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opencv/modules/core/test/test_math.cpp
Vadim Pisarevsky 299aa14c4b fixed typo in bfloat<=>float conversion test
2024-10-29 20:06:11 +03:00

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// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
//////////////////////////////////////////////////////////////////////////////////////////
/////////////////// tests for matrix operations and math functions ///////////////////////
//////////////////////////////////////////////////////////////////////////////////////////
#include "test_precomp.hpp"
#include <float.h>
#include <math.h>
#include "opencv2/core/softfloat.hpp"
#include "opencv2/core/core_c.h"
#include "opencv2/core/hal/intrin.hpp"
namespace opencv_test { namespace {
/// !!! 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 = cv::theRNG();
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 = cv::theRNG();
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 depth != CV_64F ? Base::get_success_error_level( test_case_idx, i, j ) : DBL_EPSILON*1024*1.11;
}
}
void Core_PowTest::get_minmax_bounds( int /*i*/, int /*j*/, int type, Scalar& low, Scalar& high )
{
double l, u = cvtest::randInt(cv::theRNG())%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(cubeRoot(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
cv::pow( test_mat[INPUT][0], power, test_mat[OUTPUT][0] );
}
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 = cv::theRNG();
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) = cv::trace(test_mat[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(test_mat[INPUT][0].dot(test_mat[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 = cv::theRNG();
int depth = cvtest::randInt(rng) % 2 + CV_32F;
int cn = cvtest::randInt(rng) & 1 ? 3 : 1, type = CV_MAKETYPE(depth, cn);
Size 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()
{
test_mat[OUTPUT][0] = test_mat[INPUT][0].cross(test_mat[INPUT][1]);
}
void Core_CrossProductTest::prepare_to_validation( int )
{
cv::Vec<double, 3> a, b, c;
test_mat[INPUT][0].reshape(1, 1).copyTo(a);
test_mat[INPUT][1].reshape(1, 1).copyTo(b);
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];
Mat &ref = test_mat[REF_OUTPUT][0];
Mat(c).reshape(ref.channels(), ref.rows).convertTo(ref, ref.type());
}
///////////////// 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 = cv::theRNG();
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 )
{
test_mat[INPUT][2](Rect(0, 0, 1, 1)).convertTo(Mat(1, 1, CV_64F, &alpha), CV_64F);
test_mat[INPUT][3](Rect(0, 0, 1, 1)).convertTo(Mat(1, 1, CV_64F, &beta), CV_64F);
}
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()
{
/*printf("tabc_flags=At:%d,Bt:%d,Ct:%d; A(%d x %d), B(%d x %d), C(%d x %d)\n",
(tabc_flag & GEMM_1_T) != 0, (tabc_flag & GEMM_2_T) != 0, (tabc_flag & GEMM_3_T) != 0,
test_mat[INPUT][0].rows, test_mat[INPUT][0].cols,
test_mat[INPUT][1].rows, test_mat[INPUT][1].cols,
test_mat[INPUT][4].rows, test_mat[INPUT][4].cols);*/
cv::gemm( test_mat[INPUT][0], test_mat[INPUT][1], alpha,
test_mat[INPUT][4], beta, test_mat[OUTPUT][0], tabc_flag );
}
void Core_GEMMTest::prepare_to_validation( int )
{
cvtest::gemm( test_mat[INPUT][0], test_mat[INPUT][1], alpha,
!test_mat[INPUT][4].empty() ? 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 );
bool order;
};
Core_MulTransposedTest::Core_MulTransposedTest() : Core_MatrixTest( 2, 1, false, false, 1 )
{
test_case_count = 100;
order = false;
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 = cv::theRNG();
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 = CV_32F;
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 == false) ?
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()
{
cv::mulTransposed( test_mat[INPUT][0], test_mat[OUTPUT][0], order, test_mat[INPUT][1].empty() ? noArray() : test_mat[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 = cv::theRNG();
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;
scale = 1./((cvtest::randInt(rng)%4)*50+1);
if( bits & 2 )
{
mat_cols += (bits & 4) != 0;
}
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()
{
cv::transform( test_mat[INPUT][0], test_mat[OUTPUT][0], test_mat[INPUT][1]);
}
void Core_TransformTest::prepare_to_validation( int )
{
Mat transmat = test_mat[INPUT][1];
cvtest::transform( test_mat[INPUT][0], test_mat[REF_OUTPUT][0], transmat, Mat() );
}
class Core_TransformLargeTest : public Core_TransformTest
{
public:
typedef Core_MatrixTest Base;
protected:
void get_test_array_types_and_sizes(int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types);
};
void Core_TransformLargeTest::get_test_array_types_and_sizes(int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types)
{
RNG& rng = cv::theRNG();
int bits = cvtest::randInt(rng);
int depth, dst_cn, mat_cols, mattype;
Base::get_test_array_types_and_sizes(test_case_idx, sizes, types);
for (unsigned int j = 0; j < sizes.size(); j++)
{
for (unsigned int i = 0; i < sizes[j].size(); i++)
{
sizes[j][i].width *= 4;
}
}
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;
scale = 1. / ((cvtest::randInt(rng) % 4) * 50 + 1);
if (bits & 2)
{
mat_cols += (bits & 4) != 0;
}
diagMtx = (bits & 16) != 0;
sizes[INPUT][1] = Size(mat_cols, dst_cn);
}
///////////////// 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 = cv::theRNG();
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()
{
perspectiveTransform( test_mat[INPUT][0], test_mat[OUTPUT][0], test_mat[INPUT][1] );
}
static void cvTsPerspectiveTransform( const Mat & a, Mat & b, const Mat & transmat )
{
int i, j, cols;
int cn, depth, mat_depth;
double mat[16] = {0.0};
cn = a.channels();
depth = a.depth();
mat_depth = transmat.depth();
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] = transmat.at<float>(i, j);
}
else
{
CV_Assert( mat_depth == CV_64F );
for( i = 0; i < transmat.rows; i++ )
for( j = 0; j < cols; j++ )
