254 lines
8.2 KiB
C++
254 lines
8.2 KiB
C++
|
#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<int> stitchExposureCompensation;
|
||
|
|
typedef TestBaseWithParam<tuple<string, string> > stitchDatasets;
|
||
|
|
typedef TestBaseWithParam<tuple<string, int>> stitchExposureCompMultiFeed;
|
||
|
|
|
||
|
|
#if defined(HAVE_OPENCV_XFEATURES2D) && defined(OPENCV_ENABLE_NONFREE)
|
||
|
|
#define TEST_DETECTORS testing::Values("surf", "orb", "akaze")
|
||
|
|
#else
|
||
|
|
#define TEST_DETECTORS testing::Values("orb", "akaze")
|
||
|
|
#endif
|
||
|
|
#define TEST_EXP_COMP_BS testing::Values(32, 16, 12, 10, 8)
|
||
|
|
#define TEST_EXP_COMP_NR_FEED testing::Values(1, 2, 3, 4, 5)
|
||
|
|
#define TEST_EXP_COMP_MODE testing::Values("gain", "channels", "blocks_gain", "blocks_channels")
|
||
|
|
#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<Feature2D> 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())
|
||
|
|
{
|
||
|
|
Ptr<Stitcher> stitcher = Stitcher::create();
|
||
|
|
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(stitchExposureCompensation, a123, TEST_EXP_COMP_BS)
|
||
|
|
{
|
||
|
|
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") ) );
|
||
|
|
|
||
|
|
int bs = GetParam();
|
||
|
|
|
||
|
|
declare.time(30 * 10).iterations(10);
|
||
|
|
|
||
|
|
while(next())
|
||
|
|
{
|
||
|
|
Ptr<Stitcher> stitcher = Stitcher::create();
|
||
|
|
stitcher->setWarper(makePtr<SphericalWarper>());
|
||
|
|
stitcher->setRegistrationResol(WORK_MEGAPIX);
|
||
|
|
stitcher->setExposureCompensator(
|
||
|
|
makePtr<detail::BlocksGainCompensator>(bs, bs));
|
||
|
|
|
||
|
|
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(stitchExposureCompMultiFeed, a123, testing::Combine(TEST_EXP_COMP_MODE, TEST_EXP_COMP_NR_FEED))
|
||
|
|
{
|
||
|
|
const int block_size = 32;
|
||
|
|
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") ) );
|
||
|
|
|
||
|
|
string mode = get<0>(GetParam());
|
||
|
|
int nr_feeds = get<1>(GetParam());
|
||
|
|
|
||
|
|
declare.time(30 * 10).iterations(10);
|
||
|
|
|
||
|
|
Ptr<detail::ExposureCompensator> exp_comp;
|
||
|
|
if (mode == "gain")
|
||
|
|
exp_comp = makePtr<detail::GainCompensator>(nr_feeds);
|
||
|
|
else if (mode == "channels")
|
||
|
|
exp_comp = makePtr<detail::ChannelsCompensator>(nr_feeds);
|
||
|
|
else if (mode == "blocks_gain")
|
||
|
|
exp_comp = makePtr<detail::BlocksGainCompensator>(block_size, block_size, nr_feeds);
|
||
|
|
else if (mode == "blocks_channels")
|
||
|
|
exp_comp = makePtr<detail::BlocksChannelsCompensator>(block_size, block_size, nr_feeds);
|
||
|
|
|
||
|
|
while(next())
|
||
|
|
{
|
||
|
|
Ptr<Stitcher> stitcher = Stitcher::create();
|
||
|
|
stitcher->setWarper(makePtr<SphericalWarper>());
|
||
|
|
stitcher->setRegistrationResol(WORK_MEGAPIX);
|
||
|
|
stitcher->setExposureCompensator(exp_comp);
|
||
|
|
|
||
|
|
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<Feature2D> 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())
|
||
|
|
{
|
||
|
|
Ptr<Stitcher> stitcher = Stitcher::create();
|
||
|
|
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<Feature2D> 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;
|
||
|
|
// 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 = ORB::create(1500);
|
||
|
|
}
|
||
|
|
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 = ORB::create(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);
|
||
|
|
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
|