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#include "test_precomp.hpp"
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#include<fstream>
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#include <opencv2/ts/gpu_test.hpp>
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#define DEF_PARAM_TEST(name, ...) typedef ::perf::TestBaseWithParam< std::tr1::tuple< __VA_ARGS__ > > name
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#define PARAM_TEST_CASE(name, ...) struct name : testing::TestWithParam< std::tr1::tuple< __VA_ARGS__ > >
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/// Change this parameter via CMake: cmake -DDATASETS_REPOSITORY_FOLDER=<path>
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//const static std::string datasets_repository_path = "DATASETS_REPOSITORY_FOLDER";
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const static std::string datasets_repository_path = "/home/krylov/data";
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namespace FishEye
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{
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const static cv::Size imageSize(1280, 800);
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const static cv::Matx33d K(558.478087865323, 0, 620.458515360843,
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0, 560.506767351568, 381.939424848348,
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0, 0, 1);
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const static cv::Vec4d D(-0.0014613319981768, -0.00329861110580401, 0.00605760088590183, -0.00374209380722371);
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const static cv::Matx33d R ( 9.9756700084424932e-01, 6.9698277640183867e-02, 1.4929569991321144e-03,
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-6.9711825162322980e-02, 9.9748249845531767e-01, 1.2997180766418455e-02,
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-5.8331736398316541e-04,-1.3069635393884985e-02, 9.9991441852366736e-01);
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const static cv::Vec3d T(-9.9217369356044638e-02, 3.1741831972356663e-03, 1.8551007952921010e-04);
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}
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namespace{
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std::string combine(const std::string& _item1, const std::string& _item2)
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{
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std::string item1 = _item1, item2 = _item2;
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std::replace(item1.begin(), item1.end(), '\\', '/');
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std::replace(item2.begin(), item2.end(), '\\', '/');
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if (item1.empty())
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return item2;
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if (item2.empty())
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return item1;
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char last = item1[item1.size()-1];
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return item1 + (last != '/' ? "/" : "") + item2;
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}
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std::string combine_format(const std::string& item1, const std::string& item2, ...)
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{
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std::string fmt = combine(item1, item2);
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char buffer[1 << 16];
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va_list args;
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va_start( args, item2 );
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vsprintf( buffer, fmt.c_str(), args );
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va_end( args );
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return std::string(buffer);
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}
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void readPoins(std::vector<std::vector<cv::Point3d> >& objectPoints,
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std::vector<std::vector<cv::Point2d> >& imagePoints,
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const std::string& path, const int n_images, const int n_points)
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{
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objectPoints.resize(n_images);
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imagePoints.resize(n_images);
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std::vector<cv::Point2d> image(n_points);
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std::vector<cv::Point3d> object(n_points);
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std::ifstream ipStream;
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std::ifstream opStream;
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for (int image_idx = 0; image_idx < n_images; image_idx++)
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{
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std::stringstream ss;
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ss << image_idx;
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std::string idxStr = ss.str();
