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opencv/modules/imgproc/test/test_histograms.cpp
kocheganovvm 086db9d6db Introduce CV_COMP_CHISQR_ALT, an alternative method to calculate ChiSquare Histogram comparison 
There's some disagreement about the correct formula.
has its supporters, however, for texture analysis, the newly introduced formula became
standard. The commit enables both uses without breaking backward compatibility.

First contributor of this commit was sperrholz.
2013-08-12 13:51:52 +04:00

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "test_precomp.hpp"
using namespace cv;
using namespace std;
class CV_BaseHistTest : public cvtest::BaseTest
{
public:
enum { MAX_HIST = 12 };
CV_BaseHistTest();
~CV_BaseHistTest();
void clear();
protected:
int read_params( CvFileStorage* fs );
void run_func(void);
int prepare_test_case( int test_case_idx );
int validate_test_results( int test_case_idx );
virtual void init_hist( int test_case_idx, int i );
virtual void get_hist_params( int test_case_idx );
virtual float** get_hist_ranges( int test_case_idx );
int max_log_size;
int max_cdims;
int cdims;
int dims[CV_MAX_DIM];
int total_size;
int hist_type;
int hist_count;
int uniform;
int gen_random_hist;
double gen_hist_max_val, gen_hist_sparse_nz_ratio;
int init_ranges;
int img_type;
int img_max_log_size;
double low, high, range_delta;
CvSize img_size;
vector<CvHistogram*> hist;
vector<float> _ranges;
vector<float*> ranges;
bool test_cpp;
};
CV_BaseHistTest::CV_BaseHistTest()
{
test_case_count = 100;
max_log_size = 20;
img_max_log_size = 8;
max_cdims = 6;
hist_count = 1;
init_ranges = 0;
gen_random_hist = 0;
gen_hist_max_val = 100;
test_cpp = false;
}
CV_BaseHistTest::~CV_BaseHistTest()
{
clear();
}
void CV_BaseHistTest::clear()
{
cvtest::BaseTest::clear();
for( size_t i = 0; i < hist.size(); i++ )
cvReleaseHist( &hist[i] );
}
int CV_BaseHistTest::read_params( CvFileStorage* fs )
{
int code = cvtest::BaseTest::read_params( fs );
if( code < 0 )
return code;
test_case_count = cvReadInt( find_param( fs, "struct_count" ), test_case_count );
max_log_size = cvReadInt( find_param( fs, "max_log_size" ), max_log_size );
max_log_size = cvtest::clipInt( max_log_size, 1, 20 );
img_max_log_size = cvReadInt( find_param( fs, "max_log_array_size" ), img_max_log_size );
img_max_log_size = cvtest::clipInt( img_max_log_size, 1, 9 );
max_cdims = cvReadInt( find_param( fs, "max_cdims" ), max_cdims );
max_cdims = cvtest::clipInt( max_cdims, 1, 6 );
return 0;
}
void CV_BaseHistTest::get_hist_params( int /*test_case_idx*/ )
{
RNG& rng = ts->get_rng();
int i, max_dim_size, max_ni_dim_size = 31;
double hist_size;
cdims = cvtest::randInt(rng) % max_cdims + 1;
hist_size = exp(cvtest::randReal(rng)*max_log_size*CV_LOG2);
max_dim_size = cvRound(pow(hist_size,1./cdims));
total_size = 1;
uniform = cvtest::randInt(rng) % 2;
hist_type = cvtest::randInt(rng) % 2 ? CV_HIST_SPARSE : CV_HIST_ARRAY;
for( i = 0; i < cdims; i++ )
{
dims[i] = cvtest::randInt(rng) % (max_dim_size + 2) + 2;
if( !uniform )
dims[i] = MIN(dims[i], max_ni_dim_size);
total_size *= dims[i];
}
img_type = cvtest::randInt(rng) % 2 ? CV_32F : CV_8U;
img_size.width = cvRound( exp(cvtest::randReal(rng) * img_max_log_size * CV_LOG2) );
img_size.height = cvRound( exp(cvtest::randReal(rng) * img_max_log_size * CV_LOG2) );
if( img_type < CV_32F )
{
low = cvtest::getMinVal(img_type);
high = cvtest::getMaxVal(img_type);
}
else
{
high = 1000;
low = -high;
}
range_delta = (cvtest::randInt(rng) % 2)*(high-low)*0.05;
}
float** CV_BaseHistTest::get_hist_ranges( int /*test_case_idx*/ )
{
double _low = low + range_delta, _high = high - range_delta;
if( !init_ranges )
return 0;
ranges.resize(cdims);
if( uniform )
{
_ranges.resize(cdims*2);
for( int i = 0; i < cdims; i++ )
{
_ranges[i*2] = (float)_low;
_ranges[i*2+1] = (float)_high;
ranges[i] = &_ranges[i*2];
}
}
else
{
int i, dims_sum = 0, ofs = 0;
for( i = 0; i < cdims; i++ )
dims_sum += dims[i] + 1;
_ranges.resize(dims_sum);
for( i = 0; i < cdims; i++ )
{
int j, n = dims[i];
// generate logarithmic scale
double delta, q, val;
for( j = 0; j < 10; j++ )
{
q = 1. + (j+1)*0.1;
if( (pow(q,(double)n)-1)/(q-1.) >= _high-_low )
break;
}
if( j == 0 )
{
delta = (_high-_low)/n;
q = 1.;
}
else
{
q = 1 + j*0.1;
delta = cvFloor((_high-_low)*(q-1)/(pow(q,(double)n) - 1));
delta = MAX(delta, 1.);
}
val = _low;
for( j = 0; j <= n; j++ )
{
_ranges[j+ofs] = (float)MIN(val,_high);
val += delta;
delta *= q;
}
ranges[i] = &_ranges[ofs];
ofs += n + 1;
}
}
return &ranges[0];
}
void CV_BaseHistTest::init_hist( int /*test_case_idx*/, int hist_i )
{
if( gen_random_hist )
{
RNG& rng = ts->get_rng();
if( hist_type == CV_HIST_ARRAY )
{
Mat h = cvarrToMat(hist[hist_i]->bins);
