fixed some warning under Ubuntu in gpu module
This commit is contained in:
Alexey Spizhevoy
parent
4ac4ce3e40
commit
a81b41fb08
@ -75,7 +75,7 @@ namespace cv
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//////////////////////////////// Error handling ////////////////////////
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CV_EXPORTS void error(const char *error_string, const char *file, const int line, const char *func);
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CV_EXPORTS void nppError( int err, const char *file, const int line, const char *func);
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CV_EXPORTS void nppError( int err, const char *file, const int line, const char *func);
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//////////////////////////////// GpuMat ////////////////////////////////
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class Stream;
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@ -443,11 +443,11 @@ namespace cv
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CV_EXPORTS void minMax(const GpuMat& src, double* minVal, double* maxVal, const GpuMat& mask, GpuMat& buf);
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//! finds global minimum and maximum array elements and returns their values with locations
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CV_EXPORTS void minMaxLoc(const GpuMat& src, double* minVal, double* maxVal=0, Point* minLoc=0, Point* maxLoc=0,
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CV_EXPORTS void minMaxLoc(const GpuMat& src, double* minVal, double* maxVal=0, Point* minLoc=0, Point* maxLoc=0,
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const GpuMat& mask=GpuMat());
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//! finds global minimum and maximum array elements and returns their values with locations
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CV_EXPORTS void minMaxLoc(const GpuMat& src, double* minVal, double* maxVal, Point* minLoc, Point* maxLoc,
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CV_EXPORTS void minMaxLoc(const GpuMat& src, double* minVal, double* maxVal, Point* minLoc, Point* maxLoc,
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const GpuMat& mask, GpuMat& valbuf, GpuMat& locbuf);
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//! counts non-zero array elements
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@ -532,7 +532,7 @@ namespace cv
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CV_EXPORTS void polarToCart(const GpuMat& magnitude, const GpuMat& angle, GpuMat& x, GpuMat& y, bool angleInDegrees, const Stream& stream);
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//! perfroms per-elements bit-wise inversion
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//! perfroms per-elements bit-wise inversion
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CV_EXPORTS void bitwise_not(const GpuMat& src, GpuMat& dst, const GpuMat& mask=GpuMat());
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//! async version
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CV_EXPORTS void bitwise_not(const GpuMat& src, GpuMat& dst, const GpuMat& mask, const Stream& stream);
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@ -586,11 +586,11 @@ namespace cv
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CV_EXPORTS void remap(const GpuMat& src, GpuMat& dst, const GpuMat& xmap, const GpuMat& ymap);
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//! Does mean shift filtering on GPU.
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CV_EXPORTS void meanShiftFiltering(const GpuMat& src, GpuMat& dst, int sp, int sr,
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CV_EXPORTS void meanShiftFiltering(const GpuMat& src, GpuMat& dst, int sp, int sr,
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TermCriteria criteria = TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 5, 1));
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//! Does mean shift procedure on GPU.
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CV_EXPORTS void meanShiftProc(const GpuMat& src, GpuMat& dstr, GpuMat& dstsp, int sp, int sr,
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CV_EXPORTS void meanShiftProc(const GpuMat& src, GpuMat& dstr, GpuMat& dstsp, int sp, int sr,
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TermCriteria criteria = TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 5, 1));
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//! Does mean shift segmentation with elimiation of small regions.
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@ -604,9 +604,9 @@ namespace cv
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//! async version
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CV_EXPORTS void drawColorDisp(const GpuMat& src_disp, GpuMat& dst_disp, int ndisp, const Stream& stream);
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//! Reprojects disparity image to 3D space.
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//! Reprojects disparity image to 3D space.
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//! Supports CV_8U and CV_16S types of input disparity.
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//! The output is a 4-channel floating-point (CV_32FC4) matrix.
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//! The output is a 4-channel floating-point (CV_32FC4) matrix.
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//! Each element of this matrix will contain the 3D coordinates of the point (x,y,z,1), computed from the disparity map.
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//! Q is the 4x4 perspective transformation matrix that can be obtained with cvStereoRectify.
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CV_EXPORTS void reprojectImageTo3D(const GpuMat& disp, GpuMat& xyzw, const Mat& Q);
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@ -618,7 +618,7 @@ namespace cv
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//! async version
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CV_EXPORTS void cvtColor(const GpuMat& src, GpuMat& dst, int code, int dcn, const Stream& stream);
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//! applies fixed threshold to the image.