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ipStream.open(combine(path, std::string(std::string("x_") + idxStr + std::string(".csv"))).c_str(), std::ifstream::in);
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opStream.open(combine(path, std::string(std::string("X_") + idxStr + std::string(".csv"))).c_str(), std::ifstream::in);
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CV_Assert(ipStream.is_open() && opStream.is_open());
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for (int point_idx = 0; point_idx < n_points; point_idx++)
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{
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double x, y, z;
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char delim;
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ipStream >> x >> delim >> y;
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image[point_idx] = cv::Point2d(x, y);
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opStream >> x >> delim >> y >> delim >> z;
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object[point_idx] = cv::Point3d(x, y, z);
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}
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ipStream.close();
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opStream.close();
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imagePoints[image_idx] = image;
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objectPoints[image_idx] = object;
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}
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}
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void readExtrinsics(const std::string& file, cv::OutputArray _R, cv::OutputArray _T, cv::OutputArray _R1, cv::OutputArray _R2,
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cv::OutputArray _P1, cv::OutputArray _P2, cv::OutputArray _Q)
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{
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cv::FileStorage fs(file, cv::FileStorage::READ);
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CV_Assert(fs.isOpened());
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cv::Mat R, T, R1, R2, P1, P2, Q;
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fs["R"] >> R; fs["T"] >> T; fs["R1"] >> R1; fs["R2"] >> R2; fs["P1"] >> P1; fs["P2"] >> P2; fs["Q"] >> Q;
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if (_R.needed()) R.copyTo(_R); if(_T.needed()) T.copyTo(_T); if (_R1.needed()) R1.copyTo(_R1); if (_R2.needed()) R2.copyTo(_R2);
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if(_P1.needed()) P1.copyTo(_P1); if(_P2.needed()) P2.copyTo(_P2); if(_Q.needed()) Q.copyTo(_Q);
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}
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cv::Mat mergeRectification(const cv::Mat& l, const cv::Mat& r)
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{
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CV_Assert(l.type() == r.type() && l.size() == r.size());
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cv::Mat merged(l.rows, l.cols * 2, l.type());
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cv::Mat lpart = merged.colRange(0, l.cols);
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cv::Mat rpart = merged.colRange(l.cols, merged.cols);
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l.copyTo(lpart);
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r.copyTo(rpart);
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for(int i = 0; i < l.rows; i+=20)
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cv::line(merged, cv::Point(0, i), cv::Point(merged.cols, i), CV_RGB(0, 255, 0));
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return merged;
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}
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}
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TEST(FisheyeTest, projectPoints)
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{
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double cols = FishEye::imageSize.width,
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rows = FishEye::imageSize.height;
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const int N = 20;
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cv::Mat distorted0(1, N*N, CV_64FC2), undist1, undist2, distorted1, distorted2;
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undist2.create(distorted0.size(), CV_MAKETYPE(distorted0.depth(), 3));
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cv::Vec2d* pts = distorted0.ptr<cv::Vec2d>();
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cv::Vec2d c(FishEye::K(0, 2), FishEye::K(1, 2));
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for(int y = 0, k = 0; y < N; ++y)
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for(int x = 0; x < N; ++x)
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{
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cv::Vec2d point(x*cols/(N-1.f), y*rows/(N-1.f));
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pts[k++] = (point - c) * 0.85 + c;
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}
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cv::Fisheye::undistortPoints(distorted0, undist1, FishEye::K, FishEye::D);
