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485 lines
14 KiB
485 lines
14 KiB
/*M/////////////////////////////////////////////////////////////////////////////////////// |
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// |
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. |
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// |
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// By downloading, copying, installing or using the software you agree to this license. |
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// If you do not agree to this license, do not download, install, |
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// copy or use the software. |
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// |
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// |
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// License Agreement |
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// For Open Source Computer Vision Library |
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// |
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// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. |
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// Copyright (C) 2009, Willow Garage Inc., all rights reserved. |
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// Third party copyrights are property of their respective owners. |
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// |
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// Redistribution and use in source and binary forms, with or without modification, |
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// are permitted provided that the following conditions are met: |
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// |
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// * Redistribution's of source code must retain the above copyright notice, |
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// this list of conditions and the following disclaimer. |
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// |
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// * Redistribution's in binary form must reproduce the above copyright notice, |
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// this list of conditions and the following disclaimer in the documentation |
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// and/or other materials provided with the distribution. |
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// |
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// * The name of the copyright holders may not be used to endorse or promote products |
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// derived from this software without specific prior written permission. |
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// |
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// This software is provided by the copyright holders and contributors "as is" and |
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// any express or implied warranties, including, but not limited to, the implied |
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// warranties of merchantability and fitness for a particular purpose are disclaimed. |
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// In no event shall the Intel Corporation or contributors be liable for any direct, |
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// indirect, incidental, special, exemplary, or consequential damages |
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// (including, but not limited to, procurement of substitute goods or services; |
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// loss of use, data, or profits; or business interruption) however caused |
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// and on any theory of liability, whether in contract, strict liability, |
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// or tort (including negligence or otherwise) arising in any way out of |
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// the use of this software, even if advised of the possibility of such damage. |
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// |
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//M*/ |
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#include "test_precomp.hpp" |
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namespace opencv_test { namespace { |
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class CV_ECC_BaseTest : public cvtest::BaseTest |
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{ |
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public: |
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CV_ECC_BaseTest(); |
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protected: |
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double computeRMS(const Mat& mat1, const Mat& mat2); |
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bool isMapCorrect(const Mat& mat); |
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double MAX_RMS_ECC;//upper bound for RMS error |
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int ntests;//number of tests per motion type |
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int ECC_iterations;//number of iterations for ECC |
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double ECC_epsilon; //we choose a negative value, so that |
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// ECC_iterations are always executed |
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}; |
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CV_ECC_BaseTest::CV_ECC_BaseTest() |
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{ |
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MAX_RMS_ECC=0.1; |
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ntests = 3; |
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ECC_iterations = 50; |
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ECC_epsilon = -1; //-> negative value means that ECC_Iterations will be executed |
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} |
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bool CV_ECC_BaseTest::isMapCorrect(const Mat& map) |
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{ |
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bool tr = true; |
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float mapVal; |
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for(int i =0; i<map.rows; i++) |
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for(int j=0; j<map.cols; j++){ |
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mapVal = map.at<float>(i, j); |
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tr = tr & (!cvIsNaN(mapVal) && (fabs(mapVal) < 1e9)); |
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} |
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return tr; |
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} |
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double CV_ECC_BaseTest::computeRMS(const Mat& mat1, const Mat& mat2){ |
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CV_Assert(mat1.rows == mat2.rows); |
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CV_Assert(mat1.cols == mat2.cols); |
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Mat errorMat; |
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subtract(mat1, mat2, errorMat); |
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return sqrt(errorMat.dot(errorMat)/(mat1.rows*mat1.cols)); |
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} |
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class CV_ECC_Test_Translation : public CV_ECC_BaseTest |
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{ |
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public: |
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CV_ECC_Test_Translation(); |
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protected: |
