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opencv/modules/imgproc/test/test_histograms.cpp

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
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//
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// If you do not agree to this license, do not download, install,
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//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
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// are permitted provided that the following conditions are met:
//
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// this list of conditions and the following disclaimer.
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#include "test_precomp.hpp"
namespace opencv_test { namespace {
class CV_BaseHistTest : public cvtest::BaseTest
{
public:
enum { MAX_HIST = 12 };
CV_BaseHistTest();
~CV_BaseHistTest();
void clear();
protected:
int read_params( const cv::FileStorage& 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;
Size 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( const cv::FileStorage& fs )
{
int code = cvtest::BaseTest::read_params( fs );
if( code < 0 )
return code;
read( find_param( fs, "struct_count" ), test_case_count, test_case_count );
read( find_param( fs, "max_log_size" ), max_log_size, max_log_size );
max_log_size = cvtest::clipInt( max_log_size, 1, 20 );
read( find_param( fs, "max_log_array_size" ), img_max_log_size, img_max_log_size );
img_max_log_size = cvtest::clipInt( img_max_log_size, 1, 9 );
read( find_param( fs, "max_cdims" ), 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 );
Mat indices;
Mat values;
Mat values0;
};
CV_QueryHistTest::CV_QueryHistTest()
{
hist_count = 1;
}
CV_QueryHistTest::~CV_QueryHistTest()
{
clear();
}
void CV_QueryHistTest::clear()
{
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();
int* idx;
iters = (cvtest::randInt(rng) % MAX(total_size/10,100)) + 1;
iters = MIN( iters, total_size*9/10 + 1 );
indices = Mat(1, iters*cdims, CV_32S);
values = Mat(1, iters, CV_32F );
values0 = Mat( 1, iters, CV_32F );
idx = indices.ptr<int>();
//printf( "total_size = %d, cdims = %d, iters = %d\n", total_size, cdims, iters );
Mat bit_mask(1, (total_size + 7)/8, CV_8U, Scalar(0));
#define GET_BIT(n) (bit_mask.data[(n)/8] & (1 << ((n)&7)))
#define SET_BIT(n) bit_mask.data[(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.at<float>(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.at<float>(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.at<float>(i) = (float)(lin_idx+1);
}
}
return code;
}
void CV_QueryHistTest::run_func(void)
{
int i, iters = values.cols;
CvArr* h = hist[0]->bins;
const int* idx = indices.ptr<int>();
float* val = values.ptr<float>();
float default_value = 0.f;
// stage 1: write bins
if( cdims == 1 )
for( i = 0; i < iters; i++ )
{
float v0 = values0.at<float>(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.at<float>(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.at<float>(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.at<float>(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.at<float>(i), v0 = values0.at<float>(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.at<int>(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 );
Mat indices;
Mat values;
int orig_nz_count;
double threshold;
};
CV_ThreshHistTest::CV_ThreshHistTest() : threshold(0)
{
hist_count = 1;
gen_random_hist = 1;
}
CV_ThreshHistTest::~CV_ThreshHistTest()
{
clear();
}
void CV_ThreshHistTest::clear()
{
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 = Mat( 1, total_size, CV_32F );
indices = Mat();