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//! applies fixed threshold to the image.
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//! Now supports only THRESH_TRUNC threshold type and one channels float source.
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CV_EXPORTS double threshold(const GpuMat& src, GpuMat& dst, double thresh);
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@ -662,7 +662,7 @@ namespace cv
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//! disabled until fix crash
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CV_EXPORTS void Canny(const GpuMat& image, GpuMat& edges, double threshold1, double threshold2, int apertureSize = 3);
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//! computes Harris cornerness criteria at each image pixel
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//! computes Harris cornerness criteria at each image pixel
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CV_EXPORTS void cornerHarris(const GpuMat& src, GpuMat& dst, int blockSize, int ksize, double k, int borderType=BORDER_REFLECT101);
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@ -696,7 +696,7 @@ namespace cv
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This is the base class for linear or non-linear filters that process columns of 2D arrays.
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Such filters are used for the "vertical" filtering parts in separable filters.
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*/
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*/
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class CV_EXPORTS BaseColumnFilter_GPU
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{
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public:
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@ -710,7 +710,7 @@ namespace cv
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The Base Class for Non-Separable 2D Filters.
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This is the base class for linear or non-linear 2D filters.
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*/
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*/
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class CV_EXPORTS BaseFilter_GPU
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{
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public:
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@ -739,7 +739,7 @@ namespace cv
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CV_EXPORTS Ptr<FilterEngine_GPU> createFilter2D_GPU(const Ptr<BaseFilter_GPU> filter2D, int srcType, int dstType);
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//! returns the separable filter engine with the specified filters
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CV_EXPORTS Ptr<FilterEngine_GPU> createSeparableFilter_GPU(const Ptr<BaseRowFilter_GPU>& rowFilter,
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CV_EXPORTS Ptr<FilterEngine_GPU> createSeparableFilter_GPU(const Ptr<BaseRowFilter_GPU>& rowFilter,
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const Ptr<BaseColumnFilter_GPU>& columnFilter, int srcType, int bufType, int dstType);
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//! returns horizontal 1D box filter
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@ -755,27 +755,27 @@ namespace cv
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CV_EXPORTS Ptr<BaseFilter_GPU> getBoxFilter_GPU(int srcType, int dstType, const Size& ksize, Point anchor = Point(-1, -1));
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//! returns box filter engine
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CV_EXPORTS Ptr<FilterEngine_GPU> createBoxFilter_GPU(int srcType, int dstType, const Size& ksize,
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CV_EXPORTS Ptr<FilterEngine_GPU> createBoxFilter_GPU(int srcType, int dstType, const Size& ksize,
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const Point& anchor = Point(-1,-1));
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//! returns 2D morphological filter
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//! only MORPH_ERODE and MORPH_DILATE are supported
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//! supports CV_8UC1 and CV_8UC4 types
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//! kernel must have CV_8UC1 type, one rows and cols == ksize.width * ksize.height
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CV_EXPORTS Ptr<BaseFilter_GPU> getMorphologyFilter_GPU(int op, int type, const Mat& kernel, const Size& ksize,
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CV_EXPORTS Ptr<BaseFilter_GPU> getMorphologyFilter_GPU(int op, int type, const Mat& kernel, const Size& ksize,
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Point anchor=Point(-1,-1));
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//! returns morphological filter engine. Only MORPH_ERODE and MORPH_DILATE are supported.
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CV_EXPORTS Ptr<FilterEngine_GPU> createMorphologyFilter_GPU(int op, int type, const Mat& kernel,
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CV_EXPORTS Ptr<FilterEngine_GPU> createMorphologyFilter_GPU(int op, int type, const Mat& kernel,
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const Point& anchor = Point(-1,-1), int iterations = 1);
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//! returns 2D filter with the specified kernel
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//! supports CV_8UC1 and CV_8UC4 types
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CV_EXPORTS Ptr<BaseFilter_GPU> getLinearFilter_GPU(int srcType, int dstType, const Mat& kernel, const Size& ksize,
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CV_EXPORTS Ptr<BaseFilter_GPU> getLinearFilter_GPU(int srcType, int dstType, const Mat& kernel, const Size& ksize,
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Point anchor = Point(-1, -1));
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//! returns the non-separable linear filter engine
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CV_EXPORTS Ptr<FilterEngine_GPU> createLinearFilter_GPU(int srcType, int dstType, const Mat& kernel,
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CV_EXPORTS Ptr<FilterEngine_GPU> createLinearFilter_GPU(int srcType, int dstType, const Mat& kernel,
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const Point& anchor = Point(-1,-1));
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//! returns the primitive row filter with the specified kernel.
