm_min_epi16(_minS, L0); _bestDisp = _mm_xor_si128(_bestDisp, _mm_and_si128(_mm_xor_si128(_bestDisp,_d8), mask)); _d8 = _mm_adds_epi16(_d8, _8); } - + short CV_DECL_ALIGNED(16) bestDispBuf[8]; _mm_store_si128((__m128i*)bestDispBuf, _bestDisp); - + _minL0 = _mm_min_epi16(_minL0, _mm_srli_si128(_minL0, 8)); _minL0 = _mm_min_epi16(_minL0, _mm_srli_si128(_minL0, 4)); _minL0 = _mm_min_epi16(_minL0, _mm_srli_si128(_minL0, 2)); - + __m128i qS = _mm_min_epi16(_minS, _mm_srli_si128(_minS, 8)); qS = _mm_min_epi16(qS, _mm_srli_si128(qS, 4)); qS = _mm_min_epi16(qS, _mm_srli_si128(qS, 2)); - + minLr[0][xm] = (CostType)_mm_cvtsi128_si32(_minL0); minS = (CostType)_mm_cvtsi128_si32(qS); - + qS = _mm_shuffle_epi32(_mm_unpacklo_epi16(qS, qS), 0); qS = _mm_cmpeq_epi16(_minS, qS); int idx = _mm_movemask_epi8(_mm_packs_epi16(qS, qS)) & 255; - + bestDisp = bestDispBuf[LSBTab[idx]]; } else @@ -738,10 +738,10 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2, for( d = 0; d < D; d++ ) { int L0 = Cp[d] + min((int)Lr_p0[d], min(Lr_p0[d-1] + P1, min(Lr_p0[d+1] + P1, delta0))) - delta0; - + Lr_p[d] = (CostType)L0; minL0 = min(minL0, L0); - + int Sval = Sp[d] = saturate_cast(Sp[d] + L0); if( Sval < minS ) { @@ -764,7 +764,7 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2, } } } - + for( d = 0; d < D; d++ ) { if( Sp[d]*(100 - uniquenessRatio) < minS*100 && std::abs(bestDisp - d) > 1 ) @@ -773,13 +773,13 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2, if( d < D ) continue; d = bestDisp; - int x2 = x + minX1 - d - minD; - if( disp2cost[x2] > minS ) + int _x2 = x + minX1 - d - minD; + if( disp2cost[_x2] > minS ) { - disp2cost[x2] = (CostType)minS; - disp2ptr[x2] = (DispType)(d + minD); + disp2cost[_x2] = (CostType)minS; + disp2ptr[_x2] = (DispType)(d + minD); } - + if( 0 < d && d < D-1 ) { // do subpixel quadratic interpolation: @@ -792,24 +792,24 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2, d *= DISP_SCALE; disp1ptr[x + minX1] = (DispType)(d + minD*DISP_SCALE); } - + for( x = minX1; x < maxX1; x++ ) { // we round the computed disparity both towards -inf and +inf and check // if either of the corresponding disparities in disp2 is consistent. // This is to give the computed disparity a chance to look valid if it is. - int d = disp1ptr[x]; - if( d == INVALID_DISP_SCALED ) + int d1 = disp1ptr[x]; + if( d1 == INVALID_DISP_SCALED ) continue; - int _d = d >> DISP_SHIFT; - int d_ = (d + DISP_SCALE-1) >> DISP_SHIFT; + int _d = d1 >> DISP_SHIFT; + int d_ = (d1 + DISP_SCALE-1) >> DISP_SHIFT; int _x = x - _d, x_ = x - d_; if( 0 <= _x && _x < width && disp2ptr[_x] >= minD && std::abs(disp2ptr[_x] - _d) > disp12MaxDiff && 0 <= x_ && x_ < width && disp2ptr[x_] >= minD && std::abs(disp2ptr[x_] - d_) > disp12MaxDiff ) disp1ptr[x] = (DispType)INVALID_DISP_SCALED; } } - + // now shift the cyclic buffers std::swap( Lr[0], Lr[1] ); std::swap( minLr[0], minLr[1] ); @@ -825,13 +825,13 @@ void StereoSGBM::operator ()( InputArray _left, InputArray _right, Mat left = _left.getMat(), right = _right.getMat(); CV_Assert( left.size() == right.size() && left.type() == right.type() && left.depth() == DataType::depth ); - + _disp.create( left.size(), CV_16S ); Mat disp = _disp.getMat(); - + computeDisparitySGBM( left, right, disp, *this, buffer ); medianBlur(disp, disp, 3); - + if( speckleWindowSize > 0 ) filterSpeckles(disp, (minDisparity - 1)*DISP_SCALE, speckleWindowSize, DISP_SCALE*speckleRange, buffer); } @@ -844,33 +844,33 @@ Rect getValidDisparityROI( Rect roi1, Rect roi2, { int SW2 = SADWindowSize/2; int minD = minDisparity, maxD = minDisparity + numberOfDisparities - 1; - + int xmin = max(roi1.x, roi2.x + maxD) + SW2; int xmax = min(roi1.x + roi1.width, roi2.x + roi2.width - minD) - SW2; int ymin = max(roi1.y, roi2.y) + SW2; int ymax = min(roi1.y + roi1.height, roi2.y + roi2.height) - SW2; - + Rect r(xmin, ymin, xmax - xmin, ymax - ymin); - + return r.width > 0 && r.height > 0 ? r : Rect(); -} - } - + +} + void cv::filterSpeckles( InputOutputArray _img, double _newval, int maxSpeckleSize, double _maxDiff, InputOutputArray __buf ) { Mat img = _img.getMat(); Mat temp, &_buf = __buf.needed() ? __buf.getMatRef() : temp; CV_Assert( img.type() == CV_16SC1 ); - + int newVal = cvRound(_newval); int maxDiff = cvRound(_maxDiff); int width = img.cols, height = img.rows, npixels = width*height; size_t bufSize = npixels*(int)(sizeof(Point2s) + sizeof(int) + sizeof(uchar)); if( !_buf.isContinuous() || !_buf.data || _buf.cols*_buf.rows*_buf.elemSize() < bufSize ) _buf.create(1, (int)bufSize, CV_8U); - + uchar* buf = _buf.data; int i, j, dstep = (int)(img.step/sizeof(short)); int* labels = (int*)buf; @@ -879,33 +879,33 @@ void cv::filterSpeckles( InputOutputArray _img, double _newval, int maxSpeckleSi buf += npixels*sizeof(wbuf[0]); uchar* rtype = (uchar*)buf; int curlabel = 0; - + // clear out label assignments memset(labels, 0, npixels*sizeof(labels[0])); - + for( i = 0; i < height; i++ ) { short* ds = img.ptr(i); int* ls = labels + width*i; - + for( j = 0; j < width; j++ ) { - if( ds[j] != newVal ) // not a bad disparity + if( ds[j] != newVal ) // not a bad disparity { - if( ls[j] ) // has a label, check for bad label - { + if( ls[j] ) // has a label, check for bad label + { if( rtype[ls[j]] ) // small region, zero out disparity ds[j] = (short)newVal; } // no label, assign and propagate else { - Point2s* ws = wbuf; // initialize wavefront - Point2s p((short)j, (short)i); // current pixel - curlabel++; // next label - int count = 0; // current region size + Point2s* ws = wbuf; // initialize wavefront + Point2s p((short)j, (short)i); // current pixel + curlabel++; // next label + int count = 0; // current region size ls[j] = curlabel; - + // wavefront propagation while( ws >= wbuf ) // wavefront not empty { @@ -914,50 +914,50 @@ void cv::filterSpeckles( InputOutputArray _img, double _newval, int maxSpeckleSi short* dpp = &img.at(p.y, p.x); short dp = *dpp; int* lpp = labels + width*p.y + p.x; - + if( p.x < width-1 && !lpp[+1] && dpp[+1] != newVal && std::abs(dp - dpp[+1]) <= maxDiff ) { lpp[+1] = curlabel; *ws++ = Point2s(p.x+1, p.y); } - + if( p.x > 0 && !lpp[-1] && dpp[-1] != newVal && std::abs(dp - dpp[-1]) <= maxDiff ) { lpp[-1] = curlabel; *ws++ = Point2s(p.x-1, p.y); } - + if( p.y < height-1 && !lpp[+width] && dpp[+dstep] != newVal && std::abs(dp - dpp[+dstep]) <= maxDiff ) { lpp[+width] = curlabel; *ws++ = Point2s(p.x, p.y+1); } - + if( p.y > 0 && !lpp[-width] && dpp[-dstep] != newVal && std::abs(dp - dpp[-dstep]) <= maxDiff ) { lpp[-width] = curlabel; *ws++ = Point2s(p.x, p.y-1); } - + // pop most recent and propagate // NB: could try least recent, maybe better convergence p = *--ws; } - + // assign label type - if( count <= maxSpeckleSize ) // speckle region + if( count <= maxSpeckleSize ) // speckle region { - rtype[ls[j]] = 1; // small region label + rtype[ls[j]] = 1; // small region label ds[j] = (short)newVal; } else - rtype[ls[j]] = 0; // large region label + rtype[ls[j]] = 0; // large region label } } } } -} - +} + void cv::validateDisparity( InputOutputArray _disp, InputArray _cost, int minDisparity, int numberOfDisparities, int disp12MaxDiff ) { @@ -971,32 +971,32 @@ void cv::validateDisparity( InputOutputArray _disp, InputArray _cost, int minDis const int DISP_SHIFT = 4, DISP_SCALE = 1 << DISP_SHIFT; int INVALID_DISP = minD - 1, INVALID_DISP_SCALED = INVALID_DISP*DISP_SCALE; int costType = cost.type(); - + disp12MaxDiff *= DISP_SCALE; - + CV_Assert( numberOfDisparities > 0 && disp.type() == CV_16S && (costType == CV_16S || costType == CV_32S) && disp.size() == cost.size() ); - + for( int y = 0; y < rows; y++ ) { short* dptr = disp.ptr(y); - + for( x = 0; x < cols; x++ ) { disp2buf[x] = INVALID_DISP_SCALED; disp2cost[x] = INT_MAX; } - + if( costType == CV_16S ) { const short* cptr = cost.ptr(y); - + for( x = minX1; x < maxX1; x++ ) { int d = dptr[x], c = cptr[x]; int x2 = x - ((d + DISP_SCALE/2) >> DISP_SHIFT); - + if( disp2cost[x2] > c ) { disp2cost[x2] = c; @@ -1007,12 +1007,12 @@ void cv::validateDisparity( InputOutputArray _disp, InputArray _cost, int minDis else { const int* cptr = cost.ptr(y); - + for( x = minX1; x < maxX1; x++ ) { int d = dptr[x], c = cptr[x]; int x2 = x - ((d + DISP_SCALE/2) >> DISP_SHIFT); - + if( disp2cost[x2] < c ) { disp2cost[x2] = c; @@ -1020,7 +1020,7 @@ void cv::validateDisparity( InputOutputArray _disp, InputArray _cost, int minDis } } } - + for( x = minX1; x < maxX1; x++ ) { // we round the computed disparity both towards -inf and +inf and check diff --git a/modules/calib3d/test/test_cameracalibration.cpp b/modules/calib3d/test/test_cameracalibration.cpp index 23bf5f0..0b9d794 100644 --- a/modules/calib3d/test/test_cameracalibration.cpp +++ b/modules/calib3d/test/test_cameracalibration.cpp @@ -254,13 +254,13 @@ public: protected: int compare(double* val, double* refVal, int len, double eps, const char* paramName); - virtual void calibrate( int imageCount, int* pointCounts, - CvSize imageSize, CvPoint2D64f* imagePoints, CvPoint3D64f* objectPoints, - double* distortionCoeffs, double* cameraMatrix, double* translationVectors, - double* rotationMatrices, int flags ) = 0; - virtual void project( int pointCount, CvPoint3D64f* objectPoints, - double* rotationMatrix, double* translationVector, - double* cameraMatrix, double* distortion, CvPoint2D64f* imagePoints ) = 0; + virtual void calibrate( int imageCount, int* pointCounts, + CvSize imageSize, CvPoint2D64f* imagePoints, CvPoint3D64f* objectPoints, + double* distortionCoeffs, double* cameraMatrix, double* translationVectors, + double* rotationMatrices, int flags ) = 0; + virtual void project( int pointCount, CvPoint3D64f* objectPoints, + double* rotationMatrix, double* translationVector, + double* cameraMatrix, double* distortion, CvPoint2D64f* imagePoints ) = 0; void run(int); }; @@ -276,7 +276,7 @@ CV_CameraCalibrationTest::~CV_CameraCalibrationTest() void CV_CameraCalibrationTest::clear() { - cvtest::BaseTest::clear(); + cvtest::BaseTest::clear(); } int CV_CameraCalibrationTest::compare(double* val, double* ref_val, int len, @@ -529,14 +529,14 @@ void CV_CameraCalibrationTest::run( int start_from ) /* ---- Reproject points to the image ---- */ for( currImage = 0; currImage < numImages; currImage++ ) { - int numPoints = etalonSize.width * etalonSize.height; - project( numPoints, - objectPoints + currImage * numPoints, + int nPoints = etalonSize.width * etalonSize.height; + project( nPoints, + objectPoints + currImage * nPoints, rotMatrs + currImage * 9, transVects + currImage * 3, cameraMatrix, distortion, - reprojectPoints + currImage * numPoints); + reprojectPoints + currImage * nPoints); } /* ----- Compute reprojection error ----- */ @@ -669,26 +669,26 @@ _exit_: class CV_CameraCalibrationTest_C : public CV_CameraCalibrationTest { public: - CV_CameraCalibrationTest_C(){} + CV_CameraCalibrationTest_C(){} protected: - virtual void calibrate( int imageCount, int* pointCounts, - CvSize imageSize, CvPoint2D64f* imagePoints, CvPoint3D64f* objectPoints, - double* distortionCoeffs, double* cameraMatrix, double* translationVectors, - double* rotationMatrices, int flags ); - virtual void project( int pointCount, CvPoint3D64f* objectPoints, - double* rotationMatrix, double* translationVector, - double* cameraMatrix, double* distortion, CvPoint2D64f* imagePoints ); + virtual void calibrate( int imageCount, int* pointCounts, + CvSize imageSize, CvPoint2D64f* imagePoints, CvPoint3D64f* objectPoints, + double* distortionCoeffs, double* cameraMatrix, double* translationVectors, + double* rotationMatrices, int flags ); + virtual void project( int pointCount, CvPoint3D64f* objectPoints, + double* rotationMatrix, double* translationVector, + double* cameraMatrix, double* distortion, CvPoint2D64f* imagePoints ); }; void CV_CameraCalibrationTest_C::calibrate( int imageCount, int* pointCounts, - CvSize imageSize, CvPoint2D64f* imagePoints, CvPoint3D64f* objectPoints, - double* distortionCoeffs, double* cameraMatrix, double* translationVectors, - double* rotationMatrices, int flags ) + CvSize imageSize, CvPoint2D64f* imagePoints, CvPoint3D64f* objectPoints, + double* distortionCoeffs, double* cameraMatrix, double* translationVectors, + double* rotationMatrices, int flags ) { int i, total = 0; for( i = 0; i < imageCount; i++ ) total += pointCounts[i]; - + CvMat _objectPoints = cvMat(1, total, CV_64FC3, objectPoints); CvMat _imagePoints = cvMat(1, total, CV_64FC2, imagePoints); CvMat _pointCounts = cvMat(1, imageCount, CV_32S, pointCounts); @@ -696,23 +696,23 @@ void CV_CameraCalibrationTest_C::calibrate( int imageCount, int* pointCounts, CvMat _distCoeffs = cvMat(4, 1, CV_64F, distortionCoeffs); CvMat _rotationMatrices = cvMat(imageCount, 9, CV_64F, rotationMatrices); CvMat _translationVectors = cvMat(imageCount, 3, CV_64F, translationVectors); - + cvCalibrateCamera2(&_objectPoints, &_imagePoints, &_pointCounts, imageSize, &_cameraMatrix, &_distCoeffs, &_rotationMatrices, &_translationVectors, flags); } void CV_CameraCalibrationTest_C::project( int pointCount, CvPoint3D64f* objectPoints, - double* rotationMatrix, double* translationVector, - double* cameraMatrix, double* distortion, CvPoint2D64f* imagePoints ) + double* rotationMatrix, double* translationVector, + double* cameraMatrix, double* distortion, CvPoint2D64f* imagePoints ) { - CvMat _objectPoints = cvMat(1, pointCount, CV_64FC3, objectPoints); + CvMat _objectPoints = cvMat(1, pointCount, CV_64FC3, objectPoints); CvMat _imagePoints = cvMat(1, pointCount, CV_64FC2, imagePoints); CvMat _cameraMatrix = cvMat(3, 3, CV_64F, cameraMatrix); CvMat _distCoeffs = cvMat(4, 1, CV_64F, distortion); CvMat _rotationMatrix = cvMat(3, 3, CV_64F, rotationMatrix); CvMat _translationVector = cvMat(1, 3, CV_64F, translationVector); - + cvProjectPoints2(&_objectPoints, &_rotationMatrix, &_translationVector, &_cameraMatrix, &_distCoeffs, &_imagePoints); } @@ -721,97 +721,97 @@ void CV_CameraCalibrationTest_C::project( int pointCount, CvPoint3D64f* objectPo class CV_CameraCalibrationTest_CPP : public CV_CameraCalibrationTest { public: - CV_CameraCalibrationTest_CPP(){} + CV_CameraCalibrationTest_CPP(){} protected: - virtual void calibrate( int imageCount, int* pointCounts, - CvSize imageSize, CvPoint2D64f* imagePoints, CvPoint3D64f* objectPoints, - double* distortionCoeffs, double* cameraMatrix, double* translationVectors, - double* rotationMatrices, int flags ); - virtual void project( int pointCount, CvPoint3D64f* objectPoints, - double* rotationMatrix, double* translationVector, - double* cameraMatrix, double* distortion, CvPoint2D64f* imagePoints ); + virtual void calibrate( int imageCount, int* pointCounts, + CvSize imageSize, CvPoint2D64f* imagePoints, CvPoint3D64f* objectPoints, + double* distortionCoeffs, double* cameraMatrix, double* translationVectors, + double* rotationMatrices, int flags ); + virtual void project( int pointCount, CvPoint3D64f* objectPoints, + double* rotationMatrix, double* translationVector, + double* cameraMatrix, double* distortion, CvPoint2D64f* imagePoints ); }; void CV_CameraCalibrationTest_CPP::calibrate( int imageCount, int* pointCounts, - CvSize _imageSize, CvPoint2D64f* _imagePoints, CvPoint3D64f* _objectPoints, - double* _distortionCoeffs, double* _cameraMatrix, double* translationVectors, - double* rotationMatrices, int flags ) + CvSize _imageSize, CvPoint2D64f* _imagePoints, CvPoint3D64f* _objectPoints, + double* _distortionCoeffs, double* _cameraMatrix, double* translationVectors, + double* rotationMatrices, int flags ) { - vector > objectPoints( imageCount ); - vector > imagePoints( imageCount ); - Size imageSize = _imageSize; - Mat cameraMatrix, distCoeffs(1,4,CV_64F,Scalar::all(0)); - vector rvecs, tvecs; - - CvPoint3D64f* op = _objectPoints; - CvPoint2D64f* ip = _imagePoints; - vector >::iterator objectPointsIt = objectPoints.begin(); - vector >::iterator imagePointsIt = imagePoints.begin(); - for( int i = 0; i < imageCount; ++objectPointsIt, ++imagePointsIt, i++ ) - { - int num = pointCounts[i]; - objectPointsIt->resize( num ); - imagePointsIt->resize( num ); - vector::iterator oIt = objectPointsIt->begin(); - vector::iterator iIt = imagePointsIt->begin(); - for( int j = 0; j < num; ++oIt, ++iIt, j++, op++, ip++) - { - oIt->x = (float)op->x, oIt->y = (float)op->y, oIt->z = (float)op->z; - iIt->x = (float)ip->x, iIt->y = (float)ip->y; - } - } - - calibrateCamera( objectPoints, - imagePoints, - imageSize, - cameraMatrix, - distCoeffs, - rvecs, - tvecs, - flags ); - - assert( cameraMatrix.type() == CV_64FC1 ); - memcpy( _cameraMatrix, cameraMatrix.data, 9*sizeof(double) ); - - assert( cameraMatrix.type() == CV_64FC1 ); - memcpy( _distortionCoeffs, distCoeffs.data, 4*sizeof(double) ); - - vector::iterator rvecsIt = rvecs.begin(); - vector::iterator tvecsIt = tvecs.begin(); - double *rm = rotationMatrices, - *tm = translationVectors; - assert( rvecsIt->type() == CV_64FC1 ); - assert( tvecsIt->type() == CV_64FC1 ); - for( int i = 0; i < imageCount; ++rvecsIt, ++tvecsIt, i++, rm+=9, tm+=3 ) - { - Mat r9( 3, 3, CV_64FC1 ); - Rodrigues( *rvecsIt, r9 ); - memcpy( rm, r9.data, 9*sizeof(double) ); - memcpy( tm, tvecsIt->data, 3*sizeof(double) ); - } + vector > objectPoints( imageCount ); + vector > imagePoints( imageCount ); + Size imageSize = _imageSize; + Mat cameraMatrix, distCoeffs(1,4,CV_64F,Scalar::all(0)); + vector rvecs, tvecs; + + CvPoint3D64f* op = _objectPoints; + CvPoint2D64f* ip = _imagePoints; + vector >::iterator objectPointsIt = objectPoints.begin(); + vector >::iterator imagePointsIt = imagePoints.begin(); + for( int i = 0; i < imageCount; ++objectPointsIt, ++imagePointsIt, i++ ) + { + int num = pointCounts[i]; + objectPointsIt->resize( num ); + imagePointsIt->resize( num ); + vector::iterator oIt = objectPointsIt->begin(); + vector::iterator iIt = imagePointsIt->begin(); + for( int j = 0; j < num; ++oIt, ++iIt, j++, op++, ip++) + { + oIt->x = (float)op->x, oIt->y = (float)op->y, oIt->z = (float)op->z; + iIt->x = (float)ip->x, iIt->y = (float)ip->y; + } + } + + calibrateCamera( objectPoints, + imagePoints, + imageSize, + cameraMatrix, + distCoeffs, + rvecs, + tvecs, + flags ); + + assert( cameraMatrix.type() == CV_64FC1 ); + memcpy( _cameraMatrix, cameraMatrix.data, 9*sizeof(double) ); + + assert( cameraMatrix.type() == CV_64FC1 ); + memcpy( _distortionCoeffs, distCoeffs.data, 4*sizeof(double) ); + + vector::iterator rvecsIt = rvecs.begin(); + vector::iterator tvecsIt = tvecs.begin(); + double *rm = rotationMatrices, + *tm = translationVectors; + assert( rvecsIt->type() == CV_64FC1 ); + assert( tvecsIt->type() == CV_64FC1 ); + for( int i = 0; i < imageCount; ++rvecsIt, ++tvecsIt, i++, rm+=9, tm+=3 ) + { + Mat r9( 3, 3, CV_64FC1 ); + Rodrigues( *rvecsIt, r9 ); + memcpy( rm, r9.data, 9*sizeof(double) ); + memcpy( tm, tvecsIt->data, 3*sizeof(double) ); + } } void CV_CameraCalibrationTest_CPP::project( int pointCount, CvPoint3D64f* _objectPoints, - double* rotationMatrix, double* translationVector, - double* _cameraMatrix, double* distortion, CvPoint2D64f* _imagePoints ) + double* rotationMatrix, double* translationVector, + double* _cameraMatrix, double* distortion, CvPoint2D64f* _imagePoints ) { - Mat objectPoints( pointCount, 3, CV_64FC1, _objectPoints ); - Mat rmat( 3, 3, CV_64FC1, rotationMatrix ), - rvec( 1, 3, CV_64FC1 ), - tvec( 1, 3, CV_64FC1, translationVector ); - Mat cameraMatrix( 3, 3, CV_64FC1, _cameraMatrix ); - Mat distCoeffs( 1, 4, CV_64FC1, distortion ); - vector imagePoints; - Rodrigues( rmat, rvec ); - - objectPoints.convertTo( objectPoints, CV_32FC1 ); - projectPoints( objectPoints, rvec, tvec, - cameraMatrix, distCoeffs, imagePoints ); - vector::const_iterator it = imagePoints.begin(); - for( int i = 0; it != imagePoints.end(); ++it, i++ ) - { - _imagePoints[i] = cvPoint2D64f( it->x, it->y ); - } + Mat objectPoints( pointCount, 3, CV_64FC1, _objectPoints ); + Mat rmat( 3, 3, CV_64FC1, rotationMatrix ), + rvec( 1, 3, CV_64FC1 ), + tvec( 1, 3, CV_64FC1, translationVector ); + Mat cameraMatrix( 3, 3, CV_64FC1, _cameraMatrix ); + Mat distCoeffs( 1, 4, CV_64FC1, distortion ); + vector imagePoints; + Rodrigues( rmat, rvec ); + + objectPoints.convertTo( objectPoints, CV_32FC1 ); + projectPoints( objectPoints, rvec, tvec, + cameraMatrix, distCoeffs, imagePoints ); + vector::const_iterator it = imagePoints.begin(); + for( int i = 0; it != imagePoints.end(); ++it, i++ ) + { + _imagePoints[i] = cvPoint2D64f( it->x, it->y ); + } } @@ -820,103 +820,103 @@ void CV_CameraCalibrationTest_CPP::project( int pointCount, CvPoint3D64f* _objec class CV_CalibrationMatrixValuesTest : public cvtest::BaseTest { public: - CV_CalibrationMatrixValuesTest() {} + CV_CalibrationMatrixValuesTest() {} protected: - void run(int); - virtual void calibMatrixValues( const Mat& cameraMatrix, Size imageSize, - double apertureWidth, double apertureHeight, double& fovx, double& fovy, double& focalLength, - Point2d& principalPoint, double& aspectRatio ) = 0; + void run(int); + virtual void calibMatrixValues( const Mat& cameraMatrix, Size imageSize, + double apertureWidth, double apertureHeight, double& fovx, double& fovy, double& focalLength, + Point2d& principalPoint, double& aspectRatio ) = 0; }; void CV_CalibrationMatrixValuesTest::run(int) { - int code = cvtest::TS::OK; - const double fcMinVal = 1e-5; - const double fcMaxVal = 1000; - const double apertureMaxVal = 0.01; - - RNG rng = ts->get_rng(); - - double fx, fy, cx, cy, nx, ny; - Mat cameraMatrix( 3, 3, CV_64FC1 ); - cameraMatrix.setTo( Scalar(0) ); - fx = cameraMatrix.at(0,0) = rng.uniform( fcMinVal, fcMaxVal ); - fy = cameraMatrix.at(1,1) = rng.uniform( fcMinVal, fcMaxVal ); - cx = cameraMatrix.at(0,2) = rng.uniform( fcMinVal, fcMaxVal ); - cy = cameraMatrix.at(1,2) = rng.uniform( fcMinVal, fcMaxVal ); - cameraMatrix.at(2,2) = 1; - - Size imageSize( 600, 400 ); - - double apertureWidth = (double)rng * apertureMaxVal, - apertureHeight = (double)rng * apertureMaxVal; - - double fovx, fovy, focalLength, aspectRatio, - goodFovx, goodFovy, goodFocalLength, goodAspectRatio; - Point2d principalPoint, goodPrincipalPoint; - - - calibMatrixValues( cameraMatrix, imageSize, apertureWidth, apertureHeight, - fovx, fovy, focalLength, principalPoint, aspectRatio ); - - // calculate calibration matrix values - goodAspectRatio = fy / fx; - - if( apertureWidth != 0.0 && apertureHeight != 0.0 ) - { - nx = imageSize.width / apertureWidth; - ny = imageSize.height / apertureHeight; - } - else - { - nx = 1.0; - ny = goodAspectRatio; - } - - goodFovx = 2 * atan( imageSize.width / (2 * fx)) * 180.0 / CV_PI; - goodFovy = 2 * atan( imageSize.height / (2 * fy)) * 180.0 / CV_PI; - - goodFocalLength = fx / nx; - - goodPrincipalPoint.x = cx / nx; - goodPrincipalPoint.y = cy / ny; - - // check results - if( fabs(fovx - goodFovx) > FLT_EPSILON ) - { - ts->printf( cvtest::TS::LOG, "bad fovx (real=%f, good = %f\n", fovx, goodFovx ); - code = cvtest::TS::FAIL_BAD_ACCURACY; - goto _exit_; - } - if( fabs(fovy - goodFovy) > FLT_EPSILON ) - { - ts->printf( cvtest::TS::LOG, "bad fovy (real=%f, good = %f\n", fovy, goodFovy ); - code = cvtest::TS::FAIL_BAD_ACCURACY; - goto _exit_; - } - if( fabs(focalLength - goodFocalLength) > FLT_EPSILON ) - { - ts->printf( cvtest::TS::LOG, "bad focalLength (real=%f, good = %f\n", focalLength, goodFocalLength ); - code = cvtest::TS::FAIL_BAD_ACCURACY; - goto _exit_; - } - if( fabs(aspectRatio - goodAspectRatio) > FLT_EPSILON ) - { - ts->printf( cvtest::TS::LOG, "bad aspectRatio (real=%f, good = %f\n", aspectRatio, goodAspectRatio ); - code = cvtest::TS::FAIL_BAD_ACCURACY; - goto _exit_; - } - if( norm( principalPoint - goodPrincipalPoint ) > FLT_EPSILON ) - { - ts->printf( cvtest::TS::LOG, "bad principalPoint\n" ); - code = cvtest::TS::FAIL_BAD_ACCURACY; - goto _exit_; - } + int code = cvtest::TS::OK; + const double fcMinVal = 1e-5; + const double fcMaxVal = 1000; + const double apertureMaxVal = 0.01; + + RNG rng = ts->get_rng(); + + double fx, fy, cx, cy, nx, ny; + Mat cameraMatrix( 3, 3, CV_64FC1 ); + cameraMatrix.setTo( Scalar(0) ); + fx = cameraMatrix.at(0,0) = rng.uniform( fcMinVal, fcMaxVal ); + fy = cameraMatrix.at(1,1) = rng.uniform( fcMinVal, fcMaxVal ); + cx = cameraMatrix.at(0,2) = rng.uniform( fcMinVal, fcMaxVal ); + cy = cameraMatrix.at(1,2) = rng.uniform( fcMinVal, fcMaxVal ); + cameraMatrix.at(2,2) = 1; + + Size imageSize( 600, 400 ); + + double apertureWidth = (double)rng * apertureMaxVal, + apertureHeight = (double)rng * apertureMaxVal; + + double fovx, fovy, focalLength, aspectRatio, + goodFovx, goodFovy, goodFocalLength, goodAspectRatio; + Point2d principalPoint, goodPrincipalPoint; + + + calibMatrixValues( cameraMatrix, imageSize, apertureWidth, apertureHeight, + fovx, fovy, focalLength, principalPoint, aspectRatio ); + + // calculate calibration matrix values + goodAspectRatio = fy / fx; + + if( apertureWidth != 0.0 && apertureHeight != 0.0 ) + { + nx = imageSize.width / apertureWidth; + ny = imageSize.height / apertureHeight; + } + else + { + nx = 1.0; + ny = goodAspectRatio; + } + + goodFovx = 2 * atan( imageSize.width / (2 * fx)) * 180.0 / CV_PI; + goodFovy = 2 * atan( imageSize.height / (2 * fy)) * 180.0 / CV_PI; + + goodFocalLength = fx / nx; + + goodPrincipalPoint.x = cx / nx; + goodPrincipalPoint.y = cy / ny; + + // check results + if( fabs(fovx - goodFovx) > FLT_EPSILON ) + { + ts->printf( cvtest::TS::LOG, "bad fovx (real=%f, good = %f\n", fovx, goodFovx ); + code = cvtest::TS::FAIL_BAD_ACCURACY; + goto _exit_; + } + if( fabs(fovy - goodFovy) > FLT_EPSILON ) + { + ts->printf( cvtest::TS::LOG, "bad fovy (real=%f, good = %f\n", fovy, goodFovy ); + code = cvtest::TS::FAIL_BAD_ACCURACY; + goto _exit_; + } + if( fabs(focalLength - goodFocalLength) > FLT_EPSILON ) + { + ts->printf( cvtest::TS::LOG, "bad focalLength (real=%f, good = %f\n", focalLength, goodFocalLength ); + code = cvtest::TS::FAIL_BAD_ACCURACY; + goto _exit_; + } + if( fabs(aspectRatio - goodAspectRatio) > FLT_EPSILON ) + { + ts->printf( cvtest::TS::LOG, "bad aspectRatio (real=%f, good = %f\n", aspectRatio, goodAspectRatio ); + code = cvtest::TS::FAIL_BAD_ACCURACY; + goto _exit_; + } + if( norm( principalPoint - goodPrincipalPoint ) > FLT_EPSILON ) + { + ts->printf( cvtest::TS::LOG, "bad principalPoint\n" ); + code = cvtest::TS::FAIL_BAD_ACCURACY; + goto _exit_; + } _exit_: - RNG& _rng = ts->get_rng(); - _rng = rng; - ts->set_failed_test_info( code ); + RNG& _rng = ts->get_rng(); + _rng = rng; + ts->set_failed_test_info( code ); } //----------------------------------------- CV_CalibrationMatrixValuesTest_C -------------------------------- @@ -924,24 +924,24 @@ _exit_: class CV_CalibrationMatrixValuesTest_C : public CV_CalibrationMatrixValuesTest { public: - CV_CalibrationMatrixValuesTest_C(){} + CV_CalibrationMatrixValuesTest_C(){} protected: - virtual void calibMatrixValues( const Mat& cameraMatrix, Size imageSize, - double apertureWidth, double apertureHeight, double& fovx, double& fovy, double& focalLength, - Point2d& principalPoint, double& aspectRatio ); + virtual void calibMatrixValues( const Mat& cameraMatrix, Size imageSize, + double apertureWidth, double apertureHeight, double& fovx, double& fovy, double& focalLength, + Point2d& principalPoint, double& aspectRatio ); }; void CV_CalibrationMatrixValuesTest_C::calibMatrixValues( const Mat& _cameraMatrix, Size imageSize, - double apertureWidth, double apertureHeight, - double& fovx, double& fovy, double& focalLength, - Point2d& principalPoint, double& aspectRatio ) + double apertureWidth, double apertureHeight, + double& fovx, double& fovy, double& focalLength, + Point2d& principalPoint, double& aspectRatio ) { - CvMat cameraMatrix = _cameraMatrix; - CvPoint2D64f pp; - cvCalibrationMatrixValues( &cameraMatrix, imageSize, apertureWidth, apertureHeight, - &fovx, &fovy, &focalLength, &pp, &aspectRatio ); - principalPoint.x = pp.x; - principalPoint.y = pp.y; + CvMat cameraMatrix = _cameraMatrix; + CvPoint2D64f pp; + cvCalibrationMatrixValues( &cameraMatrix, imageSize, apertureWidth, apertureHeight, + &fovx, &fovy, &focalLength, &pp, &aspectRatio ); + principalPoint.x = pp.x; + principalPoint.y = pp.y; } @@ -950,20 +950,20 @@ void CV_CalibrationMatrixValuesTest_C::calibMatrixValues( const Mat& _cameraMatr class CV_CalibrationMatrixValuesTest_CPP : public CV_CalibrationMatrixValuesTest { public: - CV_CalibrationMatrixValuesTest_CPP() {} + CV_CalibrationMatrixValuesTest_CPP() {} protected: - virtual void calibMatrixValues( const Mat& cameraMatrix, Size imageSize, - double apertureWidth, double apertureHeight, double& fovx, double& fovy, double& focalLength, - Point2d& principalPoint, double& aspectRatio ); + virtual void calibMatrixValues( const Mat& cameraMatrix, Size imageSize, + double apertureWidth, double apertureHeight, double& fovx, double& fovy, double& focalLength, + Point2d& principalPoint, double& aspectRatio ); }; void CV_CalibrationMatrixValuesTest_CPP::calibMatrixValues( const Mat& cameraMatrix, Size imageSize, - double apertureWidth, double apertureHeight, - double& fovx, double& fovy, double& focalLength, - Point2d& principalPoint, double& aspectRatio ) + double apertureWidth, double apertureHeight, + double& fovx, double& fovy, double& focalLength, + Point2d& principalPoint, double& aspectRatio ) { - calibrationMatrixValues( cameraMatrix, imageSize, apertureWidth, apertureHeight, - fovx, fovy, focalLength, principalPoint, aspectRatio ); + calibrationMatrixValues( cameraMatrix, imageSize, apertureWidth, apertureHeight, + fovx, fovy, focalLength, principalPoint, aspectRatio ); } @@ -971,17 +971,17 @@ void CV_CalibrationMatrixValuesTest_CPP::calibMatrixValues( const Mat& cameraMat void calcdfdx( const vector >& leftF, const vector >& rightF, double eps, Mat& dfdx ) { const int fdim = 2; - CV_Assert( !leftF.empty() && !rightF.empty() && !leftF[0].empty() && !rightF[0].empty() ); - CV_Assert( leftF[0].size() == rightF[0].size() ); - CV_Assert( fabs(eps) > std::numeric_limits::epsilon() ); - int fcount = (int)leftF[0].size(), xdim = (int)leftF.size(); + CV_Assert( !leftF.empty() && !rightF.empty() && !leftF[0].empty() && !rightF[0].empty() ); + CV_Assert( leftF[0].size() == rightF[0].size() ); + CV_Assert( fabs(eps) > std::numeric_limits::epsilon() ); + int fcount = (int)leftF[0].size(), xdim = (int)leftF.size(); - dfdx.create( fcount*fdim, xdim, CV_64FC1 ); + dfdx.create( fcount*fdim, xdim, CV_64FC1 ); - vector >::const_iterator arrLeftIt = leftF.begin(); - vector >::const_iterator arrRightIt = rightF.begin(); - for( int xi = 0; xi < xdim; xi++, ++arrLeftIt, ++arrRightIt ) - { + vector >::const_iterator arrLeftIt = leftF.begin(); + vector >::const_iterator arrRightIt = rightF.begin(); + for( int xi = 0; xi < xdim; xi++, ++arrLeftIt, ++arrRightIt ) + { CV_Assert( (int)arrLeftIt->size() == fcount ); CV_Assert( (int)arrRightIt->size() == fcount ); vector::const_iterator lIt = arrLeftIt->begin(); @@ -989,150 +989,150 @@ void calcdfdx( const vector >& leftF, const vector(fi, xi ) = 0.5 * ((double)(rIt->x - lIt->x)) / eps; - dfdx.at(fi+1, xi ) = 0.5 * ((double)(rIt->y - lIt->y)) / eps; - } - } + dfdx.at(fi+1, xi ) = 0.5 * ((double)(rIt->y - lIt->y)) / eps; + } + } } class CV_ProjectPointsTest : public cvtest::BaseTest { public: - CV_ProjectPointsTest() {} + CV_ProjectPointsTest() {} protected: - void run(int); - virtual void project( const Mat& objectPoints, - const Mat& rvec, const Mat& tvec, - const Mat& cameraMatrix, - const Mat& distCoeffs, - vector& imagePoints, - Mat& dpdrot, Mat& dpdt, Mat& dpdf, - Mat& dpdc, Mat& dpddist, - double aspectRatio=0 ) = 0; + void run(int); + virtual void project( const Mat& objectPoints, + const Mat& rvec, const Mat& tvec, + const Mat& cameraMatrix, + const Mat& distCoeffs, + vector& imagePoints, + Mat& dpdrot, Mat& dpdt, Mat& dpdf, + Mat& dpdc, Mat& dpddist, + double aspectRatio=0 ) = 0; }; void CV_ProjectPointsTest::run(int) { //typedef float matType; - int code = cvtest::TS::OK; - const int pointCount = 100; + int code = cvtest::TS::OK; + const int pointCount = 100; - const float zMinVal = 10.0f, zMaxVal = 100.0f, + const float zMinVal = 10.0f, zMaxVal = 100.0f, rMinVal = -0.3f, rMaxVal = 0.3f, - tMinVal = -2.0f, tMaxVal = 2.0f; + tMinVal = -2.0f, tMaxVal = 2.0f; const float imgPointErr = 1e-3f, dEps = 1e-3f; - + double err; Size imgSize( 600, 800 ); Mat_ objPoints( pointCount, 3), rvec( 1, 3), rmat, tvec( 1, 3 ), cameraMatrix( 3, 3 ), distCoeffs( 1, 4 ), leftRvec, rightRvec, leftTvec, rightTvec, leftCameraMatrix, rightCameraMatrix, leftDistCoeffs, rightDistCoeffs; - RNG rng = ts->get_rng(); + RNG rng = ts->get_rng(); - // generate data - cameraMatrix << 300.f, 0.f, imgSize.width/2.f, + // generate data + cameraMatrix << 300.f, 0.f, imgSize.width/2.f, 0.f, 300.f, imgSize.height/2.f, 0.f, 0.f, 1.f; - distCoeffs << 0.1, 0.01, 0.001, 0.001; + distCoeffs << 0.1, 0.01, 0.001, 0.001; - rvec(0,0) = rng.uniform( rMinVal, rMaxVal ); - rvec(0,1) = rng.uniform( rMinVal, rMaxVal ); - rvec(0,2) = rng.uniform( rMinVal, rMaxVal ); - Rodrigues( rvec, rmat ); + rvec(0,0) = rng.uniform( rMinVal, rMaxVal ); + rvec(0,1) = rng.uniform( rMinVal, rMaxVal ); + rvec(0,2) = rng.uniform( rMinVal, rMaxVal ); + Rodrigues( rvec, rmat ); - tvec(0,0) = rng.uniform( tMinVal, tMaxVal ); - tvec(0,1) = rng.uniform( tMinVal, tMaxVal ); - tvec(0,2) = rng.uniform( tMinVal, tMaxVal ); + tvec(0,0) = rng.uniform( tMinVal, tMaxVal ); + tvec(0,1) = rng.uniform( tMinVal, tMaxVal ); + tvec(0,2) = rng.uniform( tMinVal, tMaxVal ); for( int y = 0; y < objPoints.rows; y++ ) - { - Mat point(1, 3, CV_32FC1, objPoints.ptr(y) ); - float z = rng.uniform( zMinVal, zMaxVal ); - point.at(0,2) = z; + { + Mat point(1, 3, CV_32FC1, objPoints.ptr(y) ); + float z = rng.uniform( zMinVal, zMaxVal ); + point.at(0,2) = z; point.at(0,0) = (rng.uniform(2.f,(float)(imgSize.width-2)) - cameraMatrix(0,2)) / cameraMatrix(0,0) * z; point.at(0,1) = (rng.uniform(2.f,(float)(imgSize.height-2)) - cameraMatrix(1,2)) / cameraMatrix(1,1) * z; point = (point - tvec) * rmat; - } + } - vector imgPoints; - vector > leftImgPoints; - vector > rightImgPoints; - Mat dpdrot, dpdt, dpdf, dpdc, dpddist, - valDpdrot, valDpdt, valDpdf, valDpdc, valDpddist; + vector imgPoints; + vector > leftImgPoints; + vector > rightImgPoints; + Mat dpdrot, dpdt, dpdf, dpdc, dpddist, + valDpdrot, valDpdt, valDpdf, valDpdc, valDpddist; - project( objPoints, rvec, tvec, cameraMatrix, distCoeffs, - imgPoints, dpdrot, dpdt, dpdf, dpdc, dpddist, 0 ); + project( objPoints, rvec, tvec, cameraMatrix, distCoeffs, + imgPoints, dpdrot, dpdt, dpdf, dpdc, dpddist, 0 ); // calculate and check image points assert( (int)imgPoints.size() == pointCount ); - vector::const_iterator it = imgPoints.begin(); - for( int i = 0; i < pointCount; i++, ++it ) - { - Point3d p( objPoints(i,0), objPoints(i,1), objPoints(i,2) ); - double z = p.x*rmat(2,0) + p.y*rmat(2,1) + p.z*rmat(2,2) + tvec(0,2), + vector::const_iterator it = imgPoints.begin(); + for( int i = 0; i < pointCount; i++, ++it ) + { + Point3d p( objPoints(i,0), objPoints(i,1), objPoints(i,2) ); + double z = p.x*rmat(2,0) + p.y*rmat(2,1) + p.z*rmat(2,2) + tvec(0,2), x = (p.x*rmat(0,0) + p.y*rmat(0,1) + p.z*rmat(0,2) + tvec(0,0)) / z, y = (p.x*rmat(1,0) + p.y*rmat(1,1) + p.z*rmat(1,2) + tvec(0,1)) / z, r2 = x*x + y*y, - r4 = r2*r2; - Point2f validImgPoint; - double a1 = 2*x*y, + r4 = r2*r2; + Point2f validImgPoint; + double a1 = 2*x*y, a2 = r2 + 2*x*x, a3 = r2 + 2*y*y, cdist = 1+distCoeffs(0,0)*r2+distCoeffs(0,1)*r4; - validImgPoint.x = static_cast((double)cameraMatrix(0,0)*(x*cdist + (double)distCoeffs(0,2)*a1 + (double)distCoeffs(0,3)*a2) + validImgPoint.x = static_cast((double)cameraMatrix(0,0)*(x*cdist + (double)distCoeffs(0,2)*a1 + (double)distCoeffs(0,3)*a2) + (double)cameraMatrix(0,2)); - validImgPoint.y = static_cast((double)cameraMatrix(1,1)*(y*cdist + (double)distCoeffs(0,2)*a3 + distCoeffs(0,3)*a1) + validImgPoint.y = static_cast((double)cameraMatrix(1,1)*(y*cdist + (double)distCoeffs(0,2)*a3 + distCoeffs(0,3)*a1) + (double)cameraMatrix(1,2)); if( fabs(it->x - validImgPoint.x) > imgPointErr || fabs(it->y - validImgPoint.y) > imgPointErr ) - { - ts->printf( cvtest::TS::LOG, "bad image point\n" ); - code = cvtest::TS::FAIL_BAD_ACCURACY; - goto _exit_; - } - } - - // check derivatives - // 1. rotation - leftImgPoints.resize(3); + { + ts->printf( cvtest::TS::LOG, "bad image point\n" ); + code = cvtest::TS::FAIL_BAD_ACCURACY; + goto _exit_; + } + } + + // check derivatives + // 1. rotation + leftImgPoints.resize(3); rightImgPoints.resize(3); - for( int i = 0; i < 3; i++ ) - { + for( int i = 0; i < 3; i++ ) + { rvec.copyTo( leftRvec ); leftRvec(0,i) -= dEps; project( objPoints, leftRvec, tvec, cameraMatrix, distCoeffs, leftImgPoints[i], valDpdrot, valDpdt, valDpdf, valDpdc, valDpddist, 0 ); rvec.copyTo( rightRvec ); rightRvec(0,i) += dEps; project( objPoints, rightRvec, tvec, cameraMatrix, distCoeffs, rightImgPoints[i], valDpdrot, valDpdt, valDpdf, valDpdc, valDpddist, 0 ); - } + } calcdfdx( leftImgPoints, rightImgPoints, dEps, valDpdrot ); err = norm( dpdrot, valDpdrot, NORM_INF ); if( err > 3 ) - { - ts->printf( cvtest::TS::LOG, "bad dpdrot: too big difference = %g\n", err ); - code = cvtest::TS::FAIL_BAD_ACCURACY; - } + { + ts->printf( cvtest::TS::LOG, "bad dpdrot: too big difference = %g\n", err ); + code = cvtest::TS::FAIL_BAD_ACCURACY; + } // 2. translation for( int i = 0; i < 3; i++ ) - { + { tvec.copyTo( leftTvec ); leftTvec(0,i) -= dEps; project( objPoints, rvec, leftTvec, cameraMatrix, distCoeffs, leftImgPoints[i], valDpdrot, valDpdt, valDpdf, valDpdc, valDpddist, 0 ); tvec.copyTo( rightTvec ); rightTvec(0,i) += dEps; project( objPoints, rvec, rightTvec, cameraMatrix, distCoeffs, rightImgPoints[i], valDpdrot, valDpdt, valDpdf, valDpdc, valDpddist, 0 ); - } + } calcdfdx( leftImgPoints, rightImgPoints, dEps, valDpdt ); if( norm( dpdt, valDpdt, NORM_INF ) > 0.2 ) - { - ts->printf( cvtest::TS::LOG, "bad dpdtvec\n" ); - code = cvtest::TS::FAIL_BAD_ACCURACY; - } + { + ts->printf( cvtest::TS::LOG, "bad dpdtvec\n" ); + code = cvtest::TS::FAIL_BAD_ACCURACY; + } // 3. camera matrix // 3.1. focus @@ -1181,47 +1181,47 @@ void CV_ProjectPointsTest::run(int) // 4. distortion leftImgPoints.resize(distCoeffs.cols); rightImgPoints.resize(distCoeffs.cols); - for( int i = 0; i < distCoeffs.cols; i++ ) - { + for( int i = 0; i < distCoeffs.cols; i++ ) + { distCoeffs.copyTo( leftDistCoeffs ); leftDistCoeffs(0,i) -= dEps; project( objPoints, rvec, tvec, cameraMatrix, leftDistCoeffs, leftImgPoints[i], valDpdrot, valDpdt, valDpdf, valDpdc, valDpddist, 0 ); distCoeffs.copyTo( rightDistCoeffs ); rightDistCoeffs(0,i) += dEps; project( objPoints, rvec, tvec, cameraMatrix, rightDistCoeffs, rightImgPoints[i], valDpdrot, valDpdt, valDpdf, valDpdc, valDpddist, 0 ); - } + } calcdfdx( leftImgPoints, rightImgPoints, dEps, valDpddist ); if( norm( dpddist, valDpddist ) > 0.3 ) - { - ts->printf( cvtest::TS::LOG, "bad dpddist\n" ); - code = cvtest::TS::FAIL_BAD_ACCURACY; - } + { + ts->printf( cvtest::TS::LOG, "bad dpddist\n" ); + code = cvtest::TS::FAIL_BAD_ACCURACY; + } _exit_: - RNG& _rng = ts->get_rng(); - _rng = rng; - ts->set_failed_test_info( code ); + RNG& _rng = ts->get_rng(); + _rng = rng; + ts->set_failed_test_info( code ); } //----------------------------------------- CV_ProjectPointsTest_C -------------------------------- class CV_ProjectPointsTest_C : public CV_ProjectPointsTest { public: - CV_ProjectPointsTest_C() {} + CV_ProjectPointsTest_C() {} protected: - virtual void project( const Mat& objectPoints, - const Mat& rvec, const Mat& tvec, - const Mat& cameraMatrix, - const Mat& distCoeffs, - vector& imagePoints, - Mat& dpdrot, Mat& dpdt, Mat& dpdf, - Mat& dpdc, Mat& dpddist, - double aspectRatio=0 ); + virtual void project( const Mat& objectPoints, + const Mat& rvec, const Mat& tvec, + const Mat& cameraMatrix, + const Mat& distCoeffs, + vector& imagePoints, + Mat& dpdrot, Mat& dpdt, Mat& dpdf, + Mat& dpdc, Mat& dpddist, + double aspectRatio=0 ); }; void CV_ProjectPointsTest_C::project( const Mat& opoints, const Mat& rvec, const Mat& tvec, - const Mat& cameraMatrix, const Mat& distCoeffs, vector& ipoints, - Mat& dpdrot, Mat& dpdt, Mat& dpdf, Mat& dpdc, Mat& dpddist, double aspectRatio) + const Mat& cameraMatrix, const Mat& distCoeffs, vector& ipoints, + Mat& dpdrot, Mat& dpdt, Mat& dpdf, Mat& dpdc, Mat& dpddist, double aspectRatio) { int npoints = opoints.cols*opoints.rows*opoints.channels()/3; ipoints.resize(npoints); @@ -1234,7 +1234,7 @@ void CV_ProjectPointsTest_C::project( const Mat& opoints, const Mat& rvec, const CvMat _rvec = rvec, _tvec = tvec, _cameraMatrix = cameraMatrix, _distCoeffs = distCoeffs; CvMat _dpdrot = dpdrot, _dpdt = dpdt, _dpdf = dpdf, _dpdc = dpdc, _dpddist = dpddist; - cvProjectPoints2( &_objectPoints, &_rvec, &_tvec, &_cameraMatrix, &_distCoeffs, + cvProjectPoints2( &_objectPoints, &_rvec, &_tvec, &_cameraMatrix, &_distCoeffs, &_imagePoints, &_dpdrot, &_dpdt, &_dpdf, &_dpdc, &_dpddist, aspectRatio ); } @@ -1243,24 +1243,24 @@ void CV_ProjectPointsTest_C::project( const Mat& opoints, const Mat& rvec, const class CV_ProjectPointsTest_CPP : public CV_ProjectPointsTest { public: - CV_ProjectPointsTest_CPP() {} + CV_ProjectPointsTest_CPP() {} protected: - virtual void project( const Mat& objectPoints, - const Mat& rvec, const Mat& tvec, - const Mat& cameraMatrix, - const Mat& distCoeffs, - vector& imagePoints, - Mat& dpdrot, Mat& dpdt, Mat& dpdf, - Mat& dpdc, Mat& dpddist, - double aspectRatio=0 ); + virtual void project( const Mat& objectPoints, + const Mat& rvec, const Mat& tvec, + const Mat& cameraMatrix, + const Mat& distCoeffs, + vector& imagePoints, + Mat& dpdrot, Mat& dpdt, Mat& dpdf, + Mat& dpdc, Mat& dpddist, + double aspectRatio=0 ); }; void CV_ProjectPointsTest_CPP::project( const Mat& objectPoints, const Mat& rvec, const Mat& tvec, - const Mat& cameraMatrix, const Mat& distCoeffs, vector& imagePoints, - Mat& dpdrot, Mat& dpdt, Mat& dpdf, Mat& dpdc, Mat& dpddist, double aspectRatio) + const Mat& cameraMatrix, const Mat& distCoeffs, vector& imagePoints, + Mat& dpdrot, Mat& dpdt, Mat& dpdf, Mat& dpdc, Mat& dpddist, double aspectRatio) { Mat J; - projectPoints( objectPoints, rvec, tvec, cameraMatrix, distCoeffs, imagePoints, J, aspectRatio); + projectPoints( objectPoints, rvec, tvec, cameraMatrix, distCoeffs, imagePoints, J, aspectRatio); J.colRange(0, 3).copyTo(dpdrot); J.colRange(3, 6).copyTo(dpdt); J.colRange(6, 8).copyTo(dpdf); @@ -1273,39 +1273,39 @@ void CV_ProjectPointsTest_CPP::project( const Mat& objectPoints, const Mat& rvec class CV_StereoCalibrationTest : public cvtest::BaseTest { public: - CV_StereoCalibrationTest(); - ~CV_StereoCalibrationTest(); - void clear(); + CV_StereoCalibrationTest(); + ~CV_StereoCalibrationTest(); + void clear(); protected: - bool checkPandROI( int test_case_idx, - const Mat& M, const Mat& D, const Mat& R, - const Mat& P, Size imgsize, Rect roi ); - - // covers of tested functions - virtual double calibrateStereoCamera( const vector >& objectPoints, - const vector >& imagePoints1, - const vector >& imagePoints2, - Mat& cameraMatrix1, Mat& distCoeffs1, - Mat& cameraMatrix2, Mat& distCoeffs2, - Size imageSize, Mat& R, Mat& T, - Mat& E, Mat& F, TermCriteria criteria, int flags ) = 0; - virtual void rectify( const Mat& cameraMatrix1, const Mat& distCoeffs1, - const Mat& cameraMatrix2, const Mat& distCoeffs2, - Size imageSize, const Mat& R, const Mat& T, - Mat& R1, Mat& R2, Mat& P1, Mat& P2, Mat& Q, - double alpha, Size newImageSize, - Rect* validPixROI1, Rect* validPixROI2, int flags ) = 0; - virtual bool rectifyUncalibrated( const Mat& points1, - const Mat& points2, const Mat& F, Size imgSize, - Mat& H1, Mat& H2, double threshold=5 ) = 0; - virtual void triangulate( const Mat& P1, const Mat& P2, + bool checkPandROI( int test_case_idx, + const Mat& M, const Mat& D, const Mat& R, + const Mat& P, Size imgsize, Rect roi ); + + // covers of tested functions + virtual double calibrateStereoCamera( const vector >& objectPoints, + const vector >& imagePoints1, + const vector >& imagePoints2, + Mat& cameraMatrix1, Mat& distCoeffs1, + Mat& cameraMatrix2, Mat& distCoeffs2, + Size imageSize, Mat& R, Mat& T, + Mat& E, Mat& F, TermCriteria criteria, int flags ) = 0; + virtual void rectify( const Mat& cameraMatrix1, const Mat& distCoeffs1, + const Mat& cameraMatrix2, const Mat& distCoeffs2, + Size imageSize, const Mat& R, const Mat& T, + Mat& R1, Mat& R2, Mat& P1, Mat& P2, Mat& Q, + double alpha, Size newImageSize, + Rect* validPixROI1, Rect* validPixROI2, int flags ) = 0; + virtual bool rectifyUncalibrated( const Mat& points1, + const Mat& points2, const Mat& F, Size imgSize, + Mat& H1, Mat& H2, double threshold=5 ) = 0; + virtual void triangulate( const Mat& P1, const Mat& P2, const Mat &points1, const Mat &points2, Mat &points4D ) = 0; - virtual void correct( const Mat& F, + virtual void correct( const Mat& F, const Mat &points1, const Mat &points2, Mat &newPoints1, Mat &newPoints2 ) = 0; - void run(int); + void run(int); }; @@ -1316,196 +1316,196 @@ CV_StereoCalibrationTest::CV_StereoCalibrationTest() CV_StereoCalibrationTest::~CV_StereoCalibrationTest() { - clear(); + clear(); } void CV_StereoCalibrationTest::clear() { - cvtest::BaseTest::clear(); + cvtest::BaseTest::clear(); } bool CV_StereoCalibrationTest::checkPandROI( int test_case_idx, const Mat& M, const Mat& D, const Mat& R, - const Mat& P, Size imgsize, Rect roi ) + const Mat& P, Size imgsize, Rect roi ) { - const double eps = 0.05; - const int N = 21; - int x, y, k; - vector pts, upts; - - // step 1. check that all the original points belong to the destination image - for( y = 0; y < N; y++ ) - for( x = 0; x < N; x++ ) - pts.push_back(Point2f((float)x*imgsize.width/(N-1), (float)y*imgsize.height/(N-1))); - - undistortPoints(Mat(pts), upts, M, D, R, P ); - for( k = 0; k < N*N; k++ ) - if( upts[k].x < -imgsize.width*eps || upts[k].x > imgsize.width*(1+eps) || - upts[k].y < -imgsize.height*eps || upts[k].y > imgsize.height*(1+eps) ) - { - ts->printf(cvtest::TS::LOG, "Test #%d. The point (%g, %g) was mapped to (%g, %g) which is out of image\n", - test_case_idx, pts[k].x, pts[k].y, upts[k].x, upts[k].y); - return false; - } - - // step 2. check that all the points inside ROI belong to the original source image - Mat temp(imgsize, CV_8U), utemp, map1, map2; - temp = Scalar::all(1); - initUndistortRectifyMap(M, D, R, P, imgsize, CV_16SC2, map1, map2); - remap(temp, utemp, map1, map2, INTER_LINEAR); - - if(roi.x < 0 || roi.y < 0 || roi.x + roi.width > imgsize.width || roi.y + roi.height > imgsize.height) - { - ts->printf(cvtest::TS::LOG, "Test #%d. The ROI=(%d, %d, %d, %d) is outside of the imge rectangle\n", - test_case_idx, roi.x, roi.y, roi.width, roi.height); - return false; - } - double s = sum(utemp(roi))[0]; - if( s > roi.area() || roi.area() - s > roi.area()*(1-eps) ) - { - ts->printf(cvtest::TS::LOG, "Test #%d. The ratio of black pixels inside the valid ROI (~%g%%) is too large\n", - test_case_idx, s*100./roi.area()); - return false; - } - - return true; + const double eps = 0.05; + const int N = 21; + int x, y, k; + vector pts, upts; + + // step 1. check that all the original points belong to the destination image + for( y = 0; y < N; y++ ) + for( x = 0; x < N; x++ ) + pts.push_back(Point2f((float)x*imgsize.width/(N-1), (float)y*imgsize.height/(N-1))); + + undistortPoints(Mat(pts), upts, M, D, R, P ); + for( k = 0; k < N*N; k++ ) + if( upts[k].x < -imgsize.width*eps || upts[k].x > imgsize.width*(1+eps) || + upts[k].y < -imgsize.height*eps || upts[k].y > imgsize.height*(1+eps) ) + { + ts->printf(cvtest::TS::LOG, "Test #%d. The point (%g, %g) was mapped to (%g, %g) which is out of image\n", + test_case_idx, pts[k].x, pts[k].y, upts[k].x, upts[k].y); + return false; + } + + // step 2. check that all the points inside ROI belong to the original source image + Mat temp(imgsize, CV_8U), utemp, map1, map2; + temp = Scalar::all(1); + initUndistortRectifyMap(M, D, R, P, imgsize, CV_16SC2, map1, map2); + remap(temp, utemp, map1, map2, INTER_LINEAR); + + if(roi.x < 0 || roi.y < 0 || roi.x + roi.width > imgsize.width || roi.y + roi.height > imgsize.height) + { + ts->printf(cvtest::TS::LOG, "Test #%d. The ROI=(%d, %d, %d, %d) is outside of the imge rectangle\n", + test_case_idx, roi.x, roi.y, roi.width, roi.height); + return false; + } + double s = sum(utemp(roi))[0]; + if( s > roi.area() || roi.area() - s > roi.area()*(1-eps) ) + { + ts->printf(cvtest::TS::LOG, "Test #%d. The ratio of black pixels inside the valid ROI (~%g%%) is too large\n", + test_case_idx, s*100./roi.area()); + return false; + } + + return true; } void CV_StereoCalibrationTest::run( int ) { - const int ntests = 1; - const double maxReprojErr = 2; - const double maxScanlineDistErr_c = 3; - const double maxScanlineDistErr_uc = 4; - FILE* f = 0; - - for(int testcase = 1; testcase <= ntests; testcase++) - { - char filepath[1000]; - char buf[1000]; - sprintf( filepath, "%sstereo/case%d/stereo_calib.txt", ts->get_data_path().c_str(), testcase ); - f = fopen(filepath, "rt"); - Size patternSize; - vector imglist; - - if( !f || !fgets(buf, sizeof(buf)-3, f) || sscanf(buf, "%d%d", &patternSize.width, &patternSize.height) != 2 ) - { - ts->printf( cvtest::TS::LOG, "The file %s can not be opened or has invalid content\n", filepath ); - ts->set_failed_test_info( f ? cvtest::TS::FAIL_INVALID_TEST_DATA : cvtest::TS::FAIL_MISSING_TEST_DATA ); - return; - } - - for(;;) - { - if( !fgets( buf, sizeof(buf)-3, f )) - break; - size_t len = strlen(buf); - while( len > 0 && isspace(buf[len-1])) - buf[--len] = '\0'; - if( buf[0] == '#') - continue; - sprintf(filepath, "%sstereo/case%d/%s", ts->get_data_path().c_str(), testcase, buf ); - imglist.push_back(string(filepath)); - } - fclose(f); - - if( imglist.size() == 0 || imglist.size() % 2 != 0 ) - { - ts->printf( cvtest::TS::LOG, "The number of images is 0 or an odd number in the case #%d\n", testcase ); - ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA ); - return; - } - - int nframes = (int)(imglist.size()/2); - int npoints = patternSize.width*patternSize.height; - vector > objpt(nframes); - vector > imgpt1(nframes); - vector > imgpt2(nframes); - Size imgsize; - int total = 0; - - for( int i = 0; i < nframes; i++ ) - { - Mat left = imread(imglist[i*2]); - Mat right = imread(imglist[i*2+1]); - if(!left.data || !right.data) - { - ts->printf( cvtest::TS::LOG, "Can not load images %s and %s, testcase %d\n", - imglist[i*2].c_str(), imglist[i*2+1].c_str(), testcase ); - ts->set_failed_test_info( cvtest::TS::FAIL_MISSING_TEST_DATA ); - return; - } - imgsize = left.size(); - bool found1 = findChessboardCorners(left, patternSize, imgpt1[i]); - bool found2 = findChessboardCorners(right, patternSize, imgpt2[i]); - if(!found1 || !found2) - { - ts->printf( cvtest::TS::LOG, "The function could not detect boards on the images %s and %s, testcase %d\n", - imglist[i*2].c_str(), imglist[i*2+1].c_str(), testcase ); - ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT ); - return; - } - total += (int)imgpt1[i].size(); - for( int j = 0; j < npoints; j++ ) - objpt[i].push_back(Point3f((float)(j%patternSize.width), (float)(j/patternSize.width), 0.f)); - } - - // rectify (calibrated) - Mat M1 = Mat::eye(3,3,CV_64F), M2 = Mat::eye(3,3,CV_64F), D1(5,1,CV_64F), D2(5,1,CV_64F), R, T, E, F; - M1.at(0,2) = M2.at(0,2)=(imgsize.width-1)*0.5; - M1.at(1,2) = M2.at(1,2)=(imgsize.height-1)*0.5; - D1 = Scalar::all(0); - D2 = Scalar::all(0); - double err = calibrateStereoCamera(objpt, imgpt1, imgpt2, M1, D1, M2, D2, imgsize, R, T, E, F, - TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS, 30, 1e-6), - CV_CALIB_SAME_FOCAL_LENGTH - //+ CV_CALIB_FIX_ASPECT_RATIO - + CV_CALIB_FIX_PRINCIPAL_POINT - + CV_CALIB_ZERO_TANGENT_DIST + const int ntests = 1; + const double maxReprojErr = 2; + const double maxScanlineDistErr_c = 3; + const double maxScanlineDistErr_uc = 4; + FILE* f = 0; + + for(int testcase = 1; testcase <= ntests; testcase++) + { + char filepath[1000]; + char buf[1000]; + sprintf( filepath, "%sstereo/case%d/stereo_calib.txt", ts->get_data_path().c_str(), testcase ); + f = fopen(filepath, "rt"); + Size patternSize; + vector imglist; + + if( !f || !fgets(buf, sizeof(buf)-3, f) || sscanf(buf, "%d%d", &patternSize.width, &patternSize.height) != 2 ) + { + ts->printf( cvtest::TS::LOG, "The file %s can not be opened or has invalid content\n", filepath ); + ts->set_failed_test_info( f ? cvtest::TS::FAIL_INVALID_TEST_DATA : cvtest::TS::FAIL_MISSING_TEST_DATA ); + return; + } + + for(;;) + { + if( !fgets( buf, sizeof(buf)-3, f )) + break; + size_t len = strlen(buf); + while( len > 0 && isspace(buf[len-1])) + buf[--len] = '\0'; + if( buf[0] == '#') + continue; + sprintf(filepath, "%sstereo/case%d/%s", ts->get_data_path().c_str(), testcase, buf ); + imglist.push_back(string(filepath)); + } + fclose(f); + + if( imglist.size() == 0 || imglist.size() % 2 != 0 ) + { + ts->printf( cvtest::TS::LOG, "The number of images is 0 or an odd number in the case #%d\n", testcase ); + ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA ); + return; + } + + int nframes = (int)(imglist.size()/2); + int npoints = patternSize.width*patternSize.height; + vector > objpt(nframes); + vector > imgpt1(nframes); + vector > imgpt2(nframes); + Size imgsize; + int total = 0; + + for( int i = 0; i < nframes; i++ ) + { + Mat left = imread(imglist[i*2]); + Mat right = imread(imglist[i*2+1]); + if(!left.data || !right.data) + { + ts->printf( cvtest::TS::LOG, "Can not load images %s and %s, testcase %d\n", + imglist[i*2].c_str(), imglist[i*2+1].c_str(), testcase ); + ts->set_failed_test_info( cvtest::TS::FAIL_MISSING_TEST_DATA ); + return; + } + imgsize = left.size(); + bool found1 = findChessboardCorners(left, patternSize, imgpt1[i]); + bool found2 = findChessboardCorners(right, patternSize, imgpt2[i]); + if(!found1 || !found2) + { + ts->printf( cvtest::TS::LOG, "The function could not detect boards on the images %s and %s, testcase %d\n", + imglist[i*2].c_str(), imglist[i*2+1].c_str(), testcase ); + ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT ); + return; + } + total += (int)imgpt1[i].size(); + for( int j = 0; j < npoints; j++ ) + objpt[i].push_back(Point3f((float)(j%patternSize.width), (float)(j/patternSize.width), 0.f)); + } + + // rectify (calibrated) + Mat M1 = Mat::eye(3,3,CV_64F), M2 = Mat::eye(3,3,CV_64F), D1(5,1,CV_64F), D2(5,1,CV_64F), R, T, E, F; + M1.at(0,2) = M2.at(0,2)=(imgsize.width-1)*0.5; + M1.at(1,2) = M2.at(1,2)=(imgsize.height-1)*0.5; + D1 = Scalar::all(0); + D2 = Scalar::all(0); + double err = calibrateStereoCamera(objpt, imgpt1, imgpt2, M1, D1, M2, D2, imgsize, R, T, E, F, + TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS, 30, 1e-6), + CV_CALIB_SAME_FOCAL_LENGTH + //+ CV_CALIB_FIX_ASPECT_RATIO + + CV_CALIB_FIX_PRINCIPAL_POINT + + CV_CALIB_ZERO_TANGENT_DIST + CV_CALIB_FIX_K3 + CV_CALIB_FIX_K4 + CV_CALIB_FIX_K5 //+ CV_CALIB_FIX_K6 - ); - err /= nframes*npoints; - if( err > maxReprojErr ) - { - ts->printf( cvtest::TS::LOG, "The average reprojection error is too big (=%g), testcase %d\n", err, testcase); - ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT ); - return; - } - - Mat R1, R2, P1, P2, Q; - Rect roi1, roi2; - rectify(M1, D1, M2, D2, imgsize, R, T, R1, R2, P1, P2, Q, 1, imgsize, &roi1, &roi2, 0); - Mat eye33 = Mat::eye(3,3,CV_64F); - Mat R1t = R1.t(), R2t = R2.t(); - - if( norm(R1t*R1 - eye33) > 0.01 || - norm(R2t*R2 - eye33) > 0.01 || - abs(determinant(F)) > 0.01) - { - ts->printf( cvtest::TS::LOG, "The computed (by rectify) R1 and R2 are not orthogonal," - "or the computed (by calibrate) F is not singular, testcase %d\n", testcase); - ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT ); - return; - } - - if(!checkPandROI(testcase, M1, D1, R1, P1, imgsize, roi1)) - { - ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY ); - return; - } - - if(!checkPandROI(testcase, M2, D2, R2, P2, imgsize, roi2)) - { - ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY ); - return; - } + ); + err /= nframes*npoints; + if( err > maxReprojErr ) + { + ts->printf( cvtest::TS::LOG, "The average reprojection error is too big (=%g), testcase %d\n", err, testcase); + ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT ); + return; + } + + Mat R1, R2, P1, P2, Q; + Rect roi1, roi2; + rectify(M1, D1, M2, D2, imgsize, R, T, R1, R2, P1, P2, Q, 1, imgsize, &roi1, &roi2, 0); + Mat eye33 = Mat::eye(3,3,CV_64F); + Mat R1t = R1.t(), R2t = R2.t(); + + if( norm(R1t*R1 - eye33) > 0.01 || + norm(R2t*R2 - eye33) > 0.01 || + abs(determinant(F)) > 0.01) + { + ts->printf( cvtest::TS::LOG, "The computed (by rectify) R1 and R2 are not orthogonal," + "or the computed (by calibrate) F is not singular, testcase %d\n", testcase); + ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT ); + return; + } + + if(!checkPandROI(testcase, M1, D1, R1, P1, imgsize, roi1)) + { + ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY ); + return; + } + + if(!checkPandROI(testcase, M2, D2, R2, P2, imgsize, roi2)) + { + ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY ); + return; + } //check that Tx after rectification is equal to distance between cameras double tx = fabs(P2.at(0, 3) / P2.at(0, 0)); if (fabs(tx - norm(T)) > 1e-5) { - ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY ); - return; + ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY ); + return; } //check that Q reprojects points before the camera @@ -1514,8 +1514,8 @@ void CV_StereoCalibrationTest::run( int ) CV_Assert(reprojectedTestPoint.type() == CV_64FC1); if( reprojectedTestPoint.at(2) / reprojectedTestPoint.at(3) < 0 ) { - ts->printf( cvtest::TS::LOG, "A point after rectification is reprojected behind the camera, testcase %d\n", testcase); - ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT ); + ts->printf( cvtest::TS::LOG, "A point after rectification is reprojected behind the camera, testcase %d\n", testcase); + ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT ); } //check that Q reprojects the same points as reconstructed by triangulation @@ -1555,8 +1555,8 @@ void CV_StereoCalibrationTest::run( int ) if (norm(triangulatedPoints - reprojectedPoints) / sqrt((double)pointsCount) > requiredAccuracy) { - ts->printf( cvtest::TS::LOG, "Points reprojected with a matrix Q and points reconstructed by triangulation are different, testcase %d\n", testcase); - ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT ); + ts->printf( cvtest::TS::LOG, "Points reprojected with a matrix Q and points reconstructed by triangulation are different, testcase %d\n", testcase); + ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT ); } //check correctMatches @@ -1585,73 +1585,73 @@ void CV_StereoCalibrationTest::run( int ) } } - // rectifyUncalibrated - CV_Assert( imgpt1.size() == imgpt2.size() ); - Mat _imgpt1( total, 1, CV_32FC2 ), _imgpt2( total, 1, CV_32FC2 ); - vector >::const_iterator iit1 = imgpt1.begin(); - vector >::const_iterator iit2 = imgpt2.begin(); - for( int pi = 0; iit1 != imgpt1.end(); ++iit1, ++iit2 ) - { - vector::const_iterator pit1 = iit1->begin(); - vector::const_iterator pit2 = iit2->begin(); - CV_Assert( iit1->size() == iit2->size() ); - for( ; pit1 != iit1->end(); ++pit1, ++pit2, pi++ ) - { - _imgpt1.at(pi,0) = Point2f( pit1->x, pit1->y ); - _imgpt2.at(pi,0) = Point2f( pit2->x, pit2->y ); - } - } - - Mat _M1, _M2, _D1, _D2; - vector _R1, _R2, _T1, _T2; - calibrateCamera( objpt, imgpt1, imgsize, _M1, _D1, _R1, _T1, 0 ); - calibrateCamera( objpt, imgpt2, imgsize, _M2, _D2, _R2, _T1, 0 ); - undistortPoints( _imgpt1, _imgpt1, _M1, _D1, Mat(), _M1 ); - undistortPoints( _imgpt2, _imgpt2, _M2, _D2, Mat(), _M2 ); - - Mat matF, _H1, _H2; - matF = findFundamentalMat( _imgpt1, _imgpt2 ); - rectifyUncalibrated( _imgpt1, _imgpt2, matF, imgsize, _H1, _H2 ); - - Mat rectifPoints1, rectifPoints2; - perspectiveTransform( _imgpt1, rectifPoints1, _H1 ); - perspectiveTransform( _imgpt2, rectifPoints2, _H2 ); - - bool verticalStereo = abs(P2.at(0,3)) < abs(P2.at(1,3)); - double maxDiff_c = 0, maxDiff_uc = 0; - for( int i = 0, k = 0; i < nframes; i++ ) - { - vector temp[2]; - undistortPoints(Mat(imgpt1[i]), temp[0], M1, D1, R1, P1); - undistortPoints(Mat(imgpt2[i]), temp[1], M2, D2, R2, P2); - - for( int j = 0; j < npoints; j++, k++ ) - { - double diff_c = verticalStereo ? abs(temp[0][j].x - temp[1][j].x) : abs(temp[0][j].y - temp[1][j].y); - Point2f d = rectifPoints1.at(k,0) - rectifPoints2.at(k,0); - double diff_uc = verticalStereo ? abs(d.x) : abs(d.y); - maxDiff_c = max(maxDiff_c, diff_c); - maxDiff_uc = max(maxDiff_uc, diff_uc); - if( maxDiff_c > maxScanlineDistErr_c ) - { - ts->printf( cvtest::TS::LOG, "The distance between %s coordinates is too big(=%g) (used calibrated stereo), testcase %d\n", - verticalStereo ? "x" : "y", diff_c, testcase); - ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY ); - return; - } - if( maxDiff_uc > maxScanlineDistErr_uc ) - { - ts->printf( cvtest::TS::LOG, "The distance between %s coordinates is too big(=%g) (used uncalibrated stereo), testcase %d\n", - verticalStereo ? "x" : "y", diff_uc, testcase); - ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY ); - return; - } - } - } - - ts->printf( cvtest::TS::LOG, "Testcase %d. Max distance (calibrated) =%g\n" - "Max distance (uncalibrated) =%g\n", testcase, maxDiff_c, maxDiff_uc ); - } + // rectifyUncalibrated + CV_Assert( imgpt1.size() == imgpt2.size() ); + Mat _imgpt1( total, 1, CV_32FC2 ), _imgpt2( total, 1, CV_32FC2 ); + vector >::const_iterator iit1 = imgpt1.begin(); + vector >::const_iterator iit2 = imgpt2.begin(); + for( int pi = 0; iit1 != imgpt1.end(); ++iit1, ++iit2 ) + { + vector::const_iterator pit1 = iit1->begin(); + vector::const_iterator pit2 = iit2->begin(); + CV_Assert( iit1->size() == iit2->size() ); + for( ; pit1 != iit1->end(); ++pit1, ++pit2, pi++ ) + { + _imgpt1.at(pi,0) = Point2f( pit1->x, pit1->y ); + _imgpt2.at(pi,0) = Point2f( pit2->x, pit2->y ); + } + } + + Mat _M1, _M2, _D1, _D2; + vector _R1, _R2, _T1, _T2; + calibrateCamera( objpt, imgpt1, imgsize, _M1, _D1, _R1, _T1, 0 ); + calibrateCamera( objpt, imgpt2, imgsize, _M2, _D2, _R2, _T1, 0 ); + undistortPoints( _imgpt1, _imgpt1, _M1, _D1, Mat(), _M1 ); + undistortPoints( _imgpt2, _imgpt2, _M2, _D2, Mat(), _M2 ); + + Mat matF, _H1, _H2; + matF = findFundamentalMat( _imgpt1, _imgpt2 ); + rectifyUncalibrated( _imgpt1, _imgpt2, matF, imgsize, _H1, _H2 ); + + Mat rectifPoints1, rectifPoints2; + perspectiveTransform( _imgpt1, rectifPoints1, _H1 ); + perspectiveTransform( _imgpt2, rectifPoints2, _H2 ); + + bool verticalStereo = abs(P2.at(0,3)) < abs(P2.at(1,3)); + double maxDiff_c = 0, maxDiff_uc = 0; + for( int i = 0, k = 0; i < nframes; i++ ) + { + vector temp[2]; + undistortPoints(Mat(imgpt1[i]), temp[0], M1, D1, R1, P1); + undistortPoints(Mat(imgpt2[i]), temp[1], M2, D2, R2, P2); + + for( int j = 0; j < npoints; j++, k++ ) + { + double diff_c = verticalStereo ? abs(temp[0][j].x - temp[1][j].x) : abs(temp[0][j].y - temp[1][j].y); + Point2f d = rectifPoints1.at(k,0) - rectifPoints2.at(k,0); + double diff_uc = verticalStereo ? abs(d.x) : abs(d.y); + maxDiff_c = max(maxDiff_c, diff_c); + maxDiff_uc = max(maxDiff_uc, diff_uc); + if( maxDiff_c > maxScanlineDistErr_c ) + { + ts->printf( cvtest::TS::LOG, "The distance between %s coordinates is too big(=%g) (used calibrated stereo), testcase %d\n", + verticalStereo ? "x" : "y", diff_c, testcase); + ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY ); + return; + } + if( maxDiff_uc > maxScanlineDistErr_uc ) + { + ts->printf( cvtest::TS::LOG, "The distance between %s coordinates is too big(=%g) (used uncalibrated stereo), testcase %d\n", + verticalStereo ? "x" : "y", diff_uc, testcase); + ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY ); + return; + } + } + } + + ts->printf( cvtest::TS::LOG, "Testcase %d. Max distance (calibrated) =%g\n" + "Max distance (uncalibrated) =%g\n", testcase, maxDiff_c, maxDiff_uc ); + } } //-------------------------------- CV_StereoCalibrationTest_C ------------------------------ @@ -1659,111 +1659,111 @@ void CV_StereoCalibrationTest::run( int ) class CV_StereoCalibrationTest_C : public CV_StereoCalibrationTest { public: - CV_StereoCalibrationTest_C() {} + CV_StereoCalibrationTest_C() {} protected: - virtual double calibrateStereoCamera( const vector >& objectPoints, - const vector >& imagePoints1, - const vector >& imagePoints2, - Mat& cameraMatrix1, Mat& distCoeffs1, - Mat& cameraMatrix2, Mat& distCoeffs2, - Size imageSize, Mat& R, Mat& T, - Mat& E, Mat& F, TermCriteria criteria, int flags ); - virtual void rectify( const Mat& cameraMatrix1, const Mat& distCoeffs1, - const Mat& cameraMatrix2, const Mat& distCoeffs2, - Size imageSize, const Mat& R, const Mat& T, - Mat& R1, Mat& R2, Mat& P1, Mat& P2, Mat& Q, - double alpha, Size newImageSize, - Rect* validPixROI1, Rect* validPixROI2, int flags ); - virtual bool rectifyUncalibrated( const Mat& points1, - const Mat& points2, const Mat& F, Size imgSize, - Mat& H1, Mat& H2, double threshold=5 ); - virtual void triangulate( const Mat& P1, const Mat& P2, + virtual double calibrateStereoCamera( const vector >& objectPoints, + const vector >& imagePoints1, + const vector >& imagePoints2, + Mat& cameraMatrix1, Mat& distCoeffs1, + Mat& cameraMatrix2, Mat& distCoeffs2, + Size imageSize, Mat& R, Mat& T, + Mat& E, Mat& F, TermCriteria criteria, int flags ); + virtual void rectify( const Mat& cameraMatrix1, const Mat& distCoeffs1, + const Mat& cameraMatrix2, const Mat& distCoeffs2, + Size imageSize, const Mat& R, const Mat& T, + Mat& R1, Mat& R2, Mat& P1, Mat& P2, Mat& Q, + double alpha, Size newImageSize, + Rect* validPixROI1, Rect* validPixROI2, int flags ); + virtual bool rectifyUncalibrated( const Mat& points1, + const Mat& points2, const Mat& F, Size imgSize, + Mat& H1, Mat& H2, double threshold=5 ); + virtual void triangulate( const Mat& P1, const Mat& P2, const Mat &points1, const Mat &points2, Mat &points4D ); - virtual void correct( const Mat& F, + virtual void correct( const Mat& F, const Mat &points1, const Mat &points2, Mat &newPoints1, Mat &newPoints2 ); }; double CV_StereoCalibrationTest_C::calibrateStereoCamera( const vector >& objectPoints, - const vector >& imagePoints1, - const vector >& imagePoints2, - Mat& cameraMatrix1, Mat& distCoeffs1, - Mat& cameraMatrix2, Mat& distCoeffs2, - Size imageSize, Mat& R, Mat& T, - Mat& E, Mat& F, TermCriteria criteria, int flags ) + const vector >& imagePoints1, + const vector >& imagePoints2, + Mat& cameraMatrix1, Mat& distCoeffs1, + Mat& cameraMatrix2, Mat& distCoeffs2, + Size imageSize, Mat& R, Mat& T, + Mat& E, Mat& F, TermCriteria criteria, int flags ) { - cameraMatrix1.create( 3, 3, CV_64F ); - cameraMatrix2.create( 3, 3, CV_64F); - distCoeffs1.create( 1, 5, CV_64F); - distCoeffs2.create( 1, 5, CV_64F); - R.create(3, 3, CV_64F); - T.create(3, 1, CV_64F); - E.create(3, 3, CV_64F); - F.create(3, 3, CV_64F); - - int nimages = (int)objectPoints.size(), total = 0; - for( int i = 0; i < nimages; i++ ) - { - total += (int)objectPoints[i].size(); - } - - Mat npoints( 1, nimages, CV_32S ), - objPt( 1, total, DataType::type ), - imgPt( 1, total, DataType::type ), - imgPt2( 1, total, DataType::type ); - - Point2f* imgPtData2 = imgPt2.ptr(); - Point3f* objPtData = objPt.ptr(); - Point2f* imgPtData = imgPt.ptr(); - for( int i = 0, ni = 0, j = 0; i < nimages; i++, j += ni ) - { - ni = (int)objectPoints[i].size(); - ((int*)npoints.data)[i] = ni; - std::copy(objectPoints[i].begin(), objectPoints[i].end(), objPtData + j); - std::copy(imagePoints1[i].begin(), imagePoints1[i].end(), imgPtData + j); - std::copy(imagePoints2[i].begin(), imagePoints2[i].end(), imgPtData2 + j); - } - CvMat _objPt = objPt, _imgPt = imgPt, _imgPt2 = imgPt2, _npoints = npoints; - CvMat _cameraMatrix1 = cameraMatrix1, _distCoeffs1 = distCoeffs1; - CvMat _cameraMatrix2 = cameraMatrix2, _distCoeffs2 = distCoeffs2; - CvMat matR = R, matT = T, matE = E, matF = F; - - return cvStereoCalibrate(&_objPt, &_imgPt, &_imgPt2, &_npoints, &_cameraMatrix1, - &_distCoeffs1, &_cameraMatrix2, &_distCoeffs2, imageSize, - &matR, &matT, &matE, &matF, criteria, flags ); + cameraMatrix1.create( 3, 3, CV_64F ); + cameraMatrix2.create( 3, 3, CV_64F); + distCoeffs1.create( 1, 5, CV_64F); + distCoeffs2.create( 1, 5, CV_64F); + R.create(3, 3, CV_64F); + T.create(3, 1, CV_64F); + E.create(3, 3, CV_64F); + F.create(3, 3, CV_64F); + + int nimages = (int)objectPoints.size(), total = 0; + for( int i = 0; i < nimages; i++ ) + { + total += (int)objectPoints[i].size(); + } + + Mat npoints( 1, nimages, CV_32S ), + objPt( 1, total, DataType::type ), + imgPt( 1, total, DataType::type ), + imgPt2( 1, total, DataType::type ); + + Point2f* imgPtData2 = imgPt2.ptr(); + Point3f* objPtData = objPt.ptr(); + Point2f* imgPtData = imgPt.ptr(); + for( int i = 0, ni = 0, j = 0; i < nimages; i++, j += ni ) + { + ni = (int)objectPoints[i].size(); + ((int*)npoints.data)[i] = ni; + std::copy(objectPoints[i].begin(), objectPoints[i].end(), objPtData + j); + std::copy(imagePoints1[i].begin(), imagePoints1[i].end(), imgPtData + j); + std::copy(imagePoints2[i].begin(), imagePoints2[i].end(), imgPtData2 + j); + } + CvMat _objPt = objPt, _imgPt = imgPt, _imgPt2 = imgPt2, _npoints = npoints; + CvMat _cameraMatrix1 = cameraMatrix1, _distCoeffs1 = distCoeffs1; + CvMat _cameraMatrix2 = cameraMatrix2, _distCoeffs2 = distCoeffs2; + CvMat matR = R, matT = T, matE = E, matF = F; + + return cvStereoCalibrate(&_objPt, &_imgPt, &_imgPt2, &_npoints, &_cameraMatrix1, + &_distCoeffs1, &_cameraMatrix2, &_distCoeffs2, imageSize, + &matR, &matT, &matE, &matF, criteria, flags ); } void CV_StereoCalibrationTest_C::rectify( const Mat& cameraMatrix1, const Mat& distCoeffs1, - const Mat& cameraMatrix2, const Mat& distCoeffs2, - Size imageSize, const Mat& R, const Mat& T, - Mat& R1, Mat& R2, Mat& P1, Mat& P2, Mat& Q, - double alpha, Size newImageSize, - Rect* validPixROI1, Rect* validPixROI2, int flags ) + const Mat& cameraMatrix2, const Mat& distCoeffs2, + Size imageSize, const Mat& R, const Mat& T, + Mat& R1, Mat& R2, Mat& P1, Mat& P2, Mat& Q, + double alpha, Size newImageSize, + Rect* validPixROI1, Rect* validPixROI2, int flags ) { - int rtype = CV_64F; - R1.create(3, 3, rtype); - R2.create(3, 3, rtype); - P1.create(3, 4, rtype); - P2.create(3, 4, rtype); - Q.create(4, 4, rtype); - CvMat _cameraMatrix1 = cameraMatrix1, _distCoeffs1 = distCoeffs1; - CvMat _cameraMatrix2 = cameraMatrix2, _distCoeffs2 = distCoeffs2; - CvMat matR = R, matT = T, _R1 = R1, _R2 = R2, _P1 = P1, _P2 = P2, matQ = Q; - cvStereoRectify( &_cameraMatrix1, &_cameraMatrix2, &_distCoeffs1, &_distCoeffs2, - imageSize, &matR, &matT, &_R1, &_R2, &_P1, &_P2, &matQ, flags, - alpha, newImageSize, (CvRect*)validPixROI1, (CvRect*)validPixROI2); + int rtype = CV_64F; + R1.create(3, 3, rtype); + R2.create(3, 3, rtype); + P1.create(3, 4, rtype); + P2.create(3, 4, rtype); + Q.create(4, 4, rtype); + CvMat _cameraMatrix1 = cameraMatrix1, _distCoeffs1 = distCoeffs1; + CvMat _cameraMatrix2 = cameraMatrix2, _distCoeffs2 = distCoeffs2; + CvMat matR = R, matT = T, _R1 = R1, _R2 = R2, _P1 = P1, _P2 = P2, matQ = Q; + cvStereoRectify( &_cameraMatrix1, &_cameraMatrix2, &_distCoeffs1, &_distCoeffs2, + imageSize, &matR, &matT, &_R1, &_R2, &_P1, &_P2, &matQ, flags, + alpha, newImageSize, (CvRect*)validPixROI1, (CvRect*)validPixROI2); } bool CV_StereoCalibrationTest_C::rectifyUncalibrated( const Mat& points1, - const Mat& points2, const Mat& F, Size imgSize, Mat& H1, Mat& H2, double threshold ) + const Mat& points2, const Mat& F, Size imgSize, Mat& H1, Mat& H2, double threshold ) { - H1.create(3, 3, CV_64F); - H2.create(3, 3, CV_64F); - CvMat _pt1 = points1, _pt2 = points2, matF, *pF=0, _H1 = H1, _H2 = H2; - if( F.size() == Size(3, 3) ) - pF = &(matF = F); - return cvStereoRectifyUncalibrated(&_pt1, &_pt2, pF, imgSize, &_H1, &_H2, threshold) > 0; + H1.create(3, 3, CV_64F); + H2.create(3, 3, CV_64F); + CvMat _pt1 = points1, _pt2 = points2, matF, *pF=0, _H1 = H1, _H2 = H2; + if( F.size() == Size(3, 3) ) + pF = &(matF = F); + return cvStereoRectifyUncalibrated(&_pt1, &_pt2, pF, imgSize, &_H1, &_H2, threshold) > 0; } void CV_StereoCalibrationTest_C::triangulate( const Mat& P1, const Mat& P2, @@ -1792,25 +1792,25 @@ void CV_StereoCalibrationTest_C::correct( const Mat& F, class CV_StereoCalibrationTest_CPP : public CV_StereoCalibrationTest { public: - CV_StereoCalibrationTest_CPP() {} + CV_StereoCalibrationTest_CPP() {} protected: - virtual double calibrateStereoCamera( const vector >& objectPoints, - const vector >& imagePoints1, - const vector >& imagePoints2, - Mat& cameraMatrix1, Mat& distCoeffs1, - Mat& cameraMatrix2, Mat& distCoeffs2, - Size imageSize, Mat& R, Mat& T, - Mat& E, Mat& F, TermCriteria criteria, int flags ); - virtual void rectify( const Mat& cameraMatrix1, const Mat& distCoeffs1, - const Mat& cameraMatrix2, const Mat& distCoeffs2, - Size imageSize, const Mat& R, const Mat& T, - Mat& R1, Mat& R2, Mat& P1, Mat& P2, Mat& Q, - double alpha, Size newImageSize, - Rect* validPixROI1, Rect* validPixROI2, int flags ); - virtual bool rectifyUncalibrated( const Mat& points1, - const Mat& points2, const Mat& F, Size imgSize, - Mat& H1, Mat& H2, double threshold=5 ); - virtual void triangulate( const Mat& P1, const Mat& P2, + virtual double calibrateStereoCamera( const vector >& objectPoints, + const vector >& imagePoints1, + const vector >& imagePoints2, + Mat& cameraMatrix1, Mat& distCoeffs1, + Mat& cameraMatrix2, Mat& distCoeffs2, + Size imageSize, Mat& R, Mat& T, + Mat& E, Mat& F, TermCriteria criteria, int flags ); + virtual void rectify( const Mat& cameraMatrix1, const Mat& distCoeffs1, + const Mat& cameraMatrix2, const Mat& distCoeffs2, + Size imageSize, const Mat& R, const Mat& T, + Mat& R1, Mat& R2, Mat& P1, Mat& P2, Mat& Q, + double alpha, Size newImageSize, + Rect* validPixROI1, Rect* validPixROI2, int flags ); + virtual bool rectifyUncalibrated( const Mat& points1, + const Mat& points2, const Mat& F, Size imgSize, + Mat& H1, Mat& H2, double threshold=5 ); + virtual void triangulate( const Mat& P1, const Mat& P2, const Mat &points1, const Mat &points2, Mat &points4D ); virtual void correct( const Mat& F, @@ -1819,33 +1819,33 @@ protected: }; double CV_StereoCalibrationTest_CPP::calibrateStereoCamera( const vector >& objectPoints, - const vector >& imagePoints1, - const vector >& imagePoints2, - Mat& cameraMatrix1, Mat& distCoeffs1, - Mat& cameraMatrix2, Mat& distCoeffs2, - Size imageSize, Mat& R, Mat& T, - Mat& E, Mat& F, TermCriteria criteria, int flags ) + const vector >& imagePoints1, + const vector >& imagePoints2, + Mat& cameraMatrix1, Mat& distCoeffs1, + Mat& cameraMatrix2, Mat& distCoeffs2, + Size imageSize, Mat& R, Mat& T, + Mat& E, Mat& F, TermCriteria criteria, int flags ) { - return stereoCalibrate( objectPoints, imagePoints1, imagePoints2, - cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2, - imageSize, R, T, E, F, criteria, flags ); + return stereoCalibrate( objectPoints, imagePoints1, imagePoints2, + cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2, + imageSize, R, T, E, F, criteria, flags ); } void CV_StereoCalibrationTest_CPP::rectify( const Mat& cameraMatrix1, const Mat& distCoeffs1, - const Mat& cameraMatrix2, const Mat& distCoeffs2, - Size imageSize, const Mat& R, const Mat& T, - Mat& R1, Mat& R2, Mat& P1, Mat& P2, Mat& Q, - double alpha, Size newImageSize, - Rect* validPixROI1, Rect* validPixROI2, int flags ) + const Mat& cameraMatrix2, const Mat& distCoeffs2, + Size imageSize, const Mat& R, const Mat& T, + Mat& R1, Mat& R2, Mat& P1, Mat& P2, Mat& Q, + double alpha, Size newImageSize, + Rect* validPixROI1, Rect* validPixROI2, int flags ) { - stereoRectify( cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2, - imageSize, R, T, R1, R2, P1, P2, Q, flags, alpha, newImageSize,validPixROI1, validPixROI2 ); + stereoRectify( cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2, + imageSize, R, T, R1, R2, P1, P2, Q, flags, alpha, newImageSize,validPixROI1, validPixROI2 ); } bool CV_StereoCalibrationTest_CPP::rectifyUncalibrated( const Mat& points1, - const Mat& points2, const Mat& F, Size imgSize, Mat& H1, Mat& H2, double threshold ) + const Mat& points2, const Mat& F, Size imgSize, Mat& H1, Mat& H2, double threshold ) { - return stereoRectifyUncalibrated( points1, points2, F, imgSize, H1, H2, threshold ); + return stereoRectifyUncalibrated( points1, points2, F, imgSize, H1, H2, threshold ); } void CV_StereoCalibrationTest_CPP::triangulate( const Mat& P1, const Mat& P2, diff --git a/modules/calib3d/test/test_cameracalibration_artificial.cpp b/modules/calib3d/test/test_cameracalibration_artificial.cpp index 4b80640..e5585e9 100644 --- a/modules/calib3d/test/test_cameracalibration_artificial.cpp +++ b/modules/calib3d/test/test_cameracalibration_artificial.cpp @@ -55,22 +55,22 @@ using namespace cv; using namespace std; //template ostream& operator<<(ostream& out, const Mat_& mat) -//{ +//{ // for(Mat_::const_iterator pos = mat.begin(), end = mat.end(); pos != end; ++pos) // out << *pos << " "; // return out; //} -//ostream& operator<<(ostream& out, const Mat& mat) { return out << Mat_(mat); } +//ostream& operator<<(ostream& out, const Mat& mat) { return out << Mat_(mat); } Mat calcRvec(const vector& points, const Size& cornerSize) -{ +{ Point3f p00 = points[0]; Point3f p10 = points[1]; - Point3f p01 = points[cornerSize.width]; + Point3f p01 = points[cornerSize.width]; Vec3d ex(p10.x - p00.x, p10.y - p00.y, p10.z - p00.z); - Vec3d ey(p01.x - p00.x, p01.y - p00.y, p01.z - p00.z); - Vec3d ez = ex.cross(ey); + Vec3d ey(p01.x - p00.x, p01.y - p00.y, p01.z - p00.z); + Vec3d ez = ex.cross(ey); Mat rot(3, 3, CV_64F); *rot.ptr(0) = ex; @@ -89,7 +89,7 @@ public: { } ~CV_CalibrateCameraArtificialTest() {} -protected: +protected: int r; const static int JUST_FIND_CORNERS = 0; @@ -111,7 +111,7 @@ protected: { ts->printf( cvtest::TS::LOG, "Bad shape of camera matrix returned \n"); ts->set_failed_test_info(cvtest::TS::FAIL_MISMATCH); - } + } double fx_e = camMat_est.at(0, 0), fy_e = camMat_est.at(1, 1); double cx_e = camMat_est.at(0, 2), cy_e = camMat_est.at(1, 2); @@ -121,19 +121,19 @@ protected: const double eps = 1e-2; const double dlt = 1e-5; - bool fail = checkErr(fx_e, fx, eps, dlt) || checkErr(fy_e, fy, eps, dlt) || - checkErr(cx_e, cx, eps, dlt) || checkErr(cy_e, cy, eps, dlt); + bool fail = checkErr(fx_e, fx, eps, dlt) || checkErr(fy_e, fy, eps, dlt) || + checkErr(cx_e, cx, eps, dlt) || checkErr(cy_e, cy, eps, dlt); if (fail) { - ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY); - } + ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY); + } ts->printf( cvtest::TS::LOG, "%d) Expected [Fx Fy Cx Cy] = [%.3f %.3f %.3f %.3f]\n", r, fx, fy, cx, cy); - ts->printf( cvtest::TS::LOG, "%d) Estimated [Fx Fy Cx Cy] = [%.3f %.3f %.3f %.3f]\n", r, fx_e, fy_e, cx_e, cy_e); + ts->printf( cvtest::TS::LOG, "%d) Estimated [Fx Fy Cx Cy] = [%.3f %.3f %.3f %.3f]\n", r, fx_e, fy_e, cx_e, cy_e); } void compareDistCoeffs(const Mat_& distCoeffs, const Mat& distCoeffs_est) - { + { const double *dt_e = distCoeffs_est.ptr(); double k1_e = dt_e[0], k2_e = dt_e[1], k3_e = dt_e[4]; @@ -143,21 +143,21 @@ protected: double p1 = distCoeffs(0, 2), p2 = distCoeffs(0, 3); const double eps = 5e-2; - const double dlt = 1e-3; + const double dlt = 1e-3; const double eps_k3 = 5; - const double dlt_k3 = 1e-3; + const double dlt_k3 = 1e-3; - bool fail = checkErr(k1_e, k1, eps, dlt) || checkErr(k2_e, k2, eps, dlt) || checkErr(k3_e, k3, eps_k3, dlt_k3) || - checkErr(p1_e, p1, eps, dlt) || checkErr(p2_e, p2, eps, dlt); + bool fail = checkErr(k1_e, k1, eps, dlt) || checkErr(k2_e, k2, eps, dlt) || checkErr(k3_e, k3, eps_k3, dlt_k3) || + checkErr(p1_e, p1, eps, dlt) || checkErr(p2_e, p2, eps, dlt); if (fail) { // commented according to vp123's recomendation. TODO - improve accuaracy - //ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY); ss - } + //ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY); ss + } ts->printf( cvtest::TS::LOG, "%d) DistCoeff exp=(%.2f, %.2f, %.4f, %.4f %.2f)\n", r, k1, k2, p1, p2, k3); - ts->printf( cvtest::TS::LOG, "%d) DistCoeff est=(%.2f, %.2f, %.4f, %.4f %.2f)\n", r, k1_e, k2_e, p1_e, p2_e, k3_e); + ts->printf( cvtest::TS::LOG, "%d) DistCoeff est=(%.2f, %.2f, %.4f, %.4f %.2f)\n", r, k1_e, k2_e, p1_e, p2_e, k3_e); ts->printf( cvtest::TS::LOG, "%d) AbsError = [%.5f %.5f %.5f %.5f %.5f]\n", r, fabs(k1-k1_e), fabs(k2-k2_e), fabs(p1-p1_e), fabs(p2-p2_e), fabs(k3-k3_e)); } @@ -173,20 +173,20 @@ protected: const Point3d& tvec = *tvecs[i].ptr(); const Point3d& tvec_est = *tvecs_est[i].ptr(); - if (norm(tvec_est - tvec) > eps* (norm(tvec) + dlt)) + if (norm(tvec_est - tvec) > eps* (norm(tvec) + dlt)) { if (err_count++ < errMsgNum) { - if (err_count == errMsgNum) - ts->printf( cvtest::TS::LOG, "%d) ...\n", r); - else + if (err_count == errMsgNum) + ts->printf( cvtest::TS::LOG, "%d) ...\n", r); + else { - ts->printf( cvtest::TS::LOG, "%d) Bad accuracy in returned tvecs. Index = %d\n", r, i); + ts->printf( cvtest::TS::LOG, "%d) Bad accuracy in returned tvecs. Index = %d\n", r, i); ts->printf( cvtest::TS::LOG, "%d) norm(tvec_est - tvec) = %f, norm(tvec_exp) = %f \n", r, norm(tvec_est - tvec), norm(tvec)); } } ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY); - } + } } } @@ -199,20 +199,20 @@ protected: int err_count = 0; const int errMsgNum = 4; for(size_t i = 0; i < rvecs.size(); ++i) - { + { Rodrigues(rvecs[i], rmat); - Rodrigues(rvecs_est[i], rmat_est); + Rodrigues(rvecs_est[i], rmat_est); if (norm(rmat_est, rmat) > eps* (norm(rmat) + dlt)) { if (err_count++ < errMsgNum) { if (err_count == errMsgNum) - ts->printf( cvtest::TS::LOG, "%d) ...\n", r); + ts->printf( cvtest::TS::LOG, "%d) ...\n", r); else { - ts->printf( cvtest::TS::LOG, "%d) Bad accuracy in returned rvecs (rotation matrs). Index = %d\n", r, i); - ts->printf( cvtest::TS::LOG, "%d) norm(rot_mat_est - rot_mat_exp) = %f, norm(rot_mat_exp) = %f \n", r, norm(rmat_est, rmat), norm(rmat)); + ts->printf( cvtest::TS::LOG, "%d) Bad accuracy in returned rvecs (rotation matrs). Index = %d\n", r, i); + ts->printf( cvtest::TS::LOG, "%d) norm(rot_mat_est - rot_mat_exp) = %f, norm(rot_mat_exp) = %f \n", r, norm(rmat_est, rmat), norm(rmat)); } } @@ -221,19 +221,19 @@ protected: } } - double reprojectErrorWithoutIntrinsics(const vector& cb3d, const vector& rvecs_exp, const vector& tvecs_exp, + double reprojectErrorWithoutIntrinsics(const vector& cb3d, const vector& _rvecs_exp, const vector& _tvecs_exp, const vector& rvecs_est, const vector& tvecs_est) - { + { const static Mat eye33 = Mat::eye(3, 3, CV_64F); const static Mat zero15 = Mat::zeros(1, 5, CV_64F); - Mat chessboard3D(cb3d); + Mat _chessboard3D(cb3d); vector uv_exp, uv_est; - double res = 0; + double res = 0; - for(size_t i = 0; i < rvecs_exp.size(); ++i) - { - projectPoints(chessboard3D, rvecs_exp[i], tvecs_exp[i], eye33, zero15, uv_exp); - projectPoints(chessboard3D, rvecs_est[i], tvecs_est[i], eye33, zero15, uv_est); + for(size_t i = 0; i < rvecs_exp.size(); ++i) + { + projectPoints(_chessboard3D, _rvecs_exp[i], _tvecs_exp[i], eye33, zero15, uv_exp); + projectPoints(_chessboard3D, rvecs_est[i], tvecs_est[i], eye33, zero15, uv_est); for(size_t j = 0; j < cb3d.size(); ++j) res += norm(uv_exp[i] - uv_est[i]); } @@ -243,7 +243,7 @@ protected: Size2f sqSile; vector chessboard3D; - vector boards, rvecs_exp, tvecs_exp, rvecs_spnp, tvecs_spnp; + vector boards, rvecs_exp, tvecs_exp, rvecs_spnp, tvecs_spnp; vector< vector > objectPoints; vector< vector > imagePoints_art; vector< vector > imagePoints_findCb; @@ -268,29 +268,29 @@ protected: imagePoints_findCb.clear(); vector corners_art, corners_fcb; - for(size_t i = 0; i < brdsNum; ++i) - { + for(size_t i = 0; i < brdsNum; ++i) + { for(;;) { boards[i] = cbg(bg, camMat, distCoeffs, sqSile, corners_art); - if(findChessboardCorners(boards[i], cornersSize, corners_fcb)) - break; - } + if(findChessboardCorners(boards[i], cornersSize, corners_fcb)) + break; + } //cv::namedWindow("CB"); imshow("CB", boards[i]); cv::waitKey(); - imagePoints_art.push_back(corners_art); + imagePoints_art.push_back(corners_art); imagePoints_findCb.push_back(corners_fcb); tvecs_exp[i].create(1, 3, CV_64F); *tvecs_exp[i].ptr() = cbg.corners3d[0]; - rvecs_exp[i] = calcRvec(cbg.corners3d, cbg.cornersSize()); + rvecs_exp[i] = calcRvec(cbg.corners3d, cbg.cornersSize()); } } void runTest(const Size& imgSize, const Mat_& camMat, const Mat_& distCoeffs, size_t brdsNum, const Size& cornersSize, int flag = 0) - { + { const TermCriteria tc(TermCriteria::EPS|TermCriteria::MAX_ITER, 30, 0.1); vector< vector > imagePoints; @@ -300,9 +300,9 @@ protected: case JUST_FIND_CORNERS: imagePoints = imagePoints_findCb; break; case ARTIFICIAL_CORNERS: imagePoints = imagePoints_art; break; - case USE_CORNERS_SUBPIX: + case USE_CORNERS_SUBPIX: for(size_t i = 0; i < brdsNum; ++i) - { + { Mat gray; cvtColor(boards[i], gray, CV_BGR2GRAY); vector tmp = imagePoints_findCb[i]; @@ -312,9 +312,9 @@ protected: break; case USE_4QUAD_CORNERS: for(size_t i = 0; i < brdsNum; ++i) - { + { Mat gray; - cvtColor(boards[i], gray, CV_BGR2GRAY); + cvtColor(boards[i], gray, CV_BGR2GRAY); vector tmp = imagePoints_findCb[i]; find4QuadCornerSubpix(gray, tmp, Size(5, 5)); imagePoints.push_back(tmp); @@ -323,7 +323,7 @@ protected: default: throw std::exception(); } - + Mat camMat_est = Mat::eye(3, 3, CV_64F), distCoeffs_est = Mat::zeros(1, 5, CV_64F); vector rvecs_est, tvecs_est; @@ -342,9 +342,9 @@ protected: compareCameraMatrs(camMat, camMat_est); compareDistCoeffs(distCoeffs, distCoeffs_est); compareShiftVecs(tvecs_exp, tvecs_est); - compareRotationVecs(rvecs_exp, rvecs_est); + compareRotationVecs(rvecs_exp, rvecs_est); - double rep_errorWOI = reprojectErrorWithoutIntrinsics(chessboard3D, rvecs_exp, tvecs_exp, rvecs_est, tvecs_est); + double rep_errorWOI = reprojectErrorWithoutIntrinsics(chessboard3D, rvecs_exp, tvecs_exp, rvecs_est, tvecs_est); rep_errorWOI /= brdsNum * cornersSize.area(); const double thres2 = 0.01; @@ -352,8 +352,8 @@ protected: { ts->printf( cvtest::TS::LOG, "%d) Too big reproject error without intrinsics = %f\n", r, rep_errorWOI); ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY); - } - + } + ts->printf( cvtest::TS::LOG, "%d) Testing solvePnP...\n", r); rvecs_spnp.resize(brdsNum); tvecs_spnp.resize(brdsNum); @@ -361,11 +361,11 @@ protected: solvePnP(Mat(objectPoints[i]), Mat(imagePoints[i]), camMat, distCoeffs, rvecs_spnp[i], tvecs_spnp[i]); compareShiftVecs(tvecs_exp, tvecs_spnp); - compareRotationVecs(rvecs_exp, rvecs_spnp); + compareRotationVecs(rvecs_exp, rvecs_spnp); } void run(int) - { + { ts->set_failed_test_info(cvtest::TS::OK); RNG& rng = theRNG(); @@ -373,11 +373,11 @@ protected: int progress = 0; int repeat_num = 3; for(r = 0; r < repeat_num; ++r) - { - const int brds_num = 20; + { + const int brds_num = 20; - Mat bg(Size(640, 480), CV_8UC3); - randu(bg, Scalar::all(32), Scalar::all(255)); + Mat bg(Size(640, 480), CV_8UC3); + randu(bg, Scalar::all(32), Scalar::all(255)); GaussianBlur(bg, bg, Size(5, 5), 2); double fx = 300 + (20 * (double)rng - 10); @@ -399,20 +399,20 @@ protected: Mat_ distCoeffs(1, 5, 0.0); distCoeffs << k1, k2, p1, p2, k3; - ChessBoardGenerator cbg(Size(9, 8)); + ChessBoardGenerator cbg(Size(9, 8)); cbg.min_cos = 0.9; cbg.cov = 0.8; progress = update_progress(progress, r, repeat_num, 0); - ts->printf( cvtest::TS::LOG, "\n"); + ts->printf( cvtest::TS::LOG, "\n"); prepareForTest(bg, camMat, distCoeffs, brds_num, cbg); - ts->printf( cvtest::TS::LOG, "artificial corners\n"); - runTest(bg.size(), camMat, distCoeffs, brds_num, cbg.cornersSize(), ARTIFICIAL_CORNERS); + ts->printf( cvtest::TS::LOG, "artificial corners\n"); + runTest(bg.size(), camMat, distCoeffs, brds_num, cbg.cornersSize(), ARTIFICIAL_CORNERS); progress = update_progress(progress, r, repeat_num, 0); ts->printf( cvtest::TS::LOG, "findChessboard corners\n"); - runTest(bg.size(), camMat, distCoeffs, brds_num, cbg.cornersSize(), JUST_FIND_CORNERS); + runTest(bg.size(), camMat, distCoeffs, brds_num, cbg.cornersSize(), JUST_FIND_CORNERS); progress = update_progress(progress, r, repeat_num, 0); ts->printf( cvtest::TS::LOG, "cornersSubPix corners\n"); @@ -424,6 +424,6 @@ protected: progress = update_progress(progress, r, repeat_num, 0); } } -}; +}; TEST(Calib3d_CalibrateCamera_CPP, accuracy_on_artificial_data) { CV_CalibrateCameraArtificialTest test; test.safe_run(); } diff --git a/modules/calib3d/test/test_chesscorners.cpp b/modules/calib3d/test/test_chesscorners.cpp index f0e58e3..f6d513d 100644 --- a/modules/calib3d/test/test_chesscorners.cpp +++ b/modules/calib3d/test/test_chesscorners.cpp @@ -54,9 +54,9 @@ void show_points( const Mat& gray, const Mat& u, const vector& v, Size { Mat rgb( gray.size(), CV_8U); merge(vector(3, gray), rgb); - + for(size_t i = 0; i < v.size(); i++ ) - circle( rgb, v[i], 3, CV_RGB(255, 0, 0), CV_FILLED); + circle( rgb, v[i], 3, CV_RGB(255, 0, 0), CV_FILLED); if( !u.empty() ) { @@ -67,7 +67,7 @@ void show_points( const Mat& gray, const Mat& u, const vector& v, Size } if (!v.empty()) { - Mat corners((int)v.size(), 1, CV_32FC2, (void*)&v[0]); + Mat corners((int)v.size(), 1, CV_32FC2, (void*)&v[0]); drawChessboardCorners( rgb, pattern_size, corners, was_found ); } //namedWindow( "test", 0 ); imshow( "test", rgb ); waitKey(0); @@ -122,11 +122,11 @@ double calcError(const vector& v, const Mat& u) //printf("\n"); err = min(err, err1); } - + #if defined(_L2_ERR) err = sqrt(err/count_exp); #endif //_L2_ERR - + return err; } @@ -137,8 +137,7 @@ const double precise_success_error_level = 2; /* ///////////////////// chess_corner_test ///////////////////////// */ void CV_ChessboardDetectorTest::run( int /*start_from */) { - cvtest::TS& ts = *this->ts; - ts.set_failed_test_info( cvtest::TS::OK ); + ts->set_failed_test_info( cvtest::TS::OK ); /*if (!checkByGenerator()) return;*/ @@ -146,23 +145,23 @@ void CV_ChessboardDetectorTest::run( int /*start_from */) { case CHESSBOARD: checkByGenerator(); - if (ts.get_err_code() != cvtest::TS::OK) + if (ts->get_err_code() != cvtest::TS::OK) { break; } run_batch("negative_list.dat"); - if (ts.get_err_code() != cvtest::TS::OK) + if (ts->get_err_code() != cvtest::TS::OK) { break; } run_batch("chessboard_list.dat"); - if (ts.get_err_code() != cvtest::TS::OK) + if (ts->get_err_code() != cvtest::TS::OK) { break; } - + run_batch("chessboard_list_subpixel.dat"); break; case CIRCLES_GRID: @@ -176,36 +175,34 @@ void CV_ChessboardDetectorTest::run( int /*start_from */) void CV_ChessboardDetectorTest::run_batch( const string& filename ) { - cvtest::TS& ts = *this->ts; - - ts.printf(cvtest::TS::LOG, "\nRunning batch %s\n", filename.c_str()); + ts->printf(cvtest::TS::LOG, "\nRunning batch %s\n", filename.c_str()); //#define WRITE_POINTS 1 -#ifndef WRITE_POINTS +#ifndef WRITE_POINTS double max_rough_error = 0, max_precise_error = 0; #endif string folder; switch( pattern ) { case CHESSBOARD: - folder = string(ts.get_data_path()) + "cameracalibration/"; + folder = string(ts->get_data_path()) + "cameracalibration/"; break; case CIRCLES_GRID: - folder = string(ts.get_data_path()) + "cameracalibration/circles/"; + folder = string(ts->get_data_path()) + "cameracalibration/circles/"; break; case ASYMMETRIC_CIRCLES_GRID: - folder = string(ts.get_data_path()) + "cameracalibration/asymmetric_circles/"; + folder = string(ts->get_data_path()) + "cameracalibration/asymmetric_circles/"; break; } FileStorage fs( folder + filename, FileStorage::READ ); FileNode board_list = fs["boards"]; - + if( !fs.isOpened() || board_list.empty() || !board_list.isSeq() || board_list.size() % 2 != 0 ) { - ts.printf( cvtest::TS::LOG, "%s can not be readed or is not valid\n", (folder + filename).c_str() ); - ts.printf( cvtest::TS::LOG, "fs.isOpened=%d, board_list.empty=%d, board_list.isSeq=%d,board_list.size()%2=%d\n", + ts->printf( cvtest::TS::LOG, "%s can not be readed or is not valid\n", (folder + filename).c_str() ); + ts->printf( cvtest::TS::LOG, "fs.isOpened=%d, board_list.empty=%d, board_list.isSeq=%d,board_list.size()%2=%d\n", fs.isOpened(), (int)board_list.empty(), board_list.isSeq(), board_list.size()%2); - ts.set_failed_test_info( cvtest::TS::FAIL_MISSING_TEST_DATA ); + ts->set_failed_test_info( cvtest::TS::FAIL_MISSING_TEST_DATA ); return; } @@ -216,29 +213,29 @@ void CV_ChessboardDetectorTest::run_batch( const string& filename ) for(int idx = 0; idx < max_idx; ++idx ) { - ts.update_context( this, idx, true ); - + ts->update_context( this, idx, true ); + /* read the image */ - string img_file = board_list[idx * 2]; + string img_file = board_list[idx * 2]; Mat gray = imread( folder + img_file, 0); - + if( gray.empty() ) { - ts.printf( cvtest::TS::LOG, "one of chessboard images can't be read: %s\n", img_file.c_str() ); - ts.set_failed_test_info( cvtest::TS::FAIL_MISSING_TEST_DATA ); + ts->printf( cvtest::TS::LOG, "one of chessboard images can't be read: %s\n", img_file.c_str() ); + ts->set_failed_test_info( cvtest::TS::FAIL_MISSING_TEST_DATA ); return; } - string filename = folder + (string)board_list[idx * 2 + 1]; + string _filename = folder + (string)board_list[idx * 2 + 1]; bool doesContatinChessboard; Mat expected; { - FileStorage fs(filename, FileStorage::READ); - fs["corners"] >> expected; - fs["isFound"] >> doesContatinChessboard; - fs.release(); - } - size_t count_exp = static_cast(expected.cols * expected.rows); + FileStorage fs1(_filename, FileStorage::READ); + fs1["corners"] >> expected; + fs1["isFound"] >> doesContatinChessboard; + fs1.release(); + } + size_t count_exp = static_cast(expected.cols * expected.rows); Size pattern_size = expected.size(); vector v; @@ -256,11 +253,11 @@ void CV_ChessboardDetectorTest::run_batch( const string& filename ) break; } show_points( gray, Mat(), v, pattern_size, result ); - + if( result ^ doesContatinChessboard || v.size() != count_exp ) { - ts.printf( cvtest::TS::LOG, "chessboard is detected incorrectly in %s\n", img_file.c_str() ); - ts.set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT ); + ts->printf( cvtest::TS::LOG, "chessboard is detected incorrectly in %s\n", img_file.c_str() ); + ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT ); return; } @@ -291,45 +288,45 @@ void CV_ChessboardDetectorTest::run_batch( const string& filename ) #if 1 if( err > precise_success_error_level ) { - ts.printf( cvtest::TS::LOG, "Image %s: bad accuracy of adjusted corners %f\n", img_file.c_str(), err ); - ts.set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY ); + ts->printf( cvtest::TS::LOG, "Image %s: bad accuracy of adjusted corners %f\n", img_file.c_str(), err ); + ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY ); return; } #endif - ts.printf(cvtest::TS::LOG, "Error on %s is %f\n", img_file.c_str(), err); + ts->printf(cvtest::TS::LOG, "Error on %s is %f\n", img_file.c_str(), err); max_precise_error = MAX( max_precise_error, err ); -#endif +#endif } #ifdef WRITE_POINTS Mat mat_v(pattern_size, CV_32FC2, (void*)&v[0]); - FileStorage fs(filename, FileStorage::WRITE); + FileStorage fs(_filename, FileStorage::WRITE); fs << "isFound" << result; fs << "corners" << mat_v; fs.release(); #endif progress = update_progress( progress, idx, max_idx, 0 ); - } - + } + sum_error /= count; - ts.printf(cvtest::TS::LOG, "Average error is %f\n", sum_error); + ts->printf(cvtest::TS::LOG, "Average error is %f\n", sum_error); } double calcErrorMinError(const Size& cornSz, const vector& corners_found, const vector& corners_generated) { - Mat m1(cornSz, CV_32FC2, (Point2f*)&corners_generated[0]); + Mat m1(cornSz, CV_32FC2, (Point2f*)&corners_generated[0]); Mat m2; flip(m1, m2, 0); Mat m3; flip(m1, m3, 1); m3 = m3.t(); flip(m3, m3, 1); - + Mat m4 = m1.t(); flip(m4, m4, 1); - double min1 = min(calcError(corners_found, m1), calcError(corners_found, m2)); - double min2 = min(calcError(corners_found, m3), calcError(corners_found, m4)); + double min1 = min(calcError(corners_found, m1), calcError(corners_found, m2)); + double min2 = min(calcError(corners_found, m3), calcError(corners_found, m4)); return min(min1, min2); } -bool validateData(const ChessBoardGenerator& cbg, const Size& imgSz, +bool validateData(const ChessBoardGenerator& cbg, const Size& imgSz, const vector& corners_generated) { Size cornersSize = cbg.cornersSize(); @@ -341,7 +338,7 @@ bool validateData(const ChessBoardGenerator& cbg, const Size& imgSz, for(int j = 1; j < mat.cols - 2; ++j) { const Point2f& cur = mat(i, j); - + tmp = norm( cur - mat(i + 1, j + 1) ); if (tmp < minNeibDist) tmp = minNeibDist; @@ -361,33 +358,33 @@ bool validateData(const ChessBoardGenerator& cbg, const Size& imgSz, const double threshold = 0.25; double cbsize = (max(cornersSize.width, cornersSize.height) + 1) * minNeibDist; - int imgsize = min(imgSz.height, imgSz.width); + int imgsize = min(imgSz.height, imgSz.width); return imgsize * threshold < cbsize; } bool CV_ChessboardDetectorTest::checkByGenerator() -{ +{ bool res = true; //theRNG() = 0x58e6e895b9913160; //cv::DefaultRngAuto dra; //theRNG() = *ts->get_rng(); - Mat bg(Size(800, 600), CV_8UC3, Scalar::all(255)); - randu(bg, Scalar::all(0), Scalar::all(255)); - GaussianBlur(bg, bg, Size(7,7), 3.0); - + Mat bg(Size(800, 600), CV_8UC3, Scalar::all(255)); + randu(bg, Scalar::all(0), Scalar::all(255)); + GaussianBlur(bg, bg, Size(7,7), 3.0); + Mat_ camMat(3, 3); camMat << 300.f, 0.f, bg.cols/2.f, 0, 300.f, bg.rows/2.f, 0.f, 0.f, 1.f; - + Mat_ distCoeffs(1, 5); distCoeffs << 1.2f, 0.2f, 0.f, 0.f, 0.f; const Size sizes[] = { Size(6, 6), Size(8, 6), Size(11, 12), Size(5, 4) }; - const size_t sizes_num = sizeof(sizes)/sizeof(sizes[0]); - const int test_num = 16; + const size_t sizes_num = sizeof(sizes)/sizeof(sizes[0]); + const int test_num = 16; int progress = 0; for(int i = 0; i < test_num; ++i) - { + { progress = update_progress( progress, i, test_num, 0 ); ChessBoardGenerator cbg(sizes[i % sizes_num]); @@ -398,37 +395,37 @@ bool CV_ChessboardDetectorTest::checkByGenerator() if(!validateData(cbg, cb.size(), corners_generated)) { ts->printf( cvtest::TS::LOG, "Chess board skipped - too small" ); - continue; + continue; } - /*cb = cb * 0.8 + Scalar::all(30); + /*cb = cb * 0.8 + Scalar::all(30); GaussianBlur(cb, cb, Size(3, 3), 0.8); */ - //cv::addWeighted(cb, 0.8, bg, 0.2, 20, cb); + //cv::addWeighted(cb, 0.8, bg, 0.2, 20, cb); //cv::namedWindow("CB"); cv::imshow("CB", cb); cv::waitKey(); - + vector corners_found; int flags = i % 8; // need to check branches for all flags bool found = findChessboardCorners(cb, cbg.cornersSize(), corners_found, flags); - if (!found) - { + if (!found) + { ts->printf( cvtest::TS::LOG, "Chess board corners not found\n" ); ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY ); res = false; - return res; + return res; } - double err = calcErrorMinError(cbg.cornersSize(), corners_found, corners_generated); + double err = calcErrorMinError(cbg.cornersSize(), corners_found, corners_generated); if( err > rough_success_error_level ) { ts->printf( cvtest::TS::LOG, "bad accuracy of corner guesses" ); ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY ); res = false; return res; - } - } + } + } /* ***** negative ***** */ - { + { vector corners_found; bool found = findChessboardCorners(bg, Size(8, 7), corners_found); if (found) @@ -437,27 +434,27 @@ bool CV_ChessboardDetectorTest::checkByGenerator() ChessBoardGenerator cbg(Size(8, 7)); vector cg; - Mat cb = cbg(bg, camMat, distCoeffs, cg); + Mat cb = cbg(bg, camMat, distCoeffs, cg); found = findChessboardCorners(cb, Size(3, 4), corners_found); if (found) - res = false; + res = false; Point2f c = std::accumulate(cg.begin(), cg.end(), Point2f(), plus()) * (1.f/cg.size()); Mat_ aff(2, 3); aff << 1.0, 0.0, -(double)c.x, 0.0, 1.0, 0.0; Mat sh; - warpAffine(cb, sh, aff, cb.size()); + warpAffine(cb, sh, aff, cb.size()); found = findChessboardCorners(sh, cbg.cornersSize(), corners_found); if (found) - res = false; - + res = false; + vector< vector > cnts(1); vector& cnt = cnts[0]; - cnt.push_back(cg[ 0]); cnt.push_back(cg[0+2]); - cnt.push_back(cg[7+0]); cnt.push_back(cg[7+2]); + cnt.push_back(cg[ 0]); cnt.push_back(cg[0+2]); + cnt.push_back(cg[7+0]); cnt.push_back(cg[7+2]); cv::drawContours(cb, cnts, -1, Scalar::all(128), CV_FILLED); found = findChessboardCorners(cb, cbg.cornersSize(), corners_found); @@ -466,7 +463,7 @@ bool CV_ChessboardDetectorTest::checkByGenerator() cv::drawChessboardCorners(cb, cbg.cornersSize(), Mat(corners_found), found); } - + return res; } diff --git a/modules/calib3d/test/test_compose_rt.cpp b/modules/calib3d/test/test_compose_rt.cpp index b55318c..b71288e 100644 --- a/modules/calib3d/test/test_compose_rt.cpp +++ b/modules/calib3d/test/test_compose_rt.cpp @@ -47,87 +47,87 @@ using namespace std; class Differential { -public: - typedef Mat_ mat_t; +public: + typedef Mat_ mat_t; - Differential(double eps_, const mat_t& rv1_, const mat_t& tv1_, const mat_t& rv2_, const mat_t& tv2_) + Differential(double eps_, const mat_t& rv1_, const mat_t& tv1_, const mat_t& rv2_, const mat_t& tv2_) : rv1(rv1_), tv1(tv1_), rv2(rv2_), tv2(tv2_), eps(eps_), ev(3, 1) {} void dRv1(mat_t& dr3_dr1, mat_t& dt3_dr1) - { + { dr3_dr1.create(3, 3); dt3_dr1.create(3, 3); - - for(int i = 0; i < 3; ++i) + + for(int i = 0; i < 3; ++i) { - ev.setTo(Scalar(0)); ev(i, 0) = eps; - - composeRT( rv1 + ev, tv1, rv2, tv2, rv3_p, tv3_p); + ev.setTo(Scalar(0)); ev(i, 0) = eps; + + composeRT( rv1 + ev, tv1, rv2, tv2, rv3_p, tv3_p); composeRT( rv1 - ev, tv1, rv2, tv2, rv3_m, tv3_m); - dr3_dr1.col(i) = rv3_p - rv3_m; - dt3_dr1.col(i) = tv3_p - tv3_m; + dr3_dr1.col(i) = rv3_p - rv3_m; + dt3_dr1.col(i) = tv3_p - tv3_m; } dr3_dr1 /= 2 * eps; dt3_dr1 /= 2 * eps; } void dRv2(mat_t& dr3_dr2, mat_t& dt3_dr2) - { + { dr3_dr2.create(3, 3); dt3_dr2.create(3, 3); - - for(int i = 0; i < 3; ++i) + + for(int i = 0; i < 3; ++i) { - ev.setTo(Scalar(0)); ev(i, 0) = eps; - - composeRT( rv1, tv1, rv2 + ev, tv2, rv3_p, tv3_p); + ev.setTo(Scalar(0)); ev(i, 0) = eps; + + composeRT( rv1, tv1, rv2 + ev, tv2, rv3_p, tv3_p); composeRT( rv1, tv1, rv2 - ev, tv2, rv3_m, tv3_m); - dr3_dr2.col(i) = rv3_p - rv3_m; - dt3_dr2.col(i) = tv3_p - tv3_m; + dr3_dr2.col(i) = rv3_p - rv3_m; + dt3_dr2.col(i) = tv3_p - tv3_m; } dr3_dr2 /= 2 * eps; dt3_dr2 /= 2 * eps; } void dTv1(mat_t& drt3_dt1, mat_t& dt3_dt1) - { + { drt3_dt1.create(3, 3); dt3_dt1.create(3, 3); - - for(int i = 0; i < 3; ++i) + + for(int i = 0; i < 3; ++i) { - ev.setTo(Scalar(0)); ev(i, 0) = eps; - - composeRT( rv1, tv1 + ev, rv2, tv2, rv3_p, tv3_p); + ev.setTo(Scalar(0)); ev(i, 0) = eps; + + composeRT( rv1, tv1 + ev, rv2, tv2, rv3_p, tv3_p); composeRT( rv1, tv1 - ev, rv2, tv2, rv3_m, tv3_m); - drt3_dt1.col(i) = rv3_p - rv3_m; - dt3_dt1.col(i) = tv3_p - tv3_m; + drt3_dt1.col(i) = rv3_p - rv3_m; + dt3_dt1.col(i) = tv3_p - tv3_m; } drt3_dt1 /= 2 * eps; dt3_dt1 /= 2 * eps; } void dTv2(mat_t& dr3_dt2, mat_t& dt3_dt2) - { + { dr3_dt2.create(3, 3); dt3_dt2.create(3, 3); - - for(int i = 0; i < 3; ++i) + + for(int i = 0; i < 3; ++i) { - ev.setTo(Scalar(0)); ev(i, 0) = eps; - - composeRT( rv1, tv1, rv2, tv2 + ev, rv3_p, tv3_p); + ev.setTo(Scalar(0)); ev(i, 0) = eps; + + composeRT( rv1, tv1, rv2, tv2 + ev, rv3_p, tv3_p); composeRT( rv1, tv1, rv2, tv2 - ev, rv3_m, tv3_m); - dr3_dt2.col(i) = rv3_p - rv3_m; - dt3_dt2.col(i) = tv3_p - tv3_m; + dr3_dt2.col(i) = rv3_p - rv3_m; + dt3_dt2.col(i) = tv3_p - tv3_m; } dr3_dt2 /= 2 * eps; dt3_dt2 /= 2 * eps; } - + private: const mat_t& rv1, tv1, rv2, tv2; double eps; Mat_ ev; - + Differential& operator=(const Differential&); - Mat