/*M/////////////////////////////////////////////////////////////////////////////////////// // // IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. // // By downloading, copying, installing or using the software you agree to this license. // If you do not agree to this license, do not download, install, // copy or use the software. // // // License Agreement // For Open Source Computer Vision Library // // Copyright (C) 2010-2012, Institute Of Software Chinese Academy Of Science, all rights reserved. // Copyright (C) 2010-2012, Advanced Micro Devices, Inc., all rights reserved. // Third party copyrights are property of their respective owners. // // @Authors // Niko Li, newlife20080214@gmail.com // Jia Haipeng, jiahaipeng95@gmail.com // Shengen Yan, yanshengen@gmail.com // Rock Li, Rock.Li@amd.com // Zero Lin, Zero.Lin@amd.com // Zhang Ying, zhangying913@gmail.com // Xu Pang, pangxu010@163.com // Wu Zailong, bullet@yeah.net // // Redistribution and use in source and binary forms, with or without modification, // are permitted provided that the following conditions are met: // // * Redistribution's of source code must retain the above copyright notice, // this list of conditions and the following disclaimer. // // * Redistribution's in binary form must reproduce the above copyright notice, // this list of conditions and the following disclaimer in the documentation // and/or other oclMaterials provided with the distribution. // // * The name of the copyright holders may not be used to endorse or promote products // derived from this software without specific prior written permission. // // This software is provided by the copyright holders and contributors "as is" and // any express or implied warranties, including, but not limited to, the implied // warranties of merchantability and fitness for a particular purpose are disclaimed. // In no event shall the Intel Corporation or contributors be liable for any direct, // indirect, incidental, special, exemplary, or consequential damages // (including, but not limited to, procurement of substitute goods or services; // loss of use, data, or profits; or business interruption) however caused // and on any theory of liability, whether in contract, strict liability, // or tort (including negligence or otherwise) arising in any way out of // the use of this software, even if advised of the possibility of such damage. // //M*/ #include "precomp.hpp" #include <iomanip> using namespace cv; using namespace cv::ocl; using namespace std; #if !defined (HAVE_OPENCL) void cv::ocl::meanShiftFiltering(const oclMat &, oclMat &, int, int, TermCriteria) { throw_nogpu(); } void cv::ocl::meanShiftProc(const oclMat &, oclMat &, oclMat &, int, int, TermCriteria) { throw_nogpu(); } double cv::ocl::threshold(const oclMat &, oclMat &, double, int) { throw_nogpu(); return 0.0; } void cv::ocl::resize(const oclMat &, oclMat &, Size, double, double, int) { throw_nogpu(); } void cv::ocl::remap(const oclMat &, oclMat &, oclMat &, oclMat &, int, int , const Scalar &) { throw_nogpu(); } void cv::ocl::copyMakeBorder(const oclMat &, oclMat &, int, int, int, int, const Scalar &) { throw_nogpu(); } void cv::ocl::warpAffine(const oclMat &, oclMat &, const Mat &, Size, int) { throw_nogpu(); } void cv::ocl::warpPerspective(const oclMat &, oclMat &, const Mat &, Size, int) { throw_nogpu(); } void cv::ocl::integral(const oclMat &, oclMat &, oclMat &) { throw_nogpu(); } void cv::ocl::calcHist(const oclMat &, oclMat &hist) { throw_nogpu(); } void cv::ocl::bilateralFilter(const oclMat &, oclMat &, int, double, double, int) { throw_nogpu(); } void cv::ocl::convolve(const oclMat &, const oclMat &, oclMat &) { throw_nogpu(); } #else /* !defined (HAVE_OPENCL) */ namespace cv { namespace ocl { ////////////////////////////////////OpenCL kernel strings////////////////////////// extern const char *meanShift; extern const char *imgproc_copymakeboder; extern const char *imgproc_median; extern const char *imgproc_threshold; extern const char *imgproc_resize; extern const char *imgproc_remap; extern const char *imgproc_warpAffine; extern const char *imgproc_warpPerspective; extern const char *imgproc_integral_sum; extern const char *imgproc_integral; extern const char *imgproc_histogram; extern const char *imgproc_bilateral; extern const char *imgproc_calcHarris; extern const char *imgproc_calcMinEigenVal; extern const char *imgproc_convolve; ////////////////////////////////////OpenCL call wrappers//////////////////////////// template <typename T> struct index_and_sizeof; template <> struct index_and_sizeof<char> { enum { index = 1 }; }; template <> struct index_and_sizeof<unsigned char> { enum { index = 2 }; }; template <> struct index_and_sizeof<short> { enum { index = 3 }; }; template <> struct index_and_sizeof<unsigned short> { enum { index = 4 }; }; template <> struct index_and_sizeof<int> { enum { index = 5 }; }; template <> struct index_and_sizeof<float> { enum { index = 6 }; }; template <> struct index_and_sizeof<double> { enum { index = 7 }; }; ///////////////////////////////////////////////////////////////////////////////////// // threshold typedef void (*gpuThresh_t)(const oclMat &src, oclMat &dst, double thresh, double maxVal, int type); void threshold_8u(const oclMat &src, oclMat &dst, double thresh, double maxVal, int type) { CV_Assert( (src.cols == dst.cols) && (src.rows == dst.rows) ); Context *clCxt = src.clCxt; uchar thresh_uchar = cvFloor(thresh); uchar max_val = cvRound(maxVal); string kernelName = "threshold"; size_t cols = (dst.cols + (dst.offset % 16) + 15) / 16; size_t bSizeX = 16, bSizeY = 16; size_t gSizeX = cols % bSizeX == 0 ? cols : (cols + bSizeX - 1) / bSizeX * bSizeX; size_t gSizeY = dst.rows; size_t globalThreads[3] = {gSizeX, gSizeY, 1}; size_t localThreads[3] = {bSizeX, bSizeY, 1}; vector< pair<size_t, const void *> > args; args.push_back( make_pair(sizeof(cl_mem), &src.data)); args.push_back( make_pair(sizeof(cl_mem), &dst.data)); args.push_back( make_pair(sizeof(cl_int), (void *)&src.offset)); args.push_back( make_pair(sizeof(cl_int), (void *)&src.step)); args.push_back( make_pair(sizeof(cl_int), (void *)&dst.offset)); args.push_back( make_pair(sizeof(cl_int), (void *)&dst.rows)); args.push_back( make_pair(sizeof(cl_int), (void *)&dst.cols)); args.push_back( make_pair(sizeof(cl_int), (void *)&dst.step)); args.push_back( make_pair(sizeof(cl_uchar), (void *)&thresh_uchar)); args.push_back( make_pair(sizeof(cl_uchar), (void *)&max_val)); args.push_back( make_pair(sizeof(cl_int), (void *)&type)); openCLExecuteKernel(clCxt, &imgproc_threshold, kernelName, globalThreads, localThreads, args, src.oclchannels(), src.depth()); } void threshold_32f(const oclMat &src, oclMat &dst, double thresh, double maxVal, int type) { CV_Assert( (src.cols == dst.cols) && (src.rows == dst.rows) ); Context *clCxt = src.clCxt; float thresh_f = thresh; float max_val = maxVal; int dst_offset = (dst.offset >> 2); int dst_step = (dst.step >> 2); int src_offset = (src.offset >> 2); int src_step = (src.step >> 2); string kernelName = "threshold"; size_t cols = (dst.cols + (dst_offset & 3) + 3) / 4; //size_t cols = dst.cols; size_t bSizeX = 16, bSizeY = 16; size_t gSizeX = cols % bSizeX == 0 ? cols : (cols + bSizeX - 1) / bSizeX * bSizeX; size_t gSizeY = dst.rows; size_t globalThreads[3] = {gSizeX, gSizeY, 1}; size_t localThreads[3] = {bSizeX, bSizeY, 1}; vector< pair<size_t, const void *> > args; args.push_back( make_pair(sizeof(cl_mem), &src.data)); args.push_back( make_pair(sizeof(cl_mem), &dst.data)); args.push_back( make_pair(sizeof(cl_int), (void *)&src_offset)); args.push_back( make_pair(sizeof(cl_int), (void *)&src_step)); args.push_back( make_pair(sizeof(cl_int), (void *)&dst_offset)); args.push_back( make_pair(sizeof(cl_int), (void *)&dst.rows)); args.push_back( make_pair(sizeof(cl_int), (void *)&dst.cols)); args.push_back( make_pair(sizeof(cl_int), (void *)&dst_step)); args.push_back( make_pair(sizeof(cl_float), (void *)&thresh_f)); args.push_back( make_pair(sizeof(cl_float), (void *)&max_val)); args.push_back( make_pair(sizeof(cl_int), (void *)&type)); openCLExecuteKernel(clCxt, &imgproc_threshold, kernelName, globalThreads, localThreads, args, src.oclchannels(), src.depth()); } //threshold: support 8UC1 and 32FC1 data type and five threshold type double threshold(const oclMat &src, oclMat &dst, double thresh, double maxVal, int type) { //TODO: These limitations shall be removed later. CV_Assert(src.type() == CV_8UC1 || src.type() == CV_32FC1); CV_Assert(type == THRESH_BINARY || type == THRESH_BINARY_INV || type == THRESH_TRUNC || type == THRESH_TOZERO || type == THRESH_TOZERO_INV ); static const gpuThresh_t gpuThresh_callers[2] = {threshold_8u, threshold_32f}; dst.create( src.size(), src.type() ); gpuThresh_callers[(src.type() == CV_32FC1)](src, dst, thresh, maxVal, type); return thresh; } //////////////////////////////////////////////////////////////////////////////////////////// /////////////////////////////// remap ////////////////////////////////////////////////// //////////////////////////////////////////////////////////////////////////////////////////// void remap( const oclMat &src, oclMat &dst, oclMat &map1, oclMat &map2, int interpolation, int borderType, const Scalar &borderValue ) { Context *clCxt = src.clCxt; CV_Assert(interpolation == INTER_LINEAR || interpolation == INTER_NEAREST || interpolation == INTER_CUBIC || interpolation == INTER_LANCZOS4); CV_Assert((map1.type() == CV_16SC2 && !map2.data) || (map1.type() == CV_32FC2 && !map2.data) || (map1.type() == CV_32FC1 && map2.type() == CV_32FC1)); CV_Assert(!map2.data || map2.size() == map1.size()); CV_Assert(dst.size() == map1.size()); dst.create(map1.size(), src.type()); string kernelName; if( map1.type() == CV_32FC2 && !map2.data ) { if(interpolation == INTER_LINEAR && borderType == BORDER_CONSTANT) kernelName = "remapLNFConstant"; else if(interpolation == INTER_NEAREST && borderType == BORDER_CONSTANT) kernelName = "remapNNFConstant"; } else if(map1.type() == CV_16SC2 && !map2.data) { if(interpolation == INTER_LINEAR && borderType == BORDER_CONSTANT) kernelName = "remapLNSConstant"; else if(interpolation == INTER_NEAREST && borderType == BORDER_CONSTANT) kernelName = "remapNNSConstant"; } else if(map1.type() == CV_32FC1 && map2.type() == CV_32FC1) { if(interpolation == INTER_LINEAR && borderType == BORDER_CONSTANT) kernelName = "remapLNF1Constant"; else if (interpolation == INTER_NEAREST && borderType == BORDER_CONSTANT) kernelName = "remapNNF1Constant"; } int channels = dst.oclchannels(); int depth = dst.depth(); int type = src.type(); size_t blkSizeX = 16, blkSizeY = 16; size_t glbSizeX; int cols = dst.cols; if(src.type() == CV_8UC1) { cols = (dst.cols + dst.offset % 4 + 3) / 4; glbSizeX = cols % blkSizeX == 0 ? cols : (cols / blkSizeX + 1) * blkSizeX; } else if(src.type() == CV_8UC3 || src.type() == CV_8UC4 || src.type() == CV_32FC1) { cols = (dst.cols + (dst.offset >> 2) % 4 + 3) / 4; glbSizeX = cols % blkSizeX == 0 ? cols : (cols / blkSizeX + 1) * blkSizeX; } else { glbSizeX = dst.cols % blkSizeX == 0 ? dst.cols : (dst.cols / blkSizeX + 1) * blkSizeX; } size_t glbSizeY = dst.rows % blkSizeY == 0 ? dst.rows : (dst.rows / blkSizeY + 1) * blkSizeY; size_t globalThreads[3] = {glbSizeX, glbSizeY, 1}; size_t localThreads[3] = {blkSizeX, blkSizeY, 1}; /* ///////////////////////////// //using the image buffer ///////////////////////////// size_t image_row_pitch = 0; cl_int err1, err2, err3; cl_mem_flags flags1 = CL_MEM_READ_ONLY; cl_image_format format; if(src.type() == CV_8UC1) { format.image_channel_order = CL_R; format.image_channel_data_type = CL_UNSIGNED_INT8; } else if(src.type() == CV_8UC4) { format.image_channel_order = CL_RGBA; format.image_channel_data_type = CL_UNSIGNED_INT8; } else if(src.type() == CV_32FC1) { format.image_channel_order = CL_R; format.image_channel_data_type = CL_FLOAT; } else if(src.type() == CV_32FC4) { format.image_channel_order = CL_RGBA; format.image_channel_data_type = CL_FLOAT; } cl_mem srcImage = clCreateImage2D(clCxt->impl->clContext, flags1, &format, src.cols, src.rows, image_row_pitch, NULL, &err1); if(err1 != CL_SUCCESS) { printf("Error creating CL image buffer, error code %d\n", err1); return; } const size_t src_origin[3] = {0, 0, 0}; const size_t region[3] = {src.cols, src.rows, 1}; cl_event BtoI_event, ItoB_event; err3 = clEnqueueCopyBufferToImage(clCxt->impl->clCmdQueue, (cl_mem)src.data, srcImage, 0, src_origin, region, 0, NULL, NULL); if(err3 != CL_SUCCESS) { printf("Error copying buffer to image\n"); printf("Error code %d \n", err3); return; } // clWaitForEvents(1, &BtoI_event); cl_int ret; Mat test(src.rows, src.cols, CV_8UC1); memset(test.data, 0, src.rows*src.cols); ret = clEnqueueReadImage(clCxt->impl->clCmdQueue, srcImage, CL_TRUE, src_origin, region, 0, 0, test.data, NULL, NULL, &ItoB_event); if(ret != CL_SUCCESS) { printf("read image error, %d ", ret); return; } clWaitForEvents(1, &ItoB_event); cout << "src" << endl; cout << src << endl; cout<<"image:"<<endl; cout<< test << endl; */ vector< pair<size_t, const void *> > args; if(map1.channels() == 2) { args.push_back( make_pair(sizeof(cl_mem), (void *)&dst.data)); args.push_back( make_pair(sizeof(cl_mem), (void *)&src.data)); // args.push_back( make_pair(sizeof(cl_mem),(void*)&srcImage)); //imageBuffer args.push_back( make_pair(sizeof(cl_mem), (void *)&map1.data)); args.push_back( make_pair(sizeof(cl_int), (void *)&dst.offset)); args.push_back( make_pair(sizeof(cl_int), (void *)&src.offset)); args.push_back( make_pair(sizeof(cl_int), (void *)&map1.offset)); args.push_back( make_pair(sizeof(cl_int), (void *)&dst.step)); args.push_back( make_pair(sizeof(cl_int), (void *)&src.step)); args.push_back( make_pair(sizeof(cl_int), (void *)&map1.step)); args.push_back( make_pair(sizeof(cl_int), (void *)&src.cols)); args.push_back( make_pair(sizeof(cl_int), (void *)&src.rows)); args.push_back( make_pair(sizeof(cl_int), (void *)&dst.cols)); args.push_back( make_pair(sizeof(cl_int), (void *)&dst.rows)); args.push_back( make_pair(sizeof(cl_int), (void *)&map1.cols)); args.push_back( make_pair(sizeof(cl_int), (void *)&map1.rows)); args.push_back( make_pair(sizeof(cl_int), (void *)&cols)); if(src.clCxt -> impl -> double_support != 0) { args.push_back( make_pair(sizeof(cl_double4), (void *)&borderValue)); } else { float borderFloat[4] = {(float)borderValue[0], (float)borderValue[1], (float)borderValue[2], (float)borderValue[3]}; args.push_back( make_pair(sizeof(cl_float4), (void *)&borderFloat)); } } if(map1.channels() == 1) { args.push_back( make_pair(sizeof(cl_mem), (void *)&dst.data)); args.push_back( make_pair(sizeof(cl_mem), (void *)&src.data)); // args.push_back( make_pair(sizeof(cl_mem),(void*)&srcImage)); //imageBuffer args.push_back( make_pair(sizeof(cl_mem), (void *)&map1.data)); args.push_back( make_pair(sizeof(cl_mem), (void *)&map2.data)); args.push_back( make_pair(sizeof(cl_int), (void *)&dst.offset)); args.push_back( make_pair(sizeof(cl_int), (void *)&src.offset)); args.push_back( make_pair(sizeof(cl_int), (void *)&map1.offset)); args.push_back( make_pair(sizeof(cl_int), (void *)&dst.step)); args.push_back( make_pair(sizeof(cl_int), (void *)&src.step)); args.push_back( make_pair(sizeof(cl_int), (void *)&map1.step)); args.push_back( make_pair(sizeof(cl_int), (void *)&src.cols)); args.push_back( make_pair(sizeof(cl_int), (void *)&src.rows)); args.push_back( make_pair(sizeof(cl_int), (void *)&dst.cols)); args.push_back( make_pair(sizeof(cl_int), (void *)&dst.rows)); args.push_back( make_pair(sizeof(cl_int), (void *)&map1.cols)); args.push_back( make_pair(sizeof(cl_int), (void *)&map1.rows)); args.push_back( make_pair(sizeof(cl_int), (void *)&cols)); if(src.clCxt -> impl -> double_support != 0) { args.push_back( make_pair(sizeof(cl_double4), (void *)&borderValue)); } else { float borderFloat[4] = {(float)borderValue[0], (float)borderValue[1], (float)borderValue[2], (float)borderValue[3]}; args.push_back( make_pair(sizeof(cl_float4), (void *)&borderFloat)); } } openCLExecuteKernel(clCxt, &imgproc_remap, kernelName, globalThreads, localThreads, args, src.oclchannels(), src.depth()); } //////////////////////////////////////////////////////////////////////////////////////////// // resize void resize_gpu( const oclMat &src, oclMat &dst, double fx, double fy, int interpolation) { CV_Assert( (src.channels() == dst.channels()) ); Context *clCxt = src.clCxt; float ifx = 1. / fx; float ify = 1. / fy; double ifx_d = 1. / fx; double ify_d = 1. / fy; int srcStep_in_pixel = src.step1() / src.oclchannels(); int srcoffset_in_pixel = src.offset / src.elemSize(); int dstStep_in_pixel = dst.step1() / dst.oclchannels(); int dstoffset_in_pixel = dst.offset / dst.elemSize(); //printf("%d %d\n",src.step1() , dst.elemSize()); string kernelName; if(interpolation == INTER_LINEAR) kernelName = "resizeLN"; else if(interpolation == INTER_NEAREST) kernelName = "resizeNN"; //TODO: improve this kernel size_t blkSizeX = 16, blkSizeY = 16; size_t glbSizeX; if(src.type() == CV_8UC1) { size_t cols = (dst.cols + dst.offset % 4 + 3) / 4; glbSizeX = cols % blkSizeX == 0 && cols != 0 ? cols : (cols / blkSizeX + 1) * blkSizeX; } else { glbSizeX = dst.cols % blkSizeX == 0 && dst.cols != 0 ? dst.cols : (dst.cols / blkSizeX + 1) * blkSizeX; } size_t glbSizeY = dst.rows % blkSizeY == 0 && dst.rows != 0 ? dst.rows : (dst.rows / blkSizeY + 1) * blkSizeY; size_t globalThreads[3] = {glbSizeX, glbSizeY, 1}; size_t localThreads[3] = {blkSizeX, blkSizeY, 1}; vector< pair<size_t, const void *> > args; if(interpolation == INTER_NEAREST) { args.push_back( make_pair(sizeof(cl_mem), (void *)&dst.data)); args.push_back( make_pair(sizeof(cl_mem), (void *)&src.data)); args.push_back( make_pair(sizeof(cl_int), (void *)&dstoffset_in_pixel)); args.push_back( make_pair(sizeof(cl_int), (void *)&srcoffset_in_pixel)); args.push_back( make_pair(sizeof(cl_int), (void *)&dstStep_in_pixel)); args.push_back( make_pair(sizeof(cl_int), (void *)&srcStep_in_pixel)); args.push_back( make_pair(sizeof(cl_int), (void *)&src.cols)); args.push_back( make_pair(sizeof(cl_int), (void *)&src.rows)); args.push_back( make_pair(sizeof(cl_int), (void *)&dst.cols)); args.push_back( make_pair(sizeof(cl_int), (void *)&dst.rows)); if(src.clCxt -> impl -> double_support != 0) { args.push_back( make_pair(sizeof(cl_double), (void *)&ifx_d)); args.push_back( make_pair(sizeof(cl_double), (void *)&ify_d)); } else { args.push_back( make_pair(sizeof(cl_float), (void *)&ifx)); args.push_back( make_pair(sizeof(cl_float), (void *)&ify)); } } else { args.push_back( make_pair(sizeof(cl_mem), (void *)&dst.data)); args.push_back( make_pair(sizeof(cl_mem), (void *)&src.data)); args.push_back( make_pair(sizeof(cl_int), (void *)&dstoffset_in_pixel)); args.push_back( make_pair(sizeof(cl_int), (void *)&srcoffset_in_pixel)); args.push_back( make_pair(sizeof(cl_int), (void *)&dstStep_in_pixel)); args.push_back( make_pair(sizeof(cl_int), (void *)&srcStep_in_pixel)); args.push_back( make_pair(sizeof(cl_int), (void *)&src.cols)); args.push_back( make_pair(sizeof(cl_int), (void *)&src.rows)); args.push_back( make_pair(sizeof(cl_int), (void *)&dst.cols)); args.push_back( make_pair(sizeof(cl_int), (void *)&dst.rows)); args.push_back( make_pair(sizeof(cl_float), (void *)&ifx)); args.push_back( make_pair(sizeof(cl_float), (void *)&ify)); } openCLExecuteKernel(clCxt, &imgproc_resize, kernelName, globalThreads, localThreads, args, src.oclchannels(), src.depth()); } void resize(const oclMat &src, oclMat &dst, Size dsize, double fx, double fy, int interpolation) { CV_Assert(src.type() == CV_8UC1 || src.type() == CV_8UC3 || src.type() == CV_8UC4 || src.type() == CV_32FC1 || src.type() == CV_32FC3 || src.type() == CV_32FC4); CV_Assert(interpolation == INTER_LINEAR || interpolation == INTER_NEAREST); CV_Assert( src.size().area() > 0 ); CV_Assert( !(dsize == Size()) || (fx > 0 && fy > 0) ); if(!(dsize == Size()) && (fx > 0 && fy > 0)) { if(dsize.width != (int)(src.cols * fx) || dsize.height != (int)(src.rows * fy)) { CV_Error(CV_StsUnmatchedSizes, "invalid dsize and fx, fy!"); } } if( dsize == Size() ) { dsize = Size(saturate_cast<int>(src.cols * fx), saturate_cast<int>(src.rows * fy)); } else { fx = (double)dsize.width / src.cols; fy = (double)dsize.height / src.rows; } dst.create(dsize, src.type()); if( interpolation == INTER_NEAREST || interpolation == INTER_LINEAR ) { resize_gpu( src, dst, fx, fy, interpolation); return; } CV_Error(CV_StsUnsupportedFormat, "Non-supported interpolation method"); } //////////////////////////////////////////////////////////////////////// // medianFilter void medianFilter(const oclMat &src, oclMat &dst, int m) { CV_Assert( m % 2 == 1 && m > 1 ); CV_Assert( m <= 5 || src.depth() == CV_8U ); CV_Assert( src.cols <= dst.cols && src.rows <= dst.rows ); if(src.data == dst.data) { oclMat src1; src.copyTo(src1); return medianFilter(src1, dst, m); } int srcStep = src.step1() / src.oclchannels(); int dstStep = dst.step1() / dst.oclchannels(); int srcOffset = src.offset / src.oclchannels() / src.elemSize1(); int dstOffset = dst.offset / dst.oclchannels() / dst.elemSize1(); Context *clCxt = src.clCxt; string kernelName = "medianFilter"; vector< pair<size_t, const void *> > args; args.push_back( make_pair( sizeof(cl_mem), (void *)&src.data)); args.push_back( make_pair( sizeof(cl_mem), (void *)&dst.data)); args.push_back( make_pair( sizeof(cl_int), (void *)&srcOffset)); args.push_back( make_pair( sizeof(cl_int), (void *)&dstOffset)); args.push_back( make_pair( sizeof(cl_int), (void *)&src.cols)); args.push_back( make_pair( sizeof(cl_int), (void *)&src.rows)); args.push_back( make_pair( sizeof(cl_int), (void *)&srcStep)); args.push_back( make_pair( sizeof(cl_int), (void *)&dstStep)); size_t globalThreads[3] = {(src.cols + 18) / 16 * 16, (src.rows + 15) / 16 * 16, 1}; size_t localThreads[3] = {16, 16, 1}; if(m == 3) { string kernelName = "medianFilter3"; openCLExecuteKernel(clCxt, &imgproc_median, kernelName, globalThreads, localThreads, args, src.oclchannels(), src.depth()); } else if(m == 5) { string kernelName = "medianFilter5"; openCLExecuteKernel(clCxt, &imgproc_median, kernelName, globalThreads, localThreads, args, src.oclchannels(), src.depth()); } else { CV_Error(CV_StsUnsupportedFormat, "Non-supported filter length"); //string kernelName = "medianFilter"; //args.push_back( make_pair( sizeof(cl_int),(void*)&m)); //openCLExecuteKernel(clCxt,&imgproc_median,kernelName,globalThreads,localThreads,args,src.oclchannels(),-1); } } //////////////////////////////////////////////////////////////////////// // copyMakeBorder void copyMakeBorder(const oclMat &src, oclMat &dst, int top, int bottom, int left, int right, int bordertype, const Scalar &scalar) { //CV_Assert(src.oclchannels() != 2); CV_Assert(top >= 0 && bottom >= 0 && left >= 0 && right >= 0); if((dst.cols != dst.wholecols) || (dst.rows != dst.wholerows)) //has roi { if(((bordertype & cv::BORDER_ISOLATED) == 0) && (bordertype != cv::BORDER_CONSTANT) && (bordertype != cv::BORDER_REPLICATE)) { CV_Error(CV_StsBadArg, "unsupported border type"); } } bordertype &= ~cv::BORDER_ISOLATED; if((bordertype == cv::BORDER_REFLECT) || (bordertype == cv::BORDER_WRAP)) { CV_Assert((src.cols >= left) && (src.cols >= right) && (src.rows >= top) && (src.rows >= bottom)); } if(bordertype == cv::BORDER_REFLECT_101) { CV_Assert((src.cols > left) && (src.cols > right) && (src.rows > top) && (src.rows > bottom)); } dst.create(src.rows + top + bottom, src.cols + left + right, src.type()); int srcStep = src.step1() / src.oclchannels(); int dstStep = dst.step1() / dst.oclchannels(); int srcOffset = src.offset / src.elemSize(); int dstOffset = dst.offset / dst.elemSize(); int __bordertype[] = {cv::BORDER_CONSTANT, cv::BORDER_REPLICATE, BORDER_REFLECT, BORDER_WRAP, BORDER_REFLECT_101}; const char *borderstr[] = {"BORDER_CONSTANT", "BORDER_REPLICATE", "BORDER_REFLECT", "BORDER_WRAP", "BORDER_REFLECT_101"}; int