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Open Source Computer Vision Library
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301 lines
9.9 KiB
301 lines
9.9 KiB
#include "perf_precomp.hpp" |
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#include "opencv2/imgcodecs.hpp" |
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#include "opencv2/opencv_modules.hpp" |
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#include "opencv2/flann.hpp" |
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namespace opencv_test |
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{ |
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using namespace perf; |
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typedef TestBaseWithParam<size_t> FeaturesFinderVec; |
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typedef TestBaseWithParam<string> match; |
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typedef tuple<string, int> matchVector_t; |
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typedef TestBaseWithParam<matchVector_t> matchVector; |
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#define NUMBER_IMAGES testing::Values(1, 5, 20) |
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#define SURF_MATCH_CONFIDENCE 0.65f |
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#define ORB_MATCH_CONFIDENCE 0.3f |
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#define WORK_MEGAPIX 0.6 |
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#if defined(HAVE_OPENCV_XFEATURES2D) && defined(OPENCV_ENABLE_NONFREE) |
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#define TEST_DETECTORS testing::Values("surf", "orb") |
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#else |
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#define TEST_DETECTORS testing::Values<string>("orb") |
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#endif |
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PERF_TEST_P(FeaturesFinderVec, ParallelFeaturesFinder, NUMBER_IMAGES) |
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{ |
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Mat img = imread( getDataPath("stitching/a1.png") ); |
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vector<Mat> imgs(GetParam(), img); |
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vector<detail::ImageFeatures> features(imgs.size()); |
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Ptr<detail::FeaturesFinder> featuresFinder = makePtr<detail::OrbFeaturesFinder>(); |
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TEST_CYCLE() |
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{ |
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(*featuresFinder)(imgs, features); |
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} |
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SANITY_CHECK_NOTHING(); |
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} |
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PERF_TEST_P(FeaturesFinderVec, SerialFeaturesFinder, NUMBER_IMAGES) |
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{ |
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Mat img = imread( getDataPath("stitching/a1.png") ); |
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vector<Mat> imgs(GetParam(), img); |
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vector<detail::ImageFeatures> features(imgs.size()); |
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Ptr<detail::FeaturesFinder> featuresFinder = makePtr<detail::OrbFeaturesFinder>(); |
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TEST_CYCLE() |
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{ |
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for (size_t i = 0; i < imgs.size(); ++i) |
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(*featuresFinder)(imgs[i], features[i]); |
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} |
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SANITY_CHECK_NOTHING(); |
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} |
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PERF_TEST_P( match, bestOf2Nearest, TEST_DETECTORS) |
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{ |
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Mat img1, img1_full = imread( getDataPath("stitching/boat1.jpg") ); |
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Mat img2, img2_full = imread( getDataPath("stitching/boat2.jpg") ); |
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float scale1 = (float)std::min(1.0, sqrt(WORK_MEGAPIX * 1e6 / img1_full.total())); |
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float scale2 = (float)std::min(1.0, sqrt(WORK_MEGAPIX * 1e6 / img2_full.total())); |
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resize(img1_full, img1, Size(), scale1, scale1, INTER_LINEAR_EXACT); |
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resize(img2_full, img2, Size(), scale2, scale2, INTER_LINEAR_EXACT); |
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Ptr<detail::FeaturesFinder> finder; |
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Ptr<detail::FeaturesMatcher> matcher; |
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if (GetParam() == "surf") |
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{ |
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finder = makePtr<detail::SurfFeaturesFinder>(); |
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matcher = makePtr<detail::BestOf2NearestMatcher>(false, SURF_MATCH_CONFIDENCE); |
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} |
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else if (GetParam() == "orb") |
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{ |
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finder = makePtr<detail::OrbFeaturesFinder>(); |
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matcher = makePtr<detail::BestOf2NearestMatcher>(false, ORB_MATCH_CONFIDENCE); |
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} |
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else |
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{ |
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FAIL() << "Unknown 2D features type: " << GetParam(); |
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} |
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detail::ImageFeatures features1, features2; |
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(*finder)(img1, features1); |
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(*finder)(img2, features2); |
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detail::MatchesInfo pairwise_matches; |
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declare.in(features1.descriptors, features2.descriptors); |
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while(next()) |
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{ |
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cvflann::seed_random(42);//for predictive FlannBasedMatcher |
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startTimer(); |
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(*matcher)(features1, features2, pairwise_matches); |
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stopTimer(); |
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matcher->collectGarbage(); |
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} |
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Mat dist (pairwise_matches.H, Range::all(), Range(2, 3)); |
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Mat R (pairwise_matches.H, Range::all(), Range(0, 2)); |
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// separate transform matrix, use lower error on rotations |
