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Open Source Computer Vision Library
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335 lines
10 KiB
335 lines
10 KiB
/*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 materials 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 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 "perf_precomp.hpp" |
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#include "opencv2/ts/gpu_perf.hpp" |
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using namespace std; |
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using namespace testing; |
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using namespace perf; |
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////////////////////////////////////////////////////////////////////// |
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// FAST |
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DEF_PARAM_TEST(Image_Threshold_NonMaxSuppression, string, int, bool); |
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PERF_TEST_P(Image_Threshold_NonMaxSuppression, Features2D_FAST, |
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Combine(Values<string>("gpu/perf/aloe.png"), |
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Values(20), |
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Bool())) |
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{ |
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const cv::Mat img = readImage(GET_PARAM(0), cv::IMREAD_GRAYSCALE); |
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ASSERT_FALSE(img.empty()); |
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const int threshold = GET_PARAM(1); |
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const bool nonMaxSuppersion = GET_PARAM(2); |
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if (PERF_RUN_GPU()) |
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{ |
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cv::gpu::FAST_GPU d_fast(threshold, nonMaxSuppersion, 0.5); |
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const cv::gpu::GpuMat d_img(img); |
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cv::gpu::GpuMat d_keypoints; |
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TEST_CYCLE() d_fast(d_img, cv::gpu::GpuMat(), d_keypoints); |
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std::vector<cv::KeyPoint> gpu_keypoints; |
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d_fast.downloadKeypoints(d_keypoints, gpu_keypoints); |
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sortKeyPoints(gpu_keypoints); |
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SANITY_CHECK_KEYPOINTS(gpu_keypoints); |
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} |
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else |
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{ |
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std::vector<cv::KeyPoint> cpu_keypoints; |
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TEST_CYCLE() cv::FAST(img, cpu_keypoints, threshold, nonMaxSuppersion); |
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SANITY_CHECK_KEYPOINTS(cpu_keypoints); |
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} |
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} |
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////////////////////////////////////////////////////////////////////// |
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// ORB |
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DEF_PARAM_TEST(Image_NFeatures, string, int); |
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PERF_TEST_P(Image_NFeatures, Features2D_ORB, |
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Combine(Values<string>("gpu/perf/aloe.png"), |
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Values(4000))) |
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{ |
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declare.time(300.0); |
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const cv::Mat img = readImage(GET_PARAM(0), cv::IMREAD_GRAYSCALE); |
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ASSERT_FALSE(img.empty()); |
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const int nFeatures = GET_PARAM(1); |
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if (PERF_RUN_GPU()) |
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{ |
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cv::gpu::ORB_GPU d_orb(nFeatures); |
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const cv::gpu::GpuMat d_img(img); |
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cv::gpu::GpuMat d_keypoints, d_descriptors; |
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TEST_CYCLE() d_orb(d_img, cv::gpu::GpuMat(), d_keypoints, d_descriptors); |
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std::vector<cv::KeyPoint> gpu_keypoints; |
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d_orb.downloadKeyPoints(d_keypoints, gpu_keypoints); |
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cv::Mat gpu_descriptors(d_descriptors); |
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gpu_keypoints.resize(10); |
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gpu_descriptors = gpu_descriptors.rowRange(0, 10); |
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sortKeyPoints(gpu_keypoints, gpu_descriptors); |
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SANITY_CHECK_KEYPOINTS(gpu_keypoints, 1e-10); |
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SANITY_CHECK(gpu_descriptors); |
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} |
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else |
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{ |
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cv::ORB orb(nFeatures); |
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std::vector<cv::KeyPoint> cpu_keypoints; |
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cv::Mat cpu_descriptors; |
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TEST_CYCLE() orb(img, cv::noArray(), cpu_keypoints, cpu_descriptors); |
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SANITY_CHECK_KEYPOINTS(cpu_keypoints); |
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SANITY_CHECK(cpu_descriptors); |
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} |
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} |
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////////////////////////////////////////////////////////////////////// |
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// BFMatch |
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DEF_PARAM_TEST(DescSize_Norm, int, NormType); |
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#ifdef OPENCV_TINY_GPU_MODULE |
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PERF_TEST_P(DescSize_Norm, Features2D_BFMatch, Combine( |
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Values(64, 128, 256), |
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Values(NormType(cv::NORM_L2), NormType(cv::NORM_HAMMING)) |
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)) |
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#else |
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PERF_TEST_P(DescSize_Norm, Features2D_BFMatch, Combine( |
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Values(64, 128, 256), |
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Values(NormType(cv::NORM_L1), NormType(cv::NORM_L2), NormType(cv::NORM_HAMMING)) |
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)) |
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#endif |
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{ |
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declare.time(20.0); |
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const int desc_size = GET_PARAM(0); |
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const int normType = GET_PARAM(1); |
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const int type = normType == cv::NORM_HAMMING ? CV_8U : CV_32F; |
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cv::Mat query(3000, desc_size, type); |
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declare.in(query, WARMUP_RNG); |
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cv::Mat train(3000, desc_size, type); |
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declare.in(train, WARMUP_RNG); |
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if (PERF_RUN_GPU()) |
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{ |
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cv::gpu::BFMatcher_GPU d_matcher(normType); |
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const cv::gpu::GpuMat d_query(query); |
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const cv::gpu::GpuMat d_train(train); |
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cv::gpu::GpuMat d_trainIdx, d_distance; |
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TEST_CYCLE() d_matcher.matchSingle(d_query, d_train, d_trainIdx, d_distance); |
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std::vector<cv::DMatch> gpu_matches; |
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d_matcher.matchDownload(d_trainIdx, d_distance, gpu_matches); |
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SANITY_CHECK_MATCHES(gpu_matches); |
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} |
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else |
