OpenCV_4.2.0/opencv-4.2.0/modules/stitching/perf/perf_matchers.cpp

294 lines
9.6 KiB
C++

#include "perf_precomp.hpp"
#include "opencv2/imgcodecs.hpp"
#include "opencv2/opencv_modules.hpp"
#include "opencv2/flann.hpp"
namespace opencv_test
{
using namespace perf;
typedef TestBaseWithParam<size_t> FeaturesFinderVec;
typedef TestBaseWithParam<string> match;
typedef tuple<string, int> matchVector_t;
typedef TestBaseWithParam<matchVector_t> matchVector;
#define NUMBER_IMAGES testing::Values(1, 5, 20)
#define SURF_MATCH_CONFIDENCE 0.65f
#define ORB_MATCH_CONFIDENCE 0.3f
#define WORK_MEGAPIX 0.6
#ifdef HAVE_OPENCV_XFEATURES2D
#define TEST_DETECTORS testing::Values("surf", "orb")
#else
#define TEST_DETECTORS testing::Values<string>("orb")
#endif
PERF_TEST_P(FeaturesFinderVec, ParallelFeaturesFinder, NUMBER_IMAGES)
{
Mat img = imread( getDataPath("stitching/a1.png") );
vector<Mat> imgs(GetParam(), img);
vector<detail::ImageFeatures> features(imgs.size());
Ptr<Feature2D> finder = ORB::create();
TEST_CYCLE()
{
detail::computeImageFeatures(finder, imgs, features);
}
SANITY_CHECK_NOTHING();
}
PERF_TEST_P(FeaturesFinderVec, SerialFeaturesFinder, NUMBER_IMAGES)
{
Mat img = imread( getDataPath("stitching/a1.png") );
vector<Mat> imgs(GetParam(), img);
vector<detail::ImageFeatures> features(imgs.size());
Ptr<Feature2D> finder = ORB::create();
TEST_CYCLE()
{
for (size_t i = 0; i < imgs.size(); ++i)
detail::computeImageFeatures(finder, imgs[i], features[i]);
}
SANITY_CHECK_NOTHING();
}
PERF_TEST_P( match, bestOf2Nearest, TEST_DETECTORS)
{
Mat img1, img1_full = imread( getDataPath("stitching/boat1.jpg") );
Mat img2, img2_full = imread( getDataPath("stitching/boat2.jpg") );
float scale1 = (float)std::min(1.0, sqrt(WORK_MEGAPIX * 1e6 / img1_full.total()));
float scale2 = (float)std::min(1.0, sqrt(WORK_MEGAPIX * 1e6 / img2_full.total()));
resize(img1_full, img1, Size(), scale1, scale1, INTER_LINEAR_EXACT);
resize(img2_full, img2, Size(), scale2, scale2, INTER_LINEAR_EXACT);
Ptr<Feature2D> finder = getFeatureFinder(GetParam());
Ptr<detail::FeaturesMatcher> matcher;
if (GetParam() == "surf")
{
matcher = makePtr<detail::BestOf2NearestMatcher>(false, SURF_MATCH_CONFIDENCE);
}
else if (GetParam() == "orb")
{
matcher = makePtr<detail::BestOf2NearestMatcher>(false, ORB_MATCH_CONFIDENCE);
}
else
{
FAIL() << "Unknown 2D features type: " << GetParam();
}
detail::ImageFeatures features1, features2;
detail::computeImageFeatures(finder, img1, features1);
detail::computeImageFeatures(finder, img2, features2);
detail::MatchesInfo pairwise_matches;
declare.in(features1.descriptors, features2.descriptors);
while(next())
{
cvflann::seed_random(42);//for predictive FlannBasedMatcher
startTimer();
(*matcher)(features1, features2, pairwise_matches);
stopTimer();
matcher->collectGarbage();
}
Mat dist (pairwise_matches.H, Range::all(), Range(2, 3));
Mat R (pairwise_matches.H, Range::all(), Range(0, 2));
