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opencv/modules/core/test/test_eigen.cpp
Alexander Alekhin 6b581c4e51 build: unreachable code after CV_Error() (part 2)
2018-04-24 16:03:40 +03:00

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/*M///////////////////////////////////////////////////////////////////////////////////////
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#include "test_precomp.hpp"
namespace opencv_test { namespace {
#define sign(a) a > 0 ? 1 : a == 0 ? 0 : -1
#define CORE_EIGEN_ERROR_COUNT 1
#define CORE_EIGEN_ERROR_SIZE 2
#define CORE_EIGEN_ERROR_DIFF 3
#define CORE_EIGEN_ERROR_ORTHO 4
#define CORE_EIGEN_ERROR_ORDER 5
#define MESSAGE_ERROR_COUNT "Matrix of eigen values must have the same rows as source matrix and 1 column."
#define MESSAGE_ERROR_SIZE "Source matrix and matrix of eigen vectors must have the same sizes."
#define MESSAGE_ERROR_DIFF_1 "Accuracy of eigen values computing less than required."
#define MESSAGE_ERROR_DIFF_2 "Accuracy of eigen vectors computing less than required."
#define MESSAGE_ERROR_ORTHO "Matrix of eigen vectors is not orthogonal."
#define MESSAGE_ERROR_ORDER "Eigen values are not sorted in descending order."
const int COUNT_NORM_TYPES = 3;
const int NORM_TYPE[COUNT_NORM_TYPES] = {cv::NORM_L1, cv::NORM_L2, cv::NORM_INF};
enum TASK_TYPE_EIGEN {VALUES, VECTORS};
class Core_EigenTest: public cvtest::BaseTest
{
public:
Core_EigenTest();
~Core_EigenTest();
protected:
bool test_values(const cv::Mat& src); // complex test for eigen without vectors
bool check_full(int type); // compex test for symmetric matrix
virtual void run (int) = 0; // main testing method
protected:
float eps_val_32, eps_vec_32;
float eps_val_64, eps_vec_64;
int ntests;
bool check_pair_count(const cv::Mat& src, const cv::Mat& evalues, int low_index = -1, int high_index = -1);
bool check_pair_count(const cv::Mat& src, const cv::Mat& evalues, const cv::Mat& evectors, int low_index = -1, int high_index = -1);
bool check_pairs_order(const cv::Mat& eigen_values); // checking order of eigen values & vectors (it should be none up)
bool check_orthogonality(const cv::Mat& U); // checking is matrix of eigen vectors orthogonal
bool test_pairs(const cv::Mat& src); // complex test for eigen with vectors
void print_information(const size_t norm_idx, const cv::Mat& src, double diff, double max_diff);
};
class Core_EigenTest_Scalar : public Core_EigenTest
{
public:
Core_EigenTest_Scalar() : Core_EigenTest() {}
~Core_EigenTest_Scalar();
virtual void run(int) = 0;
};
class Core_EigenTest_Scalar_32 : public Core_EigenTest_Scalar
{
public:
Core_EigenTest_Scalar_32() : Core_EigenTest_Scalar() {}
~Core_EigenTest_Scalar_32();
void run(int);
};
class Core_EigenTest_Scalar_64 : public Core_EigenTest_Scalar
{
public:
Core_EigenTest_Scalar_64() : Core_EigenTest_Scalar() {}
~Core_EigenTest_Scalar_64();
void run(int);
};
class Core_EigenTest_32 : public Core_EigenTest
{
public:
Core_EigenTest_32(): Core_EigenTest() {}
~Core_EigenTest_32() {}
void run(int);
};
class Core_EigenTest_64 : public Core_EigenTest
{
public:
Core_EigenTest_64(): Core_EigenTest() {}
~Core_EigenTest_64() {}
void run(int);
};
Core_EigenTest_Scalar::~Core_EigenTest_Scalar() {}
Core_EigenTest_Scalar_32::~Core_EigenTest_Scalar_32() {}
Core_EigenTest_Scalar_64::~Core_EigenTest_Scalar_64() {}
void Core_EigenTest_Scalar_32::run(int)
{
for (int i = 0; i < ntests; ++i)
{
float value = cv::randu<float>();
cv::Mat src(1, 1, CV_32FC1, Scalar::all((float)value));
test_values(src);
}
}
void Core_EigenTest_Scalar_64::run(int)
{
for (int i = 0; i < ntests; ++i)
{
float value = cv::randu<float>();
cv::Mat src(1, 1, CV_64FC1, Scalar::all((double)value));
