mirror of https://github.com/opencv/opencv.git
Open Source Computer Vision Library
https://opencv.org/
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
2030 lines
56 KiB
2030 lines
56 KiB
/*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" |
|
|
|
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; |
|
} |
|
} |
|
|
|
TEST(Imgproc_Hist_Calc, badarg) |
|
{ |
|
const int channels[] = {0}; |
|
float range1[] = {0, 10}; |
|
float range2[] = {10, 20}; |
|
const float * ranges[] = {range1, range2}; |
|
Mat img = cv::Mat::zeros(10, 10, CV_8UC1); |
|
Mat imgInt = cv::Mat::zeros(10, 10, CV_32SC1); |
|
Mat hist; |
|
const int hist_size[] = { 100, 100 }; |
|
// base run |
|
EXPECT_NO_THROW(cv::calcHist(&img, 1, channels, noArray(), hist, 1, hist_size, ranges, true)); |
|
// bad parameters |
|
EXPECT_THROW(cv::calcHist(NULL, 1, channels, noArray(), hist, 1, hist_size, ranges, true), cv::Exception); |
|
EXPECT_THROW(cv::calcHist(&img, 0, channels, noArray(), hist, 1, hist_size, ranges, true), cv::Exception); |
|
EXPECT_THROW(cv::calcHist(&img, 1, NULL, noArray(), hist, 2, hist_size, ranges, true), cv::Exception); |
|
EXPECT_THROW(cv::calcHist(&img, 1, channels, noArray(), noArray(), 1, hist_size, ranges, true), cv::Exception); |
|
EXPECT_THROW(cv::calcHist(&img, 1, channels, noArray(), hist, -1, hist_size, ranges, true), cv::Exception); |
|
EXPECT_THROW(cv::calcHist(&img, 1, channels, noArray(), hist, 1, NULL, ranges, true), cv::Exception); |
|
EXPECT_THROW(cv::calcHist(&imgInt, 1, channels, noArray(), hist, 1, hist_size, NULL, true), cv::Exception); |
|
// special case |
|
EXPECT_NO_THROW(cv::calcHist(&img, 1, channels, noArray(), hist, 1, hist_size, NULL, true)); |
|
|
|
Mat backProj; |
|
// base run |
|
EXPECT_NO_THROW(cv::calcBackProject(&img, 1, channels, hist, backProj, ranges, 1, true)); |
|
// bad parameters |
|
EXPECT_THROW(cv::calcBackProject(NULL, 1, channels, hist, backProj, ranges, 1, true), cv::Exception); |
|
EXPECT_THROW(cv::calcBackProject(&img, 0, channels, hist, backProj, ranges, 1, true), cv::Exception); |
|
EXPECT_THROW(cv::calcBackProject(&img, 1, channels, noArray(), backProj, ranges, 1, true), cv::Exception); |
|
EXPECT_THROW(cv::calcBackProject(&img, 1, channels, hist, noArray(), ranges, 1, true), cv::Exception); |
|
EXPECT_THROW(cv::calcBackProject(&imgInt, 1, channels, hist, backProj, NULL, 1, true), cv::Exception); |
|
// special case |
|
EXPECT_NO_THROW(cv::calcBackProject(&img, 1, channels, hist, backProj, NULL, 1, true)); |
|
} |
|
|
|
TEST(Imgproc_Hist_Calc, IPP_ranges_with_equal_exponent_21595) |
|
{ |
|
const int channels[] = { 0 }; |
|
float range1[] = { -0.5f, 1.5f }; |
|
const float* ranges[] = { range1 }; |
|
const int hist_size[] = { 2 }; |
|
|
|
uint8_t m[1][6] = { { 0, 1, 0, 1 , 1, 1 } }; |
|
cv::Mat images_u = Mat(1, 6, CV_8UC1, m); |
|
cv::Mat histogram_u; |
|
cv::calcHist(&images_u, 1, channels, noArray(), histogram_u, 1, hist_size, ranges); |
|
|
|
ASSERT_EQ(histogram_u.at<float>(0), 2.f) << "0 not counts correctly, res: " << histogram_u.at<float>(0); |
|
ASSERT_EQ(histogram_u.at<float>(1), 4.f) << "1 not counts correctly, res: " << histogram_u.at<float>(0); |
|
} |
|
|
|
TEST(Imgproc_Hist_Calc, IPP_ranges_with_nonequal_exponent_21595) |
|
{ |
|
const int channels[] = { 0 }; |
|
float range1[] = { -1.3f, 1.5f }; |
|
const float* ranges[] = { range1 }; |
|
const int hist_size[] = { 3 }; |
|
|
|
uint8_t m[1][6] = { { 0, 1, 0, 1 , 1, 1 } }; |
|
cv::Mat images_u = Mat(1, 6, CV_8UC1, m); |
|
cv::Mat histogram_u; |
|
cv::calcHist(&images_u, 1, channels, noArray(), histogram_u, 1, hist_size, ranges); |
|
|
|
ASSERT_EQ(histogram_u.at<float>(0), 0.f) << "not equal to zero, res: " << histogram_u.at<float>(0); |
|
ASSERT_EQ(histogram_u.at<float>(1), 2.f) << "0 not counts correctly, res: " << histogram_u.at<float>(1); |
|
ASSERT_EQ(histogram_u.at<float>(2), 4.f) << "1 not counts correctly, res: " << histogram_u.at<float>(2); |
|
} |
|
|
|
}} // namespace |
|
/* End Of File */
|
|
|