2a6fb2867e
Made all STL usages explicit to be able automatically find all usages of particular class or function.
1024 lines
41 KiB
C++
1024 lines
41 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
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// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other GpuMaterials provided with the distribution.
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//
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// * The name of the copyright holders may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or bpied warranties, including, but not limited to, the bpied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#include "precomp.hpp"
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using namespace cv;
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using namespace cv::gpu;
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#if !defined (HAVE_CUDA) || defined (CUDA_DISABLER)
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cv::gpu::BFMatcher_GPU::BFMatcher_GPU(int) { throw_nogpu(); }
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void cv::gpu::BFMatcher_GPU::add(const std::vector<GpuMat>&) { throw_nogpu(); }
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const std::vector<GpuMat>& cv::gpu::BFMatcher_GPU::getTrainDescriptors() const { throw_nogpu(); return trainDescCollection; }
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void cv::gpu::BFMatcher_GPU::clear() { throw_nogpu(); }
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bool cv::gpu::BFMatcher_GPU::empty() const { throw_nogpu(); return true; }
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bool cv::gpu::BFMatcher_GPU::isMaskSupported() const { throw_nogpu(); return true; }
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void cv::gpu::BFMatcher_GPU::matchSingle(const GpuMat&, const GpuMat&, GpuMat&, GpuMat&, const GpuMat&, Stream&) { throw_nogpu(); }
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void cv::gpu::BFMatcher_GPU::matchDownload(const GpuMat&, const GpuMat&, std::vector<DMatch>&) { throw_nogpu(); }
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void cv::gpu::BFMatcher_GPU::matchConvert(const Mat&, const Mat&, std::vector<DMatch>&) { throw_nogpu(); }
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void cv::gpu::BFMatcher_GPU::match(const GpuMat&, const GpuMat&, std::vector<DMatch>&, const GpuMat&) { throw_nogpu(); }
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void cv::gpu::BFMatcher_GPU::makeGpuCollection(GpuMat&, GpuMat&, const std::vector<GpuMat>&) { throw_nogpu(); }
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void cv::gpu::BFMatcher_GPU::matchCollection(const GpuMat&, const GpuMat&, GpuMat&, GpuMat&, GpuMat&, const GpuMat&, Stream&) { throw_nogpu(); }
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void cv::gpu::BFMatcher_GPU::matchDownload(const GpuMat&, const GpuMat&, const GpuMat&, std::vector<DMatch>&) { throw_nogpu(); }
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void cv::gpu::BFMatcher_GPU::matchConvert(const Mat&, const Mat&, const Mat&, std::vector<DMatch>&) { throw_nogpu(); }
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void cv::gpu::BFMatcher_GPU::match(const GpuMat&, std::vector<DMatch>&, const std::vector<GpuMat>&) { throw_nogpu(); }
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void cv::gpu::BFMatcher_GPU::knnMatchSingle(const GpuMat&, const GpuMat&, GpuMat&, GpuMat&, GpuMat&, int, const GpuMat&, Stream&) { throw_nogpu(); }
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void cv::gpu::BFMatcher_GPU::knnMatchDownload(const GpuMat&, const GpuMat&, std::vector< std::vector<DMatch> >&, bool) { throw_nogpu(); }
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void cv::gpu::BFMatcher_GPU::knnMatchConvert(const Mat&, const Mat&, std::vector< std::vector<DMatch> >&, bool) { throw_nogpu(); }
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void cv::gpu::BFMatcher_GPU::knnMatch(const GpuMat&, const GpuMat&, std::vector< std::vector<DMatch> >&, int, const GpuMat&, bool) { throw_nogpu(); }
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void cv::gpu::BFMatcher_GPU::knnMatch2Collection(const GpuMat&, const GpuMat&, GpuMat&, GpuMat&, GpuMat&, const GpuMat&, Stream&) { throw_nogpu(); }
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void cv::gpu::BFMatcher_GPU::knnMatch2Download(const GpuMat&, const GpuMat&, const GpuMat&, std::vector< std::vector<DMatch> >&, bool) { throw_nogpu(); }
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void cv::gpu::BFMatcher_GPU::knnMatch2Convert(const Mat&, const Mat&, const Mat&, std::vector< std::vector<DMatch> >&, bool) { throw_nogpu(); }
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void cv::gpu::BFMatcher_GPU::knnMatch(const GpuMat&, std::vector< std::vector<DMatch> >&, int, const std::vector<GpuMat>&, bool) { throw_nogpu(); }
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void cv::gpu::BFMatcher_GPU::radiusMatchSingle(const GpuMat&, const GpuMat&, GpuMat&, GpuMat&, GpuMat&, float, const GpuMat&, Stream&) { throw_nogpu(); }
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void cv::gpu::BFMatcher_GPU::radiusMatchDownload(const GpuMat&, const GpuMat&, const GpuMat&, std::vector< std::vector<DMatch> >&, bool) { throw_nogpu(); }
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void cv::gpu::BFMatcher_GPU::radiusMatchConvert(const Mat&, const Mat&, const Mat&, std::vector< std::vector<DMatch> >&, bool) { throw_nogpu(); }
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void cv::gpu::BFMatcher_GPU::radiusMatch(const GpuMat&, const GpuMat&, std::vector< std::vector<DMatch> >&, float, const GpuMat&, bool) { throw_nogpu(); }
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void cv::gpu::BFMatcher_GPU::radiusMatchCollection(const GpuMat&, GpuMat&, GpuMat&, GpuMat&, GpuMat&, float, const std::vector<GpuMat>&, Stream&) { throw_nogpu(); }
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void cv::gpu::BFMatcher_GPU::radiusMatchDownload(const GpuMat&, const GpuMat&, const GpuMat&, const GpuMat&, std::vector< std::vector<DMatch> >&, bool) { throw_nogpu(); }
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void cv::gpu::BFMatcher_GPU::radiusMatchConvert(const Mat&, const Mat&, const Mat&, const Mat&, std::vector< std::vector<DMatch> >&, bool) { throw_nogpu(); }