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@ -784,9 +784,9 @@ namespace cv
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//! NPP calls when srcType == CV_8UC1 or srcType == CV_8UC4 and bufType == srcType,
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//! otherwise calls OpenCV version.
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//! NPP supports only BORDER_CONSTANT border type.
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//! OpenCV version supports only CV_32F as buffer depth and
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//! OpenCV version supports only CV_32F as buffer depth and
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//! BORDER_REFLECT101, BORDER_REPLICATE and BORDER_CONSTANT border types.
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CV_EXPORTS Ptr<BaseRowFilter_GPU> getLinearRowFilter_GPU(int srcType, int bufType, const Mat& rowKernel,
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CV_EXPORTS Ptr<BaseRowFilter_GPU> getLinearRowFilter_GPU(int srcType, int bufType, const Mat& rowKernel,
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int anchor = -1, int borderType = BORDER_CONSTANT);
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//! returns the primitive column filter with the specified kernel.
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@ -795,22 +795,22 @@ namespace cv
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//! NPP calls when dstType == CV_8UC1 or dstType == CV_8UC4 and bufType == dstType,
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//! otherwise calls OpenCV version.
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//! NPP supports only BORDER_CONSTANT border type.
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//! OpenCV version supports only CV_32F as buffer depth and
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//! OpenCV version supports only CV_32F as buffer depth and
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//! BORDER_REFLECT101, BORDER_REPLICATE and BORDER_CONSTANT border types.
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CV_EXPORTS Ptr<BaseColumnFilter_GPU> getLinearColumnFilter_GPU(int bufType, int dstType, const Mat& columnKernel,
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CV_EXPORTS Ptr<BaseColumnFilter_GPU> getLinearColumnFilter_GPU(int bufType, int dstType, const Mat& columnKernel,
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int anchor = -1, int borderType = BORDER_CONSTANT);
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//! returns the separable linear filter engine
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CV_EXPORTS Ptr<FilterEngine_GPU> createSeparableLinearFilter_GPU(int srcType, int dstType, const Mat& rowKernel,
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CV_EXPORTS Ptr<FilterEngine_GPU> createSeparableLinearFilter_GPU(int srcType, int dstType, const Mat& rowKernel,
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const Mat& columnKernel, const Point& anchor = Point(-1,-1), int rowBorderType = BORDER_DEFAULT,
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int columnBorderType = -1);
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//! returns filter engine for the generalized Sobel operator
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CV_EXPORTS Ptr<FilterEngine_GPU> createDerivFilter_GPU(int srcType, int dstType, int dx, int dy, int ksize,
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CV_EXPORTS Ptr<FilterEngine_GPU> createDerivFilter_GPU(int srcType, int dstType, int dx, int dy, int ksize,
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int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1);
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//! returns the Gaussian filter engine
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CV_EXPORTS Ptr<FilterEngine_GPU> createGaussianFilter_GPU(int type, Size ksize, double sigma1, double sigma2 = 0,
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CV_EXPORTS Ptr<FilterEngine_GPU> createGaussianFilter_GPU(int type, Size ksize, double sigma1, double sigma2 = 0,
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int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1);
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//! returns maximum filter
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@ -839,19 +839,19 @@ namespace cv
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CV_EXPORTS void filter2D(const GpuMat& src, GpuMat& dst, int ddepth, const Mat& kernel, Point anchor=Point(-1,-1));
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//! applies separable 2D linear filter to the image
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CV_EXPORTS void sepFilter2D(const GpuMat& src, GpuMat& dst, int ddepth, const Mat& kernelX, const Mat& kernelY,
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CV_EXPORTS void sepFilter2D(const GpuMat& src, GpuMat& dst, int ddepth, const Mat& kernelX, const Mat& kernelY,
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Point anchor = Point(-1,-1), int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1);
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//! applies generalized Sobel operator to the image
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CV_EXPORTS void Sobel(const GpuMat& src, GpuMat& dst, int ddepth, int dx, int dy, int ksize = 3, double scale = 1,
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CV_EXPORTS void Sobel(const GpuMat& src, GpuMat& dst, int ddepth, int dx, int dy, int ksize = 3, double scale = 1,
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int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1);
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//! applies the vertical or horizontal Scharr operator to the image
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CV_EXPORTS void Scharr(const GpuMat& src, GpuMat& dst, int ddepth, int dx, int dy, double scale = 1,
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CV_EXPORTS void Scharr(const GpuMat& src, GpuMat& dst, int ddepth, int dx, int dy, double scale = 1,
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int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1);
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//! smooths the image using Gaussian filter.