bordertype_index; for(bordertype_index = 0; bordertype_index < sizeof(__bordertype) / sizeof(int); bordertype_index++) { if(__bordertype[bordertype_index] == bordertype) break; } if(bordertype_index == sizeof(__bordertype) / sizeof(int)) { CV_Error(CV_StsBadArg, "unsupported border type"); } string kernelName = "copymakeborder"; size_t localThreads[3] = {16, 16, 1}; size_t globalThreads[3] = {(dst.cols + localThreads[0] - 1) / localThreads[0] *localThreads[0], (dst.rows + localThreads[1] - 1) / localThreads[1] *localThreads[1], 1 }; vector< pair<size_t, const void *> > args; args.push_back( make_pair( sizeof(cl_mem), (void *)&src.data)); args.push_back( make_pair( sizeof(cl_mem), (void *)&dst.data)); args.push_back( make_pair( sizeof(cl_int), (void *)&dst.cols)); args.push_back( make_pair( sizeof(cl_int), (void *)&dst.rows)); args.push_back( make_pair( sizeof(cl_int), (void *)&src.cols)); args.push_back( make_pair( sizeof(cl_int), (void *)&src.rows)); args.push_back( make_pair( sizeof(cl_int), (void *)&srcStep)); args.push_back( make_pair( sizeof(cl_int), (void *)&srcOffset)); args.push_back( make_pair( sizeof(cl_int), (void *)&dstStep)); args.push_back( make_pair( sizeof(cl_int), (void *)&dstOffset)); args.push_back( make_pair( sizeof(cl_int), (void *)&top)); args.push_back( make_pair( sizeof(cl_int), (void *)&left)); char compile_option[64]; union sc { cl_uchar4 uval; cl_char4 cval; cl_ushort4 usval; cl_short4 shval; cl_int4 ival; cl_float4 fval; cl_double4 dval; } val; switch(dst.depth()) { case CV_8U: val.uval.s[0] = saturate_cast<uchar>(scalar.val[0]); val.uval.s[1] = saturate_cast<uchar>(scalar.val[1]); val.uval.s[2] = saturate_cast<uchar>(scalar.val[2]); val.uval.s[3] = saturate_cast<uchar>(scalar.val[3]); switch(dst.oclchannels()) { case 1: sprintf(compile_option, "-D GENTYPE=uchar -D %s", borderstr[bordertype_index]); args.push_back( make_pair( sizeof(cl_uchar) , (void *)&val.uval.s[0] )); if(((dst.offset & 3) == 0) && ((dst.cols & 3) == 0)) { kernelName = "copymakeborder_C1_D0"; globalThreads[0] = (dst.cols / 4 + localThreads[0] - 1) / localThreads[0] * localThreads[0]; } break; case 4: sprintf(compile_option, "-D GENTYPE=uchar4 -D %s", borderstr[bordertype_index]); args.push_back( make_pair( sizeof(cl_uchar4) , (void *)&val.uval )); break; default: CV_Error(CV_StsUnsupportedFormat, "unsupported channels"); } break; case CV_8S: val.cval.s[0] = saturate_cast<char>(scalar.val[0]); val.cval.s[1] = saturate_cast<char>(scalar.val[1]); val.cval.s[2] = saturate_cast<char>(scalar.val[2]); val.cval.s[3] = saturate_cast<char>(scalar.val[3]); switch(dst.oclchannels()) { case 1: sprintf(compile_option, "-D GENTYPE=char -D %s", borderstr[bordertype_index]); args.push_back( make_pair( sizeof(cl_char) , (void *)&val.cval.s[0] )); break; case 4: sprintf(compile_option, "-D GENTYPE=char4 -D %s", borderstr[bordertype_index]); args.push_back( make_pair( sizeof(cl_char4) , (void *)&val.cval )); break; default: CV_Error(CV_StsUnsupportedFormat, "unsupported channels"); } break; case CV_16U: val.usval.s[0] = saturate_cast<ushort>(scalar.val[0]); val.usval.s[1] = saturate_cast<ushort>(scalar.val[1]); val.usval.s[2] = saturate_cast<ushort>(scalar.val[2]); val.usval.s[3] = saturate_cast<ushort>(scalar.val[3]); switch(dst.oclchannels()) { case 1: sprintf(compile_option, "-D GENTYPE=ushort -D %s", borderstr[bordertype_index]); args.push_back( make_pair( sizeof(cl_ushort) , (void *)&val.usval.s[0] )); break; case 4: sprintf(compile_option, "-D GENTYPE=ushort4 -D %s", borderstr[bordertype_index]); args.push_back( make_pair( sizeof(cl_ushort4) , (void *)&val.usval )); break; default: CV_Error(CV_StsUnsupportedFormat, "unsupported channels"); } break; case CV_16S: val.shval.s[0] = saturate_cast<short>(scalar.val[0]); val.shval.s[1] = saturate_cast<short>(scalar.val[1]); val.shval.s[2] = saturate_cast<short>(scalar.val[2]); val.shval.s[3] = saturate_cast<short>(scalar.val[3]); switch(dst.oclchannels()) { case 1: sprintf(compile_option, "-D GENTYPE=short -D %s", borderstr[bordertype_index]); args.push_back( make_pair( sizeof(cl_short) , (void *)&val.shval.s[0] )); break; case 4: sprintf(compile_option, "-D GENTYPE=short4 -D %s", borderstr[bordertype_index]); args.push_back( make_pair( sizeof(cl_short4) , (void *)&val.shval )); break; default: CV_Error(CV_StsUnsupportedFormat, "unsupported channels"); } break; case CV_32S: val.ival.s[0] = saturate_cast<int>(scalar.val[0]); val.ival.s[1] = saturate_cast<int>(scalar.val[1]); val.ival.s[2] = saturate_cast<int>(scalar.val[2]); val.ival.s[3] = saturate_cast<int>(scalar.val[3]); switch(dst.oclchannels()) { case 1: sprintf(compile_option, "-D GENTYPE=int -D %s", borderstr[bordertype_index]); args.push_back( make_pair( sizeof(cl_int) , (void *)&val.ival.s[0] )); break; case 2: sprintf(compile_option, "-D GENTYPE=int2 -D %s", borderstr[bordertype_index]); cl_int2 i2val; i2val.s[0] = val.ival.s[0]; i2val.s[1] = val.ival.s[1]; args.push_back( make_pair( sizeof(cl_int2) , (void *)&i2val )); break; case 4: sprintf(compile_option, "-D GENTYPE=int4 -D %s", borderstr[bordertype_index]); args.push_back( make_pair( sizeof(cl_int4) , (void *)&val.ival )); break; default: CV_Error(CV_StsUnsupportedFormat, "unsupported channels"); } break; case CV_32F: val.fval.s[0] = scalar.val[0]; val.fval.s[1] = scalar.val[1]; val.fval.s[2] = scalar.val[2]; val.fval.s[3] = scalar.val[3]; switch(dst.oclchannels()) { case 1: sprintf(compile_option, "-D GENTYPE=float -D %s", borderstr[bordertype_index]); args.push_back( make_pair( sizeof(cl_float) , (void *)&val.fval.s[0] )); break; case 4: sprintf(compile_option, "-D GENTYPE=float4 -D %s", borderstr[bordertype_index]); args.push_back( make_pair( sizeof(cl_float4) , (void *)&val.fval )); break; default: CV_Error(CV_StsUnsupportedFormat, "unsupported channels"); } break; case CV_64F: val.dval.s[0] = scalar.val[0]; val.dval.s[1] = scalar.val[1]; val.dval.s[2] = scalar.val[2]; val.dval.s[3] = scalar.val[3]; switch(dst.oclchannels()) { case 1: sprintf(compile_option, "-D GENTYPE=double -D %s", borderstr[bordertype_index]); args.push_back( make_pair( sizeof(cl_double) , (void *)&val.dval.s[0] )); break; case 4: sprintf(compile_option, "-D GENTYPE=double4 -D %s", borderstr[bordertype_index]); args.push_back( make_pair( sizeof(cl_double4) , (void *)&val.dval )); break; default: CV_Error(CV_StsUnsupportedFormat, "unsupported channels"); } break; default: CV_Error(CV_StsUnsupportedFormat, "unknown depth"); } openCLExecuteKernel(src.clCxt, &imgproc_copymakeboder, kernelName, globalThreads, localThreads, args, -1, -1, compile_option); //uchar* cputemp=new uchar[32*dst.wholerows]; ////int* cpudata=new int[this->step*this->wholerows/sizeof(int)]; //openCLSafeCall(clEnqueueReadBuffer(src.clCxt->impl->clCmdQueue, (cl_mem)dst.data, CL_TRUE, // 0, 32*dst.wholerows, cputemp, 0, NULL, NULL)); //for(int i=0;i<dst.wholerows;i++) //{ // for(int j=0;j<dst.wholecols;j++) // { // cout<< (int)cputemp[i*32+j]<<" "; // } // cout<<endl; //} //delete []cputemp; } //////////////////////////////////////////////////////////////////////// // warp namespace { #define F double void convert_coeffs(F *M) { double D = M[0] * M[4] - M[1] * M[3]; D = D != 0 ? 