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opencv/modules/core/test/test_math.cpp

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//////////////////////////////////////////////////////////////////////////////////////////
/////////////////// 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 <= 1 ? val :
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 = ((char*)a_data)[j];
((char*)b_data)[j] = (char)((val&~1)==0 ? val :
val ==-1 ? 1-2*(ipower&1) :
val == 2 && ipower == -1 ? 1 : 0);
}
else
for( j = 0; j < ncols; j++ )
{
int val = ((char*)a_data)[j];
val = ipow( val, ipower );
((char*)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&~1)==0 ? val :
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&~1)==0 ? val :
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&~1)==0 ? val :
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.data, 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 = {{0,0,0,0}}, b = {{0,0,0,0}}, c = {{0,0,0,0}};
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;
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 )
{
}
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), 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[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::Mat(&amat),umat2);
else
nr2 = cv::solveCubic(cv::Mat_<float>(cv::Mat(&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_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 = new cv::Point(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);
delete bad_pt;
}
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 = new cv::Point(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);
delete bad_pt;
}
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 = new cv::Point(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);
delete bad_pt;
}
TYPED_TEST_P(Core_CheckRange, Zero)
{
double min_bound = 0.0;
double max_bound = 0.1;
cv::Mat src = cv::Mat::zeros(3,3, cv::DataDepth<TypeParam>::value);
ASSERT_TRUE( checkRange(src, true, NULL, min_bound, max_bound) );
}
REGISTER_TYPED_TEST_CASE_P(Core_CheckRange, Negative, Positive, Bounds, Zero);
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_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(); }
// TODO: eigenvv, invsqrt, cbrt, fastarctan, (round, floor, ceil(?)),
class CV_KMeansSingularTest : public cvtest::BaseTest
{
public:
CV_KMeansSingularTest() {}
~CV_KMeansSingularTest() {}
protected:
void run(int)
{
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(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);
Mat data0(N0, dims, CV_32F);
rng.fill(data0, RNG::UNIFORM, -1, 1);
Mat data(N, dims, CV_32F);
for( i = 0; i < N; i++ )
data0.row(rng.uniform(0, N0)).copyTo(data.row(i));
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(); }
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);
}
/* End of file. */