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void cv::gpu::BFMatcher_GPU::radiusMatch(const GpuMat&, std::vector< std::vector<DMatch> >&, float, const std::vector<GpuMat>&, bool) { throw_nogpu(); }
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#else /* !defined (HAVE_CUDA) */
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namespace cv { namespace gpu { namespace device
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{
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namespace bf_match
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{
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template <typename T> void matchL1_gpu(const PtrStepSzb& query, const PtrStepSzb& train, const PtrStepSzb& mask,
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const PtrStepSzi& trainIdx, const PtrStepSzf& distance,
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cudaStream_t stream);
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template <typename T> void matchL2_gpu(const PtrStepSzb& query, const PtrStepSzb& train, const PtrStepSzb& mask,
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const PtrStepSzi& trainIdx, const PtrStepSzf& distance,
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cudaStream_t stream);
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template <typename T> void matchHamming_gpu(const PtrStepSzb& query, const PtrStepSzb& train, const PtrStepSzb& mask,
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const PtrStepSzi& trainIdx, const PtrStepSzf& distance,
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cudaStream_t stream);
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template <typename T> void matchL1_gpu(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks,
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const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance,
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cudaStream_t stream);
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template <typename T> void matchL2_gpu(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks,
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const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance,
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cudaStream_t stream);
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template <typename T> void matchHamming_gpu(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks,
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const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance,
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cudaStream_t stream);
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}
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namespace bf_knnmatch
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{
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template <typename T> void matchL1_gpu(const PtrStepSzb& query, const PtrStepSzb& train, int k, const PtrStepSzb& mask,
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const PtrStepSzb& trainIdx, const PtrStepSzb& distance, const PtrStepSzf& allDist,
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cudaStream_t stream);
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template <typename T> void matchL2_gpu(const PtrStepSzb& query, const PtrStepSzb& train, int k, const PtrStepSzb& mask,
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const PtrStepSzb& trainIdx, const PtrStepSzb& distance, const PtrStepSzf& allDist,
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cudaStream_t stream);
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template <typename T> void matchHamming_gpu(const PtrStepSzb& query, const PtrStepSzb& train, int k, const PtrStepSzb& mask,
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const PtrStepSzb& trainIdx, const PtrStepSzb& distance, const PtrStepSzf& allDist,
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cudaStream_t stream);
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template <typename T> void match2L1_gpu(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks,
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const PtrStepSzb& trainIdx, const PtrStepSzb& imgIdx, const PtrStepSzb& distance,
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cudaStream_t stream);
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template <typename T> void match2L2_gpu(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks,
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const PtrStepSzb& trainIdx, const PtrStepSzb& imgIdx, const PtrStepSzb& distance,
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cudaStream_t stream);
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template <typename T> void match2Hamming_gpu(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks,
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const PtrStepSzb& trainIdx, const PtrStepSzb& imgIdx, const PtrStepSzb& distance,
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cudaStream_t stream);
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}
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namespace bf_radius_match
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{
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template <typename T> void matchL1_gpu(const PtrStepSzb& query, const PtrStepSzb& train, float maxDistance, const PtrStepSzb& mask,
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const PtrStepSzi& trainIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches,
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cudaStream_t stream);
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template <typename T> void matchL2_gpu(const PtrStepSzb& query, const PtrStepSzb& train, float maxDistance, const PtrStepSzb& mask,
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const PtrStepSzi& trainIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches,
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cudaStream_t stream);
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template <typename T> void matchHamming_gpu(const PtrStepSzb& query, const PtrStepSzb& train, float maxDistance, const PtrStepSzb& mask,
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const PtrStepSzi& trainIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches,
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cudaStream_t stream);
