mirror of
https://github.com/opencv/opencv.git
synced 2024-12-02 16:00:17 +08:00
1ba7c728a6
[evolution] Stitching for OpenCV 4.0 * stitching: wrap Stitcher::create for bindings * provide method for consistent stitcher usage across languages * samples: add python stitching sample * port cpp stitching sample to python * stitching: consolidate Stitcher create methods * remove Stitcher::createDefault, it returns Stitcher, not Ptr<Stitcher> -> inconsistent API * deprecate cv::createStitcher and cv::createStitcherScans in favor of Stitcher::create * stitching: avoid anonymous enum in Stitcher * ORIG_RESOL should be double * add documentatiton * stitching: improve documentation in Stitcher * stitching: expose estimator in Stitcher * remove ABI hack * stitching: drop try_use_gpu flag * OCL will be used automatically through T-API in OCL-enable paths * CUDA won't be used unless user sets CUDA-enabled classes manually * stitching: drop FeaturesFinder * use Feature2D instead of FeaturesFinder * interoperability with features2d module * detach from dependency on xfeatures2d * features2d: fix compute and detect to work with UMat vectors * correctly pass UMats as UMats to allow OCL paths * support vector of UMats as output arg * stitching: use nearest interpolation for resizing masks * fix warnings
97 lines
3.5 KiB
C++
97 lines
3.5 KiB
C++
#include "perf_precomp.hpp"
|
|
#include "opencv2/imgcodecs.hpp"
|
|
#include "opencv2/opencv_modules.hpp"
|
|
|
|
namespace opencv_test
|
|
{
|
|
using namespace perf;
|
|
|
|
typedef TestBaseWithParam<tuple<string, string> > bundleAdjuster;
|
|
|
|
#ifdef HAVE_OPENCV_XFEATURES2D
|
|
#define TEST_DETECTORS testing::Values("surf", "orb")
|
|
#else
|
|
#define TEST_DETECTORS testing::Values<string>("orb")
|
|
#endif
|
|
#define WORK_MEGAPIX 0.6
|
|
#define AFFINE_FUNCTIONS testing::Values("affinePartial", "affine")
|
|
|
|
PERF_TEST_P(bundleAdjuster, affine, testing::Combine(TEST_DETECTORS, AFFINE_FUNCTIONS))
|
|
{
|
|
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 detector = get<0>(GetParam());
|
|
string affine_fun = get<1>(GetParam());
|
|
|
|
Ptr<Feature2D> finder = getFeatureFinder(detector);
|
|
Ptr<detail::FeaturesMatcher> matcher;
|
|
Ptr<detail::BundleAdjusterBase> bundle_adjuster;
|
|
if (affine_fun == "affinePartial")
|
|
{
|
|
matcher = makePtr<detail::AffineBestOf2NearestMatcher>(false);
|
|
bundle_adjuster = makePtr<detail::BundleAdjusterAffinePartial>();
|
|
}
|
|
else if (affine_fun == "affine")
|
|
{
|
|
matcher = makePtr<detail::AffineBestOf2NearestMatcher>(true);
|
|
bundle_adjuster = makePtr<detail::BundleAdjusterAffine>();
|
|
}
|
|
Ptr<detail::Estimator> estimator = makePtr<detail::AffineBasedEstimator>();
|
|
|
|
std::vector<Mat> images;
|
|
images.push_back(img1), images.push_back(img2);
|
|
std::vector<detail::ImageFeatures> features;
|
|
std::vector<detail::MatchesInfo> pairwise_matches;
|
|
std::vector<detail::CameraParams> cameras;
|
|
std::vector<detail::CameraParams> cameras2;
|
|
|
|
computeImageFeatures(finder, images, features);
|
|
(*matcher)(features, pairwise_matches);
|
|
if (!(*estimator)(features, pairwise_matches, cameras))
|
|
FAIL() << "estimation failed. this should never happen.";
|
|
// this is currently required
|
|
for (size_t i = 0; i < cameras.size(); ++i)
|
|
{
|
|
Mat R;
|
|
cameras[i].R.convertTo(R, CV_32F);
|
|
cameras[i].R = R;
|
|
}
|
|
|
|
cameras2 = cameras;
|
|
bool success = true;
|
|
while(next())
|
|
{
|
|
cameras = cameras2; // revert cameras back to original initial guess
|
|
startTimer();
|
|
success = (*bundle_adjuster)(features, pairwise_matches, cameras);
|
|
stopTimer();
|
|
}
|
|
|
|
EXPECT_TRUE(success);
|
|
EXPECT_TRUE(cameras.size() == 2);
|
|
|
|
// fist camera should be just identity
|
|
Mat &first = cameras[0].R;
|
|
SANITY_CHECK(first, 1e-3, ERROR_ABSOLUTE);
|
|
// second camera should be the estimated transform between images
|
|
// separate rotation and translation in transform matrix
|
|
Mat T_second (cameras[1].R, Range(0, 2), Range(2, 3));
|
|
Mat R_second (cameras[1].R, Range(0, 2), Range(0, 2));
|
|
Mat h (cameras[1].R, Range(2, 3), Range::all());
|
|
SANITY_CHECK(T_second, 5, ERROR_ABSOLUTE); // allow 5 pixels diff in translations
|
|
SANITY_CHECK(R_second, .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_TRUE(h.type() == CV_32F);
|
|
EXPECT_FLOAT_EQ(h.at<float>(0), 0.f);
|
|
EXPECT_FLOAT_EQ(h.at<float>(1), 0.f);
|
|
EXPECT_FLOAT_EQ(h.at<float>(2), 1.f);
|
|
}
|
|
|
|
} // namespace
|