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Open Source Computer Vision Library
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253 lines
8.2 KiB
253 lines
8.2 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/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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#ifdef HAVE_OPENCV_XFEATURES2D |
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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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