
changed buffer type in linear filters to float. added support of 1 channel image to linear filters. added support of BORDER_REFLECT101, BORDER_REPLICATE and BORDER_CONSTANT border type to gpu linear filters. minor fix in tests. update comments in gpu.hpp.
180 lines
6.8 KiB
C++
180 lines
6.8 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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// Intel License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000, Intel Corporation, 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 materials provided with the distribution.
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//
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// * The name of Intel Corporation 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 implied warranties, including, but not limited to, the implied
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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 "gputest.hpp"
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#include <algorithm>
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#include <iterator>
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using namespace cv;
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using namespace cv::gpu;
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using namespace std;
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class CV_GpuBruteForceMatcherTest : public CvTest
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{
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public:
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CV_GpuBruteForceMatcherTest() : CvTest( "GPU-BruteForceMatcher", "BruteForceMatcher" ) {}
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protected:
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void run(int)
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{
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try
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{
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BruteForceMatcher< L2<float> > matcherCPU;
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BruteForceMatcher_GPU< L2<float> > matcherGPU;
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vector<DMatch> matchesCPU, matchesGPU;
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vector< vector<DMatch> > knnMatchesCPU, knnMatchesGPU;
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vector< vector<DMatch> > radiusMatchesCPU, radiusMatchesGPU;
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RNG rng(*ts->get_rng());
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const int desc_len = rng.uniform(40, 300);
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Mat queryCPU(rng.uniform(100, 300), desc_len, CV_32F);
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rng.fill(queryCPU, cv::RNG::UNIFORM, cv::Scalar::all(0.0), cv::Scalar::all(10.0));
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GpuMat queryGPU(queryCPU);
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const int nTrains = rng.uniform(1, 5);
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vector<Mat> trainsCPU(nTrains);
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vector<GpuMat> trainsGPU(nTrains);
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vector<Mat> masksCPU(nTrains);
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vector<GpuMat> masksGPU(nTrains);
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for (int i = 0; i < nTrains; ++i)
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{
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Mat train(rng.uniform(100, 300), desc_len, CV_32F);
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rng.fill(train, cv::RNG::UNIFORM, cv::Scalar::all(0.0), cv::Scalar::all(10.0));
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trainsCPU[i] = train;
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trainsGPU[i].upload(train);
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bool with_mask = rng.uniform(0, 10) < 5;
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if (with_mask)
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{
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Mat mask(queryCPU.rows, train.rows, CV_8U);
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rng.fill(mask, cv::RNG::UNIFORM, cv::Scalar::all(0), cv::Scalar::all(200));
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masksCPU[i] = mask;
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masksGPU[i].upload(mask);
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}
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}
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matcherCPU.add(trainsCPU);
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matcherGPU.add(trainsGPU);
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matcherCPU.match(queryCPU, matchesCPU, masksCPU);
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matcherGPU.match(queryGPU, matchesGPU, masksGPU);
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if (!compareMatches(matchesCPU, matchesGPU))
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{
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ts->printf(CvTS::LOG, "Match FAIL\n");
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ts->set_failed_test_info(CvTS::FAIL_MISMATCH);
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return;
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}
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const int knn = rng.uniform(3, 10);
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matcherCPU.knnMatch(queryCPU, knnMatchesCPU, knn, masksCPU, true);
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matcherGPU.knnMatch(queryGPU, knnMatchesGPU, knn, masksGPU, true);
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if (!compareMatches(knnMatchesCPU, knnMatchesGPU))
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{
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ts->printf(CvTS::LOG, "KNN Match FAIL\n");
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ts->set_failed_test_info(CvTS::FAIL_MISMATCH);
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return;
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}
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const float maxDistance = rng.uniform(25.0f, 65.0f);
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matcherCPU.radiusMatch(queryCPU, radiusMatchesCPU, maxDistance, masksCPU, true);
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matcherGPU.radiusMatch(queryGPU, radiusMatchesGPU, maxDistance, masksGPU, true);
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if (!compareMatches(radiusMatchesCPU, radiusMatchesGPU))
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{
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ts->printf(CvTS::LOG, "Radius Match FAIL\n");
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ts->set_failed_test_info(CvTS::FAIL_MISMATCH);
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return;
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}
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}
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catch (const cv::Exception& e)
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{
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if (!check_and_treat_gpu_exception(e, ts))
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throw;
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return;
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}
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ts->set_failed_test_info(CvTS::OK);
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}
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private:
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static void convertMatches(const vector< vector<DMatch> >& knnMatches, vector<DMatch>& matches)
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{
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matches.clear();
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for (size_t i = 0; i < knnMatches.size(); ++i)
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copy(knnMatches[i].begin(), knnMatches[i].end(), back_inserter(matches));
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}
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struct DMatchEqual : public binary_function<DMatch, DMatch, bool>
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{
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bool operator()(const DMatch& m1, const DMatch& m2) const
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{
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return m1.imgIdx == m2.imgIdx && m1.queryIdx == m2.queryIdx && m1.trainIdx == m2.trainIdx;
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}
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};
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static bool compareMatches(const vector<DMatch>& matches1, const vector<DMatch>& matches2)
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{
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if (matches1.size() != matches2.size())
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return false;
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return equal(matches1.begin(), matches1.end(), matches2.begin(), DMatchEqual());
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}
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static bool compareMatches(const vector< vector<DMatch> >& matches1, const vector< vector<DMatch> >& matches2)
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{
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vector<DMatch> m1, m2;
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convertMatches(matches1, m1);
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convertMatches(matches2, m2);
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return compareMatches(m1, m2);
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}
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} brute_force_matcher_test; |