/*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" #include <string> #include <iostream> #include <fstream> #include <iterator> #include <limits> #include <numeric> using namespace cv; using namespace std; class CV_RigidTransform_Test : public cvtest::BaseTest { public: CV_RigidTransform_Test(); ~CV_RigidTransform_Test(); protected: void run(int); bool testNPoints(int); bool testImage(); }; CV_RigidTransform_Test::CV_RigidTransform_Test() { } CV_RigidTransform_Test::~CV_RigidTransform_Test() {} struct WrapAff2D { const double *F; WrapAff2D(const Mat& aff) : F(aff.ptr<double>()) {} Point2f operator()(const Point2f& p) { return Point2f( (float)(p.x * F[0] + p.y * F[1] + F[2]), (float)(p.x * F[3] + p.y * F[4] + F[5]) ); } }; bool CV_RigidTransform_Test::testNPoints(int from) { cv::RNG rng = ts->get_rng(); int progress = 0; int k, ntests = 10000; for( k = from; k < ntests; k++ ) { ts->update_context( this, k, true ); progress = update_progress(progress, k, ntests, 0); Mat aff(2, 3, CV_64F); rng.fill(aff, RNG::UNIFORM, Scalar(-2), Scalar(2)); int n = (unsigned)rng % 100 + 10; Mat fpts(1, n, CV_32FC2); Mat tpts(1, n, CV_32FC2); rng.fill(fpts, RNG::UNIFORM, Scalar(0,0), Scalar(10,10)); transform(fpts.ptr<Point2f>(), fpts.ptr<Point2f>() + n, tpts.ptr<Point2f>(), WrapAff2D(aff)); Mat noise(1, n, CV_32FC2); rng.fill(noise, RNG::NORMAL, Scalar::all(0), Scalar::all(0.001*(n<=7 ? 0 : n <= 30 ? 1 : 10))); tpts += noise; Mat aff_est = estimateRigidTransform(fpts, tpts, true); double thres = 0.1*cvtest::norm(aff, NORM_L2); double d = cvtest::norm(aff_est, aff, NORM_L2); if (d > thres) { double dB=0, nB=0; if (n <= 4) { Mat A = fpts.reshape(1, 3); Mat B = A - repeat(A.row(0), 3, 1), Bt = B.t(); B = Bt*B; dB = cv::determinant(B); nB = cvtest::norm(B, NORM_L2); if( fabs(dB) < 0.01*nB ) continue; } ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY); ts->printf( cvtest::TS::LOG, "Threshold = %f, norm of difference = %f", thres, d ); return false; } } return true; } bool CV_RigidTransform_Test::testImage() { Mat img; Mat testImg = imread( string(ts->get_data_path()) + "shared/graffiti.png", 1); if (testImg.empty()) { ts->printf( ts->LOG, "test image can not be read"); ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA); return false; } pyrDown(testImg, img); Mat aff = cv::getRotationMatrix2D(Point(img.cols/2, img.rows/2), 1, 0.99); aff.ptr<double>()[2]+=3; aff.ptr<double>()[5]+=3; Mat rotated; warpAffine(img, rotated, aff, img.size()); Mat aff_est = estimateRigidTransform(img, rotated, true); const double thres = 0.033; if (cvtest::norm(aff_est, aff, NORM_INF) > thres) { ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY); ts->printf( cvtest::TS::LOG, "Threshold = %f, norm of difference = %f", thres, cvtest::norm(aff_est, aff, NORM_INF) ); return false; } return true; } void CV_RigidTransform_Test::run( int start_from ) { cvtest::DefaultRngAuto dra; if (!testNPoints(start_from)) return; if (!testImage()) return; ts->set_failed_test_info(cvtest::TS::OK); } TEST(Video_RigidFlow, accuracy) { CV_RigidTransform_Test test; test.safe_run(); }