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template <typename T> void matchL1_gpu(const PtrStepSzb& query, const PtrStepSzb* trains, int n, float maxDistance, const PtrStepSzb* masks,
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const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches,
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cudaStream_t stream);
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template <typename T> void matchL2_gpu(const PtrStepSzb& query, const PtrStepSzb* trains, int n, float maxDistance, const PtrStepSzb* masks,
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const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches,
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cudaStream_t stream);
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template <typename T> void matchHamming_gpu(const PtrStepSzb& query, const PtrStepSzb* trains, int n, float maxDistance, const PtrStepSzb* masks,
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const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches,
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cudaStream_t stream);
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}
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}}}
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////////////////////////////////////////////////////////////////////
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// Train collection
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cv::gpu::BFMatcher_GPU::BFMatcher_GPU(int norm_) : norm(norm_)
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{
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}
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void cv::gpu::BFMatcher_GPU::add(const std::vector<GpuMat>& descCollection)
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{
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trainDescCollection.insert(trainDescCollection.end(), descCollection.begin(), descCollection.end());
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}
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const std::vector<GpuMat>& cv::gpu::BFMatcher_GPU::getTrainDescriptors() const
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{
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return trainDescCollection;
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}
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void cv::gpu::BFMatcher_GPU::clear()
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{
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trainDescCollection.clear();
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}
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bool cv::gpu::BFMatcher_GPU::empty() const
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{
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return trainDescCollection.empty();
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}
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bool cv::gpu::BFMatcher_GPU::isMaskSupported() const
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{
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return true;
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}
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////////////////////////////////////////////////////////////////////
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// Match
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void cv::gpu::BFMatcher_GPU::matchSingle(const GpuMat& query, const GpuMat& train,
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GpuMat& trainIdx, GpuMat& distance,
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const GpuMat& mask, Stream& stream)
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{
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if (query.empty() || train.empty())
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return;
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using namespace cv::gpu::device::bf_match;
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typedef void (*caller_t)(const PtrStepSzb& query, const PtrStepSzb& train, const PtrStepSzb& mask,
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const PtrStepSzi& trainIdx, const PtrStepSzf& distance,
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cudaStream_t stream);
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static const caller_t callersL1[] =
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{
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matchL1_gpu<unsigned char>, 0/*matchL1_gpu<signed char>*/,
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matchL1_gpu<unsigned short>, matchL1_gpu<short>,
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matchL1_gpu<int>, matchL1_gpu<float>
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};
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static const caller_t callersL2[] =
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{
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0/*matchL2_gpu<unsigned char>*/, 0/*matchL2_gpu<signed char>*/,
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0/*matchL2_gpu<unsigned short>*/, 0/*matchL2_gpu<short>*/,
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0/*matchL2_gpu<int>*/, matchL2_gpu<float>
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};
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static const caller_t callersHamming[] =
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{
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matchHamming_gpu<unsigned char>, 0/*matchHamming_gpu<signed char>*/,
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matchHamming_gpu<unsigned short>, 0/*matchHamming_gpu<short>*/,
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matchHamming_gpu<int>, 0/*matchHamming_gpu<float>*/
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};
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CV_Assert(query.channels() == 1 && query.depth() < CV_64F);
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CV_Assert(train.cols == query.cols && train.type() == query.type());
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CV_Assert(norm == NORM_L1 || norm == NORM_L2 || norm == NORM_HAMMING);
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const caller_t* callers = norm == NORM_L1 ? callersL1 : norm == NORM_L2 ? callersL2 : callersHamming;
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const int nQuery = query.rows;