cvtest::randUni(rng, h, Scalar::all(0), Scalar::all(gen_hist_max_val) );
}
else
{
CvArr* arr = hist[hist_i]->bins;
int i, j, totalSize = 1, nz_count;
int idx[CV_MAX_DIM];
for( i = 0; i < cdims; i++ )
totalSize *= dims[i];
nz_count = cvtest::randInt(rng) % MAX( totalSize/4, 100 );
nz_count = MIN( nz_count, totalSize );
// a zero number of non-zero elements should be allowed
for( i = 0; i < nz_count; i++ )
{
for( j = 0; j < cdims; j++ )
idx[j] = cvtest::randInt(rng) % dims[j];
cvSetRealND(arr, idx, cvtest::randReal(rng)*gen_hist_max_val);
}
}
}
}
int CV_BaseHistTest::prepare_test_case( int test_case_idx )
{
int i;
float** r;
clear();
cvtest::BaseTest::prepare_test_case( test_case_idx );
get_hist_params( test_case_idx );
r = get_hist_ranges( test_case_idx );
hist.resize(hist_count);
for( i = 0; i < hist_count; i++ )
{
hist[i] = cvCreateHist( cdims, dims, hist_type, r, uniform );
init_hist( test_case_idx, i );
}
test_cpp = (cvtest::randInt(ts->get_rng()) % 2) != 0;
return 1;
}
void CV_BaseHistTest::run_func(void)
{
}
int CV_BaseHistTest::validate_test_results( int /*test_case_idx*/ )
{
return 0;
}
////////////// testing operation for reading/writing individual histogram bins //////////////
class CV_QueryHistTest : public CV_BaseHistTest
{
public:
CV_QueryHistTest();
~CV_QueryHistTest();
void clear();
protected:
void run_func(void);
int prepare_test_case( int test_case_idx );
int validate_test_results( int test_case_idx );
void init_hist( int test_case_idx, int i );
CvMat* indices;
CvMat* values;
CvMat* values0;
};
CV_QueryHistTest::CV_QueryHistTest()
{
hist_count = 1;
indices = 0;
values = 0;
values0 = 0;
}
CV_QueryHistTest::~CV_QueryHistTest()
{
clear();
}
void CV_QueryHistTest::clear()
{
cvReleaseMat( &indices );
cvReleaseMat( &values );
cvReleaseMat( &values0 );
CV_BaseHistTest::clear();
}
void CV_QueryHistTest::init_hist( int /*test_case_idx*/, int i )
{
if( hist_type == CV_HIST_ARRAY )
cvZero( hist[i]->bins );
}
int CV_QueryHistTest::prepare_test_case( int test_case_idx )
{
int code = CV_BaseHistTest::prepare_test_case( test_case_idx );
if( code > 0 )
{
int i, j, iters;
float default_value = 0.f;
RNG& rng = ts->get_rng();
CvMat* bit_mask = 0;
int* idx;
iters = (cvtest::randInt(rng) % MAX(total_size/10,100)) + 1;
iters = MIN( iters, total_size*9/10 + 1 );
indices = cvCreateMat( 1, iters*cdims, CV_32S );
values = cvCreateMat( 1, iters, CV_32F );
values0 = cvCreateMat( 1, iters, CV_32F );
idx = indices->data.i;
//printf( "total_size = %d, cdims = %d, iters = %d\n", total_size, cdims, iters );
bit_mask = cvCreateMat( 1, (total_size + 7)/8, CV_8U );
cvZero( bit_mask );
#define GET_BIT(n) (bit_mask->data.ptr[(n)/8] & (1 << ((n)&7)))
#define SET_BIT(n) bit_mask->data.ptr[(n)/8] |= (1 << ((n)&7))
// set random histogram bins' values to the linear indices of the bins
for( i = 0; i < iters; i++ )
{
int lin_idx = 0;
for( j = 0; j < cdims; j++ )
{
int t = cvtest::randInt(rng) % dims[j];
idx[i*cdims + j] = t;
lin_idx = lin_idx*dims[j] + t;
}
if( cvtest::randInt(rng) % 8 || GET_BIT(lin_idx) )
{
values0->data.fl[i] = (float)(lin_idx+1);
SET_BIT(lin_idx);
}
else
// some histogram bins will not be initialized intentionally,
// they should be equal to the default value
values0->data.fl[i] = default_value;
}
// do the second pass to make values0 consistent with bit_mask
for( i = 0; i < iters; i++ )
{
int lin_idx = 0;
for( j = 0; j < cdims; j++ )
lin_idx = lin_idx*dims[j] + idx[i*cdims + j];
if( GET_BIT(lin_idx) )
values0->data.fl[i] = (float)(lin_idx+1);
}
cvReleaseMat( &bit_mask );
}
return code;
}
void CV_QueryHistTest::run_func(void)
{
int i, iters = values->cols;
CvArr* h = hist[0]->bins;
const int* idx = indices->data.i;
float* val = values->data.fl;
float default_value = 0.f;
// stage 1: write bins
if( cdims == 1 )
for( i = 0; i < iters; i++ )
{
float v0 = values0->data.fl[i];
if( fabs(v0 - default_value) < FLT_EPSILON )
continue;
if( !(i % 2) )
{
if( !(i % 4) )
cvSetReal1D( h, idx[i], v0 );
else
*(float*)cvPtr1D( h, idx[i] ) = v0;
}
else
cvSetRealND( h, idx+i, v0 );
}
else if( cdims == 2 )
for( i = 0; i < iters; i++ )
{
float v0 = values0->data.fl[i];
if( fabs(v0 - default_value) < FLT_EPSILON )
continue;
if( !(i % 2) )
{
if( !(i % 4) )
cvSetReal2D( h, idx[i*2], idx[i*2+1], v0 );
else
*(float*)cvPtr2D( h, idx[i*2], idx[i*2+1] ) = v0;
}
else
cvSetRealND( h, idx+i*2, v0 );
}
else if( cdims == 3 )
for( i = 0; i < iters; i++ )
{
float v0 = values0->data.fl[i];
if( fabs(v0 - default_value) < FLT_EPSILON )
continue;
if( !(i % 2) )
{
if( !(i % 4) )
cvSetReal3D( h, idx[i*3], idx[i*3+1], idx[i*3+2], v0 );
else
*(float*)cvPtr3D( h, idx[i*3], idx[i*3+1], idx[i*3+2] ) = v0;
}
else
cvSetRealND( h, idx+i*3, v0 );
}
else
for( i = 0; i < iters; i++ )
{
float v0 = values0->data.fl[i];