rv3_m, tv3_m, rv3_p, tv3_p; + Mat rv3_m, tv3_m, rv3_p, tv3_p; }; class CV_composeRT_Test : public cvtest::BaseTest @@ -135,24 +135,23 @@ class CV_composeRT_Test : public cvtest::BaseTest public: CV_composeRT_Test() {} ~CV_composeRT_Test() {} -protected: - +protected: + void run(int) { - cvtest::TS& ts = *this->ts; - ts.set_failed_test_info(cvtest::TS::OK); - - Mat_ rvec1(3, 1), tvec1(3, 1), rvec2(3, 1), tvec2(3, 1); + ts->set_failed_test_info(cvtest::TS::OK); + + Mat_ rvec1(3, 1), tvec1(3, 1), rvec2(3, 1), tvec2(3, 1); randu(rvec1, Scalar(0), Scalar(6.29)); randu(rvec2, Scalar(0), Scalar(6.29)); randu(tvec1, Scalar(-2), Scalar(2)); randu(tvec2, Scalar(-2), Scalar(2)); - + Mat rvec3, tvec3; composeRT(rvec1, tvec1, rvec2, tvec2, rvec3, tvec3); - + Mat rvec3_exp, tvec3_exp; Mat rmat1, rmat2; @@ -164,53 +163,53 @@ protected: const double thres = 1e-5; if (norm(rvec3_exp, rvec3) > thres || norm(tvec3_exp, tvec3) > thres) - ts.set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY); + ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY); const double eps = 1e-3; Differential diff(eps, rvec1, tvec1, rvec2, tvec2); - + Mat dr3dr1, dr3dt1, dr3dr2, dr3dt2, dt3dr1, dt3dt1, dt3dr2, dt3dt2; - composeRT(rvec1, tvec1, rvec2, tvec2, rvec3, tvec3, + composeRT(rvec1, tvec1, rvec2, tvec2, rvec3, tvec3, dr3dr1, dr3dt1, dr3dr2, dr3dt2, dt3dr1, dt3dt1, dt3dr2, dt3dt2); - + Mat_ dr3_dr1, dt3_dr1; diff.dRv1(dr3_dr1, dt3_dr1); if (norm(dr3_dr1, dr3dr1) > thres || norm(dt3_dr1, dt3dr1) > thres) - { - ts.printf( cvtest::TS::LOG, "Invalid derivates by r1\n" ); - ts.set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY); + { + ts->printf( cvtest::TS::LOG, "Invalid derivates by r1\n" ); + ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY); } Mat_ dr3_dr2, dt3_dr2; diff.dRv2(dr3_dr2, dt3_dr2); if (norm(dr3_dr2, dr3dr2) > thres || norm(dt3_dr2, dt3dr2) > thres) - { - ts.printf( cvtest::TS::LOG, "Invalid derivates by r2\n" ); - ts.set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY); + { + ts->printf( cvtest::TS::LOG, "Invalid derivates by r2\n" ); + ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY); } Mat_ dr3_dt1, dt3_dt1; diff.dTv1(dr3_dt1, dt3_dt1); if (norm(dr3_dt1, dr3dt1) > thres || norm(dt3_dt1, dt3dt1) > thres) - { - ts.printf( cvtest::TS::LOG, "Invalid derivates by t1\n" ); - ts.set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY); + { + ts->printf( cvtest::TS::LOG, "Invalid derivates by t1\n" ); + ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY); } - + Mat_ dr3_dt2, dt3_dt2; diff.dTv2(dr3_dt2, dt3_dt2); if (norm(dr3_dt2, dr3dt2) > thres || norm(dt3_dt2, dt3dt2) > thres) - { - ts.printf( cvtest::TS::LOG, "Invalid derivates by t2\n" ); - ts.set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY); + { + ts->printf( cvtest::TS::LOG, "Invalid derivates by t2\n" ); + ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY); } - } -}; - + } +}; + TEST(Calib3d_ComposeRT, accuracy) { CV_composeRT_Test test; test.safe_run(); } diff --git a/modules/calib3d/test/test_homography.cpp b/modules/calib3d/test/test_homography.cpp index a424782..49c20e7 100644 --- a/modules/calib3d/test/test_homography.cpp +++ b/modules/calib3d/test/test_homography.cpp @@ -86,20 +86,20 @@ protected: double sigma; private: - float max_diff, max_2diff; - bool check_matrix_size(const cv::Mat& H); - bool check_matrix_diff(const cv::Mat& original, const cv::Mat& found, const int norm_type, double &diff); + float max_diff, max_2diff; + bool check_matrix_size(const cv::Mat& H); + bool check_matrix_diff(const cv::Mat& original, const cv::Mat& found, const int norm_type, double &diff); int check_ransac_mask_1(const Mat& src, const Mat& mask); - int check_ransac_mask_2(const Mat& original_mask, const Mat& found_mask); - - void print_information_1(int j, int N, int method, const Mat& H); - void print_information_2(int j, int N, int method, const Mat& H, const Mat& H_res, int k, double diff); - void print_information_3(int j, int N, const Mat& mask); - void print_information_4(int method, int j, int N, int k, int l, double diff); - void print_information_5(int method, int j, int N, int l, double diff); - void print_information_6(int j, int N, int k, double diff, bool value); - void print_information_7(int j, int N, int k, double diff, bool original_value, bool found_value); - void print_information_8(int j, int N, int k, int l, double diff); + int check_ransac_mask_2(const Mat& original_mask, const Mat& found_mask); + + void print_information_1(int j, int N, int method, const Mat& H); + void print_information_2(int j, int N, int method, const Mat& H, const Mat& H_res, int k, double diff); + void print_information_3(int j, int N, const Mat& mask); + void print_information_4(int method, int j, int N, int k, int l, double diff); + void print_information_5(int method, int j, int N, int l, double diff); + void print_information_6(int j, int N, int k, double diff, bool value); + void print_information_7(int j, int N, int k, double diff, bool original_value, bool found_value); + void print_information_8(int j, int N, int k, int l, double diff); }; CV_HomographyTest::CV_HomographyTest() : max_diff(1e-2f), max_2diff(2e-2f) @@ -112,7 +112,7 @@ CV_HomographyTest::CV_HomographyTest() : max_diff(1e-2f), max_2diff(2e-2f) CV_HomographyTest::~CV_HomographyTest() {} -bool CV_HomographyTest::check_matrix_size(const cv::Mat& H) +bool CV_HomographyTest::check_matrix_size(const cv::Mat& H) { return (H.rows == 3) && (H.cols == 3); } @@ -138,25 +138,25 @@ int CV_HomographyTest::check_ransac_mask_2(const Mat& original_mask, const Mat& return 0; } -void CV_HomographyTest::print_information_1(int j, int N, int method, const Mat& H) +void CV_HomographyTest::print_information_1(int j, int N, int _method, const Mat& H) { cout << endl; cout << "Checking for homography matrix sizes..." << endl; cout << endl; cout << "Type of srcPoints: "; if ((j>-1) && (j<2)) cout << "Mat of CV_32FC2"; else cout << "vector "; cout << " Type of dstPoints: "; if (j % 2 == 0) cout << "Mat of CV_32FC2"; else cout << "vector "; cout << endl; cout << "Count of points: " << N << endl; cout << endl; - cout << "Method: "; if (method == 0) cout << 0; else if (method == 8) cout << "RANSAC"; else cout << "LMEDS"; cout << endl; + cout << "Method: "; if (_method == 0) cout << 0; else if (_method == 8) cout << "RANSAC"; else cout << "LMEDS"; cout << endl; cout << "Homography matrix:" << endl; cout << endl; cout << H << endl; cout << endl; cout << "Number of rows: " << H.rows << " Number of cols: " << H.cols << endl; cout << endl; } -void CV_HomographyTest::print_information_2(int j, int N, int method, const Mat& H, const Mat& H_res, int k, double diff) +void CV_HomographyTest::print_information_2(int j, int N, int _method, const Mat& H, const Mat& H_res, int k, double diff) { cout << endl; cout << "Checking for accuracy of homography matrix computing..." << endl; cout << endl; cout << "Type of srcPoints: "; if ((j>-1) && (j<2)) cout << "Mat of CV_32FC2"; else cout << "vector "; cout << " Type of dstPoints: "; if (j % 2 == 0) cout << "Mat of CV_32FC2"; else cout << "vector "; cout << endl; cout << "Count of points: " << N << endl; cout << endl; - cout << "Method: "; if (method == 0) cout << 0; else if (method == 8) cout << "RANSAC"; else cout << "LMEDS"; cout << endl; + cout << "Method: "; if (_method == 0) cout << 0; else if (_method == 8) cout << "RANSAC"; else cout << "LMEDS"; cout << endl; cout << "Original matrix:" << endl; cout << endl; cout << H << endl; cout << endl; cout << "Found matrix:" << endl; cout << endl; @@ -178,10 +178,10 @@ void CV_HomographyTest::print_information_3(int j, int N, const Mat& mask) cout << "Number of rows: " << mask.rows << " Number of cols: " << mask.cols << endl; cout << endl; } -void CV_HomographyTest::print_information_4(int method, int j, int N, int k, int l, double diff) +void CV_HomographyTest::print_information_4(int _method, int j, int N, int k, int l, double diff) { cout << endl; cout << "Checking for accuracy of reprojection error computing..." << endl; cout << endl; - cout << "Method: "; if (method == 0) cout << 0 << endl; else cout << "CV_LMEDS" << endl; + cout << "Method: "; if (_method == 0) cout << 0 << endl; else cout << "CV_LMEDS" << endl; cout << "Type of srcPoints: "; if ((j>-1) && (j<2)) cout << "Mat of CV_32FC2"; else cout << "vector "; cout << " Type of dstPoints: "; if (j % 2 == 0) cout << "Mat of CV_32FC2"; else cout << "vector "; cout << endl; cout << "Sigma of normal noise: " << sigma << endl; @@ -192,10 +192,10 @@ void CV_HomographyTest::print_information_4(int method, int j, int N, int k, int cout << "Maxumum allowed difference: " << max_2diff << endl; cout << endl; } -void CV_HomographyTest::print_information_5(int method, int j, int N, int l, double diff) -{ +void CV_HomographyTest::print_information_5(int _method, int j, int N, int l, double diff) +{ cout << endl; cout << "Checking for accuracy of reprojection error computing..." << endl; cout << endl; - cout << "Method: "; if (method == 0) cout << 0 << endl; else cout << "CV_LMEDS" << endl; + cout << "Method: "; if (_method == 0) cout << 0 << endl; else cout << "CV_LMEDS" << endl; cout << "Type of srcPoints: "; if ((j>-1) && (j<2)) cout << "Mat of CV_32FC2"; else cout << "vector "; cout << " Type of dstPoints: "; if (j % 2 == 0) cout << "Mat of CV_32FC2"; else cout << "vector "; cout << endl; cout << "Sigma of normal noise: " << sigma << endl; @@ -371,7 +371,7 @@ void CV_HomographyTest::run(int) if (code) { print_information_3(j, N, mask[j]); - + switch (code) { case 1: { CV_Error(CALIB3D_HOMOGRAPHY_ERROR_RANSAC_MASK, MESSAGE_RANSAC_MASK_1); break; } @@ -380,7 +380,7 @@ void CV_HomographyTest::run(int) default: break; } - + return; } @@ -412,7 +412,7 @@ void CV_HomographyTest::run(int) { case 0: case CV_LMEDS: - { + { Mat H_res_64 [4] = { cv::findHomography(src_mat_2f, dst_mat_2f), cv::findHomography(src_mat_2f, dst_vec), cv::findHomography(src_vec, dst_mat_2f), @@ -465,7 +465,7 @@ void CV_HomographyTest::run(int) } continue; - } + } case CV_RANSAC: { cv::Mat mask_res [4]; @@ -555,7 +555,7 @@ void CV_HomographyTest::run(int) } } } - + continue; } diff --git a/modules/calib3d/test/test_solvepnp_ransac.cpp b/modules/calib3d/test/test_solvepnp_ransac.cpp index 6683fbc..8322214 100644 --- a/modules/calib3d/test/test_solvepnp_ransac.cpp +++ b/modules/calib3d/test/test_solvepnp_ransac.cpp @@ -106,7 +106,7 @@ protected: } } - virtual bool runTest(RNG& rng, int mode, int method, const vector& points, const double* eps, double& maxError) + virtual bool runTest(RNG& rng, int mode, int method, const vector& points, const double* epsilon, double& maxError) { Mat rvec, tvec; vector inliers; @@ -136,7 +136,7 @@ protected: bool isTestSuccess = inliers.size() >= points.size()*0.95; double rvecDiff = norm(rvec-trueRvec), tvecDiff = norm(tvec-trueTvec); - isTestSuccess = isTestSuccess && rvecDiff < eps[method] && tvecDiff < eps[method]; + isTestSuccess = isTestSuccess && rvecDiff < epsilon[method] && tvecDiff < epsilon[method]; double error = rvecDiff > tvecDiff ? rvecDiff : tvecDiff; //cout << error << " " << inliers.size() << " " << eps[method] << endl; if (error > maxError) @@ -147,8 +147,7 @@ protected: void run(int) { - cvtest::TS& ts = *this->ts; - ts.set_failed_test_info(cvtest::TS::OK); + ts->set_failed_test_info(cvtest::TS::OK); vector points; const int pointsCount = 500; @@ -157,7 +156,7 @@ protected: const int methodsCount = 3; - RNG rng = ts.get_rng(); + RNG rng = ts->get_rng(); for (int mode = 0; mode < 2; mode++) @@ -174,9 +173,9 @@ protected: //cout << maxError << " " << successfulTestsCount << endl; if (successfulTestsCount < 0.7*totalTestsCount) { - ts.printf( cvtest::TS::LOG, "Invalid accuracy for method %d, failed %d tests from %d, maximum error equals %f, distortion mode equals %d\n", + ts->printf( cvtest::TS::LOG, "Invalid accuracy for method %d, failed %d tests from %d, maximum error equals %f, distortion mode equals %d\n", method, totalTestsCount - successfulTestsCount, totalTestsCount, maxError, mode); - ts.set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY); + ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY); } } } @@ -198,7 +197,7 @@ public: ~CV_solvePnP_Test() {} protected: - virtual bool runTest(RNG& rng, int mode, int method, const vector& points, const double* eps, double& maxError) + virtual bool runTest(RNG& rng, int mode, int method, const vector& points, const double* epsilon, double& maxError) { Mat rvec, tvec; Mat trueRvec, trueTvec; @@ -226,7 +225,7 @@ protected: false, method); double rvecDiff = norm(rvec-trueRvec), tvecDiff = norm(tvec-trueTvec); - bool isTestSuccess = rvecDiff < eps[method] && tvecDiff < eps[method]; + bool isTestSuccess = rvecDiff < epsilon[method] && tvecDiff < epsilon[method]; double error = rvecDiff > tvecDiff ? rvecDiff : tvecDiff; if (error > maxError) diff --git a/modules/calib3d/test/test_stereomatching.cpp b/modules/calib3d/test/test_stereomatching.cpp index a2ba91f..4b35dad 100755 --- a/modules/calib3d/test/test_stereomatching.cpp +++ b/modules/calib3d/test/test_stereomatching.cpp @@ -421,7 +421,7 @@ void CV_StereoMatchingTest::run(int) ts->set_failed_test_info( code ); return; } - + string fullResultFilename = dataPath + ALGORITHMS_DIR + algorithmName + RESULT_FILE; FileStorage resFS( fullResultFilename, FileStorage::READ ); bool isWrite = true; // write or compare results @@ -593,11 +593,11 @@ int CV_StereoMatchingTest::readDatasetsParams( FileStorage& fs ) assert(fn.isSeq()); for( int i = 0; i < (int)fn.size(); i+=3 ) { - string name = fn[i]; + string _name = fn[i]; DatasetParams params; string sf = fn[i+1]; params.dispScaleFactor = atoi(sf.c_str()); string uv = fn[i+2]; params.dispUnknVal = atoi(uv.c_str()); - datasetsParams[name] = params; + datasetsParams[_name] = params; } return cvtest::TS::OK; } @@ -713,7 +713,7 @@ class CV_StereoSGBMTest : public CV_StereoMatchingTest public: CV_StereoSGBMTest() { - name = "stereosgbm"; + name = "stereosgbm"; fill(rmsEps.begin(), rmsEps.end(), 0.25f); fill(fracEps.begin(), fracEps.end(), 0.01f); } diff --git a/modules/contrib/include/opencv2/contrib/contrib.hpp b/modules/contrib/include/opencv2/contrib/contrib.hpp index 638eadb..875dd89 100644 --- a/modules/contrib/include/opencv2/contrib/contrib.hpp +++ b/modules/contrib/include/opencv2/contrib/contrib.hpp @@ -63,48 +63,48 @@ private: GSD_INTENSITY_LT = 15, GSD_INTENSITY_UT = 250 }; - + class CV_EXPORTS Histogram { private: enum { HistogramSize = (GSD_HUE_UT - GSD_HUE_LT + 1) }; - + protected: int findCoverageIndex(double surfaceToCover, int defaultValue = 0); - + public: CvHistogram *fHistogram; Histogram(); virtual ~Histogram(); - + void findCurveThresholds(int &x1, int &x2, double percent = 0.05); void mergeWith(Histogram *source, double weight); }; - + int nStartCounter, nFrameCount, nSkinHueLowerBound, nSkinHueUpperBound, nMorphingMethod, nSamplingDivider; double fHistogramMergeFactor, fHuePercentCovered; Histogram histogramHueMotion, skinHueHistogram; IplImage *imgHueFrame, *imgSaturationFrame, *imgLastGrayFrame, *imgMotionFrame, *imgFilteredFrame; IplImage *imgShrinked, *imgTemp, *imgGrayFrame, *imgHSVFrame; - + protected: void initData(IplImage *src, int widthDivider, int heightDivider); void adaptiveFilter(); - + public: - + enum { MORPHING_METHOD_NONE = 0, MORPHING_METHOD_ERODE = 1, MORPHING_METHOD_ERODE_ERODE = 2, MORPHING_METHOD_ERODE_DILATE = 3 }; - + CvAdaptiveSkinDetector(int samplingDivider = 1, int morphingMethod = MORPHING_METHOD_NONE); virtual ~CvAdaptiveSkinDetector(); - + virtual void process(IplImage *inputBGRImage, IplImage *outputHueMask); }; @@ -116,7 +116,7 @@ public: class CV_EXPORTS CvFuzzyPoint { public: double x, y, value; - + CvFuzzyPoint(double _x, double _y); }; @@ -124,13 +124,13 @@ class CV_EXPORTS CvFuzzyCurve { private: std::vector points; double value, centre; - + bool between(double x, double x1, double x2); - + public: CvFuzzyCurve(); ~CvFuzzyCurve(); - + void setCentre(double _centre); double getCentre(); void clear(); @@ -143,7 +143,7 @@ public: class CV_EXPORTS CvFuzzyFunction { public: std::vector curves; - + CvFuzzyFunction(); ~CvFuzzyFunction(); void addCurve(CvFuzzyCurve *curve, double value = 0); @@ -186,7 +186,7 @@ private: FuzzyResizer(); int calcOutput(double edgeDensity, double density); }; - + class SearchWindow { public: @@ -200,7 +200,7 @@ private: double density; unsigned int depthLow, depthHigh; int verticalEdgeLeft, verticalEdgeRight, horizontalEdgeTop, horizontalEdgeBottom; - + SearchWindow(); ~SearchWindow(); void setSize(int _x, int _y, int _width, int _height); @@ -212,7 +212,7 @@ private: void getResizeAttribsEdgeDensityFuzzy(int &resizeDx, int &resizeDy, int &resizeDw, int &resizeDh); bool meanShift(IplImage *maskImage, IplImage *depthMap, int maxIteration, bool initDepth); }; - + public: enum TrackingState { @@ -222,40 +222,40 @@ public: tsSetWindow = 3, tsDisabled = 10 }; - + enum ResizeMethod { rmEdgeDensityLinear = 0, rmEdgeDensityFuzzy = 1, rmInnerDensity = 2 }; - + enum { MinKernelMass = 1000 }; - + SearchWindow kernel; int searchMode; - + private: enum { MaxMeanShiftIteration = 5, MaxSetSizeIteration = 5 }; - + void findOptimumSearchWindow(SearchWindow &searchWindow, IplImage *maskImage, IplImage *depthMap, int maxIteration, int resizeMethod, bool initDepth); - + public: CvFuzzyMeanShiftTracker(); ~CvFuzzyMeanShiftTracker(); - + void track(IplImage *maskImage, IplImage *depthMap, int resizeMethod, bool resetSearch, int minKernelMass = MinKernelMass); }; namespace cv { - + class CV_EXPORTS Octree { public: @@ -268,11 +268,11 @@ namespace cv bool isLeaf; int children[8]; }; - + Octree(); Octree( const vector& points, int maxLevels = 10, int minPoints = 20 ); virtual ~Octree(); - + virtual void buildTree( const vector& points, int maxLevels = 10, int minPoints = 20 ); virtual void getPointsWithinSphere( const Point3f& center, float radius, vector& points ) const; @@ -281,85 +281,85 @@ namespace cv int minPoints; vector points; vector nodes; - + virtual void buildNext(size_t node_ind); }; - - + + class CV_EXPORTS Mesh3D { public: struct EmptyMeshException {}; - + Mesh3D(); Mesh3D(const vector& vtx); ~Mesh3D(); - + void buildOctree(); void clearOctree(); float estimateResolution(float tryRatio = 0.1f); void computeNormals(float normalRadius, int minNeighbors = 20); void computeNormals(const vector& subset, float normalRadius, int minNeighbors = 20); - + void writeAsVrml(const String& file, const vector& colors = vector()) const; - + vector vtx; vector normals; float resolution; Octree octree; - + const static Point3f allzero; }; - + class CV_EXPORTS SpinImageModel { public: - + /* model parameters, leave unset for default or auto estimate */ float normalRadius; int minNeighbors; - + float binSize; int imageWidth; - + float lambda; float gamma; - + float T_GeometriccConsistency; float T_GroupingCorespondances; - + /* public interface */ SpinImageModel(); explicit SpinImageModel(const Mesh3D& mesh); ~SpinImageModel(); - + void setLogger(std::ostream* log); void selectRandomSubset(float ratio); void setSubset(const vector& subset); void compute(); - + void match(const SpinImageModel& scene, vector< vector >& result); - + Mat packRandomScaledSpins(bool separateScale = false, size_t xCount = 10, size_t yCount = 10) const; - + size_t getSpinCount() const { return spinImages.rows; } Mat getSpinImage(size_t index) const { return spinImages.row((int)index); } const Point3f& getSpinVertex(size_t index) const { return mesh.vtx[subset[index]]; } const Point3f& getSpinNormal(size_t index) const { return mesh.normals[subset[index]]; } - + const Mesh3D& getMesh() const { return mesh; } Mesh3D& getMesh() { return mesh; } - + /* static utility functions */ static bool spinCorrelation(const Mat& spin1, const Mat& spin2, float lambda, float& result); - + static Point2f calcSpinMapCoo(const Point3f& point, const Point3f& vertex, const Point3f& normal); - + static float geometricConsistency(const Point3f& pointScene1, const Point3f& normalScene1, const Point3f& pointModel1, const Point3f& normalModel1, const Point3f& pointScene2, const Point3f& normalScene2, const Point3f& pointModel2, const Point3f& normalModel2); - + static float groupingCreteria(const Point3f& pointScene1, const Point3f& normalScene1, const Point3f& pointModel1, const Point3f& normalModel1, const Point3f& pointScene2, const Point3f& normalScene2, @@ -367,40 +367,40 @@ namespace cv float gamma); protected: void defaultParams(); - + void matchSpinToModel(const Mat& spin, vector& indeces, vector& corrCoeffs, bool useExtremeOutliers = true) const; - + void repackSpinImages(const vector& mask, Mat& spinImages, bool reAlloc = true) const; - + vector subset; Mesh3D mesh; Mat spinImages; std::ostream* out; }; - + class CV_EXPORTS TickMeter { public: TickMeter(); void start(); void stop(); - + int64 getTimeTicks() const; double getTimeMicro() const; double getTimeMilli() const; double getTimeSec() const; int64 getCounter() const; - + void reset(); private: int64 counter; int64 sumTime; int64 startTime; }; - + CV_EXPORTS std::ostream& operator<<(std::ostream& out, const TickMeter& tm); - + class CV_EXPORTS SelfSimDescriptor { public: @@ -412,29 +412,29 @@ namespace cv SelfSimDescriptor(const SelfSimDescriptor& ss); virtual ~SelfSimDescriptor(); SelfSimDescriptor& operator = (const SelfSimDescriptor& ss); - + size_t getDescriptorSize() const; Size getGridSize( Size imgsize, Size winStride ) const; - + virtual void compute(const Mat& img, vector& descriptors, Size winStride=Size(), const vector& locations=vector()) const; virtual void computeLogPolarMapping(Mat& mappingMask) const; virtual void SSD(const Mat& img, Point pt, Mat& ssd) const; - + int smallSize; int largeSize; int startDistanceBucket; int numberOfDistanceBuckets; int numberOfAngles; - + enum { DEFAULT_SMALL_SIZE = 5, DEFAULT_LARGE_SIZE = 41, DEFAULT_NUM_ANGLES = 20, DEFAULT_START_DISTANCE_BUCKET = 3, DEFAULT_NUM_DISTANCE_BUCKETS = 7 }; }; - - + + typedef bool (*BundleAdjustCallback)(int iteration, double norm_error, void* user_data); - + class LevMarqSparse { public: LevMarqSparse(); @@ -447,9 +447,9 @@ namespace cv Mat& visibility, // visibility matrix. rows correspond to points, columns correspond to cameras // 1 - point is visible for the camera, 0 - invisible Mat& P0, // starting vector of parameters, first cameras then points - Mat& X, // measurements, in order of visibility. non visible cases are skipped + Mat& X, // measurements, in order of visibility. non visible cases are skipped TermCriteria criteria, // termination criteria - + // callback for estimation of Jacobian matrices void (CV_CDECL * fjac)(int i, int j, Mat& point_params, Mat& cam_params, Mat& A, Mat& B, void* data), @@ -459,9 +459,9 @@ namespace cv void* data, // user-specific data passed to the callbacks BundleAdjustCallback cb, void* user_data ); - + virtual ~LevMarqSparse(); - + virtual void run( int npoints, // number of points int ncameras, // number of cameras int nPointParams, // number of params per one point (3 in case of 3D points) @@ -471,9 +471,9 @@ namespace cv Mat& visibility, // visibility matrix. rows correspond to points, columns correspond to cameras // 1 - point is visible for the camera, 0 - invisible Mat& P0, // starting vector of parameters, first cameras then points - Mat& X, // measurements, in order of visibility. non visible cases are skipped + Mat& X, // measurements, in order of visibility. non visible cases are skipped TermCriteria criteria, // termination criteria - + // callback for estimation of Jacobian matrices void (CV_CDECL * fjac)(int i, int j, Mat& point_params, Mat& cam_params, Mat& A, Mat& B, void* data), @@ -482,13 +482,13 @@ namespace cv Mat& cam_params, Mat& estim, void* data), void* data // user-specific data passed to the callbacks ); - + virtual void clear(); - + // useful function to do simple bundle adjustment tasks static void bundleAdjust(vector& points, // positions of points in global coordinate system (input and output) const vector >& imagePoints, // projections of 3d points for every camera - const vector >& visibility, // visibility of 3d points for every camera + const vector >& visibility, // visibility of 3d points for every camera vector& cameraMatrix, // intrinsic matrices of all cameras (input and output) vector& R, // rotation matrices of all cameras (input and output) vector& T, // translation vector of all cameras (input and output) @@ -496,123 +496,123 @@ namespace cv const TermCriteria& criteria= TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, DBL_EPSILON), BundleAdjustCallback cb = 0, void* user_data = 0); - + public: virtual void optimize(CvMat &_vis); //main function that runs minimization - + //iteratively asks for measurement for visible camera-point pairs void ask_for_proj(CvMat &_vis,bool once=false); //iteratively asks for Jacobians for every camera_point pair void ask_for_projac(CvMat &_vis); - + CvMat* err; //error X-hX double prevErrNorm, errNorm; double lambda; CvTermCriteria criteria; int iters; - + CvMat** U; //size of array is equal to number of cameras CvMat** V; //size of array is equal to number of points CvMat** inv_V_star; //inverse of V* - + CvMat** A; CvMat** B; CvMat** W; - - CvMat* X; //measurement - CvMat* hX; //current measurement extimation given new parameter vector - - CvMat* prevP; //current already accepted parameter. + + CvMat* X; //measurement + CvMat* hX; //current measurement extimation given new parameter vector + + CvMat* prevP; //current already accepted parameter. CvMat* P; // parameters used to evaluate function with new params - // this parameters may be rejected - + // this parameters may be rejected + CvMat* deltaP; //computed increase of parameters (result of normal system solution ) - + CvMat** ea; // sum_i AijT * e_ij , used as right part of normal equation - // length of array is j = number of cameras + // length of array is j = number of cameras CvMat** eb; // sum_j BijT * e_ij , used as right part of normal equation // length of array is i = number of points - + CvMat** Yj; //length of array is i = num_points - - CvMat* S; //big matrix of block Sjk , each block has size num_cam_params x num_cam_params - + + CvMat* S; //big matrix of block Sjk , each block has size num_cam_params x num_cam_params + CvMat* JtJ_diag; //diagonal of JtJ, used to backup diagonal elements before augmentation - + CvMat* Vis_index; // matrix which element is index of measurement for point i and camera j - + int num_cams; int num_points; int num_err_param; int num_cam_param; int num_point_param; - - //target function and jacobian pointers, which needs to be initialized + + //target function and jacobian pointers, which needs to be initialized void (*fjac)(int i, int j, Mat& point_params, Mat& cam_params, Mat& A, Mat& B, void* data); void (*func)(int i, int j, Mat& point_params, Mat& cam_params, Mat& estim, void* data); - + void* data; - + BundleAdjustCallback cb; void* user_data; - }; - + }; + CV_EXPORTS int chamerMatching( Mat& img, Mat& templ, vector >& results, vector& cost, double templScale=1, int maxMatches = 20, double minMatchDistance = 1.0, int padX = 3, int padY = 3, int scales = 5, double minScale = 0.6, double maxScale = 1.6, double orientationWeight = 0.5, double truncate = 20); - - + + class CV_EXPORTS StereoVar { public: - // Flags + // Flags enum {USE_INITIAL_DISPARITY = 1, USE_EQUALIZE_HIST = 2, USE_SMART_ID = 4, USE_AUTO_PARAMS = 8, USE_MEDIAN_FILTERING = 16}; enum {CYCLE_O, CYCLE_V}; enum {PENALIZATION_TICHONOV, PENALIZATION_CHARBONNIER, PENALIZATION_PERONA_MALIK}; - + //! the default constructor CV_WRAP StereoVar(); - + //! the full constructor taking all the necessary algorithm parameters CV_WRAP StereoVar(int levels, double pyrScale, int nIt, int minDisp, int maxDisp, int poly_n, double poly_sigma, float fi, float lambda, int penalization, int cycle, int flags); - + //! the destructor virtual ~StereoVar(); - + //! the stereo correspondence operator that computes disparity map for the specified rectified stereo pair CV_WRAP_AS(compute) virtual void operator()(const Mat& left, const Mat& right, Mat& disp); - - CV_PROP_RW int levels; - CV_PROP_RW double pyrScale; - CV_PROP_RW int nIt; - CV_PROP_RW int minDisp; - CV_PROP_RW int maxDisp; - CV_PROP_RW int poly_n; - CV_PROP_RW double poly_sigma; - CV_PROP_RW float fi; - CV_PROP_RW float lambda; - CV_PROP_RW int penalization; - CV_PROP_RW int cycle; - CV_PROP_RW int flags; - + + CV_PROP_RW int levels; + CV_PROP_RW double pyrScale; + CV_PROP_RW int nIt; + CV_PROP_RW int minDisp; + CV_PROP_RW int maxDisp; + CV_PROP_RW int poly_n; + CV_PROP_RW double poly_sigma; + CV_PROP_RW float fi; + CV_PROP_RW float lambda; + CV_PROP_RW int penalization; + CV_PROP_RW int cycle; + CV_PROP_RW int flags; + private: void autoParams(); - void FMG(Mat &I1, Mat &I2, Mat &I2x, Mat &u, int level); + void FMG(Mat &I1, Mat &I2, Mat &I2x, Mat &u, int level); void VCycle_MyFAS(Mat &I1_h, Mat &I2_h, Mat &I2x_h, Mat &u_h, int level); void VariationalSolver(Mat &I1_h, Mat &I2_h, Mat &I2x_h, Mat &u_h, int level); }; - + CV_EXPORTS void polyfit(const Mat& srcx, const Mat& srcy, Mat& dst, int order); - class CV_EXPORTS Directory + class CV_EXPORTS Directory { - public: - static std::vector GetListFiles ( const std::string& path, const std::string & exten = "*", bool addPath = true ); - static std::vector GetListFilesR ( const std::string& path, const std::string & exten = "*", bool addPath = true ); - static std::vector GetListFolders( const std::string& path, const std::string & exten = "*", bool addPath = true ); + public: + static std::vector GetListFiles ( const std::string& path, const std::string & exten = "*", bool addPath = true ); + static std::vector GetListFilesR ( const std::string& path, const std::string & exten = "*", bool addPath = true ); + static std::vector GetListFolders( const std::string& path, const std::string & exten = "*", bool addPath = true ); }; /* @@ -654,7 +654,7 @@ namespace cv class CV_EXPORTS LogPolar_Interp { public: - + LogPolar_Interp() {} /** @@ -664,11 +664,11 @@ namespace cv *\param center the transformation center: where the output precision is maximal *\param R the number of rings of the cortical image (default value 70 pixel) *\param ro0 the radius of the blind spot (default value 3 pixel) - *\param full \a 1 (default value) means that the retinal image (the inverse transform) is computed within the circumscribing circle. + *\param full \a 1 (default value) means that the retinal image (the inverse transform) is computed within the circumscribing circle. * \a 0 means that the retinal image is computed within the inscribed circle. *\param S the number of sectors of the cortical image (default value 70 pixel). * Its value is usually internally computed to obtain a pixel aspect ratio equals to 1. - *\param sp \a 1 (default value) means that the parameter \a S is internally computed. + *\param sp \a 1 (default value) means that the parameter \a S is internally computed. * \a 0 means that the parameter \a S is provided by the user. */ LogPolar_Interp(int w, int h, Point2i center, int R=70, double ro0=3.0, @@ -689,9 +689,9 @@ namespace cv *Destructor */ ~LogPolar_Interp(); - + protected: - + Mat Rsri; Mat Csri; @@ -716,10 +716,10 @@ namespace cv *More details can be found in http://dx.doi.org/10.1007/978-3-642-23968-7_5 */ class CV_EXPORTS LogPolar_Overlapping - { + { public: LogPolar_Overlapping() {} - + /** *Constructor *\param w the width of the input image @@ -727,11 +727,11 @@ namespace cv *\param center the transformation center: where the output precision is maximal *\param R the number of rings of the cortical image (default value 70 pixel) *\param ro0 the radius of the blind spot (default value 3 pixel) - *\param full \a 1 (default value) means that the retinal image (the inverse transform) is computed within the circumscribing circle. + *\param full \a 1 (default value) means that the retinal image (the inverse transform) is computed within the circumscribing circle. * \a 0 means that the retinal image is computed within the inscribed circle. *\param S the number of sectors of the cortical image (default value 70 pixel). * Its value is usually internally computed to obtain a pixel aspect ratio equals to 1. - *\param sp \a 1 (default value) means that the parameter \a S is internally computed. + *\param sp \a 1 (default value) means that the parameter \a S is internally computed. * \a 0 means that the parameter \a S is provided by the user. */ LogPolar_Overlapping(int w, int h, Point2i center, int R=70, @@ -752,9 +752,9 @@ namespace cv *Destructor */ ~LogPolar_Overlapping(); - + protected: - + Mat Rsri; Mat Csri; vector Rsr; @@ -793,7 +793,7 @@ namespace cv { public: LogPolar_Adjacent() {} - + /** *Constructor *\param w the width of the input image @@ -802,13 +802,13 @@ namespace cv *\param R the number of rings of the cortical image (default value 70 pixel) *\param ro0 the radius of the blind spot (default value 3 pixel) *\param smin the size of the subpixel (default value 0.25 pixel) - *\param full \a 1 (default value) means that the retinal image (the inverse transform) is computed within the circumscribing circle. + *\param full \a 1 (default value) means that the retinal image (the inverse transform) is computed within the circumscribing circle. * \a 0 means that the retinal image is computed within the inscribed circle. *\param S the number of sectors of the cortical image (default value 70 pixel). * Its value is usually internally computed to obtain a pixel aspect ratio equals to 1. - *\param sp \a 1 (default value) means that the parameter \a S is internally computed. + *\param sp \a 1 (default value) means that the parameter \a S is internally computed. * \a 0 means that the parameter \a S is provided by the user. - */ + */ LogPolar_Adjacent(int w, int h, Point2i center, int R=70, double ro0=3.0, double smin=0.25, int full=1, int S=117, int sp=1); /** *Transformation from Cartesian image to cortical (log-polar) image. @@ -845,10 +845,10 @@ namespace cv bool get_uv(double x, double y, int&u, int&v); void create_map(int M, int N, int R, int S, double ro0, double smin); }; - + CV_EXPORTS Mat subspaceProject(InputArray W, InputArray mean, InputArray src); CV_EXPORTS Mat subspaceReconstruct(InputArray W, InputArray mean, InputArray src); - + class CV_EXPORTS LDA { public: @@ -908,7 +908,7 @@ namespace cv // Returns the eigenvalues of this LDA. Mat eigenvalues() const { return _eigenvalues; } - + protected: bool _dataAsRow; int _num_components; @@ -917,7 +917,7 @@ namespace cv void lda(InputArray src, InputArray labels); }; - + class CV_EXPORTS FaceRecognizer : public Algorithm { public: @@ -941,16 +941,16 @@ namespace cv // Deserializes this object from a given cv::FileStorage. virtual void load(const FileStorage& fs) = 0; - + // Returns eigenvectors (if any) virtual Mat eigenvectors() const { return Mat(); } }; - + CV_EXPORTS Ptr createEigenFaceRecognizer(int num_components = 0); CV_EXPORTS Ptr createFisherFaceRecognizer(int num_components = 0); CV_EXPORTS Ptr createLBPHFaceRecognizer(int radius=1, int neighbors=8, int grid_x=8, int grid_y=8); - + enum { COLORMAP_AUTUMN = 0, @@ -968,9 +968,9 @@ namespace cv COLORMAP_MKPJ1 = 12, COLORMAP_MKPJ2 = 13 }; - + CV_EXPORTS void applyColorMap(InputArray src, OutputArray dst, int colormap); - + CV_EXPORTS bool initModule_contrib(); } diff --git a/modules/contrib/include/opencv2/contrib/hybridtracker.hpp b/modules/contrib/include/opencv2/contrib/hybridtracker.hpp index 3173b23..418a7b8 100644 --- a/modules/contrib/include/opencv2/contrib/hybridtracker.hpp +++ b/modules/contrib/include/opencv2/contrib/hybridtracker.hpp @@ -86,10 +86,10 @@ struct CV_EXPORTS CvMeanShiftTrackerParams struct CV_EXPORTS CvFeatureTrackerParams { enum { SIFT = 0, SURF = 1, OPTICAL_FLOW = 2 }; - CvFeatureTrackerParams(int feature_type = 0, int window_size = 0) + CvFeatureTrackerParams(int featureType = 0, int windowSize = 0) { - feature_type = 0; - window_size = 0; + featureType = 0; + windowSize = 0; } int feature_type; // Feature type to use diff --git a/modules/contrib/src/ba.cpp b/modules/contrib/src/ba.cpp index f08bdda..a0f9046 100644 --- a/modules/contrib/src/ba.cpp +++ b/modules/contrib/src/ba.cpp @@ -67,15 +67,15 @@ LevMarqSparse::LevMarqSparse(int npoints, // number of points // 1 - point is visible for the camera, 0 - invisible Mat& P0, // starting vector of parameters, first cameras then points Mat& X_, // measurements, in order of visibility. non visible cases are skipped - TermCriteria criteria, // termination criteria + TermCriteria _criteria, // termination criteria // callback for estimation of Jacobian matrices - void (CV_CDECL * fjac)(int i, int j, Mat& point_params, + void (CV_CDECL * _fjac)(int i, int j, Mat& point_params, Mat& cam_params, Mat& A, Mat& B, void* data), // callback for estimation of backprojection errors - void (CV_CDECL * func)(int i, int j, Mat& point_params, + void (CV_CDECL * _func)(int i, int j, Mat& point_params, Mat& cam_params, Mat& estim, void* data), - void* data, // user-specific data passed to the callbacks + void* _data, // user-specific data passed to the callbacks BundleAdjustCallback _cb, void* _user_data ) { Vis_index = X = prevP = P = deltaP = err = JtJ_diag = S = hX = NULL; @@ -86,7 +86,7 @@ LevMarqSparse::LevMarqSparse(int npoints, // number of points user_data = _user_data; run(npoints, ncameras, nPointParams, nCameraParams, nErrParams, visibility, - P0, X_, criteria, fjac, func, data); + P0, X_, _criteria, _fjac, _func, _data); } void LevMarqSparse::clear() { @@ -443,9 +443,11 @@ void LevMarqSparse::optimize(CvMat &_vis) { //main function that runs minimizati } //U_j and ea_j computed for all j // if (!(iters%100)) - int nviz = X->rows / num_err_param; - double e2 = prevErrNorm*prevErrNorm, e2n = e2 / nviz; - std::cerr<<"Iteration: "<rows / num_err_param; + double e2 = prevErrNorm*prevErrNorm, e2n = e2 / nviz; + std::cerr<<"Iteration: "< prevErrNorm ) { //step was not accepted //increase lambda and reject change lambda *= 10; - int nviz = X->rows / num_err_param; - double e2 = errNorm*errNorm, e2_prev = prevErrNorm*prevErrNorm; - double e2n = e2/nviz, e2n_prev = e2_prev/nviz; - std::cerr<<"move failed: lambda = "< "<rows / num_err_param; + double e2 = errNorm*errNorm, e2_prev = prevErrNorm*prevErrNorm; + double e2n = e2/nviz, e2n_prev = e2_prev/nviz; + std::cerr<<"move failed: lambda = "< "< old:"<<(1-_a)*(1-_a)*(1-_a)*(1-_a)/(1+_beta)< old:"<<(1-a)*(1-a)*(1-a)*(1-a)/(1+_beta)< inline vector<_Tp> remove_dups(const vector<_Tp>& src) { @@ -82,7 +82,7 @@ inline vector<_Tp> remove_dups(const vector<_Tp>& src) { return elems; } - + // Turk, M., and Pentland, A. "Eigenfaces for recognition.". Journal of // Cognitive Neuroscience 3 (1991), 71–86. class Eigenfaces : public FaceRecognizer @@ -124,10 +124,10 @@ public: // See FaceRecognizer::save. void save(FileStorage& fs) const; - + AlgorithmInfo* info() const; }; - + // Belhumeur, P. N., Hespanha, J., and Kriegman, D. "Eigenfaces vs. Fisher- // faces: Recognition using class specific linear projection.". IEEE // Transactions on Pattern Analysis and Machine Intelligence 19, 7 (1997), @@ -208,11 +208,11 @@ public: // // radius, neighbors are used in the local binary patterns creation. // grid_x, grid_y control the grid size of the spatial histograms. - LBPH(int radius=1, int neighbors=8, int grid_x=8, int grid_y=8) : - _grid_x(grid_x), - _grid_y(grid_y), - _radius(radius), - _neighbors(neighbors) {} + LBPH(int radius_=1, int neighbors_=8, int grid_x_=8, int grid_y_=8) : + _grid_x(grid_x_), + _grid_y(grid_y_), + _radius(radius_), + _neighbors(neighbors_) {} // Initializes and computes this LBPH Model. The current implementation is // rather fixed as it uses the Extended Local Binary Patterns per default. @@ -221,12 +221,12 @@ public: // (grid_x=8), (grid_y=8) controls the grid size of the spatial histograms. LBPH(InputArray src, InputArray labels, - int radius=1, int neighbors=8, - int grid_x=8, int grid_y=8) : - _grid_x(grid_x), - _grid_y(grid_y), - _radius(radius), - _neighbors(neighbors) { + int radius_=1, int neighbors_=8, + int grid_x_=8, int grid_y_=8) : + _grid_x(grid_x_), + _grid_y(grid_y_), + _radius(radius_), + _neighbors(neighbors_) { train(src, labels); } @@ -359,9 +359,9 @@ void Fisherfaces::train(InputArray src, InputArray _lbls) { // get data Mat labels = _lbls.getMat(); Mat data = asRowMatrix(src, CV_64FC1); - + CV_Assert( labels.type() == CV_32S && (labels.cols == 1 || labels.rows == 1)); - + // dimensionality int N = data.rows; // number of samples //int D = data.cols; // dimension of samples @@ -369,7 +369,7 @@ void Fisherfaces::train(InputArray src, InputArray _lbls) { if(labels.total() != (size_t)N) CV_Error(CV_StsUnsupportedFormat, "Labels must be given as integer (CV_32SC1)."); // compute the Fisherfaces - + vector ll; labels.copyTo(ll); int C = (int)remove_dups(ll).size(); // number of unique classes @@ -570,7 +570,7 @@ static Mat histc(InputArray _src, int minVal, int maxVal, bool normed) return Mat(); } - + static Mat spatial_histogram(InputArray _src, int numPatterns, int grid_x, int grid_y, bool normed) { @@ -610,7 +610,7 @@ static Mat elbp(InputArray src, int radius, int neighbors) { elbp(src, dst, radius, neighbors); return dst; } - + void LBPH::load(const FileStorage& fs) { fs["radius"] >> _radius; fs["neighbors"] >> _neighbors; @@ -684,24 +684,24 @@ int LBPH::predict(InputArray _src) const { } return minClass; } - - + + Ptr createEigenFaceRecognizer(int num_components) { return new Eigenfaces(num_components); } - + Ptr createFisherFaceRecognizer(int num_components) { return new Fisherfaces(num_components); } - + Ptr createLBPHFaceRecognizer(int radius, int neighbors, int grid_x, int grid_y) { return new LBPH(radius, neighbors, grid_x, grid_y); } - + CV_INIT_ALGORITHM(Eigenfaces, "FaceRecognizer.Eigenfaces", obj.info()->addParam(obj, "ncomponents", obj._num_components); obj.info()->addParam(obj, "projections", obj._projections, true); @@ -716,8 +716,8 @@ CV_INIT_ALGORITHM(Fisherfaces, "FaceRecognizer.Fisherfaces", obj.info()->addParam(obj, "labels", obj._labels, true); obj.info()->addParam(obj, "eigenvectors", obj._eigenvectors, true); obj.info()->addParam(obj, "eigenvalues", obj._eigenvalues, true); - obj.info()->addParam(obj, "mean", obj._mean, true)); - + obj.info()->addParam(obj, "mean", obj._mean, true)); + CV_INIT_ALGORITHM(LBPH, "FaceRecognizer.LBPH", obj.info()->addParam(obj, "radius", obj._radius); obj.info()->addParam(obj, "neighbors", obj._neighbors); @@ -725,7 +725,7 @@ CV_INIT_ALGORITHM(LBPH, "FaceRecognizer.LBPH", obj.info()->addParam(obj, "grid_y", obj._grid_y); obj.info()->addParam(obj, "histograms", obj._histograms, true); obj.info()->addParam(obj, "labels", obj._labels, true)); - + bool initModule_contrib() { Ptr efaces = createEigenfaces(), ffaces = createFisherfaces(), lbph = createLBPH(); diff --git a/modules/contrib/src/lda.cpp b/modules/contrib/src/lda.cpp index d9c9fb1..0c95382 100644 --- a/modules/contrib/src/lda.cpp +++ b/modules/contrib/src/lda.cpp @@ -235,19 +235,19 @@ private: // Allocates memory. template - _Tp **alloc_2d(int m, int n) { + _Tp **alloc_2d(int m, int _n) { _Tp **arr = new _Tp*[m]; for (int i = 0; i < m; i++) - arr[i] = new _Tp[n]; + arr[i] = new _Tp[_n]; return arr; } // Allocates memory. template - _Tp **alloc_2d(int m, int n, _Tp val) { - _Tp **arr = alloc_2d<_Tp> (m, n); + _Tp **alloc_2d(int m, int _n, _Tp val) { + _Tp **arr = alloc_2d<_Tp> (m, _n); for (int i = 0; i < m; i++) { - for (int j = 0; j < n; j++) { + for (int j = 0; j < _n; j++) { arr[i][j] = val; } } @@ -255,17 +255,17 @@ private: } void cdiv(double xr, double xi, double yr, double yi) { - double r, d; + double r, dv; if (std::abs(yr) > std::abs(yi)) { r = yi / yr; - d = yr + r * yi; - cdivr = (xr + r * xi) / d; - cdivi = (xi - r * xr) / d; + dv = yr + r * yi; + cdivr = (xr + r * xi) / dv; + cdivi = (xi - r * xr) / dv; } else { r = yr / yi; - d = yi + r * yr; - cdivr = (r * xr + xi) / d; - cdivi = (r * xi - xr) / d; + dv = yi + r * yr; + cdivr = (r * xr + xi) / dv; + cdivi = (r * xi - xr) / dv; } } @@ -280,7 +280,7 @@ private: // Initialize int nn = this->n; - int n = nn - 1; + int n1 = nn - 1; int low = 0; int high = nn - 1; double eps = pow(2.0, -52.0); @@ -302,10 +302,10 @@ private: // Outer loop over eigenvalue index int iter = 0; - while (n >= low) { + while (n1 >= low) { // Look for single small sub-diagonal element - int l = n; + int l = n1; while (l > low) { s = std::abs(H[l - 1][l - 1]) + std::abs(H[l][l]); if (s == 0.0) { @@ -320,23 +320,23 @@ private: // Check for convergence // One root found - if (l == n) { - H[n][n] = H[n][n] + exshift; - d[n] = H[n][n]; - e[n] = 0.0; - n--; + if (l == n1) { + H[n1][n1] = H[n1][n1] + exshift; + d[n1] = H[n1][n1]; + e[n1] = 0.0; + n1--; iter = 0; // Two roots found - } else if (l == n - 1) { - w = H[n][n - 1] * H[n - 1][n]; - p = (H[n - 1][n - 1] - H[n][n]) / 2.0; + } else if (l == n1 - 1) { + w = H[n1][n1 - 1] * H[n1 - 1][n1]; + p = (H[n1 - 1][n1 - 1] - H[n1][n1]) / 2.0; q = p * p + w; z = sqrt(std::abs(q)); - H[n][n] = H[n][n] + exshift; - H[n - 1][n - 1] = H[n - 1][n - 1] + exshift; - x = H[n][n]; + H[n1][n1] = H[n1][n1] + exshift; + H[n1 - 1][n1 - 1] = H[n1 - 1][n1 - 1] + exshift; + x = H[n1][n1]; // Real pair @@ -346,14 +346,14 @@ private: } else { z = p - z; } - d[n - 1] = x + z; - d[n] = d[n - 1]; + d[n1 - 1] = x + z; + d[n1] = d[n1 - 1]; if (z != 0.0) { - d[n] = x - w / z; + d[n1] = x - w / z; } - e[n - 1] = 0.0; - e[n] = 0.0; - x = H[n][n - 1]; + e[n1 - 1] = 0.0; + e[n1] = 0.0; + x = H[n1][n1 - 1]; s = std::abs(x) + std::abs(z); p = x / s; q = z / s; @@ -363,37 +363,37 @@ private: // Row modification - for (int j = n - 1; j < nn; j++) { - z = H[n - 1][j]; - H[n - 1][j] = q * z + p * H[n][j]; - H[n][j] = q * H[n][j] - p * z; + for (int j = n1 - 1; j < nn; j++) { + z = H[n1 - 1][j]; + H[n1 - 1][j] = q * z + p * H[n1][j]; + H[n1][j] = q * H[n1][j] - p * z; } // Column modification - for (int i = 0; i <= n; i++) { - z = H[i][n - 1]; - H[i][n - 1] = q * z + p * H[i][n]; - H[i][n] = q * H[i][n] - p * z; + for (int i = 0; i <= n1; i++) { + z = H[i][n1 - 1]; + H[i][n1 - 1] = q * z + p * H[i][n1]; + H[i][n1] = q * H[i][n1] - p * z; } // Accumulate transformations for (int i = low; i <= high; i++) { - z = V[i][n - 1]; - V[i][n - 1] = q * z + p * V[i][n]; - V[i][n] = q * V[i][n] - p * z; + z = V[i][n1 - 1]; + V[i][n1 - 1] = q * z + p * V[i][n1]; + V[i][n1] = q * V[i][n1] - p * z; } // Complex pair } else { - d[n - 1] = x + p; - d[n] = x + p; - e[n - 1] = z; - e[n] = -z; + d[n1 - 1] = x + p; + d[n1] = x + p; + e[n1 - 1] = z; + e[n1] = -z; } - n = n - 2; + n1 = n1 - 2; iter = 0; // No convergence yet @@ -402,22 +402,22 @@ private: // Form shift - x = H[n][n]; + x = H[n1][n1]; y = 0.0; w = 0.0; - if (l < n) { - y = H[n - 1][n - 1]; - w = H[n][n - 1] * H[n - 1][n]; + if (l < n1) { + y = H[n1 - 1][n1 - 1]; + w = H[n1][n1 - 1] * H[n1 - 1][n1]; } // Wilkinson's original ad hoc shift if (iter == 10) { exshift += x; - for (int i = low; i <= n; i++) { + for (int i = low; i <= n1; i++) { H[i][i] -= x; } - s = std::abs(H[n][n - 1]) + std::abs(H[n - 1][n - 2]); + s = std::abs(H[n1][n1 - 1]) + std::abs(H[n1 - 1][n1 - 2]); x = y = 0.75 * s; w = -0.4375 * s * s; } @@ -433,7 +433,7 @@ private: s = -s; } s = x - w / ((y - x) / 2.0 + s); - for (int i = low; i <= n; i++) { + for (int i = low; i <= n1; i++) { H[i][i] -= s; } exshift += s; @@ -444,7 +444,7 @@ private: iter = iter + 1; // (Could check iteration count here.) // Look for two consecutive small sub-diagonal elements - int m = n - 2; + int m = n1 - 2; while (m >= l) { z = H[m][m]; r = x - z; @@ -467,7 +467,7 @@ private: m--; } - for (int i = m + 2; i <= n; i++) { + for (int i = m + 2; i <= n1; i++) { H[i][i - 2] = 0.0; if (i > m + 2) { H[i][i - 3] = 0.0; @@ -476,8 +476,8 @@ private: // Double QR step involving rows l:n and columns m:n - for (int k = m; k <= n - 1; k++) { - bool notlast = (k != n - 1); + for (int k = m; k <= n1 - 1; k++) { + bool notlast = (k != n1 - 1); if (k != m) { p = H[k][k - 1]; q = H[k + 1][k - 1]; @@ -523,7 +523,7 @@ private: // Column modification - for (int i = 0; i <= min(n, k + 3); i++) { + for (int i = 0; i <= min(n1, k + 3); i++) { p = x * H[i][k] + y * H[i][k + 1]; if (notlast) { p = p + z * H[i][k + 2]; @@ -547,7 +547,7 @@ private: } // (s != 0) } // k loop } // check convergence - } // while (n >= low) + } // while (n1 >= low) // Backsubstitute to find vectors of upper triangular form @@ -555,20 +555,20 @@ private: return; } - for (n = nn - 1; n >= 0; n--) { - p = d[n]; - q = e[n]; + for (n1 = nn - 1; n1 >= 0; n1--) { + p = d[n1]; + q = e[n1]; // Real vector if (q == 0) { - int l = n; - H[n][n] = 1.0; - for (int i = n - 1; i >= 0; i--) { + int l = n1; + H[n1][n1] = 1.0; + for (int i = n1 - 1; i >= 0; i--) { w = H[i][i] - p; r = 0.0; - for (int j = l; j <= n; j++) { - r = r + H[i][j] * H[j][n]; + for (int j = l; j <= n1; j++) { + r = r + H[i][j] * H[j][n1]; } if (e[i] < 0.0) { z = w; @@ -577,9 +577,9 @@ private: l = i; if (e[i] == 0.0) { if (w != 0.0) { - H[i][n] = -r / w; + H[i][n1] = -r / w; } else { - H[i][n] = -r / (eps * norm); + H[i][n1] = -r / (eps * norm); } // Solve real equations @@ -589,20 +589,20 @@ private: y = H[i + 1][i]; q = (d[i] - p) * (d[i] - p) + e[i] * e[i]; t = (x * s - z * r) / q; - H[i][n] = t; + H[i][n1] = t; if (std::abs(x) > std::abs(z)) { - H[i + 1][n] = (-r - w * t) / x; + H[i + 1][n1] = (-r - w * t) / x; } else { - H[i + 1][n] = (-s - y * t) / z; + H[i + 1][n1] = (-s - y * t) / z; } } // Overflow control - t = std::abs(H[i][n]); + t = std::abs(H[i][n1]); if ((eps * t) * t > 1) { - for (int j = i; j <= n; j++) { - H[j][n] = H[j][n] / t; + for (int j = i; j <= n1; j++) { + H[j][n1] = H[j][n1] / t; } } } @@ -611,27 +611,27 @@ private: // Complex vector } else if (q < 0) { - int l = n - 1; + int l = n1 - 1; // Last vector component imaginary so matrix is triangular - if (std::abs(H[n][n - 1]) > std::abs(H[n - 1][n])) { - H[n - 1][n - 1] = q / H[n][n - 1]; - H[n - 1][n] = -(H[n][n] - p) / H[n][n - 1]; + if (std::abs(H[n1][n1 - 1]) > std::abs(H[n1 - 1][n1])) { + H[n1 - 1][n1 - 1] = q / H[n1][n1 - 1]; + H[n1 - 1][n1] = -(H[n1][n1] - p) / H[n1][n1 - 1]; } else { - cdiv(0.0, -H[n - 1][n], H[n - 1][n - 1] - p, q); - H[n - 1][n - 1] = cdivr; - H[n - 1][n] = cdivi; + cdiv(0.0, -H[n1 - 1][n1], H[n1 - 1][n1 - 1] - p, q); + H[n1 - 1][n1 - 1] = cdivr; + H[n1 - 1][n1] = cdivi; } - H[n][n - 1] = 0.0; - H[n][n] = 1.0; - for (int i = n - 2; i >= 0; i--) { + H[n1][n1 - 1] = 0.0; + H[n1][n1] = 1.0; + for (int i = n1 - 2; i >= 0; i--) { double ra, sa, vr, vi; ra = 0.0; sa = 0.0; - for (int j = l; j <= n; j++) { - ra = ra + H[i][j] * H[j][n - 1]; - sa = sa + H[i][j] * H[j][n]; + for (int j = l; j <= n1; j++) { + ra = ra + H[i][j] * H[j][n1 - 1]; + sa = sa + H[i][j] * H[j][n1]; } w = H[i][i] - p; @@ -643,8 +643,8 @@ private: l = i; if (e[i] == 0) { cdiv(-ra, -sa, w, q); - H[i][n - 1] = cdivr; - H[i][n] = cdivi; + H[i][n1 - 1] = cdivr; + H[i][n1] = cdivi; } else { // Solve complex equations @@ -659,28 +659,28 @@ private: } cdiv(x * r - z * ra + q * sa, x * s - z * sa - q * ra, vr, vi); - H[i][n - 1] = cdivr; - H[i][n] = cdivi; + H[i][n1 - 1] = cdivr; + H[i][n1] = cdivi; if (std::abs(x) > (std::abs(z) + std::abs(q))) { - H[i + 1][n - 1] = (-ra - w * H[i][n - 1] + q - * H[i][n]) / x; - H[i + 1][n] = (-sa - w * H[i][n] - q * H[i][n + H[i + 1][n1 - 1] = (-ra - w * H[i][n1 - 1] + q + * H[i][n1]) / x; + H[i + 1][n1] = (-sa - w * H[i][n1] - q * H[i][n1 - 1]) / x; } else { - cdiv(-r - y * H[i][n - 1], -s - y * H[i][n], z, + cdiv(-r - y * H[i][n1 - 1], -s - y * H[i][n1], z, q); - H[i + 1][n - 1] = cdivr; - H[i + 1][n] = cdivi; + H[i + 1][n1 - 1] = cdivr; + H[i + 1][n1] = cdivi; } } // Overflow control - t = max(std::abs(H[i][n - 1]), std::abs(H[i][n])); + t = max(std::abs(H[i][n1 - 1]), std::abs(H[i][n1])); if ((eps * t) * t > 1) { - for (int j = i; j <= n; j++) { - H[j][n - 1] = H[j][n - 1] / t; - H[j][n] = H[j][n] / t; + for (int j = i; j <= n1; j++) { + H[j][n1 - 1] = H[j][n1 - 1] / t; + H[j][n1] = H[j][n1] / t; } } } diff --git a/modules/contrib/src/logpolar_bsm.cpp b/modules/contrib/src/logpolar_bsm.cpp index 52917d7..ed236fc 100644 --- a/modules/contrib/src/logpolar_bsm.cpp +++ b/modules/contrib/src/logpolar_bsm.cpp @@ -60,9 +60,9 @@ ICVS 2011, Sophia Antipolis, France, September 20-22, 2011 namespace cv { - + //------------------------------------interp------------------------------------------- -LogPolar_Interp::LogPolar_Interp(int w, int h, Point2i center, int R, double ro0, int interp, int full, int S, int sp) +LogPolar_Interp::LogPolar_Interp(int w, int h, Point2i center, int _R, double _ro0, int _interp, int full, int _S, int sp) { if ( (center.x!=w/2 || center.y!=h/2) && full==0) full=1; @@ -97,23 +97,23 @@ LogPolar_Interp::LogPolar_Interp(int w, int h, Point2i center, int R, double ro0 if (sp){ int jc=M/2-1, ic=N/2-1; - int romax=min(ic, jc); - double a=exp(log((double)(romax/2-1)/(double)ro0)/(double)R); - S=(int) floor(2*CV_PI/(a-1)+0.5); + int _romax=min(ic, jc); + double _a=exp(log((double)(_romax/2-1)/(double)ro0)/(double)R); + S=(int) floor(2*CV_PI/(_a-1)+0.5); } - this->interp=interp; + interp=_interp; - create_map(M, N, R, S, ro0); + create_map(M, N, _R, _S, _ro0); } -void LogPolar_Interp::create_map(int M, int N, int R, int S, double ro0) +void LogPolar_Interp::create_map(int _M, int _N, int _R, int _S, double _ro0) { - this->M=M; - this->N=N; - this->R=R; - this->S=S; - this->ro0=ro0; + M=_M; + N=_N; + R=_R; + S=_S; + ro0=_ro0; int jc=N/2-1, ic=M/2-1; romax=min(ic, jc); @@ -130,7 +130,7 @@ void LogPolar_Interp::create_map(int M, int N, int R, int S, double ro0) for(int u=0; u(v,u)=(float)(ro0*pow(a,u)*sin(v/q)+jc); - Csri.at(v,u)=(float)(ro0*pow(a,u)*cos(v/q)+ic); + Csri.at(v,u)=(float)(ro0*pow(a,u)*cos(v/q)+ic); } } @@ -158,7 +158,7 @@ void LogPolar_Interp::create_map(int M, int N, int R, int S, double ro0) const Mat LogPolar_Interp::to_cortical(const Mat &source) { Mat out(S,R,CV_8UC1,Scalar(0)); - + Mat source_border; copyMakeBorder(source,source_border,top,bottom,left,right,BORDER_CONSTANT,Scalar(0)); @@ -173,7 +173,7 @@ const Mat LogPolar_Interp::to_cartesian(const Mat &source) Mat out(N,M,CV_8UC1,Scalar(0)); Mat source_border; - + if (interp==INTER_NEAREST || interp==INTER_LINEAR){ copyMakeBorder(source,source_border,0,1,0,0,BORDER_CONSTANT,Scalar(0)); Mat rowS0 = source_border.row(S); @@ -208,7 +208,7 @@ LogPolar_Interp::~LogPolar_Interp() //------------------------------------overlapping---------------------------------- -LogPolar_Overlapping::LogPolar_Overlapping(int w, int h, Point2i center, int R, double ro0, int full, int S, int sp) +LogPolar_Overlapping::LogPolar_Overlapping(int w, int h, Point2i center, int _R, double _ro0, int full, int _S, int sp) { if ( (center.x!=w/2 || center.y!=h/2) && full==0) full=1; @@ -244,21 +244,21 @@ LogPolar_Overlapping::LogPolar_Overlapping(int w, int h, Point2i center, int R, if (sp){ int jc=M/2-1, ic=N/2-1; - int romax=min(ic, jc); - double a=exp(log((double)(romax/2-1)/(double)ro0)/(double)R); - S=(int) floor(2*CV_PI/(a-1)+0.5); + int _romax=min(ic, jc); + double _a=exp(log((double)(_romax/2-1)/(double)ro0)/(double)R); + S=(int) floor(2*CV_PI/(_a-1)+0.5); } - create_map(M, N, R, S, ro0); + create_map(M, N, _R, _S, _ro0); } -void LogPolar_Overlapping::create_map(int M, int N, int R, int S, double ro0) +void LogPolar_Overlapping::create_map(int _M, int _N, int _R, int _S, double _ro0) { - this->M=M; - this->N=N; - this->R=R; - this->S=S; - this->ro0=ro0; + M=_M; + N=_N; + R=_R; + S=_S; + ro0=_ro0; int jc=N/2-1, ic=M/2-1; romax=min(ic, jc); @@ -280,14 +280,14 @@ void LogPolar_Overlapping::create_map(int M, int N, int R, int S, double ro0) for(int u=0; u(v,u)=(float)(ro0*pow(a,u)*sin(v/q)+jc); - Csri.at(v,u)=(float)(ro0*pow(a,u)*cos(v/q)+ic); + Csri.at(v,u)=(float)(ro0*pow(a,u)*cos(v/q)+ic); Rsr[v*R+u]=(int)floor(Rsri.at(v,u)); - Csr[v*R+u]=(int)floor(Csri.at(v,u)); + Csr[v*R+u]=(int)floor(Csri.at(v,u)); } } bool done=false; - + for(int i=0; i(j,i)=(float)(q*theta); - + double ro2=(j-jc)*(j-jc)+(i-ic)*(i-ic); CSIyx.at(j,i)=(float)(0.5*log(ro2/(ro0*ro0))/log(a)); } @@ -387,7 +387,7 @@ const Mat LogPolar_Overlapping::to_cartesian(const Mat &source) remap(source_border,out,CSIyx,ETAyx,INTER_LINEAR); int wm=w_ker_2D[R-1].w; - + vector IMG((N+2*wm+1)*(M+2*wm+1), 0.); vector NOR((N+2*wm+1)*(M+2*wm+1), 0.); @@ -426,14 +426,14 @@ const Mat LogPolar_Overlapping::to_cartesian(const Mat &source) Mat out_cropped=out(Range(top,N-1-bottom),Range(left,M-1-right)); return out_cropped; } - + LogPolar_Overlapping::~LogPolar_Overlapping() { } //----------------------------------------adjacent--------------------------------------- -LogPolar_Adjacent::LogPolar_Adjacent(int w, int h, Point2i center, int R, double ro0, double smin, int full, int S, int sp) +LogPolar_Adjacent::LogPolar_Adjacent(int w, int h, Point2i center, int _R, double _ro0, double smin, int full, int _S, int sp) { if ( (center.x!=w/2 || center.y!=h/2) && full==0) full=1; @@ -468,22 +468,22 @@ LogPolar_Adjacent::LogPolar_Adjacent(int w, int h, Point2i center, int R, double if (sp){ int jc=M/2-1, ic=N/2-1; - int romax=min(ic, jc); - double a=exp(log((double)(romax/2-1)/(double)ro0)/(double)R); - S=(int) floor(2*CV_PI/(a-1)+0.5); + int _romax=min(ic, jc); + double _a=exp(log((double)(_romax/2-1)/(double)ro0)/(double)R); + S=(int) floor(2*CV_PI/(_a-1)+0.5); } - create_map(M, N, R, S, ro0, smin); + create_map(M, N, _R, _S, _ro0, smin); } -void LogPolar_Adjacent::create_map(int M, int N, int R, int S, double ro0, double smin) +void LogPolar_Adjacent::create_map(int _M, int _N, int _R, int _S, double _ro0, double smin) { - LogPolar_Adjacent::M=M; - LogPolar_Adjacent::N=N; - LogPolar_Adjacent::R=R; - LogPolar_Adjacent::S=S; - LogPolar_Adjacent::ro0=ro0; + M=_M; + N=_N; + R=_R; + S=_S; + ro0=_ro0; romax=min(M/2.0, N/2.0); a=exp(log(romax/ro0)/(double)R); @@ -507,7 +507,7 @@ void LogPolar_Adjacent::create_map(int M, int N, int R, int S, double ro0, doubl void LogPolar_Adjacent::subdivide_recursively(double x, double y, int i, int j, double length, double smin) -{ +{ if(length<=smin) { int u, v; @@ -576,7 +576,7 @@ const Mat LogPolar_Adjacent::to_cortical(const Mat &source) for(int j=0; j(j,i)); @@ -641,7 +641,7 @@ bool LogPolar_Adjacent::get_uv(double x, double y, int&u, int&v) else v= (int) floor(q*(theta+2*CV_PI)); return true; - } + } } LogPolar_Adjacent::~LogPolar_Adjacent() diff --git a/modules/contrib/src/octree.cpp b/modules/contrib/src/octree.cpp index cb0f12e..0808b12 100644 --- a/modules/contrib/src/octree.cpp +++ b/modules/contrib/src/octree.cpp @@ -171,9 +171,9 @@ namespace cv { } - Octree::Octree(const vector& points3d, int maxLevels, int minPoints) + Octree::Octree(const vector& points3d, int maxLevels, int _minPoints) { - buildTree(points3d, maxLevels, minPoints); + buildTree(points3d, maxLevels, _minPoints); } Octree::~Octree() @@ -256,12 +256,12 @@ namespace cv } } - void Octree::buildTree(const vector& points3d, int maxLevels, int minPoints) + void Octree::buildTree(const vector& points3d, int maxLevels, int _minPoints) { assert((size_t)maxLevels * 8 < MAX_STACK_SIZE); points.resize(points3d.size()); std::copy(points3d.begin(), points3d.end(), points.begin()); - this->minPoints = minPoints; + minPoints = _minPoints; nodes.clear(); nodes.push_back(Node()); @@ -275,7 +275,7 @@ namespace cv for (size_t i = 0; i < MAX_LEAFS; i++) root.children[i] = 0; - if (maxLevels != 1 && (root.end - root.begin) > minPoints) + if (maxLevels != 1 && (root.end - root.begin) > _minPoints) { root.isLeaf = false; buildNext(0); diff --git a/modules/contrib/src/retina.cpp b/modules/contrib/src/retina.cpp index 1280324..1464896 100644 --- a/modules/contrib/src/retina.cpp +++ b/modules/contrib/src/retina.cpp @@ -75,16 +75,16 @@ namespace cv { -Retina::Retina(const cv::Size inputSize) +Retina::Retina(const cv::Size inputSz) { _retinaFilter = 0; - _init(inputSize, true, RETINA_COLOR_BAYER, false); + _init(inputSz, true, RETINA_COLOR_BAYER, false); } -Retina::Retina(const cv::Size inputSize, const bool colorMode, RETINA_COLORSAMPLINGMETHOD colorSamplingMethod, const bool useRetinaLogSampling, const double reductionFactor, const double samplingStrenght) +Retina::Retina(const cv::Size inputSz, const bool colorMode, RETINA_COLORSAMPLINGMETHOD colorSamplingMethod, const bool useRetinaLogSampling, const double reductionFactor, const double samplingStrenght) { _retinaFilter = 0; - _init(inputSize, colorMode, colorSamplingMethod, useRetinaLogSampling, reductionFactor, samplingStrenght); + _init(inputSz, colorMode, colorSamplingMethod, useRetinaLogSampling, reductionFactor, samplingStrenght); }; Retina::~Retina() @@ -342,20 +342,20 @@ const std::valarray & Retina::getMagno() const {return _retinaFilter->get const std::valarray & Retina::getParvo() const {if (_retinaFilter->getColorMode())return _retinaFilter->getColorOutput(); /* implicite else */return _retinaFilter->getContours();} // private method called by constructirs -void Retina::_init(const cv::Size inputSize, const bool colorMode, RETINA_COLORSAMPLINGMETHOD colorSamplingMethod, const bool useRetinaLogSampling, const double reductionFactor, const double samplingStrenght) +void Retina::_init(const cv::Size inputSz, const bool colorMode, RETINA_COLORSAMPLINGMETHOD colorSamplingMethod, const bool useRetinaLogSampling, const double reductionFactor, const double samplingStrenght) { // basic error check - if (inputSize.height*inputSize.width <= 0) + if (inputSz.height*inputSz.width <= 0) throw cv::Exception(-1, "Bad retina size setup : size height and with must be superior to zero", "Retina::setup", "Retina.h", 0); - unsigned int nbPixels=inputSize.height*inputSize.width; + unsigned int nbPixels=inputSz.height*inputSz.width; // resize buffers if size does not match _inputBuffer.resize(nbPixels*3); // buffer supports gray images but also 3 channels color buffers... (larger is better...) // allocate the retina model if (_retinaFilter) delete _retinaFilter; - _retinaFilter = new RetinaFilter(inputSize.height, inputSize.width, colorMode, colorSamplingMethod, useRetinaLogSampling, reductionFactor, samplingStrenght); + _retinaFilter = new RetinaFilter(inputSz.height, inputSz.width, colorMode, colorSamplingMethod, useRetinaLogSampling, reductionFactor, samplingStrenght); // prepare the default parameter XML file with default setup setup(_retinaParameters); diff --git a/modules/contrib/src/retinacolor.cpp b/modules/contrib/src/retinacolor.cpp index 730e9b8..6418620 100644 --- a/modules/contrib/src/retinacolor.cpp +++ b/modules/contrib/src/retinacolor.cpp @@ -325,15 +325,15 @@ void RetinaColor::runColorDemultiplexing(const std::valarray &multiplexed }else { - register const float *multiplexedColorFramePTR= get_data(multiplexedColorFrame); - for (unsigned int indexc=0; indexc<_filterOutput.getNBpixels() ; ++indexc, ++chrominancePTR, ++colorLocalDensityPTR, ++luminance, ++multiplexedColorFramePTR) + register const float *multiplexedColorFramePTR1= get_data(multiplexedColorFrame); + for (unsigned int indexc=0; indexc<_filterOutput.getNBpixels() ; ++indexc, ++chrominancePTR, ++colorLocalDensityPTR, ++luminance, ++multiplexedColorFramePTR1) { // normalize by photoreceptors density float Cr=*(chrominancePTR)*_colorLocalDensity[indexc]; float Cg=*(chrominancePTR+_filterOutput.getNBpixels())*_colorLocalDensity[indexc+_filterOutput.getNBpixels()]; float Cb=*(chrominancePTR+_filterOutput.getDoubleNBpixels())*_colorLocalDensity[indexc+_filterOutput.getDoubleNBpixels()]; *luminance=(Cr+Cg+Cb)*_pG; - _demultiplexedTempBuffer[_colorSampling[indexc]] = *multiplexedColorFramePTR - *luminance; + _demultiplexedTempBuffer[_colorSampling[indexc]] = *multiplexedColorFramePTR1 - *luminance; } diff --git a/modules/contrib/src/spinimages.cpp b/modules/contrib/src/spinimages.cpp index e2818ad..73400dc 100644 --- a/modules/contrib/src/spinimages.cpp +++ b/modules/contrib/src/spinimages.cpp @@ -60,9 +60,9 @@ namespace cv using std::min; using std::sqrt; } -namespace +namespace { - const static Scalar colors[] = + const static Scalar colors[] = { CV_RGB(255, 0, 0), CV_RGB( 0, 255, 0), @@ -87,21 +87,21 @@ namespace template void iota(FwIt first, FwIt last, T value) { while(first != last) *first++ = value++; } -void computeNormals( const Octree& Octree, const vector& centers, vector& normals, +void computeNormals( const Octree& Octree, const vector& centers, vector& normals, vector& mask, float normalRadius, int minNeighbors = 20) -{ +{ size_t normals_size = centers.size(); normals.resize(normals_size); - + if (mask.size() != normals_size) { - size_t m = mask.size(); + size_t m = mask.size(); mask.resize(normals_size); if (normals_size > m) for(; m < normals_size; ++m) mask[m] = 1; } - + vector buffer; buffer.reserve(128); SVD svd; @@ -132,7 +132,7 @@ void computeNormals( const Octree& Octree, const vector& centers, vecto mean.x /= buf_size; mean.y /= buf_size; mean.z /= buf_size; - + double pxpx = 0; double pypy = 0; double pzpz = 0; @@ -162,9 +162,9 @@ void computeNormals( const Octree& Octree, const vector& centers, vecto /*normals[n] = Point3f( (float)((double*)svd.vt.data)[6], (float)((double*)svd.vt.data)[7], - (float)((double*)svd.vt.data)[8] );*/ - normals[n] = reinterpret_cast(svd.vt.data)[2]; - mask[n] = 1; + (float)((double*)svd.vt.data)[8] );*/ + normals[n] = reinterpret_cast(svd.vt.data)[2]; + mask[n] = 1; } } @@ -213,22 +213,22 @@ inline __m128 transformSSE(const __m128* matrix, const __m128& in) } inline __m128i _mm_mullo_epi32_emul(const __m128i& a, __m128i& b) -{ +{ __m128i pack = _mm_packs_epi32(a, a); - return _mm_unpacklo_epi16(_mm_mullo_epi16(pack, b), _mm_mulhi_epi16(pack, b)); + return _mm_unpacklo_epi16(_mm_mullo_epi16(pack, b), _mm_mulhi_epi16(pack, b)); } #endif -void computeSpinImages( const Octree& Octree, const vector& points, const vector& normals, +void computeSpinImages( const Octree& Octree, const vector& points, const vector& normals, vector& mask, Mat& spinImages, int imageWidth, float binSize) -{ +{ float pixelsPerMeter = 1.f / binSize; - float support = imageWidth * binSize; - + float support = imageWidth * binSize; + assert(normals.size() == points.size()); assert(mask.size() == points.size()); - + size_t points_size = points.size(); mask.resize(points_size); @@ -257,7 +257,7 @@ void computeSpinImages( const Octree& Octree, const vector& points, con int t = cvGetThreadNum(); vector& pointsInSphere = pointsInSpherePool[t]; - + const Point3f& center = points[i]; Octree.getPointsWithinSphere(center, searchRad, pointsInSphere); @@ -269,7 +269,7 @@ void computeSpinImages( const Octree& Octree, const vector& points, con } const Point3f& normal = normals[i]; - + float rotmat[9]; initRotationMat(normal, rotmat); Point3f new_center; @@ -287,7 +287,7 @@ void computeSpinImages( const Octree& Octree, const vector& points, con { __m128 rotmatSSE[3]; convertTransformMatrix(rotmat, (float*)rotmatSSE); - + __m128 center_x4 = _mm_set1_ps(new_center.x); __m128 center_y4 = _mm_set1_ps(new_center.y); __m128 center_z4 = _mm_set1_ps(new_center.z + halfSuppport); @@ -313,7 +313,7 @@ void computeSpinImages( const Octree& Octree, const vector& points, con __m128 z0 = _mm_unpackhi_ps(pt0, pt1); // z0 z1 . . __m128 z1 = _mm_unpackhi_ps(pt2, pt3); // z2 z3 . . __m128 beta4 = _mm_sub_ps(center_z4, _mm_movelh_ps(z0, z1)); // b0 b1 b2 b3 - + __m128 xy0 = _mm_unpacklo_ps(pt0, pt1); // x0 x1 y0 y1 __m128 xy1 = _mm_unpacklo_ps(pt2, pt3); // x2 x3 y2 y3 __m128 x4 = _mm_movelh_ps(xy0, xy1); // x0 x1 x2 x3 @@ -322,7 +322,7 @@ void computeSpinImages( const Octree& Octree, const vector& points, con x4 = _mm_sub_ps(x4, center_x4); y4 = _mm_sub_ps(y4, center_y4); __m128 alpha4 = _mm_sqrt_ps(_mm_add_ps(_mm_mul_ps(x4,x4),_mm_mul_ps(y4,y4))); - + __m128 n1f4 = _mm_mul_ps( beta4, ppm4); /* beta4 float */ __m128 n2f4 = _mm_mul_ps(alpha4, ppm4); /* alpha4 float */ @@ -333,21 +333,21 @@ void computeSpinImages( const Octree& Octree, const vector& points, con __m128 f1 = _mm_sub_ps( n1f4, _mm_cvtepi32_ps(n1) ); /* { beta4 } */ __m128 f2 = _mm_sub_ps( n2f4, _mm_cvtepi32_ps(n2) ); /* { alpha4 } */ - __m128 f1f2 = _mm_mul_ps(f1, f2); // f1 * f2 + __m128 f1f2 = _mm_mul_ps(f1, f2); // f1 * f2 __m128 omf1omf2 = _mm_add_ps(_mm_sub_ps(_mm_sub_ps(one4f, f2), f1), f1f2); // (1-f1) * (1-f2) - - __m128i mask = _mm_and_si128( + + __m128i _mask = _mm_and_si128( _mm_andnot_si128(_mm_cmpgt_epi32(zero4, n1), _mm_cmpgt_epi32(height4m1, n1)), _mm_andnot_si128(_mm_cmpgt_epi32(zero4, n2), _mm_cmpgt_epi32(width4m1, n2))); - __m128 maskf = _mm_cmpneq_ps(_mm_cvtepi32_ps(mask), zero4f); - + __m128 maskf = _mm_cmpneq_ps(_mm_cvtepi32_ps(_mask), zero4f); + __m128 v00 = _mm_and_ps( omf1omf2 , maskf); // a00 b00 c00 d00 __m128 v01 = _mm_and_ps( _mm_sub_ps( f2, f1f2 ), maskf); // a01 b01 c01 d01 __m128 v10 = _mm_and_ps( _mm_sub_ps( f1, f1f2 ), maskf); // a10 b10 c10 d10 __m128 v11 = _mm_and_ps( f1f2 , maskf); // a11 b11 c11 d11 - __m128i ofs4 = _mm_and_si128(_mm_add_epi32(_mm_mullo_epi32_emul(n1, step4), n2), mask); + __m128i ofs4 = _mm_and_si128(_mm_add_epi32(_mm_mullo_epi32_emul(n1, step4), n2), _mask); _mm_store_si128((__m128i*)o, ofs4); __m128 t0 = _mm_unpacklo_ps(v00, v01); // a00 a01 b00 b01 @@ -395,9 +395,9 @@ void computeSpinImages( const Octree& Octree, const vector& points, con if (beta >= support || beta < 0) continue; - alpha = sqrt( (new_center.x - pt.x) * (new_center.x - pt.x) + - (new_center.y - pt.y) * (new_center.y - pt.y) ); - + alpha = sqrt( (new_center.x - pt.x) * (new_center.x - pt.x) + + (new_center.y - pt.y) * (new_center.y - pt.y) ); + float n1f = beta * pixelsPerMeter; float n2f = alpha * pixelsPerMeter; @@ -407,7 +407,7 @@ void computeSpinImages( const Octree& Octree, const vector& points, con float f1 = n1f - n1; float f2 = n2f - n2; - if ((unsigned)n1 >= (unsigned)(spinImage.rows-1) || + if ((unsigned)n1 >= (unsigned)(spinImage.rows-1) || (unsigned)n2 >= (unsigned)(spinImage.cols-1)) continue; @@ -454,27 +454,27 @@ float cv::Mesh3D::estimateResolution(float /*tryRatio*/) vector dist(tryNum * neighbors); vector inds(tryNum * neighbors); - vector query; + vector query; - RNG& rng = theRNG(); + RNG& rng = theRNG(); for(int i = 0; i < tryNum; ++i) query.push_back(vtx[rng.next() % vtx.size()]); - + CvMat cvinds = cvMat( (int)tryNum, neighbors, CV_32S, &inds[0] ); - CvMat cvdist = cvMat( (int)tryNum, neighbors, CV_64F, &dist[0] ); + CvMat cvdist = cvMat( (int)tryNum, neighbors, CV_64F, &dist[0] ); CvMat cvquery = cvMat( (int)tryNum, 3, CV_32F, &query[0] ); - cvFindFeatures(tr, &cvquery, &cvinds, &cvdist, neighbors, 50); + cvFindFeatures(tr, &cvquery, &cvinds, &cvdist, neighbors, 50); cvReleaseFeatureTree(tr); - const int invalid_dist = -2; + const int invalid_dist = -2; for(int i = 0; i < tryNum; ++i) if (inds[i] == -1) dist[i] = invalid_dist; dist.resize(remove(dist.begin(), dist.end(), invalid_dist) - dist.begin()); - + sort(dist, less()); - + return resolution = (float)dist[ dist.size() / 2 ]; #else CV_Error(CV_StsNotImplemented, ""); @@ -494,7 +494,7 @@ void cv::Mesh3D::computeNormals(const vector& subset, float normalRadius, i { buildOctree(); vector mask(vtx.size(), 0); - for(size_t i = 0; i < subset.size(); ++i) + for(size_t i = 0; i < subset.size(); ++i) mask[subset[i]] = 1; ::computeNormals(octree, vtx, normals, mask, normalRadius, minNeighbors); } @@ -504,31 +504,31 @@ void cv::Mesh3D::writeAsVrml(const String& file, const vector& _colors) ofstream ofs(file.c_str()); ofs << "#VRML V2.0 utf8" << endl; - ofs << "Shape" << std::endl << "{" << endl; - ofs << "geometry PointSet" << endl << "{" << endl; - ofs << "coord Coordinate" << endl << "{" << endl; - ofs << "point[" << endl; + ofs << "Shape" << std::endl << "{" << endl; + ofs << "geometry PointSet" << endl << "{" << endl; + ofs << "coord Coordinate" << endl << "{" << endl; + ofs << "point[" << endl; for(size_t i = 0; i < vtx.size(); ++i) ofs << vtx[i].x << " " << vtx[i].y << " " << vtx[i].z << endl; - - ofs << "]" << endl; //point[ - ofs << "}" << endl; //Coordinate{ + + ofs << "]" << endl; //point[ + ofs << "}" << endl; //Coordinate{ if (vtx.size() == _colors.size()) { ofs << "color Color" << endl << "{" << endl; ofs << "color[" << endl; - + for(size_t i = 0; i < _colors.size(); ++i) ofs << (float)_colors[i][2] << " " << (float)_colors[i][1] << " " << (float)_colors[i][0] << endl; - + ofs << "]" << endl; //color[ - ofs << "}" << endl; //color Color{ + ofs << "}" << endl; //color Color{ } - ofs << "}" << endl; //PointSet{ - ofs << "}" << endl; //Shape{ + ofs << "}" << endl; //PointSet{ + ofs << "}" << endl; //Shape{ } @@ -538,45 +538,45 @@ void cv::Mesh3D::writeAsVrml(const String& file, const vector& _colors) bool cv::SpinImageModel::spinCorrelation(const Mat& spin1, const Mat& spin2, float lambda, float& result) { struct Math { static double atanh(double x) { return 0.5 * std::log( (1 + x) / (1 - x) ); } }; - + const float* s1 = spin1.ptr(); const float* s2 = spin2.ptr(); - int spin_sz = spin1.cols * spin1.rows; + int spin_sz = spin1.cols * spin1.rows; double sum1 = 0.0, sum2 = 0.0, sum12 = 0.0, sum11 = 0.0, sum22 = 0.0; int N = 0; int i = 0; #if CV_SSE2//____________TEMPORARY_DISABLED_____________ - float CV_DECL_ALIGNED(16) su1[4], su2[4], su11[4], su22[4], su12[4], n[4]; - + float CV_DECL_ALIGNED(16) su1[4], su2[4], su11[4], su22[4], su12[4], n[4]; + __m128 zerof4 = _mm_setzero_ps(); __m128 onef4 = _mm_set1_ps(1.f); - __m128 Nf4 = zerof4; + __m128 Nf4 = zerof4; __m128 sum1f4 = zerof4; __m128 sum2f4 = zerof4; __m128 sum11f4 = zerof4; __m128 sum22f4 = zerof4; - __m128 sum12f4 = zerof4; + __m128 sum12f4 = zerof4; for(; i < spin_sz - 5; i += 4) { - __m128 v1f4 = _mm_loadu_ps(s1 + i); - __m128 v2f4 = _mm_loadu_ps(s2 + i); + __m128 v1f4 = _mm_loadu_ps(s1 + i); + __m128 v2f4 = _mm_loadu_ps(s2 + i); __m128 mskf4 = _mm_and_ps(_mm_cmpneq_ps(v1f4, zerof4), _mm_cmpneq_ps(v2f4, zerof4)); - if( !_mm_movemask_ps(mskf4) ) + if( !_mm_movemask_ps(mskf4) ) continue; - + Nf4 = _mm_add_ps(Nf4, _mm_and_ps(onef4, mskf4)); v1f4 = _mm_and_ps(v1f4, mskf4); v2f4 = _mm_and_ps(v2f4, mskf4); - + sum1f4 = _mm_add_ps(sum1f4, v1f4); sum2f4 = _mm_add_ps(sum2f4, v2f4); sum11f4 = _mm_add_ps(sum11f4, _mm_mul_ps(v1f4, v1f4)); sum22f4 = _mm_add_ps(sum22f4, _mm_mul_ps(v2f4, v2f4)); - sum12f4 = _mm_add_ps(sum12f4, _mm_mul_ps(v1f4, v2f4)); + sum12f4 = _mm_add_ps(sum12f4, _mm_mul_ps(v1f4, v2f4)); } _mm_store_ps( su1, sum1f4 ); _mm_store_ps( su2, sum2f4 ); @@ -601,11 +601,11 @@ bool cv::SpinImageModel::spinCorrelation(const Mat& spin1, const Mat& spin2, flo if( !v1 || !v2 ) continue; N++; - - sum1 += v1; - sum2 += v2; - sum11 += v1 * v1; - sum22 += v2 * v2; + + sum1 += v1; + sum2 += v2; + sum11 += v1 * v1; + sum22 += v2 * v2; sum12 += v1 * v2; } if( N < 4 ) @@ -624,13 +624,13 @@ bool cv::SpinImageModel::spinCorrelation(const Mat& spin1, const Mat& spin2, flo double corr = (Nsum12 - sum1 * sum2) / sqrt( (Nsum11 - sum1sum1) * (Nsum22 - sum2sum2) ); double atanh = Math::atanh(corr); result = (float)( atanh * atanh - lambda * ( 1.0 / (N - 3) ) ); - return true; + return true; } inline Point2f cv::SpinImageModel::calcSpinMapCoo(const Point3f& p, const Point3f& v, const Point3f& n) -{ - /*Point3f PmV(p.x - v.x, p.y - v.y, p.z - v.z); - float normalNorm = (float)norm(n); +{ + /*Point3f PmV(p.x - v.x, p.y - v.y, p.z - v.z); + float normalNorm = (float)norm(n); float beta = PmV.dot(n) / normalNorm; float pmcNorm = (float)norm(PmV); float alpha = sqrt( pmcNorm * pmcNorm - beta * beta); @@ -639,23 +639,23 @@ inline Point2f cv::SpinImageModel::calcSpinMapCoo(const Point3f& p, const Point3 float pmv_x = p.x - v.x, pmv_y = p.y - v.y, pmv_z = p.z - v.z; float beta = (pmv_x * n.x + pmv_y + n.y + pmv_z * n.z) / sqrt(n.x * n.x + n.y * n.y + n.z * n.z); - float alpha = sqrt( pmv_x * pmv_x + pmv_y * pmv_y + pmv_z * pmv_z - beta * beta); + float alpha = sqrt( pmv_x * pmv_x + pmv_y * pmv_y + pmv_z * pmv_z - beta * beta); return Point2f(alpha, beta); } inline float cv::SpinImageModel::geometricConsistency(const Point3f& pointScene1, const Point3f& normalScene1, const Point3f& pointModel1, const Point3f& normalModel1, - const Point3f& pointScene2, const Point3f& normalScene2, + const Point3f& pointScene2, const Point3f& normalScene2, const Point3f& pointModel2, const Point3f& normalModel2) -{ +{ Point2f Sm2_to_m1, Ss2_to_s1; Point2f Sm1_to_m2, Ss1_to_s2; double n_Sm2_to_m1 = norm(Sm2_to_m1 = calcSpinMapCoo(pointModel2, pointModel1, normalModel1)); - double n_Ss2_to_s1 = norm(Ss2_to_s1 = calcSpinMapCoo(pointScene2, pointScene1, normalScene1)); + double n_Ss2_to_s1 = norm(Ss2_to_s1 = calcSpinMapCoo(pointScene2, pointScene1, normalScene1)); double gc21 = 2 * norm(Sm2_to_m1 - Ss2_to_s1) / (n_Sm2_to_m1 + n_Ss2_to_s1 ) ; - + double n_Sm1_to_m2 = norm(Sm1_to_m2 = calcSpinMapCoo(pointModel1, pointModel2, normalModel2)); double n_Ss1_to_s2 = norm(Ss1_to_s2 = calcSpinMapCoo(pointScene1, pointScene2, normalScene2)); @@ -666,10 +666,10 @@ inline float cv::SpinImageModel::geometricConsistency(const Point3f& pointScene1 inline float cv::SpinImageModel::groupingCreteria(const Point3f& pointScene1, const Point3f& normalScene1, const Point3f& pointModel1, const Point3f& normalModel1, - const Point3f& pointScene2, const Point3f& normalScene2, - const Point3f& pointModel2, const Point3f& normalModel2, + const Point3f& pointScene2, const Point3f& normalScene2, + const Point3f& pointModel2, const Point3f& normalModel2, float gamma) -{ +{ Point2f Sm2_to_m1, Ss2_to_s1; Point2f Sm1_to_m2, Ss1_to_s2; @@ -680,7 +680,7 @@ inline float cv::SpinImageModel::groupingCreteria(const Point3f& pointScene1, co double gc21 = 2 * norm(Sm2_to_m1 - Ss2_to_s1) / (n_Sm2_to_m1 + n_Ss2_to_s1 ); double wgc21 = gc21 / (1 - exp( -(n_Sm2_to_m1 + n_Ss2_to_s1) * gamma05_inv ) ); - + double n_Sm1_to_m2 = norm(Sm1_to_m2 = calcSpinMapCoo(pointModel1, pointModel2, normalModel2)); double n_Ss1_to_s2 = norm(Ss1_to_s2 = calcSpinMapCoo(pointScene1, pointScene2, normalScene2)); @@ -692,10 +692,10 @@ inline float cv::SpinImageModel::groupingCreteria(const Point3f& pointScene1, co cv::SpinImageModel::SpinImageModel(const Mesh3D& _mesh) : mesh(_mesh) , out(0) -{ +{ if (mesh.vtx.empty()) throw Mesh3D::EmptyMeshException(); - defaultParams(); + defaultParams(); } cv::SpinImageModel::SpinImageModel() : out(0) { defaultParams(); } cv::SpinImageModel::~SpinImageModel() {} @@ -708,8 +708,8 @@ void cv::SpinImageModel::defaultParams() minNeighbors = 20; binSize = 0.f; /* autodetect according to mesh resolution */ - imageWidth = 32; - + imageWidth = 32; + lambda = 0.f; /* autodetect according to medan non zero images bin */ gamma = 0.f; /* autodetect according to mesh resolution */ @@ -725,28 +725,28 @@ Mat cv::SpinImageModel::packRandomScaledSpins(bool separateScale, size_t xCount, if (num == 0) return Mat(); - RNG& rng = theRNG(); + RNG& rng = theRNG(); vector spins; for(int i = 0; i < num; ++i) - spins.push_back(getSpinImage( rng.next() % spinNum ).reshape(1, imageWidth)); - + spins.push_back(getSpinImage( rng.next() % spinNum ).reshape(1, imageWidth)); + if (separateScale) for(int i = 0; i < num; ++i) { double max; Mat spin8u; - minMaxLoc(spins[i], 0, &max); + minMaxLoc(spins[i], 0, &max); spins[i].convertTo(spin8u, CV_8U, -255.0/max, 255.0); spins[i] = spin8u; } else - { + { double totalMax = 0; for(int i = 0; i < num; ++i) { double m; - minMaxLoc(spins[i], 0, &m); + minMaxLoc(spins[i], 0, &m); totalMax = max(m, totalMax); } @@ -760,12 +760,12 @@ Mat cv::SpinImageModel::packRandomScaledSpins(bool separateScale, size_t xCount, int sz = spins.front().cols; - Mat result((int)(yCount * sz + (yCount - 1)), (int)(xCount * sz + (xCount - 1)), CV_8UC3); + Mat result((int)(yCount * sz + (yCount - 1)), (int)(xCount * sz + (xCount - 1)), CV_8UC3); result = colors[(static_cast(cvGetTickCount()/cvGetTickFrequency())/1000) % colors_mum]; int pos = 0; for(int y = 0; y < (int)yCount; ++y) - for(int x = 0; x < (int)xCount; ++x) + for(int x = 0; x < (int)xCount; ++x) if (pos < num) { int starty = (y + 0) * sz + y; @@ -778,7 +778,7 @@ Mat cv::SpinImageModel::packRandomScaledSpins(bool separateScale, size_t xCount, cvtColor(spins[pos++], color, CV_GRAY2BGR); Mat roi = result(Range(starty, endy), Range(startx, endx)); color.copyTo(roi); - } + } return result; } @@ -811,8 +811,8 @@ void cv::SpinImageModel::selectRandomSubset(float ratio) int pos = rnd.next() % left.size(); subset[i] = (int)left[pos]; - left[pos] = left.back(); - left.resize(left.size() - 1); + left[pos] = left.back(); + left.resize(left.size() - 1); } sort(subset, less()); } @@ -823,21 +823,21 @@ void cv::SpinImageModel::setSubset(const vector& ss) subset = ss; } -void cv::SpinImageModel::repackSpinImages(const vector& mask, Mat& spinImages, bool reAlloc) const -{ +void cv::SpinImageModel::repackSpinImages(const vector& mask, Mat& _spinImages, bool reAlloc) const +{ if (reAlloc) { size_t spinCount = mask.size() - count(mask.begin(), mask.end(), (uchar)0); - Mat newImgs((int)spinCount, spinImages.cols, spinImages.type()); + Mat newImgs((int)spinCount, _spinImages.cols, _spinImages.type()); int pos = 0; for(size_t t = 0; t < mask.size(); ++t) if (mask[t]) { Mat row = newImgs.row(pos++); - spinImages.row((int)t).copyTo(row); + _spinImages.row((int)t).copyTo(row); } - spinImages = newImgs; + _spinImages = newImgs; } else { @@ -849,13 +849,13 @@ void cv::SpinImageModel::repackSpinImages(const vector& mask, Mat& spinIm int first = dest + 1; for (; first != last; ++first) - if (mask[first] != 0) + if (mask[first] != 0) { - Mat row = spinImages.row(dest); - spinImages.row(first).copyTo(row); + Mat row = _spinImages.row(dest); + _spinImages.row(first).copyTo(row); ++dest; } - spinImages = spinImages.rowRange(0, dest); + _spinImages = _spinImages.rowRange(0, dest); } } @@ -865,13 +865,13 @@ void cv::SpinImageModel::compute() if (binSize == 0.f) { if (mesh.resolution == -1.f) - mesh.estimateResolution(); + mesh.estimateResolution(); binSize = mesh.resolution; } - /* estimate normalRadius */ - normalRadius = normalRadius != 0.f ? normalRadius : binSize * imageWidth / 2; + /* estimate normalRadius */ + normalRadius = normalRadius != 0.f ? normalRadius : binSize * imageWidth / 2; - mesh.buildOctree(); + mesh.buildOctree(); if (subset.empty()) { mesh.computeNormals(normalRadius, minNeighbors); @@ -881,16 +881,16 @@ void cv::SpinImageModel::compute() else mesh.computeNormals(subset, normalRadius, minNeighbors); - vector mask(mesh.vtx.size(), 0); + vector mask(mesh.vtx.size(), 0); for(size_t i = 0; i < subset.size(); ++i) - if (mesh.normals[subset[i]] == Mesh3D::allzero) - subset[i] = -1; + if (mesh.normals[subset[i]] == Mesh3D::allzero) + subset[i] = -1; else mask[subset[i]] = 1; subset.resize( remove(subset.begin(), subset.end(), -1) - subset.begin() ); - + vector vtx; - vector normals; + vector normals; for(size_t i = 0; i < mask.size(); ++i) if(mask[i]) { @@ -906,7 +906,7 @@ void cv::SpinImageModel::compute() for(size_t i = 0; i < mask.size(); ++i) if(mask[i]) if (spinMask[mask_pos++] == 0) - subset.resize( remove(subset.begin(), subset.end(), (int)i) - subset.begin() ); + subset.resize( remove(subset.begin(), subset.end(), (int)i) - subset.begin() ); } void cv::SpinImageModel::matchSpinToModel(const Mat& spin, vector& indeces, vector& corrCoeffs, bool useExtremeOutliers) const @@ -920,46 +920,46 @@ void cv::SpinImageModel::matchSpinToModel(const Mat& spin, vector& indeces, vector masks(model.spinImages.rows); vector cleanCorrs; cleanCorrs.reserve(model.spinImages.rows); - + for(int i = 0; i < model.spinImages.rows; ++i) { - masks[i] = spinCorrelation(spin, model.spinImages.row(i), model.lambda, corrs[i]); + masks[i] = spinCorrelation(spin, model.spinImages.row(i), model.lambda, corrs[i]); if (masks[i]) cleanCorrs.push_back(corrs[i]); } - + /* Filtering by measure histogram */ size_t total = cleanCorrs.size(); if(total < 5) return; sort(cleanCorrs, less()); - + float lower_fourth = cleanCorrs[(1 * total) / 4 - 1]; float upper_fourth = cleanCorrs[(3 * total) / 4 - 0]; float fourth_spread = upper_fourth - lower_fourth; //extreme or moderate? - float coef = useExtremeOutliers ? 3.0f : 1.5f; + float coef = useExtremeOutliers ? 3.0f : 1.5f; + + float histThresHi = upper_fourth + coef * fourth_spread; + //float histThresLo = lower_fourth - coef * fourth_spread; - float histThresHi = upper_fourth + coef * fourth_spread; - //float histThresLo = lower_fourth - coef * fourth_spread; - for(size_t i = 0; i < corrs.size(); ++i) if (masks[i]) if (/* corrs[i] < histThresLo || */ corrs[i] > histThresHi) { indeces.push_back((int)i); - corrCoeffs.push_back(corrs[i]); + corrCoeffs.push_back(corrs[i]); } -} +} -namespace +namespace { struct Match { - int sceneInd; + int sceneInd; int modelInd; float measure; @@ -984,7 +984,7 @@ struct WgcHelper { const float* wgcLine = mat.ptr((int)corespInd); float maximum = numeric_limits::min(); - + for(citer pos = group.begin(); pos != group.end(); ++pos) maximum = max(wgcLine[*pos], maximum); @@ -997,7 +997,7 @@ private: } void cv::SpinImageModel::match(const SpinImageModel& scene, vector< vector >& result) -{ +{ if (mesh.vtx.empty()) throw Mesh3D::EmptyMeshException(); @@ -1006,25 +1006,25 @@ private: SpinImageModel& model = *this; const float infinity = numeric_limits::infinity(); const float float_max = numeric_limits::max(); - + /* estimate gamma */ if (model.gamma == 0.f) { if (model.mesh.resolution == -1.f) - model.mesh.estimateResolution(); + model.mesh.estimateResolution(); model.gamma = 4 * model.mesh.resolution; } /* estimate lambda */ if (model.lambda == 0.f) { - vector nonzero(model.spinImages.rows); + vector nonzero(model.spinImages.rows); for(int i = 0; i < model.spinImages.rows; ++i) nonzero[i] = countNonZero(model.spinImages.row(i)); sort(nonzero, less()); model.lambda = static_cast( nonzero[ nonzero.size()/2 ] ) / 2; - } - + } + TickMeter corr_timer; corr_timer.start(); vector allMatches; @@ -1032,37 +1032,37 @@ private: { vector indeces; vector coeffs; - matchSpinToModel(scene.spinImages.row(i), indeces, coeffs); + matchSpinToModel(scene.spinImages.row(i), indeces, coeffs); for(size_t t = 0; t < indeces.size(); ++t) - allMatches.push_back(Match(i, indeces[t], coeffs[t])); + allMatches.push_back(Match(i, indeces[t], coeffs[t])); - if (out) if (i % 100 == 0) *out << "Comparing scene spinimage " << i << " of " << scene.spinImages.rows << endl; + if (out) if (i % 100 == 0) *out << "Comparing scene spinimage " << i << " of " << scene.spinImages.rows << endl; } corr_timer.stop(); if (out) *out << "Spin correlation time = " << corr_timer << endl; if (out) *out << "Matches number = " << allMatches.size() << endl; - if(allMatches.empty()) + if(allMatches.empty()) return; - + /* filtering by similarity measure */ const float fraction = 0.5f; - float maxMeasure = max_element(allMatches.begin(), allMatches.end(), less())->measure; + float maxMeasure = max_element(allMatches.begin(), allMatches.end(), less())->measure; allMatches.erase( - remove_if(allMatches.begin(), allMatches.end(), bind2nd(less(), maxMeasure * fraction)), + remove_if(allMatches.begin(), allMatches.end(), bind2nd(less(), maxMeasure * fraction)), allMatches.end()); if (out) *out << "Matches number [filtered by similarity measure] = " << allMatches.size() << endl; int matchesSize = (int)allMatches.size(); if(matchesSize == 0) return; - - /* filtering by geometric consistency */ + + /* filtering by geometric consistency */ for(int i = 0; i < matchesSize; ++i) { int consistNum = 1; float gc = float_max; - + for(int j = 0; j < matchesSize; ++j) if (i != j) { @@ -1075,31 +1075,31 @@ private: { const Point3f& pointSceneI = scene.getSpinVertex(mi.sceneInd); const Point3f& normalSceneI = scene.getSpinNormal(mi.sceneInd); - + const Point3f& pointModelI = model.getSpinVertex(mi.modelInd); const Point3f& normalModelI = model.getSpinNormal(mi.modelInd); - + const Point3f& pointSceneJ = scene.getSpinVertex(mj.sceneInd); const Point3f& normalSceneJ = scene.getSpinNormal(mj.sceneInd); - + const Point3f& pointModelJ = model.getSpinVertex(mj.modelInd); const Point3f& normalModelJ = model.getSpinNormal(mj.modelInd); - + gc = geometricConsistency(pointSceneI, normalSceneI, pointModelI, normalModelI, - pointSceneJ, normalSceneJ, pointModelJ, normalModelJ); + pointSceneJ, normalSceneJ, pointModelJ, normalModelJ); } if (gc < model.T_GeometriccConsistency) ++consistNum; } - - + + if (consistNum < matchesSize / 4) /* failed consistensy test */ - allMatches[i].measure = infinity; + allMatches[i].measure = infinity; } allMatches.erase( - remove_if(allMatches.begin(), allMatches.end(), bind2nd(equal_to(), infinity)), - allMatches.end()); + remove_if(allMatches.begin(), allMatches.end(), bind2nd(equal_to(), infinity)), + allMatches.end()); if (out) *out << "Matches number [filtered by geometric consistency] = " << allMatches.size() << endl; @@ -1110,11 +1110,11 @@ private: if (out) *out << "grouping ..." << endl; Mat groupingMat((int)matchesSize, (int)matchesSize, CV_32F); - groupingMat = Scalar(0); - + groupingMat = Scalar(0); + /* grouping */ for(int j = 0; j < matchesSize; ++j) - for(int i = j + 1; i < matchesSize; ++i) + for(int i = j + 1; i < matchesSize; ++i) { const Match& mi = allMatches[i]; const Match& mj = allMatches[j]; @@ -1128,20 +1128,20 @@ private: const Point3f& pointSceneI = scene.getSpinVertex(mi.sceneInd); const Point3f& normalSceneI = scene.getSpinNormal(mi.sceneInd); - + const Point3f& pointModelI = model.getSpinVertex(mi.modelInd); const Point3f& normalModelI = model.getSpinNormal(mi.modelInd); - + const Point3f& pointSceneJ = scene.getSpinVertex(mj.sceneInd); const Point3f& normalSceneJ = scene.getSpinNormal(mj.sceneInd); - + const Point3f& pointModelJ = model.getSpinVertex(mj.modelInd); const Point3f& normalModelJ = model.getSpinNormal(mj.modelInd); float wgc = groupingCreteria(pointSceneI, normalSceneI, pointModelI, normalModelI, pointSceneJ, normalSceneJ, pointModelJ, normalModelJ, - model.gamma); - + model.gamma); + groupingMat.ptr(i)[j] = wgc; groupingMat.ptr(j)[i] = wgc; } @@ -1149,35 +1149,35 @@ private: group_t allMatchesInds; for(int i = 0; i < matchesSize; ++i) allMatchesInds.insert(i); - + vector buf(matchesSize); float *buf_beg = &buf[0]; vector groups; - + for(int g = 0; g < matchesSize; ++g) - { + { if (out) if (g % 100 == 0) *out << "G = " << g << endl; group_t left = allMatchesInds; group_t group; - + left.erase(g); group.insert(g); - + for(;;) { size_t left_size = left.size(); if (left_size == 0) break; - + std::transform(left.begin(), left.end(), buf_beg, WgcHelper(group, groupingMat)); size_t minInd = min_element(buf_beg, buf_beg + left_size) - buf_beg; - + if (buf[minInd] < model.T_GroupingCorespondances) /* can add corespondance to group */ { iter pos = left.begin(); advance(pos, minInd); - + group.insert(*pos); left.erase(pos); } @@ -1199,16 +1199,16 @@ private: { const Match& m = allMatches[*pos]; outgrp.push_back(Vec2i(subset[m.modelInd], scene.subset[m.sceneInd])); - } + } result.push_back(outgrp); - } + } } cv::TickMeter::TickMeter() { reset(); } int64 cv::TickMeter::getTimeTicks() const { return sumTime; } double cv::TickMeter::getTimeMicro() const { return (double)getTimeTicks()/cvGetTickFrequency(); } double cv::TickMeter::getTimeMilli() const { return getTimeMicro()*1e-3; } -double cv::TickMeter::getTimeSec() const { return getTimeMilli()*1e-3; } +double cv::TickMeter::getTimeSec() const { return getTimeMilli()*1e-3; } int64 cv::TickMeter::getCounter() const { return counter; } void cv::TickMeter::reset() {startTime = 0; sumTime = 0; counter = 0; } diff --git a/modules/contrib/src/stereovar.cpp b/modules/contrib/src/stereovar.cpp index 1197acc..88640d8 100755 --- a/modules/contrib/src/stereovar.cpp +++ b/modules/contrib/src/stereovar.cpp @@ -46,14 +46,14 @@ Proceedings of the 5th International Symposium on Visual Computing, Vegas, USA This code is written by Sergey G. Kosov for "Visir PX" application as part of Project X (www.project-10.de) - */ + */ #include "precomp.hpp" #include -namespace cv +namespace cv { -StereoVar::StereoVar() : levels(3), pyrScale(0.5), nIt(5), minDisp(0), maxDisp(16), poly_n(3), poly_sigma(0), fi(25.0f), lambda(0.03f), penalization(PENALIZATION_TICHONOV), cycle(CYCLE_V), flags(USE_SMART_ID | USE_AUTO_PARAMS) +StereoVar::StereoVar() : levels(3), pyrScale(0.5), nIt(5), minDisp(0), maxDisp(16), poly_n(3), poly_sigma(0), fi(25.0f), lambda(0.03f), penalization(PENALIZATION_TICHONOV), cycle(CYCLE_V), flags(USE_SMART_ID | USE_AUTO_PARAMS) { } @@ -67,9 +67,9 @@ StereoVar::~StereoVar() static Mat diffX(Mat &src) { - register int x, y, cols = src.cols - 1; - Mat dst(src.size(), src.type()); - for(y = 0; y < src.rows; y++){ + register int x, y, cols = src.cols - 1; + Mat dst(src.size(), src.type()); + for(y = 0; y < src.rows; y++){ const float* pSrc = src.ptr(y); float* pDst = dst.ptr(y); #if CV_SSE2 @@ -92,319 +92,319 @@ static Mat diffX(Mat &src) static Mat getGradient(Mat &src) { - register int x, y; - Mat dst(src.size(), src.type()); - dst.setTo(0); - for (y = 0; y < src.rows - 1; y++) { - float *pSrc = src.ptr(y); - float *pSrcF = src.ptr(y + 1); - float *pDst = dst.ptr(y); - for (x = 0; x < src.cols - 1; x++) - pDst[x] = fabs(pSrc[x + 1] - pSrc[x]) + fabs(pSrcF[x] - pSrc[x]); - } - return dst; + register int x, y; + Mat dst(src.size(), src.type()); + dst.setTo(0); + for (y = 0; y < src.rows - 1; y++) { + float *pSrc = src.ptr(y); + float *pSrcF = src.ptr(y + 1); + float *pDst = dst.ptr(y); + for (x = 0; x < src.cols - 1; x++) + pDst[x] = fabs(pSrc[x + 1] - pSrc[x]) + fabs(pSrcF[x] - pSrc[x]); + } + return dst; } static Mat getG_c(Mat &src, float l) { - Mat dst(src.size(), src.type()); - for (register int y = 0; y < src.rows; y++) { - float *pSrc = src.ptr(y); - float *pDst = dst.ptr(y); - for (register int x = 0; x < src.cols; x++) - pDst[x] = 0.5f*l / sqrtf(l*l + pSrc[x]*pSrc[x]); - } - return dst; + Mat dst(src.size(), src.type()); + for (register int y = 0; y < src.rows; y++) { + float *pSrc = src.ptr(y); + float *pDst = dst.ptr(y); + for (register int x = 0; x < src.cols; x++) + pDst[x] = 0.5f*l / sqrtf(l*l + pSrc[x]*pSrc[x]); + } + return dst; } static Mat getG_p(Mat &src, float l) { - Mat dst(src.size(), src.type()); - for (register int y = 0; y < src.rows; y++) { - float *pSrc = src.ptr(y); - float *pDst = dst.ptr(y); - for (register int x = 0; x < src.cols; x++) - pDst[x] = 0.5f*l*l / (l*l + pSrc[x]*pSrc[x]); - } - return dst; + Mat dst(src.size(), src.type()); + for (register int y = 0; y < src.rows; y++) { + float *pSrc = src.ptr(y); + float *pDst = dst.ptr(y); + for (register int x = 0; x < src.cols; x++) + pDst[x] = 0.5f*l*l / (l*l + pSrc[x]*pSrc[x]); + } + return dst; } void StereoVar::VariationalSolver(Mat &I1, Mat &I2, Mat &I2x, Mat &u, int level) { - register int n, x, y; - float gl = 1, gr = 1, gu = 1, gd = 1, gc = 4; - Mat g_c, g_p; - Mat U; - u.copyTo(U); - - int N = nIt; - float l = lambda; - float Fi = fi; - - - if (flags & USE_SMART_ID) { - double scale = pow(pyrScale, (double) level) * (1 + pyrScale); - N = (int) (N / scale); - } - - double scale = pow(pyrScale, (double) level); - Fi /= (float) scale; - l *= (float) scale; - - int width = u.cols - 1; - int height = u.rows - 1; - for (n = 0; n < N; n++) { - if (penalization != PENALIZATION_TICHONOV) { - Mat gradient = getGradient(U); - switch (penalization) { - case PENALIZATION_CHARBONNIER: g_c = getG_c(gradient, l); break; - case PENALIZATION_PERONA_MALIK: g_p = getG_p(gradient, l); break; - } - gradient.release(); - } - for (y = 1 ; y < height; y++) { - float *pU = U.ptr(y); - float *pUu = U.ptr(y + 1); - float *pUd = U.ptr(y - 1); - float *pu = u.ptr(y); - float *pI1 = I1.ptr(y); - float *pI2 = I2.ptr(y); - float *pI2x = I2x.ptr(y); - float *pG_c = NULL, *pG_cu = NULL, *pG_cd = NULL; - float *pG_p = NULL, *pG_pu = NULL, *pG_pd = NULL; - switch (penalization) { - case PENALIZATION_CHARBONNIER: - pG_c = g_c.ptr(y); - pG_cu = g_c.ptr(y + 1); - pG_cd = g_c.ptr(y - 1); - break; - case PENALIZATION_PERONA_MALIK: - pG_p = g_p.ptr(y); - pG_pu = g_p.ptr(y + 1); - pG_pd = g_p.ptr(y - 1); - break; - } - for (x = 1; x < width; x++) { - switch (penalization) { - case PENALIZATION_CHARBONNIER: - gc = pG_c[x]; - gl = gc + pG_c[x - 1]; - gr = gc + pG_c[x + 1]; - gu = gc + pG_cu[x]; - gd = gc + pG_cd[x]; - gc = gl + gr + gu + gd; - break; - case PENALIZATION_PERONA_MALIK: - gc = pG_p[x]; - gl = gc + pG_p[x - 1]; - gr = gc + pG_p[x + 1]; - gu = gc + pG_pu[x]; - gd = gc + pG_pd[x]; - gc = gl + gr + gu + gd; - break; - } - - float fi = Fi; - if (maxDisp > minDisp) { - if (pU[x] > maxDisp * scale) {fi *= 1000; pU[x] = static_cast(maxDisp * scale);} - if (pU[x] < minDisp * scale) {fi *= 1000; pU[x] = static_cast(minDisp * scale);} - } - - int A = static_cast(pU[x]); - int neg = 0; if (pU[x] <= 0) neg = -1; - - if (x + A > width) - pu[x] = pU[width - A]; - else if (x + A + neg < 0) - pu[x] = pU[- A + 2]; - else { - pu[x] = A + (pI2x[x + A + neg] * (pI1[x] - pI2[x + A]) - + fi * (gr * pU[x + 1] + gl * pU[x - 1] + gu * pUu[x] + gd * pUd[x] - gc * A)) - / (pI2x[x + A + neg] * pI2x[x + A + neg] + gc * fi) ; - } - }// x - pu[0] = pu[1]; - pu[width] = pu[width - 1]; - }// y - for (x = 0; x <= width; x++) { - u.at(0, x) = u.at(1, x); - u.at(height, x) = u.at(height - 1, x); - } - u.copyTo(U); - if (!g_c.empty()) g_c.release(); - if (!g_p.empty()) g_p.release(); - }//n + register int n, x, y; + float gl = 1, gr = 1, gu = 1, gd = 1, gc = 4; + Mat g_c, g_p; + Mat U; + u.copyTo(U); + + int N = nIt; + float l = lambda; + float Fi = fi; + + + if (flags & USE_SMART_ID) { + double scale = pow(pyrScale, (double) level) * (1 + pyrScale); + N = (int) (N / scale); + } + + double scale = pow(pyrScale, (double) level); + Fi /= (float) scale; + l *= (float) scale; + + int width = u.cols - 1; + int height = u.rows - 1; + for (n = 0; n < N; n++) { + if (penalization != PENALIZATION_TICHONOV) { + Mat gradient = getGradient(U); + switch (penalization) { + case PENALIZATION_CHARBONNIER: g_c = getG_c(gradient, l); break; + case PENALIZATION_PERONA_MALIK: g_p = getG_p(gradient, l); break; + } + gradient.release(); + } + for (y = 1 ; y < height; y++) { + float *pU = U.ptr(y); + float *pUu = U.ptr(y + 1); + float *pUd = U.ptr(y - 1); + float *pu = u.ptr(y); + float *pI1 = I1.ptr(y); + float *pI2 = I2.ptr(y); + float *pI2x = I2x.ptr(y); + float *pG_c = NULL, *pG_cu = NULL, *pG_cd = NULL; + float *pG_p = NULL, *pG_pu = NULL, *pG_pd = NULL; + switch (penalization) { + case PENALIZATION_CHARBONNIER: + pG_c = g_c.ptr(y); + pG_cu = g_c.ptr(y + 1); + pG_cd = g_c.ptr(y - 1); + break; + case PENALIZATION_PERONA_MALIK: + pG_p = g_p.ptr(y); + pG_pu = g_p.ptr(y + 1); + pG_pd = g_p.ptr(y - 1); + break; + } + for (x = 1; x < width; x++) { + switch (penalization) { + case PENALIZATION_CHARBONNIER: + gc = pG_c[x]; + gl = gc + pG_c[x - 1]; + gr = gc + pG_c[x + 1]; + gu = gc + pG_cu[x]; + gd = gc + pG_cd[x]; + gc = gl + gr + gu + gd; + break; + case PENALIZATION_PERONA_MALIK: + gc = pG_p[x]; + gl = gc + pG_p[x - 1]; + gr = gc + pG_p[x + 1]; + gu = gc + pG_pu[x]; + gd = gc + pG_pd[x]; + gc = gl + gr + gu + gd; + break; + } + + float _fi = Fi; + if (maxDisp > minDisp) { + if (pU[x] > maxDisp * scale) {_fi *= 1000; pU[x] = static_cast(maxDisp * scale);} + if (pU[x] < minDisp * scale) {_fi *= 1000; pU[x] = static_cast(minDisp * scale);} + } + + int A = static_cast(pU[x]); + int neg = 0; if (pU[x] <= 0) neg = -1; + + if (x + A > width) + pu[x] = pU[width - A]; + else if (x + A + neg < 0) + pu[x] = pU[- A + 2]; + else { + pu[x] = A + (pI2x[x + A + neg] * (pI1[x] - pI2[x + A]) + + _fi * (gr * pU[x + 1] + gl * pU[x - 1] + gu * pUu[x] + gd * pUd[x] - gc * A)) + / (pI2x[x + A + neg] * pI2x[x + A + neg] + gc * _fi) ; + } + }// x + pu[0] = pu[1]; + pu[width] = pu[width - 1]; + }// y + for (x = 0; x <= width; x++) { + u.at(0, x) = u.at(1, x); + u.at(height, x) = u.at(height - 1, x); + } + u.copyTo(U); + if (!g_c.empty()) g_c.release(); + if (!g_p.empty()) g_p.release(); + }//n } void StereoVar::VCycle_MyFAS(Mat &I1, Mat &I2, Mat &I2x, Mat &_u, int level) { - CvSize imgSize = _u.size(); - CvSize frmSize = cvSize((int) (imgSize.width * pyrScale + 0.5), (int) (imgSize.height * pyrScale + 0.5)); - Mat I1_h, I2_h, I2x_h, u_h, U, U_h; + CvSize imgSize = _u.size(); + CvSize frmSize = cvSize((int) (imgSize.width * pyrScale + 0.5), (int) (imgSize.height * pyrScale + 0.5)); + Mat I1_h, I2_h, I2x_h, u_h, U, U_h; - //PRE relaxation - VariationalSolver(I1, I2, I2x, _u, level); + //PRE relaxation + VariationalSolver(I1, I2, I2x, _u, level); - if (level >= levels - 1) return; - level ++; + if (level >= levels - 1) return; + level ++; - //scaling DOWN - resize(I1, I1_h, frmSize, 0, 0, INTER_AREA); - resize(I2, I2_h, frmSize, 0, 0, INTER_AREA); - resize(_u, u_h, frmSize, 0, 0, INTER_AREA); - u_h.convertTo(u_h, u_h.type(), pyrScale); - I2x_h = diffX(I2_h); + //scaling DOWN + resize(I1, I1_h, frmSize, 0, 0, INTER_AREA); + resize(I2, I2_h, frmSize, 0, 0, INTER_AREA); + resize(_u, u_h, frmSize, 0, 0, INTER_AREA); + u_h.convertTo(u_h, u_h.type(), pyrScale); + I2x_h = diffX(I2_h); - //Next level - U_h = u_h.clone(); - VCycle_MyFAS(I1_h, I2_h, I2x_h, U_h, level); + //Next level + U_h = u_h.clone(); + VCycle_MyFAS(I1_h, I2_h, I2x_h, U_h, level); - subtract(U_h, u_h, U_h); - U_h.convertTo(U_h, U_h.type(), 1.0 / pyrScale); + subtract(U_h, u_h, U_h); + U_h.convertTo(U_h, U_h.type(), 1.0 / pyrScale); - //scaling UP - resize(U_h, U, imgSize); + //scaling UP + resize(U_h, U, imgSize); - //correcting the solution - add(_u, U, _u); + //correcting the solution + add(_u, U, _u); - //POST relaxation - VariationalSolver(I1, I2, I2x, _u, level - 1); + //POST relaxation + VariationalSolver(I1, I2, I2x, _u, level - 1); - if (flags & USE_MEDIAN_FILTERING) medianBlur(_u, _u, 3); + if (flags & USE_MEDIAN_FILTERING) medianBlur(_u, _u, 3); - I1_h.release(); - I2_h.release(); - I2x_h.release(); - u_h.release(); - U.release(); - U_h.release(); + I1_h.release(); + I2_h.release(); + I2x_h.release(); + u_h.release(); + U.release(); + U_h.release(); } void StereoVar::FMG(Mat &I1, Mat &I2, Mat &I2x, Mat &u, int level) { - double scale = pow(pyrScale, (double) level); - CvSize frmSize = cvSize((int) (u.cols * scale + 0.5), (int) (u.rows * scale + 0.5)); - Mat I1_h, I2_h, I2x_h, u_h; - - //scaling DOWN - resize(I1, I1_h, frmSize, 0, 0, INTER_AREA); - resize(I2, I2_h, frmSize, 0, 0, INTER_AREA); - resize(u, u_h, frmSize, 0, 0, INTER_AREA); - u_h.convertTo(u_h, u_h.type(), scale); - I2x_h = diffX(I2_h); - - switch (cycle) { - case CYCLE_O: - VariationalSolver(I1_h, I2_h, I2x_h, u_h, level); - break; - case CYCLE_V: - VCycle_MyFAS(I1_h, I2_h, I2x_h, u_h, level); - break; - } - - u_h.convertTo(u_h, u_h.type(), 1.0 / scale); - - //scaling UP - resize(u_h, u, u.size(), 0, 0, INTER_CUBIC); - - I1_h.release(); - I2_h.release(); - I2x_h.release(); - u_h.release(); - - level--; - if ((flags & USE_AUTO_PARAMS) && (level < levels / 3)) { - penalization = PENALIZATION_PERONA_MALIK; - fi *= 100; - flags -= USE_AUTO_PARAMS; - autoParams(); - } - if (flags & USE_MEDIAN_FILTERING) medianBlur(u, u, 3); - if (level >= 0) FMG(I1, I2, I2x, u, level); + double scale = pow(pyrScale, (double) level); + CvSize frmSize = cvSize((int) (u.cols * scale + 0.5), (int) (u.rows * scale + 0.5)); + Mat I1_h, I2_h, I2x_h, u_h; + + //scaling DOWN + resize(I1, I1_h, frmSize, 0, 0, INTER_AREA); + resize(I2, I2_h, frmSize, 0, 0, INTER_AREA); + resize(u, u_h, frmSize, 0, 0, INTER_AREA); + u_h.convertTo(u_h, u_h.type(), scale); + I2x_h = diffX(I2_h); + + switch (cycle) { + case CYCLE_O: + VariationalSolver(I1_h, I2_h, I2x_h, u_h, level); + break; + case CYCLE_V: + VCycle_MyFAS(I1_h, I2_h, I2x_h, u_h, level); + break; + } + + u_h.convertTo(u_h, u_h.type(), 1.0 / scale); + + //scaling UP + resize(u_h, u, u.size(), 0, 0, INTER_CUBIC); + + I1_h.release(); + I2_h.release(); + I2x_h.release(); + u_h.release(); + + level--; + if ((flags & USE_AUTO_PARAMS) && (level < levels / 3)) { + penalization = PENALIZATION_PERONA_MALIK; + fi *= 100; + flags -= USE_AUTO_PARAMS; + autoParams(); + } + if (flags & USE_MEDIAN_FILTERING) medianBlur(u, u, 3); + if (level >= 0) FMG(I1, I2, I2x, u, level); } void StereoVar::autoParams() -{ - int maxD = MAX(labs(maxDisp), labs(minDisp)); - - if (!maxD) pyrScale = 0.85; - else if (maxD < 8) pyrScale = 0.5; - else if (maxD < 64) pyrScale = 0.5 + static_cast(maxD - 8) * 0.00625; - else pyrScale = 0.85; - - if (maxD) { - levels = 0; - while ( pow(pyrScale, levels) * maxD > 1.5) levels ++; - levels++; - } - - switch(penalization) { - case PENALIZATION_TICHONOV: cycle = CYCLE_V; break; - case PENALIZATION_CHARBONNIER: cycle = CYCLE_O; break; - case PENALIZATION_PERONA_MALIK: cycle = CYCLE_O; break; - } +{ + int maxD = MAX(labs(maxDisp), labs(minDisp)); + + if (!maxD) pyrScale = 0.85; + else if (maxD < 8) pyrScale = 0.5; + else if (maxD < 64) pyrScale = 0.5 + static_cast(maxD - 8) * 0.00625; + else pyrScale = 0.85; + + if (maxD) { + levels = 0; + while ( pow(pyrScale, levels) * maxD > 1.5) levels ++; + levels++; + } + + switch(penalization) { + case PENALIZATION_TICHONOV: cycle = CYCLE_V; break; + case PENALIZATION_CHARBONNIER: cycle = CYCLE_O; break; + case PENALIZATION_PERONA_MALIK: cycle = CYCLE_O; break; + } } void StereoVar::operator ()( const Mat& left, const Mat& right, Mat& disp ) { - CV_Assert(left.size() == right.size() && left.type() == right.type()); - CvSize imgSize = left.size(); - int MaxD = MAX(labs(minDisp), labs(maxDisp)); - int SignD = 1; if (MIN(minDisp, maxDisp) < 0) SignD = -1; - if (minDisp >= maxDisp) {MaxD = 256; SignD = 1;} - - Mat u; - if ((flags & USE_INITIAL_DISPARITY) && (!disp.empty())) { - CV_Assert(disp.size() == left.size() && disp.type() == CV_8UC1); - disp.convertTo(u, CV_32FC1, static_cast(SignD * MaxD) / 256); - } else { - u.create(imgSize, CV_32FC1); - u.setTo(0); - } - - // Preprocessing - Mat leftgray, rightgray; - if (left.type() != CV_8UC1) { - cvtColor(left, leftgray, CV_BGR2GRAY); - cvtColor(right, rightgray, CV_BGR2GRAY); - } else { - left.copyTo(leftgray); - right.copyTo(rightgray); - } - if (flags & USE_EQUALIZE_HIST) { - equalizeHist(leftgray, leftgray); - equalizeHist(rightgray, rightgray); - } - if (poly_sigma > 0.0001) { - GaussianBlur(leftgray, leftgray, cvSize(poly_n, poly_n), poly_sigma); - GaussianBlur(rightgray, rightgray, cvSize(poly_n, poly_n), poly_sigma); - } - - if (flags & USE_AUTO_PARAMS) { - penalization = PENALIZATION_TICHONOV; - autoParams(); - } - - Mat I1, I2; - leftgray.convertTo(I1, CV_32FC1); - rightgray.convertTo(I2, CV_32FC1); - leftgray.release(); - rightgray.release(); - - Mat I2x = diffX(I2); - - FMG(I1, I2, I2x, u, levels - 1); - - I1.release(); - I2.release(); - I2x.release(); - - - disp.create( left.size(), CV_8UC1 ); - u = abs(u); - u.convertTo(disp, disp.type(), 256 / MaxD, 0); - - u.release(); + CV_Assert(left.size() == right.size() && left.type() == right.type()); + CvSize imgSize = left.size(); + int MaxD = MAX(labs(minDisp), labs(maxDisp)); + int SignD = 1; if (MIN(minDisp, maxDisp) < 0) SignD = -1; + if (minDisp >= maxDisp) {MaxD = 256; SignD = 1;} + + Mat u; + if ((flags & USE_INITIAL_DISPARITY) && (!disp.empty())) { + CV_Assert(disp.size() == left.size() && disp.type() == CV_8UC1); + disp.convertTo(u, CV_32FC1, static_cast(SignD * MaxD) / 256); + } else { + u.create(imgSize, CV_32FC1); + u.setTo(0); + } + + // Preprocessing + Mat leftgray, rightgray; + if (left.type() != CV_8UC1) { + cvtColor(left, leftgray, CV_BGR2GRAY); + cvtColor(right, rightgray, CV_BGR2GRAY); + } else { + left.copyTo(leftgray); + right.copyTo(rightgray); + } + if (flags & USE_EQUALIZE_HIST) { + equalizeHist(leftgray, leftgray); + equalizeHist(rightgray, rightgray); + } + if (poly_sigma > 0.0001) { + GaussianBlur(leftgray, leftgray, cvSize(poly_n, poly_n), poly_sigma); + GaussianBlur(rightgray, rightgray, cvSize(poly_n, poly_n), poly_sigma); + } + + if (flags & USE_AUTO_PARAMS) { + penalization = PENALIZATION_TICHONOV; + autoParams(); + } + + Mat I1, I2; + leftgray.convertTo(I1, CV_32FC1); + rightgray.convertTo(I2, CV_32FC1); + leftgray.release(); + rightgray.release(); + + Mat I2x = diffX(I2); + + FMG(I1, I2, I2x, u, levels - 1); + + I1.release(); + I2.release(); + I2x.release(); + + + disp.create( left.size(), CV_8UC1 ); + u = abs(u); + u.convertTo(disp, disp.type(), 256 / MaxD, 0); + + u.release(); } } // namespace \ No newline at end of file diff --git a/modules/core/CMakeLists.txt b/modules/core/CMakeLists.txt index 37e6e95..644e10e 100644 --- a/modules/core/CMakeLists.txt +++ b/modules/core/CMakeLists.txt @@ -5,13 +5,11 @@ ocv_module_include_directories(${ZLIB_INCLUDE_DIR}) if(HAVE_CUDA) file(GLOB lib_cuda "src/cuda/*.cu") source_group("Cuda" FILES "${lib_cuda}") - include_directories(AFTER SYSTEM ${CUDA_INCLUDE_DIRS}) - ocv_include_directories("${OpenCV_SOURCE_DIR}/modules/gpu/src" "${OpenCV_SOURCE_DIR}/modules/gpu/src/cuda") + ocv_include_directories("${OpenCV_SOURCE_DIR}/modules/gpu/src" "${OpenCV_SOURCE_DIR}/modules/gpu/src/cuda" ${CUDA_INCLUDE_DIRS}) ocv_warnings_disable(CMAKE_CXX_FLAGS -Wundef) + ocv_cuda_compile(cuda_objs ${lib_cuda}) - OCV_CUDA_COMPILE(cuda_objs ${lib_cuda}) - set(cuda_link_libs ${CUDA_LIBRARIES} ${CUDA_npp_LIBRARY}) else() set(lib_cuda "") diff --git a/modules/core/include/opencv2/core/gpumat.hpp b/modules/core/include/opencv2/core/gpumat.hpp index fda6990..ef86c5a 100644 --- a/modules/core/include/opencv2/core/gpumat.hpp +++ b/modules/core/include/opencv2/core/gpumat.hpp @@ -366,12 +366,12 @@ namespace cv { namespace gpu return m; } - inline void GpuMat::assignTo(GpuMat& m, int type) const + inline void GpuMat::assignTo(GpuMat& m, int _type) const { - if (type < 0) + if (_type < 0) m = *this; else - convertTo(m, type); + convertTo(m, _type); } inline size_t GpuMat::step1() const @@ -434,9 +434,9 @@ namespace cv { namespace gpu create(size_.height, size_.width, type_); } - inline GpuMat GpuMat::operator()(Range rowRange, Range colRange) const + inline GpuMat GpuMat::operator()(Range _rowRange, Range _colRange) const { - return GpuMat(*this, rowRange, colRange); + return GpuMat(*this, _rowRange, _colRange); } inline GpuMat GpuMat::operator()(Rect roi) const diff --git a/modules/core/include/opencv2/core/mat.hpp b/modules/core/include/opencv2/core/mat.hpp index 6b86cd7..1c79f50 100644 --- a/modules/core/include/opencv2/core/mat.hpp +++ b/modules/core/include/opencv2/core/mat.hpp @@ -63,7 +63,7 @@ inline void Mat::initEmpty() refcount = 0; allocator = 0; } - + inline Mat::Mat() : size(&rows) { initEmpty(); @@ -87,14 +87,14 @@ inline Mat::Mat(Size _sz, int _type) : size(&rows) initEmpty(); create( _sz.height, _sz.width, _type ); } - + inline Mat::Mat(Size _sz, int _type, const Scalar& _s) : size(&rows) { initEmpty(); create(_sz.height, _sz.width, _type); *this = _s; } - + inline Mat::Mat(int _dims, const int* _sz, int _type) : size(&rows) { initEmpty(); @@ -106,7 +106,7 @@ inline Mat::Mat(int _dims, const int* _sz, int _type, const Scalar& _s) : size(& initEmpty(); create(_dims, _sz, _type); *this = _s; -} +} inline Mat::Mat(const Mat& m) : flags(m.flags), dims(m.dims), rows(m.rows), cols(m.cols), data(m.data), @@ -187,8 +187,8 @@ template inline Mat::Mat(const vector<_Tp>& vec, bool copyData) else Mat((int)vec.size(), 1, DataType<_Tp>::type, (uchar*)&vec[0]).copyTo(*this); } - - + + template inline Mat::Mat(const Vec<_Tp, n>& vec, bool copyData) : flags(MAGIC_VAL | DataType<_Tp>::type | CV_MAT_CONT_FLAG), dims(2), rows(n), cols(1), data(0), refcount(0), @@ -218,10 +218,10 @@ template inline Mat::Mat(const Matx<_Tp,m,n>& M, boo datalimit = dataend = datastart + rows*step[0]; } else - Mat(m, n, DataType<_Tp>::type, (uchar*)M.val).copyTo(*this); + Mat(m, n, DataType<_Tp>::type, (uchar*)M.val).copyTo(*this); } - + template inline Mat::Mat(const Point_<_Tp>& pt, bool copyData) : flags(MAGIC_VAL | DataType<_Tp>::type | CV_MAT_CONT_FLAG), dims(2), rows(2), cols(1), data(0), refcount(0), @@ -240,7 +240,7 @@ template inline Mat::Mat(const Point_<_Tp>& pt, bool copyData) ((_Tp*)data)[1] = pt.y; } } - + template inline Mat::Mat(const Point3_<_Tp>& pt, bool copyData) : flags(MAGIC_VAL | DataType<_Tp>::type | CV_MAT_CONT_FLAG), @@ -262,7 +262,7 @@ template inline Mat::Mat(const Point3_<_Tp>& pt, bool copyData) } } - + template inline Mat::Mat(const MatCommaInitializer_<_Tp>& commaInitializer) : flags(MAGIC_VAL | DataType<_Tp>::type | CV_MAT_CONT_FLAG), dims(0), rows(0), cols(0), data(0), refcount(0), @@ -270,7 +270,7 @@ template inline Mat::Mat(const MatCommaInitializer_<_Tp>& commaIni { *this = *commaInitializer; } - + inline Mat::~Mat() { release(); @@ -305,7 +305,7 @@ inline Mat& Mat::operator = (const Mat& m) } return *this; } - + inline Mat Mat::row(int y) const { return Mat(*this, Range(y, y+1), Range::all()); } inline Mat Mat::col(int x) const { return Mat(*this, Range::all(), Range(x, x+1)); } inline Mat Mat::rowRange(int startrow, int endrow) const @@ -336,12 +336,12 @@ inline Mat Mat::clone() const return m; } -inline void Mat::assignTo( Mat& m, int type ) const +inline void Mat::assignTo( Mat& m, int _type ) const { - if( type < 0 ) + if( _type < 0 ) m = *this; else - convertTo(m, type); + convertTo(m, _type); } inline void Mat::create(int _rows, int _cols, int _type) @@ -370,19 +370,19 @@ inline void Mat::release() refcount = 0; } -inline Mat Mat::operator()( Range rowRange, Range colRange ) const +inline Mat Mat::operator()( Range _rowRange, Range _colRange ) const { - return Mat(*this, rowRange, colRange); + return Mat(*this, _rowRange, _colRange); } - + inline Mat Mat::operator()( const Rect& roi ) const { return Mat(*this, roi); } inline Mat Mat::operator()(const Range* ranges) const { return Mat(*this, ranges); -} - +} + inline Mat::operator CvMat() const { CV_DbgAssert(dims <= 2); @@ -435,7 +435,7 @@ template inline const _Tp* Mat::ptr(int y) const return (const _Tp*)(data + step.p[0]*y); } - + inline uchar* Mat::ptr(int i0, int i1) { CV_DbgAssert( dims >= 2 && data && @@ -505,7 +505,7 @@ template inline const _Tp* Mat::ptr(int i0, int i1, int i2) const } inline uchar* Mat::ptr(const int* idx) -{ +{ int i, d = dims; uchar* p = data; CV_DbgAssert( d >= 1 && p ); @@ -528,8 +528,8 @@ inline const uchar* Mat::ptr(const int* idx) const p += idx[i]*step.p[i]; } return p; -} - +} + template inline _Tp& Mat::at(int i0, int i1) { CV_DbgAssert( dims <= 2 && data && (unsigned)i0 < (unsigned)size.p[0] && @@ -545,7 +545,7 @@ template inline const _Tp& Mat::at(int i0, int i1) const CV_ELEM_SIZE1(DataType<_Tp>::depth) == elemSize1()); return ((const _Tp*)(data + step.p[0]*i0))[i1]; } - + template inline _Tp& Mat::at(Point pt) { CV_DbgAssert( dims <= 2 && data && (unsigned)pt.y < (unsigned)size.p[0] && @@ -574,7 +574,7 @@ template inline _Tp& Mat::at(int i0) int i = i0/cols, j = i0 - i*cols; return ((_Tp*)(data + step.p[0]*i))[j]; } - + template inline const _Tp& Mat::at(int i0) const { CV_DbgAssert( dims <= 2 && data && @@ -587,7 +587,7 @@ template inline const _Tp& Mat::at(int i0) const int i = i0/cols, j = i0 - i*cols; return ((const _Tp*)(data + step.p[0]*i))[j]; } - + template inline _Tp& Mat::at(int i0, int i1, int i2) { CV_DbgAssert( elemSize() == CV_ELEM_SIZE(DataType<_Tp>::type) ); @@ -618,8 +618,8 @@ template inline const _Tp& Mat::at(const Vec& idx) CV_DbgAssert( elemSize() == CV_ELEM_SIZE(DataType<_Tp>::type) ); return *(const _Tp*)ptr(idx.val); } - - + + template inline MatConstIterator_<_Tp> Mat::begin() const { CV_DbgAssert( elemSize() == sizeof(_Tp) ); @@ -659,18 +659,18 @@ template inline Mat::operator Vec<_Tp, n>() const { CV_Assert( data && dims <= 2 && (rows == 1 || cols == 1) && rows + cols - 1 == n && channels() == 1 ); - + if( isContinuous() && type() == DataType<_Tp>::type ) return Vec<_Tp, n>((_Tp*)data); Vec<_Tp, n> v; Mat tmp(rows, cols, DataType<_Tp>::type, v.val); convertTo(tmp, tmp.type()); return v; } - + template inline Mat::operator Matx<_Tp, m, n>() const { CV_Assert( data && dims <= 2 && rows == m && cols == n && channels() == 1 ); - + if( isContinuous() && type() == DataType<_Tp>::type ) return Matx<_Tp, m, n>((_Tp*)data); Matx<_Tp, m, n> mtx; Mat tmp(rows, cols, DataType<_Tp>::type, mtx.val); @@ -682,11 +682,11 @@ template inline Mat::operator Matx<_Tp, m, n>() cons template inline void Mat::push_back(const _Tp& elem) { if( !data ) - { - *this = Mat(1, 1, DataType<_Tp>::type, (void*)&elem).clone(); - return; - } - CV_Assert(DataType<_Tp>::type == type() && cols == 1 + { + *this = Mat(1, 1, DataType<_Tp>::type, (void*)&elem).clone(); + return; + } + CV_Assert(DataType<_Tp>::type == type() && cols == 1 /* && dims == 2 (cols == 1 implies dims == 2) */); uchar* tmp = dataend + step[0]; if( !isSubmatrix() && isContinuous() && tmp <= datalimit ) @@ -697,16 +697,16 @@ template inline void Mat::push_back(const _Tp& elem) else push_back_(&elem); } - + template inline void Mat::push_back(const Mat_<_Tp>& m) { push_back((const Mat&)m); -} - +} + inline Mat::MSize::MSize(int* _p) : p(_p) {} inline Size Mat::MSize::operator()() const { - CV_DbgAssert(p[-1] <= 2); + CV_DbgAssert(p[-1] <= 2); return Size(p[1], p[0]); } inline const int& Mat::MSize::operator[](int i) const { return p[i]; } @@ -720,18 +720,18 @@ inline bool Mat::MSize::operator == (const MSize& sz) const return false; if( d == 2 ) return p[0] == sz.p[0] && p[1] == sz.p[1]; - + for( int i = 0; i < d; i++ ) if( p[i] != sz.p[i] ) return false; return true; -} +} inline bool Mat::MSize::operator != (const MSize& sz) const { return !