/*M/////////////////////////////////////////////////////////////////////////////////////// // // IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. // // By downloading, copying, installing or using the software you agree to this license. // If you do not agree to this license, do not download, install, // copy or use the software. // // // Intel License Agreement // For Open Source Computer Vision Library // // Copyright (C) 2000, Intel Corporation, all rights reserved. // Third party copyrights are property of their respective owners. // // Redistribution and use in source and binary forms, with or without modification, // are permitted provided that the following conditions are met: // // * Redistribution's of source code must retain the above copyright notice, // this list of conditions and the following disclaimer. // // * Redistribution's in binary form must reproduce the above copyright notice, // this list of conditions and the following disclaimer in the documentation // and/or other materials provided with the distribution. // // * The name of Intel Corporation may not be used to endorse or promote products // derived from this software without specific prior written permission. // // This software is provided by the copyright holders and contributors "as is" and // any express or implied warranties, including, but not limited to, the implied // warranties of merchantability and fitness for a particular purpose are disclaimed. // In no event shall the Intel Corporation or contributors be liable for any direct, // indirect, incidental, special, exemplary, or consequential damages // (including, but not limited to, procurement of substitute goods or services; // loss of use, data, or profits; or business interruption) however caused // and on any theory of liability, whether in contract, strict liability, // or tort (including negligence or otherwise) arising in any way out of // the use of this software, even if advised of the possibility of such damage. // //M*/ #include "precomp.hpp" #include "opencl_kernels_imgproc.hpp" ////////////////////////////////////////////////// matchTemplate ////////////////////////////////////////////////////////// namespace cv { #ifdef HAVE_OPENCL /////////////////////////////////////////////////// CCORR ////////////////////////////////////////////////////////////// enum { SUM_1 = 0, SUM_2 = 1 }; static bool extractFirstChannel_32F(InputArray _image, OutputArray _result, int cn) { int depth = _image.depth(); ocl::Device dev = ocl::Device::getDefault(); int pxPerWIy = (dev.isIntel() && (dev.type() & ocl::Device::TYPE_GPU)) ? 4 : 1; ocl::Kernel k("extractFirstChannel", ocl::imgproc::match_template_oclsrc, format("-D FIRST_CHANNEL -D T1=%s -D cn=%d -D PIX_PER_WI_Y=%d", ocl::typeToStr(depth), cn, pxPerWIy)); if (k.empty()) return false; UMat image = _image.getUMat(); UMat result = _result.getUMat(); size_t globalsize[2] = {(size_t)result.cols, ((size_t)result.rows+pxPerWIy-1)/pxPerWIy}; return k.args(ocl::KernelArg::ReadOnlyNoSize(image), ocl::KernelArg::WriteOnly(result)).run( 2, globalsize, NULL, false); } static bool sumTemplate(InputArray _src, UMat & result) { int type = _src.type(), depth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type); int wdepth = CV_32F, wtype = CV_MAKE_TYPE(wdepth, cn); size_t wgs = ocl::Device::getDefault().maxWorkGroupSize(); int wgs2_aligned = 1; while (wgs2_aligned < (int)wgs) wgs2_aligned <<= 1; wgs2_aligned >>= 1; char cvt[40]; ocl::Kernel k("calcSum", ocl::imgproc::match_template_oclsrc, format("-D CALC_SUM -D T=%s -D T1=%s -D WT=%s -D cn=%d -D convertToWT=%s -D WGS=%d -D WGS2_ALIGNED=%d", ocl::typeToStr(type), ocl::typeToStr(depth), ocl::typeToStr(wtype), cn, ocl::convertTypeStr(depth, wdepth, cn, cvt), (int)wgs, wgs2_aligned)); if (k.empty()) return false; UMat src = _src.getUMat(); result.create(1, 1, CV_32FC1); ocl::KernelArg srcarg = ocl::KernelArg::ReadOnlyNoSize(src), resarg = ocl::KernelArg::PtrWriteOnly(result); k.args(srcarg, src.cols, (int)src.total(), resarg); size_t globalsize = wgs; return k.run(1, &globalsize, &wgs, false); } static bool useNaive(Size size) { int dft_size = 18; return size.height < dft_size && size.width < dft_size; } struct ConvolveBuf { Size result_size; Size block_size; Size user_block_size; Size