mat[i*cols + j] = transmat.at<double>(i, j);
}
// transform data
cols = a.cols * cn;
vector<double> buf(cols);
for( i = 0; i < a.rows; i++ )
{
switch( depth )
{
case CV_32F:
for( j = 0; j < cols; j++ )
buf[j] = a.at<float>(i, j);
break;
case CV_64F:
for( j = 0; j < cols; j++ )
buf[j] = a.at<double>(i, j);
break;
default:
CV_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:
CV_Assert(0);
}
switch( depth )
{
case CV_32F:
for( j = 0; j < cols; j++ )
b.at<float>(i, j) = (float)buf[j];
break;
case CV_64F:
for( j = 0; j < cols; j++ )
b.at<double>(i, j) = buf[j];
break;
default:
CV_Assert(0);
}
}
}
void Core_PerspectiveTransformTest::prepare_to_validation( int )
{
cvTsPerspectiveTransform( test_mat[INPUT][0], test_mat[REF_OUTPUT][0], test_mat[INPUT][1] );
}
///////////////// 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 = cv::theRNG();
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) =
cv::Mahalanobis(test_mat[INPUT][0], test_mat[INPUT][1], test_mat[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])));
}
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 test_mat[INPUT][0].depth() == 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], cv::theRNG() );
return code;
}
void Core_DetTest::run_func()
{
test_mat[OUTPUT][0].at<Scalar>(0,0) = cv::determinant(test_mat[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.;
CV_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 )
{
CV_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 = cvMat(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 = 6; // errors with larger arrays (>100x100)
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 = cv::theRNG();
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::DECOMP_SVD;
if( bits & 4 )
{
sizes[INPUT][0] = Size(min_size, min_size);
if( bits & 16 )
method = cv::DECOMP_CHOLESKY;
}
}
else
{
method = cv::DECOMP_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 test_mat[OUTPUT][0].depth() == 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], cv::theRNG() );
if( method == cv::DECOMP_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 = cv::invert(test_mat[INPUT][0], test_mat[TEMP][0], method);
}
static double cvTsSVDet( const Mat & mat, double* ratio )
{
int i, nm = MIN( mat.rows, mat.cols );
Mat w( nm, 1, mat.type() );
double det = 1.;
cv::SVD::compute( mat, w );
if( mat.type() == CV_32FC1 )
{
for( i = 0; i < nm; i++ )
det *= w.at<float>(i);
*ratio = w.at<float>(nm-1) < FLT_EPSILON ? 0 : w.at<float>(nm-1)/w.at<float>(0);
}
else
{
for( i = 0; i < nm; i++ )
det *= w.at<double>(i);
*ratio = w.at<double>(nm-1) < FLT_EPSILON ? 0 : w.at<double>(nm-1)/w.at<double>(0);
}
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];
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::DECOMP_LU || method == cv::DECOMP_CHOLESKY) && (result == 0 || ratio < threshold)) ||
((method == cv::DECOMP_SVD || method == cv::DECOMP_EIG) && 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 = cv::theRNG();
int bits = cvtest::randInt(rng);
Base::get_test_array_types_and_sizes( test_case_idx, sizes, types );
CvSize in_sz = cvSize(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::DECOMP_SVD;
if( bits & 4 )
{
sizes[INPUT][0] = Size(min_size, min_size);
/*if( bits & 8 )
method = cv::DECOMP_EIG;*/
}
}
else
{
method = cv::DECOMP_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 )
{
return Core_MatrixTest::prepare_test_case( test_case_idx );
}
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 test_mat[OUTPUT][0].depth() == CV_32F ? 5e-2 : 1e-8;
}
void Core_SolveTest::run_func()
{
result = cv::solve(test_mat[INPUT][0], test_mat[INPUT][1], test_mat[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::DECOMP_LU )
{
if( result == 0 )
{
Mat& temp1 = test_mat[TEMP][1];
cvtest::convert(input, temp1, temp1.type());
dst = Scalar::all(0);
CvMat _temp1 = cvMat(temp1);
double det = cvTsLU( &_temp1, 0, 0 );
dst0 = Scalar::all(det != 0);
return;
}
double threshold = (input.type() == CV_32F ? FLT_EPSILON : DBL_EPSILON)*1000;
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);
}
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 = cv::theRNG();
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;
}
//test x^3 = 0
cv::Mat coeffs_5623(4, 1, CV_64FC1);
cv::Mat r_5623(3, 1, CV_64FC2);
coeffs_5623.at<double>(0) = 1;
coeffs_5623.at<double>(1) = 0;
coeffs_5623.at<double>(2) = 0;
coeffs_5623.at<double>(3) = 0;
double prec_5623 = cv::solveCubic(coeffs_5623, r_5623);
pass = pass && r_5623.at<double>(0) == 0 && r_5623.at<double>(1) == 0 && r_5623.at<double>(2) == 0;
pass = pass && prec_5623 == 1;
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;
}
}
}
template<typename T>
static void checkRoot(Mat& r, T re, T im)
{
for (int i = 0; i < r.cols*r.rows; i++)
{
Vec<T, 2> v = *(Vec<T, 2>*)r.ptr(i);
if (fabs(re - v[0]) < 1e-6 && fabs(im - v[1]) < 1e-6)
{
v[0] = std::numeric_limits<T>::quiet_NaN();
v[1] = std::numeric_limits<T>::quiet_NaN();
return;
}
}
GTEST_NONFATAL_FAILURE_("Can't find root") << "(" << re << ", " << im << ")";
}
TEST(Core_SolvePoly, regression_5599)
{
// x^4 - x^2 = 0, roots: 1, -1, 0, 0
cv::Mat coefs = (cv::Mat_<float>(1,5) << 0, 0, -1, 0, 1 );
{
cv::Mat r;
double prec;
prec = cv::solvePoly(coefs, r);
EXPECT_LE(prec, 1e-6);
EXPECT_EQ(4u, r.total());
//std::cout << "Preciseness = " << prec << std::endl;
//std::cout << "roots:\n" << r << "\n" << std::endl;
ASSERT_EQ(CV_32FC2, r.type());
checkRoot<float>(r, 1, 0);
checkRoot<float>(r, -1, 0);
checkRoot<float>(r, 0, 0);
checkRoot<float>(r, 0, 0);
}
// x^2 - 2x + 1 = 0, roots: 1, 1
coefs = (cv::Mat_<float>(1,3) << 1, -2, 1 );
{
cv::Mat r;
double prec;
prec = cv::solvePoly(coefs, r);
EXPECT_LE(prec, 1e-6);
EXPECT_EQ(2u, r.total());
//std::cout << "Preciseness = " << prec << std::endl;
//std::cout << "roots:\n" << r << "\n" << std::endl;
ASSERT_EQ(CV_32FC2, r.type());
checkRoot<float>(r, 1, 0);
checkRoot<float>(r, 1, 0);
}
}
