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Partially back-port #25075 to 4.x #25149

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Merged
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Mar 5, 2024
Merged

Partially back-port #25075 to 4.x #25149

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28 changes: 14 additions & 14 deletions apps/traincascade/boost.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -86,10 +86,10 @@ static CvMat* cvPreprocessIndexArray( const CvMat* idx_arr, int data_arr_size, b
int* dsti;

if( !CV_IS_MAT(idx_arr) )
CV_ERROR( CV_StsBadArg, "Invalid index array" );
CV_ERROR( cv::Error::StsBadArg, "Invalid index array" );

if( idx_arr->rows != 1 && idx_arr->cols != 1 )
CV_ERROR( CV_StsBadSize, "the index array must be 1-dimensional" );
CV_ERROR( cv::Error::StsBadSize, "the index array must be 1-dimensional" );

idx_total = idx_arr->rows + idx_arr->cols - 1;
srcb = idx_arr->data.ptr;
Expand All @@ -105,20 +105,20 @@ static CvMat* cvPreprocessIndexArray( const CvMat* idx_arr, int data_arr_size, b
// idx_arr is array of 1's and 0's -
// i.e. it is a mask of the selected components
if( idx_total != data_arr_size )
CV_ERROR( CV_StsUnmatchedSizes,
CV_ERROR( cv::Error::StsUnmatchedSizes,
"Component mask should contain as many elements as the total number of input variables" );

for( i = 0; i < idx_total; i++ )
idx_selected += srcb[i*step] != 0;

if( idx_selected == 0 )
CV_ERROR( CV_StsOutOfRange, "No components/input_variables is selected!" );
CV_ERROR( cv::Error::StsOutOfRange, "No components/input_variables is selected!" );

break;
case CV_32SC1:
// idx_arr is array of integer indices of selected components
if( idx_total > data_arr_size )
CV_ERROR( CV_StsOutOfRange,
CV_ERROR( cv::Error::StsOutOfRange,
"index array may not contain more elements than the total number of input variables" );
idx_selected = idx_total;
// check if sorted already
Expand All @@ -134,7 +134,7 @@ static CvMat* cvPreprocessIndexArray( const CvMat* idx_arr, int data_arr_size, b
}
break;
default:
CV_ERROR( CV_StsUnsupportedFormat, "Unsupported index array data type "
CV_ERROR( cv::Error::StsUnsupportedFormat, "Unsupported index array data type "
"(it should be 8uC1, 8sC1 or 32sC1)" );
}

Expand All @@ -156,13 +156,13 @@ static CvMat* cvPreprocessIndexArray( const CvMat* idx_arr, int data_arr_size, b
qsort( dsti, idx_total, sizeof(dsti[0]), icvCmpIntegers );

if( dsti[0] < 0 || dsti[idx_total-1] >= data_arr_size )
CV_ERROR( CV_StsOutOfRange, "the index array elements are out of range" );
CV_ERROR( cv::Error::StsOutOfRange, "the index array elements are out of range" );

if( check_for_duplicates )
{
for( i = 1; i < idx_total; i++ )
if( dsti[i] <= dsti[i-1] )
CV_ERROR( CV_StsBadArg, "There are duplicated index array elements" );
CV_ERROR( cv::Error::StsBadArg, "There are duplicated index array elements" );
}
}

Expand Down Expand Up @@ -218,7 +218,7 @@ bool CvCascadeBoostParams::read( const FileNode &node )
!boostTypeStr.compare( CC_LOGIT_BOOST ) ? CvBoost::LOGIT :
!boostTypeStr.compare( CC_GENTLE_BOOST ) ? CvBoost::GENTLE : -1;
if (boost_type == -1)
CV_Error( CV_StsBadArg, "unsupported Boost type" );
CV_Error( cv::Error::StsBadArg, "unsupported Boost type" );
node[CC_MINHITRATE] >> minHitRate;
node[CC_MAXFALSEALARM] >> maxFalseAlarm;
node[CC_TRIM_RATE] >> weight_trim_rate ;
Expand All @@ -228,7 +228,7 @@ bool CvCascadeBoostParams::read( const FileNode &node )
maxFalseAlarm <= 0 || maxFalseAlarm > 1 ||
weight_trim_rate <= 0 || weight_trim_rate > 1 ||
max_depth <= 0 || weak_count <= 0 )
CV_Error( CV_StsBadArg, "bad parameters range");
CV_Error( cv::Error::StsBadArg, "bad parameters range");
return true;
}