test_values(src);
}
}
void Core_EigenTest_32::run(int) { check_full(CV_32FC1); }
void Core_EigenTest_64::run(int) { check_full(CV_64FC1); }
Core_EigenTest::Core_EigenTest()
: eps_val_32(1e-3f), eps_vec_32(1e-3f),
eps_val_64(1e-4f), eps_vec_64(1e-4f), ntests(100) {}
Core_EigenTest::~Core_EigenTest() {}
bool Core_EigenTest::check_pair_count(const cv::Mat& src, const cv::Mat& evalues, int low_index, int high_index)
{
int n = src.rows, s = sign(high_index);
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)))
{
std::cout << endl; std::cout << "Checking sizes of eigen values matrix " << evalues << "..." << endl;
std::cout << "Number of rows: " << evalues.rows << " Number of cols: " << evalues.cols << endl;
std::cout << "Size of src symmetric matrix: " << src.rows << " * " << src.cols << endl; std::cout << endl;
CV_Error(CORE_EIGEN_ERROR_COUNT, MESSAGE_ERROR_COUNT);
}
return true;
}
bool Core_EigenTest::check_pair_count(const cv::Mat& src, const cv::Mat& evalues, const cv::Mat& evectors, int low_index, int high_index)
{
int n = src.rows, s = sign(high_index);
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)));
if (!(evectors.rows == right_eigen_pair_count && evectors.cols == right_eigen_pair_count))
{
std::cout << endl; std::cout << "Checking sizes of eigen vectors matrix " << evectors << "..." << endl;
std::cout << "Number of rows: " << evectors.rows << " Number of cols: " << evectors.cols << endl;
std:: cout << "Size of src symmetric matrix: " << src.rows << " * " << src.cols << endl; std::cout << endl;
CV_Error (CORE_EIGEN_ERROR_SIZE, MESSAGE_ERROR_SIZE);
}
if (!(evalues.rows == right_eigen_pair_count && evalues.cols == 1))
{
std::cout << endl; std::cout << "Checking sizes of eigen values matrix " << evalues << "..." << endl;
std::cout << "Number of rows: " << evalues.rows << " Number of cols: " << evalues.cols << endl;
std:: cout << "Size of src symmetric matrix: " << src.rows << " * " << src.cols << endl; std::cout << endl;
CV_Error (CORE_EIGEN_ERROR_COUNT, MESSAGE_ERROR_COUNT);
}
return true;
}
void Core_EigenTest::print_information(const size_t norm_idx, const cv::Mat& src, double diff, double max_diff)
{
switch (NORM_TYPE[norm_idx])
{
case cv::NORM_L1: std::cout << "L1"; break;
case cv::NORM_L2: std::cout << "L2"; break;
case cv::NORM_INF: std::cout << "INF"; break;
default: break;
}
cout << "-criteria... " << endl;
cout << "Source size: " << src.rows << " * " << src.cols << endl;
cout << "Difference between original eigen vectors matrix and result: " << diff << endl;
cout << "Maximum allowed difference: " << max_diff << endl; cout << endl;
}
bool Core_EigenTest::check_orthogonality(const cv::Mat& U)
{
int type = U.type();
double eps_vec = type == CV_32FC1 ? eps_vec_32 : eps_vec_64;
cv::Mat UUt; cv::mulTransposed(U, UUt, false);
cv::Mat E = Mat::eye(U.rows, U.cols, type);
for (int i = 0; i < COUNT_NORM_TYPES; ++i)
{
double diff = cvtest::norm(UUt, E, NORM_TYPE[i] | cv::NORM_RELATIVE);
if (diff > eps_vec)
{
std::cout << endl; std::cout << "Checking orthogonality of matrix " << U << ": ";
print_information(i, U, diff, eps_vec);
CV_Error(CORE_EIGEN_ERROR_ORTHO, MESSAGE_ERROR_ORTHO);
}
}
return true;
}
bool Core_EigenTest::check_pairs_order(const cv::Mat& eigen_values)
{
switch (eigen_values.type())
{
case CV_32FC1:
{
for (int i = 0; i < (int)(eigen_values.total() - 1); ++i)
if (!(eigen_values.at<float>(i, 0) > eigen_values.at<float>(i+1, 0)))
{
std::cout << endl; std::cout << "Checking order of eigen values vector " << eigen_values << "..." << endl;
std::cout << "Pair of indexes with non descending of eigen values: (" << i << ", " << i+1 << ")." << endl;
std::cout << endl;
CV_Error(CORE_EIGEN_ERROR_ORDER, MESSAGE_ERROR_ORDER);