dft_size; UMat image_spect, templ_spect, result_spect; UMat image_block, templ_block, result_data; void create(Size image_size, Size templ_size); }; void ConvolveBuf::create(Size image_size, Size templ_size) { result_size = Size(image_size.width - templ_size.width + 1, image_size.height - templ_size.height + 1); const double blockScale = 4.5; const int minBlockSize = 256; block_size.width = cvRound(templ_size.width*blockScale); block_size.width = std::max( block_size.width, minBlockSize - templ_size.width + 1 ); block_size.width = std::min( block_size.width, result_size.width ); block_size.height = cvRound(templ_size.height*blockScale); block_size.height = std::max( block_size.height, minBlockSize - templ_size.height + 1 ); block_size.height = std::min( block_size.height, result_size.height ); dft_size.width = std::max(getOptimalDFTSize(block_size.width + templ_size.width - 1), 2); dft_size.height = getOptimalDFTSize(block_size.height + templ_size.height - 1); if( dft_size.width <= 0 || dft_size.height <= 0 ) CV_Error( CV_StsOutOfRange, "the input arrays are too big" ); // recompute block size block_size.width = dft_size.width - templ_size.width + 1; block_size.width = std::min( block_size.width, result_size.width); block_size.height = dft_size.height - templ_size.height + 1; block_size.height = std::min( block_size.height, result_size.height ); image_block.create(dft_size, CV_32F); templ_block.create(dft_size, CV_32F); result_data.create(dft_size, CV_32F); image_spect.create(dft_size.height, dft_size.width / 2 + 1, CV_32FC2); templ_spect.create(dft_size.height, dft_size.width / 2 + 1, CV_32FC2); result_spect.create(dft_size.height, dft_size.width / 2 + 1, CV_32FC2); // Use maximum result matrix block size for the estimated DFT block size block_size.width = std::min(dft_size.width - templ_size.width + 1, result_size.width); block_size.height = std::min(dft_size.height - templ_size.height + 1, result_size.height); } static bool convolve_dft(InputArray _image, InputArray _templ, OutputArray _result) { ConvolveBuf buf; CV_Assert(_image.type() == CV_32F); CV_Assert(_templ.type() == CV_32F); buf.create(_image.size(), _templ.size()); _result.create(buf.result_size, CV_32F); UMat image = _image.getUMat(); UMat templ = _templ.getUMat(); UMat result = _result.getUMat(); Size& block_size = buf.block_size; Size& dft_size = buf.dft_size; UMat& image_block = buf.image_block; UMat& templ_block = buf.templ_block; UMat& result_data = buf.result_data; UMat& image_spect = buf.image_spect; UMat& templ_spect = buf.templ_spect; UMat& result_spect = buf.result_spect; UMat templ_roi = templ; copyMakeBorder(templ_roi, templ_block, 0, templ_block.rows - templ_roi.rows, 0, templ_block.cols - templ_roi.cols, BORDER_ISOLATED); dft(templ_block, templ_spect, 0, templ.rows); // Process all blocks of the result matrix for (int y = 0; y < result.rows; y += block_size.height) { for (int x = 0; x < result.cols; x += block_size.width) { Size image_roi_size(std::min(x + dft_size.width, image.cols) - x, std::min(y + dft_size.height, image.rows) - y); Rect roi0(x, y, image_roi_size.width, image_roi_size.height); UMat image_roi(image, roi0); copyMakeBorder(image_roi, image_block, 0, image_block.rows - image_roi.rows, 0, image_block.cols - image_roi.cols, BORDER_ISOLATED); dft(image_block, image_spect, 0); mulSpectrums(image_spect, templ_spect, result_spect, 0, true); dft(result_spect, result_data, cv::DFT_INVERSE | cv::DFT_REAL_OUTPUT | cv::DFT_SCALE); Size result_roi_size(std::min(x + block_size.width, result.cols) - x, std::min(y + block_size.height, result.rows) - y); Rect roi1(x, y, result_roi_size.width, result_roi_size.height); Rect roi2(0, 0, result_roi_size.width, result_roi_size.height); UMat result_roi(result, roi1); UMat result_block(result_data, roi2); result_block.copyTo(result_roi); } } return true; } static bool convolve_32F(InputArray _image, InputArray _templ, OutputArray _result) { _result.create(_image.rows() - _templ.rows() + 1, _image.cols() - _templ.cols() + 1, CV_32F); if (_image.channels() == 1) return(convolve_dft(_image, _templ, _result)); else { UMat image = _image.getUMat(); UMat templ = _templ.getUMat(); UMat result_(image.rows-templ.rows+1,(image.cols-templ.cols+1)*image.channels(), CV_32F); bool ok = convolve_dft(image.reshape(1), templ.reshape(1), result_); if (ok==false) return false; UMat result = _result.getUMat(); return (extractFirstChannel_32F(result_, _result, _image.channels())); } } static bool matchTemplateNaive_CCORR(InputArray _image, InputArray _templ, OutputArray _result) { int type = _image.type(), depth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type); int wdepth = CV_32F, wtype = CV_MAKE_TYPE(wdepth, cn); ocl::Device dev = ocl::Device::getDefault(); int pxPerWIx = (cn==1 && dev.isIntel() && (dev.type() & ocl::Device::TYPE_GPU)) ? 