if( fabs(v0 - default_value) < FLT_EPSILON )
continue;
if( !(i % 2) )
cvSetRealND( h, idx+i*cdims, v0 );
else
*(float*)cvPtrND( h, idx+i*cdims ) = v0;
}
// stage 2: read bins
if( cdims == 1 )
for( i = 0; i < iters; i++ )
{
if( !(i % 2) )
val[i] = *(float*)cvPtr1D( h, idx[i] );
else
val[i] = (float)cvGetReal1D( h, idx[i] );
}
else if( cdims == 2 )
for( i = 0; i < iters; i++ )
{
if( !(i % 2) )
val[i] = *(float*)cvPtr2D( h, idx[i*2], idx[i*2+1] );
else
val[i] = (float)cvGetReal2D( h, idx[i*2], idx[i*2+1] );
}
else if( cdims == 3 )
for( i = 0; i < iters; i++ )
{
if( !(i % 2) )
val[i] = *(float*)cvPtr3D( h, idx[i*3], idx[i*3+1], idx[i*3+2] );
else
val[i] = (float)cvGetReal3D( h, idx[i*3], idx[i*3+1], idx[i*3+2] );
}
else
for( i = 0; i < iters; i++ )
{
if( !(i % 2) )
val[i] = *(float*)cvPtrND( h, idx+i*cdims );
else
val[i] = (float)cvGetRealND( h, idx+i*cdims );
}
}
int CV_QueryHistTest::validate_test_results( int /*test_case_idx*/ )
{
int code = cvtest::TS::OK;
int i, j, iters = values->cols;
for( i = 0; i < iters; i++ )
{
float v = values->data.fl[i], v0 = values0->data.fl[i];
if( cvIsNaN(v) || cvIsInf(v) )
{
ts->printf( cvtest::TS::LOG, "The bin #%d has invalid value\n", i );
code = cvtest::TS::FAIL_INVALID_OUTPUT;
}
else if( fabs(v - v0) > FLT_EPSILON )
{
ts->printf( cvtest::TS::LOG, "The bin #%d = %g, while it should be %g\n", i, v, v0 );
code = cvtest::TS::FAIL_BAD_ACCURACY;
}
if( code < 0 )
{
ts->printf( cvtest::TS::LOG, "The bin index = (" );
for( j = 0; j < cdims; j++ )
ts->printf( cvtest::TS::LOG, "%d%s", indices->data.i[i*cdims + j],
j < cdims-1 ? ", " : ")\n" );
break;
}
}
if( code < 0 )
ts->set_failed_test_info( code );
return code;
}
////////////// cvGetMinMaxHistValue //////////////
class CV_MinMaxHistTest : public CV_BaseHistTest
{
public:
CV_MinMaxHistTest();
protected:
void run_func(void);
void init_hist(int, int);
int validate_test_results( int test_case_idx );
int min_idx[CV_MAX_DIM], max_idx[CV_MAX_DIM];
float min_val, max_val;
int min_idx0[CV_MAX_DIM], max_idx0[CV_MAX_DIM];
float min_val0, max_val0;
};
CV_MinMaxHistTest::CV_MinMaxHistTest()
{
hist_count = 1;
gen_random_hist = 1;
}
void CV_MinMaxHistTest::init_hist(int test_case_idx, int hist_i)
{
int i, eq = 1;
RNG& rng = ts->get_rng();
CV_BaseHistTest::init_hist( test_case_idx, hist_i );
for(;;)
{
for( i = 0; i < cdims; i++ )
{
min_idx0[i] = cvtest::randInt(rng) % dims[i];
max_idx0[i] = cvtest::randInt(rng) % dims[i];
eq &= min_idx0[i] == max_idx0[i];
}
if( !eq || total_size == 1 )
break;
}
min_val0 = (float)(-cvtest::randReal(rng)*10 - FLT_EPSILON);
max_val0 = (float)(cvtest::randReal(rng)*10 + FLT_EPSILON + gen_hist_max_val);
if( total_size == 1 )
min_val0 = max_val0;
cvSetRealND( hist[0]->bins, min_idx0, min_val0 );
cvSetRealND( hist[0]->bins, max_idx0, max_val0 );
}
void CV_MinMaxHistTest::run_func(void)
{
if( hist_type != CV_HIST_ARRAY && test_cpp )
{
cv::SparseMat h;
((CvSparseMat*)hist[0]->bins)->copyToSparseMat(h);
double _min_val = 0, _max_val = 0;
cv::minMaxLoc(h, &_min_val, &_max_val, min_idx, max_idx );
min_val = (float)_min_val;
max_val = (float)_max_val;
}
else
cvGetMinMaxHistValue( hist[0], &min_val, &max_val, min_idx, max_idx );
}
int CV_MinMaxHistTest::validate_test_results( int /*test_case_idx*/ )
{
int code = cvtest::TS::OK;
if( cvIsNaN(min_val) || cvIsInf(min_val) ||
cvIsNaN(max_val) || cvIsInf(max_val) )
{
ts->printf( cvtest::TS::LOG,
"The extrema histogram bin values are invalid (min = %g, max = %g)\n", min_val, max_val );
code = cvtest::TS::FAIL_INVALID_OUTPUT;
}
else if( fabs(min_val - min_val0) > FLT_EPSILON ||
fabs(max_val - max_val0) > FLT_EPSILON )
{
ts->printf( cvtest::TS::LOG,
"The extrema histogram bin values are incorrect: (min = %g, should be = %g), (max = %g, should be = %g)\n",
min_val, min_val0, max_val, max_val0 );
code = cvtest::TS::FAIL_BAD_ACCURACY;
}
else
{
int i;
for( i = 0; i < cdims; i++ )
{
if( min_idx[i] != min_idx0[i] || max_idx[i] != max_idx0[i] )
{
ts->printf( cvtest::TS::LOG,
"The %d-th coordinates of extrema histogram bin values are incorrect: "
"(min = %d, should be = %d), (max = %d, should be = %d)\n",
i, min_idx[i], min_idx0[i], max_idx[i], max_idx0[i] );
code = cvtest::TS::FAIL_BAD_ACCURACY;
}
}
}
if( code < 0 )
ts->set_failed_test_info( code );
return code;
}
////////////// cvNormalizeHist //////////////
class CV_NormHistTest : public CV_BaseHistTest
{
public:
CV_NormHistTest();
protected:
int prepare_test_case( int test_case_idx );
void run_func(void);
int validate_test_results( int test_case_idx );
double factor;
};
CV_NormHistTest::CV_NormHistTest()
{
hist_count = 1;
gen_random_hist = 1;
factor = 0;
}
int CV_NormHistTest::prepare_test_case( int test_case_idx )
{
int code = CV_BaseHistTest::prepare_test_case( test_case_idx );
if( code > 0 )