memcpy( values.ptr<float>(), 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 = Mat( 1, orig_nz_count+1, CV_32F );
indices = Mat( 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.at<float>(i) = *(float*)CV_NODE_VAL(sparse,node);
for( k = 0; k < cdims; k++ )
indices.at<int>(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.ptr<float>();
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.ptr<int>() + i*cdims );
cvClearND( sparse, indices.ptr<int>() + 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 = 6 };
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);
{
if( fabs(v0) <= DBL_EPSILON )
continue;
if( fabs(v1) <= DBL_EPSILON )
v1 = 1e-10;
result0[CV_COMP_KL_DIV] += v0 * std::log( 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);
{
if (v0 <= DBL_EPSILON)
continue;
if (!v1)
v1 = 1e-10;
result0[CV_COMP_KL_DIV] += v0 * std::log( 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" :
i == CV_COMP_KL_DIV ? "Kullback-Leibler" : "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*14*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 );
vector<Mat> images;
vector<int> channels;
};
CV_CalcHistTest::CV_CalcHistTest() : images(CV_MAX_DIM+1), channels(CV_MAX_DIM+1)
{
hist_count = 2;
gen_random_hist = 0;
init_ranges = 1;
}
CV_CalcHistTest::~CV_CalcHistTest()
{
clear();
}
void CV_CalcHistTest::clear()
{
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] = Mat(img_size, CV_MAKETYPE(img_type, nch));
channels[i] = cvtest::randInt(rng) % nch;
cvtest::randUni( rng, images[i], Scalar::all(low), Scalar::all(high) );
}
else if( i == CV_MAX_DIM )
{
if( cvtest::randInt(rng) % 2 )
{
// create mask
images[i] = Mat(img_size, CV_8U);
// make ~25% pixels in the mask non-zero
cvtest::randUni( rng, images[i], Scalar::all(-2), Scalar::all(2) );
}
else
images[i] = Mat();
}
}
}
return code;
}
void CV_CalcHistTest::run_func(void)
{
int size[CV_MAX_DIM];
int hdims = cvGetDims( hist[0]->bins, size);
bool huniform = CV_IS_UNIFORM_HIST(hist[0]);
const float* uranges[CV_MAX_DIM] = {0};
const float** hranges = 0;
if( hist[0]->type & CV_HIST_RANGES_FLAG )
{
hranges = (const float**)hist[0]->thresh2;
if( huniform )
{
for(int i = 0; i < hdims; i++ )
uranges[i] = &hist[0]->thresh[i][0];
hranges = uranges;
}
}
std::vector<cv::Mat> imagesv(cdims);
copy(images.begin(), images.begin() + cdims, imagesv.begin());
Mat mask = images[CV_MAX_DIM];
if( !CV_IS_SPARSE_HIST(hist[0]) )
{
cv::Mat H = cv::cvarrToMat(hist[0]->bins);
if(huniform)
{
vector<int> emptyChannels;
vector<int> hSize(hdims);
for(int i = 0; i < hdims; i++)
hSize[i] = size[i];
vector<float> vranges;
if(hranges)
{
vranges.resize(hdims*2);
for(int i = 0; i < hdims; i++ )
{
vranges[i*2 ] = hist[0]->thresh[i][0];
vranges[i*2+1] = hist[0]->thresh[i][1];
}
}
cv::calcHist(imagesv, emptyChannels, mask, H, hSize, vranges);
}
else
{
cv::calcHist( &imagesv[0], (int)imagesv.size(), 0, mask,
H, cvGetDims(hist[0]->bins), H.size, hranges, huniform );
}
}
else
{
CvSparseMat* sparsemat = (CvSparseMat*)hist[0]->bins;
cvZero( hist[0]->bins );
cv::SparseMat sH;
sparsemat->copyToSparseMat(sH);
cv::calcHist( &imagesv[0], (int)imagesv.size(), 0, mask, sH, sH.dims(),
sH.dims() > 0 ? sH.hdr->size : 0, hranges, huniform, false);
cv::SparseMatConstIterator it = sH.begin();
int nz = (int)sH.nzcount();
for(int i = 0; i < nz; i++, ++it )
{
CV_Assert(it.ptr != NULL);
*(float*)cvPtrND(sparsemat, it.node()->idx, 0, -2) = *(const float*)it.ptr;
}
}
}
static void
cvTsCalcHist( const vector<Mat>& images, CvHistogram* hist, Mat mask, const vector<int>& channels )
{
int x, y, k;
union
{
const float* fl;
const uchar* ptr;
}
plane[CV_MAX_DIM];
int nch[CV_MAX_DIM];
int dims[CV_MAX_DIM];
int uniform = CV_IS_UNIFORM_HIST(hist);
int cdims = cvGetDims( hist->bins, dims );
