/*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" using namespace cv; using namespace std; class CV_ECC_BaseTest : public cvtest::BaseTest { public: CV_ECC_BaseTest(); protected: double computeRMS(const Mat& mat1, const Mat& mat2); bool isMapCorrect(const Mat& mat); double MAX_RMS_ECC;//upper bound for RMS error int ntests;//number of tests per motion type int ECC_iterations;//number of iterations for ECC double ECC_epsilon; //we choose a negative value, so that // ECC_iterations are always executed }; CV_ECC_BaseTest::CV_ECC_BaseTest() { MAX_RMS_ECC=0.1; ntests = 3; ECC_iterations = 50; ECC_epsilon = -1; //-> negative value means that ECC_Iterations will be executed } bool CV_ECC_BaseTest::isMapCorrect(const Mat& map) { bool tr = true; float mapVal; for(int i =0; i(i, j); tr = tr & (!cvIsNaN(mapVal) && (fabs(mapVal) < 1e9)); } return tr; } double CV_ECC_BaseTest::computeRMS(const Mat& mat1, const Mat& mat2){ CV_Assert(mat1.rows == mat2.rows); CV_Assert(mat1.cols == mat2.cols); Mat errorMat; subtract(mat1, mat2, errorMat); return sqrt(errorMat.dot(errorMat)/(mat1.rows*mat1.cols)); } class CV_ECC_Test_Translation : public CV_ECC_BaseTest { public: CV_ECC_Test_Translation(); protected: void run(int); bool testTranslation(int); }; CV_ECC_Test_Translation::CV_ECC_Test_Translation(){} bool CV_ECC_Test_Translation::testTranslation(int from) { Mat img = imread( string(ts->get_data_path()) + "shared/fruits.png", 0); if (img.empty()) { ts->printf( ts->LOG, "test image can not be read"); ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA); return false; } Mat testImg; resize(img, testImg, Size(216, 216), 0, 0, INTER_LINEAR_EXACT); cv::RNG rng = ts->get_rng(); int progress=0; for (int k=from; kupdate_context( this, k, true ); progress = update_progress(progress, k, ntests, 0); Mat translationGround = (Mat_(2,3) << 1, 0, (rng.uniform(10.f, 20.f)), 0, 1, (rng.uniform(10.f, 20.f))); Mat warpedImage; warpAffine(testImg, warpedImage, translationGround, Size(200,200), INTER_LINEAR + WARP_INVERSE_MAP); Mat mapTranslation = (Mat_(2,3) << 1, 0, 0, 0, 1, 0); findTransformECC(warpedImage, testImg, mapTranslation, 0, TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, ECC_iterations, ECC_epsilon)); if (!isMapCorrect(mapTranslation)){ ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT); return false; } if (computeRMS(mapTranslation, translationGround)>MAX_RMS_ECC){ ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY); ts->printf( ts->LOG, "RMS = %f", computeRMS(mapTranslation, translationGround)); return false; } } return true; } void CV_ECC_Test_Translation::run(int from) { if (!testTranslation(from)) return; ts->set_failed_test_info(cvtest::TS::OK); } class CV_ECC_Test_Euclidean : public CV_ECC_BaseTest { public: CV_ECC_Test_Euclidean(); protected: void run(int); bool testEuclidean(int); }; CV_ECC_Test_Euclidean::CV_ECC_Test_Euclidean() { } bool CV_ECC_Test_Euclidean::testEuclidean(int from) { Mat img = imread( string(ts->get_data_path()) + "shared/fruits.png", 0); if (img.empty()) { ts->printf( ts->LOG, "test image can not be read"); ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA); return false; } Mat testImg; resize(img, testImg, Size(216, 216), 0, 0, INTER_LINEAR_EXACT); cv::RNG rng = ts->get_rng(); int progress = 0; for (int k=from; kupdate_context( this, k, true ); progress = update_progress(progress, k, ntests, 0); double angle = CV_PI/30 + CV_PI*rng.uniform((double)-2.f, (double)2.f)/180; Mat euclideanGround = (Mat_(2,3) << cos(angle), -sin(angle), (rng.uniform(10.f, 20.f)), sin(angle), cos(angle), (rng.uniform(10.f, 20.f))); Mat warpedImage; warpAffine(testImg, warpedImage, euclideanGround, Size(200,200), INTER_LINEAR + WARP_INVERSE_MAP); Mat mapEuclidean = (Mat_(2,3) << 1, 0, 0, 0, 1, 0); findTransformECC(warpedImage, testImg, mapEuclidean, 1, TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, ECC_iterations, ECC_epsilon)); if (!isMapCorrect(mapEuclidean)){ ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT); return false; } if (computeRMS(mapEuclidean, euclideanGround)>MAX_RMS_ECC){ ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY); ts->printf( ts->LOG, "RMS = %f", computeRMS(mapEuclidean, euclideanGround)); return false; } } return true; } void CV_ECC_Test_Euclidean::run(int from) { if (!testEuclidean(from)) return; ts->set_failed_test_info(cvtest::TS::OK); } class CV_ECC_Test_Affine : public CV_ECC_BaseTest { public: CV_ECC_Test_Affine(); protected: void run(int); bool testAffine(int); }; CV_ECC_Test_Affine::CV_ECC_Test_Affine(){} bool CV_ECC_Test_Affine::testAffine(int from) { Mat img = imread( string(ts->get_data_path()) + "shared/fruits.png", 0); if (img.empty()) { ts->printf( ts->LOG, "test image can not be read"); ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA); return false; } Mat