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313 lines
9.8 KiB
C++
313 lines
9.8 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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// 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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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, 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_CUDA())
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{
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cv::Ptr<cv::cuda::FastFeatureDetector> d_fast =
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cv::cuda::FastFeatureDetector::create(threshold, nonMaxSuppersion,
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cv::FastFeatureDetector::TYPE_9_16,
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0.5 * img.size().area());
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const cv::cuda::GpuMat d_img(img);
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cv::cuda::GpuMat d_keypoints;
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TEST_CYCLE() d_fast->detectAsync(d_img, d_keypoints);
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std::vector<cv::KeyPoint> gpu_keypoints;
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d_fast->convert(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, 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_CUDA())
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{
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cv::Ptr<cv::cuda::ORB> d_orb = cv::cuda::ORB::create(nFeatures);
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const cv::cuda::GpuMat d_img(img);
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cv::cuda::GpuMat d_keypoints, d_descriptors;
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TEST_CYCLE() d_orb->detectAndComputeAsync(d_img, cv::noArray(), d_keypoints, d_descriptors);
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std::vector<cv::KeyPoint> gpu_keypoints;
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d_orb->convert(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-4);
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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::Ptr<cv::ORB> orb = cv::ORB::create(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->detectAndCompute(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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PERF_TEST_P(DescSize_Norm, BFMatch,
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Combine(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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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_CUDA())
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{
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cv::Ptr<cv::cuda::DescriptorMatcher> d_matcher = cv::cuda::DescriptorMatcher::createBFMatcher(normType);
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const cv::cuda::GpuMat d_query(query);
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const cv::cuda::GpuMat d_train(train);
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cv::cuda::GpuMat d_matches;
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TEST_CYCLE() d_matcher->matchAsync(d_query, d_train, d_matches);
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std::vector<cv::DMatch> gpu_matches;
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d_matcher->matchConvert(d_matches, 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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PERF_TEST_P(DescSize_K_Norm, BFKnnMatch,
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Combine(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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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_CUDA())
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{
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cv::Ptr<cv::cuda::DescriptorMatcher> d_matcher = cv::cuda::DescriptorMatcher::createBFMatcher(normType);
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const cv::cuda::GpuMat d_query(query);
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const cv::cuda::GpuMat d_train(train);
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cv::cuda::GpuMat d_matches;
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TEST_CYCLE() d_matcher->knnMatchAsync(d_query, d_train, d_matches, k);
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std::vector< std::vector<cv::DMatch> > matchesTbl;
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d_matcher->knnMatchConvert(d_matches, 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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PERF_TEST_P(DescSize_Norm, BFRadiusMatch,
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Combine(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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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_CUDA())
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{
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cv::Ptr<cv::cuda::DescriptorMatcher> d_matcher = cv::cuda::DescriptorMatcher::createBFMatcher(normType);
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const cv::cuda::GpuMat d_query(query);
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const cv::cuda::GpuMat d_train(train);
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cv::cuda::GpuMat d_matches;
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TEST_CYCLE() d_matcher->radiusMatchAsync(d_query, d_train, d_matches, maxDistance);
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std::vector< std::vector<cv::DMatch> > matchesTbl;
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d_matcher->radiusMatchConvert(d_matches, 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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