root/modules/core/test/test_math.cpp

DEFINITIONS

1. get_success_error_level
2. get_test_array_types_and_sizes
3. get_test_array_types_and_sizes
4. get_success_error_level
5. get_minmax_bounds
6. run_func
7. ipow
8. ipow
9. prepare_to_validation
10. max_cn
11. get_test_array_types_and_sizes
12. get_success_error_level
13. run_func
14. prepare_to_validation
15. run_func
16. prepare_to_validation
17. get_test_array_types_and_sizes
18. run_func
19. prepare_to_validation
20. get_test_array_types_and_sizes
21. prepare_test_case
22. get_minmax_bounds
23. run_func
24. prepare_to_validation
25. get_test_array_types_and_sizes
26. get_minmax_bounds
27. run_func
28. prepare_to_validation
29. get_test_array_types_and_sizes
30. prepare_test_case
31. get_success_error_level
32. run_func
33. prepare_to_validation
34. get_test_array_types_and_sizes
35. get_success_error_level
36. run_func
37. cvTsPerspectiveTransform
38. prepare_to_validation
39. get_test_array_types_and_sizes
40. prepare_test_case
41. run_func
42. prepare_to_validation
43. are_images
44. get_test_array_types_and_sizes
45. prepare_test_case
46. run_func
47. prepare_to_validation
48. cvTsFloodWithZeros
49. get_test_array_types_and_sizes
50. get_minmax_bounds
51. get_success_error_level
52. prepare_test_case
53. run_func
54. cvTsLU
55. prepare_to_validation
56. result
57. get_test_array_types_and_sizes
58. get_success_error_level
59. prepare_test_case
60. get_minmax_bounds
61. run_func
62. cvTsSVDet
63. prepare_to_validation
64. result
65. get_test_array_types_and_sizes
66. prepare_test_case
67. get_minmax_bounds
68. get_success_error_level
69. run_func
70. prepare_to_validation
71. vector_w
72. get_test_array_types_and_sizes
73. prepare_test_case
74. get_minmax_bounds
75. get_success_error_level
76. run_func
77. prepare_to_validation
78. vector_w
79. get_test_array_types_and_sizes
80. prepare_test_case
81. get_minmax_bounds
82. get_success_error_level
83. run_func
84. prepare_to_validation
85. run
86. run
87. run
88. TEST
89. run
90. TEST
91. TYPED_TEST_P
92. TYPED_TEST_P
93. TYPED_TEST_P
94. TYPED_TEST_P
95. TYPED_TEST_P
96. TEST
97. TEST
98. TEST
99. TEST
100. TEST
101. TEST
102. TEST
103. TEST
104. TEST
105. TEST
106. TEST
107. TEST
108. TEST
109. TEST
110. TEST
111. TEST
112. TEST
113. TEST
114. TEST
115. run
116. TEST
117. TEST_P
118. TEST
119. TEST
120. TEST

//////////////////////////////////////////////////////////////////////////////////////////
/////////////////// tests for matrix operations and math functions ///////////////////////
//////////////////////////////////////////////////////////////////////////////////////////

#include "test_precomp.hpp"
#include <float.h>
#include <math.h>

using namespace cv;
using namespace std;

/// !!! NOTE !!! These tests happily avoid overflow cases & out-of-range arguments
/// so that output arrays contain neigher Inf's nor Nan's.
/// Handling such cases would require special modification of check function
/// (validate_test_results) => TBD.
/// Also, need some logarithmic-scale generation of input data. Right now it is done (in some tests)
/// by generating min/max boundaries for random data in logarimithic scale, but
/// within the same test case all the input array elements are of the same order.

class Core_MathTest : public cvtest::ArrayTest
{
public:
    typedef cvtest::ArrayTest Base;
    Core_MathTest();
protected:
    void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes,
                                        vector<vector<int> >& types);
    double get_success_error_level( int /*test_case_idx*/, int i, int j );
    bool test_nd;
};


Core_MathTest::Core_MathTest()
{
    optional_mask = false;

    test_array[INPUT].push_back(NULL);
    test_array[OUTPUT].push_back(NULL);
    test_array[REF_OUTPUT].push_back(NULL);

    test_nd = false;
}


double Core_MathTest::get_success_error_level( int /*test_case_idx*/, int i, int j )
{
    return test_mat[i][j].depth() == CV_32F ? FLT_EPSILON*128 : DBL_EPSILON*1024;
}


void Core_MathTest::get_test_array_types_and_sizes( int test_case_idx,
                                                     vector<vector<Size> >& sizes,
                                                     vector<vector<int> >& types)
{
    RNG& rng = ts->get_rng();
    int depth = cvtest::randInt(rng)%2 + CV_32F;
    int cn = cvtest::randInt(rng) % 4 + 1, type = CV_MAKETYPE(depth, cn);
    size_t i, j;
    Base::get_test_array_types_and_sizes( test_case_idx, sizes, types );

    for( i = 0; i < test_array.size(); i++ )
    {
        size_t count = test_array[i].size();
        for( j = 0; j < count; j++ )
            types[i][j] = type;
    }
    test_nd = cvtest::randInt(rng)%3 == 0;
}


////////// pow /////////////

class Core_PowTest : public Core_MathTest
{
public:
    typedef Core_MathTest Base;
    Core_PowTest();
protected:
    void get_test_array_types_and_sizes( int test_case_idx,
                                        vector<vector<Size> >& sizes,
                                        vector<vector<int> >& types );
    void get_minmax_bounds( int i, int j, int type, Scalar& low, Scalar& high );
    void run_func();
    void prepare_to_validation( int test_case_idx );
    double get_success_error_level( int test_case_idx, int i, int j );
    double power;
};


Core_PowTest::Core_PowTest()
{
    power = 0;
}


void Core_PowTest::get_test_array_types_and_sizes( int test_case_idx,
                                                    vector<vector<Size> >& sizes,
                                                    vector<vector<int> >& types )
{
    RNG& rng = ts->get_rng();
    int depth = cvtest::randInt(rng) % (CV_64F+1);
    int cn = cvtest::randInt(rng) % 4 + 1;
    size_t i, j;
    Base::get_test_array_types_and_sizes( test_case_idx, sizes, types );
    depth += depth == CV_8S;

    if( depth < CV_32F || cvtest::randInt(rng)%8 == 0 )
        // integer power
        power = (int)(cvtest::randInt(rng)%21 - 10);
    else
    {
        i = cvtest::randInt(rng)%17;
        power = i == 16 ? 1./3 : i == 15 ? 0.5 : i == 14 ? -0.5 : cvtest::randReal(rng)*10 - 5;
    }

    for( i = 0; i < test_array.size(); i++ )
    {
        size_t count = test_array[i].size();
        int type = CV_MAKETYPE(depth, cn);
        for( j = 0; j < count; j++ )
            types[i][j] = type;
    }
    test_nd = cvtest::randInt(rng)%3 == 0;
}


double Core_PowTest::get_success_error_level( int test_case_idx, int i, int j )
{
    int depth = test_mat[i][j].depth();
    if( depth < CV_32F )
        return power == cvRound(power) && power >= 0 ? 0 : 1;
    else
        return Base::get_success_error_level( test_case_idx, i, j );
}


void Core_PowTest::get_minmax_bounds( int /*i*/, int /*j*/, int type, Scalar& low, Scalar& high )
{
    double l, u = cvtest::randInt(ts->get_rng())%1000 + 1;
    if( power > 0 )
    {
        double mval = cvtest::getMaxVal(type);
        double u1 = pow(mval,1./power)*2;
        u = MIN(u,u1);
    }

    l = power == cvRound(power) ? -u : FLT_EPSILON;
    low = Scalar::all(l);
    high = Scalar::all(u);
}


void Core_PowTest::run_func()
{
    if(!test_nd)
    {
        if( fabs(power-1./3) <= DBL_EPSILON && test_mat[INPUT][0].depth() == CV_32F )
        {
            Mat a = test_mat[INPUT][0], b = test_mat[OUTPUT][0];

            a = a.reshape(1);
            b = b.reshape(1);
            for( int i = 0; i < a.rows; i++ )
            {
                b.at<float>(i,0) = (float)fabs(cvCbrt(a.at<float>(i,0)));
                for( int j = 1; j < a.cols; j++ )
                    b.at<float>(i,j) = (float)fabs(cv::cubeRoot(a.at<float>(i,j)));
            }
        }
        else
            cvPow( test_array[INPUT][0], test_array[OUTPUT][0], power );
    }
    else
    {
        Mat& a = test_mat[INPUT][0];
        Mat& b = test_mat[OUTPUT][0];
        if(power == 0.5)
            cv::sqrt(a, b);
        else
            cv::pow(a, power, b);
    }
}


inline static int ipow( int a, int power )
{
    int b = 1;
    while( power > 0 )
    {
        if( power&1 )
            b *= a, power--;
        else
            a *= a, power >>= 1;
    }
    return b;
}


inline static double ipow( double a, int power )
{
    double b = 1.;
    while( power > 0 )
    {
        if( power&1 )
            b *= a, power--;
        else
            a *= a, power >>= 1;
    }
    return b;
}


void Core_PowTest::prepare_to_validation( int /*test_case_idx*/ )
{
    const Mat& a = test_mat[INPUT][0];
    Mat& b = test_mat[REF_OUTPUT][0];

    int depth = a.depth();
    int ncols = a.cols*a.channels();
    int ipower = cvRound(power), apower = abs(ipower);
    int i, j;

    for( i = 0; i < a.rows; i++ )
    {
        const uchar* a_data = a.ptr(i);
        uchar* b_data = b.ptr(i);

        switch( depth )
        {
            case CV_8U:
                if( ipower < 0 )
                    for( j = 0; j < ncols; j++ )
                    {
                        int val = ((uchar*)a_data)[j];
                        ((uchar*)b_data)[j] = (uchar)(val == 0 ? 255 : val == 1 ? 1 :
                                                      val == 2 && ipower == -1 ? 1 : 0);
                    }
                else
                    for( j = 0; j < ncols; j++ )
                    {
                        int val = ((uchar*)a_data)[j];
                        val = ipow( val, ipower );
                        ((uchar*)b_data)[j] = saturate_cast<uchar>(val);
                    }
                break;
            case CV_8S:
                if( ipower < 0 )
                    for( j = 0; j < ncols; j++ )
                    {
                        int val = ((schar*)a_data)[j];
                        ((schar*)b_data)[j] = (schar)(val == 0 ? 127 : val == 1 ? 1 :
                                                    val ==-1 ? 1-2*(ipower&1) :
                                                    val == 2 && ipower == -1 ? 1 : 0);
                    }
                else
                    for( j = 0; j < ncols; j++ )
                    {
                        int val = ((schar*)a_data)[j];
                        val = ipow( val, ipower );
                        ((schar*)b_data)[j] = saturate_cast<schar>(val);
                    }
                break;
            case CV_16U:
                if( ipower < 0 )
                    for( j = 0; j < ncols; j++ )
                    {
                        int val = ((ushort*)a_data)[j];
                        ((ushort*)b_data)[j] = (ushort)(val == 0 ? 65535 : val == 1 ? 1 :
                                                        val ==-1 ? 1-2*(ipower&1) :
                                                        val == 2 && ipower == -1 ? 1 : 0);
                    }
                else
                    for( j = 0; j < ncols; j++ )
                    {
                        int val = ((ushort*)a_data)[j];
                        val = ipow( val, ipower );
                        ((ushort*)b_data)[j] = saturate_cast<ushort>(val);
                    }
                break;
            case CV_16S:
                if( ipower < 0 )
                    for( j = 0; j < ncols; j++ )
                    {
                        int val = ((short*)a_data)[j];
                        ((short*)b_data)[j] = (short)(val == 0 ? 32767 : val == 1 ? 1 :
                                                      val ==-1 ? 1-2*(ipower&1) :
                                                      val == 2 && ipower == -1 ? 1 : 0);
                    }
                else
                    for( j = 0; j < ncols; j++ )
                    {
                        int val = ((short*)a_data)[j];
                        val = ipow( val, ipower );
                        ((short*)b_data)[j] = saturate_cast<short>(val);
                    }
                break;
            case CV_32S:
                if( ipower < 0 )
                    for( j = 0; j < ncols; j++ )
                    {
                        int val = ((int*)a_data)[j];
                        ((int*)b_data)[j] = val == 0 ? INT_MAX : val == 1 ? 1 :
                        val ==-1 ? 1-2*(ipower&1) :
                        val == 2 && ipower == -1 ? 1 : 0;
                    }
                else
                    for( j = 0; j < ncols; j++ )
                    {
                        int val = ((int*)a_data)[j];
                        val = ipow( val, ipower );
                        ((int*)b_data)[j] = val;
                    }
                break;
            case CV_32F:
                if( power != ipower )
                    for( j = 0; j < ncols; j++ )
                    {
                        double val = ((float*)a_data)[j];
                        val = pow( fabs(val), power );
                        ((float*)b_data)[j] = (float)val;
                    }
                else
                    for( j = 0; j < ncols; j++ )
                    {
                        double val = ((float*)a_data)[j];
                        if( ipower < 0 )
                            val = 1./val;
                        val = ipow( val, apower );
                        ((float*)b_data)[j] = (float)val;
                    }
                break;
            case CV_64F:
                if( power != ipower )
                    for( j = 0; j < ncols; j++ )
                    {
                        double val = ((double*)a_data)[j];
                        val = pow( fabs(val), power );
                        ((double*)b_data)[j] = (double)val;
                    }
                else
                    for( j = 0; j < ncols; j++ )
                    {
                        double val = ((double*)a_data)[j];
                        if( ipower < 0 )
                            val = 1./val;
                        val = ipow( val, apower );
                        ((double*)b_data)[j] = (double)val;
                    }
                break;
        }
    }
}

