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411 lines
15 KiB
411 lines
15 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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#include <time.h> |
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using namespace cv; |
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using namespace std; |
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#define sign(a) a > 0 ? 1 : a == 0 ? 0 : -1 |
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#define CORE_EIGEN_ERROR_COUNT 1 |
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#define CORE_EIGEN_ERROR_SIZE 2 |
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#define CORE_EIGEN_ERROR_DIFF 3 |
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#define CORE_EIGEN_ERROR_ORTHO 4 |
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#define CORE_EIGEN_ERROR_ORDER 5 |
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#define MESSAGE_ERROR_COUNT "Matrix of eigen values must have the same rows as source matrix and 1 column." |
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#define MESSAGE_ERROR_SIZE "Source matrix and matrix of eigen vectors must have the same sizes." |
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#define MESSAGE_ERROR_DIFF_1 "Accurasy of eigen values computing less than required." |
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#define MESSAGE_ERROR_DIFF_2 "Accuracy of eigen vectors computing less than required." |
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#define MESSAGE_ERROR_ORTHO "Matrix of eigen vectors is not orthogonal." |
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#define MESSAGE_ERROR_ORDER "Eigen values are not sorted in ascending order." |
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const int COUNT_NORM_TYPES = 3; |
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const int NORM_TYPE[COUNT_NORM_TYPES] = {cv::NORM_L1, cv::NORM_L2, cv::NORM_INF}; |
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enum TASK_TYPE_EIGEN {VALUES, VECTORS}; |
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class Core_EigenTest: public cvtest::BaseTest |
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{ |
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public: |
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Core_EigenTest(); |
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~Core_EigenTest(); |
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protected: |
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bool test_values(const cv::Mat& src); // complex test for eigen without vectors |
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bool check_full(int type); // compex test for symmetric matrix |
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virtual void run (int) = 0; // main testing method |
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protected: |
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float eps_val_32, eps_vec_32; |
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float eps_val_64, eps_vec_64; |
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int ntests; |
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bool check_pair_count(const cv::Mat& src, const cv::Mat& evalues, int low_index = -1, int high_index = -1); |
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bool check_pair_count(const cv::Mat& src, const cv::Mat& evalues, const cv::Mat& evectors, int low_index = -1, int high_index = -1); |
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bool check_pairs_order(const cv::Mat& eigen_values); // checking order of eigen values & vectors (it should be none up) |
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bool check_orthogonality(const cv::Mat& U); // checking is matrix of eigen vectors orthogonal |
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bool test_pairs(const cv::Mat& src); // complex test for eigen with vectors |
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void print_information(const size_t norm_idx, const cv::Mat& src, double diff, double max_diff); |
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}; |
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class Core_EigenTest_Scalar : public Core_EigenTest |
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{ |
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public: |
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Core_EigenTest_Scalar() : Core_EigenTest() {} |
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~Core_EigenTest_Scalar(); |
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virtual void run(int) = 0; |
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}; |
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class Core_EigenTest_Scalar_32 : public Core_EigenTest_Scalar |
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{ |
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public: |
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Core_EigenTest_Scalar_32() : Core_EigenTest_Scalar() {} |
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~Core_EigenTest_Scalar_32(); |
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void run(int); |
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}; |
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class Core_EigenTest_Scalar_64 : public Core_EigenTest_Scalar |
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{ |
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public: |
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Core_EigenTest_Scalar_64() : Core_EigenTest_Scalar() {} |
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~Core_EigenTest_Scalar_64(); |
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void run(int); |
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}; |
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class Core_EigenTest_32 : public Core_EigenTest |
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{ |
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public: |
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Core_EigenTest_32(): Core_EigenTest() {} |
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~Core_EigenTest_32() {} |
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void run(int); |
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}; |
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class Core_EigenTest_64 : public Core_EigenTest |
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{ |
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public: |
