more fix of mismatch
This commit is contained in:
yao
parent
ad6aae4583
commit
f36db3a037
@ -71,6 +71,9 @@ namespace cv
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void matchTemplate_SQDIFF_NORMED(
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const oclMat &image, const oclMat &templ, oclMat &result, MatchTemplateBuf &buf);
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void convolve_32F(
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const oclMat &image, const oclMat &templ, oclMat &result, MatchTemplateBuf &buf);
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void matchTemplate_CCORR(
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const oclMat &image, const oclMat &templ, oclMat &result, MatchTemplateBuf &buf);
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@ -90,41 +93,65 @@ namespace cv
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void matchTemplateNaive_CCORR(
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const oclMat &image, const oclMat &templ, oclMat &result, int cn);
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void extractFirstChannel_32F(
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const oclMat &image, oclMat &result);
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// Evaluates optimal template's area threshold. If
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// template's area is less than the threshold, we use naive match
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// template version, otherwise FFT-based (if available)
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static int getTemplateThreshold(int method, int depth)
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static bool useNaive(int , int , Size )
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{
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switch (method)
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{
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case CV_TM_CCORR:
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if (depth == CV_32F) return 250;
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if (depth == CV_8U) return 300;
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break;
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case CV_TM_SQDIFF:
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if (depth == CV_32F) return 0x7fffffff; // do naive SQDIFF for CV_32F
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if (depth == CV_8U) return 300;
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break;
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}
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CV_Error(CV_StsBadArg, "getTemplateThreshold: unsupported match template mode");
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return 0;
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// FIXME!
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// always use naive until convolve is imported
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return true;
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}
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//////////////////////////////////////////////////////////////////////
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// SQDIFF
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void matchTemplate_SQDIFF(
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const oclMat &image, const oclMat &templ, oclMat &result, MatchTemplateBuf &)
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const oclMat &image, const oclMat &templ, oclMat &result, MatchTemplateBuf & buf)
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{
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result.create(image.rows - templ.rows + 1, image.cols - templ.cols + 1, CV_32F);
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if (templ.size().area() < getTemplateThreshold(CV_TM_SQDIFF, image.depth()))
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if (useNaive(CV_TM_SQDIFF, image.depth(), templ.size()))
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{
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matchTemplateNaive_SQDIFF(image, templ, result, image.oclchannels());
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return;
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}
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else
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{
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// TODO
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CV_Error(CV_StsBadArg, "Not supported yet for this size template");
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buf.image_sqsums.resize(1);
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// TODO, add double support for ocl::integral
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// use CPU integral temporarily
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Mat sums, sqsums;
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cv::integral(Mat(image.reshape(1)), sums, sqsums);
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buf.image_sqsums[0] = sqsums;
