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Partially back-port #25075 to 4.x

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This commit is contained in:
Alexander Smorkalov
2024-03-04 15:51:05 +03:00
parent ef611df09b
commit daa8f7dfc6
111 changed files with 1124 additions and 1124 deletions
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apps/traincascade/old_ml_boost.cpp
+14 -14
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@@ -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. );
@@ -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 );
@@ -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 )
{
@@ -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" );
@@ -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" );
}
@@ -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);
@@ -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 )
@@ -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 );
@@ -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() )
@@ -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"];
@@ -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() );
@@ -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 );