///////////////////////////////////////// matrix tests ////////////////////////////////////////////

class Core_MatrixTest : public cvtest::ArrayTest
{
public:
    typedef cvtest::ArrayTest Base;
    Core_MatrixTest( int in_count, int out_count,
                       bool allow_int, bool scalar_output, int max_cn );
protected:
    void get_test_array_types_and_sizes( int test_case_idx,
                                        vector<vector<Size> >& sizes,
                                        vector<vector<int> >& types );
    double get_success_error_level( int test_case_idx, int i, int j );
    bool allow_int;
    bool scalar_output;
    int max_cn;
};


Core_MatrixTest::Core_MatrixTest( int in_count, int out_count,
                                      bool _allow_int, bool _scalar_output, int _max_cn )
: allow_int(_allow_int), scalar_output(_scalar_output), max_cn(_max_cn)
{
    int i;
    for( i = 0; i < in_count; i++ )
        test_array[INPUT].push_back(NULL);

    for( i = 0; i < out_count; i++ )
    {
        test_array[OUTPUT].push_back(NULL);
        test_array[REF_OUTPUT].push_back(NULL);
    }

    element_wise_relative_error = false;
}


void Core_MatrixTest::get_test_array_types_and_sizes( int test_case_idx,
                                                       vector<vector<Size> >& sizes,
                                                       vector<vector<int> >& types )
{
    RNG& rng = ts->get_rng();
    int depth = cvtest::randInt(rng) % (allow_int ? CV_64F+1 : 2);
    int cn = cvtest::randInt(rng) % max_cn + 1;
    size_t i, j;

    if( allow_int )
        depth += depth == CV_8S;
    else
        depth += CV_32F;

    Base::get_test_array_types_and_sizes( test_case_idx, sizes, types );

    for( i = 0; i < test_array.size(); i++ )
    {
        size_t count = test_array[i].size();
        int flag = (i == OUTPUT || i == REF_OUTPUT) && scalar_output;
        int type = !flag ? CV_MAKETYPE(depth, cn) : CV_64FC1;

        for( j = 0; j < count; j++ )
        {
            types[i][j] = type;
            if( flag )
                sizes[i][j] = Size( 4, 1 );
        }
    }
}


double Core_MatrixTest::get_success_error_level( int test_case_idx, int i, int j )
{
    int input_depth = test_mat[INPUT][0].depth();
    double input_precision = input_depth < CV_32F ? 0 : input_depth == CV_32F ? 5e-5 : 5e-10;
    double output_precision = Base::get_success_error_level( test_case_idx, i, j );
    return MAX(input_precision, output_precision);
}


///////////////// Trace /////////////////////

class Core_TraceTest : public Core_MatrixTest
{
public:
    Core_TraceTest();
protected:
    void run_func();
    void prepare_to_validation( int test_case_idx );
};


Core_TraceTest::Core_TraceTest() : Core_MatrixTest( 1, 1, true, true, 4 )
{
}


void Core_TraceTest::run_func()
{
    test_mat[OUTPUT][0].at<Scalar>(0,0) = cvTrace(test_array[INPUT][0]);
}


void Core_TraceTest::prepare_to_validation( int )
{
    Mat& mat = test_mat[INPUT][0];
    int count = MIN( mat.rows, mat.cols );
    Mat diag(count, 1, mat.type(), mat.ptr(), mat.step + mat.elemSize());
    Scalar r = cvtest::mean(diag);
    r *= (double)count;

    test_mat[REF_OUTPUT][0].at<Scalar>(0,0) = r;
}


///////// dotproduct //////////

class Core_DotProductTest : public Core_MatrixTest
{
public:
    Core_DotProductTest();
protected:
    void run_func();
    void prepare_to_validation( int test_case_idx );
};


Core_DotProductTest::Core_DotProductTest() : Core_MatrixTest( 2, 1, true, true, 4 )
{
}


void Core_DotProductTest::run_func()
{
    test_mat[OUTPUT][0].at<Scalar>(0,0) = Scalar(cvDotProduct( test_array[INPUT][0], test_array[INPUT][1] ));
}


void Core_DotProductTest::prepare_to_validation( int )
{
    test_mat[REF_OUTPUT][0].at<Scalar>(0,0) = Scalar(cvtest::crossCorr( test_mat[INPUT][0], test_mat[INPUT][1] ));
}


///////// crossproduct //////////

class Core_CrossProductTest : public Core_MatrixTest
{
public:
    Core_CrossProductTest();
protected:
    void get_test_array_types_and_sizes( int test_case_idx,
                                        vector<vector<Size> >& sizes,
                                        vector<vector<int> >& types );
    void run_func();
    void prepare_to_validation( int test_case_idx );
};


Core_CrossProductTest::Core_CrossProductTest() : Core_MatrixTest( 2, 1, false, false, 1 )
{
}


void Core_CrossProductTest::get_test_array_types_and_sizes( int,
                                                             vector<vector<Size> >& sizes,
                                                             vector<vector<int> >& types )
{
    RNG& rng = ts->get_rng();
    int depth = cvtest::randInt(rng) % 2 + CV_32F;
    int cn = cvtest::randInt(rng) & 1 ? 3 : 1, type = CV_MAKETYPE(depth, cn);
    CvSize sz;

    types[INPUT][0] = types[INPUT][1] = types[OUTPUT][0] = types[REF_OUTPUT][0] = type;

    if( cn == 3 )
        sz = Size(1,1);
    else if( cvtest::randInt(rng) & 1 )
        sz = Size(3,1);
    else
        sz = Size(1,3);

    sizes[INPUT][0] = sizes[INPUT][1] = sizes[OUTPUT][0] = sizes[REF_OUTPUT][0] = sz;
}


void Core_CrossProductTest::run_func()
{
    cvCrossProduct( test_array[INPUT][0], test_array[INPUT][1], test_array[OUTPUT][0] );
}


void Core_CrossProductTest::prepare_to_validation( int )
{
    CvScalar a(0), b(0), c(0);

    if( test_mat[INPUT][0].rows > 1 )
    {
        a.val[0] = cvGetReal2D( test_array[INPUT][0], 0, 0 );
        a.val[1] = cvGetReal2D( test_array[INPUT][0], 1, 0 );
        a.val[2] = cvGetReal2D( test_array[INPUT][0], 2, 0 );

        b.val[0] = cvGetReal2D( test_array[INPUT][1], 0, 0 );
        b.val[1] = cvGetReal2D( test_array[INPUT][1], 1, 0 );
        b.val[2] = cvGetReal2D( test_array[INPUT][1], 2, 0 );
    }
    else if( test_mat[INPUT][0].cols > 1 )
    {
        a.val[0] = cvGetReal1D( test_array[INPUT][0], 0 );
        a.val[1] = cvGetReal1D( test_array[INPUT][0], 1 );
        a.val[2] = cvGetReal1D( test_array[INPUT][0], 2 );

        b.val[0] = cvGetReal1D( test_array[INPUT][1], 0 );
        b.val[1] = cvGetReal1D( test_array[INPUT][1], 1 );
        b.val[2] = cvGetReal1D( test_array[INPUT][1], 2 );
    }
    else
    {
        a = cvGet1D( test_array[INPUT][0], 0 );
        b = cvGet1D( test_array[INPUT][1], 0 );
    }

    c.val[2] = a.val[0]*b.val[1] - a.val[1]*b.val[0];
    c.val[1] = -a.val[0]*b.val[2] + a.val[2]*b.val[0];
    c.val[0] = a.val[1]*b.val[2] - a.val[2]*b.val[1];

    if( test_mat[REF_OUTPUT][0].rows > 1 )
    {
        cvSetReal2D( test_array[REF_OUTPUT][0], 0, 0, c.val[0] );
        cvSetReal2D( test_array[REF_OUTPUT][0], 1, 0, c.val[1] );
        cvSetReal2D( test_array[REF_OUTPUT][0], 2, 0, c.val[2] );
    }
    else if( test_mat[REF_OUTPUT][0].cols > 1 )
    {
        cvSetReal1D( test_array[REF_OUTPUT][0], 0, c.val[0] );
        cvSetReal1D( test_array[REF_OUTPUT][0], 1, c.val[1] );
        cvSetReal1D( test_array[REF_OUTPUT][0], 2, c.val[2] );
    }
    else
    {
        cvSet1D( test_array[REF_OUTPUT][0], 0, c );
    }
}


///////////////// gemm /////////////////////

class Core_GEMMTest : public Core_MatrixTest
{
public:
    typedef Core_MatrixTest Base;
    Core_GEMMTest();
protected:
    void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
    void get_minmax_bounds( int /*i*/, int /*j*/, int /*type*/, Scalar& low, Scalar& high );
    int prepare_test_case( int test_case_idx );
    void run_func();
    void prepare_to_validation( int test_case_idx );
    int tabc_flag;
    double alpha, beta;
};

Core_GEMMTest::Core_GEMMTest() : Core_MatrixTest( 5, 1, false, false, 2 )
{
    test_case_count = 100;
    max_log_array_size = 10;
    tabc_flag = 0;
    alpha = beta = 0;
}


void Core_GEMMTest::get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types )
{
    RNG& rng = ts->get_rng();
    Size sizeA;
    Base::get_test_array_types_and_sizes( test_case_idx, sizes, types );
    sizeA = sizes[INPUT][0];
    Base::get_test_array_types_and_sizes( test_case_idx, sizes, types );
    sizes[INPUT][0] = sizeA;
    sizes[INPUT][2] = sizes[INPUT][3] = Size(1,1);
    types[INPUT][2] = types[INPUT][3] &= ~CV_MAT_CN_MASK;

    tabc_flag = cvtest::randInt(rng) & 7;

    switch( tabc_flag & (CV_GEMM_A_T|CV_GEMM_B_T) )
    {
        case 0:
            sizes[INPUT][1].height = sizes[INPUT][0].width;
            sizes[OUTPUT][0].height = sizes[INPUT][0].height;
            sizes[OUTPUT][0].width = sizes[INPUT][1].width;
            break;
        case CV_GEMM_B_T:
            sizes[INPUT][1].width = sizes[INPUT][0].width;
            sizes[OUTPUT][0].height = sizes[INPUT][0].height;
            sizes[OUTPUT][0].width = sizes[INPUT][1].height;
            break;
        case CV_GEMM_A_T:
            sizes[INPUT][1].height = sizes[INPUT][0].height;
            sizes[OUTPUT][0].height = sizes[INPUT][0].width;
            sizes[OUTPUT][0].width = sizes[INPUT][1].width;
            break;
        case CV_GEMM_A_T | CV_GEMM_B_T:
            sizes[INPUT][1].width = sizes[INPUT][0].height;
            sizes[OUTPUT][0].height = sizes[INPUT][0].width;
            sizes[OUTPUT][0].width = sizes[INPUT][1].height;
            break;
    }

    sizes[REF_OUTPUT][0] = sizes[OUTPUT][0];

    if( cvtest::randInt(rng) & 1 )
        sizes[INPUT][4] = Size(0,0);
    else if( !(tabc_flag & CV_GEMM_C_T) )
        sizes[INPUT][4] = sizes[OUTPUT][0];
    else
    {
        sizes[INPUT][4].width = sizes[OUTPUT][0].height;
        sizes[INPUT][4].height = sizes[OUTPUT][0].width;
    }
}


int Core_GEMMTest::prepare_test_case( int test_case_idx )
{
    int code = Base::prepare_test_case( test_case_idx );
    if( code > 0 )
    {
        alpha = cvGetReal2D( test_array[INPUT][2], 0, 0 );
        beta = cvGetReal2D( test_array[INPUT][3], 0, 0 );
    }
    return code;
}


void Core_GEMMTest::get_minmax_bounds( int /*i*/, int /*j*/, int /*type*/, Scalar& low, Scalar& high )
{
    low = Scalar::all(-10.);
    high = Scalar::all(10.);
}


void Core_GEMMTest::run_func()
{
    cvGEMM( test_array[INPUT][0], test_array[INPUT][1], alpha,
           test_array[INPUT][4], beta, test_array[OUTPUT][0], tabc_flag );
}


void Core_GEMMTest::prepare_to_validation( int )
{
    cvtest::gemm( test_mat[INPUT][0], test_mat[INPUT][1], alpha,
             test_array[INPUT][4] ? test_mat[INPUT][4] : Mat(),
             beta, test_mat[REF_OUTPUT][0], tabc_flag );
}


///////////////// multransposed /////////////////////

class Core_MulTransposedTest : public Core_MatrixTest
{
public:
    Core_MulTransposedTest();
protected:
    void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
    void get_minmax_bounds( int /*i*/, int /*j*/, int /*type*/, Scalar& low, Scalar& high );
    void run_func();
    void prepare_to_validation( int test_case_idx );
    int order;
};