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ensureSizeIsEnough(1, nQuery, CV_32S, trainIdx);
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ensureSizeIsEnough(1, nQuery, CV_32F, distance);
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caller_t func = callers[query.depth()];
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CV_Assert(func != 0);
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func(query, train, mask, trainIdx, distance, StreamAccessor::getStream(stream));
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}
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void cv::gpu::BFMatcher_GPU::matchDownload(const GpuMat& trainIdx, const GpuMat& distance, std::vector<DMatch>& matches)
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{
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if (trainIdx.empty() || distance.empty())
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return;
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Mat trainIdxCPU(trainIdx);
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Mat distanceCPU(distance);
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matchConvert(trainIdxCPU, distanceCPU, matches);
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}
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void cv::gpu::BFMatcher_GPU::matchConvert(const Mat& trainIdx, const Mat& distance, std::vector<DMatch>& matches)
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{
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if (trainIdx.empty() || distance.empty())
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return;
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CV_Assert(trainIdx.type() == CV_32SC1);
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CV_Assert(distance.type() == CV_32FC1 && distance.cols == trainIdx.cols);
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const int nQuery = trainIdx.cols;
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matches.clear();
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matches.reserve(nQuery);
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const int* trainIdx_ptr = trainIdx.ptr<int>();
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const float* distance_ptr = distance.ptr<float>();
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for (int queryIdx = 0; queryIdx < nQuery; ++queryIdx, ++trainIdx_ptr, ++distance_ptr)
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{
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int train_idx = *trainIdx_ptr;
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if (train_idx == -1)
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continue;
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float distance_local = *distance_ptr;
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DMatch m(queryIdx, train_idx, 0, distance_local);
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matches.push_back(m);
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}
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}
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void cv::gpu::BFMatcher_GPU::match(const GpuMat& query, const GpuMat& train,
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std::vector<DMatch>& matches, const GpuMat& mask)
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{
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GpuMat trainIdx, distance;
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matchSingle(query, train, trainIdx, distance, mask);
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matchDownload(trainIdx, distance, matches);
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}
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void cv::gpu::BFMatcher_GPU::makeGpuCollection(GpuMat& trainCollection, GpuMat& maskCollection,
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const std::vector<GpuMat>& masks)
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{
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if (empty())
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return;
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if (masks.empty())
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{
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Mat trainCollectionCPU(1, static_cast<int>(trainDescCollection.size()), CV_8UC(sizeof(PtrStepSzb)));
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PtrStepSzb* trainCollectionCPU_ptr = trainCollectionCPU.ptr<PtrStepSzb>();
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for (size_t i = 0, size = trainDescCollection.size(); i < size; ++i, ++trainCollectionCPU_ptr)
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*trainCollectionCPU_ptr = trainDescCollection[i];
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trainCollection.upload(trainCollectionCPU);
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maskCollection.release();
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}
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else
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{
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CV_Assert(masks.size() == trainDescCollection.size());
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Mat trainCollectionCPU(1, static_cast<int>(trainDescCollection.size()), CV_8UC(sizeof(PtrStepSzb)));
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Mat maskCollectionCPU(1, static_cast<int>(trainDescCollection.size()), CV_8UC(sizeof(PtrStepb)));
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PtrStepSzb* trainCollectionCPU_ptr = trainCollectionCPU.ptr<PtrStepSzb>();
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PtrStepb* maskCollectionCPU_ptr = maskCollectionCPU.ptr<PtrStepb>();
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for (size_t i = 0, size = trainDescCollection.size(); i < size; ++i, ++trainCollectionCPU_ptr, ++maskCollectionCPU_ptr)
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{
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const GpuMat& train = trainDescCollection[i];
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const GpuMat& mask = masks[i];
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CV_Assert(mask.empty() || (mask.type() == CV_8UC1 && mask.cols == train.rows));
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*trainCollectionCPU_ptr = train;