{
RNG& rng = ts->get_rng();
factor = cvtest::randReal(rng)*10 + 0.1;
if( hist_type == CV_HIST_SPARSE &&
((CvSparseMat*)hist[0]->bins)->heap->active_count == 0 )
factor = 0;
}
return code;
}
void CV_NormHistTest::run_func(void)
{
if( hist_type != CV_HIST_ARRAY && test_cpp )
{
cv::SparseMat h;
((CvSparseMat*)hist[0]->bins)->copyToSparseMat(h);
cv::normalize(h, h, factor, CV_L1);
cvReleaseSparseMat((CvSparseMat**)&hist[0]->bins);
hist[0]->bins = cvCreateSparseMat(h);
}
else
cvNormalizeHist( hist[0], factor );
}
int CV_NormHistTest::validate_test_results( int /*test_case_idx*/ )
{
int code = cvtest::TS::OK;
double sum = 0;
if( hist_type == CV_HIST_ARRAY )
{
int i;
const float* ptr = (float*)cvPtr1D( hist[0]->bins, 0 );
for( i = 0; i < total_size; i++ )
sum += ptr[i];
}
else
{
CvSparseMat* sparse = (CvSparseMat*)hist[0]->bins;
CvSparseMatIterator iterator;
CvSparseNode *node;
for( node = cvInitSparseMatIterator( sparse, &iterator );
node != 0; node = cvGetNextSparseNode( &iterator ))
{
sum += *(float*)CV_NODE_VAL(sparse,node);
}
}
if( cvIsNaN(sum) || cvIsInf(sum) )
{
ts->printf( cvtest::TS::LOG,
"The normalized histogram has invalid sum =%g\n", sum );
code = cvtest::TS::FAIL_INVALID_OUTPUT;
}
else if( fabs(sum - factor) > FLT_EPSILON*10*fabs(factor) )
{
ts->printf( cvtest::TS::LOG,
"The normalized histogram has incorrect sum =%g, while it should be =%g\n", sum, factor );
code = cvtest::TS::FAIL_BAD_ACCURACY;
}
if( code < 0 )
ts->set_failed_test_info( code );
return code;
}
////////////// cvThreshHist //////////////
class CV_ThreshHistTest : public CV_BaseHistTest
{
public:
CV_ThreshHistTest();
~CV_ThreshHistTest();
void clear();
protected:
int prepare_test_case( int test_case_idx );
void run_func(void);
int validate_test_results( int test_case_idx );
CvMat* indices;
CvMat* values;
int orig_nz_count;
double threshold;
};
CV_ThreshHistTest::CV_ThreshHistTest()
{
hist_count = 1;
gen_random_hist = 1;
threshold = 0;
indices = values = 0;
}
CV_ThreshHistTest::~CV_ThreshHistTest()
{
clear();
}
void CV_ThreshHistTest::clear()
{
cvReleaseMat( &indices );
cvReleaseMat( &values );
CV_BaseHistTest::clear();
}
int CV_ThreshHistTest::prepare_test_case( int test_case_idx )
{
int code = CV_BaseHistTest::prepare_test_case( test_case_idx );
if( code > 0 )
{
RNG& rng = ts->get_rng();
threshold = cvtest::randReal(rng)*gen_hist_max_val;
if( hist_type == CV_HIST_ARRAY )
{
orig_nz_count = total_size;
values = cvCreateMat( 1, total_size, CV_32F );
memcpy( values->data.fl, cvPtr1D( hist[0]->bins, 0 ), total_size*sizeof(float) );
}
else
{
CvSparseMat* sparse = (CvSparseMat*)hist[0]->bins;
CvSparseMatIterator iterator;
CvSparseNode* node;
int i, k;
orig_nz_count = sparse->heap->active_count;
values = cvCreateMat( 1, orig_nz_count+1, CV_32F );
indices = cvCreateMat( 1, (orig_nz_count+1)*cdims, CV_32S );
for( node = cvInitSparseMatIterator( sparse, &iterator ), i = 0;
node != 0; node = cvGetNextSparseNode( &iterator ), i++ )
{
const int* idx = CV_NODE_IDX(sparse,node);
OPENCV_ASSERT( i < orig_nz_count, "CV_ThreshHistTest::prepare_test_case", "Buffer overflow" );
values->data.fl[i] = *(float*)CV_NODE_VAL(sparse,node);
for( k = 0; k < cdims; k++ )
indices->data.i[i*cdims + k] = idx[k];
}
OPENCV_ASSERT( i == orig_nz_count, "Unmatched buffer size",
"CV_ThreshHistTest::prepare_test_case" );
}
}
return code;
}
void CV_ThreshHistTest::run_func(void)
{
cvThreshHist( hist[0], threshold );
}
int CV_ThreshHistTest::validate_test_results( int /*test_case_idx*/ )
{
int code = cvtest::TS::OK;
int i;
float* ptr0 = values->data.fl;
float* ptr = 0;
CvSparseMat* sparse = 0;
if( hist_type == CV_HIST_ARRAY )
ptr = (float*)cvPtr1D( hist[0]->bins, 0 );
else
sparse = (CvSparseMat*)hist[0]->bins;
if( code > 0 )
{
for( i = 0; i < orig_nz_count; i++ )
{
float v0 = ptr0[i], v;
if( hist_type == CV_HIST_ARRAY )
v = ptr[i];
else
{
v = (float)cvGetRealND( sparse, indices->data.i + i*cdims );
cvClearND( sparse, indices->data.i + i*cdims );
}
if( v0 <= threshold ) v0 = 0.f;
if( cvIsNaN(v) || cvIsInf(v) )
{
ts->printf( cvtest::TS::LOG, "The %d-th bin is invalid (=%g)\n", i, v );
code = cvtest::TS::FAIL_INVALID_OUTPUT;
break;
}
else if( fabs(v0 - v) > FLT_EPSILON*10*fabs(v0) )
{
ts->printf( cvtest::TS::LOG, "The %d-th bin is incorrect (=%g, should be =%g)\n", i, v, v0 );
code = cvtest::TS::FAIL_BAD_ACCURACY;
break;
}
}
}
if( code > 0 && hist_type == CV_HIST_SPARSE )
{
if( sparse->heap->active_count > 0 )
{
ts->printf( cvtest::TS::LOG,
"There some extra histogram bins in the sparse histogram after the thresholding\n" );
code = cvtest::TS::FAIL_INVALID_OUTPUT;
}
}
if( code < 0 )
ts->set_failed_test_info( code );
return code;
}
////////////// cvCompareHist //////////////
class CV_CompareHistTest : public CV_BaseHistTest