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CV_EXPORTS void GaussianBlur(const GpuMat& src, GpuMat& dst, Size ksize, double sigma1, double sigma2 = 0,
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CV_EXPORTS void GaussianBlur(const GpuMat& src, GpuMat& dst, Size ksize, double sigma1, double sigma2 = 0,
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int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1);
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//! applies Laplacian operator to the image
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@ -892,7 +892,7 @@ namespace cv
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class CV_EXPORTS StereoBM_GPU
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{
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public:
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public:
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enum { BASIC_PRESET = 0, PREFILTER_XSOBEL = 1 };
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enum { DEFAULT_NDISP = 64, DEFAULT_WINSZ = 19 };
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@ -948,7 +948,7 @@ namespace cv
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//! the full constructor taking the number of disparities, number of BP iterations on each level,
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//! number of levels, truncation of data cost, data weight,
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//! truncation of discontinuity cost and discontinuity single jump
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//! truncation of discontinuity cost and discontinuity single jump
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//! DataTerm = data_weight * min(fabs(I2-I1), max_data_term)
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//! DiscTerm = min(disc_single_jump * fabs(f1-f2), max_disc_term)
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//! please see paper for more details
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@ -1102,10 +1102,10 @@ namespace cv
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enum { DEFAULT_NLEVELS = 64 };
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enum { DESCR_FORMAT_ROW_BY_ROW, DESCR_FORMAT_COL_BY_COL };
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HOGDescriptor(Size win_size=Size(64, 128), Size block_size=Size(16, 16),
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Size block_stride=Size(8, 8), Size cell_size=Size(8, 8),
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int nbins=9, double win_sigma=DEFAULT_WIN_SIGMA,
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double threshold_L2hys=0.2, bool gamma_correction=true,
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HOGDescriptor(Size win_size=Size(64, 128), Size block_size=Size(16, 16),
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Size block_stride=Size(8, 8), Size cell_size=Size(8, 8),
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int nbins=9, double win_sigma=DEFAULT_WIN_SIGMA,
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double threshold_L2hys=0.2, bool gamma_correction=true,
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int nlevels=DEFAULT_NLEVELS);
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size_t getDescriptorSize() const;
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@ -1118,13 +1118,13 @@ namespace cv
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void setSVMDetector(const vector<float>& detector);
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bool checkDetectorSize() const;
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void detect(const GpuMat& img, vector<Point>& found_locations, double hit_threshold=0,
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void detect(const GpuMat& img, vector<Point>& found_locations, double hit_threshold=0,
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Size win_stride=Size(), Size padding=Size());
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void detectMultiScale(const GpuMat& img, vector<Rect>& found_locations,
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void detectMultiScale(const GpuMat& img, vector<Rect>& found_locations,
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double hit_threshold=0, Size win_stride=Size(), Size padding=Size(),
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double scale0=1.05, int group_threshold=2);
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void getDescriptors(const GpuMat& img, Size win_stride, GpuMat& descriptors,
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void getDescriptors(const GpuMat& img, Size win_stride, GpuMat& descriptors,
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int descr_format=DESCR_FORMAT_COL_BY_COL);
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Size win_size;
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@ -1134,8 +1134,8 @@ namespace cv
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int nbins;
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double win_sigma;
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double threshold_L2hys;
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int nlevels;
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bool gamma_correction;
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int nlevels;
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protected:
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void computeBlockHistograms(const GpuMat& img);
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@ -1149,14 +1149,14 @@ namespace cv
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GpuMat detector;
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// Results of the last classification step
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GpuMat labels;
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GpuMat labels;
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Mat labels_host;
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// Results of the last histogram evaluation step
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GpuMat block_hists;
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// Gradients conputation results
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GpuMat grad, qangle;
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GpuMat grad, qangle;
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};
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@ -1187,7 +1187,7 @@ namespace cv
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// Find one best match for each query descriptor.