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cv::Vec2d* u1 = undist1.ptr<cv::Vec2d>();
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cv::Vec3d* u2 = undist2.ptr<cv::Vec3d>();
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for(int i = 0; i < (int)distorted0.total(); ++i)
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u2[i] = cv::Vec3d(u1[i][0], u1[i][1], 1.0);
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cv::Fisheye::distortPoints(undist1, distorted1, FishEye::K, FishEye::D);
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cv::Fisheye::projectPoints(undist2, distorted2, cv::Vec3d::all(0), cv::Vec3d::all(0), FishEye::K, FishEye::D);
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EXPECT_MAT_NEAR(distorted0, distorted1, 1e-5);
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EXPECT_MAT_NEAR(distorted0, distorted2, 1e-5);
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}
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TEST(FisheyeTest, undistortImage)
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{
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cv::Matx33d K = FishEye::K;
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cv::Mat D = cv::Mat(FishEye::D);
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std::string file = combine(datasets_repository_path, "image000001.png");
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cv::Matx33d newK = K;
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cv::Mat distorted = cv::imread(file), undistorted;
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{
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newK(0, 0) = 100;
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newK(1, 1) = 100;
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cv::Fisheye::undistortImage(distorted, undistorted, K, D, newK);
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cv::Mat correct = cv::imread(combine(datasets_repository_path, "test_undistortImage/new_f_100.png"));
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if (correct.empty())
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CV_Assert(cv::imwrite(combine(datasets_repository_path, "test_undistortImage/new_f_100.png"), undistorted));
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else
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EXPECT_MAT_NEAR(correct, undistorted, 1e-15);
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}
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{
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double balance = 1.0;
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cv::Fisheye::estimateNewCameraMatrixForUndistortRectify(K, D, distorted.size(), cv::noArray(), newK, balance);
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cv::Fisheye::undistortImage(distorted, undistorted, K, D, newK);
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cv::Mat correct = cv::imread(combine(datasets_repository_path, "test_undistortImage/balance_1.0.png"));
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if (correct.empty())
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CV_Assert(cv::imwrite(combine(datasets_repository_path, "test_undistortImage/balance_1.0.png"), undistorted));
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else
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EXPECT_MAT_NEAR(correct, undistorted, 1e-15);
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}
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{
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double balance = 0.0;
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cv::Fisheye::estimateNewCameraMatrixForUndistortRectify(K, D, distorted.size(), cv::noArray(), newK, balance);
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cv::Fisheye::undistortImage(distorted, undistorted, K, D, newK);
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cv::Mat correct = cv::imread(combine(datasets_repository_path, "test_undistortImage/balance_0.0.png"));
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if (correct.empty())
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CV_Assert(cv::imwrite(combine(datasets_repository_path, "test_undistortImage/balance_0.0.png"), undistorted));
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else
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EXPECT_MAT_NEAR(correct, undistorted, 1e-15);
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}
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cv::waitKey();
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}
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TEST(FisheyeTest, jacobians)
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{
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int n = 10;
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cv::Mat X(1, n, CV_64FC3);
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cv::Mat om(3, 1, CV_64F), T(3, 1, CV_64F);
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cv::Mat f(2, 1, CV_64F), c(2, 1, CV_64F);
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cv::Mat k(4, 1, CV_64F);
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double alpha;
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cv::RNG& r = cv::theRNG();
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r.fill(X, cv::RNG::NORMAL, 2, 1);
