253 lines
8.2 KiB
C++
253 lines
8.2 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/core/ocl.hpp"
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namespace opencv_test
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{
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using namespace perf;
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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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typedef TestBaseWithParam<string> stitch;
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typedef TestBaseWithParam<int> stitchExposureCompensation;
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typedef TestBaseWithParam<tuple<string, string> > stitchDatasets;
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typedef TestBaseWithParam<tuple<string, int>> stitchExposureCompMultiFeed;
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#if defined(HAVE_OPENCV_XFEATURES2D) && defined(OPENCV_ENABLE_NONFREE)
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#define TEST_DETECTORS testing::Values("surf", "orb", "akaze")
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#else
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#define TEST_DETECTORS testing::Values("orb", "akaze")
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#endif
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#define TEST_EXP_COMP_BS testing::Values(32, 16, 12, 10, 8)
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#define TEST_EXP_COMP_NR_FEED testing::Values(1, 2, 3, 4, 5)
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#define TEST_EXP_COMP_MODE testing::Values("gain", "channels", "blocks_gain", "blocks_channels")
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#define AFFINE_DATASETS testing::Values("s", "budapest", "newspaper", "prague")
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PERF_TEST_P(stitch, a123, TEST_DETECTORS)
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{
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Mat pano;
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vector<Mat> imgs;
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imgs.push_back( imread( getDataPath("stitching/a1.png") ) );
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imgs.push_back( imread( getDataPath("stitching/a2.png") ) );
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imgs.push_back( imread( getDataPath("stitching/a3.png") ) );
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Ptr<Feature2D> featuresFinder = getFeatureFinder(GetParam());
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Ptr<detail::FeaturesMatcher> featuresMatcher = GetParam() == "orb"
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? makePtr<detail::BestOf2NearestMatcher>(false, ORB_MATCH_CONFIDENCE)
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: makePtr<detail::BestOf2NearestMatcher>(false, SURF_MATCH_CONFIDENCE);
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declare.time(30 * 20).iterations(20);
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while(next())
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{
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Ptr<Stitcher> stitcher = Stitcher::create();
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stitcher->setFeaturesFinder(featuresFinder);
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stitcher->setFeaturesMatcher(featuresMatcher);
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stitcher->setWarper(makePtr<SphericalWarper>());
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stitcher->setRegistrationResol(WORK_MEGAPIX);
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startTimer();
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stitcher->stitch(imgs, pano);
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stopTimer();
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}
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EXPECT_NEAR(pano.size().width, 1182, 50);
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EXPECT_NEAR(pano.size().height, 682, 30);
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SANITY_CHECK_NOTHING();
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}
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PERF_TEST_P(stitchExposureCompensation, a123, TEST_EXP_COMP_BS)
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{
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Mat pano;
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vector<Mat> imgs;
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imgs.push_back( imread( getDataPath("stitching/a1.png") ) );
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imgs.push_back( imread( getDataPath("stitching/a2.png") ) );
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imgs.push_back( imread( getDataPath("stitching/a3.png") ) );
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int bs = GetParam();
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declare.time(30 * 10).iterations(10);
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while(next())
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{
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Ptr<Stitcher> stitcher = Stitcher::create();
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stitcher->setWarper(makePtr<SphericalWarper>());
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stitcher->setRegistrationResol(WORK_MEGAPIX);
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stitcher->setExposureCompensator(
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makePtr<detail::BlocksGainCompensator>(bs, bs));
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startTimer();
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stitcher->stitch(imgs, pano);
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stopTimer();
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}
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EXPECT_NEAR(pano.size().width, 1182, 50);
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EXPECT_NEAR(pano.size().height, 682, 30);
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SANITY_CHECK_NOTHING();
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}
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PERF_TEST_P(stitchExposureCompMultiFeed, a123, testing::Combine(TEST_EXP_COMP_MODE, TEST_EXP_COMP_NR_FEED))
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{
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const int block_size = 32;
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Mat pano;
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vector<Mat> imgs;
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imgs.push_back( imread( getDataPath("stitching/a1.png") ) );
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imgs.push_back( imread( getDataPath("stitching/a2.png") ) );
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imgs.push_back( imread( getDataPath("stitching/a3.png") ) );
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string mode = get<0>(GetParam());
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int nr_feeds = get<1>(GetParam());
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declare.time(30 * 10).iterations(10);
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Ptr<detail::ExposureCompensator> exp_comp;
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if (mode == "gain")
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exp_comp = makePtr<detail::GainCompensator>(nr_feeds);
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else if (mode == "channels")
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exp_comp = makePtr<detail::ChannelsCompensator>(nr_feeds);
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else if (mode == "blocks_gain")
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exp_comp = makePtr<detail::BlocksGainCompensator>(block_size, block_size, nr_feeds);
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else if (mode == "blocks_channels")
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exp_comp = makePtr<detail::BlocksChannelsCompensator>(block_size, block_size, nr_feeds);
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while(next())
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{
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Ptr<Stitcher> stitcher = Stitcher::create();
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stitcher->setWarper(makePtr<SphericalWarper>());
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stitcher->setRegistrationResol(WORK_MEGAPIX);
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stitcher->setExposureCompensator(exp_comp);
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startTimer();
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stitcher->stitch(imgs, pano);
