412 lines
14 KiB
C++
412 lines
14 KiB
C++
/*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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