/*M/////////////////////////////////////////////////////////////////////////////////////// // // IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. // // 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 // // Copyright (C) 2000-2008, Intel Corporation, all rights reserved. // Copyright (C) 2009, Willow Garage Inc., 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: // // * Redistribution's of source code must retain the above copyright notice, // this list of conditions and the following disclaimer. // // * Redistribution's 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. // // * The name of the copyright holders may not 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 the Intel Corporation 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 { class CV_Affine3D_EstTest : public cvtest::BaseTest { public: CV_Affine3D_EstTest(); ~CV_Affine3D_EstTest(); protected: void run(int); bool test4Points(); bool testNPoints(); }; CV_Affine3D_EstTest::CV_Affine3D_EstTest() { } CV_Affine3D_EstTest::~CV_Affine3D_EstTest() {} float rngIn(float from, float to) { return from + (to-from) * (float)theRNG(); } struct WrapAff { const double *F; WrapAff(const Mat& aff) : F(aff.ptr()) {} Point3f operator()(const Point3f& p) { return Point3f( (float)(p.x * F[0] + p.y * F[1] + p.z * F[2] + F[3]), (float)(p.x * F[4] + p.y * F[5] + p.z * F[6] + F[7]), (float)(p.x * F[8] + p.y * F[9] + p.z * F[10] + F[11]) ); } }; bool CV_Affine3D_EstTest::test4Points() { Mat aff(3, 4, CV_64F); cv::randu(aff, Scalar(1), Scalar(3)); // setting points that are no in the same line Mat fpts(1, 4, CV_32FC3); Mat tpts(1, 4, CV_32FC3); fpts.ptr()[0] = Point3f( rngIn(1,2), rngIn(1,2), rngIn(5, 6) ); fpts.ptr()[1] = Point3f( rngIn(3,4), rngIn(3,4), rngIn(5, 6) ); fpts.ptr()[2] = Point3f( rngIn(1,2), rngIn(3,4), rngIn(5, 6) ); fpts.ptr()[3] = Point3f( rngIn(3,4), rngIn(1,2), rngIn(5, 6) ); std::transform(fpts.ptr(), fpts.ptr() + 4, tpts.ptr(), WrapAff(aff)); Mat aff_est; vector outliers; estimateAffine3D(fpts, tpts, aff_est, outliers); const double thres = 1e-3; if (cvtest::norm(aff_est, aff, NORM_INF) > thres) { //cout << cvtest::norm(aff_est, aff, NORM_INF) << endl; ts->set_failed_test_info(cvtest::TS::FAIL_MISMATCH); return false; } return true; } struct Noise { float l; Noise(float level) : l(level) {} Point3f operator()(const Point3f& p) { RNG& rng = theRNG(); return Point3f( p.x + l * (float)rng, p.y + l * (float)rng, p.z + l * (float)rng); } }; bool CV_Affine3D_EstTest::testNPoints() { Mat aff(3, 4, CV_64F); cv::randu(aff, Scalar(-2), Scalar(2)); // setting points that are no in the same line const int n = 100; const int m = 3*n/5; const Point3f shift_outl = Point3f(15, 15, 15); const float noise_level = 20.f; Mat fpts(1, n, CV_32FC3); Mat tpts(1, n, CV_32FC3); randu(fpts, Scalar::all(0), Scalar::all(100)); std::transform(fpts.ptr(), fpts.ptr() + n, tpts.ptr(), WrapAff(aff)); /* adding noise*/ std::transform(tpts.ptr() + m, tpts.ptr() + n, tpts.ptr() + m, [=] (const Point3f& pt) -> Point3f { return Noise(noise_level)(pt + shift_outl); }); Mat aff_est; vector outl; int res = estimateAffine3D(fpts, tpts, aff_est, outl); if (!res) { ts->set_failed_test_info(cvtest::TS::FAIL_MISMATCH); return false; } const double thres = 1e-4; if (cvtest::norm(aff_est, aff, NORM_INF) > thres) { cout << "aff est: " << aff_est << endl; cout << "aff ref: " << aff << endl; ts->set_failed_test_info(cvtest::TS::FAIL_MISMATCH); return false; } bool outl_good = count(outl.begin(), outl.end(), 1) == m && m == accumulate(outl.begin(), outl.begin() + m, 0); if (!outl_good) { ts->set_failed_test_info(cvtest::TS::FAIL_MISMATCH); return false; } return true; } void CV_Affine3D_EstTest::run( int /* start_from */) { cvtest::DefaultRngAuto dra; if (!test4Points()) return; if (!testNPoints()) return; ts->set_failed_test_info(cvtest::TS::OK); } TEST(Calib3d_EstimateAffine3D, accuracy) { CV_Affine3D_EstTest test; test.safe_run(); } TEST(Calib3d_EstimateAffine3D, regression_16007) { std::vector m1, m2; m1.push_back(Point3f(1.0f, 0.0f, 0.0f)); m2.push_back(Point3f(1.0f, 1.0f, 0.0f)); m1.push_back(Point3f(1.0f, 0.0f, 1.0f)); m2.push_back(Point3f(1.0f, 1.0f, 1.0f)); m1.push_back(Point3f(0.5f, 0.0f, 0.5f)); m2.push_back(Point3f(0.5f, 1.0f, 0.5f)); m1.push_back(Point3f(2.5f, 0.0f, 2.5f)); m2.push_back(Point3f(2.5f, 1.0f, 2.5f)); m1.push_back(Point3f(2.0f, 0.0f, 1.0f)); m2.push_back(Point3f(2.0f, 1.0f, 1.0f)); cv::Mat m3D, inl; int res = cv::estimateAffine3D(m1, m2, m3D, inl); EXPECT_EQ(1, res); } TEST(Calib3d_EstimateAffine3D, umeyama_3_pt) { std::vector points = {{{0.80549149, 0.8225781, 0.79949521}, {0.28906756, 0.57158557, 0.9864789}, {0.58266182, 0.65474983, 0.25078834}}}; cv::Mat R = (cv::Mat_(3,3) << 0.9689135, -0.0232753, 0.2463025, 0.0236362, 0.9997195, 0.0014915, -0.2462682, 0.0043765, 0.9691918); cv::Vec3d t(1., 2., 3.); cv::Affine3d transform(R, t); std::vector transformed_points(points.size()); std::transform(points.begin(), points.end(), transformed_points.begin(), [transform](const cv::Vec3d v){return transform * v;}); double scale; cv::Mat trafo_est = estimateAffine3D(points, transformed_points, &scale); Mat R_est(trafo_est(Rect(0, 0, 3, 3))); EXPECT_LE(cvtest::norm(R_est, R, NORM_INF), 1e-6); Vec3d t_est = trafo_est.col(3); EXPECT_LE(cvtest::norm(t_est, t, NORM_INF), 1e-6); EXPECT_NEAR(scale, 1.0, 1e-6); } }} // namespace