class Core_PhaseTest : public cvtest::BaseTest
{
int t;
public:
Core_PhaseTest(int t_) : t(t_) {}
~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, t),
y(axisCount + 1, dim, t);
Mat anglesInDegrees(axisCount + 1, dim, t);
// 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);
}
};
TEST(Core_CheckRange_Empty, accuracy)
{
cv::Mat m;
ASSERT_TRUE( cv::checkRange(m) );
}
TEST(Core_CheckRange_INT_MAX, accuracy)
{
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_MAX1, accuracy)
{
cv::Mat m(3, 3, CV_32SC1, cv::Scalar(INT_MAX));
ASSERT_TRUE( cv::checkRange(m, true, 0, 0, (float)((double)INT_MAX+1.0f)) );
ASSERT_TRUE( cv::checkRange(m) );
}
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, 10, 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, 2);
ASSERT_EQ(bad_pt.y, 1);
}
TYPED_TEST_P(Core_CheckRange, Negative3CN)
{
double min_bound = 4.5;
double max_bound = 16.0;
TypeParam data[] = { 5, 6, 7, 10, 11, 12, 13, 14, 15,
10, 11, 12, 10, 11, 12, 2, 5, 6,
8, 8, 8, 12, 12, 12, 14, 14, 14};
cv::Mat src = cv::Mat(3,3, CV_MAKETYPE(cv::DataDepth<TypeParam>::value, 3), data);
cv::Point bad_pt(0, 0);
ASSERT_FALSE(checkRange(src, true, &bad_pt, min_bound, max_bound));
ASSERT_EQ(bad_pt.x, 2);
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) );
}
TEST(Core_CheckRange, NaN)
{
float data[] = { 5, 6, 7, 10, 11, 12, 13, 14, 15,
10, 11, 12, 10, 11, 12, 5, 5, std::numeric_limits<float>::quiet_NaN(),
8, 8, 8, 12, 12, 12, 14, 14, 14};
cv::Mat src = cv::Mat(3,3, CV_32FC3, data);
cv::Point bad_pt(0, 0);
ASSERT_FALSE(checkRange(src, true, &bad_pt));
ASSERT_EQ(bad_pt.x, 2);
ASSERT_EQ(bad_pt.y, 1);
}
TEST(Core_CheckRange, Inf)
{
float data[] = { 5, 6, 7, 10, 11, 12, 13, 14, 15,
10, 11, 12, 10, 11, 12, 5, 5, std::numeric_limits<float>::infinity(),
8, 8, 8, 12, 12, 12, 14, 14, 14};
cv::Mat src = cv::Mat(3,3, CV_32FC3, data);
cv::Point bad_pt(0, 0);
ASSERT_FALSE(checkRange(src, true, &bad_pt));
ASSERT_EQ(bad_pt.x, 2);
ASSERT_EQ(bad_pt.y, 1);
}
TEST(Core_CheckRange, Inf_Minus)
{
float data[] = { 5, 6, 7, 10, 11, 12, 13, 14, 15,
10, 11, 12, 10, 11, 12, 5, 5, -std::numeric_limits<float>::infinity(),
8, 8, 8, 12, 12, 12, 14, 14, 14};
cv::Mat src = cv::Mat(3,3, CV_32FC3, data);
cv::Point bad_pt(0, 0);
ASSERT_FALSE(checkRange(src, true, &bad_pt));
ASSERT_EQ(bad_pt.x, 2);
ASSERT_EQ(bad_pt.y, 1);
}
REGISTER_TYPED_TEST_CASE_P(Core_CheckRange, Negative, Negative3CN, 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, NORM_INF), 0.1 );
cv::invert(b, c, cv::DECOMP_SVD); //std::cout << b*c << std::endl;
ASSERT_LT( cvtest::norm(b*c, i, NORM_INF), 0.1 );
cv::invert(b, c, cv::DECOMP_CHOLESKY); //std::cout << b*c << std::endl;
ASSERT_LT( cvtest::norm(b*c, i, NORM_INF), 0.1 );
}
/////////////////////////////////////////////////////////////////////////////////////////////////////
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_TransformLarge, accuracy) { Core_TransformLargeTest 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_Trace, accuracy) { Core_TraceTest test; test.safe_run(); }
TEST(Core_SolvePoly, accuracy) { Core_SolvePolyTest test; test.safe_run(); }
TEST(Core_Phase, accuracy32f) { Core_PhaseTest test(CV_32FC1); test.safe_run(); }
TEST(Core_Phase, accuracy64f) { Core_PhaseTest test(CV_64FC1); 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)
{
RNG& rng = cv::theRNG();
int i, iter = 0, N = 0, N0 = 0, K = 0, dims = 0;
Mat labels;
{
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(2, MAX_POINTS+1);
N0 = rng.uniform(1, MAX(N/10, 2));
K = rng.uniform(1, N+1);
Mat centers;
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, 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, centers);
}
ASSERT_EQ(centers.rows, K);
ASSERT_EQ(labels.rows, N);
Mat hist(K, 1, CV_32S, Scalar(0));
for( i = 0; i < N; i++ )
{
int l = labels.at<int>(i);
ASSERT_GE(l, 0);
ASSERT_LT(l, K);
hist.at<int>(l)++;
}
for( i = 0; i < K; i++ )
ASSERT_GT(hist.at<int>(i), 0);
}
}
}
};
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(Core_KMeans, compactness)
{
const int N = 1024;
const int attempts = 4;
const TermCriteria crit = TermCriteria(TermCriteria::COUNT, 5, 0); // low number of iterations
cvtest::TS& ts = *cvtest::TS::ptr();
for (int K = 1; K <= N; K *= 2)
{
Mat data(N, 1, CV_32FC2);
cvtest::randUni(ts.get_rng(), data, Scalar(-200, -200), Scalar(200, 200));
Mat labels, centers;
double compactness = kmeans(data, K, labels, crit, attempts, KMEANS_PP_CENTERS, centers);
centers = centers.reshape(2);
EXPECT_EQ(labels.rows, N);
EXPECT_EQ(centers.rows, K);
EXPECT_GE(compactness, 0.0);
double expected = 0.0;
for (int i = 0; i < N; ++i)
{
int l = labels.at<int>(i);
Point2f d = data.at<Point2f>(i) - centers.at<Point2f>(l);
expected += d.x * d.x + d.y * d.y;
}
EXPECT_NEAR(expected, compactness, expected * 1e-8);
if (K == N)
{
EXPECT_DOUBLE_EQ(compactness, 0.0);
}
}
}
TEST(Core_KMeans, bad_input)
{
const int N = 100;
const int attempts = 4;
const TermCriteria crit = TermCriteria(TermCriteria::COUNT, 5, 0); // low number of iterations
const int K = 3;
Mat data(N, 1, CV_32FC2);
cv::randu(data, Scalar(-200, -200), Scalar(200, 200));
{
SCOPED_TRACE("Huge value");
data.at<Vec2f>(10, 0) = Vec2f(1e20f, 0);
Mat labels, centers;
EXPECT_ANY_THROW(kmeans(data, K, labels, crit, attempts, KMEANS_PP_CENTERS, centers));
}
{
SCOPED_TRACE("Negative value");
data.at<Vec2f>(10, 0) = Vec2f(0, -1e20f);
Mat labels, centers;
EXPECT_ANY_THROW(kmeans(data, K, labels, crit, attempts, KMEANS_PP_CENTERS, centers));
}
{
SCOPED_TRACE("NaN");
data.at<Vec2f>(10, 0) = Vec2f(0, std::numeric_limits<float>::quiet_NaN());
Mat labels, centers;
EXPECT_ANY_THROW(kmeans(data, K, labels, crit, attempts, KMEANS_PP_CENTERS, centers));
}
}
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, 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);
}
}
}
}
TEST(Core_Cholesky, accuracy64f)
{
const int n = 5;
Mat A(n, n, CV_64F), refA;
Mat mean(1, 1, CV_64F);