(*this == sz); } - + inline Mat::MStep::MStep() { p = buf; p[0] = p[1] = 0; } inline Mat::MStep::MStep(size_t s) { p = buf; p[0] = s; p[1] = 0; } inline const size_t& Mat::MStep::operator[](int i) const { return p[i]; } @@ -747,7 +747,7 @@ inline Mat::MStep& Mat::MStep::operator = (size_t s) buf[0] = s; return *this; } - + static inline Mat cvarrToMatND(const CvArr* arr, bool copyData=false, int coiMode=0) { return cvarrToMat(arr, copyData, true, coiMode); @@ -773,7 +773,7 @@ template inline void SVD::compute(_a, _w, _u, _vt); CV_Assert(_w.data == (uchar*)&w.val[0] && _u.data == (uchar*)&u.val[0] && _vt.data == (uchar*)&vt.val[0]); } - + template inline void SVD::compute( const Matx<_Tp, m, n>& a, Matx<_Tp, nm, 1>& w ) { @@ -782,7 +782,7 @@ SVD::compute( const Matx<_Tp, m, n>& a, Matx<_Tp, nm, 1>& w ) SVD::compute(_a, _w); CV_Assert(_w.data == (uchar*)&w.val[0]); } - + template inline void SVD::backSubst( const Matx<_Tp, nm, 1>& w, const Matx<_Tp, m, nm>& u, const Matx<_Tp, n, nm>& vt, const Matx<_Tp, m, nb>& rhs, @@ -793,12 +793,12 @@ SVD::backSubst( const Matx<_Tp, nm, 1>& w, const Matx<_Tp, m, nm>& u, SVD::backSubst(_w, _u, _vt, _rhs, _dst); CV_Assert(_dst.data == (uchar*)&dst.val[0]); } - + ///////////////////////////////// Mat_<_Tp> //////////////////////////////////// template inline Mat_<_Tp>::Mat_() : Mat() { flags = (flags & ~CV_MAT_TYPE_MASK) | DataType<_Tp>::type; } - + template inline Mat_<_Tp>::Mat_(int _rows, int _cols) : Mat(_rows, _cols, DataType<_Tp>::type) {} @@ -807,19 +807,19 @@ template inline Mat_<_Tp>::Mat_(int _rows, int _cols, const _Tp& v template inline Mat_<_Tp>::Mat_(Size _sz) : Mat(_sz.height, _sz.width, DataType<_Tp>::type) {} - + template inline Mat_<_Tp>::Mat_(Size _sz, const _Tp& value) : Mat(_sz.height, _sz.width, DataType<_Tp>::type) { *this = value; } - + template inline Mat_<_Tp>::Mat_(int _dims, const int* _sz) : Mat(_dims, _sz, DataType<_Tp>::type) {} - + template inline Mat_<_Tp>::Mat_(int _dims, const int* _sz, const _Tp& _s) : Mat(_dims, _sz, DataType<_Tp>::type, Scalar(_s)) {} - + template inline Mat_<_Tp>::Mat_(const Mat_<_Tp>& m, const Range* ranges) : Mat(m, ranges) {} - + template inline Mat_<_Tp>::Mat_(const Mat& m) : Mat() { flags = (flags & ~CV_MAT_TYPE_MASK) | DataType<_Tp>::type; *this = m; } @@ -829,8 +829,8 @@ template inline Mat_<_Tp>::Mat_(const Mat_& m) template inline Mat_<_Tp>::Mat_(int _rows, int _cols, _Tp* _data, size_t steps) : Mat(_rows, _cols, DataType<_Tp>::type, _data, steps) {} -template inline Mat_<_Tp>::Mat_(const Mat_& m, const Range& rowRange, const Range& colRange) - : Mat(m, rowRange, colRange) {} +template inline Mat_<_Tp>::Mat_(const Mat_& m, const Range& _rowRange, const Range& _colRange) + : Mat(m, _rowRange, _colRange) {} template inline Mat_<_Tp>::Mat_(const Mat_& m, const Rect& roi) : Mat(m, roi) {} @@ -852,7 +852,7 @@ template template inline if( copyData ) *this = clone(); } - + template inline Mat_<_Tp>::Mat_(const Point_::channel_type>& pt, bool copyData) : Mat(2/DataType<_Tp>::channels, 1, DataType<_Tp>::type, (void*)&pt) { @@ -871,7 +871,7 @@ template inline Mat_<_Tp>::Mat_(const Point3_ inline Mat_<_Tp>::Mat_(const MatCommaInitializer_<_Tp>& commaInitializer) : Mat(commaInitializer) {} - + template inline Mat_<_Tp>::Mat_(const vector<_Tp>& vec, bool copyData) : Mat(vec, copyData) {} @@ -917,9 +917,9 @@ template inline void Mat_<_Tp>::create(Size _sz) template inline void Mat_<_Tp>::create(int _dims, const int* _sz) { Mat::create(_dims, _sz, DataType<_Tp>::type); -} - - +} + + template inline Mat_<_Tp> Mat_<_Tp>::cross(const Mat_& m) const { return Mat_<_Tp>(Mat::cross(m)); } @@ -967,15 +967,15 @@ template inline size_t Mat_<_Tp>::step1(int i) const { return step template inline Mat_<_Tp>& Mat_<_Tp>::adjustROI( int dtop, int dbottom, int dleft, int dright ) { return (Mat_<_Tp>&)(Mat::adjustROI(dtop, dbottom, dleft, dright)); } -template inline Mat_<_Tp> Mat_<_Tp>::operator()( const Range& rowRange, const Range& colRange ) const -{ return Mat_<_Tp>(*this, rowRange, colRange); } +template inline Mat_<_Tp> Mat_<_Tp>::operator()( const Range& _rowRange, const Range& _colRange ) const +{ return Mat_<_Tp>(*this, _rowRange, _colRange); } template inline Mat_<_Tp> Mat_<_Tp>::operator()( const Rect& roi ) const { return Mat_<_Tp>(*this, roi); } template inline Mat_<_Tp> Mat_<_Tp>::operator()( const Range* ranges ) const -{ return Mat_<_Tp>(*this, ranges); } - +{ return Mat_<_Tp>(*this, ranges); } + template inline _Tp* Mat_<_Tp>::operator [](int y) { return (_Tp*)ptr(y); } template inline const _Tp* Mat_<_Tp>::operator [](int y) const @@ -1035,8 +1035,8 @@ template template inline _Tp& Mat_<_Tp>::operator ()(const template template inline const _Tp& Mat_<_Tp>::operator ()(const Vec& idx) const { return Mat::at<_Tp>(idx); -} - +} + template inline _Tp& Mat_<_Tp>::operator ()(int i0) { return this->at<_Tp>(i0); @@ -1045,7 +1045,7 @@ template inline _Tp& Mat_<_Tp>::operator ()(int i0) template inline const _Tp& Mat_<_Tp>::operator ()(int i0) const { return this->at<_Tp>(i0); -} +} template inline _Tp& Mat_<_Tp>::operator ()(int i0, int i1, int i2) { @@ -1055,9 +1055,9 @@ template inline _Tp& Mat_<_Tp>::operator ()(int i0, int i1, int i2 template inline const _Tp& Mat_<_Tp>::operator ()(int i0, int i1, int i2) const { return this->at<_Tp>(i0, i1, i2); -} - - +} + + template inline Mat_<_Tp>::operator vector<_Tp>() const { vector<_Tp> v; @@ -1075,7 +1075,7 @@ template template inline Mat_<_Tp>::operator Matx::channels == 0); return this->Mat::operator Matx::channel_type, m, n>(); -} +} template inline void process( const Mat_& m1, Mat_& m2, Op op ) @@ -1112,9 +1112,9 @@ process( const Mat_& m1, const Mat_& m2, Mat_& m3, Op op ) } } - + /////////////////////////////// Input/Output Arrays ///////////////////////////////// - + template inline _InputArray::_InputArray(const vector<_Tp>& vec) : flags(FIXED_TYPE + STD_VECTOR + DataType<_Tp>::type), obj((void*)&vec) {} @@ -1122,11 +1122,11 @@ template inline _InputArray::_InputArray(const vector : flags(FIXED_TYPE + STD_VECTOR_VECTOR + DataType<_Tp>::type), obj((void*)&vec) {} template inline _InputArray::_InputArray(const vector >& vec) - : flags(FIXED_TYPE + STD_VECTOR_MAT + DataType<_Tp>::type), obj((void*)&vec) {} - + : flags(FIXED_TYPE + STD_VECTOR_MAT + DataType<_Tp>::type), obj((void*)&vec) {} + template inline _InputArray::_InputArray(const Matx<_Tp, m, n>& mtx) : flags(FIXED_TYPE + FIXED_SIZE + MATX + DataType<_Tp>::type), obj((void*)&mtx), sz(n, m) {} - + template inline _InputArray::_InputArray(const _Tp* vec, int n) : flags(FIXED_TYPE + FIXED_SIZE + MATX + DataType<_Tp>::type), obj((void*)vec), sz(n, 1) {} @@ -1135,7 +1135,7 @@ inline _InputArray::_InputArray(const Scalar& s) template inline _InputArray::_InputArray(const Mat_<_Tp>& m) : flags(FIXED_TYPE + MAT + DataType<_Tp>::type), obj((void*)&m) {} - + template inline _OutputArray::_OutputArray(vector<_Tp>& vec) : _InputArray(vec) {} template inline _OutputArray::_OutputArray(vector >& vec) @@ -1155,22 +1155,22 @@ template inline _OutputArray::_OutputArray(const vector inline _OutputArray::_OutputArray(const vector >& vec) : _InputArray(vec) {flags |= FIXED_SIZE;} - + template inline _OutputArray::_OutputArray(const Mat_<_Tp>& m) : _InputArray(m) {flags |= FIXED_SIZE;} template inline _OutputArray::_OutputArray(const Matx<_Tp, m, n>& mtx) : _InputArray(mtx) {} template inline _OutputArray::_OutputArray(const _Tp* vec, int n) : _InputArray(vec, n) {} - + //////////////////////////////////// Matrix Expressions ///////////////////////////////////////// class CV_EXPORTS MatOp -{ +{ public: MatOp() {}; virtual ~MatOp() {}; - + virtual bool elementWise(const MatExpr& expr) const; virtual void assign(const MatExpr& expr, Mat& m, int type=-1) const = 0; virtual void roi(const MatExpr& expr, const Range& rowRange, @@ -1183,30 +1183,30 @@ public: virtual void augAssignAnd(const MatExpr& expr, Mat& m) const; virtual void augAssignOr(const MatExpr& expr, Mat& m) const; virtual void augAssignXor(const MatExpr& expr, Mat& m) const; - + virtual void add(const MatExpr& expr1, const MatExpr& expr2, MatExpr& res) const; virtual void add(const MatExpr& expr1, const Scalar& s, MatExpr& res) const; - + virtual void subtract(const MatExpr& expr1, const MatExpr& expr2, MatExpr& res) const; virtual void subtract(const Scalar& s, const MatExpr& expr, MatExpr& res) const; - + virtual void multiply(const MatExpr& expr1, const MatExpr& expr2, MatExpr& res, double scale=1) const; virtual void multiply(const MatExpr& expr1, double s, MatExpr& res) const; - + virtual void divide(const MatExpr& expr1, const MatExpr& expr2, MatExpr& res, double scale=1) const; virtual void divide(double s, const MatExpr& expr, MatExpr& res) const; - + virtual void abs(const MatExpr& expr, MatExpr& res) const; - + virtual void transpose(const MatExpr& expr, MatExpr& res) const; virtual void matmul(const MatExpr& expr1, const MatExpr& expr2, MatExpr& res) const; virtual void invert(const MatExpr& expr, int method, MatExpr& res) const; - + virtual Size size(const MatExpr& expr) const; virtual int type(const MatExpr& expr) const; }; - + class CV_EXPORTS MatExpr { public: @@ -1221,39 +1221,39 @@ public: op->assign(*this, m); return m; } - + template operator Mat_<_Tp>() const { Mat_<_Tp> m; op->assign(*this, m, DataType<_Tp>::type); return m; } - + MatExpr row(int y) const; MatExpr col(int x) const; MatExpr diag(int d=0) const; MatExpr operator()( const Range& rowRange, const Range& colRange ) const; MatExpr operator()( const Rect& roi ) const; - + Mat cross(const Mat& m) const; double dot(const Mat& m) const; - + MatExpr t() const; MatExpr inv(int method = DECOMP_LU) const; MatExpr mul(const MatExpr& e, double scale=1) const; MatExpr mul(const Mat& m, double scale=1) const; - + Size size() const; int type() const; - + const MatOp* op; int flags; - + Mat a, b, c; double alpha, beta; Scalar s; }; - + CV_EXPORTS MatExpr operator + (const Mat& a, const Mat& b); CV_EXPORTS MatExpr operator + (const Mat& a, const Scalar& s); @@ -1284,7 +1284,7 @@ CV_EXPORTS MatExpr operator * (const Mat& m, const MatExpr& e); CV_EXPORTS MatExpr operator * (const MatExpr& e, double s); CV_EXPORTS MatExpr operator * (double s, const MatExpr& e); CV_EXPORTS MatExpr operator * (const MatExpr& e1, const MatExpr& e2); - + CV_EXPORTS MatExpr operator / (const Mat& a, const Mat& b); CV_EXPORTS MatExpr operator / (const Mat& a, double s); CV_EXPORTS MatExpr operator / (double s, const Mat& a); @@ -1292,7 +1292,7 @@ CV_EXPORTS MatExpr operator / (const MatExpr& e, const Mat& m); CV_EXPORTS MatExpr operator / (const Mat& m, const MatExpr& e); CV_EXPORTS MatExpr operator / (const MatExpr& e, double s); CV_EXPORTS MatExpr operator / (double s, const MatExpr& e); -CV_EXPORTS MatExpr operator / (const MatExpr& e1, const MatExpr& e2); +CV_EXPORTS MatExpr operator / (const MatExpr& e1, const MatExpr& e2); CV_EXPORTS MatExpr operator < (const Mat& a, const Mat& b); CV_EXPORTS MatExpr operator < (const Mat& a, double s); @@ -1316,8 +1316,8 @@ CV_EXPORTS MatExpr operator >= (double s, const Mat& a); CV_EXPORTS MatExpr operator > (const Mat& a, const Mat& b); CV_EXPORTS MatExpr operator > (const Mat& a, double s); -CV_EXPORTS MatExpr operator > (double s, const Mat& a); - +CV_EXPORTS MatExpr operator > (double s, const Mat& a); + CV_EXPORTS MatExpr min(const Mat& a, const Mat& b); CV_EXPORTS MatExpr min(const Mat& a, double s); CV_EXPORTS MatExpr min(double s, const Mat& a); @@ -1339,7 +1339,7 @@ template static inline MatExpr min(const Mat_<_Tp>& a, double s) template static inline MatExpr min(double s, const Mat_<_Tp>& a) { return cv::min((const Mat&)a, s); -} +} template static inline MatExpr max(const Mat_<_Tp>& a, const Mat_<_Tp>& b) { @@ -1354,7 +1354,7 @@ template static inline MatExpr max(const Mat_<_Tp>& a, double s) template static inline MatExpr max(double s, const Mat_<_Tp>& a) { return cv::max((const Mat&)a, s); -} +} template static inline void min(const Mat_<_Tp>& a, const Mat_<_Tp>& b, Mat_<_Tp>& c) { @@ -1386,7 +1386,7 @@ template static inline void max(double s, const Mat_<_Tp>& a, Mat_ cv::max((const Mat&)a, s, (Mat&)c); } - + CV_EXPORTS MatExpr operator & (const Mat& a, const Mat& b); CV_EXPORTS MatExpr operator & (const Mat& a, const Scalar& s); CV_EXPORTS MatExpr operator & (const Scalar& s, const Mat& a); @@ -1400,22 +1400,22 @@ CV_EXPORTS MatExpr operator ^ (const Mat& a, const Scalar& s); CV_EXPORTS MatExpr operator ^ (const Scalar& s, const Mat& a); CV_EXPORTS MatExpr operator ~(const Mat& m); - + CV_EXPORTS MatExpr abs(const Mat& m); CV_EXPORTS MatExpr abs(const MatExpr& e); - + template static inline MatExpr abs(const Mat_<_Tp>& m) { return cv::abs((const Mat&)m); } ////////////////////////////// Augmenting algebraic operations ////////////////////////////////// - + inline Mat& Mat::operator = (const MatExpr& e) { e.op->assign(e, *this); return *this; -} +} template inline Mat_<_Tp>::Mat_(const MatExpr& e) { @@ -1438,7 +1438,7 @@ static inline Mat& operator += (const Mat& a, const Scalar& s) { add(a, s, (Mat&)a); return (Mat&)a; -} +} template static inline Mat_<_Tp>& operator += (const Mat_<_Tp>& a, const Mat_<_Tp>& b) @@ -1452,11 +1452,11 @@ Mat_<_Tp>& operator += (const Mat_<_Tp>& a, const Scalar& s) { add(a, s, (Mat&)a); return (Mat_<_Tp>&)a; -} +} static inline Mat& operator += (const Mat& a, const MatExpr& b) { - b.op->augAssignAdd(b, (Mat&)a); + b.op->augAssignAdd(b, (Mat&)a); return (Mat&)a; } @@ -1466,7 +1466,7 @@ Mat_<_Tp>& operator += (const Mat_<_Tp>& a, const MatExpr& b) b.op->augAssignAdd(b, (Mat&)a); return (Mat_<_Tp>&)a; } - + static inline Mat& operator -= (const Mat& a, const Mat& b) { subtract(a, b, (Mat&)a); @@ -1477,7 +1477,7 @@ static inline Mat& operator -= (const Mat& a, const Scalar& s) { subtract(a, s, (Mat&)a); return (Mat&)a; -} +} template static inline Mat_<_Tp>& operator -= (const Mat_<_Tp>& a, const Mat_<_Tp>& b) @@ -1491,11 +1491,11 @@ Mat_<_Tp>& operator -= (const Mat_<_Tp>& a, const Scalar& s) { subtract(a, s, (Mat&)a); return (Mat_<_Tp>&)a; -} +} static inline Mat& operator -= (const Mat& a, const MatExpr& b) { - b.op->augAssignSubtract(b, (Mat&)a); + b.op->augAssignSubtract(b, (Mat&)a); return (Mat&)a; } @@ -1504,7 +1504,7 @@ Mat_<_Tp>& operator -= (const Mat_<_Tp>& a, const MatExpr& b) { b.op->augAssignSubtract(b, (Mat&)a); return (Mat_<_Tp>&)a; -} +} static inline Mat& operator *= (const Mat& a, const Mat& b) { @@ -1516,7 +1516,7 @@ static inline Mat& operator *= (const Mat& a, double s) { a.convertTo((Mat&)a, -1, s); return (Mat&)a; -} +} template static inline Mat_<_Tp>& operator *= (const Mat_<_Tp>& a, const Mat_<_Tp>& b) @@ -1530,11 +1530,11 @@ Mat_<_Tp>& operator *= (const Mat_<_Tp>& a, double s) { a.convertTo((Mat&)a, -1, s); return (Mat_<_Tp>&)a; -} +} static inline Mat& operator *= (const Mat& a, const MatExpr& b) { - b.op->augAssignMultiply(b, (Mat&)a); + b.op->augAssignMultiply(b, (Mat&)a); return (Mat&)a; } @@ -1543,8 +1543,8 @@ Mat_<_Tp>& operator *= (const Mat_<_Tp>& a, const MatExpr& b) { b.op->augAssignMultiply(b, (Mat&)a); return (Mat_<_Tp>&)a; -} - +} + static inline Mat& operator /= (const Mat& a, const Mat& b) { divide(a, b, (Mat&)a); @@ -1555,7 +1555,7 @@ static inline Mat& operator /= (const Mat& a, double s) { a.convertTo((Mat&)a, -1, 1./s); return (Mat&)a; -} +} template static inline Mat_<_Tp>& operator /= (const Mat_<_Tp>& a, const Mat_<_Tp>& b) @@ -1569,11 +1569,11 @@ Mat_<_Tp>& operator /= (const Mat_<_Tp>& a, double s) { a.convertTo((Mat&)a, -1, 1./s); return (Mat_<_Tp>&)a; -} +} static inline Mat& operator /= (const Mat& a, const MatExpr& b) { - b.op->augAssignDivide(b, (Mat&)a); + b.op->augAssignDivide(b, (Mat&)a); return (Mat&)a; } @@ -1596,22 +1596,22 @@ static inline Mat& operator &= (const Mat& a, const Scalar& s) { bitwise_and(a, s, (Mat&)a); return (Mat&)a; -} +} template static inline Mat_<_Tp>& operator &= (const Mat_<_Tp>& a, const Mat_<_Tp>& b) { bitwise_and(a, b, (Mat&)a); return (Mat_<_Tp>&)a; -} +} template static inline Mat_<_Tp>& operator &= (const Mat_<_Tp>& a, const Scalar& s) { bitwise_and(a, s, (Mat&)a); return (Mat_<_Tp>&)a; -} - +} + static inline Mat& operator |= (const Mat& a, const Mat& b) { bitwise_or(a, b, (Mat&)a); @@ -1622,22 +1622,22 @@ static inline Mat& operator |= (const Mat& a, const Scalar& s) { bitwise_or(a, s, (Mat&)a); return (Mat&)a; -} +} template static inline Mat_<_Tp>& operator |= (const Mat_<_Tp>& a, const Mat_<_Tp>& b) { bitwise_or(a, b, (Mat&)a); return (Mat_<_Tp>&)a; -} +} template static inline Mat_<_Tp>& operator |= (const Mat_<_Tp>& a, const Scalar& s) { bitwise_or(a, s, (Mat&)a); return (Mat_<_Tp>&)a; -} - +} + static inline Mat& operator ^= (const Mat& a, const Mat& b) { bitwise_xor(a, b, (Mat&)a); @@ -1648,39 +1648,39 @@ static inline Mat& operator ^= (const Mat& a, const Scalar& s) { bitwise_xor(a, s, (Mat&)a); return (Mat&)a; -} +} template static inline Mat_<_Tp>& operator ^= (const Mat_<_Tp>& a, const Mat_<_Tp>& b) { bitwise_xor(a, b, (Mat&)a); return (Mat_<_Tp>&)a; -} +} template static inline Mat_<_Tp>& operator ^= (const Mat_<_Tp>& a, const Scalar& s) { bitwise_xor(a, s, (Mat&)a); return (Mat_<_Tp>&)a; -} +} /////////////////////////////// Miscellaneous operations ////////////////////////////// - + template void split(const Mat& src, vector >& mv) { split(src, (vector&)mv ); } ////////////////////////////////////////////////////////////// - + template inline MatExpr Mat_<_Tp>::zeros(int rows, int cols) { return Mat::zeros(rows, cols, DataType<_Tp>::type); } - + template inline MatExpr Mat_<_Tp>::zeros(Size sz) { return Mat::zeros(sz, DataType<_Tp>::type); -} - +} + template inline MatExpr Mat_<_Tp>::ones(int rows, int cols) { return Mat::ones(rows, cols, DataType<_Tp>::type); @@ -1689,8 +1689,8 @@ template inline MatExpr Mat_<_Tp>::ones(int rows, int cols) template inline MatExpr Mat_<_Tp>::ones(Size sz) { return Mat::ones(sz, DataType<_Tp>::type); -} - +} + template inline MatExpr Mat_<_Tp>::eye(int rows, int cols) { return Mat::eye(rows, cols, DataType<_Tp>::type); @@ -1699,8 +1699,8 @@ template inline MatExpr Mat_<_Tp>::eye(int rows, int cols) template inline MatExpr Mat_<_Tp>::eye(Size sz) { return Mat::eye(sz, DataType<_Tp>::type); -} - +} + //////////////////////////////// Iterators & Comma initializers ////////////////////////////////// inline MatConstIterator::MatConstIterator() @@ -1742,7 +1742,7 @@ inline MatConstIterator::MatConstIterator(const Mat* _m, Point _pt) int idx[]={_pt.y, _pt.x}; seek(idx); } - + inline MatConstIterator::MatConstIterator(const MatConstIterator& it) : m(it.m), elemSize(it.elemSize), ptr(it.ptr), sliceStart(it.sliceStart), sliceEnd(it.sliceEnd) {} @@ -1755,7 +1755,7 @@ inline MatConstIterator& MatConstIterator::operator = (const MatConstIterator& i } inline uchar* MatConstIterator::operator *() const { return ptr; } - + inline MatConstIterator& MatConstIterator::operator += (ptrdiff_t ofs) { if( !m || ofs == 0 ) @@ -1778,7 +1778,7 @@ inline MatConstIterator& MatConstIterator::operator --() if( m && (ptr -= elemSize) < sliceStart ) { ptr += elemSize; - seek(-1, true); + seek(-1, true); } return *this; } @@ -1795,7 +1795,7 @@ inline MatConstIterator& MatConstIterator::operator ++() if( m && (ptr += elemSize) >= sliceEnd ) { ptr -= elemSize; - seek(1, true); + seek(1, true); } return *this; } @@ -1878,10 +1878,10 @@ template inline MatIterator_<_Tp>::MatIterator_(Mat_<_Tp>* _m, int template inline MatIterator_<_Tp>::MatIterator_(const Mat_<_Tp>* _m, Point _pt) : MatConstIterator_<_Tp>(_m, _pt) {} - + template inline MatIterator_<_Tp>::MatIterator_(const Mat_<_Tp>* _m, const int* _idx) : MatConstIterator_<_Tp>(_m, _idx) {} - + template inline MatIterator_<_Tp>::MatIterator_(const MatIterator_& it) : MatConstIterator_<_Tp>(it) {} @@ -1972,8 +1972,8 @@ operator == (const MatIterator_<_Tp>& a, const MatIterator_<_Tp>& b) template static inline bool operator != (const MatIterator_<_Tp>& a, const MatIterator_<_Tp>& b) -{ return a.m != b.m || a.ptr != b.ptr; } - +{ return a.m != b.m || a.ptr != b.ptr; } + static inline bool operator < (const MatConstIterator& a, const MatConstIterator& b) { return a.ptr < b.ptr; } @@ -1981,7 +1981,7 @@ operator < (const MatConstIterator& a, const MatConstIterator& b) static inline bool operator > (const MatConstIterator& a, const MatConstIterator& b) { return a.ptr > b.ptr; } - + static inline bool operator <= (const MatConstIterator& a, const MatConstIterator& b) { return a.ptr <= b.ptr; } @@ -2000,7 +2000,7 @@ static inline MatConstIterator operator + (ptrdiff_t ofs, const MatConstIterator static inline MatConstIterator operator - (const MatConstIterator& a, ptrdiff_t ofs) { MatConstIterator b = a; return b += -ofs; } - + template static inline MatConstIterator_<_Tp> operator + (const MatConstIterator_<_Tp>& a, ptrdiff_t ofs) { MatConstIterator t = (const MatConstIterator&)a + ofs; return (MatConstIterator_<_Tp>&)t; } @@ -2008,14 +2008,14 @@ operator + (const MatConstIterator_<_Tp>& a, ptrdiff_t ofs) template static inline MatConstIterator_<_Tp> operator + (ptrdiff_t ofs, const MatConstIterator_<_Tp>& a) { MatConstIterator t = (const MatConstIterator&)a + ofs; return (MatConstIterator_<_Tp>&)t; } - + template static inline MatConstIterator_<_Tp> operator - (const MatConstIterator_<_Tp>& a, ptrdiff_t ofs) { MatConstIterator t = (const MatConstIterator&)a - ofs; return (MatConstIterator_<_Tp>&)t; } inline uchar* MatConstIterator::operator [](ptrdiff_t i) const { return *(*this + i); } - + template inline _Tp MatConstIterator_<_Tp>::operator [](ptrdiff_t i) const { return *(_Tp*)MatConstIterator::operator [](i); } @@ -2030,7 +2030,7 @@ operator + (ptrdiff_t ofs, const MatIterator_<_Tp>& a) template static inline MatIterator_<_Tp> operator - (const MatIterator_<_Tp>& a, ptrdiff_t ofs) { MatConstIterator t = (const MatConstIterator&)a - ofs; return (MatIterator_<_Tp>&)t; } - + template inline _Tp& MatIterator_<_Tp>::operator [](ptrdiff_t i) const { return *(*this + i); } @@ -2066,8 +2066,8 @@ template inline MatCommaInitializer_<_Tp>::operator Mat_<_Tp>() co { CV_DbgAssert( this->it == ((const Mat_<_Tp>*)this->it.m)->end() ); return Mat_<_Tp>(*this->it.m); -} - +} + template static inline MatCommaInitializer_<_Tp> operator << (const Mat_<_Tp>& m, T2 val) { @@ -2123,12 +2123,12 @@ inline SparseMat SparseMat::clone() const } -inline void SparseMat::assignTo( SparseMat& m, int type ) const +inline void SparseMat::assignTo( SparseMat& m, int _type ) const { - if( type < 0 ) + if( _type < 0 ) m = *this; else - convertTo(m, type); + convertTo(m, _type); } inline void SparseMat::addref() @@ -2209,7 +2209,7 @@ inline size_t SparseMat::hash(const int* idx) const template inline _Tp& SparseMat::ref(int i0, size_t* hashval) { return *(_Tp*)((SparseMat*)this)->ptr(i0, true, hashval); } - + template inline _Tp& SparseMat::ref(int i0, int i1, size_t* hashval) { return *(_Tp*)((SparseMat*)this)->ptr(i0, i1, true, hashval); } @@ -2223,8 +2223,8 @@ template inline _Tp SparseMat::value(int i0, size_t* hashval) cons { const _Tp* p = (const _Tp*)((SparseMat*)this)->ptr(i0, false, hashval); return p ? *p : _Tp(); -} - +} + template inline _Tp SparseMat::value(int i0, int i1, size_t* hashval) const { const _Tp* p = (const _Tp*)((SparseMat*)this)->ptr(i0, i1, false, hashval); @@ -2245,7 +2245,7 @@ template inline _Tp SparseMat::value(const int* idx, size_t* hashv template inline const _Tp* SparseMat::find(int i0, size_t* hashval) const { return (const _Tp*)((SparseMat*)this)->ptr(i0, false, hashval); } - + template inline const _Tp* SparseMat::find(int i0, int i1, size_t* hashval) const { return (const _Tp*)((SparseMat*)this)->ptr(i0, i1, false, hashval); } @@ -2275,23 +2275,23 @@ inline SparseMatConstIterator SparseMat::begin() const inline SparseMatIterator SparseMat::end() { SparseMatIterator it(this); it.seekEnd(); return it; } - + inline SparseMatConstIterator SparseMat::end() const { SparseMatConstIterator it(this); it.seekEnd(); return it; } - + template inline SparseMatIterator_<_Tp> SparseMat::begin() { return SparseMatIterator_<_Tp>(this); } - + template inline SparseMatConstIterator_<_Tp> SparseMat::begin() const { return SparseMatConstIterator_<_Tp>(this); } - + template inline SparseMatIterator_<_Tp> SparseMat::end() { SparseMatIterator_<_Tp> it(this); it.seekEnd(); return it; } template inline SparseMatConstIterator_<_Tp> SparseMat::end() const { SparseMatConstIterator_<_Tp> it(this); it.seekEnd(); return it; } - - + + inline SparseMatConstIterator::SparseMatConstIterator() : m(0), hashidx(0), ptr(0) { @@ -2336,7 +2336,7 @@ inline SparseMatConstIterator SparseMatConstIterator::operator ++(int) return it; } - + inline void SparseMatConstIterator::seekEnd() { if( m && m->hdr ) @@ -2345,7 +2345,7 @@ inline void SparseMatConstIterator::seekEnd() ptr = 0; } } - + inline SparseMatIterator::SparseMatIterator() {} @@ -2482,8 +2482,8 @@ SparseMat_<_Tp>::ref(int i0, size_t* hashval) template inline _Tp SparseMat_<_Tp>::operator()(int i0, size_t* hashval) const -{ return SparseMat::value<_Tp>(i0, hashval); } - +{ return SparseMat::value<_Tp>(i0, hashval); } + template inline _Tp& SparseMat_<_Tp>::ref(int i0, int i1, size_t* hashval) { return SparseMat::ref<_Tp>(i0, i1, hashval); } @@ -2516,7 +2516,7 @@ template inline SparseMatConstIterator_<_Tp> SparseMat_<_Tp>::begi template inline SparseMatIterator_<_Tp> SparseMat_<_Tp>::end() { SparseMatIterator_<_Tp> it(this); it.seekEnd(); return it; } - + template inline SparseMatConstIterator_<_Tp> SparseMat_<_Tp>::end() const { SparseMatConstIterator_<_Tp> it(this); it.seekEnd(); return it; } @@ -2597,7 +2597,7 @@ SparseMatIterator_<_Tp>::operator ++(int) SparseMatConstIterator::operator ++(); return it; } - + } #endif diff --git a/modules/core/include/opencv2/core/opengl_interop.hpp b/modules/core/include/opencv2/core/opengl_interop.hpp index 464c821..c039a1f 100644 --- a/modules/core/include/opencv2/core/opengl_interop.hpp +++ b/modules/core/include/opencv2/core/opengl_interop.hpp @@ -47,282 +47,287 @@ #include "opencv2/core/core.hpp" -namespace cv +namespace cv { - //! Smart pointer for OpenGL buffer memory with reference counting. - class CV_EXPORTS GlBuffer +//! Smart pointer for OpenGL buffer memory with reference counting. +class CV_EXPORTS GlBuffer +{ +public: + enum Usage { - public: - enum Usage - { - ARRAY_BUFFER = 0x8892, // buffer will use for OpenGL arrays (vertices, colors, normals, etc) - TEXTURE_BUFFER = 0x88EC // buffer will ise for OpenGL textures - }; - - //! create empty buffer - explicit GlBuffer(Usage usage); - - //! create buffer - GlBuffer(int rows, int cols, int type, Usage usage); - GlBuffer(Size size, int type, Usage usage); - - //! copy from host/device memory - GlBuffer(InputArray mat, Usage usage); - - void create(int rows, int cols, int type, Usage usage); - inline void create(Size size, int type, Usage usage) { create(size.height, size.width, type, usage); } - inline void create(int rows, int cols, int type) { create(rows, cols, type, usage()); } - inline void create(Size size, int type) { create(size.height, size.width, type, usage()); } - - void release(); - - //! copy from host/device memory - void copyFrom(InputArray mat); - - void bind() const; - void unbind() const; - - //! map to host memory - Mat mapHost(); - void unmapHost(); - - //! map to device memory - gpu::GpuMat mapDevice(); - void unmapDevice(); - - inline int rows() const { return rows_; } - inline int cols() const { return cols_; } - inline Size size() const { return Size(cols_, rows_); } - inline bool empty() const { return rows_ == 0 || cols_ == 0; } - - inline int type() const { return type_; } - inline int depth() const { return CV_MAT_DEPTH(type_); } - inline int channels() const { return CV_MAT_CN(type_); } - inline int elemSize() const { return CV_ELEM_SIZE(type_); } - inline int elemSize1() const { return CV_ELEM_SIZE1(type_); } - - inline Usage usage() const { return usage_; } - - class Impl; - private: - int rows_; - int cols_; - int type_; - Usage usage_; - - Ptr impl_; + ARRAY_BUFFER = 0x8892, // buffer will use for OpenGL arrays (vertices, colors, normals, etc) + TEXTURE_BUFFER = 0x88EC // buffer will ise for OpenGL textures }; - template <> CV_EXPORTS void Ptr::delete_obj(); + //! create empty buffer + explicit GlBuffer(Usage usage); + + //! create buffer + GlBuffer(int rows, int cols, int type, Usage usage); + GlBuffer(Size size, int type, Usage usage); + + //! copy from host/device memory + GlBuffer(InputArray mat, Usage usage); + + void create(int rows, int cols, int type, Usage usage); + void create(Size size, int type, Usage usage); + void create(int rows, int cols, int type); + void create(Size size, int type); + + void release(); + + //! copy from host/device memory + void copyFrom(InputArray mat); + + void bind() const; + void unbind() const; + + //! map to host memory + Mat mapHost(); + void unmapHost(); + + //! map to device memory + gpu::GpuMat mapDevice(); + void unmapDevice(); + + inline int rows() const { return rows_; } + inline int cols() const { return cols_; } + inline Size size() const { return Size(cols_, rows_); } + inline bool empty() const { return rows_ == 0 || cols_ == 0; } + + inline int type() const { return type_; } + inline int depth() const { return CV_MAT_DEPTH(type_); } + inline int channels() const { return CV_MAT_CN(type_); } + inline int elemSize() const { return CV_ELEM_SIZE(type_); } + inline int elemSize1() const { return CV_ELEM_SIZE1(type_); } + + inline Usage usage() const { return usage_; } + + class Impl; +private: + int rows_; + int cols_; + int type_; + Usage usage_; + + Ptr impl_; +}; + +template <> CV_EXPORTS void Ptr::delete_obj(); + +//! Smart pointer for OpenGL 2d texture memory with reference counting. +class CV_EXPORTS GlTexture +{ +public: + //! create empty texture + GlTexture(); + + //! create texture + GlTexture(int rows, int cols, int type); + GlTexture(Size size, int type); + + //! copy from host/device memory + explicit GlTexture(InputArray mat, bool bgra = true); + + void create(int rows, int cols, int type); + void create(Size size, int type); + void release(); + + //! copy from host/device memory + void copyFrom(InputArray mat, bool bgra = true); + + void bind() const; + void unbind() const; + + inline int rows() const { return rows_; } + inline int cols() const { return cols_; } + inline Size size() const { return Size(cols_, rows_); } + inline bool empty() const { return rows_ == 0 || cols_ == 0; } - //! Smart pointer for OpenGL 2d texture memory with reference counting. - class CV_EXPORTS GlTexture + inline int type() const { return type_; } + inline int depth() const { return CV_MAT_DEPTH(type_); } + inline int channels() const { return CV_MAT_CN(type_); } + inline int elemSize() const { return CV_ELEM_SIZE(type_); } + inline int elemSize1() const { return CV_ELEM_SIZE1(type_); } + + class Impl; +private: + int rows_; + int cols_; + int type_; + + Ptr impl_; + GlBuffer buf_; +}; + +template <> CV_EXPORTS void Ptr::delete_obj(); + +//! OpenGL Arrays +class CV_EXPORTS GlArrays +{ +public: + inline GlArrays() + : vertex_(GlBuffer::ARRAY_BUFFER), color_(GlBuffer::ARRAY_BUFFER), bgra_(true), normal_(GlBuffer::ARRAY_BUFFER), texCoord_(GlBuffer::ARRAY_BUFFER) { - public: - //! create empty texture - GlTexture(); - - //! create texture - GlTexture(int rows, int cols, int type); - GlTexture(Size size, int type); - - //! copy from host/device memory - explicit GlTexture(InputArray mat, bool bgra = true); - - void create(int rows, int cols, int type); - inline void create(Size size, int type) { create(size.height, size.width, type); } - void release(); - - //! copy from host/device memory - void copyFrom(InputArray mat, bool bgra = true); - - void bind() const; - void unbind() const; - - inline int rows() const { return rows_; } - inline int cols() const { return cols_; } - inline Size size() const { return Size(cols_, rows_); } - inline bool empty() const { return rows_ == 0 || cols_ == 0; } - - inline int type() const { return type_; } - inline int depth() const { return CV_MAT_DEPTH(type_); } - inline int channels() const { return CV_MAT_CN(type_); } - inline int elemSize() const { return CV_ELEM_SIZE(type_); } - inline int elemSize1() const { return CV_ELEM_SIZE1(type_); } - - class Impl; - private: - int rows_; - int cols_; - int type_; - - Ptr impl_; - GlBuffer buf_; - }; + } + + void setVertexArray(InputArray vertex); + inline void resetVertexArray() { vertex_.release(); } + + void setColorArray(InputArray color, bool bgra = true); + inline void resetColorArray() { color_.release(); } + + void setNormalArray(InputArray normal); + inline void resetNormalArray() { normal_.release(); } + + void setTexCoordArray(InputArray texCoord); + inline void resetTexCoordArray() { texCoord_.release(); } + + void bind() const; + void unbind() const; - template <> CV_EXPORTS void Ptr::delete_obj(); + inline int rows() const { return vertex_.rows(); } + inline int cols() const { return vertex_.cols(); } + inline Size size() const { return vertex_.size(); } + inline bool empty() const { return vertex_.empty(); } - //! OpenGL Arrays - class CV_EXPORTS GlArrays +private: + GlBuffer vertex_; + GlBuffer color_; + bool bgra_; + GlBuffer normal_; + GlBuffer texCoord_; +}; + +//! OpenGL Font +class CV_EXPORTS GlFont +{ +public: + enum Weight { - public: - inline GlArrays() - : vertex_(GlBuffer::ARRAY_BUFFER), color_(GlBuffer::ARRAY_BUFFER), bgra_(true), normal_(GlBuffer::ARRAY_BUFFER), texCoord_(GlBuffer::ARRAY_BUFFER) - { - } - - void setVertexArray(InputArray vertex); - inline void resetVertexArray() { vertex_.release(); } - - void setColorArray(InputArray color, bool bgra = true); - inline void resetColorArray() { color_.release(); } - - void setNormalArray(InputArray normal); - inline void resetNormalArray() { normal_.release(); } - - void setTexCoordArray(InputArray texCoord); - inline void resetTexCoordArray() { texCoord_.release(); } - - void bind() const; - void unbind() const; - - inline int rows() const { return vertex_.rows(); } - inline int cols() const { return vertex_.cols(); } - inline Size size() const { return vertex_.size(); } - inline bool empty() const { return vertex_.empty(); } - - private: - GlBuffer vertex_; - GlBuffer color_; - bool bgra_; - GlBuffer normal_; - GlBuffer texCoord_; + WEIGHT_LIGHT = 300, + WEIGHT_NORMAL = 400, + WEIGHT_SEMIBOLD = 600, + WEIGHT_BOLD = 700, + WEIGHT_BLACK = 900 }; - //! OpenGL Font - class CV_EXPORTS GlFont + enum Style { - public: - enum Weight - { - WEIGHT_LIGHT = 300, - WEIGHT_NORMAL = 400, - WEIGHT_SEMIBOLD = 600, - WEIGHT_BOLD = 700, - WEIGHT_BLACK = 900 - }; - - enum Style - { - STYLE_NORMAL = 0, - STYLE_ITALIC = 1, - STYLE_UNDERLINE = 2 - }; - - static Ptr get(const std::string& family, int height = 12, Weight weight = WEIGHT_NORMAL, Style style = STYLE_NORMAL); - - void draw(const char* str, size_t len) const; - - inline const std::string& family() const { return family_; } - inline int height() const { return height_; } - inline Weight weight() const { return weight_; } - inline Style style() const { return style_; } - - private: - GlFont(const std::string& family, int height, Weight weight, Style style); - - std::string family_; - int height_; - Weight weight_; - Style style_; - - unsigned int base_; - - GlFont(const GlFont&); - GlFont& operator =(const GlFont&); + STYLE_NORMAL = 0, + STYLE_ITALIC = 1, + STYLE_UNDERLINE = 2 }; - //! render functions - - //! render texture rectangle in window - CV_EXPORTS void render(const GlTexture& tex, - Rect_ wndRect = Rect_(0.0, 0.0, 1.0, 1.0), - Rect_ texRect = Rect_(0.0, 0.0, 1.0, 1.0)); - - //! render mode - namespace RenderMode { - enum { - POINTS = 0x0000, - LINES = 0x0001, - LINE_LOOP = 0x0002, - LINE_STRIP = 0x0003, - TRIANGLES = 0x0004, - TRIANGLE_STRIP = 0x0005, - TRIANGLE_FAN = 0x0006, - QUADS = 0x0007, - QUAD_STRIP = 0x0008, - POLYGON = 0x0009 - }; - } + static Ptr get(const std::string& family, int height = 12, Weight weight = WEIGHT_NORMAL, Style style = STYLE_NORMAL); + + void draw(const char* str, size_t len) const; + + inline const std::string& family() const { return family_; } + inline int height() const { return height_; } + inline Weight weight() const { return weight_; } + inline Style style() const { return style_; } + +private: + GlFont(const std::string& family, int height, Weight weight, Style style); + + std::string family_; + int height_; + Weight weight_; + Style style_; + + unsigned int base_; + + GlFont(const GlFont&); + GlFont& operator =(const GlFont&); +}; + +//! render functions + +//! render texture rectangle in window +CV_EXPORTS void render(const GlTexture& tex, + Rect_ wndRect = Rect_(0.0, 0.0, 1.0, 1.0), + Rect_ texRect = Rect_(0.0, 0.0, 1.0, 1.0)); + +//! render mode +namespace RenderMode { + enum { + POINTS = 0x0000, + LINES = 0x0001, + LINE_LOOP = 0x0002, + LINE_STRIP = 0x0003, + TRIANGLES = 0x0004, + TRIANGLE_STRIP = 0x0005, + TRIANGLE_FAN = 0x0006, + QUADS = 0x0007, + QUAD_STRIP = 0x0008, + POLYGON = 0x0009 + }; +} - //! render OpenGL arrays - CV_EXPORTS void render(const GlArrays& arr, int mode = RenderMode::POINTS, Scalar color = Scalar::all(255)); +//! render OpenGL arrays +CV_EXPORTS void render(const GlArrays& arr, int mode = RenderMode::POINTS, Scalar color = Scalar::all(255)); - CV_EXPORTS void render(const std::string& str, const Ptr& font, Scalar color, Point2d pos); +CV_EXPORTS void render(const std::string& str, const Ptr& font, Scalar color, Point2d pos); - //! OpenGL camera - class CV_EXPORTS GlCamera - { - public: - GlCamera(); +//! OpenGL camera +class CV_EXPORTS GlCamera +{ +public: + GlCamera(); - void lookAt(Point3d eye, Point3d center, Point3d up); - void setCameraPos(Point3d pos, double yaw, double pitch, double roll); + void lookAt(Point3d eye, Point3d center, Point3d up); + void setCameraPos(Point3d pos, double yaw, double pitch, double roll); - void setScale(Point3d scale); + void setScale(Point3d scale); - void setProjectionMatrix(const Mat& projectionMatrix, bool transpose = true); - void setPerspectiveProjection(double fov, double aspect, double zNear, double zFar); - void setOrthoProjection(double left, double right, double bottom, double top, double zNear, double zFar); + void setProjectionMatrix(const Mat& projectionMatrix, bool transpose = true); + void setPerspectiveProjection(double fov, double aspect, double zNear, double zFar); + void setOrthoProjection(double left, double right, double bottom, double top, double zNear, double zFar); - void setupProjectionMatrix() const; - void setupModelViewMatrix() const; + void setupProjectionMatrix() const; + void setupModelViewMatrix() const; - private: - Point3d eye_; - Point3d center_; - Point3d up_; +private: + Point3d eye_; + Point3d center_; + Point3d up_; - Point3d pos_; - double yaw_; - double pitch_; - double roll_; + Point3d pos_; + double yaw_; + double pitch_; + double roll_; - bool useLookAtParams_; + bool useLookAtParams_; - Point3d scale_; + Point3d scale_; - Mat projectionMatrix_; + Mat projectionMatrix_; - double fov_; - double aspect_; + double fov_; + double aspect_; - double left_; - double right_; - double bottom_; - double top_; + double left_; + double right_; + double bottom_; + double top_; - double zNear_; - double zFar_; + double zNear_; + double zFar_; - bool perspectiveProjection_; - }; + bool perspectiveProjection_; +}; - namespace gpu - { - //! set a CUDA device to use OpenGL interoperability - CV_EXPORTS void setGlDevice(int device = 0); - } +inline void GlBuffer::create(Size _size, int _type, Usage _usage) { create(_size.height, _size.width, _type, _usage); } +inline void GlBuffer::create(int _rows, int _cols, int _type) { create(_rows, _cols, _type, usage()); } +inline void GlBuffer::create(Size _size, int _type) { create(_size.height, _size.width, _type, usage()); } +inline void GlTexture::create(Size _size, int _type) { create(_size.height, _size.width, _type); } + +namespace gpu +{ + //! set a CUDA device to use OpenGL interoperability + CV_EXPORTS void setGlDevice(int device = 0); +} } // namespace cv #endif // __cplusplus diff --git a/modules/core/include/opencv2/core/operations.hpp b/modules/core/include/opencv2/core/operations.hpp index 0420a75..f3e5b17 100644 --- a/modules/core/include/opencv2/core/operations.hpp +++ b/modules/core/include/opencv2/core/operations.hpp @@ -2616,20 +2616,20 @@ template inline void Ptr<_Tp>::delete_obj() template inline Ptr<_Tp>::~Ptr() { release(); } -template inline Ptr<_Tp>::Ptr(const Ptr<_Tp>& ptr) +template inline Ptr<_Tp>::Ptr(const Ptr<_Tp>& _ptr) { - obj = ptr.obj; - refcount = ptr.refcount; + obj = _ptr.obj; + refcount = _ptr.refcount; addref(); } -template inline Ptr<_Tp>& Ptr<_Tp>::operator = (const Ptr<_Tp>& ptr) +template inline Ptr<_Tp>& Ptr<_Tp>::operator = (const Ptr<_Tp>& _ptr) { - int* _refcount = ptr.refcount; + int* _refcount = _ptr.refcount; if( _refcount ) CV_XADD(_refcount, 1); release(); - obj = ptr.obj; + obj = _ptr.obj; refcount = _refcount; return *this; } @@ -3593,10 +3593,10 @@ template inline Seq<_Tp>::operator vector<_Tp>() const template inline SeqIterator<_Tp>::SeqIterator() { memset(this, 0, sizeof(*this)); } -template inline SeqIterator<_Tp>::SeqIterator(const Seq<_Tp>& seq, bool seekEnd) +template inline SeqIterator<_Tp>::SeqIterator(const Seq<_Tp>& _seq, bool seekEnd) { - cvStartReadSeq(seq.seq, this); - index = seekEnd ? seq.seq->total : 0; + cvStartReadSeq(_seq.seq, this); + index = seekEnd ? _seq.seq->total : 0; } template inline void SeqIterator<_Tp>::seek(size_t pos) @@ -3842,17 +3842,17 @@ template inline Ptr<_Tp> Algorithm::create(const string& name) return _create(name).ptr<_Tp>(); } -template inline typename ParamType<_Tp>::member_type Algorithm::get(const string& name) const +template inline typename ParamType<_Tp>::member_type Algorithm::get(const string& _name) const { typename ParamType<_Tp>::member_type value; - info()->get(this, name.c_str(), ParamType<_Tp>::type, &value); + info()->get(this, _name.c_str(), ParamType<_Tp>::type, &value); return value; } -template inline typename ParamType<_Tp>::member_type Algorithm::get(const char* name) const +template inline typename ParamType<_Tp>::member_type Algorithm::get(const char* _name) const { typename ParamType<_Tp>::member_type value; - info()->get(this, name, ParamType<_Tp>::type, &value); + info()->get(this, _name, ParamType<_Tp>::type, &value); return value; } diff --git a/modules/core/src/algorithm.cpp b/modules/core/src/algorithm.cpp index cca11cf..7c783ea 100644 --- a/modules/core/src/algorithm.cpp +++ b/modules/core/src/algorithm.cpp @@ -7,7 +7,7 @@ // copy or use the software. // // -// License Agreement +// License Agreement // For Open Source Computer Vision Library // // Copyright (C) 2000-2008, Intel Corporation, all rights reserved. @@ -46,7 +46,7 @@ namespace cv { using std::pair; - + template struct sorted_vector { sorted_vector() {} @@ -54,7 +54,7 @@ template struct sorted_vector size_t size() const { return vec.size(); } _ValueTp& operator [](size_t idx) { return vec[idx]; } const _ValueTp& operator [](size_t idx) const { return vec[idx]; } - + void add(const _KeyTp& k, const _ValueTp& val) { pair<_KeyTp, _ValueTp> p(k, val); @@ -64,7 +64,7 @@ template struct sorted_vector std::swap(vec[i-1], vec[i]); CV_Assert( i == 0 || vec[i].first != vec[i-1].first ); } - + bool find(const _KeyTp& key, _ValueTp& value) const { size_t a = 0, b = vec.size(); @@ -76,7 +76,7 @@ template struct sorted_vector else b = c; } - + if( a < vec.size() && vec[a].first == key ) { value = vec[a].second; @@ -84,26 +84,26 @@ template struct sorted_vector } return false; } - + void get_keys(vector<_KeyTp>& keys) const { size_t i = 0, n = vec.size(); keys.resize(n); - + for( i = 0; i < n; i++ ) keys[i] = vec[i].first; } - + vector > vec; }; - + template inline const _ValueTp* findstr(const sorted_vector& vec, const char* key) { if( !key ) return 0; - + size_t a = 0, b = vec.vec.size(); while( b > a ) { @@ -113,13 +113,13 @@ template inline const _ValueTp* findstr(const sorted_vector& alglist() { static sorted_vector alglist_var; @@ -171,152 +171,152 @@ Ptr Algorithm::_create(const string& name) Algorithm::Algorithm() { } - + Algorithm::~Algorithm() { } - + string Algorithm::name() const { return info()->name(); } - -void Algorithm::set(const string& name, int value) + +void Algorithm::set(const string& parameter, int value) { - info()->set(this, name.c_str(), ParamType::type, &value); + info()->set(this, parameter.c_str(), ParamType::type, &value); } -void Algorithm::set(const string& name, double value) +void Algorithm::set(const string& parameter, double value) { - info()->set(this, name.c_str(), ParamType::type, &value); + info()->set(this, parameter.c_str(), ParamType::type, &value); } -void Algorithm::set(const string& name, bool value) +void Algorithm::set(const string& parameter, bool value) { - info()->set(this, name.c_str(), ParamType::type, &value); + info()->set(this, parameter.c_str(), ParamType::type, &value); } -void Algorithm::set(const string& name, const string& value) +void Algorithm::set(const string& parameter, const string& value) { - info()->set(this, name.c_str(), ParamType::type, &value); + info()->set(this, parameter.c_str(), ParamType::type, &value); } -void Algorithm::set(const string& name, const Mat& value) +void Algorithm::set(const string& parameter, const Mat& value) { - info()->set(this, name.c_str(), ParamType::type, &value); + info()->set(this, parameter.c_str(), ParamType::type, &value); } -void Algorithm::set(const string& name, const vector& value) +void Algorithm::set(const string& parameter, const vector& value) { - info()->set(this, name.c_str(), ParamType >::type, &value); -} - -void Algorithm::set(const string& name, const Ptr& value) + info()->set(this, parameter.c_str(), ParamType >::type, &value); +} + +void Algorithm::set(const string& parameter, const Ptr& value) { - info()->set(this, name.c_str(), ParamType::type, &value); + info()->set(this, parameter.c_str(), ParamType::type, &value); } -void Algorithm::set(const char* name, int value) +void Algorithm::set(const char* parameter, int value) { - info()->set(this, name, ParamType::type, &value); + info()->set(this, parameter, ParamType::type, &value); } -void Algorithm::set(const char* name, double value) +void Algorithm::set(const char* parameter, double value) { - info()->set(this, name, ParamType::type, &value); + info()->set(this, parameter, ParamType::type, &value); } -void Algorithm::set(const char* name, bool value) +void Algorithm::set(const char* parameter, bool value) { - info()->set(this, name, ParamType::type, &value); + info()->set(this, parameter, ParamType::type, &value); } -void Algorithm::set(const char* name, const string& value) +void Algorithm::set(const char* parameter, const string& value) { - info()->set(this, name, ParamType::type, &value); + info()->set(this, parameter, ParamType::type, &value); } -void Algorithm::set(const char* name, const Mat& value) +void Algorithm::set(const char* parameter, const Mat& value) { - info()->set(this, name, ParamType::type, &value); + info()->set(this, parameter, ParamType::type, &value); } -void Algorithm::set(const char* name, const vector& value) +void Algorithm::set(const char* parameter, const vector& value) { - info()->set(this, name, ParamType >::type, &value); -} - -void Algorithm::set(const char* name, const Ptr& value) + info()->set(this, parameter, ParamType >::type, &value); +} + +void Algorithm::set(const char* parameter, const Ptr& value) { - info()->set(this, name, ParamType::type, &value); + info()->set(this, parameter, ParamType::type, &value); } - -int Algorithm::getInt(const string& name) const + +int Algorithm::getInt(const string& parameter) const { - return get(name); + return get(parameter); } - -double Algorithm::getDouble(const string& name) const + +double Algorithm::getDouble(const string& parameter) const { - return get(name); + return get(parameter); } -bool Algorithm::getBool(const string& name) const +bool Algorithm::getBool(const string& parameter) const { - return get(name); + return get(parameter); } -string Algorithm::getString(const string& name) const +string Algorithm::getString(const string& parameter) const { - return get(name); + return get(parameter); } -Mat Algorithm::getMat(const string& name) const +Mat Algorithm::getMat(const string& parameter) const { - return get(name); + return get(parameter); } -vector Algorithm::getMatVector(const string& name) const +vector Algorithm::getMatVector(const string& parameter) const { - return get >(name); + return get >(parameter); } -Ptr Algorithm::getAlgorithm(const string& name) const +Ptr Algorithm::getAlgorithm(const string& parameter) const { - return get(name); + return get(parameter); } - -string Algorithm::paramHelp(const string& name) const + +string Algorithm::paramHelp(const string& parameter) const { - return info()->paramHelp(name.c_str()); + return info()->paramHelp(parameter.c_str()); } - -int Algorithm::paramType(const string& name) const + +int Algorithm::paramType(const string& parameter) const { - return info()->paramType(name.c_str()); + return info()->paramType(parameter.c_str()); } -int Algorithm::paramType(const char* name) const +int Algorithm::paramType(const char* parameter) const { - return info()->paramType(name); -} - + return info()->paramType(parameter); +} + void Algorithm::getParams(vector& names) const { info()->getParams(names); } - + void Algorithm::write(FileStorage& fs) const { info()->write(this, fs); } - + void Algorithm::read(const FileNode& fn) { info()->read(this, fn); -} +} + - AlgorithmInfo::AlgorithmInfo(const string& _name, Algorithm::Constructor create) { data = new AlgorithmInfoData; @@ -327,8 +327,8 @@ AlgorithmInfo::AlgorithmInfo(const string& _name, Algorithm::Constructor create) AlgorithmInfo::~AlgorithmInfo() { delete data; -} - +} + void AlgorithmInfo::write(const Algorithm* algo, FileStorage& fs) const { size_t i = 0, nparams = data->params.vec.size(); @@ -364,7 +364,7 @@ void AlgorithmInfo::read(Algorithm* algo, const FileNode& fn) const { size_t i = 0, nparams = data->params.vec.size(); AlgorithmInfo* info = algo->info(); - + for( i = 0; i < nparams; i++ ) { const Param& p = data->params.vec[i].second; @@ -414,13 +414,13 @@ void AlgorithmInfo::read(Algorithm* algo, const FileNode& fn) const else CV_Error( CV_StsUnsupportedFormat, "unknown/unsupported parameter type"); } -} +} string AlgorithmInfo::name() const { return data->_name; } - + union GetSetParam { int (Algorithm::*get_int)() const; @@ -430,7 +430,7 @@ union GetSetParam Mat (Algorithm::*get_mat)() const; vector (Algorithm::*get_mat_vector)() const; Ptr (Algorithm::*get_algo)() const; - + void (Algorithm::*set_int)(int); void (Algorithm::*set_bool)(bool); void (Algorithm::*set_double)(double); @@ -440,15 +440,15 @@ union GetSetParam void (Algorithm::*set_algo)(const Ptr&); }; -void AlgorithmInfo::set(Algorithm* algo, const char* name, int argType, const void* value, bool force) const +void AlgorithmInfo::set(Algorithm* algo, const char* parameter, int argType, const void* value, bool force) const { - const Param* p = findstr(data->params, name); + const Param* p = findstr(data->params, parameter); if( !p ) - CV_Error_( CV_StsBadArg, ("No parameter '%s' is found", name ? name : "") ); + CV_Error_( CV_StsBadArg, ("No parameter '%s' is found", parameter ? parameter : "") ); if( !force && p->readonly ) - CV_Error_( CV_StsError, ("Parameter '%s' is readonly", name)); + CV_Error_( CV_StsError, ("Parameter '%s' is readonly", parameter)); GetSetParam f; f.set_int = p->setter; @@ -531,23 +531,23 @@ void AlgorithmInfo::set(Algorithm* algo, const char* name, int argType, const vo else CV_Error(CV_StsBadArg, "Unknown/unsupported parameter type"); } - -void AlgorithmInfo::get(const Algorithm* algo, const char* name, int argType, void* value) const + +void AlgorithmInfo::get(const Algorithm* algo, const char* parameter, int argType, void* value) const { - const Param* p = findstr(data->params, name); + const Param* p = findstr(data->params, parameter); if( !p ) - CV_Error_( CV_StsBadArg, ("No parameter '%s' is found", name ? name : "") ); - + CV_Error_( CV_StsBadArg, ("No parameter '%s' is found", parameter ? parameter : "") ); + GetSetParam f; f.get_int = p->getter; - + if( argType == Param::INT || argType == Param::BOOLEAN || argType == Param::REAL ) { if( p->type == Param::INT ) { CV_Assert( argType == Param::INT || argType == Param::REAL ); int val = p->getter ? (algo->*f.get_int)() : *(int*)((uchar*)algo + p->offset); - + if( argType == Param::INT ) *(int*)value = val; else @@ -557,7 +557,7 @@ void AlgorithmInfo::get(const Algorithm* algo, const char* name, int argType, vo { CV_Assert( argType == Param::INT || argType == Param::BOOLEAN || argType == Param::REAL ); bool val = p->getter ? (algo->*f.get_bool)() : *(bool*)((uchar*)algo + p->offset); - + if( argType == Param::INT ) *(int*)value = (int)val; else if( argType == Param::BOOLEAN ) @@ -569,35 +569,35 @@ void AlgorithmInfo::get(const Algorithm* algo, const char* name, int argType, vo { CV_Assert( argType == Param::REAL ); double val = p->getter ? (algo->*f.get_double)() : *(double*)((uchar*)algo + p->offset); - + *(double*)value = val; } } else if( argType == Param::STRING ) { CV_Assert( p->type == Param::STRING ); - + *(string*)value = p->getter ? (algo->*f.get_string)() : *(string*)((uchar*)algo + p->offset); } else if( argType == Param::MAT ) { CV_Assert( p->type == Param::MAT ); - + *(Mat*)value = p->getter ? (algo->*f.get_mat)() : *(Mat*)((uchar*)algo + p->offset); } else if( argType == Param::MAT_VECTOR ) { CV_Assert( p->type == Param::MAT_VECTOR ); - + *(vector*)value = p->getter ? (algo->*f.get_mat_vector)() : *(vector*)((uchar*)algo + p->offset); } else if( argType == Param::ALGORITHM ) { CV_Assert( p->type == Param::ALGORITHM ); - + *(Ptr*)value = p->getter ? (algo->*f.get_algo)() : *(Ptr*)((uchar*)algo + p->offset); } @@ -605,21 +605,21 @@ void AlgorithmInfo::get(const Algorithm* algo, const char* name, int argType, vo CV_Error(CV_StsBadArg, "Unknown/unsupported parameter type"); } - -int AlgorithmInfo::paramType(const char* name) const + +int AlgorithmInfo::paramType(const char* parameter) const { - const Param* p = findstr(data->params, name); + const Param* p = findstr(data->params, parameter); if( !p ) - CV_Error_( CV_StsBadArg, ("No parameter '%s' is found", name ? name : "") ); + CV_Error_( CV_StsBadArg, ("No parameter '%s' is found", parameter ? parameter : "") ); return p->type; } - - -string AlgorithmInfo::paramHelp(const char* name) const + + +string AlgorithmInfo::paramHelp(const char* parameter) const { - const Param* p = findstr(data->params, name); + const Param* p = findstr(data->params, parameter); if( !p ) - CV_Error_( CV_StsBadArg, ("No parameter '%s' is found", name ? name : "") ); + CV_Error_( CV_StsBadArg, ("No parameter '%s' is found", parameter ? parameter : "") ); return p->help; } @@ -628,10 +628,10 @@ void AlgorithmInfo::getParams(vector& names) const { data->params.get_keys(names); } - - -void AlgorithmInfo::addParam_(Algorithm& algo, const char* name, int argType, - void* value, bool readOnly, + + +void AlgorithmInfo::addParam_(Algorithm& algo, const char* parameter, int argType, + void* value, bool readOnly, Algorithm::Getter getter, Algorithm::Setter setter, const string& help) { @@ -639,82 +639,82 @@ void AlgorithmInfo::addParam_(Algorithm& algo, const char* name, int argType, argType == Param::REAL || argType == Param::STRING || argType == Param::MAT || argType == Param::MAT_VECTOR || argType == Param::ALGORITHM ); - data->params.add(string(name), Param(argType, readOnly, + data->params.add(string(parameter), Param(argType, readOnly, (int)((size_t)value - (size_t)(void*)&algo), getter, setter, help)); } - - -void AlgorithmInfo::addParam(Algorithm& algo, const char* name, - int& value, bool readOnly, + + +void AlgorithmInfo::addParam(Algorithm& algo, const char* parameter, + int& value, bool readOnly, int (Algorithm::*getter)(), void (Algorithm::*setter)(int), const string& help) { - addParam_(algo, name, ParamType::type, &value, readOnly, + addParam_(algo, parameter, ParamType::type, &value, readOnly, (Algorithm::Getter)getter, (Algorithm::Setter)setter, help); } -void AlgorithmInfo::addParam(Algorithm& algo, const char* name, - bool& value, bool readOnly, +void AlgorithmInfo::addParam(Algorithm& algo, const char* parameter, + bool& value, bool readOnly, int (Algorithm::*getter)(), void (Algorithm::*setter)(int), const string& help) { - addParam_(algo, name, ParamType::type, &value, readOnly, + addParam_(algo, parameter, ParamType::type, &value, readOnly, (Algorithm::Getter)getter, (Algorithm::Setter)setter, help); } - -void AlgorithmInfo::addParam(Algorithm& algo, const char* name, - double& value, bool readOnly, + +void AlgorithmInfo::addParam(Algorithm& algo, const char* parameter, + double& value, bool readOnly, double (Algorithm::*getter)(), void (Algorithm::*setter)(double), const string& help) { - addParam_(algo, name, ParamType::type, &value, readOnly, + addParam_(algo, parameter, ParamType::type, &value, readOnly, (Algorithm::Getter)getter, (Algorithm::Setter)setter, help); } -void AlgorithmInfo::addParam(Algorithm& algo, const char* name, - string& value, bool readOnly, +void AlgorithmInfo::addParam(Algorithm& algo, const char* parameter, + string& value, bool readOnly, string (Algorithm::*getter)(), void (Algorithm::*setter)(const string&), const string& help) { - addParam_(algo, name, ParamType::type, &value, readOnly, + addParam_(algo, parameter, ParamType::type, &value, readOnly, (Algorithm::Getter)getter, (Algorithm::Setter)setter, help); } -void AlgorithmInfo::addParam(Algorithm& algo, const char* name, - Mat& value, bool readOnly, +void AlgorithmInfo::addParam(Algorithm& algo, const char* parameter, + Mat& value, bool readOnly, Mat (Algorithm::*getter)(), void (Algorithm::*setter)(const Mat&), const string& help) { - addParam_(algo, name, ParamType::type, &value, readOnly, + addParam_(algo, parameter, ParamType::type, &value, readOnly, (Algorithm::Getter)getter, (Algorithm::Setter)setter, help); } -void AlgorithmInfo::addParam(Algorithm& algo, const char* name, - vector& value, bool readOnly, +void AlgorithmInfo::addParam(Algorithm& algo, const char* parameter, + vector& value, bool readOnly, vector (Algorithm::*getter)(), void (Algorithm::*setter)(const vector&), const string& help) { - addParam_(algo, name, ParamType >::type, &value, readOnly, + addParam_(algo, parameter, ParamType >::type, &value, readOnly, (Algorithm::Getter)getter, (Algorithm::Setter)setter, help); -} - -void AlgorithmInfo::addParam(Algorithm& algo, const char* name, - Ptr& value, bool readOnly, +} + +void AlgorithmInfo::addParam(Algorithm& algo, const char* parameter, + Ptr& value, bool readOnly, Ptr (Algorithm::*getter)(), void (Algorithm::*setter)(const Ptr&), const string& help) { - addParam_(algo, name, ParamType::type, &value, readOnly, + addParam_(algo, parameter, ParamType::type, &value, readOnly, (Algorithm::Getter)getter, (Algorithm::Setter)setter, help); -} +} } - + /* End of file. */ diff --git a/modules/core/src/convert.cpp b/modules/core/src/convert.cpp index 523555f..98370ea 100644 --- a/modules/core/src/convert.cpp +++ b/modules/core/src/convert.cpp @@ -88,7 +88,7 @@ split_( const T* src, T** dst, int len, int cn ) dst2[i] = src[j+2]; dst3[i] = src[j+3]; } } - + for( ; k < cn; k += 4 ) { T *dst0 = dst[k], *dst1 = dst[k+1], *dst2 = dst[k+2], *dst3 = dst[k+3]; @@ -99,7 +99,7 @@ split_( const T* src, T** dst, int len, int cn ) } } } - + template static void merge_( const T** src, T* dst, int len, int cn ) { @@ -139,7 +139,7 @@ merge_( const T** src, T* dst, int len, int cn ) dst[j+2] = src2[i]; dst[j+3] = src3[i]; } } - + for( ; k < cn; k += 4 ) { const T *src0 = src[k], *src1 = src[k+1], *src2 = src[k+2], *src3 = src[k+3]; @@ -165,7 +165,7 @@ static void split32s(const int* src, int** dst, int len, int cn ) { split_(src, dst, len, cn); } - + static void split64s(const int64* src, int64** dst, int len, int cn ) { split_(src, dst, len, cn); @@ -189,7 +189,7 @@ static void merge32s(const int** src, int* dst, int len, int cn ) static void merge64s(const int64** src, int64* dst, int len, int cn ) { merge_(src, dst, len, cn); -} +} typedef void (*SplitFunc)(const uchar* src, uchar** dst, int len, int cn); typedef void (*MergeFunc)(const uchar** src, uchar* dst, int len, int cn); @@ -205,9 +205,9 @@ static MergeFunc mergeTab[] = (MergeFunc)GET_OPTIMIZED(merge8u), (MergeFunc)GET_OPTIMIZED(merge8u), (MergeFunc)GET_OPTIMIZED(merge16u), (MergeFunc)GET_OPTIMIZED(merge16u), (MergeFunc)GET_OPTIMIZED(merge32s), (MergeFunc)GET_OPTIMIZED(merge32s), (MergeFunc)GET_OPTIMIZED(merge64s), 0 }; - + } - + void cv::split(const Mat& src, Mat* mv) { int k, depth = src.depth(), cn = src.channels(); @@ -219,30 +219,30 @@ void cv::split(const Mat& src, Mat* mv) SplitFunc func = splitTab[depth]; CV_Assert( func != 0 ); - + int esz = (int)src.elemSize(), esz1 = (int)src.elemSize1(); int blocksize0 = (BLOCK_SIZE + esz-1)/esz; AutoBuffer _buf((cn+1)*(sizeof(Mat*) + sizeof(uchar*)) + 16); const Mat** arrays = (const Mat**)(uchar*)_buf; uchar** ptrs = (uchar**)alignPtr(arrays + cn + 1, 16); - + arrays[0] = &src; for( k = 0; k < cn; k++ ) { mv[k].create(src.dims, src.size, depth); arrays[k+1] = &mv[k]; } - + NAryMatIterator it(arrays, ptrs, cn+1); int total = (int)it.size, blocksize = cn <= 4 ? total : std::min(total, blocksize0); - + for( size_t i = 0; i < it.nplanes; i++, ++it ) { for( int j = 0; j < total; j += blocksize ) { int bsz = std::min(total - j, blocksize); func( ptrs[0], &ptrs[1], bsz, cn ); - + if( j + blocksize < total ) { ptrs[0] += bsz*esz; @@ -252,7 +252,7 @@ void cv::split(const Mat& src, Mat* mv) } } } - + void cv::split(InputArray _m, OutputArrayOfArrays _mv) { Mat m = _m.getMat(); @@ -266,38 +266,38 @@ void cv::split(InputArray _m, OutputArrayOfArrays _mv) Mat* dst = &_mv.getMatRef(0); split(m, dst); } - + void cv::merge(const Mat* mv, size_t n, OutputArray _dst) { CV_Assert( mv && n > 0 ); - + int depth = mv[0].depth(); bool allch1 = true; int k, cn = 0; size_t i; - + for( i = 0; i < n; i++ ) { CV_Assert(mv[i].size == mv[0].size && mv[i].depth() == depth); allch1 = allch1 && mv[i].channels() == 1; cn += mv[i].channels(); } - + CV_Assert( 0 < cn && cn <= CV_CN_MAX ); _dst.create(mv[0].dims, mv[0].size, CV_MAKETYPE(depth, cn)); Mat dst = _dst.getMat(); - + if( n == 1 ) { mv[0].copyTo(dst); return; } - + if( !allch1 ) { AutoBuffer pairs(cn*2); int j, ni=0; - + for( i = 0, j = 0; i < n; i++, j += ni ) { ni = mv[i].channels(); @@ -310,33 +310,33 @@ void cv::merge(const Mat* mv, size_t n, OutputArray _dst) mixChannels( mv, n, &dst, 1, &pairs[0], cn ); return; } - + size_t esz = dst.elemSize(), esz1 = dst.elemSize1(); int blocksize0 = (int)((BLOCK_SIZE + esz-1)/esz); AutoBuffer _buf((cn+1)*(sizeof(Mat*) + sizeof(uchar*)) + 16); const Mat** arrays = (const Mat**)(uchar*)_buf; uchar** ptrs = (uchar**)alignPtr(arrays + cn + 1, 16); - + arrays[0] = &dst; for( k = 0; k < cn; k++ ) arrays[k+1] = &mv[k]; - + NAryMatIterator it(arrays, ptrs, cn+1); int total = (int)it.size, blocksize = cn <= 4 ? total : std::min(total, blocksize0); MergeFunc func = mergeTab[depth]; - + for( i = 0; i < it.nplanes; i++, ++it ) { for( int j = 0; j < total; j += blocksize ) { int bsz = std::min(total - j, blocksize); func( (const uchar**)&ptrs[1], ptrs[0], bsz, cn ); - + if( j + blocksize < total ) { ptrs[0] += bsz*esz; - for( int k = 0; k < cn; k++ ) - ptrs[k+1] += bsz*esz1; + for( int t = 0; t < cn; t++ ) + ptrs[t+1] += bsz*esz1; } } } @@ -347,7 +347,7 @@ void cv::merge(InputArrayOfArrays _mv, OutputArray _dst) vector mv; _mv.getMatVector(mv); merge(!mv.empty() ? &mv[0] : 0, mv.size(), _dst); -} +} /****************************************************************************************\ * Generalized split/merge: mixing channels * @@ -387,7 +387,7 @@ mixChannels_( const T** src, const int* sdelta, } } - + static void mixChannels8u( const uchar** src, const int* sdelta, uchar** dst, const int* ddelta, int len, int npairs ) @@ -408,14 +408,14 @@ static void mixChannels32s( const int** src, const int* sdelta, { mixChannels_(src, sdelta, dst, ddelta, len, npairs); } - + static void mixChannels64s( const int64** src, const int* sdelta, int64** dst, const int* ddelta, int len, int npairs ) { mixChannels_(src, sdelta, dst, ddelta, len, npairs); } - + typedef void (*MixChannelsFunc)( const uchar** src, const int* sdelta, uchar** dst, const int* ddelta, int len, int npairs ); @@ -423,17 +423,17 @@ static MixChannelsFunc mixchTab[] = { (MixChannelsFunc)mixChannels8u, (MixChannelsFunc)mixChannels8u, (MixChannelsFunc)mixChannels16u, (MixChannelsFunc)mixChannels16u, (MixChannelsFunc)mixChannels32s, (MixChannelsFunc)mixChannels32s, - (MixChannelsFunc)mixChannels64s, 0 + (MixChannelsFunc)mixChannels64s, 0 }; - + } - + void cv::mixChannels( const Mat* src, size_t nsrcs, Mat* dst, size_t ndsts, const int* fromTo, size_t npairs ) { if( npairs == 0 ) return; CV_Assert( src && nsrcs > 0 && dst && ndsts > 0 && fromTo && npairs > 0 ); - + size_t i, j, k, esz1 = dst[0].elemSize1(); int depth = dst[0].depth(); @@ -444,13 +444,13 @@ void cv::mixChannels( const Mat* src, size_t nsrcs, Mat* dst, size_t ndsts, cons uchar** dsts = (uchar**)(srcs + npairs); int* tab = (int*)(dsts + npairs); int *sdelta = (int*)(tab + npairs*4), *ddelta = sdelta + npairs; - + for( i = 0; i < nsrcs; i++ ) arrays[i] = &src[i]; for( i = 0; i < ndsts; i++ ) arrays[i + nsrcs] = &dst[i]; ptrs[nsrcs + ndsts] = 0; - + for( i = 0; i < npairs; i++ ) { int i0 = fromTo[i*2], i1 = fromTo[i*2+1]; @@ -468,7 +468,7 @@ void cv::mixChannels( const Mat* src, size_t nsrcs, Mat* dst, size_t ndsts, cons tab[i*4] = (int)(nsrcs + ndsts); tab[i*4+1] = 0; sdelta[i] = 0; } - + for( j = 0; j < ndsts; i1 -= dst[j].channels(), j++ ) if( i1 < dst[j].channels() ) break; @@ -480,7 +480,7 @@ void cv::mixChannels( const Mat* src, size_t nsrcs, Mat* dst, size_t ndsts, cons NAryMatIterator it(arrays, ptrs, (int)(nsrcs + ndsts)); int total = (int)it.size, blocksize = std::min(total, (int)((BLOCK_SIZE + esz1-1)/esz1)); MixChannelsFunc func = mixchTab[depth]; - + for( i = 0; i < it.nplanes; i++, ++it ) { for( k = 0; k < npairs; k++ ) @@ -488,13 +488,13 @@ void cv::mixChannels( const Mat* src, size_t nsrcs, Mat* dst, size_t ndsts, cons srcs[k] = ptrs[tab[k*4]] + tab[k*4+1]; dsts[k] = ptrs[tab[k*4+2]] + tab[k*4+3]; } - - for( int j = 0; j < total; j += blocksize ) + + for( int t = 0; t < total; t += blocksize ) { - int bsz = std::min(total - j, blocksize); + int bsz = std::min(total - t, blocksize); func( srcs, sdelta, dsts, ddelta, bsz, (int)npairs ); - - if( j + blocksize < total ) + + if( t + blocksize < total ) for( k = 0; k < npairs; k++ ) { srcs[k] += blocksize*sdelta[k]*esz1; @@ -524,7 +524,7 @@ void cv::mixChannels(InputArrayOfArrays src, InputArrayOfArrays dst, int i; int nsrc = src_is_mat ? 1 : (int)src.total(); int ndst = dst_is_mat ? 1 : (int)dst.total(); - + CV_Assert(fromTo.size()%2 == 0 && nsrc > 0 && ndst > 0); cv::AutoBuffer _buf(nsrc + ndst); Mat* buf = _buf; @@ -568,7 +568,7 @@ cvtScaleAbs_( const T* src, size_t sstep, { sstep /= sizeof(src[0]); dstep /= sizeof(dst[0]); - + for( ; size.height--; src += sstep, dst += dstep ) { int x = 0; @@ -583,11 +583,11 @@ cvtScaleAbs_( const T* src, size_t sstep, t1 = saturate_cast
(std::abs(src[x+3]*scale + shift)); dst[x+2] = t0; dst[x+3] = t1; } - #endif + #endif for( ; x < size.width; x++ ) dst[x] = saturate_cast
(std::abs(src[x]*scale + shift)); } -} +} template static void @@ -597,7 +597,7 @@ cvtScale_( const T* src, size_t sstep, { sstep /= sizeof(src[0]); dstep /= sizeof(dst[0]); - + for( ; size.height--; src += sstep, dst += dstep ) { int x = 0; @@ -623,38 +623,38 @@ cvtScale_( const T* src, size_t sstep, template<> void cvtScale_( const short* src, size_t sstep, short* dst, size_t dstep, Size size, - float scale, float shift ) + float scale, float shift ) { sstep /= sizeof(src[0]); dstep /= sizeof(dst[0]); - for( ; size.height--; src += sstep, dst += dstep ) + for( ; size.height--; src += sstep, dst += dstep ) { int x = 0; - #if CV_SSE2 - if(USE_SSE2) + #if CV_SSE2 + if(USE_SSE2) { __m128 scale128 = _mm_set1_ps (scale); __m128 shift128 = _mm_set1_ps (shift); - for(; x <= size.width - 8; x += 8 ) - { - __m128i r0 = _mm_loadl_epi64((const __m128i*)(src + x)); + for(; x <= size.width - 8; x += 8 ) + { + __m128i r0 = _mm_loadl_epi64((const __m128i*)(src + x)); __m128i r1 = _mm_loadl_epi64((const __m128i*)(src + x + 4)); - __m128 rf0 =_mm_cvtepi32_ps(_mm_srai_epi32(_mm_unpacklo_epi16(r0, r0), 16)); + __m128 rf0 =_mm_cvtepi32_ps(_mm_srai_epi32(_mm_unpacklo_epi16(r0, r0), 16)); __m128 rf1 =_mm_cvtepi32_ps(_mm_srai_epi32(_mm_unpacklo_epi16(r1, r1), 16)); rf0 = _mm_add_ps(_mm_mul_ps(rf0, scale128), shift128); rf1 = _mm_add_ps(_mm_mul_ps(rf1, scale128), shift128); - r0 = _mm_cvtps_epi32(rf0); - r1 = _mm_cvtps_epi32(rf1); - r0 = _mm_packs_epi32(r0, r1); - _mm_storeu_si128((__m128i*)(dst + x), r0); - } - } + r0 = _mm_cvtps_epi32(rf0); + r1 = _mm_cvtps_epi32(rf1); + r0 = _mm_packs_epi32(r0, r1); + _mm_storeu_si128((__m128i*)(dst + x), r0); + } + } #endif for(; x < size.width; x++ ) dst[x] = saturate_cast(src[x]*scale + shift); - } + } } @@ -664,21 +664,21 @@ cvt_( const T* src, size_t sstep, { sstep /= sizeof(src[0]); dstep /= sizeof(dst[0]); - + for( ; size.height--; src += sstep, dst += dstep ) { int x = 0; - #if CV_ENABLE_UNROLLED - for( ; x <= size.width - 4; x += 4 ) - { - DT t0, t1; - t0 = saturate_cast
(src[x]); - t1 = saturate_cast
(src[x+1]); - dst[x] = t0; dst[x+1] = t1; - t0 = saturate_cast
(src[x+2]); - t1 = saturate_cast
(src[x+3]); - dst[x+2] = t0; dst[x+3] = t1; - } + #if CV_ENABLE_UNROLLED + for( ; x <= size.width - 4; x += 4 ) + { + DT t0, t1; + t0 = saturate_cast
(src[x]); + t1 = saturate_cast
(src[x+1]); + dst[x] = t0; dst[x+1] = t1; + t0 = saturate_cast
(src[x+2]); + t1 = saturate_cast
(src[x+3]); + dst[x+2] = t0; dst[x+3] = t1; + } #endif for( ; x < size.width; x++ ) dst[x] = saturate_cast
(src[x]); @@ -692,24 +692,24 @@ cvt_( const float* src, size_t sstep, { sstep /= sizeof(src[0]); dstep /= sizeof(dst[0]); - + for( ; size.height--; src += sstep, dst += dstep ) { int x = 0; - #if CV_SSE2 - if(USE_SSE2){ - for( ; x <= size.width - 8; x += 8 ) - { - __m128 src128 = _mm_loadu_ps (src + x); - __m128i src_int128 = _mm_cvtps_epi32 (src128); - - src128 = _mm_loadu_ps (src + x + 4); - __m128i src1_int128 = _mm_cvtps_epi32 (src128); - - src1_int128 = _mm_packs_epi32(src_int128, src1_int128); - _mm_storeu_si128((__m128i*)(dst + x),src1_int128); - } - } + #if CV_SSE2 + if(USE_SSE2){ + for( ; x <= size.width - 8; x += 8 ) + { + __m128 src128 = _mm_loadu_ps (src + x); + __m128i src_int128 = _mm_cvtps_epi32 (src128); + + src128 = _mm_loadu_ps (src + x + 4); + __m128i src1_int128 = _mm_cvtps_epi32 (src128); + + src1_int128 = _mm_packs_epi32(src_int128, src1_int128); + _mm_storeu_si128((__m128i*)(dst + x),src1_int128); + } + } #endif for( ; x < size.width; x++ ) dst[x] = saturate_cast(src[x]); @@ -723,11 +723,11 @@ cpy_( const T* src, size_t sstep, T* dst, size_t dstep, Size size ) { sstep /= sizeof(src[0]); dstep /= sizeof(dst[0]); - + for( ; size.height--; src += sstep, dst += dstep ) memcpy(dst, src, size.width*sizeof(src[0])); } - + #define DEF_CVT_SCALE_ABS_FUNC(suffix, tfunc, stype, dtype, wtype) \ static void cvtScaleAbs##suffix( const stype* src, size_t sstep, const uchar*, size_t, \ dtype* dst, size_t dstep, Size size, double* scale) \ @@ -741,8 +741,8 @@ dtype* dst, size_t dstep, Size size, double* scale) \ { \ cvtScale_(src, sstep, dst, dstep, size, (wtype)scale[0], (wtype)scale[1]); \ } - - + + #define DEF_CVT_FUNC(suffix, stype, dtype) \ static void cvt##suffix( const stype* src, size_t sstep, const uchar*, size_t, \ dtype* dst, size_t dstep, Size size, double*) \ @@ -756,15 +756,15 @@ stype* dst, size_t dstep, Size size, double*) \ { \ cpy_(src, sstep, dst, dstep, size); \ } - - + + DEF_CVT_SCALE_ABS_FUNC(8u, cvtScaleAbs_, uchar, uchar, float); DEF_CVT_SCALE_ABS_FUNC(8s8u, cvtScaleAbs_, schar, uchar, float); DEF_CVT_SCALE_ABS_FUNC(16u8u, cvtScaleAbs_, ushort, uchar, float); DEF_CVT_SCALE_ABS_FUNC(16s8u, cvtScaleAbs_, short, uchar, float); DEF_CVT_SCALE_ABS_FUNC(32s8u, cvtScaleAbs_, int, uchar, float); DEF_CVT_SCALE_ABS_FUNC(32f8u, cvtScaleAbs_, float, uchar, float); -DEF_CVT_SCALE_ABS_FUNC(64f8u, cvtScaleAbs_, double, uchar, float); +DEF_CVT_SCALE_ABS_FUNC(64f8u, cvtScaleAbs_, double, uchar, float); DEF_CVT_SCALE_FUNC(8u, uchar, uchar, float); DEF_CVT_SCALE_FUNC(8s8u, schar, uchar, float); @@ -772,7 +772,7 @@ DEF_CVT_SCALE_FUNC(16u8u, ushort, uchar, float); DEF_CVT_SCALE_FUNC(16s8u, short, uchar, float); DEF_CVT_SCALE_FUNC(32s8u, int, uchar, float); DEF_CVT_SCALE_FUNC(32f8u, float, uchar, float); -DEF_CVT_SCALE_FUNC(64f8u, double, uchar, float); +DEF_CVT_SCALE_FUNC(64f8u, double, uchar, float); DEF_CVT_SCALE_FUNC(8u8s, uchar, schar, float); DEF_CVT_SCALE_FUNC(8s, schar, schar, float); @@ -780,7 +780,7 @@ DEF_CVT_SCALE_FUNC(16u8s, ushort, schar, float); DEF_CVT_SCALE_FUNC(16s8s, short, schar, float); DEF_CVT_SCALE_FUNC(32s8s, int, schar, float); DEF_CVT_SCALE_FUNC(32f8s, float, schar, float); -DEF_CVT_SCALE_FUNC(64f8s, double, schar, float); +DEF_CVT_SCALE_FUNC(64f8s, double, schar, float); DEF_CVT_SCALE_FUNC(8u16u, uchar, ushort, float); DEF_CVT_SCALE_FUNC(8s16u, schar, ushort, float); @@ -788,7 +788,7 @@ DEF_CVT_SCALE_FUNC(16u, ushort, ushort, float); DEF_CVT_SCALE_FUNC(16s16u, short, ushort, float); DEF_CVT_SCALE_FUNC(32s16u, int, ushort, float); DEF_CVT_SCALE_FUNC(32f16u, float, ushort, float); -DEF_CVT_SCALE_FUNC(64f16u, double, ushort, float); +DEF_CVT_SCALE_FUNC(64f16u, double, ushort, float); DEF_CVT_SCALE_FUNC(8u16s, uchar, short, float); DEF_CVT_SCALE_FUNC(8s16s, schar, short, float); @@ -797,7 +797,7 @@ DEF_CVT_SCALE_FUNC(16s, short, short, float); DEF_CVT_SCALE_FUNC(32s16s, int, short, float); DEF_CVT_SCALE_FUNC(32f16s, float, short, float); DEF_CVT_SCALE_FUNC(64f16s, double, short, float); - + DEF_CVT_SCALE_FUNC(8u32s, uchar, int, float); DEF_CVT_SCALE_FUNC(8s32s, schar, int, float); DEF_CVT_SCALE_FUNC(16u32s, ushort, int, float); @@ -874,7 +874,7 @@ DEF_CVT_FUNC(16s64f, short, double); DEF_CVT_FUNC(32s64f, int, double); DEF_CVT_FUNC(32f64f, float, double); DEF_CPY_FUNC(64s, int64); - + static BinaryFunc cvtScaleAbsTab[] = { (BinaryFunc)cvtScaleAbs8u, (BinaryFunc)cvtScaleAbs8s8u, (BinaryFunc)cvtScaleAbs16u8u, @@ -965,7 +965,7 @@ static BinaryFunc cvtTab[][8] = 0, 0, 0, 0, 0, 0, 0, 0 } }; - + BinaryFunc getConvertFunc(int sdepth, int ddepth) { return cvtTab[CV_MAT_DEPTH(ddepth)][CV_MAT_DEPTH(sdepth)]; @@ -974,10 +974,10 @@ BinaryFunc getConvertFunc(int sdepth, int ddepth) BinaryFunc getConvertScaleFunc(int sdepth, int ddepth) { return cvtScaleTab[CV_MAT_DEPTH(ddepth)][CV_MAT_DEPTH(sdepth)]; -} - } - + +} + void cv::convertScaleAbs( InputArray _src, OutputArray _dst, double alpha, double beta ) { Mat src = _src.getMat(); @@ -987,7 +987,7 @@ void cv::convertScaleAbs( InputArray _src, OutputArray _dst, double alpha, doubl Mat dst = _dst.getMat(); BinaryFunc func = cvtScaleAbsTab[src.depth()]; CV_Assert( func != 0 ); - + if( src.dims <= 2 ) { Size sz = getContinuousSize(src, dst, cn); @@ -999,7 +999,7 @@ void cv::convertScaleAbs( InputArray _src, OutputArray _dst, double alpha, doubl uchar* ptrs[2]; NAryMatIterator it(arrays, ptrs); Size sz((int)it.size*cn, 1); - + for( size_t i = 0; i < it.nplanes; i++, ++it ) func( ptrs[0], 0, 0, 0, ptrs[1], 0, sz, scale ); } @@ -1022,12 +1022,12 @@ void cv::Mat::convertTo(OutputArray _dst, int _type, double alpha, double beta) } Mat src = *this; - + BinaryFunc func = noScale ? getConvertFunc(sdepth, ddepth) : getConvertScaleFunc(sdepth, ddepth); double scale[] = {alpha, beta}; int cn = channels(); CV_Assert( func != 0 ); - + if( dims <= 2 ) { _dst.create( size(), _type ); @@ -1043,7 +1043,7 @@ void cv::Mat::convertTo(OutputArray _dst, int _type, double alpha, double beta) uchar* ptrs[2]; NAryMatIterator it(arrays, ptrs); Size sz((int)(it.size*cn), 1); - + for( size_t i = 0; i < it.nplanes; i++, ++it ) func(ptrs[0], 0, 0, 0, ptrs[1], 0, sz, scale); } @@ -1105,10 +1105,10 @@ static void LUT8u_32f( const uchar* src, const float* lut, float* dst, int len, static void LUT8u_64f( const uchar* src, const double* lut, double* dst, int len, int cn, int lutcn ) { LUT8u_( src, lut, dst, len, cn, lutcn ); -} - +} + typedef void (*LUTFunc)( const uchar* src, const uchar* lut, uchar* dst, int len, int cn, int lutcn ); - + static LUTFunc lutTab[] = { (LUTFunc)LUT8u_8u, (LUTFunc)LUT8u_8s, (LUTFunc)LUT8u_16u, (LUTFunc)LUT8u_16s, @@ -1116,7 +1116,7 @@ static LUTFunc lutTab[] = }; } - + void cv::LUT( InputArray _src, InputArray _lut, OutputArray _dst, int interpolation ) { Mat src = _src.getMat(), lut = _lut.getMat(); @@ -1132,12 +1132,12 @@ void cv::LUT( InputArray _src, InputArray _lut, OutputArray _dst, int interpolat LUTFunc func = lutTab[lut.depth()]; CV_Assert( func != 0 ); - + const Mat* arrays[] = {&src, &dst, 0}; uchar* ptrs[2]; NAryMatIterator it(arrays, ptrs); int len = (int)it.size; - + for( size_t i = 0; i < it.nplanes; i++, ++it ) func(ptrs[0], lut.data, ptrs[1], len, cn, lutcn); } @@ -1147,7 +1147,7 @@ void cv::normalize( InputArray _src, OutputArray _dst, double a, double b, int norm_type, int rtype, InputArray _mask ) { Mat src = _src.getMat(), mask = _mask.getMat(); - + double scale = 1, shift = 0; if( norm_type == CV_MINMAX ) { @@ -1165,13 +1165,13 @@ void cv::normalize( InputArray _src, OutputArray _dst, double a, double b, } else CV_Error( CV_StsBadArg, "Unknown/unsupported norm type" ); - + if( rtype < 0 ) rtype = _dst.fixedType() ? _dst.depth() : src.depth(); - + _dst.create(src.dims, src.size, CV_MAKETYPE(rtype, src.channels())); Mat dst = _dst.getMat(); - + if( !mask.data ) src.convertTo( dst, rtype, scale, shift ); else @@ -1282,7 +1282,7 @@ cvConvertScale( const void* srcarr, void* dstarr, double scale, double shift ) { cv::Mat src = cv::cvarrToMat(srcarr), dst = cv::cvarrToMat(dstarr); - + CV_Assert( src.size == dst.size && src.channels() == dst.channels() ); src.convertTo(dst, dst.type(), scale, shift); } diff --git a/modules/core/src/copy.cpp b/modules/core/src/copy.cpp index c0d0501..24e6a51 100644 --- a/modules/core/src/copy.cpp +++ b/modules/core/src/copy.cpp @@ -59,7 +59,7 @@ copyMask_(const uchar* _src, size_t sstep, const uchar* mask, size_t mstep, ucha const T* src = (const T*)_src; T* dst = (T*)_dst; int x = 0; - #if CV_ENABLE_UNROLLED + #if CV_ENABLE_UNROLLED for( ; x <= size.width - 4; x += 4 ) { if( mask[x] ) @@ -96,16 +96,16 @@ copyMaskGeneric(const uchar* _src, size_t sstep, const uchar* mask, size_t mstep } } } - - + + #define DEF_COPY_MASK(suffix, type) \ static void copyMask##suffix(const uchar* src, size_t sstep, const uchar* mask, size_t mstep, \ uchar* dst, size_t dstep, Size size, void*) \ { \ copyMask_(src, sstep, mask, mstep, dst, dstep, size); \ } - - + + DEF_COPY_MASK(8u, uchar); DEF_COPY_MASK(16u, ushort); DEF_COPY_MASK(8uC3, Vec3b); @@ -116,7 +116,7 @@ DEF_COPY_MASK(32sC3, Vec3i); DEF_COPY_MASK(32sC4, Vec4i); DEF_COPY_MASK(32sC6, Vec6i); DEF_COPY_MASK(32sC8, Vec8i); - + BinaryFunc copyMaskTab[] = { 0, @@ -137,7 +137,7 @@ BinaryFunc copyMaskTab[] = 0, 0, 0, 0, 0, 0, 0, copyMask32sC8 }; - + BinaryFunc getCopyMaskFunc(size_t esz) { return esz <= 32 && copyMaskTab[esz] ? copyMaskTab[esz] : copyMaskGeneric; @@ -152,51 +152,51 @@ void Mat::copyTo( OutputArray _dst ) const convertTo( _dst, dtype ); return; } - + if( empty() ) { _dst.release(); return; } - + if( dims <= 2 ) { _dst.create( rows, cols, type() ); Mat dst = _dst.getMat(); if( data == dst.data ) return; - + if( rows > 0 && cols > 0 ) { const uchar* sptr = data; uchar* dptr = dst.data; - + // to handle the copying 1xn matrix => nx1 std vector. Size sz = size() == dst.size() ? getContinuousSize(*this, dst) : getContinuousSize(*this); size_t len = sz.width*elemSize(); - + for( ; sz.height--; sptr += step, dptr += dst.step ) memcpy( dptr, sptr, len ); } return; } - + _dst.create( dims, size, type() ); Mat dst = _dst.getMat(); if( data == dst.data ) return; - + if( total() != 0 ) { const Mat* arrays[] = { this, &dst }; uchar* ptrs[2]; NAryMatIterator it(arrays, ptrs, 2); - size_t size = it.size*elemSize(); - + size_t sz = it.size*elemSize(); + for( size_t i = 0; i < it.nplanes; i++, ++it ) - memcpy(ptrs[1], ptrs[0], size); + memcpy(ptrs[1], ptrs[0], sz); } } @@ -208,33 +208,33 @@ void Mat::copyTo( OutputArray _dst, InputArray _mask ) const copyTo(_dst); return; } - + int cn = channels(), mcn = mask.channels(); CV_Assert( mask.depth() == CV_8U && (mcn == 1 || mcn == cn) ); bool colorMask = mcn > 1; - + size_t esz = colorMask ? elemSize1() : elemSize(); BinaryFunc copymask = getCopyMaskFunc(esz); - + uchar* data0 = _dst.getMat().data; _dst.create( dims, size, type() ); Mat dst = _dst.getMat(); - + if( dst.data != data0 ) // do not leave dst uninitialized dst = Scalar(0); - + if( dims <= 2 ) { Size sz = getContinuousSize(*this, dst, mask, mcn); copymask(data, step, mask.data, mask.step, dst.data, dst.step, sz, &esz); return; } - + const Mat* arrays[] = { this, &dst, &mask, 0 }; uchar* ptrs[3]; NAryMatIterator it(arrays, ptrs); Size sz((int)(it.size*mcn), 1); - + for( size_t i = 0; i < it.nplanes; i++, ++it ) copymask(ptrs[0], 0, ptrs[2], 0, ptrs[1], 0, sz, &esz); } @@ -242,14 +242,14 @@ void Mat::copyTo( OutputArray _dst, InputArray _mask ) const Mat& Mat::operator = (const Scalar& s) { const Mat* arrays[] = { this }; - uchar* ptr; - NAryMatIterator it(arrays, &ptr, 1); - size_t size = it.size*elemSize(); - + uchar* dptr; + NAryMatIterator it(arrays, &dptr, 1); + size_t elsize = it.size*elemSize(); + if( s[0] == 0 && s[1] == 0 && s[2] == 0 && s[3] == 0 ) { for( size_t i = 0; i < it.nplanes; i++, ++it ) - memset( ptr, 0, size ); + memset( dptr, 0, elsize ); } else { @@ -258,50 +258,50 @@ Mat& Mat::operator = (const Scalar& s) double scalar[12]; scalarToRawData(s, scalar, type(), 12); size_t blockSize = 12*elemSize1(); - - for( size_t j = 0; j < size; j += blockSize ) + + for( size_t j = 0; j < elsize; j += blockSize ) { - size_t sz = MIN(blockSize, size - j); - memcpy( ptr + j, scalar, sz ); + size_t sz = MIN(blockSize, elsize - j); + memcpy( dptr + j, scalar, sz ); } } - + for( size_t i = 1; i < it.nplanes; i++ ) { ++it; - memcpy( ptr, data, size ); + memcpy( dptr, data, elsize ); } } return *this; } - + Mat& Mat::setTo(InputArray _value, InputArray _mask) { if( !data ) return *this; - + Mat value = _value.getMat(), mask = _mask.getMat(); - + CV_Assert( checkScalar(value, type(), _value.kind(), _InputArray::MAT )); CV_Assert( mask.empty() || mask.type() == CV_8U ); - + size_t esz = elemSize(); BinaryFunc copymask = getCopyMaskFunc(esz); - + const Mat* arrays[] = { this, !mask.empty() ? &mask : 0, 0 }; uchar* ptrs[2]={0,0}; NAryMatIterator it(arrays, ptrs); - int total = (int)it.size, blockSize0 = std::min(total, (int)((BLOCK_SIZE + esz-1)/esz)); + int totalsz = (int)it.size, blockSize0 = std::min(totalsz, (int)((BLOCK_SIZE + esz-1)/esz)); AutoBuffer _scbuf(blockSize0*esz + 32); uchar* scbuf = alignPtr((uchar*)_scbuf, (int)sizeof(double)); convertAndUnrollScalar( value, type(), scbuf, blockSize0 ); - + for( size_t i = 0; i < it.nplanes; i++, ++it ) { - for( int j = 0; j < total; j += blockSize0 ) + for( int j = 0; j < totalsz; j += blockSize0 ) { - Size sz(std::min(blockSize0, total - j), 1); + Size sz(std::min(blockSize0, totalsz - j), 1); size_t blockSize = sz.width*esz; if( ptrs[1] ) { @@ -323,7 +323,7 @@ flipHoriz( const uchar* src, size_t sstep, uchar* dst, size_t dstep, Size size, int i, j, limit = (int)(((size.width + 1)/2)*esz); AutoBuffer _tab(size.width*esz); int* tab = _tab; - + for( i = 0; i < size.width; i++ ) for( size_t k = 0; k < esz; k++ ) tab[i*esz + k] = (int)((size.width - i - 1)*esz + k); @@ -403,7 +403,7 @@ flipVert( const uchar* src0, size_t sstep, uchar* dst0, size_t dstep, Size size, void flip( InputArray _src, OutputArray _dst, int flip_mode ) { Mat src = _src.getMat(); - + CV_Assert( src.dims <= 2 ); _dst.create( src.size(), src.type() ); Mat dst = _dst.getMat(); @@ -413,7 +413,7 @@ void flip( InputArray _src, OutputArray _dst, int flip_mode ) flipVert( src.data, src.step, dst.data, dst.step, src.size(), esz ); else flipHoriz( src.data, src.step, dst.data, dst.step, src.size(), esz ); - + if( flip_mode < 0 ) flipHoriz( dst.data, dst.step, dst.data, dst.step, dst.size(), esz ); } @@ -423,7 +423,7 @@ void repeat(InputArray _src, int ny, int nx, OutputArray _dst) { Mat src = _src.getMat(); CV_Assert( src.dims <= 2 ); - + _dst.create(src.rows*ny, src.cols*nx, src.type()); Mat dst = _dst.getMat(); Size ssize = src.size(), dsize = dst.size(); @@ -493,25 +493,25 @@ cvCopy( const void* srcarr, void* dstarr, const void* maskarr ) } cv::Mat src = cv::cvarrToMat(srcarr, false, true, 1), dst = cv::cvarrToMat(dstarr, false, true, 1); CV_Assert( src.depth() == dst.depth() && src.size == dst.size ); - + int coi1 = 0, coi2 = 0; if( CV_IS_IMAGE(srcarr) ) coi1 = cvGetImageCOI((const IplImage*)srcarr); if( CV_IS_IMAGE(dstarr) ) coi2 = cvGetImageCOI((const IplImage*)dstarr); - + if( coi1 || coi2 ) { CV_Assert( (coi1 != 0 || src.channels() == 1) && (coi2 != 0 || dst.channels() == 1) ); - + int pair[] = { std::max(coi1-1, 0), std::max(coi2-1, 0) }; cv::mixChannels( &src, 1, &dst, 1, pair, 1 ); return; } else CV_Assert( src.channels() == dst.channels() ); - + if( !maskarr ) src.copyTo(dst); else @@ -548,12 +548,12 @@ cvFlip( const CvArr* srcarr, CvArr* dstarr, int flip_mode ) { cv::Mat src = cv::cvarrToMat(srcarr); cv::Mat dst; - + if (!dstarr) dst = src; else dst = cv::cvarrToMat(dstarr); - + CV_Assert( src.type() == dst.type() && src.size() == dst.size() ); cv::flip( src, dst, flip_mode ); } diff --git a/modules/core/src/datastructs.cpp b/modules/core/src/datastructs.cpp index 02f188c..9438fa2 100644 --- a/modules/core/src/datastructs.cpp +++ b/modules/core/src/datastructs.cpp @@ -3349,7 +3349,7 @@ cvTreeToNodeSeq( const void* first, int header_size, CvMemStorage* storage ) } } - + return allseq; } @@ -3531,9 +3531,9 @@ namespace cv // both cv (CvFeatureTree) and ml (kNN). // The algorithm is taken from: -// J.S. Beis and D.G. Lowe. Shape indexing using approximate nearest-neighbor search -// in highdimensional spaces. In Proc. IEEE Conf. Comp. Vision Patt. Recog., -// pages 1000--1006, 1997. http://citeseer.ist.psu.edu/beis97shape.html +// J.S. Beis and D.G. Lowe. Shape indexing using approximate nearest-neighbor search +// in highdimensional spaces. In Proc. IEEE Conf. Comp. Vision Patt. Recog., +// pages 1000--1006, 1997. http://citeseer.ist.psu.edu/beis97shape.html const int MAX_TREE_DEPTH = 32; @@ -3555,8 +3555,8 @@ KDTree::KDTree(InputArray _points, InputArray _labels, bool _copyData) maxDepth = -1; normType = NORM_L2; build(_points, _labels, _copyData); -} - +} + struct SubTree { SubTree() : first(0), last(0), nodeIdx(0), depth(0) {} @@ -3596,7 +3596,7 @@ medianPartition( size_t* ofs, int a, int b, const float* vals ) else a = i0; } - + float pivot = vals[ofs[middle]]; int less = 0, more = 0; for( k = a0; k < middle; k++ ) @@ -3632,7 +3632,7 @@ computeSums( const Mat& points, const size_t* ofs, int a, int b, double* sums ) } } - + void KDTree::build(InputArray _points, bool _copyData) { build(_points, noArray(), _copyData); @@ -3652,8 +3652,8 @@ void KDTree::build(InputArray __points, InputArray __labels, bool _copyData) points.release(); points.create(_points.size(), _points.type()); } - - int i, j, n = _points.rows, dims = _points.cols, top = 0; + + int i, j, n = _points.rows, ptdims = _points.cols, top = 0; const float* data = _points.ptr(0); float* dstdata = points.ptr(0); size_t step = _points.step1(); @@ -3661,7 +3661,7 @@ void KDTree::build(InputArray __points, InputArray __labels, bool _copyData) int ptpos = 0; labels.resize(n); const int* _labels_data = 0; - + if( !