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unsigned long long templ_sqsum = (unsigned long long)sqrSum(templ.reshape(1))[0];
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matchTemplate_CCORR(image, templ, result, buf);
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//port CUDA's matchTemplatePrepared_SQDIFF_8U
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Context *clCxt = image.clCxt;
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string kernelName = "matchTemplate_Prepared_SQDIFF";
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vector< pair<size_t, const void *> > args;
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args.push_back( make_pair( sizeof(cl_mem), (void *)&buf.image_sqsums[0].data));
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args.push_back( make_pair( sizeof(cl_mem), (void *)&result.data));
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args.push_back( make_pair( sizeof(cl_ulong), (void *)&templ_sqsum));
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args.push_back( make_pair( sizeof(cl_int), (void *)&result.rows));
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args.push_back( make_pair( sizeof(cl_int), (void *)&result.cols));
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args.push_back( make_pair( sizeof(cl_int), (void *)&templ.rows));
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args.push_back( make_pair( sizeof(cl_int), (void *)&templ.cols));
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args.push_back( make_pair( sizeof(cl_int), (void *)&buf.image_sqsums[0].offset));
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args.push_back( make_pair( sizeof(cl_int), (void *)&buf.image_sqsums[0].step));
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args.push_back( make_pair( sizeof(cl_int), (void *)&result.offset));
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args.push_back( make_pair( sizeof(cl_int), (void *)&result.step));
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size_t globalThreads[3] = {result.cols, result.rows, 1};
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size_t localThreads[3] = {16, 16, 1};
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const char * build_opt = image.oclchannels() == 4 ? "-D CN4" : "";
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openCLExecuteKernel(clCxt, &match_template, kernelName, globalThreads, localThreads, args, 1, CV_8U, build_opt);
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}
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}
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@ -134,7 +161,6 @@ namespace cv
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matchTemplate_CCORR(image, templ, result, buf);
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buf.image_sums.resize(1);
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integral(image.reshape(1), buf.image_sums[0]);
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unsigned long long templ_sqsum = (unsigned long long)sqrSum(templ.reshape(1))[0];
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@ -156,7 +182,7 @@ namespace cv
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args.push_back( make_pair( sizeof(cl_int), (void *)&result.step));
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size_t globalThreads[3] = {result.cols, result.rows, 1};
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size_t localThreads[3] = {32, 8, 1};
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size_t localThreads[3] = {16, 16, 1};
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openCLExecuteKernel(clCxt, &match_template, kernelName, globalThreads, localThreads, args, 1, CV_8U);
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}
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@ -191,33 +217,39 @@ namespace cv
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args.push_back( make_pair( sizeof(cl_int), (void *)&result.step));
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size_t globalThreads[3] = {result.cols, result.rows, 1};
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size_t localThreads[3] = {32, 8, 1};
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size_t localThreads[3] = {16, 16, 1};
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openCLExecuteKernel(clCxt, &match_template, kernelName, globalThreads, localThreads, args, image.oclchannels(), image.depth());
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}
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//////////////////////////////////////////////////////////////////////
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// CCORR
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void convolve_32F(
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const oclMat &, const oclMat &, oclMat &, MatchTemplateBuf &)
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{
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CV_Error(-1, "convolve is not fully implemented yet");
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}
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void matchTemplate_CCORR(
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const oclMat &image, const oclMat &templ, oclMat &result, MatchTemplateBuf &buf)