1. / D : 0; double A11 = M[4] * D, A22 = M[0] * D; M[0] = A11; M[1] *= -D; M[3] *= -D; M[4] = A22; double b1 = -M[0] * M[2] - M[1] * M[5]; double b2 = -M[3] * M[2] - M[4] * M[5]; M[2] = b1; M[5] = b2; } double invert(double *M) { #define Sd(y,x) (Sd[y*3+x]) #define Dd(y,x) (Dd[y*3+x]) #define det3(m) (m(0,0)*(m(1,1)*m(2,2) - m(1,2)*m(2,1)) - \ m(0,1)*(m(1,0)*m(2,2) - m(1,2)*m(2,0)) + \ m(0,2)*(m(1,0)*m(2,1) - m(1,1)*m(2,0))) double *Sd = M; double *Dd = M; double d = det3(Sd); double result = 0; if( d != 0) { double t[9]; result = d; d = 1. / d; t[0] = (Sd(1, 1) * Sd(2, 2) - Sd(1, 2) * Sd(2, 1)) * d; t[1] = (Sd(0, 2) * Sd(2, 1) - Sd(0, 1) * Sd(2, 2)) * d; t[2] = (Sd(0, 1) * Sd(1, 2) - Sd(0, 2) * Sd(1, 1)) * d; t[3] = (Sd(1, 2) * Sd(2, 0) - Sd(1, 0) * Sd(2, 2)) * d; t[4] = (Sd(0, 0) * Sd(2, 2) - Sd(0, 2) * Sd(2, 0)) * d; t[5] = (Sd(0, 2) * Sd(1, 0) - Sd(0, 0) * Sd(1, 2)) * d; t[6] = (Sd(1, 0) * Sd(2, 1) - Sd(1, 1) * Sd(2, 0)) * d; t[7] = (Sd(0, 1) * Sd(2, 0) - Sd(0, 0) * Sd(2, 1)) * d; t[8] = (Sd(0, 0) * Sd(1, 1) - Sd(0, 1) * Sd(1, 0)) * d; Dd(0, 0) = t[0]; Dd(0, 1) = t[1]; Dd(0, 2) = t[2]; Dd(1, 0) = t[3]; Dd(1, 1) = t[4]; Dd(1, 2) = t[5]; Dd(2, 0) = t[6]; Dd(2, 1) = t[7]; Dd(2, 2) = t[8]; } return result; } void warpAffine_gpu(const oclMat &src, oclMat &dst, F coeffs[2][3], int interpolation) { CV_Assert( (src.oclchannels() == dst.oclchannels()) ); int srcStep = src.step1(); int dstStep = dst.step1(); float float_coeffs[2][3]; cl_mem coeffs_cm; Context *clCxt = src.clCxt; string s[3] = {"NN", "Linear", "Cubic"}; string kernelName = "warpAffine" + s[interpolation]; if(src.clCxt -> impl -> double_support != 0) { cl_int st; coeffs_cm = clCreateBuffer( clCxt->impl->clContext, CL_MEM_READ_WRITE, sizeof(F) * 2 * 3, NULL, &st ); openCLVerifyCall(st); openCLSafeCall(clEnqueueWriteBuffer(clCxt->impl->clCmdQueue, (cl_mem)coeffs_cm, 1, 0, sizeof(F) * 2 * 3, coeffs, 0, 0, 0)); } else { cl_int st; for(int m = 0; m < 2; m++) for(int n = 0; n < 3; n++) { float_coeffs[m][n] = coeffs[m][n]; } coeffs_cm = clCreateBuffer( clCxt->impl->clContext, CL_MEM_READ_WRITE, sizeof(float) * 2 * 3, NULL, &st ); openCLSafeCall(clEnqueueWriteBuffer(clCxt->impl->clCmdQueue, (cl_mem)coeffs_cm, 1, 0, sizeof(float) * 2 * 3, float_coeffs, 0, 0, 0)); } //TODO: improve this kernel size_t blkSizeX = 16, blkSizeY = 16; size_t glbSizeX; size_t cols; //if(src.type() == CV_8UC1 && interpolation != 2) if(src.type() == CV_8UC1 && interpolation != 2) { cols = (dst.cols + dst.offset % 4 + 3) / 4; glbSizeX = cols % blkSizeX == 0 ? cols : (cols / blkSizeX + 1) * blkSizeX; } else { cols = dst.cols; glbSizeX = dst.cols % blkSizeX == 0 ? dst.cols : (dst.cols / blkSizeX + 1) * blkSizeX; } size_t glbSizeY = dst.rows % blkSizeY == 0 ? dst.rows : (dst.rows / blkSizeY + 1) * blkSizeY; size_t globalThreads[3] = {glbSizeX, glbSizeY, 1}; size_t localThreads[3] = {blkSizeX, blkSizeY, 1}; vector< pair<size_t, const void *> > args; args.push_back(make_pair(sizeof(cl_mem), (void *)&src.data)); args.push_back(make_pair(sizeof(cl_mem), (void *)&dst.data)); args.push_back(make_pair(sizeof(cl_int), (void *)&src.cols)); args.push_back(make_pair(sizeof(cl_int), (void *)&src.rows)); args.push_back(make_pair(sizeof(cl_int), (void *)&dst.cols)); args.push_back(make_pair(sizeof(cl_int), (void *)&dst.rows)); args.push_back(make_pair(sizeof(cl_int), (void *)&srcStep)); args.push_back(make_pair(sizeof(cl_int), (void *)&dstStep)); args.push_back(make_pair(sizeof(cl_int), (void *)&src.offset)); args.push_back(make_pair(sizeof(cl_int), (void *)&dst.offset)); args.push_back(make_pair(sizeof(cl_mem), (void *)&coeffs_cm)); args.push_back(make_pair(sizeof(cl_int), (void *)&cols)); openCLExecuteKernel(clCxt, &imgproc_warpAffine, kernelName, globalThreads, localThreads, args, src.oclchannels(), src.depth()); openCLSafeCall(clReleaseMemObject(coeffs_cm)); } void warpPerspective_gpu(const oclMat &src, oclMat &dst, double coeffs[3][3], int interpolation) { CV_Assert( (src.oclchannels() == dst.oclchannels()) ); int srcStep = src.step1(); int dstStep = dst.step1(); float float_coeffs[3][3]; cl_mem coeffs_cm; Context *clCxt = src.clCxt; string s[3] = {"NN", "Linear", "Cubic"}; string kernelName = "warpPerspective" + s[interpolation]; if(src.clCxt -> impl -> double_support != 0) { cl_int st; coeffs_cm = clCreateBuffer( clCxt->impl->clContext, CL_MEM_READ_WRITE, sizeof(double) * 3 * 3, NULL, &st ); openCLVerifyCall(st); openCLSafeCall(clEnqueueWriteBuffer(clCxt->impl->clCmdQueue, (cl_mem)coeffs_cm, 1, 0, sizeof(double) * 3 * 3, coeffs, 0, 0, 0)); } else { cl_int st; for(int m = 0; m < 3; m++) for(int n = 0; n < 3; n++) float_coeffs[m][n] = coeffs[m][n]; coeffs_cm = clCreateBuffer( clCxt->impl->clContext, CL_MEM_READ_WRITE, sizeof(float) * 3 * 3, NULL, &st ); openCLVerifyCall(st); openCLSafeCall(clEnqueueWriteBuffer(clCxt->impl->clCmdQueue, (cl_mem)coeffs_cm, 1, 0, sizeof(float) * 3 * 3, float_coeffs, 0, 0, 0)); } //TODO: improve this kernel size_t blkSizeX = 16, blkSizeY = 16; size_t glbSizeX; size_t cols; if(src.type() == CV_8UC1 && interpolation == 0) { cols = (dst.cols + dst.offset % 4 + 3) / 4; glbSizeX = cols % blkSizeX == 0 ? cols : (cols / blkSizeX + 1) * blkSizeX; } else /* */ { cols = dst.cols; glbSizeX = dst.cols % blkSizeX == 0 ? dst.cols : (dst.cols / blkSizeX + 1) * blkSizeX; } size_t glbSizeY = dst.rows % blkSizeY == 0 ? dst.rows : (dst.rows / blkSizeY + 1) * blkSizeY; size_t globalThreads[3] = {glbSizeX, glbSizeY, 1}; size_t localThreads[3] = {blkSizeX, blkSizeY, 1}; vector< pair<size_t, const void *> > args; args.push_back(make_pair(sizeof(cl_mem), (void *)&src.data)); args.push_back(make_pair(sizeof(cl_mem), (void *)&dst.data)); args.push_back(make_pair(sizeof(cl_int), (void *)&src.cols)); args.push_back(make_pair(sizeof(cl_int), (void *)&src.rows)); args.push_back(make_pair(sizeof(cl_int), (void *)&dst.cols)); args.push_back(make_pair(sizeof(cl_int), (void *)&dst.rows)); args.push_back(make_pair(sizeof(cl_int), (void *)&srcStep)); args.push_back(make_pair(sizeof(cl_int), (void *)&dstStep)); args.push_back(make_pair(sizeof(cl_int), (void *)&src.offset)); args.push_back(make_pair(sizeof(cl_int), (void *)&dst.offset)); args.push_back(make_pair(sizeof(cl_mem), (void *)&coeffs_cm)); args.push_back(make_pair(sizeof(cl_int), (void *)&cols)); openCLExecuteKernel(clCxt, &imgproc_warpPerspective, kernelName, globalThreads, localThreads, args, src.oclchannels(), src.depth()); openCLSafeCall(clReleaseMemObject(coeffs_cm)); } } void warpAffine(const oclMat &src, oclMat &dst, const Mat &M, Size dsize, int flags) { int interpolation = flags & INTER_MAX; CV_Assert((src.depth() == CV_8U || src.depth() == CV_32F) && src.oclchannels() != 2 && src.oclchannels() != 3); CV_Assert(interpolation == INTER_NEAREST || interpolation == INTER_LINEAR || interpolation == INTER_CUBIC); dst.create(dsize, src.type()); CV_Assert(M.rows == 2 && M.cols == 3); int warpInd = (flags & WARP_INVERSE_MAP) >> 4; F coeffs[2][3]; Mat coeffsMat(2, 3, CV_64F, (void *)coeffs); M.convertTo(coeffsMat, coeffsMat.type()); if(!warpInd) { convert_coeffs((F *)(&coeffs[0][0])); } warpAffine_gpu(src, dst, coeffs, interpolation); } void warpPerspective(const oclMat &src, oclMat &dst, const Mat &M, Size dsize, int flags) { int interpolation = flags & INTER_MAX; CV_Assert((src.depth() == CV_8U || src.depth() == CV_32F) && src.oclchannels() != 2 && src.oclchannels() != 3); CV_Assert(interpolation == INTER_NEAREST || interpolation == INTER_LINEAR || interpolation == INTER_CUBIC); dst.create(dsize, src.type()); CV_Assert(M.rows == 3 && M.cols == 3); int warpInd = (flags & WARP_INVERSE_MAP) >> 4; double coeffs[3][3]; Mat coeffsMat(3, 3, CV_64F, (void *)coeffs); M.convertTo(coeffsMat, coeffsMat.type()); if(!warpInd) { invert((double *)(&coeffs[0][0])); } warpPerspective_gpu(src, dst, coeffs, interpolation); } //////////////////////////////////////////////////////////////////////// // integral void integral(const oclMat &src, oclMat &sum, oclMat &sqsum) { CV_Assert(src.type() == CV_8UC1); if(src.clCxt->impl->double_support == 0 && src.depth() == CV_64F) { CV_Error(CV_GpuNotSupported, "select device don't support double"); } int vlen = 4; int offset = src.offset / vlen; int pre_invalid = src.offset % vlen; int vcols = (pre_invalid + src.cols + vlen - 1) / vlen; oclMat t_sum , t_sqsum; t_sum.create(src.cols, src.rows, CV_32SC1); t_sqsum.create(src.cols, src.rows, CV_32FC1); int w = src.cols + 1, h = src.rows + 1; sum.create(h, w, CV_32SC1); sqsum.create(h, w, CV_32FC1); int sum_offset = sum.offset / vlen, sqsum_offset = sqsum.offset / vlen; vector<pair<size_t , const void *> > args; args.push_back( make_pair( sizeof(cl_mem) , (void *)&src.data )); args.push_back( make_pair( sizeof(cl_mem) , (void *)&t_sum.data )); args.push_back( make_pair( sizeof(cl_mem) , (void *)&t_sqsum.data )); args.push_back( make_pair( sizeof(cl_int) , (void *)&offset )); args.push_back( make_pair( sizeof(cl_int) , (void *)&pre_invalid )); args.push_back( make_pair( sizeof(cl_int) , (void *)&src.rows )); args.push_back( make_pair( sizeof(cl_int) , (void *)&src.cols )); args.push_back( make_pair( sizeof(cl_int) , (void *)&src.step )); args.push_back( make_pair( sizeof(cl_int) , (void *)&t_sum.step)); size_t gt[3] = {((vcols + 1) / 2) * 256, 1, 1}, lt[3] = {256, 1, 1}; openCLExecuteKernel(src.clCxt, &imgproc_integral, "integral_cols", gt, lt, args, -1, -1); args.clear(); args.push_back( make_pair( sizeof(cl_mem) , (void *)&t_sum.data )); args.push_back( make_pair( sizeof(cl_mem) , (void *)&t_sqsum.data )); args.push_back( make_pair( sizeof(cl_mem) , (void *)&sum.data )); args.push_back( make_pair( sizeof(cl_mem) , (void *)&sqsum.data )); args.push_back( make_pair( sizeof(cl_int) , (void *)&t_sum.rows )); args.push_back( make_pair( sizeof(cl_int) , (void *)&t_sum.cols )); args.push_back( make_pair( sizeof(cl_int) , (void *)&t_sum.step )); args.push_back( make_pair( sizeof(cl_int) , (void *)&sum.step)); args.push_back( make_pair( sizeof(cl_int) , (void *)&sqsum.step)); args.push_back( make_pair( sizeof(cl_int) , (void *)&sum_offset)); args.push_back( make_pair( sizeof(cl_int) , (void *)&sqsum_offset)); size_t gt2[3] = {t_sum.cols * 32, 1, 1}, lt2[3] = {256, 1, 1}; openCLExecuteKernel(src.clCxt, &imgproc_integral, "integral_rows", gt2, lt2, args, -1, -1); //cout << "tested" << endl; } void integral(const oclMat &src, oclMat &sum) { CV_Assert(src.type() == CV_8UC1); int vlen = 4; int offset = src.offset / vlen; int pre_invalid = src.offset % vlen; int vcols = (pre_invalid + src.cols + vlen - 1) / vlen; oclMat t_sum; t_sum.create(src.cols, src.rows, CV_32SC1); int w = src.cols + 1, h = src.rows + 1; sum.create(h, w, CV_32SC1); int sum_offset = sum.offset / vlen; vector<pair<size_t , const void *> > args; args.push_back( make_pair( sizeof(cl_mem) , (void *)&src.data )); args.push_back( make_pair( sizeof(cl_mem) , (void *)&t_sum.data )); args.push_back( make_pair( sizeof(cl_int) , (void *)&offset )); args.push_back( make_pair( sizeof(cl_int) , (void *)&pre_invalid )); args.push_back( make_pair( sizeof(cl_int) , (void *)&src.rows )); args.push_back( make_pair( sizeof(cl_int) , (void *)&src.cols )); args.push_back( make_pair( sizeof(cl_int) , (void *)&src.step )); args.push_back( make_pair( sizeof(cl_int) , (void *)&t_sum.step)); size_t gt[3] = {((vcols + 1) / 2) * 256, 1, 1}, lt[3] = {256, 1, 1}; openCLExecuteKernel(src.clCxt, &imgproc_integral_sum, "integral_sum_cols", gt, lt, args, -1, -1); args.clear(); args.push_back( make_pair( sizeof(cl_mem) , (void *)&t_sum.data )); args.push_back( make_pair( sizeof(cl_mem) , (void *)&sum.data )); args.push_back( make_pair( sizeof(cl_int) , (void *)&t_sum.rows )); args.push_back( make_pair( sizeof(cl_int) , (void *)&t_sum.cols )); args.push_back( make_pair( sizeof(cl_int) , (void *)&t_sum.step )); args.push_back( make_pair( sizeof(cl_int) , (void *)&sum.step)); args.push_back( make_pair( sizeof(cl_int) , (void *)&sum_offset)); size_t gt2[3] = {t_sum.cols * 32, 1, 1}, lt2[3] = {256, 1, 1}; openCLExecuteKernel(src.clCxt, &imgproc_integral_sum, "integral_sum_rows", gt2, lt2, args, -1, -1); //cout << "tested" << endl; } /////////////////////// corner ////////////////////////////// void extractCovData(const oclMat &src, oclMat &Dx, oclMat &Dy, int blockSize, int ksize, int borderType) { CV_Assert(src.type() == CV_8UC1 || src.type() == CV_32FC1); double scale = static_cast<double>(1 << ((ksize > 0 ? ksize : 3) - 1)) * blockSize; if (ksize < 0) scale *= 2.; if (src.depth() == CV_8U) { scale *= 255.; scale = 1. / scale; } else { scale = 1. / scale; } if (ksize > 0) { Sobel(src, Dx, CV_32F, 1, 0, ksize, scale, 0, borderType); Sobel(src, Dy, CV_32F, 0, 1, ksize, scale, 0, borderType); } else { Scharr(src, Dx, CV_32F, 1, 0, scale, 0, borderType); Scharr(src, Dy, CV_32F, 0, 1, scale, 0, borderType); } CV_Assert(Dx.offset == 0 && Dy.offset == 0); } void corner_ocl(const char *src_str, string kernelName, int block_size, float k, oclMat &Dx, oclMat &Dy, oclMat &dst, int border_type) { char borderType[30]; switch (border_type) { case cv::BORDER_CONSTANT: sprintf(borderType, "BORDER_CONSTANT"); break; case cv::BORDER_REFLECT101: sprintf(borderType, "BORDER_REFLECT101"); break; case cv::BORDER_REFLECT: sprintf(borderType, "BORDER_REFLECT"); break; case cv::BORDER_REPLICATE: sprintf(borderType, "BORDER_REPLICATE"); break; default: cout << "BORDER type is not supported!" << endl; } char build_options[150]; sprintf(build_options, "-D anX=%d -D anY=%d -D ksX=%d -D ksY=%d -D %s", block_size / 2, block_size / 2, block_size, block_size, borderType); size_t blockSizeX = 256, blockSizeY = 1; size_t gSize = blockSizeX - block_size / 2 * 2; size_t globalSizeX = (Dx.cols) % gSize == 0 ? Dx.cols / gSize * blockSizeX : (Dx.cols / gSize + 1) * blockSizeX; size_t rows_per_thread = 2; size_t globalSizeY = ((Dx.rows + rows_per_thread - 1) / rows_per_thread) % blockSizeY == 0 ? ((Dx.rows + rows_per_thread - 1) / rows_per_thread) : (((Dx.rows + rows_per_thread - 1) / rows_per_thread) / blockSizeY + 1) * blockSizeY; size_t gt[3] = { globalSizeX, globalSizeY, 1 }; size_t lt[3] = { blockSizeX, blockSizeY, 1 }; vector<pair<size_t , const void *> > args; args.push_back( make_pair( sizeof(cl_mem) , (void *)&Dx.data )); args.push_back( make_pair( sizeof(cl_mem) , (void *)&Dy.data)); args.push_back( make_pair( sizeof(cl_mem) , (void *)&dst.data)); args.push_back( make_pair( sizeof(cl_int) , (void *)&Dx.offset )); args.push_back( make_pair( sizeof(cl_int) , (void *)&Dx.wholerows )); args.push_back( make_pair( sizeof(cl_int) , (void *)&Dx.wholecols )); args.push_back( make_pair(sizeof(cl_int), (void *)&Dx.step)); args.push_back( make_pair( sizeof(cl_int) , (void *)&Dy.offset )); args.push_back( make_pair( sizeof(cl_int) , (void *)&Dy.wholerows )); args.push_back( make_pair( sizeof(cl_int) , (void *)&Dy.wholecols )); args.push_back( make_pair(sizeof(cl_int), (void *)&Dy.step)); args.push_back( make_pair(sizeof(cl_int), (void *)&dst.offset)); args.push_back( make_pair(sizeof(cl_int), (void *)&dst.rows)); args.push_back( make_pair(sizeof(cl_int), (void *)&dst.cols)); args.push_back( make_pair(sizeof(cl_int), (void *)&dst.step)); args.push_back( make_pair( sizeof(cl_float) , (void *)&k)); openCLExecuteKernel(dst.clCxt, &src_str, kernelName, gt, lt, args, -1, -1, build_options); } void cornerHarris(const oclMat &src, oclMat &dst, int blockSize, int ksize, double k, int borderType) { if(src.clCxt->impl->double_support == 0 && src.depth() == CV_64F) { CV_Error(CV_GpuNotSupported, "select device don't support double"); } CV_Assert(src.cols >= blockSize / 2 && src.rows >= blockSize / 2); oclMat Dx, Dy; CV_Assert(borderType == cv::BORDER_CONSTANT || borderType == cv::BORDER_REFLECT101 || borderType == cv::BORDER_REPLICATE || borderType == cv::BORDER_REFLECT); extractCovData(src, Dx, Dy, blockSize, ksize, borderType); dst.create(src.size(), CV_32F); corner_ocl(imgproc_calcHarris, "calcHarris", blockSize, static_cast<float>(k), Dx, Dy, dst, borderType); } void cornerMinEigenVal(const oclMat &src, oclMat &dst, int blockSize, int ksize, int borderType) { if(src.clCxt->impl->double_support == 0 && src.depth() == CV_64F) { CV_Error(CV_GpuNotSupported, "select device don't support double"); } CV_Assert(src.cols >= blockSize / 2 && src.rows >= blockSize / 2); oclMat Dx, Dy; CV_Assert(borderType == cv::BORDER_CONSTANT || borderType == cv::BORDER_REFLECT101 || borderType == cv::BORDER_REPLICATE || borderType == cv::BORDER_REFLECT); extractCovData(src, Dx, Dy, blockSize, ksize, borderType); dst.create(src.size(), CV_32F); corner_ocl(imgproc_calcMinEigenVal, "calcMinEigenVal", blockSize, 0, Dx, Dy, dst, borderType); } /////////////////////////////////// MeanShiftfiltering /////////////////////////////////////////////// void meanShiftFiltering_gpu(const oclMat &src, oclMat dst, int sp, int sr, int maxIter, float eps) { CV_Assert( (src.cols == dst.cols) && (src.rows == dst.rows) ); CV_Assert( !