Expand Down Expand Up @@ -309,7 +309,7 @@ CvDTreeNode* CvCascadeBoostTrainData::subsample_data( const CvMat* _subsample_id
bool isMakeRootCopy = true;

if( !data_root )
CV_Error( CV_StsError, "No training data has been set" );
CV_Error( cv::Error::StsError, "No training data has been set" );

if( _subsample_idx )
{
Expand Down Expand Up @@ -547,7 +547,7 @@ void CvCascadeBoostTrainData::setData( const CvFeatureEvaluator* _featureEvaluat
// TODO: check responses: elements must be 0 or 1

if( _precalcValBufSize < 0 || _precalcIdxBufSize < 0)
CV_Error( CV_StsOutOfRange, "_numPrecalcVal and _numPrecalcIdx must be positive or 0" );
CV_Error( cv::Error::StsOutOfRange, "_numPrecalcVal and _numPrecalcIdx must be positive or 0" );

var_count = var_all = featureEvaluator->getNumFeatures() * featureEvaluator->getFeatureSize();
sample_count = _numSamples;
Expand Down Expand Up @@ -602,7 +602,7 @@ void CvCascadeBoostTrainData::setData( const CvFeatureEvaluator* _featureEvaluat

if ((uint64)effective_buf_width * (uint64)effective_buf_height != effective_buf_size)
{
CV_Error(CV_StsBadArg, "The memory buffer cannot be allocated since its size exceeds integer fields limit");
CV_Error(cv::Error::StsBadArg, "The memory buffer cannot be allocated since its size exceeds integer fields limit");
}

if ( is_buf_16u )
Expand Down Expand Up @@ -914,7 +914,7 @@ CvDTreeNode* CvCascadeBoostTree::predict( int sampleIdx ) const
{
CvDTreeNode* node = root;
if( !node )
CV_Error( CV_StsError, "The tree has not been trained yet" );
CV_Error( cv::Error::StsError, "The tree has not been trained yet" );

if ( ((CvCascadeBoostTrainData*)data)->featureEvaluator->getMaxCatCount() == 0 ) // ordered
{
Expand Down
4 changes: 2 additions & 2 deletions apps/traincascade/cascadeclassifier.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -142,7 +142,7 @@ bool CvCascadeClassifier::train( const string _cascadeDirName,
double time = (double)getTickCount();

if( _cascadeDirName.empty() || _posFilename.empty() || _negFilename.empty() )
CV_Error( CV_StsBadArg, "_cascadeDirName or _bgfileName or _vecFileName is NULL" );
CV_Error( cv::Error::StsBadArg, "_cascadeDirName or _bgfileName or _vecFileName is NULL" );

string dirName;
if (_cascadeDirName.find_last_of("/\\") == (_cascadeDirName.length() - 1) )
Expand Down Expand Up @@ -452,7 +452,7 @@ void CvCascadeClassifier::save( const string filename, bool baseFormat )
//char buf[256];
CvSeq* weak;
if ( cascadeParams.featureType != CvFeatureParams::HAAR )
CV_Error( CV_StsBadFunc, "old file format is used for Haar-like features only");
CV_Error( cv::Error::StsBadFunc, "old file format is used for Haar-like features only");
fs << "{:" ICV_HAAR_TYPE_ID;
fs << ICV_HAAR_SIZE_NAME << "[:" << cascadeParams.winSize.width <<
cascadeParams.winSize.height << "]";
Expand Down
8 changes: 4 additions & 4 deletions apps/traincascade/imagestorage.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -138,7 +138,7 @@ bool CvCascadeImageReader::PosReader::create( const string _filename )
fread( &vecSize, sizeof( vecSize ), 1, file ) != 1 ||
fread( &tmp, sizeof( tmp ), 1, file ) != 1 ||
fread( &tmp, sizeof( tmp ), 1, file ) != 1 )
CV_Error_( CV_StsParseError, ("wrong file format for %s\n", _filename.c_str()) );
CV_Error_( cv::Error::StsParseError, ("wrong file format for %s\n", _filename.c_str()) );
base = sizeof( count ) + sizeof( vecSize ) + 2*sizeof( tmp );
if( feof( file ) )
return false;
Expand All @@ -154,14 +154,14 @@ bool CvCascadeImageReader::PosReader::get( Mat &_img )
uchar tmp = 0;
size_t elements_read = fread( &tmp, sizeof( tmp ), 1, file );
if( elements_read != 1 )
CV_Error( CV_StsBadArg, "Can not get new positive sample. The most possible reason is "
CV_Error( cv::Error::StsBadArg, "Can not get new positive sample. The most possible reason is "
"insufficient count of samples in given vec-file.\n");
elements_read = fread( vec, sizeof( vec[0] ), vecSize, file );
if( elements_read != (size_t)(vecSize) )
CV_Error( CV_StsBadArg, "Can not get new positive sample. Seems that vec-file has incorrect structure.\n");
CV_Error( cv::Error::StsBadArg, "Can not get new positive sample. Seems that vec-file has incorrect structure.\n");