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{
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result.create(image.rows - templ.rows + 1, image.cols - templ.cols + 1, CV_32F);
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if (templ.size().area() < getTemplateThreshold(CV_TM_SQDIFF, image.depth()))
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if (useNaive(CV_TM_CCORR, image.depth(), templ.size()))
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{
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matchTemplateNaive_CCORR(image, templ, result, image.oclchannels());
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return;
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}
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else
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{
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CV_Error(CV_StsBadArg, "Not supported yet for this size template");
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if(image.depth() == CV_8U && templ.depth() == CV_8U)
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{
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image.convertTo(buf.imagef, CV_32F);
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templ.convertTo(buf.templf, CV_32F);
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convolve_32F(buf.imagef, buf.templf, result, buf);
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}
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else
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{
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convolve_32F(image, templ, result, buf);
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}
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CV_Assert(image.oclchannels() == 1);
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oclMat o_result(image.size(), CV_MAKETYPE(CV_32F, image.oclchannels()));
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filter2D(buf.imagef, o_result, CV_32F, buf.templf, Point(0, 0));
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result = o_result(Rect(0, 0, image.rows - templ.rows + 1, image.cols - templ.cols + 1));
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}
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}
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@ -249,7 +281,7 @@ namespace cv
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args.push_back( make_pair( sizeof(cl_int), (void *)&result.step));
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size_t globalThreads[3] = {result.cols, result.rows, 1};
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size_t localThreads[3] = {32, 8, 1};
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size_t localThreads[3] = {16, 16, 1};
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openCLExecuteKernel(clCxt, &match_template, kernelName, globalThreads, localThreads, args, 1, CV_8U);
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}
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@ -284,7 +316,7 @@ namespace cv
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args.push_back( make_pair( sizeof(cl_int), (void *)&result.step));
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size_t globalThreads[3] = {result.cols, result.rows, 1};
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size_t localThreads[3] = {32, 8, 1};
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size_t localThreads[3] = {16, 16, 1};
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openCLExecuteKernel(clCxt, &match_template, kernelName, globalThreads, localThreads, args, image.oclchannels(), image.depth());
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}
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//////////////////////////////////////////////////////////////////////
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@ -301,7 +333,7 @@ namespace cv
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kernelName = "matchTemplate_Prepared_CCOFF";
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size_t globalThreads[3] = {result.cols, result.rows, 1};
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size_t localThreads[3] = {32, 8, 1};
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size_t localThreads[3] = {16, 16, 1};
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vector< pair<size_t, const void *> > args;
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args.push_back( make_pair( sizeof(cl_mem), (void *)&result.data) );
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@ -313,22 +345,22 @@ namespace cv
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args.push_back( make_pair( sizeof(cl_int), (void *)&result.cols) );
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args.push_back( make_pair( sizeof(cl_int), (void *)&result.offset));
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args.push_back( make_pair( sizeof(cl_int), (void *)&result.step));
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Vec4f templ_sum = Vec4f::all(0);
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// to be continued in the following section