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void run(int); |
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bool testTranslation(int); |
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}; |
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CV_ECC_Test_Translation::CV_ECC_Test_Translation(){} |
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bool CV_ECC_Test_Translation::testTranslation(int from) |
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{ |
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Mat img = imread( string(ts->get_data_path()) + "shared/fruits.png", 0); |
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if (img.empty()) |
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{ |
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ts->printf( ts->LOG, "test image can not be read"); |
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ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA); |
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return false; |
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} |
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Mat testImg; |
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resize(img, testImg, Size(216, 216), 0, 0, INTER_LINEAR_EXACT); |
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cv::RNG rng = ts->get_rng(); |
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int progress=0; |
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for (int k=from; k<ntests; k++){ |
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ts->update_context( this, k, true ); |
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progress = update_progress(progress, k, ntests, 0); |
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Mat translationGround = (Mat_<float>(2,3) << 1, 0, (rng.uniform(10.f, 20.f)), |
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0, 1, (rng.uniform(10.f, 20.f))); |
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Mat warpedImage; |
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warpAffine(testImg, warpedImage, translationGround, |
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Size(200,200), INTER_LINEAR + WARP_INVERSE_MAP); |
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Mat mapTranslation = (Mat_<float>(2,3) << 1, 0, 0, 0, 1, 0); |
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findTransformECC(warpedImage, testImg, mapTranslation, 0, |
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TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, ECC_iterations, ECC_epsilon)); |
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if (!isMapCorrect(mapTranslation)){ |
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ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT); |
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return false; |
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} |
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if (computeRMS(mapTranslation, translationGround)>MAX_RMS_ECC){ |
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ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY); |
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ts->printf( ts->LOG, "RMS = %f", |
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computeRMS(mapTranslation, translationGround)); |
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return false; |
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} |
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} |
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return true; |
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} |
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void CV_ECC_Test_Translation::run(int from) |
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{ |
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if (!testTranslation(from)) |
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return; |
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ts->set_failed_test_info(cvtest::TS::OK); |
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} |
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class CV_ECC_Test_Euclidean : public CV_ECC_BaseTest |
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{ |
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public: |
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CV_ECC_Test_Euclidean(); |
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protected: |
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void run(int); |
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bool testEuclidean(int); |
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}; |
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CV_ECC_Test_Euclidean::CV_ECC_Test_Euclidean() { } |
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bool CV_ECC_Test_Euclidean::testEuclidean(int from) |
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{ |
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Mat img = imread( string(ts->get_data_path()) + "shared/fruits.png", 0); |
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if (img.empty()) |
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{ |
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ts->printf( ts->LOG, "test image can not be read"); |
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ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA); |
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return false; |
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} |
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Mat testImg; |
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resize(img, testImg, Size(216, 216), 0, 0, INTER_LINEAR_EXACT); |
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cv::RNG rng = ts->get_rng(); |
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int progress = 0; |
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for (int k=from; k<ntests; k++){ |
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ts->update_context( this, k, true ); |
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progress = update_progress(progress, k, ntests, 0); |
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double angle = CV_PI/30 + CV_PI*rng.uniform((double)-2.f, (double)2.f)/180; |
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Mat euclideanGround = (Mat_<float>(2,3) << cos(angle), -sin(angle), (rng.uniform(10.f, 20.f)), |
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sin(angle), cos(angle), (rng.uniform(10.f, 20.f))); |
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Mat warpedImage; |
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warpAffine(testImg, warpedImage, euclideanGround, |
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Size(200,200), INTER_LINEAR + WARP_INVERSE_MAP); |
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Mat mapEuclidean = (Mat_<float>(2,3) << 1, 0, 0, 0, 1, 0); |
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findTransformECC(warpedImage, testImg, mapEuclidean, 1, |
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TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, ECC_iterations, ECC_epsilon)); |
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if (!isMapCorrect(mapEuclidean)){ |
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ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT); |
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return false; |
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} |
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if (computeRMS(mapEuclidean, euclideanGround)>MAX_RMS_ECC){ |
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ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY); |
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ts->printf( ts->LOG, "RMS = %f", |
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computeRMS(mapEuclidean, euclideanGround)); |
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return false; |