*mean.ptr<double>() = 10.0;
Mat dev(1, 1, CV_64F);
*dev.ptr<double>() = 10.0;
RNG rng(10);
rng.fill(A, RNG::NORMAL, mean, dev);
A = A*A.t();
A.copyTo(refA);
Cholesky(A.ptr<double>(), A.step, n, NULL, 0, 0);
for (int i = 0; i < A.rows; i++)
for (int j = i + 1; j < A.cols; j++)
A.at<double>(i, j) = 0.0;
EXPECT_LE(cvtest::norm(refA, A*A.t(), CV_RELATIVE_L2), FLT_EPSILON);
}
TEST(Core_QR_Solver, accuracy64f)
{
int m = 20, n = 18;
Mat A(m, m, CV_64F);
Mat B(m, n, CV_64F);
Mat mean(1, 1, CV_64F);
*mean.ptr<double>() = 10.0;
Mat dev(1, 1, CV_64F);
*dev.ptr<double>() = 10.0;
RNG rng(10);
rng.fill(A, RNG::NORMAL, mean, dev);
rng.fill(B, RNG::NORMAL, mean, dev);
A = A*A.t();
Mat solutionQR;
//solve system with square matrix
solve(A, B, solutionQR, DECOMP_QR);
EXPECT_LE(cvtest::norm(A*solutionQR, B, CV_RELATIVE_L2), FLT_EPSILON);
A = Mat(m, n, CV_64F);
B = Mat(m, n, CV_64F);
rng.fill(A, RNG::NORMAL, mean, dev);
rng.fill(B, RNG::NORMAL, mean, dev);
//solve normal system
solve(A, B, solutionQR, DECOMP_QR | DECOMP_NORMAL);
EXPECT_LE(cvtest::norm(A.t()*(A*solutionQR), A.t()*B, CV_RELATIVE_L2), FLT_EPSILON);
//solve overdeterminated system as a least squares problem
Mat solutionSVD;
solve(A, B, solutionQR, DECOMP_QR);
solve(A, B, solutionSVD, DECOMP_SVD);
EXPECT_LE(cvtest::norm(solutionQR, solutionSVD, CV_RELATIVE_L2), FLT_EPSILON);
//solve system with singular matrix
A = Mat(10, 10, CV_64F);
B = Mat(10, 1, CV_64F);
rng.fill(A, RNG::NORMAL, mean, dev);
rng.fill(B, RNG::NORMAL, mean, dev);
for (int i = 0; i < A.cols; i++)
A.at<double>(0, i) = A.at<double>(1, i);
ASSERT_FALSE(solve(A, B, solutionQR, DECOMP_QR));
}
TEST(Core_Solve, regression_11888)
{
cv::Matx<float, 3, 2> A(
2, 1,
3, 1,
6, 1
);
cv::Vec<float, 3> b(4, 5, 7);
cv::Matx<float, 2, 1> xQR = A.solve(b, DECOMP_QR);
cv::Matx<float, 2, 1> xSVD = A.solve(b, DECOMP_SVD);
EXPECT_LE(cvtest::norm(xQR, xSVD, NORM_L2 | NORM_RELATIVE), 0.001);
cv::Matx<float, 2, 3> iA = A.inv(DECOMP_SVD);
EXPECT_LE(cvtest::norm(iA*A, Matx<float, 2, 2>::eye(), NORM_L2), 1e-3);
EXPECT_ANY_THROW({
/*cv::Matx<float, 2, 1> xLU =*/ A.solve(b, DECOMP_LU);
std::cout << "FATAL ERROR" << std::endl;
});
}
TEST(Core_Solve, Matx_2_2)
{
cv::Matx<float, 2, 2> A(
2, 1,
1, 1
);
cv::Vec<float, 2> b(4, 5);
cv::Matx<float, 2, 1> xLU = A.solve(b, DECOMP_LU);
cv::Matx<float, 2, 1> xQR = A.solve(b, DECOMP_QR);
cv::Matx<float, 2, 1> xSVD = A.solve(b, DECOMP_SVD);
EXPECT_LE(cvtest::norm(xQR, xSVD, NORM_L2 | NORM_RELATIVE), 1e-3);
EXPECT_LE(cvtest::norm(xQR, xLU, NORM_L2 | NORM_RELATIVE), 1e-3);
cv::Matx<float, 2, 2> iA = A.inv(DECOMP_SVD);
EXPECT_LE(cvtest::norm(iA*A, Matx<float, 2, 2>::eye(), NORM_L2), 1e-3);
}
TEST(Core_Solve, Matx_3_3)
{
cv::Matx<float, 3, 3> A(
2, 1, 0,
0, 1, 1,
1, 0, 1
);
cv::Vec<float, 3> b(4, 5, 6);
cv::Matx<float, 3, 1> xLU = A.solve(b, DECOMP_LU);
cv::Matx<float, 3, 1> xQR = A.solve(b, DECOMP_QR);
cv::Matx<float, 3, 1> xSVD = A.solve(b, DECOMP_SVD);
EXPECT_LE(cvtest::norm(xQR, xSVD, NORM_L2 | NORM_RELATIVE), 1e-3);
EXPECT_LE(cvtest::norm(xQR, xLU, NORM_L2 | NORM_RELATIVE), 1e-3);
cv::Matx<float, 3, 3> iA = A.inv(DECOMP_SVD);
EXPECT_LE(cvtest::norm(iA*A, Matx<float, 3, 3>::eye(), NORM_L2), 1e-3);
}
TEST(Core_Solve, Matx_4_4)
{
cv::Matx<float, 4, 4> A(
2, 1, 0, 4,
0, 1, 1, 3,
1, 0, 1, 2,
2, 2, 0, 1
);
cv::Vec<float, 4> b(4, 5, 6, 7);
cv::Matx<float, 4, 1> xLU = A.solve(b, DECOMP_LU);
cv::Matx<float, 4, 1> xQR = A.solve(b, DECOMP_QR);
cv::Matx<float, 4, 1> xSVD = A.solve(b, DECOMP_SVD);
EXPECT_LE(cvtest::norm(xQR, xSVD, NORM_L2 | NORM_RELATIVE), 1e-3);
EXPECT_LE(cvtest::norm(xQR, xLU, NORM_L2 | NORM_RELATIVE), 1e-3);
cv::Matx<float, 4, 4> iA = A.inv(DECOMP_SVD);
EXPECT_LE(cvtest::norm(iA*A, Matx<float, 4, 4>::eye(), NORM_L2), 1e-3);
}
softdouble naiveExp(softdouble x)
{
int exponent = x.getExp();
int sign = x.getSign() ? -1 : 1;
if(sign < 0 && exponent >= 10) return softdouble::inf();
softdouble mantissa = x.getFrac();
//Taylor series for mantissa
uint64 fac[20] = {1, 2, 6, 24, 120, 720, 5040, 40320, 362880, 3628800,
39916800, 479001600, 6227020800, 87178291200, 1307674368000,
20922789888000, 355687428096000, 6402373705728000, 121645100408832000,
2432902008176640000};
softdouble sum = softdouble::one();
// 21! > (2 ** 64)
for(int i = 20; i > 0; i--)
sum += pow(mantissa, softdouble(i))/softdouble(fac[i-1]);
if(exponent >= 0)
{
exponent = (1 << exponent);
return pow(sum, softdouble(exponent*sign));
}
else
{
if(sign < 0) sum = softdouble::one()/sum;
exponent = -exponent;
for(int j = 0; j < exponent; j++)
sum = sqrt(sum);
return sum;
}
}
static float makeFP32(int sign, int exponent, int significand)
{
Cv32suf x;
x.u = (unsigned)(((sign & 1) << 31) | ((exponent&255) << 23) | (significand & 0x7fffff));
return x.f;
}
static float makeRandomFP32(RNG& rng, int sign, int exprange)
{
if( sign == -1 )
sign = rng() % 2;
int exponent = rng() % exprange;
int significand = rng() % (1 << 23);
return makeFP32(sign, exponent, significand);
}
TEST(Core_SoftFloat, exp32)
{
//special cases
EXPECT_TRUE(exp( softfloat::nan()).isNaN());
EXPECT_TRUE(exp( softfloat::inf()).isInf());
EXPECT_EQ (exp(-softfloat::inf()), softfloat::zero());
//ln(FLT_MAX) ~ 88.722
const softfloat ln_max(88.722f);
vector<softfloat> inputs;
RNG rng(0);
inputs.push_back(softfloat::zero());
inputs.push_back(softfloat::one());
inputs.push_back(softfloat::min());
for(int i = 0; i < 50000; i++)
{
float x = makeRandomFP32(rng, -1, 10+127 //bigger exponent will produce inf
);
if(softfloat(x) > ln_max)
x = rng.uniform(0.0f, (float)ln_max);
inputs.push_back(softfloat(x));
}
for(size_t i = 0; i < inputs.size(); i++)
{
softfloat x(inputs[i]);
softfloat y = exp(x);
ASSERT_TRUE(!y.isNaN());
ASSERT_TRUE(!y.isInf());
ASSERT_GE(y, softfloat::zero());
softfloat ygood = naiveExp(x);
softfloat diff = abs(ygood - y);
const softfloat eps = softfloat::eps();
if(diff > eps)
{
ASSERT_LE(diff/max(abs(y), abs(ygood)), eps);
}
}
}
