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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_tree.cpp
+34 -34
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@@ -93,17 +93,17 @@ bool CvDTreeTrainData::set_params( const CvDTreeParams& _params )
params = _params;
if( params.max_categories < 2 )
CV_ERROR( CV_StsOutOfRange, "params.max_categories should be >= 2" );
CV_ERROR( cv::Error::StsOutOfRange, "params.max_categories should be >= 2" );
params.max_categories = MIN( params.max_categories, 15 );
if( params.max_depth < 0 )
CV_ERROR( CV_StsOutOfRange, "params.max_depth should be >= 0" );
CV_ERROR( cv::Error::StsOutOfRange, "params.max_depth should be >= 0" );
params.max_depth = MIN( params.max_depth, 25 );
params.min_sample_count = MAX(params.min_sample_count,1);
if( params.cv_folds < 0 )
CV_ERROR( CV_StsOutOfRange,
CV_ERROR( cv::Error::StsOutOfRange,
"params.cv_folds should be =0 (the tree is not pruned) "
"or n>0 (tree is pruned using n-fold cross-validation)" );
@@ -111,7 +111,7 @@ bool CvDTreeTrainData::set_params( const CvDTreeParams& _params )
params.cv_folds = 0;
if( params.regression_accuracy < 0 )
CV_ERROR( CV_StsOutOfRange, "params.regression_accuracy should be >= 0" );
CV_ERROR( cv::Error::StsOutOfRange, "params.regression_accuracy should be >= 0" );
ok = true;
@@ -183,7 +183,7 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
cvNorm( data->var_type, var_type, CV_C ) < FLT_EPSILON &&
cvNorm( data->cat_count, cat_count, CV_C ) < FLT_EPSILON &&
cvNorm( data->cat_map, cat_map, CV_C ) < FLT_EPSILON) )
CV_ERROR( CV_StsBadArg,
CV_ERROR( cv::Error::StsBadArg,
"The new training data must have the same types and the input and output variables "
"and the same categories for categorical variables" );
@@ -264,7 +264,7 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
CV_MAT_TYPE(_responses->type) != CV_32FC1) ||
(_responses->rows != 1 && _responses->cols != 1) ||
_responses->rows + _responses->cols - 1 != sample_all )
CV_ERROR( CV_StsBadArg, "The array of _responses must be an integer or "
CV_ERROR( cv::Error::StsBadArg, "The array of _responses must be an integer or "
"floating-point vector containing as many elements as "
"the total number of samples in the training data matrix" );
@@ -317,7 +317,7 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
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");
}
@@ -360,7 +360,7 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
if( cv_n )
{
if( sample_count < cv_n*MAX(params.min_sample_count,10) )
CV_ERROR( CV_StsOutOfRange,
CV_ERROR( cv::Error::StsOutOfRange,
"The many folds in cross-validation for such a small dataset" );
cv_size = cvAlign( cv_n*(sizeof(int) + sizeof(double)*2), sizeof(double) );
@@ -444,7 +444,7 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
{
snprintf( err, sizeof(err), "%d-th value of %d-th (categorical) "
"variable is not an integer", i, vi );
CV_ERROR( CV_StsBadArg, err );
CV_ERROR( cv::Error::StsBadArg, err );
}
}
@@ -452,7 +452,7 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
{
snprintf( err, sizeof(err), "%d-th value of %d-th (categorical) "
"variable is too large", i, vi );
CV_ERROR( CV_StsBadArg, err );
CV_ERROR( cv::Error::StsBadArg, err );
}
num_valid++;
}
@@ -559,7 +559,7 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
{
snprintf( err, sizeof(err), "%d-th value of %d-th (ordered) "
"variable (=%g) is too large", i, vi, val );
CV_ERROR( CV_StsBadArg, err );
CV_ERROR( cv::Error::StsBadArg, err );
}
num_valid++;
}
@@ -652,7 +652,7 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
{
double val = have_priors ? params.priors[i] : 1.;
if( val <= 0 )
CV_ERROR( CV_StsOutOfRange, "Every class weight should be positive" );
CV_ERROR( cv::Error::StsOutOfRange, "Every class weight should be positive" );
priors->data.db[i] = val;
sum += val;
}
@@ -705,7 +705,7 @@ CvDTreeNode* CvDTreeTrainData::subsample_data( const CvMat* _subsample_idx )
__BEGIN__;
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 )
{
@@ -1396,7 +1396,7 @@ void CvDTreeTrainData::read_params( const cv::FileNode& node )