{
public:
enum { MAX_METHOD = 5 };
CV_CompareHistTest();
protected:
int prepare_test_case( int test_case_idx );
void run_func(void);
int validate_test_results( int test_case_idx );
double result[MAX_METHOD+1];
};
CV_CompareHistTest::CV_CompareHistTest()
{
hist_count = 2;
gen_random_hist = 1;
}
int CV_CompareHistTest::prepare_test_case( int test_case_idx )
{
int code = CV_BaseHistTest::prepare_test_case( test_case_idx );
return code;
}
void CV_CompareHistTest::run_func(void)
{
int k;
if( hist_type != CV_HIST_ARRAY && test_cpp )
{
cv::SparseMat h0, h1;
((CvSparseMat*)hist[0]->bins)->copyToSparseMat(h0);
((CvSparseMat*)hist[1]->bins)->copyToSparseMat(h1);
for( k = 0; k < MAX_METHOD; k++ )
result[k] = cv::compareHist(h0, h1, k);
}
else
for( k = 0; k < MAX_METHOD; k++ )
result[k] = cvCompareHist( hist[0], hist[1], k );
}
int CV_CompareHistTest::validate_test_results( int /*test_case_idx*/ )
{
int code = cvtest::TS::OK;
int i;
double result0[MAX_METHOD+1];
double s0 = 0, s1 = 0, sq0 = 0, sq1 = 0, t;
for( i = 0; i < MAX_METHOD; i++ )
result0[i] = 0;
if( hist_type == CV_HIST_ARRAY )
{
float* ptr0 = (float*)cvPtr1D( hist[0]->bins, 0 );
float* ptr1 = (float*)cvPtr1D( hist[1]->bins, 0 );
for( i = 0; i < total_size; i++ )
{
double v0 = ptr0[i], v1 = ptr1[i];
result0[CV_COMP_CORREL] += v0*v1;
result0[CV_COMP_INTERSECT] += MIN(v0,v1);
if( fabs(v0) > DBL_EPSILON )
result0[CV_COMP_CHISQR] += (v0 - v1)*(v0 - v1)/v0;
if( fabs(v0 + v1) > DBL_EPSILON )
result0[CV_COMP_CHISQR_ALT] += (v0 - v1)*(v0 - v1)/(v0 + v1);
s0 += v0;
s1 += v1;
sq0 += v0*v0;
sq1 += v1*v1;
result0[CV_COMP_BHATTACHARYYA] += sqrt(v0*v1);
}
}
else
{
CvSparseMat* sparse0 = (CvSparseMat*)hist[0]->bins;
CvSparseMat* sparse1 = (CvSparseMat*)hist[1]->bins;
CvSparseMatIterator iterator;
CvSparseNode* node;
for( node = cvInitSparseMatIterator( sparse0, &iterator );
node != 0; node = cvGetNextSparseNode( &iterator ) )
{
const int* idx = CV_NODE_IDX(sparse0, node);
double v0 = *(float*)CV_NODE_VAL(sparse0, node);
double v1 = (float)cvGetRealND(sparse1, idx);
result0[CV_COMP_CORREL] += v0*v1;
result0[CV_COMP_INTERSECT] += MIN(v0,v1);
if( fabs(v0) > DBL_EPSILON )
result0[CV_COMP_CHISQR] += (v0 - v1)*(v0 - v1)/v0;
if( fabs(v0 + v1) > DBL_EPSILON )
result0[CV_COMP_CHISQR_ALT] += (v0 - v1)*(v0 - v1)/(v0 + v1);
s0 += v0;
sq0 += v0*v0;
result0[CV_COMP_BHATTACHARYYA] += sqrt(v0*v1);
}
for( node = cvInitSparseMatIterator( sparse1, &iterator );
node != 0; node = cvGetNextSparseNode( &iterator ) )
{
double v1 = *(float*)CV_NODE_VAL(sparse1, node);
s1 += v1;
sq1 += v1*v1;
}
}
result0[CV_COMP_CHISQR_ALT] *= 2;
t = (sq0 - s0*s0/total_size)*(sq1 - s1*s1/total_size);
result0[CV_COMP_CORREL] = fabs(t) > DBL_EPSILON ?
(result0[CV_COMP_CORREL] - s0*s1/total_size)/sqrt(t) : 1;
s1 *= s0;
s0 = result0[CV_COMP_BHATTACHARYYA];
s0 = 1. - s0*(s1 > FLT_EPSILON ? 1./sqrt(s1) : 1.);
result0[CV_COMP_BHATTACHARYYA] = sqrt(MAX(s0,0.));
for( i = 0; i < MAX_METHOD; i++ )
{
double v = result[i], v0 = result0[i];
const char* method_name =
i == CV_COMP_CHISQR ? "Chi-Square" :
i == CV_COMP_CHISQR_ALT ? "Alternative Chi-Square" :
i == CV_COMP_CORREL ? "Correlation" :
i == CV_COMP_INTERSECT ? "Intersection" :
i == CV_COMP_BHATTACHARYYA ? "Bhattacharyya" : "Unknown";
if( cvIsNaN(v) || cvIsInf(v) )
{
ts->printf( cvtest::TS::LOG, "The comparison result using the method #%d (%s) is invalid (=%g)\n",
i, method_name, v );
code = cvtest::TS::FAIL_INVALID_OUTPUT;
break;
}
else if( fabs(v0 - v) > FLT_EPSILON*10*MAX(fabs(v0),0.1) )
{
ts->printf( cvtest::TS::LOG, "The comparison result using the method #%d (%s)\n\tis inaccurate (=%g, should be =%g)\n",
i, method_name, v, v0 );
code = cvtest::TS::FAIL_BAD_ACCURACY;
break;
}
}
if( code < 0 )
ts->set_failed_test_info( code );
return code;
}
////////////// cvCalcHist //////////////
class CV_CalcHistTest : public CV_BaseHistTest
{
public:
CV_CalcHistTest();
~CV_CalcHistTest();
void clear();
protected:
int prepare_test_case( int test_case_idx );
void run_func(void);
int validate_test_results( int test_case_idx );
IplImage* images[CV_MAX_DIM+1];
int channels[CV_MAX_DIM+1];
};
CV_CalcHistTest::CV_CalcHistTest()
{
int i;
hist_count = 2;
gen_random_hist = 0;
init_ranges = 1;
for( i = 0; i <= CV_MAX_DIM; i++ )
{
images[i] = 0;
channels[i] = 0;
}
}
CV_CalcHistTest::~CV_CalcHistTest()
{
clear();
}
void CV_CalcHistTest::clear()
{
int i;
for( i = 0; i <= CV_MAX_DIM; i++ )
cvReleaseImage( &images[i] );
CV_BaseHistTest::clear();
}
int CV_CalcHistTest::prepare_test_case( int test_case_idx )
{
int code = CV_BaseHistTest::prepare_test_case( test_case_idx );
if( code > 0 )
{
RNG& rng = ts->get_rng();
int i;
for( i = 0; i <= CV_MAX_DIM; i++ )
{
if( i < cdims )
{
int nch = 1; //cvtest::randInt(rng) % 3 + 1;
images[i] = cvCreateImage( img_size,