Core_MulTransposedTest::Core_MulTransposedTest() : Core_MatrixTest( 2, 1, false, false, 1 )
{
    test_case_count = 100;
    order = 0;
    test_array[TEMP].push_back(NULL);
}


void Core_MulTransposedTest::get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types )
{
    RNG& rng = ts->get_rng();
    int bits = cvtest::randInt(rng);
    int src_type = cvtest::randInt(rng) % 5;
    int dst_type = cvtest::randInt(rng) % 2;

    src_type = src_type == 0 ? CV_8U : src_type == 1 ? CV_16U : src_type == 2 ? CV_16S :
    src_type == 3 ? CV_32F : CV_64F;
    dst_type = dst_type == 0 ? CV_32F : CV_64F;
    dst_type = MAX( dst_type, src_type );

    Core_MatrixTest::get_test_array_types_and_sizes( test_case_idx, sizes, types );

    if( bits & 1 )
        sizes[INPUT][1] = Size(0,0);
    else
    {
        sizes[INPUT][1] = sizes[INPUT][0];
        if( bits & 2 )
            sizes[INPUT][1].height = 1;
        if( bits & 4 )
            sizes[INPUT][1].width = 1;
    }

    sizes[TEMP][0] = sizes[INPUT][0];
    types[INPUT][0] = src_type;
    types[OUTPUT][0] = types[REF_OUTPUT][0] = types[INPUT][1] = types[TEMP][0] = dst_type;

    order = (bits & 8) != 0;
    sizes[OUTPUT][0].width = sizes[OUTPUT][0].height = order == 0 ?
    sizes[INPUT][0].height : sizes[INPUT][0].width;
    sizes[REF_OUTPUT][0] = sizes[OUTPUT][0];
}


void Core_MulTransposedTest::get_minmax_bounds( int /*i*/, int /*j*/, int /*type*/, Scalar& low, Scalar& high )
{
    low = cvScalarAll(-10.);
    high = cvScalarAll(10.);
}


void Core_MulTransposedTest::run_func()
{
    cvMulTransposed( test_array[INPUT][0], test_array[OUTPUT][0],
                    order, test_array[INPUT][1] );
}


void Core_MulTransposedTest::prepare_to_validation( int )
{
    const Mat& src = test_mat[INPUT][0];
    Mat delta = test_mat[INPUT][1];
    Mat& temp = test_mat[TEMP][0];
    if( !delta.empty() )
    {
        if( delta.rows < src.rows || delta.cols < src.cols )
        {
            cv::repeat( delta, src.rows/delta.rows, src.cols/delta.cols, temp);
            delta = temp;
        }
        cvtest::add( src, 1, delta, -1, Scalar::all(0), temp, temp.type());
    }
    else
        src.convertTo(temp, temp.type());

    cvtest::gemm( temp, temp, 1., Mat(), 0, test_mat[REF_OUTPUT][0], order == 0 ? GEMM_2_T : GEMM_1_T );
}


///////////////// Transform /////////////////////

class Core_TransformTest : public Core_MatrixTest
{
public:
    typedef Core_MatrixTest Base;
    Core_TransformTest();
protected:
    void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
    double get_success_error_level( int test_case_idx, int i, int j );
    int prepare_test_case( int test_case_idx );
    void run_func();
    void prepare_to_validation( int test_case_idx );

    double scale;
    bool diagMtx;
};


Core_TransformTest::Core_TransformTest() : Core_MatrixTest( 3, 1, true, false, 4 )
{
    scale = 1;
    diagMtx = false;
}


void Core_TransformTest::get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types )
{
    RNG& rng = ts->get_rng();
    int bits = cvtest::randInt(rng);
    int depth, dst_cn, mat_cols, mattype;
    Base::get_test_array_types_and_sizes( test_case_idx, sizes, types );

    mat_cols = CV_MAT_CN(types[INPUT][0]);
    depth = CV_MAT_DEPTH(types[INPUT][0]);
    dst_cn = cvtest::randInt(rng) % 4 + 1;
    types[OUTPUT][0] = types[REF_OUTPUT][0] = CV_MAKETYPE(depth, dst_cn);

    mattype = depth < CV_32S ? CV_32F : depth == CV_64F ? CV_64F : bits & 1 ? CV_32F : CV_64F;
    types[INPUT][1] = mattype;
    types[INPUT][2] = CV_MAKETYPE(mattype, dst_cn);

    scale = 1./((cvtest::randInt(rng)%4)*50+1);

    if( bits & 2 )
    {
        sizes[INPUT][2] = Size(0,0);
        mat_cols += (bits & 4) != 0;
    }
    else if( bits & 4 )
        sizes[INPUT][2] = Size(1,1);
    else
    {
        if( bits & 8 )
            sizes[INPUT][2] = Size(dst_cn,1);
        else
            sizes[INPUT][2] = Size(1,dst_cn);
        types[INPUT][2] &= ~CV_MAT_CN_MASK;
    }
    diagMtx = (bits & 16) != 0;

    sizes[INPUT][1] = Size(mat_cols,dst_cn);
}


int Core_TransformTest::prepare_test_case( int test_case_idx )
{
    int code = Base::prepare_test_case( test_case_idx );
    if( code > 0 )
    {
        Mat& m = test_mat[INPUT][1];
        cvtest::add(m, scale, m, 0, Scalar::all(0), m, m.type() );
        if(diagMtx)
        {
            Mat mask = Mat::eye(m.rows, m.cols, CV_8U)*255;
            mask = ~mask;
            m.setTo(Scalar::all(0), mask);
        }
    }
    return code;
}


double Core_TransformTest::get_success_error_level( int test_case_idx, int i, int j )
{
    int depth = test_mat[INPUT][0].depth();
    return depth <= CV_8S ? 1 : depth <= CV_32S ? 9 : Base::get_success_error_level( test_case_idx, i, j );
}

void Core_TransformTest::run_func()
{
    CvMat _m = test_mat[INPUT][1], _shift = test_mat[INPUT][2];
    cvTransform( test_array[INPUT][0], test_array[OUTPUT][0], &_m, _shift.data.ptr ? &_shift : 0);
}


void Core_TransformTest::prepare_to_validation( int )
{
    Mat transmat = test_mat[INPUT][1];
    Mat shift = test_mat[INPUT][2];

    cvtest::transform( test_mat[INPUT][0], test_mat[REF_OUTPUT][0], transmat, shift );
}


///////////////// PerspectiveTransform /////////////////////

class Core_PerspectiveTransformTest : public Core_MatrixTest
{
public:
    Core_PerspectiveTransformTest();
protected:
    void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
    double get_success_error_level( int test_case_idx, int i, int j );
    void run_func();
    void prepare_to_validation( int test_case_idx );
};


Core_PerspectiveTransformTest::Core_PerspectiveTransformTest() : Core_MatrixTest( 2, 1, false, false, 2 )
{
}


void Core_PerspectiveTransformTest::get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types )
{
    RNG& rng = ts->get_rng();
    int bits = cvtest::randInt(rng);
    int depth, cn, mattype;
    Core_MatrixTest::get_test_array_types_and_sizes( test_case_idx, sizes, types );

    cn = CV_MAT_CN(types[INPUT][0]) + 1;
    depth = CV_MAT_DEPTH(types[INPUT][0]);
    types[INPUT][0] = types[OUTPUT][0] = types[REF_OUTPUT][0] = CV_MAKETYPE(depth, cn);

    mattype = depth == CV_64F ? CV_64F : bits & 1 ? CV_32F : CV_64F;
    types[INPUT][1] = mattype;
    sizes[INPUT][1] = Size(cn + 1, cn + 1);
}


double Core_PerspectiveTransformTest::get_success_error_level( int test_case_idx, int i, int j )
{
    int depth = test_mat[INPUT][0].depth();
    return depth == CV_32F ? 1e-4 : depth == CV_64F ? 1e-8 :
    Core_MatrixTest::get_success_error_level(test_case_idx, i, j);
}


void Core_PerspectiveTransformTest::run_func()
{
    CvMat _m = test_mat[INPUT][1];
    cvPerspectiveTransform( test_array[INPUT][0], test_array[OUTPUT][0], &_m );
}


static void cvTsPerspectiveTransform( const CvArr* _src, CvArr* _dst, const CvMat* transmat )
{
    int i, j, cols;
    int cn, depth, mat_depth;
    CvMat astub, bstub, *a, *b;
    double mat[16];

    a = cvGetMat( _src, &astub, 0, 0 );
    b = cvGetMat( _dst, &bstub, 0, 0 );

    cn = CV_MAT_CN(a->type);
    depth = CV_MAT_DEPTH(a->type);
    mat_depth = CV_MAT_DEPTH(transmat->type);
    cols = transmat->cols;

    // prepare cn x (cn + 1) transform matrix
    if( mat_depth == CV_32F )
    {
        for( i = 0; i < transmat->rows; i++ )
            for( j = 0; j < cols; j++ )
                mat[i*cols + j] = ((float*)(transmat->data.ptr + transmat->step*i))[j];
    }
    else
    {
        assert( mat_depth == CV_64F );
        for( i = 0; i < transmat->rows; i++ )
            for( j = 0; j < cols; j++ )
                mat[i*cols + j] = ((double*)(transmat->data.ptr + transmat->step*i))[j];
    }

    // transform data
    cols = a->cols * cn;
    vector<double> buf(cols);

    for( i = 0; i < a->rows; i++ )
    {
        uchar* src = a->data.ptr + i*a->step;
        uchar* dst = b->data.ptr + i*b->step;

        switch( depth )
        {
            case CV_32F:
                for( j = 0; j < cols; j++ )
                    buf[j] = ((float*)src)[j];
                break;
            case CV_64F:
                for( j = 0; j < cols; j++ )
                    buf[j] = ((double*)src)[j];
                break;
            default:
                assert(0);
        }

        switch( cn )
        {
            case 2:
                for( j = 0; j < cols; j += 2 )
                {
                    double t0 = buf[j]*mat[0] + buf[j+1]*mat[1] + mat[2];
                    double t1 = buf[j]*mat[3] + buf[j+1]*mat[4] + mat[5];
                    double w = buf[j]*mat[6] + buf[j+1]*mat[7] + mat[8];
                    w = w ? 1./w : 0;
                    buf[j] = t0*w;
                    buf[j+1] = t1*w;
                }
                break;
            case 3:
                for( j = 0; j < cols; j += 3 )
                {
                    double t0 = buf[j]*mat[0] + buf[j+1]*mat[1] + buf[j+2]*mat[2] + mat[3];
                    double t1 = buf[j]*mat[4] + buf[j+1]*mat[5] + buf[j+2]*mat[6] + mat[7];
                    double t2 = buf[j]*mat[8] + buf[j+1]*mat[9] + buf[j+2]*mat[10] + mat[11];
                    double w = buf[j]*mat[12] + buf[j+1]*mat[13] + buf[j+2]*mat[14] + mat[15];
                    w = w ? 1./w : 0;
                    buf[j] = t0*w;
                    buf[j+1] = t1*w;
                    buf[j+2] = t2*w;
                }
                break;
            default:
                assert(0);
        }

        switch( depth )
        {
            case CV_32F:
                for( j = 0; j < cols; j++ )
                    ((float*)dst)[j] = (float)buf[j];
                break;
            case CV_64F:
                for( j = 0; j < cols; j++ )
                    ((double*)dst)[j] = buf[j];
                break;
            default:
                assert(0);
        }
    }
}


void Core_PerspectiveTransformTest::prepare_to_validation( int )
{
    CvMat transmat = test_mat[INPUT][1];
    cvTsPerspectiveTransform( test_array[INPUT][0], test_array[REF_OUTPUT][0], &transmat );
}

///////////////// Mahalanobis /////////////////////

class Core_MahalanobisTest : public Core_MatrixTest
{
public:
    typedef Core_MatrixTest Base;
    Core_MahalanobisTest();
protected:
    void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
    int prepare_test_case( int test_case_idx );
    void run_func();
    void prepare_to_validation( int test_case_idx );
};