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if(image.oclchannels() == 1)
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{
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buf.image_sums.resize(1);
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integral(image, buf.image_sums[0]);
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float templ_sum = 0;
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templ_sum = (float)sum(templ)[0] / templ.size().area();
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templ_sum[0] = (float)sum(templ)[0] / templ.size().area();
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args.push_back( make_pair( sizeof(cl_mem), (void *)&buf.image_sums[0].data) );
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args.push_back( make_pair( sizeof(cl_int), (void *)&buf.image_sums[0].offset) );
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args.push_back( make_pair( sizeof(cl_int), (void *)&buf.image_sums[0].step) );
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args.push_back( make_pair( sizeof(cl_float), (void *)&templ_sum) );
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args.push_back( make_pair( sizeof(cl_float), (void *)&templ_sum[0]) );
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}
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else
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{
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Vec4f templ_sum = Vec4f::all(0);
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split(image, buf.images);
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templ_sum = sum(templ) / templ.size().area();
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buf.image_sums.resize(buf.images.size());
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@ -374,7 +406,7 @@ namespace cv
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kernelName = "matchTemplate_Prepared_CCOFF_NORMED";
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size_t globalThreads[3] = {result.cols, result.rows, 1};
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size_t localThreads[3] = {32, 8, 1};
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size_t localThreads[3] = {16, 16, 1};
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vector< pair<size_t, const void *> > args;
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args.push_back( make_pair( sizeof(cl_mem), (void *)&result.data) );
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@ -387,20 +419,22 @@ namespace cv
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args.push_back( make_pair( sizeof(cl_int), (void *)&result.offset));
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args.push_back( make_pair( sizeof(cl_int), (void *)&result.step));
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args.push_back( make_pair( sizeof(cl_float), (void *)&scale) );
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Vec4f templ_sum = Vec4f::all(0);
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Vec4f templ_sqsum = Vec4f::all(0);
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// to be continued in the following section
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if(image.oclchannels() == 1)
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{
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buf.image_sums.resize(1);
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buf.image_sqsums.resize(1);
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integral(image, buf.image_sums[0], buf.image_sqsums[0]);
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float templ_sum = 0;
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float templ_sqsum = 0;
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templ_sum = (float)sum(templ)[0];
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templ_sqsum = sqrSum(templ)[0];
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templ_sum[0] = (float)sum(templ)[0];
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templ_sqsum -= scale * templ_sum * templ_sum;
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templ_sum *= scale;
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templ_sqsum[0] = sqrSum(templ)[0];
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templ_sqsum[0] -= scale * templ_sum[0] * templ_sum[0];
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templ_sum[0] *= scale;
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args.push_back( make_pair( sizeof(cl_mem), (void *)&buf.image_sums[0].data) );
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args.push_back( make_pair( sizeof(cl_int), (void *)&buf.image_sums[0].offset) );
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@ -408,13 +442,11 @@ namespace cv
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args.push_back( make_pair( sizeof(cl_mem), (void *)&buf.image_sqsums[0].data) );
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args.push_back( make_pair( sizeof(cl_int), (void *)&buf.image_sqsums[0].offset) );
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args.push_back( make_pair( sizeof(cl_int), (void *)&buf.image_sqsums[0].step) );
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args.push_back( make_pair( sizeof(cl_float), (void *)&templ_sum) );