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*maskCollectionCPU_ptr = mask;
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}
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trainCollection.upload(trainCollectionCPU);
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maskCollection.upload(maskCollectionCPU);
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}
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}
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void cv::gpu::BFMatcher_GPU::matchCollection(const GpuMat& query, const GpuMat& trainCollection,
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GpuMat& trainIdx, GpuMat& imgIdx, GpuMat& distance,
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const GpuMat& masks, Stream& stream)
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{
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if (query.empty() || trainCollection.empty())
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return;
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using namespace cv::gpu::device::bf_match;
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typedef void (*caller_t)(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks,
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const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance,
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cudaStream_t stream);
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static const caller_t callersL1[] =
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{
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matchL1_gpu<unsigned char>, 0/*matchL1_gpu<signed char>*/,
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matchL1_gpu<unsigned short>, matchL1_gpu<short>,
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matchL1_gpu<int>, matchL1_gpu<float>
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};
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static const caller_t callersL2[] =
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{
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0/*matchL2_gpu<unsigned char>*/, 0/*matchL2_gpu<signed char>*/,
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0/*matchL2_gpu<unsigned short>*/, 0/*matchL2_gpu<short>*/,
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0/*matchL2_gpu<int>*/, matchL2_gpu<float>
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};
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static const caller_t callersHamming[] =
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{
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matchHamming_gpu<unsigned char>, 0/*matchHamming_gpu<signed char>*/,
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matchHamming_gpu<unsigned short>, 0/*matchHamming_gpu<short>*/,
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matchHamming_gpu<int>, 0/*matchHamming_gpu<float>*/
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|
};
|
|
|
|
CV_Assert(query.channels() == 1 && query.depth() < CV_64F);
|
|
CV_Assert(norm == NORM_L1 || norm == NORM_L2 || norm == NORM_HAMMING);
|
|
|
|
const caller_t* callers = norm == NORM_L1 ? callersL1 : norm == NORM_L2 ? callersL2 : callersHamming;
|
|
|
|
const int nQuery = query.rows;
|
|
|
|
ensureSizeIsEnough(1, nQuery, CV_32S, trainIdx);
|
|
ensureSizeIsEnough(1, nQuery, CV_32S, imgIdx);
|
|
ensureSizeIsEnough(1, nQuery, CV_32F, distance);
|
|
|
|
caller_t func = callers[query.depth()];
|
|
CV_Assert(func != 0);
|
|
|
|
func(query, trainCollection, masks, trainIdx, imgIdx, distance, StreamAccessor::getStream(stream));
|
|
}
|
|
|
|
void cv::gpu::BFMatcher_GPU::matchDownload(const GpuMat& trainIdx, const GpuMat& imgIdx, const GpuMat& distance, std::vector<DMatch>& matches)
|
|
{
|
|
if (trainIdx.empty() || imgIdx.empty() || distance.empty())
|
|
return;
|
|
|
|
Mat trainIdxCPU(trainIdx);
|
|
Mat imgIdxCPU(imgIdx);
|
|
Mat distanceCPU(distance);
|
|
|
|
matchConvert(trainIdxCPU, imgIdxCPU, distanceCPU, matches);
|
|
}
|
|
|
|
void cv::gpu::BFMatcher_GPU::matchConvert(const Mat& trainIdx, const Mat& imgIdx, const Mat& distance, std::vector<DMatch>& matches)
|
|
{
|
|
if (trainIdx.empty() || imgIdx.empty() || distance.empty())
|
|
return;
|
|
|
|
CV_Assert(trainIdx.type() == CV_32SC1);
|
|
CV_Assert(imgIdx.type() == CV_32SC1 && imgIdx.cols == trainIdx.cols);
|
|
CV_Assert(distance.type() == CV_32FC1 && distance.cols == trainIdx.cols);
|
|
|
|
const int nQuery = trainIdx.cols;
|
|
|
|
matches.clear();
|
|
matches.reserve(nQuery);
|
|
|
|
const int* trainIdx_ptr = trainIdx.ptr<int>();
|
|
const int* imgIdx_ptr = imgIdx.ptr<int>();
|
|
const float* distance_ptr = distance.ptr<float>();
|
|
for (int queryIdx = 0; queryIdx < nQuery; ++queryIdx, ++trainIdx_ptr, ++imgIdx_ptr, ++distance_ptr)
|
|
{
|
|
int _trainIdx = *trainIdx_ptr;
|
|
|
|
if (_trainIdx == -1)
|
|
continue;
|
|
|
|
int _imgIdx = *imgIdx_ptr;
|
|
|
|
float _distance = *distance_ptr;
|
|
|
|
DMatch m(queryIdx, _trainIdx, _imgIdx, _distance);
|
|
|
|
matches.push_back(m);
|
|
}
|
|
}
|
|
|
|
void cv::gpu::BFMatcher_GPU::match(const GpuMat& query, std::vector<DMatch>& matches, const std::vector<GpuMat>& masks)
|
|
{
|
|
GpuMat trainCollection;
|
|
GpuMat maskCollection;
|
|
|
|
makeGpuCollection(trainCollection, maskCollection, masks);
|
|
|
|
GpuMat trainIdx, imgIdx, distance;
|
|
|
|
matchCollection(query, trainCollection, trainIdx, imgIdx, distance, maskCollection);
|
|
matchDownload(trainIdx, imgIdx, distance, matches);
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////
|
|
// KnnMatch
|
|
|
|
void cv::gpu::BFMatcher_GPU::knnMatchSingle(const GpuMat& query, const GpuMat& train,
|
|
GpuMat& trainIdx, GpuMat& distance, GpuMat& allDist, int k,
|
|
const GpuMat& mask, Stream& stream)
|
|
{
|
|
if (query.empty() || train.empty())
|
|
return;
|
|
|
|
using namespace cv::gpu::device::bf_knnmatch;
|
|
|
|
typedef void (*caller_t)(const PtrStepSzb& query, const PtrStepSzb& train, int k, const PtrStepSzb& mask,
|
|
const PtrStepSzb& trainIdx, const PtrStepSzb& distance, const PtrStepSzf& allDist,
|
|
cudaStream_t stream);
|
|
|
|
static const caller_t callersL1[] =
|
|
{
|
|
matchL1_gpu<unsigned char>, 0/*matchL1_gpu<signed char>*/,
|
|
matchL1_gpu<unsigned short>, matchL1_gpu<short>,
|
|
matchL1_gpu<int>, matchL1_gpu<float>
|
|
};
|
|
static const caller_t callersL2[] =
|
|
{
|
|
0/*matchL2_gpu<unsigned char>*/, 0/*matchL2_gpu<signed char>*/,
|
|
0/*matchL2_gpu<unsigned short>*/, 0/*matchL2_gpu<short>*/,
