1793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler/*M/////////////////////////////////////////////////////////////////////////////////////// 2793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler// 3793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. 4793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler// 5793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler// By downloading, copying, installing or using the software you agree to this license. 6793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler// If you do not agree to this license, do not download, install, 7793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler// copy or use the software. 8793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler// 9793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler// 10793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler// Intel License Agreement 11793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler// For Open Source Computer Vision Library 12793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler// 13793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler// Copyright (C) 2000, Intel Corporation, all rights reserved. 14793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler// Third party copyrights are property of their respective owners. 15793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler// 16793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler// Redistribution and use in source and binary forms, with or without modification, 17793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler// are permitted provided that the following conditions are met: 18793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler// 19793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler// * Redistribution's of source code must retain the above copyright notice, 20793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler// this list of conditions and the following disclaimer. 21793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler// 22793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler// * Redistribution's in binary form must reproduce the above copyright notice, 23793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler// this list of conditions and the following disclaimer in the documentation 24793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler// and/or other materials provided with the distribution. 25793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler// 26793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler// * The name of Intel Corporation may not be used to endorse or promote products 27793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler// derived from this software without specific prior written permission. 28793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler// 29793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler// This software is provided by the copyright holders and contributors "as is" and 30793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler// any express or implied warranties, including, but not limited to, the implied 31793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler// warranties of merchantability and fitness for a particular purpose are disclaimed. 32793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler// In no event shall the Intel Corporation or contributors be liable for any direct, 33793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler// indirect, incidental, special, exemplary, or consequential damages 34793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler// (including, but not limited to, procurement of substitute goods or services; 35793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler// loss of use, data, or profits; or business interruption) however caused 36793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler// and on any theory of liability, whether in contract, strict liability, 37793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler// or tort (including negligence or otherwise) arising in any way out of 38793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler// the use of this software, even if advised of the possibility of such damage. 39793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler// 40793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler//M*/ 41793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 42793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler#include "precomp.hpp" 43793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler#include "opencl_kernels_imgproc.hpp" 44793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 45793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler////////////////////////////////////////////////// matchTemplate ////////////////////////////////////////////////////////// 46793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 47793ee12c6df9cad3806238d32528c49a3ff9331dNoah Preslernamespace cv 48793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler{ 49793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 50793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler#ifdef HAVE_OPENCL 51793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 52793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler/////////////////////////////////////////////////// CCORR ////////////////////////////////////////////////////////////// 53793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 54793ee12c6df9cad3806238d32528c49a3ff9331dNoah Preslerenum 55793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler{ 56793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler SUM_1 = 0, SUM_2 = 1 57793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler}; 58793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 59793ee12c6df9cad3806238d32528c49a3ff9331dNoah Preslerstatic bool extractFirstChannel_32F(InputArray _image, OutputArray _result, int cn) 60793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler{ 61793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler int depth = _image.depth(); 62793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 63793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler ocl::Device dev = ocl::Device::getDefault(); 64793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler int pxPerWIy = (dev.isIntel() && (dev.type() & ocl::Device::TYPE_GPU)) ? 4 : 1; 65793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 66793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 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", 67793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler ocl::typeToStr(depth), cn, pxPerWIy)); 68793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler if (k.empty()) 69793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler return false; 70793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 71793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler UMat image = _image.getUMat(); 72793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler UMat result = _result.getUMat(); 73793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 74793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 75793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler size_t globalsize[2] = {result.cols, (result.rows+pxPerWIy-1)/pxPerWIy}; 76793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler return k.args(ocl::KernelArg::ReadOnlyNoSize(image), ocl::KernelArg::WriteOnly(result)).run( 2, globalsize, NULL, false); 77793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler} 78793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 79793ee12c6df9cad3806238d32528c49a3ff9331dNoah Preslerstatic bool sumTemplate(InputArray _src, UMat & result) 80793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler{ 81793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler int type = _src.type(), depth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type); 82793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler int wdepth = CV_32F, wtype = CV_MAKE_TYPE(wdepth, cn); 83793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler size_t wgs = ocl::Device::getDefault().maxWorkGroupSize(); 84793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 85793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler int wgs2_aligned = 1; 86793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler while (wgs2_aligned < (int)wgs) 87793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler wgs2_aligned <<= 1; 88793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler wgs2_aligned >>= 1; 89793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 90793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler char cvt[40]; 91793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler ocl::Kernel k("calcSum", ocl::imgproc::match_template_oclsrc, 92793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 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", 93793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler ocl::typeToStr(type), ocl::typeToStr(depth), ocl::typeToStr(wtype), cn, 94793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler ocl::convertTypeStr(depth, wdepth, cn, cvt), 95793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler (int)wgs, wgs2_aligned)); 96793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler if (k.empty()) 97793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler return false; 98793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 99793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler UMat src = _src.getUMat(); 100793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler result.create(1, 1, CV_32FC1); 101793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 102793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler ocl::KernelArg srcarg = ocl::KernelArg::ReadOnlyNoSize(src), 103793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler resarg = ocl::KernelArg::PtrWriteOnly(result); 104793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 105793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler k.args(srcarg, src.cols, (int)src.total(), resarg); 106793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 107793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler size_t globalsize = wgs; 108793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler return k.run(1, &globalsize, &wgs, false); 109793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler} 110793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 111793ee12c6df9cad3806238d32528c49a3ff9331dNoah Preslerstatic bool useNaive(Size size) 112793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler{ 113793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler int dft_size = 18; 114793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler return size.height < dft_size && size.width < dft_size; 115793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler} 116793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 117793ee12c6df9cad3806238d32528c49a3ff9331dNoah Preslerstruct ConvolveBuf 118793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler{ 119793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler Size result_size; 120793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler Size block_size; 121793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler Size user_block_size; 122793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler Size dft_size; 123793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 124793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler UMat image_spect, templ_spect, result_spect; 125793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler UMat image_block, templ_block, result_data; 126793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 127793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler void create(Size image_size, Size templ_size); 128793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler}; 129793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 130793ee12c6df9cad3806238d32528c49a3ff9331dNoah Preslervoid ConvolveBuf::create(Size image_size, Size templ_size) 131793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler{ 132793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler result_size = Size(image_size.width - templ_size.width + 1, 133793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler image_size.height - templ_size.height + 1); 134793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 