Core_MahalanobisTest::Core_MahalanobisTest() : Core_MatrixTest( 3, 1, false, true, 1 )
{
    test_case_count = 100;
    test_array[TEMP].push_back(NULL);
    test_array[TEMP].push_back(NULL);
    test_array[TEMP].push_back(NULL);
}


void Core_MahalanobisTest::get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types )
{
    RNG& rng = ts->get_rng();
    Core_MatrixTest::get_test_array_types_and_sizes( test_case_idx, sizes, types );

    if( cvtest::randInt(rng) & 1 )
        sizes[INPUT][0].width = sizes[INPUT][1].width = 1;
    else
        sizes[INPUT][0].height = sizes[INPUT][1].height = 1;

    sizes[TEMP][0] = sizes[TEMP][1] = sizes[INPUT][0];
    sizes[INPUT][2].width = sizes[INPUT][2].height = sizes[INPUT][0].width + sizes[INPUT][0].height - 1;
    sizes[TEMP][2] = sizes[INPUT][2];
    types[TEMP][0] = types[TEMP][1] = types[TEMP][2] = types[INPUT][0];
}

int Core_MahalanobisTest::prepare_test_case( int test_case_idx )
{
    int code = Base::prepare_test_case( test_case_idx );
    if( code > 0 )
    {
        // make sure that the inverted "covariation" matrix is symmetrix and positively defined.
        cvtest::gemm( test_mat[INPUT][2], test_mat[INPUT][2], 1., Mat(), 0., test_mat[TEMP][2], GEMM_2_T );
        cvtest::copy( test_mat[TEMP][2], test_mat[INPUT][2] );
    }

    return code;
}


void Core_MahalanobisTest::run_func()
{
    test_mat[OUTPUT][0].at<Scalar>(0,0) =
    cvRealScalar(cvMahalanobis(test_array[INPUT][0], test_array[INPUT][1], test_array[INPUT][2]));
}

void Core_MahalanobisTest::prepare_to_validation( int )
{
    cvtest::add( test_mat[INPUT][0], 1., test_mat[INPUT][1], -1.,
                Scalar::all(0), test_mat[TEMP][0], test_mat[TEMP][0].type() );
    if( test_mat[INPUT][0].rows == 1 )
        cvtest::gemm( test_mat[TEMP][0], test_mat[INPUT][2], 1.,
                 Mat(), 0., test_mat[TEMP][1], 0 );
    else
        cvtest::gemm( test_mat[INPUT][2], test_mat[TEMP][0], 1.,
                 Mat(), 0., test_mat[TEMP][1], 0 );

    test_mat[REF_OUTPUT][0].at<Scalar>(0,0) = cvRealScalar(sqrt(cvtest::crossCorr(test_mat[TEMP][0], test_mat[TEMP][1])));
}


///////////////// covarmatrix /////////////////////

class Core_CovarMatrixTest : public Core_MatrixTest
{
public:
    Core_CovarMatrixTest();
protected:
    void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
    int prepare_test_case( int test_case_idx );
    void run_func();
    void prepare_to_validation( int test_case_idx );
    vector<void*> temp_hdrs;
    vector<uchar> hdr_data;
    int flags, t_flag, len, count;
    bool are_images;
};


Core_CovarMatrixTest::Core_CovarMatrixTest() : Core_MatrixTest( 1, 1, true, false, 1 ),
    flags(0), t_flag(0), len(0), count(0), are_images(false)
{
    test_case_count = 100;
    test_array[INPUT_OUTPUT].push_back(NULL);
    test_array[REF_INPUT_OUTPUT].push_back(NULL);
    test_array[TEMP].push_back(NULL);
    test_array[TEMP].push_back(NULL);
}


void Core_CovarMatrixTest::get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types )
{
    RNG& rng = ts->get_rng();
    int bits = cvtest::randInt(rng);
    int i, single_matrix;
    Core_MatrixTest::get_test_array_types_and_sizes( test_case_idx, sizes, types );

    flags = bits & (CV_COVAR_NORMAL | CV_COVAR_USE_AVG | CV_COVAR_SCALE | CV_COVAR_ROWS );
    single_matrix = flags & CV_COVAR_ROWS;
    t_flag = (bits & 256) != 0;

    const int min_count = 2;

    if( !t_flag )
    {
        len = sizes[INPUT][0].width;
        count = sizes[INPUT][0].height;
        count = MAX(count, min_count);
        sizes[INPUT][0] = Size(len, count);
    }
    else
    {
        len = sizes[INPUT][0].height;
        count = sizes[INPUT][0].width;
        count = MAX(count, min_count);
        sizes[INPUT][0] = Size(count, len);
    }

    if( single_matrix && t_flag )
        flags = (flags & ~CV_COVAR_ROWS) | CV_COVAR_COLS;

    if( CV_MAT_DEPTH(types[INPUT][0]) == CV_32S )
        types[INPUT][0] = (types[INPUT][0] & ~CV_MAT_DEPTH_MASK) | CV_32F;

    sizes[OUTPUT][0] = sizes[REF_OUTPUT][0] = flags & CV_COVAR_NORMAL ? Size(len,len) : Size(count,count);
    sizes[INPUT_OUTPUT][0] = sizes[REF_INPUT_OUTPUT][0] = !t_flag ? Size(len,1) : Size(1,len);
    sizes[TEMP][0] = sizes[INPUT][0];

    types[INPUT_OUTPUT][0] = types[REF_INPUT_OUTPUT][0] =
    types[OUTPUT][0] = types[REF_OUTPUT][0] = types[TEMP][0] =
    CV_MAT_DEPTH(types[INPUT][0]) == CV_64F || (bits & 512) ? CV_64F : CV_32F;

    are_images = (bits & 1024) != 0;
    for( i = 0; i < (single_matrix ? 1 : count); i++ )
        temp_hdrs.push_back(NULL);
}


int Core_CovarMatrixTest::prepare_test_case( int test_case_idx )
{
    int code = Core_MatrixTest::prepare_test_case( test_case_idx );
    if( code > 0 )
    {
        int i;
        int single_matrix = flags & (CV_COVAR_ROWS|CV_COVAR_COLS);
        int hdr_size = are_images ? sizeof(IplImage) : sizeof(CvMat);

        hdr_data.resize(count*hdr_size);
        uchar* _hdr_data = &hdr_data[0];
        if( single_matrix )
        {
            if( !are_images )
                *((CvMat*)_hdr_data) = test_mat[INPUT][0];
            else
                *((IplImage*)_hdr_data) = test_mat[INPUT][0];
            temp_hdrs[0] = _hdr_data;
        }
        else
            for( i = 0; i < count; i++ )
            {
                Mat part;
                void* ptr = _hdr_data + i*hdr_size;

                if( !t_flag )
                    part = test_mat[INPUT][0].row(i);
                else
                    part = test_mat[INPUT][0].col(i);

                if( !are_images )
                    *((CvMat*)ptr) = part;
                else
                    *((IplImage*)ptr) = part;

                temp_hdrs[i] = ptr;
            }
    }

    return code;
}


void Core_CovarMatrixTest::run_func()
{
    cvCalcCovarMatrix( (const void**)&temp_hdrs[0], count,
                      test_array[OUTPUT][0], test_array[INPUT_OUTPUT][0], flags );
}


void Core_CovarMatrixTest::prepare_to_validation( int )
{
    Mat& avg = test_mat[REF_INPUT_OUTPUT][0];
    double scale = 1.;

    if( !(flags & CV_COVAR_USE_AVG) )
    {
        Mat hdrs0 = cvarrToMat(temp_hdrs[0]);

        int i;
        avg = Scalar::all(0);

        for( i = 0; i < count; i++ )
        {
            Mat vec;
            if( flags & CV_COVAR_ROWS )
                vec = hdrs0.row(i);
            else if( flags & CV_COVAR_COLS )
                vec = hdrs0.col(i);
            else
                vec = cvarrToMat(temp_hdrs[i]);

            cvtest::add(avg, 1, vec, 1, Scalar::all(0), avg, avg.type());
        }

        cvtest::add(avg, 1./count, avg, 0., Scalar::all(0), avg, avg.type());
    }

    if( flags & CV_COVAR_SCALE )
    {
        scale = 1./count;
    }

    Mat& temp0 = test_mat[TEMP][0];
    cv::repeat( avg, temp0.rows/avg.rows, temp0.cols/avg.cols, temp0 );
    cvtest::add( test_mat[INPUT][0], 1, temp0, -1, Scalar::all(0), temp0, temp0.type());

    cvtest::gemm( temp0, temp0, scale, Mat(), 0., test_mat[REF_OUTPUT][0],
             t_flag ^ ((flags & CV_COVAR_NORMAL) != 0) ? CV_GEMM_A_T : CV_GEMM_B_T );
    temp_hdrs.clear();
}


static void cvTsFloodWithZeros( Mat& mat, RNG& rng )
{
    int k, total = mat.rows*mat.cols, type = mat.type();
    int zero_total = cvtest::randInt(rng) % total;
    CV_Assert( type == CV_32FC1 || type == CV_64FC1 );

    for( k = 0; k < zero_total; k++ )
    {
        int i = cvtest::randInt(rng) % mat.rows;
        int j = cvtest::randInt(rng) % mat.cols;

        if( type == CV_32FC1 )
            mat.at<float>(i,j) = 0.f;
        else
            mat.at<double>(i,j) = 0.;
    }
}


///////////////// determinant /////////////////////

class Core_DetTest : public Core_MatrixTest
{
public:
    typedef Core_MatrixTest Base;
    Core_DetTest();
protected:
    void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
    double get_success_error_level( int test_case_idx, int i, int j );
    void get_minmax_bounds( int /*i*/, int /*j*/, int /*type*/, Scalar& low, Scalar& high );
    int prepare_test_case( int test_case_idx );
    void run_func();
    void prepare_to_validation( int test_case_idx );
};


Core_DetTest::Core_DetTest() : Core_MatrixTest( 1, 1, false, true, 1 )
{
    test_case_count = 100;
    max_log_array_size = 7;
    test_array[TEMP].push_back(NULL);
}


void Core_DetTest::get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types )
{
    Base::get_test_array_types_and_sizes( test_case_idx, sizes, types );

    sizes[INPUT][0].width = sizes[INPUT][0].height;
    sizes[TEMP][0] = sizes[INPUT][0];
    types[TEMP][0] = CV_64FC1;
}


void Core_DetTest::get_minmax_bounds( int /*i*/, int /*j*/, int /*type*/, Scalar& low, Scalar& high )
{
    low = cvScalarAll(-2.);
    high = cvScalarAll(2.);
}


double Core_DetTest::get_success_error_level( int /*test_case_idx*/, int /*i*/, int /*j*/ )
{
    return CV_MAT_DEPTH(cvGetElemType(test_array[INPUT][0])) == CV_32F ? 1e-2 : 1e-5;
}


int Core_DetTest::prepare_test_case( int test_case_idx )
{
    int code = Core_MatrixTest::prepare_test_case( test_case_idx );
    if( code > 0 )
        cvTsFloodWithZeros( test_mat[INPUT][0], ts->get_rng() );

    return code;
}


void Core_DetTest::run_func()
{
    test_mat[OUTPUT][0].at<Scalar>(0,0) = cvRealScalar(cvDet(test_array[INPUT][0]));
}


// LU method that chooses the optimal in a column pivot element
static double cvTsLU( CvMat* a, CvMat* b=NULL, CvMat* x=NULL, int* rank=0 )
{
    int i, j, k, N = a->rows, N1 = a->cols, Nm = MIN(N, N1), step = a->step/sizeof(double);
    int M = b ? b->cols : 0, b_step = b ? b->step/sizeof(double) : 0;
    int x_step = x ? x->step/sizeof(double) : 0;
    double *a0 = a->data.db, *b0 = b ? b->data.db : 0;
    double *x0 = x ? x->data.db : 0;
    double t, det = 1.;
    assert( CV_MAT_TYPE(a->type) == CV_64FC1 &&
           (!b || CV_ARE_TYPES_EQ(a,b)) && (!x || CV_ARE_TYPES_EQ(a,x)));

    for( i = 0; i < Nm; i++ )
    {
        double max_val = fabs(a0[i*step + i]);
        double *a1, *a2, *b1 = 0, *b2 = 0;
        k = i;

        for( j = i+1; j < N; j++ )
        {
            t = fabs(a0[j*step + i]);
            if( max_val < t )
            {
                max_val = t;
                k = j;
            }
        }

        if( k != i )
        {
            for( j = i; j < N1; j++ )
                CV_SWAP( a0[i*step + j], a0[k*step + j], t );

            for( j = 0; j < M; j++ )
                CV_SWAP( b0[i*b_step + j], b0[k*b_step + j], t );
            det = -det;
        }

        if( max_val == 0 )
        {
            if( rank )
                *rank = i;
            return 0.;
        }

        a1 = a0 + i*step;
        a2 = a1 + step;
        b1 = b0 + i*b_step;
        b2 = b1 + b_step;

        for( j = i+1; j < N; j++, a2 += step, b2 += b_step )
        {
            t = a2[i]/a1[i];
            for( k = i+1; k < N1; k++ )
                a2[k] -= t*a1[k];

            for( k = 0; k < M; k++ )
                b2[k] -= t*b1[k];
        }

        det *= a1[i];
    }

    if( x )
    {
        assert( b );

        for( i = N-1; i >= 0; i-- )
        {
            double* a1 = a0 + i*step;
            double* b1 = b0 + i*b_step;
            for( j = 0; j < M; j++ )
            {
                t = b1[j];
                for( k = i+1; k < N1; k++ )
                    t -= a1[k]*x0[k*x_step + j];
                x0[i*x_step + j] = t/a1[i];
            }
        }
    }

    if( rank )
        *rank = i;
    return det;
}


void Core_DetTest::prepare_to_validation( int )
{
    test_mat[INPUT][0].convertTo(test_mat[TEMP][0], test_mat[TEMP][0].type());
    CvMat temp0 = test_mat[TEMP][0];
    test_mat[REF_OUTPUT][0].at<Scalar>(0,0) = cvRealScalar(cvTsLU(&temp0, 0, 0));
}