|
|
0/*matchL2_gpu<int>*/, matchL2_gpu<float>
|
|
};
|
|
static const caller_t callersHamming[] =
|
|
{
|
|
matchHamming_gpu<unsigned char>, 0/*matchHamming_gpu<signed char>*/,
|
|
matchHamming_gpu<unsigned short>, 0/*matchHamming_gpu<short>*/,
|
|
matchHamming_gpu<int>, 0/*matchHamming_gpu<float>*/
|
|
};
|
|
|
|
CV_Assert(query.channels() == 1 && query.depth() < CV_64F);
|
|
CV_Assert(train.type() == query.type() && train.cols == query.cols);
|
|
CV_Assert(norm == NORM_L1 || norm == NORM_L2 || norm == NORM_HAMMING);
|
|
|
|
const caller_t* callers = norm == NORM_L1 ? callersL1 : norm == NORM_L2 ? callersL2 : callersHamming;
|
|
|
|
const int nQuery = query.rows;
|
|
const int nTrain = train.rows;
|
|
|
|
if (k == 2)
|
|
{
|
|
ensureSizeIsEnough(1, nQuery, CV_32SC2, trainIdx);
|
|
ensureSizeIsEnough(1, nQuery, CV_32FC2, distance);
|
|
}
|
|
else
|
|
{
|
|
ensureSizeIsEnough(nQuery, k, CV_32S, trainIdx);
|
|
ensureSizeIsEnough(nQuery, k, CV_32F, distance);
|
|
ensureSizeIsEnough(nQuery, nTrain, CV_32FC1, allDist);
|
|
}
|
|
|
|
if (stream)
|
|
stream.enqueueMemSet(trainIdx, Scalar::all(-1));
|
|
else
|
|
trainIdx.setTo(Scalar::all(-1));
|
|
|
|
caller_t func = callers[query.depth()];
|
|
CV_Assert(func != 0);
|
|
|
|
func(query, train, k, mask, trainIdx, distance, allDist, StreamAccessor::getStream(stream));
|
|
}
|
|
|
|
void cv::gpu::BFMatcher_GPU::knnMatchDownload(const GpuMat& trainIdx, const GpuMat& distance,
|
|
std::vector< std::vector<DMatch> >& matches, bool compactResult)
|
|
{
|
|
if (trainIdx.empty() || distance.empty())
|
|
return;
|
|
|
|
Mat trainIdxCPU(trainIdx);
|
|
Mat distanceCPU(distance);
|
|
|
|
knnMatchConvert(trainIdxCPU, distanceCPU, matches, compactResult);
|
|
}
|
|
|
|
void cv::gpu::BFMatcher_GPU::knnMatchConvert(const Mat& trainIdx, const Mat& distance,
|
|
std::vector< std::vector<DMatch> >& matches, bool compactResult)
|
|
{
|
|
if (trainIdx.empty() || distance.empty())
|
|
return;
|
|
|
|
CV_Assert(trainIdx.type() == CV_32SC2 || trainIdx.type() == CV_32SC1);
|
|
CV_Assert(distance.type() == CV_32FC2 || distance.type() == CV_32FC1);
|
|
CV_Assert(distance.size() == trainIdx.size());
|
|
CV_Assert(trainIdx.isContinuous() && distance.isContinuous());
|
|
|
|
const int nQuery = trainIdx.type() == CV_32SC2 ? trainIdx.cols : trainIdx.rows;
|
|
const int k = trainIdx.type() == CV_32SC2 ? 2 :trainIdx.cols;
|
|
|
|
matches.clear();
|
|
matches.reserve(nQuery);
|
|
|
|
const int* trainIdx_ptr = trainIdx.ptr<int>();
|
|
const float* distance_ptr = distance.ptr<float>();
|
|
|
|
for (int queryIdx = 0; queryIdx < nQuery; ++queryIdx)
|
|
{
|
|
matches.push_back(std::vector<DMatch>());
|
|
std::vector<DMatch>& curMatches = matches.back();
|
|
curMatches.reserve(k);
|
|
|
|
for (int i = 0; i < k; ++i, ++trainIdx_ptr, ++distance_ptr)
|
|
{
|
|
int _trainIdx = *trainIdx_ptr;
|
|
|
|
if (_trainIdx != -1)
|
|
{
|
|
float _distance = *distance_ptr;
|
|
|
|
DMatch m(queryIdx, _trainIdx, 0, _distance);
|
|
|
|
curMatches.push_back(m);
|
|
}
|
|
}
|
|
|
|
if (compactResult && curMatches.empty())
|
|
matches.pop_back();
|
|
}
|
|
}
|
|
|
|
void cv::gpu::BFMatcher_GPU::knnMatch(const GpuMat& query, const GpuMat& train,
|
|
std::vector< std::vector<DMatch> >& matches, int k, const GpuMat& mask, bool compactResult)
|
|
{
|
|
GpuMat trainIdx, distance, allDist;
|
|
knnMatchSingle(query, train, trainIdx, distance, allDist, k, mask);
|
|
knnMatchDownload(trainIdx, distance, matches, compactResult);
|
|
}
|
|
|
|
void cv::gpu::BFMatcher_GPU::knnMatch2Collection(const GpuMat& query, const GpuMat& trainCollection,
|
|
GpuMat& trainIdx, GpuMat& imgIdx, GpuMat& distance,
|
|
const GpuMat& maskCollection, Stream& stream)
|
|
{
|
|
if (query.empty() || trainCollection.empty())
|
|
return;
|
|
|
|
using namespace cv::gpu::device::bf_knnmatch;
|
|
|
|
typedef void (*caller_t)(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks,
|
|
const PtrStepSzb& trainIdx, const PtrStepSzb& imgIdx, const PtrStepSzb& distance,
|
|
cudaStream_t stream);
|
|
|
|
static const caller_t callersL1[] =
|
|
{
|
|
match2L1_gpu<unsigned char>, 0/*match2L1_gpu<signed char>*/,
|
|
match2L1_gpu<unsigned short>, match2L1_gpu<short>,
|
|
match2L1_gpu<int>, match2L1_gpu<float>
|
|
};
|
|
static const caller_t callersL2[] =
|
|
{
|
|
0/*match2L2_gpu<unsigned char>*/, 0/*match2L2_gpu<signed char>*/,
|
|
0/*match2L2_gpu<unsigned short>*/, 0/*match2L2_gpu<short>*/,
|
|
0/*match2L2_gpu<int>*/, match2L2_gpu<float>
|
|
};
|
|
static const caller_t callersHamming[] =
|
|
{
|
|
match2Hamming_gpu<unsigned char>, 0/*match2Hamming_gpu<signed char>*/,
|
|
match2Hamming_gpu<unsigned short>, 0/*match2Hamming_gpu<short>*/,
|
|
match2Hamming_gpu<int>, 0/*match2Hamming_gpu<float>*/
|
|
};
|
|
|
|
CV_Assert(query.channels() == 1 && query.depth() < CV_64F);
|
|
CV_Assert(norm == NORM_L1 || norm == NORM_L2 || norm == NORM_HAMMING);
|
|
|
|
const caller_t* callers = norm == NORM_L1 ? callersL1 : norm == NORM_L2 ? callersL2 : callersHamming;
|
|
|
|
const int nQuery = query.rows;
|
|
|
|
ensureSizeIsEnough(1, nQuery, CV_32SC2, trainIdx);
|
|
ensureSizeIsEnough(1, nQuery, CV_32SC2, imgIdx);
|
|
ensureSizeIsEnough(1, nQuery, CV_32FC2, distance);
|
|
|
|
if (stream)
|
|
stream.enqueueMemSet(trainIdx, Scalar::all(-1));
|
|
else
|
|
trainIdx.setTo(Scalar::all(-1));
|
|
|
|
caller_t func = callers[query.depth()];
|
|
CV_Assert(func != 0);
|
|
|
|
func(query, trainCollection, maskCollection, trainIdx, imgIdx, distance, StreamAccessor::getStream(stream));
|
|
}
|
|
|
|
void cv::gpu::BFMatcher_GPU::knnMatch2Download(const GpuMat& trainIdx, const GpuMat& imgIdx, const GpuMat& distance,
|
|
std::vector< std::vector<DMatch> >& matches, bool compactResult)
|
|
{
|
|
if (trainIdx.empty() || imgIdx.empty() || distance.empty())
|
|
return;
|
|
|
|
Mat trainIdxCPU(trainIdx);
|
|
Mat imgIdxCPU(imgIdx);
|
|
Mat distanceCPU(distance);
|
|
|
|
knnMatch2Convert(trainIdxCPU, imgIdxCPU, distanceCPU, matches, compactResult);
|
|
}
|
|
|
|
void cv::gpu::BFMatcher_GPU::knnMatch2Convert(const Mat& trainIdx, const Mat& imgIdx, const Mat& distance,
|
|
std::vector< std::vector<DMatch> >& matches, bool compactResult)
|
|
{
|
|
if (trainIdx.empty() || imgIdx.empty() || distance.empty())
|
|
return;
|
|
|
|
CV_Assert(trainIdx.type() == CV_32SC2);
|
|
CV_Assert(imgIdx.type() == CV_32SC2 && imgIdx.cols == trainIdx.cols);
|
|
CV_Assert(distance.type() == CV_32FC2 && distance.cols == trainIdx.cols);
|
|
|
|
const int nQuery = trainIdx.cols;