if( feof( file ) || last++ >= count )
CV_Error( CV_StsBadArg, "Can not get new positive sample. vec-file is over.\n");
CV_Error( cv::Error::StsBadArg, "Can not get new positive sample. vec-file is over.\n");

for( int r = 0; r < _img.rows; r++ )
{
Expand Down
28 changes: 14 additions & 14 deletions apps/traincascade/old_ml_boost.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -991,7 +991,7 @@ CvBoost::set_params( const CvBoostParams& _params )
params = _params;
if( params.boost_type != DISCRETE && params.boost_type != REAL &&
params.boost_type != LOGIT && params.boost_type != GENTLE )
CV_ERROR( CV_StsBadArg, "Unknown/unsupported boosting type" );
CV_ERROR( cv::Error::StsBadArg, "Unknown/unsupported boosting type" );

params.weak_count = MAX( params.weak_count, 1 );
params.weight_trim_rate = MAX( params.weight_trim_rate, 0. );
Expand Down Expand Up @@ -1045,7 +1045,7 @@ CvBoost::train( const CvMat* _train_data, int _tflag,
_sample_idx, _var_type, _missing_mask, _params, true, true );

if( data->get_num_classes() != 2 )
CV_ERROR( CV_StsNotImplemented,
CV_ERROR( cv::Error::StsNotImplemented,
"Boosted trees can only be used for 2-class classification." );
CV_CALL( storage = cvCreateMemStorage() );
weak = cvCreateSeq( 0, sizeof(CvSeq), sizeof(CvBoostTree*), storage );
Expand Down Expand Up @@ -1482,7 +1482,7 @@ CvBoost::get_active_vars( bool absolute_idx )
__BEGIN__;

if( !weak )
CV_ERROR( CV_StsError, "The boosted tree ensemble has not been trained yet" );
CV_ERROR( cv::Error::StsError, "The boosted tree ensemble has not been trained yet" );

if( !active_vars || !active_vars_abs )
{
Expand Down Expand Up @@ -1612,13 +1612,13 @@ CvBoost::predict( const CvMat* _sample, const CvMat* _missing,
const float* sample_data;

if( !weak )
CV_Error( CV_StsError, "The boosted tree ensemble has not been trained yet" );
CV_Error( cv::Error::StsError, "The boosted tree ensemble has not been trained yet" );

if( !CV_IS_MAT(_sample) || CV_MAT_TYPE(_sample->type) != CV_32FC1 ||
(_sample->cols != 1 && _sample->rows != 1) ||
(_sample->cols + _sample->rows - 1 != data->var_all && !raw_mode) ||
(active_vars && _sample->cols + _sample->rows - 1 != active_vars->cols && raw_mode) )
CV_Error( CV_StsBadArg,
CV_Error( cv::Error::StsBadArg,
"the input sample must be 1d floating-point vector with the same "
"number of elements as the total number of variables or "
"as the number of variables used for training" );
Expand All @@ -1627,7 +1627,7 @@ CvBoost::predict( const CvMat* _sample, const CvMat* _missing,
{
if( !CV_IS_MAT(_missing) || !CV_IS_MASK_ARR(_missing) ||
!CV_ARE_SIZES_EQ(_missing, _sample) )
CV_Error( CV_StsBadArg,
CV_Error( cv::Error::StsBadArg,
"the missing data mask must be 8-bit vector of the same size as input sample" );
}