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{ |
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cv::BFMatcher matcher(normType); |
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std::vector<cv::DMatch> cpu_matches; |
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TEST_CYCLE() matcher.match(query, train, cpu_matches); |
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SANITY_CHECK_MATCHES(cpu_matches); |
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} |
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} |
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////////////////////////////////////////////////////////////////////// |
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// BFKnnMatch |
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static void toOneRowMatches(const std::vector< std::vector<cv::DMatch> >& src, std::vector<cv::DMatch>& dst) |
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{ |
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dst.clear(); |
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for (size_t i = 0; i < src.size(); ++i) |
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for (size_t j = 0; j < src[i].size(); ++j) |
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dst.push_back(src[i][j]); |
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} |
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DEF_PARAM_TEST(DescSize_K_Norm, int, int, NormType); |
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#ifdef OPENCV_TINY_GPU_MODULE |
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PERF_TEST_P(DescSize_K_Norm, Features2D_BFKnnMatch, Combine( |
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Values(64, 128, 256), |
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Values(2, 3), |
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Values(NormType(cv::NORM_L2)) |
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)) |
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#else |
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PERF_TEST_P(DescSize_K_Norm, Features2D_BFKnnMatch, Combine( |
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Values(64, 128, 256), |
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Values(2, 3), |
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Values(NormType(cv::NORM_L1), NormType(cv::NORM_L2)) |
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)) |
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#endif |
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{ |
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declare.time(30.0); |
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const int desc_size = GET_PARAM(0); |
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const int k = GET_PARAM(1); |
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const int normType = GET_PARAM(2); |
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const int type = normType == cv::NORM_HAMMING ? CV_8U : CV_32F; |
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cv::Mat query(3000, desc_size, type); |
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declare.in(query, WARMUP_RNG); |
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cv::Mat train(3000, desc_size, type); |
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declare.in(train, WARMUP_RNG); |
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if (PERF_RUN_GPU()) |
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{ |
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cv::gpu::BFMatcher_GPU d_matcher(normType); |
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const cv::gpu::GpuMat d_query(query); |
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const cv::gpu::GpuMat d_train(train); |
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cv::gpu::GpuMat d_trainIdx, d_distance, d_allDist; |
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TEST_CYCLE() d_matcher.knnMatchSingle(d_query, d_train, d_trainIdx, d_distance, d_allDist, k); |
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std::vector< std::vector<cv::DMatch> > matchesTbl; |
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d_matcher.knnMatchDownload(d_trainIdx, d_distance, matchesTbl); |
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std::vector<cv::DMatch> gpu_matches; |
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toOneRowMatches(matchesTbl, gpu_matches); |
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SANITY_CHECK_MATCHES(gpu_matches); |
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} |
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else |
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{ |
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cv::BFMatcher matcher(normType); |
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std::vector< std::vector<cv::DMatch> > matchesTbl; |
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TEST_CYCLE() matcher.knnMatch(query, train, matchesTbl, k); |
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std::vector<cv::DMatch> cpu_matches; |
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toOneRowMatches(matchesTbl, cpu_matches); |
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SANITY_CHECK_MATCHES(cpu_matches); |
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} |
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} |
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////////////////////////////////////////////////////////////////////// |
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// BFRadiusMatch |
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#ifdef OPENCV_TINY_GPU_MODULE |
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PERF_TEST_P(DescSize_Norm, Features2D_BFRadiusMatch, Combine( |
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Values(64, 128, 256), |
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Values(NormType(cv::NORM_L2)) |
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)) |
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#else |
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PERF_TEST_P(DescSize_Norm, Features2D_BFRadiusMatch, Combine( |
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Values(64, 128, 256), |
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Values(NormType(cv::NORM_L1), NormType(cv::NORM_L2)) |
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)) |
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#endif |
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{ |
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declare.time(30.0); |
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const int desc_size = GET_PARAM(0); |
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const int normType = GET_PARAM(1); |
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const int type = normType == cv::NORM_HAMMING ? CV_8U : CV_32F; |
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const float maxDistance = 10000; |
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cv::Mat query(3000, desc_size, type); |
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declare.in(query, WARMUP_RNG); |
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cv::Mat train(3000, desc_size, type); |
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declare.in(train, WARMUP_RNG); |
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if (PERF_RUN_GPU()) |
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{ |
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cv::gpu::BFMatcher_GPU d_matcher(normType); |
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const cv::gpu::GpuMat d_query(query); |
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const cv::gpu::GpuMat d_train(train); |
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cv::gpu::GpuMat d_trainIdx, d_nMatches, d_distance; |
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TEST_CYCLE() d_matcher.radiusMatchSingle(d_query, d_train, d_trainIdx, d_distance, d_nMatches, maxDistance); |
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std::vector< std::vector<cv::DMatch> > matchesTbl; |
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d_matcher.radiusMatchDownload(d_trainIdx, d_distance, d_nMatches, matchesTbl); |
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std::vector<cv::DMatch> gpu_matches; |
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toOneRowMatches(matchesTbl, gpu_matches); |
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SANITY_CHECK_MATCHES(gpu_matches); |
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} |
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else |
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{ |
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cv::BFMatcher matcher(normType); |
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std::vector< std::vector<cv::DMatch> > matchesTbl; |
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TEST_CYCLE() matcher.radiusMatch(query, train, matchesTbl, maxDistance); |
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std::vector<cv::DMatch> cpu_matches; |
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toOneRowMatches(matchesTbl, cpu_matches); |
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SANITY_CHECK_MATCHES(cpu_matches); |
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} |
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}
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