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} |
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} |
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return true; |
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} |
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void CV_ECC_Test_Euclidean::run(int from) |
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{ |
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if (!testEuclidean(from)) |
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return; |
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ts->set_failed_test_info(cvtest::TS::OK); |
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} |
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class CV_ECC_Test_Affine : public CV_ECC_BaseTest |
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{ |
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public: |
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CV_ECC_Test_Affine(); |
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protected: |
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void run(int); |
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bool testAffine(int); |
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}; |
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CV_ECC_Test_Affine::CV_ECC_Test_Affine(){} |
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bool CV_ECC_Test_Affine::testAffine(int from) |
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{ |
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Mat img = imread( string(ts->get_data_path()) + "shared/fruits.png", 0); |
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if (img.empty()) |
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{ |
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ts->printf( ts->LOG, "test image can not be read"); |
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ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA); |
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return false; |
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} |
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Mat testImg; |
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resize(img, testImg, Size(216, 216), 0, 0, INTER_LINEAR_EXACT); |
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cv::RNG rng = ts->get_rng(); |
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int progress = 0; |
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for (int k=from; k<ntests; k++){ |
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ts->update_context( this, k, true ); |
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progress = update_progress(progress, k, ntests, 0); |
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Mat affineGround = (Mat_<float>(2,3) << (1-rng.uniform(-0.05f, 0.05f)), |
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(rng.uniform(-0.03f, 0.03f)), (rng.uniform(10.f, 20.f)), |
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(rng.uniform(-0.03f, 0.03f)), (1-rng.uniform(-0.05f, 0.05f)), |
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(rng.uniform(10.f, 20.f))); |
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Mat warpedImage; |
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warpAffine(testImg, warpedImage, affineGround, |
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Size(200,200), INTER_LINEAR + WARP_INVERSE_MAP); |
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Mat mapAffine = (Mat_<float>(2,3) << 1, 0, 0, 0, 1, 0); |
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findTransformECC(warpedImage, testImg, mapAffine, 2, |
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TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, ECC_iterations, ECC_epsilon)); |
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if (!isMapCorrect(mapAffine)){ |
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ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT); |
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return false; |
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} |
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if (computeRMS(mapAffine, affineGround)>MAX_RMS_ECC){ |
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ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY); |
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ts->printf( ts->LOG, "RMS = %f", |
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computeRMS(mapAffine, affineGround)); |
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return false; |
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} |
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} |
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return true; |
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} |
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void CV_ECC_Test_Affine::run(int from) |
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{ |
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if (!testAffine(from)) |
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return; |
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ts->set_failed_test_info(cvtest::TS::OK); |
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} |
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class CV_ECC_Test_Homography : public CV_ECC_BaseTest |
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{ |
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public: |
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CV_ECC_Test_Homography(); |
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protected: |
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void run(int); |
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bool testHomography(int); |
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}; |
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CV_ECC_Test_Homography::CV_ECC_Test_Homography(){} |
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bool CV_ECC_Test_Homography::testHomography(int from) |
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{ |
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Mat img = imread( string(ts->get_data_path()) + "shared/fruits.png", 0); |
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if (img.empty()) |
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{ |
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ts->printf( ts->LOG, "test image can not be read"); |
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ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA); |
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return false; |
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} |
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Mat testImg; |
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resize(img, testImg, Size(216, 216), 0, 0, INTER_LINEAR_EXACT); |
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cv::RNG rng = ts->get_rng(); |
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int progress = 0; |
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for (int k=from; k<ntests; k++){ |
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ts->update_context( this, k, true ); |
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progress = update_progress(progress, k, ntests, 0); |
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Mat homoGround = (Mat_<float>(3,3) << (1-rng.uniform(-0.05f, 0.05f)), |
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(rng.uniform(-0.03f, 0.03f)), (rng.uniform(10.f, 20.f)), |
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(rng.uniform(-0.03f, 0.03f)), (1-rng.uniform(-0.05f, 0.05f)),(rng.uniform(10.f, 20.f)), |
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(rng.uniform(0.0001f, 0.0003f)), (rng.uniform(0.0001f, 0.0003f)), 1.f); |
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Mat warpedImage; |
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warpPerspective(testImg, warpedImage, homoGround, |