TEST(Core_SoftFloat, exp64)
{
//special cases
EXPECT_TRUE(exp( softdouble::nan()).isNaN());
EXPECT_TRUE(exp( softdouble::inf()).isInf());
EXPECT_EQ (exp(-softdouble::inf()), softdouble::zero());
//ln(DBL_MAX) ~ 709.7827
const softdouble ln_max(709.7827);
vector<softdouble> inputs;
RNG rng(0);
inputs.push_back(softdouble::zero());
inputs.push_back(softdouble::one());
inputs.push_back(softdouble::min());
for(int i = 0; i < 50000; i++)
{
Cv64suf x;
uint64 sign = rng() % 2;
uint64 exponent = rng() % (10 + 1023); //bigger exponent will produce inf
uint64 mantissa = (((long long int)((unsigned int)(rng)) << 32 ) | (unsigned int)(rng)) & ((1LL << 52) - 1);
x.u = (sign << 63) | (exponent << 52) | mantissa;
if(softdouble(x.f) > ln_max)
x.f = rng.uniform(0.0, (double)ln_max);
inputs.push_back(softdouble(x.f));
}
for(size_t i = 0; i < inputs.size(); i++)
{
softdouble x(inputs[i]);
softdouble y = exp(x);
ASSERT_TRUE(!y.isNaN());
ASSERT_TRUE(!y.isInf());
ASSERT_GE(y, softdouble::zero());
softdouble ygood = naiveExp(x);
softdouble diff = abs(ygood - y);
const softdouble eps = softdouble::eps();
if(diff > eps)
{
ASSERT_LE(diff/max(abs(y), abs(ygood)), softdouble(8192)*eps);
}
}
}
TEST(Core_SoftFloat, log32)
{
const int nValues = 50000;
RNG rng(0);
//special cases
EXPECT_TRUE(log(softfloat::nan()).isNaN());
for(int i = 0; i < nValues; i++)
{
softfloat x32(makeRandomFP32(rng, 1, 255));
ASSERT_TRUE(log(x32).isNaN());
}
EXPECT_TRUE(log(softfloat::zero()).isInf());
vector<softfloat> inputs;
inputs.push_back(softfloat::one());
inputs.push_back(softfloat(exp(softfloat::one())));
inputs.push_back(softfloat::min());
inputs.push_back(softfloat::max());
for(int i = 0; i < nValues; i++)
{
inputs.push_back(softfloat(makeRandomFP32(rng, 0, 255)));
}
for(size_t i = 0; i < inputs.size(); i++)
{
softfloat x(inputs[i]);
softfloat y = log(x);
ASSERT_TRUE(!y.isNaN());
ASSERT_TRUE(!y.isInf());
softfloat ex = exp(y);
softfloat diff = abs(ex - x);
// 88 is approx estimate of max exp() argument
ASSERT_TRUE(!ex.isInf() || (y > softfloat(88)));
const softfloat eps2 = softfloat().setExp(-17);
if(!ex.isInf() && diff > softfloat::eps())
{
ASSERT_LT(diff/max(abs(ex), x), eps2);
}
}
}
TEST(Core_SoftFloat, log64)
{
const int nValues = 50000;
RNG rng(0);
//special cases
EXPECT_TRUE(log(softdouble::nan()).isNaN());
for(int i = 0; i < nValues; i++)
{
Cv64suf x;
uint64 sign = 1;
uint64 exponent = rng() % 2047;
uint64 mantissa = (((long long int)((unsigned int)(rng)) << 32 ) | (unsigned int)(rng)) & ((1LL << 52) - 1);
x.u = (sign << 63) | (exponent << 52) | mantissa;
softdouble x64(x.f);
ASSERT_TRUE(log(x64).isNaN());
}
EXPECT_TRUE(log(softdouble::zero()).isInf());
vector<softdouble> inputs;
inputs.push_back(softdouble::one());
inputs.push_back(exp(softdouble::one()));
inputs.push_back(softdouble::min());
inputs.push_back(softdouble::max());
for(int i = 0; i < nValues; i++)
{
Cv64suf x;
uint64 sign = 0;
uint64 exponent = rng() % 2047;
uint64 mantissa = (((long long int)((unsigned int)(rng)) << 32 ) | (unsigned int)(rng)) & ((1LL << 52) - 1);
x.u = (sign << 63) | (exponent << 52) | mantissa;
inputs.push_back(softdouble(x.f));
}
for(size_t i = 0; i < inputs.size(); i++)
{
softdouble x(inputs[i]);
softdouble y = log(x);
ASSERT_TRUE(!y.isNaN());
ASSERT_TRUE(!y.isInf());
softdouble ex = exp(y);
softdouble diff = abs(ex - x);
// 700 is approx estimate of max exp() argument
ASSERT_TRUE(!ex.isInf() || (y > softdouble(700)));
const softdouble eps2 = softdouble().setExp(-41);
if(!ex.isInf() && diff > softdouble::eps())
{
ASSERT_LT(diff/max(abs(ex), x), eps2);
}
}
}
TEST(Core_SoftFloat, cbrt32)
{
vector<softfloat> inputs;
RNG rng(0);
inputs.push_back(softfloat::zero());
inputs.push_back(softfloat::one());
inputs.push_back(softfloat::max());
inputs.push_back(softfloat::min());
for(int i = 0; i < 50000; i++)
{
inputs.push_back(softfloat(makeRandomFP32(rng, -1, 255)));
}
for(size_t i = 0; i < inputs.size(); i++)
{
softfloat x(inputs[i]);
softfloat y = cbrt(x);
ASSERT_TRUE(!y.isNaN());
ASSERT_TRUE(!y.isInf());
softfloat cube = y*y*y;
softfloat diff = abs(x - cube);
const softfloat eps = softfloat::eps();
if(diff > eps)
{
ASSERT_LT(diff/max(abs(x), abs(cube)), softfloat(4)*eps);
}
}
}
TEST(Core_SoftFloat, pow32)
{
const softfloat zero = softfloat::zero(), one = softfloat::one();
const softfloat inf = softfloat::inf(), nan = softfloat::nan();
const size_t nValues = 5000;
RNG rng(0);
//x ** nan == nan
for(size_t i = 0; i < nValues; i++)
{
Cv32suf x;
x.u = rng();
ASSERT_TRUE(pow(softfloat(x.f), nan).isNaN());
}
//x ** inf check
for(size_t i = 0; i < nValues; i++)
{
Cv32suf x;
x.u = rng();
softfloat x32(x.f);
softfloat ax = abs(x32);
if(x32.isNaN())
{
ASSERT_TRUE(pow(x32, inf).isNaN());
}
if(ax > one)
{
ASSERT_TRUE(pow(x32, inf).isInf());
ASSERT_EQ (pow(x32, -inf), zero);
}
if(ax < one && ax > zero)
{
ASSERT_TRUE(pow(x32, -inf).isInf());
ASSERT_EQ (pow(x32, inf), zero);
}
}
//+-1 ** inf
EXPECT_TRUE(pow( one, inf).isNaN());
EXPECT_TRUE(pow(-one, inf).isNaN());
// x ** 0 == 1
for(size_t i = 0; i < nValues; i++)
{
Cv32suf x;
x.u = rng();
ASSERT_EQ(pow(softfloat(x.f), zero), one);
}
// x ** 1 == x
for(size_t i = 0; i < nValues; i++)
{
Cv32suf x;
x.u = rng();
softfloat x32(x.f);
softfloat val = pow(x32, one);
// don't compare val and x32 directly because x != x if x is nan
ASSERT_EQ(val.v, x32.v);
}
// nan ** y == nan, if y != 0
for(size_t i = 0; i < nValues; i++)
{
unsigned u = rng();
softfloat x32 = softfloat::fromRaw(u);
x32 = (x32 != softfloat::zero()) ? x32 : softfloat::min();
ASSERT_TRUE(pow(nan, x32).isNaN());
}
// nan ** 0 == 1
EXPECT_EQ(pow(nan, zero), one);
// inf ** y == 0, if y < 0
// inf ** y == inf, if y > 0
for(size_t i = 0; i < nValues; i++)
{
float x = makeRandomFP32(rng, 0, 255);
softfloat x32 = softfloat(x);
ASSERT_TRUE(pow( inf, x32).isInf());
ASSERT_TRUE(pow(-inf, x32).isInf());
ASSERT_EQ(pow( inf, -x32), zero);
ASSERT_EQ(pow(-inf, -x32), zero);