Expand All @@ -1644,7 +1644,7 @@ CvBoost::predict( const CvMat* _sample, const CvMat* _missing,
CV_MAT_TYPE(weak_responses->type) != CV_32FC1 ||
(weak_responses->cols != 1 && weak_responses->rows != 1) ||
weak_responses->cols + weak_responses->rows - 1 != weak_count )
CV_Error( CV_StsBadArg,
CV_Error( cv::Error::StsBadArg,
"The output matrix of weak classifier responses must be valid "
"floating-point vector of the same number of components as the length of input slice" );
wstep = CV_IS_MAT_CONT(weak_responses->type) ? 1 : weak_responses->step/sizeof(float);
Expand Down Expand Up @@ -1700,7 +1700,7 @@ CvBoost::predict( const CvMat* _sample, const CvMat* _missing,
c = a;
int ival = cvRound(val);
if ( (ival != val) && (!m) )
CV_Error( CV_StsBadArg,
CV_Error( cv::Error::StsBadArg,
"one of input categorical variable is not an integer" );

while( a < b )
Expand Down Expand Up @@ -1735,7 +1735,7 @@ CvBoost::predict( const CvMat* _sample, const CvMat* _missing,
else
{
if( !CV_IS_MAT_CONT(_sample->type & (_missing ? _missing->type : -1)) )
CV_Error( CV_StsBadArg, "In raw mode the input vectors must be continuous" );
CV_Error( cv::Error::StsBadArg, "In raw mode the input vectors must be continuous" );
}

cvStartReadSeq( weak, &reader );
Expand Down Expand Up @@ -1951,7 +1951,7 @@ void CvBoost::read_params( cv::FileNode& fnode )
params.boost_type = temp.empty() ? -1 : (int)temp;

if( params.boost_type < DISCRETE || params.boost_type > GENTLE )
CV_ERROR( CV_StsBadArg, "Unknown boosting type" );
CV_ERROR( cv::Error::StsBadArg, "Unknown boosting type" );

temp = fnode[ "splitting_criteria" ];
if( !temp.empty() && temp.isString() )
Expand All @@ -1966,7 +1966,7 @@ void CvBoost::read_params( cv::FileNode& fnode )
params.split_criteria = temp.empty() ? -1 : (int) temp;

if( params.split_criteria < DEFAULT || params.boost_type > SQERR )
CV_ERROR( CV_StsBadArg, "Unknown boosting type" );
CV_ERROR( cv::Error::StsBadArg, "Unknown boosting type" );

params.weak_count = (int) fnode[ "ntrees" ];
params.weight_trim_rate = (double)fnode["weight_trimming_rate"];
Expand Down Expand Up @@ -1996,13 +1996,13 @@ CvBoost::read( cv::FileNode& node )

trees_fnode = node[ "trees" ];
if( trees_fnode.empty() || !trees_fnode.isSeq() )
CV_ERROR( CV_StsParseError, "<trees> tag is missing" );
CV_ERROR( cv::Error::StsParseError, "<trees> tag is missing" );

reader = trees_fnode.begin();
ntrees = (int) trees_fnode.size();

if( ntrees != params.weak_count )
CV_ERROR( CV_StsUnmatchedSizes,
CV_ERROR( cv::Error::StsUnmatchedSizes,
"The number of trees stored does not match <ntrees> tag value" );

CV_CALL( storage = cvCreateMemStorage() );
Expand Down Expand Up @@ -2034,7 +2034,7 @@ CvBoost::write( cv::FileStorage& fs, const char* name ) const
fs.startWriteStruct( name, cv::FileNode::MAP, CV_TYPE_NAME_ML_BOOSTING );

if( !weak )
CV_ERROR( CV_StsBadArg, "The classifier has not been trained yet" );
CV_ERROR( cv::Error::StsBadArg, "The classifier has not been trained yet" );

write_params( fs );
fs.startWriteStruct( "trees", cv::FileNode::SEQ );
Expand Down
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