// separate transform matrix, use lower error on rotations
SANITY_CHECK(dist, 3., ERROR_ABSOLUTE);
SANITY_CHECK(R, .06, ERROR_ABSOLUTE);
}
PERF_TEST_P( matchVector, bestOf2NearestVectorFeatures, testing::Combine(
TEST_DETECTORS,
testing::Values(2, 4, 8))
)
{
Mat img1, img1_full = imread( getDataPath("stitching/boat1.jpg") );
Mat img2, img2_full = imread( getDataPath("stitching/boat2.jpg") );
float scale1 = (float)std::min(1.0, sqrt(WORK_MEGAPIX * 1e6 / img1_full.total()));
float scale2 = (float)std::min(1.0, sqrt(WORK_MEGAPIX * 1e6 / img2_full.total()));
resize(img1_full, img1, Size(), scale1, scale1, INTER_LINEAR_EXACT);
resize(img2_full, img2, Size(), scale2, scale2, INTER_LINEAR_EXACT);
string detectorName = get<0>(GetParam());
int featuresVectorSize = get<1>(GetParam());
Ptr<Feature2D> finder = getFeatureFinder(detectorName);
Ptr<detail::FeaturesMatcher> matcher;
if (detectorName == "surf")
{
matcher = makePtr<detail::BestOf2NearestMatcher>(false, SURF_MATCH_CONFIDENCE);
}
else if (detectorName == "orb")
{
matcher = makePtr<detail::BestOf2NearestMatcher>(false, ORB_MATCH_CONFIDENCE);
}
else
{
FAIL() << "Unknown 2D features type: " << get<0>(GetParam());
}
detail::ImageFeatures features1, features2;
detail::computeImageFeatures(finder, img1, features1);
detail::computeImageFeatures(finder, img2, features2);
vector<detail::ImageFeatures> features;
vector<detail::MatchesInfo> pairwise_matches;
for(int i = 0; i < featuresVectorSize/2; i++)
{
features.push_back(features1);
features.push_back(features2);
}
declare.time(200);
while(next())
{
cvflann::seed_random(42);//for predictive FlannBasedMatcher
startTimer();
(*matcher)(features, pairwise_matches);
stopTimer();
matcher->collectGarbage();
}
size_t matches_count = 0;
for (size_t i = 0; i < pairwise_matches.size(); ++i)
{
if (pairwise_matches[i].src_img_idx < 0)
continue;
EXPECT_GT(pairwise_matches[i].matches.size(), 95u);
EXPECT_FALSE(pairwise_matches[i].H.empty());
++matches_count;
}
EXPECT_GT(matches_count, 0u);
SANITY_CHECK_NOTHING();
}
PERF_TEST_P( match, affineBestOf2Nearest, TEST_DETECTORS)
{
Mat img1, img1_full = imread( getDataPath("stitching/s1.jpg") );
Mat img2, img2_full = imread( getDataPath("stitching/s2.jpg") );
float scale1 = (float)std::min(1.0, sqrt(WORK_MEGAPIX * 1e6 / img1_full.total()));
float scale2 = (float)std::min(1.0, sqrt(WORK_MEGAPIX * 1e6 / img2_full.total()));
resize(img1_full, img1, Size(), scale1, scale1, INTER_LINEAR_EXACT);
resize(img2_full, img2, Size(), scale2, scale2, INTER_LINEAR_EXACT);
Ptr<Feature2D> finder = getFeatureFinder(GetParam());
Ptr<detail::FeaturesMatcher> matcher;
if (GetParam() == "surf")
{
matcher = makePtr<detail::AffineBestOf2NearestMatcher>(false, false, SURF_MATCH_CONFIDENCE);
}
else if (GetParam() == "orb")
{
matcher = makePtr<detail::AffineBestOf2NearestMatcher>(false, false, ORB_MATCH_CONFIDENCE);
}
else
{
FAIL() << "Unknown 2D features type: " << GetParam();
}
detail::ImageFeatures features1, features2;