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// trainIdx.at<int>(0, queryIdx) will contain best train index for queryIdx
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// distance.at<float>(0, queryIdx) will contain distance
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void matchSingle(const GpuMat& queryDescs, const GpuMat& trainDescs,
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void matchSingle(const GpuMat& queryDescs, const GpuMat& trainDescs,
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GpuMat& trainIdx, GpuMat& distance,
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const GpuMat& mask = GpuMat());
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@ -1195,7 +1195,7 @@ namespace cv
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static void matchDownload(const GpuMat& trainIdx, const GpuMat& distance, std::vector<DMatch>& matches);
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// Find one best match for each query descriptor.
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void match(const GpuMat& queryDescs, const GpuMat& trainDescs, std::vector<DMatch>& matches,
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void match(const GpuMat& queryDescs, const GpuMat& trainDescs, std::vector<DMatch>& matches,
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const GpuMat& mask = GpuMat());
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// Make gpu collection of trains and masks in suitable format for matchCollection function
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@ -1206,16 +1206,16 @@ namespace cv
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// trainIdx.at<int>(0, queryIdx) will contain best train index for queryIdx
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// imgIdx.at<int>(0, queryIdx) will contain best image index for queryIdx
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// distance.at<float>(0, queryIdx) will contain distance
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void matchCollection(const GpuMat& queryDescs, const GpuMat& trainCollection,
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GpuMat& trainIdx, GpuMat& imgIdx, GpuMat& distance,
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void matchCollection(const GpuMat& queryDescs, const GpuMat& trainCollection,
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GpuMat& trainIdx, GpuMat& imgIdx, GpuMat& distance,
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const GpuMat& maskCollection);
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// Download trainIdx, imgIdx and distance to CPU vector with DMatch
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static void matchDownload(const GpuMat& trainIdx, GpuMat& imgIdx, const GpuMat& distance,
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static void matchDownload(const GpuMat& trainIdx, GpuMat& imgIdx, const GpuMat& distance,
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std::vector<DMatch>& matches);
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// Find one best match from train collection for each query descriptor.
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void match(const GpuMat& queryDescs, std::vector<DMatch>& matches,
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void match(const GpuMat& queryDescs, std::vector<DMatch>& matches,
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const std::vector<GpuMat>& masks = std::vector<GpuMat>());
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// Find k best matches for each query descriptor (in increasing order of distances).
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@ -1223,9 +1223,9 @@ namespace cv
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// distance.at<float>(queryIdx, i) will contain distance.
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// allDist is a buffer to store all distance between query descriptors and train descriptors
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// it have size (nQuery,nTrain) and CV_32F type
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// allDist.at<float>(queryIdx, trainIdx) will contain FLT_MAX, if trainIdx is one from k best,
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// allDist.at<float>(queryIdx, trainIdx) will contain FLT_MAX, if trainIdx is one from k best,
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// otherwise it will contain distance between queryIdx and trainIdx descriptors
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void knnMatch(const GpuMat& queryDescs, const GpuMat& trainDescs,
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void knnMatch(const GpuMat& queryDescs, const GpuMat& trainDescs,
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GpuMat& trainIdx, GpuMat& distance, GpuMat& allDist, int k, const GpuMat& mask = GpuMat());
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// Download trainIdx and distance to CPU vector with DMatch
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@ -1239,15 +1239,15 @@ namespace cv
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// compactResult is used when mask is not empty. If compactResult is false matches
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// vector will have the same size as queryDescriptors rows. If compactResult is true
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// matches vector will not contain matches for fully masked out query descriptors.
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void knnMatch(const GpuMat& queryDescs, const GpuMat& trainDescs,
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std::vector< std::vector<DMatch> >& matches, int k, const GpuMat& mask = GpuMat(),
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bool compactResult = false);
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void knnMatch(const GpuMat& queryDescs, const GpuMat& trainDescs,
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std::vector< std::vector<DMatch> >& matches, int k, const GpuMat& mask = GpuMat(),
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bool compactResult = false);
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// Find k best matches for each query descriptor (in increasing order of distances).