_labels.empty() ) { int nlabels = _labels.checkVector(1, CV_32S, true); @@ -3669,9 +3669,9 @@ void KDTree::build(InputArray __points, InputArray __labels, bool _copyData) _labels_data = (const int*)_labels.data; } - Mat sumstack(MAX_TREE_DEPTH*2, dims*2, CV_64F); + Mat sumstack(MAX_TREE_DEPTH*2, ptdims*2, CV_64F); SubTree stack[MAX_TREE_DEPTH*2]; - + vector _ptofs(n); size_t* ptofs = &_ptofs[0]; @@ -3682,7 +3682,7 @@ void KDTree::build(InputArray __points, InputArray __labels, bool _copyData) computeSums(points, ptofs, 0, n-1, sumstack.ptr(top)); stack[top++] = SubTree(0, n-1, 0, 0); int _maxDepth = 0; - + while( --top >= 0 ) { int first = stack[top].first, last = stack[top].last; @@ -3700,16 +3700,16 @@ void KDTree::build(InputArray __points, InputArray __labels, bool _copyData) { const float* src = data + ptofs[first]; float* dst = dstdata + idx*dstep; - for( j = 0; j < dims; j++ ) + for( j = 0; j < ptdims; j++ ) dst[j] = src[j]; } - labels[idx] = _labels_data ? _labels_data[idx0] : idx0; + labels[idx] = _labels_data ? _labels_data[idx0] : idx0; _maxDepth = std::max(_maxDepth, depth); continue; } // find the dimensionality with the biggest variance - for( j = 0; j < dims; j++ ) + for( j = 0; j < ptdims; j++ ) { double m = sums[j*2]*invCount; double varj = sums[j*2+1]*invCount - m*m; @@ -3729,9 +3729,9 @@ void KDTree::build(InputArray __points, InputArray __labels, bool _copyData) nodes[nidx].boundary = medianPartition(ptofs, first, last, data + dim); int middle = (first + last)/2; - double *lsums = (double*)sums, *rsums = lsums + dims*2; + double *lsums = (double*)sums, *rsums = lsums + ptdims*2; computeSums(points, ptofs, middle+1, last, rsums); - for( j = 0; j < dims*2; j++ ) + for( j = 0; j < ptdims*2; j++ ) lsums[j] = sums[j] - rsums[j]; stack[top++] = SubTree(first, middle, left, depth+1); stack[top++] = SubTree(middle+1, last, right, depth+1); @@ -3752,13 +3752,13 @@ struct PQueueElem int KDTree::findNearest(InputArray _vec, int K, int emax, OutputArray _neighborsIdx, OutputArray _neighbors, OutputArray _dist, OutputArray _labels) const - + { Mat vecmat = _vec.getMat(); CV_Assert( vecmat.isContinuous() && vecmat.type() == CV_32F && vecmat.total() == (size_t)points.cols ); const float* vec = vecmat.ptr(); K = std::min(K, points.rows); - int dims = points.cols; + int ptdims = points.cols; CV_Assert(K > 0 && (normType == NORM_L2 || normType == NORM_L1)); @@ -3776,7 +3776,7 @@ int KDTree::findNearest(InputArray _vec, int K, int emax, { float d, alt_d = 0.f; int nidx; - + if( e == 0 ) nidx = 0; else @@ -3803,7 +3803,7 @@ int KDTree::findNearest(InputArray _vec, int K, int emax, i = left; } } - + if( ncount == K && alt_d > dist[ncount-1] ) continue; } @@ -3813,21 +3813,21 @@ int KDTree::findNearest(InputArray _vec, int K, int emax, if( nidx < 0 ) break; const Node& n = nodes[nidx]; - + if( n.idx < 0 ) { i = ~n.idx; const float* row = points.ptr(i); if( normType == NORM_L2 ) - for( j = 0, d = 0.f; j < dims; j++ ) + for( j = 0, d = 0.f; j < ptdims; j++ ) { float t = vec[j] - row[j]; d += t*t; } else - for( j = 0, d = 0.f; j < dims; j++ ) + for( j = 0, d = 0.f; j < ptdims; j++ ) d += std::abs(vec[j] - row[j]); - + dist[ncount] = d; idx[ncount] = i; for( i = ncount-1; i >= 0; i-- ) @@ -3839,9 +3839,9 @@ int KDTree::findNearest(InputArray _vec, int K, int emax, } ncount += ncount < K; e++; - break; + break; } - + int alt; if( vec[n.idx] <= n.boundary ) { @@ -3853,7 +3853,7 @@ int KDTree::findNearest(InputArray _vec, int K, int emax, nidx = n.right; alt = n.left; } - + d = vec[n.idx] - n.boundary; if( normType == NORM_L2 ) d = d*d + alt_d; @@ -3898,22 +3898,22 @@ void KDTree::findOrthoRange(InputArray _lowerBound, OutputArray _neighbors, OutputArray _labels ) const { - int dims = points.cols; + int ptdims = points.cols; Mat lowerBound = _lowerBound.getMat(), upperBound = _upperBound.getMat(); CV_Assert( lowerBound.size == upperBound.size && lowerBound.isContinuous() && upperBound.isContinuous() && lowerBound.type() == upperBound.type() && lowerBound.type() == CV_32F && - lowerBound.total() == (size_t)dims ); + lowerBound.total() == (size_t)ptdims ); const float* L = lowerBound.ptr(); const float* R = upperBound.ptr(); - + vector idx; AutoBuffer _stack(MAX_TREE_DEPTH*2 + 1); int* stack = _stack; int top = 0; - + stack[top++] = 0; while( --top >= 0 ) @@ -3926,10 +3926,10 @@ void KDTree::findOrthoRange(InputArray _lowerBound, { int j, i = ~n.idx; const float* row = points.ptr(i); - for( j = 0; j < dims; j++ ) + for( j = 0; j < ptdims; j++ ) if( row[j] < L[j] || row[j] >= R[j] ) break; - if( j == dims ) + if( j == ptdims ) idx.push_back(i); continue; } @@ -3948,7 +3948,7 @@ void KDTree::findOrthoRange(InputArray _lowerBound, getPoints( idx, _neighbors, _labels ); } - + void KDTree::getPoints(InputArray _idx, OutputArray _pts, OutputArray _labels) const { Mat idxmat = _idx.getMat(), pts, labelsmat; @@ -3956,8 +3956,8 @@ void KDTree::getPoints(InputArray _idx, OutputArray _pts, OutputArray _labels) c (idxmat.cols == 1 || idxmat.rows == 1) ); const int* idx = idxmat.ptr(); int* dstlabels = 0; - - int dims = points.cols; + + int ptdims = points.cols; int i, nidx = (int)idxmat.total(); if( nidx == 0 ) { @@ -3965,13 +3965,13 @@ void KDTree::getPoints(InputArray _idx, OutputArray _pts, OutputArray _labels) c _labels.release(); return; } - + if( _pts.needed() ) { - _pts.create( nidx, dims, points.type()); + _pts.create( nidx, ptdims, points.type()); pts = _pts.getMat(); } - + if(_labels.needed()) { _labels.create(nidx, 1, CV_32S, -1, true); @@ -3980,14 +3980,14 @@ void KDTree::getPoints(InputArray _idx, OutputArray _pts, OutputArray _labels) c dstlabels = labelsmat.ptr(); } const int* srclabels = !labels.empty() ? &labels[0] : 0; - + for( i = 0; i < nidx; i++ ) { int k = idx[i]; CV_Assert( (unsigned)k < (unsigned)points.rows ); const float* src = points.ptr(k); if( pts.data ) - std::copy(src, src + dims, pts.ptr(i)); + std::copy(src, src + ptdims, pts.ptr(i)); if( dstlabels ) dstlabels[i] = srclabels ? srclabels[k] : k; } @@ -4007,9 +4007,9 @@ int KDTree::dims() const { return !points.empty() ? points.cols : 0; } - + //////////////////////////////////////////////////////////////////////////////// - + schar* seqPush( CvSeq* seq, const void* element ) { return cvSeqPush(seq, element); diff --git a/modules/core/src/drawing.cpp b/modules/core/src/drawing.cpp index b1b87ad..c48b048 100644 --- a/modules/core/src/drawing.cpp +++ b/modules/core/src/drawing.cpp @@ -169,7 +169,7 @@ LineIterator::LineIterator(const Mat& img, Point pt1, Point pt2, } int bt_pix0 = (int)img.elemSize(), bt_pix = bt_pix0; - size_t step = img.step; + size_t istep = img.step; int dx = pt2.x - pt1.x; int dy = pt2.y - pt1.y; @@ -188,11 +188,11 @@ LineIterator::LineIterator(const Mat& img, Point pt1, Point pt2, bt_pix = (bt_pix ^ s) - s; } - ptr = (uchar*)(img.data + pt1.y * step + pt1.x * bt_pix0); + ptr = (uchar*)(img.data + pt1.y * istep + pt1.x * bt_pix0); s = dy < 0 ? -1 : 0; dy = (dy ^ s) - s; - step = (step ^ s) - s; + istep = (istep ^ s) - s; s = dy > dx ? -1 : 0; @@ -201,9 +201,9 @@ LineIterator::LineIterator(const Mat& img, Point pt1, Point pt2, dy ^= dx & s; dx ^= dy & s; - bt_pix ^= step & s; - step ^= bt_pix & s; - bt_pix ^= step & s; + bt_pix ^= istep & s; + istep ^= bt_pix & s; + bt_pix ^= istep & s; if( connectivity == 8 ) { @@ -212,7 +212,7 @@ LineIterator::LineIterator(const Mat& img, Point pt1, Point pt2, err = dx - (dy + dy); plusDelta = dx + dx; minusDelta = -(dy + dy); - plusStep = (int)step; + plusStep = (int)istep; minusStep = bt_pix; count = dx + 1; } @@ -223,7 +223,7 @@ LineIterator::LineIterator(const Mat& img, Point pt1, Point pt2, err = 0; plusDelta = (dx + dx) + (dy + dy); minusDelta = -(dy + dy); - plusStep = (int)step - bt_pix; + plusStep = (int)istep - bt_pix; minusStep = bt_pix; count = dx + dy + 1; } diff --git a/modules/core/src/gpumat.cpp b/modules/core/src/gpumat.cpp index 781d143..25a3e76 100644 --- a/modules/core/src/gpumat.cpp +++ b/modules/core/src/gpumat.cpp @@ -524,30 +524,30 @@ cv::gpu::GpuMat::GpuMat(Size size_, int type_, void* data_, size_t step_) : dataend += step * (rows - 1) + minstep; } -cv::gpu::GpuMat::GpuMat(const GpuMat& m, Range rowRange, Range colRange) +cv::gpu::GpuMat::GpuMat(const GpuMat& m, Range _rowRange, Range _colRange) { flags = m.flags; step = m.step; refcount = m.refcount; data = m.data; datastart = m.datastart; dataend = m.dataend; - if (rowRange == Range::all()) + if (_rowRange == Range::all()) rows = m.rows; else { - CV_Assert(0 <= rowRange.start && rowRange.start <= rowRange.end && rowRange.end <= m.rows); + CV_Assert(0 <= _rowRange.start && _rowRange.start <= _rowRange.end && _rowRange.end <= m.rows); - rows = rowRange.size(); - data += step*rowRange.start; + rows = _rowRange.size(); + data += step*_rowRange.start; } - if (colRange == Range::all()) + if (_colRange == Range::all()) cols = m.cols; else { - CV_Assert(0 <= colRange.start && colRange.start <= colRange.end && colRange.end <= m.cols); + CV_Assert(0 <= _colRange.start && _colRange.start <= _colRange.end && _colRange.end <= m.cols); - cols = colRange.size(); - data += colRange.start*elemSize(); + cols = _colRange.size(); + data += _colRange.start*elemSize(); flags &= cols < m.cols ? ~Mat::CONTINUOUS_FLAG : -1; } diff --git a/modules/core/src/matmul.cpp b/modules/core/src/matmul.cpp index a55fab3..fe138f0 100644 --- a/modules/core/src/matmul.cpp +++ b/modules/core/src/matmul.cpp @@ -63,7 +63,7 @@ GEMM_CopyBlock( const uchar* src, size_t src_step, for( ; size.height--; src += src_step, dst += dst_step ) { - j=0; + j=0; #if CV_ENABLE_UNROLLED for( ; j <= size.width - 4; j += 4 ) { @@ -345,7 +345,7 @@ GEMMSingleMul( const T* a_data, size_t a_step, for( k = 0; k < n; k++, b_data += b_step ) { WT al(a_data[k]); - j=0; + j=0; #if CV_ENABLE_UNROLLED for(; j <= m - 4; j += 4 ) { @@ -513,8 +513,8 @@ GEMMStore( const T* c_data, size_t c_step, if( _c_data ) { c_data = _c_data; - j=0; - #if CV_ENABLE_UNROLLED + j=0; + #if CV_ENABLE_UNROLLED for(; j <= d_size.width - 4; j += 4, c_data += 4*c_step1 ) { WT t0 = alpha*d_buf[j]; @@ -539,8 +539,8 @@ GEMMStore( const T* c_data, size_t c_step, } else { - j = 0; - #if CV_ENABLE_UNROLLED + j = 0; + #if CV_ENABLE_UNROLLED for( ; j <= d_size.width - 4; j += 4 ) { WT t0 = alpha*d_buf[j]; @@ -552,7 +552,7 @@ GEMMStore( const T* c_data, size_t c_step, d_data[j+2] = T(t0); d_data[j+3] = T(t1); } - #endif + #endif for( ; j < d_size.width; j++ ) d_data[j] = T(alpha*d_buf[j]); } @@ -597,7 +597,7 @@ static void GEMMSingleMul_64f( const double* a_data, size_t a_step, alpha, beta, flags); } - + static void GEMMSingleMul_32fc( const Complexf* a_data, size_t a_step, const Complexf* b_data, size_t b_step, const Complexf* c_data, size_t c_step, @@ -620,7 +620,7 @@ static void GEMMSingleMul_64fc( const Complexd* a_data, size_t a_step, GEMMSingleMul(a_data, a_step, b_data, b_step, c_data, c_step, d_data, d_step, a_size, d_size, alpha, beta, flags); -} +} static void GEMMBlockMul_32f( const float* a_data, size_t a_step, const float* b_data, size_t b_step, @@ -696,7 +696,7 @@ static void GEMMStore_64fc( const Complexd* c_data, size_t c_step, } void cv::gemm( InputArray matA, InputArray matB, double alpha, - InputArray matC, double beta, OutputArray matD, int flags ) + InputArray matC, double beta, OutputArray _matD, int flags ) { const int block_lin_size = 128; const int block_size = block_lin_size * block_lin_size; @@ -741,8 +741,8 @@ void cv::gemm( InputArray matA, InputArray matB, double alpha, ((flags&GEMM_3_T) != 0 && C.rows == d_size.width && C.cols == d_size.height))); } - matD.create( d_size.height, d_size.width, type ); - Mat D = matD.getMat(); + _matD.create( d_size.height, d_size.width, type ); + Mat D = _matD.getMat(); if( (flags & GEMM_3_T) != 0 && C.data == D.data ) { transpose( C, C ); @@ -2008,7 +2008,7 @@ static void scaleAdd_32f(const float* src1, const float* src2, float* dst, t1 = src1[i+3]*alpha + src2[i+3]; dst[i+2] = t0; dst[i+3] = t1; } - for(; i < len; i++ ) + for(; i < len; i++ ) dst[i] = src1[i]*alpha + src2[i]; } @@ -2035,7 +2035,7 @@ static void scaleAdd_64f(const double* src1, const double* src2, double* dst, } else #endif - //vz why do we need unroll here? + //vz why do we need unroll here? for( ; i <= len - 4; i += 4 ) { double t0, t1; @@ -2046,7 +2046,7 @@ static void scaleAdd_64f(const double* src1, const double* src2, double* dst, t1 = src1[i+3]*alpha + src2[i+3]; dst[i+2] = t0; dst[i+3] = t1; } - for(; i < len; i++ ) + for(; i < len; i++ ) dst[i] = src1[i]*alpha + src2[i]; } @@ -2072,7 +2072,7 @@ void cv::scaleAdd( InputArray _src1, double alpha, InputArray _src2, OutputArray float falpha = (float)alpha; void* palpha = depth == CV_32F ? (void*)&falpha : (void*)α - ScaleAddFunc func = depth == CV_32F ? (ScaleAddFunc)scaleAdd_32f : (ScaleAddFunc)scaleAdd_64f; + ScaleAddFunc func = depth == CV_32F ? (ScaleAddFunc)scaleAdd_32f : (ScaleAddFunc)scaleAdd_64f; if( src1.isContinuous() && src2.isContinuous() && dst.isContinuous() ) { @@ -2134,12 +2134,12 @@ void cv::calcCovarMatrix( const Mat* data, int nsamples, Mat& covar, Mat& _mean, _mean = mean.reshape(1, size.height); } -void cv::calcCovarMatrix( InputArray _data, OutputArray _covar, InputOutputArray _mean, int flags, int ctype ) +void cv::calcCovarMatrix( InputArray _src, OutputArray _covar, InputOutputArray _mean, int flags, int ctype ) { - if(_data.kind() == _InputArray::STD_VECTOR_MAT) + if(_src.kind() == _InputArray::STD_VECTOR_MAT) { std::vector src; - _data.getMatVector(src); + _src.getMatVector(src); CV_Assert( src.size() > 0 ); @@ -2185,7 +2185,7 @@ void cv::calcCovarMatrix( InputArray _data, OutputArray _covar, InputOutputArray return; } - Mat data = _data.getMat(), mean; + Mat data = _src.getMat(), mean; CV_Assert( ((flags & CV_COVAR_ROWS) != 0) ^ ((flags & CV_COVAR_COLS) != 0) ); bool takeRows = (flags & CV_COVAR_ROWS) != 0; int type = data.type(); @@ -2209,7 +2209,7 @@ void cv::calcCovarMatrix( InputArray _data, OutputArray _covar, InputOutputArray else { ctype = std::max(CV_MAT_DEPTH(ctype >= 0 ? ctype : type), CV_32F); - reduce( _data, _mean, takeRows ? 0 : 1, CV_REDUCE_AVG, ctype ); + reduce( _src, _mean, takeRows ? 0 : 1, CV_REDUCE_AVG, ctype ); mean = _mean.getMat(); } @@ -2223,7 +2223,7 @@ void cv::calcCovarMatrix( InputArray _data, OutputArray _covar, InputOutputArray double cv::Mahalanobis( InputArray _v1, InputArray _v2, InputArray _icovar ) { - Mat v1 = _v1.getMat(), v2 = _v2.getMat(), icovar = _icovar.getMat(); + Mat v1 = _v1.getMat(), v2 = _v2.getMat(), icovar = _icovar.getMat(); int type = v1.type(), depth = v1.depth(); Size sz = v1.size(); int i, j, len = sz.width*sz.height*v1.channels(); @@ -2261,7 +2261,7 @@ double cv::Mahalanobis( InputArray _v1, InputArray _v2, InputArray _icovar ) { double row_sum = 0; j = 0; - #if CV_ENABLE_UNROLLED + #if CV_ENABLE_UNROLLED for(; j <= len - 4; j += 4 ) row_sum += diff[j]*mat[j] + diff[j+1]*mat[j+1] + diff[j+2]*mat[j+2] + diff[j+3]*mat[j+3]; @@ -2292,7 +2292,7 @@ double cv::Mahalanobis( InputArray _v1, InputArray _v2, InputArray _icovar ) { double row_sum = 0; j = 0; - #if CV_ENABLE_UNROLLED + #if CV_ENABLE_UNROLLED for(; j <= len - 4; j += 4 ) row_sum += diff[j]*mat[j] + diff[j+1]*mat[j+1] + diff[j+2]*mat[j+2] + diff[j+3]*mat[j+3]; @@ -2642,7 +2642,7 @@ dotProd_(const T* src1, const T* src2, int len) { int i = 0; double result = 0; - #if CV_ENABLE_UNROLLED + #if CV_ENABLE_UNROLLED for( ; i <= len - 4; i += 4 ) result += (double)src1[i]*src2[i] + (double)src1[i+1]*src2[i+1] + (double)src1[i+2]*src2[i+2] + (double)src1[i+3]*src2[i+3]; @@ -2674,7 +2674,7 @@ static double dotProd_8u(const uchar* src1, const uchar* src2, int len) { blockSize = std::min(len0 - i, blockSize0); __m128i s = _mm_setzero_si128(); - j = 0; + j = 0; for( ; j <= blockSize - 16; j += 16 ) { __m128i b0 = _mm_loadu_si128((const __m128i*)(src1 + j)); @@ -2806,9 +2806,9 @@ double Mat::dot(InputArray _mat) const PCA::PCA() {} -PCA::PCA(InputArray data, InputArray mean, int flags, int maxComponents) +PCA::PCA(InputArray data, InputArray _mean, int flags, int maxComponents) { - operator()(data, mean, flags, maxComponents); + operator()(data, _mean, flags, maxComponents); } PCA& PCA::operator()(InputArray _data, InputArray __mean, int flags, int maxComponents) @@ -2964,7 +2964,7 @@ void cv::PCACompute(InputArray data, InputOutputArray mean, pca.mean.copyTo(mean); pca.eigenvectors.copyTo(eigenvectors); } - + void cv::PCAProject(InputArray data, InputArray mean, InputArray eigenvectors, OutputArray result) { diff --git a/modules/core/src/matop.cpp b/modules/core/src/matop.cpp index e4ee189..c7fb841 100644 --- a/modules/core/src/matop.cpp +++ b/modules/core/src/matop.cpp @@ -59,82 +59,82 @@ public: bool elementWise(const MatExpr& /*expr*/) const { return true; } void assign(const MatExpr& expr, Mat& m, int type=-1) const; - + static void makeExpr(MatExpr& res, const Mat& m); }; static MatOp_Identity g_MatOp_Identity; - + class MatOp_AddEx : public MatOp { public: MatOp_AddEx() {} virtual ~MatOp_AddEx() {} - + bool elementWise(const MatExpr& /*expr*/) const { return true; } void assign(const MatExpr& expr, Mat& m, int type=-1) const; - + void add(const MatExpr& e1, const Scalar& s, MatExpr& res) const; void subtract(const Scalar& s, const MatExpr& expr, MatExpr& res) const; void multiply(const MatExpr& e1, double s, MatExpr& res) const; void divide(double s, const MatExpr& e, MatExpr& res) const; - + void transpose(const MatExpr& e1, MatExpr& res) const; void abs(const MatExpr& expr, MatExpr& res) const; - + static void makeExpr(MatExpr& res, const Mat& a, const Mat& b, double alpha, double beta, const Scalar& s=Scalar()); }; static MatOp_AddEx g_MatOp_AddEx; - + class MatOp_Bin : public MatOp { public: MatOp_Bin() {} virtual ~MatOp_Bin() {} - + bool elementWise(const MatExpr& /*expr*/) const { return true; } void assign(const MatExpr& expr, Mat& m, int type=-1) const; - + void multiply(const MatExpr& e1, double s, MatExpr& res) const; void divide(double s, const MatExpr& e, MatExpr& res) const; - + static void makeExpr(MatExpr& res, char op, const Mat& a, const Mat& b, double scale=1); static void makeExpr(MatExpr& res, char op, const Mat& a, const Scalar& s); }; static MatOp_Bin g_MatOp_Bin; - + class MatOp_Cmp : public MatOp { public: MatOp_Cmp() {} virtual ~MatOp_Cmp() {} - + bool elementWise(const MatExpr& /*expr*/) const { return true; } void assign(const MatExpr& expr, Mat& m, int type=-1) const; - + static void makeExpr(MatExpr& res, int cmpop, const Mat& a, const Mat& b); static void makeExpr(MatExpr& res, int cmpop, const Mat& a, double alpha); }; - + static MatOp_Cmp g_MatOp_Cmp; - + class MatOp_GEMM : public MatOp { public: MatOp_GEMM() {} virtual ~MatOp_GEMM() {} - + bool elementWise(const MatExpr& /*expr*/) const { return false; } void assign(const MatExpr& expr, Mat& m, int type=-1) const; - + void add(const MatExpr& e1, const MatExpr& e2, MatExpr& res) const; void subtract(const MatExpr& e1, const MatExpr& e2, MatExpr& res) const; void multiply(const MatExpr& e, double s, MatExpr& res) const; - + void transpose(const MatExpr& expr, MatExpr& res) const; - + static void makeExpr(MatExpr& res, int flags, const Mat& a, const Mat& b, double alpha=1, const Mat& c=Mat(), double beta=1); }; @@ -146,14 +146,14 @@ class MatOp_Invert : public MatOp public: MatOp_Invert() {} virtual ~MatOp_Invert() {} - + bool elementWise(const MatExpr& /*expr*/) const { return false; } void assign(const MatExpr& expr, Mat& m, int type=-1) const; - + void matmul(const MatExpr& expr1, const MatExpr& expr2, MatExpr& res) const; - + static void makeExpr(MatExpr& res, int method, const Mat& m); -}; +}; static MatOp_Invert g_MatOp_Invert; @@ -162,13 +162,13 @@ class MatOp_T : public MatOp public: MatOp_T() {} virtual ~MatOp_T() {} - + bool elementWise(const MatExpr& /*expr*/) const { return false; } void assign(const MatExpr& expr, Mat& m, int type=-1) const; - + void multiply(const MatExpr& e1, double s, MatExpr& res) const; void transpose(const MatExpr& expr, MatExpr& res) const; - + static void makeExpr(MatExpr& res, const Mat& a, double alpha=1); }; @@ -179,10 +179,10 @@ class MatOp_Solve : public MatOp public: MatOp_Solve() {} virtual ~MatOp_Solve() {} - + bool elementWise(const MatExpr& /*expr*/) const { return false; } void assign(const MatExpr& expr, Mat& m, int type=-1) const; - + static void makeExpr(MatExpr& res, int method, const Mat& a, const Mat& b); }; @@ -193,17 +193,17 @@ class MatOp_Initializer : public MatOp public: MatOp_Initializer() {} virtual ~MatOp_Initializer() {} - + bool elementWise(const MatExpr& /*expr*/) const { return false; } void assign(const MatExpr& expr, Mat& m, int type=-1) const; - + void multiply(const MatExpr& e, double s, MatExpr& res) const; - + static void makeExpr(MatExpr& res, int method, Size sz, int type, double alpha=1); }; static MatOp_Initializer g_MatOp_Initializer; - + static inline bool isIdentity(const MatExpr& e) { return e.op == &g_MatOp_Identity; } static inline bool isAddEx(const MatExpr& e) { return e.op == &g_MatOp_AddEx; } static inline bool isScaled(const MatExpr& e) { return isAddEx(e) && (!e.b.data || e.beta == 0) && e.s == Scalar(); } @@ -216,14 +216,14 @@ static inline bool isSolve(const MatExpr& e) { return e.op == &g_MatOp_Solve; } static inline bool isGEMM(const MatExpr& e) { return e.op == &g_MatOp_GEMM; } static inline bool isMatProd(const MatExpr& e) { return e.op == &g_MatOp_GEMM && (!e.c.data || e.beta == 0); } static inline bool isInitializer(const MatExpr& e) { return e.op == &g_MatOp_Initializer; } - + ///////////////////////////////////////////////////////////////////////////////////////////////////// - + bool MatOp::elementWise(const MatExpr& /*expr*/) const { return false; } - + void MatOp::roi(const MatExpr& expr, const Range& rowRange, const Range& colRange, MatExpr& e) const { if( elementWise(expr) ) @@ -244,7 +244,7 @@ void MatOp::roi(const MatExpr& expr, const Range& rowRange, const Range& colRang e = MatExpr(&g_MatOp_Identity, 0, m(rowRange, colRange), Mat(), Mat()); } } - + void MatOp::diag(const MatExpr& expr, int d, MatExpr& e) const { if( elementWise(expr) ) @@ -266,7 +266,7 @@ void MatOp::diag(const MatExpr& expr, int d, MatExpr& e) const } } - + void MatOp::augAssignAdd(const MatExpr& expr, Mat& m) const { Mat temp; @@ -274,7 +274,7 @@ void MatOp::augAssignAdd(const MatExpr& expr, Mat& m) const m += temp; } - + void MatOp::augAssignSubtract(const MatExpr& expr, Mat& m) const { Mat temp; @@ -282,7 +282,7 @@ void MatOp::augAssignSubtract(const MatExpr& expr, Mat& m) const m -= temp; } - + void MatOp::augAssignMultiply(const MatExpr& expr, Mat& m) const { Mat temp; @@ -290,15 +290,15 @@ void MatOp::augAssignMultiply(const MatExpr& expr, Mat& m) const m *= temp; } - + void MatOp::augAssignDivide(const MatExpr& expr, Mat& m) const { Mat temp; expr.op->assign(expr, temp); m /= temp; } - - + + void MatOp::augAssignAnd(const MatExpr& expr, Mat& m) const { Mat temp; @@ -306,7 +306,7 @@ void MatOp::augAssignAnd(const MatExpr& expr, Mat& m) const m &= temp; } - + void MatOp::augAssignOr(const MatExpr& expr, Mat& m) const { Mat temp; @@ -314,14 +314,14 @@ void MatOp::augAssignOr(const MatExpr& expr, Mat& m) const m |= temp; } - + void MatOp::augAssignXor(const MatExpr& expr, Mat& m) const { Mat temp; expr.op->assign(expr, temp); m /= temp; } - + void MatOp::add(const MatExpr& e1, const MatExpr& e2, MatExpr& res) const { @@ -338,13 +338,13 @@ void MatOp::add(const MatExpr& e1, const MatExpr& e2, MatExpr& res) const } else e1.op->assign(e1, m1); - + if( isAddEx(e2) && (!e2.b.data || e2.beta == 0) ) { m2 = e2.a; beta = e2.alpha; s += e2.s; - } + } else e2.op->assign(e2, m2); MatOp_AddEx::makeExpr(res, m1, m2, alpha, beta, s); @@ -353,7 +353,7 @@ void MatOp::add(const MatExpr& e1, const MatExpr& e2, MatExpr& res) const e2.op->add(e1, e2, res); } - + void MatOp::add(const MatExpr& expr1, const Scalar& s, MatExpr& res) const { Mat m1; @@ -361,7 +361,7 @@ void MatOp::add(const MatExpr& expr1, const Scalar& s, MatExpr& res) const MatOp_AddEx::makeExpr(res, m1, Mat(), 1, 0, s); } - + void MatOp::subtract(const MatExpr& e1, const MatExpr& e2, MatExpr& res) const { if( this == e2.op ) @@ -377,13 +377,13 @@ void MatOp::subtract(const MatExpr& e1, const MatExpr& e2, MatExpr& res) const } else e1.op->assign(e1, m1); - + if( isAddEx(e2) && (!e2.b.data || e2.beta == 0) ) { m2 = e2.a; beta = -e2.alpha; s -= e2.s; - } + } else e2.op->assign(e2, m2); MatOp_AddEx::makeExpr(res, m1, m2, alpha, beta, s); @@ -392,7 +392,7 @@ void MatOp::subtract(const MatExpr& e1, const MatExpr& e2, MatExpr& res) const e2.op->subtract(e1, e2, res); } - + void MatOp::subtract(const Scalar& s, const MatExpr& expr, MatExpr& res) const { Mat m; @@ -400,13 +400,13 @@ void MatOp::subtract(const Scalar& s, const MatExpr& expr, MatExpr& res) const MatOp_AddEx::makeExpr(res, m, Mat(), -1, 0, s); } - + void MatOp::multiply(const MatExpr& e1, const MatExpr& e2, MatExpr& res, double scale) const { if( this == e2.op ) { Mat m1, m2; - + if( isReciprocal(e1) ) { if( isScaled(e2) ) @@ -429,7 +429,7 @@ void MatOp::multiply(const MatExpr& e1, const MatExpr& e2, MatExpr& res, double } else e1.op->assign(e1, m1); - + if( isScaled(e2) ) { m2 = e2.a; @@ -443,23 +443,23 @@ void MatOp::multiply(const MatExpr& e1, const MatExpr& e2, MatExpr& res, double } else e2.op->assign(e2, m2); - + MatOp_Bin::makeExpr(res, op, m1, m2, scale); } } else e2.op->multiply(e1, e2, res, scale); } - - + + void MatOp::multiply(const MatExpr& expr, double s, MatExpr& res) const { Mat m; expr.op->assign(expr, m); - MatOp_AddEx::makeExpr(res, m, Mat(), s, 0); + MatOp_AddEx::makeExpr(res, m, Mat(), s, 0); } - - + + void MatOp::divide(const MatExpr& e1, const MatExpr& e2, MatExpr& res, double scale) const { if( this == e2.op ) @@ -470,7 +470,7 @@ void MatOp::divide(const MatExpr& e1, const MatExpr& e2, MatExpr& res, double sc { Mat m1, m2; char op = '/'; - + if( isScaled(e1) ) { m1 = e1.a; @@ -478,7 +478,7 @@ void MatOp::divide(const MatExpr& e1, const MatExpr& e2, MatExpr& res, double sc } else e1.op->assign(e1, m1); - + if( isScaled(e2) ) { m2 = e2.a; @@ -499,7 +499,7 @@ void MatOp::divide(const MatExpr& e1, const MatExpr& e2, MatExpr& res, double sc e2.op->divide(e1, e2, res, scale); } - + void MatOp::divide(double s, const MatExpr& expr, MatExpr& res) const { Mat m; @@ -507,7 +507,7 @@ void MatOp::divide(double s, const MatExpr& expr, MatExpr& res) const MatOp_Bin::makeExpr(res, '/', m, Mat(), s); } - + void MatOp::abs(const MatExpr& expr, MatExpr& res) const { Mat m; @@ -515,7 +515,7 @@ void MatOp::abs(const MatExpr& expr, MatExpr& res) const MatOp_Bin::makeExpr(res, 'a', m, Mat()); } - + void MatOp::transpose(const MatExpr& expr, MatExpr& res) const { Mat m; @@ -523,7 +523,7 @@ void MatOp::transpose(const MatExpr& expr, MatExpr& res) const MatOp_T::makeExpr(res, m, 1); } - + void MatOp::matmul(const MatExpr& e1, const MatExpr& e2, MatExpr& res) const { if( this == e2.op ) @@ -531,7 +531,7 @@ void MatOp::matmul(const MatExpr& e1, const MatExpr& e2, MatExpr& res) const double scale = 1; int flags = 0; Mat m1, m2; - + if( isT(e1) ) { flags = CV_GEMM_A_T; @@ -545,7 +545,7 @@ void MatOp::matmul(const MatExpr& e1, const MatExpr& e2, MatExpr& res) const } else e1.op->assign(e1, m1); - + if( isT(e2) ) { flags |= CV_GEMM_B_T; @@ -559,22 +559,22 @@ void MatOp::matmul(const MatExpr& e1, const MatExpr& e2, MatExpr& res) const } else e2.op->assign(e2, m2); - + MatOp_GEMM::makeExpr(res, flags, m1, m2, scale); } else e2.op->matmul(e1, e2, res); } - + void MatOp::invert(const MatExpr& expr, int method, MatExpr& res) const { Mat m; expr.op->assign(expr, m); MatOp_Invert::makeExpr(res, method, m); } - - + + Size MatOp::size(const MatExpr& expr) const { return !expr.a.empty() ? expr.a.size() : expr.b.empty() ? expr.b.size() : expr.c.size(); @@ -583,14 +583,14 @@ Size MatOp::size(const MatExpr& expr) const int MatOp::type(const MatExpr& expr) const { return !expr.a.empty() ? expr.a.type() : expr.b.empty() ? expr.b.type() : expr.c.type(); -} - +} + ////////////////////////////////////////////////////////////////////////////////////////////////// MatExpr::MatExpr(const Mat& m) : op(&g_MatOp_Identity), flags(0), a(m), b(Mat()), c(Mat()), alpha(1), beta(0), s(Scalar()) { } - + MatExpr MatExpr::row(int y) const { MatExpr e; @@ -670,42 +670,42 @@ MatExpr operator + (const Mat& a, const Mat& b) MatOp_AddEx::makeExpr(e, a, b, 1, 1); return e; } - + MatExpr operator + (const Mat& a, const Scalar& s) { MatExpr e; MatOp_AddEx::makeExpr(e, a, Mat(), 1, 0, s); return e; } - + MatExpr operator + (const Scalar& s, const Mat& a) { MatExpr e; MatOp_AddEx::makeExpr(e, a, Mat(), 1, 0, s); return e; -} - +} + MatExpr operator + (const MatExpr& e, const Mat& m) { MatExpr en; e.op->add(e, MatExpr(m), en); return en; } - + MatExpr operator + (const Mat& m, const MatExpr& e) { MatExpr en; e.op->add(e, MatExpr(m), en); return en; -} - +} + MatExpr operator + (const MatExpr& e, const Scalar& s) { MatExpr en; e.op->add(e, s, en); return en; } - + MatExpr operator + (const Scalar& s, const MatExpr& e) { MatExpr en; @@ -726,49 +726,49 @@ MatExpr operator - (const Mat& a, const Mat& b) MatOp_AddEx::makeExpr(e, a, b, 1, -1); return e; } - + MatExpr operator - (const Mat& a, const Scalar& s) { MatExpr e; MatOp_AddEx::makeExpr(e, a, Mat(), 1, 0, -s); return e; } - + MatExpr operator - (const Scalar& s, const Mat& a) { MatExpr e; MatOp_AddEx::makeExpr(e, a, Mat(), -1, 0, s); return e; } - + MatExpr operator - (const MatExpr& e, const Mat& m) { MatExpr en; e.op->subtract(e, MatExpr(m), en); return en; } - + MatExpr operator - (const Mat& m, const MatExpr& e) { MatExpr en; e.op->subtract(MatExpr(m), e, en); return en; } - + MatExpr operator - (const MatExpr& e, const Scalar& s) { MatExpr en; e.op->add(e, -s, en); return en; } - + MatExpr operator - (const Scalar& s, const MatExpr& e) { MatExpr en; e.op->subtract(s, e, en); return en; } - + MatExpr operator - (const MatExpr& e1, const MatExpr& e2) { MatExpr en; @@ -782,7 +782,7 @@ MatExpr operator - (const Mat& m) MatOp_AddEx::makeExpr(e, m, Mat(), -1, 0); return e; } - + MatExpr operator - (const MatExpr& e) { MatExpr en; @@ -852,49 +852,49 @@ MatExpr operator / (const Mat& a, const Mat& b) MatOp_Bin::makeExpr(e, '/', a, b); return e; } - + MatExpr operator / (const Mat& a, double s) { MatExpr e; MatOp_AddEx::makeExpr(e, a, Mat(), 1./s, 0); return e; } - + MatExpr operator / (double s, const Mat& a) { MatExpr e; MatOp_Bin::makeExpr(e, '/', a, Mat(), s); return e; } - + MatExpr operator / (const MatExpr& e, const Mat& m) { MatExpr en; e.op->divide(e, MatExpr(m), en); return en; } - + MatExpr operator / (const Mat& m, const MatExpr& e) { MatExpr en; e.op->divide(MatExpr(m), e, en); return en; } - + MatExpr operator / (const MatExpr& e, double s) { MatExpr en; e.op->multiply(e, 1./s, en); return en; } - + MatExpr operator / (double s, const MatExpr& e) { MatExpr en; e.op->divide(s, e, en); return en; } - + MatExpr operator / (const MatExpr& e1, const MatExpr& e2) { MatExpr en; @@ -908,14 +908,14 @@ MatExpr operator < (const Mat& a, const Mat& b) MatOp_Cmp::makeExpr(e, CV_CMP_LT, a, b); return e; } - + MatExpr operator < (const Mat& a, double s) { MatExpr e; MatOp_Cmp::makeExpr(e, CV_CMP_LT, a, s); return e; } - + MatExpr operator < (double s, const Mat& a) { MatExpr e; @@ -985,7 +985,7 @@ MatExpr operator != (double s, const Mat& a) MatOp_Cmp::makeExpr(e, CV_CMP_NE, a, s); return e; } - + MatExpr operator >= (const Mat& a, const Mat& b) { MatExpr e; @@ -1026,22 +1026,22 @@ MatExpr operator > (double s, const Mat& a) MatExpr e; MatOp_Cmp::makeExpr(e, CV_CMP_LT, a, s); return e; -} - +} + MatExpr min(const Mat& a, const Mat& b) { MatExpr e; MatOp_Bin::makeExpr(e, 'm', a, b); return e; } - + MatExpr min(const Mat& a, double s) { MatExpr e; MatOp_Bin::makeExpr(e, 'm', a, s); return e; } - + MatExpr min(double s, const Mat& a) { MatExpr e; @@ -1055,14 +1055,14 @@ MatExpr max(const Mat& a, const Mat& b) MatOp_Bin::makeExpr(e, 'M', a, b); return e; } - + MatExpr max(const Mat& a, double s) { MatExpr e; MatOp_Bin::makeExpr(e, 'M', a, s); return e; } - + MatExpr max(double s, const Mat& a) { MatExpr e; @@ -1076,35 +1076,35 @@ MatExpr operator & (const Mat& a, const Mat& b) MatOp_Bin::makeExpr(e, '&', a, b); return e; } - + MatExpr operator & (const Mat& a, const Scalar& s) { MatExpr e; MatOp_Bin::makeExpr(e, '&', a, s); return e; } - + MatExpr operator & (const Scalar& s, const Mat& a) { MatExpr e; MatOp_Bin::makeExpr(e, '&', a, s); return e; } - + MatExpr operator | (const Mat& a, const Mat& b) { MatExpr e; MatOp_Bin::makeExpr(e, '|', a, b); return e; } - + MatExpr operator | (const Mat& a, const Scalar& s) { MatExpr e; MatOp_Bin::makeExpr(e, '|', a, s); return e; } - + MatExpr operator | (const Scalar& s, const Mat& a) { MatExpr e; @@ -1154,7 +1154,7 @@ MatExpr abs(const MatExpr& e) return en; } - + Size MatExpr::size() const { if( isT(*this) || isInv(*this) ) @@ -1167,8 +1167,8 @@ Size MatExpr::size() const return a.size(); return op ? op->size(*this) : Size(); } - - + + int MatExpr::type() const { if( isInitializer(*this) ) @@ -1177,31 +1177,31 @@ int MatExpr::type() const return CV_8U; return op ? op->type(*this) : -1; } - - + + ///////////////////////////////////////////////////////////////////////////////////////////////////// - -void MatOp_Identity::assign(const MatExpr& e, Mat& m, int type) const + +void MatOp_Identity::assign(const MatExpr& e, Mat& m, int _type) const { - if( type == -1 || type == e.a.type() ) + if( _type == -1 || _type == e.a.type() ) m = e.a; else { - CV_Assert( CV_MAT_CN(type) == e.a.channels() ); - e.a.convertTo(m, type); + CV_Assert( CV_MAT_CN(_type) == e.a.channels() ); + e.a.convertTo(m, _type); } } inline void MatOp_Identity::makeExpr(MatExpr& res, const Mat& m) { res = MatExpr(&g_MatOp_Identity, 0, m, Mat(), Mat(), 1, 0); -} - +} + ///////////////////////////////////////////////////////////////////////////////////////////////////// -void MatOp_AddEx::assign(const MatExpr& e, Mat& m, int type) const -{ - Mat temp, &dst = type == -1 || e.a.type() == type ? m : temp; +void MatOp_AddEx::assign(const MatExpr& e, Mat& m, int _type) const +{ + Mat temp, &dst = _type == -1 || e.a.type() == _type ? m : temp; if( e.b.data ) { if( e.s == Scalar() || !e.s.isReal() ) @@ -1224,7 +1224,7 @@ void MatOp_AddEx::assign(const MatExpr& e, Mat& m, int type) const } else cv::addWeighted(e.a, e.alpha, e.b, e.beta, 0, dst); - + if( !e.s.isReal() ) cv::add(dst, e.s, dst); } @@ -1233,7 +1233,7 @@ void MatOp_AddEx::assign(const MatExpr& e, Mat& m, int type) const } else if( e.s.isReal() && (dst.data != m.data || fabs(e.alpha) != 1)) { - e.a.convertTo(m, type, e.alpha, e.s[0]); + e.a.convertTo(m, _type, e.alpha, e.s[0]); return; } else if( e.alpha == 1 ) @@ -1245,19 +1245,19 @@ void MatOp_AddEx::assign(const MatExpr& e, Mat& m, int type) const e.a.convertTo(dst, e.a.type(), e.alpha); cv::add(dst, e.s, dst); } - + if( dst.data != m.data ) dst.convertTo(m, m.type()); } - + void MatOp_AddEx::add(const MatExpr& e, const Scalar& s, MatExpr& res) const { res = e; res.s += s; } - + void MatOp_AddEx::subtract(const Scalar& s, const MatExpr& e, MatExpr& res) const { res = e; @@ -1265,7 +1265,7 @@ void MatOp_AddEx::subtract(const Scalar& s, const MatExpr& e, MatExpr& res) cons res.beta = -res.beta; res.s = s - res.s; } - + void MatOp_AddEx::multiply(const MatExpr& e, double s, MatExpr& res) const { res = e; @@ -1273,7 +1273,7 @@ void MatOp_AddEx::multiply(const MatExpr& e, double s, MatExpr& res) const res.beta *= s; res.s *= s; } - + void MatOp_AddEx::divide(double s, const MatExpr& e, MatExpr& res) const { if( isScaled(e) ) @@ -1290,7 +1290,7 @@ void MatOp_AddEx::transpose(const MatExpr& e, MatExpr& res) const else MatOp::transpose(e, res); } - + void MatOp_AddEx::abs(const MatExpr& e, MatExpr& res) const { if( (!e.b.data || e.beta == 0) && fabs(e.alpha) == 1 ) @@ -1300,18 +1300,18 @@ void MatOp_AddEx::abs(const MatExpr& e, MatExpr& res) const else MatOp::abs(e, res); } - + inline void MatOp_AddEx::makeExpr(MatExpr& res, const Mat& a, const Mat& b, double alpha, double beta, const Scalar& s) { res = MatExpr(&g_MatOp_AddEx, 0, a, b, Mat(), alpha, beta, s); } - + ////////////////////////////////////////////////////////////////////////////////////////////////////////////////// - -void MatOp_Bin::assign(const MatExpr& e, Mat& m, int type) const + +void MatOp_Bin::assign(const MatExpr& e, Mat& m, int _type) const { - Mat temp, &dst = type == -1 || e.a.type() == type ? m : temp; - + Mat temp, &dst = _type == -1 || e.a.type() == _type ? m : temp; + if( e.flags == '*' ) cv::multiply(e.a, e.b, dst, e.alpha); else if( e.flags == '/' && e.b.data ) @@ -1346,9 +1346,9 @@ void MatOp_Bin::assign(const MatExpr& e, Mat& m, int type) const cv::absdiff(e.a, e.s, dst); else CV_Error(CV_StsError, "Unknown operation"); - + if( dst.data != m.data ) - dst.convertTo(m, type); + dst.convertTo(m, _type); } void MatOp_Bin::multiply(const MatExpr& e, double s, MatExpr& res) const @@ -1379,42 +1379,42 @@ inline void MatOp_Bin::makeExpr(MatExpr& res, char op, const Mat& a, const Scala { res = MatExpr(&g_MatOp_Bin, op, a, Mat(), Mat(), 1, 0, s); } - + /////////////////////////////////////////////////////////////////////////////////////////////////////// - -void MatOp_Cmp::assign(const MatExpr& e, Mat& m, int type) const + +void MatOp_Cmp::assign(const MatExpr& e, Mat& m, int _type) const { - Mat temp, &dst = type == -1 || type == CV_8U ? m : temp; - + Mat temp, &dst = _type == -1 || _type == CV_8U ? m : temp; + if( e.b.data ) cv::compare(e.a, e.b, dst, e.flags); else cv::compare(e.a, e.alpha, dst, e.flags); - + if( dst.data != m.data ) - dst.convertTo(m, type); + dst.convertTo(m, _type); } inline void MatOp_Cmp::makeExpr(MatExpr& res, int cmpop, const Mat& a, const Mat& b) { res = MatExpr(&g_MatOp_Cmp, cmpop, a, b, Mat(), 1, 1); } - + inline void MatOp_Cmp::makeExpr(MatExpr& res, int cmpop, const Mat& a, double alpha) { res = MatExpr(&g_MatOp_Cmp, cmpop, a, Mat(), Mat(), alpha, 1); } ///////////////////////////////////////////////////////////////////////////////////////////////////////// - -void MatOp_T::assign(const MatExpr& e, Mat& m, int type) const + +void MatOp_T::assign(const MatExpr& e, Mat& m, int _type) const { - Mat temp, &dst = type == -1 || type == e.a.type() ? m : temp; - + Mat temp, &dst = _type == -1 || _type == e.a.type() ? m : temp; + cv::transpose(e.a, dst); - + if( dst.data != m.data || e.alpha != 1 ) - dst.convertTo(m, type, e.alpha); + dst.convertTo(m, _type, e.alpha); } void MatOp_T::multiply(const MatExpr& e, double s, MatExpr& res) const @@ -1422,7 +1422,7 @@ void MatOp_T::multiply(const MatExpr& e, double s, MatExpr& res) const res = e; res.alpha *= s; } - + void MatOp_T::transpose(const MatExpr& e, MatExpr& res) const { if( e.alpha == 1 ) @@ -1430,28 +1430,28 @@ void MatOp_T::transpose(const MatExpr& e, MatExpr& res) const else MatOp_AddEx::makeExpr(res, e.a, Mat(), e.alpha, 0); } - + inline void MatOp_T::makeExpr(MatExpr& res, const Mat& a, double alpha) { res = MatExpr(&g_MatOp_T, 0, a, Mat(), Mat(), alpha, 0); } ///////////////////////////////////////////////////////////////////////////////////////////////////////// - -void MatOp_GEMM::assign(const MatExpr& e, Mat& m, int type) const + +void MatOp_GEMM::assign(const MatExpr& e, Mat& m, int _type) const { - Mat temp, &dst = type == -1 || type == e.a.type() ? m : temp; - + Mat temp, &dst = _type == -1 || _type == e.a.type() ? m : temp; + cv::gemm(e.a, e.b, e.alpha, e.c, e.beta, dst, e.flags); if( dst.data != m.data ) - dst.convertTo(m, type); + dst.convertTo(m, _type); } void MatOp_GEMM::add(const MatExpr& e1, const MatExpr& e2, MatExpr& res) const { bool i1 = isIdentity(e1), i2 = isIdentity(e2); double alpha1 = i1 ? 1 : e1.alpha, alpha2 = i2 ? 1 : e2.alpha; - + if( isMatProd(e1) && (i2 || isScaled(e2) || isT(e2)) ) MatOp_GEMM::makeExpr(res, (e1.flags & ~CV_GEMM_C_T)|(isT(e2) ? CV_GEMM_C_T : 0), e1.a, e1.b, alpha1, e2.a, alpha2); @@ -1463,12 +1463,12 @@ void MatOp_GEMM::add(const MatExpr& e1, const MatExpr& e2, MatExpr& res) const else e2.op->add(e1, e2, res); } - + void MatOp_GEMM::subtract(const MatExpr& e1, const MatExpr& e2, MatExpr& res) const { bool i1 = isIdentity(e1), i2 = isIdentity(e2); double alpha1 = i1 ? 1 : e1.alpha, alpha2 = i2 ? 1 : e2.alpha; - + if( isMatProd(e1) && (i2 || isScaled(e2) || isT(e2)) ) MatOp_GEMM::makeExpr(res, (e1.flags & ~CV_GEMM_C_T)|(isT(e2) ? CV_GEMM_C_T : 0), e1.a, e1.b, alpha1, e2.a, -alpha2); @@ -1487,7 +1487,7 @@ void MatOp_GEMM::multiply(const MatExpr& e, double s, MatExpr& res) const res.alpha *= s; res.beta *= s; } - + void MatOp_GEMM::transpose(const MatExpr& e, MatExpr& res) const { res = e; @@ -1502,14 +1502,14 @@ inline void MatOp_GEMM::makeExpr(MatExpr& res, int flags, const Mat& a, const Ma } /////////////////////////////////////////////////////////////////////////////////////////////////////// - -void MatOp_Invert::assign(const MatExpr& e, Mat& m, int type) const + +void MatOp_Invert::assign(const MatExpr& e, Mat& m, int _type) const { - Mat temp, &dst = type == -1 || type == e.a.type() ? m : temp; - + Mat temp, &dst = _type == -1 || _type == e.a.type() ? m : temp; + cv::invert(e.a, dst, e.flags); if( dst.data != m.data ) - dst.convertTo(m, type); + dst.convertTo(m, _type); } void MatOp_Invert::matmul(const MatExpr& e1, const MatExpr& e2, MatExpr& res) const @@ -1528,14 +1528,14 @@ inline void MatOp_Invert::makeExpr(MatExpr& res, int method, const Mat& m) } ///////////////////////////////////////////////////////////////////////////////////////////////////////// - -void MatOp_Solve::assign(const MatExpr& e, Mat& m, int type) const + +void MatOp_Solve::assign(const MatExpr& e, Mat& m, int _type) const { - Mat temp, &dst = type == -1 || type == e.a.type() ? m : temp; - + Mat temp, &dst = _type == -1 || _type == e.a.type() ? m : temp; + cv::solve(e.a, e.b, dst, e.flags); if( dst.data != m.data ) - dst.convertTo(m, type); + dst.convertTo(m, _type); } inline void MatOp_Solve::makeExpr(MatExpr& res, int method, const Mat& a, const Mat& b) @@ -1544,12 +1544,12 @@ inline void MatOp_Solve::makeExpr(MatExpr& res, int method, const Mat& a, const } ////////////////////////////////////////////////////////////////////////////////////////////////////////// - -void MatOp_Initializer::assign(const MatExpr& e, Mat& m, int type) const + +void MatOp_Initializer::assign(const MatExpr& e, Mat& m, int _type) const { - if( type == -1 ) - type = e.a.type(); - m.create(e.a.size(), type); + if( _type == -1 ) + _type = e.a.type(); + m.create(e.a.size(), _type); if( e.flags == 'I' ) setIdentity(m, Scalar(e.alpha)); else if( e.flags == '0' ) @@ -1565,13 +1565,13 @@ void MatOp_Initializer::multiply(const MatExpr& e, double s, MatExpr& res) const res = e; res.alpha *= s; } - + inline void MatOp_Initializer::makeExpr(MatExpr& res, int method, Size sz, int type, double alpha) { res = MatExpr(&g_MatOp_Initializer, method, Mat(sz, type, (void*)0), Mat(), Mat(), alpha, 0); -} +} + - /////////////////////////////////////////////////////////////////////////////////////////////////////////// MatExpr Mat::t() const @@ -1580,14 +1580,14 @@ MatExpr Mat::t() const MatOp_T::makeExpr(e, *this); return e; } - + MatExpr Mat::inv(int method) const { MatExpr e; MatOp_Invert::makeExpr(e, method, *this); return e; } - + MatExpr Mat::mul(InputArray m, double scale) const { @@ -1608,21 +1608,21 @@ MatExpr Mat::zeros(int rows, int cols, int type) MatOp_Initializer::makeExpr(e, '0', Size(cols, rows), type); return e; } - + MatExpr Mat::zeros(Size size, int type) { MatExpr e; MatOp_Initializer::makeExpr(e, '0', size, type); return e; } - + MatExpr Mat::ones(int rows, int cols, int type) { MatExpr e; MatOp_Initializer::makeExpr(e, '1', Size(cols, rows), type); return e; } - + MatExpr Mat::ones(Size size, int type) { MatExpr e; @@ -1643,7 +1643,7 @@ MatExpr Mat::eye(Size size, int type) MatOp_Initializer::makeExpr(e, 'I', size, type); return e; } - + } /* End of file. */ diff --git a/modules/core/src/matrix.cpp b/modules/core/src/matrix.cpp index 5778ee3..0fba2cb 100644 --- a/modules/core/src/matrix.cpp +++ b/modules/core/src/matrix.cpp @@ -210,9 +210,9 @@ void Mat::create(int d, const int* _sizes, int _type) #endif if( !allocator ) { - size_t total = alignSize(step.p[0]*size.p[0], (int)sizeof(*refcount)); - data = datastart = (uchar*)fastMalloc(total + (int)sizeof(*refcount)); - refcount = (int*)(data + total); + size_t totalsize = alignSize(step.p[0]*size.p[0], (int)sizeof(*refcount)); + data = datastart = (uchar*)fastMalloc(totalsize + (int)sizeof(*refcount)); + refcount = (int*)(data + totalsize); *refcount = 1; } else @@ -262,15 +262,15 @@ void Mat::deallocate() } -Mat::Mat(const Mat& m, const Range& rowRange, const Range& colRange) : size(&rows) +Mat::Mat(const Mat& m, const Range& _rowRange, const Range& _colRange) : size(&rows) { initEmpty(); CV_Assert( m.dims >= 2 ); if( m.dims > 2 ) { AutoBuffer rs(m.dims); - rs[0] = rowRange; - rs[1] = colRange; + rs[0] = _rowRange; + rs[1] = _colRange; for( int i = 2; i < m.dims; i++ ) rs[i] = Range::all(); *this = m(rs); @@ -278,19 +278,19 @@ Mat::Mat(const Mat& m, const Range& rowRange, const Range& colRange) : size(&row } *this = m; - if( rowRange != Range::all() && rowRange != Range(0,rows) ) + if( _rowRange != Range::all() && _rowRange != Range(0,rows) ) { - CV_Assert( 0 <= rowRange.start && rowRange.start <= rowRange.end && rowRange.end <= m.rows ); - rows = rowRange.size(); - data += step*rowRange.start; + CV_Assert( 0 <= _rowRange.start && _rowRange.start <= _rowRange.end && _rowRange.end <= m.rows ); + rows = _rowRange.size(); + data += step*_rowRange.start; flags |= SUBMATRIX_FLAG; } - if( colRange != Range::all() && colRange != Range(0,cols) ) + if( _colRange != Range::all() && _colRange != Range(0,cols) ) { - CV_Assert( 0 <= colRange.start && colRange.start <= colRange.end && colRange.end <= m.cols ); - cols = colRange.size(); - data += colRange.start*elemSize(); + CV_Assert( 0 <= _colRange.start && _colRange.start <= _colRange.end && _colRange.end <= m.cols ); + cols = _colRange.size(); + data += _colRange.start*elemSize(); flags &= cols < m.cols ? ~CONTINUOUS_FLAG : -1; flags |= SUBMATRIX_FLAG; } @@ -473,14 +473,14 @@ Mat::Mat(const IplImage* img, bool copyData) : size(&rows) dims = 2; CV_DbgAssert(CV_IS_IMAGE(img) && img->imageData != 0); - int depth = IPL2CV_DEPTH(img->depth); + int imgdepth = IPL2CV_DEPTH(img->depth); size_t esz; step[0] = img->widthStep; if(!img->roi) { CV_Assert(img->dataOrder == IPL_DATA_ORDER_PIXEL); - flags = MAGIC_VAL + CV_MAKETYPE(depth, img->nChannels); + flags = MAGIC_VAL + CV_MAKETYPE(imgdepth, img->nChannels); rows = img->height; cols = img->width; datastart = data = (uchar*)img->imageData; esz = CV_ELEM_SIZE(flags); @@ -489,7 +489,7 @@ Mat::Mat(const IplImage* img, bool copyData) : size(&rows) { CV_Assert(img->dataOrder == IPL_DATA_ORDER_PIXEL || img->roi->coi != 0); bool selectedPlane = img->roi->coi && img->dataOrder == IPL_DATA_ORDER_PLANE; - flags = MAGIC_VAL + CV_MAKETYPE(depth, selectedPlane ? 1 : img->nChannels); + flags = MAGIC_VAL + CV_MAKETYPE(imgdepth, selectedPlane ? 1 : img->nChannels); rows = img->roi->height; cols = img->roi->width; esz = CV_ELEM_SIZE(flags); data = datastart = (uchar*)img->imageData + @@ -1299,38 +1299,38 @@ bool _OutputArray::fixedType() const return (flags & FIXED_TYPE) == FIXED_TYPE; } -void _OutputArray::create(Size _sz, int type, int i, bool allowTransposed, int fixedDepthMask) const +void _OutputArray::create(Size _sz, int mtype, int i, bool allowTransposed, int fixedDepthMask) const { int k = kind(); if( k == MAT && i < 0 && !allowTransposed && fixedDepthMask == 0 ) { CV_Assert(!fixedSize() || ((Mat*)obj)->size.operator()() == _sz); - CV_Assert(!fixedType() || ((Mat*)obj)->type() == type); - ((Mat*)obj)->create(_sz, type); + CV_Assert(!fixedType() || ((Mat*)obj)->type() == mtype); + ((Mat*)obj)->create(_sz, mtype); return; } - int sz[] = {_sz.height, _sz.width}; - create(2, sz, type, i, allowTransposed, fixedDepthMask); + int sizes[] = {_sz.height, _sz.width}; + create(2, sizes, mtype, i, allowTransposed, fixedDepthMask); } -void _OutputArray::create(int rows, int cols, int type, int i, bool allowTransposed, int fixedDepthMask) const +void _OutputArray::create(int rows, int cols, int mtype, int i, bool allowTransposed, int fixedDepthMask) const { int k = kind(); if( k == MAT && i < 0 && !allowTransposed && fixedDepthMask == 0 ) { CV_Assert(!fixedSize() || ((Mat*)obj)->size.operator()() == Size(cols, rows)); - CV_Assert(!fixedType() || ((Mat*)obj)->type() == type); - ((Mat*)obj)->create(rows, cols, type); + CV_Assert(!fixedType() || ((Mat*)obj)->type() == mtype); + ((Mat*)obj)->create(rows, cols, mtype); return; } - int sz[] = {rows, cols}; - create(2, sz, type, i, allowTransposed, fixedDepthMask); + int sizes[] = {rows, cols}; + create(2, sizes, mtype, i, allowTransposed, fixedDepthMask); } -void _OutputArray::create(int dims, const int* size, int type, int i, bool allowTransposed, int fixedDepthMask) const +void _OutputArray::create(int dims, const int* sizes, int mtype, int i, bool allowTransposed, int fixedDepthMask) const { int k = kind(); - type = CV_MAT_TYPE(type); + mtype = CV_MAT_TYPE(mtype); if( k == MAT ) { @@ -1345,24 +1345,24 @@ void _OutputArray::create(int dims, const int* size, int type, int i, bool allow } if( dims == 2 && m.dims == 2 && m.data && - m.type() == type && m.rows == size[1] && m.cols == size[0] ) + m.type() == mtype && m.rows == sizes[1] && m.cols == sizes[0] ) return; } if(fixedType()) { - if(CV_MAT_CN(type) == m.channels() && ((1 << CV_MAT_TYPE(flags)) & fixedDepthMask) != 0 ) - type = m.type(); + if(CV_MAT_CN(mtype) == m.channels() && ((1 << CV_MAT_TYPE(flags)) & fixedDepthMask) != 0 ) + mtype = m.type(); else - CV_Assert(CV_MAT_TYPE(type) == m.type()); + CV_Assert(CV_MAT_TYPE(mtype) == m.type()); } if(fixedSize()) { CV_Assert(m.dims == dims); for(int j = 0; j < dims; ++j) - CV_Assert(m.size[j] == size[j]); + CV_Assert(m.size[j] == sizes[j]); } - m.create(dims, size, type); + m.create(dims, sizes, mtype); return; } @@ -1370,16 +1370,16 @@ void _OutputArray::create(int dims, const int* size, int type, int i, bool allow { CV_Assert( i < 0 ); int type0 = CV_MAT_TYPE(flags); - CV_Assert( type == type0 || (CV_MAT_CN(type) == 1 && ((1 << type0) & fixedDepthMask) != 0) ); - CV_Assert( dims == 2 && ((size[0] == sz.height && size[1] == sz.width) || - (allowTransposed && size[0] == sz.width && size[1] == sz.height))); + CV_Assert( mtype == type0 || (CV_MAT_CN(mtype) == 1 && ((1 << type0) & fixedDepthMask) != 0) ); + CV_Assert( dims == 2 && ((sizes[0] == sz.height && sizes[1] == sz.width) || + (allowTransposed && sizes[0] == sz.width && sizes[1] == sz.height))); return; } if( k == STD_VECTOR || k == STD_VECTOR_VECTOR ) { - CV_Assert( dims == 2 && (size[0] == 1 || size[1] == 1 || size[0]*size[1] == 0) ); - size_t len = size[0]*size[1] > 0 ? size[0] + size[1] - 1 : 0; + CV_Assert( dims == 2 && (sizes[0] == 1 || sizes[1] == 1 || sizes[0]*sizes[1] == 0) ); + size_t len = sizes[0]*sizes[1] > 0 ? sizes[0] + sizes[1] - 1 : 0; vector* v = (vector*)obj; if( k == STD_VECTOR_VECTOR ) @@ -1398,7 +1398,7 @@ void _OutputArray::create(int dims, const int* size, int type, int i, bool allow CV_Assert( i < 0 ); int type0 = CV_MAT_TYPE(flags); - CV_Assert( type == type0 || (CV_MAT_CN(type) == CV_MAT_CN(type0) && ((1 << type0) & fixedDepthMask) != 0) ); + CV_Assert( mtype == type0 || (CV_MAT_CN(mtype) == CV_MAT_CN(type0) && ((1 << type0) & fixedDepthMask) != 0) ); int esz = CV_ELEM_SIZE(type0); CV_Assert(!fixedSize() || len == ((vector*)v)->size() / esz); @@ -1471,20 +1471,20 @@ void _OutputArray::create(int dims, const int* size, int type, int i, bool allow if( i < 0 ) { - CV_Assert( dims == 2 && (size[0] == 1 || size[1] == 1 || size[0]*size[1] == 0) ); - size_t len = size[0]*size[1] > 0 ? size[0] + size[1] - 1 : 0, len0 = v.size(); + CV_Assert( dims == 2 && (sizes[0] == 1 || sizes[1] == 1 || sizes[0]*sizes[1] == 0) ); + size_t len = sizes[0]*sizes[1] > 0 ? sizes[0] + sizes[1] - 1 : 0, len0 = v.size(); CV_Assert(!fixedSize() || len == len0); v.resize(len); if( fixedType() ) { - int type = CV_MAT_TYPE(flags); + int _type = CV_MAT_TYPE(flags); for( size_t j = len0; j < len; j++ ) { - if( v[i].type() == type ) + if( v[i].type() == _type ) continue; CV_Assert( v[i].empty() ); - v[i].flags = (v[i].flags & ~CV_MAT_TYPE_MASK) | type; + v[i].flags = (v[i].flags & ~CV_MAT_TYPE_MASK) | _type; } } return; @@ -1502,25 +1502,25 @@ void _OutputArray::create(int dims, const int* size, int type, int i, bool allow } if( dims == 2 && m.dims == 2 && m.data && - m.type() == type && m.rows == size[1] && m.cols == size[0] ) + m.type() == mtype && m.rows == sizes[1] && m.cols == sizes[0] ) return; } if(fixedType()) { - if(CV_MAT_CN(type) == m.channels() && ((1 << CV_MAT_TYPE(flags)) & fixedDepthMask) != 0 ) - type = m.type(); + if(CV_MAT_CN(mtype) == m.channels() && ((1 << CV_MAT_TYPE(flags)) & fixedDepthMask) != 0 ) + mtype = m.type(); else - CV_Assert(!fixedType() || (CV_MAT_CN(type) == m.channels() && ((1 << CV_MAT_TYPE(flags)) & fixedDepthMask) != 0)); + CV_Assert(!fixedType() || (CV_MAT_CN(mtype) == m.channels() && ((1 << CV_MAT_TYPE(flags)) & fixedDepthMask) != 0)); } if(fixedSize()) { CV_Assert(m.dims == dims); for(int j = 0; j < dims; ++j) - CV_Assert(m.size[j] == size[j]); + CV_Assert(m.size[j] == sizes[j]); } - m.create(dims, size, type); + m.create(dims, sizes, mtype); } } @@ -1929,10 +1929,10 @@ void cv::completeSymm( InputOutputArray _m, bool LtoR ) cv::Mat cv::Mat::cross(InputArray _m) const { Mat m = _m.getMat(); - int t = type(), d = CV_MAT_DEPTH(t); - CV_Assert( dims <= 2 && m.dims <= 2 && size() == m.size() && t == m.type() && + int tp = type(), d = CV_MAT_DEPTH(tp); + CV_Assert( dims <= 2 && m.dims <= 2 && size() == m.size() && tp == m.type() && ((rows == 3 && cols == 1) || (cols*channels() == 3 && rows == 1))); - Mat result(rows, cols, t); + Mat result(rows, cols, tp); if( d == CV_32F ) { @@ -2845,7 +2845,7 @@ cvRange( CvArr* arr, double start, double end ) CV_IMPL void cvSort( const CvArr* _src, CvArr* _dst, CvArr* _idx, int flags ) { - cv::Mat src = cv::cvarrToMat(_src), dst, idx; + cv::Mat src = cv::cvarrToMat(_src); if( _idx ) { @@ -3410,22 +3410,22 @@ SparseMat::SparseMat(const Mat& m) int i, idx[CV_MAX_DIM] = {0}, d = m.dims, lastSize = m.size[d - 1]; size_t esz = m.elemSize(); - uchar* ptr = m.data; + uchar* dptr = m.data; for(;;) { - for( i = 0; i < lastSize; i++, ptr += esz ) + for( i = 0; i < lastSize; i++, dptr += esz ) { - if( isZeroElem(ptr, esz) ) + if( isZeroElem(dptr, esz) ) continue; idx[d-1] = i; uchar* to = newNode(idx, hash(idx)); - copyElem( ptr, to, esz ); + copyElem( dptr, to, esz ); } for( i = d - 2; i >= 0; i-- ) { - ptr += m.step[i] - m.size[i+1]*m.step[i+1]; + dptr += m.step[i] - m.size[i+1]*m.step[i+1]; if( ++idx[i] < m.size[i] ) break; idx[i] = 0; diff --git a/modules/core/src/opengl_interop.cpp b/modules/core/src/opengl_interop.cpp index 2de7874..bf9deaa 100644 --- a/modules/core/src/opengl_interop.cpp +++ b/modules/core/src/opengl_interop.cpp @@ -163,11 +163,11 @@ void icvSetOpenGlFuncTab(const CvOpenGlFuncTab* tab) void cv::gpu::setGlDevice(int device) { #ifndef HAVE_CUDA - (void)device; + (void)device; throw_nocuda; #else #ifndef HAVE_OPENGL - (void)device; + (void)device; throw_nogl; #else if (!glFuncTab()->isGlContextInitialized()) @@ -287,7 +287,7 @@ class cv::GlBuffer::Impl { public: static const Ptr& empty(); - + Impl(int rows, int cols, int type, unsigned int target); Impl(const Mat& m, unsigned int target); ~Impl(); @@ -311,7 +311,7 @@ public: private: Impl(); - + unsigned int buffer_; #ifdef HAVE_CUDA @@ -484,57 +484,57 @@ inline void cv::GlBuffer::Impl::unmapDevice(cudaStream_t stream) #endif // HAVE_OPENGL -cv::GlBuffer::GlBuffer(Usage usage) : rows_(0), cols_(0), type_(0), usage_(usage) +cv::GlBuffer::GlBuffer(Usage _usage) : rows_(0), cols_(0), type_(0), usage_(_usage) { #ifndef HAVE_OPENGL - (void)usage; + (void)_usage; throw_nogl; #else impl_ = Impl::empty(); #endif } -cv::GlBuffer::GlBuffer(int rows, int cols, int type, Usage usage) : rows_(0), cols_(0), type_(0), usage_(usage) +cv::GlBuffer::GlBuffer(int _rows, int _cols, int _type, Usage _usage) : rows_(0), cols_(0), type_(0), usage_(_usage) { #ifndef HAVE_OPENGL - (void)rows; - (void)cols; - (void)type; - (void)usage; + (void)_rows; + (void)_cols; + (void)_type; + (void)_usage; throw_nogl; #else - impl_ = new Impl(rows, cols, type, usage); - rows_ = rows; - cols_ = cols; - type_ = type; + impl_ = new Impl(_rows, _cols, _type, _usage); + rows_ = _rows; + cols_ = _cols; + type_ = _type; #endif } -cv::GlBuffer::GlBuffer(Size size, int type, Usage usage) : rows_(0), cols_(0), type_(0), usage_(usage) +cv::GlBuffer::GlBuffer(Size _size, int _type, Usage _usage) : rows_(0), cols_(0), type_(0), usage_(_usage) { #ifndef HAVE_OPENGL - (void)size; - (void)type; - (void)usage; + (void)_size; + (void)_type; + (void)_usage; throw_nogl; #else - impl_ = new Impl(size.height, size.width, type, usage); - rows_ = size.height; - cols_ = size.width; - type_ = type; + impl_ = new Impl(_size.height, _size.width, _type, _usage); + rows_ = _size.height; + cols_ = _size.width; + type_ = _type; #endif } -cv::GlBuffer::GlBuffer(InputArray mat_, Usage usage) : rows_(0), cols_(0), type_(0), usage_(usage) +cv::GlBuffer::GlBuffer(InputArray mat_, Usage _usage) : rows_(0), cols_(0), type_(0), usage_(_usage) { #ifndef HAVE_OPENGL - (void)mat_; - (void)usage; + (void)mat_; + (void)_usage; throw_nogl; #else int kind = mat_.kind(); - Size size = mat_.size(); - int type = mat_.type(); + Size _size = mat_.size(); + int _type = mat_.type(); if (kind == _InputArray::GPU_MAT) { @@ -542,38 +542,38 @@ cv::GlBuffer::GlBuffer(InputArray mat_, Usage usage) : rows_(0), cols_(0), type_ throw_nocuda; #else GpuMat d_mat = mat_.getGpuMat(); - impl_ = new Impl(d_mat.rows, d_mat.cols, d_mat.type(), usage); + impl_ = new Impl(d_mat.rows, d_mat.cols, d_mat.type(), _usage); impl_->copyFrom(d_mat); #endif } else { Mat mat = mat_.getMat(); - impl_ = new Impl(mat, usage); + impl_ = new Impl(mat, _usage); } - rows_ = size.height; - cols_ = size.width; - type_ = type; + rows_ = _size.height; + cols_ = _size.width; + type_ = _type; #endif } -void cv::GlBuffer::create(int rows, int cols, int type, Usage usage) +void cv::GlBuffer::create(int _rows, int _cols, int _type, Usage _usage) { #ifndef HAVE_OPENGL - (void)rows; - (void)cols; - (void)type; - (void)usage; + (void)_rows; + (void)_cols; + (void)_type; + (void)_usage; throw_nogl; #else - if (rows_ != rows || cols_ != cols || type_ != type || usage_ != usage) + if (rows_ != _rows || cols_ != _cols || type_ != _type || usage_ != _usage) { - impl_ = new Impl(rows, cols, type, usage); - rows_ = rows; - cols_ = cols; - type_ = type; - usage_ = usage; + impl_ = new Impl(_rows, _cols, _type, _usage); + rows_ = _rows; + cols_ = _cols; + type_ = _type; + usage_ = _usage; } #endif } @@ -590,14 +590,14 @@ void cv::GlBuffer::release() void cv::GlBuffer::copyFrom(InputArray mat_) { #ifndef HAVE_OPENGL - (void)mat_; + (void)mat_; throw_nogl; #else int kind = mat_.kind(); - Size size = mat_.size(); - int type = mat_.type(); + Size _size = mat_.size(); + int _type = mat_.type(); - create(size, type); + create(_size, _type); switch (kind) { @@ -728,7 +728,7 @@ public: private: Impl(); - + GLuint tex_; }; @@ -926,45 +926,45 @@ cv::GlTexture::GlTexture() : rows_(0), cols_(0), type_(0), buf_(GlBuffer::TEXTUR #endif } -cv::GlTexture::GlTexture(int rows, int cols, int type) : rows_(0), cols_(0), type_(0), buf_(GlBuffer::TEXTURE_BUFFER) +cv::GlTexture::GlTexture(int _rows, int _cols, int _type) : rows_(0), cols_(0), type_(0), buf_(GlBuffer::TEXTURE_BUFFER) { #ifndef HAVE_OPENGL - (void)rows; - (void)cols; - (void)type; + (void)_rows; + (void)_cols; + (void)_type; throw_nogl; #else - impl_ = new Impl(rows, cols, type); - rows_ = rows; - cols_ = cols; - type_ = type; + impl_ = new Impl(_rows, _cols, _type); + rows_ = _rows; + cols_ = _cols; + type_ = _type; #endif } -cv::GlTexture::GlTexture(Size size, int type) : rows_(0), cols_(0), type_(0), buf_(GlBuffer::TEXTURE_BUFFER) +cv::GlTexture::GlTexture(Size _size, int _type) : rows_(0), cols_(0), type_(0), buf_(GlBuffer::TEXTURE_BUFFER) { #ifndef HAVE_OPENGL - (void)size; - (void)type; + (void)_size; + (void)_type; throw_nogl; #else - impl_ = new Impl(size.height, size.width, type); - rows_ = size.height; - cols_ = size.width; - type_ = type; + impl_ = new Impl(_size.height, _size.width, _type); + rows_ = _size.height; + cols_ = _size.width; + type_ = _type; #endif } cv::GlTexture::GlTexture(InputArray mat_, bool bgra) : rows_(0), cols_(0), type_(0), buf_(GlBuffer::TEXTURE_BUFFER) { #ifndef HAVE_OPENGL - (void)mat_; - (void)bgra; + (void)mat_; + (void)bgra; throw_nogl; -#else +#else int kind = mat_.kind(); - Size size = mat_.size(); - int type = mat_.type(); + Size _size = mat_.size(); + int _type = mat_.type(); switch (kind) { @@ -994,26 +994,26 @@ cv::GlTexture::GlTexture(InputArray mat_, bool bgra) : rows_(0), cols_(0), type_ } } - rows_ = size.height; - cols_ = size.width; - type_ = type; + rows_ = _size.height; + cols_ = _size.width; + type_ = _type; #endif } -void cv::GlTexture::create(int rows, int cols, int type) +void cv::GlTexture::create(int _rows, int _cols, int _type) { #ifndef HAVE_OPENGL - (void)rows; - (void)cols; - (void)type; + (void)_rows; + (void)_cols; + (void)_type; throw_nogl; #else - if (rows_ != rows || cols_ != cols || type_ != type) + if (rows_ != _rows || cols_ != _cols || type_ != _type) { - impl_ = new Impl(rows, cols, type); - rows_ = rows; - cols_ = cols; - type_ = type; + impl_ = new Impl(_rows, _cols, _type); + rows_ = _rows; + cols_ = _cols; + type_ = _type; } #endif } @@ -1030,15 +1030,15 @@ void cv::GlTexture::release() void cv::GlTexture::copyFrom(InputArray mat_, bool bgra) { #ifndef HAVE_OPENGL - (void)mat_; - (void)bgra; + (void)mat_; + (void)bgra; throw_nogl; #else int kind = mat_.kind(); - Size size = mat_.size(); - int type = mat_.type(); + Size _size = mat_.size(); + int _type = mat_.type(); - create(size, type); + create(_size, _type); switch(kind) { @@ -1244,8 +1244,8 @@ void cv::GlArrays::unbind() const //////////////////////////////////////////////////////////////////////// // GlFont -cv::GlFont::GlFont(const string& family, int height, Weight weight, Style style) - : family_(family), height_(height), weight_(weight), style_(style), base_(0) +cv::GlFont::GlFont(const string& _family, int _height, Weight _weight, Style _style) + : family_(_family), height_(_height), weight_(_weight), style_(_style), base_(0) { #ifndef HAVE_OPENGL throw_nogl; @@ -1253,7 +1253,7 @@ cv::GlFont::GlFont(const string& family, int height, Weight weight, Style style) base_ = glGenLists(256); CV_CheckGlError(); - glFuncTab()->generateBitmapFont(family, height, weight, (style & STYLE_ITALIC) != 0, (style & STYLE_UNDERLINE) != 0, 0, 256, base_); + glFuncTab()->generateBitmapFont(family_, height_, weight_, (style_ & STYLE_ITALIC) != 0, (style_ & STYLE_UNDERLINE) != 0, 0, 256, base_); #endif } @@ -1283,7 +1283,7 @@ namespace class FontCompare : public unary_function, bool> { public: - inline FontCompare(const string& family, int height, GlFont::Weight weight, GlFont::Style style) + inline FontCompare(const string& family, int height, GlFont::Weight weight, GlFont::Style style) : family_(family), height_(height), weight_(weight), style_(style) { } @@ -1304,10 +1304,10 @@ namespace Ptr cv::GlFont::get(const std::string& family, int height, Weight weight, Style style) { #ifndef HAVE_OPENGL - (void)family; - (void)height; - (void)weight; - (void)style; + (void)family; + (void)height; + (void)weight; + (void)style; throw_nogl; return Ptr(); #else @@ -1333,9 +1333,9 @@ Ptr cv::GlFont::get(const std::string& family, int height, Weight weight void cv::render(const GlTexture& tex, Rect_ wndRect, Rect_ texRect) { #ifndef HAVE_OPENGL - (void)tex; - (void)wndRect; - (void)texRect; + (void)tex; + (void)wndRect; + (void)texRect; throw_nogl; #else if (!tex.empty()) @@ -1368,9 +1368,9 @@ void cv::render(const GlTexture& tex, Rect_ wndRect, Rect_ texRe void cv::render(const GlArrays& arr, int mode, Scalar color) { #ifndef HAVE_OPENGL - (void)arr; - (void)mode; - (void)color; + (void)arr; + (void)mode; + (void)color; throw_nogl; #else glColor3d(color[0] / 255.0, color[1] / 255.0, color[2] / 255.0); @@ -1386,10 +1386,10 @@ void cv::render(const GlArrays& arr, int mode, Scalar color) void cv::render(const string& str, const Ptr& font, Scalar color, Point2d pos) { #ifndef HAVE_OPENGL - (void)str; - (void)font; - (void)color; - (void)pos; + (void)str; + (void)font; + (void)color; + (void)pos; throw_nogl; #else glPushAttrib(GL_DEPTH_BUFFER_BIT); @@ -1544,9 +1544,9 @@ void cv::GlCamera::setupModelViewMatrix() const bool icvCheckGlError(const char* file, const int line, const char* func) { #ifndef HAVE_OPENGL - (void)file; - (void)line; - (void)func; + (void)file; + (void)line; + (void)func; return true; #else GLenum err = glGetError(); diff --git a/modules/core/test/test_ds.cpp b/modules/core/test/test_ds.cpp index 91c9727..764653c 100644 --- a/modules/core/test/test_ds.cpp +++ b/modules/core/test/test_ds.cpp @@ -1262,7 +1262,7 @@ int Core_SetTest::test_set_ops( int iters ) if( iter > iters/10 && cvtest::randInt(rng)%200 == 0 ) // clear set { - int prev_count = cvset->total; + prev_count = cvset->total; cvClearSet( cvset ); cvTsClearSimpleSet( sset ); @@ -1482,19 +1482,19 @@ int Core_GraphTest::test_graph_ops( int iters ) if( cvtest::randInt(rng) % 200 == 0 ) // clear graph { - int prev_vtx_count = graph->total, prev_edge_count = graph->edges->total; + int prev_vtx_count2 = graph->total, prev_edge_count2 = graph->edges->total; cvClearGraph( graph ); cvTsClearSimpleGraph( sgraph ); CV_TS_SEQ_CHECK_CONDITION( graph->active_count == 0 && graph->total == 0 && graph->first == 0 && graph->free_elems == 0 && - (graph->free_blocks != 0 || prev_vtx_count == 0), + (graph->free_blocks != 0 || prev_vtx_count2 == 0), "The graph is not empty after clearing" ); CV_TS_SEQ_CHECK_CONDITION( edges->active_count == 0 && edges->total == 0 && edges->first == 0 && edges->free_elems == 0 && - (edges->free_blocks != 0 || prev_edge_count == 0), + (edges->free_blocks != 0 || prev_edge_count2 == 0), "The graph is not empty after clearing" ); } else if( op == 0 ) // add vertex diff --git a/modules/core/test/test_mat.cpp b/modules/core/test/test_mat.cpp index f588ae4..6b5d09f 100644 --- a/modules/core/test/test_mat.cpp +++ b/modules/core/test/test_mat.cpp @@ -22,25 +22,25 @@ void testReduce( const Mat& src, Mat& sum, Mat& avg, Mat& max, Mat& min, int dim assert( src.channels() == 1 ); if( dim == 0 ) // row { - sum.create( 1, src.cols, CV_64FC1 ); + sum.create( 1, src.cols, CV_64FC1 ); max.create( 1, src.cols, CV_64FC1 ); min.create( 1, src.cols, CV_64FC1 ); } else { - sum.create( src.rows, 1, CV_64FC1 ); + sum.create( src.rows, 1, CV_64FC1 ); max.create( src.rows, 1, CV_64FC1 ); min.create( src.rows, 1, CV_64FC1 ); } sum.setTo(Scalar(0)); max.setTo(Scalar(-DBL_MAX)); min.setTo(Scalar(DBL_MAX)); - + const Mat_& src_ = src; Mat_& sum_ = (Mat_&)sum; Mat_& min_ = (Mat_&)min; Mat_& max_ = (Mat_&)max; - + if( dim == 0 ) { for( int ri = 0; ri < src.rows; ri++ ) @@ -128,7 +128,7 @@ int Core_ReduceTest::checkOp( const Mat& src, int dstType, int opType, const Mat else if ( dstType == CV_32S ) eps = 0.6; } - + assert( opRes.type() == CV_64FC1 ); Mat _dst, dst, diff; reduce( src, _dst, dim, opType, dstType ); @@ -151,7 +151,7 @@ int Core_ReduceTest::checkOp( const Mat& src, int dstType, int opType, const Mat getMatTypeStr( src.type(), srcTypeStr ); getMatTypeStr( dstType, dstTypeStr ); const char* dimStr = dim == 0 ? "ROWS" : "COLS"; - + sprintf( msg, "bad accuracy with srcType = %s, dstType = %s, opType = %s, dim = %s", srcTypeStr.c_str(), dstTypeStr.c_str(), opTypeStr, dimStr ); ts->printf( cvtest::TS::LOG, msg ); @@ -164,10 +164,10 @@ int Core_ReduceTest::checkCase( int srcType, int dstType, int dim, Size sz ) { int code = cvtest::TS::OK, tempCode; Mat src, sum, avg, max, min; - + src.create( sz, srcType ); randu( src, Scalar(0), Scalar(100) ); - + if( srcType == CV_8UC1 ) testReduce( src, sum, avg, max, min, dim ); else if( srcType == CV_8SC1 ) @@ -182,110 +182,108 @@ int Core_ReduceTest::checkCase( int srcType, int dstType, int dim, Size sz ) testReduce( src, sum, avg, max, min, dim ); else if( srcType == CV_64FC1 ) testReduce( src, sum, avg, max, min, dim ); - else + else assert( 0 ); - + // 1. sum tempCode = checkOp( src, dstType, CV_REDUCE_SUM, sum, dim ); code = tempCode != cvtest::TS::OK ? tempCode : code; - + // 2. avg tempCode = checkOp( src, dstType, CV_REDUCE_AVG, avg, dim ); code = tempCode != cvtest::TS::OK ? tempCode : code; - + // 3. max tempCode = checkOp( src, dstType, CV_REDUCE_MAX, max, dim ); code = tempCode != cvtest::TS::OK ? tempCode : code; - + // 4. min tempCode = checkOp( src, dstType, CV_REDUCE_MIN, min, dim ); code = tempCode != cvtest::TS::OK ? tempCode : code; - + return code; } int Core_ReduceTest::checkDim( int dim, Size sz ) { int code = cvtest::TS::OK, tempCode; - + // CV_8UC1 tempCode = checkCase( CV_8UC1, CV_8UC1, dim, sz ); code = tempCode != cvtest::TS::OK ? tempCode : code; - + tempCode = checkCase( CV_8UC1, CV_32SC1, dim, sz ); code = tempCode != cvtest::TS::OK ? tempCode : code; - + tempCode = checkCase( CV_8UC1, CV_32FC1, dim, sz ); code = tempCode != cvtest::TS::OK ? tempCode : code; - + tempCode = checkCase( CV_8UC1, CV_64FC1, dim, sz ); code = tempCode != cvtest::TS::OK ? tempCode : code; - + // CV_16UC1 tempCode = checkCase( CV_16UC1, CV_32FC1, dim, sz ); code = tempCode != cvtest::TS::OK ? tempCode : code; - + tempCode = checkCase( CV_16UC1, CV_64FC1, dim, sz ); code = tempCode != cvtest::TS::OK ? tempCode : code; - + // CV_16SC1 tempCode = checkCase( CV_16SC1, CV_32FC1, dim, sz ); code = tempCode != cvtest::TS::OK ? tempCode : code; - + tempCode = checkCase( CV_16SC1, CV_64FC1, dim, sz ); code = tempCode != cvtest::TS::OK ? tempCode : code; - + // CV_32FC1 tempCode = checkCase( CV_32FC1, CV_32FC1, dim, sz ); code = tempCode != cvtest::TS::OK ? tempCode : code; - + tempCode = checkCase( CV_32FC1, CV_64FC1, dim, sz ); code = tempCode != cvtest::TS::OK ? tempCode : code; - + // CV_64FC1 tempCode = checkCase( CV_64FC1, CV_64FC1, dim, sz ); code = tempCode != cvtest::TS::OK ? tempCode : code; - + return code; } int Core_ReduceTest::checkSize( Size sz ) { int code = cvtest::TS::OK, tempCode; - + tempCode = checkDim( 0, sz ); // rows code = tempCode != cvtest::TS::OK ? tempCode : code; - - tempCode = checkDim( 1, sz ); // cols + + tempCode = checkDim( 1, sz ); // cols code = tempCode != cvtest::TS::OK ? tempCode : code; - + return code; } void Core_ReduceTest::run( int ) { int code = cvtest::TS::OK, tempCode; - + tempCode = checkSize( Size(1,1) ); code = tempCode != cvtest::TS::OK ? tempCode : code; - + tempCode = checkSize( Size(1,100) ); code = tempCode != cvtest::TS::OK ? tempCode : code; - + tempCode = checkSize( Size(100,1) ); code = tempCode != cvtest::TS::OK ? tempCode : code; - + tempCode = checkSize( Size(1000,500) ); code = tempCode != cvtest::TS::OK ? tempCode : code; - + ts->set_failed_test_info( code ); } #define CHECK_C -Size sz(200, 500); - class Core_PCATest : public cvtest::BaseTest { public: @@ -293,41 +291,43 @@ public: protected: void run(int) { + const Size sz(200, 500); + double diffPrjEps, diffBackPrjEps, prjEps, backPrjEps, evalEps, evecEps; int maxComponents = 100; Mat rPoints(sz, CV_32FC1), rTestPoints(sz, CV_32FC1); - RNG& rng = ts->get_rng(); - + RNG& rng = ts->get_rng(); + rng.fill( rPoints, RNG::UNIFORM, Scalar::all(0.0), Scalar::all(1.0) ); rng.fill( rTestPoints, RNG::UNIFORM, Scalar::all(0.0), Scalar::all(1.0) ); - + PCA rPCA( rPoints, Mat(), CV_PCA_DATA_AS_ROW, maxComponents ), cPCA; - + // 1. check C++ PCA & ROW Mat rPrjTestPoints = rPCA.project( rTestPoints ); Mat rBackPrjTestPoints = rPCA.backProject( rPrjTestPoints ); - + Mat avg(1, sz.width, CV_32FC1 ); reduce( rPoints, avg, 0, CV_REDUCE_AVG ); Mat Q = rPoints - repeat( avg, rPoints.rows, 1 ), Qt = Q.t(), eval, evec; Q = Qt * Q; Q = Q /(float)rPoints.rows; - + eigen( Q, eval, evec ); /*SVD svd(Q); evec = svd.vt; eval = svd.w;*/ - + Mat subEval( maxComponents, 1, eval.type(), eval.data ), subEvec( maxComponents, evec.cols, evec.type(), evec.data ); - + #ifdef CHECK_C Mat prjTestPoints, backPrjTestPoints, cPoints = rPoints.t(), cTestPoints = rTestPoints.t(); CvMat _points, _testPoints, _avg, _eval, _evec, _prjTestPoints, _backPrjTestPoints; #endif - + // check eigen() double eigenEps = 1e-6; double err; @@ -335,7 +335,7 @@ protected: { Mat v = evec.row(i).t(); Mat Qv = Q * v; - + Mat lv = eval.at(i,0) * v; err = norm( Qv, lv ); if( err > eigenEps ) @@ -370,7 +370,7 @@ protected: absdiff(rPCA.eigenvectors, subEvec, tmp); double mval = 0; Point mloc; minMaxLoc(tmp, 0, &mval, 0, &mloc); - + ts->printf( cvtest::TS::LOG, "pca.eigenvectors is incorrect (CV_PCA_DATA_AS_ROW); err = %f\n", err ); ts->printf( cvtest::TS::LOG, "max diff is %g at (i=%d, j=%d) (%g vs %g)\n", mval, mloc.y, mloc.x, rPCA.eigenvectors.at(mloc.y, mloc.x), @@ -380,7 +380,7 @@ protected: } } } - + prjEps = 1.265, backPrjEps = 1.265; for( int i = 0; i < rTestPoints.rows; i++ ) { @@ -404,7 +404,7 @@ protected: return; } } - + // 2. check C++ PCA & COL cPCA( rPoints.t(), Mat(), CV_PCA_DATA_AS_COL, maxComponents ); diffPrjEps = 1, diffBackPrjEps = 1; @@ -423,7 +423,7 @@ protected: ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY ); return; } - + #ifdef CHECK_C // 3. check C PCA & ROW _points = rPoints; @@ -435,11 +435,11 @@ protected: backPrjTestPoints.create(rPoints.size(), rPoints.type() ); _prjTestPoints = prjTestPoints; _backPrjTestPoints = backPrjTestPoints; - + cvCalcPCA( &_points, &_avg, &_eval, &_evec, CV_PCA_DATA_AS_ROW ); cvProjectPCA( &_testPoints, &_avg, &_evec, &_prjTestPoints ); cvBackProjectPCA( &_prjTestPoints, &_avg, &_evec, &_backPrjTestPoints ); - + err = norm(prjTestPoints, rPrjTestPoints, CV_RELATIVE_L2); if( err > diffPrjEps ) { @@ -454,7 +454,7 @@ protected: ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY ); return; } - + // 3. check C PCA & COL _points = cPoints; _testPoints = cTestPoints; @@ -463,11 +463,11 @@ protected: evec = evec.t(); _evec = evec; prjTestPoints = prjTestPoints.t(); _prjTestPoints = prjTestPoints; backPrjTestPoints = backPrjTestPoints.t(); _backPrjTestPoints = backPrjTestPoints; - + cvCalcPCA( &_points, &_avg, &_eval, &_evec, CV_PCA_DATA_AS_COL ); cvProjectPCA( &_testPoints, &_avg, &_evec, &_prjTestPoints ); cvBackProjectPCA( &_prjTestPoints, &_avg, &_evec, &_backPrjTestPoints ); - + err = norm(cv::abs(prjTestPoints), cv::abs(rPrjTestPoints.t()), CV_RELATIVE_L2 ); if( err > diffPrjEps ) { @@ -490,9 +490,9 @@ class Core_ArrayOpTest : public cvtest::BaseTest { public: Core_ArrayOpTest(); - ~Core_ArrayOpTest(); + ~Core_ArrayOpTest(); protected: - void run(int); + void run(int); }; @@ -536,7 +536,7 @@ static double getValue(SparseMat& M, const int* idx, RNG& rng) d == 3 ? M.hash(idx[0], idx[1], idx[2]) : M.hash(idx); phv = &hv; } - + const uchar* ptr = d == 2 ? M.ptr(idx[0], idx[1], false, phv) : d == 3 ? M.ptr(idx[0], idx[1], idx[2], false, phv) : M.ptr(idx, false, phv); @@ -560,7 +560,7 @@ static void eraseValue(SparseMat& M, const int* idx, RNG& rng) d == 3 ? M.hash(idx[0], idx[1], idx[2]) : M.hash(idx); phv = &hv; } - + if( d == 2 ) M.erase(idx[0], idx[1], phv); else if( d == 3 ) @@ -584,7 +584,7 @@ static void setValue(SparseMat& M, const int* idx, double value, RNG& rng) d == 3 ? M.hash(idx[0], idx[1], idx[2]) : M.hash(idx); phv = &hv; } - + uchar* ptr = d == 2 ? M.ptr(idx[0], idx[1], true, phv) : d == 3 ? M.ptr(idx[0], idx[1], idx[2], true, phv) : M.ptr(idx, true, phv); @@ -599,7 +599,7 @@ static void setValue(SparseMat& M, const int* idx, double value, RNG& rng) void Core_ArrayOpTest::run( int /* start_from */) { int errcount = 0; - + // dense matrix operations { int sz3[] = {5, 10, 15}; @@ -608,7 +608,7 @@ void Core_ArrayOpTest::run( int /* start_from */) RNG rng; rng.fill(A, CV_RAND_UNI, Scalar::all(-10), Scalar::all(10)); rng.fill(B, CV_RAND_UNI, Scalar::all(-10), Scalar::all(10)); - + int idx0[] = {3,4,5}, idx1[] = {0, 9, 7}; float val0 = 130; Scalar val1(-1000, 30, 3, 8); @@ -617,12 +617,12 @@ void Core_ArrayOpTest::run( int /* start_from */) cvSetND(&matB, idx0, val1); cvSet3D(&matB, idx1[0], idx1[1], idx1[2], -val1); Ptr matC = cvCloneMatND(&matB); - + if( A.at(idx0[0], idx0[1], idx0[2]) != val0 || A.at(idx1[0], idx1[1], idx1[2]) != -val0 || cvGetReal3D(&matA, idx0[0], idx0[1], idx0[2]) != val0 || cvGetRealND(&matA, idx1) != -val0 || - + Scalar(B.at(idx0[0], idx0[1], idx0[2])) != val1 || Scalar(B.at(idx1[0], idx1[1], idx1[2])) != -val1 || Scalar(cvGet3D(matC, idx0[0], idx0[1], idx0[2])) != val1 || @@ -633,7 +633,7 @@ void Core_ArrayOpTest::run( int /* start_from */) errcount++; } } - + RNG rng; const int MAX_DIM = 5, MAX_DIM_SZ = 10; // sparse matrix operations @@ -647,7 +647,7 @@ void Core_ArrayOpTest::run( int /* start_from */) vector all_vals2; string sidx, min_sidx, max_sidx; double min_val=0, max_val=0; - + int p = 1; for( k = 0; k < dims; k++ ) { @@ -656,7 +656,7 @@ void Core_ArrayOpTest::run( int /* start_from */) } SparseMat M( dims, size, depth ); map M0; - + int nz0 = (unsigned)rng % max(p/5,10); nz0 = min(max(nz0, 1), p); all_vals.resize(nz0); @@ -676,12 +676,12 @@ void Core_ArrayOpTest::run( int /* start_from */) _all_vals2.convertTo(_all_vals2_f, CV_32F); _all_vals2_f.convertTo(_all_vals2, CV_64F); } - + minMaxLoc(_all_vals, &min_val, &max_val); double _norm0 = norm(_all_vals, CV_C); double _norm1 = norm(_all_vals, CV_L1); double _norm2 = norm(_all_vals, CV_L2); - + for( i = 0; i < nz0; i++ ) { for(;;) @@ -708,18 +708,18 @@ void Core_ArrayOpTest::run( int /* start_from */) break; } } - + Ptr M2 = (CvSparseMat*)M; MatND Md; M.copyTo(Md); SparseMat M3; SparseMat(Md).convertTo(M3, Md.type(), 2); - + int nz1 = (int)M.nzcount(), nz2 = (int)M3.nzcount(); double norm0 = norm(M, CV_C); double norm1 = norm(M, CV_L1); double norm2 = norm(M, CV_L2); double eps = depth == CV_32F ? FLT_EPSILON*100 : DBL_EPSILON*1000; - + if( nz1 != nz0 || nz2 != nz0) { errcount++; @@ -727,7 +727,7 @@ void Core_ArrayOpTest::run( int /* start_from */) si, nz1, nz2, nz0 ); break; } - + if( fabs(norm0 - _norm0) > fabs(_norm0)*eps || fabs(norm1 - _norm1) > fabs(_norm1)*eps || fabs(norm2 - _norm2) > fabs(_norm2)*eps ) @@ -737,10 +737,10 @@ void Core_ArrayOpTest::run( int /* start_from */) si, norm0, norm1, norm2, _norm0, _norm1, _norm2 ); break; } - + int n = (unsigned)rng % max(p/5,10); n = min(max(n, 1), p) + nz0; - + for( i = 0; i < n; i++ ) { double val1, val2, val3, val0; @@ -760,7 +760,7 @@ void Core_ArrayOpTest::run( int /* start_from */) val1 = getValue(M, idx, rng); val2 = getValue(M2, idx); val3 = getValue(M3, idx, rng); - + if( val1 != val0 || val2 != val0 || fabs(val3 - val0*2) > fabs(val0*2)*FLT_EPSILON ) { errcount++; @@ -768,7 +768,7 @@ void Core_ArrayOpTest::run( int /* start_from */) break; } } - + for( i = 0; i < n; i++ ) { double val1, val2; @@ -792,9 +792,9 @@ void Core_ArrayOpTest::run( int /* start_from */) errcount++; ts->printf(cvtest::TS::LOG, "SparseMat: after deleting M[%s], it is =%g/%g (while it should be 0)\n", sidx.c_str(), val1, val2 ); break; - } + } } - + int nz = (int)M.nzcount(); if( nz != 0 ) { @@ -802,7 +802,7 @@ void Core_ArrayOpTest::run( int /* start_from */) ts->printf(cvtest::TS::LOG, "The number of non-zero elements after removing all the elements = %d (while it should be 0)\n", nz ); break; } - + int idx1[MAX_DIM], idx2[MAX_DIM]; double val1 = 0, val2 = 0; M3 = SparseMat(Md); @@ -816,7 +816,7 @@ void Core_ArrayOpTest::run( int /* start_from */) min_val, max_val, min_sidx.c_str(), max_sidx.c_str()); break; } - + minMaxIdx(Md, &val1, &val2, idx1, idx2); s1 = idx2string(idx1, dims), s2 = idx2string(idx2, dims); if( (min_val < 0 && (val1 != min_val || s1 != min_sidx)) || @@ -829,7 +829,7 @@ void Core_ArrayOpTest::run( int /* start_from */) break; } } - + ts->set_failed_test_info(errcount == 0 ? cvtest::TS::OK : cvtest::TS::FAIL_INVALID_OUTPUT); } diff --git a/modules/core/test/test_rand.cpp b/modules/core/test/test_rand.cpp index 616264d..8fab165 100644 --- a/modules/core/test/test_rand.cpp +++ b/modules/core/test/test_rand.cpp @@ -27,7 +27,7 @@ static double chi2_p95(int n) 36.42f, 37.65f, 38.89f, 40.11f, 41.34f, 42.56f, 43.77f }; static const double xp = 1.64; CV_Assert(n >= 1); - + if( n <= 30 ) return chi2_tab95[n-1]; return n + sqrt((double)2*n)*xp + 0.6666666666666*(xp*xp - 1); @@ -40,12 +40,12 @@ bool Core_RandTest::check_pdf(const Mat& hist, double scale, const int* H = (const int*)hist.data; float* H0 = ((float*)hist0.data); int i, hsz = hist.cols; - + double sum = 0; for( i = 0; i < hsz; i++ ) sum += H[i]; CV_Assert( fabs(1./sum - scale) < FLT_EPSILON ); - + if( dist_type == CV_RAND_UNI ) { float scale0 = (float)(1./hsz); @@ -54,19 +54,19 @@ bool Core_RandTest::check_pdf(const Mat& hist, double scale, } else { - double sum = 0, r = (hsz-1.)/2; + double sum2 = 0, r = (hsz-1.)/2; double alpha = 2*sqrt(2.)/r, beta = -alpha*r; for( i = 0; i < hsz; i++ ) { double x = i*alpha + beta; H0[i] = (float)exp(-x*x); - sum += H0[i]; + sum2 += H0[i]; } - sum = 1./sum; + sum2 = 1./sum2; for( i = 0; i < hsz; i++ ) - H0[i] = (float)(H0[i]*sum); + H0[i] = (float)(H0[i]*sum2); } - + double chi2 = 0; for( i = 0; i < hsz; i++ ) { @@ -76,7 +76,7 @@ bool Core_RandTest::check_pdf(const Mat& hist, double scale, chi2 += (a - b)*(a - b)/(a + b); } realval = chi2; - + double chi2_pval = chi2_p95(hsz - 1 - (dist_type == CV_RAND_NORMAL ? 2 : 0)); refval = chi2_pval*0.01; return realval <= refval; @@ -87,22 +87,22 @@ void Core_RandTest::run( int ) static int _ranges[][2] = {{ 0, 256 }, { -128, 128 }, { 0, 65536 }, { -32768, 32768 }, { -1000000, 1000000 }, { -1000, 1000 }, { -1000, 1000 }}; - + const int MAX_SDIM = 10; const int N = 2000000; const int maxSlice = 1000; const int MAX_HIST_SIZE = 1000; int progress = 0; - + RNG& rng = ts->get_rng(); RNG tested_rng = theRNG(); test_case_count = 200; - + for( int idx = 0; idx < test_case_count; idx++ ) { progress = update_progress( progress, idx, test_case_count, 0 ); ts->update_context( this, idx, false ); - + int depth = cvtest::randInt(rng) % (CV_64F+1); int c, cn = (cvtest::randInt(rng) % 4) + 1; int type = CV_MAKETYPE(depth, cn); @@ -113,15 +113,15 @@ void Core_RandTest::run( int ) double eps = 1.e-4; if (depth == CV_64F) eps = 1.e-7; - + bool do_sphere_test = dist_type == CV_RAND_UNI; Mat arr[2], hist[4]; int W[] = {0,0,0,0}; - + arr[0].create(1, SZ, type); arr[1].create(1, SZ, type); bool fast_algo = dist_type == CV_RAND_UNI && depth < CV_32F; - + for( c = 0; c < cn; c++ ) { int a, b, hsz; @@ -137,7 +137,7 @@ void Core_RandTest::run( int ) while( abs(a-b) <= 1 ); if( a > b ) std::swap(a, b); - + unsigned r = (unsigned)(b - a); fast_algo = fast_algo && r <= 256 && (r & (r-1)) == 0; hsz = min((unsigned)(b - a), (unsigned)MAX_HIST_SIZE); @@ -149,7 +149,7 @@ void Core_RandTest::run( int ) int meanrange = vrange/16; int mindiv = MAX(vrange/20, 5); int maxdiv = MIN(vrange/8, 10000); - + a = cvtest::randInt(rng) % meanrange - meanrange/2 + (_ranges[depth][0] + _ranges[depth][1])/2; b = cvtest::randInt(rng) % (maxdiv - mindiv) + mindiv; @@ -157,9 +157,9 @@ void Core_RandTest::run( int ) } A[c] = a; B[c] = b; - hist[c].create(1, hsz, CV_32S); + hist[c].create(1, hsz, CV_32S); } - + cv::RNG saved_rng = tested_rng; int maxk = fast_algo ? 0 : 1; for( k = 0; k <= maxk; k++ ) @@ -173,14 +173,14 @@ void Core_RandTest::run( int ) tested_rng.fill(aslice, dist_type, A, B); } } - + if( maxk >= 1 && norm(arr[0], arr[1], NORM_INF) > eps) { ts->printf( cvtest::TS::LOG, "RNG output depends on the array lengths (some generated numbers get lost?)" ); ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT ); return; } - + for( c = 0; c < cn; c++ ) { const uchar* data = arr[0].data; @@ -190,9 +190,9 @@ void Core_RandTest::run( int ) double maxVal = dist_type == CV_RAND_UNI ? B[c] : A[c] + B[c]*4; double scale = HSZ/(maxVal - minVal); double delta = -minVal*scale; - + hist[c] = Scalar::all(0); - + for( i = c; i < SZ*cn; i += cn ) { double val = depth == CV_8U ? ((const uchar*)data)[i] : @@ -221,7 +221,7 @@ void Core_RandTest::run( int ) } } } - + if( dist_type == CV_RAND_UNI && W[c] != SZ ) { ts->printf( cvtest::TS::LOG, "Uniform RNG gave values out of the range [%g,%g) on channel %d/%d\n", @@ -237,7 +237,7 @@ void Core_RandTest::run( int ) return; } double refval = 0, realval = 0; - + if( !check_pdf(hist[c], 1./W[c], dist_type, refval, realval) ) { ts->printf( cvtest::TS::LOG, "RNG failed Chi-square test " @@ -247,13 +247,13 @@ void Core_RandTest::run( int ) return; } } - + // Monte-Carlo test. Compute volume of SDIM-dimensional sphere // inscribed in [-1,1]^SDIM cube. if( do_sphere_test ) { int SDIM = cvtest::randInt(rng) % (MAX_SDIM-1) + 2; - int N0 = (SZ*cn/SDIM), N = 0; + int N0 = (SZ*cn/SDIM), n = 0; double r2 = 0; const uchar* data = arr[0].data; double scale[4], delta[4]; @@ -262,7 +262,7 @@ void Core_RandTest::run( int ) scale[c] = 2./(B[c] - A[c]); delta[c] = -A[c]*scale[c] - 1; } - + for( i = k = c = 0; i <= SZ*cn - SDIM; i++, k++, c++ ) { double val = depth == CV_8U ? ((const uchar*)data)[i] : @@ -276,20 +276,20 @@ void Core_RandTest::run( int ) r2 += val*val; if( k == SDIM-1 ) { - N += r2 <= 1; + n += r2 <= 1; r2 = 0; k = -1; } } - - double V = ((double)N/N0)*(1 << SDIM); - + + double V = ((double)n/N0)*(1 << SDIM); + // the theoretically computed volume int sdim = SDIM % 2; double V0 = sdim + 1; for( sdim += 2; sdim <= SDIM; sdim += 2 ) V0 *= 2*CV_PI/sdim; - + if( fabs(V - V0) > 0.3*fabs(V0) ) { ts->printf( cvtest::TS::LOG, "RNG failed %d-dim sphere volume test (got %g instead of %g)\n", @@ -309,7 +309,7 @@ class Core_RandRangeTest : public cvtest::BaseTest { public: Core_RandRangeTest() {} - ~Core_RandRangeTest() {} + ~Core_RandRangeTest() {} protected: void run(int) { @@ -319,7 +319,7 @@ protected: theRNG().fill(af, RNG::UNIFORM, -DBL_MAX, DBL_MAX); int n0 = 0, n255 = 0, nx = 0; int nfmin = 0, nfmax = 0, nfx = 0; - + for( int i = 0; i < a.rows; i++ ) for( int j = 0; j < a.cols; j++ ) { diff --git a/modules/features2d/src/bagofwords.cpp b/modules/features2d/src/bagofwords.cpp index 384d2bc..9770064 100755 --- a/modules/features2d/src/bagofwords.cpp +++ b/modules/features2d/src/bagofwords.cpp @@ -110,10 +110,10 @@ Mat BOWKMeansTrainer::cluster() const BOWKMeansTrainer::~BOWKMeansTrainer() {} -Mat BOWKMeansTrainer::cluster( const Mat& descriptors ) const +Mat BOWKMeansTrainer::cluster( const Mat& _descriptors ) const { Mat labels, vocabulary; - kmeans( descriptors, clusterCount, labels, termcrit, attempts, flags, vocabulary ); + kmeans( _descriptors, clusterCount, labels, termcrit, attempts, flags, vocabulary ); return vocabulary; } diff --git a/modules/features2d/src/brief.cpp b/modules/features2d/src/brief.cpp index 98759be..95ed868 100644 --- a/modules/features2d/src/brief.cpp +++ b/modules/features2d/src/brief.cpp @@ -127,8 +127,8 @@ int BriefDescriptorExtractor::descriptorType() const void BriefDescriptorExtractor::read( const FileNode& fn) { - int descriptorSize = fn["descriptorSize"]; - switch (descriptorSize) + int dSize = fn["descriptorSize"]; + switch (dSize) { case 16: test_fn_ = pixelTests16; @@ -142,7 +142,7 @@ void BriefDescriptorExtractor::read( const FileNode& fn) default: CV_Error(CV_StsBadArg, "descriptorSize must be 16, 32, or 64"); } - bytes_ = descriptorSize; + bytes_ = dSize; } void BriefDescriptorExtractor::write( FileStorage& fs) const diff --git a/modules/features2d/src/descriptors.cpp b/modules/features2d/src/descriptors.cpp index e90bf9d..2fb43b7 100644 --- a/modules/features2d/src/descriptors.cpp +++ b/modules/features2d/src/descriptors.cpp @@ -223,8 +223,8 @@ void OpponentColorDescriptorExtractor::computeImpl( const Mat& bgrImage, vector< vector outKeypoints; outKeypoints.reserve( keypoints.size() ); - int descriptorSize = descriptorExtractor->descriptorSize(); - Mat mergedDescriptors( maxKeypointsCount, 3*descriptorSize, descriptorExtractor->descriptorType() ); + int dSize = descriptorExtractor->descriptorSize(); + Mat mergedDescriptors( maxKeypointsCount, 3*dSize, descriptorExtractor->descriptorType() ); int mergedCount = 0; // cp - current channel position size_t cp[] = {0, 0, 0}; @@ -250,7 +250,7 @@ void OpponentColorDescriptorExtractor::computeImpl( const Mat& bgrImage, vector< // merge descriptors for( int ci = 0; ci < N; ci++ ) { - Mat dst = mergedDescriptors(Range(mergedCount, mergedCount+1), Range(ci*descriptorSize, (ci+1)*descriptorSize)); + Mat dst = mergedDescriptors(Range(mergedCount, mergedCount+1), Range(ci*dSize, (ci+1)*dSize)); channelDescriptors[ci].row( idxs[ci][cp[ci]] ).copyTo( dst ); cp[ci]++; } diff --git a/modules/features2d/src/draw.cpp b/modules/features2d/src/draw.cpp index ec14911..c123526 100755 --- a/modules/features2d/src/draw.cpp +++ b/modules/features2d/src/draw.cpp @@ -156,11 +156,11 @@ static void _prepareImgAndDrawKeypoints( const Mat& img1, const vector // draw keypoints if( !(flags & DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS) ) { - Mat outImg1 = outImg( Rect(0, 0, img1.cols, img1.rows) ); - drawKeypoints( outImg1, keypoints1, outImg1, singlePointColor, flags + DrawMatchesFlags::DRAW_OVER_OUTIMG ); + Mat _outImg1 = outImg( Rect(0, 0, img1.cols, img1.rows) ); + drawKeypoints( _outImg1, keypoints1, _outImg1, singlePointColor, flags + DrawMatchesFlags::DRAW_OVER_OUTIMG ); - Mat outImg2 = outImg( Rect(img1.cols, 0, img2.cols, img2.rows) ); - drawKeypoints( outImg2, keypoints2, outImg2, singlePointColor, flags + DrawMatchesFlags::DRAW_OVER_OUTIMG ); + Mat _outImg2 = outImg( Rect(img1.cols, 0, img2.cols, img2.rows) ); + drawKeypoints( _outImg2, keypoints2, _outImg2, singlePointColor, flags + DrawMatchesFlags::DRAW_OVER_OUTIMG ); } } @@ -178,9 +178,9 @@ static inline void _drawMatch( Mat& outImg, Mat& outImg1, Mat& outImg2 , pt2 = kp2.pt, dpt2 = Point2f( std::min(pt2.x+outImg1.cols, float(outImg.cols-1)), pt2.y ); - line( outImg, - Point(cvRound(pt1.x*draw_multiplier), cvRound(pt1.y*draw_multiplier)), - Point(cvRound(dpt2.x*draw_multiplier), cvRound(dpt2.y*draw_multiplier)), + line( outImg, + Point(cvRound(pt1.x*draw_multiplier), cvRound(pt1.y*draw_multiplier)), + Point(cvRound(dpt2.x*draw_multiplier), cvRound(dpt2.y*draw_multiplier)), color, 1, CV_AA, draw_shift_bits ); } diff --git a/modules/features2d/src/features2d_init.cpp b/modules/features2d/src/features2d_init.cpp index 09b9322..631f5c7 100644 --- a/modules/features2d/src/features2d_init.cpp +++ b/modules/features2d/src/features2d_init.cpp @@ -109,11 +109,14 @@ class CV_EXPORTS HarrisDetector : public GFTTDetector { public: HarrisDetector( int maxCorners=1000, double qualityLevel=0.01, double minDistance=1, - int blockSize=3, bool useHarrisDetector=true, double k=0.04 ) - : GFTTDetector( maxCorners, qualityLevel, minDistance, blockSize, useHarrisDetector, k ) {} + int blockSize=3, bool useHarrisDetector=true, double k=0.04 ); AlgorithmInfo* info() const; }; +inline HarrisDetector::HarrisDetector( int _maxCorners, double _qualityLevel, double _minDistance, + int _blockSize, bool _useHarrisDetector, double _k ) + : GFTTDetector( _maxCorners, _qualityLevel, _minDistance, _blockSize, _useHarrisDetector, _k ) {} + CV_INIT_ALGORITHM(HarrisDetector, "Feature2D.HARRIS", obj.info()->addParam(obj, "nfeatures", obj.nfeatures); obj.info()->addParam(obj, "qualityLevel", obj.qualityLevel); diff --git a/modules/features2d/src/matchers.cpp b/modules/features2d/src/matchers.cpp index 74ab4b8..f1a97dc 100755 --- a/modules/features2d/src/matchers.cpp +++ b/modules/features2d/src/matchers.cpp @@ -539,7 +539,7 @@ void FlannBasedMatcher::read( const FileNode& fn) for(int i = 0; i < (int)ip.size(); ++i) { CV_Assert(ip[i].type() == FileNode::MAP); - std::string name = (std::string)ip[i]["name"]; + std::string _name = (std::string)ip[i]["name"]; int type = (int)ip[i]["type"]; switch(type) @@ -549,19 +549,19 @@ void FlannBasedMatcher::read( const FileNode& fn) case CV_16U: case CV_16S: case CV_32S: - indexParams->setInt(name, (int) ip[i]["value"]); + indexParams->setInt(_name, (int) ip[i]["value"]); break; case CV_32F: - indexParams->setFloat(name, (float) ip[i]["value"]); + indexParams->setFloat(_name, (float) ip[i]["value"]); break; case CV_64F: - indexParams->setDouble(name, (double) ip[i]["value"]); + indexParams->setDouble(_name, (double) ip[i]["value"]); break; case CV_USRTYPE1: - indexParams->setString(name, (std::string) ip[i]["value"]); + indexParams->setString(_name, (std::string) ip[i]["value"]); break; case CV_MAKETYPE(CV_USRTYPE1,2): - indexParams->setBool(name, (int) ip[i]["value"] != 0); + indexParams->setBool(_name, (int) ip[i]["value"] != 0); break; case CV_MAKETYPE(CV_USRTYPE1,3): indexParams->setAlgorithm((int) ip[i]["value"]); @@ -578,7 +578,7 @@ void FlannBasedMatcher::read( const FileNode& fn) for(int i = 0; i < (int)sp.size(); ++i) { CV_Assert(sp[i].type() == FileNode::MAP); - std::string name = (std::string)sp[i]["name"]; + std::string _name = (std::string)sp[i]["name"]; int type = (int)sp[i]["type"]; switch(type) @@ -588,19 +588,19 @@ void FlannBasedMatcher::read( const FileNode& fn) case CV_16U: case CV_16S: case CV_32S: - searchParams->setInt(name, (int) sp[i]["value"]); + searchParams->setInt(_name, (int) sp[i]["value"]); break; case CV_32F: - searchParams->setFloat(name, (float) ip[i]["value"]); + searchParams->setFloat(_name, (float) ip[i]["value"]); break; case CV_64F: - searchParams->setDouble(name, (double) ip[i]["value"]); + searchParams->setDouble(_name, (double) ip[i]["value"]); break; case CV_USRTYPE1: - searchParams->setString(name, (std::string) ip[i]["value"]); + searchParams->setString(_name, (std::string) ip[i]["value"]); break; case CV_MAKETYPE(CV_USRTYPE1,2): - searchParams->setBool(name, (int) ip[i]["value"] != 0); + searchParams->setBool(_name, (int) ip[i]["value"] != 0); break; case CV_MAKETYPE(CV_USRTYPE1,3): searchParams->setAlgorithm((int) ip[i]["value"]); diff --git a/modules/features2d/src/mser.cpp b/modules/features2d/src/mser.cpp index f7bd7a8..c2a2749 100644 --- a/modules/features2d/src/mser.cpp +++ b/modules/features2d/src/mser.cpp @@ -539,8 +539,8 @@ static void extractMSER_8UC1_Pass( int* ioptr, } *imgptr += 0x10000; } - int i = (int)(imgptr-ioptr); - ptsptr->pt = cvPoint( i&stepmask, i>>stepgap ); + int imsk = (int)(imgptr-ioptr); + ptsptr->pt = cvPoint( imsk&stepmask, imsk>>stepgap ); // get the current location accumulateMSERComp( comptr, ptsptr ); ptsptr++; diff --git a/modules/features2d/src/orb.cpp b/modules/features2d/src/orb.cpp index 32b4849..19491f1 100644 --- a/modules/features2d/src/orb.cpp +++ b/modules/features2d/src/orb.cpp @@ -555,9 +555,9 @@ static inline float getScale(int level, int firstLevel, double scaleFactor) * @param detector_params parameters to use */ ORB::ORB(int _nfeatures, float _scaleFactor, int _nlevels, int _edgeThreshold, - int _firstLevel, int WTA_K, int _scoreType, int _patchSize) : + int _firstLevel, int _WTA_K, int _scoreType, int _patchSize) : nfeatures(_nfeatures), scaleFactor(_scaleFactor), nlevels(_nlevels), - edgeThreshold(_edgeThreshold), firstLevel(_firstLevel), WTA_K(WTA_K), + edgeThreshold(_edgeThreshold), firstLevel(_firstLevel), WTA_K(_WTA_K), scoreType(_scoreType), patchSize(_patchSize) {} @@ -653,8 +653,8 @@ static void computeKeyPoints(const vector& imagePyramid, for (int level = 0; level < nlevels; ++level) { - int nfeatures = nfeaturesPerLevel[level]; - allKeypoints[level].reserve(nfeatures*2); + int featuresNum = nfeaturesPerLevel[level]; + allKeypoints[level].reserve(featuresNum*2); vector & keypoints = allKeypoints[level]; @@ -668,14 +668,14 @@ static void computeKeyPoints(const vector& imagePyramid, if( scoreType == ORB::HARRIS_SCORE ) { // Keep more points than necessary as FAST does not give amazing corners - KeyPointsFilter::retainBest(keypoints, 2 * nfeatures); + KeyPointsFilter::retainBest(keypoints, 2 * featuresNum); // Compute the Harris cornerness (better scoring than FAST) HarrisResponses(imagePyramid[level], keypoints, 7, HARRIS_K); } //cull to the final desired level, using the new Harris scores or the original FAST scores. - KeyPointsFilter::retainBest(keypoints, nfeatures); + KeyPointsFilter::retainBest(keypoints, featuresNum); float sf = getScale(level, firstLevel, scaleFactor); @@ -738,7 +738,7 @@ void ORB::operator()( InputArray _image, InputArray _mask, vector& _ke if( image.type() != CV_8UC1 ) cvtColor(_image, image, CV_BGR2GRAY); - int nlevels = this->nlevels; + int levelsNum = this->nlevels; if( !do_keypoints ) { @@ -751,15 +751,15 @@ void ORB::operator()( InputArray _image, InputArray _mask, vector& _ke // // In short, ultimately the descriptor should // ignore octave parameter and deal only with the keypoint size. - nlevels = 0; + levelsNum = 0; for( size_t i = 0; i < _keypoints.size(); i++ ) - nlevels = std::max(nlevels, std::max(_keypoints[i].octave, 0)); - nlevels++; + levelsNum = std::max(levelsNum, std::max(_keypoints[i].octave, 0)); + levelsNum++; } // Pre-compute the scale pyramids - vector imagePyramid(nlevels), maskPyramid(nlevels); - for (int level = 0; level < nlevels; ++level) + vector imagePyramid(levelsNum), maskPyramid(levelsNum); + for (int level = 0; level < levelsNum; ++level) { float scale = 1/getScale(level, firstLevel, scaleFactor); Size sz(cvRound(image.cols*scale), cvRound(image.rows*scale)); @@ -839,13 +839,13 @@ void ORB::operator()( InputArray _image, InputArray _mask, vector& _ke KeyPointsFilter::runByImageBorder(_keypoints, image.size(), edgeThreshold); // Cluster the input keypoints depending on the level they were computed at - allKeypoints.resize(nlevels); + allKeypoints.resize(levelsNum); for (vector::iterator keypoint = _keypoints.begin(), keypointEnd = _keypoints.end(); keypoint != keypointEnd; ++keypoint) allKeypoints[keypoint->octave].push_back(*keypoint); // Make sure we rescale the coordinates - for (int level = 0; level < nlevels; ++level) + for (int level = 0; level < levelsNum; ++level) { if (level == firstLevel) continue; @@ -864,7 +864,7 @@ void ORB::operator()( InputArray _image, InputArray _mask, vector& _ke if( do_descriptors ) { int nkeypoints = 0; - for (int level = 0; level < nlevels; ++level) + for (int level = 0; level < levelsNum; ++level) nkeypoints += (int)allKeypoints[level].size(); if( nkeypoints == 0 ) _descriptors.release(); @@ -897,7 +897,7 @@ void ORB::operator()( InputArray _image, InputArray _mask, vector& _ke _keypoints.clear(); int offset = 0; - for (int level = 0; level < nlevels; ++level) + for (int level = 0; level < levelsNum; ++level) { // Get the features and compute their orientation vector& keypoints = allKeypoints[level]; diff --git a/modules/features2d/src/stardetector.cpp b/modules/features2d/src/stardetector.cpp index 3775ebd..0ceb3f8 100644 --- a/modules/features2d/src/stardetector.cpp +++ b/modules/features2d/src/stardetector.cpp @@ -48,13 +48,13 @@ static void computeIntegralImages( const Mat& matI, Mat& matS, Mat& matT, Mat& _FT ) { CV_Assert( matI.type() == CV_8U ); - + int x, y, rows = matI.rows, cols = matI.cols; - + matS.create(rows + 1, cols + 1, CV_32S); matT.create(rows + 1, cols + 1, CV_32S); _FT.create(rows + 1, cols + 1, CV_32S); - + const uchar* I = matI.ptr(); int *S = matS.ptr(), *T = matT.ptr(), *FT = _FT.ptr(); int istep = (int)matI.step, step = (int)(matS.step/sizeof(S[0])); @@ -121,29 +121,28 @@ StarDetectorComputeResponses( const Mat& img, Mat& responses, Mat& sizes, int ma StarFeature f[MAX_PATTERN]; Mat sum, tilted, flatTilted; - int y, i=0, rows = img.rows, cols = img.cols; + int y, rows = img.rows, cols = img.cols; int border, npatterns=0, maxIdx=0; CV_Assert( img.type() == CV_8UC1 ); - + responses.create( img.size(), CV_32F ); sizes.create( img.size(), CV_16S ); - while( pairs[i][0] >= 0 && ! - ( sizes0[pairs[i][0]] >= maxSize - || sizes0[pairs[i+1][0]] + sizes0[pairs[i+1][0]]/2 >= std::min(rows, cols) ) ) + while( pairs[npatterns][0] >= 0 && ! + ( sizes0[pairs[npatterns][0]] >= maxSize + || sizes0[pairs[npatterns+1][0]] + sizes0[pairs[npatterns+1][0]]/2 >= std::min(rows, cols) ) ) { - ++i; + ++npatterns; } - - npatterns = i; + npatterns += (pairs[npatterns-1][0] >= 0); maxIdx = pairs[npatterns-1][0]; - + computeIntegralImages( img, sum, tilted, flatTilted ); int step = (int)(sum.step/sum.elemSize()); - for( i = 0; i <= maxIdx; i++ ) + for(int i = 0; i <= maxIdx; i++ ) { int ur_size = sizes0[i], t_size = sizes0[i] + sizes0[i]/2; int ur_area = (2*ur_size + 1)*(2*ur_size + 1); @@ -169,24 +168,24 @@ StarDetectorComputeResponses( const Mat& img, Mat& responses, Mat& sizes, int ma sizes1[maxIdx] = -sizes1[maxIdx]; border = sizes0[maxIdx] + sizes0[maxIdx]/2; - for( i = 0; i < npatterns; i++ ) + for(int i = 0; i < npatterns; i++ ) { int innerArea = f[pairs[i][1]].area; int outerArea = f[pairs[i][0]].area - innerArea; invSizes[i][0] = 1.f/outerArea; invSizes[i][1] = 1.f/innerArea; } - + #if CV_SSE2 if( useSIMD ) { - for( i = 0; i < npatterns; i++ ) + for(int i = 0; i < npatterns; i++ ) { _mm_store_ps((float*)&invSizes4[i][0], _mm_set1_ps(invSizes[i][0])); _mm_store_ps((float*)&invSizes4[i][1], _mm_set1_ps(invSizes[i][1])); } - for( i = 0; i <= maxIdx; i++ ) + for(int i = 0; i <= maxIdx; i++ ) _mm_store_ps((float*)&sizes1_4[i], _mm_set1_ps((float)sizes1[i])); } #endif @@ -197,7 +196,7 @@ StarDetectorComputeResponses( const Mat& img, Mat& responses, Mat& sizes, int ma float* r_ptr2 = responses.ptr(rows - 1 - y); short* s_ptr = sizes.ptr(y); short* s_ptr2 = sizes.ptr(rows - 1 - y); - + memset( r_ptr, 0, cols*sizeof(r_ptr[0])); memset( r_ptr2, 0, cols*sizeof(r_ptr2[0])); memset( s_ptr, 0, cols*sizeof(s_ptr[0])); @@ -206,10 +205,10 @@ StarDetectorComputeResponses( const Mat& img, Mat& responses, Mat& sizes, int ma for( y = border; y < rows - border; y++ ) { - int x = border, i; + int x = border; float* r_ptr = responses.ptr(y); short* s_ptr = sizes.ptr(y); - + memset( r_ptr, 0, border*sizeof(r_ptr[0])); memset( s_ptr, 0, border*sizeof(s_ptr[0])); memset( r_ptr + cols - border, 0, border*sizeof(r_ptr[0])); @@ -226,7 +225,7 @@ StarDetectorComputeResponses( const Mat& img, Mat& responses, Mat& sizes, int ma __m128 bestResponse = _mm_setzero_ps(); __m128 bestSize = _mm_setzero_ps(); - for( i = 0; i <= maxIdx; i++ ) + for(int i = 0; i <= maxIdx; i++ ) { const int** p = (const int**)&f[i].p[0]; __m128i r0 = _mm_sub_epi32(_mm_loadu_si128((const __m128i*)(p[0]+ofs)), @@ -241,7 +240,7 @@ StarDetectorComputeResponses( const Mat& img, Mat& responses, Mat& sizes, int ma _mm_store_ps((float*)&vals[i], _mm_cvtepi32_ps(r0)); } - for( i = 0; i < npatterns; i++ ) + for(int i = 0; i < npatterns; i++ ) { __m128 inner_sum = vals[pairs[i][1]]; __m128 outer_sum = _mm_sub_ps(vals[pairs[i][0]], inner_sum); @@ -260,7 +259,7 @@ StarDetectorComputeResponses( const Mat& img, Mat& responses, Mat& sizes, int ma _mm_packs_epi32(_mm_cvtps_epi32(bestSize),_mm_setzero_si128())); } } -#endif +#endif for( ; x < cols - border; x++ ) { int ofs = y*step + x; @@ -268,13 +267,13 @@ StarDetectorComputeResponses( const Mat& img, Mat& responses, Mat& sizes, int ma float bestResponse = 0; int bestSize = 0; - for( i = 0; i <= maxIdx; i++ ) + for(int i = 0; i <= maxIdx; i++ ) { const int** p = (const int**)&f[i].p[0]; vals[i] = p[0][ofs] - p[1][ofs] - p[2][ofs] + p[3][ofs] + p[4][ofs] - p[5][ofs] - p[6][ofs] + p[7][ofs]; } - for( i = 0; i < npatterns; i++ ) + for(int i = 0; i < npatterns; i++ ) { int inner_sum = vals[pairs[i][1]]; int outer_sum = vals[pairs[i][0]] - inner_sum; @@ -306,7 +305,7 @@ static bool StarDetectorSuppressLines( const Mat& responses, const Mat& sizes, P int x, y, delta = sz/4, radius = delta*4; float Lxx = 0, Lyy = 0, Lxy = 0; int Lxxb = 0, Lyyb = 0, Lxyb = 0; - + for( y = pt.y - radius; y <= pt.y + radius; y += delta ) for( x = pt.x - radius; x <= pt.x + radius; x += delta ) { @@ -314,7 +313,7 @@ static bool StarDetectorSuppressLines( const Mat& responses, const Mat& sizes, P float Ly = r_ptr[(y+1)*rstep + x] - r_ptr[(y-1)*rstep + x]; Lxx += Lx*Lx; Lyy += Ly*Ly; Lxy += Lx*Ly; } - + if( (Lxx + Lyy)*(Lxx + Lyy) >= lineThresholdProjected*(Lxx*Lyy - Lxy*Lxy) ) return true; @@ -415,7 +414,7 @@ StarDetectorSuppressNonmax( const Mat& responses, const Mat& sizes, ; } } - + StarDetector::StarDetector(int _maxSize, int _responseThreshold, int _lineThresholdProjected, int _lineThresholdBinarized, @@ -431,10 +430,10 @@ void StarDetector::detectImpl( const Mat& image, vector& keypoints, co { Mat grayImage = image; if( image.type() != CV_8U ) cvtColor( image, grayImage, CV_BGR2GRAY ); - + (*this)(grayImage, keypoints); KeyPointsFilter::runByPixelsMask( keypoints, mask ); -} +} void StarDetector::operator()(const Mat& img, vector& keypoints) const { @@ -446,5 +445,5 @@ void StarDetector::operator()(const Mat& img, vector& keypoints) const responseThreshold, lineThresholdProjected, lineThresholdBinarized, suppressNonmaxSize ); } - + } diff --git a/modules/flann/include/opencv2/flann/allocator.h b/modules/flann/include/opencv2/flann/allocator.h index 6ca44fc..26091d0 100644 --- a/modules/flann/include/opencv2/flann/allocator.h +++ b/modules/flann/include/opencv2/flann/allocator.h @@ -92,9 +92,9 @@ public: /** Default constructor. Initializes a new pool. */ - PooledAllocator(int blocksize = BLOCKSIZE) + PooledAllocator(int blockSize = BLOCKSIZE) { - this->blocksize = blocksize; + blocksize = blockSize; remaining = 0; base = NULL; @@ -122,7 +122,7 @@ public: */ void* allocateMemory(int size) { - int blocksize; + int blockSize; /* Round size up to a multiple of wordsize. The following expression only works for WORDSIZE that is a power of 2, by masking last bits of @@ -138,11 +138,11 @@ public: wastedMemory += remaining; /* Allocate new storage. */ - blocksize = (size + sizeof(void*) + (WORDSIZE-1) > BLOCKSIZE) ? + blockSize = (size + sizeof(void*) + (WORDSIZE-1) > BLOCKSIZE) ? size + sizeof(void*) + (WORDSIZE-1) : BLOCKSIZE; // use the standard C malloc to allocate memory - void* m = ::malloc(blocksize); + void* m = ::malloc(blockSize); if (!m) { fprintf(stderr,"Failed to allocate memory.