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Core_EigenTest_64(): Core_EigenTest() {} |
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~Core_EigenTest_64() {} |
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void run(int); |
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}; |
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Core_EigenTest_Scalar::~Core_EigenTest_Scalar() {} |
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Core_EigenTest_Scalar_32::~Core_EigenTest_Scalar_32() {} |
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Core_EigenTest_Scalar_64::~Core_EigenTest_Scalar_64() {} |
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void Core_EigenTest_Scalar_32::run(int) |
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{ |
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for (int i = 0; i < ntests; ++i) |
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{ |
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float value = cv::randu<float>(); |
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cv::Mat src(1, 1, CV_32FC1, Scalar::all((float)value)); |
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test_values(src); |
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} |
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} |
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void Core_EigenTest_Scalar_64::run(int) |
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{ |
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for (int i = 0; i < ntests; ++i) |
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{ |
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float value = cv::randu<float>(); |
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cv::Mat src(1, 1, CV_64FC1, Scalar::all((double)value)); |
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test_values(src); |
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} |
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} |
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void Core_EigenTest_32::run(int) { check_full(CV_32FC1); } |
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void Core_EigenTest_64::run(int) { check_full(CV_64FC1); } |
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Core_EigenTest::Core_EigenTest() |
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: eps_val_32(1e-3f), eps_vec_32(1e-2f), |
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eps_val_64(1e-4f), eps_vec_64(1e-3f), ntests(100) {} |
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Core_EigenTest::~Core_EigenTest() {} |
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bool Core_EigenTest::check_pair_count(const cv::Mat& src, const cv::Mat& evalues, int low_index, int high_index) |
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{ |
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int n = src.rows, s = sign(high_index); |
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if (!( (evalues.rows == n - max<int>(0, low_index) - ((int)((n/2.0)*(s*s-s)) + (1+s-s*s)*(n - (high_index+1)))) && (evalues.cols == 1))) |
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{ |
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std::cout << endl; std::cout << "Checking sizes of eigen values matrix " << evalues << "..." << endl; |
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std::cout << "Number of rows: " << evalues.rows << " Number of cols: " << evalues.cols << endl; |
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std:: cout << "Size of src symmetric matrix: " << src.rows << " * " << src.cols << endl; std::cout << endl; |
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CV_Error(CORE_EIGEN_ERROR_COUNT, MESSAGE_ERROR_COUNT); |
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return false; |
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} |
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return true; |
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} |
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bool Core_EigenTest::check_pair_count(const cv::Mat& src, const cv::Mat& evalues, const cv::Mat& evectors, int low_index, int high_index) |
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{ |
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int n = src.rows, s = sign(high_index); |
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int right_eigen_pair_count = n - max<int>(0, low_index) - ((int)((n/2.0)*(s*s-s)) + (1+s-s*s)*(n - (high_index+1))); |
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if (!((evectors.rows == right_eigen_pair_count) && (evectors.cols == right_eigen_pair_count))) |
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{ |
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std::cout << endl; std::cout << "Checking sizes of eigen vectors matrix " << evectors << "..." << endl; |
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std::cout << "Number of rows: " << evectors.rows << " Number of cols: " << evectors.cols << endl; |
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std:: cout << "Size of src symmetric matrix: " << src.rows << " * " << src.cols << endl; std::cout << endl; |
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CV_Error (CORE_EIGEN_ERROR_SIZE, MESSAGE_ERROR_SIZE); |
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return false; |
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} |
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if (!((evalues.rows == right_eigen_pair_count) && (evalues.cols == 1))) |
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{ |
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std::cout << endl; std::cout << "Checking sizes of eigen values matrix " << evalues << "..." << endl; |
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std::cout << "Number of rows: " << evalues.rows << " Number of cols: " << evalues.cols << endl; |
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std:: cout << "Size of src symmetric matrix: " << src.rows << " * " << src.cols << endl; std::cout << endl; |
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CV_Error (CORE_EIGEN_ERROR_COUNT, MESSAGE_ERROR_COUNT); |
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return false; |
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} |
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return true; |
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} |
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void Core_EigenTest::print_information(const size_t norm_idx, const cv::Mat& src, double diff, double max_diff) |