(dst.step & 0x3) ); Context *clCxt = src.clCxt; //Arrange the NDRange int col = src.cols, row = src.rows; int ltx = 16, lty = 8; if(src.cols % ltx != 0) col = (col / ltx + 1) * ltx; if(src.rows % lty != 0) row = (row / lty + 1) * lty; size_t globalThreads[3] = {col, row, 1}; size_t localThreads[3] = {ltx, lty, 1}; //set args vector<pair<size_t , const void *> > args; args.push_back( make_pair( sizeof(cl_mem) , (void *)&dst.data )); args.push_back( make_pair( sizeof(cl_int) , (void *)&dst.step )); args.push_back( make_pair( sizeof(cl_mem) , (void *)&src.data )); args.push_back( make_pair( sizeof(cl_int) , (void *)&src.step )); args.push_back( make_pair( sizeof(cl_int) , (void *)&dst.offset )); args.push_back( make_pair( sizeof(cl_int) , (void *)&src.offset )); args.push_back( make_pair( sizeof(cl_int) , (void *)&dst.cols )); args.push_back( make_pair( sizeof(cl_int) , (void *)&dst.rows )); args.push_back( make_pair( sizeof(cl_int) , (void *)&sp )); args.push_back( make_pair( sizeof(cl_int) , (void *)&sr )); args.push_back( make_pair( sizeof(cl_int) , (void *)&maxIter )); args.push_back( make_pair( sizeof(cl_float) , (void *)&eps )); openCLExecuteKernel(clCxt, &meanShift, "meanshift_kernel", globalThreads, localThreads, args, -1, -1); } void meanShiftFiltering(const oclMat &src, oclMat &dst, int sp, int sr, TermCriteria criteria) { if( src.empty() ) CV_Error( CV_StsBadArg, "The input image is empty" ); if( src.depth() != CV_8U || src.oclchannels() != 4 ) CV_Error( CV_StsUnsupportedFormat, "Only 8-bit, 4-channel images are supported" ); if(src.clCxt->impl->double_support == 0) { CV_Error( CV_GpuNotSupported, "Selected device doesn't support double, so a deviation is exists.\nIf the accuracy is acceptable, the error can be ignored.\n"); } dst.create( src.size(), CV_8UC4 ); if( !(criteria.type & TermCriteria::MAX_ITER) ) criteria.maxCount = 5; int maxIter = std::min(std::max(criteria.maxCount, 1), 100); float eps; if( !(criteria.type & TermCriteria::EPS) ) eps = 1.f; eps = (float)std::max(criteria.epsilon, 0.0); meanShiftFiltering_gpu(src, dst, sp, sr, maxIter, eps); } void meanShiftProc_gpu(const oclMat &src, oclMat dstr, oclMat dstsp, int sp, int sr, int maxIter, float eps) { //sanity checks CV_Assert( (src.cols == dstr.cols) && (src.rows == dstr.rows) && (src.rows == dstsp.rows) && (src.cols == dstsp.cols)); CV_Assert( !(dstsp.step & 0x3) ); Context *clCxt = src.clCxt; //Arrange the NDRange int col = src.cols, row = src.rows; int ltx = 16, lty = 8; if(src.cols % ltx != 0) col = (col / ltx + 1) * ltx; if(src.rows % lty != 0) row = (row / lty + 1) * lty; size_t globalThreads[3] = {col, row, 1}; size_t localThreads[3] = {ltx, lty, 1}; //set args vector<pair<size_t , const void *> > args; args.push_back( make_pair( sizeof(cl_mem) , (void *)&src.data )); args.push_back( make_pair( sizeof(cl_mem) , (void *)&dstr.data )); args.push_back( make_pair( sizeof(cl_mem) , (void *)&dstsp.data )); args.push_back( make_pair( sizeof(cl_int) , (void *)&src.step )); args.push_back( make_pair( sizeof(cl_int) , (void *)&dstr.step )); args.push_back( make_pair( sizeof(cl_int) , (void *)&dstsp.step )); args.push_back( make_pair( sizeof(cl_int) , (void *)&src.offset )); args.push_back( make_pair( sizeof(cl_int) , (void *)&dstr.offset )); args.push_back( make_pair( sizeof(cl_int) , (void *)&dstsp.offset )); args.push_back( make_pair( sizeof(cl_int) , (void *)&dstr.cols )); args.push_back( make_pair( sizeof(cl_int) , (void *)&dstr.rows )); args.push_back( make_pair( sizeof(cl_int) , (void *)&sp )); args.push_back( make_pair( sizeof(cl_int) , (void *)&sr )); args.push_back( make_pair( sizeof(cl_int) , (void *)&maxIter )); args.push_back( make_pair( sizeof(cl_float) , (void *)&eps )); openCLExecuteKernel(clCxt, &meanShift, "meanshiftproc_kernel", globalThreads, localThreads, args, -1, -1); } void meanShiftProc(const oclMat &src, oclMat &dstr, oclMat &dstsp, int sp, int sr, TermCriteria criteria) { if( src.empty() ) CV_Error( CV_StsBadArg, "The input image is empty" ); if( src.depth() != CV_8U || src.oclchannels() != 4 ) CV_Error( CV_StsUnsupportedFormat, "Only 8-bit, 4-channel images are supported" ); if(src.clCxt->impl->double_support == 0) { CV_Error( CV_GpuNotSupported, "Selected device doesn't support double, so a deviation is exists.\nIf the accuracy is acceptable, the error can be ignored.\n"); } dstr.create( src.size(), CV_8UC4 ); dstsp.create( src.size(), CV_16SC2 ); if( !(criteria.type & TermCriteria::MAX_ITER) ) criteria.maxCount = 5; int maxIter = std::min(std::max(criteria.maxCount, 1), 100); float eps; if( !(criteria.type & TermCriteria::EPS) ) eps = 1.f; eps = (float)std::max(criteria.epsilon, 0.0); meanShiftProc_gpu(src, dstr, dstsp, sp, sr, maxIter, eps); } /////////////////////////////////////////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////hist/////////////////////////////////////////////// ///////////////////////////////////////////////////////////////////////////////////////////////////// namespace histograms { const int PARTIAL_HISTOGRAM256_COUNT = 256; const int HISTOGRAM256_BIN_COUNT = 256; } ///////////////////////////////calcHist///////////////////////////////////////////////////////////////// void calc_sub_hist(const oclMat &mat_src, const oclMat &mat_sub_hist) { using namespace histograms; Context *clCxt = mat_src.clCxt; int depth = mat_src.depth(); string kernelName = "calc_sub_hist"; size_t localThreads[3] = { HISTOGRAM256_BIN_COUNT, 1, 1 }; size_t globalThreads[3] = { PARTIAL_HISTOGRAM256_COUNT *localThreads[0], 1, 1}; int dataWidth = 16; int dataWidth_bits = 4; int mask = dataWidth - 1; int cols = mat_src.cols * mat_src.oclchannels(); int src_offset = mat_src.offset; int hist_step = mat_sub_hist.step >> 2; int left_col = 0, right_col = 0; if(cols >= dataWidth * 2 - 1) { left_col = dataWidth - (src_offset & mask); left_col &= mask; src_offset += left_col; cols -= left_col; right_col = cols & mask; cols -= right_col; } else { left_col = cols; right_col = 0; cols = 0; globalThreads[0] = 0; } vector<pair<size_t , const void *> > args; if(globalThreads[0] != 0) { int tempcols = cols >> dataWidth_bits; int inc_x = globalThreads[0] % tempcols; int inc_y = globalThreads[0] / tempcols; src_offset >>= dataWidth_bits; int src_step = mat_src.step >> dataWidth_bits; int datacount = tempcols * mat_src.rows; args.push_back( make_pair( sizeof(cl_mem), (void *)&mat_src.data)); args.push_back( make_pair( sizeof(cl_int), (void *)&src_step)); args.push_back( make_pair( sizeof(cl_int), (void *)&src_offset)); args.push_back( make_pair( sizeof(cl_mem), (void *)&mat_sub_hist.data)); args.push_back( make_pair( sizeof(cl_int), (void *)&datacount)); args.push_back( make_pair( sizeof(cl_int), (void *)&tempcols)); args.push_back( make_pair( sizeof(cl_int), (void *)&inc_x)); args.push_back( make_pair( sizeof(cl_int), (void *)&inc_y)); args.push_back( make_pair( sizeof(cl_int), (void *)&hist_step)); openCLExecuteKernel(clCxt, &imgproc_histogram, kernelName, globalThreads, localThreads, args, -1, depth); } if(left_col != 0 || right_col != 0) { kernelName = "calc_sub_hist_border"; src_offset = mat_src.offset; localThreads[0] = 1; localThreads[1] = 256; globalThreads[0] = left_col + right_col; globalThreads[1] = (mat_src.rows + localThreads[1] - 1) / localThreads[1] * localThreads[1]; args.clear(); args.push_back( make_pair( sizeof(cl_mem), (void *)&mat_src.data)); args.push_back( make_pair( sizeof(cl_int), (void *)&mat_src.step)); args.push_back( make_pair( sizeof(cl_int), (void *)&src_offset)); args.push_back( make_pair( sizeof(cl_mem), (void *)&mat_sub_hist.data)); args.push_back( make_pair( sizeof(cl_int), (void *)&left_col)); args.push_back( make_pair( sizeof(cl_int), (void *)&cols)); args.push_back( make_pair( sizeof(cl_int), (void *)&mat_src.rows)); args.push_back( make_pair( sizeof(cl_int), (void *)&hist_step)); openCLExecuteKernel(clCxt, &imgproc_histogram, kernelName, globalThreads, localThreads, args, -1, depth); } } void merge_sub_hist(const oclMat &sub_hist, oclMat &mat_hist) { using namespace histograms; Context *clCxt = sub_hist.clCxt; string kernelName = "merge_hist"; size_t localThreads[3] = { 256, 1, 1 }; size_t globalThreads[3] = { HISTOGRAM256_BIN_COUNT *localThreads[0], 1, 1}; int src_step = sub_hist.step >> 2; vector<pair<size_t , const void *> > args; args.push_back( make_pair( sizeof(cl_mem), (void *)&sub_hist.data)); args.push_back( make_pair( sizeof(cl_mem), (void *)&mat_hist.data)); args.push_back( make_pair( sizeof(cl_int), (void *)&src_step)); openCLExecuteKernel(clCxt, &imgproc_histogram, kernelName, globalThreads, localThreads, args, -1, -1); } void calcHist(const oclMat &mat_src, oclMat &mat_hist) { using namespace histograms; CV_Assert(mat_src.type() == CV_8UC1); mat_hist.create(1, 256, CV_32SC1); oclMat buf(PARTIAL_HISTOGRAM256_COUNT, HISTOGRAM256_BIN_COUNT, CV_32SC1); buf.setTo(0); calc_sub_hist(mat_src, buf); merge_sub_hist(buf, mat_hist); } ///////////////////////////////////equalizeHist///////////////////////////////////////////////////// void equalizeHist(const oclMat &mat_src, oclMat &mat_dst) { mat_dst.create(mat_src.rows, mat_src.cols, CV_8UC1); oclMat mat_hist(1, 256, CV_32SC1); //mat_hist.setTo(0); calcHist(mat_src, mat_hist); Context *clCxt = mat_src.clCxt; string kernelName = "calLUT"; size_t localThreads[3] = { 256, 1, 1}; size_t globalThreads[3] = { 256, 1, 1}; oclMat lut(1, 256, CV_8UC1); vector<pair<size_t , const void *> > args; float scale = 255.f / (mat_src.rows * mat_src.cols); args.push_back( make_pair( sizeof(cl_mem), (void *)&lut.data)); args.push_back( make_pair( sizeof(cl_mem), (void *)&mat_hist.data)); args.push_back( make_pair( sizeof(cl_float), (void *)&scale)); openCLExecuteKernel(clCxt, &imgproc_histogram, kernelName, globalThreads, localThreads, args, -1, -1); LUT(mat_src, lut, mat_dst); } //////////////////////////////////bilateralFilter//////////////////////////////////////////////////// static void oclbilateralFilter_8u( const oclMat &src, oclMat &dst, int d, double sigma_color, double sigma_space, int borderType ) { int cn = src.channels(); int i, j, k, maxk, radius; Size size = src.size(); CV_Assert( (src.channels() == 1 || src.channels() == 3) && src.type() == dst.type() && src.size() == dst.size() && src.data != dst.data ); if( sigma_color <= 0 ) sigma_color = 1; if( sigma_space <= 0 ) sigma_space = 1; double gauss_color_coeff = -0.5 / (sigma_color * sigma_color); double gauss_space_coeff = -0.5 / (sigma_space * sigma_space); if( d <= 0 ) radius = cvRound(sigma_space * 1.5); else radius = d / 2; radius = MAX(radius, 1); d = radius * 2 + 1; oclMat temp; copyMakeBorder( src, temp, radius, radius, radius, radius, borderType ); vector<float> _color_weight(cn * 256); vector<float> _space_weight(d * d); vector<int> _space_ofs(d * d); float *color_weight = &_color_weight[0]; float *space_weight = &_space_weight[0]; int *space_ofs = &_space_ofs[0]; int dst_step_in_pixel = dst.step / dst.elemSize(); int dst_offset_in_pixel = dst.offset / dst.elemSize(); int temp_step_in_pixel = temp.step / temp.elemSize(); // initialize color-related bilateral filter coefficients for( i = 0; i < 256 * cn; i++ ) color_weight[i] = (float)std::exp(i * i * gauss_color_coeff); // initialize space-related bilateral filter coefficients for( i = -radius, maxk = 0; i <= radius; i++ ) for( j = -radius; j <= radius; j++ ) { double r = std::sqrt((double)i * i + (double)j * j); if( r > radius ) continue; space_weight[maxk] = (float)std::exp(r * r * gauss_space_coeff); space_ofs[maxk++] = (int)(i * temp_step_in_pixel + j); } oclMat oclcolor_weight(1, cn * 256, CV_32FC1, color_weight); oclMat oclspace_weight(1, d * d, CV_32FC1, space_weight); oclMat oclspace_ofs(1, d * d, CV_32SC1, space_ofs); string kernelName = "bilateral"; size_t localThreads[3] = { 16, 16, 1 }; size_t globalThreads[3] = { (dst.cols + localThreads[0] - 1) / localThreads[0] *localThreads[0], (dst.rows + localThreads[1] - 1) / localThreads[1] *localThreads[1], 1 }; if((dst.type() == CV_8UC1) && ((dst.offset & 3) == 0) && ((dst.cols & 3) == 0)) { kernelName = "bilateral2"; globalThreads[0] = (dst.cols / 4 + localThreads[0] - 1) / localThreads[0] * localThreads[0]; } vector<pair<size_t , const void *> > args; args.push_back( make_pair( sizeof(cl_mem), (void *)&dst.data )); args.push_back( make_pair( sizeof(cl_mem), (void *)&temp.data )); args.push_back( make_pair( sizeof(cl_int), (void *)&dst.rows )); args.push_back( make_pair( sizeof(cl_int), (void *)&dst.cols )); args.push_back( make_pair( sizeof(cl_int), (void *)&maxk )); args.push_back( make_pair( sizeof(cl_int), (void *)&radius )); args.push_back( make_pair( sizeof(cl_int), (void *)&dst_step_in_pixel )); args.push_back( make_pair( sizeof(cl_int), (void *)&dst_offset_in_pixel )); args.push_back( make_pair( sizeof(cl_int), (void *)&temp_step_in_pixel )); args.push_back( make_pair( sizeof(cl_int), (void *)&temp.rows )); args.push_back( make_pair( sizeof(cl_int), (void *)&temp.cols )); args.push_back( make_pair( sizeof(cl_mem), (void *)&oclcolor_weight.data )); args.push_back( make_pair( sizeof(cl_mem), (void *)&oclspace_weight.data )); args.push_back( make_pair( sizeof(cl_mem), (void *)&oclspace_ofs.data )); openCLExecuteKernel(src.clCxt, &imgproc_bilateral, kernelName, globalThreads, localThreads, args, dst.oclchannels(), dst.depth()); } void bilateralFilter(const oclMat &src, oclMat &dst, int radius, double sigmaclr, double sigmaspc, int borderType) { dst.create( src.size(), src.type() ); if( src.depth() == CV_8U ) oclbilateralFilter_8u( src, dst, radius, sigmaclr, sigmaspc, borderType ); else CV_Error( CV_StsUnsupportedFormat, "Bilateral filtering is only implemented for 8uimages" ); } } } //////////////////////////////////convolve//////////////////////////////////////////////////// inline int divUp(int total, int grain) { return (total + grain - 1) / grain; } void convolve_run(const oclMat &src, const oclMat &temp1, oclMat &dst, string kernelName, const char **kernelString) { CV_Assert(src.depth() == CV_32FC1); CV_Assert(temp1.depth() == CV_32F); CV_Assert(temp1.cols <= 17 && temp1.rows <= 17); dst.create(src.size(), src.type()); CV_Assert(src.cols == dst.cols && src.rows == dst.rows); CV_Assert(src.type() == dst.type()); Context *clCxt = src.clCxt; int channels = dst.oclchannels(); int depth = dst.depth(); size_t vector_length = 1; int offset_cols = ((dst.offset % dst.step) / dst.elemSize1()) & (vector_length - 1); int cols = divUp(dst.cols * channels + offset_cols, vector_length); int rows = dst.rows; size_t localThreads[3] = { 16, 16, 1 }; size_t globalThreads[3] = { divUp(cols, localThreads[0]) *localThreads[0], divUp(rows, localThreads[1]) *localThreads[1], 1 }; vector<pair<size_t , const void *> > args; args.push_back( make_pair( sizeof(cl_mem), (void *)&src.data )); args.push_back( make_pair( sizeof(cl_mem), (void *)&temp1.data )); args.push_back( make_pair( sizeof(cl_mem), (void *)&dst.data )); args.push_back( make_pair( sizeof(cl_int), (void *)&src.rows )); args.push_back( make_pair( sizeof(cl_int), (void *)&cols )); args.push_back( make_pair( sizeof(cl_int), (void *)&src.step )); args.push_back( make_pair( sizeof(cl_int), (void *)&dst.step )); args.push_back( make_pair( sizeof(cl_int), (void *)&temp1.step )); args.push_back( make_pair( sizeof(cl_int), (void *)&temp1.rows )); args.push_back( make_pair( sizeof(cl_int), (void *)&temp1.cols )); openCLExecuteKernel(clCxt, kernelString, kernelName, globalThreads, localThreads, args, -1, depth); } void cv::ocl::convolve(const oclMat &x, const oclMat &t, oclMat &y) { CV_Assert(x.depth() == CV_32F); CV_Assert(t.depth() == CV_32F); CV_Assert(x.type() == y.type() && x.size() == y.size()); y.create(x.size(), x.type()); string kernelName = "convolve"; convolve_run(x, t, y, kernelName, &imgproc_convolve); } #endif /* !defined (HAVE_OPENCL) */