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311 lines
11 KiB
C++
311 lines
11 KiB
C++
#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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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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private:
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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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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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};
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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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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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src.~Mat();
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}
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void Core_EigenTest_Scalar_64::run(int)
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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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src.~Mat();
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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() : eps_val_32(1e-3), eps_vec_32(1e-2), eps_val_64(1e-4), eps_vec_64(1e-3) {}
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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 << "Checking sizes of eigen values matrix " << evalues << "..." << endl;
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CV_Error(CORE_EIGEN_ERROR_COUNT, "Matrix of eigen values must have the same rows as source matrix and 1 column.");
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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 << "Checking sizes of eigen vectors matrix " << evectors << "..." << endl;
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CV_Error (CORE_EIGEN_ERROR_SIZE, "Source matrix and matrix of eigen vectors must have the same sizes.");
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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 << "Checking sizes of eigen values matrix " << evalues << "..." << endl;
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CV_Error (CORE_EIGEN_ERROR_COUNT, "Matrix of eigen values must have the same rows as source matrix and 1 column.");
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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_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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double diff_L1 = cv::norm(UUt, E, NORM_L1);
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double diff_L2 = cv::norm(UUt, E, NORM_L2);
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double diff_INF = cv::norm(UUt, E, NORM_INF);
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if (diff_L1 > eps_vec) { std::cout << "Checking orthogonality of matrix " << U << "..." << endl; CV_Error(CORE_EIGEN_ERROR_ORTHO, "Matrix of eigen vectors is not orthogonal."); return false; }
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if (diff_L2 > eps_vec) { std::cout << "Checking orthogonality of matrix " << U << "..." << endl; CV_Error(CORE_EIGEN_ERROR_ORTHO, "Matrix of eigen vectors is not orthogonal."); return false; }
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if (diff_INF > eps_vec) { std::cout << "Checking orthogonality of matrix " << U << "..." << endl; CV_Error(CORE_EIGEN_ERROR_ORTHO, "Matrix of eigen vectors is not orthogonal."); return false; }
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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 < 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 << "Checking order of eigen values vector " << eigen_values << "..." << 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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case CV_64FC1:
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{
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for (int i = 0; i < 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 << "Checking order of eigen values vector " << eigen_values << "..." << 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 (size_t 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 (size_t 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 (size_t 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 (size_t 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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double diff_L1 = cv::norm(disparity, NORM_L1);
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double diff_L2 = cv::norm(disparity, NORM_L2);
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double diff_INF = cv::norm(disparity, NORM_INF);
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if (diff_L1 > eps_vec) { std::cout << "Checking accuracy of eigen vectors computing for matrix " << src << ": L1-criteria..." << endl; CV_Error(CORE_EIGEN_ERROR_DIFF, "Accuracy of eigen vectors computing less than required."); return false; }
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if (diff_L2 > eps_vec) { std::cout << "Checking accuracy of eigen vectors computing for matrix " << src << ": L2-criteria..." << endl; CV_Error(CORE_EIGEN_ERROR_DIFF, "Accuracy of eigen vectors computing less than required."); return false; }
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if (diff_INF > eps_vec) { std::cout << "Checking accuracy of eigen vectors computing for matrix " << src << ": INF-criteria..." << endl; CV_Error(CORE_EIGEN_ERROR_DIFF, "Accuracy of eigen vectors computing less than required."); return false; }
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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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double diff_L1 = cv::norm(eigen_values_1, eigen_values_2, NORM_L1);
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double diff_L2 = cv::norm(eigen_values_1, eigen_values_2, NORM_L2);
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double diff_INF = cv::norm(eigen_values_1, eigen_values_2, NORM_INF);
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if (diff_L1 > eps_val) { std::cout << "Checking accuracy of eigen values computing for matrix " << src << ": L1-criteria..." << endl; CV_Error(CORE_EIGEN_ERROR_DIFF, "Accuracy of eigen values computing less than required."); return false; }
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if (diff_L2 > eps_val) { std::cout << "Checking accuracy of eigen values computing for matrix " << src << ": L2-criteria..." << endl; CV_Error(CORE_EIGEN_ERROR_DIFF, "Accuracy of eigen vectors computing less than required."); return false; }
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if (diff_INF > eps_val) { std::cout << "Checking accuracy of eigen values computing for matrix " << src << ": INF-criteria..." << endl; CV_Error(CORE_EIGEN_ERROR_DIFF, "Accuracy of eigen vectors computing less than required."); return false; }
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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 MATRIX_COUNT = 500;
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const int MAX_DEGREE = 7;
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srand(time(0));
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for (size_t i = 1; i <= MATRIX_COUNT; ++i)
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{
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size_t src_size = (int)(std::pow(2.0, (rand()%MAX_DEGREE+1)*1.0));
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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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src.~Mat();
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}
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return true;
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}
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// TEST(Core_Eigen_Scalar_32, single_complex) {Core_EigenTest_Scalar_32 test; test.safe_run(); }
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// TEST(Core_Eigen_Scalar_64, single_complex) {Core_EigenTest_Scalar_64 test; test.safe_run(); }
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TEST(Core_Eigen_32, complex) { Core_EigenTest_32 test; test.safe_run(); }
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TEST(Core_Eigen_64, complex) { Core_EigenTest_64 test; test.safe_run(); } |