/*M/////////////////////////////////////////////////////////////////////////////////////// // // By downloading, copying, installing or using the software you agree to this license. // If you do not agree to this license, do not download, install, // copy or use the software. // // // License Agreement // For Open Source Computer Vision Library // (3-clause BSD License) // // Copyright (C) 2015-2016, OpenCV Foundation, all rights reserved. // Third party copyrights are property of their respective owners. // // Redistribution and use in source and binary forms, with or without modification, // are permitted provided that the following conditions are met: // // * Redistributions of source code must retain the above copyright notice, // this list of conditions and the following disclaimer. // // * Redistributions in binary form must reproduce the above copyright notice, // this list of conditions and the following disclaimer in the documentation // and/or other materials provided with the distribution. // // * Neither the names of the copyright holders nor the names of the contributors // may be used to endorse or promote products derived from this software // without specific prior written permission. // // This software is provided by the copyright holders and contributors "as is" and // any express or implied warranties, including, but not limited to, the implied // warranties of merchantability and fitness for a particular purpose are disclaimed. // In no event shall copyright holders or contributors be liable for any direct, // indirect, incidental, special, exemplary, or consequential damages // (including, but not limited to, procurement of substitute goods or services; // loss of use, data, or profits; or business interruption) however caused // and on any theory of liability, whether in contract, strict liability, // or tort (including negligence or otherwise) arising in any way out of // the use of this software, even if advised of the possibility of such damage. // //M*/ #include "test_precomp.hpp" namespace opencv_test { namespace { CV_ENUM(Method, RANSAC, LMEDS) typedef TestWithParam EstimateAffine2D; static float rngIn(float from, float to) { return from + (to-from) * (float)theRNG(); } TEST_P(EstimateAffine2D, test3Points) { // try more transformations for (size_t i = 0; i < 500; ++i) { Mat aff(2, 3, CV_64F); cv::randu(aff, 1., 3.); Mat fpts(1, 3, CV_32FC2); Mat tpts(1, 3, CV_32FC2); // setting points that are not in the same line fpts.at(0) = Point2f( rngIn(1,2), rngIn(5,6) ); fpts.at(1) = Point2f( rngIn(3,4), rngIn(3,4) ); fpts.at(2) = Point2f( rngIn(1,2), rngIn(3,4) ); transform(fpts, tpts, aff); vector inliers; Mat aff_est = estimateAffine2D(fpts, tpts, inliers, GetParam() /* method */); EXPECT_NEAR(0., cvtest::norm(aff_est, aff, NORM_INF), 1e-3); // all must be inliers EXPECT_EQ(countNonZero(inliers), 3); } } TEST_P(EstimateAffine2D, testNPoints) { // try more transformations for (size_t i = 0; i < 500; ++i) { Mat aff(2, 3, CV_64F); cv::randu(aff, -2., 2.); const int method = GetParam(); const int n = 100; int m; // LMEDS can't handle more than 50% outliers (by design) if (method == LMEDS) m = 3*n/5; else m = 2*n/5; const float shift_outl = 15.f; const float noise_level = 20.f; Mat fpts(1, n, CV_32FC2); Mat tpts(1, n, CV_32FC2); randu(fpts, 0., 100.); transform(fpts, tpts, aff); /* adding noise to some points */ Mat outliers = tpts.colRange(m, n); outliers.reshape(1) += shift_outl; Mat noise (outliers.size(), outliers.type()); randu(noise, 0., noise_level); outliers += noise; vector inliers; Mat aff_est = estimateAffine2D(fpts, tpts, inliers, method); EXPECT_FALSE(aff_est.empty()) << "estimation failed, unable to estimate transform"; EXPECT_NEAR(0., cvtest::norm(aff_est, aff, NORM_INF), 1e-4); bool inliers_good = count(inliers.begin(), inliers.end(), 1) == m && m == accumulate(inliers.begin(), inliers.begin() + m, 0); EXPECT_TRUE(inliers_good); } } // test conversion from other datatypes than float TEST_P(EstimateAffine2D, testConversion) { Mat aff(2, 3, CV_32S); cv::randu(aff, 1., 3.); std::vector fpts(3); std::vector tpts(3); // setting points that are not in the same line fpts[0] = Point2f( rngIn(1,2), rngIn(5,6) ); fpts[1] = Point2f( rngIn(3,4), rngIn(3,4) ); fpts[2] = Point2f( rngIn(1,2), rngIn(3,4) ); transform(fpts, tpts, aff); vector inliers; Mat aff_est = estimateAffine2D(fpts, tpts, inliers, GetParam() /* method */); ASSERT_FALSE(aff_est.empty()); aff.convertTo(aff, CV_64F); // need to convert before compare EXPECT_NEAR(0., cvtest::norm(aff_est, aff, NORM_INF), 1e-3); // all must be inliers EXPECT_EQ(countNonZero(inliers), 3); } INSTANTIATE_TEST_CASE_P(Calib3d, EstimateAffine2D, Method::all()); // https://github.com/opencv/opencv/issues/14259 TEST(EstimateAffine2D, issue_14259_dont_change_inputs) { /*const static*/ float pts0_[10] = { 0.0f, 0.0f, 0.0f, 8.0f, 4.0f, 0.0f, // outlier 8.0f, 8.0f, 8.0f, 0.0f }; /*const static*/ float pts1_[10] = { 0.1f, 0.1f, 0.1f, 8.1f, 0.0f, 4.0f, // outlier 8.1f, 8.1f, 8.1f, 0.1f }; Mat pts0(Size(1, 5), CV_32FC2, (void*)pts0_); Mat pts1(Size(1, 5), CV_32FC2, (void*)pts1_); Mat pts0_copy = pts0.clone(); Mat pts1_copy = pts1.clone(); Mat inliers; cv::Mat A = cv::estimateAffine2D(pts0, pts1, inliers); for(int i = 0; i < pts0.rows; ++i) { EXPECT_EQ(pts0_copy.at(i), pts0.at(i)) << "pts0: i=" << i; } for(int i = 0; i < pts1.rows; ++i) { EXPECT_EQ(pts1_copy.at(i), pts1.at(i)) << "pts1: i=" << i; } EXPECT_EQ(0, (int)inliers.at(2)); } }} // namespace