135793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler const double blockScale = 4.5; 136793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler const int minBlockSize = 256; 137793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 138793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler block_size.width = cvRound(result_size.width*blockScale); 139793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler block_size.width = std::max( block_size.width, minBlockSize - templ_size.width + 1 ); 140793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler block_size.width = std::min( block_size.width, result_size.width ); 141793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler block_size.height = cvRound(templ_size.height*blockScale); 142793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler block_size.height = std::max( block_size.height, minBlockSize - templ_size.height + 1 ); 143793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler block_size.height = std::min( block_size.height, result_size.height ); 144793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 145793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler dft_size.width = std::max(getOptimalDFTSize(block_size.width + templ_size.width - 1), 2); 146793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler dft_size.height = getOptimalDFTSize(block_size.height + templ_size.height - 1); 147793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler if( dft_size.width <= 0 || dft_size.height <= 0 ) 148793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler CV_Error( CV_StsOutOfRange, "the input arrays are too big" ); 149793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 150793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler // recompute block size 151793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler block_size.width = dft_size.width - templ_size.width + 1; 152793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler block_size.width = std::min( block_size.width, result_size.width); 153793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler block_size.height = dft_size.height - templ_size.height + 1; 154793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler block_size.height = std::min( block_size.height, result_size.height ); 155793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 156793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler image_block.create(dft_size, CV_32F); 157793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler templ_block.create(dft_size, CV_32F); 158793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler result_data.create(dft_size, CV_32F); 159793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 160793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler image_spect.create(dft_size.height, dft_size.width / 2 + 1, CV_32FC2); 161793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler templ_spect.create(dft_size.height, dft_size.width / 2 + 1, CV_32FC2); 162793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler result_spect.create(dft_size.height, dft_size.width / 2 + 1, CV_32FC2); 163793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 164793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler // Use maximum result matrix block size for the estimated DFT block size 165793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler block_size.width = std::min(dft_size.width - templ_size.width + 1, result_size.width); 166793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler block_size.height = std::min(dft_size.height - templ_size.height + 1, result_size.height); 167793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler} 168793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 169793ee12c6df9cad3806238d32528c49a3ff9331dNoah Preslerstatic bool convolve_dft(InputArray _image, InputArray _templ, OutputArray _result) 170793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler{ 171793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler ConvolveBuf buf; 172793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler CV_Assert(_image.type() == CV_32F); 173793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler CV_Assert(_templ.type() == CV_32F); 174793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 175793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler buf.create(_image.size(), _templ.size()); 176793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler _result.create(buf.result_size, CV_32F); 177793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 178793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler UMat image = _image.getUMat(); 179793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler UMat templ = _templ.getUMat(); 180793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 181793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler UMat result = _result.getUMat(); 182793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 183793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler Size& block_size = buf.block_size; 184793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler Size& dft_size = buf.dft_size; 185793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 186793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler UMat& image_block = buf.image_block; 187793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler UMat& templ_block = buf.templ_block; 188793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler UMat& result_data = buf.result_data; 189793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 190793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler UMat& image_spect = buf.image_spect; 191793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler UMat& templ_spect = buf.templ_spect; 192793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler UMat& result_spect = buf.result_spect; 193793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 194793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler UMat templ_roi = templ; 195793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler copyMakeBorder(templ_roi, templ_block, 0, templ_block.rows - templ_roi.rows, 0, 196793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler templ_block.cols - templ_roi.cols, BORDER_ISOLATED); 197793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 198793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler dft(templ_block, templ_spect, 0, templ.rows); 199793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 200793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler // Process all blocks of the result matrix 201793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler for (int y = 0; y < result.rows; y += block_size.height) 202793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler { 203793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler for (int x = 0; x < result.cols; x += block_size.width) 204793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler { 205793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler Size image_roi_size(std::min(x + dft_size.width, image.cols) - x, 206793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler std::min(y + dft_size.height, image.rows) - y); 207793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler Rect roi0(x, y, image_roi_size.width, image_roi_size.height); 208793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 209793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler UMat image_roi(image, roi0); 210793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 211793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler copyMakeBorder(image_roi, image_block, 0, image_block.rows - image_roi.rows, 212793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 0, image_block.cols - image_roi.cols, BORDER_ISOLATED); 213793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 214793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler dft(image_block, image_spect, 0); 215793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 216793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler mulSpectrums(image_spect, templ_spect, result_spect, 0, true); 217793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 218793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler dft(result_spect, result_data, cv::DFT_INVERSE | cv::DFT_REAL_OUTPUT | cv::DFT_SCALE); 219793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 220793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler Size result_roi_size(std::min(x + block_size.width, result.cols) - x, 221793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler std::min(y + block_size.height, result.rows) - y); 222793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 223793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler Rect roi1(x, y, result_roi_size.width, result_roi_size.height); 224793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler Rect roi2(0, 0, result_roi_size.width, result_roi_size.height); 225793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 226793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler UMat result_roi(result, roi1); 227793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler UMat result_block(result_data, roi2); 228793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 229793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler result_block.copyTo(result_roi); 230793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler } 231793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler } 232793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler return true; 233793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler} 234793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 235793ee12c6df9cad3806238d32528c49a3ff9331dNoah Preslerstatic bool convolve_32F(InputArray _image, InputArray _templ, OutputArray _result) 236793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler{ 237793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler _result.create(_image.rows() - _templ.rows() + 1, _image.cols() - _templ.cols() + 1, CV_32F); 238793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 239793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler if (_image.channels() == 1) 240793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler return(convolve_dft(_image, _templ, _result)); 241793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler else 242793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler { 243793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler UMat image = _image.getUMat(); 244793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler UMat templ = _templ.getUMat(); 245793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler UMat result_(image.rows-templ.rows+1,(image.cols-templ.cols+1)*image.channels(), CV_32F); 246793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler bool ok = convolve_dft(image.reshape(1), templ.reshape(1), result_); 247793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler if (ok==false) 248793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler return false; 249793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler UMat result = _result.getUMat(); 250793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler return (extractFirstChannel_32F(result_, _result, _image.channels())); 251793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler } 252793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler} 253793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 254793ee12c6df9cad3806238d32528c49a3ff9331dNoah Preslerstatic bool matchTemplateNaive_CCORR(InputArray _image, InputArray _templ, OutputArray _result) 255793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler{ 256793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler int type = _image.type(), depth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type); 257793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler int wdepth = CV_32F, wtype = CV_MAKE_TYPE(wdepth, cn); 258793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 259793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler ocl::Device dev = ocl::Device::getDefault(); 260793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler int pxPerWIx = (cn==1 && dev.isIntel() && (dev.type() & ocl::Device::TYPE_GPU)) ? 4 : 1; 261793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler int rated_cn = cn; 262793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler int wtype1 = wtype; 263793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 264793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler if (pxPerWIx!