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args.push_back( make_pair( sizeof(cl_float), (void *)&templ_sqsum) );
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args.push_back( make_pair( sizeof(cl_float), (void *)&templ_sum[0]) );
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args.push_back( make_pair( sizeof(cl_float), (void *)&templ_sqsum[0]) );
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}
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else
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{
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Vec4f templ_sum = Vec4f::all(0);
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Vec4f templ_sqsum = Vec4f::all(0);
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split(image, buf.images);
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templ_sum = sum(templ);
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@ -465,7 +497,27 @@ namespace cv
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}
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openCLExecuteKernel(clCxt, &match_template, kernelName, globalThreads, localThreads, args, image.oclchannels(), image.depth());
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}
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void extractFirstChannel_32F(const oclMat &image, oclMat &result)
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{
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Context *clCxt = image.clCxt;
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string kernelName;
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kernelName = "extractFirstChannel";
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size_t globalThreads[3] = {result.cols, result.rows, 1};
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size_t localThreads[3] = {16, 16, 1};
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vector< pair<size_t, const void *> > args;
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args.push_back( make_pair( sizeof(cl_mem), (void *)&image.data) );
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args.push_back( make_pair( sizeof(cl_mem), (void *)&result.data) );
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args.push_back( make_pair( sizeof(cl_int), (void *)&result.rows) );
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args.push_back( make_pair( sizeof(cl_int), (void *)&result.cols) );
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args.push_back( make_pair( sizeof(cl_int), (void *)&image.offset));
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args.push_back( make_pair( sizeof(cl_int), (void *)&result.offset));
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args.push_back( make_pair( sizeof(cl_int), (void *)&image.step));
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args.push_back( make_pair( sizeof(cl_int), (void *)&result.step));
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openCLExecuteKernel(clCxt, &match_template, kernelName, globalThreads, localThreads, args, -1, -1);
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}
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}/*ocl*/
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} /*cv*/
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@ -45,22 +45,28 @@
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#pragma OPENCL EXTENSION cl_amd_printf : enable
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#if defined (__ATI__)
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#pragma OPENCL EXTENSION cl_amd_fp64:enable
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#if defined (DOUBLE_SUPPORT)
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#elif defined (__NVIDIA__)
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#ifdef cl_khr_fp64
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#pragma OPENCL EXTENSION cl_khr_fp64:enable
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#elif defined (cl_amd_fp64)
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#pragma OPENCL EXTENSION cl_amd_fp64:enable
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#endif
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#if !defined(USE_SQR_INTEGRAL) && (defined (__ATI__) || defined (__NVIDIA__))
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#define TYPE_IMAGE_SQSUM double
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#else
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#define TYPE_IMAGE_SQSUM ulong
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#define TYPE_IMAGE_SQSUM float
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#endif
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#ifndef CN4
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#define CN4 1
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#else
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#define CN4 4
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#endif
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//////////////////////////////////////////////////
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// utilities