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SANITY_CHECK(dist, 3., ERROR_ABSOLUTE); |
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SANITY_CHECK(R, .06, ERROR_ABSOLUTE); |
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} |
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PERF_TEST_P( matchVector, bestOf2NearestVectorFeatures, testing::Combine( |
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TEST_DETECTORS, |
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testing::Values(2, 4, 8)) |
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) |
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{ |
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Mat img1, img1_full = imread( getDataPath("stitching/boat1.jpg") ); |
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Mat img2, img2_full = imread( getDataPath("stitching/boat2.jpg") ); |
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float scale1 = (float)std::min(1.0, sqrt(WORK_MEGAPIX * 1e6 / img1_full.total())); |
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float scale2 = (float)std::min(1.0, sqrt(WORK_MEGAPIX * 1e6 / img2_full.total())); |
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resize(img1_full, img1, Size(), scale1, scale1, INTER_LINEAR_EXACT); |
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resize(img2_full, img2, Size(), scale2, scale2, INTER_LINEAR_EXACT); |
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Ptr<detail::FeaturesFinder> finder; |
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Ptr<detail::FeaturesMatcher> matcher; |
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string detectorName = get<0>(GetParam()); |
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int featuresVectorSize = get<1>(GetParam()); |
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if (detectorName == "surf") |
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{ |
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finder = makePtr<detail::SurfFeaturesFinder>(); |
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matcher = makePtr<detail::BestOf2NearestMatcher>(false, SURF_MATCH_CONFIDENCE); |
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} |
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else if (detectorName == "orb") |
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{ |
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finder = makePtr<detail::OrbFeaturesFinder>(); |
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matcher = makePtr<detail::BestOf2NearestMatcher>(false, ORB_MATCH_CONFIDENCE); |
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} |
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else |
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{ |
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FAIL() << "Unknown 2D features type: " << get<0>(GetParam()); |
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} |
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detail::ImageFeatures features1, features2; |
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(*finder)(img1, features1); |
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(*finder)(img2, features2); |
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vector<detail::ImageFeatures> features; |
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vector<detail::MatchesInfo> pairwise_matches; |
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for(int i = 0; i < featuresVectorSize/2; i++) |
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{ |
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features.push_back(features1); |
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features.push_back(features2); |
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} |
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declare.time(200); |
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while(next()) |
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{ |
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cvflann::seed_random(42);//for predictive FlannBasedMatcher |
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startTimer(); |
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(*matcher)(features, pairwise_matches); |
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stopTimer(); |
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matcher->collectGarbage(); |
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} |
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size_t matches_count = 0; |
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for (size_t i = 0; i < pairwise_matches.size(); ++i) |
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{ |
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if (pairwise_matches[i].src_img_idx < 0) |
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continue; |
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EXPECT_GT(pairwise_matches[i].matches.size(), 95u); |
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EXPECT_FALSE(pairwise_matches[i].H.empty()); |
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++matches_count; |
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} |
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EXPECT_GT(matches_count, 0u); |
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SANITY_CHECK_NOTHING(); |
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} |
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PERF_TEST_P( match, affineBestOf2Nearest, TEST_DETECTORS) |
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{ |
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Mat img1, img1_full = imread( getDataPath("stitching/s1.jpg") ); |
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Mat img2, img2_full = imread( getDataPath("stitching/s2.jpg") ); |
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float scale1 = (float)std::min(1.0, sqrt(WORK_MEGAPIX * 1e6 / img1_full.total())); |
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float scale2 = (float)std::min(1.0, sqrt(WORK_MEGAPIX * 1e6 / img2_full.total())); |
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resize(img1_full, img1, Size(), scale1, scale1, INTER_LINEAR_EXACT); |
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resize(img2_full, img2, Size(), scale2, scale2, INTER_LINEAR_EXACT); |
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Ptr<detail::FeaturesFinder> finder; |
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Ptr<detail::FeaturesMatcher> matcher; |
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if (GetParam() == "surf") |
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{ |
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finder = makePtr<detail::SurfFeaturesFinder>(); |
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matcher = makePtr<detail::AffineBestOf2NearestMatcher>(false, false, SURF_MATCH_CONFIDENCE); |
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} |
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else if (GetParam() == "orb") |
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{ |
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finder = makePtr<detail::OrbFeaturesFinder>(); |
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matcher = makePtr<detail::AffineBestOf2NearestMatcher>(false, false, ORB_MATCH_CONFIDENCE); |
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} |
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else |
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{ |
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FAIL() << "Unknown 2D features type: " << GetParam(); |