}
break;
}
case CV_64FC1:
{
for (int i = 0; i < (int)(eigen_values.total() - 1); ++i)
if (!(eigen_values.at<double>(i, 0) > eigen_values.at<double>(i+1, 0)))
{
std::cout << endl; std::cout << "Checking order of eigen values vector " << eigen_values << "..." << endl;
std::cout << "Pair of indexes with non descending of eigen values: (" << i << ", " << i+1 << ")." << endl;
std::cout << endl;
CV_Error(CORE_EIGEN_ERROR_ORDER, "Eigen values are not sorted in descending order.");
}
break;
}
default:;
}
return true;
}
bool Core_EigenTest::test_pairs(const cv::Mat& src)
{
int type = src.type();
double eps_vec = type == CV_32FC1 ? eps_vec_32 : eps_vec_64;
cv::Mat eigen_values, eigen_vectors;
cv::eigen(src, eigen_values, eigen_vectors);
if (!check_pair_count(src, eigen_values, eigen_vectors))
return false;
if (!check_orthogonality (eigen_vectors))
return false;
if (!check_pairs_order(eigen_values))
return false;
cv::Mat eigen_vectors_t; cv::transpose(eigen_vectors, eigen_vectors_t);
// Check:
// src * eigenvector = eigenval * eigenvector
cv::Mat lhs(src.rows, src.cols, type);
cv::Mat rhs(src.rows, src.cols, type);
lhs = src*eigen_vectors_t;
for (int i = 0; i < src.cols; ++i)
{
double eigenval = 0;
switch (type)
{
case CV_32FC1: eigenval = eigen_values.at<float>(i, 0); break;
case CV_64FC1: eigenval = eigen_values.at<double>(i, 0); break;
}
cv::Mat rhs_v = eigenval * eigen_vectors_t.col(i);
rhs_v.copyTo(rhs.col(i));
}
for (int i = 0; i < COUNT_NORM_TYPES; ++i)
{
double diff = cvtest::norm(lhs, rhs, NORM_TYPE[i] | cv::NORM_RELATIVE);
if (diff > eps_vec)
{
std::cout << endl; std::cout << "Checking accuracy of eigen vectors computing for matrix " << src << ": ";
print_information(i, src, diff, eps_vec);
CV_Error(CORE_EIGEN_ERROR_DIFF, MESSAGE_ERROR_DIFF_2);
}
}
return true;
}
bool Core_EigenTest::test_values(const cv::Mat& src)
{
int type = src.type();
double eps_val = type == CV_32FC1 ? eps_val_32 : eps_val_64;
cv::Mat eigen_values_1, eigen_values_2, eigen_vectors;
if (!test_pairs(src)) return false;
cv::eigen(src, eigen_values_1, eigen_vectors);
cv::eigen(src, eigen_values_2);
if (!check_pair_count(src, eigen_values_2)) return false;
for (int i = 0; i < COUNT_NORM_TYPES; ++i)
{
double diff = cvtest::norm(eigen_values_1, eigen_values_2, NORM_TYPE[i] | cv::NORM_RELATIVE);
if (diff > eps_val)
{
std::cout << endl; std::cout << "Checking accuracy of eigen values computing for matrix " << src << ": ";
print_information(i, src, diff, eps_val);
CV_Error(CORE_EIGEN_ERROR_DIFF, MESSAGE_ERROR_DIFF_1);
}
}
return true;
}
bool Core_EigenTest::check_full(int type)
{
const int MAX_DEGREE = 7;
RNG rng = cv::theRNG(); // fix the seed
for (int i = 0; i < ntests; ++i)
{
int src_size = (int)(std::pow(2.0, (rng.uniform(0, MAX_DEGREE) + 1.)));
cv::Mat src(src_size, src_size, type);
for (int j = 0; j < src.rows; ++j)
for (int k = j; k < src.cols; ++k)
if (type == CV_32FC1) src.at<float>(k, j) = src.at<float>(j, k) = cv::randu<float>();
else src.at<double>(k, j) = src.at<double>(j, k) = cv::randu<double>();
if (!test_values(src)) return false;
}
return true;
}
TEST(Core_Eigen, scalar_32) {Core_EigenTest_Scalar_32 test; test.safe_run(); }
TEST(Core_Eigen, scalar_64) {Core_EigenTest_Scalar_64 test; test.safe_run(); }
TEST(Core_Eigen, vector_32) { Core_EigenTest_32 test; test.safe_run(); }
TEST(Core_Eigen, vector_64) { Core_EigenTest_64 test; test.safe_run(); }
template<typename T>
static void testEigen(const Mat_<T>& src, const Mat_<T>& expected_eigenvalues, bool runSymmetric = false)
{
SCOPED_TRACE(runSymmetric ? "cv::eigen" : "cv::eigenNonSymmetric");
int type = traits::Type<T>::value;
const T eps = src.type() == CV_32F ? 1e-4f : 1e-6f;
