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302 lines
9.7 KiB
C++
302 lines
9.7 KiB
C++
#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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using namespace std;
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using namespace cv;
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using namespace perf;
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using std::tr1::make_tuple;
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using std::tr1::get;
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typedef TestBaseWithParam<size_t> FeaturesFinderVec;
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typedef TestBaseWithParam<string> match;
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typedef std::tr1::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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#ifdef HAVE_OPENCV_XFEATURES2D
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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);
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resize(img2_full, img2, Size(), scale2, scale2);
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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, 1., ERROR_ABSOLUTE);
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SANITY_CHECK(R, .015, 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);
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resize(img2_full, img2, Size(), scale2, scale2);
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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(), 100);
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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);
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resize(img2_full, img2, Size(), scale2, scale2);
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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);
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resize(img2_full, img2, Size(), scale2, scale2);
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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_TRUE(pairwise_matches[i].matches.size() > 400);
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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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