/*M/////////////////////////////////////////////////////////////////////////////////////// // // IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. // // By downloading, copying, installing or using the software you agree to this license. // If you do not agree to this license, do not download, install, // copy or use the software. // // // License Agreement // For Open Source Computer Vision Library // // Copyright (C) 2000-2008, Intel Corporation, all rights reserved. // Copyright (C) 2009, Willow Garage Inc., all rights reserved. // Third party copyrights are property of their respective owners. // // Redistribution and use in source and binary forms, with or without modification, // are permitted provided that the following conditions are met: // // * Redistribution's of source code must retain the above copyright notice, // this list of conditions and the following disclaimer. // // * Redistribution's in binary form must reproduce the above copyright notice, // this list of conditions and the following disclaimer in the documentation // and/or other materials provided with the distribution. // // * The name of the copyright holders may not be used to endorse or promote products // derived from this software without specific prior written permission. // // This software is provided by the copyright holders and contributors "as is" and // any express or implied warranties, including, but not limited to, the implied // warranties of merchantability and fitness for a particular purpose are disclaimed. // In no event shall the Intel Corporation or contributors be liable for any direct, // indirect, incidental, special, exemplary, or consequential damages // (including, but not limited to, procurement of substitute goods or services; // loss of use, data, or profits; or business interruption) however caused // and on any theory of liability, whether in contract, strict liability, // or tort (including negligence or otherwise) arising in any way out of // the use of this software, even if advised of the possibility of such damage. // //M*/ #include "precomp.hpp" #include #include using namespace cv; using namespace cv::gpu; #define BLOCK_VERSION #if !defined (HAVE_CUDA) void cv::gpu::matchTemplate(const GpuMat&, const GpuMat&, GpuMat&, int) { throw_nogpu(); } #else #include namespace cv { namespace gpu { namespace imgproc { void multiplyAndNormalizeSpects(int n, float scale, const cufftComplex* a, const cufftComplex* b, cufftComplex* c); void matchTemplateNaive_8U_SQDIFF(const DevMem2D image, const DevMem2D templ, DevMem2Df result); void matchTemplateNaive_32F_SQDIFF(const DevMem2D image, const DevMem2D templ, DevMem2Df result); }}} namespace { template void matchTemplate(const GpuMat& image, const GpuMat& templ, GpuMat& result); #ifdef BLOCK_VERSION void estimateBlockSize(int w, int h, int tw, int th, int& bw, int& bh) { const int scale = 40; const int bh_min = 1024; const int bw_min = 1024; bw = std::max(tw * scale, bw_min); bh = std::max(th * scale, bh_min); bw = std::min(bw, w); bh = std::min(bh, h); } #endif template <> void matchTemplate(const GpuMat& image, const GpuMat& templ, GpuMat& result) { result.create(image.rows - templ.rows + 1, image.cols - templ.cols + 1, CV_32F); imgproc::matchTemplateNaive_8U_SQDIFF(image, templ, result); } template <> void matchTemplate(const GpuMat& image, const GpuMat& templ, GpuMat& result) { result.create(image.rows - templ.rows + 1, image.cols - templ.cols + 1, CV_32F); imgproc::matchTemplateNaive_32F_SQDIFF(image, templ, result); } #ifdef BLOCK_VERSION template <> void matchTemplate(const GpuMat& image, const GpuMat& templ, GpuMat& result) { result.create(image.rows - templ.rows + 1, image.cols - templ.cols + 1, CV_32F); Size block_size; estimateBlockSize(result.cols, result.rows, templ.cols, templ.rows, block_size.width, block_size.height); Size dft_size; dft_size.width = getOptimalDFTSize(block_size.width + templ.cols - 1); dft_size.height = getOptimalDFTSize(block_size.width + templ.rows - 1); block_size.width = std::min(dft_size.width - templ.cols + 1, result.cols); block_size.height = std::min(dft_size.height - templ.rows + 1, result.rows); cufftReal* image_data; cufftReal* templ_data; cufftReal* result_data; cudaMalloc((void**)&image_data, sizeof(cufftReal) * dft_size.area()); cudaMalloc((void**)&templ_data, sizeof(cufftReal) * dft_size.area()); cudaMalloc((void**)&result_data, sizeof(cufftReal) * dft_size.area()); int spect_len = dft_size.height * (dft_size.width / 2 + 1); cufftComplex* image_spect; cufftComplex* templ_spect; cufftComplex* result_spect; cudaMalloc((void**)&image_spect, sizeof(cufftComplex) * spect_len); cudaMalloc((void**)&templ_spect, sizeof(cufftComplex) * spect_len); cudaMalloc((void**)&result_spect, sizeof(cufftComplex) * spect_len); cufftHandle