\n"); return NULL; @@ -155,7 +155,7 @@ public: int shift = 0; //int shift = (WORDSIZE - ( (((size_t)m) + sizeof(void*)) & (WORDSIZE-1))) & (WORDSIZE-1); - remaining = blocksize - sizeof(void*) - shift; + remaining = blockSize - sizeof(void*) - shift; loc = ((char*)m + sizeof(void*) + shift); } void* rloc = loc; diff --git a/modules/flann/include/opencv2/flann/dynamic_bitset.h b/modules/flann/include/opencv2/flann/dynamic_bitset.h index b94a089..bfd39ce 100644 --- a/modules/flann/include/opencv2/flann/dynamic_bitset.h +++ b/modules/flann/include/opencv2/flann/dynamic_bitset.h @@ -66,9 +66,9 @@ public: /** @param only constructor we use in our code * @param the size of the bitset (in bits) */ - DynamicBitset(size_t size) + DynamicBitset(size_t sz) { - resize(size); + resize(sz); reset(); } @@ -116,10 +116,10 @@ public: /** @param resize the bitset so that it contains at least size bits * @param size */ - void resize(size_t size) + void resize(size_t sz) { - size_ = size; - bitset_.resize(size / cell_bit_size_ + 1); + size_ = sz; + bitset_.resize(sz / cell_bit_size_ + 1); } /** @param set a bit to true diff --git a/modules/flann/include/opencv2/flann/heap.h b/modules/flann/include/opencv2/flann/heap.h index dc0a6f7..92a6ea6 100644 --- a/modules/flann/include/opencv2/flann/heap.h +++ b/modules/flann/include/opencv2/flann/heap.h @@ -67,12 +67,12 @@ public: * Constructor. * * Params: - * size = heap size + * sz = heap size */ - Heap(int size) + Heap(int sz) { - length = size; + length = sz; heap.reserve(length); count = 0; } diff --git a/modules/flann/include/opencv2/flann/hierarchical_clustering_index.h b/modules/flann/include/opencv2/flann/hierarchical_clustering_index.h index 103d24d..3b61bd2 100644 --- a/modules/flann/include/opencv2/flann/hierarchical_clustering_index.h +++ b/modules/flann/include/opencv2/flann/hierarchical_clustering_index.h @@ -106,7 +106,7 @@ private: * indices_length = length of indices vector * */ - void chooseCentersRandom(int k, int* indices, int indices_length, int* centers, int& centers_length) + void chooseCentersRandom(int k, int* dsindices, int indices_length, int* centers, int& centers_length) { UniqueRandom r(indices_length); @@ -122,7 +122,7 @@ private: return; } - centers[index] = indices[rnd]; + centers[index] = dsindices[rnd]; for (int j=0; j=0 && rnd < n); - centers[0] = indices[rnd]; + centers[0] = dsindices[rnd]; int index; for (index=1; index=0 && index < n); - centers[0] = indices[index]; + centers[0] = dsindices[index]; for (int i = 0; i < n; i++) { - closestDistSq[i] = distance(dataset[indices[i]], dataset[indices[index]], dataset.cols); + closestDistSq[i] = distance(dataset[dsindices[i]], dataset[dsindices[index]], dataset.cols); currentPot += closestDistSq[i]; } @@ -237,7 +237,7 @@ private: // Compute the new potential double newPot = 0; - for (int i = 0; i < n; i++) newPot += std::min( distance(dataset[indices[i]], dataset[indices[index]], dataset.cols), closestDistSq[i] ); + for (int i = 0; i < n; i++) newPot += std::min( distance(dataset[dsindices[i]], dataset[dsindices[index]], dataset.cols), closestDistSq[i] ); // Store the best result if ((bestNewPot < 0)||(newPot < bestNewPot)) { @@ -247,9 +247,9 @@ private: } // Add the appropriate center - centers[centerCount] = indices[bestNewIndex]; + centers[centerCount] = dsindices[bestNewIndex]; currentPot = bestNewPot; - for (int i = 0; i < n; i++) closestDistSq[i] = std::min( distance(dataset[indices[i]], dataset[indices[bestNewIndex]], dataset.cols), closestDistSq[i] ); + for (int i = 0; i < n; i++) closestDistSq[i] = std::min( distance(dataset[dsindices[i]], dataset[dsindices[bestNewIndex]], dataset.cols), closestDistSq[i] ); } centers_length = centerCount; @@ -518,11 +518,11 @@ private: - void computeLabels(int* indices, int indices_length, int* centers, int centers_length, int* labels, DistanceType& cost) + void computeLabels(int* dsindices, int indices_length, int* centers, int centers_length, int* labels, DistanceType& cost) { cost = 0; for (int i=0; isize = indices_length; node->level = level; if (indices_length < leaf_size_) { // leaf node - node->indices = indices; + node->indices = dsindices; std::sort(node->indices,node->indices+indices_length); node->childs = NULL; return; @@ -563,10 +563,10 @@ private: std::vector labels(indices_length); int centers_length; - (this->*chooseCenters)(branching, indices, indices_length, ¢ers[0], centers_length); + (this->*chooseCenters)(branching, dsindices, indices_length, ¢ers[0], centers_length); if (centers_lengthindices = indices; + node->indices = dsindices; std::sort(node->indices,node->indices+indices_length); node->childs = NULL; return; @@ -575,7 +575,7 @@ private: // assign points to clusters DistanceType cost; - computeLabels(indices, indices_length, ¢ers[0], centers_length, &labels[0], cost); + computeLabels(dsindices, indices_length, ¢ers[0], centers_length, &labels[0], cost); node->childs = pool.allocate(branching); int start = 0; @@ -583,7 +583,7 @@ private: for (int i=0; ichilds[i] = pool.allocate(); node->childs[i]->pivot = centers[i]; node->childs[i]->indices = NULL; - computeClustering(node->childs[i],indices+start, end-start, branching, level+1); + computeClustering(node->childs[i],dsindices+start, end-start, branching, level+1); start=end; } } diff --git a/modules/gpu/CMakeLists.txt b/modules/gpu/CMakeLists.txt index b5f19f8..f241536 100644 --- a/modules/gpu/CMakeLists.txt +++ b/modules/gpu/CMakeLists.txt @@ -5,9 +5,7 @@ endif() set(the_description "GPU-accelerated Computer Vision") ocv_add_module(gpu opencv_imgproc opencv_calib3d opencv_objdetect opencv_video opencv_nonfree opencv_legacy) -ocv_module_include_directories("${CMAKE_CURRENT_SOURCE_DIR}/src/cuda") - -ocv_module_include_directories("${CMAKE_CURRENT_SOURCE_DIR}/../highgui/src") +ocv_module_include_directories("${CMAKE_CURRENT_SOURCE_DIR}/src/cuda" "${CMAKE_CURRENT_SOURCE_DIR}/../highgui/src") file(GLOB lib_hdrs "include/opencv2/${name}/*.hpp" "include/opencv2/${name}/*.h") file(GLOB lib_int_hdrs "src/*.hpp" "src/*.h") @@ -30,17 +28,14 @@ if (HAVE_CUDA) set(ncv_files ${ncv_srcs} ${ncv_hdrs} ${ncv_cuda}) source_group("Src\\NVidia" FILES ${ncv_files}) - include_directories(AFTER SYSTEM ${CUDA_INCLUDE_DIRS}) - ocv_include_directories("src/nvidia" "src/nvidia/core" "src/nvidia/NPP_staging") - ocv_warnings_disable(CMAKE_CXX_FLAGS -Wundef) + ocv_include_directories("src/nvidia" "src/nvidia/core" "src/nvidia/NPP_staging" ${CUDA_INCLUDE_DIRS}) + ocv_warnings_disable(CMAKE_CXX_FLAGS -Wundef /wd4211 /wd4201 /wd4100 /wd4505 /wd4408) #set (CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS} "-keep") #set (CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS} "-Xcompiler;/EHsc-;") if(MSVC) if(NOT ENABLE_NOISY_WARNINGS) - set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /wd4211 /wd4201 /wd4100 /wd4505 /wd4408") - foreach(var CMAKE_CXX_FLAGS CMAKE_CXX_FLAGS_RELEASE CMAKE_CXX_FLAGS_DEBUG) string(REPLACE "/W4" "/W3" ${var} "${${var}}") endforeach() @@ -52,10 +47,8 @@ if (HAVE_CUDA) ocv_cuda_compile(cuda_objs ${lib_cuda} ${ncv_cuda}) #CUDA_BUILD_CLEAN_TARGET() - set(cuda_link_libs ${CUDA_LIBRARIES} ${CUDA_npp_LIBRARY}) - if(NOT APPLE) unset(CUDA_nvcuvid_LIBRARY CACHE) find_cuda_helper_libs(nvcuvid) @@ -106,11 +99,11 @@ ocv_add_precompiled_headers(${the_module}) ################################################################################################################ file(GLOB test_srcs "test/*.cpp") file(GLOB test_hdrs "test/*.hpp" "test/*.h") + +set(nvidia "") if(HAVE_CUDA) file(GLOB nvidia "test/nvidia/*.cpp" "test/nvidia/*.hpp" "test/nvidia/*.h") set(nvidia FILES "Src\\\\\\\\NVidia" ${nvidia}) # 8 ugly backslashes :'( -else() - set(nvidia "") endif() ocv_add_accuracy_tests(FILES "Include" ${test_hdrs} diff --git a/modules/highgui/CMakeLists.txt b/modules/highgui/CMakeLists.txt index b1e2627..4bb4c62 100644 --- a/modules/highgui/CMakeLists.txt +++ b/modules/highgui/CMakeLists.txt @@ -134,7 +134,7 @@ endif() if(HAVE_OPENNI) list(APPEND highgui_srcs src/cap_openni.cpp) - include_directories(AFTER SYSTEM ${OPENNI_INCLUDE_DIR}) + ocv_include_directories(${OPENNI_INCLUDE_DIR}) list(APPEND HIGHGUI_LIBRARIES ${OPENNI_LIBRARY}) endif(HAVE_OPENNI) diff --git a/modules/highgui/src/cap_gstreamer.cpp b/modules/highgui/src/cap_gstreamer.cpp index bb9efa1..fd55435 100644 --- a/modules/highgui/src/cap_gstreamer.cpp +++ b/modules/highgui/src/cap_gstreamer.cpp @@ -283,7 +283,7 @@ void CvCapture_GStreamer::newPad(GstElement *uridecodebin, sinkpad = gst_element_get_static_pad (color, "sink"); - + // printf("linking dynamic pad to colourconverter %p %p\n", uridecodebin, pad); gst_pad_link (pad, sinkpad); @@ -357,13 +357,13 @@ bool CvCapture_GStreamer::open( int type, const char* filename ) if(manualpipeline) { GstIterator *it = gst_bin_iterate_sinks(GST_BIN(uridecodebin)); if(gst_iterator_next(it, (gpointer *)&sink) != GST_ITERATOR_OK) { - CV_ERROR(CV_StsError, "GStreamer: cannot find appsink in manual pipeline\n"); - return false; + CV_ERROR(CV_StsError, "GStreamer: cannot find appsink in manual pipeline\n"); + return false; } - pipeline = uridecodebin; + pipeline = uridecodebin; } else { - pipeline = gst_pipeline_new (NULL); + pipeline = gst_pipeline_new (NULL); color = gst_element_factory_make("ffmpegcolorspace", NULL); sink = gst_element_factory_make("appsink", NULL); @@ -381,16 +381,12 @@ bool CvCapture_GStreamer::open( int type, const char* filename ) gst_app_sink_set_max_buffers (GST_APP_SINK(sink), 1); gst_app_sink_set_drop (GST_APP_SINK(sink), stream); - { - GstCaps* caps; - caps = gst_caps_new_simple("video/x-raw-rgb", - "red_mask", G_TYPE_INT, 0x0000FF, - "green_mask", G_TYPE_INT, 0x00FF00, - "blue_mask", G_TYPE_INT, 0xFF0000, - NULL); - gst_app_sink_set_caps(GST_APP_SINK(sink), caps); + gst_app_sink_set_caps(GST_APP_SINK(sink), gst_caps_new_simple("video/x-raw-rgb", + "red_mask", G_TYPE_INT, 0x0000FF, + "green_mask", G_TYPE_INT, 0x00FF00, + "blue_mask", G_TYPE_INT, 0xFF0000, + NULL)); gst_caps_unref(caps); - } if(gst_element_set_state(GST_ELEMENT(pipeline), GST_STATE_READY) == GST_STATE_CHANGE_FAILURE) { diff --git a/modules/highgui/src/cap_libv4l.cpp b/modules/highgui/src/cap_libv4l.cpp index 233e6f6..c27224d 100644 --- a/modules/highgui/src/cap_libv4l.cpp +++ b/modules/highgui/src/cap_libv4l.cpp @@ -779,9 +779,9 @@ static int _capture_V4L2 (CvCaptureCAM_V4L *capture, char *deviceName) return -1; } else { buffer_number--; - fprintf (stderr, "Insufficient buffer memory on %s -- decreaseing buffers\n", deviceName); + fprintf (stderr, "Insufficient buffer memory on %s -- decreaseing buffers\n", deviceName); - goto try_again; + goto try_again; } } @@ -824,8 +824,8 @@ static int _capture_V4L2 (CvCaptureCAM_V4L *capture, char *deviceName) if (capture->buffers[MAX_V4L_BUFFERS].start) { free(capture->buffers[MAX_V4L_BUFFERS].start); capture->buffers[MAX_V4L_BUFFERS].start = NULL; - } - + } + capture->buffers[MAX_V4L_BUFFERS].start = malloc(buf.length); capture->buffers[MAX_V4L_BUFFERS].length = buf.length; }; @@ -1080,11 +1080,11 @@ static int read_frame_v4l2(CvCaptureCAM_V4L* capture) { #ifdef USE_TEMP_BUFFER memcpy(capture->buffers[MAX_V4L_BUFFERS].start, - capture->buffers[buf.index].start, - capture->buffers[MAX_V4L_BUFFERS].length ); + capture->buffers[buf.index].start, + capture->buffers[MAX_V4L_BUFFERS].length ); capture->bufferIndex = MAX_V4L_BUFFERS; //printf("got data in buff %d, len=%d, flags=0x%X, seq=%d, used=%d)\n", - // buf.index, buf.length, buf.flags, buf.sequence, buf.bytesused); + // buf.index, buf.length, buf.flags, buf.sequence, buf.bytesused); #else capture->bufferIndex = buf.index; #endif @@ -1211,9 +1211,9 @@ static int icvGrabFrameCAM_V4L(CvCaptureCAM_V4L* capture) { capture->mmaps[capture->bufferIndex].format = capture->imageProperties.palette; if (v4l1_ioctl (capture->deviceHandle, VIDIOCMCAPTURE, - &capture->mmaps[capture->bufferIndex]) == -1) { - /* capture is on the way, so just exit */ - return 1; + &capture->mmaps[capture->bufferIndex]) == -1) { + /* capture is on the way, so just exit */ + return 1; } ++capture->bufferIndex; @@ -1273,11 +1273,11 @@ static IplImage* icvRetrieveFrameCAM_V4L( CvCaptureCAM_V4L* capture, int) { if (capture->is_v4l2_device == 1) { - if(capture->buffers[capture->bufferIndex].start){ - memcpy((char *)capture->frame.imageData, - (char *)capture->buffers[capture->bufferIndex].start, - capture->frame.imageSize); - } + if(capture->buffers[capture->bufferIndex].start){ + memcpy((char *)capture->frame.imageData, + (char *)capture->buffers[capture->bufferIndex].start, + capture->frame.imageSize); + } } else #endif /* HAVE_CAMV4L2 */ @@ -1353,7 +1353,7 @@ static double icvGetPropertyCAM_V4L (CvCaptureCAM_V4L* capture, sprintf(name, ""); capture->control.id = property_id; } - + if(v4l2_ioctl(capture->deviceHandle, VIDIOC_G_CTRL, &capture->control) == 0) { /* all went well */ is_v4l2_device = 1; @@ -1519,7 +1519,7 @@ static int icvSetControl (CvCaptureCAM_V4L* capture, int property_id, double val CLEAR (capture->control); CLEAR (capture->queryctrl); - + /* get current values */ switch (property_id) { case CV_CAP_PROP_BRIGHTNESS: @@ -1688,8 +1688,8 @@ static void icvCloseCAM_V4L( CvCaptureCAM_V4L* capture ){ if (xioctl(capture->deviceHandle, VIDIOC_STREAMOFF, &capture->type) < 0) { perror ("Unable to stop the stream."); } - for (unsigned int n_buffers = 0; n_buffers < capture->req.count; ++n_buffers) { - if (-1 == v4l2_munmap (capture->buffers[n_buffers].start, capture->buffers[n_buffers].length)) { + for (unsigned int n_buffers2 = 0; n_buffers2 < capture->req.count; ++n_buffers2) { + if (-1 == v4l2_munmap (capture->buffers[n_buffers2].start, capture->buffers[n_buffers2].length)) { perror ("munmap"); } } diff --git a/modules/highgui/src/grfmt_png.cpp b/modules/highgui/src/grfmt_png.cpp index dac017f..3a05993 100644 --- a/modules/highgui/src/grfmt_png.cpp +++ b/modules/highgui/src/grfmt_png.cpp @@ -157,22 +157,22 @@ bool PngDecoder::readHeader() if( !m_buf.empty() || m_f ) { - png_uint_32 width, height; + png_uint_32 wdth, hght; int bit_depth, color_type; png_read_info( png_ptr, info_ptr ); - png_get_IHDR( png_ptr, info_ptr, &width, &height, + png_get_IHDR( png_ptr, info_ptr, &wdth, &hght, &bit_depth, &color_type, 0, 0, 0 ); - m_width = (int)width; - m_height = (int)height; + m_width = (int)wdth; + m_height = (int)hght; m_color_type = color_type; m_bit_depth = bit_depth; if( bit_depth <= 8 || bit_depth == 16 ) { - switch(color_type) + switch(color_type) { case PNG_COLOR_TYPE_RGB: case PNG_COLOR_TYPE_PALETTE: @@ -224,7 +224,7 @@ bool PngDecoder::readData( Mat& img ) else if( !isBigEndian() ) png_set_swap( png_ptr ); - if(img.channels() < 4) + if(img.channels() < 4) { /* observation: png_read_image() writes 400 bytes beyond * end of data when reading a 400x118 color png @@ -247,7 +247,7 @@ bool PngDecoder::readData( Mat& img ) #else png_set_gray_1_2_4_to_8( png_ptr ); #endif - + if( CV_MAT_CN(m_type) > 1 && color ) png_set_bgr( png_ptr ); // convert RGB to BGR else if( color ) @@ -330,7 +330,7 @@ bool PngEncoder::write( const Mat& img, const vector& params ) if( params[i] == CV_IMWRITE_PNG_STRATEGY ) { compression_strategy = params[i+1]; - compression_strategy = MIN(MAX(compression_strategy, 0), Z_FIXED); + compression_strategy = MIN(MAX(compression_strategy, 0), Z_FIXED); } } diff --git a/modules/highgui/src/grfmt_tiff.cpp b/modules/highgui/src/grfmt_tiff.cpp index 686acf0..eba54bc 100644 --- a/modules/highgui/src/grfmt_tiff.cpp +++ b/modules/highgui/src/grfmt_tiff.cpp @@ -115,19 +115,19 @@ bool TiffDecoder::readHeader() if( tif ) { - int width = 0, height = 0, photometric = 0; + int wdth = 0, hght = 0, photometric = 0; m_tif = tif; - if( TIFFGetField( tif, TIFFTAG_IMAGEWIDTH, &width ) && - TIFFGetField( tif, TIFFTAG_IMAGELENGTH, &height ) && + if( TIFFGetField( tif, TIFFTAG_IMAGEWIDTH, &wdth ) && + TIFFGetField( tif, TIFFTAG_IMAGELENGTH, &hght ) && TIFFGetField( tif, TIFFTAG_PHOTOMETRIC, &photometric )) { int bpp=8, ncn = photometric > 1 ? 3 : 1; TIFFGetField( tif, TIFFTAG_BITSPERSAMPLE, &bpp ); TIFFGetField( tif, TIFFTAG_SAMPLESPERPIXEL, &ncn ); - - m_width = width; - m_height = height; + + m_width = wdth; + m_height = hght; if( bpp > 8 && ((photometric != 2 && photometric != 1) || (ncn != 1 && ncn != 3 && ncn != 4))) @@ -169,7 +169,7 @@ bool TiffDecoder::readData( Mat& img ) bool color = img.channels() > 1; uchar* data = img.data; int step = (int)img.step; - + if( img.depth() != CV_8U && img.depth() != CV_16U && img.depth() != CV_32F && img.depth() != CV_64F ) return false; @@ -422,9 +422,9 @@ bool TiffEncoder::writeLibTiff( const Mat& img, const vector& /*params*/) default: { return false; - } + } } - + const int bitsPerByte = 8; size_t fileStep = (width * channels * bitsPerChannel) / bitsPerByte; int rowsPerStrip = (int)((1 << 13)/fileStep); @@ -443,7 +443,7 @@ bool TiffEncoder::writeLibTiff( const Mat& img, const vector& /*params*/) { return false; } - + // defaults for now, maybe base them on params in the future int compression = COMPRESSION_LZW; int predictor = PREDICTOR_HORIZONTAL; @@ -516,7 +516,7 @@ bool TiffEncoder::writeLibTiff( const Mat& img, const vector& /*params*/) return false; } } - + TIFFClose(pTiffHandle); return true; } @@ -546,7 +546,7 @@ bool TiffEncoder::write( const Mat& img, const vector& /*params*/) if( !strm.open(*m_buf) ) return false; } - else + else { #ifdef HAVE_TIFF return writeLibTiff(img, params); diff --git a/modules/highgui/test/test_drawing.cpp b/modules/highgui/test/test_drawing.cpp index 4b45a7b..09590b3 100644 --- a/modules/highgui/test/test_drawing.cpp +++ b/modules/highgui/test/test_drawing.cpp @@ -60,9 +60,9 @@ protected: void CV_DrawingTest::run( int ) { Mat testImg, valImg; - const string name = "drawing/image.jpg"; + const string fname = "drawing/image.jpg"; string path = ts->get_data_path(), filename; - filename = path + name; + filename = path + fname; draw( testImg ); @@ -415,30 +415,30 @@ class CV_FillConvexPolyTest : public cvtest::BaseTest { public: CV_FillConvexPolyTest() {} - ~CV_FillConvexPolyTest() {} + ~CV_FillConvexPolyTest() {} protected: void run(int) { vector line1; vector line2; - + line1.push_back(Point(1, 1)); line1.push_back(Point(5, 1)); line1.push_back(Point(5, 8)); line1.push_back(Point(1, 8)); - + line2.push_back(Point(2, 2)); line2.push_back(Point(10, 2)); line2.push_back(Point(10, 16)); line2.push_back(Point(2, 16)); - + Mat gray0(10,10,CV_8U, Scalar(0)); fillConvexPoly(gray0, line1, Scalar(255), 8, 0); int nz1 = countNonZero(gray0); - + fillConvexPoly(gray0, line2, Scalar(0), 8, 1); int nz2 = countNonZero(gray0)/255; - + CV_Assert( nz1 == 40 && nz2 == 0 ); } }; diff --git a/modules/highgui/test/test_video_io.cpp b/modules/highgui/test/test_video_io.cpp index 51c6e26..3134b8e 100644 --- a/modules/highgui/test/test_video_io.cpp +++ b/modules/highgui/test/test_video_io.cpp @@ -53,7 +53,7 @@ string fourccToString(int fourcc) { return format("%c%c%c%c", fourcc & 255, (fourcc >> 8) & 255, (fourcc >> 16) & 255, (fourcc >> 24) & 255); } - + const VideoFormat g_specific_fmt_list[] = { VideoFormat("avi", CV_FOURCC('X', 'V', 'I', 'D')), @@ -63,11 +63,11 @@ const VideoFormat g_specific_fmt_list[] = VideoFormat("mkv", CV_FOURCC('X', 'V', 'I', 'D')), VideoFormat("mkv", CV_FOURCC('M', 'P', 'E', 'G')), VideoFormat("mkv", CV_FOURCC('M', 'J', 'P', 'G')), - + VideoFormat("mov", CV_FOURCC('m', 'p', '4', 'v')), VideoFormat() }; - + } class CV_HighGuiTest : public cvtest::BaseTest @@ -246,7 +246,7 @@ void CV_HighGuiTest::VideoTest(const string& dir, const cvtest::VideoFormat& fmt if (!img) break; - + frames.push_back(Mat(img).clone()); if (writer == 0) @@ -393,7 +393,7 @@ void CV_HighGuiTest::SpecificVideoTest(const string& dir, const cvtest::VideoFor { string ext = fmt.ext; int fourcc = fmt.fourcc; - + string fourcc_str = cvtest::fourccToString(fourcc); const string video_file = "video_" + fourcc_str + "." + ext; @@ -403,7 +403,7 @@ void CV_HighGuiTest::SpecificVideoTest(const string& dir, const cvtest::VideoFor if (!writer.isOpened()) { // call it repeatedly for easier debugging - VideoWriter writer(video_file, fourcc, 25, frame_size, true); + VideoWriter writer2(video_file, fourcc, 25, frame_size, true); ts->printf(ts->LOG, "Creating a video in %s...\n", video_file.c_str()); ts->printf(ts->LOG, "Cannot create VideoWriter object with codec %s.\n", fourcc_str.c_str()); ts->set_failed_test_info(ts->FAIL_MISMATCH); @@ -412,7 +412,7 @@ void CV_HighGuiTest::SpecificVideoTest(const string& dir, const cvtest::VideoFor const size_t IMAGE_COUNT = 30; vector images; - + for( size_t i = 0; i < IMAGE_COUNT; ++i ) { string file_path = format("%s../python/images/QCIF_%02d.bmp", dir.c_str(), i); @@ -432,7 +432,7 @@ void CV_HighGuiTest::SpecificVideoTest(const string& dir, const cvtest::VideoFor if (img.at(k, l) == Vec3b::all(0)) img.at(k, l) = Vec3b(0, 255, 0); else img.at(k, l) = Vec3b(0, 0, 255); - + resize(img, img, frame_size, 0.0, 0.0, INTER_CUBIC); images.push_back(img); diff --git a/modules/imgproc/src/color.cpp b/modules/imgproc/src/color.cpp index f887837..42f7009 100644 --- a/modules/imgproc/src/color.cpp +++ b/modules/imgproc/src/color.cpp @@ -63,18 +63,18 @@ 44141 Dortmund Germany www.md-it.de - + Redistribution and use in source and binary forms, with or without modification, are permitted provided - that the following conditions are met: + that the following conditions are met: Redistributions of source code must retain - the above copyright notice, this list of conditions and the following disclaimer. + the above copyright notice, this list of conditions and the following disclaimer. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation - and/or other materials provided with the distribution. + and/or other materials provided with the distribution. The name of Contributor may not be used to endorse or promote products - derived from this software without specific prior written permission. + derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, @@ -102,14 +102,14 @@ template static void splineBuild(const _Tp* f, int n, _Tp* tab) _Tp cn = 0; int i; tab[0] = tab[1] = (_Tp)0; - + for(i = 1; i < n-1; i++) { _Tp t = 3*(f[i+1] - 2*f[i] + f[i-1]); _Tp l = 1/(4 - tab[(i-1)*4]); tab[i*4] = l; tab[i*4+1] = (t - tab[(i-1)*4+1])*l; } - + for(i = n-1; i >= 0; i--) { _Tp c = tab[i*4+1] - tab[i*4]*cn; @@ -129,14 +129,14 @@ template static inline _Tp splineInterpolate(_Tp x, const _Tp* tab x -= ix; tab += ix*4; return ((tab[3]*x + tab[2])*x + tab[1])*x + tab[0]; -} +} + - template struct ColorChannel { typedef float worktype_f; static _Tp max() { return std::numeric_limits<_Tp>::max(); } - static _Tp half() { return (_Tp)(max()/2 + 1); } + static _Tp half() { return (_Tp)(max()/2 + 1); } }; template<> struct ColorChannel @@ -153,7 +153,7 @@ template<> struct ColorChannel static double half() { return 0.5; } };*/ - + ///////////////////////////// Top-level template function //////////////////////////////// template void CvtColorLoop(const Mat& srcmat, Mat& dstmat, const Cvt& cvt) @@ -163,24 +163,24 @@ template void CvtColorLoop(const Mat& srcmat, Mat& dstmat, const Cvt& const uchar* src = srcmat.data; uchar* dst = dstmat.data; size_t srcstep = srcmat.step, dststep = dstmat.step; - + if( srcmat.isContinuous() && dstmat.isContinuous() ) { sz.width *= sz.height; sz.height = 1; - } - + } + for( ; sz.height--; src += srcstep, dst += dststep ) cvt((const _Tp*)src, (_Tp*)dst, sz.width); } - - + + ////////////////// Various 3/4-channel to 3/4-channel RGB transformations ///////////////// - + template struct RGB2RGB { typedef _Tp channel_type; - + RGB2RGB(int _srccn, int _dstcn, int _blueIdx) : srccn(_srccn), dstcn(_dstcn), blueIdx(_blueIdx) {} void operator()(const _Tp* src, _Tp* dst, int n) const { @@ -214,19 +214,19 @@ template struct RGB2RGB } } } - + int srccn, dstcn, blueIdx; }; - + /////////// Transforming 16-bit (565 or 555) RGB to/from 24/32-bit (888[8]) RGB ////////// struct RGB5x52RGB { typedef uchar channel_type; - + RGB5x52RGB(int _dstcn, int _blueIdx, int _greenBits) - : dstcn(_dstcn), blueIdx(_blueIdx), greenBits(_greenBits) {} - + : dstcn(_dstcn), blueIdx(_blueIdx), greenBits(_greenBits) {} + void operator()(const uchar* src, uchar* dst, int n) const { int dcn = dstcn, bidx = blueIdx; @@ -251,18 +251,18 @@ struct RGB5x52RGB dst[3] = t & 0x8000 ? 255 : 0; } } - + int dstcn, blueIdx, greenBits; }; - + struct RGB2RGB5x5 { typedef uchar channel_type; - + RGB2RGB5x5(int _srccn, int _blueIdx, int _greenBits) - : srccn(_srccn), blueIdx(_blueIdx), greenBits(_greenBits) {} - + : srccn(_srccn), blueIdx(_blueIdx), greenBits(_greenBits) {} + void operator()(const uchar* src, uchar* dst, int n) const { int scn = srccn, bidx = blueIdx; @@ -283,17 +283,17 @@ struct RGB2RGB5x5 ((src[bidx^2]&~7) << 7)|(src[3] ? 0x8000 : 0)); } } - + int srccn, blueIdx, greenBits; }; - + ///////////////////////////////// Color to/from Grayscale //////////////////////////////// template struct Gray2RGB { typedef _Tp channel_type; - + Gray2RGB(int _dstcn) : dstcn(_dstcn) {} void operator()(const _Tp* src, _Tp* dst, int n) const { @@ -312,15 +312,15 @@ struct Gray2RGB } } } - + int dstcn; }; - + struct Gray2RGB5x5 { typedef uchar channel_type; - + Gray2RGB5x5(int _greenBits) : greenBits(_greenBits) {} void operator()(const uchar* src, uchar* dst, int n) const { @@ -344,7 +344,7 @@ struct Gray2RGB5x5 #undef R2Y #undef G2Y #undef B2Y - + enum { yuv_shift = 14, @@ -359,7 +359,7 @@ enum struct RGB5x52Gray { typedef uchar channel_type; - + RGB5x52Gray(int _greenBits) : greenBits(_greenBits) {} void operator()(const uchar* src, uchar* dst, int n) const { @@ -387,7 +387,7 @@ struct RGB5x52Gray template struct RGB2Gray { typedef _Tp channel_type; - + RGB2Gray(int _srccn, int blueIdx, const float* _coeffs) : srccn(_srccn) { static const float coeffs0[] = { 0.299f, 0.587f, 0.114f }; @@ -395,7 +395,7 @@ template struct RGB2Gray if(blueIdx == 0) std::swap(coeffs[0], coeffs[2]); } - + void operator()(const _Tp* src, _Tp* dst, int n) const { int scn = srccn; @@ -407,19 +407,19 @@ template struct RGB2Gray float coeffs[3]; }; - + template<> struct RGB2Gray { typedef uchar channel_type; - + RGB2Gray(int _srccn, int blueIdx, const int* coeffs) : srccn(_srccn) { const int coeffs0[] = { R2Y, G2Y, B2Y }; if(!coeffs) coeffs = coeffs0; - + int b = 0, g = 0, r = (1 << (yuv_shift-1)); int db = coeffs[blueIdx^2], dg = coeffs[1], dr = coeffs[blueIdx]; - + for( int i = 0; i < 256; i++, b += db, g += dg, r += dr ) { tab[i] = b; @@ -430,19 +430,19 @@ template<> struct RGB2Gray void operator()(const uchar* src, uchar* dst, int n) const { int scn = srccn; - const int* _tab = tab; + const int* _tab = tab; for(int i = 0; i < n; i++, src += scn) dst[i] = (uchar)((_tab[src[0]] + _tab[src[1]+256] + _tab[src[2]+512]) >> yuv_shift); } - int srccn, blueIdx; + int srccn; int tab[256*3]; }; - + template<> struct RGB2Gray { typedef ushort channel_type; - + RGB2Gray(int _srccn, int blueIdx, const int* _coeffs) : srccn(_srccn) { static const int coeffs0[] = { R2Y, G2Y, B2Y }; @@ -450,7 +450,7 @@ template<> struct RGB2Gray if( blueIdx == 0 ) std::swap(coeffs[0], coeffs[2]); } - + void operator()(const ushort* src, ushort* dst, int n) const { int scn = srccn, cb = coeffs[0], cg = coeffs[1], cr = coeffs[2]; @@ -461,25 +461,25 @@ template<> struct RGB2Gray int coeffs[3]; }; - + ///////////////////////////////////// RGB <-> YCrCb ////////////////////////////////////// template struct RGB2YCrCb_f { typedef _Tp channel_type; - + RGB2YCrCb_f(int _srccn, int _blueIdx, const float* _coeffs) : srccn(_srccn), blueIdx(_blueIdx) - { - static const float coeffs0[] = {0.299f, 0.587f, 0.114f, 0.713f, 0.564f}; - memcpy(coeffs, _coeffs ? _coeffs : coeffs0, 5*sizeof(coeffs[0])); - if(blueIdx==0) std::swap(coeffs[0], coeffs[2]); - } - + { + static const float coeffs0[] = {0.299f, 0.587f, 0.114f, 0.713f, 0.564f}; + memcpy(coeffs, _coeffs ? _coeffs : coeffs0, 5*sizeof(coeffs[0])); + if(blueIdx==0) std::swap(coeffs[0], coeffs[2]); + } + void operator()(const _Tp* src, _Tp* dst, int n) const { int scn = srccn, bidx = blueIdx; const _Tp delta = ColorChannel<_Tp>::half(); - float C0 = coeffs[0], C1 = coeffs[1], C2 = coeffs[2], C3 = coeffs[3], C4 = coeffs[4]; + float C0 = coeffs[0], C1 = coeffs[1], C2 = coeffs[2], C3 = coeffs[3], C4 = coeffs[4]; n *= 3; for(int i = 0; i < n; i += 3, src += scn) { @@ -490,25 +490,25 @@ template struct RGB2YCrCb_f } } int srccn, blueIdx; - float coeffs[5]; + float coeffs[5]; }; template struct RGB2YCrCb_i { typedef _Tp channel_type; - + RGB2YCrCb_i(int _srccn, int _blueIdx, const int* _coeffs) - : srccn(_srccn), blueIdx(_blueIdx) - { - static const int coeffs0[] = {R2Y, G2Y, B2Y, 11682, 9241}; - memcpy(coeffs, _coeffs ? _coeffs : coeffs0, 5*sizeof(coeffs[0])); - if(blueIdx==0) std::swap(coeffs[0], coeffs[2]); - } + : srccn(_srccn), blueIdx(_blueIdx) + { + static const int coeffs0[] = {R2Y, G2Y, B2Y, 11682, 9241}; + memcpy(coeffs, _coeffs ? _coeffs : coeffs0, 5*sizeof(coeffs[0])); + if(blueIdx==0) std::swap(coeffs[0], coeffs[2]); + } void operator()(const _Tp* src, _Tp* dst, int n) const { int scn = srccn, bidx = blueIdx; - int C0 = coeffs[0], C1 = coeffs[1], C2 = coeffs[2], C3 = coeffs[3], C4 = coeffs[4]; + int C0 = coeffs[0], C1 = coeffs[1], C2 = coeffs[2], C3 = coeffs[3], C4 = coeffs[4]; int delta = ColorChannel<_Tp>::half()*(1 << yuv_shift); n *= 3; for(int i = 0; i < n; i += 3, src += scn) @@ -522,20 +522,20 @@ template struct RGB2YCrCb_i } } int srccn, blueIdx; - int coeffs[5]; -}; + int coeffs[5]; +}; template struct YCrCb2RGB_f { typedef _Tp channel_type; - + YCrCb2RGB_f(int _dstcn, int _blueIdx, const float* _coeffs) - : dstcn(_dstcn), blueIdx(_blueIdx) - { - static const float coeffs0[] = {1.403f, -0.714f, -0.344f, 1.773f}; - memcpy(coeffs, _coeffs ? _coeffs : coeffs0, 4*sizeof(coeffs[0])); - } + : dstcn(_dstcn), blueIdx(_blueIdx) + { + static const float coeffs0[] = {1.403f, -0.714f, -0.344f, 1.773f}; + memcpy(coeffs, _coeffs ? _coeffs : coeffs0, 4*sizeof(coeffs[0])); + } void operator()(const _Tp* src, _Tp* dst, int n) const { int dcn = dstcn, bidx = blueIdx; @@ -547,32 +547,32 @@ template struct YCrCb2RGB_f _Tp Y = src[i]; _Tp Cr = src[i+1]; _Tp Cb = src[i+2]; - + _Tp b = saturate_cast<_Tp>(Y + (Cb - delta)*C3); _Tp g = saturate_cast<_Tp>(Y + (Cb - delta)*C2 + (Cr - delta)*C1); _Tp r = saturate_cast<_Tp>(Y + (Cr - delta)*C0); - + dst[bidx] = b; dst[1] = g; dst[bidx^2] = r; if( dcn == 4 ) dst[3] = alpha; } } int dstcn, blueIdx; - float coeffs[4]; -}; + float coeffs[4]; +}; template struct YCrCb2RGB_i { typedef _Tp channel_type; - + YCrCb2RGB_i(int _dstcn, int _blueIdx, const int* _coeffs) : dstcn(_dstcn), blueIdx(_blueIdx) { - static const int coeffs0[] = {22987, -11698, -5636, 29049}; - memcpy(coeffs, _coeffs ? _coeffs : coeffs0, 4*sizeof(coeffs[0])); + static const int coeffs0[] = {22987, -11698, -5636, 29049}; + memcpy(coeffs, _coeffs ? _coeffs : coeffs0, 4*sizeof(coeffs[0])); } - + void operator()(const _Tp* src, _Tp* dst, int n) const { int dcn = dstcn, bidx = blueIdx; @@ -584,11 +584,11 @@ template struct YCrCb2RGB_i _Tp Y = src[i]; _Tp Cr = src[i+1]; _Tp Cb = src[i+2]; - + int b = Y + CV_DESCALE((Cb - delta)*C3, yuv_shift); int g = Y + CV_DESCALE((Cb - delta)*C2 + (Cr - delta)*C1, yuv_shift); int r = Y + CV_DESCALE((Cr - delta)*C0, yuv_shift); - + dst[bidx] = saturate_cast<_Tp>(b); dst[1] = saturate_cast<_Tp>(g); dst[bidx^2] = saturate_cast<_Tp>(r); @@ -598,9 +598,9 @@ template struct YCrCb2RGB_i } int dstcn, blueIdx; int coeffs[4]; -}; +}; + - ////////////////////////////////////// RGB <-> XYZ /////////////////////////////////////// static const float sRGB2XYZ_D65[] = @@ -609,18 +609,18 @@ static const float sRGB2XYZ_D65[] = 0.212671f, 0.715160f, 0.072169f, 0.019334f, 0.119193f, 0.950227f }; - + static const float XYZ2sRGB_D65[] = { 3.240479f, -1.53715f, -0.498535f, -0.969256f, 1.875991f, 0.041556f, 0.055648f, -0.204043f, 1.057311f }; - + template struct RGB2XYZ_f { typedef _Tp channel_type; - + RGB2XYZ_f(int _srccn, int blueIdx, const float* _coeffs) : srccn(_srccn) { memcpy(coeffs, _coeffs ? _coeffs : sRGB2XYZ_D65, 9*sizeof(coeffs[0])); @@ -637,13 +637,13 @@ template struct RGB2XYZ_f float C0 = coeffs[0], C1 = coeffs[1], C2 = coeffs[2], C3 = coeffs[3], C4 = coeffs[4], C5 = coeffs[5], C6 = coeffs[6], C7 = coeffs[7], C8 = coeffs[8]; - + n *= 3; for(int i = 0; i < n; i += 3, src += scn) { - _Tp X = saturate_cast<_Tp>(src[0]*C0 + src[1]*C1 + src[2]*C2); - _Tp Y = saturate_cast<_Tp>(src[0]*C3 + src[1]*C4 + src[2]*C5); - _Tp Z = saturate_cast<_Tp>(src[0]*C6 + src[1]*C7 + src[2]*C8); + _Tp X = saturate_cast<_Tp>(src[0]*C0 + src[1]*C1 + src[2]*C2); + _Tp Y = saturate_cast<_Tp>(src[0]*C3 + src[1]*C4 + src[2]*C5); + _Tp Z = saturate_cast<_Tp>(src[0]*C6 + src[1]*C7 + src[2]*C8); dst[i] = X; dst[i+1] = Y; dst[i+2] = Z; } } @@ -655,13 +655,13 @@ template struct RGB2XYZ_f template struct RGB2XYZ_i { typedef _Tp channel_type; - + RGB2XYZ_i(int _srccn, int blueIdx, const float* _coeffs) : srccn(_srccn) { static const int coeffs0[] = { - 1689, 1465, 739, - 871, 2929, 296, + 1689, 1465, 739, + 871, 2929, 296, 79, 488, 3892 }; for( int i = 0; i < 9; i++ ) @@ -692,12 +692,12 @@ template struct RGB2XYZ_i int srccn; int coeffs[9]; }; - - + + template struct XYZ2RGB_f { typedef _Tp channel_type; - + XYZ2RGB_f(int _dstcn, int _blueIdx, const float* _coeffs) : dstcn(_dstcn), blueIdx(_blueIdx) { @@ -709,23 +709,23 @@ template struct XYZ2RGB_f std::swap(coeffs[2], coeffs[8]); } } - + void operator()(const _Tp* src, _Tp* dst, int n) const { int dcn = dstcn; - _Tp alpha = ColorChannel<_Tp>::max(); + _Tp alpha = ColorChannel<_Tp>::max(); float C0 = coeffs[0], C1 = coeffs[1], C2 = coeffs[2], C3 = coeffs[3], C4 = coeffs[4], C5 = coeffs[5], C6 = coeffs[6], C7 = coeffs[7], C8 = coeffs[8]; n *= 3; for(int i = 0; i < n; i += 3, dst += dcn) { - _Tp B = saturate_cast<_Tp>(src[i]*C0 + src[i+1]*C1 + src[i+2]*C2); - _Tp G = saturate_cast<_Tp>(src[i]*C3 + src[i+1]*C4 + src[i+2]*C5); - _Tp R = saturate_cast<_Tp>(src[i]*C6 + src[i+1]*C7 + src[i+2]*C8); + _Tp B = saturate_cast<_Tp>(src[i]*C0 + src[i+1]*C1 + src[i+2]*C2); + _Tp G = saturate_cast<_Tp>(src[i]*C3 + src[i+1]*C4 + src[i+2]*C5); + _Tp R = saturate_cast<_Tp>(src[i]*C6 + src[i+1]*C7 + src[i+2]*C8); dst[0] = B; dst[1] = G; dst[2] = R; - if( dcn == 4 ) - dst[3] = alpha; + if( dcn == 4 ) + dst[3] = alpha; } } int dstcn, blueIdx; @@ -736,19 +736,19 @@ template struct XYZ2RGB_f template struct XYZ2RGB_i { typedef _Tp channel_type; - + XYZ2RGB_i(int _dstcn, int _blueIdx, const int* _coeffs) : dstcn(_dstcn), blueIdx(_blueIdx) { static const int coeffs0[] = { - 13273, -6296, -2042, - -3970, 7684, 170, + 13273, -6296, -2042, + -3970, 7684, 170, 228, -836, 4331 }; for(int i = 0; i < 9; i++) coeffs[i] = _coeffs ? cvRound(_coeffs[i]*(1 << xyz_shift)) : coeffs0[i]; - + if(blueIdx == 0) { std::swap(coeffs[0], coeffs[6]); @@ -772,13 +772,13 @@ template struct XYZ2RGB_i dst[0] = saturate_cast<_Tp>(B); dst[1] = saturate_cast<_Tp>(G); dst[2] = saturate_cast<_Tp>(R); if( dcn == 4 ) - dst[3] = alpha; + dst[3] = alpha; } } int dstcn, blueIdx; int coeffs[9]; }; - + ////////////////////////////////////// RGB <-> HSV /////////////////////////////////////// @@ -786,27 +786,27 @@ template struct XYZ2RGB_i struct RGB2HSV_b { typedef uchar channel_type; - + RGB2HSV_b(int _srccn, int _blueIdx, int _hrange) : srccn(_srccn), blueIdx(_blueIdx), hrange(_hrange) { CV_Assert( hrange == 180 || hrange == 256 ); } - + void operator()(const uchar* src, uchar* dst, int n) const { int i, bidx = blueIdx, scn = srccn; const int hsv_shift = 12; - + static int sdiv_table[256]; static int hdiv_table180[256]; static int hdiv_table256[256]; static volatile bool initialized = false; - + int hr = hrange; const int* hdiv_table = hr == 180 ? hdiv_table180 : hdiv_table256; n *= 3; - + if( !initialized ) { sdiv_table[0] = hdiv_table180[0] = hdiv_table256[0] = 0; @@ -818,65 +818,65 @@ struct RGB2HSV_b } initialized = true; } - + for( i = 0; i < n; i += 3, src += scn ) { int b = src[bidx], g = src[1], r = src[bidx^2]; int h, s, v = b; int vmin = b, diff; int vr, vg; - + CV_CALC_MAX_8U( v, g ); CV_CALC_MAX_8U( v, r ); CV_CALC_MIN_8U( vmin, g ); CV_CALC_MIN_8U( vmin, r ); - + diff = v - vmin; vr = v == r ? -1 : 0; vg = v == g ? -1 : 0; - + s = (diff * sdiv_table[v] + (1 << (hsv_shift-1))) >> hsv_shift; h = (vr & (g - b)) + (~vr & ((vg & (b - r + 2 * diff)) + ((~vg) & (r - g + 4 * diff)))); h = (h * hdiv_table[diff] + (1 << (hsv_shift-1))) >> hsv_shift; h += h < 0 ? hr : 0; - + dst[i] = saturate_cast(h); dst[i+1] = (uchar)s; dst[i+2] = (uchar)v; } } - + int srccn, blueIdx, hrange; -}; +}; + - struct RGB2HSV_f { typedef float channel_type; - + RGB2HSV_f(int _srccn, int _blueIdx, float _hrange) : srccn(_srccn), blueIdx(_blueIdx), hrange(_hrange) {} - + void operator()(const float* src, float* dst, int n) const { int i, bidx = blueIdx, scn = srccn; float hscale = hrange*(1.f/360.f); n *= 3; - + for( i = 0; i < n; i += 3, src += scn ) { float b = src[bidx], g = src[1], r = src[bidx^2]; float h, s, v; - + float vmin, diff; - + v = vmin = r; if( v < g ) v = g; if( v < b ) v = b; if( vmin > g ) vmin = g; if( vmin > b ) vmin = b; - + diff = v - vmin; s = diff/(float)(fabs(v) + FLT_EPSILON); diff = (float)(60./(diff + FLT_EPSILON)); @@ -886,15 +886,15 @@ struct RGB2HSV_f h = (b - r)*diff + 120.f; else h = (r - g)*diff + 240.f; - + if( h < 0 ) h += 360.f; - + dst[i] = h*hscale; dst[i+1] = s; dst[i+2] = v; } } - + int srccn, blueIdx; float hrange; }; @@ -903,17 +903,17 @@ struct RGB2HSV_f struct HSV2RGB_f { typedef float channel_type; - + HSV2RGB_f(int _dstcn, int _blueIdx, float _hrange) : dstcn(_dstcn), blueIdx(_blueIdx), hscale(6.f/_hrange) {} - + void operator()(const float* src, float* dst, int n) const { int i, bidx = blueIdx, dcn = dstcn; float _hscale = hscale; float alpha = ColorChannel::max(); n *= 3; - + for( i = 0; i < n; i += 3, dst += dcn ) { float h = src[i], s = src[i+1], v = src[i+2]; @@ -939,7 +939,7 @@ struct HSV2RGB_f tab[1] = v*(1.f - s); tab[2] = v*(1.f - s*h); tab[3] = v*(1.f - s*(1.f - h)); - + b = tab[sector_data[sector][0]]; g = tab[sector_data[sector][1]]; r = tab[sector_data[sector][2]]; @@ -956,26 +956,26 @@ struct HSV2RGB_f int dstcn, blueIdx; float hscale; }; - + struct HSV2RGB_b { typedef uchar channel_type; - + HSV2RGB_b(int _dstcn, int _blueIdx, int _hrange) : dstcn(_dstcn), cvt(3, _blueIdx, (float)_hrange) {} - + void operator()(const uchar* src, uchar* dst, int n) const { int i, j, dcn = dstcn; uchar alpha = ColorChannel::max(); float buf[3*BLOCK_SIZE]; - + for( i = 0; i < n; i += BLOCK_SIZE, src += BLOCK_SIZE*3 ) { int dn = std::min(n - i, (int)BLOCK_SIZE); - + for( j = 0; j < dn*3; j += 3 ) { buf[j] = src[j]; @@ -983,7 +983,7 @@ struct HSV2RGB_b buf[j+2] = src[j+2]*(1.f/255.f); } cvt(buf, buf, dn); - + for( j = 0; j < dn*3; j += 3, dst += dcn ) { dst[0] = saturate_cast(buf[j]*255.f); @@ -994,84 +994,84 @@ struct HSV2RGB_b } } } - + int dstcn; HSV2RGB_f cvt; }; - + ///////////////////////////////////// RGB <-> HLS //////////////////////////////////////// struct RGB2HLS_f { typedef float channel_type; - + RGB2HLS_f(int _srccn, int _blueIdx, float _hrange) : srccn(_srccn), blueIdx(_blueIdx), hrange(_hrange) {} - + void operator()(const float* src, float* dst, int n) const { int i, bidx = blueIdx, scn = srccn; float hscale = hrange*(1.f/360.f); n *= 3; - + for( i = 0; i < n; i += 3, src += scn ) { float b = src[bidx], g = src[1], r = src[bidx^2]; float h = 0.f, s = 0.f, l; float vmin, vmax, diff; - + vmax = vmin = r; if( vmax < g ) vmax = g; if( vmax < b ) vmax = b; if( vmin > g ) vmin = g; if( vmin > b ) vmin = b; - + diff = vmax - vmin; l = (vmax + vmin)*0.5f; - + if( diff > FLT_EPSILON ) { s = l < 0.5f ? diff/(vmax + vmin) : diff/(2 - vmax - vmin); diff = 60.f/diff; - + if( vmax == r ) h = (g - b)*diff; else if( vmax == g ) h = (b - r)*diff + 120.f; else h = (r - g)*diff + 240.f; - + if( h < 0.f ) h += 360.f; } - + dst[i] = h*hscale; dst[i+1] = l; dst[i+2] = s; } } - + int srccn, blueIdx; float hrange; }; - - + + struct RGB2HLS_b { typedef uchar channel_type; - + RGB2HLS_b(int _srccn, int _blueIdx, int _hrange) : srccn(_srccn), cvt(3, _blueIdx, (float)_hrange) {} - + void operator()(const uchar* src, uchar* dst, int n) const { int i, j, scn = srccn; float buf[3*BLOCK_SIZE]; - + for( i = 0; i < n; i += BLOCK_SIZE, dst += BLOCK_SIZE*3 ) { int dn = std::min(n - i, (int)BLOCK_SIZE); - + for( j = 0; j < dn*3; j += 3, src += scn ) { buf[j] = src[0]*(1.f/255.f); @@ -1079,7 +1079,7 @@ struct RGB2HLS_b buf[j+2] = src[2]*(1.f/255.f); } cvt(buf, buf, dn); - + for( j = 0; j < dn*3; j += 3 ) { dst[j] = saturate_cast(buf[j]); @@ -1088,31 +1088,31 @@ struct RGB2HLS_b } } } - + int srccn; RGB2HLS_f cvt; }; - + struct HLS2RGB_f { typedef float channel_type; - + HLS2RGB_f(int _dstcn, int _blueIdx, float _hrange) : dstcn(_dstcn), blueIdx(_blueIdx), hscale(6.f/_hrange) {} - + void operator()(const float* src, float* dst, int n) const { int i, bidx = blueIdx, dcn = dstcn; float _hscale = hscale; float alpha = ColorChannel::max(); n *= 3; - + for( i = 0; i < n; i += 3, dst += dcn ) { float h = src[i], l = src[i+1], s = src[i+2]; float b, g, r; - + if( s == 0 ) b = g = r = l; else @@ -1121,30 +1121,30 @@ struct HLS2RGB_f {{1,3,0}, {1,0,2}, {3,0,1}, {0,2,1}, {0,1,3}, {2,1,0}}; float tab[4]; int sector; - + float p2 = l <= 0.5f ? l*(1 + s) : l + s - l*s; float p1 = 2*l - p2; - + h *= _hscale; if( h < 0 ) do h += 6; while( h < 0 ); else if( h >= 6 ) do h -= 6; while( h >= 6 ); - + assert( 0 <= h && h < 6 ); sector = cvFloor(h); h -= sector; - + tab[0] = p2; tab[1] = p1; tab[2] = p1 + (p2 - p1)*(1-h); tab[3] = p1 + (p2 - p1)*h; - + b = tab[sector_data[sector][0]]; g = tab[sector_data[sector][1]]; r = tab[sector_data[sector][2]]; } - + dst[bidx] = b; dst[1] = g; dst[bidx^2] = r; @@ -1152,30 +1152,30 @@ struct HLS2RGB_f dst[3] = alpha; } } - + int dstcn, blueIdx; float hscale; }; - + struct HLS2RGB_b { typedef uchar channel_type; - + HLS2RGB_b(int _dstcn, int _blueIdx, int _hrange) : dstcn(_dstcn), cvt(3, _blueIdx, (float)_hrange) {} - + void operator()(const uchar* src, uchar* dst, int n) const { int i, j, dcn = dstcn; uchar alpha = ColorChannel::max(); float buf[3*BLOCK_SIZE]; - + for( i = 0; i < n; i += BLOCK_SIZE, src += BLOCK_SIZE*3 ) { int dn = std::min(n - i, (int)BLOCK_SIZE); - + for( j = 0; j < dn*3; j += 3 ) { buf[j] = src[j]; @@ -1183,7 +1183,7 @@ struct HLS2RGB_b buf[j+2] = src[j+2]*(1.f/255.f); } cvt(buf, buf, dn); - + for( j = 0; j < dn*3; j += 3, dst += dcn ) { dst[0] = saturate_cast(buf[j]*255.f); @@ -1194,12 +1194,12 @@ struct HLS2RGB_b } } } - + int dstcn; HLS2RGB_f cvt; }; - + ///////////////////////////////////// RGB <-> L*a*b* ///////////////////////////////////// static const float D65[] = { 0.950456f, 1.f, 1.088754f }; @@ -1210,15 +1210,15 @@ static const float LabCbrtTabScale = LAB_CBRT_TAB_SIZE/1.5f; static float sRGBGammaTab[GAMMA_TAB_SIZE*4], sRGBInvGammaTab[GAMMA_TAB_SIZE*4]; static const float GammaTabScale = (float)GAMMA_TAB_SIZE; - -static ushort sRGBGammaTab_b[256], linearGammaTab_b[256]; + +static ushort sRGBGammaTab_b[256], linearGammaTab_b[256]; #undef lab_shift #define lab_shift xyz_shift #define gamma_shift 3 #define lab_shift2 (lab_shift + gamma_shift) #define LAB_CBRT_TAB_SIZE_B (256*3/2*(1<(255.f*(1 << gamma_shift)*(x <= 0.04045f ? x*(1.f/12.92f) : (float)pow((double)(x + 0.055)*(1./1.055), 2.4))); linearGammaTab_b[i] = (ushort)(i*(1 << gamma_shift)); } - + for(i = 0; i < LAB_CBRT_TAB_SIZE_B; i++) { float x = i*(1.f/(255.f*(1 << gamma_shift))); @@ -1262,14 +1262,14 @@ static void initLabTabs() struct RGB2Lab_b { typedef uchar channel_type; - + RGB2Lab_b(int _srccn, int blueIdx, const float* _coeffs, const float* _whitept, bool _srgb) : srccn(_srccn), srgb(_srgb) { static volatile int _3 = 3; initLabTabs(); - + if(!_coeffs) _coeffs = sRGB2XYZ_D65; if(!_whitept) _whitept = D65; float scale[] = @@ -1278,18 +1278,18 @@ struct RGB2Lab_b (float)(1 << lab_shift), (1 << lab_shift)/_whitept[2] }; - + for( int i = 0; i < _3; i++ ) { coeffs[i*3+(blueIdx^2)] = cvRound(_coeffs[i*3]*scale[i]); coeffs[i*3+1] = cvRound(_coeffs[i*3+1]*scale[i]); coeffs[i*3+blueIdx] = cvRound(_coeffs[i*3+2]*scale[i]); - + CV_Assert( coeffs[i] >= 0 && coeffs[i*3+1] >= 0 && coeffs[i*3+2] >= 0 && coeffs[i*3] + coeffs[i*3+1] + coeffs[i*3+2] < 2*(1 << lab_shift) ); } } - + void operator()(const uchar* src, uchar* dst, int n) const { const int Lscale = (116*255+50)/100; @@ -1300,45 +1300,45 @@ struct RGB2Lab_b C3 = coeffs[3], C4 = coeffs[4], C5 = coeffs[5], C6 = coeffs[6], C7 = coeffs[7], C8 = coeffs[8]; n *= 3; - + for( i = 0; i < n; i += 3, src += scn ) { int R = tab[src[0]], G = tab[src[1]], B = tab[src[2]]; int fX = LabCbrtTab_b[CV_DESCALE(R*C0 + G*C1 + B*C2, lab_shift)]; int fY = LabCbrtTab_b[CV_DESCALE(R*C3 + G*C4 + B*C5, lab_shift)]; int fZ = LabCbrtTab_b[CV_DESCALE(R*C6 + G*C7 + B*C8, lab_shift)]; - + int L = CV_DESCALE( Lscale*fY + Lshift, lab_shift2 ); int a = CV_DESCALE( 500*(fX - fY) + 128*(1 << lab_shift2), lab_shift2 ); int b = CV_DESCALE( 200*(fY - fZ) + 128*(1 << lab_shift2), lab_shift2 ); - + dst[i] = saturate_cast(L); dst[i+1] = saturate_cast(a); dst[i+2] = saturate_cast(b); } } - + int srccn; int coeffs[9]; bool srgb; }; - - + + struct RGB2Lab_f { typedef float channel_type; - + RGB2Lab_f(int _srccn, int blueIdx, const float* _coeffs, const float* _whitept, bool _srgb) : srccn(_srccn), srgb(_srgb) { volatile int _3 = 3; initLabTabs(); - + if(!_coeffs) _coeffs = sRGB2XYZ_D65; if(!_whitept) _whitept = D65; float scale[] = { LabCbrtTabScale/_whitept[0], LabCbrtTabScale, LabCbrtTabScale/_whitept[2] }; - + for( int i = 0; i < _3; i++ ) { coeffs[i*3+(blueIdx^2)] = _coeffs[i*3]*scale[i]; @@ -1348,7 +1348,7 @@ struct RGB2Lab_f coeffs[i*3] + coeffs[i*3+1] + coeffs[i*3+2] < 1.5f*LabCbrtTabScale ); } } - + void operator()(const float* src, float* dst, int n) const { int i, scn = srccn; @@ -1358,7 +1358,7 @@ struct RGB2Lab_f C3 = coeffs[3], C4 = coeffs[4], C5 = coeffs[5], C6 = coeffs[6], C7 = coeffs[7], C8 = coeffs[8]; n *= 3; - + for( i = 0; i < n; i += 3, src += scn ) { float R = src[0], G = src[1], B = src[2]; @@ -1368,37 +1368,37 @@ struct RGB2Lab_f G = splineInterpolate(G*gscale, gammaTab, GAMMA_TAB_SIZE); B = splineInterpolate(B*gscale, gammaTab, GAMMA_TAB_SIZE); } - float fX = splineInterpolate(R*C0 + G*C1 + B*C2, LabCbrtTab, LAB_CBRT_TAB_SIZE); + float fX = splineInterpolate(R*C0 + G*C1 + B*C2, LabCbrtTab, LAB_CBRT_TAB_SIZE); float fY = splineInterpolate(R*C3 + G*C4 + B*C5, LabCbrtTab, LAB_CBRT_TAB_SIZE); float fZ = splineInterpolate(R*C6 + G*C7 + B*C8, LabCbrtTab, LAB_CBRT_TAB_SIZE); - + float L = 116.f*fY - 16.f; float a = 500.f*(fX - fY); float b = 200.f*(fY - fZ); - + dst[i] = L; dst[i+1] = a; dst[i+2] = b; } } - + int srccn; float coeffs[9]; bool srgb; }; - + struct Lab2RGB_f { typedef float channel_type; - + Lab2RGB_f( int _dstcn, int blueIdx, const float* _coeffs, const float* _whitept, bool _srgb ) : dstcn(_dstcn), srgb(_srgb) { initLabTabs(); - + if(!_coeffs) _coeffs = XYZ2sRGB_D65; if(!_whitept) _whitept = D65; - + for( int i = 0; i < 3; i++ ) { coeffs[i+(blueIdx^2)*3] = _coeffs[i]*_whitept[i]; @@ -1406,7 +1406,7 @@ struct Lab2RGB_f coeffs[i+blueIdx*3] = _coeffs[i+6]*_whitept[i]; } } - + void operator()(const float* src, float* dst, int n) const { int i, dcn = dstcn; @@ -1417,7 +1417,7 @@ struct Lab2RGB_f C6 = coeffs[6], C7 = coeffs[7], C8 = coeffs[8]; float alpha = ColorChannel::max(); n *= 3; - + for( i = 0; i < n; i += 3, dst += dcn ) { float L = src[i], a = src[i+1], b = src[i+2]; @@ -1427,48 +1427,48 @@ struct Lab2RGB_f Y = Y*Y*Y; X = X*X*X; Z = Z*Z*Z; - + float R = X*C0 + Y*C1 + Z*C2; float G = X*C3 + Y*C4 + Z*C5; float B = X*C6 + Y*C7 + Z*C8; - + if( gammaTab ) { R = splineInterpolate(R*gscale, gammaTab, GAMMA_TAB_SIZE); G = splineInterpolate(G*gscale, gammaTab, GAMMA_TAB_SIZE); B = splineInterpolate(B*gscale, gammaTab, GAMMA_TAB_SIZE); } - + dst[0] = R; dst[1] = G; dst[2] = B; if( dcn == 4 ) dst[3] = alpha; } } - + int dstcn; float coeffs[9]; bool srgb; }; - + struct Lab2RGB_b { typedef uchar channel_type; - + Lab2RGB_b( int _dstcn, int blueIdx, const float* _coeffs, const float* _whitept, bool _srgb ) : dstcn(_dstcn), cvt(3, blueIdx, _coeffs, _whitept, _srgb ) {} - + void operator()(const uchar* src, uchar* dst, int n) const { int i, j, dcn = dstcn; uchar alpha = ColorChannel::max(); float buf[3*BLOCK_SIZE]; - + for( i = 0; i < n; i += BLOCK_SIZE, src += BLOCK_SIZE*3 ) { int dn = std::min(n - i, (int)BLOCK_SIZE); - + for( j = 0; j < dn*3; j += 3 ) { buf[j] = src[j]*(100.f/255.f); @@ -1476,7 +1476,7 @@ struct Lab2RGB_b buf[j+2] = (float)(src[j+2] - 128); } cvt(buf, buf, dn); - + for( j = 0; j < dn*3; j += 3, dst += dcn ) { dst[0] = saturate_cast(buf[j]*255.f); @@ -1487,28 +1487,28 @@ struct Lab2RGB_b } } } - + int dstcn; Lab2RGB_f cvt; }; - - + + ///////////////////////////////////// RGB <-> L*u*v* ///////////////////////////////////// struct RGB2Luv_f { typedef float channel_type; - + RGB2Luv_f( int _srccn, int blueIdx, const float* _coeffs, const float* whitept, bool _srgb ) : srccn(_srccn), srgb(_srgb) { - volatile int i; + volatile int i; initLabTabs(); - + if(!_coeffs) _coeffs = sRGB2XYZ_D65; if(!whitept) whitept = D65; - + for( i = 0; i < 3; i++ ) { coeffs[i*3] = _coeffs[i*3]; @@ -1519,14 +1519,14 @@ struct RGB2Luv_f CV_Assert( coeffs[i*3] >= 0 && coeffs[i*3+1] >= 0 && coeffs[i*3+2] >= 0 && coeffs[i*3] + coeffs[i*3+1] + coeffs[i*3+2] < 1.5f ); } - + float d = 1.f/(whitept[0] + whitept[1]*15 + whitept[2]*3); un = 4*whitept[0]*d; vn = 9*whitept[1]*d; - + CV_Assert(whitept[1] == 1.f); } - + void operator()(const float* src, float* dst, int n) const { int i, scn = srccn; @@ -1537,7 +1537,7 @@ struct RGB2Luv_f C6 = coeffs[6], C7 = coeffs[7], C8 = coeffs[8]; float _un = 13*un, _vn = 13*vn; n *= 3; - + for( i = 0; i < n; i += 3, src += scn ) { float R = src[0], G = src[1], B = src[2]; @@ -1547,55 +1547,55 @@ struct RGB2Luv_f G = splineInterpolate(G*gscale, gammaTab, GAMMA_TAB_SIZE); B = splineInterpolate(B*gscale, gammaTab, GAMMA_TAB_SIZE); } - + float X = R*C0 + G*C1 + B*C2; float Y = R*C3 + G*C4 + B*C5; float Z = R*C6 + G*C7 + B*C8; - + float L = splineInterpolate(Y*LabCbrtTabScale, LabCbrtTab, LAB_CBRT_TAB_SIZE); L = 116.f*L - 16.f; - - float d = (4*13) / std::max(X + 15 * Y + 3 * Z, FLT_EPSILON); + + float d = (4*13) / std::max(X + 15 * Y + 3 * Z, FLT_EPSILON); float u = L*(X*d - _un); float v = L*((9*0.25f)*Y*d - _vn); - + dst[i] = L; dst[i+1] = u; dst[i+2] = v; } } - + int srccn; float coeffs[9], un, vn; bool srgb; }; - + struct Luv2RGB_f { typedef float channel_type; - + Luv2RGB_f( int _dstcn, int blueIdx, const float* _coeffs, const float* whitept, bool _srgb ) : dstcn(_dstcn), srgb(_srgb) { initLabTabs(); - + if(!_coeffs) _coeffs = XYZ2sRGB_D65; if(!whitept) whitept = D65; - + for( int i = 0; i < 3; i++ ) { coeffs[i+(blueIdx^2)*3] = _coeffs[i]; coeffs[i+3] = _coeffs[i+3]; coeffs[i+blueIdx*3] = _coeffs[i+6]; } - + float d = 1.f/(whitept[0] + whitept[1]*15 + whitept[2]*3); un = 4*whitept[0]*d; vn = 9*whitept[1]*d; - + CV_Assert(whitept[1] == 1.f); } - + void operator()(const float* src, float* dst, int n) const { int i, dcn = dstcn; @@ -1607,7 +1607,7 @@ struct Luv2RGB_f float alpha = ColorChannel::max(); float _un = un, _vn = vn; n *= 3; - + for( i = 0; i < n; i += 3, dst += dcn ) { float L = src[i], u = src[i+1], v = src[i+2], d, X, Y, Z; @@ -1618,48 +1618,48 @@ struct Luv2RGB_f v = v*d + _vn; float iv = 1.f/v; X = 2.25f * u * Y * iv ; - Z = (12 - 3 * u - 20 * v) * Y * 0.25f * iv; - + Z = (12 - 3 * u - 20 * v) * Y * 0.25f * iv; + float R = X*C0 + Y*C1 + Z*C2; float G = X*C3 + Y*C4 + Z*C5; float B = X*C6 + Y*C7 + Z*C8; - + if( gammaTab ) { R = splineInterpolate(R*gscale, gammaTab, GAMMA_TAB_SIZE); G = splineInterpolate(G*gscale, gammaTab, GAMMA_TAB_SIZE); B = splineInterpolate(B*gscale, gammaTab, GAMMA_TAB_SIZE); } - + dst[0] = R; dst[1] = G; dst[2] = B; if( dcn == 4 ) dst[3] = alpha; } } - + int dstcn; float coeffs[9], un, vn; bool srgb; }; - + struct RGB2Luv_b { typedef uchar channel_type; - + RGB2Luv_b( int _srccn, int blueIdx, const float* _coeffs, const float* _whitept, bool _srgb ) : srccn(_srccn), cvt(3, blueIdx, _coeffs, _whitept, _srgb) {} - + void operator()(const uchar* src, uchar* dst, int n) const { int i, j, scn = srccn; float buf[3*BLOCK_SIZE]; - + for( i = 0; i < n; i += BLOCK_SIZE, dst += BLOCK_SIZE*3 ) { int dn = std::min(n - i, (int)BLOCK_SIZE); - + for( j = 0; j < dn*3; j += 3, src += scn ) { buf[j] = src[0]*(1.f/255.f); @@ -1667,7 +1667,7 @@ struct RGB2Luv_b buf[j+2] = (float)(src[2]*(1.f/255.f)); } cvt(buf, buf, dn); - + for( j = 0; j < dn*3; j += 3 ) { dst[j] = saturate_cast(buf[j]*2.55f); @@ -1676,30 +1676,30 @@ struct RGB2Luv_b } } } - + int srccn; RGB2Luv_f cvt; }; - + struct Luv2RGB_b { typedef uchar channel_type; - + Luv2RGB_b( int _dstcn, int blueIdx, const float* _coeffs, const float* _whitept, bool _srgb ) : dstcn(_dstcn), cvt(3, blueIdx, _coeffs, _whitept, _srgb ) {} - + void operator()(const uchar* src, uchar* dst, int n) const { int i, j, dcn = dstcn; uchar alpha = ColorChannel::max(); float buf[3*BLOCK_SIZE]; - + for( i = 0; i < n; i += BLOCK_SIZE, src += BLOCK_SIZE*3 ) { int dn = std::min(n - i, (int)BLOCK_SIZE); - + for( j = 0; j < dn*3; j += 3 ) { buf[j] = src[j]*(100.f/255.f); @@ -1707,7 +1707,7 @@ struct Luv2RGB_b buf[j+2] = (float)(src[j+2]*1.003921568627451f - 140.f); } cvt(buf, buf, dn); - + for( j = 0; j < dn*3; j += 3, dst += dcn ) { dst[0] = saturate_cast(buf[j]*255.f); @@ -1718,12 +1718,12 @@ struct Luv2RGB_b } } } - + int dstcn; Luv2RGB_f cvt; }; - + //////////////////////////// Bayer Pattern -> RGB conversion ///////////////////////////// template @@ -1734,13 +1734,13 @@ public: { return 0; } - + int bayer2RGB(const T*, int, T*, int, int) const { return 0; } -}; - +}; + #if CV_SSE2 class SIMDBayerInterpolator_8u { @@ -1749,34 +1749,34 @@ public: { use_simd = checkHardwareSupport(CV_CPU_SSE2); } - + int bayer2Gray(const uchar* bayer, int bayer_step, uchar* dst, int width, int bcoeff, int gcoeff, int rcoeff) const { if( !use_simd ) return 0; - + __m128i _b2y = _mm_set1_epi16((short)(rcoeff*2)); __m128i _g2y = _mm_set1_epi16((short)(gcoeff*2)); __m128i _r2y = _mm_set1_epi16((short)(bcoeff*2)); const uchar* bayer_end = bayer + width; - + for( ; bayer <= bayer_end - 18; bayer += 14, dst += 14 ) { __m128i r0 = _mm_loadu_si128((const __m128i*)bayer); __m128i r1 = _mm_loadu_si128((const __m128i*)(bayer+bayer_step)); __m128i r2 = _mm_loadu_si128((const __m128i*)(bayer+bayer_step*2)); - + __m128i b1 = _mm_add_epi16(_mm_srli_epi16(_mm_slli_epi16(r0, 8), 7), _mm_srli_epi16(_mm_slli_epi16(r2, 8), 7)); __m128i b0 = _mm_add_epi16(b1, _mm_srli_si128(b1, 2)); b1 = _mm_slli_epi16(_mm_srli_si128(b1, 2), 1); - + __m128i g0 = _mm_add_epi16(_mm_srli_epi16(r0, 7), _mm_srli_epi16(r2, 7)); __m128i g1 = _mm_srli_epi16(_mm_slli_epi16(r1, 8), 7); g0 = _mm_add_epi16(g0, _mm_add_epi16(g1, _mm_srli_si128(g1, 2))); g1 = _mm_slli_epi16(_mm_srli_si128(g1, 2), 2); - + r0 = _mm_srli_epi16(r1, 8); r1 = _mm_slli_epi16(_mm_add_epi16(r0, _mm_srli_si128(r0, 2)), 2); r0 = _mm_slli_epi16(r0, 3); @@ -1792,10 +1792,10 @@ public: g0 = _mm_unpacklo_epi8(g0, g1); _mm_storeu_si128((__m128i*)dst, g0); } - + return (int)(bayer - (bayer_end - width)); } - + int bayer2RGB(const uchar* bayer, int bayer_step, uchar* dst, int width, int blue) const { if( !use_simd ) @@ -1809,82 +1809,82 @@ public: __m128i mask = _mm_set1_epi16(blue < 0 ? -1 : 0), z = _mm_setzero_si128(); __m128i masklo = _mm_set1_epi16(0x00ff); const uchar* bayer_end = bayer + width; - + for( ; bayer <= bayer_end - 18; bayer += 14, dst += 42 ) { __m128i r0 = _mm_loadu_si128((const __m128i*)bayer); __m128i r1 = _mm_loadu_si128((const __m128i*)(bayer+bayer_step)); __m128i r2 = _mm_loadu_si128((const __m128i*)(bayer+bayer_step*2)); - + __m128i b1 = _mm_add_epi16(_mm_and_si128(r0, masklo), _mm_and_si128(r2, masklo)); __m128i b0 = _mm_add_epi16(b1, _mm_srli_si128(b1, 2)); b1 = _mm_srli_si128(b1, 2); b1 = _mm_srli_epi16(_mm_add_epi16(b1, delta1), 1); b0 = _mm_srli_epi16(_mm_add_epi16(b0, delta2), 2); b0 = _mm_packus_epi16(b0, b1); - + __m128i g0 = _mm_add_epi16(_mm_srli_epi16(r0, 8), _mm_srli_epi16(r2, 8)); __m128i g1 = _mm_and_si128(r1, masklo); g0 = _mm_add_epi16(g0, _mm_add_epi16(g1, _mm_srli_si128(g1, 2))); g1 = _mm_srli_si128(g1, 2); g0 = _mm_srli_epi16(_mm_add_epi16(g0, delta2), 2); g0 = _mm_packus_epi16(g0, g1); - + r0 = _mm_srli_epi16(r1, 8); r1 = _mm_add_epi16(r0, _mm_srli_si128(r0, 2)); r1 = _mm_srli_epi16(_mm_add_epi16(r1, delta1), 1); r0 = _mm_packus_epi16(r0, r1); - + b1 = _mm_and_si128(_mm_xor_si128(b0, r0), mask); b0 = _mm_xor_si128(b0, b1); r0 = _mm_xor_si128(r0, b1); - + // b1 g1 b1 g1 ... b1 = _mm_unpackhi_epi8(b0, g0); // b0 g0 b2 g2 b4 g4 .... b0 = _mm_unpacklo_epi8(b0, g0); - + // r1 0 r3 0 ... r1 = _mm_unpackhi_epi8(r0, z); // r0 0 r2 0 r4 0 ... r0 = _mm_unpacklo_epi8(r0, z); - + // 0 b0 g0 r0 0 b2 g2 r2 0 ... g0 = _mm_slli_si128(_mm_unpacklo_epi16(b0, r0), 1); // 0 b8 g8 r8 0 b10 g10 r10 0 ... g1 = _mm_slli_si128(_mm_unpackhi_epi16(b0, r0), 1); - + // b1 g1 r1 0 b3 g3 r3 .... r0 = _mm_unpacklo_epi16(b1, r1); // b9 g9 r9 0 ... r1 = _mm_unpackhi_epi16(b1, r1); - + b0 = _mm_srli_si128(_mm_unpacklo_epi32(g0, r0), 1); b1 = _mm_srli_si128(_mm_unpackhi_epi32(g0, r0), 1); - + _mm_storel_epi64((__m128i*)(dst-1+0), b0); _mm_storel_epi64((__m128i*)(dst-1+6*1), _mm_srli_si128(b0, 8)); _mm_storel_epi64((__m128i*)(dst-1+6*2), b1); _mm_storel_epi64((__m128i*)(dst-1+6*3), _mm_srli_si128(b1, 8)); - + g0 = _mm_srli_si128(_mm_unpacklo_epi32(g1, r1), 1); g1 = _mm_srli_si128(_mm_unpackhi_epi32(g1, r1), 1); - + _mm_storel_epi64((__m128i*)(dst-1+6*4), g0); _mm_storel_epi64((__m128i*)(dst-1+6*5), _mm_srli_si128(g0, 8)); - + _mm_storel_epi64((__m128i*)(dst-1+6*6), g1); } - + return (int)(bayer - (bayer_end - width)); } - + bool use_simd; }; #else typedef SIMDBayerStubInterpolator_ SIMDBayerInterpolator_8u; #endif - + template static void Bayer2Gray_( const Mat& srcmat, Mat& dstmat, int code ) { @@ -1893,7 +1893,7 @@ static void Bayer2Gray_( const Mat& srcmat, Mat& dstmat, int code ) const int G2Y = 9617; const int B2Y = 1868; const int SHIFT = 14; - + const T* bayer0 = (const T*)srcmat.data; int bayer_step = (int)(srcmat.step/sizeof(T)); T* dst0 = (T*)dstmat.data; @@ -1902,58 +1902,58 @@ static void Bayer2Gray_( const Mat& srcmat, Mat& dstmat, int code ) int bcoeff = B2Y, rcoeff = R2Y; int start_with_green = code == CV_BayerGB2GRAY || code == CV_BayerGR2GRAY; bool brow = true; - + if( code != CV_BayerBG2GRAY && code != CV_BayerGB2GRAY ) { brow = false; std::swap(bcoeff, rcoeff); } - + dst0 += dst_step + 1; size.height -= 2; size.width -= 2; - + for( ; size.height-- > 0; bayer0 += bayer_step, dst0 += dst_step ) { unsigned t0, t1, t2; const T* bayer = bayer0; T* dst = dst0; const T* bayer_end = bayer + size.width; - + if( size.width <= 0 ) { dst[-1] = dst[size.width] = 0; continue; } - + if( start_with_green ) { t0 = (bayer[1] + bayer[bayer_step*2+1])*rcoeff; t1 = (bayer[bayer_step] + bayer[bayer_step+2])*bcoeff; t2 = bayer[bayer_step+1]*(2*G2Y); - + dst[0] = (T)CV_DESCALE(t0 + t1 + t2, SHIFT+1); bayer++; dst++; } - + int delta = vecOp.bayer2Gray(bayer, bayer_step, dst, size.width, bcoeff, G2Y, rcoeff); bayer += delta; dst += delta; - + for( ; bayer <= bayer_end - 2; bayer += 2, dst += 2 ) { t0 = (bayer[0] + bayer[2] + bayer[bayer_step*2] + bayer[bayer_step*2+2])*rcoeff; t1 = (bayer[1] + bayer[bayer_step] + bayer[bayer_step+2] + bayer[bayer_step*2+1])*G2Y; t2 = bayer[bayer_step+1]*(4*bcoeff); dst[0] = (T)CV_DESCALE(t0 + t1 + t2, SHIFT+2); - + t0 = (bayer[2] + bayer[bayer_step*2+2])*rcoeff; t1 = (bayer[bayer_step+1] + bayer[bayer_step+3])*bcoeff; t2 = bayer[bayer_step+2]*(2*G2Y); dst[1] = (T)CV_DESCALE(t0 + t1 + t2, SHIFT+1); } - + if( bayer < bayer_end ) { t0 = (bayer[0] + bayer[2] + bayer[bayer_step*2] + bayer[bayer_step*2+2])*rcoeff; @@ -1963,15 +1963,15 @@ static void Bayer2Gray_( const Mat& srcmat, Mat& dstmat, int code ) bayer++; dst++; } - + dst0[-1] = dst0[0]; dst0[size.width] = dst0[size.width-1]; - + brow = !brow; std::swap(bcoeff, rcoeff); start_with_green = !start_with_green; } - + size = dstmat.size(); dst0 = (T*)dstmat.data; if( size.height > 2 ) @@ -1987,7 +1987,7 @@ static void Bayer2Gray_( const Mat& srcmat, Mat& dstmat, int code ) } } -template +template static void Bayer2RGB_( const Mat& srcmat, Mat& dstmat, int code ) { SIMDInterpolator vecOp; @@ -1998,25 +1998,25 @@ static void Bayer2RGB_( const Mat& srcmat, Mat& dstmat, int code ) Size size = srcmat.size(); int blue = code == CV_BayerBG2BGR || code == CV_BayerGB2BGR ? -1 : 1; int start_with_green = code == CV_BayerGB2BGR || code == CV_BayerGR2BGR; - + dst0 += dst_step + 3 + 1; size.height -= 2; size.width -= 2; - + for( ; size.height-- > 0; bayer0 += bayer_step, dst0 += dst_step ) { int t0, t1; const T* bayer = bayer0; T* dst = dst0; const T* bayer_end = bayer + size.width; - + if( size.width <= 0 ) { dst[-4] = dst[-3] = dst[-2] = dst[size.width*3-1] = dst[size.width*3] = dst[size.width*3+1] = 0; continue; } - + if( start_with_green ) { t0 = (bayer[1] + bayer[bayer_step*2+1] + 1) >> 1; @@ -2027,11 +2027,11 @@ static void Bayer2RGB_( const Mat& srcmat, Mat& dstmat, int code ) bayer++; dst += 3; } - + int delta = vecOp.bayer2RGB(bayer, bayer_step, dst, size.width, blue); bayer += delta; dst += delta*3; - + if( blue > 0 ) { for( ; bayer <= bayer_end - 2; bayer += 2, dst += 6 ) @@ -2043,7 +2043,7 @@ static void Bayer2RGB_( const Mat& srcmat, Mat& dstmat, int code ) dst[-1] = (T)t0; dst[0] = (T)t1; dst[1] = bayer[bayer_step+1]; - + t0 = (bayer[2] + bayer[bayer_step*2+2] + 1) >> 1; t1 = (bayer[bayer_step+1] + bayer[bayer_step+3] + 1) >> 1; dst[2] = (T)t0; @@ -2062,7 +2062,7 @@ static void Bayer2RGB_( const Mat& srcmat, Mat& dstmat, int code ) dst[1] = (T)t0; dst[0] = (T)t1; dst[-1] = bayer[bayer_step+1]; - + t0 = (bayer[2] + bayer[bayer_step*2+2] + 1) >> 1; t1 = (bayer[bayer_step+1] + bayer[bayer_step+3] + 1) >> 1; dst[4] = (T)t0; @@ -2070,7 +2070,7 @@ static void Bayer2RGB_( const Mat& srcmat, Mat& dstmat, int code ) dst[2] = (T)t1; } } - + if( bayer < bayer_end ) { t0 = (bayer[0] + bayer[2] + bayer[bayer_step*2] + @@ -2083,18 +2083,18 @@ static void Bayer2RGB_( const Mat& srcmat, Mat& dstmat, int code ) bayer++; dst += 3; } - + dst0[-4] = dst0[-1]; dst0[-3] = dst0[0]; dst0[-2] = dst0[1]; dst0[size.width*3-1] = dst0[size.width*3-4]; dst0[size.width*3] = dst0[size.width*3-3]; dst0[size.width*3+1] = dst0[size.width*3-2]; - + blue = -blue; start_with_green = !start_with_green; } - + size = dstmat.size(); dst0 = (T*)dstmat.data; if( size.height > 2 ) @@ -2110,9 +2110,9 @@ static void Bayer2RGB_( const Mat& srcmat, Mat& dstmat, int code ) } } - + /////////////////// Demosaicing using Variable Number of Gradients /////////////////////// - + static void Bayer2RGB_VNG_8u( const Mat& srcmat, Mat& dstmat, int code ) { const uchar* bayer = srcmat.data; @@ -2120,45 +2120,45 @@ static void Bayer2RGB_VNG_8u( const Mat& srcmat, Mat& dstmat, int code ) uchar* dst = dstmat.data; int dststep = (int)dstmat.step; Size size = srcmat.size(); - + int blueIdx = code == CV_BayerBG2BGR_VNG || code == CV_BayerGB2BGR_VNG ? 0 : 2; bool greenCell0 = code != CV_BayerBG2BGR_VNG && code != CV_BayerRG2BGR_VNG; - + // for too small images use the simple interpolation algorithm if( MIN(size.width, size.height) < 8 ) { Bayer2RGB_( srcmat, dstmat, code ); return; } - + const int brows = 3, bcn = 7; - int N = size.width, N2 = N*2, N3 = N*3, N4 = N*4, N5 = N*5, N6 = N*6, N7 = N*7; + int N = size.width, N2 = N*2, N3 = N*3, N4 = N*4, N5 = N*5, N6 = N*6, N7 = N*7; int i, bufstep = N7*bcn; cv::AutoBuffer _buf(bufstep*brows); ushort* buf = (ushort*)_buf; - + bayer += bstep*2; - + #if CV_SSE2 bool haveSSE = cv::checkHardwareSupport(CV_CPU_SSE2); #define _mm_absdiff_epu16(a,b) _mm_adds_epu16(_mm_subs_epu16(a, b), _mm_subs_epu16(b, a)) #endif - + for( int y = 2; y < size.height - 4; y++ ) { uchar* dstrow = dst + dststep*y + 6; const uchar* srow; - + for( int dy = (y == 2 ? -1 : 1); dy <= 1; dy++ ) { ushort* brow = buf + ((y + dy - 1)%brows)*bufstep + 1; srow = bayer + (y+dy)*bstep + 1; - + for( i = 0; i < bcn; i++ ) brow[N*i-1] = brow[(N-2) + N*i] = 0; - + i = 1; - + #if CV_SSE2 if( haveSSE ) { @@ -2166,20 +2166,20 @@ static void Bayer2RGB_VNG_8u( const Mat& srcmat, Mat& dstmat, int code ) for( ; i <= N-9; i += 8, srow += 8, brow += 8 ) { __m128i s1, s2, s3, s4, s6, s7, s8, s9; - + s1 = _mm_unpacklo_epi8(_mm_loadl_epi64((__m128i*)(srow-1-bstep)),z); s2 = _mm_unpacklo_epi8(_mm_loadl_epi64((__m128i*)(srow-bstep)),z); s3 = _mm_unpacklo_epi8(_mm_loadl_epi64((__m128i*)(srow+1-bstep)),z); - + s4 = _mm_unpacklo_epi8(_mm_loadl_epi64((__m128i*)(srow-1)),z); s6 = _mm_unpacklo_epi8(_mm_loadl_epi64((__m128i*)(srow+1)),z); - + s7 = _mm_unpacklo_epi8(_mm_loadl_epi64((__m128i*)(srow-1+bstep)),z); s8 = _mm_unpacklo_epi8(_mm_loadl_epi64((__m128i*)(srow+bstep)),z); s9 = _mm_unpacklo_epi8(_mm_loadl_epi64((__m128i*)(srow+1+bstep)),z); - + __m128i b0, b1, b2, b3, b4, b5, b6; - + b0 = _mm_adds_epu16(_mm_slli_epi16(_mm_absdiff_epu16(s2,s8),1), _mm_adds_epu16(_mm_absdiff_epu16(s1, s7), _mm_absdiff_epu16(s3, s9))); @@ -2188,26 +2188,26 @@ static void Bayer2RGB_VNG_8u( const Mat& srcmat, Mat& dstmat, int code ) _mm_absdiff_epu16(s7, s9))); b2 = _mm_slli_epi16(_mm_absdiff_epu16(s3,s7),1); b3 = _mm_slli_epi16(_mm_absdiff_epu16(s1,s9),1); - + _mm_storeu_si128((__m128i*)brow, b0); _mm_storeu_si128((__m128i*)(brow + N), b1); _mm_storeu_si128((__m128i*)(brow + N2), b2); _mm_storeu_si128((__m128i*)(brow + N3), b3); - + b4 = _mm_adds_epu16(b2,_mm_adds_epu16(_mm_absdiff_epu16(s2, s4), _mm_absdiff_epu16(s6, s8))); b5 = _mm_adds_epu16(b3,_mm_adds_epu16(_mm_absdiff_epu16(s2, s6), _mm_absdiff_epu16(s4, s8))); b6 = _mm_adds_epu16(_mm_adds_epu16(s2, s4), _mm_adds_epu16(s6, s8)); b6 = _mm_srli_epi16(b6, 1); - + _mm_storeu_si128((__m128i*)(brow + N4), b4); _mm_storeu_si128((__m128i*)(brow + N5), b5); _mm_storeu_si128((__m128i*)(brow + N6), b6); } } #endif - + for( ; i < N-1; i++, srow++, brow++ ) { brow[0] = (ushort)(std::abs(srow[-1-bstep] - srow[-1+bstep]) + @@ -2225,21 +2225,21 @@ static void Bayer2RGB_VNG_8u( const Mat& srcmat, Mat& dstmat, int code ) brow[N6] = (ushort)((srow[-bstep] + srow[-1] + srow[1] + srow[+bstep])>>1); } } - + const ushort* brow0 = buf + ((y - 2) % brows)*bufstep + 2; const ushort* brow1 = buf + ((y - 1) % brows)*bufstep + 2; const ushort* brow2 = buf + (y % brows)*bufstep + 2; static const float scale[] = { 0.f, 0.5f, 0.25f, 0.1666666666667f, 0.125f, 0.1f, 0.08333333333f, 0.0714286f, 0.0625f }; srow = bayer + y*bstep + 2; bool greenCell = greenCell0; - + i = 2; #if CV_SSE2 int limit = !haveSSE ? N-2 : greenCell ? std::min(3, N-2) : 2; #else int limit = N - 2; #endif - + do { for( ; i < limit; i++, srow++, brow0++, brow1++, brow2++, dstrow += 3 ) @@ -2251,18 +2251,18 @@ static void Bayer2RGB_VNG_8u( const Mat& srcmat, Mat& dstmat, int code ) int minGrad = std::min(std::min(std::min(gradN, gradS), gradW), gradE); int maxGrad = std::max(std::max(std::max(gradN, gradS), gradW), gradE); int R, G, B; - + if( !greenCell ) { int gradNE = brow0[N4+1] + brow1[N4]; int gradSW = brow1[N4] + brow2[N4-1]; int gradNW = brow0[N5-1] + brow1[N5]; int gradSE = brow1[N5] + brow2[N5+1]; - + minGrad = std::min(std::min(std::min(std::min(minGrad, gradNE), gradSW), gradNW), gradSE); maxGrad = std::max(std::max(std::max(std::max(maxGrad, gradNE), gradSW), gradNW), gradSE); int T = minGrad + maxGrad/2; - + int Rs = 0, Gs = 0, Bs = 0, ng = 0; if( gradN < T ) { @@ -2322,7 +2322,7 @@ static void Bayer2RGB_VNG_8u( const Mat& srcmat, Mat& dstmat, int code ) } R = srow[0]; G = R + cvRound((Gs - Rs)*scale[ng]); - B = R + cvRound((Bs - Rs)*scale[ng]); + B = R + cvRound((Bs - Rs)*scale[ng]); } else { @@ -2330,11 +2330,11 @@ static void Bayer2RGB_VNG_8u( const Mat& srcmat, Mat& dstmat, int code ) int gradSW = brow1[N2] + brow1[N2-1] + brow2[N2] + brow2[N2-1]; int gradNW = brow0[N3] + brow0[N3-1] + brow1[N3] + brow1[N3-1]; int gradSE = brow1[N3] + brow1[N3+1] + brow2[N3] + brow2[N3+1]; - + minGrad = std::min(std::min(std::min(std::min(minGrad, gradNE), gradSW), gradNW), gradSE); maxGrad = std::max(std::max(std::max(std::max(maxGrad, gradNE), gradSW), gradNW), gradSE); int T = minGrad + maxGrad/2; - + int Rs = 0, Gs = 0, Bs = 0, ng = 0; if( gradN < T ) { @@ -2405,17 +2405,17 @@ static void Bayer2RGB_VNG_8u( const Mat& srcmat, Mat& dstmat, int code ) #if CV_SSE2 if( !haveSSE ) break; - + __m128i emask = _mm_set1_epi32(0x0000ffff), omask = _mm_set1_epi32(0xffff0000), z = _mm_setzero_si128(); __m128 _0_5 = _mm_set1_ps(0.5f); - + #define _mm_merge_epi16(a, b) _mm_or_si128(_mm_and_si128(a, emask), _mm_and_si128(b, omask)) //(aA_aA_aA_aA) * (bB_bB_bB_bB) => (bA_bA_bA_bA) #define _mm_cvtloepi16_ps(a) _mm_cvtepi32_ps(_mm_srai_epi32(_mm_unpacklo_epi16(a,a), 16)) //(1,2,3,4,5,6,7,8) => (1f,2f,3f,4f) #define _mm_cvthiepi16_ps(a) _mm_cvtepi32_ps(_mm_srai_epi32(_mm_unpackhi_epi16(a,a), 16)) //(1,2,3,4,5,6,7,8) => (5f,6f,7f,8f) #define _mm_loadl_u8_s16(ptr, offset) _mm_unpacklo_epi8(_mm_loadl_epi64((__m128i*)((ptr) + (offset))), z) //load 8 uchars to 8 shorts - + // process 8 pixels at once for( ; i <= N - 10; i += 8, srow += 8, brow0 += 8, brow1 += 8, brow2 += 8 ) { @@ -2430,28 +2430,28 @@ static void Bayer2RGB_VNG_8u( const Mat& srcmat, Mat& dstmat, int code ) //int gradE = brow1[N+1] + brow1[N]; __m128i gradE = _mm_adds_epi16(_mm_loadu_si128((__m128i*)(brow1+N+1)), _mm_loadu_si128((__m128i*)(brow1+N))); - + //int minGrad = std::min(std::min(std::min(gradN, gradS), gradW), gradE); //int maxGrad = std::max(std::max(std::max(gradN, gradS), gradW), gradE); __m128i minGrad = _mm_min_epi16(_mm_min_epi16(gradN, gradS), _mm_min_epi16(gradW, gradE)); __m128i maxGrad = _mm_max_epi16(_mm_max_epi16(gradN, gradS), _mm_max_epi16(gradW, gradE)); - + __m128i grad0, grad1; - + //int gradNE = brow0[N4+1] + brow1[N4]; //int gradNE = brow0[N2] + brow0[N2+1] + brow1[N2] + brow1[N2+1]; grad0 = _mm_adds_epi16(_mm_loadu_si128((__m128i*)(brow0+N4+1)), _mm_loadu_si128((__m128i*)(brow1+N4))); grad1 = _mm_adds_epi16( _mm_adds_epi16(_mm_loadu_si128((__m128i*)(brow0+N2)), _mm_loadu_si128((__m128i*)(brow0+N2+1))), _mm_adds_epi16(_mm_loadu_si128((__m128i*)(brow1+N2)), _mm_loadu_si128((__m128i*)(brow1+N2+1)))); __m128i gradNE = _mm_merge_epi16(grad0, grad1); - + //int gradSW = brow1[N4] + brow2[N4-1]; //int gradSW = brow1[N2] + brow1[N2-1] + brow2[N2] + brow2[N2-1]; grad0 = _mm_adds_epi16(_mm_loadu_si128((__m128i*)(brow2+N4-1)), _mm_loadu_si128((__m128i*)(brow1+N4))); grad1 = _mm_adds_epi16(_mm_adds_epi16(_mm_loadu_si128((__m128i*)(brow2+N2)), _mm_loadu_si128((__m128i*)(brow2+N2-1))), _mm_adds_epi16(_mm_loadu_si128((__m128i*)(brow1+N2)), _mm_loadu_si128((__m128i*)(brow1+N2-1)))); __m128i gradSW = _mm_merge_epi16(grad0, grad1); - + minGrad = _mm_min_epi16(_mm_min_epi16(minGrad, gradNE), gradSW); maxGrad = _mm_max_epi16(_mm_max_epi16(maxGrad, gradNE), gradSW); @@ -2461,22 +2461,22 @@ static void Bayer2RGB_VNG_8u( const Mat& srcmat, Mat& dstmat, int code ) grad1 = _mm_adds_epi16(_mm_adds_epi16(_mm_loadu_si128((__m128i*)(brow0+N3)), _mm_loadu_si128((__m128i*)(brow0+N3-1))), _mm_adds_epi16(_mm_loadu_si128((__m128i*)(brow1+N3)), _mm_loadu_si128((__m128i*)(brow1+N3-1)))); __m128i gradNW = _mm_merge_epi16(grad0, grad1); - + //int gradSE = brow1[N5] + brow2[N5+1]; //int gradSE = brow1[N3] + brow1[N3+1] + brow2[N3] + brow2[N3+1]; grad0 = _mm_adds_epi16(_mm_loadu_si128((__m128i*)(brow2+N5+1)), _mm_loadu_si128((__m128i*)(brow1+N5))); grad1 = _mm_adds_epi16(_mm_adds_epi16(_mm_loadu_si128((__m128i*)(brow2+N3)), _mm_loadu_si128((__m128i*)(brow2+N3+1))), _mm_adds_epi16(_mm_loadu_si128((__m128i*)(brow1+N3)), _mm_loadu_si128((__m128i*)(brow1+N3+1)))); __m128i gradSE = _mm_merge_epi16(grad0, grad1); - + minGrad = _mm_min_epi16(_mm_min_epi16(minGrad, gradNW), gradSE); maxGrad = _mm_max_epi16(_mm_max_epi16(maxGrad, gradNW), gradSE); - + //int T = minGrad + maxGrad/2; __m128i T = _mm_adds_epi16(_mm_srli_epi16(maxGrad, 1), minGrad); __m128i RGs = z, GRs = z, Bs = z, ng = z; - + __m128i x0 = _mm_loadl_u8_s16(srow, +0 ); __m128i x1 = _mm_loadl_u8_s16(srow, -1 - bstep ); __m128i x2 = _mm_loadl_u8_s16(srow, -1 - bstep*2); @@ -2496,39 +2496,39 @@ static void Bayer2RGB_VNG_8u( const Mat& srcmat, Mat& dstmat, int code ) __m128i x16 = _mm_loadl_u8_s16(srow, -2 - bstep ); __m128i t0, t1, mask; - + // gradN *********************************************** mask = _mm_cmpgt_epi16(T, gradN); // mask = T>gradN ng = _mm_sub_epi16(ng, mask); // ng += (T>gradN) - + t0 = _mm_slli_epi16(x3, 1); // srow[-bstep]*2 t1 = _mm_adds_epi16(_mm_loadl_u8_s16(srow, -bstep*2), x0); // srow[-bstep*2] + srow[0] - + // RGs += (srow[-bstep*2] + srow[0]) * (T>gradN) RGs = _mm_adds_epi16(RGs, _mm_and_si128(t1, mask)); // GRs += {srow[-bstep]*2; (srow[-bstep*2-1] + srow[-bstep*2+1])} * (T>gradN) GRs = _mm_adds_epi16(GRs, _mm_and_si128(_mm_merge_epi16(t0, _mm_adds_epi16(x2,x4)), mask)); // Bs += {(srow[-bstep-1]+srow[-bstep+1]); srow[-bstep]*2 } * (T>gradN) Bs = _mm_adds_epi16(Bs, _mm_and_si128(_mm_merge_epi16(_mm_adds_epi16(x1,x5), t0), mask)); - + // gradNE ********************************************** mask = _mm_cmpgt_epi16(T, gradNE); // mask = T>gradNE ng = _mm_sub_epi16(ng, mask); // ng += (T>gradNE) t0 = _mm_slli_epi16(x5, 1); // srow[-bstep+1]*2 t1 = _mm_adds_epi16(_mm_loadl_u8_s16(srow, -bstep*2+2), x0); // srow[-bstep*2+2] + srow[0] - + // RGs += {(srow[-bstep*2+2] + srow[0]); srow[-bstep+1]*2} * (T>gradNE) RGs = _mm_adds_epi16(RGs, _mm_and_si128(_mm_merge_epi16(t1, t0), mask)); // GRs += {brow0[N6+1]; (srow[-bstep*2+1] + srow[1])} * (T>gradNE) GRs = _mm_adds_epi16(GRs, _mm_and_si128(_mm_merge_epi16(_mm_loadu_si128((__m128i*)(brow0+N6+1)), _mm_adds_epi16(x4,x7)), mask)); // Bs += {srow[-bstep+1]*2; (srow[-bstep] + srow[-bstep+2])} * (T>gradNE) Bs = _mm_adds_epi16(Bs, _mm_and_si128(_mm_merge_epi16(t0,_mm_adds_epi16(x3,x6)), mask)); - + // gradE *********************************************** mask = _mm_cmpgt_epi16(T, gradE); // mask = T>gradE ng = _mm_sub_epi16(ng, mask); // ng += (T>gradE) - + t0 = _mm_slli_epi16(x7, 1); // srow[1]*2 t1 = _mm_adds_epi16(_mm_loadl_u8_s16(srow, 2), x0); // srow[2] + srow[0] @@ -2538,11 +2538,11 @@ static void Bayer2RGB_VNG_8u( const Mat& srcmat, Mat& dstmat, int code ) GRs = _mm_adds_epi16(GRs, _mm_and_si128(t0, mask)); // Bs += {(srow[-bstep+1]+srow[bstep+1]); (srow[-bstep+2]+srow[bstep+2])} * (T>gradE) Bs = _mm_adds_epi16(Bs, _mm_and_si128(_mm_merge_epi16(_mm_adds_epi16(x5,x9), _mm_adds_epi16(x6,x8)), mask)); - + // gradSE ********************************************** mask = _mm_cmpgt_epi16(T, gradSE); // mask = T>gradSE ng = _mm_sub_epi16(ng, mask); // ng += (T>gradSE) - + t0 = _mm_slli_epi16(x9, 1); // srow[bstep+1]*2 t1 = _mm_adds_epi16(_mm_loadl_u8_s16(srow, bstep*2+2), x0); // srow[bstep*2+2] + srow[0] @@ -2552,11 +2552,11 @@ static void Bayer2RGB_VNG_8u( const Mat& srcmat, Mat& dstmat, int code ) GRs = _mm_adds_epi16(GRs, _mm_and_si128(_mm_merge_epi16(_mm_loadu_si128((__m128i*)(brow2+N6+1)), _mm_adds_epi16(x7,x10)), mask)); // Bs += {srow[-bstep+1]*2; (srow[bstep+2]+srow[bstep])} * (T>gradSE) Bs = _mm_adds_epi16(Bs, _mm_and_si128(_mm_merge_epi16(_mm_slli_epi16(x5, 1), _mm_adds_epi16(x8,x11)), mask)); - + // gradS *********************************************** mask = _mm_cmpgt_epi16(T, gradS); // mask = T>gradS ng = _mm_sub_epi16(ng, mask); // ng += (T>gradS) - + t0 = _mm_slli_epi16(x11, 1); // srow[bstep]*2 t1 = _mm_adds_epi16(_mm_loadl_u8_s16(srow,bstep*2), x0); // srow[bstep*2]+srow[0] @@ -2566,11 +2566,11 @@ static void Bayer2RGB_VNG_8u( const Mat& srcmat, Mat& dstmat, int code ) GRs = _mm_adds_epi16(GRs, _mm_and_si128(_mm_merge_epi16(t0, _mm_adds_epi16(x10,x12)), mask)); // Bs += {(srow[bstep+1]+srow[bstep-1]); srow[bstep]*2} * (T>gradS) Bs = _mm_adds_epi16(Bs, _mm_and_si128(_mm_merge_epi16(_mm_adds_epi16(x9,x13), t0), mask)); - + // gradSW ********************************************** mask = _mm_cmpgt_epi16(T, gradSW); // mask = T>gradSW ng = _mm_sub_epi16(ng, mask); // ng += (T>gradSW) - + t0 = _mm_slli_epi16(x13, 1); // srow[bstep-1]*2 t1 = _mm_adds_epi16(_mm_loadl_u8_s16(srow, bstep*2-2), x0); // srow[bstep*2-2]+srow[0] @@ -2580,11 +2580,11 @@ static void Bayer2RGB_VNG_8u( const Mat& srcmat, Mat& dstmat, int code ) GRs = _mm_adds_epi16(GRs, _mm_and_si128(_mm_merge_epi16(_mm_loadu_si128((__m128i*)(brow2+N6-1)), _mm_adds_epi16(x12,x15)), mask)); // Bs += {srow[bstep-1]*2; (srow[bstep]+srow[bstep-2])} * (T>gradSW) Bs = _mm_adds_epi16(Bs, _mm_and_si128(_mm_merge_epi16(t0,_mm_adds_epi16(x11,x14)), mask)); - + // gradW *********************************************** mask = _mm_cmpgt_epi16(T, gradW); // mask = T>gradW ng = _mm_sub_epi16(ng, mask); // ng += (T>gradW) - + t0 = _mm_slli_epi16(x15, 1); // srow[-1]*2 t1 = _mm_adds_epi16(_mm_loadl_u8_s16(srow, -2), x0); // srow[-2]+srow[0] @@ -2594,11 +2594,11 @@ static void Bayer2RGB_VNG_8u( const Mat& srcmat, Mat& dstmat, int code ) GRs = _mm_adds_epi16(GRs, _mm_and_si128(t0, mask)); // Bs += {(srow[-bstep-1]+srow[bstep-1]); (srow[bstep-2]+srow[-bstep-2])} * (T>gradW) Bs = _mm_adds_epi16(Bs, _mm_and_si128(_mm_merge_epi16(_mm_adds_epi16(x1,x13), _mm_adds_epi16(x14,x16)), mask)); - + // gradNW ********************************************** mask = _mm_cmpgt_epi16(T, gradNW); // mask = T>gradNW ng = _mm_sub_epi16(ng, mask); // ng += (T>gradNW) - + t0 = _mm_slli_epi16(x1, 1); // srow[-bstep-1]*2 t1 = _mm_adds_epi16(_mm_loadl_u8_s16(srow,-bstep*2-2), x0); // srow[-bstep*2-2]+srow[0] @@ -2612,49 +2612,49 @@ static void Bayer2RGB_VNG_8u( const Mat& srcmat, Mat& dstmat, int code ) __m128 ngf0, ngf1; ngf0 = _mm_div_ps(_0_5, _mm_cvtloepi16_ps(ng)); ngf1 = _mm_div_ps(_0_5, _mm_cvthiepi16_ps(ng)); - + // now interpolate r, g & b t0 = _mm_sub_epi16(GRs, RGs); t1 = _mm_sub_epi16(Bs, RGs); - + t0 = _mm_add_epi16(x0, _mm_packs_epi32( _mm_cvtps_epi32(_mm_mul_ps(_mm_cvtloepi16_ps(t0), ngf0)), _mm_cvtps_epi32(_mm_mul_ps(_mm_cvthiepi16_ps(t0), ngf1)))); - + t1 = _mm_add_epi16(x0, _mm_packs_epi32( _mm_cvtps_epi32(_mm_mul_ps(_mm_cvtloepi16_ps(t1), ngf0)), _mm_cvtps_epi32(_mm_mul_ps(_mm_cvthiepi16_ps(t1), ngf1)))); - + x1 = _mm_merge_epi16(x0, t0); x2 = _mm_merge_epi16(t0, x0); - + uchar R[8], G[8], B[8]; - + _mm_storel_epi64(blueIdx ? (__m128i*)B : (__m128i*)R, _mm_packus_epi16(x1, z)); _mm_storel_epi64((__m128i*)G, _mm_packus_epi16(x2, z)); _mm_storel_epi64(blueIdx ? (__m128i*)R : (__m128i*)B, _mm_packus_epi16(t1, z)); - + for( int j = 0; j < 8; j++, dstrow += 3 ) { dstrow[0] = B[j]; dstrow[1] = G[j]; dstrow[2] = R[j]; } } #endif - + limit = N - 2; } while( i < N - 2 ); - + for( i = 0; i < 6; i++ ) { dst[dststep*y + 5 - i] = dst[dststep*y + 8 - i]; dst[dststep*y + (N - 2)*3 + i] = dst[dststep*y + (N - 3)*3 + i]; } - + greenCell0 = !greenCell0; blueIdx ^= 2; } - + for( i = 0; i < size.width*3; i++ ) { dst[i] = dst[i + dststep] = dst[i + dststep*2]; @@ -2831,14 +2831,14 @@ struct YUV420p2RGB888Invoker { const int rangeBegin = range.begin() * 2; const int rangeEnd = range.end() * 2; - + size_t uvsteps[2] = {width/2, stride - width/2}; int usIdx = ustepIdx, vsIdx = vstepIdx; const uchar* y1 = my1 + rangeBegin * stride; const uchar* u1 = mu + (range.begin() / 2) * stride; const uchar* v1 = mv + (range.begin() / 2) * stride; - + if(range.begin() % 2 == 1) { u1 += uvsteps[(usIdx++) & 1]; @@ -2906,7 +2906,7 @@ struct YUV420p2RGBA8888Invoker const uchar* y1 = my1 + rangeBegin * stride; const uchar* u1 = mu + (range.begin() / 2) * stride; const uchar* v1 = mv + (range.begin() / 2) * stride; - + if(range.begin() % 2 == 1) { u1 += uvsteps[(usIdx++) & 1]; @@ -3139,9 +3139,9 @@ void cv::cvtColor( InputArray _src, OutputArray _dst, int code, int dcn ) Mat src = _src.getMat(), dst; Size sz = src.size(); int scn = src.channels(), depth = src.depth(), bidx; - + CV_Assert( depth == CV_8U || depth == CV_16U || depth == CV_32F ); - + switch( code ) { case CV_BGR2BGRA: case CV_RGB2BGRA: case CV_BGRA2BGR: @@ -3149,10 +3149,10 @@ void cv::cvtColor( InputArray _src, OutputArray _dst, int code, int dcn ) CV_Assert( scn == 3 || scn == 4 ); dcn = code == CV_BGR2BGRA || code == CV_RGB2BGRA || code == CV_BGRA2RGBA ? 4 : 3; bidx = code == CV_BGR2BGRA || code == CV_BGRA2BGR ? 0 : 2; - + _dst.create( sz, CV_MAKETYPE(depth, dcn)); dst = _dst.getMat(); - + if( depth == CV_8U ) { #ifdef HAVE_TEGRA_OPTIMIZATION @@ -3165,7 +3165,7 @@ void cv::cvtColor( InputArray _src, OutputArray _dst, int code, int dcn ) else CvtColorLoop(src, dst, RGB2RGB(scn, dcn, bidx)); break; - + case CV_BGR2BGR565: case CV_BGR2BGR555: case CV_RGB2BGR565: case CV_RGB2BGR555: case CV_BGRA2BGR565: case CV_BGRA2BGR555: case CV_RGBA2BGR565: case CV_RGBA2BGR555: CV_Assert( (scn == 3 || scn == 4) && depth == CV_8U ); @@ -3177,7 +3177,7 @@ void cv::cvtColor( InputArray _src, OutputArray _dst, int code, int dcn ) if(tegra::cvtRGB2RGB565(src, dst, code == CV_RGB2BGR565 || code == CV_RGBA2BGR565 ? 0 : 2)) break; #endif - + CvtColorLoop(src, dst, RGB2RGB5x5(scn, code == CV_BGR2BGR565 || code == CV_BGR2BGR555 || code == CV_BGRA2BGR565 || code == CV_BGRA2BGR555 ? 0 : 2, @@ -3185,14 +3185,14 @@ void cv::cvtColor( InputArray _src, OutputArray _dst, int code, int dcn ) code == CV_BGRA2BGR565 || code == CV_RGBA2BGR565 ? 6 : 5 // green bits )); break; - + case CV_BGR5652BGR: case CV_BGR5552BGR: case CV_BGR5652RGB: case CV_BGR5552RGB: case CV_BGR5652BGRA: case CV_BGR5552BGRA: case CV_BGR5652RGBA: case CV_BGR5552RGBA: if(dcn <= 0) dcn = (code==CV_BGR5652BGRA || code==CV_BGR5552BGRA || code==CV_BGR5652RGBA || code==CV_BGR5552RGBA) ? 4 : 3; CV_Assert( (dcn == 3 || dcn == 4) && scn == 2 && depth == CV_8U ); _dst.create(sz, CV_MAKETYPE(depth, dcn)); dst = _dst.getMat(); - + CvtColorLoop(src, dst, RGB5x52RGB(dcn, code == CV_BGR5652BGR || code == CV_BGR5552BGR || code == CV_BGR5652BGRA || code == CV_BGR5552BGRA ? 0 : 2, // blue idx @@ -3200,14 +3200,14 @@ void cv::cvtColor( InputArray _src, OutputArray _dst, int code, int dcn ) code == CV_BGR5652BGRA || code == CV_BGR5652RGBA ? 6 : 5 // green bits )); break; - + case CV_BGR2GRAY: case CV_BGRA2GRAY: case CV_RGB2GRAY: case CV_RGBA2GRAY: CV_Assert( scn == 3 || scn == 4 ); _dst.create(sz, CV_MAKETYPE(depth, 1)); dst = _dst.getMat(); - + bidx = code == CV_BGR2GRAY || code == CV_BGRA2GRAY ? 0 : 2; - + if( depth == CV_8U ) { #ifdef HAVE_TEGRA_OPTIMIZATION @@ -3220,21 +3220,21 @@ void cv::cvtColor( InputArray _src, OutputArray _dst, int code, int dcn ) else CvtColorLoop(src, dst, RGB2Gray(scn, bidx, 0)); break; - + case CV_BGR5652GRAY: case CV_BGR5552GRAY: CV_Assert( scn == 2 && depth == CV_8U ); _dst.create(sz, CV_8UC1); dst = _dst.getMat(); - + CvtColorLoop(src, dst, RGB5x52Gray(code == CV_BGR5652GRAY ? 6 : 5)); break; - + case CV_GRAY2BGR: case CV_GRAY2BGRA: if( dcn <= 0 ) dcn = (code==CV_GRAY2BGRA) ? 4 : 3; CV_Assert( scn == 1 && (dcn == 3 || dcn == 4)); _dst.create(sz, CV_MAKETYPE(depth, dcn)); dst = _dst.getMat(); - + if( depth == CV_8U ) { #ifdef HAVE_TEGRA_OPTIMIZATION @@ -3247,15 +3247,15 @@ void cv::cvtColor( InputArray _src, OutputArray _dst, int code, int dcn ) else CvtColorLoop(src, dst, Gray2RGB(dcn)); break; - + case CV_GRAY2BGR565: case CV_GRAY2BGR555: CV_Assert( scn == 1 && depth == CV_8U ); _dst.create(sz, CV_8UC2); dst = _dst.getMat(); - + CvtColorLoop(src, dst, Gray2RGB5x5(code == CV_GRAY2BGR565 ? 6 : 5)); break; - + case CV_BGR2YCrCb: case CV_RGB2YCrCb: case CV_BGR2YUV: case CV_RGB2YUV: { @@ -3265,10 +3265,10 @@ void cv::cvtColor( InputArray _src, OutputArray _dst, int code, int dcn ) static const int yuv_i[] = { B2Y, G2Y, R2Y, 8061, 14369 }; const float* coeffs_f = code == CV_BGR2YCrCb || code == CV_RGB2YCrCb ? 0 : yuv_f; const int* coeffs_i = code == CV_BGR2YCrCb || code == CV_RGB2YCrCb ? 0 : yuv_i; - + _dst.create(sz, CV_MAKETYPE(depth, 3)); dst = _dst.getMat(); - + if( depth == CV_8U ) { #ifdef HAVE_TEGRA_OPTIMIZATION @@ -3283,7 +3283,7 @@ void cv::cvtColor( InputArray _src, OutputArray _dst, int code, int dcn ) CvtColorLoop(src, dst, RGB2YCrCb_f(scn, bidx, coeffs_f)); } break; - + case CV_YCrCb2BGR: case CV_YCrCb2RGB: case CV_YUV2BGR: case CV_YUV2RGB: { @@ -3291,13 +3291,13 @@ void cv::cvtColor( InputArray _src, OutputArray _dst, int code, int dcn ) CV_Assert( scn == 3 && (dcn == 3 || dcn == 4) ); bidx = code == CV_YCrCb2BGR || code == CV_YUV2RGB ? 0 : 2; static const float yuv_f[] = { 2.032f, -0.395f, -0.581f, 1.140f }; - static const int yuv_i[] = { 33292, -6472, -9519, 18678 }; + static const int yuv_i[] = { 33292, -6472, -9519, 18678 }; const float* coeffs_f = code == CV_YCrCb2BGR || code == CV_YCrCb2RGB ? 0 : yuv_f; const int* coeffs_i = code == CV_YCrCb2BGR || code == CV_YCrCb2RGB ? 0 : yuv_i; - + _dst.create(sz, CV_MAKETYPE(depth, dcn)); dst = _dst.getMat(); - + if( depth == CV_8U ) CvtColorLoop(src, dst, YCrCb2RGB_i(dcn, bidx, coeffs_i)); else if( depth == CV_16U ) @@ -3306,14 +3306,14 @@ void cv::cvtColor( InputArray _src, OutputArray _dst, int code, int dcn ) CvtColorLoop(src, dst, YCrCb2RGB_f(dcn, bidx, coeffs_f)); } break; - + case CV_BGR2XYZ: case CV_RGB2XYZ: CV_Assert( scn == 3 || scn == 4 ); bidx = code == CV_BGR2XYZ ? 0 : 2; - + _dst.create(sz, CV_MAKETYPE(depth, 3)); dst = _dst.getMat(); - + if( depth == CV_8U ) CvtColorLoop(src, dst, RGB2XYZ_i(scn, bidx, 0)); else if( depth == CV_16U ) @@ -3321,15 +3321,15 @@ void cv::cvtColor( InputArray _src, OutputArray _dst, int code, int dcn ) else CvtColorLoop(src, dst, RGB2XYZ_f(scn, bidx, 0)); break; - + case CV_XYZ2BGR: case CV_XYZ2RGB: if( dcn <= 0 ) dcn = 3; CV_Assert( scn == 3 && (dcn == 3 || dcn == 4) ); bidx = code == CV_XYZ2BGR ? 0 : 2; - + _dst.create(sz, CV_MAKETYPE(depth, dcn)); dst = _dst.getMat(); - + if( depth == CV_8U ) CvtColorLoop(src, dst, XYZ2RGB_i(dcn, bidx, 0)); else if( depth == CV_16U ) @@ -3337,7 +3337,7 @@ void cv::cvtColor( InputArray _src, OutputArray _dst, int code, int dcn ) else CvtColorLoop(src, dst, XYZ2RGB_f(dcn, bidx, 0)); break; - + case CV_BGR2HSV: case CV_RGB2HSV: case CV_BGR2HSV_FULL: case CV_RGB2HSV_FULL: case CV_BGR2HLS: case CV_RGB2HLS: case CV_BGR2HLS_FULL: case CV_RGB2HLS_FULL: { @@ -3346,15 +3346,15 @@ void cv::cvtColor( InputArray _src, OutputArray _dst, int code, int dcn ) code == CV_BGR2HSV_FULL || code == CV_BGR2HLS_FULL ? 0 : 2; int hrange = depth == CV_32F ? 360 : code == CV_BGR2HSV || code == CV_RGB2HSV || code == CV_BGR2HLS || code == CV_RGB2HLS ? 180 : 256; - + _dst.create(sz, CV_MAKETYPE(depth, 3)); dst = _dst.getMat(); - + if( code == CV_BGR2HSV || code == CV_RGB2HSV || code == CV_BGR2HSV_FULL || code == CV_RGB2HSV_FULL ) { #ifdef HAVE_TEGRA_OPTIMIZATION - if(tegra::cvtRGB2HSV(src, dst, bidx, hrange)) + if(tegra::cvtRGB2HSV(src, dst, bidx, hrange)) break; #endif if( depth == CV_8U ) @@ -3371,7 +3371,7 @@ void cv::cvtColor( InputArray _src, OutputArray _dst, int code, int dcn ) } } break; - + case CV_HSV2BGR: case CV_HSV2RGB: case CV_HSV2BGR_FULL: case CV_HSV2RGB_FULL: case CV_HLS2BGR: case CV_HLS2RGB: case CV_HLS2BGR_FULL: case CV_HLS2RGB_FULL: { @@ -3381,10 +3381,10 @@ void cv::cvtColor( InputArray _src, OutputArray _dst, int code, int dcn ) code == CV_HSV2BGR_FULL || code == CV_HLS2BGR_FULL ? 0 : 2; int hrange = depth == CV_32F ? 360 : code == CV_HSV2BGR || code == CV_HSV2RGB || code == CV_HLS2BGR || code == CV_HLS2RGB ? 180 : 255; - + _dst.create(sz, CV_MAKETYPE(depth, dcn)); dst = _dst.getMat(); - + if( code == CV_HSV2BGR || code == CV_HSV2RGB || code == CV_HSV2BGR_FULL || code == CV_HSV2RGB_FULL ) { @@ -3402,7 +3402,7 @@ void cv::cvtColor( InputArray _src, OutputArray _dst, int code, int dcn ) } } break; - + case CV_BGR2Lab: case CV_RGB2Lab: case CV_LBGR2Lab: case CV_LRGB2Lab: case CV_BGR2Luv: case CV_RGB2Luv: case CV_LBGR2Luv: case CV_LRGB2Luv: { @@ -3411,10 +3411,10 @@ void cv::cvtColor( InputArray _src, OutputArray _dst, int code, int dcn ) code == CV_LBGR2Lab || code == CV_LBGR2Luv ? 0 : 2; bool srgb = code == CV_BGR2Lab || code == CV_RGB2Lab || code == CV_BGR2Luv || code == CV_RGB2Luv; - + _dst.create(sz, CV_MAKETYPE(depth, 3)); dst = _dst.getMat(); - + if( code == CV_BGR2Lab || code == CV_RGB2Lab || code == CV_LBGR2Lab || code == CV_LRGB2Lab ) { @@ -3432,7 +3432,7 @@ void cv::cvtColor( InputArray _src, OutputArray _dst, int code, int dcn ) } } break; - + case CV_Lab2BGR: case CV_Lab2RGB: case CV_Lab2LBGR: case CV_Lab2LRGB: case CV_Luv2BGR: case CV_Luv2RGB: case CV_Luv2LBGR: case CV_Luv2LRGB: { @@ -3442,10 +3442,10 @@ void cv::cvtColor( InputArray _src, OutputArray _dst, int code, int dcn ) code == CV_Lab2LBGR || code == CV_Luv2LBGR ? 0 : 2; bool srgb = code == CV_Lab2BGR || code == CV_Lab2RGB || code == CV_Luv2BGR || code == CV_Luv2RGB; - + _dst.create(sz, CV_MAKETYPE(depth, dcn)); dst = _dst.getMat(); - + if( code == CV_Lab2BGR || code == CV_Lab2RGB || code == CV_Lab2LBGR || code == CV_Lab2LRGB ) { @@ -3463,14 +3463,14 @@ void cv::cvtColor( InputArray _src, OutputArray _dst, int code, int dcn ) } } break; - + case CV_BayerBG2GRAY: case CV_BayerGB2GRAY: case CV_BayerRG2GRAY: case CV_BayerGR2GRAY: if(dcn <= 0) dcn = 1; CV_Assert( scn == 1 && dcn == 1 ); - + _dst.create(sz, depth); dst = _dst.getMat(); - + if( depth == CV_8U ) Bayer2Gray_(src, dst, code); else if( depth == CV_16U ) @@ -3478,15 +3478,15 @@ void cv::cvtColor( InputArray _src, OutputArray _dst, int code, int dcn ) else CV_Error(CV_StsUnsupportedFormat, "Bayer->Gray demosaicing only supports 8u and 16u types"); break; - + case CV_BayerBG2BGR: case CV_BayerGB2BGR: case CV_BayerRG2BGR: case CV_BayerGR2BGR: case CV_BayerBG2BGR_VNG: case CV_BayerGB2BGR_VNG: case CV_BayerRG2BGR_VNG: case CV_BayerGR2BGR_VNG: if(dcn <= 0) dcn = 3; CV_Assert( scn == 1 && dcn == 3 ); - + _dst.create(sz, CV_MAKETYPE(depth, dcn)); dst = _dst.getMat(); - + if( code == CV_BayerBG2BGR || code == CV_BayerGB2BGR || code == CV_BayerRG2BGR || code == CV_BayerGR2BGR ) { @@ -3508,14 +3508,14 @@ void cv::cvtColor( InputArray _src, OutputArray _dst, int code, int dcn ) { // http://www.fourcc.org/yuv.php#NV21 == yuv420sp -> a plane of 8 bit Y samples followed by an interleaved V/U plane containing 8 bit 2x2 subsampled chroma samples // http://www.fourcc.org/yuv.php#NV12 -> a plane of 8 bit Y samples followed by an interleaved U/V plane containing 8 bit 2x2 subsampled colour difference samples - + if (dcn <= 0) dcn = (code==CV_YUV420sp2BGRA || code==CV_YUV420sp2RGBA || code==CV_YUV2BGRA_NV12 || code==CV_YUV2RGBA_NV12) ? 4 : 3; - const int bidx = (code==CV_YUV2BGR_NV21 || code==CV_YUV2BGRA_NV21 || code==CV_YUV2BGR_NV12 || code==CV_YUV2BGRA_NV12) ? 0 : 2; - const int uidx = (code==CV_YUV2BGR_NV21 || code==CV_YUV2BGRA_NV21 || code==CV_YUV2RGB_NV21 || code==CV_YUV2RGBA_NV21) ? 1 : 0; - + const int bIdx = (code==CV_YUV2BGR_NV21 || code==CV_YUV2BGRA_NV21 || code==CV_YUV2BGR_NV12 || code==CV_YUV2BGRA_NV12) ? 0 : 2; + const int uIdx = (code==CV_YUV2BGR_NV21 || code==CV_YUV2BGRA_NV21 || code==CV_YUV2RGB_NV21 || code==CV_YUV2RGBA_NV21) ? 1 : 0; + CV_Assert( dcn == 3 || dcn == 4 ); CV_Assert( sz.width % 2 == 0 && sz.height % 3 == 0 && depth == CV_8U ); - + Size dstSz(sz.width, sz.height * 2 / 3); _dst.create(dstSz, CV_MAKETYPE(depth, dcn)); dst = _dst.getMat(); @@ -3523,8 +3523,8 @@ void cv::cvtColor( InputArray _src, OutputArray _dst, int code, int dcn ) int srcstep = (int)src.step; const uchar* y = src.ptr(); const uchar* uv = y + srcstep * dstSz.height; - - switch(dcn*100 + bidx * 10 + uidx) + + switch(dcn*100 + bIdx * 10 + uIdx) { case 300: cvtYUV420sp2RGB<0, 0> (dst, srcstep, y, uv); break; case 301: cvtYUV420sp2RGB<0, 1> (dst, srcstep, y, uv); break; @@ -3543,29 +3543,29 @@ void cv::cvtColor( InputArray _src, OutputArray _dst, int code, int dcn ) { //http://www.fourcc.org/yuv.php#YV12 == yuv420p -> It comprises an NxM Y plane followed by (N/2)x(M/2) V and U planes. //http://www.fourcc.org/yuv.php#IYUV == I420 -> It comprises an NxN Y plane followed by (N/2)x(N/2) U and V planes - + if (dcn <= 0) dcn = (code==CV_YUV2BGRA_YV12 || code==CV_YUV2RGBA_YV12 || code==CV_YUV2RGBA_IYUV || code==CV_YUV2BGRA_IYUV) ? 4 : 3; - const int bidx = (code==CV_YUV2BGR_YV12 || code==CV_YUV2BGRA_YV12 || code==CV_YUV2BGR_IYUV || code==CV_YUV2BGRA_IYUV) ? 0 : 2; - const int uidx = (code==CV_YUV2BGR_YV12 || code==CV_YUV2RGB_YV12 || code==CV_YUV2BGRA_YV12 || code==CV_YUV2RGBA_YV12) ? 1 : 0; - + const int bIdx = (code==CV_YUV2BGR_YV12 || code==CV_YUV2BGRA_YV12 || code==CV_YUV2BGR_IYUV || code==CV_YUV2BGRA_IYUV) ? 0 : 2; + const int uIdx = (code==CV_YUV2BGR_YV12 || code==CV_YUV2RGB_YV12 || code==CV_YUV2BGRA_YV12 || code==CV_YUV2RGBA_YV12) ? 1 : 0; + CV_Assert( dcn == 3 || dcn == 4 ); CV_Assert( sz.width % 2 == 0 && sz.height % 3 == 0 && depth == CV_8U ); Size dstSz(sz.width, sz.height * 2 / 3); _dst.create(dstSz, CV_MAKETYPE(depth, dcn)); dst = _dst.getMat(); - + int srcstep = (int)src.step; const uchar* y = src.ptr(); const uchar* u = y + srcstep * dstSz.height; const uchar* v = y + srcstep * (dstSz.height + dstSz.height/4) + (dstSz.width/2) * ((dstSz.height % 4)/2); - + int ustepIdx = 0; int vstepIdx = dstSz.height % 4 == 2 ? 1 : 0; - - if(uidx == 1) { std::swap(u ,v), std::swap(ustepIdx, vstepIdx); }; - - switch(dcn*10 + bidx) + + if(uIdx == 1) { std::swap(u ,v), std::swap(ustepIdx, vstepIdx); }; + + switch(dcn*10 + bIdx) { case 30: cvtYUV420p2RGB<0>(dst, srcstep, y, u, v, ustepIdx, vstepIdx); break; case 32: cvtYUV420p2RGB<2>(dst, srcstep, y, u, v, ustepIdx, vstepIdx); break; @@ -3578,14 +3578,14 @@ void cv::cvtColor( InputArray _src, OutputArray _dst, int code, int dcn ) case CV_YUV2GRAY_420: { if (dcn <= 0) dcn = 1; - + CV_Assert( dcn == 1 ); CV_Assert( sz.width % 2 == 0 && sz.height % 3 == 0 && depth == CV_8U ); Size dstSz(sz.width, sz.height * 2 / 3); _dst.create(dstSz, CV_MAKETYPE(depth, dcn)); dst = _dst.getMat(); - + src(Range(0, dstSz.height), Range::all()).copyTo(dst); } break; @@ -3595,20 +3595,20 @@ void cv::cvtColor( InputArray _src, OutputArray _dst, int code, int dcn ) { //http://www.fourcc.org/yuv.php#UYVY //http://www.fourcc.org/yuv.php#YUY2 - //http://www.fourcc.org/yuv.php#YVYU - + //http://www.fourcc.org/yuv.php#YVYU + if (dcn <= 0) dcn = (code==CV_YUV2RGBA_UYVY || code==CV_YUV2BGRA_UYVY || code==CV_YUV2RGBA_YUY2 || code==CV_YUV2BGRA_YUY2 || code==CV_YUV2RGBA_YVYU || code==CV_YUV2BGRA_YVYU) ? 4 : 3; - const int bidx = (code==CV_YUV2BGR_UYVY || code==CV_YUV2BGRA_UYVY || code==CV_YUV2BGR_YUY2 || code==CV_YUV2BGRA_YUY2 || code==CV_YUV2BGR_YVYU || code==CV_YUV2BGRA_YVYU) ? 0 : 2; - const int ycn = (code==CV_YUV2RGB_UYVY || code==CV_YUV2BGR_UYVY || code==CV_YUV2RGBA_UYVY || code==CV_YUV2BGRA_UYVY) ? 1 : 0; - const int uidx = (code==CV_YUV2RGB_YVYU || code==CV_YUV2BGR_YVYU || code==CV_YUV2RGBA_YVYU || code==CV_YUV2BGRA_YVYU) ? 1 : 0; - + const int bIdx = (code==CV_YUV2BGR_UYVY || code==CV_YUV2BGRA_UYVY || code==CV_YUV2BGR_YUY2 || code==CV_YUV2BGRA_YUY2 || code==CV_YUV2BGR_YVYU || code==CV_YUV2BGRA_YVYU) ? 0 : 2; + const int ycn = (code==CV_YUV2RGB_UYVY || code==CV_YUV2BGR_UYVY || code==CV_YUV2RGBA_UYVY || code==CV_YUV2BGRA_UYVY) ? 1 : 0; + const int uIdx = (code==CV_YUV2RGB_YVYU || code==CV_YUV2BGR_YVYU || code==CV_YUV2RGBA_YVYU || code==CV_YUV2BGRA_YVYU) ? 