detail::computeImageFeatures(finder, img1, features1);
detail::computeImageFeatures(finder, img2, features2);
detail::MatchesInfo pairwise_matches;
declare.in(features1.descriptors, features2.descriptors);
while(next())
{
cvflann::seed_random(42);//for predictive FlannBasedMatcher
startTimer();
(*matcher)(features1, features2, pairwise_matches);
stopTimer();
matcher->collectGarbage();
}
// separate rotation and translation in transform matrix
Mat T (pairwise_matches.H, Range(0, 2), Range(2, 3));
Mat R (pairwise_matches.H, Range(0, 2), Range(0, 2));
Mat h (pairwise_matches.H, Range(2, 3), Range::all());
SANITY_CHECK(T, 5, ERROR_ABSOLUTE); // allow 5 pixels diff in translations
SANITY_CHECK(R, .01, ERROR_ABSOLUTE); // rotations must be more precise
// last row should be precisely (0, 0, 1) as it is just added for representation in homogeneous
// coordinates
EXPECT_DOUBLE_EQ(h.at<double>(0), 0.);
EXPECT_DOUBLE_EQ(h.at<double>(1), 0.);
EXPECT_DOUBLE_EQ(h.at<double>(2), 1.);
}
PERF_TEST_P( matchVector, affineBestOf2NearestVectorFeatures, testing::Combine(
TEST_DETECTORS,
testing::Values(2, 4, 8))
)
{
Mat img1, img1_full = imread( getDataPath("stitching/s1.jpg") );
Mat img2, img2_full = imread( getDataPath("stitching/s2.jpg") );
float scale1 = (float)std::min(1.0, sqrt(WORK_MEGAPIX * 1e6 / img1_full.total()));
float scale2 = (float)std::min(1.0, sqrt(WORK_MEGAPIX * 1e6 / img2_full.total()));
resize(img1_full, img1, Size(), scale1, scale1, INTER_LINEAR_EXACT);
resize(img2_full, img2, Size(), scale2, scale2, INTER_LINEAR_EXACT);
string detectorName = get<0>(GetParam());
int featuresVectorSize = get<1>(GetParam());
Ptr<Feature2D> finder = getFeatureFinder(detectorName);
Ptr<detail::FeaturesMatcher> matcher;
if (detectorName == "surf")
{
matcher = makePtr<detail::AffineBestOf2NearestMatcher>(false, false, SURF_MATCH_CONFIDENCE);
}
else if (detectorName == "orb")
{
matcher = makePtr<detail::AffineBestOf2NearestMatcher>(false, false, ORB_MATCH_CONFIDENCE);
}
else
{
FAIL() << "Unknown 2D features type: " << get<0>(GetParam());
}
detail::ImageFeatures features1, features2;
detail::computeImageFeatures(finder, img1, features1);
detail::computeImageFeatures(finder, img2, features2);
vector<detail::ImageFeatures> features;
vector<detail::MatchesInfo> pairwise_matches;
for(int i = 0; i < featuresVectorSize/2; i++)
{
features.push_back(features1);
features.push_back(features2);
}
declare.time(200);
while(next())
{
cvflann::seed_random(42);//for predictive FlannBasedMatcher
startTimer();
(*matcher)(features, pairwise_matches);
stopTimer();
matcher->collectGarbage();
}
size_t matches_count = 0;
for (size_t i = 0; i < pairwise_matches.size(); ++i)
{
if (pairwise_matches[i].src_img_idx < 0)
continue;
EXPECT_GT(pairwise_matches[i].matches.size(), 150u);
EXPECT_FALSE(pairwise_matches[i].H.empty());
++matches_count;
}
EXPECT_GT(matches_count, 0u);
SANITY_CHECK_NOTHING();
}
} // namespace