=1) 265793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler { 266793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler rated_cn = pxPerWIx; 267793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler type = CV_MAKE_TYPE(depth, rated_cn); 268793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler wtype1 = CV_MAKE_TYPE(wdepth, rated_cn); 269793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler } 270793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 271793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler char cvt[40]; 272793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler char cvt1[40]; 273793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler const char* convertToWT1 = ocl::convertTypeStr(depth, wdepth, cn, cvt); 274793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler const char* convertToWT = ocl::convertTypeStr(depth, wdepth, rated_cn, cvt1); 275793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 276793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler ocl::Kernel k("matchTemplate_Naive_CCORR", ocl::imgproc::match_template_oclsrc, 277793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 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), 278793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler convertToWT, convertToWT1, cn, pxPerWIx)); 279793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler if (k.empty()) 280793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler return false; 281793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 282793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler UMat image = _image.getUMat(), templ = _templ.getUMat(); 283793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler _result.create(image.rows - templ.rows + 1, image.cols - templ.cols + 1, CV_32FC1); 284793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler UMat result = _result.getUMat(); 285793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 286793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler k.args(ocl::KernelArg::ReadOnlyNoSize(image), ocl::KernelArg::ReadOnly(templ), 287793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler ocl::KernelArg::WriteOnly(result)); 288793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 289793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler size_t globalsize[2] = { (result.cols+pxPerWIx-1)/pxPerWIx, result.rows}; 290793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler return k.run(2, globalsize, NULL, false); 291793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler} 292793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 293793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 294793ee12c6df9cad3806238d32528c49a3ff9331dNoah Preslerstatic bool matchTemplate_CCORR(InputArray _image, InputArray _templ, OutputArray _result) 295793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler{ 296793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler if (useNaive(_templ.size())) 297793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler return( matchTemplateNaive_CCORR(_image, _templ, _result)); 298793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler else 299793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler { 300793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler if(_image.depth() == CV_8U) 301793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler { 302793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler UMat imagef, templf; 303793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler UMat image = _image.getUMat(); 304793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler UMat templ = _templ.getUMat(); 305793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler image.convertTo(imagef, CV_32F); 306793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler templ.convertTo(templf, CV_32F); 307793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler return(convolve_32F(imagef, templf, _result)); 308793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler } 309793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler else 310793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler { 311793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler return(convolve_32F(_image, _templ, _result)); 312793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler } 313793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler } 314793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler} 315793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 316793ee12c6df9cad3806238d32528c49a3ff9331dNoah Preslerstatic bool matchTemplate_CCORR_NORMED(InputArray _image, InputArray _templ, OutputArray _result) 317793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler{ 318793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler matchTemplate(_image, _templ, _result, CV_TM_CCORR); 319793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 320793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler int type = _image.type(), cn = CV_MAT_CN(type); 321793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 322793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler ocl::Kernel k("matchTemplate_CCORR_NORMED", ocl::imgproc::match_template_oclsrc, 323793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler format("-D CCORR_NORMED -D T=%s -D cn=%d", ocl::typeToStr(type), cn)); 324793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler if (k.empty()) 325793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler return false; 326793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 327793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler UMat image = _image.getUMat(), templ = _templ.getUMat(); 328793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler _result.create(image.rows - templ.rows + 1, image.cols - templ.cols + 1, CV_32FC1); 329793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler UMat result = _result.getUMat(); 330793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 331793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler UMat image_sums, image_sqsums; 332793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler integral(image.reshape(1), image_sums, image_sqsums, CV_32F, CV_32F); 333793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 334793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler UMat templ_sqsum; 335793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler if (!sumTemplate(templ, templ_sqsum)) 336793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler return false; 337793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 338793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler k.args(ocl::KernelArg::ReadOnlyNoSize(image_sqsums), ocl::KernelArg::ReadWrite(result), 339793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler templ.rows, templ.cols, ocl::KernelArg::PtrReadOnly(templ_sqsum)); 340793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 341793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler size_t globalsize[2] = { result.cols, result.rows }; 342793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler return k.run(2, globalsize, NULL, false); 343793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler} 344793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 345793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler////////////////////////////////////// SQDIFF ////////////////////////////////////////////////////////////// 346793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 347793ee12c6df9cad3806238d32528c49a3ff9331dNoah Preslerstatic bool matchTemplateNaive_SQDIFF(InputArray _image, InputArray _templ, OutputArray _result) 348793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler{ 349793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler int type = _image.type(), depth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type); 350793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler int wdepth = CV_32F, wtype = CV_MAKE_TYPE(wdepth, cn); 351793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 352793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler char cvt[40]; 353793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler ocl::Kernel k("matchTemplate_Naive_SQDIFF", ocl::imgproc::match_template_oclsrc, 354793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler format("-D SQDIFF -D T=%s -D T1=%s -D WT=%s -D convertToWT=%s -D cn=%d", ocl::typeToStr(type), ocl::typeToStr(depth), 355793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler ocl::typeToStr(wtype), ocl::convertTypeStr(depth, wdepth, cn, cvt), cn)); 356793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler if (k.empty()) 357793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler return false; 358793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 359793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler UMat image = _image.getUMat(), templ = _templ.getUMat(); 360793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler _result.create(image.rows - templ.rows + 1, image.cols - templ.cols + 1, CV_32F); 361793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler UMat result = _result.getUMat(); 362793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 363793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler k.args(ocl::KernelArg::ReadOnlyNoSize(image), ocl::KernelArg::ReadOnly(templ), 364793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler ocl::KernelArg::WriteOnly(result)); 365793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 366793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler size_t globalsize[2] = { result.cols, result.rows }; 367793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler return k.run(2, globalsize, NULL, false); 368793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler} 369793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 370793ee12c6df9cad3806238d32528c49a3ff9331dNoah Preslerstatic bool matchTemplate_SQDIFF(InputArray _image, InputArray _templ, OutputArray _result) 371793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler{ 372793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler if (useNaive(_templ.size())) 373793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler return( matchTemplateNaive_SQDIFF(_image, _templ, _result)); 374793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler else 375793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler { 376793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler matchTemplate(_image, _templ, _result, CV_TM_CCORR); 377793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 378793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler int type = _image.type(), cn = CV_MAT_CN(type); 379793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 380793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler ocl::Kernel k("matchTemplate_Prepared_SQDIFF", ocl::imgproc::match_template_oclsrc, 381793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler format("-D SQDIFF_PREPARED -D T=%s -D cn=%d", ocl::typeToStr(type), cn)); 382793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler if (k.empty()) 383793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler return false; 384793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 385793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler UMat image = _image.getUMat(), templ = _templ.getUMat(); 386793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler _result.create(image.rows - templ.rows + 1, image.cols - templ.cols + 1, CV_32F); 387793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler UMat result = _result.getUMat(); 388793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 389793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler UMat image_sums, image_sqsums; 390793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler integral(image.reshape(1), image_sums, image_sqsums, CV_32F, CV_32F); 391793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 392793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler UMat templ_sqsum; 393793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler if (!sumTemplate(_templ, templ_sqsum)) 394793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler return false; 395793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 396793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler k.args(ocl::KernelArg::ReadOnlyNoSize(image_sqsums), ocl::KernelArg::ReadWrite(result), 397793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler templ.rows, templ.cols, ocl::KernelArg::PtrReadOnly(templ_sqsum)); 398793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 399793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler size_t globalsize[2] = { result.cols, result.rows }; 400793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 401793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler return k.run(2, globalsize, NULL, false); 402793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler } 403793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler} 404793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 405793ee12c6df9cad3806238d32528c49a3ff9331dNoah Preslerstatic bool matchTemplate_SQDIFF_NORMED(InputArray _image, InputArray _templ, OutputArray _result) 406793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler{ 407793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler matchTemplate(_image, _templ, _result, CV_TM_CCORR); 408793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 409793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler int type = _image.type(), cn = CV_MAT_CN(type); 410793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 411793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler ocl::Kernel k("matchTemplate_SQDIFF_NORMED", ocl::imgproc::match_template_oclsrc, 412793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler format("-D SQDIFF_NORMED -D T=%s -D cn=%d", ocl::typeToStr(type), cn)); 413793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler if (k.empty()) 414793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler return false; 415793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 416793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler UMat image = _image.getUMat(), templ = _templ.getUMat(); 417793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler _result.create(image.rows - templ.rows + 1, image.cols - templ.cols + 1, CV_32F); 418793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler UMat result = _result.getUMat(); 419793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 420793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler UMat image_sums, image_sqsums; 421793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler integral(image.reshape(1), image_sums, image_sqsums, CV_32F, CV_32F); 422793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 423793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler UMat templ_sqsum; 424793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler if (!sumTemplate(_templ, templ_sqsum)) 425793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler return false; 426793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 427793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler k.args(ocl::KernelArg::ReadOnlyNoSize(image_sqsums), ocl::KernelArg::ReadWrite(result), 428793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler templ.rows, templ.cols, ocl::KernelArg::PtrReadOnly(templ_sqsum)); 429793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 430793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler size_t globalsize[2] = { result.cols, result.rows }; 431793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 432793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler return k.run(2, globalsize, NULL, false); 433793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler} 434793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 435793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler///////////////////////////////////// CCOEFF ///////////////////////////////////////////////////////////////// 436793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 437793ee12c6df9cad3806238d32528c49a3ff9331dNoah Preslerstatic bool matchTemplate_CCOEFF(InputArray _image, InputArray _templ, OutputArray _result) 438793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler{ 439793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler matchTemplate(_image, _templ, _result, CV_TM_CCORR); 440793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 441793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler UMat image_sums, temp; 442793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler integral(_image, image_sums, CV_32F); 443793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 444793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler int type = image_sums.type(), depth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type); 445793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 446793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler ocl::Kernel k("matchTemplate_Prepared_CCOEFF", ocl::imgproc::match_template_oclsrc, 447793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler format("-D CCOEFF -D T=%s -D T1=%s -D cn=%d", ocl::typeToStr(type), ocl::typeToStr(depth), cn)); 448793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler if (k.empty()) 449793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler return false; 450793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 451793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler UMat templ = _templ.getUMat(); 452793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler UMat result = _result.getUMat(); 453793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 454793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler if (cn==1) 455793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler { 456793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler Scalar templMean = mean(templ); 457793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler float templ_sum = (float)templMean[0]; 458793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 459793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler k.args(ocl::KernelArg::ReadOnlyNoSize(image_sums), ocl::KernelArg::ReadWrite(result), templ.rows, templ.cols, templ_sum); 460793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler } 461793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler else 462793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler { 463793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler Vec4f templ_sum = Vec4f::all(0); 464793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler templ_sum = (Vec4f)mean(templ); 465793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 466793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler k.args(ocl::KernelArg::ReadOnlyNoSize(image_sums), ocl::KernelArg::ReadWrite(result), templ.rows, templ.cols, templ_sum); } 467793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 468793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler size_t globalsize[2] = { result.cols, result.rows }; 469793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler return k.run(2, globalsize, NULL, false); 470793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler} 471793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 472793ee12c6df9cad3806238d32528c49a3ff9331dNoah Preslerstatic bool matchTemplate_CCOEFF_NORMED(InputArray _image, InputArray _templ, OutputArray _result) 473793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler{ 474793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler matchTemplate(_image, _templ, _result, CV_TM_CCORR); 475793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 476793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler UMat temp, image_sums, image_sqsums; 477793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler integral(_image, image_sums, image_sqsums, CV_32F, CV_32F); 478793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 479793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler int type = image_sums.type(), depth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type); 480793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 481793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler ocl::Kernel k("matchTemplate_CCOEFF_NORMED", ocl::imgproc::match_template_oclsrc, 482793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler format("-D CCOEFF_NORMED -D T=%s -D T1=%s -D cn=%d", ocl::typeToStr(type), ocl::typeToStr(depth), cn)); 483793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler if (k.empty()) 484793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler return false; 485793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 486793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler UMat templ = _templ.getUMat(); 487793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler Size size = _image.size(), tsize = templ.size(); 488793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler _result.create(size.height - templ.rows + 1, size.width - templ.cols + 1, CV_32F); 489793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler UMat result = _result.getUMat(); 490793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 491793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler float scale = 1.f / tsize.area(); 492793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 493793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler if (cn == 1) 494793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler { 495793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler float templ_sum = (float)sum(templ)[0]; 496793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 497793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler multiply(templ, templ, temp, 1, CV_32F); 498793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler float templ_sqsum = (float)sum(temp)[0]; 499793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 500793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler templ_sqsum -= scale * templ_sum * templ_sum; 501793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler templ_sum *= scale; 502793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 503793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler if (templ_sqsum < DBL_EPSILON) 504793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler { 505793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler result = Scalar::all(1); 506793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler return true; 507793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler } 508793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 509793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler k.args(ocl::KernelArg::ReadOnlyNoSize(image_sums), ocl::KernelArg::ReadOnlyNoSize(image_sqsums), 510793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler ocl::KernelArg::ReadWrite(result), templ.rows, templ.cols, scale, templ_sum, templ_sqsum); 511793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler } 512793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler else 513793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler { 514793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler Vec4f templ_sum = Vec4f::all(0), templ_sqsum = Vec4f::all(0); 515793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler templ_sum = sum(templ); 516793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 517793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler multiply(templ, templ, temp, 1, CV_32F); 518793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler templ_sqsum = sum(temp); 519793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 520793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler float templ_sqsum_sum = 0; 521793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler for (int i = 0; i < cn; i ++) 522793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler templ_sqsum_sum += templ_sqsum[i] - scale * templ_sum[i] * templ_sum[i]; 523793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 524793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler templ_sum *= scale; 525793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 526793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler if (templ_sqsum_sum < DBL_EPSILON) 527793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler { 528793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler result = Scalar::all(1); 529793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler return true; 530793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler } 531793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 532793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler k.args(ocl::KernelArg::ReadOnlyNoSize(image_sums), ocl::KernelArg::ReadOnlyNoSize(image_sqsums), 