img_type == CV_8U ? IPL_DEPTH_8U : IPL_DEPTH_32F, nch );
channels[i] = cvtest::randInt(rng) % nch;
Mat images_i = cvarrToMat(images[i]);
cvtest::randUni( rng, images_i, Scalar::all(low), Scalar::all(high) );
}
else if( i == CV_MAX_DIM && cvtest::randInt(rng) % 2 )
{
// create mask
images[i] = cvCreateImage( img_size, IPL_DEPTH_8U, 1 );
Mat images_i = cvarrToMat(images[i]);
// make ~25% pixels in the mask non-zero
cvtest::randUni( rng, images_i, Scalar::all(-2), Scalar::all(2) );
}
}
}
return code;
}
void CV_CalcHistTest::run_func(void)
{
cvCalcHist( images, hist[0], 0, images[CV_MAX_DIM] );
}
static void
cvTsCalcHist( IplImage** _images, CvHistogram* hist, IplImage* _mask, int* channels )
{
int x, y, k, cdims;
union
{
float* fl;
uchar* ptr;
}
plane[CV_MAX_DIM];
int nch[CV_MAX_DIM];
int dims[CV_MAX_DIM];
int uniform = CV_IS_UNIFORM_HIST(hist);
CvSize img_size = cvGetSize(_images[0]);
CvMat images[CV_MAX_DIM], mask = cvMat(1,1,CV_8U);
int img_depth = _images[0]->depth;
cdims = cvGetDims( hist->bins, dims );
cvZero( hist->bins );
for( k = 0; k < cdims; k++ )
{
cvGetMat( _images[k], &images[k] );
nch[k] = _images[k]->nChannels;
}
if( _mask )
cvGetMat( _mask, &mask );
for( y = 0; y < img_size.height; y++ )
{
const uchar* mptr = _mask ? &CV_MAT_ELEM(mask, uchar, y, 0 ) : 0;
if( img_depth == IPL_DEPTH_8U )
for( k = 0; k < cdims; k++ )
plane[k].ptr = &CV_MAT_ELEM(images[k], uchar, y, 0 ) + channels[k];
else
for( k = 0; k < cdims; k++ )
plane[k].fl = &CV_MAT_ELEM(images[k], float, y, 0 ) + channels[k];
for( x = 0; x < img_size.width; x++ )
{
float val[CV_MAX_DIM];
int idx[CV_MAX_DIM];
if( mptr && !mptr[x] )
continue;
if( img_depth == IPL_DEPTH_8U )
for( k = 0; k < cdims; k++ )
val[k] = plane[k].ptr[x*nch[k]];
else
for( k = 0; k < cdims; k++ )
val[k] = plane[k].fl[x*nch[k]];
idx[cdims-1] = -1;
if( uniform )
{
for( k = 0; k < cdims; k++ )
{
double v = val[k], lo = hist->thresh[k][0], hi = hist->thresh[k][1];
idx[k] = cvFloor((v - lo)*dims[k]/(hi - lo));
if( idx[k] < 0 || idx[k] >= dims[k] )
break;
}
}
else
{
for( k = 0; k < cdims; k++ )
{
float v = val[k];
float* t = hist->thresh2[k];
int j, n = dims[k];
for( j = 0; j <= n; j++ )
if( v < t[j] )
break;
if( j <= 0 || j > n )
break;
idx[k] = j-1;
}
}
if( k < cdims )
continue;
(*(float*)cvPtrND( hist->bins, idx ))++;
}
}
}
int CV_CalcHistTest::validate_test_results( int /*test_case_idx*/ )
{
int code = cvtest::TS::OK;
double diff;
cvTsCalcHist( images, hist[1], images[CV_MAX_DIM], channels );
diff = cvCompareHist( hist[0], hist[1], CV_COMP_CHISQR );
if( diff > DBL_EPSILON )
{
ts->printf( cvtest::TS::LOG, "The histogram does not match to the reference one\n" );
code = cvtest::TS::FAIL_BAD_ACCURACY;
}
if( code < 0 )
ts->set_failed_test_info( code );
return code;
}
CV_CalcHistTest hist_calc_test;
////////////// cvCalcBackProject //////////////
class CV_CalcBackProjectTest : public CV_BaseHistTest
{
public:
CV_CalcBackProjectTest();
~CV_CalcBackProjectTest();
void clear();
protected:
int prepare_test_case( int test_case_idx );
void run_func(void);
int validate_test_results( int test_case_idx );
IplImage* images[CV_MAX_DIM+3];
int channels[CV_MAX_DIM+3];
};
CV_CalcBackProjectTest::CV_CalcBackProjectTest()
{
int i;
hist_count = 1;
gen_random_hist = 0;
init_ranges = 1;
for( i = 0; i < CV_MAX_DIM+3; i++ )
{
images[i] = 0;
channels[i] = 0;
}
}
CV_CalcBackProjectTest::~CV_CalcBackProjectTest()
{
clear();
}
void CV_CalcBackProjectTest::clear()
{
int i;
for( i = 0; i < CV_MAX_DIM+3; i++ )
cvReleaseImage( &images[i] );
CV_BaseHistTest::clear();
}
int CV_CalcBackProjectTest::prepare_test_case( int test_case_idx )
{
int code = CV_BaseHistTest::prepare_test_case( test_case_idx );
if( code > 0 )
{
RNG& rng = ts->get_rng();
int i, j, n, img_len = img_size.width*img_size.height;
for( i = 0; i < CV_MAX_DIM + 3; i++ )
{
if( i < cdims )
{
int nch = 1; //cvtest::randInt(rng) % 3 + 1;
images[i] = cvCreateImage( img_size,
img_type == CV_8U ? IPL_DEPTH_8U : IPL_DEPTH_32F, nch );
channels[i] = cvtest::randInt(rng) % nch;
Mat images_i = cvarrToMat(images[i]);
cvtest::randUni( rng, images_i, Scalar::all(low), Scalar::all(high) );
}
else if( i == CV_MAX_DIM && cvtest::randInt(rng) % 2 )
{
// create mask
images[i] = cvCreateImage( img_size, IPL_DEPTH_8U, 1 );
Mat images_i = cvarrToMat(images[i]);
// make ~25% pixels in the mask non-zero
cvtest::randUni( rng, images_i, Scalar::all(-2), Scalar::all(2) );
}
else if( i > CV_MAX_DIM )
{
images[i] = cvCreateImage( img_size, images[0]->depth, 1 );
}
}
cvTsCalcHist( images, hist[0], images[CV_MAX_DIM], channels );
// now modify the images a bit to add some zeros go to the backprojection
n = cvtest::randInt(rng) % (img_len/20+1);
for( i = 0; i < cdims; i++ )
{
char* data = images[i]->imageData;