|
|
|
|
matches.clear();
|
|
matches.reserve(nQuery);
|
|
|
|
const int* trainIdx_ptr = trainIdx.ptr<int>();
|
|
const int* imgIdx_ptr = imgIdx.ptr<int>();
|
|
const float* distance_ptr = distance.ptr<float>();
|
|
|
|
for (int queryIdx = 0; queryIdx < nQuery; ++queryIdx)
|
|
{
|
|
matches.push_back(std::vector<DMatch>());
|
|
std::vector<DMatch>& curMatches = matches.back();
|
|
curMatches.reserve(2);
|
|
|
|
for (int i = 0; i < 2; ++i, ++trainIdx_ptr, ++imgIdx_ptr, ++distance_ptr)
|
|
{
|
|
int _trainIdx = *trainIdx_ptr;
|
|
|
|
if (_trainIdx != -1)
|
|
{
|
|
int _imgIdx = *imgIdx_ptr;
|
|
|
|
float _distance = *distance_ptr;
|
|
|
|
DMatch m(queryIdx, _trainIdx, _imgIdx, _distance);
|
|
|
|
curMatches.push_back(m);
|
|
}
|
|
}
|
|
|
|
if (compactResult && curMatches.empty())
|
|
matches.pop_back();
|
|
}
|
|
}
|
|
|
|
namespace
|
|
{
|
|
struct ImgIdxSetter
|
|
{
|
|
explicit inline ImgIdxSetter(int imgIdx_) : imgIdx(imgIdx_) {}
|
|
inline void operator()(DMatch& m) const {m.imgIdx = imgIdx;}
|
|
int imgIdx;
|
|
};
|
|
}
|
|
|
|
void cv::gpu::BFMatcher_GPU::knnMatch(const GpuMat& query, std::vector< std::vector<DMatch> >& matches, int k,
|
|
const std::vector<GpuMat>& masks, bool compactResult)
|
|
{
|
|
if (k == 2)
|
|
{
|
|
GpuMat trainCollection;
|
|
GpuMat maskCollection;
|
|
|
|
makeGpuCollection(trainCollection, maskCollection, masks);
|
|
|
|
GpuMat trainIdx, imgIdx, distance;
|
|
|
|
knnMatch2Collection(query, trainCollection, trainIdx, imgIdx, distance, maskCollection);
|
|
knnMatch2Download(trainIdx, imgIdx, distance, matches);
|
|
}
|
|
else
|
|
{
|
|
if (query.empty() || empty())
|
|
return;
|
|
|
|
std::vector< std::vector<DMatch> > curMatches;
|
|
std::vector<DMatch> temp;
|
|
temp.reserve(2 * k);
|
|
|
|
matches.resize(query.rows);
|
|
for_each(matches.begin(), matches.end(), bind2nd(mem_fun_ref(&std::vector<DMatch>::reserve), k));
|
|
|
|
for (size_t imgIdx = 0, size = trainDescCollection.size(); imgIdx < size; ++imgIdx)
|
|
{
|
|
knnMatch(query, trainDescCollection[imgIdx], curMatches, k, masks.empty() ? GpuMat() : masks[imgIdx]);
|
|
|
|
for (int queryIdx = 0; queryIdx < query.rows; ++queryIdx)
|
|
{
|
|
std::vector<DMatch>& localMatch = curMatches[queryIdx];
|
|
std::vector<DMatch>& globalMatch = matches[queryIdx];
|
|
|
|
for_each(localMatch.begin(), localMatch.end(), ImgIdxSetter(static_cast<int>(imgIdx)));
|
|
|
|
temp.clear();
|
|
merge(globalMatch.begin(), globalMatch.end(), localMatch.begin(), localMatch.end(), back_inserter(temp));
|
|
|
|
globalMatch.clear();
|
|
const size_t count = std::min((size_t)k, temp.size());
|
|
copy(temp.begin(), temp.begin() + count, back_inserter(globalMatch));
|
|
}
|
|
}
|
|
|
|
if (compactResult)
|
|
{
|
|
std::vector< std::vector<DMatch> >::iterator new_end = remove_if(matches.begin(), matches.end(), mem_fun_ref(&std::vector<DMatch>::empty));
|
|
matches.erase(new_end, matches.end());
|
|
}
|
|
}
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////
|
|
// RadiusMatch
|
|
|
|
void cv::gpu::BFMatcher_GPU::radiusMatchSingle(const GpuMat& query, const GpuMat& train,
|
|
GpuMat& trainIdx, GpuMat& distance, GpuMat& nMatches, float maxDistance,
|
|
const GpuMat& mask, Stream& stream)
|
|
{
|
|
if (query.empty() || train.empty())
|
|
return;
|
|
|
|
using namespace cv::gpu::device::bf_radius_match;
|
|
|
|
typedef void (*caller_t)(const PtrStepSzb& query, const PtrStepSzb& train, float maxDistance, const PtrStepSzb& mask,
|
|
const PtrStepSzi& trainIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches,
|
|
cudaStream_t stream);
|
|
|
|
static const caller_t callersL1[] =
|
|
{
|
|
matchL1_gpu<unsigned char>, 0/*matchL1_gpu<signed char>*/,
|
|
matchL1_gpu<unsigned short>, matchL1_gpu<short>,
|
|
matchL1_gpu<int>, matchL1_gpu<float>
|
|
};
|
|
static const caller_t callersL2[] =
|
|
{
|
|
0/*matchL2_gpu<unsigned char>*/, 0/*matchL2_gpu<signed char>*/,
|
|
0/*matchL2_gpu<unsigned short>*/, 0/*matchL2_gpu<short>*/,
|
|
0/*matchL2_gpu<int>*/, matchL2_gpu<float>
|
|
};
|
|
static const caller_t callersHamming[] =
|
|
{
|
|
matchHamming_gpu<unsigned char>, 0/*matchHamming_gpu<signed char>*/,
|
|
matchHamming_gpu<unsigned short>, 0/*matchHamming_gpu<short>*/,
|
|
matchHamming_gpu<int>, 0/*matchHamming_gpu<float>*/
|
|
};
|
|
|
|
const int nQuery = query.rows;
|
|
const int nTrain = train.rows;
|
|
|
|
CV_Assert(query.channels() == 1 && query.depth() < CV_64F);
|
|
CV_Assert(train.type() == query.type() && train.cols == query.cols);
|
|
CV_Assert(trainIdx.empty() || (trainIdx.rows == nQuery && trainIdx.size() == distance.size()));
|
|
CV_Assert(norm == NORM_L1 || norm == NORM_L2 || norm == NORM_HAMMING);
|
|
|
|
const caller_t* callers = norm == NORM_L1 ? callersL1 : norm == NORM_L2 ? callersL2 : callersHamming;
|
|
|
|
ensureSizeIsEnough(1, nQuery, CV_32SC1, nMatches);
|
|
if (trainIdx.empty())
|
|
{
|
|
ensureSizeIsEnough(nQuery, std::max((nTrain / 100), 10), CV_32SC1, trainIdx);
|
|
ensureSizeIsEnough(nQuery, std::max((nTrain / 100), 10), CV_32FC1, distance);
|
|
}
|
|
|
|
if (stream)
|
|
stream.enqueueMemSet(nMatches, Scalar::all(0));
|
|
else
|
|
nMatches.setTo(Scalar::all(0));
|
|
|
|
caller_t func = callers[query.depth()];
|
|
CV_Assert(func != 0);
|
|
|
|
func(query, train, maxDistance, mask, trainIdx, distance, nMatches, StreamAccessor::getStream(stream));
|
|
}
|
|
|
|
void cv::gpu::BFMatcher_GPU::radiusMatchDownload(const GpuMat& trainIdx, const GpuMat& distance, const GpuMat& nMatches,
|
|
std::vector< std::vector<DMatch> >& matches, bool compactResult)
|
|
{
|
|
if (trainIdx.empty() || distance.empty() || nMatches.empty())
|
|
return;
|
|
|
|
Mat trainIdxCPU(trainIdx);
|
|
Mat distanceCPU(distance);
|
|
Mat nMatchesCPU(nMatches);
|
|
|
|
radiusMatchConvert(trainIdxCPU, distanceCPU, nMatchesCPU, matches, compactResult);
|
|
}
|
|
|
|
void cv::gpu::BFMatcher_GPU::radiusMatchConvert(const Mat& trainIdx, const Mat& distance, const Mat& nMatches,
|
|
std::vector< std::vector<DMatch> >& matches, bool compactResult)
|
|
{
|
|
if (trainIdx.empty() || distance.empty() || nMatches.empty())
|
|
return;
|
|
|
|
CV_Assert(trainIdx.type() == CV_32SC1);
|
|
CV_Assert(distance.type() == CV_32FC1 && distance.size() == trainIdx.size());
|
|
CV_Assert(nMatches.type() == CV_32SC1 && nMatches.cols == trainIdx.rows);
|
|
|
|
const int nQuery = trainIdx.rows;
|
|
|
|
matches.clear();
|
|
matches.reserve(nQuery);
|
|
|
|
const int* nMatches_ptr = nMatches.ptr<int>();
|
|
|
|
for (int queryIdx = 0; queryIdx < nQuery; ++queryIdx)