533793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler ocl::KernelArg::ReadWrite(result), templ.rows, templ.cols, scale, 534793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler templ_sum, templ_sqsum_sum); } 535793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 536793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler size_t globalsize[2] = { result.cols, result.rows }; 537793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler return k.run(2, globalsize, NULL, false); 538793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler} 539793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 540793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler/////////////////////////////////////////////////////////////////////////////////////////////////////////// 541793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 542793ee12c6df9cad3806238d32528c49a3ff9331dNoah Preslerstatic bool ocl_matchTemplate( InputArray _img, InputArray _templ, OutputArray _result, int method) 543793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler{ 544793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler int cn = _img.channels(); 545793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 546793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler if (cn > 4) 547793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler return false; 548793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 549793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler typedef bool (*Caller)(InputArray _img, InputArray _templ, OutputArray _result); 550793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 551793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler static const Caller callers[] = 552793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler { 553793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler matchTemplate_SQDIFF, matchTemplate_SQDIFF_NORMED, matchTemplate_CCORR, 554793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler matchTemplate_CCORR_NORMED, matchTemplate_CCOEFF, matchTemplate_CCOEFF_NORMED 555793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler }; 556793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler const Caller caller = callers[method]; 557793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 558793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler return caller(_img, _templ, _result); 559793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler} 560793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 561793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler#endif 562793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 563793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler#if defined HAVE_IPP 564793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 565793ee12c6df9cad3806238d32528c49a3ff9331dNoah Preslertypedef IppStatus (CV_STDCALL * ippimatchTemplate)(const void*, int, IppiSize, const void*, int, IppiSize, Ipp32f* , int , IppEnum , Ipp8u*); 566793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 567793ee12c6df9cad3806238d32528c49a3ff9331dNoah Preslerstatic bool ipp_crossCorr(const Mat& src, const Mat& tpl, Mat& dst) 568793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler{ 569793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler IppStatus status; 570793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 571793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler IppiSize srcRoiSize = {src.cols,src.rows}; 572793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler IppiSize tplRoiSize = {tpl.cols,tpl.rows}; 573793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 574793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler Ipp8u *pBuffer; 575793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler int bufSize=0; 576793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 577793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler int depth = src.depth(); 578793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 579793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler ippimatchTemplate ippFunc = 580793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler depth==CV_8U ? (ippimatchTemplate)ippiCrossCorrNorm_8u32f_C1R: 581793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler depth==CV_32F? (ippimatchTemplate)ippiCrossCorrNorm_32f_C1R: 0; 582793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 583793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler if (ippFunc==0) 584793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler return false; 585793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 586793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler IppEnum funCfg = (IppEnum)(ippAlgAuto | ippiNormNone | ippiROIValid); 587793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 588793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler status = ippiCrossCorrNormGetBufferSize(srcRoiSize, tplRoiSize, funCfg, &bufSize); 589793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler if ( status < 0 ) 590793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler return false; 591793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 592793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler pBuffer = ippsMalloc_8u( bufSize ); 593793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 594793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler status = ippFunc(src.ptr(), (int)src.step, srcRoiSize, tpl.ptr(), (int)tpl.step, tplRoiSize, dst.ptr<Ipp32f>(), (int)dst.step, funCfg, pBuffer); 595793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 596793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler ippsFree( pBuffer ); 597793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler return status >= 0; 598793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler} 599793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 600793ee12c6df9cad3806238d32528c49a3ff9331dNoah Preslerstatic bool ipp_sqrDistance(const Mat& src, const Mat& tpl, Mat& dst) 601793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler{ 602793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler IppStatus status; 603793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 604793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler IppiSize srcRoiSize = {src.cols,src.rows}; 605793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler IppiSize tplRoiSize = {tpl.cols,tpl.rows}; 606793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 607793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler Ipp8u *pBuffer; 608793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler int bufSize=0; 609793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 610793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler int depth = src.depth(); 611793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 612793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler ippimatchTemplate ippFunc = 613793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler depth==CV_8U ? (ippimatchTemplate)ippiSqrDistanceNorm_8u32f_C1R: 614793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler depth==CV_32F? (ippimatchTemplate)ippiSqrDistanceNorm_32f_C1R: 0; 615793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 616793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler if (ippFunc==0) 617793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler return false; 618793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 619793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler IppEnum funCfg = (IppEnum)(ippAlgAuto | ippiNormNone | ippiROIValid); 620793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 621793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler status = ippiSqrDistanceNormGetBufferSize(srcRoiSize, tplRoiSize, funCfg, &bufSize); 622793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler if ( status < 0 ) 623793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler return false; 624793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 625793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler pBuffer = ippsMalloc_8u( bufSize ); 626793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 627793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler status = ippFunc(src.ptr(), (int)src.step, srcRoiSize, tpl.ptr(), (int)tpl.step, tplRoiSize, dst.ptr<Ipp32f>(), (int)dst.step, funCfg, pBuffer); 628793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 629793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler ippsFree( pBuffer ); 630793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler return status >= 0; 631793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler} 632793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 633793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler#endif 634793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 635793ee12c6df9cad3806238d32528c49a3ff9331dNoah Preslervoid crossCorr( const Mat& img, const Mat& _templ, Mat& corr, 636793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler Size corrsize, int ctype, 637793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler Point anchor, double delta, int borderType ) 638793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler{ 639793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler const double blockScale = 4.5; 640793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler const int minBlockSize = 256; 641793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler std::vector<uchar> buf; 642793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 643793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler Mat templ = _templ; 644793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler int depth = img.depth(), cn = img.channels(); 645793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler int tdepth = templ.depth(), tcn = templ.channels(); 646793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler int cdepth = CV_MAT_DEPTH(ctype), ccn = CV_MAT_CN(ctype); 647793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 648793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler CV_Assert( img.dims <= 2 && templ.dims <= 2 && corr.dims <= 2 ); 649793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 650793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler if( depth != tdepth && tdepth != std::max(CV_32F, depth) ) 651793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler { 652793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler _templ.convertTo(templ, std::max(CV_32F, depth)); 653793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler tdepth = templ.depth(); 654793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler } 655793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 656793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler CV_Assert( depth == tdepth || tdepth == CV_32F); 657793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler CV_Assert( corrsize.height <= img.rows + templ.rows - 1 && 658793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler corrsize.width <= img.cols + templ.cols - 1 ); 659793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 660793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler CV_Assert( ccn == 1 || delta == 0 ); 661793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 662793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler corr.create(corrsize, ctype); 663793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 664793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler int maxDepth = depth > CV_8S ? CV_64F : std::max(std::max(CV_32F, tdepth), cdepth); 665793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler Size blocksize, dftsize; 666793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 667793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler blocksize.width = cvRound(templ.cols*blockScale); 668793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler blocksize.width = std::max( blocksize.width, minBlockSize - templ.cols + 1 ); 669793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler blocksize.width = std::min( blocksize.width, corr.cols ); 670793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler blocksize.height = cvRound(templ.rows*blockScale); 671793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler blocksize.height = std::max( blocksize.height, minBlockSize - templ.rows + 1 ); 672793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler blocksize.height = std::min( blocksize.height, corr.rows ); 673793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 674793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler dftsize.width = std::max(getOptimalDFTSize(blocksize.width + templ.cols - 1), 2); 675793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler dftsize.height = getOptimalDFTSize(blocksize.height + templ.rows - 1); 676793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler if( dftsize.width <= 0 || dftsize.height <= 0 ) 677793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler CV_Error( CV_StsOutOfRange, "the input arrays are too big" ); 678793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 679793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler // recompute block size 680793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler blocksize.width = dftsize.width - templ.cols + 1; 681793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler blocksize.width = MIN( blocksize.width, corr.cols ); 682793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler blocksize.height = dftsize.height - templ.rows + 1; 683793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler blocksize.height = MIN( blocksize.height, corr.rows ); 684793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 685793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler Mat dftTempl( dftsize.height*tcn, dftsize.width, maxDepth ); 686793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler Mat dftImg( dftsize, maxDepth ); 687793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 688793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler int i, k, bufSize = 0; 689793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler if( tcn > 1 && tdepth != maxDepth ) 690793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler bufSize = templ.cols*templ.rows*CV_ELEM_SIZE(tdepth); 691793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 692793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler if( cn > 1 && depth != maxDepth ) 693793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler bufSize = std::max( bufSize, (blocksize.width + templ.cols - 1)* 694793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler (blocksize.height + templ.rows - 1)*CV_ELEM_SIZE(depth)); 695793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 696793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler if( (ccn > 1 || cn > 1) && cdepth != maxDepth ) 697793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler bufSize = std::max( bufSize, blocksize.width*blocksize.height*CV_ELEM_SIZE(cdepth)); 698793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 699793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler buf.resize(bufSize); 700793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 701793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler // compute DFT of each template plane 702793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler for( k = 0; k < tcn; k++ ) 703793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler { 704793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler int yofs = k*dftsize.height; 705793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler Mat src = templ; 706793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler Mat dst(dftTempl, Rect(0, yofs, dftsize.width, dftsize.height)); 707793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler Mat dst1(dftTempl, Rect(0, yofs, templ.cols, templ.rows)); 708793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 709793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler if( tcn > 1 ) 710793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler { 711793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler src = tdepth == maxDepth ? dst1 : Mat(templ.size(), tdepth, &buf[0]); 712793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler int pairs[] = {k, 0}; 713793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler mixChannels(&templ, 1, &src, 1, pairs, 1); 714793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler } 715793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 716793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler if( dst1.data != src.data ) 717793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler src.convertTo(dst1, dst1.depth()); 718793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 719793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler if( dst.cols > templ.cols ) 720793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler { 721793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler Mat part(dst, Range(0, templ.rows), Range(templ.cols, dst.cols)); 722793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler part = Scalar::all(0); 723793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler } 724793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler dft(dst, dst, 0, templ.rows); 725793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler } 726793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 727793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler int tileCountX = (corr.cols + blocksize.width - 1)/blocksize.width; 728793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler int tileCountY = (corr.rows + blocksize.height - 1)/blocksize.height; 729793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler int tileCount = tileCountX * tileCountY; 730793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 731793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler Size wholeSize = img.size(); 732793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler Point roiofs(0,0); 733793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler Mat img0 = img; 734793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 735793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler if( !(borderType & BORDER_ISOLATED) ) 736793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler { 737793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler img.locateROI(wholeSize, roiofs); 738793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler img0.adjustROI(roiofs.y, wholeSize.height-img.rows-roiofs.y, 739793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler roiofs.x, wholeSize.width-img.cols-roiofs.x); 740793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler } 741793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler borderType |= BORDER_ISOLATED; 742793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 743793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler // calculate correlation by blocks 744793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler for( i = 0; i < tileCount; i++ ) 745793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler { 746793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler int x = (i%tileCountX)*blocksize.width; 747793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler int y = (i/tileCountX)*blocksize.height; 748793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 749793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler Size bsz(std::min(blocksize.width, corr.cols - x), 750793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler std::min(blocksize.height, corr.rows - y)); 751793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler Size dsz(bsz.width + templ.cols - 1, bsz.height + templ.rows - 1); 752793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler int x0 = x - anchor.x + roiofs.x, y0 = y - anchor.y + roiofs.y; 753793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler int x1 = std::max(0, x0), y1 = std::max(0, y0); 754793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler int x2 = std::min(img0.cols, x0 + dsz.width); 755793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler int y2 = std::min(img0.rows, y0 + dsz.height); 756793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler Mat src0(img0, Range(y1, y2), Range(x1, x2)); 757793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler Mat dst(dftImg, Rect(0, 0, dsz.width, dsz.height)); 758793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler Mat dst1(dftImg, Rect(x1-x0, y1-y0, x2-x1, y2-y1)); 759793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler Mat cdst(corr, Rect(x, y, bsz.width, bsz.height)); 760793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 761793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler for( k = 0; k < cn; k++ ) 762793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler { 763793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler Mat src = src0; 764793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler dftImg = Scalar::all(0); 765793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 766793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler if( cn > 1 ) 767793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler { 768793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler src = depth == maxDepth ? dst1 : Mat(y2-y1, x2-x1, depth, &buf[0]); 769793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler int pairs[] = {k, 0}; 770793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler mixChannels(&src0, 1, &src, 1, pairs, 1); 771793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler } 772793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 773793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler if( dst1.data != src.data ) 774793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler src.convertTo(dst1, dst1.depth()); 775793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 776793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler if( x2 - x1 < dsz.width || y2 - y1 < dsz.height ) 777793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler copyMakeBorder(dst1, dst, y1-y0, dst.rows-dst1.rows-(y1-y0), 778793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler x1-x0, dst.cols-dst1.cols-(x1-x0), borderType); 779793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 780793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler dft( dftImg, dftImg, 0, dsz.height ); 781793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler Mat dftTempl1(dftTempl, Rect(0, tcn > 1 ? k*dftsize.height : 0, 782793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler dftsize.width, dftsize.height)); 783793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler mulSpectrums(dftImg, dftTempl1, dftImg, 0, true); 784793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler dft( dftImg, dftImg, DFT_INVERSE + DFT_SCALE, bsz.height ); 785793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 786793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler src = dftImg(Rect(0, 0, bsz.width, bsz.height)); 787793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 788793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler if( ccn > 1 ) 789793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler { 790793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler if( cdepth != maxDepth ) 791793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler { 792793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler Mat plane(bsz, cdepth, &buf[0]); 793793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler src.convertTo(plane, cdepth, 1, delta); 794793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler src = plane; 795793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler } 796793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler int pairs[] = {0, k}; 797793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler mixChannels(&src, 1, &cdst, 1, pairs, 1); 798793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler } 799793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler else 800793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler { 801793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler if( k == 0 ) 802793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler src.convertTo(cdst, cdepth, 1, delta); 803793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler else 804793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler { 805793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler if( maxDepth != cdepth ) 806793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler { 807793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler Mat plane(bsz, cdepth, &buf[0]); 808793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler src.convertTo(plane, cdepth); 809793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler src = plane; 810793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler } 811793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler add(src, cdst, cdst); 812793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler } 813793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler } 814793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler } 815793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler } 816793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler} 817793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 818793ee12c6df9cad3806238d32528c49a3ff9331dNoah Preslerstatic void matchTemplateMask( InputArray _img, InputArray _templ, OutputArray _result, int method, InputArray _mask ) 819793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler{ 820793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler int type = _img.type(), depth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type); 821793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler CV_Assert( CV_TM_SQDIFF <= method && method <= CV_TM_CCOEFF_NORMED ); 822793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler CV_Assert( (depth == CV_8U || depth == CV_32F) && type == _templ.type() && _img.dims() <= 2 ); 823793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 824793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler Mat img = _img.getMat(), templ = _templ.getMat(), mask = _mask.getMat(); 825793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler int ttype = templ.type(), tdepth = CV_MAT_DEPTH(ttype), tcn = CV_MAT_CN(ttype); 826793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler int mtype = img.type(), mdepth = CV_MAT_DEPTH(type), mcn = CV_MAT_CN(mtype); 827793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 828793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler if (depth == CV_8U) 829793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler { 830793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler depth = CV_32F; 831793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler type = CV_MAKETYPE(CV_32F, cn); 832793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler img.convertTo(img, type, 1.0 / 255); 833793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler } 834793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 835793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler if (tdepth == CV_8U) 836793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler { 837793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler tdepth = CV_32F; 838793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler ttype = CV_MAKETYPE(CV_32F, tcn); 839793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler templ.convertTo(templ, ttype, 1.0 / 255); 840793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler } 841793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 842793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler if (mdepth == CV_8U) 843793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler { 844793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler mdepth = CV_32F; 845793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler mtype = CV_MAKETYPE(CV_32F, mcn); 846793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler compare(mask, Scalar::all(0), mask, CMP_NE); 847793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler mask.convertTo(mask, mtype, 1.0 / 255); 848793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler } 849793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 850793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler Size corrSize(img.cols - templ.cols + 1, img.rows - templ.rows + 1); 851793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler _result.create(corrSize, CV_32F); 852793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler Mat result = _result.getMat(); 853793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 854793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler Mat img2 = img.mul(img); 855793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler Mat mask2 = mask.mul(mask); 856793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler Mat mask_templ = templ.mul(mask); 857793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler Scalar templMean, templSdv; 858793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 859793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler double templSum2 = 0; 860793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler meanStdDev( mask_templ, templMean, templSdv ); 861793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 862793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler templSum2 = templSdv[0]*templSdv[0] + templSdv[1]*templSdv[1] + templSdv[2]*templSdv[2] + templSdv[3]*templSdv[3]; 863793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler templSum2 += templMean[0]*templMean[0] + templMean[1]*templMean[1] + templMean[2]*templMean[2] + templMean[3]*templMean[3]; 864793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler templSum2 *= ((double)templ.rows * templ.cols); 865793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 866793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler if (method == CV_TM_SQDIFF) 867793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler { 868793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler Mat mask2_templ = templ.mul(mask2); 869793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 870793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler Mat corr(corrSize, CV_32F); 871793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler crossCorr( img, mask2_templ, corr, corr.size(), corr.type(), Point(0,0), 0, 0 ); 872793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler crossCorr( img2, mask, result, result.size(), result.type(), Point(0,0), 0, 0 ); 873793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 874793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler result -= corr * 2; 875793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler result += templSum2; 876793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler } 877793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler else if (method == CV_TM_CCORR_NORMED) 878793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler { 879793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler if (templSum2 < DBL_EPSILON) 880793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler { 881793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler result = Scalar::all(1); 882793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler return; 883793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler } 884793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 885793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler Mat corr(corrSize, CV_32F); 886793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler crossCorr( img2, mask2, corr, corr.size(), corr.type(), Point(0,0), 0, 0 ); 887793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler crossCorr( img, mask_templ, result, result.size(), result.type(), Point(0,0), 0, 0 ); 888793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 889793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler sqrt(corr, corr); 890793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler result = result.mul(1/corr); 891793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler result /= std::sqrt(templSum2); 892793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler } 893793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler else 894793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler CV_Error(Error::StsNotImplemented, ""); 895793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler} 896793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler} 897793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 898793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler//////////////////////////////////////////////////////////////////////////////////////////////////////// 899793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 900793ee12c6df9cad3806238d32528c49a3ff9331dNoah Preslervoid cv::matchTemplate( InputArray _img, InputArray _templ, OutputArray _result, int method, InputArray _mask ) 901793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler{ 902793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler if (!_mask.empty()) 903793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler { 904793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler cv::matchTemplateMask(_img, _templ, _result, method, _mask); 905793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler return; 906793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler } 907793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 908793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler int type = _img.type(), depth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type); 909793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler CV_Assert( CV_TM_SQDIFF <= method && method <= CV_TM_CCOEFF_NORMED ); 910793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler CV_Assert( (depth == CV_8U || depth == CV_32F) && type == _templ.type() && _img.dims() <= 2 ); 911793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 912793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler bool needswap = _img.size().height < _templ.size().height || _img.size().width < _templ.size().width; 913793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler if (needswap) 914793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler { 915793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler CV_Assert(_img.size().height <= _templ.size().height && _img.size().width <= _templ.size().width); 916793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler } 917793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 918793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler CV_OCL_RUN(_img.dims() <= 2 && _result.isUMat(), 919793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler (!needswap ? ocl_matchTemplate(_img, _templ, _result, method) : ocl_matchTemplate(_templ, _img, _result, method))) 920793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 921793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler int numType = method == CV_TM_CCORR || method == CV_TM_CCORR_NORMED ? 0 : 922793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler method == CV_TM_CCOEFF || method == CV_TM_CCOEFF_NORMED ? 1 : 2; 923793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler bool isNormed = method == CV_TM_CCORR_NORMED || 924793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler method == CV_TM_SQDIFF_NORMED || 925793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler method == CV_TM_CCOEFF_NORMED; 926793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 927793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler Mat img = _img.getMat(), templ = _templ.getMat(); 928793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler if (needswap) 929793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler std::swap(img, templ); 930793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 931793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler Size corrSize(img.cols - templ.cols + 1, img.rows - templ.rows + 1); 932793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler _result.create(corrSize, CV_32F); 933793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler Mat result = _result.getMat(); 934793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 935793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler#ifdef HAVE_TEGRA_OPTIMIZATION 936793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler if (tegra::useTegra() && tegra::matchTemplate(img, templ, result, method)) 937793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler return; 938793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler#endif 939793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 940793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler#if defined HAVE_IPP 941793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler bool useIppMT = false; 942793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler CV_IPP_CHECK() 943793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler { 944793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler useIppMT = (templ.rows < img.rows/2 && templ.cols < img.cols/2); 945793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 946793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler if (method == CV_TM_SQDIFF && cn == 1 && useIppMT) 