}
// x ** y == (-x) ** y, if y % 2 == 0
// x ** y == - (-x) ** y, if y % 2 == 1
// x ** y == nan, if x < 0 and y is not integer
for(size_t i = 0; i < nValues; i++)
{
softfloat x32(makeRandomFP32(rng, 1, 255));
softfloat y32(makeRandomFP32(rng, -1, 23+127 //bigger exponent produces integer numbers only
));
int yi = cvRound(y32);
if(y32 != softfloat(yi))
ASSERT_TRUE(pow(x32, y32).isNaN());
else if(yi % 2)
ASSERT_EQ(pow(-x32, y32), -pow(x32, y32));
else
ASSERT_EQ(pow(-x32, y32), pow(x32, y32));
}
// (0 ** 0) == 1
EXPECT_EQ(pow(zero, zero), one);
// 0 ** y == inf, if y < 0
// 0 ** y == 0, if y > 0
for(size_t i = 0; i < nValues; i++)
{
softfloat x32(makeRandomFP32(rng, 0, 255));
ASSERT_TRUE(pow(zero, -x32).isInf());
if(x32 != one)
{
ASSERT_EQ(pow(zero, x32), zero);
}
}
}
TEST(Core_SoftFloat, pow64)
{
const softdouble zero = softdouble::zero(), one = softdouble::one();
const softdouble inf = softdouble::inf(), nan = softdouble::nan();
const size_t nValues = 5000;
RNG rng(0);
//x ** nan == nan
for(size_t i = 0; i < nValues; i++)
{
Cv64suf x;
x.u = ((long long int)((unsigned int)(rng)) << 32 ) | (unsigned int)(rng);
ASSERT_TRUE(pow(softdouble(x.f), nan).isNaN());
}
//x ** inf check
for(size_t i = 0; i < nValues; i++)
{
Cv64suf x;
x.u = ((long long int)((unsigned int)(rng)) << 32 ) | (unsigned int)(rng);
softdouble x64(x.f);
softdouble ax = abs(x64);
if(x64.isNaN())
{
ASSERT_TRUE(pow(x64, inf).isNaN());
}
if(ax > one)
{
ASSERT_TRUE(pow(x64, inf).isInf());
ASSERT_EQ(pow(x64, -inf), zero);
}
if(ax < one && ax > zero)
{
ASSERT_TRUE(pow(x64, -inf).isInf());
ASSERT_EQ(pow(x64, inf), zero);
}
}
//+-1 ** inf
EXPECT_TRUE(pow( one, inf).isNaN());
EXPECT_TRUE(pow(-one, inf).isNaN());
// x ** 0 == 1
for(size_t i = 0; i < nValues; i++)
{
Cv64suf x;
x.u = ((long long int)((unsigned int)(rng)) << 32 ) | (unsigned int)(rng);
ASSERT_EQ(pow(softdouble(x.f), zero), one);
}
// x ** 1 == x
for(size_t i = 0; i < nValues; i++)
{
Cv64suf x;
x.u = ((long long int)((unsigned int)(rng)) << 32 ) | (unsigned int)(rng);
softdouble x64(x.f);
softdouble val = pow(x64, one);
// don't compare val and x64 directly because x != x if x is nan
ASSERT_EQ(val.v, x64.v);
}
// nan ** y == nan, if y != 0
for(size_t i = 0; i < nValues; i++)
{
uint64 u = ((long long int)((unsigned int)(rng)) << 32 ) | (unsigned int)(rng);
softdouble x64 = softdouble::fromRaw(u);
x64 = (x64 != softdouble::zero()) ? x64 : softdouble::min();
ASSERT_TRUE(pow(nan, x64).isNaN());
}
// nan ** 0 == 1
EXPECT_EQ(pow(nan, zero), one);
// inf ** y == 0, if y < 0
// inf ** y == inf, if y > 0
for(size_t i = 0; i < nValues; i++)
{
Cv64suf x;
uint64 sign = 0;
uint64 exponent = rng() % 2047;
uint64 mantissa = (((long long int)((unsigned int)(rng)) << 32 ) | (unsigned int)(rng)) & ((1LL << 52) - 1);
x.u = (sign << 63) | (exponent << 52) | mantissa;
softdouble x64(x.f);
ASSERT_TRUE(pow( inf, x64).isInf());
ASSERT_TRUE(pow(-inf, x64).isInf());
ASSERT_EQ(pow( inf, -x64), zero);
ASSERT_EQ(pow(-inf, -x64), zero);
}
// x ** y == (-x) ** y, if y % 2 == 0
// x ** y == - (-x) ** y, if y % 2 == 1
// x ** y == nan, if x < 0 and y is not integer
for(size_t i = 0; i < nValues; i++)
{
Cv64suf x;
uint64 sign = 1;
uint64 exponent = rng() % 2047;
uint64 mantissa = (((long long int)((unsigned int)(rng)) << 32 ) | (unsigned int)(rng)) & ((1LL << 52) - 1);
x.u = (sign << 63) | (exponent << 52) | mantissa;
softdouble x64(x.f);
Cv64suf y;
sign = rng() % 2;
//bigger exponent produces integer numbers only
//exponent = rng() % (52 + 1023);
//bigger exponent is too big
exponent = rng() % (23 + 1023);
mantissa = (((long long int)((unsigned int)(rng)) << 32 ) | (unsigned int)(rng)) & ((1LL << 52) - 1);
y.u = (sign << 63) | (exponent << 52) | mantissa;
softdouble y64(y.f);
uint64 yi = cvRound(y64);
if(y64 != softdouble(yi))
ASSERT_TRUE(pow(x64, y64).isNaN());
else if(yi % 2)
ASSERT_EQ(pow(-x64, y64), -pow(x64, y64));
else
ASSERT_EQ(pow(-x64, y64), pow(x64, y64));
}
// (0 ** 0) == 1
EXPECT_EQ(pow(zero, zero), one);
// 0 ** y == inf, if y < 0
// 0 ** y == 0, if y > 0
for(size_t i = 0; i < nValues; i++)
{
Cv64suf x;
uint64 sign = 0;
uint64 exponent = rng() % 2047;
uint64 mantissa = (((long long int)((unsigned int)(rng)) << 32 ) | (unsigned int)(rng)) & ((1LL << 52) - 1);
x.u = (sign << 63) | (exponent << 52) | mantissa;
softdouble x64(x.f);
ASSERT_TRUE(pow(zero, -x64).isInf());
if(x64 != one)
{
ASSERT_EQ(pow(zero, x64), zero);
}
}
}
TEST(Core_SoftFloat, sincos64)
{
static const softdouble
two = softdouble(2), three = softdouble(3),
half = softdouble::one()/two,
zero = softdouble::zero(), one = softdouble::one(),
pi = softdouble::pi(), piby2 = pi/two, eps = softdouble::eps(),
sin45 = sqrt(two)/two, sin60 = sqrt(three)/two;
softdouble vstdAngles[] =
//x, sin(x), cos(x)
{
zero, zero, one,
pi/softdouble(6), half, sin60,
pi/softdouble(4), sin45, sin45,
pi/three, sin60, half,
};
vector<softdouble> stdAngles;
stdAngles.assign(vstdAngles, vstdAngles + 3*4);
static const softdouble stdEps = eps.setExp(-39);
const size_t nStdValues = 5000;
for(size_t i = 0; i < nStdValues; i++)
{
for(size_t k = 0; k < stdAngles.size()/3; k++)
{
softdouble x = stdAngles[k*3] + pi*softdouble(2*((int)i-(int)nStdValues/2));
softdouble s = stdAngles[k*3+1];
softdouble c = stdAngles[k*3+2];
ASSERT_LE(abs(sin(x) - s), stdEps);
ASSERT_LE(abs(cos(x) - c), stdEps);
//sin(x+pi/2) = cos(x)
ASSERT_LE(abs(sin(x + piby2) - c), stdEps);
//sin(x+pi) = -sin(x)
ASSERT_LE(abs(sin(x + pi) + s), stdEps);
//cos(x+pi/2) = -sin(x)
ASSERT_LE(abs(cos(x+piby2) + s), stdEps);
//cos(x+pi) = -cos(x)
ASSERT_LE(abs(cos(x+pi) + c), stdEps);
}
}
// sin(x) is NaN iff x ix NaN or Inf
EXPECT_TRUE(sin(softdouble::inf()).isNaN());
EXPECT_TRUE(sin(softdouble::nan()).isNaN());
vector<int> exponents;
exponents.push_back(0);
for(int i = 1; i < 52; i++)
{
exponents.push_back( i);
exponents.push_back(-i);
}
exponents.push_back(256); exponents.push_back(-256);