|
|
{
|
|
const int* trainIdx_ptr = trainIdx.ptr<int>(queryIdx);
|
|
const float* distance_ptr = distance.ptr<float>(queryIdx);
|
|
|
|
const int nMatched = std::min(nMatches_ptr[queryIdx], trainIdx.cols);
|
|
|
|
if (nMatched == 0)
|
|
{
|
|
if (!compactResult)
|
|
matches.push_back(std::vector<DMatch>());
|
|
continue;
|
|
}
|
|
|
|
matches.push_back(std::vector<DMatch>(nMatched));
|
|
std::vector<DMatch>& curMatches = matches.back();
|
|
|
|
for (int i = 0; i < nMatched; ++i, ++trainIdx_ptr, ++distance_ptr)
|
|
{
|
|
int _trainIdx = *trainIdx_ptr;
|
|
|
|
float _distance = *distance_ptr;
|
|
|
|
DMatch m(queryIdx, _trainIdx, 0, _distance);
|
|
|
|
curMatches[i] = m;
|
|
}
|
|
|
|
sort(curMatches.begin(), curMatches.end());
|
|
}
|
|
}
|
|
|
|
void cv::gpu::BFMatcher_GPU::radiusMatch(const GpuMat& query, const GpuMat& train,
|
|
std::vector< std::vector<DMatch> >& matches, float maxDistance, const GpuMat& mask, bool compactResult)
|
|
{
|
|
GpuMat trainIdx, distance, nMatches;
|
|
radiusMatchSingle(query, train, trainIdx, distance, nMatches, maxDistance, mask);
|
|
radiusMatchDownload(trainIdx, distance, nMatches, matches, compactResult);
|
|
}
|
|
|
|
void cv::gpu::BFMatcher_GPU::radiusMatchCollection(const GpuMat& query, GpuMat& trainIdx, GpuMat& imgIdx, GpuMat& distance, GpuMat& nMatches,
|
|
float maxDistance, const std::vector<GpuMat>& masks, Stream& stream)
|
|
{
|
|
if (query.empty() || empty())
|
|
return;
|
|
|
|
using namespace cv::gpu::device::bf_radius_match;
|
|
|
|
typedef void (*caller_t)(const PtrStepSzb& query, const PtrStepSzb* trains, int n, float maxDistance, const PtrStepSzb* masks,
|
|
const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches,
|
|
cudaStream_t stream);
|
|
|
|
static const caller_t callersL1[] =
|
|
{
|
|
matchL1_gpu<unsigned char>, 0/*matchL1_gpu<signed char>*/,
|
|
matchL1_gpu<unsigned short>, matchL1_gpu<short>,
|
|
matchL1_gpu<int>, matchL1_gpu<float>
|
|
};
|
|
static const caller_t callersL2[] =
|
|
{
|
|
0/*matchL2_gpu<unsigned char>*/, 0/*matchL2_gpu<signed char>*/,
|
|
0/*matchL2_gpu<unsigned short>*/, 0/*matchL2_gpu<short>*/,
|
|
0/*matchL2_gpu<int>*/, matchL2_gpu<float>
|
|
};
|
|
static const caller_t callersHamming[] =
|
|
{
|
|
matchHamming_gpu<unsigned char>, 0/*matchHamming_gpu<signed char>*/,
|
|
matchHamming_gpu<unsigned short>, 0/*matchHamming_gpu<short>*/,
|
|
matchHamming_gpu<int>, 0/*matchHamming_gpu<float>*/
|
|
};
|
|
|
|
const int nQuery = query.rows;
|
|
|
|
CV_Assert(query.channels() == 1 && query.depth() < CV_64F);
|
|
CV_Assert(trainIdx.empty() || (trainIdx.rows == nQuery && trainIdx.size() == distance.size() && trainIdx.size() == imgIdx.size()));
|
|
CV_Assert(norm == NORM_L1 || norm == NORM_L2 || norm == NORM_HAMMING);
|
|
|
|
const caller_t* callers = norm == NORM_L1 ? callersL1 : norm == NORM_L2 ? callersL2 : callersHamming;
|
|
|
|
ensureSizeIsEnough(1, nQuery, CV_32SC1, nMatches);
|
|
if (trainIdx.empty())
|
|
{
|
|
ensureSizeIsEnough(nQuery, std::max((nQuery / 100), 10), CV_32SC1, trainIdx);
|
|
ensureSizeIsEnough(nQuery, std::max((nQuery / 100), 10), CV_32SC1, imgIdx);
|
|
ensureSizeIsEnough(nQuery, std::max((nQuery / 100), 10), CV_32FC1, distance);
|
|
}
|
|
|
|
if (stream)
|
|
stream.enqueueMemSet(nMatches, Scalar::all(0));
|
|
else
|
|
nMatches.setTo(Scalar::all(0));
|
|
|
|
caller_t func = callers[query.depth()];
|
|
CV_Assert(func != 0);
|
|
|
|
std::vector<PtrStepSzb> trains_(trainDescCollection.begin(), trainDescCollection.end());
|
|
std::vector<PtrStepSzb> masks_(masks.begin(), masks.end());
|
|
|
|
func(query, &trains_[0], static_cast<int>(trains_.size()), maxDistance, masks_.size() == 0 ? 0 : &masks_[0],
|
|
trainIdx, imgIdx, distance, nMatches, StreamAccessor::getStream(stream));
|
|
}
|
|
|
|
void cv::gpu::BFMatcher_GPU::radiusMatchDownload(const GpuMat& trainIdx, const GpuMat& imgIdx, const GpuMat& distance, const GpuMat& nMatches,
|
|
std::vector< std::vector<DMatch> >& matches, bool compactResult)
|
|
{
|
|
if (trainIdx.empty() || imgIdx.empty() || distance.empty() || nMatches.empty())
|
|
return;
|
|
|
|
Mat trainIdxCPU(trainIdx);
|
|
Mat imgIdxCPU(imgIdx);
|
|
Mat distanceCPU(distance);
|
|
Mat nMatchesCPU(nMatches);
|
|
|
|
radiusMatchConvert(trainIdxCPU, imgIdxCPU, distanceCPU, nMatchesCPU, matches, compactResult);
|
|
}
|
|
|
|
void cv::gpu::BFMatcher_GPU::radiusMatchConvert(const Mat& trainIdx, const Mat& imgIdx, const Mat& distance, const Mat& nMatches,
|
|
std::vector< std::vector<DMatch> >& matches, bool compactResult)
|
|
{
|
|
if (trainIdx.empty() || imgIdx.empty() || distance.empty() || nMatches.empty())
|
|
return;
|
|
|
|
CV_Assert(trainIdx.type() == CV_32SC1);
|
|
CV_Assert(imgIdx.type() == CV_32SC1 && imgIdx.size() == trainIdx.size());
|
|
CV_Assert(distance.type() == CV_32FC1 && distance.size() == trainIdx.size());
|
|
CV_Assert(nMatches.type() == CV_32SC1 && nMatches.cols == trainIdx.rows);
|
|
|
|
const int nQuery = trainIdx.rows;
|
|
|
|
matches.clear();
|
|
matches.reserve(nQuery);
|
|
|
|
const int* nMatches_ptr = nMatches.ptr<int>();
|
|
|
|
for (int queryIdx = 0; queryIdx < nQuery; ++queryIdx)
|
|
{
|
|
const int* trainIdx_ptr = trainIdx.ptr<int>(queryIdx);
|
|
const int* imgIdx_ptr = imgIdx.ptr<int>(queryIdx);
|
|
const float* distance_ptr = distance.ptr<float>(queryIdx);
|
|
|
|
const int nMatched = std::min(nMatches_ptr[queryIdx], trainIdx.cols);
|
|
|
|
if (nMatched == 0)
|
|
{
|
|
if (!compactResult)
|
|
matches.push_back(std::vector<DMatch>());
|
|
continue;
|
|
}
|
|
|
|
matches.push_back(std::vector<DMatch>());
|
|
std::vector<DMatch>& curMatches = matches.back();
|
|
curMatches.reserve(nMatched);
|
|
|
|
for (int i = 0; i < nMatched; ++i, ++trainIdx_ptr, ++imgIdx_ptr, ++distance_ptr)
|
|
{
|
|
int _trainIdx = *trainIdx_ptr;
|
|
int _imgIdx = *imgIdx_ptr;
|
|
float _distance = *distance_ptr;
|
|
|
|
DMatch m(queryIdx, _trainIdx, _imgIdx, _distance);
|
|
|
|
curMatches.push_back(m);
|
|
}
|
|
|
|
sort(curMatches.begin(), curMatches.end());
|
|
}
|
|
}
|
|
|
|
void cv::gpu::BFMatcher_GPU::radiusMatch(const GpuMat& query, std::vector< std::vector<DMatch> >& matches,
|
|
float maxDistance, const std::vector<GpuMat>& masks, bool compactResult)
|
|
{
|
|
GpuMat trainIdx, imgIdx, distance, nMatches;
|
|
radiusMatchCollection(query, trainIdx, imgIdx, distance, nMatches, maxDistance, masks);
|
|
radiusMatchDownload(trainIdx, imgIdx, distance, nMatches, matches, compactResult);
|
|
}
|
|
|
|
#endif /* !defined (HAVE_CUDA) */
|