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#include "perf_precomp.hpp"
#include "opencv2/imgcodecs.hpp"
#include "opencv2/opencv_modules.hpp"
using namespace std;
using namespace cv;
using namespace perf;
using std::tr1::tuple;
using std::tr1::get;
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);
resize(img2_full, img2, Size(), scale2, scale2);
string detector = get<0>(GetParam());
string affine_fun = get<1>(GetParam());
Ptr<detail::FeaturesFinder> finder;
Ptr<detail::FeaturesMatcher> matcher;
Ptr<detail::BundleAdjusterBase> bundle_adjuster;
if (detector == "surf")
finder = makePtr<detail::SurfFeaturesFinder>();
else if (detector == "orb")
finder = makePtr<detail::OrbFeaturesFinder>();
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;
(*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);
}
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