exponents.push_back(512); exponents.push_back(-512);
exponents.push_back(1022); exponents.push_back(-1022);
vector<softdouble> inputs;
RNG rng(0);
static const size_t nValues = 1 << 18;
for(size_t i = 0; i < nValues; i++)
{
softdouble x;
uint64 mantissa = (((long long int)((unsigned int)(rng)) << 32 ) | (unsigned int)(rng)) & ((1LL << 52) - 1);
x.v = mantissa;
x = x.setSign((rng() % 2) != 0);
x = x.setExp(exponents[rng() % exponents.size()]);
inputs.push_back(x);
}
for(size_t i = 0; i < inputs.size(); i++)
{
softdouble x = inputs[i];
int xexp = x.getExp();
softdouble randEps = eps.setExp(std::max(xexp-52, -46));
softdouble sx = sin(x);
softdouble cx = cos(x);
ASSERT_FALSE(sx.isInf()); ASSERT_FALSE(cx.isInf());
ASSERT_FALSE(sx.isNaN()); ASSERT_FALSE(cx.isNaN());
ASSERT_LE(abs(sx), one); ASSERT_LE(abs(cx), one);
ASSERT_LE(abs((sx*sx + cx*cx) - one), eps);
ASSERT_LE(abs(sin(x*two) - two*sx*cx), randEps);
ASSERT_LE(abs(cos(x*two) - (cx*cx - sx*sx)), randEps);
ASSERT_LE(abs(sin(-x) + sx), eps);
ASSERT_LE(abs(cos(-x) - cx), eps);
ASSERT_LE(abs(sin(x + piby2) - cx), randEps);
ASSERT_LE(abs(sin(x + pi) + sx), randEps);
ASSERT_LE(abs(cos(x+piby2) + sx), randEps);
ASSERT_LE(abs(cos(x+pi) + cx), randEps);
}
}
TEST(Core_SoftFloat, CvRound)
{
struct
{
uint64_t inVal;
int64_t out64;
int32_t out32;
} _values[] =
{
{ 0x0123456789abcdefU, 0, 0 }, // 3.51270056408850369812238561681E-303
{ 0x0000000000000000U, 0, 0 }, // 0
{ 0x8000000000000000U, 0, 0 }, // -0
{ 0x000123456789abcdU, 0, 0 }, // 1.5822747438273385725152200433E-309
{ 0x800123456789abcdU, 0, 0 }, // -1.5822747438273385725152200433E-309
{ 0x7ff0000000000000U, INT64_MAX, INT32_MAX }, // +inf
{ 0xfff0000000000000U, INT64_MIN, INT32_MIN }, // -inf
{ 0x7ff0000000000001U, INT64_MAX, INT32_MAX }, // nan(casts to maximum value)
{ 0xfff0000000000001U, INT64_MAX, INT32_MAX }, // nan(casts to maximum value)
{ 0x7ffa5a5a5a5a5a5aU, INT64_MAX, INT32_MAX }, // nan(casts to maximum value)
{ 0xfffa5a5a5a5a5a5aU, INT64_MAX, INT32_MAX }, // nan(casts to maximum value)
{ 0x7fe123456789abcdU, INT64_MAX, INT32_MAX }, // 9.627645455595956656406699747E307
{ 0xffe123456789abcdU, INT64_MIN, INT32_MIN }, // -9.627645455595956656406699747E307
{ 0x43ffffffffffffffU, INT64_MAX, INT32_MAX }, // (2^53-1)*2^12
{ 0xc3ffffffffffffffU, INT64_MIN, INT32_MIN }, // -(2^53-1)*2^12
{ 0x43f0000000000000U, INT64_MAX, INT32_MAX }, // 2^64
{ 0xc3f0000000000000U, INT64_MIN, INT32_MIN }, // -2^64
{ 0x43efffffffffffffU, INT64_MAX, INT32_MAX }, // (2^53-1)*2^11
{ 0xc3efffffffffffffU, INT64_MIN, INT32_MIN }, // -(2^53-1)*2^11
{ 0x43e0000000000000U, INT64_MAX, INT32_MAX }, // 2^63
{ 0xc3e0000000000000U, -0x7fffffffffffffff-1, INT32_MIN }, // -2^63
{ 0x43dfffffffffffffU, 0x7ffffffffffffc00, INT32_MAX }, // (2^53-1)*2^10
{ 0xc3dfffffffffffffU, -0x7ffffffffffffc00, INT32_MIN }, // -(2^53-1)*2^10
{ 0x433fffffffffffffU, 0x1fffffffffffff, INT32_MAX }, // (2^53-1)
{ 0xc33fffffffffffffU, -0x1fffffffffffff, INT32_MIN }, // -(2^53-1)
{ 0x432fffffffffffffU, 0x10000000000000, INT32_MAX }, // (2^52-1) + 0.5
{ 0xc32fffffffffffffU, -0x10000000000000, INT32_MIN }, // -(2^52-1) - 0.5
{ 0x431fffffffffffffU, 0x8000000000000, INT32_MAX }, // (2^51-1) + 0.75
{ 0xc31fffffffffffffU, -0x8000000000000, INT32_MIN }, // -(2^51-1) - 0.75
{ 0x431ffffffffffffeU, 0x8000000000000, INT32_MAX }, // (2^51-1) + 0.5
{ 0xc31ffffffffffffeU, -0x8000000000000, INT32_MIN }, // -(2^51-1) - 0.5
{ 0x431ffffffffffffdU, 0x7ffffffffffff, INT32_MAX }, // (2^51-1) + 0.25
{ 0xc31ffffffffffffdU, -0x7ffffffffffff, INT32_MIN }, // -(2^51-1) - 0.25
{ 0x41f0000000000000U, 0x100000000, INT32_MAX }, // 2^32 = 4294967296
{ 0xc1f0000000000000U, -0x100000000, INT32_MIN }, // -2^32 = -4294967296
{ 0x41efffffffffffffU, 0x100000000, INT32_MAX }, // 4294967295.99999952316284179688
{ 0xc1efffffffffffffU, -0x100000000, INT32_MIN }, // -4294967295.99999952316284179688
{ 0x41effffffff00000U, 0x100000000, INT32_MAX }, // (2^32-1) + 0.5 = 4294967295.5
{ 0xc1effffffff00000U, -0x100000000, INT32_MIN }, // -(2^32-1) - 0.5 = -4294967295.5
{ 0x41efffffffe00000U, 0xffffffffll, INT32_MAX }, // (2^32-1)
{ 0xc1efffffffe00000U, -0xffffffffll, INT32_MIN }, // -(2^32-1)
{ 0x41e0000000000000U, 0x80000000ll, INT32_MAX }, // 2^31 = 2147483648
{ 0xc1e0000000000000U, -0x80000000ll, -0x7fffffff-1 }, // -2^31 = -2147483648
{ 0x41dfffffffffffffU, 0x80000000ll, INT32_MAX }, // 2147483647.99999976158142089844
{ 0xc1dfffffffffffffU, -0x80000000ll, -0x7fffffff-1 }, // -2147483647.99999976158142089844
{ 0x41dffffffff00000U, 0x80000000ll, INT32_MAX }, // (2^31-1) + 0.75
{ 0xc1dffffffff00000U, -0x80000000ll, -0x7fffffff-1 }, // -(2^31-1) - 0.75
{ 0x41dfffffffe00001U, 0x80000000ll, INT32_MAX }, // (2^31-1) + 0.5 + 2^-22
{ 0xc1dfffffffe00001U, -0x80000000ll, -0x7fffffff-1 }, // -(2^31-1) - 0.5 - 2^-22
{ 0x41dfffffffe00000U, 0x80000000ll, INT32_MAX }, // (2^31-1) + 0.5
{ 0xc1dfffffffe00000U, -0x80000000ll, -0x7fffffff-1 }, // -(2^31-1) - 0.5
{ 0x41dfffffffdfffffU, 0x7fffffff, 0x7fffffff }, // (2^31-1) + 0.5 - 2^-22
{ 0xc1dfffffffdfffffU, -0x7fffffff, -0x7fffffff }, // -(2^31-1) - 0.5 + 2^-22
{ 0x41dfffffffd00000U, 0x7fffffff, 0x7fffffff }, // (2^31-1) + 0.25
{ 0xc1dfffffffd00000U, -0x7fffffff, -0x7fffffff }, // -(2^31-1) - 0.25
{ 0x41dfffffffc00000U, 0x7fffffff, 0x7fffffff }, // (2^31-1)
{ 0xc1dfffffffc00000U, -0x7fffffff, -0x7fffffff }, // -(2^31-1)
{ 0x41d0000000000000U, 0x40000000, 0x40000000 }, // 2^30 = 2147483648
{ 0xc1d0000000000000U, -0x40000000, -0x40000000 }, // -2^30 = -2147483648
{ 0x4006000000000000U, 3, 3 }, // 2.75
{ 0xc006000000000000U, -3, -3 }, // -2.75
{ 0x4004000000000001U, 3, 3 }, // 2.5 + 2^-51
{ 0xc004000000000001U, -3, -3 }, // -2.5 - 2^-51
{ 0x4004000000000000U, 2, 2 }, // 2.5
{ 0xc004000000000000U, -2, -2 }, // -2.5
{ 0x4003ffffffffffffU, 2, 2 }, // 2.5 - 2^-51
{ 0xc003ffffffffffffU, -2, -2 }, // -2.5 + 2^-51
{ 0x4002000000000000U, 2, 2 }, // 2.25