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stopTimer();
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}
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EXPECT_NEAR(pano.size().width, 1182, 50);
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EXPECT_NEAR(pano.size().height, 682, 30);
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SANITY_CHECK_NOTHING();
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}
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PERF_TEST_P(stitch, b12, TEST_DETECTORS)
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{
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Mat pano;
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vector<Mat> imgs;
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imgs.push_back( imread( getDataPath("stitching/b1.png") ) );
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imgs.push_back( imread( getDataPath("stitching/b2.png") ) );
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Ptr<Feature2D> featuresFinder = getFeatureFinder(GetParam());
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Ptr<detail::FeaturesMatcher> featuresMatcher = GetParam() == "orb"
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? makePtr<detail::BestOf2NearestMatcher>(false, ORB_MATCH_CONFIDENCE)
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: makePtr<detail::BestOf2NearestMatcher>(false, SURF_MATCH_CONFIDENCE);
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declare.time(30 * 20).iterations(20);
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while(next())
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{
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Ptr<Stitcher> stitcher = Stitcher::create();
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stitcher->setFeaturesFinder(featuresFinder);
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stitcher->setFeaturesMatcher(featuresMatcher);
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stitcher->setWarper(makePtr<SphericalWarper>());
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stitcher->setRegistrationResol(WORK_MEGAPIX);
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startTimer();
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stitcher->stitch(imgs, pano);
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stopTimer();
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}
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EXPECT_NEAR(pano.size().width, 1117, GetParam() == "surf" ? 100 : 50);
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EXPECT_NEAR(pano.size().height, 642, GetParam() == "surf" ? 60 : 30);
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SANITY_CHECK_NOTHING();
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}
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PERF_TEST_P(stitchDatasets, affine, testing::Combine(AFFINE_DATASETS, TEST_DETECTORS))
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{
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string dataset = get<0>(GetParam());
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string detector = get<1>(GetParam());
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Mat pano;
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vector<Mat> imgs;
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int width, height, allowed_diff = 20;
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Ptr<Feature2D> featuresFinder = getFeatureFinder(detector);
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if(dataset == "budapest")
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{
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imgs.push_back(imread(getDataPath("stitching/budapest1.jpg")));
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imgs.push_back(imread(getDataPath("stitching/budapest2.jpg")));
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imgs.push_back(imread(getDataPath("stitching/budapest3.jpg")));
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imgs.push_back(imread(getDataPath("stitching/budapest4.jpg")));
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imgs.push_back(imread(getDataPath("stitching/budapest5.jpg")));
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imgs.push_back(imread(getDataPath("stitching/budapest6.jpg")));
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width = 2313;
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height = 1158;
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// this dataset is big, the results between surf and orb differ slightly,
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// but both are still good
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allowed_diff = 50;
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// we need to boost ORB number of features to be able to stitch this dataset
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// SURF works just fine with default settings
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if(detector == "orb")
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featuresFinder = ORB::create(1500);
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}
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else if (dataset == "newspaper")
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{
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imgs.push_back(imread(getDataPath("stitching/newspaper1.jpg")));
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imgs.push_back(imread(getDataPath("stitching/newspaper2.jpg")));
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imgs.push_back(imread(getDataPath("stitching/newspaper3.jpg")));
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imgs.push_back(imread(getDataPath("stitching/newspaper4.jpg")));
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width = 1791;
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height = 1136;
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// we need to boost ORB number of features to be able to stitch this dataset
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// SURF works just fine with default settings
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if(detector == "orb")
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featuresFinder = ORB::create(3000);
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}
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else if (dataset == "prague")
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{
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imgs.push_back(imread(getDataPath("stitching/prague1.jpg")));
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imgs.push_back(imread(getDataPath("stitching/prague2.jpg")));
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width = 983;
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height = 1759;
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}
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else // dataset == "s"
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{
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imgs.push_back(imread(getDataPath("stitching/s1.jpg")));
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imgs.push_back(imread(getDataPath("stitching/s2.jpg")));
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width = 1815;
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height = 700;
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}
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declare.time(30 * 20).iterations(20);
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while(next())
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{
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Ptr<Stitcher> stitcher = Stitcher::create(Stitcher::SCANS);
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stitcher->setFeaturesFinder(featuresFinder);
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if (cv::ocl::useOpenCL())
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cv::theRNG() = cv::RNG(12345); // prevent fails of Windows OpenCL builds (see #8294)
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startTimer();
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stitcher->stitch(imgs, pano);
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stopTimer();
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}
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EXPECT_NEAR(pano.size().width, width, allowed_diff);
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EXPECT_NEAR(pano.size().height, height, allowed_diff);
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SANITY_CHECK_NOTHING();
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}
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} // namespace
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