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#define SQSUMS_PTR(ox, oy) mad24(gidy + oy, img_sqsums_step, gidx + img_sqsums_offset + ox)
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#define SQSUMS_PTR(ox, oy) mad24(gidy + oy, img_sqsums_step, (gidx + img_sqsums_offset + ox) * CN4)
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#define SUMS_PTR(ox, oy) mad24(gidy + oy, img_sums_step, gidx + img_sums_offset + ox)
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// normAcc* are accurate normalization routines which make GPU matchTemplate
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// consistent with CPU one
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@ -95,7 +101,7 @@ float normAcc_SQDIFF(float num, float denum)
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__kernel
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void normalizeKernel_C1_D0
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(
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__global const TYPE_IMAGE_SQSUM * img_sqsums,
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__global const float * img_sqsums,
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__global float * res,
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ulong tpl_sqsum,
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int res_rows,
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@ -119,8 +125,8 @@ void normalizeKernel_C1_D0
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if(gidx < res_cols && gidy < res_rows)
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{
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float image_sqsum_ = (float)(
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(img_sqsums[SQSUMS_PTR(tpl_cols, tpl_rows)] - img_sqsums[SQSUMS_PTR(tpl_cols, 0)]) -
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(img_sqsums[SQSUMS_PTR(0, tpl_rows)] - img_sqsums[SQSUMS_PTR(0, 0)]));
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(img_sqsums[SQSUMS_PTR(tpl_cols, tpl_rows)] - img_sqsums[SQSUMS_PTR(tpl_cols, 0)]) -
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(img_sqsums[SQSUMS_PTR(0, tpl_rows)] - img_sqsums[SQSUMS_PTR(0, 0)]));
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res[res_idx] = normAcc(res[res_idx], sqrt(image_sqsum_ * tpl_sqsum));
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}
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}
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@ -152,8 +158,8 @@ void matchTemplate_Prepared_SQDIFF_C1_D0
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if(gidx < res_cols && gidy < res_rows)
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{
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float image_sqsum_ = (float)(
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(img_sqsums[SQSUMS_PTR(tpl_cols, tpl_rows)] - img_sqsums[SQSUMS_PTR(tpl_cols, 0)]) -
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(img_sqsums[SQSUMS_PTR(0, tpl_rows)] - img_sqsums[SQSUMS_PTR(0, 0)]));
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(img_sqsums[SQSUMS_PTR(tpl_cols, tpl_rows)] - img_sqsums[SQSUMS_PTR(tpl_cols, 0)]) -
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(img_sqsums[SQSUMS_PTR(0, tpl_rows)] - img_sqsums[SQSUMS_PTR(0, 0)]));
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res[res_idx] = image_sqsum_ - 2.f * res[res_idx] + tpl_sqsum;
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}
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}
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@ -161,7 +167,7 @@ void matchTemplate_Prepared_SQDIFF_C1_D0
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__kernel
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void matchTemplate_Prepared_SQDIFF_NORMED_C1_D0
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(
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__global const TYPE_IMAGE_SQSUM * img_sqsums,
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__global const float * img_sqsums,
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__global float * res,
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ulong tpl_sqsum,
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int res_rows,
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@ -185,10 +191,10 @@ void matchTemplate_Prepared_SQDIFF_NORMED_C1_D0
|
||||
if(gidx < res_cols && gidy < res_rows)
|
||||
{
|
||||
float image_sqsum_ = (float)(
|
||||
(img_sqsums[SQSUMS_PTR(tpl_cols, tpl_rows)] - img_sqsums[SQSUMS_PTR(tpl_cols, 0)]) -
|
||||
(img_sqsums[SQSUMS_PTR(0, tpl_rows)] - img_sqsums[SQSUMS_PTR(0, 0)]));
|
||||
(img_sqsums[SQSUMS_PTR(tpl_cols, tpl_rows)] - img_sqsums[SQSUMS_PTR(tpl_cols, 0)]) -
|
||||
(img_sqsums[SQSUMS_PTR(0, tpl_rows)] - img_sqsums[SQSUMS_PTR(0, 0)]));
|
||||
res[res_idx] = normAcc_SQDIFF(image_sqsum_ - 2.f * res[res_idx] + tpl_sqsum,
|
||||
sqrt(image_sqsum_ * tpl_sqsum));
|
||||
sqrt(image_sqsum_ * tpl_sqsum));
|
||||
}
|
||||
}
|
||||
|
||||
@ -628,8 +634,8 @@ void matchTemplate_Prepared_CCOFF_C1_D0
|
||||
if(gidx < res_cols && gidy < res_rows)
|
||||
{
|
||||
float sum = (float)(
|
||||
(img_sums[SUMS_PTR(tpl_cols, tpl_rows)] - img_sums[SUMS_PTR(tpl_cols, 0)])
|
||||
