int CV_ThreshHistTest::validate_test_results( int /*test_case_idx*/ )
{
int code = CvTS::OK;
int i;
float* ptr0 = values->data.fl;
float* ptr = 0;
CvSparseMat* sparse = 0;
if( hist_type == CV_HIST_ARRAY )
ptr = (float*)cvPtr1D( hist[0]->bins, 0 );
else
sparse = (CvSparseMat*)hist[0]->bins;
if( code > 0 )
{
for( i = 0; i < orig_nz_count; i++ )
{
float v0 = ptr0[i], v;
if( hist_type == CV_HIST_ARRAY )
v = ptr[i];
else
{
v = (float)cvGetRealND( sparse, indices->data.i + i*cdims );
cvClearND( sparse, indices->data.i + i*cdims );
}
if( v0 <= threshold ) v0 = 0.f;
if( cvIsNaN(v) || cvIsInf(v) )
{
ts->printf( CvTS::LOG, "The %d-th bin is invalid (=%g)\n", i, v );
code = CvTS::FAIL_INVALID_OUTPUT;
break;
}
else if( fabs(v0 - v) > FLT_EPSILON*10*fabs(v0) )
{
ts->printf( CvTS::LOG, "The %d-th bin is incorrect (=%g, should be =%g)\n", i, v, v0 );
code = CvTS::FAIL_BAD_ACCURACY;
break;
}
}
}
if( code > 0 && hist_type == CV_HIST_SPARSE )
{
if( sparse->heap->active_count > 0 )
{
ts->printf( CvTS::LOG,
"There some extra histogram bins in the sparse histogram after the thresholding\n" );
code = CvTS::FAIL_INVALID_OUTPUT;
}
}
if( code < 0 )
ts->set_failed_test_info( code );
return code;
}
void cveClearND(CvArr* arr, int* idx)
{
cvClearND(arr, idx);
}