{ 0xc002000000000000U, -2, -2 }, // -2.25
{ 0x3ffc000000000000U, 2, 2 }, // 1.75
{ 0xbffc000000000000U, -2, -2 }, // -1.75
{ 0x3ff8000000000001U, 2, 2 }, // 1.5 + 2^-52
{ 0xbff8000000000001U, -2, -2 }, // -1.5 - 2^-52
{ 0x3ff8000000000000U, 2, 2 }, // 1.5
{ 0xbff8000000000000U, -2, -2 }, // -1.5
{ 0x3ff7ffffffffffffU, 1, 1 }, // 1.5 - 2^-52
{ 0xbff7ffffffffffffU, -1, -1 }, // -1.5 + 2^-52
{ 0x3ff4000000000000U, 1, 1 }, // 1.25
{ 0xbff4000000000000U, -1, -1 }, // -1.25
{ 0x3fe8000000000000U, 1, 1 }, // 0.75
{ 0xbfe8000000000000U, -1, -1 }, // -0.75
{ 0x3fe0000000000001U, 1, 1 }, // 0.5 + 2^-53
{ 0xbfe0000000000001U, -1, -1 }, // -0.5 - 2^-53
{ 0x3fe0000000000000U, 0, 0 }, // 0.5
{ 0xbfe0000000000000U, 0, 0 }, // -0.5
{ 0x3fd8000000000000U, 0, 0 }, // 0.375
{ 0xbfd8000000000000U, 0, 0 }, // -0.375
{ 0x3fd0000000000000U, 0, 0 }, // 0.25
{ 0xbfd0000000000000U, 0, 0 }, // -0.25
{ 0x0ff123456789abcdU, 0, 0 }, // 6.89918601543515033558134828315E-232
{ 0x8ff123456789abcdU, 0, 0 } // -6.89918601543515033558134828315E-232
};
struct testvalues
{
softdouble inVal;
int64_t out64;
int32_t out32;
} *values = (testvalues*)_values;
for (int i = 0, maxi = sizeof(_values) / sizeof(_values[0]); i < maxi; i++)
{
EXPECT_EQ(values[i].out64, cvRound64(values[i].inVal));
EXPECT_EQ(values[i].out64, saturate_cast<int64_t>(values[i].inVal));
EXPECT_EQ((uint64_t)(values[i].out64), saturate_cast<uint64_t>(values[i].inVal));
EXPECT_EQ(values[i].out32, cvRound(values[i].inVal));
EXPECT_EQ(values[i].out32, saturate_cast<int32_t>(values[i].inVal));
EXPECT_EQ((uint32_t)(values[i].out32), saturate_cast<uint32_t>(values[i].inVal));
}
}
template<typename T>
static void checkRounding(T in, int outCeil, int outFloor)
{
EXPECT_EQ(outCeil,cvCeil(in));
EXPECT_EQ(outFloor,cvFloor(in));
/* cvRound is not expected to be IEEE compliant. The implementation
should round to one of the above. */
EXPECT_TRUE((cvRound(in) == outCeil) || (cvRound(in) == outFloor));
}
TEST(Core_FastMath, InlineRoundingOps)
{
struct
{
double in;
int outCeil;
int outFloor;
} values[] =
{
// Values are chosen to convert to binary float 32/64 exactly
{ 1.0, 1, 1 },
{ 1.5, 2, 1 },
{ -1.5, -1, -2}
};
for (int i = 0, maxi = sizeof(values) / sizeof(values[0]); i < maxi; i++)
{
checkRounding<double>(values[i].in, values[i].outCeil, values[i].outFloor);
checkRounding<float>((float)values[i].in, values[i].outCeil, values[i].outFloor);
}
}
TEST(Core_FastMath, InlineNaN)
{
EXPECT_EQ( cvIsNaN((float) NAN), 1);
EXPECT_EQ( cvIsNaN((float) -NAN), 1);
EXPECT_EQ( cvIsNaN(0.0f), 0);
EXPECT_EQ( cvIsNaN((double) NAN), 1);
EXPECT_EQ( cvIsNaN((double) -NAN), 1);
EXPECT_EQ( cvIsNaN(0.0), 0);
// Regression: check the +/-Inf cases
Cv64suf suf;
suf.u = 0x7FF0000000000000UL;
EXPECT_EQ( cvIsNaN(suf.f), 0);
suf.u = 0xFFF0000000000000UL;
EXPECT_EQ( cvIsNaN(suf.f), 0);
}
TEST(Core_FastMath, InlineIsInf)
{
// Assume HUGE_VAL is infinity. Strictly speaking, may not always be true.
EXPECT_EQ( cvIsInf((float) HUGE_VAL), 1);
EXPECT_EQ( cvIsInf((float) -HUGE_VAL), 1);
EXPECT_EQ( cvIsInf(0.0f), 0);
EXPECT_EQ( cvIsInf((double) HUGE_VAL), 1);
EXPECT_EQ( cvIsInf((double) -HUGE_VAL), 1);
EXPECT_EQ( cvIsInf(0.0), 0);
// Regression: check the cases of 0x7FF00000xxxxxxxx
Cv64suf suf;
suf.u = 0x7FF0000000000001UL;
EXPECT_EQ( cvIsInf(suf.f), 0);
suf.u = 0x7FF0000012345678UL;
EXPECT_EQ( cvIsInf(suf.f), 0);
}
TEST(Core_BFloat, CornerCases)
{
float data[] = {0.f, -0.f, 1.f, -1.f, expf(1.f), FLT_MAX, -FLT_MAX,
std::numeric_limits<float>::infinity(),
-std::numeric_limits<float>::infinity(),
std::numeric_limits<float>::quiet_NaN()
};
size_t n0 = sizeof(data)/sizeof(data[0]);
for (size_t i = 0; i < n0; i++) {
float x = data[i];
Cv32suf suf0, suf1;
suf0.f = x;
uint16_t x0 = (uint16_t)(suf0.u >> 16);
bfloat x1 = bfloat(x);
suf0.u = x0 << 16;
suf1.f = (float)x1;
//printf("%zu. orig = %f, restored (old) = %f, restored (new) = %f\n", i, x, suf0.f, suf1.f);
if (suf0.u != suf1.u) {
EXPECT_LE(fabs(suf1.f - x), fabs(suf0.f - x));
}
}
}
TEST(Core_BFloat, convert)
{
size_t N = 1 << 20;
std::vector<float> bigdata(N);
RNG& rng = theRNG();
int m_max = 1 << 24;
double m_scale = 1./m_max;
for (size_t i = 0; i < N; i++) {
double m = (m_max + rng.uniform(0, m_max))*m_scale;
double e = pow(2., rng.uniform(-127, 127));
double s = rng.uniform(0, 2)*2 - 1;
float x = (float)(s*m*e);
bigdata[i] = x;
}
double sum0 = 0, sqsum0 = 0, maxerr0 = 0, maxerr1 = 0, sum1 = 0, sqsum1 = 0;
for (size_t i = 0; i < N; i++) {
float x = bigdata[i];
Cv32suf suf0, suf1;
suf0.f = suf1.f = x;
uint16_t x0 = (uint16_t)(suf0.u >> 16);
bfloat x1 = bfloat(suf1.f);
suf0.u = x0 << 16;
suf1.f = (float)x1;
double err0 = fabs(x - suf0.f)/(fabs(x) + DBL_EPSILON);
double err1 = fabs(x - suf1.f)/(fabs(x) + DBL_EPSILON);
maxerr0 = std::max(maxerr0, err0);
maxerr1 = std::max(maxerr1, err1);
sum0 += err0;
sqsum0 += err0*err0;
sum1 += err1;
sqsum1 += err1*err1;
}
double mean0 = sum0/N;
double stddev0 = sqrt(std::max(sqsum0 - N*mean0*mean0, 0.)/(N-1));
double mean1 = sum1/N;
double stddev1 = sqrt(std::max(sqsum1 - N*mean1*mean1, 0.)/(N-1));
//if (maxerr1 > maxerr0 || mean1 > mean0 || stddev1 > stddev0)
{
printf("maxerr0 = %g, mean0 = %g, stddev0 = %g\nmaxerr1 = %g, mean1 = %g, stddev1 = %g\n",
maxerr0, mean0, stddev0, maxerr1, mean1, stddev1);
}
EXPECT_LE(maxerr1, maxerr0);
EXPECT_LE(mean1, mean0);
EXPECT_LE(stddev1, stddev0);
#if CV_SIMD || CV_SIMD_SCALABLE
//printf("checking vector part ...\n");
std::vector<bfloat> bfdata(N);
int vlanes = VTraits<v_float32>::vlanes();
for (size_t i = 0; i < N; i += vlanes)
{
v_float32 x = vx_load(&bigdata[i]);
v_pack_store(&bfdata[i], x);
}
int vecerr = 0;
for (size_t i = 0; i < N; i++) {
Cv32suf suf0, suf1;
suf0.f = (float)bfloat(bigdata[i]);
suf1.f = (float)bfdata[i];
vecerr += suf0.u != suf1.u;
}
EXPECT_EQ(0, vecerr);
#endif
}
}} // namespace
/* End of file. */
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