for( j = 0; j < n; j++ )
{
int idx = cvtest::randInt(rng) % img_len;
double val = cvtest::randReal(rng)*(high - low) + low;
if( img_type == CV_8U )
((uchar*)data)[idx] = (uchar)cvRound(val);
else
((float*)data)[idx] = (float)val;
}
}
}
return code;
}
void CV_CalcBackProjectTest::run_func(void)
{
cvCalcBackProject( images, images[CV_MAX_DIM+1], hist[0] );
}
static void
cvTsCalcBackProject( IplImage** images, IplImage* dst, CvHistogram* hist, int* channels )
{
int x, y, k, cdims;
union
{
float* fl;
uchar* ptr;
}
plane[CV_MAX_DIM];
int nch[CV_MAX_DIM];
int dims[CV_MAX_DIM];
int uniform = CV_IS_UNIFORM_HIST(hist);
CvSize img_size = cvGetSize(images[0]);
int img_depth = images[0]->depth;
cdims = cvGetDims( hist->bins, dims );
for( k = 0; k < cdims; k++ )
nch[k] = images[k]->nChannels;
for( y = 0; y < img_size.height; y++ )
{
if( img_depth == IPL_DEPTH_8U )
for( k = 0; k < cdims; k++ )
plane[k].ptr = &CV_IMAGE_ELEM(images[k], uchar, y, 0 ) + channels[k];
else
for( k = 0; k < cdims; k++ )
plane[k].fl = &CV_IMAGE_ELEM(images[k], float, y, 0 ) + channels[k];
for( x = 0; x < img_size.width; x++ )
{
float val[CV_MAX_DIM];
float bin_val = 0;
int idx[CV_MAX_DIM];
if( img_depth == IPL_DEPTH_8U )
for( k = 0; k < cdims; k++ )
val[k] = plane[k].ptr[x*nch[k]];
else
for( k = 0; k < cdims; k++ )
val[k] = plane[k].fl[x*nch[k]];
idx[cdims-1] = -1;
if( uniform )
{
for( k = 0; k < cdims; k++ )
{
double v = val[k], lo = hist->thresh[k][0], hi = hist->thresh[k][1];
idx[k] = cvFloor((v - lo)*dims[k]/(hi - lo));
if( idx[k] < 0 || idx[k] >= dims[k] )
break;
}
}
else
{
for( k = 0; k < cdims; k++ )
{
float v = val[k];
float* t = hist->thresh2[k];
int j, n = dims[k];
for( j = 0; j <= n; j++ )
if( v < t[j] )
break;
if( j <= 0 || j > n )
break;
idx[k] = j-1;
}
}
if( k == cdims )
bin_val = (float)cvGetRealND( hist->bins, idx );
if( img_depth == IPL_DEPTH_8U )
{
int t = cvRound(bin_val);
CV_IMAGE_ELEM( dst, uchar, y, x ) = saturate_cast<uchar>(t);
}
else
CV_IMAGE_ELEM( dst, float, y, x ) = bin_val;
}
}
}
int CV_CalcBackProjectTest::validate_test_results( int /*test_case_idx*/ )
{
int code = cvtest::TS::OK;
cvTsCalcBackProject( images, images[CV_MAX_DIM+2], hist[0], channels );
Mat a = cvarrToMat(images[CV_MAX_DIM+1]), b = cvarrToMat(images[CV_MAX_DIM+2]);
double threshold = a.depth() == CV_8U ? 2 : FLT_EPSILON;
code = cvtest::cmpEps2( ts, a, b, threshold, true, "Back project image" );
if( code < 0 )
ts->set_failed_test_info( code );
return code;
}
////////////// cvCalcBackProjectPatch //////////////
class CV_CalcBackProjectPatchTest : public CV_BaseHistTest
{
public:
CV_CalcBackProjectPatchTest();
~CV_CalcBackProjectPatchTest();
void clear();
protected:
int prepare_test_case( int test_case_idx );
void run_func(void);
int validate_test_results( int test_case_idx );
IplImage* images[CV_MAX_DIM+2];
int channels[CV_MAX_DIM+2];
CvSize patch_size;
double factor;
int method;
};
CV_CalcBackProjectPatchTest::CV_CalcBackProjectPatchTest()
{
int i;
hist_count = 1;
gen_random_hist = 0;
init_ranges = 1;
img_max_log_size = 6;
for( i = 0; i < CV_MAX_DIM+2; i++ )
{
images[i] = 0;
channels[i] = 0;
}
}
CV_CalcBackProjectPatchTest::~CV_CalcBackProjectPatchTest()
{
clear();
}
void CV_CalcBackProjectPatchTest::clear()
{
int i;
for( i = 0; i < CV_MAX_DIM+2; i++ )
cvReleaseImage( &images[i] );
CV_BaseHistTest::clear();
}
int CV_CalcBackProjectPatchTest::prepare_test_case( int test_case_idx )
{
int code = CV_BaseHistTest::prepare_test_case( test_case_idx );
if( code > 0 )
{
RNG& rng = ts->get_rng();
int i, j, n, img_len = img_size.width*img_size.height;
patch_size.width = cvtest::randInt(rng) % img_size.width + 1;
patch_size.height = cvtest::randInt(rng) % img_size.height + 1;
patch_size.width = MIN( patch_size.width, 30 );
patch_size.height = MIN( patch_size.height, 30 );
factor = 1.;
method = cvtest::randInt(rng) % CV_CompareHistTest::MAX_METHOD;
for( i = 0; i < CV_MAX_DIM + 2; i++ )
{
if( i < cdims )
{
int nch = 1; //cvtest::randInt(rng) % 3 + 1;
images[i] = cvCreateImage( img_size,
img_type == CV_8U ? IPL_DEPTH_8U : IPL_DEPTH_32F, nch );
channels[i] = cvtest::randInt(rng) % nch;
Mat images_i = cvarrToMat(images[i]);
cvtest::randUni( rng, images_i, Scalar::all(low), Scalar::all(high) );
}
else if( i >= CV_MAX_DIM )
{
images[i] = cvCreateImage(
cvSize(img_size.width - patch_size.width + 1,
img_size.height - patch_size.height + 1),
IPL_DEPTH_32F, 1 );
}
}
cvTsCalcHist( images, hist[0], 0, channels );
cvNormalizeHist( hist[0], factor );
// now modify the images a bit
n = cvtest::randInt(rng) % (img_len/10+1);
for( i = 0; i < cdims; i++ )
{
char* data = images[i]->imageData;
for( j = 0; j < n; j++ )
{
int idx = cvtest::randInt(rng) % img_len;
double val = cvtest::randReal(rng)*(high - low) + low;