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// compactResult is used when mask is not empty. If compactResult is false matches
|
||||
// vector will have the same size as queryDescriptors rows. If compactResult is true
|
||||
// matches vector will not contain matches for fully masked out query descriptors.
|
||||
void knnMatch(const GpuMat& queryDescs, std::vector< std::vector<DMatch> >& matches, int knn,
|
||||
void knnMatch(const GpuMat& queryDescs, std::vector< std::vector<DMatch> >& matches, int knn,
|
||||
const std::vector<GpuMat>& masks = std::vector<GpuMat>(), bool compactResult = false );
|
||||
|
||||
// Find best matches for each query descriptor which have distance less than maxDistance.
|
||||
@ -1259,8 +1259,8 @@ namespace cv
|
||||
// If trainIdx is empty, then trainIdx and distance will be created with size nQuery x nTrain,
|
||||
// otherwize user can pass own allocated trainIdx and distance with size nQuery x nMaxMatches
|
||||
// Matches doesn't sorted.
|
||||
void radiusMatch(const GpuMat& queryDescs, const GpuMat& trainDescs,
|
||||
GpuMat& trainIdx, GpuMat& nMatches, GpuMat& distance, float maxDistance,
|
||||
void radiusMatch(const GpuMat& queryDescs, const GpuMat& trainDescs,
|
||||
GpuMat& trainIdx, GpuMat& nMatches, GpuMat& distance, float maxDistance,
|
||||
const GpuMat& mask = GpuMat());
|
||||
|
||||
// Download trainIdx, nMatches and distance to CPU vector with DMatch.
|
||||
@ -1271,17 +1271,17 @@ namespace cv
|
||||
static void radiusMatchDownload(const GpuMat& trainIdx, const GpuMat& nMatches, const GpuMat& distance,
|
||||
std::vector< std::vector<DMatch> >& matches, bool compactResult = false);
|
||||
|
||||
// Find best matches for each query descriptor which have distance less than maxDistance
|
||||
// Find best matches for each query descriptor which have distance less than maxDistance
|
||||
// in increasing order of distances).
|
||||
void radiusMatch(const GpuMat& queryDescs, const GpuMat& trainDescs,
|
||||
std::vector< std::vector<DMatch> >& matches, float maxDistance,
|
||||
void radiusMatch(const GpuMat& queryDescs, const GpuMat& trainDescs,
|
||||
std::vector< std::vector<DMatch> >& matches, float maxDistance,
|
||||
const GpuMat& mask = GpuMat(), bool compactResult = false);
|
||||
|
||||
// Find best matches from train collection for each query descriptor which have distance less than
|
||||
// maxDistance (in increasing order of distances).
|
||||
void radiusMatch(const GpuMat& queryDescs, std::vector< std::vector<DMatch> >& matches, float maxDistance,
|
||||
const std::vector<GpuMat>& masks = std::vector<GpuMat>(), bool compactResult = false);
|
||||
|
||||
void radiusMatch(const GpuMat& queryDescs, std::vector< std::vector<DMatch> >& matches, float maxDistance,
|
||||
const std::vector<GpuMat>& masks = std::vector<GpuMat>(), bool compactResult = false);
|
||||
|
||||
private:
|
||||
DistType distType;
|
||||
|
||||
|
||||
@ -57,8 +57,8 @@ void cv::gpu::matchTemplate(const GpuMat&, const GpuMat&, GpuMat&, int) { throw_
|
||||
|
||||
#include <cufft.h>
|
||||
|
||||
namespace cv { namespace gpu { namespace imgproc
|
||||
{
|
||||
namespace cv { namespace gpu { namespace imgproc
|
||||
{
|
||||
void multiplyAndNormalizeSpects(int n, float scale, const cufftComplex* a,
|
||||
const cufftComplex* b, cufftComplex* c);
|
||||
|
||||
@ -74,7 +74,7 @@ namespace cv { namespace gpu { namespace imgproc