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X = cv::abs(X) * 10;
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r.fill(om, cv::RNG::NORMAL, 0, 1);
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om = cv::abs(om);
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r.fill(T, cv::RNG::NORMAL, 0, 1);
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T = cv::abs(T); T.at<double>(2) = 4; T *= 10;
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r.fill(f, cv::RNG::NORMAL, 0, 1);
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f = cv::abs(f) * 1000;
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r.fill(c, cv::RNG::NORMAL, 0, 1);
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c = cv::abs(c) * 1000;
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r.fill(k, cv::RNG::NORMAL, 0, 1);
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k*= 0.5;
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alpha = 0.01*r.gaussian(1);
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cv::Mat x1, x2, xpred;
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cv::Matx33d K(f.at<double>(0), alpha * f.at<double>(0), c.at<double>(0),
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0, f.at<double>(1), c.at<double>(1),
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0, 0, 1);
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cv::Mat jacobians;
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cv::Fisheye::projectPoints(X, x1, om, T, K, k, alpha, jacobians);
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//test on T:
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cv::Mat dT(3, 1, CV_64FC1);
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r.fill(dT, cv::RNG::NORMAL, 0, 1);
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dT *= 1e-9*cv::norm(T);
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cv::Mat T2 = T + dT;
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cv::Fisheye::projectPoints(X, x2, om, T2, K, k, alpha, cv::noArray());
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xpred = x1 + cv::Mat(jacobians.colRange(11,14) * dT).reshape(2, 1);
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CV_Assert (cv::norm(x2 - xpred) < 1e-12);
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//test on om:
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cv::Mat dom(3, 1, CV_64FC1);
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r.fill(dom, cv::RNG::NORMAL, 0, 1);
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dom *= 1e-9*cv::norm(om);
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cv::Mat om2 = om + dom;
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cv::Fisheye::projectPoints(X, x2, om2, T, K, k, alpha, cv::noArray());
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xpred = x1 + cv::Mat(jacobians.colRange(8,11) * dom).reshape(2, 1);
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CV_Assert (cv::norm(x2 - xpred) < 1e-12);
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//test on f:
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cv::Mat df(2, 1, CV_64FC1);
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r.fill(df, cv::RNG::NORMAL, 0, 1);
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df *= 1e-9*cv::norm(f);
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cv::Matx33d K2 = K + cv::Matx33d(df.at<double>(0), df.at<double>(0) * alpha, 0, 0, df.at<double>(1), 0, 0, 0, 0);
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cv::Fisheye::projectPoints(X, x2, om, T, K2, k, alpha, cv::noArray());
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xpred = x1 + cv::Mat(jacobians.colRange(0,2) * df).reshape(2, 1);
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CV_Assert (cv::norm(x2 - xpred) < 1e-12);
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//test on c:
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cv::Mat dc(2, 1, CV_64FC1);
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r.fill(dc, cv::RNG::NORMAL, 0, 1);
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dc *= 1e-9*cv::norm(c);
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K2 = K + cv::Matx33d(0, 0, dc.at<double>(0), 0, 0, dc.at<double>(1), 0, 0, 0);
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cv::Fisheye::projectPoints(X, x2, om, T, K2, k, alpha, cv::noArray());
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xpred = x1 + cv::Mat(jacobians.colRange(2,4) * dc).reshape(2, 1);
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CV_Assert (cv::norm(x2 - xpred) < 1e-12);
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//test on k:
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cv::Mat dk(4, 1, CV_64FC1);
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r.fill(dk, cv::RNG::NORMAL, 0, 1);
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dk *= 1e-9*cv::norm(k);
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cv::Mat k2 = k + dk;
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cv::Fisheye::projectPoints(X, x2, om, T, K, k2, alpha, cv::noArray());
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xpred = x1 + cv::Mat(jacobians.colRange(4,8) * dk).reshape(2, 1);
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CV_Assert (cv::norm(x2 - xpred) < 1e-12);
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//test on alpha:
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cv::Mat dalpha(1, 1, CV_64FC1);
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r.fill(dalpha, cv::RNG::NORMAL, 0, 1);
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dalpha *= 1e-9*cv::norm(f);
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double alpha2 = alpha + dalpha.at<double>(0);
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K2 = K + cv::Matx33d(0, f.at<double>(0) * dalpha.at<double>(0), 0, 0, 0, 0, 0, 0, 0);
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cv::Fisheye::projectPoints(X, x2, om, T, K, k, alpha2, cv::noArray());
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xpred = x1 + cv::Mat(jacobians.col(14) * dalpha).reshape(2, 1);
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CV_Assert (cv::norm(x2 - xpred) < 1e-12);
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}
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TEST(FisheyeTest, Calibration)
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{
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const int n_images = 34;
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const int n_points = 48;
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cv::Size imageSize = cv::Size(1280, 800);
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std::vector<std::vector<cv::Point2d> > imagePoints;
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std::vector<std::vector<cv::Point3d> > objectPoints;
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readPoins(objectPoints, imagePoints, combine(datasets_repository_path, "calib-3_stereo_from_JY/left"), n_images, n_points);
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int flag = 0;
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flag |= cv::Fisheye::CALIB_RECOMPUTE_EXTRINSIC;
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flag |= cv::Fisheye::CALIB_CHECK_COND;
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flag |= cv::Fisheye::CALIB_FIX_SKEW;
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cv::Matx33d K;
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cv::Vec4d D;
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cv::Fisheye::calibrate(objectPoints, imagePoints, imageSize, K, D,
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cv::noArray(), cv::noArray(), flag, cv::TermCriteria(3, 20, 1e-6));
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EXPECT_MAT_NEAR(K, FishEye::K, 1e-11);
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EXPECT_MAT_NEAR(D, FishEye::D, 1e-12);
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}
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TEST(FisheyeTest, Homography)
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{
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const int n_images = 1;
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const int n_points = 48;
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cv::Size imageSize = cv::Size(1280, 800);
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std::vector<std::vector<cv::Point2d> > imagePoints;
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std::vector<std::vector<cv::Point3d> > objectPoints;
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readPoins(objectPoints, imagePoints, combine(datasets_repository_path, "calib-3_stereo_from_JY/left"), n_images, n_points);
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cv::internal::IntrinsicParams param;
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param.Init(cv::Vec2d(cv::max(imageSize.width, imageSize.height) / CV_PI, cv::max(imageSize.width, imageSize.height) / CV_PI),
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cv::Vec2d(imageSize.width / 2.0 - 0.5, imageSize.height / 2.0 - 0.5));
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cv::Mat _imagePoints (imagePoints[0]);
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cv::Mat _objectPoints(objectPoints[0]);
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cv::Mat imagePointsNormalized = NormalizePixels(_imagePoints, param).reshape(1).t();
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_objectPoints = _objectPoints.reshape(1).t();
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cv::Mat objectPointsMean, covObjectPoints;
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int Np = imagePointsNormalized.cols;
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cv::calcCovarMatrix(_objectPoints, covObjectPoints, objectPointsMean, CV_COVAR_NORMAL | CV_COVAR_COLS);
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cv::SVD svd(covObjectPoints);
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cv::Mat R(svd.vt);
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if (cv::norm(R(cv::Rect(2, 0, 1, 2))) < 1e-6)
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R = cv::Mat::eye(3,3, CV_64FC1);
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if (cv::determinant(R) < 0)
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R = -R;
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cv::Mat T = -R * objectPointsMean;
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cv::Mat X_new = R * _objectPoints + T * cv::Mat::ones(1, Np, CV_64FC1);
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cv::Mat H = cv::internal::ComputeHomography(imagePointsNormalized, X_new.rowRange(0, 2));