///////////////// invert /////////////////////

class Core_InvertTest : public Core_MatrixTest
{
public:
    typedef Core_MatrixTest Base;
    Core_InvertTest();
protected:
    void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
    void get_minmax_bounds( int /*i*/, int /*j*/, int /*type*/, Scalar& low, Scalar& high );
    double get_success_error_level( int test_case_idx, int i, int j );
    int prepare_test_case( int test_case_idx );
    void run_func();
    void prepare_to_validation( int test_case_idx );
    int method, rank;
    double result;
};


Core_InvertTest::Core_InvertTest()
: Core_MatrixTest( 1, 1, false, false, 1 ), method(0), rank(0), result(0.)
{
    test_case_count = 100;
    max_log_array_size = 7;
    test_array[TEMP].push_back(NULL);
    test_array[TEMP].push_back(NULL);
}


void Core_InvertTest::get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types )
{
    RNG& rng = ts->get_rng();
    int bits = cvtest::randInt(rng);
    Base::get_test_array_types_and_sizes( test_case_idx, sizes, types );
    int min_size = MIN( sizes[INPUT][0].width, sizes[INPUT][0].height );

    if( (bits & 3) == 0 )
    {
        method = CV_SVD;
        if( bits & 4 )
        {
            sizes[INPUT][0] = Size(min_size, min_size);
            if( bits & 16 )
                method = CV_CHOLESKY;
        }
    }
    else
    {
        method = CV_LU;
        sizes[INPUT][0] = Size(min_size, min_size);
    }

    sizes[TEMP][0].width = sizes[INPUT][0].height;
    sizes[TEMP][0].height = sizes[INPUT][0].width;
    sizes[TEMP][1] = sizes[INPUT][0];
    types[TEMP][0] = types[INPUT][0];
    types[TEMP][1] = CV_64FC1;
    sizes[OUTPUT][0] = sizes[REF_OUTPUT][0] = Size(min_size, min_size);
}


double Core_InvertTest::get_success_error_level( int /*test_case_idx*/, int, int )
{
    return CV_MAT_DEPTH(cvGetElemType(test_array[OUTPUT][0])) == CV_32F ? 1e-2 : 1e-6;
}

int Core_InvertTest::prepare_test_case( int test_case_idx )
{
    int code = Core_MatrixTest::prepare_test_case( test_case_idx );
    if( code > 0 )
    {
        cvTsFloodWithZeros( test_mat[INPUT][0], ts->get_rng() );

        if( method == CV_CHOLESKY )
        {
            cvtest::gemm( test_mat[INPUT][0], test_mat[INPUT][0], 1.,
                     Mat(), 0., test_mat[TEMP][0], CV_GEMM_B_T );
            cvtest::copy( test_mat[TEMP][0], test_mat[INPUT][0] );
        }
    }

    return code;
}



void Core_InvertTest::get_minmax_bounds( int /*i*/, int /*j*/, int /*type*/, Scalar& low, Scalar& high )
{
    low = cvScalarAll(-1.);
    high = cvScalarAll(1.);
}


void Core_InvertTest::run_func()
{
    result = cvInvert(test_array[INPUT][0], test_array[TEMP][0], method);
}


static double cvTsSVDet( CvMat* mat, double* ratio )
{
    int type = CV_MAT_TYPE(mat->type);
    int i, nm = MIN( mat->rows, mat->cols );
    CvMat* w = cvCreateMat( nm, 1, type );
    double det = 1.;

    cvSVD( mat, w, 0, 0, 0 );

    if( type == CV_32FC1 )
    {
        for( i = 0; i < nm; i++ )
            det *= w->data.fl[i];
        *ratio = w->data.fl[nm-1] < FLT_EPSILON ? 0 : w->data.fl[nm-1]/w->data.fl[0];
    }
    else
    {
        for( i = 0; i < nm; i++ )
            det *= w->data.db[i];
        *ratio = w->data.db[nm-1] < FLT_EPSILON ? 0 : w->data.db[nm-1]/w->data.db[0];
    }

    cvReleaseMat( &w );
    return det;
}

void Core_InvertTest::prepare_to_validation( int )
{
    Mat& input = test_mat[INPUT][0];
    Mat& temp0 = test_mat[TEMP][0];
    Mat& temp1 = test_mat[TEMP][1];
    Mat& dst0 = test_mat[REF_OUTPUT][0];
    Mat& dst = test_mat[OUTPUT][0];
    CvMat _input = input;
    double ratio = 0, det = cvTsSVDet( &_input, &ratio );
    double threshold = (input.depth() == CV_32F ? FLT_EPSILON : DBL_EPSILON)*1000;

    cvtest::convert( input, temp1, temp1.type() );

    if( det < threshold ||
       ((method == CV_LU || method == CV_CHOLESKY) && (result == 0 || ratio < threshold)) ||
       ((method == CV_SVD || method == CV_SVD_SYM) && result < threshold) )
    {
        dst = Scalar::all(0);
        dst0 = Scalar::all(0);
        return;
    }

    if( input.rows >= input.cols )
        cvtest::gemm( temp0, input, 1., Mat(), 0., dst, 0 );
    else
        cvtest::gemm( input, temp0, 1., Mat(), 0., dst, 0 );

    cv::setIdentity( dst0, Scalar::all(1) );
}


///////////////// solve /////////////////////

class Core_SolveTest : public Core_MatrixTest
{
public:
    typedef Core_MatrixTest Base;
    Core_SolveTest();
protected:
    void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
    void get_minmax_bounds( int /*i*/, int /*j*/, int /*type*/, Scalar& low, Scalar& high );
    double get_success_error_level( int test_case_idx, int i, int j );
    int prepare_test_case( int test_case_idx );
    void run_func();
    void prepare_to_validation( int test_case_idx );
    int method, rank;
    double result;
};


Core_SolveTest::Core_SolveTest() : Core_MatrixTest( 2, 1, false, false, 1 ), method(0), rank(0), result(0.)
{
    test_case_count = 100;
    max_log_array_size = 7;
    test_array[TEMP].push_back(NULL);
    test_array[TEMP].push_back(NULL);
}


void Core_SolveTest::get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types )
{
    RNG& rng = ts->get_rng();
    int bits = cvtest::randInt(rng);
    Base::get_test_array_types_and_sizes( test_case_idx, sizes, types );
    CvSize in_sz = sizes[INPUT][0];
    if( in_sz.width > in_sz.height )
        in_sz = cvSize(in_sz.height, in_sz.width);
    Base::get_test_array_types_and_sizes( test_case_idx, sizes, types );
    sizes[INPUT][0] = in_sz;
    int min_size = MIN( sizes[INPUT][0].width, sizes[INPUT][0].height );

    if( (bits & 3) == 0 )
    {
        method = CV_SVD;
        if( bits & 4 )
        {
            sizes[INPUT][0] = Size(min_size, min_size);
            /*if( bits & 8 )
             method = CV_SVD_SYM;*/
        }
    }
    else
    {
        method = CV_LU;
        sizes[INPUT][0] = Size(min_size, min_size);
    }

    sizes[INPUT][1].height = sizes[INPUT][0].height;
    sizes[TEMP][0].width = sizes[INPUT][1].width;
    sizes[TEMP][0].height = sizes[INPUT][0].width;
    sizes[TEMP][1] = sizes[INPUT][0];
    types[TEMP][0] = types[INPUT][0];
    types[TEMP][1] = CV_64FC1;
    sizes[OUTPUT][0] = sizes[REF_OUTPUT][0] = Size(sizes[INPUT][1].width, min_size);
}


int Core_SolveTest::prepare_test_case( int test_case_idx )
{
    int code = Core_MatrixTest::prepare_test_case( test_case_idx );

    /*if( method == CV_SVD_SYM )
     {
     cvTsGEMM( test_array[INPUT][0], test_array[INPUT][0], 1.,
     0, 0., test_array[TEMP][0], CV_GEMM_B_T );
     cvTsCopy( test_array[TEMP][0], test_array[INPUT][0] );
     }*/

    return code;
}


void Core_SolveTest::get_minmax_bounds( int /*i*/, int /*j*/, int /*type*/, Scalar& low, Scalar& high )
{
    low = cvScalarAll(-1.);
    high = cvScalarAll(1.);
}


double Core_SolveTest::get_success_error_level( int /*test_case_idx*/, int, int )
{
    return CV_MAT_DEPTH(cvGetElemType(test_array[OUTPUT][0])) == CV_32F ? 5e-2 : 1e-8;
}


void Core_SolveTest::run_func()
{
    result = cvSolve(test_array[INPUT][0], test_array[INPUT][1], test_array[TEMP][0], method);
}

void Core_SolveTest::prepare_to_validation( int )
{
    //int rank = test_mat[REF_OUTPUT][0].rows;
    Mat& input = test_mat[INPUT][0];
    Mat& dst = test_mat[OUTPUT][0];
    Mat& dst0 = test_mat[REF_OUTPUT][0];

    if( method == CV_LU )
    {
        if( result == 0 )
        {
            Mat& temp1 = test_mat[TEMP][1];
            cvtest::convert(input, temp1, temp1.type());
            dst = Scalar::all(0);
            CvMat _temp1 = temp1;
            double det = cvTsLU( &_temp1, 0, 0 );
            dst0 = Scalar::all(det != 0);
            return;
        }

        double threshold = (input.type() == CV_32F ? FLT_EPSILON : DBL_EPSILON)*1000;
        CvMat _input = input;
        double ratio = 0, det = cvTsSVDet( &_input, &ratio );
        if( det < threshold || ratio < threshold )
        {
            dst = Scalar::all(0);
            dst0 = Scalar::all(0);
            return;
        }
    }

    Mat* pdst = input.rows <= input.cols ? &test_mat[OUTPUT][0] : &test_mat[INPUT][1];

    cvtest::gemm( input, test_mat[TEMP][0], 1., test_mat[INPUT][1], -1., *pdst, 0 );
    if( pdst != &dst )
        cvtest::gemm( input, *pdst, 1., Mat(), 0., dst, CV_GEMM_A_T );
    dst0 = Scalar::all(0);
}


///////////////// SVD /////////////////////

class Core_SVDTest : public Core_MatrixTest
{
public:
    typedef Core_MatrixTest Base;
    Core_SVDTest();
protected:
    void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
    double get_success_error_level( int test_case_idx, int i, int j );
    void get_minmax_bounds( int /*i*/, int /*j*/, int /*type*/, Scalar& low, Scalar& high );
    int prepare_test_case( int test_case_idx );
    void run_func();
    void prepare_to_validation( int test_case_idx );
    int flags;
    bool have_u, have_v, symmetric, compact, vector_w;
};


Core_SVDTest::Core_SVDTest() :
Core_MatrixTest( 1, 4, false, false, 1 ),
flags(0), have_u(false), have_v(false), symmetric(false), compact(false), vector_w(false)
{
    test_case_count = 100;
    max_log_array_size = 8;
    test_array[TEMP].push_back(NULL);
    test_array[TEMP].push_back(NULL);
    test_array[TEMP].push_back(NULL);
    test_array[TEMP].push_back(NULL);
}


void Core_SVDTest::get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types )
{
    RNG& rng = ts->get_rng();
    int bits = cvtest::randInt(rng);
    Core_MatrixTest::get_test_array_types_and_sizes( test_case_idx, sizes, types );
    int min_size, i, m, n;

    min_size = MIN( sizes[INPUT][0].width, sizes[INPUT][0].height );

    flags = bits & (CV_SVD_MODIFY_A+CV_SVD_U_T+CV_SVD_V_T);
    have_u = (bits & 8) != 0;
    have_v = (bits & 16) != 0;
    symmetric = (bits & 32) != 0;
    compact = (bits & 64) != 0;
    vector_w = (bits & 128) != 0;

    if( symmetric )
        sizes[INPUT][0] = Size(min_size, min_size);

    m = sizes[INPUT][0].height;
    n = sizes[INPUT][0].width;

    if( compact )
        sizes[TEMP][0] = Size(min_size, min_size);
    else
        sizes[TEMP][0] = sizes[INPUT][0];
    sizes[TEMP][3] = Size(0,0);

    if( vector_w )
    {
        sizes[TEMP][3] = sizes[TEMP][0];
        if( bits & 256 )
            sizes[TEMP][0] = Size(1, min_size);
        else
            sizes[TEMP][0] = Size(min_size, 1);
    }

    if( have_u )
    {
        sizes[TEMP][1] = compact ? Size(min_size, m) : Size(m, m);

        if( flags & CV_SVD_U_T )
            CV_SWAP( sizes[TEMP][1].width, sizes[TEMP][1].height, i );
    }
    else
        sizes[TEMP][1] = Size(0,0);

    if( have_v )
    {
        sizes[TEMP][2] = compact ? Size(n, min_size) : Size(n, n);

        if( !(flags & CV_SVD_V_T) )
            CV_SWAP( sizes[TEMP][2].width, sizes[TEMP][2].height, i );
    }
    else
        sizes[TEMP][2] = Size(0,0);

    types[TEMP][0] = types[TEMP][1] = types[TEMP][2] = types[TEMP][3] = types[INPUT][0];
    types[OUTPUT][0] = types[OUTPUT][1] = types[OUTPUT][2] = types[INPUT][0];
    types[OUTPUT][3] = CV_8UC1;
    sizes[OUTPUT][0] = !have_u || !have_v ? Size(0,0) : sizes[INPUT][0];
    sizes[OUTPUT][1] = !have_u ? Size(0,0) : compact ? Size(min_size,min_size) : Size(m,m);
    sizes[OUTPUT][2] = !have_v ? Size(0,0) : compact ? Size(min_size,min_size) : Size(n,n);
    sizes[OUTPUT][3] = Size(min_size,1);