|
||||
}}}
|
||||
|
||||
|
||||
namespace
|
||||
namespace
|
||||
{
|
||||
void matchTemplate_32F_SQDIFF(const GpuMat&, const GpuMat&, GpuMat&);
|
||||
void matchTemplate_32F_CCORR(const GpuMat&, const GpuMat&, GpuMat&);
|
||||
@ -94,7 +94,7 @@ namespace
|
||||
bh = std::min(bh, h);
|
||||
}
|
||||
#endif
|
||||
|
||||
|
||||
void matchTemplate_32F_SQDIFF(const GpuMat& image, const GpuMat& templ, GpuMat& result)
|
||||
{
|
||||
result.create(image.rows - templ.rows + 1, image.cols - templ.cols + 1, CV_32F);
|
||||
@ -108,7 +108,7 @@ namespace
|
||||
result.create(image.rows - templ.rows + 1, image.cols - templ.cols + 1, CV_32F);
|
||||
|
||||
Size block_size;
|
||||
estimateBlockSize(result.cols, result.rows, templ.cols, templ.rows,
|
||||
estimateBlockSize(result.cols, result.rows, templ.cols, templ.rows,
|
||||
block_size.width, block_size.height);
|
||||
|
||||
Size dft_size;
|
||||
@ -139,7 +139,7 @@ namespace
|
||||
|
||||
GpuMat templ_roi(templ.size(), CV_32S, templ.data, templ.step);
|
||||
GpuMat templ_block(dft_size, CV_32S, templ_data, dft_size.width * sizeof(cufftReal));
|
||||
copyMakeBorder(templ_roi, templ_block, 0, templ_block.rows - templ_roi.rows, 0,
|
||||
copyMakeBorder(templ_roi, templ_block, 0, templ_block.rows - templ_roi.rows, 0,
|
||||
templ_block.cols - templ_roi.cols, 0);
|
||||
CV_Assert(cufftExecR2C(planR2C, templ_data, templ_spect) == CUFFT_SUCCESS);
|
||||
|
||||
@ -148,16 +148,16 @@ namespace
|
||||
for (int y = 0; y < result.rows; y += block_size.height)
|
||||
{
|
||||
for (int x = 0; x < result.cols; x += block_size.width)
|
||||
{
|
||||
{
|
||||
Size image_roi_size;
|
||||
image_roi_size.width = min(x + dft_size.width, image.cols) - x;
|
||||
image_roi_size.height = min(y + dft_size.height, image.rows) - y;
|
||||
GpuMat image_roi(image_roi_size, CV_32S, (void*)(image.ptr<float>(y) + x), image.step);
|
||||
copyMakeBorder(image_roi, image_block, 0, image_block.rows - image_roi.rows, 0,
|
||||
copyMakeBorder(image_roi, image_block, 0, image_block.rows - image_roi.rows, 0,
|
||||
image_block.cols - image_roi.cols, 0);
|
||||
|
||||
CV_Assert(cufftExecR2C(planR2C, image_data, image_spect) == CUFFT_SUCCESS);
|
||||
imgproc::multiplyAndNormalizeSpects(spect_len, 1.f / dft_size.area(),
|
||||
imgproc::multiplyAndNormalizeSpects(spect_len, 1.f / dft_size.area(),
|
||||
image_spect, templ_spect, result_spect);
|
||||
CV_Assert(cufftExecC2R(planC2R, result_spect, result_data) == CUFFT_SUCCESS);
|
||||
|
||||
@ -204,12 +204,12 @@ namespace
|
||||
|
||||
GpuMat image_(image.size(), CV_32S, image.data, image.step);
|
||||
GpuMat image_cont(opt_size, CV_32S, image_data, opt_size.width * sizeof(cufftReal));
|
||||
copyMakeBorder(image_, image_cont, 0, image_cont.rows - image.rows, 0,
|
||||
copyMakeBorder(image_, image_cont, 0, image_cont.rows - image.rows, 0,
|
||||
image_cont.cols - image.cols, 0);
|
||||
|
||||
GpuMat templ_(templ.size(), CV_32S, templ.data, templ.step);
|
||||
GpuMat templ_cont(opt_size, CV_32S, templ_data, opt_size.width * sizeof(cufftReal));
|
||||
copyMakeBorder(templ_, templ_cont, 0, templ_cont.rows - templ.rows, 0,
|
||||
copyMakeBorder(templ_, templ_cont, 0, templ_cont.rows - templ.rows, 0,
|
||||
templ_cont.cols - templ.cols, 0);
|
||||
|
||||
cufftHandle planR2C, planC2R;
|
||||
@ -218,7 +218,7 @@ namespace
|
||||
|
||||