cvZero( hist->bins );
Size img_size = images[0].size();
int img_depth = images[0].depth();
for( k = 0; k < cdims; k++ )
{
nch[k] = images[k].channels();
}
for( y = 0; y < img_size.height; y++ )
{
const uchar* mptr = mask.empty() ? 0 : mask.ptr<uchar>(y);
if( img_depth == CV_8U )
for( k = 0; k < cdims; k++ )
plane[k].ptr = images[k].ptr<uchar>(y) + channels[k];
else
for( k = 0; k < cdims; k++ )
plane[k].fl = images[k].ptr<float>(y) + 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 == CV_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];
if (v < lo || v >= hi)
break;
double idx_ = (v - lo)*dims[k]/(hi - lo);
idx[k] = cvFloor(idx_);
if (idx[k] < 0)
{
idx[k] = 0;
}
if (idx[k] >= dims[k])
{
idx[k] = dims[k] - 1;
}
}
}
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 );
vector<Mat> images;
vector<int> channels;
};
CV_CalcBackProjectTest::CV_CalcBackProjectTest() : images(CV_MAX_DIM+3), channels(CV_MAX_DIM+3)
{
hist_count = 1;
gen_random_hist = 0;
init_ranges = 1;
}
CV_CalcBackProjectTest::~CV_CalcBackProjectTest()
{
clear();
}
void CV_CalcBackProjectTest::clear()
{
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.area();
for( i = 0; i < CV_MAX_DIM + 3; i++ )
{
if( i < cdims )
{
int nch = 1; //cvtest::randInt(rng) % 3 + 1;
images[i] = Mat(img_size, CV_MAKETYPE(img_type, nch));
channels[i] = cvtest::randInt(rng) % nch;
cvtest::randUni( rng, images[i], Scalar::all(low), Scalar::all(high) );
}
else if( i == CV_MAX_DIM )
{
if(cvtest::randInt(rng) % 2 )
{
// create mask
images[i] = Mat(img_size, CV_8U);
// make ~25% pixels in the mask non-zero
cvtest::randUni( rng, images[i], Scalar::all(-2), Scalar::all(2) );
}
else
{
images[i] = Mat();
}
}
else if( i > CV_MAX_DIM )
{
images[i] = Mat(img_size, images[0].type());
}
}
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++ )
{
uchar* data = images[i].data;
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)
{
int size[CV_MAX_DIM];
int hdims = cvGetDims( hist[0]->bins, size );
bool huniform = CV_IS_UNIFORM_HIST(hist[0]);
const float* uranges[CV_MAX_DIM] = {0};
const float** hranges = 0;
if( hist[0]->type & CV_HIST_RANGES_FLAG )
{
hranges = (const float**)hist[0]->thresh2;
if( huniform )
{
for(int i = 0; i < hdims; i++ )
uranges[i] = &hist[0]->thresh[i][0];
hranges = uranges;
}
}
std::vector<cv::Mat> imagesv(hdims);
copy(images.begin(), images.begin() + hdims, imagesv.begin());
cv::Mat dst = images[CV_MAX_DIM+1];
CV_Assert( dst.size() == imagesv[0].size() && dst.depth() == imagesv[0].depth() );
if( !CV_IS_SPARSE_HIST(hist[0]) )
{
cv::Mat H = cv::cvarrToMat(hist[0]->bins);
if(huniform)
{
vector<int> emptyChannels;
vector<float> vranges;
if(hranges)
{
vranges.resize(hdims*2);
for(int i = 0; i < hdims; i++ )
{
vranges[i*2 ] = hist[0]->thresh[i][0];
vranges[i*2+1] = hist[0]->thresh[i][1];
}
}
cv::calcBackProject(imagesv, emptyChannels, H, dst, vranges, 1);
}
else
{
cv::calcBackProject( &imagesv[0], (int)imagesv.size(),
0, H, dst, hranges, 1, false );
}
}
else
{
cv::SparseMat sH;
((const CvSparseMat*)hist[0]->bins)->copyToSparseMat(sH);
cv::calcBackProject( &imagesv[0], (int)imagesv.size(),
0, sH, dst, hranges, 1, huniform );
}
}
static void
cvTsCalcBackProject( const vector<Mat>& images, Mat dst, CvHistogram* hist, const vector<int>& channels )
{
int x, y, k, cdims;
union
{
const float* fl;
const uchar* ptr;
}
plane[CV_MAX_DIM];
int nch[CV_MAX_DIM];
int dims[CV_MAX_DIM];
int uniform = CV_IS_UNIFORM_HIST(hist);
Size img_size = images[0].size();
int img_depth = images[0].depth();
cdims = cvGetDims( hist->bins, dims );
for( k = 0; k < cdims; k++ )
nch[k] = images[k].channels();
for( y = 0; y < img_size.height; y++ )
{
if( img_depth == CV_8U )
for( k = 0; k < cdims; k++ )