1 : 0; + CV_Assert( dcn == 3 || dcn == 4 ); CV_Assert( scn == 2 && depth == CV_8U ); _dst.create(sz, CV_8UC(dcn)); dst = _dst.getMat(); - - switch(dcn*1000 + bidx*100 + uidx*10 + ycn) + + switch(dcn*1000 + bIdx*100 + uIdx*10 + ycn) { case 3000: cvtYUV422toRGB<0,0,0>(dst, (int)src.step, src.ptr()); break; case 3001: cvtYUV422toRGB<0,0,1>(dst, (int)src.step, src.ptr()); break; @@ -3633,7 +3633,7 @@ void cv::cvtColor( InputArray _src, OutputArray _dst, int code, int dcn ) case CV_YUV2GRAY_UYVY: case CV_YUV2GRAY_YUY2: { if (dcn <= 0) dcn = 1; - + CV_Assert( dcn == 1 ); CV_Assert( scn == 2 && depth == CV_8U ); @@ -3644,13 +3644,13 @@ void cv::cvtColor( InputArray _src, OutputArray _dst, int code, int dcn ) CV_Error( CV_StsBadFlag, "Unknown/unsupported color conversion code" ); } } - + CV_IMPL void cvCvtColor( const CvArr* srcarr, CvArr* dstarr, int code ) { cv::Mat src = cv::cvarrToMat(srcarr), dst0 = cv::cvarrToMat(dstarr), dst = dst0; CV_Assert( src.depth() == dst.depth() ); - + cv::cvtColor(src, dst, code, dst.channels()); CV_Assert( dst.data == dst0.data ); } diff --git a/modules/imgproc/src/filter.cpp b/modules/imgproc/src/filter.cpp index 6a998e5..a2bfa6a 100644 --- a/modules/imgproc/src/filter.cpp +++ b/modules/imgproc/src/filter.cpp @@ -48,11 +48,11 @@ /* Various border types, image boundaries are denoted with '|' - + * BORDER_REPLICATE: aaaaaa|abcdefgh|hhhhhhh * BORDER_REFLECT: fedcba|abcdefgh|hgfedcb * BORDER_REFLECT_101: gfedcb|abcdefgh|gfedcba - * BORDER_WRAP: cdefgh|abcdefgh|abcdefg + * BORDER_WRAP: cdefgh|abcdefgh|abcdefg * BORDER_CONSTANT: iiiiii|abcdefgh|iiiiiii with some specified 'i' */ int cv::borderInterpolate( int p, int len, int borderType ) @@ -113,7 +113,7 @@ FilterEngine::FilterEngine() wholeSize = Size(-1,-1); } - + FilterEngine::FilterEngine( const Ptr& _filter2D, const Ptr& _rowFilter, @@ -125,7 +125,7 @@ FilterEngine::FilterEngine( const Ptr& _filter2D, init(_filter2D, _rowFilter, _columnFilter, _srcType, _dstType, _bufType, _rowBorderType, _columnBorderType, _borderValue); } - + FilterEngine::~FilterEngine() { } @@ -141,24 +141,24 @@ void FilterEngine::init( const Ptr& _filter2D, _srcType = CV_MAT_TYPE(_srcType); _bufType = CV_MAT_TYPE(_bufType); _dstType = CV_MAT_TYPE(_dstType); - + srcType = _srcType; int srcElemSize = (int)getElemSize(srcType); dstType = _dstType; bufType = _bufType; - + filter2D = _filter2D; rowFilter = _rowFilter; columnFilter = _columnFilter; if( _columnBorderType < 0 ) _columnBorderType = _rowBorderType; - + rowBorderType = _rowBorderType; columnBorderType = _columnBorderType; - + CV_Assert( columnBorderType != BORDER_WRAP ); - + if( isSeparable() ) { CV_Assert( !rowFilter.empty() && !columnFilter.empty() ); @@ -175,7 +175,7 @@ void FilterEngine::init( const Ptr& _filter2D, CV_Assert( 0 <= anchor.x && anchor.x < ksize.width && 0 <= anchor.y && anchor.y < ksize.height ); - borderElemSize = srcElemSize/(CV_MAT_DEPTH(srcType) >= CV_32S ? sizeof(int) : 1); + borderElemSize = srcElemSize/(CV_MAT_DEPTH(srcType) >= CV_32S ? sizeof(int) : 1); int borderLength = std::max(ksize.width - 1, 1); borderTab.resize(borderLength*borderElemSize); @@ -198,7 +198,7 @@ static const int VEC_ALIGN = CV_MALLOC_ALIGN; int FilterEngine::start(Size _wholeSize, Rect _roi, int _maxBufRows) { int i, j; - + wholeSize = _wholeSize; roi = _roi; CV_Assert( roi.x >= 0 && roi.y >= 0 && roi.width >= 0 && roi.height >= 0 && @@ -226,7 +226,7 @@ int FilterEngine::start(Size _wholeSize, Rect _roi, int _maxBufRows) int n = (int)constBorderValue.size(), N; N = (maxWidth + ksize.width - 1)*esz; tdst = isSeparable() ? &srcRow[0] : dst; - + for( i = 0; i < N; i += n ) { n = std::min( n, N - i ); @@ -237,7 +237,7 @@ int FilterEngine::start(Size _wholeSize, Rect _roi, int _maxBufRows) if( isSeparable() ) (*rowFilter)(&srcRow[0], dst, maxWidth, cn); } - + int maxBufStep = bufElemSize*(int)alignSize(maxWidth + (!isSeparable() ? ksize.width - 1 : 0),VEC_ALIGN); ringBuf.resize(maxBufStep*rows.size()+VEC_ALIGN); @@ -265,10 +265,10 @@ int FilterEngine::start(Size _wholeSize, Rect _roi, int _maxBufRows) else { int xofs1 = std::min(roi.x, anchor.x) - roi.x; - + int btab_esz = borderElemSize, wholeWidth = wholeSize.width; int* btab = (int*)&borderTab[0]; - + for( i = 0; i < dx1; i++ ) { int p0 = (borderInterpolate(i-dx1, wholeWidth, rowBorderType) + xofs1)*btab_esz; @@ -301,20 +301,20 @@ int FilterEngine::start(const Mat& src, const Rect& _srcRoi, bool isolated, int maxBufRows) { Rect srcRoi = _srcRoi; - + if( srcRoi == Rect(0,0,-1,-1) ) srcRoi = Rect(0,0,src.cols,src.rows); - + CV_Assert( srcRoi.x >= 0 && srcRoi.y >= 0 && srcRoi.width >= 0 && srcRoi.height >= 0 && srcRoi.x + srcRoi.width <= src.cols && srcRoi.y + srcRoi.height <= src.rows ); Point ofs; - Size wholeSize(src.cols, src.rows); + Size wsz(src.cols, src.rows); if( !isolated ) - src.locateROI( wholeSize, ofs ); - start( wholeSize, srcRoi + ofs, maxBufRows ); + src.locateROI( wsz, ofs ); + start( wsz, srcRoi + ofs, maxBufRows ); return startY - ofs.y; } @@ -334,7 +334,7 @@ int FilterEngine::proceed( const uchar* src, int srcstep, int count, uchar* dst, int dststep ) { CV_Assert( wholeSize.width > 0 && wholeSize.height > 0 ); - + const int *btab = &borderTab[0]; int esz = (int)getElemSize(srcType), btab_esz = borderElemSize; uchar** brows = &rows[0]; @@ -365,7 +365,7 @@ int FilterEngine::proceed( const uchar* src, int srcstep, int count, int bi = (startY - startY0 + rowCount) % bufRows; uchar* brow = alignPtr(&ringBuf[0], VEC_ALIGN) + bi*bufStep; uchar* row = isSep ? &srcRow[0] : brow; - + if( ++rowCount > bufRows ) { --rowCount; @@ -394,7 +394,7 @@ int FilterEngine::proceed( const uchar* src, int srcstep, int count, row[i + (width1 - _dx2)*esz] = src[btab[i+_dx1*esz]]; } } - + if( isSep ) (*rowFilter)(row, brow, width, CV_MAT_CN(srcType)); } @@ -434,11 +434,11 @@ void FilterEngine::apply(const Mat& src, Mat& dst, const Rect& _srcRoi, Point dstOfs, bool isolated) { CV_Assert( src.type() == srcType && dst.type() == dstType ); - + Rect srcRoi = _srcRoi; if( srcRoi == Rect(0,0,-1,-1) ) srcRoi = Rect(0,0,src.cols,src.rows); - + if( srcRoi.area() == 0 ) return; @@ -560,7 +560,7 @@ struct RowVec_8u32s { if( !checkHardwareSupport(CV_CPU_SSE2) ) return 0; - + int i = 0, k, _ksize = kernel.rows + kernel.cols - 1; int* dst = (int*)_dst; const int* _kx = (const int*)kernel.data; @@ -593,7 +593,7 @@ struct RowVec_8u32s s2 = _mm_add_epi32(s2, _mm_unpacklo_epi16(x2, x3)); s3 = _mm_add_epi32(s3, _mm_unpackhi_epi16(x2, x3)); } - + _mm_store_si128((__m128i*)(dst + i), s0); _mm_store_si128((__m128i*)(dst + i + 4), s1); _mm_store_si128((__m128i*)(dst + i + 8), s2); @@ -652,7 +652,7 @@ struct SymmRowSmallVec_8u32s { if( !checkHardwareSupport(CV_CPU_SSE2) ) return 0; - + int i = 0, j, k, _ksize = kernel.rows + kernel.cols - 1; int* dst = (int*)_dst; bool symmetrical = (symmetryType & KERNEL_SYMMETRICAL) != 0; @@ -973,7 +973,7 @@ struct SymmColumnVec_32s8u { if( !checkHardwareSupport(CV_CPU_SSE2) ) return 0; - + int ksize2 = (kernel.rows + kernel.cols - 1)/2; const float* ky = (const float*)kernel.data + ksize2; int i = 0, k; @@ -1121,7 +1121,7 @@ struct SymmColumnSmallVec_32s16s { if( !checkHardwareSupport(CV_CPU_SSE2) ) return 0; - + int ksize2 = (kernel.rows + kernel.cols - 1)/2; const float* ky = (const float*)kernel.data + ksize2; int i = 0; @@ -1237,9 +1237,9 @@ struct SymmColumnSmallVec_32s16s Mat kernel; }; - + /////////////////////////////////////// 16s ////////////////////////////////// - + struct RowVec_16s32f { RowVec_16s32f() {} @@ -1248,17 +1248,17 @@ struct RowVec_16s32f kernel = _kernel; sse2_supported = checkHardwareSupport(CV_CPU_SSE2); } - + int operator()(const uchar* _src, uchar* _dst, int width, int cn) const { if( !sse2_supported ) return 0; - + int i = 0, k, _ksize = kernel.rows + kernel.cols - 1; float* dst = (float*)_dst; const float* _kx = (const float*)kernel.data; width *= cn; - + for( ; i <= width - 8; i += 8 ) { const short* src = (const short*)_src + i; @@ -1267,7 +1267,7 @@ struct RowVec_16s32f { f = _mm_load_ss(_kx+k); f = _mm_shuffle_ps(f, f, 0); - + __m128i x0i = _mm_loadu_si128((const __m128i*)src); __m128i x1i = _mm_srai_epi32(_mm_unpackhi_epi16(x0i, x0i), 16); x0i = _mm_srai_epi32(_mm_unpacklo_epi16(x0i, x0i), 16); @@ -1281,12 +1281,12 @@ struct RowVec_16s32f } return i; } - + Mat kernel; bool sse2_supported; }; - - + + struct SymmColumnVec_32f16s { SymmColumnVec_32f16s() { symmetryType=0; } @@ -1298,12 +1298,12 @@ struct SymmColumnVec_32f16s CV_Assert( (symmetryType & (KERNEL_SYMMETRICAL | KERNEL_ASYMMETRICAL)) != 0 ); sse2_supported = checkHardwareSupport(CV_CPU_SSE2); } - + int operator()(const uchar** _src, uchar* _dst, int width) const { if( !sse2_supported ) return 0; - + int ksize2 = (kernel.rows + kernel.cols - 1)/2; const float* ky = (const float*)kernel.data + ksize2; int i = 0, k; @@ -1312,7 +1312,7 @@ struct SymmColumnVec_32f16s const float *S, *S2; short* dst = (short*)_dst; __m128 d4 = _mm_set1_ps(delta); - + if( symmetrical ) { for( ; i <= width - 16; i += 16 ) @@ -1330,7 +1330,7 @@ struct SymmColumnVec_32f16s s3 = _mm_load_ps(S+12); s2 = _mm_add_ps(_mm_mul_ps(s2, f), d4); s3 = _mm_add_ps(_mm_mul_ps(s3, f), d4); - + for( k = 1; k <= ksize2; k++ ) { S = src[k] + i; @@ -1346,23 +1346,23 @@ struct SymmColumnVec_32f16s s2 = _mm_add_ps(s2, _mm_mul_ps(x0, f)); s3 = _mm_add_ps(s3, _mm_mul_ps(x1, f)); } - + __m128i s0i = _mm_cvtps_epi32(s0); __m128i s1i = _mm_cvtps_epi32(s1); __m128i s2i = _mm_cvtps_epi32(s2); __m128i s3i = _mm_cvtps_epi32(s3); - + _mm_storeu_si128((__m128i*)(dst + i), _mm_packs_epi32(s0i, s1i)); _mm_storeu_si128((__m128i*)(dst + i + 8), _mm_packs_epi32(s2i, s3i)); } - + for( ; i <= width - 4; i += 4 ) { __m128 f = _mm_load_ss(ky); f = _mm_shuffle_ps(f, f, 0); __m128 x0, s0 = _mm_load_ps(src[0] + i); s0 = _mm_add_ps(_mm_mul_ps(s0, f), d4); - + for( k = 1; k <= ksize2; k++ ) { f = _mm_load_ss(ky+k); @@ -1372,7 +1372,7 @@ struct SymmColumnVec_32f16s x0 = _mm_add_ps(_mm_load_ps(src[k]+i), _mm_load_ps(src[-k] + i)); s0 = _mm_add_ps(s0, _mm_mul_ps(x0, f)); } - + __m128i s0i = _mm_cvtps_epi32(s0); _mm_storel_epi64((__m128i*)(dst + i), _mm_packs_epi32(s0i, s0i)); } @@ -1384,7 +1384,7 @@ struct SymmColumnVec_32f16s __m128 f, s0 = d4, s1 = d4, s2 = d4, s3 = d4; __m128 x0, x1; S = src[0] + i; - + for( k = 1; k <= ksize2; k++ ) { S = src[k] + i; @@ -1400,20 +1400,20 @@ struct SymmColumnVec_32f16s s2 = _mm_add_ps(s2, _mm_mul_ps(x0, f)); s3 = _mm_add_ps(s3, _mm_mul_ps(x1, f)); } - + __m128i s0i = _mm_cvtps_epi32(s0); __m128i s1i = _mm_cvtps_epi32(s1); __m128i s2i = _mm_cvtps_epi32(s2); __m128i s3i = _mm_cvtps_epi32(s3); - + _mm_storeu_si128((__m128i*)(dst + i), _mm_packs_epi32(s0i, s1i)); _mm_storeu_si128((__m128i*)(dst + i + 8), _mm_packs_epi32(s2i, s3i)); } - + for( ; i <= width - 4; i += 4 ) { __m128 f, x0, s0 = d4; - + for( k = 1; k <= ksize2; k++ ) { f = _mm_load_ss(ky+k); @@ -1421,21 +1421,21 @@ struct SymmColumnVec_32f16s x0 = _mm_sub_ps(_mm_load_ps(src[k]+i), _mm_load_ps(src[-k] + i)); s0 = _mm_add_ps(s0, _mm_mul_ps(x0, f)); } - + __m128i s0i = _mm_cvtps_epi32(s0); _mm_storel_epi64((__m128i*)(dst + i), _mm_packs_epi32(s0i, s0i)); } } - + return i; } - + int symmetryType; float delta; Mat kernel; bool sse2_supported; -}; - +}; + /////////////////////////////////////// 32f ////////////////////////////////// @@ -1451,7 +1451,7 @@ struct RowVec_32f { if( !checkHardwareSupport(CV_CPU_SSE) ) return 0; - + int i = 0, k, _ksize = kernel.rows + kernel.cols - 1; float* dst = (float*)_dst; const float* _kx = (const float*)kernel.data; @@ -1494,7 +1494,7 @@ struct SymmRowSmallVec_32f { if( !checkHardwareSupport(CV_CPU_SSE) ) return 0; - + int i = 0, _ksize = kernel.rows + kernel.cols - 1; float* dst = (float*)_dst; const float* src = (const float*)_src + (_ksize/2)*cn; @@ -1594,12 +1594,12 @@ struct SymmRowSmallVec_32f y0 = _mm_mul_ps(_mm_add_ps(y0, y2), k1); x0 = _mm_add_ps(x0, _mm_mul_ps(x1, k0)); y0 = _mm_add_ps(y0, _mm_mul_ps(y1, k0)); - + x2 = _mm_add_ps(_mm_loadu_ps(src + cn*2), _mm_loadu_ps(src - cn*2)); y2 = _mm_add_ps(_mm_loadu_ps(src + cn*2 + 4), _mm_loadu_ps(src - cn*2 + 4)); x0 = _mm_add_ps(x0, _mm_mul_ps(x2, k2)); y0 = _mm_add_ps(y0, _mm_mul_ps(y2, k2)); - + _mm_store_ps(dst + i, x0); _mm_store_ps(dst + i + 4, y0); } @@ -1654,12 +1654,12 @@ struct SymmRowSmallVec_32f x0 = _mm_mul_ps(_mm_sub_ps(x0, x2), k1); y0 = _mm_mul_ps(_mm_sub_ps(y0, y2), k1); - + x2 = _mm_sub_ps(_mm_loadu_ps(src + cn*2), _mm_loadu_ps(src - cn*2)); y2 = _mm_sub_ps(_mm_loadu_ps(src + cn*2 + 4), _mm_loadu_ps(src - cn*2 + 4)); x0 = _mm_add_ps(x0, _mm_mul_ps(x2, k2)); y0 = _mm_add_ps(y0, _mm_mul_ps(y2, k2)); - + _mm_store_ps(dst + i, x0); _mm_store_ps(dst + i + 4, y0); } @@ -1689,7 +1689,7 @@ struct SymmColumnVec_32f { if( !checkHardwareSupport(CV_CPU_SSE) ) return 0; - + int ksize2 = (kernel.rows + kernel.cols - 1)/2; const float* ky = (const float*)kernel.data + ksize2; int i = 0, k; @@ -1829,7 +1829,7 @@ struct SymmColumnSmallVec_32f { if( !checkHardwareSupport(CV_CPU_SSE) ) return 0; - + int ksize2 = (kernel.rows + kernel.cols - 1)/2; const float* ky = (const float*)kernel.data + ksize2; int i = 0; @@ -1963,7 +1963,7 @@ struct FilterVec_8u { if( !checkHardwareSupport(CV_CPU_SSE2) ) return 0; - + const float* kf = (const float*)&coeffs[0]; int i = 0, k, nz = _nz; __m128 d4 = _mm_set1_ps(delta); @@ -2046,7 +2046,7 @@ struct FilterVec_8u16s { if( !checkHardwareSupport(CV_CPU_SSE2) ) return 0; - + const float* kf = (const float*)&coeffs[0]; short* dst = (short*)_dst; int i = 0, k, nz = _nz; @@ -2127,7 +2127,7 @@ struct FilterVec_32f { if( !checkHardwareSupport(CV_CPU_SSE) ) return 0; - + const float* kf = (const float*)&coeffs[0]; const float** src = (const float**)_src; float* dst = (float*)_dst; @@ -2217,7 +2217,7 @@ template struct RowFilter : public BaseRo (kernel.rows == 1 || kernel.cols == 1)); vecOp = _vecOp; } - + void operator()(const uchar* src, uchar* dst, int width, int cn) { int _ksize = ksize; @@ -2242,7 +2242,7 @@ template struct RowFilter : public BaseRo s0 += f*S[0]; s1 += f*S[1]; s2 += f*S[2]; s3 += f*S[3]; } - + D[i] = s0; D[i+1] = s1; D[i+2] = s2; D[i+3] = s3; } @@ -2275,7 +2275,7 @@ template struct SymmRowSmallFilter : symmetryType = _symmetryType; CV_Assert( (symmetryType & (KERNEL_SYMMETRICAL | KERNEL_ASYMMETRICAL)) != 0 && this->ksize <= 5 ); } - + void operator()(const uchar* src, uchar* dst, int width, int cn) { int ksize2 = this->ksize/2, ksize2n = ksize2*cn; @@ -2397,7 +2397,7 @@ template struct ColumnFilter : public BaseColumnFilte { typedef typename CastOp::type1 ST; typedef typename CastOp::rtype DT; - + ColumnFilter( const Mat& _kernel, int _anchor, double _delta, const CastOp& _castOp=CastOp(), const VecOp& _vecOp=VecOp() ) @@ -2427,7 +2427,7 @@ template struct ColumnFilter : public BaseColumnFilte { DT* D = (DT*)dst; i = vecOp(src, dst, width); - #if CV_ENABLE_UNROLLED + #if CV_ENABLE_UNROLLED for( ; i <= width - 4; i += 4 ) { ST f = ky[0]; @@ -2574,7 +2574,7 @@ struct SymmColumnSmallFilter : public SymmColumnFilter { typedef typename CastOp::type1 ST; typedef typename CastOp::rtype DT; - + SymmColumnSmallFilter( const Mat& _kernel, int _anchor, double _delta, int _symmetryType, const CastOp& _castOp=CastOp(), @@ -2610,7 +2610,7 @@ struct SymmColumnSmallFilter : public SymmColumnFilter { if( is_1_2_1 ) { - #if CV_ENABLE_UNROLLED + #if CV_ENABLE_UNROLLED for( ; i <= width - 4; i += 4 ) { ST s0 = S0[i] + S1[i]*2 + S2[i] + _delta; @@ -2624,7 +2624,7 @@ struct SymmColumnSmallFilter : public SymmColumnFilter D[i+3] = castOp(s1); } #else - for( ; i < width; i ++ ) + for( ; i < width; i ++ ) { ST s0 = S0[i] + S1[i]*2 + S2[i] + _delta; D[i] = castOp(s0); @@ -2633,7 +2633,7 @@ struct SymmColumnSmallFilter : public SymmColumnFilter } else if( is_1_m2_1 ) { - #if CV_ENABLE_UNROLLED + #if CV_ENABLE_UNROLLED for( ; i <= width - 4; i += 4 ) { ST s0 = S0[i] - S1[i]*2 + S2[i] + _delta; @@ -2647,7 +2647,7 @@ struct SymmColumnSmallFilter : public SymmColumnFilter D[i+3] = castOp(s1); } #else - for( ; i < width; i ++ ) + for( ; i < width; i ++ ) { ST s0 = S0[i] - S1[i]*2 + S2[i] + _delta; D[i] = castOp(s0); @@ -2700,7 +2700,7 @@ struct SymmColumnSmallFilter : public SymmColumnFilter D[i+3] = castOp(s1); } #else - for( ; i < width; i ++ ) + for( ; i < width; i ++ ) { ST s0 = S2[i] - S0[i] + _delta; D[i] = castOp(s0); @@ -2763,7 +2763,7 @@ template struct FixedPtCastEx }; } - + cv::Ptr cv::getLinearRowFilter( int srcType, int bufType, InputArray _kernel, int anchor, int symmetryType ) @@ -2785,7 +2785,7 @@ cv::Ptr cv::getLinearRowFilter( int srcType, int bufType, return Ptr(new SymmRowSmallFilter (kernel, anchor, symmetryType, SymmRowSmallVec_32f(kernel, symmetryType))); } - + if( sdepth == CV_8U && ddepth == CV_32S ) return Ptr(new RowFilter (kernel, anchor, RowVec_8u32s(kernel))); @@ -2820,7 +2820,7 @@ cv::Ptr cv::getLinearRowFilter( int srcType, int bufType, cv::Ptr cv::getLinearColumnFilter( int bufType, int dstType, InputArray _kernel, int anchor, - int symmetryType, double delta, + int symmetryType, double delta, int bits ) { Mat kernel = _kernel.getMat(); @@ -3045,7 +3045,7 @@ template struct Filter2D : public BaseFi { typedef typename CastOp::type1 KT; typedef typename CastOp::rtype DT; - + Filter2D( const Mat& _kernel, Point _anchor, double _delta, const CastOp& _castOp=CastOp(), const VecOp& _vecOp=VecOp() ) @@ -3143,7 +3143,7 @@ cv::Ptr cv::getLinearFilter(int srcType, int dstType, kernel = _kernel; else _kernel.convertTo(kernel, kdepth, _kernel.type() == CV_32S ? 1./(1 << bits) : 1.); - + if( sdepth == CV_8U && ddepth == CV_8U ) return Ptr(new Filter2D, FilterVec_8u> (kernel, anchor, delta, Cast(), FilterVec_8u(kernel, 0, delta))); @@ -3203,7 +3203,7 @@ cv::Ptr cv::createLinearFilter( int _srcType, int _dstType, { Mat _kernel = filter_kernel.getMat(); _srcType = CV_MAT_TYPE(_srcType); - _dstType = CV_MAT_TYPE(_dstType); + _dstType = CV_MAT_TYPE(_dstType); int cn = CV_MAT_CN(_srcType); CV_Assert( cn == CV_MAT_CN(_dstType) ); @@ -3211,14 +3211,14 @@ cv::Ptr cv::createLinearFilter( int _srcType, int _dstType, int bits = 0; /*int sdepth = CV_MAT_DEPTH(_srcType), ddepth = CV_MAT_DEPTH(_dstType); - int ktype = _kernel.depth() == CV_32S ? KERNEL_INTEGER : getKernelType(_kernel, _anchor); + int ktype = _kernel.depth() == CV_32S ? KERNEL_INTEGER : getKernelType(_kernel, _anchor); if( sdepth == CV_8U && (ddepth == CV_8U || ddepth == CV_16S) && _kernel.rows*_kernel.cols <= (1 << 10) ) { bits = (ktype & KERNEL_INTEGER) ? 0 : 11; _kernel.convertTo(kernel, CV_32S, 1 << bits); }*/ - + Ptr _filter2D = getLinearFilter(_srcType, _dstType, kernel, _anchor, _delta, bits); @@ -3233,7 +3233,7 @@ void cv::filter2D( InputArray _src, OutputArray _dst, int ddepth, double delta, int borderType ) { Mat src = _src.getMat(), kernel = _kernel.getMat(); - + if( ddepth < 0 ) ddepth = src.depth(); @@ -3279,7 +3279,7 @@ void cv::sepFilter2D( InputArray _src, OutputArray _dst, int ddepth, double delta, int borderType ) { Mat src = _src.getMat(), kernelX = _kernelX.getMat(), kernelY = _kernelY.getMat(); - + if( ddepth < 0 ) ddepth = src.depth(); diff --git a/modules/imgproc/src/gcgraph.hpp b/modules/imgproc/src/gcgraph.hpp index 7f0f501..59c9744 100644 --- a/modules/imgproc/src/gcgraph.hpp +++ b/modules/imgproc/src/gcgraph.hpp @@ -64,7 +64,7 @@ private: int ts; int dist; TWeight weight; - uchar t; + uchar t; }; class Edge { @@ -174,7 +174,7 @@ TWeight GCGraph::maxFlow() v->t = v->weight < 0; } else - v->parent = 0; + v->parent = 0; } first = first->next; last->next = nilNode; @@ -290,14 +290,14 @@ TWeight GCGraph::maxFlow() curr_ts++; while( !orphans.empty() ) { - Vtx* v = orphans.back(); + Vtx* v2 = orphans.back(); orphans.pop_back(); int d, minDist = INT_MAX; e0 = 0; - vt = v->t; + vt = v2->t; - for( ei = v->first; ei != 0; ei = edgePtr[ei].next ) + for( ei = v2->first; ei != 0; ei = edgePtr[ei].next ) { if( edgePtr[ei^(vt^1)].weight == 0 ) continue; @@ -344,16 +344,16 @@ TWeight GCGraph::maxFlow() } } - if( (v->parent = e0) > 0 ) + if( (v2->parent = e0) > 0 ) { - v->ts = curr_ts; - v->dist = minDist; + v2->ts = curr_ts; + v2->dist = minDist; continue; } /* no parent is found */ - v->ts = 0; - for( ei = v->first; ei != 0; ei = edgePtr[ei].next ) + v2->ts = 0; + for( ei = v2->first; ei != 0; ei = edgePtr[ei].next ) { u = vtxPtr+edgePtr[ei].dst; ej = u->parent; @@ -364,7 +364,7 @@ TWeight GCGraph::maxFlow() u->next = nilNode; last = last->next = u; } - if( ej > 0 && vtxPtr+edgePtr[ej].dst == v ) + if( ej > 0 && vtxPtr+edgePtr[ej].dst == v2 ) { orphans.push_back(u); u->parent = ORPHAN; diff --git a/modules/imgproc/src/histogram.cpp b/modules/imgproc/src/histogram.cpp index 546c215..edcb240 100644 --- a/modules/imgproc/src/histogram.cpp +++ b/modules/imgproc/src/histogram.cpp @@ -7,8 +7,8 @@ // copy or use the software. // // -// Intel License Agreement -// For Open Source Computer Vision Library +// Intel License Agreement +// For Open Source Computer Vision Library // // Copyright (C) 2000, Intel Corporation, all rights reserved. // Third party copyrights are property of their respective owners. @@ -46,7 +46,7 @@ namespace cv template<> void Ptr::delete_obj() { cvReleaseHist(&obj); } - + ////////////////// Helper functions ////////////////////// static const size_t OUT_OF_RANGE = (size_t)1 << (sizeof(size_t)*8 - 2); @@ -60,7 +60,7 @@ calcHistLookupTables_8u( const Mat& hist, const SparseMat& shist, int i, j; _tab.resize((high-low)*dims); size_t* tab = &_tab[0]; - + if( uniform ) { for( i = 0; i < dims; i++ ) @@ -69,7 +69,7 @@ calcHistLookupTables_8u( const Mat& hist, const SparseMat& shist, double b = uniranges[i*2+1]; int sz = !issparse ? hist.size[i] : shist.size(i); size_t step = !issparse ? hist.step[i] : 1; - + for( j = low; j < high; j++ ) { int idx = cvFloor(j*a + b); @@ -78,7 +78,7 @@ calcHistLookupTables_8u( const Mat& hist, const SparseMat& shist, written_idx = idx*step; else written_idx = OUT_OF_RANGE; - + tab[i*(high - low) + j - low] = written_idx; } } @@ -91,12 +91,12 @@ calcHistLookupTables_8u( const Mat& hist, const SparseMat& shist, int idx = -1, sz = !issparse ? hist.size[i] : shist.size(i); size_t written_idx = OUT_OF_RANGE; size_t step = !issparse ? hist.step[i] : 1; - + for(j = low;;) { for( ; j < limit; j++ ) tab[i*(high - low) + j - low] = written_idx; - + if( (unsigned)(++idx) < (unsigned)sz ) { limit = std::min(cvCeil(ranges[i][idx+1]), high); @@ -122,14 +122,14 @@ static void histPrepareImages( const Mat* images, int nimages, const int* channe { int i, j, c; CV_Assert( channels != 0 || nimages == dims ); - + imsize = images[0].size(); int depth = images[0].depth(), esz1 = (int)images[0].elemSize1(); bool isContinuous = true; - + ptrs.resize(dims + 1); deltas.resize((dims + 1)*2); - + for( i = 0; i < dims; i++ ) { if(!channels) @@ -147,7 +147,7 @@ static void histPrepareImages( const Mat* images, int nimages, const int* channe break; CV_Assert( j < nimages ); } - + CV_Assert( images[j].size() == imsize && images[j].depth() == depth ); if( !images[j].isContinuous() ) isContinuous = false; @@ -155,7 +155,7 @@ static void histPrepareImages( const Mat* images, int nimages, const int* channe deltas[i*2] = images[j].channels(); deltas[i*2+1] = (int)(images[j].step/esz1 - imsize.width*deltas[i*2]); } - + if( mask.data ) { CV_Assert( mask.size() == imsize && mask.channels() == 1 ); @@ -164,17 +164,17 @@ static void histPrepareImages( const Mat* images, int nimages, const int* channe deltas[dims*2] = 1; deltas[dims*2 + 1] = (int)(mask.step/mask.elemSize1()); } - + if( isContinuous ) { imsize.width *= imsize.height; imsize.height = 1; } - + if( !ranges ) { CV_Assert( depth == CV_8U ); - + uniranges.resize( dims*2 ); for( i = 0; i < dims; i++ ) { @@ -198,16 +198,16 @@ static void histPrepareImages( const Mat* images, int nimages, const int* channe { for( i = 0; i < dims; i++ ) { - size_t j, n = histSize[i]; - for( j = 0; j < n; j++ ) - CV_Assert( ranges[i][j] < ranges[i][j+1] ); + size_t n = histSize[i]; + for(size_t k = 0; k < n; k++ ) + CV_Assert( ranges[i][k] < ranges[i][k+1] ); } } } - - -////////////////////////////////// C A L C U L A T E H I S T O G R A M //////////////////////////////////// - + + +////////////////////////////////// C A L C U L A T E H I S T O G R A M //////////////////////////////////// + template static void calcHist_( vector& _ptrs, const vector& _deltas, Size imsize, Mat& hist, int dims, const float** _ranges, @@ -221,23 +221,23 @@ calcHist_( vector& _ptrs, const vector& _deltas, int mstep = _deltas[dims*2 + 1]; int size[CV_MAX_DIM]; size_t hstep[CV_MAX_DIM]; - + for( i = 0; i < dims; i++ ) { size[i] = hist.size[i]; hstep[i] = hist.step[i]; } - + if( uniform ) { const double* uniranges = &_uniranges[0]; - + if( dims == 1 ) { double a = uniranges[0], b = uniranges[1]; int sz = size[0], d0 = deltas[0], step0 = deltas[1]; const T* p0 = (const T*)ptrs[0]; - + for( ; imsize.height--; p0 += step0, mask += mstep ) { if( !mask ) @@ -266,7 +266,7 @@ calcHist_( vector& _ptrs, const vector& _deltas, size_t hstep0 = hstep[0]; const T* p0 = (const T*)ptrs[0]; const T* p1 = (const T*)ptrs[1]; - + for( ; imsize.height--; p0 += step0, p1 += step1, mask += mstep ) { if( !mask ) @@ -300,8 +300,8 @@ calcHist_( vector& _ptrs, const vector& _deltas, size_t hstep0 = hstep[0], hstep1 = hstep[1]; const T* p0 = (const T*)ptrs[0]; const T* p1 = (const T*)ptrs[1]; - const T* p2 = (const T*)ptrs[2]; - + const T* p2 = (const T*)ptrs[2]; + for( ; imsize.height--; p0 += step0, p1 += step1, p2 += step2, mask += mstep ) { if( !mask ) @@ -345,7 +345,7 @@ calcHist_( vector& _ptrs, const vector& _deltas, ptrs[i] += deltas[i*2]; Hptr += idx*hstep[i]; } - + if( i == dims ) ++*((int*)Hptr); else @@ -366,7 +366,7 @@ calcHist_( vector& _ptrs, const vector& _deltas, ptrs[i] += deltas[i*2]; Hptr += idx*hstep[i]; } - + if( i == dims ) ++*((int*)Hptr); else @@ -384,45 +384,45 @@ calcHist_( vector& _ptrs, const vector& _deltas, const float* ranges[CV_MAX_DIM]; for( i = 0; i < dims; i++ ) ranges[i] = &_ranges[i][0]; - + for( ; imsize.height--; mask += mstep ) { for( x = 0; x < imsize.width; x++ ) { uchar* Hptr = H; i = 0; - + if( !mask || mask[x] ) for( ; i < dims; i++ ) { float v = (float)*ptrs[i]; const float* R = ranges[i]; int idx = -1, sz = size[i]; - + while( v >= R[idx+1] && ++idx < sz ) ; // nop - + if( (unsigned)idx >= (unsigned)sz ) break; ptrs[i] += deltas[i*2]; Hptr += idx*hstep[i]; } - + if( i == dims ) ++*((int*)Hptr); else for( ; i < dims; i++ ) ptrs[i] += deltas[i*2]; } - + for( i = 0; i < dims; i++ ) ptrs[i] += deltas[i*2 + 1]; } - } + } } - - + + static void calcHist_8u( vector& _ptrs, const vector& _deltas, Size imsize, Mat& hist, int dims, const float** _ranges, @@ -431,20 +431,20 @@ calcHist_8u( vector& _ptrs, const vector& _deltas, uchar** ptrs = &_ptrs[0]; const int* deltas = &_deltas[0]; uchar* H = hist.data; - int i, x; + int x; const uchar* mask = _ptrs[dims]; int mstep = _deltas[dims*2 + 1]; vector _tab; - + calcHistLookupTables_8u( hist, SparseMat(), dims, _ranges, _uniranges, uniform, false, _tab ); const size_t* tab = &_tab[0]; - + if( dims == 1 ) { int d0 = deltas[0], step0 = deltas[1]; int matH[256] = {0}; const uchar* p0 = (const uchar*)ptrs[0]; - + for( ; imsize.height--; p0 += step0, mask += mstep ) { if( !mask ) @@ -460,7 +460,7 @@ calcHist_8u( vector& _ptrs, const vector& _deltas, } p0 += x; } - else + else for( x = 0; x <= imsize.width - 4; x += 4 ) { int t0 = p0[0], t1 = p0[d0]; @@ -470,7 +470,7 @@ calcHist_8u( vector& _ptrs, const vector& _deltas, matH[t0]++; matH[t1]++; p0 += d0*2; } - + for( ; x < imsize.width; x++, p0 += d0 ) matH[*p0]++; } @@ -479,8 +479,8 @@ calcHist_8u( vector& _ptrs, const vector& _deltas, if( mask[x] ) matH[*p0]++; } - - for( i = 0; i < 256; i++ ) + + for(int i = 0; i < 256; i++ ) { size_t hidx = tab[i]; if( hidx < OUT_OF_RANGE ) @@ -493,7 +493,7 @@ calcHist_8u( vector& _ptrs, const vector& _deltas, d1 = deltas[2], step1 = deltas[3]; const uchar* p0 = (const uchar*)ptrs[0]; const uchar* p1 = (const uchar*)ptrs[1]; - + for( ; imsize.height--; p0 += step0, p1 += step1, mask += mstep ) { if( !mask ) @@ -517,11 +517,11 @@ calcHist_8u( vector& _ptrs, const vector& _deltas, int d0 = deltas[0], step0 = deltas[1], d1 = deltas[2], step1 = deltas[3], d2 = deltas[4], step2 = deltas[5]; - + const uchar* p0 = (const uchar*)ptrs[0]; const uchar* p1 = (const uchar*)ptrs[1]; const uchar* p2 = (const uchar*)ptrs[2]; - + for( ; imsize.height--; p0 += step0, p1 += step1, p2 += step2, mask += mstep ) { if( !mask ) @@ -548,7 +548,8 @@ calcHist_8u( vector& _ptrs, const vector& _deltas, for( x = 0; x < imsize.width; x++ ) { uchar* Hptr = H; - for( i = 0; i < dims; i++ ) + int i = 0; + for( ; i < dims; i++ ) { size_t idx = tab[*ptrs[i] + i*256]; if( idx >= OUT_OF_RANGE ) @@ -556,7 +557,7 @@ calcHist_8u( vector& _ptrs, const vector& _deltas, Hptr += idx; ptrs[i] += deltas[i*2]; } - + if( i == dims ) ++*((int*)Hptr); else @@ -577,14 +578,14 @@ calcHist_8u( vector& _ptrs, const vector& _deltas, Hptr += idx; ptrs[i] += deltas[i*2]; } - + if( i == dims ) ++*((int*)Hptr); else for( ; i < dims; i++ ) ptrs[i] += deltas[i*2]; } - for( i = 0; i < dims; i++ ) + for(int i = 0; i < dims; i++ ) ptrs[i] += deltas[i*2 + 1]; } } @@ -597,31 +598,31 @@ void cv::calcHist( const Mat* images, int nimages, const int* channels, const float** ranges, bool uniform, bool accumulate ) { Mat mask = _mask.getMat(); - + CV_Assert(dims > 0 && histSize); - + uchar* histdata = _hist.getMat().data; _hist.create(dims, histSize, CV_32F); Mat hist = _hist.getMat(), ihist = hist; ihist.flags = (ihist.flags & ~CV_MAT_TYPE_MASK)|CV_32S; - + if( !accumulate || histdata != hist.data ) hist = Scalar(0.); else hist.convertTo(ihist, CV_32S); - + vector ptrs; vector deltas; vector uniranges; Size imsize; - + CV_Assert( !mask.data || mask.type() == CV_8UC1 ); histPrepareImages( images, nimages, channels, mask, dims, hist.size, ranges, uniform, ptrs, deltas, imsize, uniranges ); const double* _uniranges = uniform ? &uniranges[0] : 0; - + int depth = images[0].depth(); - + if( depth == CV_8U ) calcHist_8u(ptrs, deltas, imsize, ihist, dims, ranges, _uniranges, uniform ); else if( depth == CV_16U ) @@ -630,7 +631,7 @@ void cv::calcHist( const Mat* images, int nimages, const int* channels, calcHist_(ptrs, deltas, imsize, ihist, dims, ranges, _uniranges, uniform ); else CV_Error(CV_StsUnsupportedFormat, ""); - + ihist.convertTo(hist, CV_32F); } @@ -650,11 +651,11 @@ calcSparseHist_( vector& _ptrs, const vector& _deltas, int mstep = _deltas[dims*2 + 1]; const int* size = hist.hdr->size; int idx[CV_MAX_DIM]; - + if( uniform ) { const double* uniranges = &_uniranges[0]; - + for( ; imsize.height--; mask += mstep ) { for( x = 0; x < imsize.width; x++ ) @@ -668,7 +669,7 @@ calcSparseHist_( vector& _ptrs, const vector& _deltas, break; ptrs[i] += deltas[i*2]; } - + if( i == dims ) ++*(int*)hist.ptr(idx, true); else @@ -685,43 +686,43 @@ calcSparseHist_( vector& _ptrs, const vector& _deltas, const float* ranges[CV_MAX_DIM]; for( i = 0; i < dims; i++ ) ranges[i] = &_ranges[i][0]; - + for( ; imsize.height--; mask += mstep ) { for( x = 0; x < imsize.width; x++ ) { i = 0; - + if( !mask || mask[x] ) for( ; i < dims; i++ ) { float v = (float)*ptrs[i]; const float* R = ranges[i]; int j = -1, sz = size[i]; - + while( v >= R[j+1] && ++j < sz ) ; // nop - + if( (unsigned)j >= (unsigned)sz ) break; - ptrs[i] += deltas[i*2]; + ptrs[i] += deltas[i*2]; idx[i] = j; } - + if( i == dims ) ++*(int*)hist.ptr(idx, true); else for( ; i < dims; i++ ) ptrs[i] += deltas[i*2]; } - + for( i = 0; i < dims; i++ ) ptrs[i] += deltas[i*2 + 1]; } - } -} + } +} + - static void calcSparseHist_8u( vector& _ptrs, const vector& _deltas, Size imsize, SparseMat& hist, int dims, const float** _ranges, @@ -729,15 +730,15 @@ calcSparseHist_8u( vector& _ptrs, const vector& _deltas, { uchar** ptrs = (uchar**)&_ptrs[0]; const int* deltas = &_deltas[0]; - int i, x; + int x; const uchar* mask = _ptrs[dims]; int mstep = _deltas[dims*2 + 1]; int idx[CV_MAX_DIM]; vector _tab; - + calcHistLookupTables_8u( Mat(), hist, dims, _ranges, _uniranges, uniform, true, _tab ); const size_t* tab = &_tab[0]; - + for( ; imsize.height--; mask += mstep ) { for( x = 0; x < imsize.width; x++ ) @@ -752,25 +753,25 @@ calcSparseHist_8u( vector& _ptrs, const vector& _deltas, ptrs[i] += deltas[i*2]; idx[i] = (int)hidx; } - + if( i == dims ) ++*(int*)hist.ptr(idx,true); else for( ; i < dims; i++ ) ptrs[i] += deltas[i*2]; } - for( i = 0; i < dims; i++ ) + for(int i = 0; i < dims; i++ ) ptrs[i] += deltas[i*2 + 1]; } -} - +} + static void calcHist( const Mat* images, int nimages, const int* channels, const Mat& mask, SparseMat& hist, int dims, const int* histSize, const float** ranges, bool uniform, bool accumulate, bool keepInt ) { size_t i, N; - + if( !accumulate ) hist.create(dims, histSize, CV_32F); else @@ -782,17 +783,17 @@ static void calcHist( const Mat* images, int nimages, const int* channels, val->i = cvRound(val->f); } } - + vector ptrs; vector deltas; vector uniranges; Size imsize; - + CV_Assert( !mask.data || mask.type() == CV_8UC1 ); histPrepareImages( images, nimages, channels, mask, dims, hist.hdr->size, ranges, uniform, ptrs, deltas, imsize, uniranges ); const double* _uniranges = uniform ? &uniranges[0] : 0; - + int depth = images[0].depth(); if( depth == CV_8U ) calcSparseHist_8u(ptrs, deltas, imsize, hist, dims, ranges, _uniranges, uniform ); @@ -802,7 +803,7 @@ static void calcHist( const Mat* images, int nimages, const int* channels, calcSparseHist_(ptrs, deltas, imsize, hist, dims, ranges, _uniranges, uniform ); else CV_Error(CV_StsUnsupportedFormat, ""); - + if( !keepInt ) { SparseMatIterator it = hist.begin(); @@ -813,9 +814,9 @@ static void calcHist( const Mat* images, int nimages, const int* channels, } } } - + } - + void cv::calcHist( const Mat* images, int nimages, const int* channels, InputArray _mask, SparseMat& hist, int dims, const int* histSize, const float** ranges, bool uniform, bool accumulate ) @@ -834,7 +835,7 @@ void cv::calcHist( InputArrayOfArrays images, const vector& channels, { int i, dims = (int)histSize.size(), rsz = (int)ranges.size(), csz = (int)channels.size(); int nimages = (int)images.total(); - + CV_Assert(nimages > 0 && dims > 0); CV_Assert(rsz == dims*2 || (rsz == 0 && images.depth(0) == CV_8U)); CV_Assert(csz == 0 || csz == dims); @@ -844,19 +845,19 @@ void cv::calcHist( InputArrayOfArrays images, const vector& channels, for( i = 0; i < rsz/2; i++ ) _ranges[i] = (float*)&ranges[i*2]; } - + AutoBuffer buf(nimages); for( i = 0; i < nimages; i++ ) buf[i] = images.getMat(i); - + calcHist(&buf[0], nimages, csz ? &channels[0] : 0, mask, hist, dims, &histSize[0], rsz ? (const float**)_ranges : 0, true, accumulate); } -/////////////////////////////////////// B A C K P R O J E C T //////////////////////////////////// - +/////////////////////////////////////// B A C K P R O J E C T //////////////////////////////////// + namespace cv { @@ -873,23 +874,23 @@ calcBackProj_( vector& _ptrs, const vector& _deltas, int bpstep = _deltas[dims*2 + 1]; int size[CV_MAX_DIM]; size_t hstep[CV_MAX_DIM]; - + for( i = 0; i < dims; i++ ) { size[i] = hist.size[i]; hstep[i] = hist.step[i]; } - + if( uniform ) { const double* uniranges = &_uniranges[0]; - + if( dims == 1 ) { double a = uniranges[0], b = uniranges[1]; int sz = size[0], d0 = deltas[0], step0 = deltas[1]; const T* p0 = (const T*)ptrs[0]; - + for( ; imsize.height--; p0 += step0, bproj += bpstep ) { for( x = 0; x < imsize.width; x++, p0 += d0 ) @@ -909,7 +910,7 @@ calcBackProj_( vector& _ptrs, const vector& _deltas, size_t hstep0 = hstep[0]; const T* p0 = (const T*)ptrs[0]; const T* p1 = (const T*)ptrs[1]; - + for( ; imsize.height--; p0 += step0, p1 += step1, bproj += bpstep ) { for( x = 0; x < imsize.width; x++, p0 += d0, p1 += d1 ) @@ -934,8 +935,8 @@ calcBackProj_( vector& _ptrs, const vector& _deltas, size_t hstep0 = hstep[0], hstep1 = hstep[1]; const T* p0 = (const T*)ptrs[0]; const T* p1 = (const T*)ptrs[1]; - const T* p2 = (const T*)ptrs[2]; - + const T* p2 = (const T*)ptrs[2]; + for( ; imsize.height--; p0 += step0, p1 += step1, p2 += step2, bproj += bpstep ) { for( x = 0; x < imsize.width; x++, p0 += d0, p1 += d1, p2 += d2 ) @@ -965,7 +966,7 @@ calcBackProj_( vector& _ptrs, const vector& _deltas, ptrs[i] += deltas[i*2]; Hptr += idx*hstep[i]; } - + if( i == dims ) bproj[x] = saturate_cast(*(float*)Hptr*scale); else @@ -986,7 +987,7 @@ calcBackProj_( vector& _ptrs, const vector& _deltas, const float* ranges[CV_MAX_DIM]; for( i = 0; i < dims; i++ ) ranges[i] = &_ranges[i][0]; - + for( ; imsize.height--; bproj += bpstep ) { for( x = 0; x < imsize.width; x++ ) @@ -997,17 +998,17 @@ calcBackProj_( vector& _ptrs, const vector& _deltas, float v = (float)*ptrs[i]; const float* R = ranges[i]; int idx = -1, sz = size[i]; - + while( v >= R[idx+1] && ++idx < sz ) ; // nop - + if( (unsigned)idx >= (unsigned)sz ) break; ptrs[i] += deltas[i*2]; Hptr += idx*hstep[i]; } - + if( i == dims ) bproj[x] = saturate_cast(*(float*)Hptr*scale); else @@ -1017,11 +1018,11 @@ calcBackProj_( vector& _ptrs, const vector& _deltas, ptrs[i] += deltas[i*2]; } } - + for( i = 0; i < dims; i++ ) ptrs[i] += deltas[i*2 + 1]; } - } + } } @@ -1037,23 +1038,23 @@ calcBackProj_8u( vector& _ptrs, const vector& _deltas, uchar* bproj = _ptrs[dims]; int bpstep = _deltas[dims*2 + 1]; vector _tab; - + calcHistLookupTables_8u( hist, SparseMat(), dims, _ranges, _uniranges, uniform, false, _tab ); const size_t* tab = &_tab[0]; - + if( dims == 1 ) { int d0 = deltas[0], step0 = deltas[1]; uchar matH[256] = {0}; const uchar* p0 = (const uchar*)ptrs[0]; - + for( i = 0; i < 256; i++ ) { size_t hidx = tab[i]; if( hidx < OUT_OF_RANGE ) matH[i] = saturate_cast(*(float*)(H + hidx)*scale); } - + for( ; imsize.height--; p0 += step0, bproj += bpstep ) { if( d0 == 1 ) @@ -1067,7 +1068,7 @@ calcBackProj_8u( vector& _ptrs, const vector& _deltas, } p0 += x; } - else + else for( x = 0; x <= imsize.width - 4; x += 4 ) { uchar t0 = matH[p0[0]], t1 = matH[p0[d0]]; @@ -1077,7 +1078,7 @@ calcBackProj_8u( vector& _ptrs, const vector& _deltas, bproj[x+2] = t0; bproj[x+3] = t1; p0 += d0*2; } - + for( ; x < imsize.width; x++, p0 += d0 ) bproj[x] = matH[*p0]; } @@ -1088,7 +1089,7 @@ calcBackProj_8u( vector& _ptrs, const vector& _deltas, d1 = deltas[2], step1 = deltas[3]; const uchar* p0 = (const uchar*)ptrs[0]; const uchar* p1 = (const uchar*)ptrs[1]; - + for( ; imsize.height--; p0 += step0, p1 += step1, bproj += bpstep ) { for( x = 0; x < imsize.width; x++, p0 += d0, p1 += d1 ) @@ -1106,7 +1107,7 @@ calcBackProj_8u( vector& _ptrs, const vector& _deltas, const uchar* p0 = (const uchar*)ptrs[0]; const uchar* p1 = (const uchar*)ptrs[1]; const uchar* p2 = (const uchar*)ptrs[2]; - + for( ; imsize.height--; p0 += step0, p1 += step1, p2 += step2, bproj += bpstep ) { for( x = 0; x < imsize.width; x++, p0 += d0, p1 += d1, p2 += d2 ) @@ -1131,7 +1132,7 @@ calcBackProj_8u( vector& _ptrs, const vector& _deltas, ptrs[i] += deltas[i*2]; Hptr += idx; } - + if( i == dims ) bproj[x] = saturate_cast(*(float*)Hptr*scale); else @@ -1145,10 +1146,10 @@ calcBackProj_8u( vector& _ptrs, const vector& _deltas, ptrs[i] += deltas[i*2 + 1]; } } -} +} } - + void cv::calcBackProject( const Mat* images, int nimages, const int* channels, InputArray _hist, OutputArray _backProject, const float** ranges, double scale, bool uniform ) @@ -1159,14 +1160,14 @@ void cv::calcBackProject( const Mat* images, int nimages, const int* channels, vector uniranges; Size imsize; int dims = hist.dims == 2 && hist.size[1] == 1 ? 1 : hist.dims; - + CV_Assert( dims > 0 && hist.data ); _backProject.create( images[0].size(), images[0].depth() ); Mat backProject = _backProject.getMat(); histPrepareImages( images, nimages, channels, backProject, dims, hist.size, ranges, uniform, ptrs, deltas, imsize, uniranges ); const double* _uniranges = uniform ? &uniranges[0] : 0; - + int depth = images[0].depth(); if( depth == CV_8U ) calcBackProj_8u(ptrs, deltas, imsize, hist, dims, ranges, _uniranges, (float)scale, uniform); @@ -1195,7 +1196,7 @@ calcSparseBackProj_( vector& _ptrs, const vector& _deltas, const int* size = hist.hdr->size; int idx[CV_MAX_DIM]; const SparseMat_& hist_ = (const SparseMat_&)hist; - + if( uniform ) { const double* uniranges = &_uniranges[0]; @@ -1210,7 +1211,7 @@ calcSparseBackProj_( vector& _ptrs, const vector& _deltas, break; ptrs[i] += deltas[i*2]; } - + if( i == dims ) bproj[x] = saturate_cast(hist_(idx)*scale); else @@ -1230,7 +1231,7 @@ calcSparseBackProj_( vector& _ptrs, const vector& _deltas, const float* ranges[CV_MAX_DIM]; for( i = 0; i < dims; i++ ) ranges[i] = &_ranges[i][0]; - + for( ; imsize.height--; bproj += bpstep ) { for( x = 0; x < imsize.width; x++ ) @@ -1240,16 +1241,16 @@ calcSparseBackProj_( vector& _ptrs, const vector& _deltas, float v = (float)*ptrs[i]; const float* R = ranges[i]; int j = -1, sz = size[i]; - + while( v >= R[j+1] && ++j < sz ) ; // nop - + if( (unsigned)j >= (unsigned)sz ) break; idx[i] = j; ptrs[i] += deltas[i*2]; } - + if( i == dims ) bproj[x] = saturate_cast(hist_(idx)*scale); else @@ -1259,11 +1260,11 @@ calcSparseBackProj_( vector& _ptrs, const vector& _deltas, ptrs[i] += deltas[i*2]; } } - + for( i = 0; i < dims; i++ ) ptrs[i] += deltas[i*2 + 1]; } - } + } } @@ -1279,10 +1280,10 @@ calcSparseBackProj_8u( vector& _ptrs, const vector& _deltas, int bpstep = _deltas[dims*2 + 1]; vector _tab; int idx[CV_MAX_DIM]; - + calcHistLookupTables_8u( Mat(), hist, dims, _ranges, _uniranges, uniform, true, _tab ); const size_t* tab = &_tab[0]; - + for( ; imsize.height--; bproj += bpstep ) { for( x = 0; x < imsize.width; x++ ) @@ -1295,7 +1296,7 @@ calcSparseBackProj_8u( vector& _ptrs, const vector& _deltas, idx[i] = (int)hidx; ptrs[i] += deltas[i*2]; } - + if( i == dims ) bproj[x] = saturate_cast(hist.value(idx)*scale); else @@ -1308,7 +1309,7 @@ calcSparseBackProj_8u( vector& _ptrs, const vector& _deltas, for( i = 0; i < dims; i++ ) ptrs[i] += deltas[i*2 + 1]; } -} +} } @@ -1321,7 +1322,7 @@ void cv::calcBackProject( const Mat* images, int nimages, const int* channels, vector uniranges; Size imsize; int dims = hist.dims(); - + CV_Assert( dims > 0 ); _backProject.create( images[0].size(), images[0].depth() ); Mat backProject = _backProject.getMat(); @@ -1329,7 +1330,7 @@ void cv::calcBackProject( const Mat* images, int nimages, const int* channels, dims, hist.hdr->size, ranges, uniform, ptrs, deltas, imsize, uniranges ); const double* _uniranges = uniform ? &uniranges[0] : 0; - + int depth = images[0].depth(); if( depth == CV_8U ) calcSparseBackProj_8u(ptrs, deltas, imsize, hist, dims, ranges, @@ -1344,7 +1345,7 @@ void cv::calcBackProject( const Mat* images, int nimages, const int* channels, CV_Error(CV_StsUnsupportedFormat, ""); } - + void cv::calcBackProject( InputArrayOfArrays images, const vector& channels, InputArray hist, OutputArray dst, const vector& ranges, @@ -1374,17 +1375,17 @@ void cv::calcBackProject( InputArrayOfArrays images, const vector& channels for( i = 0; i < rsz/2; i++ ) _ranges[i] = (float*)&ranges[i*2]; } - + AutoBuffer buf(nimages); for( i = 0; i < nimages; i++ ) buf[i] = images.getMat(i); - + calcBackProject(&buf[0], nimages, csz ? &channels[0] : 0, hist, dst, rsz ? (const float**)_ranges : 0, scale, true); } - -////////////////// C O M P A R E H I S T O G R A M S //////////////////////// + +////////////////// C O M P A R E H I S T O G R A M S //////////////////////// double cv::compareHist( InputArray _H1, InputArray _H2, int method ) { @@ -1394,19 +1395,19 @@ double cv::compareHist( InputArray _H1, InputArray _H2, int method ) NAryMatIterator it(arrays, planes); double result = 0; int j, len = (int)it.size; - + CV_Assert( H1.type() == H2.type() && H1.type() == CV_32F ); - + double s1 = 0, s2 = 0, s11 = 0, s12 = 0, s22 = 0; - + CV_Assert( it.planes[0].isContinuous() && it.planes[1].isContinuous() ); - + for( size_t i = 0; i < it.nplanes; i++, ++it ) { const float* h1 = (const float*)it.planes[0].data; const float* h2 = (const float*)it.planes[1].data; len = it.planes[0].rows*it.planes[0].cols; - + if( method == CV_COMP_CHISQR ) { for( j = 0; j < len; j++ ) @@ -1423,7 +1424,7 @@ double cv::compareHist( InputArray _H1, InputArray _H2, int method ) { double a = h1[j]; double b = h2[j]; - + s12 += a*b; s1 += a; s11 += a*a; @@ -1450,7 +1451,7 @@ double cv::compareHist( InputArray _H1, InputArray _H2, int method ) else CV_Error( CV_StsBadArg, "Unknown comparison method" ); } - + if( method == CV_COMP_CORREL ) { size_t total = H1.total(); @@ -1465,27 +1466,27 @@ double cv::compareHist( InputArray _H1, InputArray _H2, int method ) s1 = fabs(s1) > FLT_EPSILON ? 1./std::sqrt(s1) : 1.; result = std::sqrt(std::max(1. - result*s1, 0.)); } - + return result; } - + double cv::compareHist( const SparseMat& H1, const SparseMat& H2, int method ) { double result = 0; int i, dims = H1.dims(); - + CV_Assert( dims > 0 && dims == H2.dims() && H1.type() == H2.type() && H1.type() == CV_32F ); for( i = 0; i < dims; i++ ) CV_Assert( H1.size(i) == H2.size(i) ); - + const SparseMat *PH1 = &H1, *PH2 = &H2; if( PH1->nzcount() > PH2->nzcount() && method != CV_COMP_CHISQR ) std::swap(PH1, PH2); - + SparseMatConstIterator it = PH1->begin(); int N1 = (int)PH1->nzcount(), N2 = (int)PH2->nzcount(); - + if( method == CV_COMP_CHISQR ) { for( i = 0; i < N1; i++, ++it ) @@ -1502,7 +1503,7 @@ double cv::compareHist( const SparseMat& H1, const SparseMat& H2, int method ) else if( method == CV_COMP_CORREL ) { double s1 = 0, s2 = 0, s11 = 0, s12 = 0, s22 = 0; - + for( i = 0; i < N1; i++, ++it ) { double v1 = it.value(); @@ -1511,7 +1512,7 @@ double cv::compareHist( const SparseMat& H1, const SparseMat& H2, int method ) s1 += v1; s11 += v1*v1; } - + it = PH2->begin(); for( i = 0; i < N2; i++, ++it ) { @@ -1519,7 +1520,7 @@ double cv::compareHist( const SparseMat& H1, const SparseMat& H2, int method ) s2 += v2; s22 += v2*v2; } - + size_t total = 1; for( i = 0; i < H1.dims(); i++ ) total *= H1.size(i); @@ -1542,7 +1543,7 @@ double cv::compareHist( const SparseMat& H1, const SparseMat& H2, int method ) else if( method == CV_COMP_BHATTACHARYYA ) { double s1 = 0, s2 = 0; - + for( i = 0; i < N1; i++, ++it ) { double v1 = it.value(); @@ -1551,22 +1552,22 @@ double cv::compareHist( const SparseMat& H1, const SparseMat& H2, int method ) result += std::sqrt(v1*v2); s1 += v1; } - + it = PH2->begin(); for( i = 0; i < N2; i++, ++it ) s2 += it.value(); - + s1 *= s2; s1 = fabs(s1) > FLT_EPSILON ? 1./std::sqrt(s1) : 1.; result = std::sqrt(std::max(1. - result*s1, 0.)); } else CV_Error( CV_StsBadArg, "Unknown comparison method" ); - + return result; } - + const int CV_HIST_DEFAULT_TYPE = CV_32F; /* Creates new histogram */ @@ -1583,7 +1584,7 @@ cvCreateHist( int dims, int *sizes, CvHistType type, float** ranges, int uniform hist = (CvHistogram *)cvAlloc( sizeof( CvHistogram )); hist->type = CV_HIST_MAGIC_VAL + ((int)type & 1); - if (uniform) hist->type|= CV_HIST_UNIFORM_FLAG; + if (uniform) hist->type|= CV_HIST_UNIFORM_FLAG; hist->thresh2 = 0; hist->bins = 0; if( type == CV_HIST_ARRAY ) @@ -1652,7 +1653,7 @@ cvReleaseHist( CvHistogram **hist ) cvReleaseData( temp->bins ); temp->bins = 0; } - + if( temp->thresh2 ) cvFree( &temp->thresh2 ); cvFree( &temp ); @@ -1686,7 +1687,7 @@ cvThreshHist( CvHistogram* hist, double thresh ) CvSparseMat* mat = (CvSparseMat*)hist->bins; CvSparseMatIterator iterator; CvSparseNode *node; - + for( node = cvInitSparseMatIterator( mat, &iterator ); node != 0; node = cvGetNextSparseNode( &iterator )) { @@ -1722,7 +1723,7 @@ cvNormalizeHist( CvHistogram* hist, double factor ) CvSparseMatIterator iterator; CvSparseNode *node; float scale; - + for( node = cvInitSparseMatIterator( mat, &iterator ); node != 0; node = cvGetNextSparseNode( &iterator )) { @@ -1749,7 +1750,7 @@ cvGetMinMaxHistValue( const CvHistogram* hist, int* idx_min, int* idx_max ) { double minVal, maxVal; - int i, dims, size[CV_MAX_DIM]; + int dims, size[CV_MAX_DIM]; if( !CV_IS_HIST(hist) ) CV_Error( CV_StsBadArg, "Invalid histogram header" ); @@ -1782,9 +1783,8 @@ cvGetMinMaxHistValue( const CvHistogram* hist, { int imin = minPt.y*mat.cols + minPt.x; int imax = maxPt.y*mat.cols + maxPt.x; - int i; - - for( i = dims - 1; i >= 0; i-- ) + + for(int i = dims - 1; i >= 0; i-- ) { if( idx_min ) { @@ -1844,7 +1844,7 @@ cvGetMinMaxHistValue( const CvHistogram* hist, minVal = maxVal = 0; } - for( i = 0; i < dims; i++ ) + for(int i = 0; i < dims; i++ ) { if( idx_min ) idx_min[i] = _idx_min ? _idx_min[i] : -1; @@ -1869,7 +1869,7 @@ cvCompareHist( const CvHistogram* hist1, { int i; int size1[CV_MAX_DIM], size2[CV_MAX_DIM], total = 1; - + if( !CV_IS_HIST(hist1) || !CV_IS_HIST(hist2) ) CV_Error( CV_StsBadArg, "Invalid histogram header[s]" ); @@ -1881,14 +1881,14 @@ cvCompareHist( const CvHistogram* hist1, cv::Mat H1((const CvMatND*)hist1->bins), H2((const CvMatND*)hist2->bins); return cv::compareHist(H1, H2, method); } - + int dims1 = cvGetDims( hist1->bins, size1 ); int dims2 = cvGetDims( hist2->bins, size2 ); - + if( dims1 != dims2 ) CV_Error( CV_StsUnmatchedSizes, "The histograms have different numbers of dimensions" ); - + for( i = 0; i < dims1; i++ ) { if( size1[i] != size2[i] ) @@ -1928,7 +1928,7 @@ cvCompareHist( const CvHistogram* hist1, double s2 = 0, s22 = 0; double s12 = 0; double num, denom2, scale = 1./total; - + for( node1 = cvInitSparseMatIterator( mat1, &iterator ); node1 != 0; node1 = cvGetNextSparseNode( &iterator )) { @@ -1977,7 +1977,7 @@ cvCompareHist( const CvHistogram* hist1, else if( method == CV_COMP_BHATTACHARYYA ) { double s1 = 0, s2 = 0; - + for( node1 = cvInitSparseMatIterator( mat1, &iterator ); node1 != 0; node1 = cvGetNextSparseNode( &iterator )) { @@ -2006,7 +2006,7 @@ cvCompareHist( const CvHistogram* hist1, } else CV_Error( CV_StsBadArg, "Unknown comparison method" ); - + return result; } @@ -2021,7 +2021,7 @@ cvCopyHist( const CvHistogram* src, CvHistogram** _dst ) float* ranges[CV_MAX_DIM]; float** thresh = 0; CvHistogram* dst; - + if( !_dst ) CV_Error( CV_StsNullPtr, "Destination double pointer is NULL" ); @@ -2038,7 +2038,7 @@ cvCopyHist( const CvHistogram* src, CvHistogram** _dst ) if( dst && is_sparse == CV_IS_SPARSE_MAT(dst->bins)) { dims2 = cvGetDims( dst->bins, size2 ); - + if( dims1 == dims2 ) { for( i = 0; i < dims1; i++ ) @@ -2089,7 +2089,7 @@ cvSetHistBinRanges( CvHistogram* hist, float** ranges, int uniform ) dims = cvGetDims( hist->bins, size ); for( i = 0; i < dims; i++ ) total += size[i]+1; - + if( uniform ) { for( i = 0; i < dims; i++ ) @@ -2120,7 +2120,7 @@ cvSetHistBinRanges( CvHistogram* hist, float** ranges, int uniform ) if( !ranges[i] ) CV_Error( CV_StsNullPtr, "One of elements is NULL" ); - + for( j = 0; j <= size[i]; j++ ) { float val = ranges[i][j]; @@ -2151,18 +2151,18 @@ cvCalcArrHist( CvArr** img, CvHistogram* hist, int accumulate, const CvArr* mask int size[CV_MAX_DIM]; int i, dims = cvGetDims( hist->bins, size); bool uniform = CV_IS_UNIFORM_HIST(hist); - + cv::vector images(dims); for( i = 0; i < dims; i++ ) images[i] = cv::cvarrToMat(img[i]); - + cv::Mat _mask; if( mask ) _mask = cv::cvarrToMat(mask); - + const float* uranges[CV_MAX_DIM] = {0}; const float** ranges = 0; - + if( hist->type & CV_HIST_RANGES_FLAG ) { ranges = (const float**)hist->thresh2; @@ -2173,7 +2173,7 @@ cvCalcArrHist( CvArr** img, CvHistogram* hist, int accumulate, const CvArr* mask ranges = uranges; } } - + if( !CV_IS_SPARSE_HIST(hist) ) { cv::Mat H((const CvMatND*)hist->bins); @@ -2183,16 +2183,16 @@ cvCalcArrHist( CvArr** img, CvHistogram* hist, int accumulate, const CvArr* mask else { CvSparseMat* sparsemat = (CvSparseMat*)hist->bins; - + if( !accumulate ) cvZero( hist->bins ); cv::SparseMat sH(sparsemat); cv::calcHist( &images[0], (int)images.size(), 0, _mask, sH, sH.dims(), sH.dims() > 0 ? sH.hdr->size : 0, ranges, uniform, accumulate != 0, true ); - + if( accumulate ) cvZero( sparsemat ); - + cv::SparseMatConstIterator it = sH.begin(); int nz = (int)sH.nzcount(); for( i = 0; i < nz; i++, ++it ) @@ -2212,11 +2212,11 @@ cvCalcArrBackProject( CvArr** img, CvArr* dst, const CvHistogram* hist ) int size[CV_MAX_DIM]; int i, dims = cvGetDims( hist->bins, size ); - + bool uniform = CV_IS_UNIFORM_HIST(hist); const float* uranges[CV_MAX_DIM] = {0}; const float** ranges = 0; - + if( hist->type & CV_HIST_RANGES_FLAG ) { ranges = (const float**)hist->thresh2; @@ -2227,15 +2227,15 @@ cvCalcArrBackProject( CvArr** img, CvArr* dst, const CvHistogram* hist ) ranges = uranges; } } - + cv::vector images(dims); for( i = 0; i < dims; i++ ) images[i] = cv::cvarrToMat(img[i]); - + cv::Mat _dst = cv::cvarrToMat(dst); - + CV_Assert( _dst.size() == images[0].size() && _dst.depth() == images[0].depth() ); - + if( !CV_IS_SPARSE_HIST(hist) ) { cv::Mat H((const CvMatND*)hist->bins); @@ -2331,13 +2331,13 @@ CV_IMPL void cvCalcBayesianProb( CvHistogram** src, int count, CvHistogram** dst ) { int i; - + if( !src || !dst ) CV_Error( CV_StsNullPtr, "NULL histogram array pointer" ); if( count < 2 ) CV_Error( CV_StsOutOfRange, "Too small number of histograms" ); - + for( i = 0; i < count; i++ ) { if( !CV_IS_HIST(src[i]) || !CV_IS_HIST(dst[i]) ) @@ -2346,7 +2346,7 @@ cvCalcBayesianProb( CvHistogram** src, int count, CvHistogram** dst ) if( !CV_IS_MATND(src[i]->bins) || !CV_IS_MATND(dst[i]->bins) ) CV_Error( CV_StsBadArg, "The function supports dense histograms only" ); } - + cvZero( dst[0]->bins ); // dst[0] = src[0] + ... + src[count-1] for( i = 0; i < count; i++ ) @@ -2409,7 +2409,7 @@ CV_IMPL void cvEqualizeHist( const CvArr* srcarr, CvArr* dstarr ) { CvMat sstub, *src = cvGetMat(srcarr, &sstub); CvMat dstub, *dst = cvGetMat(dstarr, &dstub); - + CV_Assert( CV_ARE_SIZES_EQ(src, dst) && CV_ARE_TYPES_EQ(src, dst) && CV_MAT_TYPE(src->type) == CV_8UC1 ); CvSize size = cvGetMatSize(src); @@ -2422,14 +2422,14 @@ CV_IMPL void cvEqualizeHist( const CvArr* srcarr, CvArr* dstarr ) const int hist_sz = 256; int hist[hist_sz]; memset(hist, 0, sizeof(hist)); - + for( y = 0; y < size.height; y++ ) { const uchar* sptr = src->data.ptr + src->step*y; for( x = 0; x < size.width; x++ ) hist[sptr[x]]++; } - + float scale = 255.f/(size.width*size.height); int sum = 0; uchar lut[hist_sz+1]; @@ -2511,7 +2511,7 @@ static void *icvReadHist( CvFileStorage * fs, CvFileNode * node ) h->mat.refcount = mat->refcount; // increase refcount so freeing temp header doesn't free data - cvIncRefData( mat ); + cvIncRefData( mat ); // free temporary header cvReleaseMatND( &mat ); diff --git a/modules/imgproc/src/hough.cpp b/modules/imgproc/src/hough.cpp index 462f712..9395595 100644 --- a/modules/imgproc/src/hough.cpp +++ b/modules/imgproc/src/hough.cpp @@ -92,8 +92,6 @@ icvHoughLinesStandard( const CvMat* img, float rho, float theta, int step, width, height; int numangle, numrho; int total = 0; - float ang; - int r, n; int i, j; float irho = 1 / rho; double scale; @@ -117,7 +115,8 @@ icvHoughLinesStandard( const CvMat* img, float rho, float theta, memset( accum, 0, sizeof(accum[0]) * (numangle+2) * (numrho+2) ); - for( ang = 0, n = 0; n < numangle; ang += theta, n++ ) + float ang = 0; + for(int n = 0; n < numangle; ang += theta, n++ ) { tabSin[n] = (float)(sin(ang) * irho); tabCos[n] = (float)(cos(ang) * irho); @@ -128,17 +127,17 @@ icvHoughLinesStandard( const CvMat* img, float rho, float theta, for( j = 0; j < width; j++ ) { if( image[i * step + j] != 0 ) - for( n = 0; n < numangle; n++ ) + for(int n = 0; n < numangle; n++ ) { - r = cvRound( j * tabCos[n] + i * tabSin[n] ); + int r = cvRound( j * tabCos[n] + i * tabSin[n] ); r += (numrho - 1) / 2; accum[(n+1) * (numrho+2) + r+1]++; } } // stage 2. find local maximums - for( r = 0; r < numrho; r++ ) - for( n = 0; n < numangle; n++ ) + for(int r = 0; r < numrho; r++ ) + for(int n = 0; n < numangle; n++ ) { int base = (n+1) * (numrho+2) + r+1; if( accum[base] > threshold && @@ -529,7 +528,7 @@ icvHoughLinesProbabilistic( CvMat* image, // choose random point out of the remaining ones int idx = cvRandInt(&rng) % count; int max_val = threshold-1, max_n = 0; - CvPoint* pt = (CvPoint*)cvGetSeqElem( seq, idx ); + CvPoint* point = (CvPoint*)cvGetSeqElem( seq, idx ); CvPoint line_end[2] = {{0,0}, {0,0}}; float a, b; int* adata = (int*)accum.data; @@ -537,11 +536,11 @@ icvHoughLinesProbabilistic( CvMat* image, int good_line; const int shift = 16; - i = pt->y; - j = pt->x; + i = point->y; + j = point->x; // "remove" it by overriding it with the last element - *pt = *(CvPoint*)cvGetSeqElem( seq, count-1 ); + *point = *(CvPoint*)cvGetSeqElem( seq, count-1 ); // check if it has been excluded already (i.e. belongs to some other line) if( !mdata0[i*width + j] ) @@ -852,7 +851,7 @@ icvHoughCirclesGradient( CvMat* img, float dp, float min_dist, for( x = 0; x < cols; x++ ) { float vx, vy; - int sx, sy, x0, y0, x1, y1, r, k; + int sx, sy, x0, y0, x1, y1, r; CvPoint pt; vx = dx_row[x]; @@ -869,7 +868,7 @@ icvHoughCirclesGradient( CvMat* img, float dp, float min_dist, x0 = cvRound((x*idp)*ONE); y0 = cvRound((y*idp)*ONE); // Step from min_radius to max_radius in both directions of the gradient - for( k = 0; k < 2; k++ ) + for(int k1 = 0; k1 < 2; k1++ ) { x1 = x0 + min_radius * sx; y1 = y0 + min_radius * sy; @@ -934,7 +933,7 @@ icvHoughCirclesGradient( CvMat* img, float dp, float min_dist, //Calculate circle's center in pixels float cx = (float)((x + 0.5f)*dp), cy = (float)(( y + 0.5f )*dp); float start_dist, dist_sum; - float r_best = 0, c[3]; + float r_best = 0; int max_count = 0; // Check distance with previously detected circles for( j = 0; j < circles->total; j++ ) @@ -996,6 +995,7 @@ icvHoughCirclesGradient( CvMat* img, float dp, float min_dist, // Check if the circle has enough support if( max_count > acc_threshold ) { + float c[3]; c[0] = cx; c[1] = cy; c[2] = (float)r_best; diff --git a/modules/imgproc/src/imgwarp.cpp b/modules/imgproc/src/imgwarp.cpp index be21ae9..78b567d 100644 --- a/modules/imgproc/src/imgwarp.cpp +++ b/modules/imgproc/src/imgwarp.cpp @@ -97,7 +97,6 @@ static inline void interpolateLanczos4( float x, float* coeffs ) static const double cs[][2]= {{1, 0}, {-s45, -s45}, {0, 1}, {s45, -s45}, {-1, 0}, {s45, s45}, {0, -1}, {-s45, s45}}; - int i; if( x < FLT_EPSILON ) { for( int i = 0; i < 8; i++ ) @@ -108,7 +107,7 @@ static inline void interpolateLanczos4( float x, float* coeffs ) float sum = 0; double y0=-(x+3)*CV_PI*0.25, s0 = sin(y0), c0=cos(y0); - for( i = 0; i < 8; i++ ) + for(int i = 0; i < 8; i++ ) { double y = -(x+3-i)*CV_PI*0.25; coeffs[i] = (float)((cs[i][0]*s0 + cs[i][1]*c0)/(y*y)); @@ -116,7 +115,7 @@ static inline void interpolateLanczos4( float x, float* coeffs ) } sum = 1.f/sum; - for( i = 0; i < 8; i++ ) + for(int i = 0; i < 8; i++ ) coeffs[i] *= sum; } @@ -1091,14 +1090,14 @@ static void resizeGeneric_( const Mat& src, Mat& dst, const T* srows[MAX_ESIZE]={0}; WT* rows[MAX_ESIZE]={0}; int prev_sy[MAX_ESIZE]; - int k, dy; + int dy; xmin *= cn; xmax *= cn; HResize hresize; VResize vresize; - for( k = 0; k < ksize; k++ ) + for(int k = 0; k < ksize; k++ ) { prev_sy[k] = -1; rows[k] = (WT*)_buffer + bufstep*k; @@ -1107,9 +1106,9 @@ static void resizeGeneric_( const Mat& src, Mat& dst, // image resize is a separable operation. In case of not too strong for( dy = 0; dy < dsize.height; dy++, beta += ksize ) { - int sy0 = yofs[dy], k, k0=ksize, k1=0, ksize2 = ksize/2; + int sy0 = yofs[dy], k0=ksize, k1=0, ksize2 = ksize/2; - for( k = 0; k < ksize; k++ ) + for(int k = 0; k < ksize; k++ ) { int sy = clip(sy0 - ksize2 + 1 + k, 0, ssize.height); for( k1 = std::max(k1, k); k1 < ksize; k1++ ) @@ -2374,25 +2373,25 @@ static void remapLanczos4( const Mat& _src, Mat& _dst, const Mat& _xy, for( i = 0; i < 8; i++, w += 8 ) { int yi = y[i]; - const T* S = S0 + yi*sstep; + const T* S1 = S0 + yi*sstep; if( yi < 0 ) continue; if( x[0] >= 0 ) - sum += (S[x[0]] - cv)*w[0]; + sum += (S1[x[0]] - cv)*w[0]; if( x[1] >= 0 ) - sum += (S[x[1]] - cv)*w[1]; + sum += (S1[x[1]] - cv)*w[1]; if( x[2] >= 0 ) - sum += (S[x[2]] - cv)*w[2]; + sum += (S1[x[2]] - cv)*w[2]; if( x[3] >= 0 ) - sum += (S[x[3]] - cv)*w[3]; + sum += (S1[x[3]] - cv)*w[3]; if( x[4] >= 0 ) - sum += (S[x[4]] - cv)*w[4]; + sum += (S1[x[4]] - cv)*w[4]; if( x[5] >= 0 ) - sum += (S[x[5]] - cv)*w[5]; + sum += (S1[x[5]] - cv)*w[5]; if( x[6] >= 0 ) - sum += (S[x[6]] - cv)*w[6]; + sum += (S1[x[6]] - cv)*w[6]; if( x[7] >= 0 ) - sum += (S[x[7]] - cv)*w[7]; + sum += (S1[x[7]] - cv)*w[7]; } D[k] = castOp(sum); } @@ -2966,8 +2965,8 @@ void cv::warpAffine( InputArray _src, OutputArray _dst, remap( src, dpart, _XY, Mat(), interpolation, borderType, borderValue ); else { - Mat matA(bh, bw, CV_16U, A); - remap( src, dpart, _XY, matA, interpolation, borderType, borderValue ); + Mat _matA(bh, bw, CV_16U, A); + remap( src, dpart, _XY, _matA, interpolation, borderType, borderValue ); } } } @@ -3064,8 +3063,8 @@ void cv::warpPerspective( InputArray _src, OutputArray _dst, InputArray _M0, remap( src, dpart, _XY, Mat(), interpolation, borderType, borderValue ); else { - Mat matA(bh, bw, CV_16U, A); - remap( src, dpart, _XY, matA, interpolation, borderType, borderValue ); + Mat _matA(bh, bw, CV_16U, A); + remap( src, dpart, _XY, _matA, interpolation, borderType, borderValue ); } } } diff --git a/modules/imgproc/src/moments.cpp b/modules/imgproc/src/moments.cpp index aa1a569..784a61b 100644 --- a/modules/imgproc/src/moments.cpp +++ b/modules/imgproc/src/moments.cpp @@ -106,7 +106,7 @@ static void icvContourMoments( CvSeq* contour, CvMoments* moments ) yi_1 = ((CvPoint2D32f*)(reader.ptr))->y; } CV_NEXT_SEQ_ELEM( contour->elem_size, reader ); - + xi_12 = xi_1 * xi_1; yi_12 = yi_1 * yi_1; @@ -208,7 +208,7 @@ static void momentsInTile( const cv::Mat& img, double* moments ) const T* ptr = (const T*)(img.data + y*img.step); WT x0 = 0, x1 = 0, x2 = 0; MT x3 = 0; - + for( x = 0; x < size.width; x++ ) { WT p = ptr[x]; @@ -248,21 +248,21 @@ template<> void momentsInTile( const cv::Mat& img, double* mome typedef int WT; typedef int MT; cv::Size size = img.size(); - int x, y; + int y; MT mom[10] = {0,0,0,0,0,0,0,0,0,0}; bool useSIMD = cv::checkHardwareSupport(CV_CPU_SSE2); - + for( y = 0; y < size.height; y++ ) { const T* ptr = img.ptr(y); int x0 = 0, x1 = 0, x2 = 0, x3 = 0, x = 0; - + if( useSIMD ) { __m128i qx_init = _mm_setr_epi16(0, 1, 2, 3, 4, 5, 6, 7); __m128i dx = _mm_set1_epi16(8); __m128i z = _mm_setzero_si128(), qx0 = z, qx1 = z, qx2 = z, qx3 = z, qx = qx_init; - + for( ; x <= size.width - 8; x += 8 ) { __m128i p = _mm_unpacklo_epi8(_mm_loadl_epi64((const __m128i*)(ptr + x)), z); @@ -272,34 +272,34 @@ template<> void momentsInTile( const cv::Mat& img, double* mome qx1 = _mm_add_epi32(qx1, _mm_madd_epi16(p, qx)); qx2 = _mm_add_epi32(qx2, _mm_madd_epi16(p, sx)); qx3 = _mm_add_epi32(qx3, _mm_madd_epi16(px, sx)); - + qx = _mm_add_epi16(qx, dx); } int CV_DECL_ALIGNED(16) buf[4]; _mm_store_si128((__m128i*)buf, qx0); x0 = buf[0] + buf[1] + buf[2] + buf[3]; _mm_store_si128((__m128i*)buf, qx1); - x1 = buf[0] + buf[1] + buf[2] + buf[3]; + x1 = buf[0] + buf[1] + buf[2] + buf[3]; _mm_store_si128((__m128i*)buf, qx2); x2 = buf[0] + buf[1] + buf[2] + buf[3]; _mm_store_si128((__m128i*)buf, qx3); x3 = buf[0] + buf[1] + buf[2] + buf[3]; } - + for( ; x < size.width; x++ ) { WT p = ptr[x]; WT xp = x * p, xxp; - + x0 += p; x1 += xp; xxp = xp * x; x2 += xxp; x3 += xxp * x; } - + WT py = y * x0, sy = y*y; - + mom[9] += ((MT)py) * sy; // m03 mom[8] += ((MT)x1) * sy; // m12 mom[7] += ((MT)x2) * y; // m21 @@ -311,8 +311,8 @@ template<> void momentsInTile( const cv::Mat& img, double* mome mom[1] += x1; // m10 mom[0] += x0; // m00 } - - for( x = 0; x < 10; x++ ) + + for(int x = 0; x < 10; x++ ) moments[x] = (double)mom[x]; } @@ -366,7 +366,7 @@ CV_IMPL void cvMoments( const void* array, CvMoments* moments, int binary ) type = CV_MAT_TYPE( mat->type ); depth = CV_MAT_DEPTH( type ); cn = CV_MAT_CN( type ); - + cv::Size size = cvGetMatSize( mat ); if( cn > 1 && coi == 0 ) @@ -387,14 +387,14 @@ CV_IMPL void cvMoments( const void* array, CvMoments* moments, int binary ) func = momentsInTile; else CV_Error( CV_StsUnsupportedFormat, "" ); - + cv::Mat src0(mat); for( int y = 0; y < size.height; y += TILE_SIZE ) { cv::Size tileSize; tileSize.height = std::min(TILE_SIZE, size.height - y); - + for( int x = 0; x < size.width; x += TILE_SIZE ) { tileSize.width = std::min(TILE_SIZE, size.width - x); @@ -413,20 +413,20 @@ CV_IMPL void cvMoments( const void* array, CvMoments* moments, int binary ) cv::compare( src, 0, tmp, CV_CMP_NE ); src = tmp; } - + double mom[10]; func( src, mom ); - + if(binary) { double s = 1./255; for( int k = 0; k < 10; k++ ) mom[k] *= s; } - + double xm = x * mom[0], ym = y * mom[0]; - // accumulate moments computed in each tile + // accumulate moments computed in each tile // + m00 ( = m00' ) moments->m00 += mom[0]; @@ -451,7 +451,7 @@ CV_IMPL void cvMoments( const void* array, CvMoments* moments, int binary ) // + m21 ( = m21' + x*(2*m11' + 2*y*m10' + x*m01' + x*y*m00') + y*m20') moments->m21 += mom[7] + x * (2 * (mom[4] + y * mom[1]) + x * (mom[2] + ym)) + y * mom[3]; - + // + m12 ( = m12' + y*(2*m11' + 2*x*m01' + y*m10' + x*y*m00') + x*m02') moments->m12 += mom[8] + y * (2 * (mom[4] + x * mom[2]) + y * (mom[1] + xm)) + x * mom[5]; @@ -601,9 +601,9 @@ Moments::operator CvMoments() const return m; } - + } - + cv::Moments cv::moments( InputArray _array, bool binaryImage ) { CvMoments om; diff --git a/modules/imgproc/src/shapedescr.cpp b/modules/imgproc/src/shapedescr.cpp index efc11b0..36c0c6c 100644 --- a/modules/imgproc/src/shapedescr.cpp +++ b/modules/imgproc/src/shapedescr.cpp @@ -49,7 +49,7 @@ cvArcLength( const void *array, CvSlice slice, int is_closed ) int i, j = 0, count; const int N = 16; float buf[N]; - CvMat buffer = cvMat( 1, N, CV_32F, buf ); + CvMat buffer = cvMat( 1, N, CV_32F, buf ); CvSeqReader reader; CvContour contour_header; CvSeq* contour = 0; @@ -74,7 +74,7 @@ cvArcLength( const void *array, CvSlice slice, int is_closed ) if( contour->total > 1 ) { int is_float = CV_SEQ_ELTYPE( contour ) == CV_32FC2; - + cvStartReadSeq( contour, &reader, 0 ); cvSetSeqReaderPos( &reader, slice.start_index ); count = cvSliceLength( slice, contour ); @@ -110,7 +110,7 @@ cvArcLength( const void *array, CvSlice slice, int is_closed ) CV_NEXT_SEQ_ELEM( contour->elem_size, reader ); // Bugfix by Axel at rubico.com 2010-03-22, affects closed slices only // wraparound not handled by CV_NEXT_SEQ_ELEM - if( is_closed && i == count - 2 ) + if( is_closed && i == count - 2 ) cvSetSeqReaderPos( &reader, slice.start_index ); buffer.data.fl[j] = dx * dx + dy * dy; @@ -287,7 +287,7 @@ cvMinEnclosingCircle( const void* array, CvPoint2D32f * _center, float *_radius *_radius = 0; CvSeqReader reader; - int i, k, count; + int k, count; CvPoint2D32f pts[8]; CvContour contour_header; CvSeqBlock block; @@ -324,7 +324,7 @@ cvMinEnclosingCircle( const void* array, CvPoint2D32f * _center, float *_radius pt_left = pt_right = pt_top = pt_bottom = (CvPoint *)(reader.ptr); CV_READ_SEQ_ELEM( pt, reader ); - for( i = 1; i < count; i++ ) + for(int i = 1; i < count; i++ ) { CvPoint* pt_ptr = (CvPoint*)reader.ptr; CV_READ_SEQ_ELEM( pt, reader ); @@ -351,7 +351,7 @@ cvMinEnclosingCircle( const void* array, CvPoint2D32f * _center, float *_radius pt_left = pt_right = pt_top = pt_bottom = (CvPoint2D32f *) (reader.ptr); CV_READ_SEQ_ELEM( pt, reader ); - for( i = 1; i < count; i++ ) + for(int i = 1; i < count; i++ ) { CvPoint2D32f* pt_ptr = (CvPoint2D32f*)reader.ptr; CV_READ_SEQ_ELEM( pt, reader ); @@ -375,14 +375,14 @@ cvMinEnclosingCircle( const void* array, CvPoint2D32f * _center, float *_radius for( k = 0; k < max_iters; k++ ) { double min_delta = 0, delta; - CvPoint2D32f ptfl, farAway = { 0, 0}; - /*only for first iteration because the alg is repared at the loop's foot*/ - if(k==0) - icvFindEnslosingCicle4pts_32f( pts, ¢er, &radius ); + CvPoint2D32f ptfl, farAway = { 0, 0}; + /*only for first iteration because the alg is repared at the loop's foot*/ + if(k==0) + icvFindEnslosingCicle4pts_32f( pts, ¢er, &radius ); cvStartReadSeq( sequence, &reader, 0 ); - for( i = 0; i < count; i++ ) + for(int i = 0; i < count; i++ ) { if( !is_float ) { @@ -406,22 +406,22 @@ cvMinEnclosingCircle( const void* array, CvPoint2D32f * _center, float *_radius if( result ) break; - CvPoint2D32f ptsCopy[4]; - /* find good replacement partner for the point which is at most far away, - starting with the one that lays in the actual circle (i=3) */ - for(int i = 3; i >=0; i-- ) - { - for(int j = 0; j < 4; j++ ) - { - ptsCopy[j]=(i != j)? pts[j]: farAway; - } - - icvFindEnslosingCicle4pts_32f(ptsCopy, ¢er, &radius ); - if( icvIsPtInCircle( pts[i], center, radius )>=0){ // replaced one again in the new circle? - pts[i] = farAway; - break; - } - } + CvPoint2D32f ptsCopy[4]; + /* find good replacement partner for the point which is at most far away, + starting with the one that lays in the actual circle (i=3) */ + for(int i = 3; i >=0; i-- ) + { + for(int j = 0; j < 4; j++ ) + { + ptsCopy[j]=(i != j)? pts[j]: farAway; + } + + icvFindEnslosingCicle4pts_32f(ptsCopy, ¢er, &radius ); + if( icvIsPtInCircle( pts[i], center, radius )>=0){ // replaced one again in the new circle? + pts[i] = farAway; + break; + } + } } if( !result ) @@ -429,7 +429,7 @@ cvMinEnclosingCircle( const void* array, CvPoint2D32f * _center, float *_radius cvStartReadSeq( sequence, &reader, 0 ); radius = 0.f; - for( i = 0; i < count; i++ ) + for(int i = 0; i < count; i++ ) { CvPoint2D32f ptfl; float t, dx, dy; @@ -486,7 +486,7 @@ icvContourArea( const CvSeq* contour, double *area ) yi_1 = ((CvPoint2D32f*)(reader.ptr))->y; } CV_NEXT_SEQ_ELEM( contour->elem_size, reader ); - + while( lpt-- > 0 ) { double dxy, xi, yi; @@ -520,7 +520,7 @@ icvContourArea( const CvSeq* contour, double *area ) /****************************************************************************************\ - copy data from one buffer to other buffer + copy data from one buffer to other buffer \****************************************************************************************/ @@ -797,9 +797,9 @@ cvFitEllipse2( const CvArr* array ) n = ptseq->total; if( n < 5 ) CV_Error( CV_StsBadSize, "Number of points should be >= 5" ); - + /* - * New fitellipse algorithm, contributed by Dr. Daniel Weiss + * New fitellipse algorithm, contributed by Dr. Daniel Weiss */ CvPoint2D32f c = {0,0}; double gfp[5], rp[5], t; @@ -818,7 +818,7 @@ cvFitEllipse2( const CvArr* array ) cvStartReadSeq( ptseq, &reader ); is_float = CV_SEQ_ELTYPE(ptseq) == CV_32FC2; - + for( i = 0; i < n; i++ ) { CvPoint2D32f p; @@ -857,7 +857,7 @@ cvFitEllipse2( const CvArr* array ) Ad[i*5 + 3] = p.x; Ad[i*5 + 4] = p.y; } - + cvSolve( &A, &b, &x, CV_SVD ); // now use general-form parameters A - E to find the ellipse center: @@ -1069,7 +1069,7 @@ cvBoundingRect( CvArr* array, int update ) xmin = ymin = 0; } else if( ptseq->total ) - { + { int is_float = CV_SEQ_ELTYPE(ptseq) == CV_32FC2; cvStartReadSeq( ptseq, &reader, 0 ); @@ -1082,12 +1082,12 @@ cvBoundingRect( CvArr* array, int update ) ymin = ymax = pt.y; for( i = 1; i < ptseq->total; i++ ) - { + { CV_READ_SEQ_ELEM( pt, reader ); - + if( xmin > pt.x ) xmin = pt.x; - + if( xmax < pt.x ) xmax = pt.x; @@ -1108,14 +1108,14 @@ cvBoundingRect( CvArr* array, int update ) ymin = ymax = CV_TOGGLE_FLT(pt.y); for( i = 1; i < ptseq->total; i++ ) - { + { CV_READ_SEQ_ELEM( pt, reader ); pt.x = CV_TOGGLE_FLT(pt.x); pt.y = CV_TOGGLE_FLT(pt.y); - + if( xmin > pt.x ) xmin = pt.x; - + if( xmax < pt.x ) xmax = pt.x; @@ -1144,7 +1144,7 @@ cvBoundingRect( CvArr* array, int update ) if( update ) ((CvContour*)ptseq)->rect = rect; - + return rect; } diff --git a/modules/imgproc/src/undistort.cpp b/modules/imgproc/src/undistort.cpp index d84cfef..fc13b50 100644 --- a/modules/imgproc/src/undistort.cpp +++ b/modules/imgproc/src/undistort.cpp @@ -48,7 +48,7 @@ cv::Mat cv::getDefaultNewCameraMatrix( InputArray _cameraMatrix, Size imgsize, Mat cameraMatrix = _cameraMatrix.getMat(); if( !centerPrincipalPoint && cameraMatrix.type() == CV_64F ) return cameraMatrix; - + Mat newCameraMatrix; cameraMatrix.convertTo(newCameraMatrix, CV_64F); if( centerPrincipalPoint ) @@ -65,7 +65,7 @@ void cv::initUndistortRectifyMap( InputArray _cameraMatrix, InputArray _distCoef { Mat cameraMatrix = _cameraMatrix.getMat(), distCoeffs = _distCoeffs.getMat(); Mat matR = _matR.getMat(), newCameraMatrix = _newCameraMatrix.getMat(); - + if( m1type <= 0 ) m1type = CV_16SC2; CV_Assert( m1type == CV_16SC2 || m1type == CV_32FC1 || m1type == CV_32FC2 ); @@ -106,7 +106,7 @@ void cv::initUndistortRectifyMap( InputArray _cameraMatrix, InputArray _distCoef double u0 = A(0, 2), v0 = A(1, 2); double fx = A(0, 0), fy = A(1, 1); - CV_Assert( distCoeffs.size() == Size(1, 4) || distCoeffs.size() == Size(4, 1) || + CV_Assert( distCoeffs.size() == Size(1, 4) || distCoeffs.size() == Size(4, 1) || distCoeffs.size() == Size(1, 5) || distCoeffs.size() == Size(5, 1) || distCoeffs.size() == Size(1, 8) || distCoeffs.size() == Size(8, 1)); @@ -166,10 +166,10 @@ void cv::undistort( InputArray _src, OutputArray _dst, InputArray _cameraMatrix, { Mat src = _src.getMat(), cameraMatrix = _cameraMatrix.getMat(); Mat distCoeffs = _distCoeffs.getMat(), newCameraMatrix = _newCameraMatrix.getMat(); - + _dst.create( src.size(), src.type() ); Mat dst = _dst.getMat(); - + CV_Assert( dst.data != src.data ); int stripe_size0 = std::min(std::max(1, (1 << 12) / std::max(src.cols, 1)), src.rows); @@ -289,11 +289,11 @@ void cvUndistortPoints( const CvMat* _src, CvMat* _dst, const CvMat* _cameraMatr (_distCoeffs->rows == 1 || _distCoeffs->cols == 1) && (_distCoeffs->rows*_distCoeffs->cols == 4 || _distCoeffs->rows*_distCoeffs->cols == 5 || - _distCoeffs->rows*_distCoeffs->cols == 8)); + _distCoeffs->rows*_distCoeffs->cols == 8)); _Dk = cvMat( _distCoeffs->rows, _distCoeffs->cols, CV_MAKETYPE(CV_64F,CV_MAT_CN(_distCoeffs->type)), k); - + cvConvert( _distCoeffs, &_Dk ); iters = 5; } @@ -389,13 +389,13 @@ void cv::undistortPoints( InputArray _src, OutputArray _dst, { Mat src = _src.getMat(), cameraMatrix = _cameraMatrix.getMat(); Mat distCoeffs = _distCoeffs.getMat(), R = _Rmat.getMat(), P = _Pmat.getMat(); - + CV_Assert( src.isContinuous() && (src.depth() == CV_32F || src.depth() == CV_64F) && ((src.rows == 1 && src.channels() == 2) || src.cols*src.channels() == 2)); - + _dst.create(src.size(), src.type(), -1, true); Mat dst = _dst.getMat(); - + CvMat _csrc = src, _cdst = dst, _ccameraMatrix = cameraMatrix; CvMat matR, matP, _cdistCoeffs, *pR=0, *pP=0, *pD=0; if( R.data ) @@ -416,11 +416,11 @@ static Point2f mapPointSpherical(const Point2f& p, float alpha, Vec4d* J, int pr double beta = 1 + 2*alpha; double v = x*x + y*y + 1, iv = 1/v; double u = sqrt(beta*v + alpha*alpha); - + double k = (u - alpha)*iv; double kv = (v*beta/u - (u - alpha)*2)*iv*iv; double kx = kv*x, ky = kv*y; - + if( projType == PROJ_SPHERICAL_ORTHO ) { if(J) @@ -433,7 +433,7 @@ static Point2f mapPointSpherical(const Point2f& p, float alpha, Vec4d* J, int pr double iR = 1/(alpha + 1); double x1 = std::max(std::min(x*k*iR, 1.), -1.); double y1 = std::max(std::min(y*k*iR, 1.), -1.); - + if(J) { double fx1 = iR/sqrt(1 - x1*x1); @@ -446,35 +446,35 @@ static Point2f mapPointSpherical(const Point2f& p, float alpha, Vec4d* J, int pr return Point2f(); } - + static Point2f invMapPointSpherical(Point2f _p, float alpha, int projType) { static int avgiter = 0, avgn = 0; - + double eps = 1e-12; Vec2d p(_p.x, _p.y), q(_p.x, _p.y), err; Vec4d J; int i, maxiter = 5; - + for( i = 0; i < maxiter; i++ ) { Point2f p1 = mapPointSpherical(Point2f((float)q[0], (float)q[1]), alpha, &J, projType); err = Vec2d(p1.x, p1.y) - p; if( err[0]*err[0] + err[1]*err[1] < eps ) break; - + Vec4d JtJ(J[0]*J[0] + J[2]*J[2], J[0]*J[1] + J[2]*J[3], J[0]*J[1] + J[2]*J[3], J[1]*J[1] + J[3]*J[3]); double d = JtJ[0]*JtJ[3] - JtJ[1]*JtJ[2]; d = d ? 1./d : 0; Vec4d iJtJ(JtJ[3]*d, -JtJ[1]*d, -JtJ[2]*d, JtJ[0]*d); Vec2d JtErr(J[0]*err[0] + J[2]*err[1], J[1]*err[0] + J[3]*err[1]); - + q -= Vec2d(iJtJ[0]*JtErr[0] + iJtJ[1]*JtErr[1], iJtJ[2]*JtErr[0] + iJtJ[3]*JtErr[1]); //Matx22d J(kx*x + k, kx*y, ky*x, ky*y + k); //q -= Vec2d((J.t()*J).inv()*(J.t()*err)); } - + if( i < maxiter ) { avgiter += i; @@ -482,12 +482,12 @@ static Point2f invMapPointSpherical(Point2f _p, float alpha, int projType) if( avgn == 1500 ) printf("avg iters = %g\n", (double)avgiter/avgn); } - + return i < maxiter ? Point2f((float)q[0], (float)q[1]) : Point2f(-FLT_MAX, -FLT_MAX); } } - + float cv::initWideAngleProjMap( InputArray _cameraMatrix0, InputArray _distCoeffs0, Size imageSize, int destImageWidth, int m1type, OutputArray _map1, OutputArray _map2, int projType, double _alpha ) @@ -500,40 +500,40 @@ float cv::initWideAngleProjMap( InputArray _cameraMatrix0, InputArray _distCoeff Point2f dcenter((destImageWidth-1)*0.5f, 0.f); float xmin = FLT_MAX, xmax = -FLT_MAX, ymin = FLT_MAX, ymax = -FLT_MAX; int N = 9; - std::vector u(1), v(1); - Mat _u(u), I = Mat::eye(3,3,CV_64F); + std::vector uvec(1), vvec(1); + Mat I = Mat::eye(3,3,CV_64F); float alpha = (float)_alpha; - + int ndcoeffs = distCoeffs0.cols*distCoeffs0.rows*distCoeffs0.channels(); CV_Assert((distCoeffs0.cols == 1 || distCoeffs0.rows == 1) && (ndcoeffs == 4 || ndcoeffs == 5 || ndcoeffs == 8)); CV_Assert(cameraMatrix0.size() == Size(3,3)); distCoeffs0.convertTo(distCoeffs,CV_64F); cameraMatrix0.convertTo(cameraMatrix,CV_64F); - + alpha = std::min(alpha, 0.999f); - + for( int i = 0; i < N; i++ ) for( int j = 0; j < N; j++ ) { Point2f p((float)j*imageSize.width/(N-1), (float)i*imageSize.height/(N-1)); - u[0] = p; - undistortPoints(_u, v, cameraMatrix, distCoeffs, I, I); - Point2f q = mapPointSpherical(v[0], alpha, 0, projType); + uvec[0] = p; + undistortPoints(uvec, vvec, cameraMatrix, distCoeffs, I, I); + Point2f q = mapPointSpherical(vvec[0], alpha, 0, projType); if( xmin > q.x ) xmin = q.x; if( xmax < q.x ) xmax = q.x; if( ymin > q.y ) ymin = q.y; if( ymax < q.y ) ymax = q.y; } - + float scale = (float)std::min(dcenter.x/fabs(xmax), dcenter.x/fabs(xmin)); Size dsize(destImageWidth, cvCeil(std::max(scale*fabs(ymin)*2, scale*fabs(ymax)*2))); dcenter.y = (dsize.height - 1)*0.5f; - + Mat mapxy(dsize, CV_32FC2); double k1 = k[0], k2 = k[1], k3 = k[2], p1 = k[3], p2 = k[4], k4 = k[5], k5 = k[6], k6 = k[7]; double fx = cameraMatrix.at(0,0), fy = cameraMatrix.at(1,1), cx = scenter.x, cy = scenter.y; - + for( int y = 0; y < dsize.height; y++ ) { Point2f* mxy = mapxy.ptr(y); @@ -551,11 +551,11 @@ float cv::initWideAngleProjMap( InputArray _cameraMatrix0, InputArray _distCoeff double kr = 1 + ((k3*r2 + k2)*r2 + k1)*r2/(1 + ((k6*r2 + k5)*r2 + k4)*r2); double u = fx*(q.x*kr + p1*_2xy + p2*(r2 + 2*x2)) + cx; double v = fy*(q.y*kr + p1*(r2 + 2*y2) + p2*_2xy) + cy; - + mxy[x] = Point2f((float)u, (float)v); } } - + if(m1type == CV_32FC2) { _map1.create(mapxy.size(), mapxy.type()); @@ -565,7 +565,7 @@ float cv::initWideAngleProjMap( InputArray _cameraMatrix0, InputArray _distCoeff } else convertMaps(mapxy, Mat(), _map1, _map2, m1type, false); - + return scale; } diff --git a/modules/imgproc/test/test_contours.cpp b/modules/imgproc/test/test_contours.cpp index 3e07488..a8c9a72 100644 --- a/modules/imgproc/test/test_contours.cpp +++ b/modules/imgproc/test/test_contours.cpp @@ -266,7 +266,7 @@ void CV_FindContourTest::run_func() // the whole testing is done here, run_func() is not utilized in this test int CV_FindContourTest::validate_test_results( int /*test_case_idx*/ ) { - int i, code = cvtest::TS::OK; + int code = cvtest::TS::OK; cvCmpS( img[0], 0, img[0], CV_CMP_GT ); @@ -284,7 +284,7 @@ int CV_FindContourTest::validate_test_results( int /*test_case_idx*/ ) Mat _img[4]; for( int i = 0; i < 4; i++ ) _img[i] = cvarrToMat(img[i]); - + code = cvtest::cmpEps2(ts, _img[0], _img[3], 0, true, "Comparing original image with the map of filled contours" ); if( code < 0 ) @@ -303,7 +303,7 @@ int CV_FindContourTest::validate_test_results( int /*test_case_idx*/ ) CvTreeNodeIterator iterator2; int count3; - for( i = 0; i < 2; i++ ) + for(int i = 0; i < 2; i++ ) { CvTreeNodeIterator iterator; cvInitTreeNodeIterator( &iterator, i == 0 ? contours : contours2, INT_MAX ); @@ -353,7 +353,7 @@ int CV_FindContourTest::validate_test_results( int /*test_case_idx*/ ) goto _exit_; } - for( i = 0; i < seq1->total; i++ ) + for(int i = 0; i < seq1->total; i++ ) { CvPoint pt1; CvPoint pt2; diff --git a/modules/imgproc/test/test_convhull.cpp b/modules/imgproc/test/test_convhull.cpp index 84fa0cf..19f536a 100644 --- a/modules/imgproc/test/test_convhull.cpp +++ b/modules/imgproc/test/test_convhull.cpp @@ -193,7 +193,7 @@ protected: void* result; double low_high_range; CvScalar low, high; - + bool test_cpp; }; @@ -254,7 +254,7 @@ int CV_BaseShapeDescrTest::read_params( CvFileStorage* fs ) } -void CV_BaseShapeDescrTest::generate_point_set( void* points ) +void CV_BaseShapeDescrTest::generate_point_set( void* pointsSet ) { RNG& rng = ts->get_rng(); int i, k, n, total, point_type; @@ -269,16 +269,16 @@ void CV_BaseShapeDescrTest::generate_point_set( void* points ) } memset( &reader, 0, sizeof(reader) ); - if( CV_IS_SEQ(points) ) + if( CV_IS_SEQ(pointsSet) ) { - CvSeq* ptseq = (CvSeq*)points; + CvSeq* ptseq = (CvSeq*)pointsSet; total = ptseq->total; point_type = CV_SEQ_ELTYPE(ptseq); cvStartReadSeq( ptseq, &reader ); } else { - CvMat* ptm = (CvMat*)points; + CvMat* ptm = (CvMat*)pointsSet; assert( CV_IS_MAT(ptm) && CV_IS_MAT_CONT(ptm->type) ); total = ptm->rows + ptm->cols - 1; point_type = CV_MAT_TYPE(ptm->type); @@ -362,7 +362,7 @@ int CV_BaseShapeDescrTest::prepare_test_case( int test_case_idx ) } generate_point_set( points ); - + test_cpp = (cvtest::randInt(rng) & 16) == 0; return 1; } @@ -614,16 +614,16 @@ int CV_ConvHullTest::validate_test_results( int test_case_idx ) for( i = 0; i < point_count; i++ ) { int idx = 0, on_edge = 0; - double result = cvTsPointPolygonTest( p[i], h, hull_count, &idx, &on_edge ); + double pptresult = cvTsPointPolygonTest( p[i], h, hull_count, &idx, &on_edge ); - if( result < 0 ) + if( pptresult < 0 ) { ts->printf( cvtest::TS::LOG, "The point #%d is outside of the convex hull\n", i ); code = cvtest::TS::FAIL_BAD_ACCURACY; goto _exit_; } - if( result < FLT_EPSILON && !on_edge ) + if( pptresult < FLT_EPSILON && !on_edge ) mask->data.ptr[idx] = (uchar)1; } @@ -735,15 +735,15 @@ int CV_MinAreaRectTest::validate_test_results( int test_case_idx ) for( i = 0; i < point_count; i++ ) { int idx = 0, on_edge = 0; - double result = cvTsPointPolygonTest( p[i], box_pt, 4, &idx, &on_edge ); - if( result < -eps ) + double pptresult = cvTsPointPolygonTest( p[i], box_pt, 4, &idx, &on_edge ); + if( pptresult < -eps ) { ts->printf( cvtest::TS::LOG, "The point #%d is outside of the box\n", i ); code = cvtest::TS::FAIL_BAD_ACCURACY; goto _exit_; } - if( result < eps ) + if( pptresult < eps ) { for( j = 0; j < 4; j++ ) { @@ -997,7 +997,7 @@ CV_FitEllipseTest::CV_FitEllipseTest() } -void CV_FitEllipseTest::generate_point_set( void* points ) +void CV_FitEllipseTest::generate_point_set( void* pointsSet ) { RNG& rng = ts->get_rng(); int i, total, point_type; @@ -1020,16 +1020,16 @@ void CV_FitEllipseTest::generate_point_set( void* points ) } memset( &reader, 0, sizeof(reader) ); - if( CV_IS_SEQ(points) ) + if( CV_IS_SEQ(pointsSet) ) { - CvSeq* ptseq = (CvSeq*)points; + CvSeq* ptseq = (CvSeq*)pointsSet; total = ptseq->total; point_type = CV_SEQ_ELTYPE(ptseq); cvStartReadSeq( ptseq, &reader ); } else { - CvMat* ptm = (CvMat*)points; + CvMat* ptm = (CvMat*)pointsSet; assert( CV_IS_MAT(ptm) && CV_IS_MAT_CONT(ptm->type) ); total = ptm->rows + ptm->cols - 1; point_type = CV_MAT_TYPE(ptm->type); @@ -1171,7 +1171,7 @@ class CV_FitEllipseSmallTest : public cvtest::BaseTest { public: CV_FitEllipseSmallTest() {} - ~CV_FitEllipseSmallTest() {} + ~CV_FitEllipseSmallTest() {} protected: void run(int) { @@ -1188,7 +1188,7 @@ protected: c[0].push_back(Point(8, 6)*scale+ofs); c[0].push_back(Point(8, 2)*scale+ofs); c[0].push_back(Point(6, 0)*scale+ofs); - + RotatedRect e = fitEllipse(c[0]); CV_Assert( fabs(e.center.x - 4) <= 1. && fabs(e.center.y - 4) <= 1. && @@ -1226,7 +1226,7 @@ CV_FitLineTest::CV_FitLineTest() } -void CV_FitLineTest::generate_point_set( void* points ) +void CV_FitLineTest::generate_point_set( void* pointsSet ) { RNG& rng = ts->get_rng(); int i, k, n, total, point_type; @@ -1250,16 +1250,16 @@ void CV_FitLineTest::generate_point_set( void* points ) memset( &reader, 0, sizeof(reader) ); - if( CV_IS_SEQ(points) ) + if( CV_IS_SEQ(pointsSet) ) { - CvSeq* ptseq = (CvSeq*)points; + CvSeq* ptseq = (CvSeq*)pointsSet; total = ptseq->total; point_type = CV_MAT_DEPTH(CV_SEQ_ELTYPE(ptseq)); cvStartReadSeq( ptseq, &reader ); } else { - CvMat* ptm = (CvMat*)points; + CvMat* ptm = (CvMat*)pointsSet; assert( CV_IS_MAT(ptm) && CV_IS_MAT_CONT(ptm->type) ); total = ptm->rows + ptm->cols - 1; point_type = CV_MAT_DEPTH(CV_MAT_TYPE(ptm->type)); @@ -1498,7 +1498,7 @@ CV_ContourMomentsTest::CV_ContourMomentsTest() } -void CV_ContourMomentsTest::generate_point_set( void* points ) +void CV_ContourMomentsTest::generate_point_set( void* pointsSet ) { RNG& rng = ts->get_rng(); float max_sz; @@ -1518,7 +1518,7 @@ void CV_ContourMomentsTest::generate_point_set( void* points ) max_r_scale = cvtest::randReal(rng)*max_max_r_scale*0.01; angle = cvtest::randReal(rng)*360; - cvTsGenerateTousledBlob( center, axes, max_r_scale, angle, points, rng ); + cvTsGenerateTousledBlob( center, axes, max_r_scale, angle, pointsSet, rng ); if( points1 ) points1->flags = CV_SEQ_MAGIC_VAL + CV_SEQ_POLYGON; @@ -1614,8 +1614,8 @@ class CV_PerimeterAreaSliceTest : public cvtest::BaseTest { public: CV_PerimeterAreaSliceTest(); - ~CV_PerimeterAreaSliceTest(); -protected: + ~CV_PerimeterAreaSliceTest(); +protected: void run(int); }; @@ -1629,7 +1629,7 @@ void CV_PerimeterAreaSliceTest::run( int ) Ptr storage = cvCreateMemStorage(); RNG& rng = theRNG(); const double min_r = 90, max_r = 120; - + for( int i = 0; i < 100; i++ ) { ts->update_context( this, i, true ); @@ -1640,7 +1640,7 @@ void CV_PerimeterAreaSliceTest::run( int ) CvPoint center; center.x = rng.uniform(cvCeil(max_r), cvFloor(640-max_r)); center.y = rng.uniform(cvCeil(max_r), cvFloor(480-max_r)); - + for( int j = 0; j < n; j++ ) { CvPoint pt; @@ -1650,7 +1650,7 @@ void CV_PerimeterAreaSliceTest::run( int ) pt.y = cvRound(center.y - r*sin(phi)); cvSeqPush(contour, &pt); } - + CvSlice slice; for(;;) { @@ -1664,14 +1664,14 @@ void CV_PerimeterAreaSliceTest::run( int ) /*printf( "%d. (%d, %d) of %d, length = %d, length1 = %d\n", i, slice.start_index, slice.end_index, contour->total, cvSliceLength(slice, contour), cslice->total ); - + double area0 = cvContourArea(cslice); - double area1 = cvContourArea(contour, slice); + double area1 = cvContourArea(contour, slice); if( area0 != area1 ) { ts->printf(cvtest::TS::LOG, "The contour area slice is computed differently (%g vs %g)\n", area0, area1 ); - ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY ); + ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY ); return; }*/ @@ -1681,7 +1681,7 @@ void CV_PerimeterAreaSliceTest::run( int ) { ts->printf(cvtest::TS::LOG, "The contour arc length is computed differently (%g vs %g)\n", len0, len1 ); - ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY ); + ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY ); return; } } diff --git a/modules/imgproc/test/test_filter.cpp b/modules/imgproc/test/test_filter.cpp index 49f0f6b..0260bdf 100644 --- a/modules/imgproc/test/test_filter.cpp +++ b/modules/imgproc/test/test_filter.cpp @@ -253,46 +253,46 @@ void CV_MorphologyBaseTest::prepare_to_validation( int /*test_case_idx*/ ) Mat _ielement(element->nRows, element->nCols, CV_32S, element->values); Mat _element; _ielement.convertTo(_element, CV_8U); - Point anchor(element->anchorX, element->anchorY); - int border = BORDER_REPLICATE; + Point _anchor(element->anchorX, element->anchorY); + int _border = BORDER_REPLICATE; if( optype == CV_MOP_ERODE ) { - cvtest::erode( src, dst, _element, anchor, border ); + cvtest::erode( src, dst, _element, _anchor, _border ); } else if( optype == CV_MOP_DILATE ) { - cvtest::dilate( src, dst, _element, anchor, border ); + cvtest::dilate( src, dst, _element, _anchor, _border ); } else { Mat temp; if( optype == CV_MOP_OPEN ) { - cvtest::erode( src, temp, _element, anchor, border ); - cvtest::dilate( temp, dst, _element, anchor, border ); + cvtest::erode( src, temp, _element, _anchor, _border ); + cvtest::dilate( temp, dst, _element, _anchor, _border ); } else if( optype == CV_MOP_CLOSE ) { - cvtest::dilate( src, temp, _element, anchor, border ); - cvtest::erode( temp, dst, _element, anchor, border ); + cvtest::dilate( src, temp, _element, _anchor, _border ); + cvtest::erode( temp, dst, _element, _anchor, _border ); } else if( optype == CV_MOP_GRADIENT ) { - cvtest::erode( src, temp, _element, anchor, border ); - cvtest::dilate( src, dst, _element, anchor, border ); + cvtest::erode( src, temp, _element, _anchor, _border ); + cvtest::dilate( src, dst, _element, _anchor, _border ); cvtest::add( dst, 1, temp, -1, Scalar::all(0), dst, dst.type() ); } else if( optype == CV_MOP_TOPHAT ) { - cvtest::erode( src, temp, _element, anchor, border ); - cvtest::dilate( temp, dst, _element, anchor, border ); + cvtest::erode( src, temp, _element, _anchor, _border ); + cvtest::dilate( temp, dst, _element, _anchor, _border ); cvtest::add( src, 1, dst, -1, Scalar::all(0), dst, dst.type() ); } else if( optype == CV_MOP_BLACKHAT ) { - cvtest::dilate( src, temp, _element, anchor, border ); - cvtest::erode( temp, dst, _element, anchor, border ); + cvtest::dilate( src, temp, _element, _anchor, _border ); + cvtest::erode( temp, dst, _element, _anchor, _border ); cvtest::add( dst, 1, src, -1, Scalar::all(0), dst, dst.type() ); } else diff --git a/modules/imgproc/test/test_floodfill.cpp b/modules/imgproc/test/test_floodfill.cpp index f185ec5..e46e9e1 100644 --- a/modules/imgproc/test/test_floodfill.cpp +++ b/modules/imgproc/test/test_floodfill.cpp @@ -56,7 +56,7 @@ protected: void prepare_to_validation( int ); void fill_array( int test_case_idx, int i, int j, Mat& arr ); - + /*int write_default_params(CvFileStorage* fs); void get_timing_test_array_types_and_sizes( int test_case_idx, vector >& sizes, vector >& types CvSize** whole_sizes, bool *are_images ); @@ -94,7 +94,7 @@ void CV_FloodFillTest::get_test_array_types_and_sizes( int test_case_idx, RNG& rng = ts->get_rng(); int depth, cn; int i; - double buf[8]; + double buff[8]; cvtest::ArrayTest::get_test_array_types_and_sizes( test_case_idx, sizes, types ); depth = cvtest::randInt(rng) % 3; @@ -111,7 +111,7 @@ void CV_FloodFillTest::get_test_array_types_and_sizes( int test_case_idx, types[INPUT_OUTPUT][1] = types[REF_INPUT_OUTPUT][1] = CV_8UC1; types[OUTPUT][0] = types[REF_OUTPUT][0] = CV_64FC1; sizes[OUTPUT][0] = sizes[REF_OUTPUT][0] = cvSize(9,1); - + if( !use_mask ) sizes[INPUT_OUTPUT][1] = sizes[REF_INPUT_OUTPUT][1] = cvSize(0,0); else @@ -119,7 +119,7 @@ void CV_FloodFillTest::get_test_array_types_and_sizes( int test_case_idx, CvSize sz = sizes[INPUT_OUTPUT][0]; sizes[INPUT_OUTPUT][1] = sizes[REF_INPUT_OUTPUT][1] = cvSize(sz.width+2,sz.height+2); } - + seed_pt.x = cvtest::randInt(rng) % sizes[INPUT_OUTPUT][0].width; seed_pt.y = cvtest::randInt(rng) % sizes[INPUT_OUTPUT][0].height; @@ -127,7 +127,7 @@ void CV_FloodFillTest::get_test_array_types_and_sizes( int test_case_idx, l_diff = u_diff = Scalar::all(0.); else { - Mat m( 1, 8, CV_16S, buf ); + Mat m( 1, 8, CV_16S, buff ); rng.fill( m, RNG::NORMAL, Scalar::all(0), Scalar::all(32) ); for( i = 0; i < 4; i++ ) { @@ -139,7 +139,7 @@ void CV_FloodFillTest::get_test_array_types_and_sizes( int test_case_idx, new_val = Scalar::all(0.); for( i = 0; i < cn; i++ ) new_val.val[i] = cvtest::randReal(rng)*255; - + test_cpp = (cvtest::randInt(rng) & 256) == 0; } @@ -153,13 +153,13 @@ double CV_FloodFillTest::get_success_error_level( int /*test_case_idx*/, int i, void CV_FloodFillTest::fill_array( int test_case_idx, int i, int j, Mat& arr ) { RNG& rng = ts->get_rng(); - + if( i != INPUT && i != INPUT_OUTPUT ) { cvtest::ArrayTest::fill_array( test_case_idx, i, j, arr ); return; } - + if( j == 0 ) { Mat tmp = arr; @@ -191,7 +191,7 @@ void CV_FloodFillTest::run_func() int flags = connectivity + (mask_only ? CV_FLOODFILL_MASK_ONLY : 0) + (range_type == 1 ? CV_FLOODFILL_FIXED_RANGE : 0) + (new_mask_val << 8); double* odata = test_mat[OUTPUT][0].ptr(); - + if(!test_cpp) { CvConnectedComp comp; @@ -255,7 +255,7 @@ cvTsFloodFill( CvMat* _img, CvPoint seed_pt, CvScalar new_val, int cols = _img->cols, rows = _img->rows; int u0 = 0, u1 = 0, u2 = 0; double s0 = 0, s1 = 0, s2 = 0; - + if( CV_MAT_DEPTH(_img->type) == CV_8U || CV_MAT_DEPTH(_img->type) == CV_32S ) { tmp = cvCreateMat( rows, cols, CV_MAKETYPE(CV_32F,CV_MAT_CN(_img->type)) ); @@ -395,7 +395,7 @@ cvTsFloodFill( CvMat* _img, CvPoint seed_pt, CvScalar new_val, cvSeqPush( seq, &p ); } } - } + } } r.x = r.width = seed_pt.x; diff --git a/modules/imgproc/test/test_histograms.cpp b/modules/imgproc/test/test_histograms.cpp index 2f55a5f..3ac1e94b 100644 --- a/modules/imgproc/test/test_histograms.cpp +++ b/modules/imgproc/test/test_histograms.cpp @@ -59,7 +59,7 @@ protected: int prepare_test_case( int test_case_idx ); int validate_test_results( int test_case_idx ); virtual void init_hist( int test_case_idx, int i ); - + virtual void get_hist_params( int test_case_idx ); virtual float** get_hist_ranges( int test_case_idx ); @@ -73,7 +73,7 @@ protected: int uniform; int gen_random_hist; double gen_hist_max_val, gen_hist_sparse_nz_ratio; - + int init_ranges; int img_type; int img_max_log_size; @@ -127,7 +127,7 @@ int CV_BaseHistTest::read_params( CvFileStorage* fs ) max_log_size = cvtest::clipInt( max_log_size, 1, 20 ); img_max_log_size = cvReadInt( find_param( fs, "max_log_array_size" ), img_max_log_size ); img_max_log_size = cvtest::clipInt( img_max_log_size, 1, 9 ); - + max_cdims = cvReadInt( find_param( fs, "max_cdims" ), max_cdims ); max_cdims = cvtest::clipInt( max_cdims, 1, 6 ); @@ -146,13 +146,13 @@ void CV_BaseHistTest::get_hist_params( int /*test_case_idx*/ ) max_dim_size = cvRound(pow(hist_size,1./cdims)); total_size = 1; uniform = cvtest::randInt(rng) % 2; - hist_type = cvtest::randInt(rng) % 2 ? CV_HIST_SPARSE : CV_HIST_ARRAY; - + hist_type = cvtest::randInt(rng) % 2 ? CV_HIST_SPARSE : CV_HIST_ARRAY; + for( i = 0; i < cdims; i++ ) { dims[i] = cvtest::randInt(rng) % (max_dim_size + 2) + 2; if( !uniform ) - dims[i] = MIN(dims[i], max_ni_dim_size); + dims[i] = MIN(dims[i], max_ni_dim_size); total_size *= dims[i]; } @@ -178,12 +178,12 @@ void CV_BaseHistTest::get_hist_params( int /*test_case_idx*/ ) float** CV_BaseHistTest::get_hist_ranges( int /*test_case_idx*/ ) { double _low = low + range_delta, _high = high - range_delta; - + if( !init_ranges ) return 0; - + ranges.resize(cdims); - + if( uniform ) { _ranges.resize(cdims*2); @@ -200,7 +200,7 @@ float** CV_BaseHistTest::get_hist_ranges( int /*test_case_idx*/ ) for( i = 0; i < cdims; i++ ) dims_sum += dims[i] + 1; _ranges.resize(dims_sum); - + for( i = 0; i < cdims; i++ ) { int j, n = dims[i]; @@ -212,7 +212,7 @@ float** CV_BaseHistTest::get_hist_ranges( int /*test_case_idx*/ ) if( (pow(q,(double)n)-1)/(q-1.) >= _high-_low ) break; } - + if( j == 0 ) { delta = (_high-_low)/n; @@ -223,9 +223,9 @@ float** CV_BaseHistTest::get_hist_ranges( int /*test_case_idx*/ ) q = 1 + j*0.1; delta = cvFloor((_high-_low)*(q-1)/(pow(q,(double)n) - 1)); delta = MAX(delta, 1.); - } + } val = _low; - + for( j = 0; j <= n; j++ ) { _ranges[j+ofs] = (float)MIN(val,_high); @@ -236,7 +236,7 @@ float** CV_BaseHistTest::get_hist_ranges( int /*test_case_idx*/ ) ofs += n + 1; } } - + return &ranges[0]; } @@ -246,7 +246,7 @@ void CV_BaseHistTest::init_hist( int /*test_case_idx*/, int hist_i ) if( gen_random_hist ) { RNG& rng = ts->get_rng(); - + if( hist_type == CV_HIST_ARRAY ) { Mat h = cvarrToMat(hist[hist_i]->bins); @@ -255,13 +255,13 @@ void CV_BaseHistTest::init_hist( int /*test_case_idx*/, int hist_i ) else { CvArr* arr = hist[hist_i]->bins; - int i, j, total_size = 1, nz_count; + int i, j, totalSize = 1, nz_count; int idx[CV_MAX_DIM]; for( i = 0; i < cdims; i++ ) - total_size *= dims[i]; + totalSize *= dims[i]; - nz_count = cvtest::randInt(rng) % MAX( total_size/4, 100 ); - nz_count = MIN( nz_count, total_size ); + nz_count = cvtest::randInt(rng) % MAX( totalSize/4, 100 ); + nz_count = MIN( nz_count, totalSize ); // a zero number of non-zero elements should be allowed for( i = 0; i < nz_count; i++ ) @@ -286,7 +286,7 @@ int CV_BaseHistTest::prepare_test_case( int test_case_idx ) get_hist_params( test_case_idx ); r = get_hist_ranges( test_case_idx ); hist.resize(hist_count); - + for( i = 0; i < hist_count; i++ ) { hist[i] = cvCreateHist( cdims, dims, hist_type, r, uniform ); @@ -323,7 +323,7 @@ protected: int prepare_test_case( int test_case_idx ); int validate_test_results( int test_case_idx ); void init_hist( int test_case_idx, int i ); - + CvMat* indices; CvMat* values; CvMat* values0; @@ -376,7 +376,7 @@ int CV_QueryHistTest::prepare_test_case( int test_case_idx ) iters = (cvtest::randInt(rng) % MAX(total_size/10,100)) + 1; iters = MIN( iters, total_size*9/10 + 1 ); - + indices = cvCreateMat( 1, iters*cdims, CV_32S ); values = cvCreateMat( 1, iters, CV_32F ); values0 = cvCreateMat( 1, iters, CV_32F ); @@ -422,7 +422,7 @@ int CV_QueryHistTest::prepare_test_case( int test_case_idx ) if( GET_BIT(lin_idx) ) values0->data.fl[i] = (float)(lin_idx+1); } - + cvReleaseMat( &bit_mask ); } @@ -539,7 +539,7 @@ int CV_QueryHistTest::validate_test_results( int /*test_case_idx*/ ) { int code = cvtest::TS::OK; int i, j, iters = values->cols; - + for( i = 0; i < iters; i++ ) { float v = values->data.fl[i], v0 = values0->data.fl[i]; @@ -613,7 +613,7 @@ void CV_MinMaxHistTest::init_hist(int test_case_idx, int hist_i) } if( !eq || total_size == 1 ) break; - } + } min_val0 = (float)(-cvtest::randReal(rng)*10 - FLT_EPSILON); max_val0 = (float)(cvtest::randReal(rng)*10 + FLT_EPSILON + gen_hist_max_val); @@ -644,7 +644,7 @@ void CV_MinMaxHistTest::run_func(void) int CV_MinMaxHistTest::validate_test_results( int /*test_case_idx*/ ) { int code = cvtest::TS::OK; - + if( cvIsNaN(min_val) || cvIsInf(min_val) || cvIsNaN(max_val) || cvIsInf(max_val) ) { @@ -728,7 +728,7 @@ void CV_NormHistTest::run_func(void) if( hist_type != CV_HIST_ARRAY && test_cpp ) { cv::SparseMat h((CvSparseMat*)hist[0]->bins); - cv::normalize(h, h, factor, CV_L1); + cv::normalize(h, h, factor, CV_L1); cvReleaseSparseMat((CvSparseMat**)&hist[0]->bins); hist[0]->bins = (CvSparseMat*)h; } @@ -741,7 +741,7 @@ int CV_NormHistTest::validate_test_results( int /*test_case_idx*/ ) { int code = cvtest::TS::OK; double sum = 0; - + if( hist_type == CV_HIST_ARRAY ) { int i; @@ -755,7 +755,7 @@ int CV_NormHistTest::validate_test_results( int /*test_case_idx*/ ) CvSparseMat* sparse = (CvSparseMat*)hist[0]->bins; CvSparseMatIterator iterator; CvSparseNode *node; - + for( node = cvInitSparseMatIterator( sparse, &iterator ); node != 0; node = cvGetNextSparseNode( &iterator )) { @@ -839,7 +839,7 @@ int CV_ThreshHistTest::prepare_test_case( int test_case_idx ) if( hist_type == CV_HIST_ARRAY ) { orig_nz_count = total_size; - + values = cvCreateMat( 1, total_size, CV_32F ); memcpy( values->data.fl, cvPtr1D( hist[0]->bins, 0 ), total_size*sizeof(float) ); } @@ -859,7 +859,7 @@ int CV_ThreshHistTest::prepare_test_case( int test_case_idx ) node != 0; node = cvGetNextSparseNode( &iterator ), i++ ) { const int* idx = CV_NODE_IDX(sparse,node); - + OPENCV_ASSERT( i < orig_nz_count, "CV_ThreshHistTest::prepare_test_case", "Buffer overflow" ); values->data.fl[i] = *(float*)CV_NODE_VAL(sparse,node); @@ -924,7 +924,7 @@ int CV_ThreshHistTest::validate_test_results( int /*test_case_idx*/ ) } } } - + if( code > 0 && hist_type == CV_HIST_SPARSE ) { if( sparse->heap->active_count > 0 ) @@ -1003,7 +1003,7 @@ int CV_CompareHistTest::validate_test_results( int /*test_case_idx*/ ) { float* ptr0 = (float*)cvPtr1D( hist[0]->bins, 0 ); float* ptr1 = (float*)cvPtr1D( hist[1]->bins, 0 ); - + for( i = 0; i < total_size; i++ ) { double v0 = ptr0[i], v1 = ptr1[i]; @@ -1031,7 +1031,7 @@ int CV_CompareHistTest::validate_test_results( int /*test_case_idx*/ ) const int* idx = CV_NODE_IDX(sparse0, node); double v0 = *(float*)CV_NODE_VAL(sparse0, node); double v1 = (float)cvGetRealND(sparse1, idx); - + result0[CV_COMP_CORREL] += v0*v1; result0[CV_COMP_INTERSECT] += MIN(v0,v1); if( fabs(v0) > DBL_EPSILON ) @@ -1134,7 +1134,7 @@ CV_CalcHistTest::~CV_CalcHistTest() void CV_CalcHistTest::clear() { int i; - + for( i = 0; i <= CV_MAX_DIM; i++ ) cvReleaseImage( &images[i] ); @@ -1160,7 +1160,7 @@ int CV_CalcHistTest::prepare_test_case( int test_case_idx ) img_type == CV_8U ? IPL_DEPTH_8U : IPL_DEPTH_32F, nch ); channels[i] = cvtest::randInt(rng) % nch; Mat images_i = cvarrToMat(images[i]); - + cvtest::randUni( rng, images_i, Scalar::all(low), Scalar::all(high) ); } else if( i == CV_MAX_DIM && cvtest::randInt(rng) % 2 ) @@ -1168,7 +1168,7 @@ int CV_CalcHistTest::prepare_test_case( int test_case_idx ) // create mask images[i] = cvCreateImage( img_size, IPL_DEPTH_8U, 1 ); Mat images_i = cvarrToMat(images[i]); - + // make ~25% pixels in the mask non-zero cvtest::randUni( rng, images_i, Scalar::all(-2), Scalar::all(2) ); } @@ -1230,7 +1230,7 @@ cvTsCalcHist( IplImage** _images, CvHistogram* hist, IplImage* _mask, int* chann { float val[CV_MAX_DIM]; int idx[CV_MAX_DIM]; - + if( mptr && !mptr[x] ) continue; if( img_depth == IPL_DEPTH_8U ) @@ -1288,7 +1288,7 @@ int CV_CalcHistTest::validate_test_results( int /*test_case_idx*/ ) { ts->printf( cvtest::TS::LOG, "The histogram does not match to the reference one\n" ); code = cvtest::TS::FAIL_BAD_ACCURACY; - + } if( code < 0 ) @@ -1345,7 +1345,7 @@ CV_CalcBackProjectTest::~CV_CalcBackProjectTest() void CV_CalcBackProjectTest::clear() { int i; - + for( i = 0; i < CV_MAX_DIM+3; i++ ) cvReleaseImage( &images[i] ); @@ -1399,7 +1399,7 @@ int CV_CalcBackProjectTest::prepare_test_case( int test_case_idx ) { int idx = cvtest::randInt(rng) % img_len; double val = cvtest::randReal(rng)*(high - low) + low; - + if( img_type == CV_8U ) ((uchar*)data)[idx] = (uchar)cvRound(val); else @@ -1453,7 +1453,7 @@ cvTsCalcBackProject( IplImage** images, IplImage* dst, CvHistogram* hist, int* c float val[CV_MAX_DIM]; float bin_val = 0; int idx[CV_MAX_DIM]; - + if( img_depth == IPL_DEPTH_8U ) for( k = 0; k < cdims; k++ ) val[k] = plane[k].ptr[x*nch[k]]; @@ -1569,7 +1569,7 @@ CV_CalcBackProjectPatchTest::~CV_CalcBackProjectPatchTest() void CV_CalcBackProjectPatchTest::clear() { int i; - + for( i = 0; i < CV_MAX_DIM+2; i++ ) cvReleaseImage( &images[i] ); @@ -1627,7 +1627,7 @@ int CV_CalcBackProjectPatchTest::prepare_test_case( int test_case_idx ) { int idx = cvtest::randInt(rng) % img_len; double val = cvtest::randReal(rng)*(high - low) + low; - + if( img_type == CV_8U ) ((uchar*)data)[idx] = (uchar)cvRound(val); else @@ -1652,7 +1652,7 @@ cvTsCalcBackProjectPatch( IplImage** images, IplImage* dst, CvSize patch_size, double factor, int* channels ) { CvHistogram* model = 0; - + IplImage imgstub[CV_MAX_DIM], *img[CV_MAX_DIM]; IplROI roi; int i, dims; @@ -1679,7 +1679,7 @@ cvTsCalcBackProjectPatch( IplImage** images, IplImage* dst, CvSize patch_size, for( x = 0; x < size.width; x++ ) { double result; - + roi.xOffset = x; roi.yOffset = y; roi.width = patch_size.width; @@ -1703,7 +1703,7 @@ int CV_CalcBackProjectPatchTest::validate_test_results( int /*test_case_idx*/ ) cvTsCalcBackProjectPatch( images, images[CV_MAX_DIM+1], patch_size, hist[0], method, factor, channels ); - + Mat a = cvarrToMat(images[CV_MAX_DIM]), b = cvarrToMat(images[CV_MAX_DIM+1]); code = cvtest::cmpEps2( ts, a, b, err_level, true, "BackProjectPatch result" ); @@ -1756,7 +1756,7 @@ void CV_BayesianProbTest::init_hist( int test_case_idx, int hist_i ) int CV_BayesianProbTest::prepare_test_case( int test_case_idx ) { RNG& rng = ts->get_rng(); - + hist_count = (cvtest::randInt(rng) % (MAX_HIST/2-1) + 2)*2; hist_count = MIN( hist_count, MAX_HIST ); int code = CV_BaseHistTest::prepare_test_case( test_case_idx ); @@ -1833,5 +1833,5 @@ TEST(Imgproc_Hist_MinMaxVal, accuracy) { CV_MinMaxHistTest test; test.safe_run() TEST(Imgproc_Hist_CalcBackProject, accuracy) { CV_CalcBackProjectTest test; test.safe_run(); } TEST(Imgproc_Hist_CalcBackProjectPatch, accuracy) { CV_CalcBackProjectPatchTest test; test.safe_run(); } TEST(Imgproc_Hist_BayesianProb, accuracy) { CV_BayesianProbTest test; test.safe_run(); } - + /* End Of File */ diff --git a/modules/imgproc/test/test_imgwarp.cpp b/modules/imgproc/test/test_imgwarp.cpp index afc0bc5..c43ecd4 100644 --- a/modules/imgproc/test/test_imgwarp.cpp +++ b/modules/imgproc/test/test_imgwarp.cpp @@ -135,7 +135,7 @@ int CV_ImgWarpBaseTest::prepare_test_case( int test_case_idx ) if( test_mat[INPUT_OUTPUT][0].cols >= img.cols && test_mat[INPUT_OUTPUT][0].rows >= img.rows ) space_scale = spatial_scale_zoom; - + for( i = 0; i < img.rows; i++ ) { uchar* ptr = img.ptr(i); @@ -192,7 +192,7 @@ int CV_ImgWarpBaseTest::prepare_test_case( int test_case_idx ) }*/ cv::Mat src(1, cols*cn, CV_32F, &buffer[0]); cv::Mat dst(1, cols*cn, depth, ptr); - src.convertTo(dst, dst.type()); + src.convertTo(dst, dst.type()); } return code; @@ -279,7 +279,7 @@ void CV_ResizeTest::prepare_to_validation( int /*test_case_idx*/ ) CvMat* x_idx = cvCreateMat( 1, dst->cols, CV_32SC1 ); CvMat* y_idx = cvCreateMat( 1, dst->rows, CV_32SC1 ); int* x_tab = x_idx->data.i; - int elem_size = CV_ELEM_SIZE(src->type); + int elem_size = CV_ELEM_SIZE(src->type); int drows = dst->rows, dcols = dst->cols; if( interpolation == CV_INTER_NN ) @@ -302,7 +302,7 @@ void CV_ResizeTest::prepare_to_validation( int /*test_case_idx*/ ) { double scale_x = (double)src->cols/dcols; double scale_y = (double)src->rows/drows; - + for( j = 0; j < dcols; j++ ) { double f = ((j+0.5)*scale_x - 0.5); @@ -322,7 +322,7 @@ void CV_ResizeTest::prepare_to_validation( int /*test_case_idx*/ ) { uchar* dptr = dst->data.ptr + dst->step*i; const uchar* sptr0 = src->data.ptr + src->step*y_idx->data.i[i]; - + for( j = 0; j < dcols; j++, dptr += elem_size ) { const uchar* sptr = sptr0 + x_tab[j]; @@ -394,7 +394,7 @@ static void test_remap( const Mat& src, Mat& dst, const Mat& mapx, const Mat& ma xs -= ixs; ys -= iys; - + switch( depth ) { case CV_8U: @@ -508,7 +508,7 @@ int CV_WarpAffineTest::prepare_test_case( int test_case_idx ) RNG& rng = ts->get_rng(); int code = CV_ImgWarpBaseTest::prepare_test_case( test_case_idx ); const Mat& src = test_mat[INPUT][0]; - const Mat& dst = test_mat[INPUT_OUTPUT][0]; + const Mat& dst = test_mat[INPUT_OUTPUT][0]; Mat& mat = test_mat[INPUT][1]; CvPoint2D32f center; double scale, angle; @@ -516,8 +516,8 @@ int CV_WarpAffineTest::prepare_test_case( int test_case_idx ) if( code <= 0 ) return code; - double buf[6]; - Mat tmp( 2, 3, mat.type(), buf ); + double buffer[6]; + Mat tmp( 2, 3, mat.type(), buffer ); center.x = (float)((cvtest::randReal(rng)*1.2 - 0.1)*src.cols); center.y = (float)((cvtest::randReal(rng)*1.2 - 0.1)*src.rows); @@ -619,7 +619,7 @@ int CV_WarpPerspectiveTest::prepare_test_case( int test_case_idx ) RNG& rng = ts->get_rng(); int code = CV_ImgWarpBaseTest::prepare_test_case( test_case_idx ); const CvMat& src = test_mat[INPUT][0]; - const CvMat& dst = test_mat[INPUT_OUTPUT][0]; + const CvMat& dst = test_mat[INPUT_OUTPUT][0]; Mat& mat = test_mat[INPUT][1]; Point2f s[4], d[4]; int i; @@ -636,17 +636,17 @@ int CV_WarpPerspectiveTest::prepare_test_case( int test_case_idx ) s[3] = Point2f(0,src.rows-1.f); d[3] = Point2f(0,dst.rows-1.f); - float buf[16]; - Mat tmp( 1, 16, CV_32FC1, buf ); + float bufer[16]; + Mat tmp( 1, 16, CV_32FC1, bufer ); rng.fill( tmp, CV_RAND_NORMAL, Scalar::all(0.), Scalar::all(0.1) ); for( i = 0; i < 4; i++ ) { - s[i].x += buf[i*4]*src.cols/2; - s[i].y += buf[i*4+1]*src.rows/2; - d[i].x += buf[i*4+2]*dst.cols/2; - d[i].y += buf[i*4+3]*dst.rows/2; + s[i].x += bufer[i*4]*src.cols/2; + s[i].y += bufer[i*4+1]*src.rows/2; + d[i].x += bufer[i*4+2]*dst.cols/2; + d[i].y += bufer[i*4+3]*dst.rows/2; } cv::getPerspectiveTransform( s, d ).convertTo( mat, mat.depth() ); @@ -675,11 +675,11 @@ void CV_WarpPerspectiveTest::prepare_to_validation( int /*test_case_idx*/ ) double xs = x*m[0] + y*m[1] + m[2]; double ys = x*m[3] + y*m[4] + m[5]; double ds = x*m[6] + y*m[7] + m[8]; - + ds = ds ? 1./ds : 0; xs *= ds; ys *= ds; - + mapx.at(y, x) = (float)xs; mapy.at(y, x) = (float)ys; } @@ -806,15 +806,15 @@ protected: void fill_array( int test_case_idx, int i, int j, Mat& arr ); private: - bool useCPlus; - cv::Mat input0; - cv::Mat input1; - cv::Mat input2; - cv::Mat input_new_cam; - cv::Mat input_output; - - bool zero_new_cam; - bool zero_distortion; + bool useCPlus; + cv::Mat input0; + cv::Mat input1; + cv::Mat input2; + cv::Mat input_new_cam; + cv::Mat input_output; + + bool zero_new_cam; + bool zero_distortion; }; @@ -823,7 +823,7 @@ CV_UndistortTest::CV_UndistortTest() : CV_ImgWarpBaseTest( false ) //spatial_scale_zoom = spatial_scale_decimate; test_array[INPUT].push_back(NULL); test_array[INPUT].push_back(NULL); - test_array[INPUT].push_back(NULL); + test_array[INPUT].push_back(NULL); spatial_scale_decimate = spatial_scale_zoom; } @@ -834,14 +834,14 @@ void CV_UndistortTest::get_test_array_types_and_sizes( int test_case_idx, vector RNG& rng = ts->get_rng(); CV_ImgWarpBaseTest::get_test_array_types_and_sizes( test_case_idx, sizes, types ); int type = types[INPUT][0]; - type = CV_MAKETYPE( CV_8U, CV_MAT_CN(type) ); + type = CV_MAKETYPE( CV_8U, CV_MAT_CN(type) ); types[INPUT][0] = types[INPUT_OUTPUT][0] = types[REF_INPUT_OUTPUT][0] = type; types[INPUT][1] = cvtest::randInt(rng)%2 ? CV_64F : CV_32F; types[INPUT][2] = cvtest::randInt(rng)%2 ? CV_64F : CV_32F; sizes[INPUT][1] = cvSize(3,3); sizes[INPUT][2] = cvtest::randInt(rng)%2 ? cvSize(4,1) : cvSize(1,4); - types[INPUT][3] = types[INPUT][1]; - sizes[INPUT][3] = sizes[INPUT][1]; + types[INPUT][3] = types[INPUT][1]; + sizes[INPUT][3] = sizes[INPUT][1]; interpolation = CV_INTER_LINEAR; } @@ -855,22 +855,22 @@ void CV_UndistortTest::fill_array( int test_case_idx, int i, int j, Mat& arr ) void CV_UndistortTest::run_func() { - if (!useCPlus) - { + if (!useCPlus) + { CvMat a = test_mat[INPUT][1], k = test_mat[INPUT][2]; - cvUndistort2( test_array[INPUT][0], test_array[INPUT_OUTPUT][0], &a, &k); - } - else - { - if (zero_distortion) - { - cv::undistort(input0,input_output,input1,cv::Mat()); - } - else - { - cv::undistort(input0,input_output,input1,input2); - } - } + cvUndistort2( test_array[INPUT][0], test_array[INPUT_OUTPUT][0], &a, &k); + } + else + { + if (zero_distortion) + { + cv::undistort(input0,input_output,input1,cv::Mat()); + } + else + { + cv::undistort(input0,input_output,input1,input2); + } + } } @@ -888,10 +888,10 @@ int CV_UndistortTest::prepare_test_case( int test_case_idx ) const Mat& src = test_mat[INPUT][0]; double k[4], a[9] = {0,0,0,0,0,0,0,0,1}; - double new_cam[9] = {0,0,0,0,0,0,0,0,1}; + double new_cam[9] = {0,0,0,0,0,0,0,0,1}; double sz = MAX(src.rows, src.cols); - - Mat& _new_cam0 = test_mat[INPUT][3]; + + Mat& _new_cam0 = test_mat[INPUT][3]; Mat _new_cam(test_mat[INPUT][3].rows,test_mat[INPUT][3].cols,CV_64F,new_cam); Mat& _a0 = test_mat[INPUT][1]; Mat _a(3,3,CV_64F,a); @@ -925,21 +925,21 @@ int CV_UndistortTest::prepare_test_case( int test_case_idx ) _a.convertTo(_a0, _a0.depth()); - zero_distortion = (cvtest::randInt(rng)%2) == 0 ? false : true; + zero_distortion = (cvtest::randInt(rng)%2) == 0 ? false : true; _k.convertTo(_k0, _k0.depth()); - zero_new_cam = (cvtest::randInt(rng)%2) == 0 ? false : true; + zero_new_cam = (cvtest::randInt(rng)%2) == 0 ? false : true; _new_cam.convertTo(_new_cam0, _new_cam0.depth()); - //Testing C++ code - useCPlus = ((cvtest::randInt(rng) % 2)!