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| // For Open Source Computer Vision Library |
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| //M*/ |
| |
| #include "test_precomp.hpp" |
| #include "opencv2/core/affine.hpp" |
| #include "opencv2/calib3d.hpp" |
| #include <iostream> |
| |
| TEST(Calib3d_Affine3f, accuracy) |
| { |
| cv::Vec3d rvec(0.2, 0.5, 0.3); |
| cv::Affine3d affine(rvec); |
| |
| cv::Mat expected; |
| cv::Rodrigues(rvec, expected); |
| |
| |
| ASSERT_EQ(0, cvtest::norm(cv::Mat(affine.matrix, false).colRange(0, 3).rowRange(0, 3) != expected, cv::NORM_L2)); |
| ASSERT_EQ(0, cvtest::norm(cv::Mat(affine.linear()) != expected, cv::NORM_L2)); |
| |
| |
| cv::Matx33d R = cv::Matx33d::eye(); |
| |
| double angle = 50; |
| R.val[0] = R.val[4] = std::cos(CV_PI*angle/180.0); |
| R.val[3] = std::sin(CV_PI*angle/180.0); |
| R.val[1] = -R.val[3]; |
| |
| |
| cv::Affine3d affine1(cv::Mat(cv::Vec3d(0.2, 0.5, 0.3)).reshape(1, 1), cv::Vec3d(4, 5, 6)); |
| cv::Affine3d affine2(R, cv::Vec3d(1, 1, 0.4)); |
| |
| cv::Affine3d result = affine1.inv() * affine2; |
| |
| expected = cv::Mat(affine1.matrix.inv(cv::DECOMP_SVD)) * cv::Mat(affine2.matrix, false); |
| |
| |
| cv::Mat diff; |
| cv::absdiff(expected, result.matrix, diff); |
| |
| ASSERT_LT(cvtest::norm(diff, cv::NORM_INF), 1e-15); |
| } |
| |
| TEST(Calib3d_Affine3f, accuracy_rvec) |
| { |
| cv::RNG rng; |
| typedef float T; |
| |
| cv::Affine3<T>::Vec3 w; |
| cv::Affine3<T>::Mat3 u, vt, R; |
| |
| for(int i = 0; i < 100; ++i) |
| { |
| rng.fill(R, cv::RNG::UNIFORM, -10, 10, true); |
| cv::SVD::compute(R, w, u, vt, cv::SVD::FULL_UV + cv::SVD::MODIFY_A); |
| R = u * vt; |
| |
| //double s = (double)cv::getTickCount(); |
| cv::Affine3<T>::Vec3 va = cv::Affine3<T>(R).rvec(); |
| //std::cout << "M:" <<(cv::getTickCount() - s)*1000/cv::getTickFrequency() << std::endl; |
| |
| cv::Affine3<T>::Vec3 vo; |
| //s = (double)cv::getTickCount(); |
| cv::Rodrigues(R, vo); |
| //std::cout << "O:" <<(cv::getTickCount() - s)*1000/cv::getTickFrequency() << std::endl; |
| |
| ASSERT_LT(cvtest::norm(va, vo, cv::NORM_L2), 1e-9); |
| } |
| } |