- (img_sums[SUMS_PTR(0, tpl_rows)] - img_sums[SUMS_PTR(0, 0)]));
|
||||
(img_sums[SUMS_PTR(tpl_cols, tpl_rows)] - img_sums[SUMS_PTR(tpl_cols, 0)])
|
||||
- (img_sums[SUMS_PTR(0, tpl_rows)] - img_sums[SUMS_PTR(0, 0)]));
|
||||
res[res_idx] -= sum * tpl_sum;
|
||||
}
|
||||
}
|
||||
@ -671,17 +677,17 @@ void matchTemplate_Prepared_CCOFF_C4_D0
|
||||
{
|
||||
float ccorr = res[res_idx];
|
||||
ccorr -= tpl_sum_c0*(float)(
|
||||
(img_sums_c0[SUMS_PTR(tpl_cols, tpl_rows)] - img_sums_c0[SUMS_PTR(tpl_cols, 0)])
|
||||
- (img_sums_c0[SUMS_PTR(0, tpl_rows)] - img_sums_c0[SUMS_PTR(0, 0)]));
|
||||
(img_sums_c0[SUMS_PTR(tpl_cols, tpl_rows)] - img_sums_c0[SUMS_PTR(tpl_cols, 0)])
|
||||
- (img_sums_c0[SUMS_PTR(0, tpl_rows)] - img_sums_c0[SUMS_PTR(0, 0)]));
|
||||
ccorr -= tpl_sum_c1*(float)(
|
||||
(img_sums_c1[SUMS_PTR(tpl_cols, tpl_rows)] - img_sums_c1[SUMS_PTR(tpl_cols, 0)])
|
||||
- (img_sums_c1[SUMS_PTR(0, tpl_rows)] - img_sums_c1[SUMS_PTR(0, 0)]));
|
||||
(img_sums_c1[SUMS_PTR(tpl_cols, tpl_rows)] - img_sums_c1[SUMS_PTR(tpl_cols, 0)])
|
||||
- (img_sums_c1[SUMS_PTR(0, tpl_rows)] - img_sums_c1[SUMS_PTR(0, 0)]));
|
||||
ccorr -= tpl_sum_c2*(float)(
|
||||
(img_sums_c2[SUMS_PTR(tpl_cols, tpl_rows)] - img_sums_c2[SUMS_PTR(tpl_cols, 0)])
|
||||
- (img_sums_c2[SUMS_PTR(0, tpl_rows)] - img_sums_c2[SUMS_PTR(0, 0)]));
|
||||
(img_sums_c2[SUMS_PTR(tpl_cols, tpl_rows)] - img_sums_c2[SUMS_PTR(tpl_cols, 0)])
|
||||
- (img_sums_c2[SUMS_PTR(0, tpl_rows)] - img_sums_c2[SUMS_PTR(0, 0)]));
|
||||
ccorr -= tpl_sum_c3*(float)(
|
||||
(img_sums_c3[SUMS_PTR(tpl_cols, tpl_rows)] - img_sums_c3[SUMS_PTR(tpl_cols, 0)])
|
||||
- (img_sums_c3[SUMS_PTR(0, tpl_rows)] - img_sums_c3[SUMS_PTR(0, 0)]));
|
||||
(img_sums_c3[SUMS_PTR(tpl_cols, tpl_rows)] - img_sums_c3[SUMS_PTR(tpl_cols, 0)])
|
||||
- (img_sums_c3[SUMS_PTR(0, tpl_rows)] - img_sums_c3[SUMS_PTR(0, 0)]));
|
||||
res[res_idx] = ccorr;
|
||||
}
|
||||
}
|
||||
@ -702,7 +708,7 @@ void matchTemplate_Prepared_CCOFF_NORMED_C1_D0
|
||||
__global const uint * img_sums,
|
||||
int img_sums_offset,
|
||||
int img_sums_step,
|
||||
__global const TYPE_IMAGE_SQSUM * img_sqsums,
|
||||
__global const float * img_sqsums,
|
||||
int img_sqsums_offset,
|
||||
int img_sqsums_step,
|
||||
float tpl_sum,
|
||||
@ -725,12 +731,12 @@ void matchTemplate_Prepared_CCOFF_NORMED_C1_D0
|
||||
if(gidx < res_cols && gidy < res_rows)
|
||||
{
|
||||
float image_sum_ = (float)(
|
||||
(img_sums[SUMS_PTR(tpl_cols, tpl_rows)] - img_sums[SUMS_PTR(tpl_cols, 0)])
|
||||
- (img_sums[SUMS_PTR(0, tpl_rows)] - img_sums[SUMS_PTR(0, 0)]));
|
||||
(img_sums[SUMS_PTR(tpl_cols, tpl_rows)] - img_sums[SUMS_PTR(tpl_cols, 0)])
|
||||
- (img_sums[SUMS_PTR(0, tpl_rows)] - img_sums[SUMS_PTR(0, 0)]));
|
||||
|
||||
float image_sqsum_ = (float)(
|
||||
(img_sqsums[SQSUMS_PTR(tpl_cols, tpl_rows)] - img_sqsums[SQSUMS_PTR(tpl_cols, 0)]) -
|
||||
(img_sqsums[SQSUMS_PTR(0, tpl_rows)] - img_sqsums[SQSUMS_PTR(0, 0)]));
|
||||
(img_sqsums[SQSUMS_PTR(tpl_cols, tpl_rows)] - img_sqsums[SQSUMS_PTR(tpl_cols, 0)]) -
|
||||
(img_sqsums[SQSUMS_PTR(0, tpl_rows)] - img_sqsums[SQSUMS_PTR(0, 0)]));
|
||||
res[res_idx] = normAcc(res[res_idx] - image_sum_ * tpl_sum,
|
||||
sqrt(tpl_sqsum * (image_sqsum_ - weight * image_sum_ * image_sum_)));
|
||||
}
|
||||
@ -754,10 +760,10 @@ void matchTemplate_Prepared_CCOFF_NORMED_C4_D0
|
||||
__global const uint * img_sums_c3,
|
||||
int img_sums_offset,
|
||||
int img_sums_step,
|
||||
__global const TYPE_IMAGE_SQSUM * img_sqsums_c0,
|
||||
__global const TYPE_IMAGE_SQSUM * img_sqsums_c1,
|
||||
__global const TYPE_IMAGE_SQSUM * img_sqsums_c2,
|
||||
__global const TYPE_IMAGE_SQSUM * img_sqsums_c3,
|
||||
__global const float * img_sqsums_c0,
|
||||
__global const float * img_sqsums_c1,
|
||||
__global const float * img_sqsums_c2,
|
||||
__global const float * img_sqsums_c3,
|
||||
int img_sqsums_offset,
|
||||
int img_sqsums_step,
|
||||
float tpl_sum_c0,
|
||||
@ -782,42 +788,71 @@ void matchTemplate_Prepared_CCOFF_NORMED_C4_D0
|
||||
if(gidx < res_cols && gidy < res_rows)
|
||||
{
|
||||
float image_sum_c0 = (float)(
|
||||
(img_sums_c0[SUMS_PTR(tpl_cols, tpl_rows)] - img_sums_c0[SUMS_PTR(tpl_cols, 0)])
|
||||
- (img_sums_c0[SUMS_PTR(0, tpl_rows)] - img_sums_c0[SUMS_PTR(0, 0)]));
|
||||
(img_sums_c0[SUMS_PTR(tpl_cols, tpl_rows)] - img_sums_c0[SUMS_PTR(tpl_cols, 0)])
|
||||
- (img_sums_c0[SUMS_PTR(0, tpl_rows)] - img_sums_c0[SUMS_PTR(0, 0)]));
|
||||
float image_sum_c1 = (float)(
|
||||
(img_sums_c1[SUMS_PTR(tpl_cols, tpl_rows)] - img_sums_c1[SUMS_PTR(tpl_cols, 0)])
|
||||
- (img_sums_c1[SUMS_PTR(0, tpl_rows)] - img_sums_c1[SUMS_PTR(0, 0)]));
|
||||
(img_sums_c1[SUMS_PTR(tpl_cols, tpl_rows)] - img_sums_c1[SUMS_PTR(tpl_cols, 0)])
|
||||
- (img_sums_c1[SUMS_PTR(0, tpl_rows)] - img_sums_c1[SUMS_PTR(0, 0)]));
|
||||
float image_sum_c2 = (float)(
|
||||
(img_sums_c2[SUMS_PTR(tpl_cols, tpl_rows)] - img_sums_c2[SUMS_PTR(tpl_cols, 0)])
|
||||
- (img_sums_c2[SUMS_PTR(0, tpl_rows)] - img_sums_c2[SUMS_PTR(0, 0)]));
|
||||
(img_sums_c2[SUMS_PTR(tpl_cols, tpl_rows)] - img_sums_c2[SUMS_PTR(tpl_cols, 0)])
|
||||
- (img_sums_c2[SUMS_PTR(0, tpl_rows)] - img_sums_c2[SUMS_PTR(0, 0)]));
|
||||
float image_sum_c3 = (float)(
|
||||
(img_sums_c3[SUMS_PTR(tpl_cols, tpl_rows)] - img_sums_c3[SUMS_PTR(tpl_cols, 0)])