=0); - if (useCPlus) - { - input0 = test_mat[INPUT][0]; - input1 = test_mat[INPUT][1]; - input2 = test_mat[INPUT][2]; - input_new_cam = test_mat[INPUT][3]; - } + //Testing C++ code + useCPlus = ((cvtest::randInt(rng) % 2)!=0); + if (useCPlus) + { + input0 = test_mat[INPUT][0]; + input1 = test_mat[INPUT][1]; + input2 = test_mat[INPUT][2]; + input_new_cam = test_mat[INPUT][3]; + } return code; } @@ -947,11 +947,11 @@ int CV_UndistortTest::prepare_test_case( int test_case_idx ) void CV_UndistortTest::prepare_to_validation( int /*test_case_idx*/ ) { - if (useCPlus) - { + if (useCPlus) + { Mat& output = test_mat[INPUT_OUTPUT][0]; input_output.convertTo(output, output.type()); - } + } Mat& src = test_mat[INPUT][0]; Mat& dst = test_mat[REF_INPUT_OUTPUT][0]; Mat& dst0 = test_mat[INPUT_OUTPUT][0]; @@ -978,7 +978,7 @@ protected: void fill_array( int test_case_idx, int i, int j, Mat& arr ); private: - bool dualChannel; + bool dualChannel; }; @@ -1003,8 +1003,8 @@ void CV_UndistortMapTest::get_test_array_types_and_sizes( int test_case_idx, vec CvSize sz = sizes[OUTPUT][0]; types[INPUT][0] = types[INPUT][1] = depth; - dualChannel = cvtest::randInt(rng)%2 == 0; - types[OUTPUT][0] = types[OUTPUT][1] = + dualChannel = cvtest::randInt(rng)%2 == 0; + types[OUTPUT][0] = types[OUTPUT][1] = types[REF_OUTPUT][0] = types[REF_OUTPUT][1] = dualChannel ? CV_32FC2 : CV_32F; sizes[INPUT][0] = cvSize(3,3); sizes[INPUT][1] = cvtest::randInt(rng)%2 ? cvSize(4,1) : cvSize(1,4); @@ -1026,11 +1026,11 @@ void CV_UndistortMapTest::fill_array( int test_case_idx, int i, int j, Mat& arr void CV_UndistortMapTest::run_func() { CvMat a = test_mat[INPUT][0], k = test_mat[INPUT][1]; - - if (!dualChannel ) - cvInitUndistortMap( &a, &k, test_array[OUTPUT][0], test_array[OUTPUT][1] ); - else - cvInitUndistortMap( &a, &k, test_array[OUTPUT][0], 0 ); + + if (!dualChannel ) + cvInitUndistortMap( &a, &k, test_array[OUTPUT][0], test_array[OUTPUT][1] ); + else + cvInitUndistortMap( &a, &k, test_array[OUTPUT][0], 0 ); } @@ -1069,11 +1069,11 @@ int CV_UndistortMapTest::prepare_test_case( int test_case_idx ) _a.convertTo(_a0, _a0.depth()); _k.convertTo(_k0, _k0.depth()); - if (dualChannel) - { + if (dualChannel) + { test_mat[REF_OUTPUT][1] = Scalar::all(0); - test_mat[OUTPUT][1] = Scalar::all(0); - } + test_mat[OUTPUT][1] = Scalar::all(0); + } return code; } @@ -1102,7 +1102,7 @@ test_getQuadrangeSubPix( const Mat& src, Mat& dst, double* a ) { int sstep = (int)(src.step / sizeof(float)); int scols = src.cols, srows = src.rows; - + CV_Assert( src.depth() == CV_32F && src.type() == dst.type() ); int cn = dst.channels(); @@ -1167,11 +1167,11 @@ void CV_GetRectSubPixTest::get_test_array_types_and_sizes( int test_case_idx, ve int src_depth = cvtest::randInt(rng) % 2, dst_depth; int cn = cvtest::randInt(rng) % 2 ? 3 : 1; CvSize src_size, dst_size; - + dst_depth = src_depth = src_depth == 0 ? CV_8U : CV_32F; if( src_depth < CV_32F && cvtest::randInt(rng) % 2 ) dst_depth = CV_32F; - + types[INPUT][0] = CV_MAKETYPE(src_depth,cn); types[INPUT_OUTPUT][0] = types[REF_INPUT_OUTPUT][0] = CV_MAKETYPE(dst_depth,cn); @@ -1181,11 +1181,11 @@ void CV_GetRectSubPixTest::get_test_array_types_and_sizes( int test_case_idx, ve dst_size.width = MIN(dst_size.width,src_size.width); dst_size.height = MIN(dst_size.width,src_size.height); sizes[INPUT_OUTPUT][0] = sizes[REF_INPUT_OUTPUT][0] = dst_size; - + center.x = (float)(cvtest::randReal(rng)*src_size.width); center.y = (float)(cvtest::randReal(rng)*src_size.height); interpolation = CV_INTER_LINEAR; - + test_cpp = (cvtest::randInt(rng) & 256) == 0; } @@ -1274,11 +1274,11 @@ void CV_GetQuadSubPixTest::get_test_array_types_and_sizes( int test_case_idx, ve RNG& rng = ts->get_rng(); int msz, src_depth = cvtest::randInt(rng) % 2, dst_depth; int cn = cvtest::randInt(rng) % 2 ? 3 : 1; - + dst_depth = src_depth = src_depth == 0 ? CV_8U : CV_32F; if( src_depth < CV_32F && cvtest::randInt(rng) % 2 ) dst_depth = CV_32F; - + types[INPUT][0] = CV_MAKETYPE(src_depth,cn); types[INPUT_OUTPUT][0] = types[REF_INPUT_OUTPUT][0] = CV_MAKETYPE(dst_depth,cn); @@ -1333,7 +1333,7 @@ int CV_GetQuadSubPixTest::prepare_test_case( int test_case_idx ) center.y = (float)((cvtest::randReal(rng)*1.2 - 0.1)*src.rows); angle = cvtest::randReal(rng)*360; scale = cvtest::randReal(rng)*0.2 + 0.9; - + // y = Ax + b -> x = A^-1(y - b) = A^-1*y - A^-1*b scale = 1./scale; angle = angle*(CV_PI/180.); @@ -1413,7 +1413,7 @@ TEST(Imgproc_fitLine_vector_2d, regression) points_vector.push_back(p21); points_vector.push_back(p22); - points_vector.push_back(p23); + points_vector.push_back(p23); std::vector line; diff --git a/modules/imgproc/test/test_thresh.cpp b/modules/imgproc/test/test_thresh.cpp index 0b8d381..80f06b8 100644 --- a/modules/imgproc/test/test_thresh.cpp +++ b/modules/imgproc/test/test_thresh.cpp @@ -91,7 +91,8 @@ void CV_ThreshTest::get_test_array_types_and_sizes( int test_case_idx, } else if( depth == CV_16S ) { - float min_val = SHRT_MIN-100.f, max_val = SHRT_MAX+100.f; + float min_val = SHRT_MIN-100.f; + max_val = SHRT_MAX+100.f; thresh_val = (float)(cvtest::randReal(rng)*(max_val - min_val) + min_val); max_val = (float)(cvtest::randReal(rng)*(max_val - min_val) + min_val); if( cvtest::randInt(rng)%4 == 0 ) diff --git a/modules/legacy/include/opencv2/legacy/legacy.hpp b/modules/legacy/include/opencv2/legacy/legacy.hpp index 3911ebd..88e47ed 100644 --- a/modules/legacy/include/opencv2/legacy/legacy.hpp +++ b/modules/legacy/include/opencv2/legacy/legacy.hpp @@ -1347,9 +1347,9 @@ class CV_EXPORTS CvImage { public: CvImage() : image(0), refcount(0) {} - CvImage( CvSize size, int depth, int channels ) + CvImage( CvSize _size, int _depth, int _channels ) { - image = cvCreateImage( size, depth, channels ); + image = cvCreateImage( _size, _depth, _channels ); refcount = image ? new int(1) : 0; } @@ -1383,12 +1383,12 @@ public: CvImage clone() { return CvImage(image ? cvCloneImage(image) : 0); } - void create( CvSize size, int depth, int channels ) + void create( CvSize _size, int _depth, int _channels ) { if( !image || !refcount || - image->width != size.width || image->height != size.height || - image->depth != depth || image->nChannels != channels ) - attach( cvCreateImage( size, depth, channels )); + image->width != _size.width || image->height != _size.height || + image->depth != _depth || image->nChannels != _channels ) + attach( cvCreateImage( _size, _depth, _channels )); } void release() { detach(); } @@ -1447,9 +1447,9 @@ public: int coi() const { return !image || !image->roi ? 0 : image->roi->coi; } - void set_roi(CvRect roi) { cvSetImageROI(image,roi); } + void set_roi(CvRect _roi) { cvSetImageROI(image,_roi); } void reset_roi() { cvResetImageROI(image); } - void set_coi(int coi) { cvSetImageCOI(image,coi); } + void set_coi(int _coi) { cvSetImageCOI(image,_coi); } int depth() const { return image ? image->depth : 0; } int channels() const { return image ? image->nChannels : 0; } int pix_size() const { return image ? ((image->depth & 255)>>3)*image->nChannels : 0; } @@ -1511,18 +1511,18 @@ class CV_EXPORTS CvMatrix { public: CvMatrix() : matrix(0) {} - CvMatrix( int rows, int cols, int type ) - { matrix = cvCreateMat( rows, cols, type ); } + CvMatrix( int _rows, int _cols, int _type ) + { matrix = cvCreateMat( _rows, _cols, _type ); } - CvMatrix( int rows, int cols, int type, CvMat* hdr, - void* data=0, int step=CV_AUTOSTEP ) - { matrix = cvInitMatHeader( hdr, rows, cols, type, data, step ); } + CvMatrix( int _rows, int _cols, int _type, CvMat* hdr, + void* _data=0, int _step=CV_AUTOSTEP ) + { matrix = cvInitMatHeader( hdr, _rows, _cols, _type, _data, _step ); } CvMatrix( int rows, int cols, int type, CvMemStorage* storage, bool alloc_data=true ); - CvMatrix( int rows, int cols, int type, void* data, int step=CV_AUTOSTEP ) - { matrix = cvCreateMatHeader( rows, cols, type ); - cvSetData( matrix, data, step ); } + CvMatrix( int _rows, int _cols, int _type, void* _data, int _step=CV_AUTOSTEP ) + { matrix = cvCreateMatHeader( _rows, _cols, _type ); + cvSetData( matrix, _data, _step ); } CvMatrix( CvMat* m ) { matrix = m; } @@ -1557,12 +1557,12 @@ public: addref(); } - void create( int rows, int cols, int type ) + void create( int _rows, int _cols, int _type ) { if( !matrix || !matrix->refcount || - matrix->rows != rows || matrix->cols != cols || - CV_MAT_TYPE(matrix->type) != type ) - set( cvCreateMat( rows, cols, type ), false ); + matrix->rows != _rows || matrix->cols != _cols || + CV_MAT_TYPE(matrix->type) != _type ) + set( cvCreateMat( _rows, _cols, _type ), false ); } void addref() const @@ -1626,8 +1626,8 @@ public: const uchar* data() const { return matrix ? matrix->data.ptr : 0; } int step() const { return matrix ? matrix->step : 0; } - void set_data( void* data, int step=CV_AUTOSTEP ) - { cvSetData( matrix, data, step ); } + void set_data( void* _data, int _step=CV_AUTOSTEP ) + { cvSetData( matrix, _data, _step ); } uchar* row(int i) { return !matrix ? 0 : matrix->data.ptr + i*matrix->step; } const uchar* row(int i) const @@ -2014,8 +2014,8 @@ struct CV_EXPORTS BaseKeypoint : x(0), y(0), image(NULL) {} - BaseKeypoint(int x, int y, IplImage* image) - : x(x), y(y), image(image) + BaseKeypoint(int _x, int _y, IplImage* _image) + : x(_x), y(_y), image(_image) {} }; diff --git a/modules/legacy/src/blobtrackanalysistrackdist.cpp b/modules/legacy/src/blobtrackanalysistrackdist.cpp index 3c8a346..be34ab1 100644 --- a/modules/legacy/src/blobtrackanalysistrackdist.cpp +++ b/modules/legacy/src/blobtrackanalysistrackdist.cpp @@ -350,11 +350,10 @@ public: virtual void Process(IplImage* pImg, IplImage* /*pFG*/) { - int i; double MinTv = pImg->width/1440.0; /* minimal threshold for speed difference */ double MinTv2 = MinTv*MinTv; - for(i=m_Tracks.GetBlobNum(); i>0; --i) + for(int i=m_Tracks.GetBlobNum(); i>0; --i) { DefTrackForDist* pF = (DefTrackForDist*)m_Tracks.GetBlob(i-1); pF->state = 0; @@ -466,14 +465,13 @@ public: if(m_Wnd) { /* Debug output: */ - int i; if(m_pDebugImg==NULL) m_pDebugImg = cvCloneImage(pImg); else cvCopy(pImg, m_pDebugImg); - for(i=m_TrackDataBase.GetBlobNum(); i>0; --i) + for(int i=m_TrackDataBase.GetBlobNum(); i>0; --i) { /* Draw all elements in track data base: */ int j; DefTrackForDist* pF = (DefTrackForDist*)m_TrackDataBase.GetBlob(i-1); @@ -497,7 +495,7 @@ public: pF->close = 0; } /* Draw all elements in track data base. */ - for(i=m_Tracks.GetBlobNum(); i>0; --i) + for(int i=m_Tracks.GetBlobNum(); i>0; --i) { /* Draw all elements for all trajectories: */ DefTrackForDist* pF = (DefTrackForDist*)m_Tracks.GetBlob(i-1); int j; diff --git a/modules/legacy/src/blobtrackingcc.cpp b/modules/legacy/src/blobtrackingcc.cpp index 753628d..c2279a2 100644 --- a/modules/legacy/src/blobtrackingcc.cpp +++ b/modules/legacy/src/blobtrackingcc.cpp @@ -301,8 +301,8 @@ public: { /* Find a neighbour on current frame * for each blob from previous frame: */ - CvBlob* pB = m_BlobList.GetBlob(i-1); - DefBlobTracker* pBT = (DefBlobTracker*)pB; + CvBlob* pBl = m_BlobList.GetBlob(i-1); + DefBlobTracker* pBT = (DefBlobTracker*)pBl; //int BlobID = CV_BLOB_ID(pB); //CvBlob* pBBest = NULL; //double DistBest = -1; diff --git a/modules/legacy/src/kdtree.cpp b/modules/legacy/src/kdtree.cpp index 1e9abf7..63426dd 100644 --- a/modules/legacy/src/kdtree.cpp +++ b/modules/legacy/src/kdtree.cpp @@ -93,18 +93,19 @@ class CvKDTreeWrap : public CvFeatureTree { assert(results->cols == k); assert(dist->cols == k); - for (int j = 0; j < d->rows; ++j) { - const typename __treetype::scalar_type* dj = - (const typename __treetype::scalar_type*) dptr; + for (int j = 0; j < d->rows; ++j) + { + const typename __treetype::scalar_type* dj = (const typename __treetype::scalar_type*) dptr; int* resultsj = (int*) resultsptr; double* distj = (double*) distptr; tr->find_nn_bbf(dj, k, emax, nn); assert((int)nn.size() <= k); - for (unsigned int j = 0; j < nn.size(); ++j) { - *resultsj++ = *nn[j].p; - *distj++ = nn[j].dist; + for (unsigned int i = 0; i < nn.size(); ++i) + { + *resultsj++ = *nn[i].p; + *distj++ = nn[i].dist; } std::fill(resultsj, resultsj + k - nn.size(), -1); std::fill(distj, distj + k - nn.size(), 0); diff --git a/modules/ml/include/opencv2/ml/ml.hpp b/modules/ml/include/opencv2/ml/ml.hpp index a38d044..790aea9 100644 --- a/modules/ml/include/opencv2/ml/ml.hpp +++ b/modules/ml/include/opencv2/ml/ml.hpp @@ -170,12 +170,7 @@ struct CV_EXPORTS_W_MAP CvParamGrid min_val = max_val = step = 0; } - CvParamGrid( double min_val, double max_val, double log_step ) - { - this->min_val = min_val; - this->max_val = max_val; - step = log_step; - } + CvParamGrid( double min_val, double max_val, double log_step ); //CvParamGrid( int param_id ); bool check() const; @@ -184,6 +179,13 @@ struct CV_EXPORTS_W_MAP CvParamGrid CV_PROP_RW double step; }; +inline CvParamGrid::CvParamGrid( double _min_val, double _max_val, double _log_step ) +{ + min_val = _min_val; + max_val = _max_val; + step = _log_step; +} + class CV_EXPORTS_W CvNormalBayesClassifier : public CvStatModel { public: @@ -192,10 +194,10 @@ public: CvNormalBayesClassifier( const CvMat* trainData, const CvMat* responses, const CvMat* varIdx=0, const CvMat* sampleIdx=0 ); - + virtual bool train( const CvMat* trainData, const CvMat* responses, const CvMat* varIdx = 0, const CvMat* sampleIdx=0, bool update=false ); - + virtual float predict( const CvMat* samples, CV_OUT CvMat* results=0 ) const; CV_WRAP virtual void clear(); @@ -207,7 +209,7 @@ public: bool update=false ); CV_WRAP virtual float predict( const cv::Mat& samples, CV_OUT cv::Mat* results=0 ) const; #endif - + virtual void write( CvFileStorage* storage, const char* name ) const; virtual void read( CvFileStorage* storage, CvFileNode* node ); @@ -243,31 +245,31 @@ public: virtual bool train( const CvMat* trainData, const CvMat* responses, const CvMat* sampleIdx=0, bool is_regression=false, int maxK=32, bool updateBase=false ); - + virtual float find_nearest( const CvMat* samples, int k, CV_OUT CvMat* results=0, const float** neighbors=0, CV_OUT CvMat* neighborResponses=0, CV_OUT CvMat* dist=0 ) const; - + #ifndef SWIG CV_WRAP CvKNearest( const cv::Mat& trainData, const cv::Mat& responses, const cv::Mat& sampleIdx=cv::Mat(), bool isRegression=false, int max_k=32 ); - + CV_WRAP virtual bool train( const cv::Mat& trainData, const cv::Mat& responses, const cv::Mat& sampleIdx=cv::Mat(), bool isRegression=false, - int maxK=32, bool updateBase=false ); - + int maxK=32, bool updateBase=false ); + virtual float find_nearest( const cv::Mat& samples, int k, cv::Mat* results=0, const float** neighbors=0, cv::Mat* neighborResponses=0, cv::Mat* dist=0 ) const; CV_WRAP virtual float find_nearest( const cv::Mat& samples, int k, CV_OUT cv::Mat& results, CV_OUT cv::Mat& neighborResponses, CV_OUT cv::Mat& dists) const; #endif - + virtual void clear(); int get_max_k() const; int get_var_count() const; int get_sample_count() const; bool is_regression() const; - + virtual float write_results( int k, int k1, int start, int end, const float* neighbor_responses, const float* dist, CvMat* _results, CvMat* _neighbor_responses, CvMat* _dist, Cv32suf* sort_buf ) const; @@ -473,7 +475,7 @@ public: virtual bool train( const CvMat* trainData, const CvMat* responses, const CvMat* varIdx=0, const CvMat* sampleIdx=0, CvSVMParams params=CvSVMParams() ); - + virtual bool train_auto( const CvMat* trainData, const CvMat* responses, const CvMat* varIdx, const CvMat* sampleIdx, CvSVMParams params, int kfold = 10, @@ -487,16 +489,16 @@ public: virtual float predict( const CvMat* sample, bool returnDFVal=false ) const; virtual float predict( const CvMat* samples, CvMat* results ) const; - + #ifndef SWIG CV_WRAP CvSVM( const cv::Mat& trainData, const cv::Mat& responses, const cv::Mat& varIdx=cv::Mat(), const cv::Mat& sampleIdx=cv::Mat(), CvSVMParams params=CvSVMParams() ); - + CV_WRAP virtual bool train( const cv::Mat& trainData, const cv::Mat& responses, const cv::Mat& varIdx=cv::Mat(), const cv::Mat& sampleIdx=cv::Mat(), CvSVMParams params=CvSVMParams() ); - + CV_WRAP virtual bool train_auto( const cv::Mat& trainData, const cv::Mat& responses, const cv::Mat& varIdx, const cv::Mat& sampleIdx, CvSVMParams params, int k_fold = 10, @@ -509,7 +511,7 @@ public: bool balanced=false); CV_WRAP virtual float predict( const cv::Mat& sample, bool returnDFVal=false ) const; #endif - + CV_WRAP virtual int get_support_vector_count() const; virtual const float* get_support_vector(int i) const; virtual CvSVMParams get_params() const { return params; }; @@ -564,14 +566,14 @@ public: // Default parameters enum {DEFAULT_NCLUSTERS=5, DEFAULT_MAX_ITERS=100}; - + // The initial step enum {START_E_STEP=1, START_M_STEP=2, START_AUTO_STEP=0}; CV_WRAP EM(int nclusters=EM::DEFAULT_NCLUSTERS, int covMatType=EM::COV_MAT_DIAGONAL, const TermCriteria& termCrit=TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, EM::DEFAULT_MAX_ITERS, FLT_EPSILON)); - + virtual ~EM(); CV_WRAP virtual void clear(); @@ -579,7 +581,7 @@ public: OutputArray logLikelihoods=noArray(), OutputArray labels=noArray(), OutputArray probs=noArray()); - + CV_WRAP virtual bool trainE(InputArray samples, InputArray means0, InputArray covs0=noArray(), @@ -587,13 +589,13 @@ public: OutputArray logLikelihoods=noArray(), OutputArray labels=noArray(), OutputArray probs=noArray()); - + CV_WRAP virtual bool trainM(InputArray samples, InputArray probs0, OutputArray logLikelihoods=noArray(), OutputArray labels=noArray(), OutputArray probs=noArray()); - + CV_WRAP Vec2d predict(InputArray sample, OutputArray probs=noArray()) const; @@ -603,7 +605,7 @@ public: virtual void read(const FileNode& fn); protected: - + virtual void setTrainData(int startStep, const Mat& samples, const Mat* probs0, const Mat* means0, @@ -802,7 +804,7 @@ struct CV_EXPORTS CvDTreeTrainData int buf_count, buf_size; bool shared; int is_buf_16u; - + CvMat* cat_count; CvMat* cat_ofs; CvMat* cat_map; @@ -871,12 +873,12 @@ public: const cv::Mat& sampleIdx=cv::Mat(), const cv::Mat& varType=cv::Mat(), const cv::Mat& missingDataMask=cv::Mat(), CvDTreeParams params=CvDTreeParams() ); - + CV_WRAP virtual CvDTreeNode* predict( const cv::Mat& sample, const cv::Mat& missingDataMask=cv::Mat(), bool preprocessedInput=false ) const; CV_WRAP virtual cv::Mat getVarImportance(); #endif - + virtual const CvMat* get_var_importance(); CV_WRAP virtual void clear(); @@ -900,13 +902,13 @@ protected: virtual void try_split_node( CvDTreeNode* n ); virtual void split_node_data( CvDTreeNode* n ); virtual CvDTreeSplit* find_best_split( CvDTreeNode* n ); - virtual CvDTreeSplit* find_split_ord_class( CvDTreeNode* n, int vi, + virtual CvDTreeSplit* find_split_ord_class( CvDTreeNode* n, int vi, float init_quality = 0, CvDTreeSplit* _split = 0, uchar* ext_buf = 0 ); virtual CvDTreeSplit* find_split_cat_class( CvDTreeNode* n, int vi, float init_quality = 0, CvDTreeSplit* _split = 0, uchar* ext_buf = 0 ); - virtual CvDTreeSplit* find_split_ord_reg( CvDTreeNode* n, int vi, + virtual CvDTreeSplit* find_split_ord_reg( CvDTreeNode* n, int vi, float init_quality = 0, CvDTreeSplit* _split = 0, uchar* ext_buf = 0 ); - virtual CvDTreeSplit* find_split_cat_reg( CvDTreeNode* n, int vi, + virtual CvDTreeSplit* find_split_cat_reg( CvDTreeNode* n, int vi, float init_quality = 0, CvDTreeSplit* _split = 0, uchar* ext_buf = 0 ); virtual CvDTreeSplit* find_surrogate_split_ord( CvDTreeNode* n, int vi, uchar* ext_buf = 0 ); virtual CvDTreeSplit* find_surrogate_split_cat( CvDTreeNode* n, int vi, uchar* ext_buf = 0 ); @@ -1003,7 +1005,7 @@ public: const CvMat* sampleIdx=0, const CvMat* varType=0, const CvMat* missingDataMask=0, CvRTParams params=CvRTParams() ); - + virtual bool train( CvMLData* data, CvRTParams params=CvRTParams() ); virtual float predict( const CvMat* sample, const CvMat* missing = 0 ) const; virtual float predict_prob( const CvMat* sample, const CvMat* missing = 0 ) const; @@ -1018,16 +1020,16 @@ public: CV_WRAP virtual float predict_prob( const cv::Mat& sample, const cv::Mat& missing = cv::Mat() ) const; CV_WRAP virtual cv::Mat getVarImportance(); #endif - + CV_WRAP virtual void clear(); virtual const CvMat* get_var_importance(); virtual float get_proximity( const CvMat* sample1, const CvMat* sample2, const CvMat* missing1 = 0, const CvMat* missing2 = 0 ) const; - + virtual float calc_error( CvMLData* data, int type , std::vector* resp = 0 ); // type in {CV_TRAIN_ERROR, CV_TEST_ERROR} - virtual float get_train_error(); + virtual float get_train_error(); virtual void read( CvFileStorage* fs, CvFileNode* node ); virtual void write( CvFileStorage* fs, const char* name ) const; @@ -1083,13 +1085,13 @@ class CV_EXPORTS CvForestERTree : public CvForestTree { protected: virtual double calc_node_dir( CvDTreeNode* node ); - virtual CvDTreeSplit* find_split_ord_class( CvDTreeNode* n, int vi, + virtual CvDTreeSplit* find_split_ord_class( CvDTreeNode* n, int vi, float init_quality = 0, CvDTreeSplit* _split = 0, uchar* ext_buf = 0 ); virtual CvDTreeSplit* find_split_cat_class( CvDTreeNode* n, int vi, float init_quality = 0, CvDTreeSplit* _split = 0, uchar* ext_buf = 0 ); - virtual CvDTreeSplit* find_split_ord_reg( CvDTreeNode* n, int vi, + virtual CvDTreeSplit* find_split_ord_reg( CvDTreeNode* n, int vi, float init_quality = 0, CvDTreeSplit* _split = 0, uchar* ext_buf = 0 ); - virtual CvDTreeSplit* find_split_cat_reg( CvDTreeNode* n, int vi, + virtual CvDTreeSplit* find_split_cat_reg( CvDTreeNode* n, int vi, float init_quality = 0, CvDTreeSplit* _split = 0, uchar* ext_buf = 0 ); virtual void split_node_data( CvDTreeNode* n ); }; @@ -1169,13 +1171,13 @@ protected: virtual void try_split_node( CvDTreeNode* n ); virtual CvDTreeSplit* find_surrogate_split_ord( CvDTreeNode* n, int vi, uchar* ext_buf = 0 ); virtual CvDTreeSplit* find_surrogate_split_cat( CvDTreeNode* n, int vi, uchar* ext_buf = 0 ); - virtual CvDTreeSplit* find_split_ord_class( CvDTreeNode* n, int vi, + virtual CvDTreeSplit* find_split_ord_class( CvDTreeNode* n, int vi, float init_quality = 0, CvDTreeSplit* _split = 0, uchar* ext_buf = 0 ); virtual CvDTreeSplit* find_split_cat_class( CvDTreeNode* n, int vi, float init_quality = 0, CvDTreeSplit* _split = 0, uchar* ext_buf = 0 ); - virtual CvDTreeSplit* find_split_ord_reg( CvDTreeNode* n, int vi, + virtual CvDTreeSplit* find_split_ord_reg( CvDTreeNode* n, int vi, float init_quality = 0, CvDTreeSplit* _split = 0, uchar* ext_buf = 0 ); - virtual CvDTreeSplit* find_split_cat_reg( CvDTreeNode* n, int vi, + virtual CvDTreeSplit* find_split_cat_reg( CvDTreeNode* n, int vi, float init_quality = 0, CvDTreeSplit* _split = 0, uchar* ext_buf = 0 ); virtual void calc_node_value( CvDTreeNode* n ); virtual double calc_node_dir( CvDTreeNode* n ); @@ -1201,14 +1203,14 @@ public: const CvMat* sampleIdx=0, const CvMat* varType=0, const CvMat* missingDataMask=0, CvBoostParams params=CvBoostParams() ); - + virtual bool train( const CvMat* trainData, int tflag, const CvMat* responses, const CvMat* varIdx=0, const CvMat* sampleIdx=0, const CvMat* varType=0, const CvMat* missingDataMask=0, CvBoostParams params=CvBoostParams(), bool update=false ); - + virtual bool train( CvMLData* data, CvBoostParams params=CvBoostParams(), bool update=false ); @@ -1223,19 +1225,19 @@ public: const cv::Mat& sampleIdx=cv::Mat(), const cv::Mat& varType=cv::Mat(), const cv::Mat& missingDataMask=cv::Mat(), CvBoostParams params=CvBoostParams() ); - + CV_WRAP virtual bool train( const cv::Mat& trainData, int tflag, const cv::Mat& responses, const cv::Mat& varIdx=cv::Mat(), const cv::Mat& sampleIdx=cv::Mat(), const cv::Mat& varType=cv::Mat(), const cv::Mat& missingDataMask=cv::Mat(), CvBoostParams params=CvBoostParams(), bool update=false ); - + CV_WRAP virtual float predict( const cv::Mat& sample, const cv::Mat& missing=cv::Mat(), const cv::Range& slice=cv::Range::all(), bool rawMode=false, bool returnSum=false ) const; #endif - + virtual float calc_error( CvMLData* _data, int type , std::vector *resp = 0 ); // type in {CV_TRAIN_ERROR, CV_TEST_ERROR} CV_WRAP virtual void prune( CvSlice slice ); @@ -1315,7 +1317,7 @@ struct CV_EXPORTS_W_MAP CvGBTreesParams : public CvDTreeParams // DataType: CLASS CvGBTrees // Gradient Boosting Trees (GBT) algorithm implementation. -// +// // data - training dataset // params - parameters of the CvGBTrees // weak - array[0..(class_count-1)] of CvSeq @@ -1347,7 +1349,7 @@ struct CV_EXPORTS_W_MAP CvGBTreesParams : public CvDTreeParams // missing - mask of the missing values in the training set. This // matrix has the same size as train_data. 1 - missing // value, 0 - not a missing value. -// class_labels - output class labels map. +// class_labels - output class labels map. // rng - random number generator. Used for spliting the // training set. // class_count - count of output classes. @@ -1368,15 +1370,15 @@ public: /* // DataType: ENUM // Loss functions implemented in CvGBTrees. - // + // // SQUARED_LOSS // problem: regression // loss = (x - x')^2 - // + // // ABSOLUTE_LOSS // problem: regression // loss = abs(x - x') - // + // // HUBER_LOSS // problem: regression // loss = delta*( abs(x - x') - delta/2), if abs(x - x') > delta @@ -1386,18 +1388,18 @@ public: // // DEVIANCE_LOSS // problem: classification - // - */ + // + */ enum {SQUARED_LOSS=0, ABSOLUTE_LOSS, HUBER_LOSS=3, DEVIANCE_LOSS}; - - + + /* // Default constructor. Creates a model only (without training). // Should be followed by one form of the train(...) function. // // API // CvGBTrees(); - + // INPUT // OUTPUT // RESULT @@ -1415,7 +1417,7 @@ public: const CvMat* sampleIdx=0, const CvMat* varType=0, const CvMat* missingDataMask=0, CvGBTreesParams params=CvGBTreesParams() ); - + // INPUT // trainData - a set of input feature vectors. // size of matrix is @@ -1448,13 +1450,13 @@ public: const CvMat* missingDataMask=0, CvGBTreesParams params=CvGBTreesParams() ); - + /* // Destructor. */ virtual ~CvGBTrees(); - - + + /* // Gradient tree boosting model training // @@ -1465,7 +1467,7 @@ public: const CvMat* missingDataMask=0, CvGBTreesParams params=CvGBTreesParams(), bool update=false ); - + // INPUT // trainData - a set of input feature vectors. // size of matrix is @@ -1500,8 +1502,8 @@ public: const CvMat* missingDataMask=0, CvGBTreesParams params=CvGBTreesParams(), bool update=false ); - - + + /* // Gradient tree boosting model training // @@ -1509,7 +1511,7 @@ public: // virtual bool train( CvMLData* data, CvGBTreesParams params=CvGBTreesParams(), bool update=false ) {return false;}; - + // INPUT // data - training set. // params - parameters of GTB algorithm. @@ -1522,7 +1524,7 @@ public: CvGBTreesParams params=CvGBTreesParams(), bool update=false ); - + /* // Response value prediction // @@ -1530,7 +1532,7 @@ public: // virtual float predict_serial( const CvMat* sample, const CvMat* missing=0, CvMat* weak_responses=0, CvSlice slice = CV_WHOLE_SEQ, int k=-1 ) const; - + // INPUT // sample - input sample of the same type as in the training set. // missing - missing values mask. missing=0 if there are no @@ -1541,7 +1543,7 @@ public: // slice = CV_WHOLE_SEQ when all trees are used. // k - number of ensemble used. // k is in {-1,0,1,..,}. - // in the case of classification problem + // in the case of classification problem // ensembles are built. // If k = -1 ordinary prediction is the result, // otherwise function gives the prediction of the @@ -1553,7 +1555,7 @@ public: virtual float predict_serial( const CvMat* sample, const CvMat* missing=0, CvMat* weakResponses=0, CvSlice slice = CV_WHOLE_SEQ, int k=-1 ) const; - + /* // Response value prediction. // Parallel version (in the case of TBB existence) @@ -1562,7 +1564,7 @@ public: // virtual float predict( const CvMat* sample, const CvMat* missing=0, CvMat* weak_responses=0, CvSlice slice = CV_WHOLE_SEQ, int k=-1 ) const; - + // INPUT // sample - input sample of the same type as in the training set. // missing - missing values mask. missing=0 if there are no @@ -1573,7 +1575,7 @@ public: // slice = CV_WHOLE_SEQ when all trees are used. // k - number of ensemble used. // k is in {-1,0,1,..,}. - // in the case of classification problem + // in the case of classification problem // ensembles are built. // If k = -1 ordinary prediction is the result, // otherwise function gives the prediction of the @@ -1581,7 +1583,7 @@ public: // OUTPUT // RESULT // Predicted value. - */ + */ virtual float predict( const CvMat* sample, const CvMat* missing=0, CvMat* weakResponses=0, CvSlice slice = CV_WHOLE_SEQ, int k=-1 ) const; @@ -1591,7 +1593,7 @@ public: // // API // virtual void clear(); - + // INPUT // OUTPUT // delete data, weak, orig_response, sum_response, @@ -1622,7 +1624,7 @@ public: std::vector *resp = 0 ); /* - // + // // Write parameters of the gtb model and data. Write learned model. // // API @@ -1638,7 +1640,7 @@ public: /* - // + // // Read parameters of the gtb model and data. Read learned model. // // API @@ -1652,14 +1654,14 @@ public: */ virtual void read( CvFileStorage* fs, CvFileNode* node ); - + // new-style C++ interface CV_WRAP CvGBTrees( const cv::Mat& trainData, int tflag, const cv::Mat& responses, const cv::Mat& varIdx=cv::Mat(), const cv::Mat& sampleIdx=cv::Mat(), const cv::Mat& varType=cv::Mat(), const cv::Mat& missingDataMask=cv::Mat(), CvGBTreesParams params=CvGBTreesParams() ); - + CV_WRAP virtual bool train( const cv::Mat& trainData, int tflag, const cv::Mat& responses, const cv::Mat& varIdx=cv::Mat(), const cv::Mat& sampleIdx=cv::Mat(), const cv::Mat& varType=cv::Mat(), @@ -1670,7 +1672,7 @@ public: CV_WRAP virtual float predict( const cv::Mat& sample, const cv::Mat& missing=cv::Mat(), const cv::Range& slice = cv::Range::all(), int k=-1 ) const; - + protected: /* @@ -1678,7 +1680,7 @@ protected: // // API // virtual void find_gradient( const int k = 0); - + // INPUT // k - used for classification problem, determining current // tree ensemble. @@ -1690,9 +1692,9 @@ protected: */ virtual void find_gradient( const int k = 0); - + /* - // + // // Change values in tree leaves according to the used loss function. // // API @@ -1711,7 +1713,7 @@ protected: /* - // + // // Find optimal constant prediction value according to the used loss // function. // The goal is to find a constant which gives the minimal summary loss @@ -1728,9 +1730,9 @@ protected: */ virtual float find_optimal_value( const CvMat* _Idx ); - + /* - // + // // Randomly split the whole training set in two parts according // to params.portion. // @@ -1747,7 +1749,7 @@ protected: /* - // + // // Internal recursive function giving an array of subtree tree leaves. // // API @@ -1761,10 +1763,10 @@ protected: // RESULT */ void leaves_get( CvDTreeNode** leaves, int& count, CvDTreeNode* node ); - - + + /* - // + // // Get leaves of the tree. // // API @@ -1779,9 +1781,9 @@ protected: */ CvDTreeNode** GetLeaves( const CvDTree* dtree, int& len ); - + /* - // + // // Is it a regression or a classification. // // API @@ -1797,7 +1799,7 @@ protected: /* - // + // // Write parameters of the gtb model. // // API @@ -1812,7 +1814,7 @@ protected: /* - // + // // Read parameters of the gtb model and data. // // API @@ -1829,9 +1831,9 @@ protected: // RESULT */ virtual void read_params( CvFileStorage* fs, CvFileNode* fnode ); - int get_len(const CvMat* mat) const; + int get_len(const CvMat* mat) const; + - CvDTreeTrainData* data; CvGBTreesParams params; @@ -1894,30 +1896,30 @@ public: virtual void create( const CvMat* layerSizes, int activateFunc=CvANN_MLP::SIGMOID_SYM, double fparam1=0, double fparam2=0 ); - + virtual int train( const CvMat* inputs, const CvMat* outputs, const CvMat* sampleWeights, const CvMat* sampleIdx=0, CvANN_MLP_TrainParams params = CvANN_MLP_TrainParams(), int flags=0 ); virtual float predict( const CvMat* inputs, CV_OUT CvMat* outputs ) const; - + #ifndef SWIG CV_WRAP CvANN_MLP( const cv::Mat& layerSizes, int activateFunc=CvANN_MLP::SIGMOID_SYM, double fparam1=0, double fparam2=0 ); - + CV_WRAP virtual void create( const cv::Mat& layerSizes, int activateFunc=CvANN_MLP::SIGMOID_SYM, - double fparam1=0, double fparam2=0 ); - + double fparam1=0, double fparam2=0 ); + CV_WRAP virtual int train( const cv::Mat& inputs, const cv::Mat& outputs, const cv::Mat& sampleWeights, const cv::Mat& sampleIdx=cv::Mat(), CvANN_MLP_TrainParams params = CvANN_MLP_TrainParams(), - int flags=0 ); - + int flags=0 ); + CV_WRAP virtual float predict( const cv::Mat& inputs, CV_OUT cv::Mat& outputs ) const; #endif - + CV_WRAP virtual void clear(); // possible activation functions @@ -2031,7 +2033,7 @@ public: virtual ~CvMLData(); // returns: - // 0 - OK + // 0 - OK // -1 - file can not be opened or is not correct int read_csv( const char* filename ); @@ -2039,8 +2041,8 @@ public: const CvMat* get_responses(); const CvMat* get_missing() const; - void set_header_lines_number( int n ); - int get_header_lines_number() const; + void set_header_lines_number( int n ); + int get_header_lines_number() const; void set_response_idx( int idx ); // old response become predictors, new response_idx = idx // if idx < 0 there will be no response @@ -2050,7 +2052,7 @@ public: const CvMat* get_train_sample_idx() const; const CvMat* get_test_sample_idx() const; void mix_train_and_test_idx(); - + const CvMat* get_var_idx(); void chahge_var_idx( int vi, bool state ); // misspelled (saved for back compitability), // use change_var_idx @@ -2064,14 +2066,14 @@ public: void set_var_types( const char* str ); // str examples: // "ord[0-17],cat[18]", "ord[0,2,4,10-12], cat[1,3,5-9,13,14]", // "cat", "ord" (all vars are categorical/ordered) - void change_var_type( int var_idx, int type); // type in { CV_VAR_ORDERED, CV_VAR_CATEGORICAL } - + void change_var_type( int var_idx, int type); // type in { CV_VAR_ORDERED, CV_VAR_CATEGORICAL } + void set_delimiter( char ch ); char get_delimiter() const; void set_miss_ch( char ch ); char get_miss_ch() const; - + const std::map& get_class_labels_map() const; protected: @@ -2079,7 +2081,7 @@ protected: void str_to_flt_elem( const char* token, float& flt_elem, int& type); void free_train_test_idx(); - + char delimiter; char miss_ch; //char flt_separator; @@ -2093,13 +2095,13 @@ protected: CvMat* var_idx_out; // mat CvMat* var_types_out; // mat - int header_lines_number; + int header_lines_number; int response_idx; int train_sample_count; bool mix; - + int total_class_count; std::map class_map; @@ -2113,7 +2115,7 @@ protected: namespace cv { - + typedef CvStatModel StatModel; typedef CvParamGrid ParamGrid; typedef CvNormalBayesClassifier NormalBayesClassifier; @@ -2142,7 +2144,7 @@ typedef CvGBTrees GradientBoostingTrees; template<> CV_EXPORTS void Ptr::delete_obj(); CV_EXPORTS bool initModule_ml(void); - + } #endif // __cplusplus diff --git a/modules/ml/src/ann_mlp.cpp b/modules/ml/src/ann_mlp.cpp index 0b9db71..438872a 100644 --- a/modules/ml/src/ann_mlp.cpp +++ b/modules/ml/src/ann_mlp.cpp @@ -504,7 +504,7 @@ void CvANN_MLP::calc_activ_func_deriv( CvMat* _xf, CvMat* _df, n *= cols; xf -= n; df -= n; - + for( i = 0; i < n; i++ ) df[i] *= xf[i]; } @@ -517,7 +517,7 @@ void CvANN_MLP::calc_activ_func_deriv( CvMat* _xf, CvMat* _df, xf[j] = (xf[j] + bias[j])*scale; df[j] = -fabs(xf[j]); } - + cvExp( _df, _df ); n *= cols; @@ -1023,9 +1023,9 @@ int CvANN_MLP::train_backprop( CvVectors x0, CvVectors u, const double* sw ) } struct rprop_loop { - rprop_loop(const CvANN_MLP* _point, double**& _weights, int& _count, int& _ivcount, CvVectors* _x0, + rprop_loop(const CvANN_MLP* _point, double**& _weights, int& _count, int& _ivcount, CvVectors* _x0, int& _l_count, CvMat*& _layer_sizes, int& _ovcount, int& _max_count, - CvVectors* _u, const double*& _sw, double& _inv_count, CvMat*& _dEdw, int& _dcount0, double* _E, int _buf_sz) + CvVectors* _u, const double*& _sw, double& _inv_count, CvMat*& _dEdw, int& _dcount0, double* _E, int _buf_sz) { point = _point; weights = _weights; @@ -1044,7 +1044,7 @@ struct rprop_loop { E = _E; buf_sz = _buf_sz; } - + const CvANN_MLP* point; double** weights; int count; @@ -1062,14 +1062,14 @@ struct rprop_loop { double* E; int buf_sz; - + void operator()( const cv::BlockedRange& range ) const { double* buf_ptr; double** x = 0; - double **df = 0; + double **df = 0; int total = 0; - + for(int i = 0; i < l_count; i++ ) total += layer_sizes->data.i[i]; CvMat* buf; @@ -1087,7 +1087,7 @@ struct rprop_loop { for(int si = range.begin(); si < range.end(); si++ ) { if (si % dcount0 != 0) continue; - int n1, n2, j, k; + int n1, n2, k; double* w; CvMat _w, _dEdw, hdr1, hdr2, ghdr1, ghdr2, _df; CvMat *x1, *x2, *grad1, *grad2, *temp; @@ -1100,23 +1100,23 @@ struct rprop_loop { // grab and preprocess input data if( x0->type == CV_32F ) - { + { for(int i = 0; i < dcount; i++ ) { const float* x0data = x0->data.fl[si+i]; double* xdata = x[0]+i*ivcount; - for( j = 0; j < ivcount; j++ ) + for(int j = 0; j < ivcount; j++ ) xdata[j] = x0data[j]*w[j*2] + w[j*2+1]; } - } + } else for(int i = 0; i < dcount; i++ ) { const double* x0data = x0->data.db[si+i]; double* xdata = x[0]+i*ivcount; - for( j = 0; j < ivcount; j++ ) + for(int j = 0; j < ivcount; j++ ) xdata[j] = x0data[j]*w[j*2] + w[j*2+1]; - } + } cvInitMatHeader( x1, dcount, ivcount, CV_64F, x[0] ); // forward pass, compute y[i]=w*x[i-1], x[i]=f(y[i]), df[i]=f'(y[i]) @@ -1144,7 +1144,7 @@ struct rprop_loop { double* gdata = grad1->data.db + i*ovcount; double sweight = sw ? sw[si+i] : inv_count, E1 = 0; - for( j = 0; j < ovcount; j++ ) + for(int j = 0; j < ovcount; j++ ) { double t = udata[j]*w[j*2] + w[j*2+1] - xdata[j]; gdata[j] = t*sweight; @@ -1168,7 +1168,7 @@ struct rprop_loop { } *E += sweight*E1; } - + // backward pass, update dEdw #ifdef HAVE_TBB static tbb::spin_mutex mutex; @@ -1191,10 +1191,10 @@ struct rprop_loop { { double* dst = _dEdw.data.db + n1*n2; const double* src = grad1->data.db + k*n2; - for( j = 0; j < n2; j++ ) + for(int j = 0; j < n2; j++ ) dst[j] += src[j]; } - + if (i > 1) cvInitMatHeader( &_w, n1, n2, CV_64F, weights[i] ); #ifdef HAVE_TBB @@ -1215,7 +1215,7 @@ struct rprop_loop { int CvANN_MLP::train_rprop( CvVectors x0, CvVectors u, const double* sw ) { - const int max_buf_sz = 1 << 16; + const int max_buf_size = 1 << 16; CvMat* dw = 0; CvMat* dEdw = 0; CvMat* prev_dEdw_sign = 0; @@ -1256,7 +1256,7 @@ int CvANN_MLP::train_rprop( CvVectors x0, CvVectors u, const double* sw ) cvZero( prev_dEdw_sign ); inv_count = 1./count; - dcount0 = max_buf_sz/(2*total); + dcount0 = max_buf_size/(2*total); dcount0 = MAX( dcount0, 1 ); dcount0 = MIN( dcount0, count ); buf_sz = dcount0*(total + max_count)*2; @@ -1297,8 +1297,8 @@ int CvANN_MLP::train_rprop( CvVectors x0, CvVectors u, const double* sw ) double E = 0; // first, iterate through all the samples and compute dEdw - cv::parallel_for(cv::BlockedRange(0, count), - rprop_loop(this, weights, count, ivcount, &x0, l_count, layer_sizes, + cv::parallel_for(cv::BlockedRange(0, count), + rprop_loop(this, weights, count, ivcount, &x0, l_count, layer_sizes, ovcount, max_count, &u, sw, inv_count, dEdw, dcount0, &E, buf_sz) ); @@ -1600,8 +1600,8 @@ CvANN_MLP::CvANN_MLP( const Mat& _layer_sizes, int _activ_func, void CvANN_MLP::create( const Mat& _layer_sizes, int _activ_func, double _f_param1, double _f_param2 ) { - CvMat layer_sizes = _layer_sizes; - create( &layer_sizes, _activ_func, _f_param1, _f_param2 ); + CvMat cvlayer_sizes = _layer_sizes; + create( &cvlayer_sizes, _activ_func, _f_param1, _f_param2 ); } int CvANN_MLP::train( const Mat& _inputs, const Mat& _outputs, @@ -1610,7 +1610,7 @@ int CvANN_MLP::train( const Mat& _inputs, const Mat& _outputs, { CvMat inputs = _inputs, outputs = _outputs, sweights = _sample_weights, sidx = _sample_idx; return train(&inputs, &outputs, sweights.data.ptr ? &sweights : 0, - sidx.data.ptr ? &sidx : 0, _params, flags); + sidx.data.ptr ? &sidx : 0, _params, flags); } float CvANN_MLP::predict( const Mat& _inputs, Mat& _outputs ) const @@ -1618,8 +1618,8 @@ float CvANN_MLP::predict( const Mat& _inputs, Mat& _outputs ) const CV_Assert(layer_sizes != 0); _outputs.create(_inputs.rows, layer_sizes->data.i[layer_sizes->cols-1], _inputs.type()); CvMat inputs = _inputs, outputs = _outputs; - - return predict(&inputs, &outputs); + + return predict(&inputs, &outputs); } /* End of file. */ diff --git a/modules/ml/src/boost.cpp b/modules/ml/src/boost.cpp index dec8c96..447a998 100644 --- a/modules/ml/src/boost.cpp +++ b/modules/ml/src/boost.cpp @@ -129,7 +129,7 @@ CvBoostTree::train( CvDTreeTrainData*, const CvMat* ) void -CvBoostTree::scale( double scale ) +CvBoostTree::scale( double _scale ) { CvDTreeNode* node = root; @@ -139,7 +139,7 @@ CvBoostTree::scale( double scale ) CvDTreeNode* parent; for(;;) { - node->value *= scale; + node->value *= _scale; if( !node->left ) break; node = node->left; @@ -501,7 +501,7 @@ CvBoostTree::find_split_ord_reg( CvDTreeNode* node, int vi, float init_quality, int i, best_i = -1; double L = 0, R = weights[n]; double best_val = init_quality, lsum = 0, rsum = node->value*R; - + // compensate for missing values for( i = n1; i < n; i++ ) { @@ -590,7 +590,7 @@ CvBoostTree::find_split_cat_reg( CvDTreeNode* node, int vi, float init_quality, { R += counts[i]; rsum += sum[i]; - sum[i] = fabs(counts[i]) > DBL_EPSILON ? sum[i]/counts[i] : 0; + sum[i] = fabs(counts[i]) > DBL_EPSILON ? sum[i]/counts[i] : 0; sum_ptr[i] = sum + i; } @@ -1030,7 +1030,7 @@ CvBoost::train( const CvMat* _train_data, int _tflag, __BEGIN__; int i; - + set_params( _params ); cvReleaseMat( &active_vars ); @@ -1057,7 +1057,7 @@ CvBoost::train( const CvMat* _train_data, int _tflag, if ( (_params.boost_type == LOGIT) || (_params.boost_type == GENTLE) ) data->do_responses_copy(); - + update_weights( 0 ); for( i = 0; i < params.weak_count; i++ ) @@ -1088,7 +1088,7 @@ CvBoost::train( const CvMat* _train_data, int _tflag, } bool CvBoost::train( CvMLData* _data, - CvBoostParams params, + CvBoostParams _params, bool update ) { bool result = false; @@ -1105,7 +1105,7 @@ bool CvBoost::train( CvMLData* _data, const CvMat* var_idx = _data->get_var_idx(); CV_CALL( result = train( values, CV_ROW_SAMPLE, response, var_idx, - train_sidx, var_types, missing, params, update ) ); + train_sidx, var_types, missing, _params, update ) ); __END__; @@ -1258,7 +1258,7 @@ CvBoost::update_weights( CvBoostTree* tree ) // invert the subsample mask cvXorS( subsample_mask, cvScalar(1.), subsample_mask ); data->get_vectors( subsample_mask, values, missing, 0 ); - + _sample = cvMat( 1, data->var_count, CV_32F ); _mask = cvMat( 1, data->var_count, CV_8U ); @@ -1458,17 +1458,17 @@ CvBoost::trim_weights() } -const CvMat* +const CvMat* CvBoost::get_active_vars( bool absolute_idx ) { CvMat* mask = 0; CvMat* inv_map = 0; CvMat* result = 0; - + CV_FUNCNAME( "CvBoost::get_active_vars" ); __BEGIN__; - + if( !weak ) CV_ERROR( CV_StsError, "The boosted tree ensemble has not been trained yet" ); @@ -1478,7 +1478,7 @@ CvBoost::get_active_vars( bool absolute_idx ) int i, j, nactive_vars; CvBoostTree* wtree; const CvDTreeNode* node; - + assert(!active_vars && !active_vars_abs); mask = cvCreateMat( 1, data->var_count, CV_8U ); inv_map = cvCreateMat( 1, data->var_count, CV_32S ); @@ -1518,7 +1518,7 @@ CvBoost::get_active_vars( bool absolute_idx ) } nactive_vars = cvCountNonZero(mask); - + //if ( nactive_vars > 0 ) { active_vars = cvCreateMat( 1, nactive_vars, CV_32S ); @@ -1538,7 +1538,7 @@ CvBoost::get_active_vars( bool absolute_idx ) j++; } } - + // second pass: now compute the condensed indices cvStartReadSeq( weak, &reader ); @@ -1638,7 +1638,7 @@ CvBoost::predict( const CvMat* _sample, const CvMat* _missing, "floating-point vector of the same number of components as the length of input slice" ); wstep = CV_IS_MAT_CONT(weak_responses->type) ? 1 : weak_responses->step/sizeof(float); } - + int var_count = active_vars->cols; const int* vtype = data->var_type->data.i; const int* cmap = data->cat_map->data.i; @@ -1738,7 +1738,7 @@ CvBoost::predict( const CvMat* _sample, const CvMat* _missing, CvBoostTree* wtree; const CvDTreeNode* node; CV_READ_SEQ_ELEM( wtree, reader ); - + node = wtree->get_root(); while( node->left ) { @@ -1757,14 +1757,14 @@ CvBoost::predict( const CvMat* _sample, const CvMat* _missing, { const int* avars = active_vars->data.i; const uchar* m = _missing ? _missing->data.ptr : 0; - + // full-featured version for( i = 0; i < weak_count; i++ ) { CvBoostTree* wtree; const CvDTreeNode* node; CV_READ_SEQ_ELEM( wtree, reader ); - + node = wtree->get_root(); while( node->left ) { @@ -1841,9 +1841,9 @@ float CvBoost::calc_error( CvMLData* _data, int type, std::vector *resp ) { CvMat sample, miss; int si = sidx ? sidx[i] : i; - cvGetRow( values, &sample, si ); - if( missing ) - cvGetRow( missing, &miss, si ); + cvGetRow( values, &sample, si ); + if( missing ) + cvGetRow( missing, &miss, si ); float r = (float)predict( &sample, missing ? &miss : 0 ); if( pred_resp ) pred_resp[i] = r; @@ -1859,15 +1859,15 @@ float CvBoost::calc_error( CvMLData* _data, int type, std::vector *resp ) CvMat sample, miss; int si = sidx ? sidx[i] : i; cvGetRow( values, &sample, si ); - if( missing ) - cvGetRow( missing, &miss, si ); + if( missing ) + cvGetRow( missing, &miss, si ); float r = (float)predict( &sample, missing ? &miss : 0 ); if( pred_resp ) pred_resp[i] = r; float d = r - response->data.fl[si*r_step]; err += d*d; } - err = sample_count ? err / (float)sample_count : -FLT_MAX; + err = sample_count ? err / (float)sample_count : -FLT_MAX; } return err; } @@ -2097,10 +2097,10 @@ CvBoost::CvBoost( const Mat& _train_data, int _tflag, default_model_name = "my_boost_tree"; active_vars = active_vars_abs = orig_response = sum_response = weak_eval = subsample_mask = weights = subtree_weights = 0; - + train( _train_data, _tflag, _responses, _var_idx, _sample_idx, _var_type, _missing_mask, _params ); -} +} bool @@ -2130,7 +2130,7 @@ CvBoost::predict( const Mat& _sample, const Mat& _missing, weak_count = weak->total; slice.start_index = 0; } - + if( !(weak_responses->data && weak_responses->type() == CV_32FC1 && (weak_responses->cols == 1 || weak_responses->rows == 1) && weak_responses->cols + weak_responses->rows - 1 == weak_count) ) diff --git a/modules/ml/src/ertrees.cpp b/modules/ml/src/ertrees.cpp index 0460527..8a3828d 100644 --- a/modules/ml/src/ertrees.cpp +++ b/modules/ml/src/ertrees.cpp @@ -71,7 +71,7 @@ void CvERTreeTrainData::set_data( const CvMat* _train_data, int _tflag, CV_FUNCNAME( "CvERTreeTrainData::set_data" ); __BEGIN__; - + int sample_all = 0, r_type, cv_n; int total_c_count = 0; int tree_block_size, temp_block_size, max_split_size, nv_size, cv_size = 0; @@ -79,10 +79,10 @@ void CvERTreeTrainData::set_data( const CvMat* _train_data, int _tflag, int vi, i, size; char err[100]; const int *sidx = 0, *vidx = 0; - + if ( _params.use_surrogates ) CV_ERROR(CV_StsBadArg, "CvERTrees do not support surrogate splits"); - + if( _update_data && data_root ) { CV_ERROR(CV_StsBadArg, "CvERTrees do not support data update"); @@ -143,17 +143,17 @@ void CvERTreeTrainData::set_data( const CvMat* _train_data, int _tflag, CV_ERROR( CV_StsBadArg, "The array of _responses must be an integer or " "floating-point vector containing as many elements as " "the total number of samples in the training data matrix" ); - + is_buf_16u = false; - if ( sample_count < 65536 ) - is_buf_16u = true; - + if ( sample_count < 65536 ) + is_buf_16u = true; + r_type = CV_VAR_CATEGORICAL; if( _var_type ) CV_CALL( var_type0 = cvPreprocessVarType( _var_type, var_idx, var_count, &r_type )); CV_CALL( var_type = cvCreateMat( 1, var_count+2, CV_32SC1 )); - + cat_var_count = 0; ord_var_count = -1; @@ -182,7 +182,7 @@ void CvERTreeTrainData::set_data( const CvMat* _train_data, int _tflag, buf_size = (work_var_count + 1)*sample_count; shared = _shared; buf_count = shared ? 2 : 1; - + if ( is_buf_16u ) { CV_CALL( buf = cvCreateMat( buf_count, buf_size, CV_16UC1 )); @@ -192,13 +192,13 @@ void CvERTreeTrainData::set_data( const CvMat* _train_data, int _tflag, { CV_CALL( buf = cvCreateMat( buf_count, buf_size, CV_32SC1 )); CV_CALL( int_ptr = (int**)cvAlloc( sample_count*sizeof(int_ptr[0]) )); - } + } size = is_classifier ? cat_var_count+1 : cat_var_count; size = !size ? 1 : size; CV_CALL( cat_count = cvCreateMat( 1, size, CV_32SC1 )); CV_CALL( cat_ofs = cvCreateMat( 1, size, CV_32SC1 )); - + size = is_classifier ? (cat_var_count + 1)*params.max_categories : cat_var_count*params.max_categories; size = !size ? 1 : size; CV_CALL( cat_map = cvCreateMat( 1, size, CV_32SC1 )); @@ -283,12 +283,12 @@ void CvERTreeTrainData::set_data( const CvMat* _train_data, int _tflag, { int c_count, prev_label; int* c_map; - + if (is_buf_16u) udst = (unsigned short*)(buf->data.s + ci*sample_count); else idst = buf->data.i + ci*sample_count; - + // copy data for( i = 0; i < sample_count; i++ ) { @@ -322,7 +322,7 @@ void CvERTreeTrainData::set_data( const CvMat* _train_data, int _tflag, _idst[i] = val; pair16u32s_ptr[i].u = udst + i; pair16u32s_ptr[i].i = _idst + i; - } + } else { idst[i] = val; @@ -397,7 +397,7 @@ void CvERTreeTrainData::set_data( const CvMat* _train_data, int _tflag, // replace labels for missing values with -1 for( ; i < sample_count; i++ ) *int_ptr[i] = -1; - } + } } else if( ci < 0 ) // process ordered variable { @@ -442,15 +442,15 @@ void CvERTreeTrainData::set_data( const CvMat* _train_data, int _tflag, if( cv_n ) { - unsigned short* udst = 0; - int* idst = 0; + unsigned short* usdst = 0; + int* idst2 = 0; if (is_buf_16u) { - udst = (unsigned short*)(buf->data.s + (get_work_var_count()-1)*sample_count); + usdst = (unsigned short*)(buf->data.s + (get_work_var_count()-1)*sample_count); for( i = vi = 0; i < sample_count; i++ ) { - udst[i] = (unsigned short)vi++; + usdst[i] = (unsigned short)vi++; vi &= vi < cv_n ? -1 : 0; } @@ -459,15 +459,15 @@ void CvERTreeTrainData::set_data( const CvMat* _train_data, int _tflag, int a = (*rng)(sample_count); int b = (*rng)(sample_count); unsigned short unsh = (unsigned short)vi; - CV_SWAP( udst[a], udst[b], unsh ); + CV_SWAP( usdst[a], usdst[b], unsh ); } } else { - idst = buf->data.i + (get_work_var_count()-1)*sample_count; + idst2 = buf->data.i + (get_work_var_count()-1)*sample_count; for( i = vi = 0; i < sample_count; i++ ) { - idst[i] = vi++; + idst2[i] = vi++; vi &= vi < cv_n ? -1 : 0; } @@ -475,12 +475,12 @@ void CvERTreeTrainData::set_data( const CvMat* _train_data, int _tflag, { int a = (*rng)(sample_count); int b = (*rng)(sample_count); - CV_SWAP( idst[a], idst[b], vi ); + CV_SWAP( idst2[a], idst2[b], vi ); } } } - if ( cat_map ) + if ( cat_map ) cat_map->cols = MAX( total_c_count, 1 ); max_split_size = cvAlign(sizeof(CvDTreeSplit) + @@ -532,7 +532,7 @@ void CvERTreeTrainData::get_ord_var_data( CvDTreeNode* n, int vi, float* ord_val const float** ord_values, const int** missing, int* sample_indices_buf ) { int vidx = var_idx ? var_idx->data.i[vi] : vi; - int node_sample_count = n->sample_count; + int node_sample_count = n->sample_count; // may use missing_buf as buffer for sample indices! const int* sample_indices = get_sample_indices(n, sample_indices_buf ? sample_indices_buf : missing_buf); @@ -580,7 +580,7 @@ const int* CvERTreeTrainData::get_cat_var_data( CvDTreeNode* n, int vi, int* cat if( !is_buf_16u ) cat_values = buf->data.i + n->buf_idx*buf->cols + ci*sample_count + n->offset; else { - const unsigned short* short_values = (const unsigned short*)(buf->data.s + n->buf_idx*buf->cols + + const unsigned short* short_values = (const unsigned short*)(buf->data.s + n->buf_idx*buf->cols + ci*sample_count + n->offset); for( int i = 0; i < n->sample_count; i++ ) cat_values_buf[i] = short_values[i]; @@ -591,7 +591,7 @@ const int* CvERTreeTrainData::get_cat_var_data( CvDTreeNode* n, int vi, int* cat void CvERTreeTrainData::get_vectors( const CvMat* _subsample_idx, float* values, uchar* missing, - float* responses, bool get_class_idx ) + float* _responses, bool get_class_idx ) { CvMat* subsample_idx = 0; CvMat* subsample_co = 0; @@ -664,7 +664,7 @@ void CvERTreeTrainData::get_vectors( const CvMat* _subsample_idx, } // copy responses - if( responses ) + if( _responses ) { if( is_classifier ) { @@ -675,10 +675,10 @@ void CvERTreeTrainData::get_vectors( const CvMat* _subsample_idx, int idx = sidx ? sidx[i] : i; int val = get_class_idx ? src[idx] : cat_map->data.i[cat_ofs->data.i[cat_var_count]+src[idx]]; - responses[i] = (float)val; + _responses[i] = (float)val; } } - else + else { float* _values_buf = (float*)(uchar*)inn_buf; int* sample_idx_buf = (int*)(_values_buf + sample_count); @@ -686,7 +686,7 @@ void CvERTreeTrainData::get_vectors( const CvMat* _subsample_idx, for( i = 0; i < count; i++ ) { int idx = sidx ? sidx[i] : i; - responses[i] = _values[idx]; + _responses[i] = _values[idx]; } } } @@ -700,7 +700,7 @@ void CvERTreeTrainData::get_vectors( const CvMat* _subsample_idx, CvDTreeNode* CvERTreeTrainData::subsample_data( const CvMat* _subsample_idx ) { CvDTreeNode* root = 0; - + CV_FUNCNAME( "CvERTreeTrainData::subsample_data" ); __BEGIN__; @@ -853,7 +853,7 @@ CvDTreeSplit* CvForestERTree::find_split_ord_class( CvDTreeNode* node, int vi, f const float epsilon = FLT_EPSILON*2; const float split_delta = (1 + FLT_EPSILON) * FLT_EPSILON; - int n = node->sample_count, i; + int n = node->sample_count; int m = data->get_num_classes(); cv::AutoBuffer inn_buf; @@ -882,8 +882,8 @@ CvDTreeSplit* CvForestERTree::find_split_ord_class( CvDTreeNode* node, int vi, f for (; smpi < n; smpi++) { float ptemp = values[smpi]; - int m = missing[smpi]; - if (m) continue; + int ms = missing[smpi]; + if (ms) continue; if ( ptemp < pmin) pmin = ptemp; if ( ptemp > pmax) @@ -898,7 +898,7 @@ CvDTreeSplit* CvForestERTree::find_split_ord_class( CvDTreeNode* node, int vi, f if (split_val - pmin <= FLT_EPSILON) split_val = pmin + split_delta; if (pmax - split_val <= FLT_EPSILON) - split_val = pmax - split_delta; + split_val = pmax - split_delta; // calculate Gini index if ( !priors ) @@ -906,9 +906,9 @@ CvDTreeSplit* CvForestERTree::find_split_ord_class( CvDTreeNode* node, int vi, f cv::AutoBuffer lrc(m*2); int *lc = lrc, *rc = lc + m; int L = 0, R = 0; - + // init arrays of class instance counters on both sides of the split - for( i = 0; i < m; i++ ) + for(int i = 0; i < m; i++ ) { lc[i] = 0; rc[i] = 0; @@ -917,8 +917,8 @@ CvDTreeSplit* CvForestERTree::find_split_ord_class( CvDTreeNode* node, int vi, f { int r = responses[si]; float val = values[si]; - int m = missing[si]; - if (m) continue; + int ms = missing[si]; + if (ms) continue; if ( val < split_val ) { lc[r]++; @@ -942,9 +942,9 @@ CvDTreeSplit* CvForestERTree::find_split_ord_class( CvDTreeNode* node, int vi, f cv::AutoBuffer lrc(m*2); double *lc = lrc, *rc = lc + m; double L = 0, R = 0; - + // init arrays of class instance counters on both sides of the split - for( i = 0; i < m; i++ ) + for(int i = 0; i < m; i++ ) { lc[i] = 0; rc[i] = 0; @@ -953,9 +953,9 @@ CvDTreeSplit* CvForestERTree::find_split_ord_class( CvDTreeNode* node, int vi, f { int r = responses[si]; float val = values[si]; - int m = missing[si]; + int ms = missing[si]; double p = priors[r]; - if (m) continue; + if (ms) continue; if ( val < split_val ) { lc[r] += p; @@ -974,7 +974,7 @@ CvDTreeSplit* CvForestERTree::find_split_ord_class( CvDTreeNode* node, int vi, f } best_val = (lbest_val*R + rbest_val*L) / (L*R); } - + } CvDTreeSplit* split = 0; @@ -995,7 +995,7 @@ CvDTreeSplit* CvForestERTree::find_split_cat_class( CvDTreeNode* node, int vi, f { int ci = data->get_var_type(vi); int n = node->sample_count; - int cm = data->get_num_classes(); + int cm = data->get_num_classes(); int vm = data->cat_count->data.i[ci]; double best_val = init_quality; CvDTreeSplit *split = 0; @@ -1009,8 +1009,8 @@ CvDTreeSplit* CvForestERTree::find_split_cat_class( CvDTreeNode* node, int vi, f const int* labels = data->get_cat_var_data( node, vi, ext_buf ); const int* responses = data->get_class_labels( node, ext_buf + n ); - - const double* priors = data->have_priors ? data->priors_mult->data.db : 0; + + const double* priors = data->have_priors ? data->priors_mult->data.db : 0; // create random class mask cv::AutoBuffer valid_cidx(vm); @@ -1078,7 +1078,7 @@ CvDTreeSplit* CvForestERTree::find_split_cat_class( CvDTreeNode* node, int vi, f if (var_class_mask->data.ptr[mask_class_idx]) { lc[r]++; - L++; + L++; split->subset[var_class_idx >> 5] |= 1 << (var_class_idx & 31); } else @@ -1091,7 +1091,7 @@ CvDTreeSplit* CvForestERTree::find_split_cat_class( CvDTreeNode* node, int vi, f { lbest_val += lc[i]*lc[i]; rbest_val += rc[i]*rc[i]; - } + } best_val = (lbest_val*R + rbest_val*L) / ((double)(L*R)); } else @@ -1113,11 +1113,11 @@ CvDTreeSplit* CvForestERTree::find_split_cat_class( CvDTreeNode* node, int vi, f continue; double p = priors[si]; int mask_class_idx = valid_cidx[var_class_idx]; - + if (var_class_mask->data.ptr[mask_class_idx]) { lc[r]+=(int)p; - L+=p; + L+=p; split->subset[var_class_idx >> 5] |= 1 << (var_class_idx & 31); } else @@ -1136,8 +1136,8 @@ CvDTreeSplit* CvForestERTree::find_split_cat_class( CvDTreeNode* node, int vi, f split->quality = (float)best_val; cvReleaseMat(&var_class_mask); - } - } + } + } return split; } @@ -1193,7 +1193,7 @@ CvDTreeSplit* CvForestERTree::find_split_ord_reg( CvDTreeNode* node, int vi, flo if (split_val - pmin <= FLT_EPSILON) split_val = pmin + split_delta; if (pmax - split_val <= FLT_EPSILON) - split_val = pmax - split_delta; + split_val = pmax - split_delta; for (int si = 0; si < n; si++) { @@ -1209,7 +1209,7 @@ CvDTreeSplit* CvForestERTree::find_split_ord_reg( CvDTreeNode* node, int vi, flo else { rsum += r; - R++; + R++; } } best_val = (lsum*lsum*R + rsum*rsum*L)/((double)L*R); @@ -1306,7 +1306,7 @@ CvDTreeSplit* CvForestERTree::find_split_cat_reg( CvDTreeNode* node, int vi, flo if (var_class_mask->data.ptr[mask_class_idx]) { lsum += r; - L++; + L++; split->subset[var_class_idx >> 5] |= 1 << (var_class_idx & 31); } else @@ -1320,8 +1320,8 @@ CvDTreeSplit* CvForestERTree::find_split_cat_reg( CvDTreeNode* node, int vi, flo split->quality = (float)best_val; cvReleaseMat(&var_class_mask); - } - } + } + } return split; } @@ -1358,7 +1358,7 @@ void CvForestERTree::split_node_data( CvDTreeNode* node ) { int ci = data->get_var_type(vi); if (ci >= 0) continue; - + int n1 = node->get_num_valid(vi), nr1 = 0; float* values_buf = (float*)(uchar*)inn_buf; int* missing_buf = (int*)(values_buf + n); @@ -1369,7 +1369,7 @@ void CvForestERTree::split_node_data( CvDTreeNode* node ) for( i = 0; i < n; i++ ) nr1 += ((!missing[i]) & dir[i]); left->set_num_valid(vi, n1 - nr1); - right->set_num_valid(vi, nr1); + right->set_num_valid(vi, nr1); } // split categorical vars, responses and cv_labels using new_idx relocation table for( vi = 0; vi < data->get_work_var_count() + data->ord_var_count; vi++ ) @@ -1385,11 +1385,11 @@ void CvForestERTree::split_node_data( CvDTreeNode* node ) if (data->is_buf_16u) { - unsigned short *ldst = (unsigned short *)(buf->data.s + left->buf_idx*buf->cols + + unsigned short *ldst = (unsigned short *)(buf->data.s + left->buf_idx*buf->cols + ci*scount + left->offset); - unsigned short *rdst = (unsigned short *)(buf->data.s + right->buf_idx*buf->cols + + unsigned short *rdst = (unsigned short *)(buf->data.s + right->buf_idx*buf->cols + ci*scount + right->offset); - + for( i = 0; i < n; i++ ) { int d = dir[i]; @@ -1415,11 +1415,11 @@ void CvForestERTree::split_node_data( CvDTreeNode* node ) } else { - int *ldst = buf->data.i + left->buf_idx*buf->cols + + int *ldst = buf->data.i + left->buf_idx*buf->cols + ci*scount + left->offset; - int *rdst = buf->data.i + right->buf_idx*buf->cols + + int *rdst = buf->data.i + right->buf_idx*buf->cols + ci*scount + right->offset; - + for( i = 0; i < n; i++ ) { int d = dir[i]; @@ -1435,7 +1435,7 @@ void CvForestERTree::split_node_data( CvDTreeNode* node ) *ldst = idx; ldst++; } - + } if( vi < data->var_count ) @@ -1443,7 +1443,7 @@ void CvForestERTree::split_node_data( CvDTreeNode* node ) left->set_num_valid(vi, n1 - nr1); right->set_num_valid(vi, nr1); } - } + } } // split sample indices @@ -1457,14 +1457,14 @@ void CvForestERTree::split_node_data( CvDTreeNode* node ) temp_buf[i] = sample_idx_src[i]; int pos = data->get_work_var_count(); - + if (data->is_buf_16u) { - unsigned short* ldst = (unsigned short*)(buf->data.s + left->buf_idx*buf->cols + + unsigned short* ldst = (unsigned short*)(buf->data.s + left->buf_idx*buf->cols + pos*scount + left->offset); - unsigned short* rdst = (unsigned short*)(buf->data.s + right->buf_idx*buf->cols + + unsigned short* rdst = (unsigned short*)(buf->data.s + right->buf_idx*buf->cols + pos*scount + right->offset); - + for (i = 0; i < n; i++) { int d = dir[i]; @@ -1483,9 +1483,9 @@ void CvForestERTree::split_node_data( CvDTreeNode* node ) } else { - int* ldst = buf->data.i + left->buf_idx*buf->cols + + int* ldst = buf->data.i + left->buf_idx*buf->cols + pos*scount + left->offset; - int* rdst = buf->data.i + right->buf_idx*buf->cols + + int* rdst = buf->data.i + right->buf_idx*buf->cols + pos*scount + right->offset; for (i = 0; i < n; i++) { @@ -1504,9 +1504,9 @@ void CvForestERTree::split_node_data( CvDTreeNode* node ) } } } - + // deallocate the parent node data that is not needed anymore - data->free_node_data(node); + data->free_node_data(node); } CvERTrees::CvERTrees() @@ -1576,10 +1576,10 @@ bool CvERTrees::train( const CvMat* _train_data, int _tflag, __END__ return result; - + } -bool CvERTrees::train( CvMLData* data, CvRTParams params) +bool CvERTrees::train( CvMLData* _data, CvRTParams params) { bool result = false; @@ -1587,7 +1587,7 @@ bool CvERTrees::train( CvMLData* data, CvRTParams params) __BEGIN__; - CV_CALL( result = CvRTrees::train( data, params) ); + CV_CALL( result = CvRTrees::train( _data, params) ); __END__; @@ -1609,7 +1609,7 @@ bool CvERTrees::grow_forest( const CvTermCriteria term_crit ) const int dims = data->var_count; float maximal_response = 0; - CvMat* oob_sample_votes = 0; + CvMat* oob_sample_votes = 0; CvMat* oob_responses = 0; float* oob_samples_perm_ptr= 0; @@ -1625,7 +1625,7 @@ bool CvERTrees::grow_forest( const CvTermCriteria term_crit ) // initialize these variable to avoid warning C4701 CvMat oob_predictions_sum = cvMat( 1, 1, CV_32FC1 ); CvMat oob_num_of_predictions = cvMat( 1, 1, CV_32FC1 ); - + nsamples = data->sample_count; nclasses = data->get_num_classes(); @@ -1647,11 +1647,11 @@ bool CvERTrees::grow_forest( const CvTermCriteria term_crit ) cvGetRow( oob_responses, &oob_predictions_sum, 0 ); cvGetRow( oob_responses, &oob_num_of_predictions, 1 ); } - + CV_CALL(oob_samples_perm_ptr = (float*)cvAlloc( sizeof(float)*nsamples*dims )); CV_CALL(samples_ptr = (float*)cvAlloc( sizeof(float)*nsamples*dims )); CV_CALL(missing_ptr = (uchar*)cvAlloc( sizeof(uchar)*nsamples*dims )); - CV_CALL(true_resp_ptr = (float*)cvAlloc( sizeof(float)*nsamples )); + CV_CALL(true_resp_ptr = (float*)cvAlloc( sizeof(float)*nsamples )); CV_CALL(data->get_vectors( 0, samples_ptr, missing_ptr, true_resp_ptr )); { @@ -1661,7 +1661,7 @@ bool CvERTrees::grow_forest( const CvTermCriteria term_crit ) maximal_response = (float)MAX( MAX( fabs(minval), fabs(maxval) ), 0 ); } } - + trees = (CvForestTree**)cvAlloc( sizeof(trees[0])*max_ntrees ); memset( trees, 0, sizeof(trees[0])*max_ntrees ); @@ -1692,7 +1692,7 @@ bool CvERTrees::grow_forest( const CvTermCriteria term_crit ) sample.data.fl += dims, missing.data.ptr += dims ) { CvDTreeNode* predicted_node = 0; - + // predict oob samples if( !predicted_node ) CV_CALL(predicted_node = tree->predict(&sample, &missing, true)); @@ -1796,12 +1796,12 @@ bool CvERTrees::grow_forest( const CvTermCriteria term_crit ) } result = true; - + cvFree( &oob_samples_perm_ptr ); cvFree( &samples_ptr ); cvFree( &missing_ptr ); cvFree( &true_resp_ptr ); - + cvReleaseMat( &sample_idx_for_tree ); cvReleaseMat( &oob_sample_votes ); diff --git a/modules/ml/src/gbt.cpp b/modules/ml/src/gbt.cpp index 3ef3e5e..7b08d4c 100644 --- a/modules/ml/src/gbt.cpp +++ b/modules/ml/src/gbt.cpp @@ -166,13 +166,13 @@ bool CvGBTrees::problem_type() const //=========================================================================== bool -CvGBTrees::train( CvMLData* data, CvGBTreesParams params, bool update ) +CvGBTrees::train( CvMLData* _data, CvGBTreesParams _params, bool update ) { bool result; - result = train ( data->get_values(), CV_ROW_SAMPLE, - data->get_responses(), data->get_var_idx(), - data->get_train_sample_idx(), data->get_var_types(), - data->get_missing(), params, update); + result = train ( _data->get_values(), CV_ROW_SAMPLE, + _data->get_responses(), _data->get_var_idx(), + _data->get_train_sample_idx(), _data->get_var_types(), + _data->get_missing(), _params, update); //update is not supported return result; } @@ -1294,12 +1294,12 @@ CvGBTrees::calc_error( CvMLData* _data, int type, std::vector *resp ) { float err = 0.0f; - const CvMat* sample_idx = (type == CV_TRAIN_ERROR) ? + const CvMat* _sample_idx = (type == CV_TRAIN_ERROR) ? _data->get_train_sample_idx() : _data->get_test_sample_idx(); const CvMat* response = _data->get_responses(); - int n = sample_idx ? get_len(sample_idx) : 0; + int n = _sample_idx ? get_len(_sample_idx) : 0; n = (type == CV_TRAIN_ERROR && n == 0) ? _data->get_values()->rows : n; if (!n) @@ -1315,7 +1315,7 @@ CvGBTrees::calc_error( CvMLData* _data, int type, std::vector *resp ) pred_resp = new float[n]; Sample_predictor predictor = Sample_predictor(this, pred_resp, _data->get_values(), - _data->get_missing(), sample_idx); + _data->get_missing(), _sample_idx); //#ifdef HAVE_TBB // tbb::parallel_for(cv::BlockedRange(0,n), predictor, tbb::auto_partitioner()); @@ -1323,7 +1323,7 @@ CvGBTrees::calc_error( CvMLData* _data, int type, std::vector *resp ) cv::parallel_for(cv::BlockedRange(0,n), predictor); //#endif - int* sidx = sample_idx ? sample_idx->data.i : 0; + int* sidx = _sample_idx ? _sample_idx->data.i : 0; int r_step = CV_IS_MAT_CONT(response->type) ? 1 : response->step / CV_ELEM_SIZE(response->type); @@ -1357,7 +1357,7 @@ CvGBTrees::CvGBTrees( const cv::Mat& trainData, int tflag, const cv::Mat& responses, const cv::Mat& varIdx, const cv::Mat& sampleIdx, const cv::Mat& varType, const cv::Mat& missingDataMask, - CvGBTreesParams params ) + CvGBTreesParams _params ) { data = 0; weak = 0; @@ -1371,14 +1371,14 @@ CvGBTrees::CvGBTrees( const cv::Mat& trainData, int tflag, clear(); - train(trainData, tflag, responses, varIdx, sampleIdx, varType, missingDataMask, params, false); + train(trainData, tflag, responses, varIdx, sampleIdx, varType, missingDataMask, _params, false); } bool CvGBTrees::train( const cv::Mat& trainData, int tflag, const cv::Mat& responses, const cv::Mat& varIdx, const cv::Mat& sampleIdx, const cv::Mat& varType, const cv::Mat& missingDataMask, - CvGBTreesParams params, + CvGBTreesParams _params, bool update ) { CvMat _trainData = trainData, _responses = responses; @@ -1387,13 +1387,13 @@ bool CvGBTrees::train( const cv::Mat& trainData, int tflag, return train( &_trainData, tflag, &_responses, varIdx.empty() ? 0 : &_varIdx, sampleIdx.empty() ? 0 : &_sampleIdx, varType.empty() ? 0 : &_varType, - missingDataMask.empty() ? 0 : &_missingDataMask, params, update); + missingDataMask.empty() ? 0 : &_missingDataMask, _params, update); } -float CvGBTrees::predict( const cv::Mat& sample, const cv::Mat& missing, +float CvGBTrees::predict( const cv::Mat& sample, const cv::Mat& _missing, const cv::Range& slice, int k ) const { - CvMat _sample = sample, _missing = missing; - return predict(&_sample, missing.empty() ? 0 : &_missing, 0, + CvMat _sample = sample, miss = _missing; + return predict(&_sample, _missing.empty() ? 0 : &miss, 0, slice==cv::Range::all() ? CV_WHOLE_SEQ : cvSlice(slice.start, slice.end), k); } diff --git a/modules/ml/src/knearest.cpp b/modules/ml/src/knearest.cpp index e073d1e..fa6b15e 100644 --- a/modules/ml/src/knearest.cpp +++ b/modules/ml/src/knearest.cpp @@ -141,7 +141,7 @@ bool CvKNearest::train( const CvMat* _train_data, const CvMat* _responses, ok = true; __END__; - + if( responses && responses->data.ptr != _responses->data.ptr ) cvReleaseMat(&responses); @@ -318,7 +318,7 @@ struct P1 { result = _result; buf_sz = _buf_sz; } - + const CvKNearest* pointer; int k; const CvMat* _samples; @@ -329,7 +329,7 @@ struct P1 { CvMat* _dist; float* result; int buf_sz; - + void operator()( const cv::BlockedRange& range ) const { cv::AutoBuffer buf(buf_sz); @@ -429,7 +429,7 @@ bool CvKNearest::train( const Mat& _train_data, const Mat& _responses, int _max_k, bool _update_base ) { CvMat tdata = _train_data, responses = _responses, sidx = _sample_idx; - + return train(&tdata, &responses, sidx.data.ptr ? &sidx : 0, _is_regression, _max_k, _update_base ); } @@ -439,7 +439,7 @@ float CvKNearest::find_nearest( const Mat& _samples, int k, Mat* _results, Mat* _dist ) const { CvMat s = _samples, results, *presults = 0, nresponses, *pnresponses = 0, dist, *pdist = 0; - + if( _results ) { if(!(_results->data && (_results->type() == CV_32F || @@ -449,7 +449,7 @@ float CvKNearest::find_nearest( const Mat& _samples, int k, Mat* _results, _results->create(_samples.rows, 1, CV_32F); presults = &(results = *_results); } - + if( _neighbor_responses ) { if(!(_neighbor_responses->data && _neighbor_responses->type() == CV_32F && @@ -457,7 +457,7 @@ float CvKNearest::find_nearest( const Mat& _samples, int k, Mat* _results, _neighbor_responses->create(_samples.rows, k, CV_32F); pnresponses = &(nresponses = *_neighbor_responses); } - + if( _dist ) { if(!(_dist->data && _dist->type() == CV_32F && @@ -465,15 +465,15 @@ float CvKNearest::find_nearest( const Mat& _samples, int k, Mat* _results, _dist->create(_samples.rows, k, CV_32F); pdist = &(dist = *_dist); } - + return find_nearest(&s, k, presults, _neighbors, pnresponses, pdist ); } -float CvKNearest::find_nearest( const cv::Mat& samples, int k, CV_OUT cv::Mat& results, +float CvKNearest::find_nearest( const cv::Mat& _samples, int k, CV_OUT cv::Mat& results, CV_OUT cv::Mat& neighborResponses, CV_OUT cv::Mat& dists) const { - return find_nearest(samples, k, &results, 0, &neighborResponses, &dists); + return find_nearest(_samples, k, &results, 0, &neighborResponses, &dists); } /* End of file */ diff --git a/modules/ml/src/nbayes.cpp b/modules/ml/src/nbayes.cpp index 594007b..15146d6 100644 --- a/modules/ml/src/nbayes.cpp +++ b/modules/ml/src/nbayes.cpp @@ -241,13 +241,13 @@ bool CvNormalBayesClassifier::train( const CvMat* _train_data, const CvMat* _res double* cov_data = cov->data.db + i*_var_count; double s1val = sum1[i]; double avg1 = avg_data[i]; - int count = count_data[i]; + int _count = count_data[i]; for( j = 0; j <= i; j++ ) { double avg2 = avg2_data[j]; - double cov_val = prod_data[j] - avg1 * sum2[j] - avg2 * s1val + avg1 * avg2 * count; - cov_val = (count > 1) ? cov_val / (count - 1) : cov_val; + double cov_val = prod_data[j] - avg1 * sum2[j] - avg2 * s1val + avg1 * avg2 * _count; + cov_val = (_count > 1) ? cov_val / (_count - 1) : cov_val; cov_data[j] = cov_val; } } @@ -294,7 +294,7 @@ struct predict_body { value = _value; var_count1 = _var_count1; } - + CvMat* c; CvMat** cov_rotate_mats; CvMat** inv_eigen_values; @@ -306,15 +306,15 @@ struct predict_body { CvMat* results; float* value; int var_count1; - + void operator()( const cv::BlockedRange& range ) const { int cls = -1; - int rtype = 0, rstep = 0; + int rtype = 0, rstep = 0; int nclasses = cls_labels->cols; int _var_count = avg[0]->cols; - + if (results) { rtype = CV_MAT_TYPE(results->type); @@ -323,7 +323,7 @@ struct predict_body { // allocate memory and initializing headers for calculating cv::AutoBuffer buffer(nclasses + var_count1); CvMat diff = cvMat( 1, var_count1, CV_64FC1, &buffer[0] ); - + for(int k = range.begin(); k < range.end(); k += 1 ) { int ival; @@ -592,7 +592,7 @@ CvNormalBayesClassifier::CvNormalBayesClassifier( const Mat& _train_data, const cov_rotate_mats = 0; c = 0; default_model_name = "my_nb"; - + CvMat tdata = _train_data, responses = _responses, vidx = _var_idx, sidx = _sample_idx; train(&tdata, &responses, vidx.data.ptr ? &vidx : 0, sidx.data.ptr ? &sidx : 0); @@ -609,7 +609,7 @@ bool CvNormalBayesClassifier::train( const Mat& _train_data, const Mat& _respons float CvNormalBayesClassifier::predict( const Mat& _samples, Mat* _results ) const { CvMat samples = _samples, results, *presults = 0; - + if( _results ) { if( !(_results->data && _results->type() == CV_32F && @@ -618,7 +618,7 @@ float CvNormalBayesClassifier::predict( const Mat& _samples, Mat* _results ) con _results->create(_samples.rows, 1, CV_32F); presults = &(results = *_results); } - + return predict(&samples, presults); } diff --git a/modules/ml/src/rtrees.cpp b/modules/ml/src/rtrees.cpp index 81576c3..d88611b 100644 --- a/modules/ml/src/rtrees.cpp +++ b/modules/ml/src/rtrees.cpp @@ -307,14 +307,14 @@ bool CvRTrees::train( const CvMat* _train_data, int _tflag, return grow_forest( params.term_crit ); } -bool CvRTrees::train( CvMLData* data, CvRTParams params ) +bool CvRTrees::train( CvMLData* _data, CvRTParams params ) { - const CvMat* values = data->get_values(); - const CvMat* response = data->get_responses(); - const CvMat* missing = data->get_missing(); - const CvMat* var_types = data->get_var_types(); - const CvMat* train_sidx = data->get_train_sample_idx(); - const CvMat* var_idx = data->get_var_idx(); + const CvMat* values = _data->get_values(); + const CvMat* response = _data->get_responses(); + const CvMat* missing = _data->get_missing(); + const CvMat* var_types = _data->get_var_types(); + const CvMat* train_sidx = _data->get_train_sample_idx(); + const CvMat* var_idx = _data->get_var_idx(); return train( values, CV_ROW_SAMPLE, response, var_idx, train_sidx, var_types, missing, params ); @@ -331,7 +331,7 @@ bool CvRTrees::grow_forest( const CvTermCriteria term_crit ) const int dims = data->var_count; float maximal_response = 0; - CvMat* oob_sample_votes = 0; + CvMat* oob_sample_votes = 0; CvMat* oob_responses = 0; float* oob_samples_perm_ptr= 0; @@ -347,7 +347,7 @@ bool CvRTrees::grow_forest( const CvTermCriteria term_crit ) // initialize these variable to avoid warning C4701 CvMat oob_predictions_sum = cvMat( 1, 1, CV_32FC1 ); CvMat oob_num_of_predictions = cvMat( 1, 1, CV_32FC1 ); - + nsamples = data->sample_count; nclasses = data->get_num_classes(); @@ -369,14 +369,14 @@ bool CvRTrees::grow_forest( const CvTermCriteria term_crit ) cvGetRow( oob_responses, &oob_predictions_sum, 0 ); cvGetRow( oob_responses, &oob_num_of_predictions, 1 ); } - + oob_samples_perm_ptr = (float*)cvAlloc( sizeof(float)*nsamples*dims ); samples_ptr = (float*)cvAlloc( sizeof(float)*nsamples*dims ); missing_ptr = (uchar*)cvAlloc( sizeof(uchar)*nsamples*dims ); - true_resp_ptr = (float*)cvAlloc( sizeof(float)*nsamples ); + true_resp_ptr = (float*)cvAlloc( sizeof(float)*nsamples ); data->get_vectors( 0, samples_ptr, missing_ptr, true_resp_ptr ); - + double minval, maxval; CvMat responses = cvMat(1, nsamples, CV_32FC1, true_resp_ptr); cvMinMaxLoc( &responses, &minval, &maxval ); @@ -536,7 +536,7 @@ bool CvRTrees::grow_forest( const CvTermCriteria term_crit ) cvFree( &samples_ptr ); cvFree( &missing_ptr ); cvFree( &true_resp_ptr ); - + cvReleaseMat( &sample_idx_mask_for_tree ); cvReleaseMat( &sample_idx_for_tree ); @@ -592,9 +592,9 @@ float CvRTrees::calc_error( CvMLData* _data, int type , std::vector *resp { CvMat sample, miss; int si = sidx ? sidx[i] : i; - cvGetRow( values, &sample, si ); - if( missing ) - cvGetRow( missing, &miss, si ); + cvGetRow( values, &sample, si ); + if( missing ) + cvGetRow( missing, &miss, si ); float r = (float)predict( &sample, missing ? &miss : 0 ); if( pred_resp ) pred_resp[i] = r; @@ -610,15 +610,15 @@ float CvRTrees::calc_error( CvMLData* _data, int type , std::vector *resp CvMat sample, miss; int si = sidx ? sidx[i] : i; cvGetRow( values, &sample, si ); - if( missing ) - cvGetRow( missing, &miss, si ); + if( missing ) + cvGetRow( missing, &miss, si ); float r = (float)predict( &sample, missing ? &miss : 0 ); if( pred_resp ) pred_resp[i] = r; float d = r - response->data.fl[si*r_step]; err += d*d; } - err = sample_count ? err / (float)sample_count : -FLT_MAX; + err = sample_count ? err / (float)sample_count : -FLT_MAX; } return err; } @@ -635,12 +635,12 @@ float CvRTrees::get_train_error() float *responses_ptr = (float*)cvAlloc( sizeof(float)*sample_count ); data->get_vectors( 0, values_ptr, missing_ptr, responses_ptr); - + if (data->is_classifier) { int err_count = 0; float *vp = values_ptr; - uchar *mp = missing_ptr; + uchar *mp = missing_ptr; for (int si = 0; si < sample_count; si++, vp += var_count, mp += var_count) { CvMat sample = cvMat( 1, var_count, CV_32FC1, vp ); @@ -653,10 +653,10 @@ float CvRTrees::get_train_error() } else CV_Error( CV_StsBadArg, "This method is not supported for regression problems" ); - + cvFree( &values_ptr ); cvFree( &missing_ptr ); - cvFree( &responses_ptr ); + cvFree( &responses_ptr ); return err; } @@ -701,7 +701,7 @@ float CvRTrees::predict( const CvMat* sample, const CvMat* missing ) const float CvRTrees::predict_prob( const CvMat* sample, const CvMat* missing) const { - if( nclasses == 2 ) //classification + if( nclasses == 2 ) //classification { cv::AutoBuffer _votes(nclasses); int* votes = _votes; @@ -711,15 +711,15 @@ float CvRTrees::predict_prob( const CvMat* sample, const CvMat* missing) const CvDTreeNode* predicted_node = trees[k]->predict( sample, missing ); int class_idx = predicted_node->class_idx; CV_Assert( 0 <= class_idx && class_idx < nclasses ); - + ++votes[class_idx]; } - - return float(votes[1])/ntrees; + + return float(votes[1])/ntrees; } else // regression - CV_Error(CV_StsBadArg, "This function works for binary classification problems only..."); - + CV_Error(CV_StsBadArg, "This function works for binary classification problems only..."); + return -1; } @@ -809,15 +809,15 @@ void CvRTrees::read( CvFileStorage* fs, CvFileNode* fnode ) { // initialize active variables mask CvMat submask1; - cvGetCols( active_var_mask, &submask1, 0, nactive_vars ); + cvGetCols( active_var_mask, &submask1, 0, nactive_vars ); cvSet( &submask1, cvScalar(1) ); - if( nactive_vars < var_count ) - { - CvMat submask2; - cvGetCols( active_var_mask, &submask2, nactive_vars, var_count ); - cvZero( &submask2 ); - } + if( nactive_vars < var_count ) + { + CvMat submask2; + cvGetCols( active_var_mask, &submask2, nactive_vars, var_count ); + cvZero( &submask2 ); + } } } diff --git a/modules/ml/src/svm.cpp b/modules/ml/src/svm.cpp index bfe1ed6..5a500b3 100644 --- a/modules/ml/src/svm.cpp +++ b/modules/ml/src/svm.cpp @@ -1065,10 +1065,10 @@ bool CvSVMSolver::solve_eps_svr( int _sample_count, int _var_count, const float* CvSVMKernel* _kernel, double* _alpha, CvSVMSolutionInfo& _si ) { int i; - double p = _kernel->params->p, C = _kernel->params->C; + double p = _kernel->params->p, _C = _kernel->params->C; if( !create( _sample_count, _var_count, _samples, 0, - _sample_count*2, 0, C, C, _storage, _kernel, &CvSVMSolver::get_row_svr, + _sample_count*2, 0, _C, _C, _storage, _kernel, &CvSVMSolver::get_row_svr, &CvSVMSolver::select_working_set, &CvSVMSolver::calc_rho )) return false; @@ -1101,7 +1101,7 @@ bool CvSVMSolver::solve_nu_svr( int _sample_count, int _var_count, const float** CvSVMKernel* _kernel, double* _alpha, CvSVMSolutionInfo& _si ) { int i; - double C = _kernel->params->C, sum; + double _C = _kernel->params->C, sum; if( !create( _sample_count, _var_count, _samples, 0, _sample_count*2, 0, 1., 1., _storage, _kernel, &CvSVMSolver::get_row_svr, @@ -1110,11 +1110,11 @@ bool CvSVMSolver::solve_nu_svr( int _sample_count, int _var_count, const float** y = (schar*)cvMemStorageAlloc( storage, sample_count*2*sizeof(y[0]) ); alpha = (double*)cvMemStorageAlloc( storage, alpha_count*sizeof(alpha[0]) ); - sum = C * _kernel->params->nu * sample_count * 0.5; + sum = _C * _kernel->params->nu * sample_count * 0.5; for( i = 0; i < sample_count; i++ ) { - alpha[i] = alpha[i + sample_count] = MIN(sum, C); + alpha[i] = alpha[i + sample_count] = MIN(sum, _C); sum -= alpha[i]; b[i] = -_y[i]; @@ -1628,12 +1628,11 @@ bool CvSVM::train_auto( const CvMat* _train_data, const CvMat* _responses, int svm_type, sample_count, var_count, sample_size; int block_size = 1 << 16; double* alpha; - int i, k; RNG* rng = &theRNG(); // all steps are logarithmic and must be > 1 double degree_step = 10, g_step = 10, coef_step = 10, C_step = 10, nu_step = 10, p_step = 10; - double gamma = 0, C = 0, degree = 0, coef = 0, p = 0, nu = 0; + double gamma = 0, _C = 0, degree = 0, coef = 0, p = 0, nu = 0; double best_degree = 0, best_gamma = 0, best_coef = 0, best_C = 0, best_nu = 0, best_p = 0; float min_error = FLT_MAX, error; @@ -1760,7 +1759,7 @@ bool CvSVM::train_auto( const CvMat* _train_data, const CvMat* _responses, cvZero( responses_local ); // randomly permute samples and responses - for( i = 0; i < sample_count; i++ ) + for(int i = 0; i < sample_count; i++ ) { int i1 = (*rng)(sample_count); int i2 = (*rng)(sample_count); @@ -1779,7 +1778,7 @@ bool CvSVM::train_auto( const CvMat* _train_data, const CvMat* _responses, { // count class samples int num_0=0,num_1=0; - for (i=0; idata.i[i]==class_labels->data.i[0]) ++num_0; @@ -1875,10 +1874,10 @@ bool CvSVM::train_auto( const CvMat* _train_data, const CvMat* _responses, } int* cls_lbls = class_labels ? class_labels->data.i : 0; - C = C_grid.min_val; + _C = C_grid.min_val; do { - params.C = C; + params.C = _C; gamma = gamma_grid.min_val; do { @@ -1906,7 +1905,7 @@ bool CvSVM::train_auto( const CvMat* _train_data, const CvMat* _responses, int train_size = trainset_size; error = 0; - for( k = 0; k < k_fold; k++ ) + for(int k = 0; k < k_fold; k++ ) { memcpy( samples_local, samples, sizeof(samples[0])*test_size*k ); memcpy( samples_local + test_size*k, test_samples_ptr + test_size, @@ -1930,7 +1929,7 @@ bool CvSVM::train_auto( const CvMat* _train_data, const CvMat* _responses, EXIT; // Compute test set error on samples - for( i = 0; i < test_size; i++, true_resp += resp_elem_size, test_samples_ptr++ ) + for(int i = 0; i < test_size; i++, true_resp += resp_elem_size, test_samples_ptr++ ) { float resp = predict( *test_samples_ptr, var_count ); error += is_regression ? powf( resp - *(float*)true_resp, 2 ) @@ -1943,7 +1942,7 @@ bool CvSVM::train_auto( const CvMat* _train_data, const CvMat* _responses, best_degree = degree; best_gamma = gamma; best_coef = coef; - best_C = C; + best_C = _C; best_nu = nu; best_p = p; } @@ -1962,9 +1961,9 @@ bool CvSVM::train_auto( const CvMat* _train_data, const CvMat* _responses, gamma *= gamma_grid.step; } while( gamma < gamma_grid.max_val ); - C *= C_grid.step; + _C *= C_grid.step; } - while( C < C_grid.max_val ); + while( _C < C_grid.max_val ); } min_error /= (float) sample_count; diff --git a/modules/ml/src/tree.cpp b/modules/ml/src/tree.cpp index e69f888..936f552 100644 --- a/modules/ml/src/tree.cpp +++ b/modules/ml/src/tree.cpp @@ -156,7 +156,7 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag, int vi, i, size; char err[100]; const int *sidx = 0, *vidx = 0; - + if( _update_data && data_root ) { data = new CvDTreeTrainData( _train_data, _tflag, _responses, _var_idx, @@ -224,7 +224,7 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag, sample_count = sample_all; var_count = var_all; - + if( _sample_idx ) { CV_CALL( sample_indices = cvPreprocessIndexArray( _sample_idx, sample_all )); @@ -239,10 +239,10 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag, var_count = var_idx->rows + var_idx->cols - 1; } - is_buf_16u = false; - if ( sample_count < 65536 ) - is_buf_16u = true; - + is_buf_16u = false; + if ( sample_count < 65536 ) + is_buf_16u = true; + if( !CV_IS_MAT(_responses) || (CV_MAT_TYPE(_responses->type) != CV_32SC1 && CV_MAT_TYPE(_responses->type) != CV_32FC1) || @@ -251,13 +251,13 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag, CV_ERROR( CV_StsBadArg, "The array of _responses must be an integer or " "floating-point vector containing as many elements as " "the total number of samples in the training data matrix" ); - + r_type = CV_VAR_CATEGORICAL; if( _var_type ) CV_CALL( var_type0 = cvPreprocessVarType( _var_type, var_idx, var_count, &r_type )); CV_CALL( var_type = cvCreateMat( 1, var_count+2, CV_32SC1 )); - + cat_var_count = 0; ord_var_count = -1; @@ -284,11 +284,11 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag, work_var_count = var_count + (is_classifier ? 1 : 0) // for responses class_labels + (have_labels ? 1 : 0); // for cv_labels - + buf_size = (work_var_count + 1 /*for sample_indices*/) * sample_count; shared = _shared; buf_count = shared ? 2 : 1; - + if ( is_buf_16u ) { CV_CALL( buf = cvCreateMat( buf_count, buf_size, CV_16UC1 )); @@ -298,13 +298,13 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag, { CV_CALL( buf = cvCreateMat( buf_count, buf_size, CV_32SC1 )); CV_CALL( int_ptr = (int**)cvAlloc( sample_count*sizeof(int_ptr[0]) )); - } + } size = is_classifier ? (cat_var_count+1) : cat_var_count; size = !size ? 1 : size; CV_CALL( cat_count = cvCreateMat( 1, size, CV_32SC1 )); CV_CALL( cat_ofs = cvCreateMat( 1, size, CV_32SC1 )); - + size = is_classifier ? (cat_var_count + 1)*params.max_categories : cat_var_count*params.max_categories; size = !size ? 1 : size; CV_CALL( cat_map = cvCreateMat( 1, size, CV_32SC1 )); @@ -389,12 +389,12 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag, { int c_count, prev_label; int* c_map; - + if (is_buf_16u) udst = (unsigned short*)(buf->data.s + vi*sample_count); else idst = buf->data.i + vi*sample_count; - + // copy data for( i = 0; i < sample_count; i++ ) { @@ -428,7 +428,7 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag, _idst[i] = val; pair16u32s_ptr[i].u = udst + i; pair16u32s_ptr[i].i = _idst + i; - } + } else { idst[i] = val; @@ -502,7 +502,7 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag, // replace labels for missing values with -1 for( ; i < sample_count; i++ ) *int_ptr[i] = -1; - } + } } else if( ci < 0 ) // process ordered variable { @@ -536,14 +536,14 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag, else idst[i] = i; _fdst[i] = val; - + } if (is_buf_16u) icvSortUShAux( udst, sample_count, _fdst); else icvSortIntAux( idst, sample_count, _fdst ); } - + if( vi < var_count ) data_root->set_num_valid(vi, num_valid); } @@ -564,15 +564,15 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag, if( cv_n ) { - unsigned short* udst = 0; - int* idst = 0; + unsigned short* usdst = 0; + int* idst2 = 0; if (is_buf_16u) { - udst = (unsigned short*)(buf->data.s + (get_work_var_count()-1)*sample_count); + usdst = (unsigned short*)(buf->data.s + (get_work_var_count()-1)*sample_count); for( i = vi = 0; i < sample_count; i++ ) { - udst[i] = (unsigned short)vi++; + usdst[i] = (unsigned short)vi++; vi &= vi < cv_n ? -1 : 0; } @@ -581,15 +581,15 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag, int a = (*rng)(sample_count); int b = (*rng)(sample_count); unsigned short unsh = (unsigned short)vi; - CV_SWAP( udst[a], udst[b], unsh ); + CV_SWAP( usdst[a], usdst[b], unsh ); } } else { - idst = buf->data.i + (get_work_var_count()-1)*sample_count; + idst2 = buf->data.i + (get_work_var_count()-1)*sample_count; for( i = vi = 0; i < sample_count; i++ ) { - idst[i] = vi++; + idst2[i] = vi++; vi &= vi < cv_n ? -1 : 0; } @@ -597,12 +597,12 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag, { int a = (*rng)(sample_count); int b = (*rng)(sample_count); - CV_SWAP( idst[a], idst[b], vi ); + CV_SWAP( idst2[a], idst2[b], vi ); } } } - if ( cat_map ) + if ( cat_map ) cat_map->cols = MAX( total_c_count, 1 ); max_split_size = cvAlign(sizeof(CvDTreeSplit) + @@ -751,7 +751,7 @@ CvDTreeNode* CvDTreeTrainData::subsample_data( const CvMat* _subsample_idx ) if (is_buf_16u) { - unsigned short* udst = (unsigned short*)(buf->data.s + root->buf_idx*buf->cols + + unsigned short* udst = (unsigned short*)(buf->data.s + root->buf_idx*buf->cols + vi*sample_count + root->offset); for( i = 0; i < count; i++ ) { @@ -762,7 +762,7 @@ CvDTreeNode* CvDTreeTrainData::subsample_data( const CvMat* _subsample_idx ) } else { - int* idst = buf->data.i + root->buf_idx*buf->cols + + int* idst = buf->data.i + root->buf_idx*buf->cols + vi*sample_count + root->offset; for( i = 0; i < count; i++ ) { @@ -788,7 +788,7 @@ CvDTreeNode* CvDTreeTrainData::subsample_data( const CvMat* _subsample_idx ) if (is_buf_16u) { - unsigned short* udst_idx = (unsigned short*)(buf->data.s + root->buf_idx*buf->cols + + unsigned short* udst_idx = (unsigned short*)(buf->data.s + root->buf_idx*buf->cols + vi*sample_count + data_root->offset); for( i = 0; i < num_valid; i++ ) { @@ -812,7 +812,7 @@ CvDTreeNode* CvDTreeTrainData::subsample_data( const CvMat* _subsample_idx ) } else { - int* idst_idx = buf->data.i + root->buf_idx*buf->cols + + int* idst_idx = buf->data.i + root->buf_idx*buf->cols + vi*sample_count + root->offset; for( i = 0; i < num_valid; i++ ) { @@ -840,14 +840,14 @@ CvDTreeNode* CvDTreeTrainData::subsample_data( const CvMat* _subsample_idx ) const int* sample_idx_src = get_sample_indices(data_root, (int*)(uchar*)inn_buf); if (is_buf_16u) { - unsigned short* sample_idx_dst = (unsigned short*)(buf->data.s + root->buf_idx*buf->cols + + unsigned short* sample_idx_dst = (unsigned short*)(buf->data.s + root->buf_idx*buf->cols + workVarCount*sample_count + root->offset); for (i = 0; i < count; i++) sample_idx_dst[i] = (unsigned short)sample_idx_src[sidx[i]]; } else { - int* sample_idx_dst = buf->data.i + root->buf_idx*buf->cols + + int* sample_idx_dst = buf->data.i + root->buf_idx*buf->cols + workVarCount*sample_count + root->offset; for (i = 0; i < count; i++) sample_idx_dst[i] = sample_idx_src[sidx[i]]; @@ -865,7 +865,7 @@ CvDTreeNode* CvDTreeTrainData::subsample_data( const CvMat* _subsample_idx ) void CvDTreeTrainData::get_vectors( const CvMat* _subsample_idx, float* values, uchar* missing, - float* responses, bool get_class_idx ) + float* _responses, bool get_class_idx ) { CvMat* subsample_idx = 0; CvMat* subsample_co = 0; @@ -962,7 +962,7 @@ void CvDTreeTrainData::get_vectors( const CvMat* _subsample_idx, } // copy responses - if( responses ) + if( _responses ) { if( is_classifier ) { @@ -972,7 +972,7 @@ void CvDTreeTrainData::get_vectors( const CvMat* _subsample_idx, int idx = sidx ? sidx[i] : i; int val = get_class_idx ? src[idx] : cat_map->data.i[cat_ofs->data.i[cat_var_count]+src[idx]]; - responses[i] = (float)val; + _responses[i] = (float)val; } } else @@ -983,7 +983,7 @@ void CvDTreeTrainData::get_vectors( const CvMat* _subsample_idx, for( i = 0; i < count; i++ ) { int idx = sidx ? sidx[i] : i; - responses[i] = _values[idx]; + _responses[i] = _values[idx]; } } } @@ -1122,7 +1122,7 @@ void CvDTreeTrainData::clear() cvReleaseMat( &cat_map ); cvReleaseMat( &priors ); cvReleaseMat( &priors_mult ); - + node_heap = split_heap = 0; sample_count = var_all = var_count = max_c_count = ord_var_count = cat_var_count = 0; @@ -1130,7 +1130,7 @@ void CvDTreeTrainData::clear() buf_count = buf_size = 0; shared = false; - + data_root = 0; rng = &cv::theRNG(); @@ -1152,7 +1152,7 @@ void CvDTreeTrainData::get_ord_var_data( CvDTreeNode* n, int vi, float* ord_valu const float** ord_values, const int** sorted_indices, int* sample_indices_buf ) { int vidx = var_idx ? var_idx->data.i[vi] : vi; - int node_sample_count = n->sample_count; + int node_sample_count = n->sample_count; int td_step = train_data->step/CV_ELEM_SIZE(train_data->type); const int* sample_indices = get_sample_indices(n, sample_indices_buf); @@ -1161,16 +1161,16 @@ void CvDTreeTrainData::get_ord_var_data( CvDTreeNode* n, int vi, float* ord_valu *sorted_indices = buf->data.i + n->buf_idx*buf->cols + vi*sample_count + n->offset; else { - const unsigned short* short_indices = (const unsigned short*)(buf->data.s + n->buf_idx*buf->cols + + const unsigned short* short_indices = (const unsigned short*)(buf->data.s + n->buf_idx*buf->cols + vi*sample_count + n->offset ); for( int i = 0; i < node_sample_count; i++ ) sorted_indices_buf[i] = short_indices[i]; *sorted_indices = sorted_indices_buf; } - + if( tflag == CV_ROW_SAMPLE ) { - for( int i = 0; i < node_sample_count && + for( int i = 0; i < node_sample_count && ((((*sorted_indices)[i] >= 0) && !is_buf_16u) || (((*sorted_indices)[i] != 65535) && is_buf_16u)); i++ ) { int idx = (*sorted_indices)[i]; @@ -1179,14 +1179,14 @@ void CvDTreeTrainData::get_ord_var_data( CvDTreeNode* n, int vi, float* ord_valu } } else - for( int i = 0; i < node_sample_count && + for( int i = 0; i < node_sample_count && ((((*sorted_indices)[i] >= 0) && !is_buf_16u) || (((*sorted_indices)[i] != 65535) && is_buf_16u)); i++ ) { int idx = (*sorted_indices)[i]; idx = sample_indices[idx]; ord_values_buf[i] = *(train_data->data.fl + vidx* td_step + idx); } - + *ord_values = ord_values_buf; } @@ -1205,17 +1205,17 @@ const int* CvDTreeTrainData::get_sample_indices( CvDTreeNode* n, int* indices_bu const float* CvDTreeTrainData::get_ord_responses( CvDTreeNode* n, float* values_buf, int*sample_indices_buf ) { - int sample_count = n->sample_count; + int _sample_count = n->sample_count; int r_step = CV_IS_MAT_CONT(responses->type) ? 