    for( i = 0; i < 4; i++ )
    {
        sizes[REF_OUTPUT][i] = sizes[OUTPUT][i];
        types[REF_OUTPUT][i] = types[OUTPUT][i];
    }
}


int Core_SVDTest::prepare_test_case( int test_case_idx )
{
    int code = Core_MatrixTest::prepare_test_case( test_case_idx );
    if( code > 0 )
    {
        Mat& input = test_mat[INPUT][0];
        cvTsFloodWithZeros( input, ts->get_rng() );

        if( symmetric && (have_u || have_v) )
        {
            Mat& temp = test_mat[TEMP][have_u ? 1 : 2];
            cvtest::gemm( input, input, 1., Mat(), 0., temp, CV_GEMM_B_T );
            cvtest::copy( temp, input );
        }

        if( (flags & CV_SVD_MODIFY_A) && test_array[OUTPUT][0] )
            cvtest::copy( input, test_mat[OUTPUT][0] );
    }

    return code;
}


void Core_SVDTest::get_minmax_bounds( int /*i*/, int /*j*/, int /*type*/, Scalar& low, Scalar& high )
{
    low = cvScalarAll(-2.);
    high = cvScalarAll(2.);
}

double Core_SVDTest::get_success_error_level( int test_case_idx, int i, int j )
{
    int input_depth = CV_MAT_DEPTH(cvGetElemType( test_array[INPUT][0] ));
    double input_precision = input_depth < CV_32F ? 0 : input_depth == CV_32F ? 1e-5 : 5e-11;
    double output_precision = Base::get_success_error_level( test_case_idx, i, j );
    return MAX(input_precision, output_precision);
}

void Core_SVDTest::run_func()
{
    CvArr* src = test_array[!(flags & CV_SVD_MODIFY_A) ? INPUT : OUTPUT][0];
    if( !src )
        src = test_array[INPUT][0];
    cvSVD( src, test_array[TEMP][0], test_array[TEMP][1], test_array[TEMP][2], flags );
}


void Core_SVDTest::prepare_to_validation( int /*test_case_idx*/ )
{
    Mat& input = test_mat[INPUT][0];
    int depth = input.depth();
    int i, m = input.rows, n = input.cols, min_size = MIN(m, n);
    Mat *src, *dst, *w;
    double prev = 0, threshold = depth == CV_32F ? FLT_EPSILON : DBL_EPSILON;

    if( have_u )
    {
        src = &test_mat[TEMP][1];
        dst = &test_mat[OUTPUT][1];
        cvtest::gemm( *src, *src, 1., Mat(), 0., *dst, src->rows == dst->rows ? CV_GEMM_B_T : CV_GEMM_A_T );
        cv::setIdentity( test_mat[REF_OUTPUT][1], Scalar::all(1.) );
    }

    if( have_v )
    {
        src = &test_mat[TEMP][2];
        dst = &test_mat[OUTPUT][2];
        cvtest::gemm( *src, *src, 1., Mat(), 0., *dst, src->rows == dst->rows ? CV_GEMM_B_T : CV_GEMM_A_T );
        cv::setIdentity( test_mat[REF_OUTPUT][2], Scalar::all(1.) );
    }

    w = &test_mat[TEMP][0];
    for( i = 0; i < min_size; i++ )
    {
        double normval = 0, aii;
        if( w->rows > 1 && w->cols > 1 )
        {
            normval = cvtest::norm( w->row(i), NORM_L1 );
            aii = depth == CV_32F ? w->at<float>(i,i) : w->at<double>(i,i);
        }
        else
        {
            normval = aii = depth == CV_32F ? w->at<float>(i) : w->at<double>(i);
        }

        normval = fabs(normval - aii);
        test_mat[OUTPUT][3].at<uchar>(i) = aii >= 0 && normval < threshold && (i == 0 || aii <= prev);
        prev = aii;
    }

    test_mat[REF_OUTPUT][3] = Scalar::all(1);

    if( have_u && have_v )
    {
        if( vector_w )
        {
            test_mat[TEMP][3] = Scalar::all(0);
            for( i = 0; i < min_size; i++ )
            {
                double val = depth == CV_32F ? w->at<float>(i) : w->at<double>(i);
                cvSetReal2D( test_array[TEMP][3], i, i, val );
            }
            w = &test_mat[TEMP][3];
        }

        if( m >= n )
        {
            cvtest::gemm( test_mat[TEMP][1], *w, 1., Mat(), 0., test_mat[REF_OUTPUT][0],
                     flags & CV_SVD_U_T ? CV_GEMM_A_T : 0 );
            cvtest::gemm( test_mat[REF_OUTPUT][0], test_mat[TEMP][2], 1., Mat(), 0.,
                     test_mat[OUTPUT][0], flags & CV_SVD_V_T ? 0 : CV_GEMM_B_T );
        }
        else
        {
            cvtest::gemm( *w, test_mat[TEMP][2], 1., Mat(), 0., test_mat[REF_OUTPUT][0],
                     flags & CV_SVD_V_T ? 0 : CV_GEMM_B_T );
            cvtest::gemm( test_mat[TEMP][1], test_mat[REF_OUTPUT][0], 1., Mat(), 0.,
                     test_mat[OUTPUT][0], flags & CV_SVD_U_T ? CV_GEMM_A_T : 0 );
        }

        cvtest::copy( test_mat[INPUT][0], test_mat[REF_OUTPUT][0] );
    }
}



///////////////// SVBkSb /////////////////////

class Core_SVBkSbTest : public Core_MatrixTest
{
public:
    typedef Core_MatrixTest Base;
    Core_SVBkSbTest();
protected:
    void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
    double get_success_error_level( int test_case_idx, int i, int j );
    void get_minmax_bounds( int /*i*/, int /*j*/, int /*type*/, Scalar& low, Scalar& high );
    int prepare_test_case( int test_case_idx );
    void run_func();
    void prepare_to_validation( int test_case_idx );
    int flags;
    bool have_b, symmetric, compact, vector_w;
};


Core_SVBkSbTest::Core_SVBkSbTest() : Core_MatrixTest( 2, 1, false, false, 1 ),
flags(0), have_b(false), symmetric(false), compact(false), vector_w(false)
{
    test_case_count = 100;
    test_array[TEMP].push_back(NULL);
    test_array[TEMP].push_back(NULL);
    test_array[TEMP].push_back(NULL);
}


void Core_SVBkSbTest::get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes,
                                                      vector<vector<int> >& types )
{
    RNG& rng = ts->get_rng();
    int bits = cvtest::randInt(rng);
    Base::get_test_array_types_and_sizes( test_case_idx, sizes, types );
    int min_size, i, m, n;
    CvSize b_size;

    min_size = MIN( sizes[INPUT][0].width, sizes[INPUT][0].height );

    flags = bits & (CV_SVD_MODIFY_A+CV_SVD_U_T+CV_SVD_V_T);
    have_b = (bits & 16) != 0;
    symmetric = (bits & 32) != 0;
    compact = (bits & 64) != 0;
    vector_w = (bits & 128) != 0;

    if( symmetric )
        sizes[INPUT][0] = Size(min_size, min_size);

    m = sizes[INPUT][0].height;
    n = sizes[INPUT][0].width;

    sizes[INPUT][1] = Size(0,0);
    b_size = Size(m,m);
    if( have_b )
    {
        sizes[INPUT][1].height = sizes[INPUT][0].height;
        sizes[INPUT][1].width = cvtest::randInt(rng) % 100 + 1;
        b_size = sizes[INPUT][1];
    }

    if( compact )
        sizes[TEMP][0] = Size(min_size, min_size);
    else
        sizes[TEMP][0] = sizes[INPUT][0];

    if( vector_w )
    {
        if( bits & 256 )
            sizes[TEMP][0] = Size(1, min_size);
        else
            sizes[TEMP][0] = Size(min_size, 1);
    }

    sizes[TEMP][1] = compact ? Size(min_size, m) : Size(m, m);

    if( flags & CV_SVD_U_T )
        CV_SWAP( sizes[TEMP][1].width, sizes[TEMP][1].height, i );

    sizes[TEMP][2] = compact ? Size(n, min_size) : Size(n, n);

    if( !(flags & CV_SVD_V_T) )
        CV_SWAP( sizes[TEMP][2].width, sizes[TEMP][2].height, i );

    types[TEMP][0] = types[TEMP][1] = types[TEMP][2] = types[INPUT][0];
    types[OUTPUT][0] = types[REF_OUTPUT][0] = types[INPUT][0];
    sizes[OUTPUT][0] = sizes[REF_OUTPUT][0] = Size( b_size.width, n );
}


int Core_SVBkSbTest::prepare_test_case( int test_case_idx )
{
    int code = Base::prepare_test_case( test_case_idx );
    if( code > 0 )
    {
        Mat& input = test_mat[INPUT][0];
        cvTsFloodWithZeros( input, ts->get_rng() );

        if( symmetric )
        {
            Mat& temp = test_mat[TEMP][1];
            cvtest::gemm( input, input, 1., Mat(), 0., temp, CV_GEMM_B_T );
            cvtest::copy( temp, input );
        }

        CvMat _input = input;
        cvSVD( &_input, test_array[TEMP][0], test_array[TEMP][1], test_array[TEMP][2], flags );
    }

    return code;
}


void Core_SVBkSbTest::get_minmax_bounds( int /*i*/, int /*j*/, int /*type*/, Scalar& low, Scalar& high )
{
    low = cvScalarAll(-2.);
    high = cvScalarAll(2.);
}


double Core_SVBkSbTest::get_success_error_level( int /*test_case_idx*/, int /*i*/, int /*j*/ )
{
    return CV_MAT_DEPTH(cvGetElemType(test_array[INPUT][0])) == CV_32F ? 1e-3 : 1e-7;
}


void Core_SVBkSbTest::run_func()
{
    cvSVBkSb( test_array[TEMP][0], test_array[TEMP][1], test_array[TEMP][2],
             test_array[INPUT][1], test_array[OUTPUT][0], flags );
}


void Core_SVBkSbTest::prepare_to_validation( int )
{
    Mat& input = test_mat[INPUT][0];
    int i, m = input.rows, n = input.cols, min_size = MIN(m, n);
    bool is_float = input.type() == CV_32F;
    Size w_size = compact ? Size(min_size,min_size) : Size(m,n);
    Mat& w = test_mat[TEMP][0];
    Mat wdb( w_size.height, w_size.width, CV_64FC1 );
    CvMat _w = w, _wdb = wdb;
    // use exactly the same threshold as in icvSVD... ,
    // so the changes in the library and here should be synchronized.
    double threshold = cv::sum(w)[0]*(DBL_EPSILON*2);//(is_float ? FLT_EPSILON*10 : DBL_EPSILON*2);

    wdb = Scalar::all(0);
    for( i = 0; i < min_size; i++ )
    {
        double wii = vector_w ? cvGetReal1D(&_w,i) : cvGetReal2D(&_w,i,i);
        cvSetReal2D( &_wdb, i, i, wii > threshold ? 1./wii : 0. );
    }

    Mat u = test_mat[TEMP][1];
    Mat v = test_mat[TEMP][2];
    Mat b = test_mat[INPUT][1];

    if( is_float )
    {
        test_mat[TEMP][1].convertTo(u, CV_64F);
        test_mat[TEMP][2].convertTo(v, CV_64F);
        if( !b.empty() )
            test_mat[INPUT][1].convertTo(b, CV_64F);
    }

    Mat t0, t1;

    if( !b.empty() )
        cvtest::gemm( u, b, 1., Mat(), 0., t0, !(flags & CV_SVD_U_T) ? CV_GEMM_A_T : 0 );
    else if( flags & CV_SVD_U_T )
        cvtest::copy( u, t0 );
    else
        cvtest::transpose( u, t0 );

    cvtest::gemm( wdb, t0, 1, Mat(), 0, t1, 0 );

    cvtest::gemm( v, t1, 1, Mat(), 0, t0, flags & CV_SVD_V_T ? CV_GEMM_A_T : 0 );
    Mat& dst0 = test_mat[REF_OUTPUT][0];
    t0.convertTo(dst0, dst0.type() );
}


typedef std::complex<double> complex_type;

struct pred_complex
{
    bool operator() (const complex_type& lhs, const complex_type& rhs) const
    {
        return fabs(lhs.real() - rhs.real()) > fabs(rhs.real())*FLT_EPSILON ? lhs.real() < rhs.real() : lhs.imag() < rhs.imag();
    }
};

struct pred_double
{
    bool operator() (const double& lhs, const double& rhs) const
    {
        return lhs < rhs;
    }
};

class Core_SolvePolyTest : public cvtest::BaseTest
{
public:
    Core_SolvePolyTest();
    ~Core_SolvePolyTest();
protected:
    virtual void run( int start_from );
};