4 : 1; int rated_cn = cn; int wtype1 = wtype; if (pxPerWIx!=1) { rated_cn = pxPerWIx; type = CV_MAKE_TYPE(depth, rated_cn); wtype1 = CV_MAKE_TYPE(wdepth, rated_cn); } char cvt[40]; char cvt1[40]; const char* convertToWT1 = ocl::convertTypeStr(depth, wdepth, cn, cvt); const char* convertToWT = ocl::convertTypeStr(depth, wdepth, rated_cn, cvt1); ocl::Kernel k("matchTemplate_Naive_CCORR", ocl::imgproc::match_template_oclsrc, format("-D CCORR -D T=%s -D T1=%s -D WT=%s -D WT1=%s -D convertToWT=%s -D convertToWT1=%s -D cn=%d -D PIX_PER_WI_X=%d", ocl::typeToStr(type), ocl::typeToStr(depth), ocl::typeToStr(wtype1), ocl::typeToStr(wtype), convertToWT, convertToWT1, cn, pxPerWIx)); if (k.empty()) return false; UMat image = _image.getUMat(), templ = _templ.getUMat(); _result.create(image.rows - templ.rows + 1, image.cols - templ.cols + 1, CV_32FC1); UMat result = _result.getUMat(); k.args(ocl::KernelArg::ReadOnlyNoSize(image), ocl::KernelArg::ReadOnly(templ), ocl::KernelArg::WriteOnly(result)); size_t globalsize[2] = { ((size_t)result.cols+pxPerWIx-1)/pxPerWIx, (size_t)result.rows}; return k.run(2, globalsize, NULL, false); } static bool matchTemplate_CCORR(InputArray _image, InputArray _templ, OutputArray _result) { if (useNaive(_templ.size())) return( matchTemplateNaive_CCORR(_image, _templ, _result)); else { if(_image.depth() == CV_8U) { UMat imagef, templf; UMat image = _image.getUMat(); UMat templ = _templ.getUMat(); image.convertTo(imagef, CV_32F); templ.convertTo(templf, CV_32F); return(convolve_32F(imagef, templf, _result)); } else { return(convolve_32F(_image, _templ, _result)); } } } static bool matchTemplate_CCORR_NORMED(InputArray _image, InputArray _templ, OutputArray _result) { matchTemplate(_image, _templ, _result, CV_TM_CCORR); int type = _image.type(), cn = CV_MAT_CN(type); ocl::Kernel k("matchTemplate_CCORR_NORMED", ocl::imgproc::match_template_oclsrc, format("-D CCORR_NORMED -D T=%s -D cn=%d", ocl::typeToStr(type), cn)); if (k.empty()) return false; UMat image = _image.getUMat(), templ = _templ.getUMat(); _result.create(image.rows - templ.rows + 1, image.cols - templ.cols + 1, CV_32FC1); UMat result = _result.getUMat(); UMat image_sums, image_sqsums; integral(image.reshape(1), image_sums, image_sqsums, CV_32F, CV_32F); UMat templ_sqsum; if (!sumTemplate(templ, templ_sqsum)) return false; k.args(ocl::KernelArg::ReadOnlyNoSize(image_sqsums), ocl::KernelArg::ReadWrite(result), templ.rows, templ.cols, ocl::KernelArg::PtrReadOnly(templ_sqsum)); size_t globalsize[2] = { (size_t)result.cols, (size_t)result.rows }; return k.run(2, globalsize, NULL, false); } ////////////////////////////////////// SQDIFF ////////////////////////////////////////////////////////////// static bool matchTemplateNaive_SQDIFF(InputArray _image, InputArray _templ, OutputArray _result) { int type = _image.type(), depth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type); int wdepth = CV_32F, wtype = CV_MAKE_TYPE(wdepth, cn); char cvt[40]; ocl::Kernel k("matchTemplate_Naive_SQDIFF", ocl::imgproc::match_template_oclsrc, format("-D SQDIFF -D T=%s -D T1=%s -D WT=%s -D convertToWT=%s -D cn=%d", ocl::typeToStr(type), ocl::typeToStr(depth), ocl::typeToStr(wtype), ocl::convertTypeStr(depth, wdepth, cn, cvt), cn)); if (k.empty()) return false; UMat image = _image.getUMat(), templ = _templ.getUMat(); _result.create(image.rows - templ.rows + 1, image.cols - templ.cols + 1, CV_32F); UMat result = _result.getUMat(); k.args(ocl::KernelArg::ReadOnlyNoSize(image), ocl::KernelArg::ReadOnly(templ), ocl::KernelArg::WriteOnly(result)); size_t globalsize[2] = { (size_t)result.cols, (size_t)result.rows }; return k.run(2, globalsize, NULL, false); } static bool matchTemplate_SQDIFF(InputArray _image, InputArray _templ, OutputArray _result) { if (useNaive(_templ.size())) return( matchTemplateNaive_SQDIFF(_image, _templ, _result)); else { matchTemplate(_image, _templ, _result, CV_TM_CCORR); int type = _image.type(), cn = CV_MAT_CN(type); ocl::Kernel k("matchTemplate_Prepared_SQDIFF", ocl::imgproc::match_template_oclsrc, format("-D SQDIFF_PREPARED -D T=%s -D cn=%d", ocl::typeToStr(type), cn)); if (k.empty()) return false; UMat image = _image.getUMat(), templ = _templ.getUMat(); _result.create(image.rows - templ.rows + 1, image.cols - templ.cols + 1, CV_32F); UMat result = _result.getUMat(); UMat image_sums, image_sqsums; integral(image.reshape(1), image_sums, image_sqsums, CV_32F, CV_32F); UMat templ_sqsum; if (!sumTemplate(_templ, templ_sqsum)) return false; k.args(ocl::KernelArg::ReadOnlyNoSize(image_sqsums), ocl::KernelArg::ReadWrite(result), templ.rows, templ.cols, ocl::KernelArg::PtrReadOnly(templ_sqsum)); size_t globalsize[2] = { (size_t)result.cols, (size_t)result.rows }; return k.run(2, globalsize, NULL, false); } } static bool matchTemplate_SQDIFF_NORMED(InputArray _image, InputArray _templ, OutputArray _result) { matchTemplate(_image, _templ, _result, CV_TM_CCORR); int type = _image.type(), cn = CV_MAT_CN(type); ocl::Kernel k("matchTemplate_SQDIFF_NORMED", ocl::imgproc::match_template_oclsrc, format("-D SQDIFF_NORMED -D T=%s -D cn=%d", ocl::typeToStr(type), cn)); if (k.empty()) return false; UMat image = _image.getUMat(), templ = _templ.getUMat(); _result.create(image.rows - templ.rows + 1, image.cols - templ.cols + 1, CV_32F); UMat result = _result.getUMat(); UMat image_sums, image_sqsums; integral(image.reshape(1), image_sums, image_sqsums, CV_32F, CV_32F); UMat templ_sqsum; if (!sumTemplate(_templ, templ_sqsum)) return false; k.args(ocl::KernelArg::ReadOnlyNoSize(image_sqsums), ocl::KernelArg::ReadWrite(result), templ.rows, templ.cols, ocl::KernelArg::PtrReadOnly(templ_sqsum)); size_t globalsize[2] = { (size_t)result.cols, (size_t)result.rows }; return k.run(2, globalsize, NULL, false); } ///////////////////////////////////// CCOEFF ///////////////////////////////////////////////////////////////// static bool matchTemplate_CCOEFF(InputArray _image, InputArray _templ, OutputArray _result) { matchTemplate(_image, _templ, _result, CV_TM_CCORR); UMat image_sums, temp; integral(_image, image_sums, CV_32F); int type = image_sums.type(), depth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type); ocl::Kernel k("matchTemplate_Prepared_CCOEFF", ocl::imgproc::match_template_oclsrc, format("-D CCOEFF -D T=%s -D T1=%s -D cn=%d", ocl::typeToStr(type), ocl::typeToStr(depth), cn)); if (k.empty()) return false; UMat templ = _templ.getUMat(); UMat result = _result.getUMat(); if (cn==1) { Scalar templMean = mean(templ); float templ_sum = (float)templMean[0]; k.args(ocl::KernelArg::ReadOnlyNoSize(image_sums), ocl::KernelArg::ReadWrite(result), templ.rows, templ.cols, templ_sum); } else { Vec4f templ_sum = Vec4f::all(0); templ_sum = (Vec4f)mean(templ); k.args(ocl::KernelArg::ReadOnlyNoSize(image_sums), ocl::KernelArg::ReadWrite(result), templ.rows, templ.cols, templ_sum); } size_t globalsize[2] = { (size_t)result.cols, (size_t)result.rows }; return k.run(2, globalsize, NULL, false); } static bool matchTemplate_CCOEFF_NORMED(InputArray _image, InputArray _templ, OutputArray _result) { matchTemplate(_image, _templ, _result, CV_TM_CCORR); UMat temp, image_sums, image_sqsums; integral(_image, image_sums, image_sqsums, CV_32F, CV_32F); int type = image_sums.type(), depth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type); CV_Assert(cn >= 1 && cn <= 4); ocl::Kernel k("matchTemplate_CCOEFF_NORMED", ocl::imgproc::match_template_oclsrc, format("-D CCOEFF_NORMED -D T=%s -D T1=%s -D cn=%d", ocl::typeToStr(type), ocl::typeToStr(depth), cn)); if (k.empty()) return false; UMat templ = _templ.getUMat(); Size size = _image.size(), tsize = templ.size(); _result.create(size.height - templ.rows + 1, size.width - templ.cols + 1, CV_32F); UMat result = _result.getUMat(); float scale = 1.f / tsize.area(); if (cn == 1) { float templ_sum = (float)sum(templ)[0]; multiply(templ, templ, temp, 1, CV_32F); float templ_sqsum = (float)sum(temp)[0]; templ_sqsum -= scale * templ_sum * templ_sum; templ_sum *= scale; if (templ_sqsum < DBL_EPSILON) { result = Scalar::all(1); return true; } k.args(ocl::KernelArg::ReadOnlyNoSize(image_sums), ocl::KernelArg::ReadOnlyNoSize(image_sqsums), ocl::KernelArg::ReadWrite(result), templ.rows, templ.cols, scale, templ_sum, templ_sqsum); } else { Vec4f templ_sum = Vec4f::all(0), templ_sqsum = Vec4f::all(0); templ_sum = sum(templ); multiply(templ, templ, temp, 1, CV_32F); templ_sqsum = sum(temp); float templ_sqsum_sum = 0; for (int i = 0; i < cn; i ++) templ_sqsum_sum += templ_sqsum[i] - scale * templ_sum[i] * templ_sum[i]; templ_sum *= scale; if (templ_sqsum_sum < DBL_EPSILON) { result = Scalar::all(1); return true; } k.args(ocl::KernelArg::ReadOnlyNoSize(image_sums), ocl::KernelArg::ReadOnlyNoSize(image_sqsums), ocl::KernelArg::ReadWrite(result), templ.rows, templ.cols, scale, templ_sum, templ_sqsum_sum); } size_t globalsize[2] = { (size_t)result.cols, (size_t)result.rows }; return k.run(2, globalsize, NULL, false); } /////////////////////////////////////////////////////////////////////////////////////////////////////////// static bool ocl_matchTemplate( InputArray _img, InputArray _templ, OutputArray _result, int method) { int cn = _img.channels(); if (cn > 4) return false; typedef bool (*Caller)(InputArray _img, InputArray _templ, OutputArray _result); static const Caller callers[] = { matchTemplate_SQDIFF, matchTemplate_SQDIFF_NORMED, matchTemplate_CCORR, matchTemplate_CCORR_NORMED, matchTemplate_CCOEFF, matchTemplate_CCOEFF_NORMED }; const Caller caller = callers[method]; return caller(_img, _templ, _result); } #endif #include "opencv2/core/hal/hal.hpp" void crossCorr( const Mat& img, const Mat& _templ, Mat& corr, Point anchor, double delta, int borderType ) { const double blockScale = 4.5; const int minBlockSize = 256; std::vector buf; Mat templ = _templ; int depth = img.depth(), cn = img.channels(); int tdepth = templ.depth(), tcn = templ.channels(); int cdepth = corr.depth(), ccn = corr.channels(); CV_Assert( img.dims <= 2 && templ.dims <= 2 && corr.dims <= 2 ); if( depth != tdepth && tdepth != std::max(CV_32F, depth) ) { _templ.convertTo(templ, std::max(CV_32F, depth)); tdepth = templ.depth(); } CV_Assert( depth == tdepth || tdepth == CV_32F); CV_Assert( corr.rows <= img.rows + templ.rows - 1 && corr.cols <= img.cols + templ.cols - 1 ); CV_Assert( ccn == 1 || delta == 0 ); int maxDepth = depth > CV_8S ? CV_64F : std::max(std::max(CV_32F, tdepth), cdepth); Size blocksize, dftsize; blocksize.width = cvRound(templ.cols*blockScale); blocksize.width = std::max( blocksize.width, minBlockSize - templ.cols + 1 ); blocksize.width = std::min( blocksize.width, corr.cols ); blocksize.height = cvRound(templ.rows*blockScale); blocksize.height = std::max( blocksize.height, minBlockSize - templ.rows + 1 ); blocksize.height = std::min( blocksize.height, corr.rows ); dftsize.width = std::max(getOptimalDFTSize(blocksize.width + templ.cols - 1), 2); dftsize.height = getOptimalDFTSize(blocksize.height + templ.rows - 1); if( dftsize.width <= 0 || dftsize.height <= 0 ) CV_Error( CV_StsOutOfRange, "the input arrays are too big" ); // recompute block size blocksize.width = dftsize.width - templ.cols + 1; blocksize.width = MIN( blocksize.width, corr.cols ); blocksize.height = dftsize.height - templ.rows + 1; blocksize.height = MIN( blocksize.height, corr.rows ); Mat dftTempl( dftsize.height*tcn, dftsize.width, maxDepth ); Mat dftImg( dftsize, maxDepth ); int i, k, bufSize = 0; if( tcn > 1 && tdepth != maxDepth ) bufSize = templ.cols*templ.rows*CV_ELEM_SIZE(tdepth); if( cn > 1 && depth != maxDepth ) bufSize = std::max( bufSize, (blocksize.width + templ.cols - 1)* (blocksize.height + templ.rows - 1)*CV_ELEM_SIZE(depth)); if( (ccn > 1 || cn > 1) && cdepth != maxDepth ) bufSize = std::max( bufSize, blocksize.width*blocksize.height*CV_ELEM_SIZE(cdepth)); buf.resize(bufSize); Ptr c = hal::DFT2D::create(dftsize.width, dftsize.height, dftTempl.depth(), 1, 1, CV_HAL_DFT_IS_INPLACE, templ.rows); // compute DFT of each template plane for( k = 0; k < tcn; k++ ) { int yofs = k*dftsize.height; Mat src = templ; Mat dst(dftTempl, Rect(0, yofs, dftsize.width, dftsize.height)); Mat dst1(dftTempl, Rect(0, yofs, templ.cols, templ.rows)); if( tcn > 1 ) { src = tdepth == maxDepth ? dst1 : Mat(templ.size(), tdepth, &buf[0]); int pairs[] = {k, 0}; mixChannels(&templ, 1, &src, 1, pairs, 1); } if( dst1.data != src.data ) src.convertTo(dst1, dst1.depth()); if( dst.cols > templ.cols ) { Mat part(dst, Range(0, templ.rows), Range(templ.cols, dst.cols)); part = Scalar::all(0); } c->apply(dst.data, (int)dst.step, dst.data, (int)dst.step); } int tileCountX = (corr.cols + blocksize.width - 1)/blocksize.width; int tileCountY = (corr.rows + blocksize.height - 1)/blocksize.height; int tileCount = tileCountX * tileCountY; Size wholeSize = img.size(); Point roiofs(0,0); Mat img0 = img; if( !