testImg; resize(img, testImg, Size(216, 216), 0, 0, INTER_LINEAR_EXACT); cv::RNG rng = ts->get_rng(); int progress = 0; for (int k=from; kupdate_context( this, k, true ); progress = update_progress(progress, k, ntests, 0); Mat affineGround = (Mat_(2,3) << (1-rng.uniform(-0.05f, 0.05f)), (rng.uniform(-0.03f, 0.03f)), (rng.uniform(10.f, 20.f)), (rng.uniform(-0.03f, 0.03f)), (1-rng.uniform(-0.05f, 0.05f)), (rng.uniform(10.f, 20.f))); Mat warpedImage; warpAffine(testImg, warpedImage, affineGround, Size(200,200), INTER_LINEAR + WARP_INVERSE_MAP); Mat mapAffine = (Mat_(2,3) << 1, 0, 0, 0, 1, 0); findTransformECC(warpedImage, testImg, mapAffine, 2, TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, ECC_iterations, ECC_epsilon)); if (!isMapCorrect(mapAffine)){ ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT); return false; } if (computeRMS(mapAffine, affineGround)>MAX_RMS_ECC){ ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY); ts->printf( ts->LOG, "RMS = %f", computeRMS(mapAffine, affineGround)); return false; } } return true; } void CV_ECC_Test_Affine::run(int from) { if (!testAffine(from)) return; ts->set_failed_test_info(cvtest::TS::OK); } class CV_ECC_Test_Homography : public CV_ECC_BaseTest { public: CV_ECC_Test_Homography(); protected: void run(int); bool testHomography(int); }; CV_ECC_Test_Homography::CV_ECC_Test_Homography(){} bool CV_ECC_Test_Homography::testHomography(int from) { Mat img = imread( string(ts->get_data_path()) + "shared/fruits.png", 0); if (img.empty()) { ts->printf( ts->LOG, "test image can not be read"); ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA); return false; } Mat testImg; resize(img, testImg, Size(216, 216), 0, 0, INTER_LINEAR_EXACT); cv::RNG rng = ts->get_rng(); int progress = 0; for (int k=from; kupdate_context( this, k, true ); progress = update_progress(progress, k, ntests, 0); Mat homoGround = (Mat_(3,3) << (1-rng.uniform(-0.05f, 0.05f)), (rng.uniform(-0.03f, 0.03f)), (rng.uniform(10.f, 20.f)), (rng.uniform(-0.03f, 0.03f)), (1-rng.uniform(-0.05f, 0.05f)),(rng.uniform(10.f, 20.f)), (rng.uniform(0.0001f, 0.0003f)), (rng.uniform(0.0001f, 0.0003f)), 1.f); Mat warpedImage; warpPerspective(testImg, warpedImage, homoGround, Size(200,200), INTER_LINEAR + WARP_INVERSE_MAP); Mat mapHomography = Mat::eye(3, 3, CV_32F); findTransformECC(warpedImage, testImg, mapHomography, 3, TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, ECC_iterations, ECC_epsilon)); if (!isMapCorrect(mapHomography)){ ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT); return false; } if (computeRMS(mapHomography, homoGround)>MAX_RMS_ECC){ ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY); ts->printf( ts->LOG, "RMS = %f", computeRMS(mapHomography, homoGround)); return false; } } return true; } void CV_ECC_Test_Homography::run(int from) { if (!testHomography(from)) return; ts->set_failed_test_info(cvtest::TS::OK); } class CV_ECC_Test_Mask : public CV_ECC_BaseTest { public: CV_ECC_Test_Mask(); protected: void run(int); bool testMask(int); }; CV_ECC_Test_Mask::CV_ECC_Test_Mask(){} bool CV_ECC_Test_Mask::testMask(int from) { Mat img = imread( string(ts->get_data_path()) + "shared/fruits.png", 0); if (img.empty()) { ts->printf( ts->LOG, "test image can not be read"); ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA); return false; } Mat scaledImage; resize(img, scaledImage, Size(216, 216), 0, 0, INTER_LINEAR_EXACT ); Mat_ testImg; scaledImage.convertTo(testImg, testImg.type()); cv::RNG rng = ts->get_rng(); int progress=0; for (int k=from; kupdate_context( this, k, true ); progress = update_progress(progress, k, ntests, 0); Mat translationGround = (Mat_(2,3) << 1, 0, (rng.uniform(10.f, 20.f)), 0, 1, (rng.uniform(10.f, 20.f))); Mat warpedImage; warpAffine(testImg, warpedImage, translationGround, Size(200,200), INTER_LINEAR + WARP_INVERSE_MAP); Mat mapTranslation = (Mat_(2,3) << 1, 0, 0, 0, 1, 0); Mat_ mask = Mat_::ones(testImg.rows, testImg.cols); for (int i=testImg.rows*2/3; iset_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT); return false; } if (computeRMS(mapTranslation, translationGround)>MAX_RMS_ECC){ ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY); ts->printf( ts->LOG, "RMS = %f", computeRMS(mapTranslation, translationGround)); return false; } } return true; } void CV_ECC_Test_Mask::run(int from) { if (!testMask(from)) return; ts->set_failed_test_info(cvtest::TS::OK); } TEST(Video_ECC_Translation, accuracy) { CV_ECC_Test_Translation test; test.safe_run();} TEST(Video_ECC_Euclidean, accuracy) { CV_ECC_Test_Euclidean test; test.safe_run(); } TEST(Video_ECC_Affine, accuracy) { CV_ECC_Test_Affine test; test.safe_run(); } TEST(Video_ECC_Homography, accuracy) { CV_ECC_Test_Homography test; test.safe_run(); } TEST(Video_ECC_Mask, accuracy) { CV_ECC_Test_Mask test; test.safe_run(); }