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test_math.cpp
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2770 lines (2286 loc) · 84 KB
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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];