947793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler { 948793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler if (ipp_sqrDistance(img, templ, result)) 949793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler { 950793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler CV_IMPL_ADD(CV_IMPL_IPP); 951793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler return; 952793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler } 953793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler setIppErrorStatus(); 954793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler } 955793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler } 956793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler#endif 957793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 958793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler#if defined HAVE_IPP 959793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler if (cn == 1 && useIppMT) 960793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler { 961793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler if (!ipp_crossCorr(img, templ, result)) 962793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler { 963793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler setIppErrorStatus(); 964793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler crossCorr( img, templ, result, result.size(), result.type(), Point(0,0), 0, 0); 965793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler } 966793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler else 967793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler { 968793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler CV_IMPL_ADD(CV_IMPL_IPP); 969793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler } 970793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler } 971793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler else 972793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler#endif 973793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler crossCorr( img, templ, result, result.size(), result.type(), Point(0,0), 0, 0); 974793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 975793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler if( method == CV_TM_CCORR ) 976793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler return; 977793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 978793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler double invArea = 1./((double)templ.rows * templ.cols); 979793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 980793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler Mat sum, sqsum; 981793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler Scalar templMean, templSdv; 982793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler double *q0 = 0, *q1 = 0, *q2 = 0, *q3 = 0; 983793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler double templNorm = 0, templSum2 = 0; 984793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 985793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler if( method == CV_TM_CCOEFF ) 986793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler { 987793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler integral(img, sum, CV_64F); 988793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler templMean = mean(templ); 989793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler } 990793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler else 991793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler { 992793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler integral(img, sum, sqsum, CV_64F); 993793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler meanStdDev( templ, templMean, templSdv ); 994793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 995793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler templNorm = templSdv[0]*templSdv[0] + templSdv[1]*templSdv[1] + templSdv[2]*templSdv[2] + templSdv[3]*templSdv[3]; 996793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 997793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler if( templNorm < DBL_EPSILON && method == CV_TM_CCOEFF_NORMED ) 998793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler { 999793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler result = Scalar::all(1); 1000793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler return; 1001793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler } 1002793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 1003793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler templSum2 = templNorm + templMean[0]*templMean[0] + templMean[1]*templMean[1] + templMean[2]*templMean[2] + templMean[3]*templMean[3]; 1004793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 1005793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler if( numType != 1 ) 1006793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler { 1007793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler templMean = Scalar::all(0); 1008793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler templNorm = templSum2; 1009793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler } 1010793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 1011793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler templSum2 /= invArea; 1012793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler templNorm = std::sqrt(templNorm); 1013793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler templNorm /= std::sqrt(invArea); // care of accuracy here 1014793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 1015793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler q0 = (double*)sqsum.data; 1016793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler q1 = q0 + templ.cols*cn; 1017793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler q2 = (double*)(sqsum.data + templ.rows*sqsum.step); 1018793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler q3 = q2 + templ.cols*cn; 1019793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler } 1020793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 1021793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler double* p0 = (double*)sum.data; 1022793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler double* p1 = p0 + templ.cols*cn; 1023793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler double* p2 = (double*)(sum.data + templ.rows*sum.step); 1024793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler double* p3 = p2 + templ.cols*cn; 1025793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 1026793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler int sumstep = sum.data ? (int)(sum.step / sizeof(double)) : 0; 1027793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler int sqstep = sqsum.data ? (int)(sqsum.step / sizeof(double)) : 0; 1028793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 1029793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler int i, j, k; 1030793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 1031793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler for( i = 0; i < result.rows; i++ ) 1032793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler { 1033793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler float* rrow = result.ptr<float>(i); 1034793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler int idx = i * sumstep; 1035793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler int idx2 = i * sqstep; 1036793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 1037793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler for( j = 0; j < result.cols; j++, idx += cn, idx2 += cn ) 1038793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler { 1039793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler double num = rrow[j], t; 1040793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler double wndMean2 = 0, wndSum2 = 0; 1041793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 1042793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler if( numType == 1 ) 1043793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler { 1044793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler for( k = 0; k < cn; k++ ) 1045793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler { 1046793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler t = p0[idx+k] - p1[idx+k] - p2[idx+k] + p3[idx+k]; 1047793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler wndMean2 += t*t; 1048793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler num -= t*templMean[k]; 1049793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler } 1050793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 1051793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler wndMean2 *= invArea; 1052793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler } 1053793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 1054793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler if( isNormed || numType == 2 ) 1055793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler { 1056793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler for( k = 0; k < cn; k++ ) 1057793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler { 1058793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler t = q0[idx2+k] - q1[idx2+k] - q2[idx2+k] + q3[idx2+k]; 1059793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler wndSum2 += t; 1060793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler } 1061793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 1062793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler if( numType == 2 ) 1063793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler { 1064793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler num = wndSum2 - 2*num + templSum2; 1065793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler num = MAX(num, 0.); 1066793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler } 1067793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler } 1068793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 1069793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler if( isNormed ) 1070793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler { 1071793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler t = std::sqrt(MAX(wndSum2 - wndMean2,0))*templNorm; 1072793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler if( fabs(num) < t ) 1073793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler num /= t; 1074793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler else if( fabs(num) < t*1.125 ) 1075793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler num = num > 0 ? 1 : -1; 1076793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler else 1077793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler num = method != CV_TM_SQDIFF_NORMED ? 0 : 1; 1078793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler } 1079793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 1080793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler rrow[j] = (float)num; 1081793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler } 1082793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler } 1083793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler} 1084793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 1085793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 1086793ee12c6df9cad3806238d32528c49a3ff9331dNoah PreslerCV_IMPL void 1087793ee12c6df9cad3806238d32528c49a3ff9331dNoah PreslercvMatchTemplate( const CvArr* _img, const CvArr* _templ, CvArr* _result, int method ) 1088793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler{ 1089793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler cv::Mat img = cv::cvarrToMat(_img), templ = cv::cvarrToMat(_templ), 1090793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler result = cv::cvarrToMat(_result); 1091793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler CV_Assert( result.size() == cv::Size(std::abs(img.cols - templ.cols) + 1, 1092793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler std::abs(img.rows - templ.rows) + 1) && 1093793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler result.type() == CV_32F ); 1094793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler matchTemplate(img, templ, result, method); 1095793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler} 1096793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler 1097793ee12c6df9cad3806238d32528c49a3ff9331dNoah Presler/* End of file. */ 1098