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{ |
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switch (NORM_TYPE[norm_idx]) |
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{ |
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case cv::NORM_L1: {std::cout << "L1"; break;} |
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case cv::NORM_L2: {std::cout << "L2"; break;} |
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case cv::NORM_INF: {std::cout << "INF"; break;} |
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default: break; |
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} |
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cout << "-criteria... " << endl; |
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cout << "Source size: " << src.rows << " * " << src.cols << endl; |
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cout << "Difference between original eigen vectors matrix and result: " << diff << endl; |
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cout << "Maximum allowed difference: " << max_diff << endl; cout << endl; |
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} |
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bool Core_EigenTest::check_orthogonality(const cv::Mat& U) |
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{ |
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int type = U.type(); |
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double eps_vec = type == CV_32FC1 ? eps_vec_32 : eps_vec_64; |
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cv::Mat UUt; cv::mulTransposed(U, UUt, false); |
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cv::Mat E = Mat::eye(U.rows, U.cols, type); |
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for (int i = 0; i < COUNT_NORM_TYPES; ++i) |
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{ |
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double diff = cv::norm(UUt, E, NORM_TYPE[i]); |
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if (diff > eps_vec) |
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{ |
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std::cout << endl; std::cout << "Checking orthogonality of matrix " << U << ": "; |
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print_information(i, U, diff, eps_vec); |
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CV_Error(CORE_EIGEN_ERROR_ORTHO, MESSAGE_ERROR_ORTHO); |
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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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bool Core_EigenTest::check_pairs_order(const cv::Mat& eigen_values) |
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{ |
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switch (eigen_values.type()) |
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{ |
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case CV_32FC1: |
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{ |
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for (int i = 0; i < (int)(eigen_values.total() - 1); ++i) |
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if (!(eigen_values.at<float>(i, 0) > eigen_values.at<float>(i+1, 0))) |
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{ |
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std::cout << endl; std::cout << "Checking order of eigen values vector " << eigen_values << "..." << endl; |
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std::cout << "Pair of indexes with non ascending of eigen values: (" << i << ", " << i+1 << ")." << endl; |
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std::cout << endl; |
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CV_Error(CORE_EIGEN_ERROR_ORDER, MESSAGE_ERROR_ORDER); |
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return false; |
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} |
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break; |
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} |
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case CV_64FC1: |
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{ |
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for (int i = 0; i < (int)(eigen_values.total() - 1); ++i) |
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if (!(eigen_values.at<double>(i, 0) > eigen_values.at<double>(i+1, 0))) |
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{ |
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std::cout << endl; std::cout << "Checking order of eigen values vector " << eigen_values << "..." << endl; |
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std::cout << "Pair of indexes with non ascending of eigen values: (" << i << ", " << i+1 << ")." << endl; |
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std::cout << endl; |
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CV_Error(CORE_EIGEN_ERROR_ORDER, "Eigen values are not sorted in ascending order."); |
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return false; |
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} |
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break; |
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} |
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default:; |
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} |
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return true; |
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} |
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bool Core_EigenTest::test_pairs(const cv::Mat& src) |
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{ |
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int type = src.type(); |
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double eps_vec = type == CV_32FC1 ? eps_vec_32 : eps_vec_64; |
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cv::Mat eigen_values, eigen_vectors; |
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cv::eigen(src, true, eigen_values, eigen_vectors); |
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if (!check_pair_count(src, eigen_values, eigen_vectors)) return false; |
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if (!check_orthogonality (eigen_vectors)) return false; |
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if (!check_pairs_order(eigen_values)) return false; |
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cv::Mat eigen_vectors_t; cv::transpose(eigen_vectors, eigen_vectors_t); |
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cv::Mat src_evec(src.rows, src.cols, type); |