(borderType & BORDER_ISOLATED) ) { img.locateROI(wholeSize, roiofs); img0.adjustROI(roiofs.y, wholeSize.height-img.rows-roiofs.y, roiofs.x, wholeSize.width-img.cols-roiofs.x); } borderType |= BORDER_ISOLATED; Ptr cF, cR; int f = CV_HAL_DFT_IS_INPLACE; int f_inv = f | CV_HAL_DFT_INVERSE | CV_HAL_DFT_SCALE; cF = hal::DFT2D::create(dftsize.width, dftsize.height, maxDepth, 1, 1, f, blocksize.height + templ.rows - 1); cR = hal::DFT2D::create(dftsize.width, dftsize.height, maxDepth, 1, 1, f_inv, blocksize.height); // calculate correlation by blocks for( i = 0; i < tileCount; i++ ) { int x = (i%tileCountX)*blocksize.width; int y = (i/tileCountX)*blocksize.height; Size bsz(std::min(blocksize.width, corr.cols - x), std::min(blocksize.height, corr.rows - y)); Size dsz(bsz.width + templ.cols - 1, bsz.height + templ.rows - 1); int x0 = x - anchor.x + roiofs.x, y0 = y - anchor.y + roiofs.y; int x1 = std::max(0, x0), y1 = std::max(0, y0); int x2 = std::min(img0.cols, x0 + dsz.width); int y2 = std::min(img0.rows, y0 + dsz.height); Mat src0(img0, Range(y1, y2), Range(x1, x2)); Mat dst(dftImg, Rect(0, 0, dsz.width, dsz.height)); Mat dst1(dftImg, Rect(x1-x0, y1-y0, x2-x1, y2-y1)); Mat cdst(corr, Rect(x, y, bsz.width, bsz.height)); for( k = 0; k < cn; k++ ) { Mat src = src0; dftImg = Scalar::all(0); if( cn > 1 ) { src = depth == maxDepth ? dst1 : Mat(y2-y1, x2-x1, depth, &buf[0]); int pairs[] = {k, 0}; mixChannels(&src0, 1, &src, 1, pairs, 1); } if( dst1.data != src.data ) src.convertTo(dst1, dst1.depth()); if( x2 - x1 < dsz.width || y2 - y1 < dsz.height ) copyMakeBorder(dst1, dst, y1-y0, dst.rows-dst1.rows-(y1-y0), x1-x0, dst.cols-dst1.cols-(x1-x0), borderType); if (bsz.height == blocksize.height) cF->apply(dftImg.data, (int)dftImg.step, dftImg.data, (int)dftImg.step); else dft( dftImg, dftImg, 0, dsz.height ); Mat dftTempl1(dftTempl, Rect(0, tcn > 1 ? k*dftsize.height : 0, dftsize.width, dftsize.height)); mulSpectrums(dftImg, dftTempl1, dftImg, 0, true); if (bsz.height == blocksize.height) cR->apply(dftImg.data, (int)dftImg.step, dftImg.data, (int)dftImg.step); else dft( dftImg, dftImg, DFT_INVERSE + DFT_SCALE, bsz.height ); src = dftImg(Rect(0, 0, bsz.width, bsz.height)); if( ccn > 1 ) { if( cdepth != maxDepth ) { Mat plane(bsz, cdepth, &buf[0]); src.convertTo(plane, cdepth, 1, delta); src = plane; } int pairs[] = {0, k}; mixChannels(&src, 1, &cdst, 1, pairs, 1); } else { if( k == 0 ) src.convertTo(cdst, cdepth, 1, delta); else { if( maxDepth != cdepth ) { Mat plane(bsz, cdepth, &buf[0]); src.convertTo(plane, cdepth); src = plane; } add(src, cdst, cdst); } } } } } static void matchTemplateMask( InputArray _img, InputArray _templ, OutputArray _result, int method, InputArray _mask ) { int type = _img.type(), depth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type); CV_Assert( CV_TM_SQDIFF <= method && method <= CV_TM_CCOEFF_NORMED ); CV_Assert( (depth == CV_8U || depth == CV_32F) && type == _templ.type() && _img.dims() <= 2 ); Mat img = _img.getMat(), templ = _templ.getMat(), mask = _mask.getMat(); int ttype = templ.type(), tdepth = CV_MAT_DEPTH(ttype), tcn = CV_MAT_CN(ttype); int mtype = img.type(), mdepth = CV_MAT_DEPTH(type), mcn = CV_MAT_CN(mtype); if (depth == CV_8U) { depth = CV_32F; type = CV_MAKETYPE(CV_32F, cn); img.convertTo(img, type, 1.0 / 255); } if (tdepth == CV_8U) { tdepth = CV_32F; ttype = CV_MAKETYPE(CV_32F, tcn); templ.convertTo(templ, ttype, 1.0 / 255); } if (mdepth == CV_8U) { mdepth = CV_32F; mtype = CV_MAKETYPE(CV_32F, mcn); compare(mask, Scalar::all(0), mask, CMP_NE); mask.convertTo(mask, mtype, 1.0 / 255); } Size corrSize(img.cols - templ.cols + 1, img.rows - templ.rows + 1); _result.create(corrSize, CV_32F); Mat result = _result.getMat(); Mat img2 = img.mul(img); Mat mask2 = mask.mul(mask); Mat