|
||||
- (img_sums_c3[SUMS_PTR(0, tpl_rows)] - img_sums_c3[SUMS_PTR(0, 0)]));
|
||||
(img_sums_c3[SUMS_PTR(tpl_cols, tpl_rows)] - img_sums_c3[SUMS_PTR(tpl_cols, 0)])
|
||||
- (img_sums_c3[SUMS_PTR(0, tpl_rows)] - img_sums_c3[SUMS_PTR(0, 0)]));
|
||||
|
||||
float image_sqsum_c0 = (float)(
|
||||
(img_sqsums_c0[SQSUMS_PTR(tpl_cols, tpl_rows)] - img_sqsums_c0[SQSUMS_PTR(tpl_cols, 0)]) -
|
||||
(img_sqsums_c0[SQSUMS_PTR(0, tpl_rows)] - img_sqsums_c0[SQSUMS_PTR(0, 0)]));
|
||||
(img_sqsums_c0[SQSUMS_PTR(tpl_cols, tpl_rows)] - img_sqsums_c0[SQSUMS_PTR(tpl_cols, 0)]) -
|
||||
(img_sqsums_c0[SQSUMS_PTR(0, tpl_rows)] - img_sqsums_c0[SQSUMS_PTR(0, 0)]));
|
||||
float image_sqsum_c1 = (float)(
|
||||
(img_sqsums_c1[SQSUMS_PTR(tpl_cols, tpl_rows)] - img_sqsums_c1[SQSUMS_PTR(tpl_cols, 0)]) -
|
||||
(img_sqsums_c1[SQSUMS_PTR(0, tpl_rows)] - img_sqsums_c1[SQSUMS_PTR(0, 0)]));
|
||||
(img_sqsums_c1[SQSUMS_PTR(tpl_cols, tpl_rows)] - img_sqsums_c1[SQSUMS_PTR(tpl_cols, 0)]) -
|
||||
(img_sqsums_c1[SQSUMS_PTR(0, tpl_rows)] - img_sqsums_c1[SQSUMS_PTR(0, 0)]));
|
||||
float image_sqsum_c2 = (float)(
|
||||
(img_sqsums_c2[SQSUMS_PTR(tpl_cols, tpl_rows)] - img_sqsums_c2[SQSUMS_PTR(tpl_cols, 0)]) -
|
||||
(img_sqsums_c2[SQSUMS_PTR(0, tpl_rows)] - img_sqsums_c2[SQSUMS_PTR(0, 0)]));
|
||||
(img_sqsums_c2[SQSUMS_PTR(tpl_cols, tpl_rows)] - img_sqsums_c2[SQSUMS_PTR(tpl_cols, 0)]) -
|
||||
(img_sqsums_c2[SQSUMS_PTR(0, tpl_rows)] - img_sqsums_c2[SQSUMS_PTR(0, 0)]));
|
||||
float image_sqsum_c3 = (float)(
|
||||
(img_sqsums_c3[SQSUMS_PTR(tpl_cols, tpl_rows)] - img_sqsums_c3[SQSUMS_PTR(tpl_cols, 0)]) -
|
||||
(img_sqsums_c3[SQSUMS_PTR(0, tpl_rows)] - img_sqsums_c3[SQSUMS_PTR(0, 0)]));
|
||||
(img_sqsums_c3[SQSUMS_PTR(tpl_cols, tpl_rows)] - img_sqsums_c3[SQSUMS_PTR(tpl_cols, 0)]) -
|
||||
(img_sqsums_c3[SQSUMS_PTR(0, tpl_rows)] - img_sqsums_c3[SQSUMS_PTR(0, 0)]));
|
||||
|
||||
float num = res[res_idx] -
|
||||
image_sum_c0 * tpl_sum_c0 -
|
||||
image_sum_c1 * tpl_sum_c1 -
|
||||
image_sum_c2 * tpl_sum_c2 -
|
||||
image_sum_c3 * tpl_sum_c3;
|
||||
image_sum_c0 * tpl_sum_c0 -
|
||||
image_sum_c1 * tpl_sum_c1 -
|
||||
image_sum_c2 * tpl_sum_c2 -
|
||||
image_sum_c3 * tpl_sum_c3;
|
||||
float denum = sqrt( tpl_sqsum * (
|
||||
image_sqsum_c0 - weight * image_sum_c0 * image_sum_c0 +
|
||||
image_sqsum_c1 - weight * image_sum_c1 * image_sum_c1 +
|
||||
image_sqsum_c2 - weight * image_sum_c2 * image_sum_c2 +
|
||||
image_sqsum_c3 - weight * image_sum_c0 * image_sum_c3)
|
||||
);
|
||||
image_sqsum_c0 - weight * image_sum_c0 * image_sum_c0 +
|
||||
image_sqsum_c1 - weight * image_sum_c1 * image_sum_c1 +
|
||||
image_sqsum_c2 - weight * image_sum_c2 * image_sum_c2 +
|
||||
image_sqsum_c3 - weight * image_sum_c0 * image_sum_c3)
|
||||
);
|
||||
res[res_idx] = normAcc(num, denum);
|
||||
}
|
||||
}
|
||||
|
||||
//////////////////////////////////////////////////////////////////////
|
||||
// extractFirstChannel
|
||||
__kernel
|
||||
void extractFirstChannel
|
||||
(
|
||||
const __global float4* img,
|
||||
__global float* res,
|
||||
int rows,
|
||||
int cols,
|
||||
int img_offset,
|
||||
int res_offset,
|
||||
int img_step,
|
||||
int res_step
|
||||
)
|
||||
{
|
||||
img_step /= sizeof(float4);
|
||||
res_step /= sizeof(float);
|
||||
img_offset /= sizeof(float4);
|
||||
res_offset /= sizeof(float);
|
||||
img += img_offset;
|
||||
res += res_offset;
|
||||
int gidx = get_global_id(0);
|
||||
int gidy = get_global_id(1);
|
||||
if(gidx < cols && gidy < rows)
|
||||
{
|
||||
res[gidx + gidy * res_step] = img[gidx + gidy * img_step].x;
|
||||
}
|
||||
}
|
||||
|
||||
@ -75,7 +75,7 @@ PARAM_TEST_CASE(MatchTemplate8U, cv::Size, TemplateSize, Channels, TemplateMetho
|
||||
}
|
||||
};
|
||||
|
||||
TEST_P(MatchTemplate8U, DISABLED_Accuracy)
|
||||
TEST_P(MatchTemplate8U, Accuracy)
|
||||
{
|
||||
|
||||
std::cout << "Method: " << TEMPLATE_METHOD_NAMES[method] << std::endl;
|
||||
@ -138,18 +138,18 @@ TEST_P(MatchTemplate32F, Accuracy)
|
||||
EXPECT_MAT_NEAR(dst_gold, mat_dst, templ_size.area() * 1e-1, sss);
|
||||
}
|
||||
|
||||
INSTANTIATE_TEST_CASE_P(GPU_ImgProc, MatchTemplate8U,
|
||||
INSTANTIATE_TEST_CASE_P(OCL_ImgProc, MatchTemplate8U,
|
||||
testing::Combine(
|
||||
MTEMP_SIZES,
|
||||
testing::Values(TemplateSize(cv::Size(5, 5)), TemplateSize(cv::Size(16, 16))/*, TemplateSize(cv::Size(30, 30))*/),
|
||||
testing::Values(TemplateSize(cv::Size(5, 5)), TemplateSize(cv::Size(16, 16)), TemplateSize(cv::Size(30, 30))),
|
||||
testing::Values(Channels(1), Channels(3), Channels(4)),
|
||||
ALL_TEMPLATE_METHODS
|
||||
)
|
||||
);
|
||||
|
||||
INSTANTIATE_TEST_CASE_P(GPU_ImgProc, MatchTemplate32F, testing::Combine(
|
||||
INSTANTIATE_TEST_CASE_P(OCL_ImgProc, MatchTemplate32F, testing::Combine(
|
||||
MTEMP_SIZES,
|
||||
testing::Values(TemplateSize(cv::Size(5, 5)), TemplateSize(cv::Size(16, 16))/*, TemplateSize(cv::Size(30, 30))*/),
|
||||
testing::Values(TemplateSize(cv::Size(5, 5)), TemplateSize(cv::Size(16, 16)), TemplateSize(cv::Size(30, 30))),
|
||||
testing::Values(Channels(1), Channels(3), Channels(4)),
|
||||
testing::Values(TemplateMethod(cv::TM_SQDIFF), TemplateMethod(cv::TM_CCORR))));
|
||||
#endif
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user