Core_SolvePolyTest::Core_SolvePolyTest() {}

Core_SolvePolyTest::~Core_SolvePolyTest() {}

void Core_SolvePolyTest::run( int )
{
    RNG& rng = ts->get_rng();
    int fig = 100;
    double range = 50;
    double err_eps = 1e-4;

    for (int idx = 0, max_idx = 1000, progress = 0; idx < max_idx; ++idx)
    {
        progress = update_progress(progress, idx-1, max_idx, 0);
        int n = cvtest::randInt(rng) % 13 + 1;
        std::vector<complex_type> r(n), ar(n), c(n + 1, 0);
        std::vector<double> a(n + 1), u(n * 2), ar1(n), ar2(n);

        int rr_odds = 3; // odds that we get a real root
        for (int j = 0; j < n;)
        {
            if (cvtest::randInt(rng) % rr_odds == 0 || j == n - 1)
                r[j++] = cvtest::randReal(rng) * range;
            else
            {
                r[j] = complex_type(cvtest::randReal(rng) * range,
                                    cvtest::randReal(rng) * range + 1);
                r[j + 1] = std::conj(r[j]);
                j += 2;
            }
        }

        for (int j = 0, k = 1 << n, jj, kk; j < k; ++j)
        {
            int p = 0;
            complex_type v(1);
            for (jj = 0, kk = 1; jj < n && !(j & kk); ++jj, ++p, kk <<= 1)
                ;
            for (; jj < n; ++jj, kk <<= 1)
            {
                if (j & kk)
                    v *= -r[jj];
                else
                    ++p;
            }
            c[p] += v;
        }

        bool pass = false;
        double div = 0, s = 0;
        int cubic_case = idx & 1;
        for (int maxiter = 100; !pass && maxiter < 10000; maxiter *= 2, cubic_case = (cubic_case + 1) % 2)
        {
            for (int j = 0; j < n + 1; ++j)
                a[j] = c[j].real();

            CvMat amat, umat;
            cvInitMatHeader(&amat, n + 1, 1, CV_64FC1, &a[0]);
            cvInitMatHeader(&umat, n, 1, CV_64FC2, &u[0]);
            cvSolvePoly(&amat, &umat, maxiter, fig);

            for (int j = 0; j < n; ++j)
                ar[j] = complex_type(u[j * 2], u[j * 2 + 1]);

            std::sort(r.begin(), r.end(), pred_complex());
            std::sort(ar.begin(), ar.end(), pred_complex());

            pass = true;
            if( n == 3 )
            {
                ar2.resize(n);
                cv::Mat _umat2(3, 1, CV_64F, &ar2[0]), umat2 = _umat2;
                cvFlip(&amat, &amat, 0);
                int nr2;
                if( cubic_case == 0 )
                    nr2 = cv::solveCubic(cv::cvarrToMat(&amat),umat2);
                else
                    nr2 = cv::solveCubic(cv::Mat_<float>(cv::cvarrToMat(&amat)), umat2);
                cvFlip(&amat, &amat, 0);
                if(nr2 > 0)
                    std::sort(ar2.begin(), ar2.begin()+nr2, pred_double());
                ar2.resize(nr2);

                int nr1 = 0;
                for(int j = 0; j < n; j++)
                    if( fabs(r[j].imag()) < DBL_EPSILON )
                        ar1[nr1++] = r[j].real();

                pass = pass && nr1 == nr2;
                if( nr2 > 0 )
                {
                    div = s = 0;
                    for(int j = 0; j < nr1; j++)
                    {
                        s += fabs(ar1[j]);
                        div += fabs(ar1[j] - ar2[j]);
                    }
                    div /= s;
                    pass = pass && div < err_eps;
                }
            }

            div = s = 0;
            for (int j = 0; j < n; ++j)
            {
                s += fabs(r[j].real()) + fabs(r[j].imag());
                div += sqrt(pow(r[j].real() - ar[j].real(), 2) + pow(r[j].imag() - ar[j].imag(), 2));
            }
            div /= s;
            pass = pass && div < err_eps;
        }

        if (!pass)
        {
            ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT);
            ts->printf( cvtest::TS::LOG, "too big diff = %g\n", div );

            for (size_t j=0;j<ar2.size();++j)
                ts->printf( cvtest::TS::LOG, "ar2[%d]=%g\n", j, ar2[j]);
            ts->printf(cvtest::TS::LOG, "\n");

            for (size_t j=0;j<r.size();++j)
                ts->printf( cvtest::TS::LOG, "r[%d]=(%g, %g)\n", j, r[j].real(), r[j].imag());
            ts->printf( cvtest::TS::LOG, "\n" );
            for (size_t j=0;j<ar.size();++j)
                ts->printf( cvtest::TS::LOG, "ar[%d]=(%g, %g)\n", j, ar[j].real(), ar[j].imag());
            break;
        }
    }
}

class Core_PhaseTest : public cvtest::BaseTest
{
public:
    Core_PhaseTest() {}
    ~Core_PhaseTest() {}
protected:
    virtual void run(int)
    {
        const float maxAngleDiff = 0.5; //in degrees
        const int axisCount = 8;
        const int dim = theRNG().uniform(1,10);
        const float scale = theRNG().uniform(1.f, 100.f);
        Mat x(axisCount + 1, dim, CV_32FC1),
            y(axisCount + 1, dim, CV_32FC1);
        Mat anglesInDegrees(axisCount + 1, dim, CV_32FC1);

        // fill the data
        x.row(0).setTo(Scalar(0));
        y.row(0).setTo(Scalar(0));
        anglesInDegrees.row(0).setTo(Scalar(0));

        x.row(1).setTo(Scalar(scale));
        y.row(1).setTo(Scalar(0));
        anglesInDegrees.row(1).setTo(Scalar(0));

        x.row(2).setTo(Scalar(scale));
        y.row(2).setTo(Scalar(scale));
        anglesInDegrees.row(2).setTo(Scalar(45));

        x.row(3).setTo(Scalar(0));
        y.row(3).setTo(Scalar(scale));
        anglesInDegrees.row(3).setTo(Scalar(90));

        x.row(4).setTo(Scalar(-scale));
        y.row(4).setTo(Scalar(scale));
        anglesInDegrees.row(4).setTo(Scalar(135));

        x.row(5).setTo(Scalar(-scale));
        y.row(5).setTo(Scalar(0));
        anglesInDegrees.row(5).setTo(Scalar(180));

        x.row(6).setTo(Scalar(-scale));
        y.row(6).setTo(Scalar(-scale));
        anglesInDegrees.row(6).setTo(Scalar(225));

        x.row(7).setTo(Scalar(0));
        y.row(7).setTo(Scalar(-scale));
        anglesInDegrees.row(7).setTo(Scalar(270));

        x.row(8).setTo(Scalar(scale));
        y.row(8).setTo(Scalar(-scale));
        anglesInDegrees.row(8).setTo(Scalar(315));

        Mat resInRad, resInDeg;
        phase(x, y, resInRad, false);
        phase(x, y, resInDeg, true);

        CV_Assert(resInRad.size() == x.size());
        CV_Assert(resInRad.type() == x.type());

        CV_Assert(resInDeg.size() == x.size());
        CV_Assert(resInDeg.type() == x.type());

        // check the result
        int outOfRangeCount = countNonZero((resInDeg > 360) | (resInDeg < 0));
        if(outOfRangeCount > 0)
        {
            ts->printf(cvtest::TS::LOG, "There are result angles that are out of range [0, 360] (part of them is %f)\n",
                       static_cast<float>(outOfRangeCount)/resInDeg.total());
            ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT);
        }

        Mat diff = abs(anglesInDegrees - resInDeg);
        size_t errDegCount = diff.total() - countNonZero((diff < maxAngleDiff) | ((360 - diff) < maxAngleDiff));
        if(errDegCount > 0)
        {
            ts->printf(cvtest::TS::LOG, "There are incorrect result angles (in degrees) (part of them is %f)\n",
                       static_cast<float>(errDegCount)/resInDeg.total());
            ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT);
        }

        Mat convertedRes = resInRad * 180. / CV_PI;
        double normDiff = cvtest::norm(convertedRes - resInDeg, NORM_INF);
        if(normDiff > FLT_EPSILON * 180.)
        {
            ts->printf(cvtest::TS::LOG, "There are incorrect result angles (in radians)\n");
            ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT);
        }

        ts->set_failed_test_info(cvtest::TS::OK);
    }
};

class Core_CheckRange_Empty : public cvtest::BaseTest
{
public:
    Core_CheckRange_Empty(){}
    ~Core_CheckRange_Empty(){}
protected:
    virtual void run( int start_from );
};

void Core_CheckRange_Empty::run( int )
{
    cv::Mat m;
    ASSERT_TRUE( cv::checkRange(m) );
}

TEST(Core_CheckRange_Empty, accuracy) { Core_CheckRange_Empty test; test.safe_run(); }

class Core_CheckRange_INT_MAX : public cvtest::BaseTest
{
public:
    Core_CheckRange_INT_MAX(){}
    ~Core_CheckRange_INT_MAX(){}
protected:
    virtual void run( int start_from );
};

void Core_CheckRange_INT_MAX::run( int )
{
    cv::Mat m(3, 3, CV_32SC1, cv::Scalar(INT_MAX));
    ASSERT_FALSE( cv::checkRange(m, true, 0, 0, INT_MAX) );
    ASSERT_TRUE( cv::checkRange(m) );
}

TEST(Core_CheckRange_INT_MAX, accuracy) { Core_CheckRange_INT_MAX test; test.safe_run(); }

template <typename T> class Core_CheckRange : public testing::Test {};

TYPED_TEST_CASE_P(Core_CheckRange);

TYPED_TEST_P(Core_CheckRange, Negative)
{
    double min_bound = 4.5;
    double max_bound = 16.0;

    TypeParam data[] = {5, 10, 15, 4, 10, 2, 8, 12, 14};
    cv::Mat src = cv::Mat(3,3, cv::DataDepth<TypeParam>::value, data);

    cv::Point bad_pt(0, 0);

    ASSERT_FALSE(checkRange(src, true, &bad_pt, min_bound, max_bound));
    ASSERT_EQ(bad_pt.x, 0);
    ASSERT_EQ(bad_pt.y, 1);
}

TYPED_TEST_P(Core_CheckRange, Positive)
{
    double min_bound = -1;
    double max_bound = 16.0;

    TypeParam data[] = {5, 10, 15, 4, 10, 2, 8, 12, 14};
    cv::Mat src = cv::Mat(3,3, cv::DataDepth<TypeParam>::value, data);

    cv::Point bad_pt(0, 0);

    ASSERT_TRUE(checkRange(src, true, &bad_pt, min_bound, max_bound));
    ASSERT_EQ(bad_pt.x, 0);
    ASSERT_EQ(bad_pt.y, 0);
}

TYPED_TEST_P(Core_CheckRange, Bounds)
{
    double min_bound = 24.5;
    double max_bound = 1.0;

    TypeParam data[] = {5, 10, 15, 4, 10, 2, 8, 12, 14};
    cv::Mat src = cv::Mat(3,3, cv::DataDepth<TypeParam>::value, data);

    cv::Point bad_pt(0, 0);

    ASSERT_FALSE(checkRange(src, true, &bad_pt, min_bound, max_bound));
    ASSERT_EQ(bad_pt.x, 0);
    ASSERT_EQ(bad_pt.y, 0);
}

TYPED_TEST_P(Core_CheckRange, Zero)
{
    double min_bound = 0.0;
    double max_bound = 0.1;

    cv::Mat src1 = cv::Mat::zeros(3, 3, cv::DataDepth<TypeParam>::value);

    int sizes[] = {5, 6, 7};
    cv::Mat src2 = cv::Mat::zeros(3, sizes, cv::DataDepth<TypeParam>::value);

    ASSERT_TRUE( checkRange(src1, true, NULL, min_bound, max_bound) );
    ASSERT_TRUE( checkRange(src2, true, NULL, min_bound, max_bound) );
}

TYPED_TEST_P(Core_CheckRange, One)
{
    double min_bound = 1.0;
    double max_bound = 1.1;

    cv::Mat src1 = cv::Mat::ones(3, 3, cv::DataDepth<TypeParam>::value);

    int sizes[] = {5, 6, 7};
    cv::Mat src2 = cv::Mat::ones(3, sizes, cv::DataDepth<TypeParam>::value);

    ASSERT_TRUE( checkRange(src1, true, NULL, min_bound, max_bound) );
    ASSERT_TRUE( checkRange(src2, true, NULL, min_bound, max_bound) );
}

REGISTER_TYPED_TEST_CASE_P(Core_CheckRange, Negative, Positive, Bounds, Zero, One);

typedef ::testing::Types<signed char,unsigned char, signed short, unsigned short, signed int> mat_data_types;
INSTANTIATE_TYPED_TEST_CASE_P(Negative_Test, Core_CheckRange, mat_data_types);