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src_evec = src*eigen_vectors_t; |
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cv::Mat eval_evec(src.rows, src.cols, type); |
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switch (type) |
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{ |
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case CV_32FC1: |
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{ |
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for (int i = 0; i < src.cols; ++i) |
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{ |
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cv::Mat tmp = eigen_values.at<float>(i, 0) * eigen_vectors_t.col(i); |
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for (int j = 0; j < src.rows; ++j) eval_evec.at<float>(j, i) = tmp.at<float>(j, 0); |
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} |
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break; |
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} |
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case CV_64FC1: |
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{ |
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for (int i = 0; i < src.cols; ++i) |
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{ |
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cv::Mat tmp = eigen_values.at<double>(i, 0) * eigen_vectors_t.col(i); |
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for (int j = 0; j < src.rows; ++j) eval_evec.at<double>(j, i) = tmp.at<double>(j, 0); |
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} |
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break; |
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} |
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default:; |
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} |
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cv::Mat disparity = src_evec - eval_evec; |
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for (int i = 0; i < COUNT_NORM_TYPES; ++i) |
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{ |
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double diff = cv::norm(disparity, NORM_TYPE[i]); |
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if (diff > eps_vec) |
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{ |
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std::cout << endl; std::cout << "Checking accuracy of eigen vectors computing for matrix " << src << ": "; |
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print_information(i, src, diff, eps_vec); |
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CV_Error(CORE_EIGEN_ERROR_DIFF, MESSAGE_ERROR_DIFF_2); |
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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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bool Core_EigenTest::test_values(const cv::Mat& src) |
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{ |
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int type = src.type(); |
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double eps_val = type == CV_32FC1 ? eps_val_32 : eps_val_64; |
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cv::Mat eigen_values_1, eigen_values_2, eigen_vectors; |
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if (!test_pairs(src)) return false; |
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cv::eigen(src, true, eigen_values_1, eigen_vectors); |
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cv::eigen(src, false, eigen_values_2, eigen_vectors); |
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if (!check_pair_count(src, eigen_values_2)) return false; |
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for (int i = 0; i < COUNT_NORM_TYPES; ++i) |
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{ |
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double diff = cv::norm(eigen_values_1, eigen_values_2, NORM_TYPE[i]); |
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if (diff > eps_val) |
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{ |
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std::cout << endl; std::cout << "Checking accuracy of eigen values computing for matrix " << src << ": "; |
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print_information(i, src, diff, eps_val); |
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CV_Error(CORE_EIGEN_ERROR_DIFF, MESSAGE_ERROR_DIFF_1); |
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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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bool Core_EigenTest::check_full(int type) |
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{ |
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const int MAX_DEGREE = 7; |
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srand((unsigned int)time(0)); |
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for (int i = 0; i < ntests; ++i) |
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{ |
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int src_size = (int)(std::pow(2.0, (rand()%MAX_DEGREE)+1.)); |
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cv::Mat src(src_size, src_size, type); |
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for (int j = 0; j < src.rows; ++j) |
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for (int k = j; k < src.cols; ++k) |
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if (type == CV_32FC1) src.at<float>(k, j) = src.at<float>(j, k) = cv::randu<float>(); |
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else src.at<double>(k, j) = src.at<double>(j, k) = cv::randu<double>(); |
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if (!test_values(src)) return false; |
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} |
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return true; |
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} |
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TEST(Core_Eigen, scalar_32) {Core_EigenTest_Scalar_32 test; test.safe_run(); } |
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TEST(Core_Eigen, scalar_64) {Core_EigenTest_Scalar_64 test; test.safe_run(); } |
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TEST(Core_Eigen, vector_32) { Core_EigenTest_32 test; test.safe_run(); } |
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TEST(Core_Eigen, vector_64) { Core_EigenTest_64 test; test.safe_run(); }
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