mask_templ = templ.mul(mask); Scalar templMean, templSdv; double templSum2 = 0; meanStdDev( mask_templ, templMean, templSdv ); templSum2 = templSdv[0]*templSdv[0] + templSdv[1]*templSdv[1] + templSdv[2]*templSdv[2] + templSdv[3]*templSdv[3]; templSum2 += templMean[0]*templMean[0] + templMean[1]*templMean[1] + templMean[2]*templMean[2] + templMean[3]*templMean[3]; templSum2 *= ((double)templ.rows * templ.cols); if (method == CV_TM_SQDIFF) { Mat mask2_templ = templ.mul(mask2); Mat corr(corrSize, CV_32F); crossCorr( img, mask2_templ, corr, Point(0,0), 0, 0 ); crossCorr( img2, mask, result, Point(0,0), 0, 0 ); result -= corr * 2; result += templSum2; } else if (method == CV_TM_CCORR_NORMED) { if (templSum2 < DBL_EPSILON) { result = Scalar::all(1); return; } Mat corr(corrSize, CV_32F); crossCorr( img2, mask2, corr, Point(0,0), 0, 0 ); crossCorr( img, mask_templ, result, Point(0,0), 0, 0 ); sqrt(corr, corr); result = result.mul(1/corr); result /= std::sqrt(templSum2); } else CV_Error(Error::StsNotImplemented, ""); } static void common_matchTemplate( Mat& img, Mat& templ, Mat& result, int method, int cn ) { if( method == CV_TM_CCORR ) return; int numType = method == CV_TM_CCORR || method == CV_TM_CCORR_NORMED ? 0 : method == CV_TM_CCOEFF || method == CV_TM_CCOEFF_NORMED ? 1 : 2; bool isNormed = method == CV_TM_CCORR_NORMED || method == CV_TM_SQDIFF_NORMED || method == CV_TM_CCOEFF_NORMED; double invArea = 1./((double)templ.rows * templ.cols); Mat sum, sqsum; Scalar templMean, templSdv; double *q0 = 0, *q1 = 0, *q2 = 0, *q3 = 0; double templNorm = 0, templSum2 = 0; if( method == CV_TM_CCOEFF ) { integral(img, sum, CV_64F); templMean = mean(templ); } else { integral(img, sum, sqsum, CV_64F); meanStdDev( templ, templMean, templSdv ); templNorm = templSdv[0]*templSdv[0] + templSdv[1]*templSdv[1] + templSdv[2]*templSdv[2] + templSdv[3]*templSdv[3]; if( templNorm < DBL_EPSILON && method == CV_TM_CCOEFF_NORMED ) { result = Scalar::all(1); return; } templSum2 = templNorm + templMean[0]*templMean[0] + templMean[1]*templMean[1] + templMean[2]*templMean[2] + templMean[3]*templMean[3]; if( numType != 1 ) { templMean = Scalar::all(0); templNorm = templSum2; } templSum2 /= invArea; templNorm = std::sqrt(templNorm); templNorm /= std::sqrt(invArea); // care of accuracy here CV_Assert(sqsum.data != NULL); q0 = (double*)sqsum.data; q1 = q0 + templ.cols*cn; q2 = (double*)(sqsum.data + templ.rows*sqsum.step); q3 = q2 + templ.cols*cn; } CV_Assert(sum.data != NULL); double* p0 = (double*)sum.data; double* p1 = p0 + templ.cols*cn; double* p2 = (double*)(sum.data + templ.rows*sum.step); double* p3 = p2 + templ.cols*cn; int sumstep = sum.data ? (int)(sum.step / sizeof(double)) : 0; int sqstep = sqsum.data ? (int)(sqsum.step / sizeof(double)) : 0; int i, j, k; for( i = 0; i < result.rows; i++ ) { float* rrow = result.ptr(i); int idx = i * sumstep; int idx2 = i * sqstep; for( j = 0; j < result.cols; j++, idx += cn, idx2 += cn ) { double num = rrow[j], t; double wndMean2 = 0, wndSum2 = 0; if( numType == 1 ) { for( k = 0; k < cn; k++ ) { t = p0[idx+k] - p1[idx+k] - p2[idx+k] + p3[idx+k]; wndMean2 += t*t; num -= t*templMean[k]; } wndMean2 *= invArea; } if( isNormed || numType == 2 ) { for( k = 0; k < cn; k++ ) { t = q0[idx2+k] - q1[idx2+k] - q2[idx2+k] + q3[idx2+k]; wndSum2 += t; } if( numType == 2 ) { num = wndSum2 - 2*num + templSum2; num = MAX(num, 0.); } } if( isNormed ) { double diff2 = MAX(wndSum2 - wndMean2, 0); if (diff2 <= std::min(0.5, 10 * FLT_EPSILON * wndSum2)) t = 0; // avoid rounding errors else t = std::sqrt(diff2)*templNorm; if( fabs(num) < t ) num /= t; else if( fabs(num) < t*1.125 ) num = num > 0 ? 1 : -1; else num = method != CV_TM_SQDIFF_NORMED ? 