TEST(Core_Invert, small)
{
    cv::Mat a = (cv::Mat_<float>(3,3) << 2.42104644730331, 1.81444796521479, -3.98072565304758, 0, 7.08389214348967e-3, 5.55326770986007e-3, 0,0, 7.44556154284261e-3);
    //cv::randu(a, -1, 1);

    cv::Mat b = a.t()*a;
    cv::Mat c, i = Mat_<float>::eye(3, 3);
    cv::invert(b, c, cv::DECOMP_LU); //std::cout << b*c << std::endl;
    ASSERT_LT( cvtest::norm(b*c, i, CV_C), 0.1 );
    cv::invert(b, c, cv::DECOMP_SVD); //std::cout << b*c << std::endl;
    ASSERT_LT( cvtest::norm(b*c, i, CV_C), 0.1 );
    cv::invert(b, c, cv::DECOMP_CHOLESKY); //std::cout << b*c << std::endl;
    ASSERT_LT( cvtest::norm(b*c, i, CV_C), 0.1 );
}

/////////////////////////////////////////////////////////////////////////////////////////////////////

TEST(Core_CovarMatrix, accuracy) { Core_CovarMatrixTest test; test.safe_run(); }
TEST(Core_CrossProduct, accuracy) { Core_CrossProductTest test; test.safe_run(); }
TEST(Core_Determinant, accuracy) { Core_DetTest test; test.safe_run(); }
TEST(Core_DotProduct, accuracy) { Core_DotProductTest test; test.safe_run(); }
TEST(Core_GEMM, accuracy) { Core_GEMMTest test; test.safe_run(); }
TEST(Core_Invert, accuracy) { Core_InvertTest test; test.safe_run(); }
TEST(Core_Mahalanobis, accuracy) { Core_MahalanobisTest test; test.safe_run(); }
TEST(Core_MulTransposed, accuracy) { Core_MulTransposedTest test; test.safe_run(); }
TEST(Core_Transform, accuracy) { Core_TransformTest test; test.safe_run(); }
TEST(Core_PerspectiveTransform, accuracy) { Core_PerspectiveTransformTest test; test.safe_run(); }
TEST(Core_Pow, accuracy) { Core_PowTest test; test.safe_run(); }
TEST(Core_SolveLinearSystem, accuracy) { Core_SolveTest test; test.safe_run(); }
TEST(Core_SVD, accuracy) { Core_SVDTest test; test.safe_run(); }
TEST(Core_SVBkSb, accuracy) { Core_SVBkSbTest test; test.safe_run(); }
TEST(Core_Trace, accuracy) { Core_TraceTest test; test.safe_run(); }
TEST(Core_SolvePoly, accuracy) { Core_SolvePolyTest test; test.safe_run(); }
TEST(Core_Phase, accuracy) { Core_PhaseTest test; test.safe_run(); }


TEST(Core_SVD, flt)
{
    float a[] = {
    1.23377746e+011f, -7.05490125e+010f, -4.18380882e+010f, -11693456.f,
    -39091328.f, 77492224.f, -7.05490125e+010f, 2.36211143e+011f,
    -3.51093473e+010f, 70773408.f, -4.83386156e+005f, -129560368.f,
    -4.18380882e+010f, -3.51093473e+010f, 9.25311222e+010f, -49052424.f,
    43922752.f, 12176842.f, -11693456.f, 70773408.f, -49052424.f, 8.40836094e+004f,
    5.17475293e+003f, -1.16122949e+004f, -39091328.f, -4.83386156e+005f,
    43922752.f, 5.17475293e+003f, 5.16047969e+004f, 5.68887842e+003f, 77492224.f,
    -129560368.f, 12176842.f, -1.16122949e+004f, 5.68887842e+003f,
    1.28060578e+005f
    };

    float b[] = {
    283751232.f, 2.61604198e+009f, -745033216.f, 2.31125625e+005f,
    -4.52429188e+005f, -1.37596525e+006f
    };

    Mat A(6, 6, CV_32F, a);
    Mat B(6, 1, CV_32F, b);
    Mat X, B1;
    solve(A, B, X, DECOMP_SVD);
    B1 = A*X;
    EXPECT_LE(cvtest::norm(B1, B, NORM_L2 + NORM_RELATIVE), FLT_EPSILON*10);
}


// TODO: eigenvv, invsqrt, cbrt, fastarctan, (round, floor, ceil(?)),

enum
{
    MAT_N_DIM_C1,
    MAT_N_1_CDIM,
    MAT_1_N_CDIM,
    MAT_N_DIM_C1_NONCONT,
    MAT_N_1_CDIM_NONCONT,
    VECTOR
};

class CV_KMeansSingularTest : public cvtest::BaseTest
{
public:
    CV_KMeansSingularTest() {}
    ~CV_KMeansSingularTest() {}
protected:
    void run(int inVariant)
    {
        int i, iter = 0, N = 0, N0 = 0, K = 0, dims = 0;
        Mat labels;
        try
        {
            RNG& rng = theRNG();
            const int MAX_DIM=5;
            int MAX_POINTS = 100, maxIter = 100;
            for( iter = 0; iter < maxIter; iter++ )
            {
                ts->update_context(this, iter, true);
                dims = rng.uniform(inVariant == MAT_1_N_CDIM ? 2 : 1, MAX_DIM+1);
                N = rng.uniform(1, MAX_POINTS+1);
                N0 = rng.uniform(1, MAX(N/10, 2));
                K = rng.uniform(1, N+1);

                if (inVariant == VECTOR)
                {
                    dims = 2;

                    std::vector<cv::Point2f> data0(N0);
                    rng.fill(data0, RNG::UNIFORM, -1, 1);

                    std::vector<cv::Point2f> data(N);
                    for( i = 0; i < N; i++ )
                        data[i] = data0[rng.uniform(0, N0)];

                    kmeans(data, K, labels, TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS, 30, 0),
                           5, KMEANS_PP_CENTERS);
                }
                else
                {
                    Mat data0(N0, dims, CV_32F);
                    rng.fill(data0, RNG::UNIFORM, -1, 1);

                    Mat data;

                    switch (inVariant)
                    {
                    case MAT_N_DIM_C1:
                        data.create(N, dims, CV_32F);
                        for( i = 0; i < N; i++ )
                            data0.row(rng.uniform(0, N0)).copyTo(data.row(i));
                        break;

                    case MAT_N_1_CDIM:
                        data.create(N, 1, CV_32FC(dims));
                        for( i = 0; i < N; i++ )
                            memcpy(data.ptr(i), data0.ptr(rng.uniform(0, N0)), dims * sizeof(float));
                        break;

                    case MAT_1_N_CDIM:
                        data.create(1, N, CV_32FC(dims));
                        for( i = 0; i < N; i++ )
                            memcpy(data.ptr() + i * dims * sizeof(float), data0.ptr(rng.uniform(0, N0)), dims * sizeof(float));
                        break;

                    case MAT_N_DIM_C1_NONCONT:
                        data.create(N, dims + 5, CV_32F);
                        data = data(Range(0, N), Range(0, dims));
                        for( i = 0; i < N; i++ )
                            data0.row(rng.uniform(0, N0)).copyTo(data.row(i));
                        break;

                    case MAT_N_1_CDIM_NONCONT:
                        data.create(N, 3, CV_32FC(dims));
                        data = data.colRange(0, 1);
                        for( i = 0; i < N; i++ )
                            memcpy(data.ptr(i), data0.ptr(rng.uniform(0, N0)), dims * sizeof(float));
                        break;
                    }

                    kmeans(data, K, labels, TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS, 30, 0),
                           5, KMEANS_PP_CENTERS);
                }

                Mat hist(K, 1, CV_32S, Scalar(0));
                for( i = 0; i < N; i++ )
                {
                    int l = labels.at<int>(i);
                    CV_Assert(0 <= l && l < K);
                    hist.at<int>(l)++;
                }
                for( i = 0; i < K; i++ )
                    CV_Assert( hist.at<int>(i) != 0 );
            }
        }
        catch(...)
        {
            ts->printf(cvtest::TS::LOG,
                       "context: iteration=%d, N=%d, N0=%d, K=%d\n",
                       iter, N, N0, K);
            std::cout << labels << std::endl;
            ts->set_failed_test_info(cvtest::TS::FAIL_MISMATCH);
        }
    }
};

TEST(Core_KMeans, singular) { CV_KMeansSingularTest test; test.safe_run(MAT_N_DIM_C1); }

CV_ENUM(KMeansInputVariant, MAT_N_DIM_C1, MAT_N_1_CDIM, MAT_1_N_CDIM, MAT_N_DIM_C1_NONCONT, MAT_N_1_CDIM_NONCONT, VECTOR)

typedef testing::TestWithParam<KMeansInputVariant> Core_KMeans_InputVariants;

TEST_P(Core_KMeans_InputVariants, singular)
{
    CV_KMeansSingularTest test;
    test.safe_run(GetParam());
}

INSTANTIATE_TEST_CASE_P(AllVariants, Core_KMeans_InputVariants, KMeansInputVariant::all());

TEST(CovariationMatrixVectorOfMat, accuracy)
{
    unsigned int col_problem_size = 8, row_problem_size = 8, vector_size = 16;
    cv::Mat src(vector_size, col_problem_size * row_problem_size, CV_32F);
    int singleMatFlags = CV_COVAR_ROWS;

    cv::Mat gold;
    cv::Mat goldMean;
    cv::randu(src,cv::Scalar(-128), cv::Scalar(128));
    cv::calcCovarMatrix(src,gold,goldMean,singleMatFlags,CV_32F);
    std::vector<cv::Mat> srcVec;
    for(size_t i = 0; i < vector_size; i++)
    {
        srcVec.push_back(src.row(static_cast<int>(i)).reshape(0,col_problem_size));
    }

    cv::Mat actual;
    cv::Mat actualMean;
    cv::calcCovarMatrix(srcVec, actual, actualMean,singleMatFlags,CV_32F);

    cv::Mat diff;
    cv::absdiff(gold, actual, diff);
    cv::Scalar s = cv::sum(diff);
    ASSERT_EQ(s.dot(s), 0.0);

    cv::Mat meanDiff;
    cv::absdiff(goldMean, actualMean.reshape(0,1), meanDiff);
    cv::Scalar sDiff = cv::sum(meanDiff);
    ASSERT_EQ(sDiff.dot(sDiff), 0.0);
}

TEST(CovariationMatrixVectorOfMatWithMean, accuracy)
{
    unsigned int col_problem_size = 8, row_problem_size = 8, vector_size = 16;
    cv::Mat src(vector_size, col_problem_size * row_problem_size, CV_32F);
    int singleMatFlags = CV_COVAR_ROWS | CV_COVAR_USE_AVG;

    cv::Mat gold;
    cv::randu(src,cv::Scalar(-128), cv::Scalar(128));
    cv::Mat goldMean;

    cv::reduce(src,goldMean,0 ,CV_REDUCE_AVG, CV_32F);

    cv::calcCovarMatrix(src,gold,goldMean,singleMatFlags,CV_32F);

    std::vector<cv::Mat> srcVec;
    for(size_t i = 0; i < vector_size; i++)
    {
        srcVec.push_back(src.row(static_cast<int>(i)).reshape(0,col_problem_size));
    }

    cv::Mat actual;
    cv::Mat actualMean = goldMean.reshape(0, row_problem_size);
    cv::calcCovarMatrix(srcVec, actual, actualMean,singleMatFlags,CV_32F);

    cv::Mat diff;
    cv::absdiff(gold, actual, diff);
    cv::Scalar s = cv::sum(diff);
    ASSERT_EQ(s.dot(s), 0.0);

    cv::Mat meanDiff;
    cv::absdiff(goldMean, actualMean.reshape(0,1), meanDiff);
    cv::Scalar sDiff = cv::sum(meanDiff);
    ASSERT_EQ(sDiff.dot(sDiff), 0.0);
}

TEST(Core_Pow, special)
{
    for( int i = 0; i < 100; i++ )
    {
        int n = theRNG().uniform(1, 30);
        Mat mtx0(1, n, CV_8S), mtx, result;
        randu(mtx0, -5, 5);

        int type = theRNG().uniform(0, 2) ? CV_64F : CV_32F;
        double eps = type == CV_32F ? 1e-3 : 1e-10;
        mtx0.convertTo(mtx, type);
        // generate power from [-n, n] interval with 1/8 step - enough to check various cases.
        const int max_pf = 3;
        int pf = theRNG().uniform(0, max_pf*2+1);
        double power = ((1 << pf) - (1 << (max_pf*2-1)))/16.;
        int ipower = cvRound(power);
        bool is_ipower = ipower == power;
        cv::pow(mtx, power, result);
        for( int j = 0; j < n; j++ )
        {
            double val = type == CV_32F ? (double)mtx.at<float>(j) : mtx.at<double>(j);
            double r = type == CV_32F ? (double)result.at<float>(j) : result.at<double>(j);
            double r0;
            if( power == 0. )
                r0 = 1;
            else if( is_ipower )
            {
                r0 = 1;
                for( int k = 0; k < std::abs(ipower); k++ )
                    r0 *= val;
                if( ipower < 0 )
                    r0 = 1./r0;
            }
            else
                r0 = std::pow(val, power);
            if( cvIsInf(r0) )
            {
                ASSERT_TRUE(cvIsInf(r) != 0);
            }
            else if( cvIsNaN(r0) )
            {
                ASSERT_TRUE(cvIsNaN(r) != 0);
            }
            else
            {
                ASSERT_TRUE(cvIsInf(r) == 0 && cvIsNaN(r) == 0);
                ASSERT_LT(fabs(r - r0), eps);
            }
        }
    }
}

/* End of file. */