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} |
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detail::ImageFeatures features1, features2; |
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(*finder)(img1, features1); |
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(*finder)(img2, features2); |
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detail::MatchesInfo pairwise_matches; |
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declare.in(features1.descriptors, features2.descriptors); |
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while(next()) |
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{ |
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cvflann::seed_random(42);//for predictive FlannBasedMatcher |
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startTimer(); |
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(*matcher)(features1, features2, pairwise_matches); |
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stopTimer(); |
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matcher->collectGarbage(); |
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} |
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// separate rotation and translation in transform matrix |
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Mat T (pairwise_matches.H, Range(0, 2), Range(2, 3)); |
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Mat R (pairwise_matches.H, Range(0, 2), Range(0, 2)); |
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Mat h (pairwise_matches.H, Range(2, 3), Range::all()); |
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SANITY_CHECK(T, 5, ERROR_ABSOLUTE); // allow 5 pixels diff in translations |
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SANITY_CHECK(R, .01, ERROR_ABSOLUTE); // rotations must be more precise |
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// last row should be precisely (0, 0, 1) as it is just added for representation in homogeneous |
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// coordinates |
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EXPECT_DOUBLE_EQ(h.at<double>(0), 0.); |
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EXPECT_DOUBLE_EQ(h.at<double>(1), 0.); |
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EXPECT_DOUBLE_EQ(h.at<double>(2), 1.); |
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} |
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PERF_TEST_P( matchVector, affineBestOf2NearestVectorFeatures, testing::Combine( |
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TEST_DETECTORS, |
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testing::Values(2, 4, 8)) |
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) |
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{ |
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Mat img1, img1_full = imread( getDataPath("stitching/s1.jpg") ); |
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Mat img2, img2_full = imread( getDataPath("stitching/s2.jpg") ); |
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float scale1 = (float)std::min(1.0, sqrt(WORK_MEGAPIX * 1e6 / img1_full.total())); |
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float scale2 = (float)std::min(1.0, sqrt(WORK_MEGAPIX * 1e6 / img2_full.total())); |
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resize(img1_full, img1, Size(), scale1, scale1, INTER_LINEAR_EXACT); |
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resize(img2_full, img2, Size(), scale2, scale2, INTER_LINEAR_EXACT); |
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Ptr<detail::FeaturesFinder> finder; |
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Ptr<detail::FeaturesMatcher> matcher; |
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string detectorName = get<0>(GetParam()); |
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int featuresVectorSize = get<1>(GetParam()); |
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if (detectorName == "surf") |
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{ |
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finder = makePtr<detail::SurfFeaturesFinder>(); |
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matcher = makePtr<detail::AffineBestOf2NearestMatcher>(false, false, SURF_MATCH_CONFIDENCE); |
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} |
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else if (detectorName == "orb") |
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{ |
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finder = makePtr<detail::OrbFeaturesFinder>(); |
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matcher = makePtr<detail::AffineBestOf2NearestMatcher>(false, false, ORB_MATCH_CONFIDENCE); |
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} |
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else |
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{ |
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FAIL() << "Unknown 2D features type: " << get<0>(GetParam()); |
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} |
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detail::ImageFeatures features1, features2; |
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(*finder)(img1, features1); |
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(*finder)(img2, features2); |
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vector<detail::ImageFeatures> features; |
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vector<detail::MatchesInfo> pairwise_matches; |
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for(int i = 0; i < featuresVectorSize/2; i++) |
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{ |
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features.push_back(features1); |
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features.push_back(features2); |
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} |
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declare.time(200); |
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while(next()) |
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{ |
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cvflann::seed_random(42);//for predictive FlannBasedMatcher |
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startTimer(); |
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(*matcher)(features, pairwise_matches); |
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stopTimer(); |
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matcher->collectGarbage(); |
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} |
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size_t matches_count = 0; |
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for (size_t i = 0; i < pairwise_matches.size(); ++i) |
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{ |
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if (pairwise_matches[i].src_img_idx < 0) |
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continue; |
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EXPECT_GT(pairwise_matches[i].matches.size(), (size_t)300); |
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EXPECT_FALSE(pairwise_matches[i].H.empty()); |
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++matches_count; |
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} |
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EXPECT_TRUE(matches_count > 0); |
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SANITY_CHECK_NOTHING(); |
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} |
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} // namespace
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