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#include "perf_precomp.hpp"
#include "opencv2/imgcodecs.hpp"
#include "opencv2/opencv_modules.hpp"
#include "opencv2/core/ocl.hpp"
namespace opencv_test
{
using namespace perf;
#define SURF_MATCH_CONFIDENCE 0.65f
#define ORB_MATCH_CONFIDENCE 0.3f
#define WORK_MEGAPIX 0.6
typedef TestBaseWithParam<string> stitch;
typedef TestBaseWithParam<tuple<string, string> > stitchDatasets;
#ifdef HAVE_OPENCV_XFEATURES2D
#define TEST_DETECTORS testing::Values("surf", "orb", "akaze")
#else
#define TEST_DETECTORS testing::Values("orb", "akaze")
#endif
#define AFFINE_DATASETS testing::Values("s", "budapest", "newspaper", "prague")
PERF_TEST_P(stitch, a123, TEST_DETECTORS)
{
Mat pano;
vector<Mat> imgs;
imgs.push_back( imread( getDataPath("stitching/a1.png") ) );
imgs.push_back( imread( getDataPath("stitching/a2.png") ) );
imgs.push_back( imread( getDataPath("stitching/a3.png") ) );
Ptr<detail::FeaturesFinder> featuresFinder = getFeatureFinder(GetParam());
Ptr<detail::FeaturesMatcher> featuresMatcher = GetParam() == "orb"
? makePtr<detail::BestOf2NearestMatcher>(false, ORB_MATCH_CONFIDENCE)
: makePtr<detail::BestOf2NearestMatcher>(false, SURF_MATCH_CONFIDENCE);
declare.time(30 * 20).iterations(20);
while(next())
{
Stitcher stitcher = Stitcher::createDefault();
stitcher.setFeaturesFinder(featuresFinder);
stitcher.setFeaturesMatcher(featuresMatcher);
stitcher.setWarper(makePtr<SphericalWarper>());
stitcher.setRegistrationResol(WORK_MEGAPIX);
startTimer();
stitcher.stitch(imgs, pano);
stopTimer();
}
EXPECT_NEAR(pano.size().width, 1182, 50);
EXPECT_NEAR(pano.size().height, 682, 30);
SANITY_CHECK_NOTHING();
}
PERF_TEST_P(stitch, b12, TEST_DETECTORS)
{
Mat pano;
vector<Mat> imgs;
imgs.push_back( imread( getDataPath("stitching/b1.png") ) );
imgs.push_back( imread( getDataPath("stitching/b2.png") ) );
Ptr<detail::FeaturesFinder> featuresFinder = getFeatureFinder(GetParam());
Ptr<detail::FeaturesMatcher> featuresMatcher = GetParam() == "orb"
? makePtr<detail::BestOf2NearestMatcher>(false, ORB_MATCH_CONFIDENCE)
: makePtr<detail::BestOf2NearestMatcher>(false, SURF_MATCH_CONFIDENCE);
declare.time(30 * 20).iterations(20);
while(next())
{
Stitcher stitcher = Stitcher::createDefault();
stitcher.setFeaturesFinder(featuresFinder);
stitcher.setFeaturesMatcher(featuresMatcher);
stitcher.setWarper(makePtr<SphericalWarper>());
stitcher.setRegistrationResol(WORK_MEGAPIX);
startTimer();
stitcher.stitch(imgs, pano);
stopTimer();
}
EXPECT_NEAR(pano.size().width, 1117, GetParam() == "surf" ? 100 : 50);
EXPECT_NEAR(pano.size().height, 642, GetParam() == "surf" ? 60 : 30);
SANITY_CHECK_NOTHING();
}
PERF_TEST_P(stitchDatasets, affine, testing::Combine(AFFINE_DATASETS, TEST_DETECTORS))
{
string dataset = get<0>(GetParam());
string detector = get<1>(GetParam());
Mat pano;
vector<Mat> imgs;
int width, height, allowed_diff = 20;
Ptr<detail::FeaturesFinder> featuresFinder = getFeatureFinder(detector);
if(dataset == "budapest")
{
imgs.push_back(imread(getDataPath("stitching/budapest1.jpg")));
imgs.push_back(imread(getDataPath("stitching/budapest2.jpg")));
imgs.push_back(imread(getDataPath("stitching/budapest3.jpg")));
imgs.push_back(imread(getDataPath("stitching/budapest4.jpg")));
imgs.push_back(imread(getDataPath("stitching/budapest5.jpg")));
imgs.push_back(imread(getDataPath("stitching/budapest6.jpg")));
width = 2313;
height = 1158;
// this dataset is big, the results between surf and orb differ slightly,
// but both are still good
allowed_diff = 50;
}
else if (dataset == "newspaper")
{
imgs.push_back(imread(getDataPath("stitching/newspaper1.jpg")));
imgs.push_back(imread(getDataPath("stitching/newspaper2.jpg")));
imgs.push_back(imread(getDataPath("stitching/newspaper3.jpg")));
imgs.push_back(imread(getDataPath("stitching/newspaper4.jpg")));
width = 1791;
height = 1136;
// we need to boost ORB number of features to be able to stitch this dataset
// SURF works just fine with default settings
if(detector == "orb")
featuresFinder = makePtr<detail::OrbFeaturesFinder>(Size(3,1), 3000);
}
else if (dataset == "prague")
{
imgs.push_back(imread(getDataPath("stitching/prague1.jpg")));
imgs.push_back(imread(getDataPath("stitching/prague2.jpg")));
width = 983;
height = 1759;
}
else // dataset == "s"
{
imgs.push_back(imread(getDataPath("stitching/s1.jpg")));
imgs.push_back(imread(getDataPath("stitching/s2.jpg")));
width = 1815;
height = 700;
}
declare.time(30 * 20).iterations(20);
while(next())
{
Ptr<Stitcher> stitcher = Stitcher::create(Stitcher::SCANS, false);
stitcher->setFeaturesFinder(featuresFinder);
if (cv::ocl::useOpenCL())
cv::theRNG() = cv::RNG(12345); // prevent fails of Windows OpenCL builds (see #8294)
startTimer();
stitcher->stitch(imgs, pano);
stopTimer();
}
EXPECT_NEAR(pano.size().width, width, allowed_diff);
EXPECT_NEAR(pano.size().height, height, allowed_diff);
SANITY_CHECK_NOTHING();
}
} // namespace