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Size(200,200), INTER_LINEAR + WARP_INVERSE_MAP); |
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Mat mapHomography = Mat::eye(3, 3, CV_32F); |
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findTransformECC(warpedImage, testImg, mapHomography, 3, |
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TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, ECC_iterations, ECC_epsilon)); |
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if (!isMapCorrect(mapHomography)){ |
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ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT); |
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return false; |
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} |
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if (computeRMS(mapHomography, homoGround)>MAX_RMS_ECC){ |
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ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY); |
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ts->printf( ts->LOG, "RMS = %f", |
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computeRMS(mapHomography, homoGround)); |
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return false; |
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} |
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} |
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return true; |
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} |
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void CV_ECC_Test_Homography::run(int from) |
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{ |
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if (!testHomography(from)) |
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return; |
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ts->set_failed_test_info(cvtest::TS::OK); |
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} |
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class CV_ECC_Test_Mask : public CV_ECC_BaseTest |
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{ |
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public: |
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CV_ECC_Test_Mask(); |
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protected: |
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void run(int); |
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bool testMask(int); |
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}; |
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CV_ECC_Test_Mask::CV_ECC_Test_Mask(){} |
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bool CV_ECC_Test_Mask::testMask(int from) |
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{ |
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Mat img = imread( string(ts->get_data_path()) + "shared/fruits.png", 0); |
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if (img.empty()) |
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{ |
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ts->printf( ts->LOG, "test image can not be read"); |
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ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA); |
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return false; |
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} |
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Mat scaledImage; |
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resize(img, scaledImage, Size(216, 216), 0, 0, INTER_LINEAR_EXACT ); |
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Mat_<float> testImg; |
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scaledImage.convertTo(testImg, testImg.type()); |
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cv::RNG rng = ts->get_rng(); |
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int progress=0; |
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for (int k=from; k<ntests; k++){ |
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ts->update_context( this, k, true ); |
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progress = update_progress(progress, k, ntests, 0); |
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Mat translationGround = (Mat_<float>(2,3) << 1, 0, (rng.uniform(10.f, 20.f)), |
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0, 1, (rng.uniform(10.f, 20.f))); |
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Mat warpedImage; |
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warpAffine(testImg, warpedImage, translationGround, |
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Size(200,200), INTER_LINEAR + WARP_INVERSE_MAP); |
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Mat mapTranslation = (Mat_<float>(2,3) << 1, 0, 0, 0, 1, 0); |
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Mat_<unsigned char> mask = Mat_<unsigned char>::ones(testImg.rows, testImg.cols); |
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for (int i=testImg.rows*2/3; i<testImg.rows; i++) { |
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for (int j=testImg.cols*2/3; j<testImg.cols; j++) { |
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testImg(i, j) = 0; |
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mask(i, j) = 0; |
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} |
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} |
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findTransformECC(warpedImage, testImg, mapTranslation, 0, |
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TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, ECC_iterations, ECC_epsilon), mask); |
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if (!isMapCorrect(mapTranslation)){ |
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ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT); |
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return false; |
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} |
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if (computeRMS(mapTranslation, translationGround)>MAX_RMS_ECC){ |
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ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY); |
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ts->printf( ts->LOG, "RMS = %f", |
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computeRMS(mapTranslation, translationGround)); |
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return false; |
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} |
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} |
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return true; |
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} |
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void CV_ECC_Test_Mask::run(int from) |
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{ |
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if (!testMask(from)) |
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return; |
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ts->set_failed_test_info(cvtest::TS::OK); |
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} |
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TEST(Video_ECC_Translation, accuracy) { CV_ECC_Test_Translation test; test.safe_run();} |
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TEST(Video_ECC_Euclidean, accuracy) { CV_ECC_Test_Euclidean test; test.safe_run(); } |
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TEST(Video_ECC_Affine, accuracy) { CV_ECC_Test_Affine test; test.safe_run(); } |
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TEST(Video_ECC_Homography, accuracy) { CV_ECC_Test_Homography test; test.safe_run(); } |
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TEST(Video_ECC_Mask, accuracy) { CV_ECC_Test_Mask test; test.safe_run(); } |
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}} // namespace
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