CV_Assert(cufftExecR2C(planR2C, image_data, image_spect) == CUFFT_SUCCESS);
|
||||
CV_Assert(cufftExecR2C(planR2C, templ_data, templ_spect) == CUFFT_SUCCESS);
|
||||
imgproc::multiplyAndNormalizeSpects(spect_len, 1.f / opt_size.area(),
|
||||
imgproc::multiplyAndNormalizeSpects(spect_len, 1.f / opt_size.area(),
|
||||
image_spect, templ_spect, result_spect);
|
||||
|
||||
CV_Assert(cufftExecC2R(planC2R, result_spect, result_data) == CUFFT_SUCCESS);
|
||||
@ -226,7 +226,7 @@ namespace
|
||||
cufftDestroy(planR2C);
|
||||
cufftDestroy(planC2R);
|
||||
|
||||
GpuMat result_cont(image.rows - templ.rows + 1, image.cols - templ.cols + 1, CV_32F,
|
||||
GpuMat result_cont(image.rows - templ.rows + 1, image.cols - templ.cols + 1, CV_32F,
|
||||
result_data, opt_size.width * sizeof(cufftReal));
|
||||
result_cont.copyTo(result);
|
||||
|
||||
@ -246,7 +246,7 @@ namespace
|
||||
imgproc::matchTemplateNaive_8U_SQDIFF(image, templ, result);
|
||||
}
|
||||
|
||||
|
||||
|
||||
void matchTemplate_8U_CCORR(const GpuMat& image, const GpuMat& templ, GpuMat& result)
|
||||
{
|
||||
GpuMat imagef, templf;
|
||||
@ -264,12 +264,12 @@ void cv::gpu::matchTemplate(const GpuMat& image, const GpuMat& templ, GpuMat& re
|
||||
|
||||
typedef void (*Caller)(const GpuMat&, const GpuMat&, GpuMat&);
|
||||
|
||||
static const Caller callers8U[] = { ::matchTemplate_8U_SQDIFF, 0,
|
||||
static const Caller callers8U[] = { ::matchTemplate_8U_SQDIFF, 0,
|
||||
::matchTemplate_8U_CCORR, 0, 0, 0 };
|
||||
static const Caller callers32F[] = { ::matchTemplate_32F_SQDIFF, 0,
|
||||
static const Caller callers32F[] = { ::matchTemplate_32F_SQDIFF, 0,
|
||||
::matchTemplate_32F_CCORR, 0, 0, 0 };
|
||||
|
||||
const Caller* callers;
|
||||
const Caller* callers = 0;
|
||||
switch (image.type())
|
||||
{
|
||||
case CV_8U: callers = callers8U; break;
|
||||
|
||||
@ -69,8 +69,8 @@ public:
|
||||
vector<int> rank;
|
||||
vector<int> size;
|
||||
private:
|
||||
DjSets(const DjSets&) {}
|
||||
DjSets operator =(const DjSets&) {}
|
||||
DjSets(const DjSets&);
|
||||
void operator =(const DjSets&);
|
||||
};
|
||||
|
||||
|
||||
@ -123,9 +123,9 @@ struct SegmLinkVal
|
||||
struct SegmLink
|
||||
{
|
||||
SegmLink() {}
|
||||
SegmLink(int from, int to, const SegmLinkVal& val)
|
||||
SegmLink(int from, int to, const SegmLinkVal& val)
|
||||
: from(from), to(to), val(val) {}
|
||||
bool operator <(const SegmLink& other) const
|
||||
bool operator <(const SegmLink& other) const
|
||||
{
|
||||
return val < other.val;
|
||||
}
|
||||
@ -199,25 +199,25 @@ inline void Graph<T>::addEdge(int from, int to, const T& val)
|
||||
}
|
||||
|
||||
|
||||
inline int pix(int y, int x, int ncols)
|
||||
inline int pix(int y, int x, int ncols)
|
||||
{
|
||||
return y * ncols + x;
|
||||
}
|
||||
|
||||
|
||||
inline int sqr(int x)
|
||||
inline int sqr(int x)
|
||||
{
|
||||
return x * x;
|
||||
}
|
||||
|
||||
|
||||
inline int dist2(const cv::Vec4b& lhs, const cv::Vec4b& rhs)
|
||||
inline int dist2(const cv::Vec4b& lhs, const cv::Vec4b& rhs)
|
||||
{
|
||||
return sqr(lhs[0] - rhs[0]) + sqr(lhs[1] - rhs[1]) + sqr(lhs[2] - rhs[2]);
|
||||
}
|
||||
|
||||
|
||||
inline int dist2(const cv::Vec2s& lhs, const cv::Vec2s& rhs)
|
||||
inline int dist2(const cv::Vec2s& lhs, const cv::Vec2s& rhs)
|
||||
{
|
||||
return sqr(lhs[0] - rhs[0]) + sqr(lhs[1] - rhs[1]);
|
||||
}
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user