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| // For Open Source Computer Vision Library | |
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| //M*/ | |
| namespace opencv_test { namespace { | |
| TEST(Calib3d_Affine3f, accuracy) | |
| { | |
| const double eps = 1e-5; | |
| cv::Vec3d rvec(0.2, 0.5, 0.3); | |
| cv::Affine3d affine(rvec); | |
| cv::Mat expected; | |
| cv::Rodrigues(rvec, expected); | |
| ASSERT_LE(cvtest::norm(cv::Mat(affine.matrix, false).colRange(0, 3).rowRange(0, 3), expected, cv::NORM_L2), eps); | |
| ASSERT_LE(cvtest::norm(cv::Mat(affine.linear()), expected, cv::NORM_L2), eps); | |
| 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); | |
| } | |
| } | |
| }} // namespace | |