Mat eigenvalues, eigenvectors, eigenvalues0;
if (runSymmetric)
{
cv::eigen(src, eigenvalues0, noArray());
cv::eigen(src, eigenvalues, eigenvectors);
}
else
{
cv::eigenNonSymmetric(src, eigenvalues0, noArray());
cv::eigenNonSymmetric(src, eigenvalues, eigenvectors);
}
#if 0
std::cout << "src = " << src << std::endl;
std::cout << "eigenvalues.t() = " << eigenvalues.t() << std::endl;
std::cout << "eigenvectors = " << eigenvectors << std::endl;
#endif
ASSERT_EQ(type, eigenvalues0.type());
ASSERT_EQ(type, eigenvalues.type());
ASSERT_EQ(type, eigenvectors.type());
ASSERT_EQ(src.rows, eigenvalues.rows);
ASSERT_EQ(eigenvalues.rows, eigenvectors.rows);
ASSERT_EQ(src.rows, eigenvectors.cols);
EXPECT_LT(cvtest::norm(eigenvalues, eigenvalues0, NORM_INF), eps);
// check definition: src*eigenvectors.row(i).t() = eigenvalues.at<srcType>(i)*eigenvectors.row(i).t()
for (int i = 0; i < src.rows; i++)
{
EXPECT_NEAR(eigenvalues.at<T>(i), expected_eigenvalues(i), eps) << "i=" << i;
Mat lhs = src*eigenvectors.row(i).t();
Mat rhs = eigenvalues.at<T>(i)*eigenvectors.row(i).t();
EXPECT_LT(cvtest::norm(lhs, rhs, NORM_INF), eps)
<< "i=" << i << " eigenvalue=" << eigenvalues.at<T>(i) << std::endl
<< "lhs=" << lhs.t() << std::endl
<< "rhs=" << rhs.t();
}
}
template<typename T>
static void testEigenSymmetric3x3()
{
/*const*/ T values_[] = {
2, -1, 0,
-1, 2, -1,
0, -1, 2
};
Mat_<T> src(3, 3, values_);
/*const*/ T expected_eigenvalues_[] = { 3.414213562373095f, 2, 0.585786437626905f };
Mat_<T> expected_eigenvalues(3, 1, expected_eigenvalues_);
testEigen(src, expected_eigenvalues);
testEigen(src, expected_eigenvalues, true);
}
TEST(Core_EigenSymmetric, float3x3) { testEigenSymmetric3x3<float>(); }
TEST(Core_EigenSymmetric, double3x3) { testEigenSymmetric3x3<double>(); }
template<typename T>
static void testEigenSymmetric5x5()
{
/*const*/ T values_[5*5] = {
5, -1, 0, 2, 1,
-1, 4, -1, 0, 0,
0, -1, 3, 1, -1,
2, 0, 1, 4, 0,
1, 0, -1, 0, 1
};
Mat_<T> src(5, 5, values_);
/*const*/ T expected_eigenvalues_[] = { 7.028919644935684f, 4.406130784616501f, 3.73626552682258f, 1.438067799899037f, 0.390616243726198f };
Mat_<T> expected_eigenvalues(5, 1, expected_eigenvalues_);
testEigen(src, expected_eigenvalues);
testEigen(src, expected_eigenvalues, true);
}
TEST(Core_EigenSymmetric, float5x5) { testEigenSymmetric5x5<float>(); }
TEST(Core_EigenSymmetric, double5x5) { testEigenSymmetric5x5<double>(); }
template<typename T>
static void testEigen2x2()
{
/*const*/ T values_[] = { 4, 1, 6, 3 };
Mat_<T> src(2, 2, values_);
/*const*/ T expected_eigenvalues_[] = { 6, 1 };
Mat_<T> expected_eigenvalues(2, 1, expected_eigenvalues_);
testEigen(src, expected_eigenvalues);
}
TEST(Core_EigenNonSymmetric, float2x2) { testEigen2x2<float>(); }
TEST(Core_EigenNonSymmetric, double2x2) { testEigen2x2<double>(); }
template<typename T>
static void testEigen3x3()
{
/*const*/ T values_[3*3] = {
3,1,0,
0,3,1,
0,0,3
};
Mat_<T> src(3, 3, values_);
/*const*/ T expected_eigenvalues_[] = { 3, 3, 3 };
Mat_<T> expected_eigenvalues(3, 1, expected_eigenvalues_);
testEigen(src, expected_eigenvalues);
}
TEST(Core_EigenNonSymmetric, float3x3) { testEigen3x3<float>(); }
TEST(Core_EigenNonSymmetric, double3x3) { testEigen3x3<double>(); }
typedef testing::TestWithParam<int> Core_EigenZero;
TEST_P(Core_EigenZero, double)
{
int N = GetParam();
Mat_<double> srcZero = Mat_<double>::zeros(N, N);
Mat_<double> expected_eigenvalueZero = Mat_<double>::zeros(N, 1); // 1D Mat
testEigen(srcZero, expected_eigenvalueZero);
testEigen(srcZero, expected_eigenvalueZero, true);
}
INSTANTIATE_TEST_CASE_P(/**/, Core_EigenZero, testing::Values(2, 3, 5));
}} // namespace
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