plane[k].ptr = images[k].ptr<uchar>(y) + channels[k];
else
for( k = 0; k < cdims; k++ )
plane[k].fl = images[k].ptr<float>(y) + 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 == CV_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 == CV_8U )
{
int t = cvRound(bin_val);
dst.at<uchar>(y, x) = saturate_cast<uchar>(t);
}
else
dst.at<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 = images[CV_MAX_DIM+1], b = 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 );
vector<Mat> images;
vector<int> channels;
Size patch_size;
double factor;
int method;
};
CV_CalcBackProjectPatchTest::CV_CalcBackProjectPatchTest() :
images(CV_MAX_DIM+2), channels(CV_MAX_DIM+2)
{
hist_count = 1;
gen_random_hist = 0;
init_ranges = 1;
img_max_log_size = 6;
}
CV_CalcBackProjectPatchTest::~CV_CalcBackProjectPatchTest()
{
clear();
}
void CV_CalcBackProjectPatchTest::clear()
{
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.area();
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] = Mat(img_size, CV_MAKETYPE(img_type, nch));
channels[i] = cvtest::randInt(rng) % nch;
cvtest::randUni( rng, images[i], Scalar::all(low), Scalar::all(high) );
}
else if( i >= CV_MAX_DIM )
{
images[i] = Mat(img_size - patch_size + Size(1, 1), CV_32F);
}
}
cvTsCalcHist( images, hist[0], Mat(), 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++ )
{
uchar* data = images[i].data;
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)
{
CvMat dst = cvMat(images[CV_MAX_DIM]);
vector<CvMat > img(cdims);
vector<CvMat*> pimg(cdims);
for(int i = 0; i < cdims; i++)
{
img[i] = cvMat(images[i]);
pimg[i] = &img[i];
}
cvCalcArrBackProjectPatch( (CvArr**)&pimg[0], &dst, cvSize(patch_size), hist[0], method, factor );
}
static void
cvTsCalcBackProjectPatch( const vector<Mat>& images, Mat dst, Size patch_size,
CvHistogram* hist, int method,
double factor, const vector<int>& channels )
{
CvHistogram* model = 0;
int x, y;
Size size = dst.size();
cvCopyHist( hist, &model );
cvNormalizeHist( hist, factor );
vector<Mat> img(images.size());
for( y = 0; y < size.height; y++ )
{
for( x = 0; x < size.width; x++ )
{
double result;
Rect roi(Point(x, y), patch_size);
for(size_t i = 0; i < img.size(); i++)
img[i] = images[i](roi);
cvTsCalcHist( img, model, Mat(), channels );
cvNormalizeHist( model, factor );
result = cvCompareHist( model, hist, method );
dst.at<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;
Mat dst = images[CV_MAX_DIM+1];
vector<Mat> imagesv(cdims);
for(int i = 0; i < cdims; i++)
imagesv[i] = images[i];
cvTsCalcBackProjectPatch( imagesv, dst, patch_size, hist[0],
method, factor, channels );
Mat a = images[CV_MAX_DIM], b = 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(); }
TEST(Imgproc_Hist_Calc, calcHist_regression_11544)
{
cv::Mat1w m = cv::Mat1w::zeros(10, 10);
int n_images = 1;
int channels[] = { 0 };
cv::Mat mask;
cv::MatND hist1, hist2;
cv::MatND hist1_opt, hist2_opt;
int dims = 1;
int hist_size[] = { 1000 };
float range1[] = { 0, 900 };
float range2[] = { 0, 1000 };
const float* ranges1[] = { range1 };
const float* ranges2[] = { range2 };
setUseOptimized(false);
cv::calcHist(&m, n_images, channels, mask, hist1, dims, hist_size, ranges1);
cv::calcHist(&m, n_images, channels, mask, hist2, dims, hist_size, ranges2);
setUseOptimized(true);
cv::calcHist(&m, n_images, channels, mask, hist1_opt, dims, hist_size, ranges1);
cv::calcHist(&m, n_images, channels, mask, hist2_opt, dims, hist_size, ranges2);
for(int i = 0; i < 1000; i++)
{
EXPECT_EQ(hist1.at<float>(i, 0), hist1_opt.at<float>(i, 0)) << i;
EXPECT_EQ(hist2.at<float>(i, 0), hist2_opt.at<float>(i, 0)) << i;
}
}
}} // namespace
/* End Of File */
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