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init - 初始化项目

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Lee Nony
2022-05-06 01:58:53 +08:00
commit 90a5cc7cb6
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modules/stitching/perf/perf_stich.cpp Normal file
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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<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