1 : responses->step/CV_ELEM_SIZE(responses->type); const int* indices = get_sample_indices(n, sample_indices_buf); - for( int i = 0; i < sample_count && + for( int i = 0; i < _sample_count && (((indices[i] >= 0) && !is_buf_16u) || ((indices[i] != 65535) && is_buf_16u)); i++ ) { int idx = indices[i]; values_buf[i] = *(responses->data.fl + idx * r_step); } - + return values_buf; } @@ -1235,7 +1235,7 @@ const int* CvDTreeTrainData::get_cat_var_data( CvDTreeNode* n, int vi, int* cat_ cat_values = buf->data.i + n->buf_idx*buf->cols + vi*sample_count + n->offset; else { - const unsigned short* short_values = (const unsigned short*)(buf->data.s + n->buf_idx*buf->cols + + const unsigned short* short_values = (const unsigned short*)(buf->data.s + n->buf_idx*buf->cols + vi*sample_count + n->offset); for( int i = 0; i < n->sample_count; i++ ) cat_values_buf[i] = short_values[i]; @@ -1562,7 +1562,7 @@ bool CvDTree::train( const Mat& _train_data, int _tflag, const Mat& _missing_mask, CvDTreeParams _params ) { CvMat tdata = _train_data, responses = _responses, vidx=_var_idx, - sidx=_sample_idx, vtype=_var_type, mmask=_missing_mask; + sidx=_sample_idx, vtype=_var_type, mmask=_missing_mask; return train(&tdata, _tflag, &responses, vidx.data.ptr ? &vidx : 0, sidx.data.ptr ? &sidx : 0, vtype.data.ptr ? &vtype : 0, mmask.data.ptr ? &mmask : 0, _params); } @@ -1794,7 +1794,7 @@ double CvDTree::calc_node_dir( CvDTreeNode* node ) const float* val = 0; const int* sorted = 0; data->get_ord_var_data( node, vi, val_buf, sorted_buf, &val, &sorted, sample_idx_buf); - + assert( 0 <= split_point && split_point < n1-1 ); if( !data->have_priors ) @@ -1848,7 +1848,7 @@ template<> CV_EXPORTS void Ptr::delete_obj() { fastFree(obj); } - + DTreeBestSplitFinder::DTreeBestSplitFinder( CvDTree* _tree, CvDTreeNode* _node) { tree = _tree; @@ -2310,7 +2310,7 @@ CvDTreeSplit* CvDTree::find_split_cat_class( CvDTreeNode* node, int vi, float in } CvDTreeSplit* split = 0; - if( best_subset >= 0 ) + if( best_subset >= 0 ) { split = _split ? _split : data->new_split_cat( 0, -1.0f ); split->var_idx = vi; @@ -2933,7 +2933,7 @@ void CvDTree::complete_node_dir( CvDTreeNode* node ) { int idx = labels[i]; if( !dir[i] && ( ((idx >= 0)&&(!data->is_buf_16u)) || ((idx != 65535)&&(data->is_buf_16u)) )) - + { int d = CV_DTREE_CAT_DIR(idx,subset); dir[i] = (char)((d ^ inversed_mask) - inversed_mask); @@ -3049,7 +3049,7 @@ void CvDTree::split_node_data( CvDTreeNode* node ) { unsigned short *ldst, *rdst, *ldst0, *rdst0; //unsigned short tl, tr; - ldst0 = ldst = (unsigned short*)(buf->data.s + left->buf_idx*buf->cols + + ldst0 = ldst = (unsigned short*)(buf->data.s + left->buf_idx*buf->cols + vi*scount + left->offset); rdst0 = rdst = (unsigned short*)(ldst + nl); @@ -3095,9 +3095,9 @@ void CvDTree::split_node_data( CvDTreeNode* node ) else { int *ldst0, *ldst, *rdst0, *rdst; - ldst0 = ldst = buf->data.i + left->buf_idx*buf->cols + + ldst0 = ldst = buf->data.i + left->buf_idx*buf->cols + vi*scount + left->offset; - rdst0 = rdst = buf->data.i + right->buf_idx*buf->cols + + rdst0 = rdst = buf->data.i + right->buf_idx*buf->cols + vi*scount + right->offset; // split sorted @@ -3146,7 +3146,7 @@ void CvDTree::split_node_data( CvDTreeNode* node ) { int ci = data->get_var_type(vi); int n1 = node->get_num_valid(vi), nr1 = 0; - + if( ci < 0 || (vi < data->var_count && !split_input_data) ) continue; @@ -3158,11 +3158,11 @@ void CvDTree::split_node_data( CvDTreeNode* node ) if (data->is_buf_16u) { - unsigned short *ldst = (unsigned short *)(buf->data.s + left->buf_idx*buf->cols + + unsigned short *ldst = (unsigned short *)(buf->data.s + left->buf_idx*buf->cols + vi*scount + left->offset); - unsigned short *rdst = (unsigned short *)(buf->data.s + right->buf_idx*buf->cols + + unsigned short *rdst = (unsigned short *)(buf->data.s + right->buf_idx*buf->cols + vi*scount + right->offset); - + for( i = 0; i < n; i++ ) { int d = dir[i]; @@ -3188,11 +3188,11 @@ void CvDTree::split_node_data( CvDTreeNode* node ) } else { - int *ldst = buf->data.i + left->buf_idx*buf->cols + + int *ldst = buf->data.i + left->buf_idx*buf->cols + vi*scount + left->offset; - int *rdst = buf->data.i + right->buf_idx*buf->cols + + int *rdst = buf->data.i + right->buf_idx*buf->cols + vi*scount + right->offset; - + for( i = 0; i < n; i++ ) { int d = dir[i]; @@ -3208,7 +3208,7 @@ void CvDTree::split_node_data( CvDTreeNode* node ) *ldst = idx; ldst++; } - + } if( vi < data->var_count ) @@ -3216,7 +3216,7 @@ void CvDTree::split_node_data( CvDTreeNode* node ) left->set_num_valid(vi, n1 - nr1); right->set_num_valid(vi, nr1); } - } + } } @@ -3230,9 +3230,9 @@ void CvDTree::split_node_data( CvDTreeNode* node ) int pos = data->get_work_var_count(); if (data->is_buf_16u) { - unsigned short* ldst = (unsigned short*)(buf->data.s + left->buf_idx*buf->cols + + unsigned short* ldst = (unsigned short*)(buf->data.s + left->buf_idx*buf->cols + pos*scount + left->offset); - unsigned short* rdst = (unsigned short*)(buf->data.s + right->buf_idx*buf->cols + + unsigned short* rdst = (unsigned short*)(buf->data.s + right->buf_idx*buf->cols + pos*scount + right->offset); for (i = 0; i < n; i++) { @@ -3252,9 +3252,9 @@ void CvDTree::split_node_data( CvDTreeNode* node ) } else { - int* ldst = buf->data.i + left->buf_idx*buf->cols + + int* ldst = buf->data.i + left->buf_idx*buf->cols + pos*scount + left->offset; - int* rdst = buf->data.i + right->buf_idx*buf->cols + + int* rdst = buf->data.i + right->buf_idx*buf->cols + pos*scount + right->offset; for (i = 0; i < n; i++) { @@ -3272,9 +3272,9 @@ void CvDTree::split_node_data( CvDTreeNode* node ) } } } - + // deallocate the parent node data that is not needed anymore - data->free_node_data(node); + data->free_node_data(node); } float CvDTree::calc_error( CvMLData* _data, int type, vector *resp ) @@ -3304,9 +3304,9 @@ float CvDTree::calc_error( CvMLData* _data, int type, vector *resp ) { CvMat sample, miss; int si = sidx ? sidx[i] : i; - cvGetRow( values, &sample, si ); - if( missing ) - cvGetRow( missing, &miss, si ); + cvGetRow( values, &sample, si ); + if( missing ) + cvGetRow( missing, &miss, si ); float r = (float)predict( &sample, missing ? &miss : 0 )->value; if( pred_resp ) pred_resp[i] = r; @@ -3321,16 +3321,16 @@ float CvDTree::calc_error( CvMLData* _data, int type, vector *resp ) { CvMat sample, miss; int si = sidx ? sidx[i] : i; - cvGetRow( values, &sample, si ); - if( missing ) - cvGetRow( missing, &miss, si ); + cvGetRow( values, &sample, si ); + if( missing ) + cvGetRow( missing, &miss, si ); float r = (float)predict( &sample, missing ? &miss : 0 )->value; if( pred_resp ) pred_resp[i] = r; float d = r - response->data.fl[si*r_step]; err += d*d; } - err = sample_count ? err / (float)sample_count : -FLT_MAX; + err = sample_count ? err / (float)sample_count : -FLT_MAX; } return err; } @@ -3527,7 +3527,7 @@ int CvDTree::cut_tree( int T, int fold, double min_alpha ) } -void CvDTree::free_prune_data(bool cut_tree) +void CvDTree::free_prune_data(bool _cut_tree) { CvDTreeNode* node = root; @@ -3548,7 +3548,7 @@ void CvDTree::free_prune_data(bool cut_tree) for( parent = node->parent; parent && parent->right == node; node = parent, parent = parent->parent ) { - if( cut_tree && parent->Tn <= pruned_tree_idx ) + if( _cut_tree && parent->Tn <= pruned_tree_idx ) { data->free_node( parent->left ); data->free_node( parent->right ); @@ -3650,12 +3650,12 @@ CvDTreeNode* CvDTree::predict( const CvMat* _sample, { int a = c = cofs[ci]; int b = (ci+1 >= data->cat_ofs->cols) ? data->cat_map->cols : cofs[ci+1]; - + int ival = cvRound(val); if( ival != val ) CV_Error( CV_StsBadArg, "one of input categorical variable is not an integer" ); - + int sh = 0; while( a < b ) { diff --git a/modules/ml/test/test_emknearestkmeans.cpp b/modules/ml/test/test_emknearestkmeans.cpp index 3dedb3a..a6a6f08 100644 --- a/modules/ml/test/test_emknearestkmeans.cpp +++ b/modules/ml/test/test_emknearestkmeans.cpp @@ -80,7 +80,7 @@ void generateData( Mat& data, Mat& labels, const vector& sizes, const Mat& CV_Assert( _means.rows == (int)sizes.size() && covs.size() == sizes.size() ); CV_Assert( !data.empty() && data.rows == total ); CV_Assert( data.type() == dataType ); - + labels.create( data.rows, 1, labelType ); randn( data, Scalar::all(-1.0), Scalar::all(1.0) ); @@ -99,7 +99,7 @@ void generateData( Mat& data, Mat& labels, const vector& sizes, const Mat& for( int i = bi; i < ei; i++, p++ ) { Mat r = data.row(i); - r = r * (*cit) + *mit; + r = r * (*cit) + *mit; if( labelType == CV_32FC1 ) labels.at(p, 0) = (float)l; else if( labelType == CV_32SC1 ) @@ -224,14 +224,14 @@ void CV_KMeansTest::run( int /*start_from*/ ) const int iters = 100; int sizesArr[] = { 5000, 7000, 8000 }; int pointsCount = sizesArr[0]+ sizesArr[1] + sizesArr[2]; - + Mat data( pointsCount, 2, CV_32FC1 ), labels; vector sizes( sizesArr, sizesArr + sizeof(sizesArr) / sizeof(sizesArr[0]) ); Mat means; vector covs; defaultDistribs( means, covs ); generateData( data, labels, sizes, means, covs, CV_32FC1, CV_32SC1 ); - + int code = cvtest::TS::OK; float err; Mat bestLabels; @@ -327,24 +327,24 @@ void CV_KNearestTest::run( int /*start_from*/ ) class EM_Params { public: - EM_Params(int nclusters=10, int covMatType=EM::COV_MAT_DIAGONAL, int startStep=EM::START_AUTO_STEP, - const cv::TermCriteria& termCrit=cv::TermCriteria(cv::TermCriteria::COUNT+cv::TermCriteria::EPS, 100, FLT_EPSILON), - const cv::Mat* probs=0, const cv::Mat* weights=0, - const cv::Mat* means=0, const std::vector* covs=0) - : nclusters(nclusters), covMatType(covMatType), startStep(startStep), - probs(probs), weights(weights), means(means), covs(covs), termCrit(termCrit) + EM_Params(int _nclusters=10, int _covMatType=EM::COV_MAT_DIAGONAL, int _startStep=EM::START_AUTO_STEP, + const cv::TermCriteria& _termCrit=cv::TermCriteria(cv::TermCriteria::COUNT+cv::TermCriteria::EPS, 100, FLT_EPSILON), + const cv::Mat* _probs=0, const cv::Mat* _weights=0, + const cv::Mat* _means=0, const std::vector* _covs=0) + : nclusters(_nclusters), covMatType(_covMatType), startStep(_startStep), + probs(_probs), weights(_weights), means(_means), covs(_covs), termCrit(_termCrit) {} - + int nclusters; int covMatType; int startStep; - + // all 4 following matrices should have type CV_32FC1 const cv::Mat* probs; const cv::Mat* weights; const cv::Mat* means; const std::vector* covs; - + cv::TermCriteria termCrit; }; @@ -497,7 +497,7 @@ void CV_EMTest::run( int /*start_from*/ ) int currCode = runCase(caseIndex++, params, trainData, trainLabels, testData, testLabels, sizes); code = currCode == cvtest::TS::OK ? code : currCode; } - + ts->set_failed_test_info( code ); } diff --git a/modules/ml/test/test_gbttest.cpp b/modules/ml/test/test_gbttest.cpp index f9ecc4f..1e6d0fb 100644 --- a/modules/ml/test/test_gbttest.cpp +++ b/modules/ml/test/test_gbttest.cpp @@ -12,8 +12,8 @@ class CV_GBTreesTest : public cvtest::BaseTest { public: CV_GBTreesTest(); - ~CV_GBTreesTest(); - + ~CV_GBTreesTest(); + protected: void run(int); @@ -21,21 +21,21 @@ protected: int TestSaveLoad(); int checkPredictError(int test_num); - int checkLoadSave(); - + int checkLoadSave(); + string model_file_name1; string model_file_name2; string* datasets; string data_path; - + CvMLData* data; CvGBTrees* gtb; - + vector test_resps1; vector test_resps2; - int64 initSeed; + int64 initSeed; }; @@ -47,7 +47,7 @@ int _get_len(const CvMat* mat) CV_GBTreesTest::CV_GBTreesTest() { - int64 seeds[] = { CV_BIG_INT(0x00009fff4f9c8d52), + int64 seeds[] = { CV_BIG_INT(0x00009fff4f9c8d52), CV_BIG_INT(0x0000a17166072c7c), CV_BIG_INT(0x0201b32115cd1f9a), CV_BIG_INT(0x0513cb37abcd1234), @@ -55,7 +55,7 @@ CV_GBTreesTest::CV_GBTreesTest() }; int seedCount = sizeof(seeds)/sizeof(seeds[0]); - cv::RNG& rng = cv::theRNG(); + cv::RNG& rng = cv::theRNG(); initSeed = rng.state; rng.state = seeds[rng(seedCount)]; @@ -69,14 +69,14 @@ CV_GBTreesTest::~CV_GBTreesTest() if (data) delete data; delete[] datasets; - cv::theRNG().state = initSeed; + cv::theRNG().state = initSeed; } int CV_GBTreesTest::TestTrainPredict(int test_num) { int code = cvtest::TS::OK; - + int weak_count = 200; float shrinkage = 0.1f; float subsample_portion = 0.5f; @@ -89,7 +89,7 @@ int CV_GBTreesTest::TestTrainPredict(int test_num) case (2) : loss_function_type = CvGBTrees::ABSOLUTE_LOSS; break; case (3) : loss_function_type = CvGBTrees::HUBER_LOSS; break; case (0) : loss_function_type = CvGBTrees::DEVIANCE_LOSS; break; - default : + default : { ts->printf( cvtest::TS::LOG, "Bad test_num value in CV_GBTreesTest::TestTrainPredict(..) function." ); return cvtest::TS::FAIL_BAD_ARG_CHECK; @@ -101,7 +101,7 @@ int CV_GBTreesTest::TestTrainPredict(int test_num) { data = new CvMLData(); data->set_delimiter(','); - + if (data->read_csv(datasets[dataset_num].c_str())) { ts->printf( cvtest::TS::LOG, "File reading error." ); @@ -124,25 +124,25 @@ int CV_GBTreesTest::TestTrainPredict(int test_num) CvTrainTestSplit spl( train_sample_count ); data->set_train_test_split( &spl ); } - - data->mix_train_and_test_idx(); - - + + data->mix_train_and_test_idx(); + + if (gtb) delete gtb; gtb = new CvGBTrees(); bool tmp_code = true; tmp_code = gtb->train(data, CvGBTreesParams(loss_function_type, weak_count, shrinkage, subsample_portion, max_depth, use_surrogates)); - + if (!tmp_code) { ts->printf( cvtest::TS::LOG, "Model training was failed."); return cvtest::TS::FAIL_INVALID_OUTPUT; } - + code = checkPredictError(test_num); - + return code; } @@ -152,14 +152,14 @@ int CV_GBTreesTest::checkPredictError(int test_num) { if (!gtb) return cvtest::TS::FAIL_GENERIC; - + //float mean[] = {5.430247f, 13.5654f, 12.6569f, 13.1661f}; //float sigma[] = {0.4162694f, 3.21161f, 3.43297f, 3.00624f}; - float mean[] = {5.80226f, 12.68689f, 13.49095f, 13.19628f}; + float mean[] = {5.80226f, 12.68689f, 13.49095f, 13.19628f}; float sigma[] = {0.4764534f, 3.166919f, 3.022405f, 2.868722f}; - + float current_error = gtb->calc_error(data, CV_TEST_ERROR); - + if ( abs( current_error - mean[test_num]) > 6*sigma[test_num] ) { ts->printf( cvtest::TS::LOG, "Test error is out of range:\n" @@ -177,7 +177,7 @@ int CV_GBTreesTest::TestSaveLoad() { if (!gtb) return cvtest::TS::FAIL_GENERIC; - + model_file_name1 = cv::tempfile(); model_file_name2 = cv::tempfile(); @@ -186,9 +186,9 @@ int CV_GBTreesTest::TestSaveLoad() gtb->load(model_file_name1.c_str()); gtb->calc_error(data, CV_TEST_ERROR, &test_resps2); gtb->save(model_file_name2.c_str()); - + return checkLoadSave(); - + } @@ -200,7 +200,7 @@ int CV_GBTreesTest::checkLoadSave() // 1. compare files ifstream f1( model_file_name1.c_str() ), f2( model_file_name2.c_str() ); string s1, s2; - int lineIdx = 0; + int lineIdx = 0; CV_Assert( f1.is_open() && f2.is_open() ); for( ; !f1.eof() && !f2.eof(); lineIdx++ ) { @@ -244,23 +244,23 @@ int CV_GBTreesTest::checkLoadSave() void CV_GBTreesTest::run(int) { - string data_path = string(ts->get_data_path()); + string dataPath = string(ts->get_data_path()); datasets = new string[2]; - datasets[0] = data_path + string("spambase.data"); /*string("dataset_classification.csv");*/ - datasets[1] = data_path + string("housing_.data"); /*string("dataset_regression.csv");*/ + datasets[0] = dataPath + string("spambase.data"); /*string("dataset_classification.csv");*/ + datasets[1] = dataPath + string("housing_.data"); /*string("dataset_regression.csv");*/ int code = cvtest::TS::OK; for (int i = 0; i < 4; i++) { - + int temp_code = TestTrainPredict(i); if (temp_code != cvtest::TS::OK) { code = temp_code; break; } - + else if (i==0) { temp_code = TestSaveLoad(); @@ -269,13 +269,13 @@ void CV_GBTreesTest::run(int) delete data; data = 0; } - + delete gtb; gtb = 0; } delete data; data = 0; - + ts->set_failed_test_info( code ); } diff --git a/modules/nonfree/src/sift.cpp b/modules/nonfree/src/sift.cpp index 7edba13..0bb7848 100644 --- a/modules/nonfree/src/sift.cpp +++ b/modules/nonfree/src/sift.cpp @@ -43,7 +43,7 @@ /**********************************************************************************************\ Implementation of SIFT is based on the code from http://blogs.oregonstate.edu/hess/code/sift/ Below is the original copyright. - + // Copyright (c) 2006-2010, Rob Hess // All rights reserved. @@ -101,7 +101,7 @@ // NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS // SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. \**********************************************************************************************/ - + #include "precomp.hpp" #include #include @@ -161,10 +161,10 @@ static const float SIFT_DESCR_MAG_THR = 0.2f; // factor used to convert floating-point descriptor to unsigned char static const float SIFT_INT_DESCR_FCTR = 512.f; - + static const int SIFT_FIXPT_SCALE = 48; - - + + static Mat createInitialImage( const Mat& img, bool doubleImageSize, float sigma ) { Mat gray, gray_fpt; @@ -173,9 +173,9 @@ static Mat createInitialImage( const Mat& img, bool doubleImageSize, float sigma else img.copyTo(gray); gray.convertTo(gray_fpt, CV_16S, SIFT_FIXPT_SCALE, 0); - + float sig_diff; - + if( doubleImageSize ) { sig_diff = sqrtf( std::max(sigma * sigma - SIFT_INIT_SIGMA * SIFT_INIT_SIGMA * 4, 0.01f) ); @@ -191,13 +191,13 @@ static Mat createInitialImage( const Mat& img, bool doubleImageSize, float sigma return gray_fpt; } } - - + + void SIFT::buildGaussianPyramid( const Mat& base, vector& pyr, int nOctaves ) const { vector sig(nOctaveLayers + 3); pyr.resize(nOctaves*(nOctaveLayers + 3)); - + // precompute Gaussian sigmas using the following formula: // \sigma_{total}^2 = \sigma_{i}^2 + \sigma_{i-1}^2 sig[0] = sigma; @@ -208,7 +208,7 @@ void SIFT::buildGaussianPyramid( const Mat& base, vector& pyr, int nOctaves double sig_total = sig_prev*k; sig[i] = std::sqrt(sig_total*sig_total - sig_prev*sig_prev); } - + for( int o = 0; o < nOctaves; o++ ) { for( int i = 0; i < nOctaveLayers + 3; i++ ) @@ -237,7 +237,7 @@ void SIFT::buildDoGPyramid( const vector& gpyr, vector& dogpyr ) const { int nOctaves = (int)gpyr.size()/(nOctaveLayers + 3); dogpyr.resize( nOctaves*(nOctaveLayers + 2) ); - + for( int o = 0; o < nOctaves; o++ ) { for( int i = 0; i < nOctaveLayers + 2; i++ ) @@ -250,21 +250,21 @@ void SIFT::buildDoGPyramid( const vector& gpyr, vector& dogpyr ) const } } - + // Computes a gradient orientation histogram at a specified pixel static float calcOrientationHist( const Mat& img, Point pt, int radius, float sigma, float* hist, int n ) { int i, j, k, len = (radius*2+1)*(radius*2+1); - + float expf_scale = -1.f/(2.f * sigma * sigma); AutoBuffer buf(len*4 + n+4); float *X = buf, *Y = X + len, *Mag = X, *Ori = Y + len, *W = Ori + len; float* temphist = W + len + 2; - + for( i = 0; i < n; i++ ) temphist[i] = 0.f; - + for( i = -radius, k = 0; i <= radius; i++ ) { int y = pt.y + i; @@ -275,22 +275,22 @@ static float calcOrientationHist( const Mat& img, Point pt, int radius, int x = pt.x + j; if( x <= 0 || x >= img.cols - 1 ) continue; - + float dx = (float)(img.at(y, x+1) - img.at(y, x-1)); float dy = (float)(img.at(y-1, x) - img.at(y+1, x)); - + X[k] = dx; Y[k] = dy; W[k] = (i*i + j*j)*expf_scale; k++; } } - + len = k; - + // compute gradient values, orientations and the weights over the pixel neighborhood exp(W, W, len); fastAtan2(Y, X, Ori, len, true); magnitude(X, Y, Mag, len); - + for( k = 0; k < len; k++ ) { int bin = cvRound((n/360.f)*Ori[k]); @@ -300,7 +300,7 @@ static float calcOrientationHist( const Mat& img, Point pt, int radius, bin += n; temphist[bin] += W[k]*Mag[k]; } - + // smooth the histogram temphist[-1] = temphist[n-1]; temphist[-2] = temphist[n-2]; @@ -312,19 +312,19 @@ static float calcOrientationHist( const Mat& img, Point pt, int radius, (temphist[i-1] + temphist[i+1])*(4.f/16.f) + temphist[i]*(6.f/16.f); } - + float maxval = hist[0]; for( i = 1; i < n; i++ ) maxval = std::max(maxval, hist[i]); - + return maxval; } - + // // Interpolates a scale-space extremum's location and scale to subpixel // accuracy to form an image feature. Rejects features with low contrast. -// Based on Section 4 of Lowe's paper. +// Based on Section 4 of Lowe's paper. static bool adjustLocalExtrema( const vector& dog_pyr, KeyPoint& kpt, int octv, int& layer, int& r, int& c, int nOctaveLayers, float contrastThreshold, float edgeThreshold, float sigma ) @@ -333,21 +333,21 @@ static bool adjustLocalExtrema( const vector& dog_pyr, KeyPoint& kpt, int o const float deriv_scale = img_scale*0.5f; const float second_deriv_scale = img_scale; const float cross_deriv_scale = img_scale*0.25f; - + float xi=0, xr=0, xc=0, contr; int i = 0; - + for( ; i < SIFT_MAX_INTERP_STEPS; i++ ) { int idx = octv*(nOctaveLayers+2) + layer; const Mat& img = dog_pyr[idx]; const Mat& prev = dog_pyr[idx-1]; const Mat& next = dog_pyr[idx+1]; - + Vec3f dD((img.at(r, c+1) - img.at(r, c-1))*deriv_scale, (img.at(r+1, c) - img.at(r-1, c))*deriv_scale, (next.at(r, c) - prev.at(r, c))*deriv_scale); - + float v2 = (float)img.at(r, c)*2; float dxx = (img.at(r, c+1) + img.at(r, c-1) - v2)*second_deriv_scale; float dyy = (img.at(r+1, c) + img.at(r-1, c) - v2)*second_deriv_scale; @@ -358,34 +358,34 @@ static bool adjustLocalExtrema( const vector& dog_pyr, KeyPoint& kpt, int o prev.at(r, c+1) + prev.at(r, c-1))*cross_deriv_scale; float dys = (next.at(r+1, c) - next.at(r-1, c) - prev.at(r+1, c) + prev.at(r-1, c))*cross_deriv_scale; - + Matx33f H(dxx, dxy, dxs, dxy, dyy, dys, dxs, dys, dss); - + Vec3f X = H.solve(dD, DECOMP_LU); - + xi = -X[2]; xr = -X[1]; xc = -X[0]; - + if( std::abs( xi ) < 0.5f && std::abs( xr ) < 0.5f && std::abs( xc ) < 0.5f ) break; - + c += cvRound( xc ); r += cvRound( xr ); layer += cvRound( xi ); - + if( layer < 1 || layer > nOctaveLayers || c < SIFT_IMG_BORDER || c >= img.cols - SIFT_IMG_BORDER || r < SIFT_IMG_BORDER || r >= img.rows - SIFT_IMG_BORDER ) return false; } - + /* ensure convergence of interpolation */ if( i >= SIFT_MAX_INTERP_STEPS ) return false; - + { int idx = octv*(nOctaveLayers+2) + layer; const Mat& img = dog_pyr[idx]; @@ -395,11 +395,11 @@ static bool adjustLocalExtrema( const vector& dog_pyr, KeyPoint& kpt, int o (img.at(r+1, c) - img.at(r-1, c))*deriv_scale, (next.at(r, c) - prev.at(r, c))*deriv_scale); float t = dD.dot(Matx31f(xc, xr, xi)); - + contr = img.at(r, c)*img_scale + t * 0.5f; if( std::abs( contr ) * nOctaveLayers < contrastThreshold ) return false; - + /* principal curvatures are computed using the trace and det of Hessian */ float v2 = img.at(r, c)*2.f; float dxx = (img.at(r, c+1) + img.at(r, c-1) - v2)*second_deriv_scale; @@ -408,20 +408,20 @@ static bool adjustLocalExtrema( const vector& dog_pyr, KeyPoint& kpt, int o img.at(r-1, c+1) + img.at(r-1, c-1)) * cross_deriv_scale; float tr = dxx + dyy; float det = dxx * dyy - dxy * dxy; - + if( det <= 0 || tr*tr*edgeThreshold >= (edgeThreshold + 1)*(edgeThreshold + 1)*det ) return false; } - + kpt.pt.x = (c + xc) * (1 << octv); kpt.pt.y = (r + xr) * (1 << octv); kpt.octave = octv + (layer << 8) + (cvRound((xi + 0.5)*255) << 16); kpt.size = sigma*powf(2.f, (layer + xi) / nOctaveLayers)*(1 << octv)*2; - + return true; } - - + + // // Detects features at extrema in DoG scale space. Bad features are discarded // based on contrast and ratio of principal curvatures. @@ -433,9 +433,9 @@ void SIFT::findScaleSpaceExtrema( const vector& gauss_pyr, const vector& gauss_pyr, const vector(r); const short* prevptr = prev.ptr(r); const short* nextptr = next.ptr(r); - + for( int c = SIFT_IMG_BORDER; c < cols-SIFT_IMG_BORDER; c++) { int val = currptr[c]; - + // find local extrema with pixel accuracy if( std::abs(val) > threshold && ((val > 0 && val >= currptr[c-1] && val >= currptr[c+1] && @@ -492,11 +492,11 @@ void SIFT::findScaleSpaceExtrema( const vector& gauss_pyr, const vector 0 ? j - 1 : n - 1; - int r = j < n-1 ? j + 1 : 0; - - if( hist[j] > hist[l] && hist[j] > hist[r] && hist[j] >= mag_thr ) + int r2 = j < n-1 ? j + 1 : 0; + + if( hist[j] > hist[l] && hist[j] > hist[r2] && hist[j] >= mag_thr ) { - float bin = j + 0.5f * (hist[l]-hist[r]) / (hist[l] - 2*hist[j] + hist[r]); + float bin = j + 0.5f * (hist[l]-hist[r2]) / (hist[l] - 2*hist[j] + hist[r2]); bin = bin < 0 ? n + bin : bin >= n ? bin - n : bin; kpt.angle = (float)((360.f/n) * bin); keypoints.push_back(kpt); @@ -506,9 +506,9 @@ void SIFT::findScaleSpaceExtrema( const vector& gauss_pyr, const vector buf(len*6 + histlen); float *X = buf, *Y = X + len, *Mag = Y, *Ori = Mag + len, *W = Ori + len; float *RBin = W + len, *CBin = RBin + len, *hist = CBin + len; - + for( i = 0; i < d+2; i++ ) { for( j = 0; j < d+2; j++ ) for( k = 0; k < n+2; k++ ) hist[(i*(d+2) + j)*(n+2) + k] = 0.; } - + for( i = -radius, k = 0; i <= radius; i++ ) for( j = -radius; j <= radius; j++ ) { @@ -549,7 +549,7 @@ static void calcSIFTDescriptor( const Mat& img, Point2f ptf, float ori, float sc float rbin = r_rot + d/2 - 0.5f; float cbin = c_rot + d/2 - 0.5f; int r = pt.y + i, c = pt.x + j; - + if( rbin > -1 && rbin < d && cbin > -1 && cbin < d && r > 0 && r < rows - 1 && c > 0 && c < cols - 1 ) { @@ -560,30 +560,30 @@ static void calcSIFTDescriptor( const Mat& img, Point2f ptf, float ori, float sc k++; } } - + len = k; fastAtan2(Y, X, Ori, len, true); magnitude(X, Y, Mag, len); exp(W, W, len); - + for( k = 0; k < len; k++ ) { float rbin = RBin[k], cbin = CBin[k]; float obin = (Ori[k] - ori)*bins_per_rad; float mag = Mag[k]*W[k]; - + int r0 = cvFloor( rbin ); int c0 = cvFloor( cbin ); int o0 = cvFloor( obin ); rbin -= r0; cbin -= c0; obin -= o0; - + if( o0 < 0 ) o0 += n; if( o0 >= n ) o0 -= n; - + // histogram update using tri-linear interpolation float v_r1 = mag*rbin, v_r0 = mag - v_r1; float v_rc11 = v_r1*cbin, v_rc10 = v_r1 - v_rc11; @@ -592,7 +592,7 @@ static void calcSIFTDescriptor( const Mat& img, Point2f ptf, float ori, float sc float v_rco101 = v_rc10*obin, v_rco100 = v_rc10 - v_rco101; float v_rco011 = v_rc01*obin, v_rco010 = v_rc01 - v_rco011; float v_rco001 = v_rc00*obin, v_rco000 = v_rc00 - v_rco001; - + int idx = ((r0+1)*(d+2) + c0+1)*(n+2) + o0; hist[idx] += v_rco000; hist[idx+1] += v_rco001; @@ -603,7 +603,7 @@ static void calcSIFTDescriptor( const Mat& img, Point2f ptf, float ori, float sc hist[idx+(d+3)*(n+2)] += v_rco110; hist[idx+(d+3)*(n+2)+1] += v_rco111; } - + // finalize histogram, since the orientation histograms are circular for( i = 0; i < d; i++ ) for( j = 0; j < d; j++ ) @@ -635,12 +635,12 @@ static void calcSIFTDescriptor( const Mat& img, Point2f ptf, float ori, float sc dst[k] = saturate_cast(dst[k]*nrm2); } } - + static void calcDescriptors(const vector& gpyr, const vector& keypoints, Mat& descriptors, int nOctaveLayers ) { int d = SIFT_DESCR_WIDTH, n = SIFT_DESCR_HIST_BINS; - + for( size_t i = 0; i < keypoints.size(); i++ ) { KeyPoint kpt = keypoints[i]; @@ -649,7 +649,7 @@ static void calcDescriptors(const vector& gpyr, const vector& key float size=kpt.size*scale; Point2f ptf(kpt.pt.x*scale, kpt.pt.y*scale); const Mat& img = gpyr[octv*(nOctaveLayers + 3) + layer]; - + calcSIFTDescriptor(img, ptf, kpt.angle, size*0.5f, d, n, descriptors.ptr((int)i)); } } @@ -687,34 +687,34 @@ void SIFT::operator()(InputArray _image, InputArray _mask, bool useProvidedKeypoints) const { Mat image = _image.getMat(), mask = _mask.getMat(); - + if( image.empty() || image.depth() != CV_8U ) CV_Error( CV_StsBadArg, "image is empty or has incorrect depth (!=CV_8U)" ); - + if( !mask.empty() && mask.type() != CV_8UC1 ) CV_Error( CV_StsBadArg, "mask has incorrect type (!=CV_8UC1)" ); - + Mat base = createInitialImage(image, false, (float)sigma); vector gpyr, dogpyr; int nOctaves = cvRound(log( (double)std::min( base.cols, base.rows ) ) / log(2.) - 2); - + //double t, tf = getTickFrequency(); //t = (double)getTickCount(); buildGaussianPyramid(base, gpyr, nOctaves); buildDoGPyramid(gpyr, dogpyr); - + //t = (double)getTickCount() - t; //printf("pyramid construction time: %g\n", t*1000./tf); - + if( !useProvidedKeypoints ) { //t = (double)getTickCount(); findScaleSpaceExtrema(gpyr, dogpyr, keypoints); KeyPointsFilter::removeDuplicated( keypoints ); - + if( !mask.empty() ) KeyPointsFilter::runByPixelsMask( keypoints, mask ); - + if( nfeatures > 0 ) KeyPointsFilter::retainBest(keypoints, nfeatures); //t = (double)getTickCount() - t; @@ -725,14 +725,14 @@ void SIFT::operator()(InputArray _image, InputArray _mask, // filter keypoints by mask //KeyPointsFilter::runByPixelsMask( keypoints, mask ); } - + if( _descriptors.needed() ) { //t = (double)getTickCount(); int dsize = descriptorSize(); _descriptors.create((int)keypoints.size(), dsize, CV_32F); Mat descriptors = _descriptors.getMat(); - + calcDescriptors(gpyr, keypoints, descriptors, nOctaveLayers); //t = (double)getTickCount() - t; //printf("descriptor extraction time: %g\n", t*1000./tf); @@ -742,8 +742,8 @@ void SIFT::operator()(InputArray _image, InputArray _mask, void SIFT::detectImpl( const Mat& image, vector& keypoints, const Mat& mask) const { (*this)(image, mask, keypoints, noArray()); -} - +} + void SIFT::computeImpl( const Mat& image, vector& keypoints, Mat& descriptors) const { (*this)(image, Mat(), keypoints, descriptors, true); diff --git a/modules/nonfree/src/surf.cpp b/modules/nonfree/src/surf.cpp index b6e776d..2690b35 100644 --- a/modules/nonfree/src/surf.cpp +++ b/modules/nonfree/src/surf.cpp @@ -592,7 +592,7 @@ struct SURFInvoker Ptr winbuf = cvCreateMat( 1, imaxSize*imaxSize, CV_8U ); for( k = k1; k < k2; k++ ) { - int i, j, kk, x, y, nangle; + int i, j, kk, nangle; float* vec; SurfHF dx_t[NX], dy_t[NY]; KeyPoint& kp = (*keypoints)[k]; @@ -622,8 +622,8 @@ struct SURFInvoker resizeHaarPattern( dy_s, dy_t, NY, 4, grad_wav_size, sum->cols ); for( kk = 0, nangle = 0; kk < nOriSamples; kk++ ) { - x = cvRound( center.x + apt[kk].x*s - (float)(grad_wav_size-1)/2 ); - y = cvRound( center.y + apt[kk].y*s - (float)(grad_wav_size-1)/2 ); + int x = cvRound( center.x + apt[kk].x*s - (float)(grad_wav_size-1)/2 ); + int y = cvRound( center.y + apt[kk].y*s - (float)(grad_wav_size-1)/2 ); if( y < 0 || y >= sum->rows - grad_wav_size || x < 0 || x >= sum->cols - grad_wav_size ) continue; @@ -729,8 +729,8 @@ struct SURFInvoker int pixel_y = start_y; for( j = 0; j < win_size; j++, pixel_y-- ) { - x = MAX( pixel_x, 0 ); - y = MAX( pixel_y, 0 ); + int x = MAX( pixel_x, 0 ); + int y = MAX( pixel_y, 0 ); x = MIN( x, img->cols-1 ); y = MIN( y, img->rows-1 ); WIN[i*win_size + j] = img->at(y, x); @@ -763,9 +763,9 @@ struct SURFInvoker for( i = 0; i < 4; i++ ) for( j = 0; j < 4; j++ ) { - for( y = i*5; y < i*5+5; y++ ) + for(int y = i*5; y < i*5+5; y++ ) { - for( x = j*5; x < j*5+5; x++ ) + for(int x = j*5; x < j*5+5; x++ ) { float tx = DX[y][x], ty = DY[y][x]; if( ty >= 0 ) @@ -797,9 +797,9 @@ struct SURFInvoker for( i = 0; i < 4; i++ ) for( j = 0; j < 4; j++ ) { - for( y = i*5; y < i*5+5; y++ ) + for(int y = i*5; y < i*5+5; y++ ) { - for( x = j*5; x < j*5+5; x++ ) + for(int x = j*5; x < j*5+5; x++ ) { float tx = DX[y][x], ty = DY[y][x]; vec[0] += tx; vec[1] += ty; diff --git a/modules/objdetect/include/opencv2/objdetect/objdetect.hpp b/modules/objdetect/include/opencv2/objdetect/objdetect.hpp index 7568986..a754238 100644 --- a/modules/objdetect/include/opencv2/objdetect/objdetect.hpp +++ b/modules/objdetect/include/opencv2/objdetect/objdetect.hpp @@ -636,12 +636,14 @@ struct CV_EXPORTS Feature int label; ///< Quantization Feature() : x(0), y(0), label(0) {} - Feature(int x, int y, int label) : x(x), y(y), label(label) {} + Feature(int x, int y, int label); void read(const FileNode& fn); void write(FileStorage& fs) const; }; +inline Feature::Feature(int _x, int _y, int _label) : x(_x), y(_y), label(_label) {} + struct CV_EXPORTS Template { int width; @@ -688,10 +690,7 @@ protected: /// Candidate feature with a score struct Candidate { - Candidate(int x, int y, int label, float score) - : f(x, y, label), score(score) - { - } + Candidate(int x, int y, int label, float score); /// Sort candidates with high score to the front bool operator<(const Candidate& rhs) const @@ -716,6 +715,8 @@ protected: size_t num_features, float distance); }; +inline QuantizedPyramid::Candidate::Candidate(int x, int y, int label, float _score) : f(x, y, label), score(_score) {} + /** * \brief Interface for modalities that plug into the LINE template matching representation. * @@ -853,10 +854,7 @@ struct CV_EXPORTS Match { } - Match(int x, int y, float similarity, const std::string& class_id, int template_id) - : x(x), y(y), similarity(similarity), class_id(class_id), template_id(template_id) - { - } + Match(int x, int y, float similarity, const std::string& class_id, int template_id); /// Sort matches with high similarity to the front bool operator<(const Match& rhs) const @@ -880,6 +878,11 @@ struct CV_EXPORTS Match int template_id; }; +inline Match::Match(int _x, int _y, float _similarity, const std::string& _class_id, int _template_id) + : x(_x), y(_y), similarity(_similarity), class_id(_class_id), template_id(_template_id) + { + } + /** * \brief Object detector using the LINE template matching algorithm with any set of * modalities. diff --git a/modules/objdetect/src/cascadedetect.cpp b/modules/objdetect/src/cascadedetect.cpp index 7cb9708..91b8ba5 100644 --- a/modules/objdetect/src/cascadedetect.cpp +++ b/modules/objdetect/src/cascadedetect.cpp @@ -46,12 +46,12 @@ namespace cv { - + // class for grouping object candidates, detected by Cascade Classifier, HOG etc. // instance of the class is to be passed to cv::partition (see cxoperations.hpp) class CV_EXPORTS SimilarRects { -public: +public: SimilarRects(double _eps) : eps(_eps) {} inline bool operator()(const Rect& r1, const Rect& r2) const { @@ -62,8 +62,8 @@ public: std::abs(r1.y + r1.height - r2.y - r2.height) <= delta; } double eps; -}; - +}; + void groupRectangles(vector& rectList, int groupThreshold, double eps, vector* weights, vector* levelWeights) { @@ -78,13 +78,13 @@ void groupRectangles(vector& rectList, int groupThreshold, double eps, vec } return; } - + vector labels; int nclasses = partition(rectList, labels, SimilarRects(eps)); - + vector rrects(nclasses); vector rweights(nclasses, 0); - vector rejectLevels(nclasses, 0); + vector rejectLevels(nclasses, 0); vector rejectWeights(nclasses, DBL_MIN); int i, j, nlabels = (int)labels.size(); for( i = 0; i < nlabels; i++ ) @@ -97,10 +97,10 @@ void groupRectangles(vector& rectList, int groupThreshold, double eps, vec rweights[cls]++; } if ( levelWeights && weights && !weights->empty() && !levelWeights->empty() ) - { - for( i = 0; i < nlabels; i++ ) - { - int cls = labels[i]; + { + for( i = 0; i < nlabels; i++ ) + { + int cls = labels[i]; if( (*weights)[i] > rejectLevels[cls] ) { rejectLevels[cls] = (*weights)[i]; @@ -108,9 +108,9 @@ void groupRectangles(vector& rectList, int groupThreshold, double eps, vec } else if( ( (*weights)[i] == rejectLevels[cls] ) && ( (*levelWeights)[i] > rejectWeights[cls] ) ) rejectWeights[cls] = (*levelWeights)[i]; - } - } - + } + } + for( i = 0; i < nclasses; i++ ) { Rect r = rrects[i]; @@ -120,32 +120,32 @@ void groupRectangles(vector& rectList, int groupThreshold, double eps, vec saturate_cast(r.width*s), saturate_cast(r.height*s)); } - + rectList.clear(); if( weights ) weights->clear(); - if( levelWeights ) - levelWeights->clear(); - + if( levelWeights ) + levelWeights->clear(); + for( i = 0; i < nclasses; i++ ) { Rect r1 = rrects[i]; int n1 = levelWeights ? rejectLevels[i] : rweights[i]; - double w1 = rejectWeights[i]; + double w1 = rejectWeights[i]; if( n1 <= groupThreshold ) continue; // filter out small face rectangles inside large rectangles for( j = 0; j < nclasses; j++ ) { int n2 = rweights[j]; - + if( j == i || n2 <= groupThreshold ) continue; Rect r2 = rrects[j]; - + int dx = saturate_cast( r2.width * eps ); int dy = saturate_cast( r2.height * eps ); - + if( i != j && r1.x >= r2.x - dx && r1.y >= r2.y - dy && @@ -154,14 +154,14 @@ void groupRectangles(vector& rectList, int groupThreshold, double eps, vec (n2 > std::max(3, n1) || n1 < 3) ) break; } - + if( j == nclasses ) { rectList.push_back(r1); if( weights ) weights->push_back(n1); - if( levelWeights ) - levelWeights->push_back(w1); + if( levelWeights ) + levelWeights->push_back(w1); } } } @@ -169,158 +169,158 @@ void groupRectangles(vector& rectList, int groupThreshold, double eps, vec class MeanshiftGrouping { public: - MeanshiftGrouping(const Point3d& densKer, const vector& posV, - const vector& wV, double, int maxIter = 20) + MeanshiftGrouping(const Point3d& densKer, const vector& posV, + const vector& wV, double, int maxIter = 20) { - densityKernel = densKer; + densityKernel = densKer; weightsV = wV; positionsV = posV; positionsCount = (int)posV.size(); - meanshiftV.resize(positionsCount); + meanshiftV.resize(positionsCount); distanceV.resize(positionsCount); - iterMax = maxIter; - - for (unsigned i = 0; i& modesV, vector& resWeightsV, const double eps) + + void getModes(vector& modesV, vector& resWeightsV, const double eps) { - for (size_t i=0; i positionsV; - vector weightsV; + vector positionsV; + vector weightsV; - Point3d densityKernel; - int positionsCount; + Point3d densityKernel; + int positionsCount; - vector meanshiftV; - vector distanceV; - int iterMax; - double modeEps; + vector meanshiftV; + vector distanceV; + int iterMax; + double modeEps; - Point3d getNewValue(const Point3d& inPt) const + Point3d getNewValue(const Point3d& inPt) const { - Point3d resPoint(.0); - Point3d ratPoint(.0); - for (size_t i=0; i& rectList, double detectThreshold, vector* foundWeights, - vector& scales, Size winDetSize) +static void groupRectangles_meanshift(vector& rectList, double detectThreshold, vector* foundWeights, + vector& scales, Size winDetSize) { int detectionCount = (int)rectList.size(); vector hits(detectionCount), resultHits; vector hitWeights(detectionCount), resultWeights; Point2d hitCenter; - for (int i=0; i < detectionCount; i++) + for (int i=0; i < detectionCount; i++) { hitWeights[i] = (*foundWeights)[i]; hitCenter = (rectList[i].tl() + rectList[i].br())*(0.5); //center of rectangles @@ -338,17 +338,17 @@ static void groupRectangles_meanshift(vector& rectList, double detectThres msGrouping.getModes(resultHits, resultWeights, 1); - for (unsigned i=0; i < resultHits.size(); ++i) + for (unsigned i=0; i < resultHits.size(); ++i) { double scale = exp(resultHits[i].z); hitCenter.x = resultHits[i].x; hitCenter.y = resultHits[i].y; Size s( int(winDetSize.width * scale), int(winDetSize.height * scale) ); - Rect resultRect( int(hitCenter.x-s.width/2), int(hitCenter.y-s.height/2), - int(s.width), int(s.height) ); + Rect resultRect( int(hitCenter.x-s.width/2), int(hitCenter.y-s.height/2), + int(s.width), int(s.height) ); - if (resultWeights[i] > detectThreshold) + if (resultWeights[i] > detectThreshold) { rectList.push_back(resultRect); foundWeights->push_back(resultWeights[i]); @@ -371,13 +371,13 @@ void groupRectangles(vector& rectList, vector& rejectLevels, vector& rectList, vector& foundWeights, - vector& foundScales, double detectThreshold, Size winDetSize) +void groupRectangles_meanshift(vector& rectList, vector& foundWeights, + vector& foundScales, double detectThreshold, Size winDetSize) { - groupRectangles_meanshift(rectList, detectThreshold, &foundWeights, foundScales, winDetSize); + groupRectangles_meanshift(rectList, detectThreshold, &foundWeights, foundScales, winDetSize); } - + FeatureEvaluator::~FeatureEvaluator() {} bool FeatureEvaluator::read(const FileNode&) {return true;} @@ -394,21 +394,21 @@ bool HaarEvaluator::Feature :: read( const FileNode& node ) { FileNode rnode = node[CC_RECTS]; FileNodeIterator it = rnode.begin(), it_end = rnode.end(); - + int ri; for( ri = 0; ri < RECT_NUM; ri++ ) { rect[ri].r = Rect(); rect[ri].weight = 0.f; } - + for(ri = 0; it != it_end; ++it, ri++) { FileNodeIterator it2 = (*it).begin(); it2 >> rect[ri].r.x >> rect[ri].r.y >> rect[ri].r.width >> rect[ri].r.height >> rect[ri].weight; } - + tilted = (int)node[CC_TILTED] != 0; return true; } @@ -427,7 +427,7 @@ bool HaarEvaluator::read(const FileNode& node) featuresPtr = &(*features)[0]; FileNodeIterator it = node.begin(), it_end = node.end(); hasTiltedFeatures = false; - + for(int i = 0; it != it_end; ++it, i++) { if(!featuresPtr[i].read(*it)) @@ -437,7 +437,7 @@ bool HaarEvaluator::read(const FileNode& node) } return true; } - + Ptr HaarEvaluator::clone() const { HaarEvaluator* ret = new HaarEvaluator; @@ -451,7 +451,7 @@ Ptr HaarEvaluator::clone() const memcpy( ret->p, p, 4*sizeof(p[0]) ); memcpy( ret->pq, pq, 4*sizeof(pq[0]) ); ret->offset = offset; - ret->varianceNormFactor = varianceNormFactor; + ret->varianceNormFactor = varianceNormFactor; return ret; } @@ -460,10 +460,10 @@ bool HaarEvaluator::setImage( const Mat &image, Size _origWinSize ) int rn = image.rows+1, cn = image.cols+1; origWinSize = _origWinSize; normrect = Rect(1, 1, origWinSize.width-2, origWinSize.height-2); - + if (image.cols < origWinSize.width || image.rows < origWinSize.height) return false; - + if( sum0.rows < rn || sum0.cols < cn ) { sum0.create(rn, cn, CV_32S); @@ -485,10 +485,10 @@ bool HaarEvaluator::setImage( const Mat &image, Size _origWinSize ) const double* sqdata = (const double*)sqsum.data; size_t sumStep = sum.step/sizeof(sdata[0]); size_t sqsumStep = sqsum.step/sizeof(sqdata[0]); - + CV_SUM_PTRS( p[0], p[1], p[2], p[3], sdata, normrect, sumStep ); CV_SUM_PTRS( pq[0], pq[1], pq[2], pq[3], sqdata, normrect, sqsumStep ); - + size_t fi, nfeatures = features->size(); for( fi = 0; fi < nfeatures; fi++ ) @@ -568,19 +568,19 @@ bool LBPEvaluator::setImage( const Mat& image, Size _origWinSize ) if( image.cols < origWinSize.width || image.rows < origWinSize.height ) return false; - + if( sum0.rows < rn || sum0.cols < cn ) sum0.create(rn, cn, CV_32S); sum = Mat(rn, cn, CV_32S, sum0.data); integral(image, sum); - + size_t fi, nfeatures = features->size(); - + for( fi = 0; fi < nfeatures; fi++ ) featuresPtr[fi].updatePtrs( sum ); return true; } - + bool LBPEvaluator::setWindow( Point pt ) { if( pt.x < 0 || pt.y < 0 || @@ -589,7 +589,7 @@ bool LBPEvaluator::setWindow( Point pt ) return false; offset = pt.y * ((int)sum.step/sizeof(int)) + pt.x; return true; -} +} //---------------------------------------------- HOGEvaluator --------------------------------------- bool HOGEvaluator::Feature :: read( const FileNode& node ) @@ -638,7 +638,7 @@ Ptr HOGEvaluator::clone() const ret->featuresPtr = &(*ret->features)[0]; ret->offset = offset; ret->hist = hist; - ret->normSum = normSum; + ret->normSum = normSum; return ret; } @@ -756,7 +756,7 @@ void HOGEvaluator::integralHistogram(const Mat &img, vector &histogram, Mat memset( histBuf, 0, histSize.width * sizeof(histBuf[0]) ); histBuf += histStep + 1; for( y = 0; y < qangle.rows; y++ ) - { + { histBuf[-1] = 0.f; float strSum = 0.f; for( x = 0; x < qangle.cols; x++ ) @@ -775,7 +775,7 @@ void HOGEvaluator::integralHistogram(const Mat &img, vector &histogram, Mat Ptr FeatureEvaluator::create( int featureType ) { return featureType == HAAR ? Ptr(new HaarEvaluator) : - featureType == LBP ? Ptr(new LBPEvaluator) : + featureType == LBP ? Ptr(new LBPEvaluator) : featureType == HOG ? Ptr(new HOGEvaluator) : Ptr(); } @@ -787,13 +787,13 @@ CascadeClassifier::CascadeClassifier() } CascadeClassifier::CascadeClassifier(const string& filename) -{ - load(filename); +{ + load(filename); } CascadeClassifier::~CascadeClassifier() { -} +} bool CascadeClassifier::empty() const { @@ -805,57 +805,57 @@ bool CascadeClassifier::load(const string& filename) oldCascade.release(); data = Data(); featureEvaluator.release(); - + FileStorage fs(filename, FileStorage::READ); if( !fs.isOpened() ) return false; - + if( read(fs.getFirstTopLevelNode()) ) return true; - + fs.release(); - + oldCascade = Ptr((CvHaarClassifierCascade*)cvLoad(filename.c_str(), 0, 0, 0)); return !oldCascade.empty(); } - -int CascadeClassifier::runAt( Ptr& featureEvaluator, Point pt, double& weight ) + +int CascadeClassifier::runAt( Ptr& evaluator, Point pt, double& weight ) { CV_Assert( oldCascade.empty() ); - + assert( data.featureType == FeatureEvaluator::HAAR || data.featureType == FeatureEvaluator::LBP || data.featureType == FeatureEvaluator::HOG ); - if( !featureEvaluator->setWindow(pt) ) + if( !evaluator->setWindow(pt) ) return -1; if( data.isStumpBased ) { if( data.featureType == FeatureEvaluator::HAAR ) - return predictOrderedStump( *this, featureEvaluator, weight ); + return predictOrderedStump( *this, evaluator, weight ); else if( data.featureType == FeatureEvaluator::LBP ) - return predictCategoricalStump( *this, featureEvaluator, weight ); + return predictCategoricalStump( *this, evaluator, weight ); else if( data.featureType == FeatureEvaluator::HOG ) - return predictOrderedStump( *this, featureEvaluator, weight ); + return predictOrderedStump( *this, evaluator, weight ); else return -2; } else { if( data.featureType == FeatureEvaluator::HAAR ) - return predictOrdered( *this, featureEvaluator, weight ); + return predictOrdered( *this, evaluator, weight ); else if( data.featureType == FeatureEvaluator::LBP ) - return predictCategorical( *this, featureEvaluator, weight ); + return predictCategorical( *this, evaluator, weight ); else if( data.featureType == FeatureEvaluator::HOG ) - return predictOrdered( *this, featureEvaluator, weight ); + return predictOrdered( *this, evaluator, weight ); else return -2; } } - -bool CascadeClassifier::setImage( Ptr& featureEvaluator, const Mat& image ) + +bool CascadeClassifier::setImage( Ptr& evaluator, const Mat& image ) { - return empty() ? false : featureEvaluator->setImage(image, data.origWinSize); + return empty() ? false : evaluator->setImage(image, data.origWinSize); } void CascadeClassifier::setMaskGenerator(Ptr _maskGenerator) @@ -878,7 +878,7 @@ void CascadeClassifier::setFaceDetectionMaskGenerator() struct CascadeClassifierInvoker { - CascadeClassifierInvoker( CascadeClassifier& _cc, Size _sz1, int _stripSize, int _yStep, double _factor, + CascadeClassifierInvoker( CascadeClassifier& _cc, Size _sz1, int _stripSize, int _yStep, double _factor, ConcurrentRectVector& _vec, vector& _levels, vector& _weights, bool outputLevels, const Mat& _mask) { classifier = &_cc; @@ -891,7 +891,7 @@ struct CascadeClassifierInvoker levelWeights = outputLevels ? &_weights : 0; mask=_mask; } - + void operator()(const BlockedRange& range) const { Ptr evaluator = classifier->featureEvaluator->clone(); @@ -916,11 +916,11 @@ struct CascadeClassifierInvoker result = -(int)classifier->data.stages.size(); if( classifier->data.stages.size() + result < 4 ) { - rectangles->push_back(Rect(cvRound(x*scalingFactor), cvRound(y*scalingFactor), winSize.width, winSize.height)); + rectangles->push_back(Rect(cvRound(x*scalingFactor), cvRound(y*scalingFactor), winSize.width, winSize.height)); rejectLevels->push_back(-result); levelWeights->push_back(gypWeight); } - } + } else if( result > 0 ) rectangles->push_back(Rect(cvRound(x*scalingFactor), cvRound(y*scalingFactor), winSize.width, winSize.height)); @@ -929,7 +929,7 @@ struct CascadeClassifierInvoker } } } - + CascadeClassifier* classifier; ConcurrentRectVector* rectangles; Size processingRectSize; @@ -939,7 +939,7 @@ struct CascadeClassifierInvoker vector *levelWeights; Mat mask; }; - + struct getRect { Rect operator ()(const CvAvgComp& e) const { return e.rect; } }; bool CascadeClassifier::detectSingleScale( const Mat& image, int stripCount, Size processingRectSize, @@ -995,17 +995,17 @@ bool CascadeClassifier::setImage(const Mat& image) return featureEvaluator->setImage(image, data.origWinSize); } -void CascadeClassifier::detectMultiScale( const Mat& image, vector& objects, +void CascadeClassifier::detectMultiScale( const Mat& image, vector& objects, vector& rejectLevels, vector& levelWeights, double scaleFactor, int minNeighbors, - int flags, Size minObjectSize, Size maxObjectSize, + int flags, Size minObjectSize, Size maxObjectSize, bool outputRejectLevels ) { const double GROUP_EPS = 0.2; - + CV_Assert( scaleFactor > 1 && image.depth() == CV_8U ); - + if( empty() ) return; @@ -1031,7 +1031,7 @@ void CascadeClassifier::detectMultiScale( const Mat& image, vector& object if( maxObjectSize.height == 0 || maxObjectSize.width == 0 ) maxObjectSize = image.size(); - + Mat grayImage = image; if( grayImage.channels() > 1 ) { @@ -1039,7 +1039,7 @@ void CascadeClassifier::detectMultiScale( const Mat& image, vector& object cvtColor(grayImage, temp, CV_BGR2GRAY); grayImage = temp; } - + Mat imageBuffer(image.rows + 1, image.cols + 1, CV_8U); vector candidates; @@ -1050,14 +1050,14 @@ void CascadeClassifier::detectMultiScale( const Mat& image, vector& object Size windowSize( cvRound(originalWindowSize.width*factor), cvRound(originalWindowSize.height*factor) ); Size scaledImageSize( cvRound( grayImage.cols/factor ), cvRound( grayImage.rows/factor ) ); Size processingRectSize( scaledImageSize.width - originalWindowSize.width + 1, scaledImageSize.height - originalWindowSize.height + 1 ); - + if( processingRectSize.width <= 0 || processingRectSize.height <= 0 ) break; if( windowSize.width > maxObjectSize.width || windowSize.height > maxObjectSize.height ) break; if( windowSize.width < minObjectSize.width || windowSize.height < minObjectSize.height ) continue; - + Mat scaledImage( scaledImageSize, CV_8U, imageBuffer.data ); resize( grayImage, scaledImage, scaledImageSize, 0, 0, CV_INTER_LINEAR ); @@ -1083,12 +1083,12 @@ void CascadeClassifier::detectMultiScale( const Mat& image, vector& object stripSize = processingRectSize.height; #endif - if( !detectSingleScale( scaledImage, stripCount, processingRectSize, stripSize, yStep, factor, candidates, + if( !detectSingleScale( scaledImage, stripCount, processingRectSize, stripSize, yStep, factor, candidates, rejectLevels, levelWeights, outputRejectLevels ) ) break; } - + objects.resize(candidates.size()); std::copy(candidates.begin(), candidates.end(), objects.begin()); @@ -1108,14 +1108,14 @@ void CascadeClassifier::detectMultiScale( const Mat& image, vector& object { vector fakeLevels; vector fakeWeights; - detectMultiScale( image, objects, fakeLevels, fakeWeights, scaleFactor, + detectMultiScale( image, objects, fakeLevels, fakeWeights, scaleFactor, minNeighbors, flags, minObjectSize, maxObjectSize, false ); -} +} bool CascadeClassifier::Data::read(const FileNode &root) { static const float THRESHOLD_EPS = 1e-5f; - + // load stage params string stageTypeStr = (string)root[CC_STAGE_TYPE]; if( stageTypeStr == CC_BOOST ) @@ -1232,11 +1232,11 @@ bool CascadeClassifier::read(const FileNode& root) FileNode fn = root[CC_FEATURES]; if( fn.empty() ) return false; - + return featureEvaluator->read(fn); } - + template<> void Ptr::delete_obj() -{ cvReleaseHaarClassifierCascade(&obj); } +{ cvReleaseHaarClassifierCascade(&obj); } } // namespace cv diff --git a/modules/objdetect/src/datamatrix.cpp b/modules/objdetect/src/datamatrix.cpp index a4258bd..39f3616 100644 --- a/modules/objdetect/src/datamatrix.cpp +++ b/modules/objdetect/src/datamatrix.cpp @@ -256,14 +256,14 @@ static int decode(Sampler &sa, code &cc) { uchar binary[8] = {0,0,0,0,0,0,0,0}; uchar b = 0; - int i, sum; + int sum; sum = 0; - for (i = 0; i < 64; i++) + for (int i = 0; i < 64; i++) sum += sa.getpixel(1 + (i & 7), 1 + (i >> 3)); uchar mean = (uchar)(sum / 64); - for (i = 0; i < 64; i++) { + for (int i = 0; i < 64; i++) { b = (b << 1) + (sa.getpixel(pickup[i].x, pickup[i].y) <= mean); if ((i & 7) == 7) { binary[i >> 3] = b; @@ -275,12 +275,11 @@ static int decode(Sampler &sa, code &cc) uchar c[5] = {0,0,0,0,0}; { - int i, j; uchar a[5] = {228, 48, 15, 111, 62}; int k = 5; - for (i = 0; i < 3; i++) { + for (int i = 0; i < 3; i++) { uchar t = binary[i] ^ c[4]; - for (j = k - 1; j != -1; j--) { + for (int j = k - 1; j != -1; j--) { if (t == 0) c[j] = 0; else @@ -390,12 +389,12 @@ deque cvFindDataMatrix(CvMat *im) deque candidates; { int x, y; - int r = cxy->rows; - int c = cxy->cols; - for (y = 0; y < r; y++) { + int rows = cxy->rows; + int cols = cxy->cols; + for (y = 0; y < rows; y++) { const short *cd = (const short*)cvPtr2D(cxy, y, 0); const short *ccd = (const short*)cvPtr2D(ccxy, y, 0); - for (x = 0; x < c; x += 4, cd += 8, ccd += 8) { + for (x = 0; x < cols; x += 4, cd += 8, ccd += 8) { __m128i v = _mm_loadu_si128((const __m128i*)cd); __m128 cyxyxA = _mm_cvtepi32_ps(_mm_srai_epi32(_mm_unpacklo_epi16(v, v), 16)); __m128 cyxyxB = _mm_cvtepi32_ps(_mm_srai_epi32(_mm_unpackhi_epi16(v, v), 16)); @@ -496,7 +495,7 @@ endo: ; // end search for this o namespace cv { - + void findDataMatrix(InputArray _image, vector& codes, OutputArray _corners, @@ -507,23 +506,23 @@ void findDataMatrix(InputArray _image, deque rc = cvFindDataMatrix(&m); int i, n = (int)rc.size(); Mat corners; - + if( _corners.needed() ) { _corners.create(n, 4, CV_32SC2); corners = _corners.getMat(); } - + if( _dmtx.needed() ) _dmtx.create(n, 1, CV_8U); - + codes.resize(n); - + for( i = 0; i < n; i++ ) { CvDataMatrixCode& rc_i = rc[i]; codes[i] = string(rc_i.msg); - + if( corners.data ) { const Point* srcpt = (Point*)rc_i.corners->data.ptr; @@ -532,7 +531,7 @@ void findDataMatrix(InputArray _image, dstpt[k] = srcpt[k]; } cvReleaseMat(&rc_i.corners); - + if( _dmtx.needed() ) { _dmtx.create(rc_i.original->rows, rc_i.original->cols, rc_i.original->type, i); @@ -550,20 +549,20 @@ void drawDataMatrixCodes(InputOutputArray _image, Mat image = _image.getMat(); Mat corners = _corners.getMat(); int i, n = corners.rows; - + if( n > 0 ) { CV_Assert( corners.depth() == CV_32S && corners.cols*corners.channels() == 8 && n == (int)codes.size() ); } - + for( i = 0; i < n; i++ ) { Scalar c(0, 255, 0); Scalar c2(255, 0,0); const Point* pt = (const Point*)corners.ptr(i); - + for( int k = 0; k < 4; k++ ) line(image, pt[k], pt[(k+1)%4], c); //int baseline = 0; @@ -571,5 +570,5 @@ void drawDataMatrixCodes(InputOutputArray _image, putText(image, codes[i], pt[0], CV_FONT_HERSHEY_SIMPLEX, 0.8, c2, 1, CV_AA, false); } } - + } diff --git a/modules/objdetect/src/hog.cpp b/modules/objdetect/src/hog.cpp index 053b988..d96386d 100644 --- a/modules/objdetect/src/hog.cpp +++ b/modules/objdetect/src/hog.cpp @@ -456,7 +456,6 @@ void HOGCache::init(const HOGDescriptor* _descriptor, Size blockSize = descriptor->blockSize; Size blockStride = descriptor->blockStride; Size cellSize = descriptor->cellSize; - Size winSize = descriptor->winSize; int i, j, nbins = descriptor->nbins; int rawBlockSize = blockSize.width*blockSize.height; @@ -471,10 +470,10 @@ void HOGCache::init(const HOGDescriptor* _descriptor, (winSize.height/cacheStride.height)+1); blockCache.create(cacheSize.height, cacheSize.width*blockHistogramSize); blockCacheFlags.create(cacheSize); - size_t i, cacheRows = blockCache.rows; + size_t cacheRows = blockCache.rows; ymaxCached.resize(cacheRows); - for( i = 0; i < cacheRows; i++ ) - ymaxCached[i] = -1; + for(size_t ii = 0; ii < cacheRows; ii++ ) + ymaxCached[ii] = -1; } Mat_ weights(blockSize); diff --git a/modules/objdetect/src/linemod.cpp b/modules/objdetect/src/linemod.cpp index 7a83190..b204f58 100644 --- a/modules/objdetect/src/linemod.cpp +++ b/modules/objdetect/src/linemod.cpp @@ -451,15 +451,15 @@ protected: float strong_threshold; }; -ColorGradientPyramid::ColorGradientPyramid(const Mat& src, const Mat& mask, - float weak_threshold, size_t num_features, - float strong_threshold) - : src(src), - mask(mask), +ColorGradientPyramid::ColorGradientPyramid(const Mat& _src, const Mat& _mask, + float _weak_threshold, size_t _num_features, + float _strong_threshold) + : src(_src), + mask(_mask), pyramid_level(0), - weak_threshold(weak_threshold), - num_features(num_features), - strong_threshold(strong_threshold) + weak_threshold(_weak_threshold), + num_features(_num_features), + strong_threshold(_strong_threshold) { update(); } @@ -557,10 +557,10 @@ ColorGradient::ColorGradient() { } -ColorGradient::ColorGradient(float weak_threshold, size_t num_features, float strong_threshold) - : weak_threshold(weak_threshold), - num_features(num_features), - strong_threshold(strong_threshold) +ColorGradient::ColorGradient(float _weak_threshold, size_t _num_features, float _strong_threshold) + : weak_threshold(_weak_threshold), + num_features(_num_features), + strong_threshold(_strong_threshold) { } @@ -751,13 +751,13 @@ protected: int extract_threshold; }; -DepthNormalPyramid::DepthNormalPyramid(const Mat& src, const Mat& mask, - int distance_threshold, int difference_threshold, size_t num_features, - int extract_threshold) - : mask(mask), +DepthNormalPyramid::DepthNormalPyramid(const Mat& src, const Mat& _mask, + int distance_threshold, int difference_threshold, size_t _num_features, + int _extract_threshold) + : mask(_mask), pyramid_level(0), - num_features(num_features), - extract_threshold(extract_threshold) + num_features(_num_features), + extract_threshold(_extract_threshold) { quantizedNormals(src, normal, distance_threshold, difference_threshold); } @@ -876,12 +876,12 @@ DepthNormal::DepthNormal() { } -DepthNormal::DepthNormal(int distance_threshold, int difference_threshold, size_t num_features, - int extract_threshold) - : distance_threshold(distance_threshold), - difference_threshold(difference_threshold), - num_features(num_features), - extract_threshold(extract_threshold) +DepthNormal::DepthNormal(int _distance_threshold, int _difference_threshold, size_t _num_features, + int _extract_threshold) + : distance_threshold(_distance_threshold), + difference_threshold(_difference_threshold), + num_features(_num_features), + extract_threshold(_extract_threshold) { } @@ -1388,9 +1388,9 @@ Detector::Detector() { } -Detector::Detector(const std::vector< Ptr >& modalities, +Detector::Detector(const std::vector< Ptr >& _modalities, const std::vector& T_pyramid) - : modalities(modalities), + : modalities(_modalities), pyramid_levels(static_cast(T_pyramid.size())), T_at_level(T_pyramid) { @@ -1480,7 +1480,7 @@ void Detector::match(const std::vector& sources, float threshold, std::vect // Used to filter out weak matches struct MatchPredicate { - MatchPredicate(float threshold) : threshold(threshold) {} + MatchPredicate(float _threshold) : threshold(_threshold) {} bool operator() (const Match& m) { return m.similarity < threshold; } float threshold; }; @@ -1554,13 +1554,13 @@ void Detector::matchClass(const LinearMemoryPyramid& lm_pyramid, int max_x = size.width - tp[start].width - border; int max_y = size.height - tp[start].height - border; - std::vector similarities(modalities.size()); - Mat total_similarity; + std::vector similarities2(modalities.size()); + Mat total_similarity2; for (int m = 0; m < (int)candidates.size(); ++m) { - Match& match = candidates[m]; - int x = match.x * 2 + 1; /// @todo Support other pyramid distance - int y = match.y * 2 + 1; + Match& match2 = candidates[m]; + int x = match2.x * 2 + 1; /// @todo Support other pyramid distance + int y = match2.y * 2 + 1; // Require 8 (reduced) row/cols to the up/left x = std::max(x, border); @@ -1571,22 +1571,22 @@ void Detector::matchClass(const LinearMemoryPyramid& lm_pyramid, y = std::min(y, max_y); // Compute local similarity maps for each modality - int num_features = 0; + int numFeatures = 0; for (int i = 0; i < (int)modalities.size(); ++i) { const Template& templ = tp[start + i]; - num_features += static_cast(templ.features.size()); - similarityLocal(lms[i], templ, similarities[i], size, T, Point(x, y)); + numFeatures += static_cast(templ.features.size()); + similarityLocal(lms[i], templ, similarities2[i], size, T, Point(x, y)); } - addSimilarities(similarities, total_similarity); + addSimilarities(similarities2, total_similarity2); // Find best local adjustment int best_score = 0; int best_r = -1, best_c = -1; - for (int r = 0; r < total_similarity.rows; ++r) + for (int r = 0; r < total_similarity2.rows; ++r) { - ushort* row = total_similarity.ptr(r); - for (int c = 0; c < total_similarity.cols; ++c) + ushort* row = total_similarity2.ptr(r); + for (int c = 0; c < total_similarity2.cols; ++c) { int score = row[c]; if (score > best_score) @@ -1598,9 +1598,9 @@ void Detector::matchClass(const LinearMemoryPyramid& lm_pyramid, } } // Update current match - match.x = (x / T - 8 + best_c) * T + offset; - match.y = (y / T - 8 + best_r) * T + offset; - match.similarity = (best_score * 100.f) / (4 * num_features); + match2.x = (x / T - 8 + best_c) * T + offset; + match2.y = (y / T - 8 + best_r) * T + offset; + match2.similarity = (best_score * 100.f) / (4 * numFeatures); } // Filter out any matches that drop below the similarity threshold @@ -1763,10 +1763,10 @@ void Detector::write(FileStorage& fs) const tps[template_id].resize(templates_fn.size()); FileNodeIterator templ_it = templates_fn.begin(), templ_it_end = templates_fn.end(); - int i = 0; + int idx = 0; for ( ; templ_it != templ_it_end; ++templ_it) { - tps[template_id][i++].read(*templ_it); + tps[template_id][idx++].read(*templ_it); } } diff --git a/modules/objdetect/test/test_cascadeandhog.cpp b/modules/objdetect/test/test_cascadeandhog.cpp index dd7f060..e41314a 100644 --- a/modules/objdetect/test/test_cascadeandhog.cpp +++ b/modules/objdetect/test/test_cascadeandhog.cpp @@ -53,12 +53,12 @@ using namespace std; //#define TOTAL_NO_PAIR_E "totalNoPairE" #define DETECTOR_NAMES "detector_names" -#define DETECTORS "detectors" +#define DETECTORS "detectors" #define IMAGE_FILENAMES "image_filenames" #define VALIDATION "validation" -#define FILENAME "fn" +#define FILENAME "fn" -#define C_SCALE_CASCADE "scale_cascade" +#define C_SCALE_CASCADE "scale_cascade" class CV_DetectorTest : public cvtest::BaseTest { @@ -68,9 +68,9 @@ protected: virtual int prepareData( FileStorage& fs ); virtual void run( int startFrom ); virtual string& getValidationFilename(); - - virtual void readDetector( const FileNode& fn ) = 0; - virtual void writeDetector( FileStorage& fs, int di ) = 0; + + virtual void readDetector( const FileNode& fn ) = 0; + virtual void writeDetector( FileStorage& fs, int di ) = 0; int runTestCase( int detectorIdx, vector >& objects ); virtual int detectMultiScale( int di, const Mat& img, vector& objects ) = 0; int validate( int detectorIdx, vector >& objects ); @@ -118,10 +118,10 @@ int CV_DetectorTest::prepareData( FileStorage& _fs ) FileNodeIterator it = fn[DETECTOR_NAMES].begin(); for( ; it != fn[DETECTOR_NAMES].end(); ) { - string name; - it >> name; - detectorNames.push_back(name); - readDetector(fn[DETECTORS][name]); + string _name; + it >> _name; + detectorNames.push_back(_name); + readDetector(fn[DETECTORS][_name]); } } test_case_count = (int)detectorNames.size(); @@ -175,18 +175,18 @@ void CV_DetectorTest::run( int ) } validationFS << "]"; // DETECTOR_NAMES - // write detectors - validationFS << DETECTORS << "{"; - assert( detectorNames.size() == detectorFilenames.size() ); - nit = detectorNames.begin(); - for( int di = 0; di < detectorNames.size(), nit != detectorNames.end(); ++nit, di++ ) - { - validationFS << *nit << "{"; - writeDetector( validationFS, di ); - validationFS << "}"; - } - validationFS << "}"; - + // write detectors + validationFS << DETECTORS << "{"; + assert( detectorNames.size() == detectorFilenames.size() ); + nit = detectorNames.begin(); + for( int di = 0; di < detectorNames.size(), nit != detectorNames.end(); ++nit, di++ ) + { + validationFS << *nit << "{"; + writeDetector( validationFS, di ); + validationFS << "}"; + } + validationFS << "}"; + // write image filenames validationFS << IMAGE_FILENAMES << "["; vector::const_iterator it = imageFilenames.begin(); @@ -252,8 +252,8 @@ int CV_DetectorTest::runTestCase( int detectorIdx, vector >& object return cvtest::TS::FAIL_INVALID_TEST_DATA; } int code = detectMultiScale( detectorIdx, image, imgObjects ); - if( code != cvtest::TS::OK ) - return code; + if( code != cvtest::TS::OK ) + return code; objects.push_back( imgObjects ); @@ -300,17 +300,17 @@ int CV_DetectorTest::validate( int detectorIdx, vector >& objects ) vector valRects; if( node.node->data.seq != 0 ) { - for( FileNodeIterator it = node.begin(); it != node.end(); ) + for( FileNodeIterator it2 = node.begin(); it2 != node.end(); ) { Rect r; - it >> r.x >> r.y >> r.width >> r.height; + it2 >> r.x >> r.y >> r.width >> r.height; valRects.push_back(r); } } totalValRectCount += (int)valRects.size(); - + // compare rectangles - vector map(valRects.size(), 0); + vector map(valRects.size(), 0); for( vector::const_iterator cr = it->begin(); cr != it->end(); ++cr ) { @@ -337,10 +337,10 @@ int CV_DetectorTest::validate( int detectorIdx, vector >& objects ) { Rect vr = valRects[minIdx]; if( map[minIdx] != 0 || (minDist > dist) || (abs(cr->width - vr.width) > wDiff) || - (abs(cr->height - vr.height) > hDiff) ) + (abs(cr->height - vr.height) > hDiff) ) noPair++; - else - map[minIdx] = 1; + else + map[minIdx] = 1; } } noPair += (int)count_if( map.begin(), map.end(), isZero ); @@ -371,10 +371,10 @@ class CV_CascadeDetectorTest : public CV_DetectorTest public: CV_CascadeDetectorTest(); protected: - virtual void readDetector( const FileNode& fn ); - virtual void writeDetector( FileStorage& fs, int di ); + virtual void readDetector( const FileNode& fn ); + virtual void writeDetector( FileStorage& fs, int di ); virtual int detectMultiScale( int di, const Mat& img, vector& objects ); - vector flags; + vector flags; }; CV_CascadeDetectorTest::CV_CascadeDetectorTest() @@ -384,40 +384,40 @@ CV_CascadeDetectorTest::CV_CascadeDetectorTest() void CV_CascadeDetectorTest::readDetector( const FileNode& fn ) { - string filename; - int flag; - fn[FILENAME] >> filename; - detectorFilenames.push_back(filename); - fn[C_SCALE_CASCADE] >> flag; - if( flag ) - flags.push_back( 0 ); - else - flags.push_back( CV_HAAR_SCALE_IMAGE ); + string filename; + int flag; + fn[FILENAME] >> filename; + detectorFilenames.push_back(filename); + fn[C_SCALE_CASCADE] >> flag; + if( flag ) + flags.push_back( 0 ); + else + flags.push_back( CV_HAAR_SCALE_IMAGE ); } void CV_CascadeDetectorTest::writeDetector( FileStorage& fs, int di ) { - int sc = flags[di] & CV_HAAR_SCALE_IMAGE ? 0 : 1; - fs << FILENAME << detectorFilenames[di]; - fs << C_SCALE_CASCADE << sc; + int sc = flags[di] & CV_HAAR_SCALE_IMAGE ? 0 : 1; + fs << FILENAME << detectorFilenames[di]; + fs << C_SCALE_CASCADE << sc; } int CV_CascadeDetectorTest::detectMultiScale( int di, const Mat& img, vector& objects) { - string dataPath = ts->get_data_path(), filename; - filename = dataPath + detectorFilenames[di]; + string dataPath = ts->get_data_path(), filename; + filename = dataPath + detectorFilenames[di]; CascadeClassifier cascade( filename ); - if( cascade.empty() ) - { - ts->printf( cvtest::TS::LOG, "cascade %s can not be opened"); - return cvtest::TS::FAIL_INVALID_TEST_DATA; - } + if( cascade.empty() ) + { + ts->printf( cvtest::TS::LOG, "cascade %s can not be opened"); + return cvtest::TS::FAIL_INVALID_TEST_DATA; + } Mat grayImg; cvtColor( img, grayImg, CV_BGR2GRAY ); equalizeHist( grayImg, grayImg ); cascade.detectMultiScale( grayImg, objects, 1.1, 3, flags[di] ); - return cvtest::TS::OK; + return cvtest::TS::OK; } //----------------------------------------------- HOGDetectorTest ----------------------------------- @@ -426,8 +426,8 @@ class CV_HOGDetectorTest : public CV_DetectorTest public: CV_HOGDetectorTest(); protected: - virtual void readDetector( const FileNode& fn ); - virtual void writeDetector( FileStorage& fs, int di ); + virtual void readDetector( const FileNode& fn ); + virtual void writeDetector( FileStorage& fs, int di ); virtual int detectMultiScale( int di, const Mat& img, vector& objects ); }; @@ -438,15 +438,15 @@ CV_HOGDetectorTest::CV_HOGDetectorTest() void CV_HOGDetectorTest::readDetector( const FileNode& fn ) { - string filename; - if( fn[FILENAME].node->data.seq != 0 ) - fn[FILENAME] >> filename; - detectorFilenames.push_back( filename); + string filename; + if( fn[FILENAME].node->data.seq != 0 ) + fn[FILENAME] >> filename; + detectorFilenames.push_back( filename); } void CV_HOGDetectorTest::writeDetector( FileStorage& fs, int di ) { - fs << FILENAME << detectorFilenames[di]; + fs << FILENAME << detectorFilenames[di]; } int CV_HOGDetectorTest::detectMultiScale( int di, const Mat& img, @@ -458,7 +458,7 @@ int CV_HOGDetectorTest::detectMultiScale( int di, const Mat& img, else assert(0); hog.detectMultiScale(img, objects); - return cvtest::TS::OK; + return cvtest::TS::OK; } TEST(Objdetect_CascadeDetector, regression) { CV_CascadeDetectorTest test; test.safe_run(); } diff --git a/modules/stitching/include/opencv2/stitching/detail/blenders.hpp b/modules/stitching/include/opencv2/stitching/detail/blenders.hpp index 7f7c7fe..a4e09ed 100644 --- a/modules/stitching/include/opencv2/stitching/detail/blenders.hpp +++ b/modules/stitching/include/opencv2/stitching/detail/blenders.hpp @@ -58,7 +58,7 @@ public: enum { NO, FEATHER, MULTI_BAND }; static Ptr createDefault(int type, bool try_gpu = false); - void prepare(const std::vector &corners, const std::vector &sizes); + void prepare(const std::vector &corners, const std::vector &sizes); virtual void prepare(Rect dst_roi); virtual void feed(const Mat &img, const Mat &mask, Point tl); virtual void blend(Mat &dst, Mat &dst_mask); @@ -72,7 +72,7 @@ protected: class CV_EXPORTS FeatherBlender : public Blender { public: - FeatherBlender(float sharpness = 0.02f) { setSharpness(sharpness); } + FeatherBlender(float sharpness = 0.02f); float sharpness() const { return sharpness_; } void setSharpness(float val) { sharpness_ = val; } @@ -92,6 +92,8 @@ private: Mat dst_weight_map_; }; +inline FeatherBlender::FeatherBlender(float _sharpness) { setSharpness(_sharpness); } + class CV_EXPORTS MultiBandBlender : public Blender { diff --git a/modules/stitching/include/opencv2/stitching/detail/util.hpp b/modules/stitching/include/opencv2/stitching/detail/util.hpp index b33292e..bce390b 100644 --- a/modules/stitching/include/opencv2/stitching/detail/util.hpp +++ b/modules/stitching/include/opencv2/stitching/detail/util.hpp @@ -75,8 +75,8 @@ { \ LOG_STITCHING_MSG(_msg); \ } \ - break; \ - } + break; \ + } #define LOG(msg) LOG_(1, msg) @@ -115,8 +115,7 @@ private: struct CV_EXPORTS GraphEdge { - GraphEdge(int from, int to, float weight) - : from(from), to(to), weight(weight) {} + GraphEdge(int from, int to, float weight); bool operator <(const GraphEdge& other) const { return weight < other.weight; } bool operator >(const GraphEdge& other) const { return weight > other.weight; } @@ -124,6 +123,8 @@ struct CV_EXPORTS GraphEdge float weight; }; +inline GraphEdge::GraphEdge(int _from, int _to, float _weight) : from(_from), to(_to), weight(_weight) {} + class CV_EXPORTS Graph { @@ -134,7 +135,7 @@ public: void addEdge(int from, int to, float weight); template B forEach(B body) const; template B walkBreadthFirst(int from, B body) const; - + private: std::vector< std::list > edges_; }; diff --git a/modules/stitching/include/opencv2/stitching/stitcher.hpp b/modules/stitching/include/opencv2/stitching/stitcher.hpp index aab2cca..c535c52 100644 --- a/modules/stitching/include/opencv2/stitching/stitcher.hpp +++ b/modules/stitching/include/opencv2/stitching/stitcher.hpp @@ -103,9 +103,9 @@ public: const cv::Mat& matchingMask() const { return matching_mask_; } void setMatchingMask(const cv::Mat &mask) - { + { CV_Assert(mask.type() == CV_8U && mask.cols == mask.rows); - matching_mask_ = mask.clone(); + matching_mask_ = mask.clone(); } Ptr bundleAdjuster() { return bundle_adjuster_; } @@ -115,7 +115,7 @@ public: Ptr warper() { return warper_; } const Ptr warper() const { return warper_; } - void setWarper(Ptr warper) { warper_ = warper; } + void setWarper(Ptr creator) { warper_ = creator; } Ptr exposureCompensator() { return exposure_comp_; } const Ptr exposureCompensator() const { return exposure_comp_; } @@ -128,7 +128,7 @@ public: Ptr blender() { return blender_; } const Ptr blender() const { return blender_; } - void setBlender(Ptr blender) { blender_ = blender; } + void setBlender(Ptr b) { blender_ = b; } private: Stitcher() {} diff --git a/modules/stitching/src/matchers.cpp b/modules/stitching/src/matchers.cpp index 854db00..f876897 100644 --- a/modules/stitching/src/matchers.cpp +++ b/modules/stitching/src/matchers.cpp @@ -72,10 +72,10 @@ struct MatchPairsBody : matcher(other.matcher), features(other.features), pairwise_matches(other.pairwise_matches), near_pairs(other.near_pairs) {} - MatchPairsBody(FeaturesMatcher &matcher, const vector &features, - vector &pairwise_matches, vector > &near_pairs) - : matcher(matcher), features(features), - pairwise_matches(pairwise_matches), near_pairs(near_pairs) {} + MatchPairsBody(FeaturesMatcher &_matcher, const vector &_features, + vector &_pairwise_matches, vector > &_near_pairs) + : matcher(_matcher), features(_features), + pairwise_matches(_pairwise_matches), near_pairs(_near_pairs) {} void operator ()(const BlockedRange &r) const { diff --git a/modules/stitching/src/motion_estimators.cpp b/modules/stitching/src/motion_estimators.cpp index f061059..e9bb3eb 100644 --- a/modules/stitching/src/motion_estimators.cpp +++ b/modules/stitching/src/motion_estimators.cpp @@ -50,7 +50,7 @@ namespace { struct IncDistance { - IncDistance(vector &dists) : dists(&dists[0]) {} + IncDistance(vector &vdists) : dists(&vdists[0]) {} void operator ()(const GraphEdge &edge) { dists[edge.to] = dists[edge.from] + 1; } int* dists; }; @@ -58,8 +58,8 @@ struct IncDistance struct CalcRotation { - CalcRotation(int num_images, const vector &pairwise_matches, vector &cameras) - : num_images(num_images), pairwise_matches(&pairwise_matches[0]), cameras(&cameras[0]) {} + CalcRotation(int _num_images, const vector &_pairwise_matches, vector &_cameras) + : num_images(_num_images), pairwise_matches(&_pairwise_matches[0]), cameras(&_cameras[0]) {} void operator ()(const GraphEdge &edge) { @@ -195,10 +195,10 @@ void BundleAdjusterBase::estimate(const vector &features, // Compute number of correspondences total_num_matches_ = 0; for (size_t i = 0; i < edges_.size(); ++i) - total_num_matches_ += static_cast(pairwise_matches[edges_[i].first * num_images_ + + total_num_matches_ += static_cast(pairwise_matches[edges_[i].first * num_images_ + edges_[i].second].num_inliers); - CvLevMarq solver(num_images_ * num_params_per_cam_, + CvLevMarq solver(num_images_ * num_params_per_cam_, total_num_matches_ * num_errs_per_measurement_, term_criteria_); @@ -270,7 +270,7 @@ void BundleAdjusterReproj::setUpInitialCameraParams(const vector & svd(cameras[i].R, SVD::FULL_UV); Mat R = svd.u * svd.vt; - if (determinant(R) < 0) + if (determinant(R) < 0) R *= -1; Mat rvec; @@ -392,7 +392,7 @@ void BundleAdjusterReproj::calcJacobian(Mat &jac) calcDeriv(err1_, err2_, 2 * step, jac.col(i * 7)); cam_params_.at(i * 7, 0) = val; } - if (refinement_mask_.at(0, 2)) + if (refinement_mask_.at(0, 2)) { val = cam_params_.at(i * 7 + 1, 0); cam_params_.at(i * 7 + 1, 0) = val - step; @@ -402,7 +402,7 @@ void BundleAdjusterReproj::calcJacobian(Mat &jac) calcDeriv(err1_, err2_, 2 * step, jac.col(i * 7 + 1)); cam_params_.at(i * 7 + 1, 0) = val; } - if (refinement_mask_.at(1, 2)) + if (refinement_mask_.at(1, 2)) { val = cam_params_.at(i * 7 + 2, 0); cam_params_.at(i * 7 + 2, 0) = val - step; @@ -448,7 +448,7 @@ void BundleAdjusterRay::setUpInitialCameraParams(const vector &cam svd(cameras[i].R, SVD::FULL_UV); Mat R = svd.u * svd.vt; - if (determinant(R) < 0) + if (determinant(R) < 0) R *= -1; Mat rvec; @@ -546,7 +546,7 @@ void BundleAdjusterRay::calcError(Mat &err) double mult = sqrt(f1 * f2); err.at(3 * match_idx, 0) = mult * (x1 - x2); err.at(3 * match_idx + 1, 0) = mult * (y1 - y2); - err.at(3 * match_idx + 2, 0) = mult * (z1 - z2); + err.at(3 * match_idx + 2, 0) = mult * (z1 - z2); match_idx++; } @@ -728,7 +728,7 @@ vector leaveBiggestComponent(vector &features, vector leaveBiggestComponent(vector &features, vector indices_removed; for (int i = 0; i < num_images; ++i) if (comps.findSetByElem(i) == max_comp) - indices.push_back(i); + indices.push_back(i); else indices_removed.push_back(i); @@ -761,7 +761,7 @@ vector leaveBiggestComponent(vector &features, vector warper = warper_->create(float(warped_image_scale_ * seam_work_aspect_)); + Ptr w = warper_->create(float(warped_image_scale_ * seam_work_aspect_)); for (size_t i = 0; i < imgs_.size(); ++i) { Mat_ K; cameras_[i].K().convertTo(K, CV_32F); - K(0,0) *= (float)seam_work_aspect_; + K(0,0) *= (float)seam_work_aspect_; K(0,2) *= (float)seam_work_aspect_; - K(1,1) *= (float)seam_work_aspect_; + K(1,1) *= (float)seam_work_aspect_; K(1,2) *= (float)seam_work_aspect_; - corners[i] = warper->warp(seam_est_imgs_[i], K, cameras_[i].R, INTER_LINEAR, BORDER_REFLECT, images_warped[i]); + corners[i] = w->warp(seam_est_imgs_[i], K, cameras_[i].R, INTER_LINEAR, BORDER_REFLECT, images_warped[i]); sizes[i] = images_warped[i].size(); - warper->warp(masks[i], K, cameras_[i].R, INTER_NEAREST, BORDER_CONSTANT, masks_warped[i]); + w->warp(masks[i], K, cameras_[i].R, INTER_NEAREST, BORDER_CONSTANT, masks_warped[i]); } vector images_warped_f(imgs_.size()); @@ -232,7 +232,7 @@ Stitcher::Status Stitcher::composePanorama(InputArray images, OutputArray pano) // Update warped image scale warped_image_scale_ *= static_cast(compose_work_aspect); - warper = warper_->create((float)warped_image_scale_); + w = warper_->create((float)warped_image_scale_); // Update corners and sizes for (size_t i = 0; i < imgs_.size(); ++i) @@ -252,7 +252,7 @@ Stitcher::Status Stitcher::composePanorama(InputArray images, OutputArray pano) Mat K; cameras_[i].K().convertTo(K, CV_32F); - Rect roi = warper->warpRoi(sz, K, cameras_[i].R); + Rect roi = w->warpRoi(sz, K, cameras_[i].R); corners[i] = roi.tl(); sizes[i] = roi.size(); } @@ -268,12 +268,12 @@ Stitcher::Status Stitcher::composePanorama(InputArray images, OutputArray pano) cameras_[img_idx].K().convertTo(K, CV_32F); // Warp the current image - warper->warp(img, K, cameras_[img_idx].R, INTER_LINEAR, BORDER_REFLECT, img_warped); + w->warp(img, K, cameras_[img_idx].R, INTER_LINEAR, BORDER_REFLECT, img_warped); // Warp the current image mask mask.create(img_size, CV_8U); mask.setTo(Scalar::all(255)); - warper->warp(mask, K, cameras_[img_idx].R, INTER_NEAREST, BORDER_CONSTANT, mask_warped); + w->warp(mask, K, cameras_[img_idx].R, INTER_NEAREST, BORDER_CONSTANT, mask_warped); // Compensate exposure exposure_comp_->apply((int)img_idx, corners[img_idx], img_warped, mask_warped); diff --git a/modules/video/src/lkpyramid.cpp b/modules/video/src/lkpyramid.cpp index 14ba66f..552bfbd 100644 --- a/modules/video/src/lkpyramid.cpp +++ b/modules/video/src/lkpyramid.cpp @@ -43,7 +43,7 @@ #include #include "lkpyramid.hpp" -namespace +namespace { static void calcSharrDeriv(const cv::Mat& src, cv::Mat& dst) { @@ -56,23 +56,23 @@ static void calcSharrDeriv(const cv::Mat& src, cv::Mat& dst) #ifdef HAVE_TEGRA_OPTIMIZATION if (tegra::calcSharrDeriv(src, dst)) return; -#endif - +#endif + int x, y, delta = (int)alignSize((cols + 2)*cn, 16); AutoBuffer _tempBuf(delta*2 + 64); deriv_type *trow0 = alignPtr(_tempBuf + cn, 16), *trow1 = alignPtr(trow0 + delta, 16); - + #if CV_SSE2 __m128i z = _mm_setzero_si128(), c3 = _mm_set1_epi16(3), c10 = _mm_set1_epi16(10); #endif - + for( y = 0; y < rows; y++ ) { const uchar* srow0 = src.ptr(y > 0 ? y-1 : rows > 1 ? 1 : 0); const uchar* srow1 = src.ptr(y); const uchar* srow2 = src.ptr(y < rows-1 ? y+1 : rows > 1 ? rows-2 : 0); deriv_type* drow = dst.ptr(y); - + // do vertical convolution x = 0; #if CV_SSE2 @@ -94,7 +94,7 @@ static void calcSharrDeriv(const cv::Mat& src, cv::Mat& dst) trow0[x] = (deriv_type)t0; trow1[x] = (deriv_type)t1; } - + // make border int x0 = (cols > 1 ? 1 : 0)*cn, x1 = (cols > 1 ? cols-2 : 0)*cn; for( int k = 0; k < cn; k++ ) @@ -102,7 +102,7 @@ static void calcSharrDeriv(const cv::Mat& src, cv::Mat& dst) trow0[-cn + k] = trow0[x0 + k]; trow0[colsn + k] = trow0[x1 + k]; trow1[-cn + k] = trow1[x0 + k]; trow1[colsn + k] = trow1[x1 + k]; } - + // do horizontal convolution, interleave the results and store them to dst x = 0; #if CV_SSE2 @@ -113,7 +113,7 @@ static void calcSharrDeriv(const cv::Mat& src, cv::Mat& dst) __m128i s2 = _mm_loadu_si128((const __m128i*)(trow1 + x - cn)); __m128i s3 = _mm_load_si128((const __m128i*)(trow1 + x)); __m128i s4 = _mm_loadu_si128((const __m128i*)(trow1 + x + cn)); - + __m128i t0 = _mm_sub_epi16(s1, s0); __m128i t1 = _mm_add_epi16(_mm_mullo_epi16(_mm_add_epi16(s2, s4), c3), _mm_mullo_epi16(s3, c10)); __m128i t2 = _mm_unpacklo_epi16(t0, t1); @@ -122,7 +122,7 @@ static void calcSharrDeriv(const cv::Mat& src, cv::Mat& dst) _mm_storeu_si128((__m128i*)(drow + x*2), t2); _mm_storeu_si128((__m128i*)(drow + x*2 + 8), t0); } -#endif +#endif for( ; x < colsn; x++ ) { deriv_type t0 = (deriv_type)(trow0[x+cn] - trow0[x-cn]); @@ -133,7 +133,7 @@ static void calcSharrDeriv(const cv::Mat& src, cv::Mat& dst) } }//namespace - + cv::detail::LKTrackerInvoker::LKTrackerInvoker( const Mat& _prevImg, const Mat& _prevDeriv, const Mat& _nextImg, const Point2f* _prevPts, Point2f* _nextPts, @@ -162,14 +162,14 @@ void cv::detail::LKTrackerInvoker::operator()(const BlockedRange& range) const const Mat& I = *prevImg; const Mat& J = *nextImg; const Mat& derivI = *prevDeriv; - + int j, cn = I.channels(), cn2 = cn*2; cv::AutoBuffer _buf(winSize.area()*(cn + cn2)); int derivDepth = DataType::depth; - + Mat IWinBuf(winSize, CV_MAKETYPE(derivDepth, cn), (deriv_type*)_buf); Mat derivIWinBuf(winSize, CV_MAKETYPE(derivDepth, cn2), (deriv_type*)_buf + winSize.area()*cn); - + for( int ptidx = range.begin(); ptidx < range.end(); ptidx++ ) { Point2f prevPt = prevPts[ptidx]*(float)(1./(1 << level)); @@ -184,12 +184,12 @@ void cv::detail::LKTrackerInvoker::operator()(const BlockedRange& range) const else nextPt = nextPts[ptidx]*2.f; nextPts[ptidx] = nextPt; - + Point2i iprevPt, inextPt; prevPt -= halfWin; iprevPt.x = cvFloor(prevPt.x); iprevPt.y = cvFloor(prevPt.y); - + if( iprevPt.x < -winSize.width || iprevPt.x >= derivI.cols || iprevPt.y < -winSize.height || iprevPt.y >= derivI.rows ) { @@ -202,7 +202,7 @@ void cv::detail::LKTrackerInvoker::operator()(const BlockedRange& range) const } continue; } - + float a = prevPt.x - iprevPt.x; float b = prevPt.y - iprevPt.y; const int W_BITS = 14, W_BITS1 = 14; @@ -211,12 +211,12 @@ void cv::detail::LKTrackerInvoker::operator()(const BlockedRange& range) const int iw01 = cvRound(a*(1.f - b)*(1 << W_BITS)); int iw10 = cvRound((1.f - a)*b*(1 << W_BITS)); int iw11 = (1 << W_BITS) - iw00 - iw01 - iw10; - + int dstep = (int)(derivI.step/derivI.elemSize1()); int step = (int)(I.step/I.elemSize1()); CV_Assert( step == (int)(J.step/J.elemSize1()) ); float A11 = 0, A12 = 0, A22 = 0; - + #if CV_SSE2 __m128i qw0 = _mm_set1_epi32(iw00 + (iw01 << 16)); __m128i qw1 = _mm_set1_epi32(iw10 + (iw11 << 16)); @@ -225,39 +225,39 @@ void cv::detail::LKTrackerInvoker::operator()(const BlockedRange& range) const __m128i qdelta = _mm_set1_epi32(1 << (W_BITS1-5-1)); __m128 qA11 = _mm_setzero_ps(), qA12 = _mm_setzero_ps(), qA22 = _mm_setzero_ps(); #endif - + // extract the patch from the first image, compute covariation matrix of derivatives int x, y; for( y = 0; y < winSize.height; y++ ) { const uchar* src = (const uchar*)I.data + (y + iprevPt.y)*step + iprevPt.x*cn; const deriv_type* dsrc = (const deriv_type*)derivI.data + (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); - + x = 0; - + #if CV_SSE2 for( ; x <= winSize.width*cn - 4; x += 4, dsrc += 4*2, dIptr += 4*2 ) { __m128i v00, v01, v10, v11, t0, t1; - + v00 = _mm_unpacklo_epi8(_mm_cvtsi32_si128(*(const int*)(src + x)), z); v01 = _mm_unpacklo_epi8(_mm_cvtsi32_si128(*(const int*)(src + x + cn)), z); v10 = _mm_unpacklo_epi8(_mm_cvtsi32_si128(*(const int*)(src + x + step)), z); v11 = _mm_unpacklo_epi8(_mm_cvtsi32_si128(*(const int*)(src + x + step + cn)), z); - + t0 = _mm_add_epi32(_mm_madd_epi16(_mm_unpacklo_epi16(v00, v01), qw0), _mm_madd_epi16(_mm_unpacklo_epi16(v10, v11), qw1)); t0 = _mm_srai_epi32(_mm_add_epi32(t0, qdelta), W_BITS1-5); _mm_storel_epi64((__m128i*)(Iptr + x), _mm_packs_epi32(t0,t0)); - + v00 = _mm_loadu_si128((const __m128i*)(dsrc)); v01 = _mm_loadu_si128((const __m128i*)(dsrc + cn2)); v10 = _mm_loadu_si128((const __m128i*)(dsrc + dstep)); v11 = _mm_loadu_si128((const __m128i*)(dsrc + dstep + cn2)); - + t0 = _mm_add_epi32(_mm_madd_epi16(_mm_unpacklo_epi16(v00, v01), qw0), _mm_madd_epi16(_mm_unpacklo_epi16(v10, v11), qw1)); t1 = _mm_add_epi32(_mm_madd_epi16(_mm_unpackhi_epi16(v00, v01), qw0), @@ -265,20 +265,20 @@ void cv::detail::LKTrackerInvoker::operator()(const BlockedRange& range) const t0 = _mm_srai_epi32(_mm_add_epi32(t0, qdelta_d), W_BITS1); t1 = _mm_srai_epi32(_mm_add_epi32(t1, qdelta_d), W_BITS1); v00 = _mm_packs_epi32(t0, t1); // Ix0 Iy0 Ix1 Iy1 ... - + _mm_storeu_si128((__m128i*)dIptr, v00); t0 = _mm_srai_epi32(v00, 16); // Iy0 Iy1 Iy2 Iy3 t1 = _mm_srai_epi32(_mm_slli_epi32(v00, 16), 16); // Ix0 Ix1 Ix2 Ix3 - + __m128 fy = _mm_cvtepi32_ps(t0); __m128 fx = _mm_cvtepi32_ps(t1); - + qA22 = _mm_add_ps(qA22, _mm_mul_ps(fy, fy)); qA12 = _mm_add_ps(qA12, _mm_mul_ps(fx, fy)); qA11 = _mm_add_ps(qA11, _mm_mul_ps(fx, fx)); } #endif - + for( ; x < winSize.width*cn; x++, dsrc += 2, dIptr += 2 ) { int ival = CV_DESCALE(src[x]*iw00 + src[x+cn]*iw01 + @@ -287,17 +287,17 @@ void cv::detail::LKTrackerInvoker::operator()(const BlockedRange& range) const dsrc[dstep]*iw10 + dsrc[dstep+cn2]*iw11, W_BITS1); int iyval = CV_DESCALE(dsrc[1]*iw00 + dsrc[cn2+1]*iw01 + dsrc[dstep+1]*iw10 + dsrc[dstep+cn2+1]*iw11, W_BITS1); - + Iptr[x] = (short)ival; dIptr[0] = (short)ixval; dIptr[1] = (short)iyval; - + A11 += (float)(ixval*ixval); A12 += (float)(ixval*iyval); A22 += (float)(iyval*iyval); } } - + #if CV_SSE2 float CV_DECL_ALIGNED(16) A11buf[4], A12buf[4], A22buf[4]; _mm_store_ps(A11buf, qA11); @@ -307,35 +307,35 @@ void cv::detail::LKTrackerInvoker::operator()(const BlockedRange& range) const A12 += A12buf[0] + A12buf[1] + A12buf[2] + A12buf[3]; A22 += A22buf[0] + A22buf[1] + A22buf[2] + A22buf[3]; #endif - + A11 *= FLT_SCALE; A12 *= FLT_SCALE; A22 *= FLT_SCALE; - + float D = A11*A22 - A12*A12; float minEig = (A22 + A11 - std::sqrt((A11-A22)*(A11-A22) + 4.f*A12*A12))/(2*winSize.width*winSize.height); - + if( err && (flags & CV_LKFLOW_GET_MIN_EIGENVALS) != 0 ) err[ptidx] = (float)minEig; - + if( minEig < minEigThreshold || D < FLT_EPSILON ) { if( level == 0 && status ) status[ptidx] = false; continue; } - + D = 1.f/D; - + nextPt -= halfWin; Point2f prevDelta; - + for( j = 0; j < criteria.maxCount; j++ ) { inextPt.x = cvFloor(nextPt.x); inextPt.y = cvFloor(nextPt.y); - + if( inextPt.x < -winSize.width || inextPt.x >= J.cols || inextPt.y < -winSize.height || inextPt.y >= J.rows ) { @@ -343,7 +343,7 @@ void cv::detail::LKTrackerInvoker::operator()(const BlockedRange& range) const status[ptidx] = false; break; } - + a = nextPt.x - inextPt.x; b = nextPt.y - inextPt.y; iw00 = cvRound((1.f - a)*(1.f - b)*(1 << W_BITS)); @@ -356,15 +356,15 @@ void cv::detail::LKTrackerInvoker::operator()(const BlockedRange& range) const qw1 = _mm_set1_epi32(iw10 + (iw11 << 16)); __m128 qb0 = _mm_setzero_ps(), qb1 = _mm_setzero_ps(); #endif - + for( y = 0; y < winSize.height; y++ ) { const uchar* Jptr = (const uchar*)J.data + (y + inextPt.y)*step + 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); - + x = 0; - + #if CV_SSE2 for( ; x <= winSize.width*cn - 8; x += 8, dIptr += 8*2 ) { @@ -373,7 +373,7 @@ void cv::detail::LKTrackerInvoker::operator()(const BlockedRange& range) const __m128i v01 = _mm_unpacklo_epi8(_mm_loadl_epi64((const __m128i*)(Jptr + x + cn)), z); __m128i v10 = _mm_unpacklo_epi8(_mm_loadl_epi64((const __m128i*)(Jptr + x + step)), z); __m128i v11 = _mm_unpacklo_epi8(_mm_loadl_epi64((const __m128i*)(Jptr + x + step + cn)), z); - + __m128i t0 = _mm_add_epi32(_mm_madd_epi16(_mm_unpacklo_epi16(v00, v01), qw0), _mm_madd_epi16(_mm_unpacklo_epi16(v10, v11), qw1)); __m128i t1 = _mm_add_epi32(_mm_madd_epi16(_mm_unpackhi_epi16(v00, v01), qw0), @@ -383,7 +383,7 @@ void cv::detail::LKTrackerInvoker::operator()(const BlockedRange& range) const diff0 = _mm_subs_epi16(_mm_packs_epi32(t0, t1), diff0); diff1 = _mm_unpackhi_epi16(diff0, diff0); diff0 = _mm_unpacklo_epi16(diff0, diff0); // It0 It0 It1 It1 ... - v00 = _mm_loadu_si128((const __m128i*)(dIptr)); // Ix0 Iy0 Ix1 Iy1 ... + v00 = _mm_loadu_si128((const __m128i*)(dIptr)); // Ix0 Iy0 Ix1 Iy1 ... v01 = _mm_loadu_si128((const __m128i*)(dIptr + 8)); v10 = _mm_mullo_epi16(v00, diff0); v11 = _mm_mulhi_epi16(v00, diff0); @@ -399,7 +399,7 @@ void cv::detail::LKTrackerInvoker::operator()(const BlockedRange& range) const qb1 = _mm_add_ps(qb1, _mm_cvtepi32_ps(v10)); } #endif - + for( ; x < winSize.width*cn; x++, dIptr += 2 ) { int diff = CV_DESCALE(Jptr[x]*iw00 + Jptr[x+cn]*iw01 + @@ -409,7 +409,7 @@ void cv::detail::LKTrackerInvoker::operator()(const BlockedRange& range) const b2 += (float)(diff*dIptr[1]); } } - + #if CV_SSE2 float CV_DECL_ALIGNED(16) bbuf[4]; _mm_store_ps(bbuf, _mm_add_ps(qb0, qb1)); @@ -419,17 +419,17 @@ void cv::detail::LKTrackerInvoker::operator()(const BlockedRange& range) const b1 *= FLT_SCALE; b2 *= FLT_SCALE; - + Point2f delta( (float)((A12*b2 - A22*b1) * D), (float)((A12*b1 - A11*b2) * D)); //delta = -delta; - + nextPt += delta; nextPts[ptidx] = nextPt + halfWin; - + if( delta.ddot(delta) <= criteria.epsilon ) break; - + if( j > 0 && std::abs(delta.x + prevDelta.x) < 0.01 && std::abs(delta.y + prevDelta.y) < 0.01 ) { @@ -438,36 +438,36 @@ void cv::detail::LKTrackerInvoker::operator()(const BlockedRange& range) const } prevDelta = delta; } - + if( status[ptidx] && err && level == 0 && (flags & CV_LKFLOW_GET_MIN_EIGENVALS) == 0 ) { - Point2f nextPt = nextPts[ptidx] - halfWin; - Point inextPt; - - inextPt.x = cvFloor(nextPt.x); - inextPt.y = cvFloor(nextPt.y); - - if( inextPt.x < -winSize.width || inextPt.x >= J.cols || - inextPt.y < -winSize.height || inextPt.y >= J.rows ) + Point2f nextPoint = nextPts[ptidx] - halfWin; + Point inextPoint; + + inextPoint.x = cvFloor(nextPoint.x); + inextPoint.y = cvFloor(nextPoint.y); + + if( inextPoint.x < -winSize.width || inextPoint.x >= J.cols || + inextPoint.y < -winSize.height || inextPoint.y >= J.rows ) { if( status ) status[ptidx] = false; continue; } - - float a = nextPt.x - inextPt.x; - float b = nextPt.y - inextPt.y; - iw00 = cvRound((1.f - a)*(1.f - b)*(1 << W_BITS)); - iw01 = cvRound(a*(1.f - b)*(1 << W_BITS)); - iw10 = cvRound((1.f - a)*b*(1 << W_BITS)); + + float aa = nextPoint.x - inextPoint.x; + float bb = nextPoint.y - inextPoint.y; + iw00 = cvRound((1.f - aa)*(1.f - bb)*(1 << W_BITS)); + iw01 = cvRound(aa*(1.f - bb)*(1 << W_BITS)); + iw10 = cvRound((1.f - aa)*bb*(1 << W_BITS)); iw11 = (1 << W_BITS) - iw00 - iw01 - iw10; float errval = 0.f; - + for( y = 0; y < winSize.height; y++ ) { - const uchar* Jptr = (const uchar*)J.data + (y + inextPt.y)*step + inextPt.x*cn; + const uchar* Jptr = (const uchar*)J.data + (y + inextPoint.y)*step + inextPoint.x*cn; const deriv_type* Iptr = (const deriv_type*)(IWinBuf.data + y*IWinBuf.step); - + for( x = 0; x < winSize.width*cn; x++ ) { int diff = CV_DESCALE(Jptr[x]*iw00 + Jptr[x+cn]*iw01 + @@ -480,7 +480,7 @@ void cv::detail::LKTrackerInvoker::operator()(const BlockedRange& range) const } } } - + int cv::buildOpticalFlowPyramid(InputArray _img, OutputArrayOfArrays pyramid, Size winSize, int maxLevel, bool withDerivatives, int pyrBorder, int derivBorder, bool tryReuseInputImage) { @@ -511,7 +511,7 @@ int cv::buildOpticalFlowPyramid(InputArray _img, OutputArrayOfArrays pyramid, Si if(!lvl0IsSet) { Mat& temp = pyramid.getMatRef(0); - + if(!temp.empty()) temp.adjustROI(winSize.height, winSize.height, winSize.width, winSize.width); if(temp.type() != img.type() || temp.cols != winSize.width*2 + img.cols || temp.rows != winSize.height * 2 + img.rows) @@ -533,7 +533,7 @@ int cv::buildOpticalFlowPyramid(InputArray _img, OutputArrayOfArrays pyramid, Si if (level != 0) { Mat& temp = pyramid.getMatRef(level * pyrstep); - + if(!temp.empty()) temp.adjustROI(winSize.height, winSize.height, winSize.width, winSize.width); if(temp.type() != img.type() || temp.cols != winSize.width*2 + sz.width || temp.rows != winSize.height * 2 + sz.height) @@ -591,7 +591,7 @@ void cv::calcOpticalFlowPyrLK( InputArray _prevImg, InputArray _nextImg, int level=0, i, npoints; CV_Assert( (npoints = prevPtsMat.checkVector(2, CV_32F, true)) >= 0 ); - + if( npoints == 0 ) { _nextPts.release(); @@ -599,25 +599,25 @@ void cv::calcOpticalFlowPyrLK( InputArray _prevImg, InputArray _nextImg, _err.release(); return; } - + if( !(flags & OPTFLOW_USE_INITIAL_FLOW) ) _nextPts.create(prevPtsMat.size(), prevPtsMat.type(), -1, true); - + 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; - + _status.create((int)npoints, 1, CV_8U, -1, true); Mat statusMat = _status.getMat(), errMat; CV_Assert( statusMat.isContinuous() ); uchar* status = statusMat.data; float* err = 0; - + for( i = 0; i < npoints; i++ ) status[i] = true; - + if( _err.needed() ) { _err.create((int)npoints, 1, CV_32F, -1, true); @@ -723,14 +723,14 @@ void cv::calcOpticalFlowPyrLK( InputArray _prevImg, InputArray _nextImg, } else derivI = prevPyr[level * lvlStep1 + 1]; - + CV_Assert(prevPyr[level * lvlStep1].size() == nextPyr[level * lvlStep2].size()); CV_Assert(prevPyr[level * lvlStep1].type() == nextPyr[level * lvlStep2].type()); #ifdef HAVE_TEGRA_OPTIMIZATION typedef tegra::LKTrackerInvoker LKTrackerInvoker; #else - typedef cv::detail::LKTrackerInvoker LKTrackerInvoker; + typedef cv::detail::LKTrackerInvoker LKTrackerInvoker; #endif parallel_for(BlockedRange(0, npoints), LKTrackerInvoker(prevPyr[level * lvlStep1], derivI, @@ -936,7 +936,7 @@ icvAdjustRect( const void* srcptr, int src_step, int pix_size, { CvRect rect; const char* src = (const char*)srcptr; - + if( ip.x >= 0 ) { src += ip.x*pix_size; @@ -948,7 +948,7 @@ icvAdjustRect( const void* srcptr, int src_step, int pix_size, if( rect.x > win_size.width ) rect.x = win_size.width; } - + if( ip.x + win_size.width < src_size.width ) rect.width = win_size.width; else @@ -961,7 +961,7 @@ icvAdjustRect( const void* srcptr, int src_step, int pix_size, } assert( rect.width <= win_size.width ); } - + if( ip.y >= 0 ) { src += ip.y * src_step; @@ -969,7 +969,7 @@ icvAdjustRect( const void* srcptr, int src_step, int pix_size, } else rect.y = -ip.y; - + if( ip.y + win_size.height < src_size.height ) rect.height = win_size.height; else @@ -981,7 +981,7 @@ icvAdjustRect( const void* srcptr, int src_step, int pix_size, rect.height = 0; } } - + *pRect = rect; return src - rect.x*pix_size; } @@ -996,16 +996,16 @@ static CvStatus CV_STDCALL icvGetRectSubPix_8u32f_C1R float a, b; double s = 0; int i, j; - + center.x -= (win_size.width-1)*0.5f; center.y -= (win_size.height-1)*0.5f; - + ip.x = cvFloor( center.x ); ip.y = cvFloor( center.y ); - + if( win_size.width <= 0 || win_size.height <= 0 ) return CV_BADRANGE_ERR; - + a = center.x - ip.x; b = center.y - ip.y; a = MAX(a,0.0001f); @@ -1014,23 +1014,23 @@ static CvStatus CV_STDCALL icvGetRectSubPix_8u32f_C1R b1 = 1.f - b; b2 = b; s = (1. - a)/a; - + src_step /= sizeof(src[0]); dst_step /= sizeof(dst[0]); - + if( 0 <= ip.x && ip.x + win_size.width < src_size.width && 0 <= ip.y && ip.y + win_size.height < src_size.height ) { // extracted rectangle is totally inside the image src += ip.y * src_step + ip.x; - + #if 0 if( icvCopySubpix_8u32f_C1R_p && icvCopySubpix_8u32f_C1R_p( src, src_step, dst, dst_step*sizeof(dst[0]), win_size, a, b ) >= 0 ) return CV_OK; #endif - + for( ; win_size.height--; src += src_step, dst += dst_step ) { float prev = (1 - a)*(b1*CV_8TO32F(src[0]) + b2*CV_8TO32F(src[src_step])); @@ -1045,29 +1045,29 @@ static CvStatus CV_STDCALL icvGetRectSubPix_8u32f_C1R else { CvRect r; - + src = (const uchar*)icvAdjustRect( src, src_step*sizeof(*src), sizeof(*src), src_size, win_size,ip, &r); - + for( i = 0; i < win_size.height; i++, dst += dst_step ) { const uchar *src2 = src + src_step; - + if( i < r.y || i >= r.height ) src2 -= src_step; - + for( j = 0; j < r.x; j++ ) { float s0 = CV_8TO32F(src[r.x])*b1 + CV_8TO32F(src2[r.x])*b2; - + dst[j] = (float)(s0); } - + if( j < r.width ) { float prev = (1 - a)*(b1*CV_8TO32F(src[j]) + b2*CV_8TO32F(src2[j])); - + for( ; j < r.width; j++ ) { float t = a12*CV_8TO32F(src[j+1]) + a22*CV_8TO32F(src2[j+1]); @@ -1075,20 +1075,20 @@ static CvStatus CV_STDCALL icvGetRectSubPix_8u32f_C1R prev = (float)(t*s); } } - + for( ; j < win_size.width; j++ ) { float s0 = CV_8TO32F(src[r.width])*b1 + CV_8TO32F(src2[r.width])*b2; - + dst[j] = (float)(s0); } - + if( i < r.height ) src = src2; } } - + return CV_OK; } @@ -1189,7 +1189,7 @@ cvCalcOpticalFlowPyrLK( const void* arrA, const void* arrB, cv::Mat ptA(count, 1, CV_32FC2, (void*)featuresA); cv::Mat ptB(count, 1, CV_32FC2, (void*)featuresB); cv::Mat st, err; - + if( status ) st = cv::Mat(count, 1, CV_8U, (void*)status); if( error ) @@ -1549,7 +1549,7 @@ cvCalcAffineFlowPyrLK( const void* arrA, const void* arrB, status[i] = (char)pt_status; featuresB[i].x = Av[2]; featuresB[i].y = Av[5]; - + matrices[i*4] = Av[0]; matrices[i*4+1] = Av[1]; matrices[i*4+2] = Av[3]; @@ -1745,7 +1745,7 @@ cvEstimateRigidTransform( const CvArr* matA, const CvArr* matB, CvMat* matM, int cvResize( A, sA, CV_INTER_AREA ); cvResize( B, sB, CV_INTER_AREA ); } - + A = sA; B = sB; } @@ -1801,8 +1801,8 @@ cvEstimateRigidTransform( const CvArr* matA, const CvArr* matB, CvMat* matM, int if( count < RANSAC_SIZE0 ) return 0; - - CvMat _pB = cvMat(1, count, CV_32FC2, pB); + + CvMat _pB = cvMat(1, count, CV_32FC2, pB); brect = cvBoundingRect(&_pB, 1); // RANSAC stuff: @@ -1822,7 +1822,7 @@ cvEstimateRigidTransform( const CvArr* matA, const CvArr* matB, CvMat* matM, int for( k1 = 0; k1 < RANSAC_MAX_ITERS; k1++ ) { idx[i] = cvRandInt(&rng) % count; - + for( j = 0; j < i; j++ ) { if( idx[j] == idx[i] ) @@ -1849,7 +1849,7 @@ cvEstimateRigidTransform( const CvArr* matA, const CvArr* matB, CvMat* matM, int b[0] = pB[idx[0]]; b[1] = pB[idx[1]]; b[2] = pB[idx[2]]; - + double dax1 = a[1].x - a[0].x, day1 = a[1].y - a[0].y; double dax2 = a[2].x - a[0].x, day2 = a[2].y - a[0].y; double dbx1 = b[1].x - b[0].x, dby1 = b[1].y - b[0].y; @@ -1901,7 +1901,7 @@ cvEstimateRigidTransform( const CvArr* matA, const CvArr* matB, CvMat* matM, int m[2] /= scale; m[5] /= scale; cvConvert( &M, matM ); - + return 1; } diff --git a/modules/video/src/optflowgf.cpp b/modules/video/src/optflowgf.cpp index f15eb19..0f63bb4 100644 --- a/modules/video/src/optflowgf.cpp +++ b/modules/video/src/optflowgf.cpp @@ -55,7 +55,7 @@ static void FarnebackPolyExp( const Mat& src, Mat& dst, int n, double sigma ) { int k, x, y; - + assert( src.type() == CV_32FC1 ); int width = src.cols; int height = src.rows; @@ -64,17 +64,17 @@ FarnebackPolyExp( const Mat& src, Mat& dst, int n, double sigma ) float* xg = g + n*2 + 1; float* xxg = xg + n*2 + 1; float *row = (float*)_row + n*3; - + if( sigma < FLT_EPSILON ) sigma = n*0.3; - + double s = 0.; for( x = -n; x <= n; x++ ) { g[x] = (float)std::exp(-x*x/(2*sigma*sigma)); s += g[x]; } - + s = 1./s; for( x = -n; x <= n; x++ ) { @@ -84,7 +84,7 @@ FarnebackPolyExp( const Mat& src, Mat& dst, int n, double sigma ) } Mat_ G = Mat_::zeros(6, 6); - + for( y = -n; y <= n; y++ ) for( x = -n; x <= n; x++ ) { @@ -93,7 +93,7 @@ FarnebackPolyExp( const Mat& src, Mat& dst, int n, double sigma ) G(3,3) += g[y]*g[x]*x*x*x*x; G(5,5) += g[y]*g[x]*x*x*y*y; } - + //G[0][0] = 1.; G(2,2) = G(0,3) = G(0,4) = G(3,0) = G(4,0) = G(1,1); G(4,4) = G(3,3); @@ -110,53 +110,53 @@ FarnebackPolyExp( const Mat& src, Mat& dst, int n, double sigma ) double ig11 = invG(1,1), ig03 = invG(0,3), ig33 = invG(3,3), ig55 = invG(5,5); dst.create( height, width, CV_32FC(5)); - + 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); - + // vertical part of convolution for( x = 0; x < width; x++ ) { row[x*3] = srow0[x]*g0; row[x*3+1] = row[x*3+2] = 0.f; } - + 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)); - + for( x = 0; x < width; x++ ) { float p = srow0[x] + srow1[x]; float t0 = row[x*3] + g0*p; float t1 = row[x*3+1] + g1*(srow1[x] - srow0[x]); float t2 = row[x*3+2] + g2*p; - + row[x*3] = t0; row[x*3+1] = t1; row[x*3+2] = t2; } } - + // horizontal part of convolution for( x = 0; x < n*3; x++ ) { row[-1-x] = row[2-x]; row[width*3+x] = row[width*3+x-3]; } - + for( x = 0; x < width; x++ ) { g0 = g[0]; // r1 ~ 1, r2 ~ x, r3 ~ y, r4 ~ x^2, r5 ~ y^2, r6 ~ xy double b1 = row[x*3]*g0, b2 = 0, b3 = row[x*3+1]*g0, b4 = 0, b5 = row[x*3+2]*g0, b6 = 0; - + for( k = 1; k <= n; k++ ) { double tg = row[(x+k)*3] + row[(x-k)*3]; @@ -168,7 +168,7 @@ FarnebackPolyExp( const Mat& src, Mat& dst, int n, double sigma ) b6 += (row[(x+k)*3+1] - row[(x-k)*3+1])*xg[k]; b5 += (row[(x+k)*3+2] + row[(x-k)*3+2])*g0; } - + // do not store r1 drow[x*5+1] = (float)(b2*ig11); drow[x*5] = (float)(b3*ig11); @@ -177,7 +177,7 @@ FarnebackPolyExp( const Mat& src, Mat& dst, int n, double sigma ) drow[x*5+4] = (float)(b6*ig55); } } - + row -= n*3; } @@ -202,7 +202,7 @@ FarnebackUpdateMatrices( const Mat& _R0, const Mat& _R1, const Mat& _flow, Mat& int x, y, width = _flow.cols, height = _flow.rows; const float* R1 = (float*)_R1.data; size_t step1 = _R1.step/sizeof(R1[0]); - + matM.create(height, width, CV_32FC(5)); for( y = _y0; y < _y1; y++ ) @@ -210,7 +210,7 @@ FarnebackUpdateMatrices( const Mat& _R0, const Mat& _R1, const Mat& _flow, Mat& 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); - + for( x = 0; x < width; x++ ) { float dx = flow[x*2], dy = flow[x*2+1]; @@ -222,7 +222,7 @@ FarnebackUpdateMatrices( const Mat& _R0, const Mat& _R1, const Mat& _flow, Mat& float r2, r3, r4, r5, r6; fx -= x1; fy -= y1; - + if( (unsigned)x1 < (unsigned)(width-1) && (unsigned)y1 < (unsigned)(height-1) ) { @@ -300,7 +300,7 @@ FarnebackUpdateFlow_Blur( const Mat& _R0, const Mat& _R1, int y0 = 0, y1; int min_update_stripe = std::max((1 << 10)/width, block_size); double scale = 1./(block_size*block_size); - + AutoBuffer _vsum((width+m*2+2)*5); double* vsum = _vsum + (m+1)*5; @@ -324,7 +324,7 @@ FarnebackUpdateFlow_Blur( const Mat& _R0, const Mat& _R1, 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)); - + // vertical blur for( x = 0; x < width*5; x++ ) vsum[x] += srow1[x] - srow0[x]; @@ -368,7 +368,7 @@ FarnebackUpdateFlow_Blur( const Mat& _R0, const Mat& _R1, double h2_ = h2*scale; double idet = 1./(g11_*g22_ - g12_*g12_+1e-3); - + flow[x*2] = (float)((g11_*h2_-g12_*h1_)*idet); flow[x*2+1] = (float)((g22_*h1_-g12_*h2_)*idet); } @@ -393,7 +393,7 @@ FarnebackUpdateFlow_GaussianBlur( const Mat& _R0, const Mat& _R1, int y0 = 0, y1; int min_update_stripe = std::max((1 << 10)/width, block_size); double sigma = m*0.3, s = 1; - + AutoBuffer _vsum((width+m*2+2)*5 + 16), _hsum(width*5 + 16); AutoBuffer _kernel((m+1)*5 + 16); AutoBuffer _srow(m*2+1); @@ -464,7 +464,7 @@ FarnebackUpdateFlow_GaussianBlur( const Mat& _R0, const Mat& _R1, s2 = _mm_add_ps(s2, _mm_mul_ps(x0, g4)); s3 = _mm_add_ps(s3, _mm_mul_ps(x1, g4)); } - + _mm_store_ps(vsum + x, s0); _mm_store_ps(vsum + x + 4, s1); _mm_store_ps(vsum + x + 8, s2); @@ -532,10 +532,10 @@ FarnebackUpdateFlow_GaussianBlur( const Mat& _R0, const Mat& _R1, #endif for( ; x < width*5; x++ ) { - float s = vsum[x]*kernel[0]; + float sum = vsum[x]*kernel[0]; for( i = 1; i <= m; i++ ) - s += kernel[i]*(vsum[x - i*5] + vsum[x + i*5]); - hsum[x] = s; + sum += kernel[i]*(vsum[x - i*5] + vsum[x + i*5]); + hsum[x] = sum; } for( x = 0; x < width; x++ ) @@ -547,7 +547,7 @@ FarnebackUpdateFlow_GaussianBlur( const Mat& _R0, const Mat& _R1, h2 = hsum[x*5+4]; double idet = 1./(g11*g22 - g12*g12 + 1e-3); - + flow[x*2] = (float)((g11*h2-g12*h1)*idet); flow[x*2+1] = (float)((g22*h1-g12*h2)*idet); } @@ -630,7 +630,7 @@ void cv::calcOpticalFlowFarneback( InputArray _prev0, InputArray _next0, resize( fimg, I, Size(width, height), CV_INTER_LINEAR ); FarnebackPolyExp( I, R[i], poly_n, poly_sigma ); } - + FarnebackUpdateMatrices( R[0], R[1], flow, M, 0, flow.rows ); for( i = 0; i < iterations; i++ ) diff --git a/modules/videostab/include/opencv2/videostab/fast_marching.hpp b/modules/videostab/include/opencv2/videostab/fast_marching.hpp index 45663d4..23c5df3 100644 --- a/modules/videostab/include/opencv2/videostab/fast_marching.hpp +++ b/modules/videostab/include/opencv2/videostab/fast_marching.hpp @@ -73,7 +73,7 @@ private: int x, y; DXY() : dist(0), x(0), y(0) {} - DXY(float dist, int x, int y) : dist(dist), x(x), y(y) {} + DXY(float _dist, int _x, int _y) : dist(_dist), x(_x), y(_y) {} bool operator <(const DXY &dxy) const { return dist < dxy.dist; } }; diff --git a/modules/videostab/include/opencv2/videostab/inpainting.hpp b/modules/videostab/include/opencv2/videostab/inpainting.hpp index 580413a..6455b14 100644 --- a/modules/videostab/include/opencv2/videostab/inpainting.hpp +++ b/modules/videostab/include/opencv2/videostab/inpainting.hpp @@ -180,8 +180,7 @@ private: class CV_EXPORTS ColorInpainter : public InpainterBase { public: - ColorInpainter(int method = INPAINT_TELEA, double radius = 2.) - : method_(method), radius_(radius) {} + ColorInpainter(int method = INPAINT_TELEA, double radius = 2.); virtual void inpaint(int idx, Mat &frame, Mat &mask); @@ -191,6 +190,9 @@ private: Mat invMask_; }; +inline ColorInpainter::ColorInpainter(int _method, double _radius) + : method_(_method), radius_(_radius) {} + CV_EXPORTS void calcFlowMask( const Mat &flowX, const Mat &flowY, const Mat &errors, float maxError, const Mat &mask0, const Mat &mask1, Mat &flowMask); diff --git a/modules/videostab/include/opencv2/videostab/motion_core.hpp b/modules/videostab/include/opencv2/videostab/motion_core.hpp index 4102412..b9f3614 100644 --- a/modules/videostab/include/opencv2/videostab/motion_core.hpp +++ b/modules/videostab/include/opencv2/videostab/motion_core.hpp @@ -70,8 +70,7 @@ struct CV_EXPORTS RansacParams float prob; // probability of success RansacParams() : size(0), thresh(0), eps(0), prob(0) {} - RansacParams(int size, float thresh, float eps, float prob) - : size(size), thresh(thresh), eps(eps), prob(prob) {} + RansacParams(int size, float thresh, float eps, float prob); int niters() const { @@ -96,6 +95,9 @@ struct CV_EXPORTS RansacParams } }; +inline RansacParams::RansacParams(int _size, float _thresh, float _eps, float _prob) + : size(_size), thresh(_thresh), eps(_eps), prob(_prob) {} + } // namespace videostab } // namespace cv diff --git a/modules/videostab/include/opencv2/videostab/motion_stabilizing.hpp b/modules/videostab/include/opencv2/videostab/motion_stabilizing.hpp index 69d7ac2..eb31994 100644 --- a/modules/videostab/include/opencv2/videostab/motion_stabilizing.hpp +++ b/modules/videostab/include/opencv2/videostab/motion_stabilizing.hpp @@ -94,7 +94,7 @@ public: class CV_EXPORTS GaussianMotionFilter : public MotionFilterBase { public: - GaussianMotionFilter(int radius = 15, float stdev = -1.f) { setParams(radius, stdev); } + GaussianMotionFilter(int radius = 15, float stdev = -1.f); void setParams(int radius, float stdev = -1.f); int radius() const { return radius_; } @@ -109,6 +109,8 @@ private: std::vector weight_; }; +inline GaussianMotionFilter::GaussianMotionFilter(int _radius, float _stdev) { setParams(_radius, _stdev); } + class CV_EXPORTS LpMotionStabilizer : public IMotionStabilizer { public: diff --git a/modules/videostab/include/opencv2/videostab/stabilizer.hpp b/modules/videostab/include/opencv2/videostab/stabilizer.hpp index d6827da..cdc3cfe 100644 --- a/modules/videostab/include/opencv2/videostab/stabilizer.hpp +++ b/modules/videostab/include/opencv2/videostab/stabilizer.hpp @@ -65,7 +65,7 @@ class CV_EXPORTS StabilizerBase public: virtual ~StabilizerBase() {} - void setLog(Ptr log) { log_ = log; } + void setLog(Ptr ilog) { log_ = ilog; } Ptr log() const { return log_; } void setRadius(int val) { radius_ = val; } diff --git a/modules/videostab/src/global_motion.cpp b/modules/videostab/src/global_motion.cpp index e464a03..ede72d8 100644 --- a/modules/videostab/src/global_motion.cpp +++ b/modules/videostab/src/global_motion.cpp @@ -355,7 +355,7 @@ Mat estimateGlobalMotionRobust( Mat_ M = estimateGlobalMotionLeastSquares(subset0, subset1, model, 0); - int ninliers = 0; + int numinliers = 0; for (int i = 0; i < npoints; ++i) { p0 = points0_[i]; @@ -363,12 +363,12 @@ Mat estimateGlobalMotionRobust( x = M(0,0)*p0.x + M(0,1)*p0.y + M(0,2); y = M(1,0)*p0.x + M(1,1)*p0.y + M(1,2); if (sqr(x - p1.x) + sqr(y - p1.y) < params.thresh * params.thresh) - ninliers++; + numinliers++; } - if (ninliers >= ninliersMax) + if (numinliers >= ninliersMax) { bestM = M; - ninliersMax = ninliers; + ninliersMax = numinliers; subset0best.swap(subset0); subset1best.swap(subset1); } @@ -657,8 +657,8 @@ Mat KeypointBasedMotionEstimator::estimate(const Mat &frame0, const Mat &frame1, // perform outlier rejection - IOutlierRejector *outlierRejector = static_cast(outlierRejector_); - if (!dynamic_cast(outlierRejector)) + IOutlierRejector *outlRejector = static_cast(outlierRejector_); + if (!dynamic_cast(outlRejector)) { pointsPrev_.swap(pointsPrevGood_); points_.swap(pointsGood_); diff --git a/modules/videostab/src/inpainting.cpp b/modules/videostab/src/inpainting.cpp index b97cd0f..929c28a 100644 --- a/modules/videostab/src/inpainting.cpp +++ b/modules/videostab/src/inpainting.cpp @@ -130,9 +130,9 @@ void ConsistentMosaicInpainter::inpaint(int idx, Mat &frame, Mat &mask) CV_Assert(mask.size() == frame.size() && mask.type() == CV_8U); Mat invS = at(idx, *stabilizationMotions_).inv(); - vector > motions(2*radius_ + 1); + vector > vmotions(2*radius_ + 1); for (int i = -radius_; i <= radius_; ++i) - motions[radius_ + i] = getMotion(idx, idx + i, *motions_) * invS; + vmotions[radius_ + i] = getMotion(idx, idx + i, *motions_) * invS; int n; float mean, var; @@ -154,7 +154,7 @@ void ConsistentMosaicInpainter::inpaint(int idx, Mat &frame, Mat &mask) for (int i = -radius_; i <= radius_; ++i) { const Mat_ > &framei = at(idx + i, *frames_); - const Mat_ &Mi = motions[radius_ + i]; + const Mat_ &Mi = vmotions[radius_ + i]; int xi = cvRound(Mi(0,0)*x + Mi(0,1)*y + Mi(0,2)); int yi = cvRound(Mi(1,0)*x + Mi(1,1)*y + Mi(1,2)); if (xi >= 0 && xi < framei.cols && yi >= 0 && yi < framei.rows) @@ -339,12 +339,12 @@ MotionInpainter::MotionInpainter() void MotionInpainter::inpaint(int idx, Mat &frame, Mat &mask) { priority_queue > neighbors; - vector motions(2*radius_ + 1); + vector vmotions(2*radius_ + 1); for (int i = -radius_; i <= radius_; ++i) { Mat motion0to1 = getMotion(idx, idx + i, *motions_) * at(idx, *stabilizationMotions_).inv(); - motions[radius_ + i] = motion0to1; + vmotions[radius_ + i] = motion0to1; if (i != 0) { @@ -370,7 +370,7 @@ void MotionInpainter::inpaint(int idx, Mat &frame, Mat &mask) int neighbor = neighbors.top().second; neighbors.pop(); - Mat motion1to0 = motions[radius_ + neighbor - idx].inv(); + Mat motion1to0 = vmotions[radius_ + neighbor - idx].inv(); // warp frame diff --git a/modules/videostab/src/motion_stabilizing.cpp b/modules/videostab/src/motion_stabilizing.cpp index 800ac80..be73991 100644 --- a/modules/videostab/src/motion_stabilizing.cpp +++ b/modules/videostab/src/motion_stabilizing.cpp @@ -69,8 +69,8 @@ void MotionStabilizationPipeline::stabilize( { stabilizers_[i]->stabilize(size, updatedMotions, range, &stabilizationMotions_[0]); - for (int i = 0; i < size; ++i) - stabilizationMotions[i] = stabilizationMotions_[i] * stabilizationMotions[i]; + for (int k = 0; k < size; ++k) + stabilizationMotions[k] = stabilizationMotions_[k] * stabilizationMotions[k]; for (int j = 0; j + 1 < size; ++j) { @@ -90,10 +90,10 @@ void MotionFilterBase::stabilize( } -void GaussianMotionFilter::setParams(int radius, float stdev) +void GaussianMotionFilter::setParams(int _radius, float _stdev) { - radius_ = radius; - stdev_ = stdev > 0.f ? stdev : sqrt(static_cast(radius)); + radius_ = _radius; + stdev_ = _stdev > 0.f ? _stdev : sqrt(static_cast(_radius)); float sum = 0; weight_.resize(2*radius_ + 1); diff --git a/modules/videostab/src/stabilizer.cpp b/modules/videostab/src/stabilizer.cpp index e893571..47c221e 100644 --- a/modules/videostab/src/stabilizer.cpp +++ b/modules/videostab/src/stabilizer.cpp @@ -158,8 +158,8 @@ bool StabilizerBase::doOneIteration() void StabilizerBase::setUp(const Mat &firstFrame) { - InpainterBase *inpainter = static_cast(inpainter_); - doInpainting_ = dynamic_cast(inpainter) == 0; + InpainterBase *inpaint = static_cast(inpainter_); + doInpainting_ = dynamic_cast(inpaint) == 0; if (doInpainting_) { inpainter_->setMotionModel(motionEstimator_->motionModel()); @@ -370,11 +370,11 @@ static void saveMotions( void TwoPassStabilizer::runPrePassIfNecessary() { if (!isPrePassDone_) - { + { // check if we must do wobble suppression - WobbleSuppressorBase *wobbleSuppressor = static_cast(wobbleSuppressor_); - doWobbleSuppression_ = dynamic_cast(wobbleSuppressor) == 0; + WobbleSuppressorBase *wobble = static_cast(wobbleSuppressor_); + doWobbleSuppression_ = dynamic_cast(wobble) == 0; // estimate motions @@ -471,8 +471,8 @@ void TwoPassStabilizer::setUp(const Mat &firstFrame) for (int i = -radius_; i <= 0; ++i) at(i, frames_) = firstFrame; - WobbleSuppressorBase *wobbleSuppressor = static_cast(wobbleSuppressor_); - doWobbleSuppression_ = dynamic_cast(wobbleSuppressor) == 0; + WobbleSuppressorBase *wobble = static_cast(wobbleSuppressor_); + doWobbleSuppression_ = dynamic_cast(wobble) == 0; if (doWobbleSuppression_) { wobbleSuppressor_->setFrameCount(frameCount_); -- 2.7.4