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cv::Mat M = cv::Mat::ones(3, X_new.cols, CV_64FC1);
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X_new.rowRange(0, 2).copyTo(M.rowRange(0, 2));
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cv::Mat mrep = H * M;
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cv::divide(mrep, cv::Mat::ones(3,1, CV_64FC1) * mrep.row(2).clone(), mrep);
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|
cv::Mat merr = (mrep.rowRange(0, 2) - imagePointsNormalized).t();
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|
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|
cv::Vec2d std_err;
|
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|
|
cv::meanStdDev(merr.reshape(2), cv::noArray(), std_err);
|
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|
|
std_err *= sqrt((double)merr.reshape(2).total() / (merr.reshape(2).total() - 1));
|
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|
|
cv::Vec2d correct_std_err(0.00516740156010384, 0.00644205331553901);
|
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|
EXPECT_MAT_NEAR(std_err, correct_std_err, 1e-16);
|
|
|
|
}
|
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|
|
TEST(TestFisheye, EtimateUncertainties)
|
|
|
|
{
|
|
|
|
const int n_images = 34;
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|
|
const int n_points = 48;
|
|
|
|
|
|
|
|
cv::Size imageSize = cv::Size(1280, 800);
|
|
|
|
std::vector<std::vector<cv::Point2d> > imagePoints;
|
|
|
|
std::vector<std::vector<cv::Point3d> > objectPoints;
|
|
|
|
|
|
|
|
readPoins(objectPoints, imagePoints, combine(datasets_repository_path, "calib-3_stereo_from_JY/left"), n_images, n_points);
|
|
|
|
|
|
|
|
int flag = 0;
|
|
|
|
flag |= cv::Fisheye::CALIB_RECOMPUTE_EXTRINSIC;
|
|
|
|
flag |= cv::Fisheye::CALIB_CHECK_COND;
|
|
|
|
flag |= cv::Fisheye::CALIB_FIX_SKEW;
|
|
|
|
|
|
|
|
cv::Matx33d K;
|
|
|
|
cv::Vec4d D;
|
|
|
|
std::vector<cv::Vec3d> rvec;
|
|
|
|
std::vector<cv::Vec3d> tvec;
|
|
|
|
|
|
|
|
cv::Fisheye::calibrate(objectPoints, imagePoints, imageSize, K, D,
|
|
|
|
cv::noArray(), cv::noArray(), flag, cv::TermCriteria(3, 20, 1e-6));
|
|
|
|
|
|
|
|
cv::internal::IntrinsicParams param, errors;
|
|
|
|
cv::Vec2d err_std;
|
|
|
|
double thresh_cond = 1e6;
|
|
|
|
int check_cond = 1;
|
|
|
|
param.Init(cv::Vec2d(K(0,0), K(1,1)), cv::Vec2d(K(0,2), K(1, 2)), D);
|
|
|
|
param.isEstimate = std::vector<int>(9, 1);
|
|
|
|
param.isEstimate[4] = 0;
|
|
|
|
|
|
|
|
errors.isEstimate = param.isEstimate;
|
|
|
|
|
|
|
|
double rms;
|
|
|
|
|
|
|
|
cv::internal::EstimateUncertainties(objectPoints, imagePoints, param, rvec, tvec,
|
|
|
|
errors, err_std, thresh_cond, check_cond, rms);
|
|
|
|
|
|
|
|
EXPECT_MAT_NEAR(errors.f, cv::Vec2d(1.29837104202046, 1.31565641071524), 1e-14);
|
|
|
|
EXPECT_MAT_NEAR(errors.c, cv::Vec2d(0.890439368129246, 0.816096854937896), 1e-15);
|
|
|
|
EXPECT_MAT_NEAR(errors.k, cv::Vec4d(0.00516248605191506, 0.0168181467500934, 0.0213118690274604, 0.00916010877545648), 1e-15);
|
|
|
|
EXPECT_MAT_NEAR(err_std, cv::Vec2d(0.187475975266883, 0.185678953263995), 1e-15);
|
|
|
|
CV_Assert(abs(rms - 0.263782587133546) < 1e-15);
|
|
|
|
CV_Assert(errors.alpha == 0);
|
|
|
|
}
|
|
|
|
|
|
|
|
TEST(FisheyeTest, rectify)
|
|
|
|
{
|
|
|
|
const std::string folder =combine(datasets_repository_path, "calib-3_stereo_from_JY");
|
|
|
|
|
|
|
|
cv::Size calibration_size = FishEye::imageSize, requested_size = calibration_size;
|
|
|
|
cv::Matx33d K1 = FishEye::K, K2 = K1;
|
|
|
|
cv::Mat D1 = cv::Mat(FishEye::D), D2 = D1;
|
|
|
|
|
|
|
|
cv::Vec3d T = FishEye::T;
|
|
|
|
cv::Matx33d R = FishEye::R;
|
|
|
|
|
|
|
|
double balance = 0.0, fov_scale = 1.1;
|
|
|
|
cv::Mat R1, R2, P1, P2, Q;
|
|
|
|
cv::Fisheye::stereoRectify(K1, D1, K2, D2, calibration_size, R, T, R1, R2, P1, P2, Q,
|
|
|
|
cv::CALIB_ZERO_DISPARITY, requested_size, balance, fov_scale);
|
|
|
|
|
|
|
|
cv::Mat lmapx, lmapy, rmapx, rmapy;
|
|
|
|
//rewrite for fisheye
|
|
|
|
cv::Fisheye::initUndistortRectifyMap(K1, D1, R1, P1, requested_size, CV_32F, lmapx, lmapy);
|
|
|
|
cv::Fisheye::initUndistortRectifyMap(K2, D2, R2, P2, requested_size, CV_32F, rmapx, rmapy);
|
|
|
|
|
|
|
|
cv::Mat l, r, lundist, rundist;
|
|
|
|
cv::VideoCapture lcap(combine(folder, "left/stereo_pair_%03d.jpg")),
|
|
|
|
rcap(combine(folder, "right/stereo_pair_%03d.jpg"));
|
|
|
|
|
|
|
|
for(int i = 0;; ++i)
|
|
|
|
{
|
|
|
|
lcap >> l; rcap >> r;
|
|
|
|
if (l.empty() || r.empty())
|
|
|
|
break;
|
|
|
|
|
|
|
|
int ndisp = 128;
|
|
|
|
cv::rectangle(l, cv::Rect(255, 0, 829, l.rows-1), CV_RGB(255, 0, 0));
|
|
|
|
cv::rectangle(r, cv::Rect(255, 0, 829, l.rows-1), CV_RGB(255, 0, 0));
|
|
|
|
cv::rectangle(r, cv::Rect(255-ndisp, 0, 829+ndisp ,l.rows-1), CV_RGB(255, 0, 0));
|
|
|
|
cv::remap(l, lundist, lmapx, lmapy, cv::INTER_LINEAR);
|
|
|
|
cv::remap(r, rundist, rmapx, rmapy, cv::INTER_LINEAR);
|
|
|
|
|
|
|
|
cv::Mat rectification = mergeRectification(lundist, rundist);
|
|
|
|
|
|
|
|
cv::Mat correct = cv::imread(combine_format(folder, "test_rectify/rectification_AB_%03d.png", i));
|
|
|
|
if (correct.empty())
|
|
|
|
cv::imwrite(combine_format(folder, "test_rectify/rectification_AB_%03d.png", i), rectification);
|
|
|
|
else
|
|
|
|
EXPECT_MAT_NEAR(correct, rectification, 1e-15);
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|