0 : 1; } rrow[j] = (float)num; } } } } #if defined HAVE_IPP namespace cv { typedef IppStatus (CV_STDCALL * ippimatchTemplate)(const void*, int, IppiSize, const void*, int, IppiSize, Ipp32f* , int , IppEnum , Ipp8u*); static bool ipp_crossCorr(const Mat& src, const Mat& tpl, Mat& dst, bool normed) { CV_INSTRUMENT_REGION_IPP(); IppStatus status; IppiSize srcRoiSize = {src.cols,src.rows}; IppiSize tplRoiSize = {tpl.cols,tpl.rows}; IppAutoBuffer buffer; int bufSize=0; int depth = src.depth(); ippimatchTemplate ippiCrossCorrNorm = depth==CV_8U ? (ippimatchTemplate)ippiCrossCorrNorm_8u32f_C1R: depth==CV_32F? (ippimatchTemplate)ippiCrossCorrNorm_32f_C1R: 0; if (ippiCrossCorrNorm==0) return false; IppEnum funCfg = (IppEnum)(ippAlgAuto | ippiROIValid); if(normed) funCfg |= ippiNorm; else funCfg |= ippiNormNone; status = ippiCrossCorrNormGetBufferSize(srcRoiSize, tplRoiSize, funCfg, &bufSize); if ( status < 0 ) return false; buffer.allocate( bufSize ); status = CV_INSTRUMENT_FUN_IPP(ippiCrossCorrNorm, src.ptr(), (int)src.step, srcRoiSize, tpl.ptr(), (int)tpl.step, tplRoiSize, dst.ptr(), (int)dst.step, funCfg, buffer); return status >= 0; } static bool ipp_sqrDistance(const Mat& src, const Mat& tpl, Mat& dst) { CV_INSTRUMENT_REGION_IPP(); IppStatus status; IppiSize srcRoiSize = {src.cols,src.rows}; IppiSize tplRoiSize = {tpl.cols,tpl.rows}; IppAutoBuffer buffer; int bufSize=0; int depth = src.depth(); ippimatchTemplate ippiSqrDistanceNorm = depth==CV_8U ? (ippimatchTemplate)ippiSqrDistanceNorm_8u32f_C1R: depth==CV_32F? (ippimatchTemplate)ippiSqrDistanceNorm_32f_C1R: 0; if (ippiSqrDistanceNorm==0) return false; IppEnum funCfg = (IppEnum)(ippAlgAuto | ippiROIValid | ippiNormNone); status = ippiSqrDistanceNormGetBufferSize(srcRoiSize, tplRoiSize, funCfg, &bufSize); if ( status < 0 ) return false; buffer.allocate( bufSize ); status = CV_INSTRUMENT_FUN_IPP(ippiSqrDistanceNorm, src.ptr(), (int)src.step, srcRoiSize, tpl.ptr(), (int)tpl.step, tplRoiSize, dst.ptr(), (int)dst.step, funCfg, buffer); return status >= 0; } static bool ipp_matchTemplate( Mat& img, Mat& templ, Mat& result, int method) { CV_INSTRUMENT_REGION_IPP(); if(img.channels() != 1) return false; // These functions are not efficient if template size is comparable with image size if(templ.size().area()*4 > img.size().area()) return false; if(method == CV_TM_SQDIFF) { if(ipp_sqrDistance(img, templ, result)) return true; } else if(method == CV_TM_SQDIFF_NORMED) { if(ipp_crossCorr(img, templ, result, false)) { common_matchTemplate(img, templ, result, CV_TM_SQDIFF_NORMED, 1); return true; } } else if(method == CV_TM_CCORR) { if(ipp_crossCorr(img, templ, result, false)) return true; } else if(method == CV_TM_CCORR_NORMED) { if(ipp_crossCorr(img, templ, result, true)) return true; } else if(method == CV_TM_CCOEFF || method == CV_TM_CCOEFF_NORMED) { if(ipp_crossCorr(img, templ, result, false)) { common_matchTemplate(img, templ, result, method, 1); return true; } } return false; } } #endif //////////////////////////////////////////////////////////////////////////////////////////////////////// void cv::matchTemplate( InputArray _img, InputArray _templ, OutputArray _result, int method, InputArray _mask ) { CV_INSTRUMENT_REGION(); if (!_mask.empty()) { cv::matchTemplateMask(_img, _templ, _result, method, _mask); return; } int type = _img.type(), depth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type); CV_Assert( CV_TM_SQDIFF <= method && method <= CV_TM_CCOEFF_NORMED ); CV_Assert( (depth == CV_8U || depth == CV_32F) && type == _templ.type() && _img.dims() <= 2 ); bool needswap = _img.size().height < _templ.size().height || _img.size().width < _templ.size().width; if (needswap) { CV_Assert(_img.size().height <= _templ.size().height && _img.size().width <= _templ.size().width); } CV_OCL_RUN(_img.dims() <= 2 && _result.isUMat(), (!needswap ? ocl_matchTemplate(_img, _templ, _result, method) : ocl_matchTemplate(_templ, _img, _result, method))) Mat img = _img.getMat(), templ = _templ.getMat(); if (needswap) std::swap(img, templ); Size corrSize(img.cols - templ.cols + 1, img.rows - templ.rows + 1); _result.create(corrSize, CV_32F); Mat result = _result.getMat(); #ifdef HAVE_TEGRA_OPTIMIZATION if (tegra::useTegra() && tegra::matchTemplate(img, templ, result, method)) return; #endif CV_IPP_RUN_FAST(ipp_matchTemplate(img, templ, result, method)) crossCorr( img, templ, result, Point(0,0), 0, 0); common_matchTemplate(img, templ, result, method, cn); } CV_IMPL void cvMatchTemplate( const CvArr* _img, const CvArr* _templ, CvArr* _result, int method ) { cv::Mat img = cv::cvarrToMat(_img), templ = cv::cvarrToMat(_templ), result = cv::cvarrToMat(_result); CV_Assert( result.size() == cv::Size(std::abs(img.cols - templ.cols) + 1, std::abs(img.rows - templ.rows) + 1) && result.type() == CV_32F ); matchTemplate(img, templ, result, method); } /* End of file. */