planR2C, planC2R; CV_Assert(cufftPlan2d(&planC2R, dft_size.height, dft_size.width, CUFFT_C2R) == CUFFT_SUCCESS); CV_Assert(cufftPlan2d(&planR2C, dft_size.height, dft_size.width, CUFFT_R2C) == CUFFT_SUCCESS); GpuMat templ_roi(templ.size(), CV_32S, templ.data, templ.step); GpuMat templ_block(dft_size, CV_32S, templ_data, dft_size.width * sizeof(cufftReal)); copyMakeBorder(templ_roi, templ_block, 0, templ_block.rows - templ_roi.rows, 0, templ_block.cols - templ_roi.cols, 0); CV_Assert(cufftExecR2C(planR2C, templ_data, templ_spect) == CUFFT_SUCCESS); GpuMat image_block(dft_size, CV_32S, image_data, dft_size.width * sizeof(cufftReal)); for (int y = 0; y < result.rows; y += block_size.height) { for (int x = 0; x < result.cols; x += block_size.width) { Size image_roi_size; image_roi_size.width = min(x + dft_size.width, image.cols) - x; image_roi_size.height = min(y + dft_size.height, image.rows) - y; GpuMat image_roi(image_roi_size, CV_32S, (void*)(image.ptr(y) + x), image.step); copyMakeBorder(image_roi, image_block, 0, image_block.rows - image_roi.rows, 0, image_block.cols - image_roi.cols, 0); CV_Assert(cufftExecR2C(planR2C, image_data, image_spect) == CUFFT_SUCCESS); imgproc::multiplyAndNormalizeSpects(spect_len, 1.f / dft_size.area(), image_spect, templ_spect, result_spect); CV_Assert(cufftExecC2R(planC2R, result_spect, result_data) == CUFFT_SUCCESS); Size result_roi_size; result_roi_size.width = min(x + block_size.width, result.cols) - x; result_roi_size.height = min(y + block_size.height, result.rows) - y; GpuMat result_roi(result_roi_size, CV_32F, (void*)(result.ptr(y) + x), result.step); GpuMat result_block(result_roi_size, CV_32F, result_data, dft_size.width * sizeof(cufftReal)); result_block.copyTo(result_roi); } } cufftDestroy(planR2C); cufftDestroy(planC2R); cudaFree(image_spect); cudaFree(templ_spect); cudaFree(result_spect); cudaFree(image_data); cudaFree(templ_data); cudaFree(result_data); } #else template <> void matchTemplate(const GpuMat& image, const GpuMat& templ, GpuMat& result) { Size opt_size; opt_size.width = getOptimalDFTSize(image.cols); opt_size.height = getOptimalDFTSize(image.rows); cufftReal* image_data; cufftReal* templ_data; cufftReal* result_data; cudaMalloc((void**)&image_data, sizeof(cufftReal) * opt_size.area()); cudaMalloc((void**)&templ_data, sizeof(cufftReal) * opt_size.area()); cudaMalloc((void**)&result_data, sizeof(cufftReal) * opt_size.area()); int spect_len = opt_size.height * (opt_size.width / 2 + 1); cufftComplex* image_spect; cufftComplex* templ_spect; cufftComplex* result_spect; cudaMalloc((void**)&image_spect, sizeof(cufftComplex) * spect_len); cudaMalloc((void**)&templ_spect, sizeof(cufftComplex) * spect_len); cudaMalloc((void**)&result_spect, sizeof(cufftComplex) * spect_len); GpuMat image_(image.size(), CV_32S, image.data, image.step); GpuMat image_cont(opt_size, CV_32S, image_data, opt_size.width * sizeof(cufftReal)); copyMakeBorder(image_, image_cont, 0, image_cont.rows - image.rows, 0, image_cont.cols - image.cols, 0); GpuMat templ_(templ.size(), CV_32S, templ.data, templ.step); GpuMat templ_cont(opt_size, CV_32S, templ_data, opt_size.width * sizeof(cufftReal)); copyMakeBorder(templ_, templ_cont, 0, templ_cont.rows - templ.rows, 0, templ_cont.cols - templ.cols, 0); cufftHandle planR2C, planC2R; CV_Assert(cufftPlan2d(&planC2R, opt_size.height, opt_size.width, CUFFT_C2R) == CUFFT_SUCCESS); CV_Assert(cufftPlan2d(&planR2C, opt_size.height, opt_size.width, CUFFT_R2C) == CUFFT_SUCCESS); CV_Assert(cufftExecR2C(planR2C, image_data, image_spect) == CUFFT_SUCCESS); CV_Assert(cufftExecR2C(planR2C, templ_data, templ_spect) == CUFFT_SUCCESS); imgproc::multiplyAndNormalizeSpects(spect_len, 1.f / opt_size.area(), image_spect, templ_spect, result_spect); CV_Assert(cufftExecC2R(planC2R, result_spect, result_data) == CUFFT_SUCCESS); cufftDestroy(planR2C); cufftDestroy(planC2R); GpuMat result_cont(image.rows - templ.rows + 1, image.cols - templ.cols + 1, CV_32F, result_data, opt_size.width * sizeof(cufftReal)); result_cont.copyTo(result); cudaFree(image_spect); cudaFree(templ_spect); cudaFree(result_spect); cudaFree(image_data); cudaFree(templ_data); cudaFree(result_data); } #endif } void cv::gpu::matchTemplate(const GpuMat& image, const GpuMat& templ, GpuMat& result, int method) { CV_Assert(image.type() == templ.type()); CV_Assert(image.cols >= templ.cols && image.rows >= templ.rows); typedef void (*Caller)(const GpuMat&, const GpuMat&, GpuMat&); static const Caller callers8U[] = { ::matchTemplate, 0, 0, 0, 0, 0 }; static const Caller callers32F[] = { ::matchTemplate, 0, ::matchTemplate, 0, 0, 0 }; const Caller* callers; switch (image.type()) { case CV_8U: callers = callers8U; break; case CV_32F: callers = callers32F; break; default: CV_Error(CV_StsBadArg, "matchTemplate: unsupported data type"); } Caller caller = callers[method]; CV_Assert(caller); caller(image, templ, result); } #endif