if( img_type == CV_8U )
((uchar*)data)[idx] = (uchar)cvRound(val);
else
((float*)data)[idx] = (float)val;
}
}
}
return code;
}
void CV_CalcBackProjectPatchTest::run_func(void)
{
cvCalcBackProjectPatch( images, images[CV_MAX_DIM], patch_size, hist[0], method, factor );
}
static void
cvTsCalcBackProjectPatch( IplImage** images, IplImage* dst, CvSize patch_size,
CvHistogram* hist, int method,
double factor, int* channels )
{
CvHistogram* model = 0;
IplImage imgstub[CV_MAX_DIM], *img[CV_MAX_DIM];
IplROI roi;
int i, dims;
int x, y;
CvSize size = cvGetSize(dst);
dims = cvGetDims( hist->bins );
cvCopyHist( hist, &model );
cvNormalizeHist( hist, factor );
cvZero( dst );
for( i = 0; i < dims; i++ )
{
CvMat stub, *mat;
mat = cvGetMat( images[i], &stub, 0, 0 );
img[i] = cvGetImage( mat, &imgstub[i] );
img[i]->roi = &roi;
}
roi.coi = 0;
for( y = 0; y < size.height; y++ )
{
for( x = 0; x < size.width; x++ )
{
double result;
roi.xOffset = x;
roi.yOffset = y;
roi.width = patch_size.width;
roi.height = patch_size.height;
cvTsCalcHist( img, model, 0, channels );
cvNormalizeHist( model, factor );
result = cvCompareHist( model, hist, method );
CV_IMAGE_ELEM( dst, float, y, x ) = (float)result;
}
}
cvReleaseHist( &model );
}
int CV_CalcBackProjectPatchTest::validate_test_results( int /*test_case_idx*/ )
{
int code = cvtest::TS::OK;
double err_level = 5e-3;
cvTsCalcBackProjectPatch( images, images[CV_MAX_DIM+1],
patch_size, hist[0], method, factor, channels );
Mat a = cvarrToMat(images[CV_MAX_DIM]), b = cvarrToMat(images[CV_MAX_DIM+1]);
code = cvtest::cmpEps2( ts, a, b, err_level, true, "BackProjectPatch result" );
if( code < 0 )
ts->set_failed_test_info( code );
return code;
}
////////////// cvCalcBayesianProb //////////////
class CV_BayesianProbTest : public CV_BaseHistTest
{
public:
enum { MAX_METHOD = 4 };
CV_BayesianProbTest();
protected:
int prepare_test_case( int test_case_idx );
void run_func(void);
int validate_test_results( int test_case_idx );
void init_hist( int test_case_idx, int i );
void get_hist_params( int test_case_idx );
};
CV_BayesianProbTest::CV_BayesianProbTest()
{
hist_count = CV_MAX_DIM;
gen_random_hist = 1;
}
void CV_BayesianProbTest::get_hist_params( int test_case_idx )
{
CV_BaseHistTest::get_hist_params( test_case_idx );
hist_type = CV_HIST_ARRAY;
}
void CV_BayesianProbTest::init_hist( int test_case_idx, int hist_i )
{
if( hist_i < hist_count/2 )
CV_BaseHistTest::init_hist( test_case_idx, hist_i );
}
int CV_BayesianProbTest::prepare_test_case( int test_case_idx )
{
RNG& rng = ts->get_rng();
hist_count = (cvtest::randInt(rng) % (MAX_HIST/2-1) + 2)*2;
hist_count = MIN( hist_count, MAX_HIST );
int code = CV_BaseHistTest::prepare_test_case( test_case_idx );
return code;
}
void CV_BayesianProbTest::run_func(void)
{
cvCalcBayesianProb( &hist[0], hist_count/2, &hist[hist_count/2] );
}
int CV_BayesianProbTest::validate_test_results( int /*test_case_idx*/ )
{
int code = cvtest::TS::OK;
int i, j, n = hist_count/2;
double s[CV_MAX_DIM];
const double err_level = 1e-5;
for( i = 0; i < total_size; i++ )
{
double sum = 0;
for( j = 0; j < n; j++ )
{
double v = hist[j]->mat.data.fl[i];
sum += v;
s[j] = v;
}
sum = sum > DBL_EPSILON ? 1./sum : 0;
for( j = 0; j < n; j++ )
{
double v0 = s[j]*sum;
double v = hist[j+n]->mat.data.fl[i];
if( cvIsNaN(v) || cvIsInf(v) )
{
ts->printf( cvtest::TS::LOG,
"The element #%d in the destination histogram #%d is invalid (=%g)\n",
i, j, v );
code = cvtest::TS::FAIL_INVALID_OUTPUT;
break;
}
else if( fabs(v0 - v) > err_level*fabs(v0) )
{
ts->printf( cvtest::TS::LOG,
"The element #%d in the destination histogram #%d is inaccurate (=%g, should be =%g)\n",
i, j, v, v0 );
code = cvtest::TS::FAIL_BAD_ACCURACY;
break;
}
}
if( j < n )
break;
}
if( code < 0 )
ts->set_failed_test_info( code );
return code;
}
//////////////////////////////////////////////////////////////////////////////////////////////////////
TEST(Imgproc_Hist_Calc, accuracy) { CV_CalcHistTest test; test.safe_run(); }
TEST(Imgproc_Hist_Query, accuracy) { CV_QueryHistTest test; test.safe_run(); }
TEST(Imgproc_Hist_Compare, accuracy) { CV_CompareHistTest test; test.safe_run(); }
TEST(Imgproc_Hist_Threshold, accuracy) { CV_ThreshHistTest test; test.safe_run(); }
TEST(Imgproc_Hist_Normalize, accuracy) { CV_NormHistTest test; test.safe_run(); }
TEST(Imgproc_Hist_MinMaxVal, accuracy) { CV_MinMaxHistTest test; test.safe_run(); }
TEST(Imgproc_Hist_CalcBackProject, accuracy) { CV_CalcBackProjectTest test; test.safe_run(); }
TEST(Imgproc_Hist_CalcBackProjectPatch, accuracy) { CV_CalcBackProjectPatchTest test; test.safe_run(); }
TEST(Imgproc_Hist_BayesianProb, accuracy) { CV_BayesianProbTest test; test.safe_run(); }
/* End Of File */
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