auto tmat = cvMat( tparams_node[ "priors" ].mat() );
priors = cvCloneMat( &tmat );
if( !CV_IS_MAT(priors) )
CV_ERROR( CV_StsParseError, "priors must stored as a matrix" );
CV_ERROR( cv::Error::StsParseError, "priors must stored as a matrix" );
priors_mult = cvCloneMat( priors );
}
}
@@ -1413,12 +1413,12 @@ void CvDTreeTrainData::read_params( const cv::FileNode& node )
(var_idx->cols != 1 && var_idx->rows != 1) ||
var_idx->cols + var_idx->rows - 1 != var_count ||
CV_MAT_TYPE(var_idx->type) != CV_32SC1 )
CV_ERROR( CV_StsParseError,
CV_ERROR( cv::Error::StsParseError,
"var_idx (if exist) must be valid 1d integer vector containing <var_count> elements" );
for( vi = 0; vi < var_count; vi++ )
if( (unsigned)var_idx->data.i[vi] >= (unsigned)var_all )
CV_ERROR( CV_StsOutOfRange, "some of var_idx elements are out of range" );
CV_ERROR( cv::Error::StsOutOfRange, "some of var_idx elements are out of range" );
}
////// read var type
@@ -1434,7 +1434,7 @@ void CvDTreeTrainData::read_params( const cv::FileNode& node )
{
if( vartype_node.empty() || !vartype_node.isSeq() ||
vartype_node.size() != (size_t) var_count )
CV_ERROR( CV_StsParseError, "var_type must exist and be a sequence of 0's and 1's" );
CV_ERROR( cv::Error::StsParseError, "var_type must exist and be a sequence of 0's and 1's" );
reader = vartype_node.begin();
@@ -1442,7 +1442,7 @@ void CvDTreeTrainData::read_params( const cv::FileNode& node )
{
cv::FileNode n = *reader;
if( !n.isInt() || ((int) n & ~1) )
CV_ERROR( CV_StsParseError, "var_type must exist and be a sequence of 0's and 1's" );
CV_ERROR( cv::Error::StsParseError, "var_type must exist and be a sequence of 0's and 1's" );
var_type->data.i[vi] = (int) n ? cat_var_count++ : ord_var_count--;
reader++;
}
@@ -1468,7 +1468,7 @@ void CvDTreeTrainData::read_params( const cv::FileNode& node )
cat_count->cols + cat_count->rows - 1 != cat_var_count + is_classifier ||
(cat_map->cols != 1 && cat_map->rows != 1) ||
CV_MAT_TYPE(cat_map->type) != CV_32SC1 )
CV_ERROR( CV_StsParseError,
CV_ERROR( cv::Error::StsParseError,
"Both cat_count and cat_map must exist and be valid 1d integer vectors of an appropriate size" );
ccount = cat_var_count + is_classifier;
@@ -1481,13 +1481,13 @@ void CvDTreeTrainData::read_params( const cv::FileNode& node )
{
int val = cat_count->data.i[vi];
if( val <= 0 )
CV_ERROR( CV_StsOutOfRange, "some of cat_count elements are out of range" );
CV_ERROR( cv::Error::StsOutOfRange, "some of cat_count elements are out of range" );
max_c_count = MAX( max_c_count, val );
cat_ofs->data.i[vi+1] = total_c_count += val;
}
if( cat_map->cols + cat_map->rows - 1 != total_c_count )
CV_ERROR( CV_StsBadSize,
CV_ERROR( cv::Error::StsBadSize,
"cat_map vector length is not equal to the total number of categories in all categorical vars" );
}
@@ -3631,13 +3631,13 @@ CvDTreeNode* CvDTree::predict( const CvMat* _sample,
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( !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 && !preprocessed_input) ||
(_sample->cols + _sample->rows - 1 != data->var_count && preprocessed_input) )
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 used for training" );
@@ -3656,7 +3656,7 @@ CvDTreeNode* CvDTree::predict( const CvMat* _sample,
{
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" );
m = _missing->data.ptr;
mstep = CV_IS_MAT_CONT(_missing->type) ? 1 : _missing->step/sizeof(m[0]);
@@ -3696,7 +3696,7 @@ CvDTreeNode* CvDTree::predict( const CvMat* _sample,
int ival = cvRound(val);
if( ival != val )
CV_Error( CV_StsBadArg,
CV_Error( cv::Error::StsBadArg,
"one of input categorical variable is not an integer" );
while( a < b )
@@ -3936,11 +3936,11 @@ CvDTreeSplit* CvDTree::read_split( const cv::FileNode& fnode )
int vi, ci;
if( fnode.empty() || !fnode.isMap() )
CV_ERROR( CV_StsParseError, "some of the splits are not stored properly" );
CV_ERROR( cv::Error::StsParseError, "some of the splits are not stored properly" );
vi = fnode[ "var" ].empty() ? -1 : (int) fnode[ "var" ];
if( (unsigned)vi >= (unsigned)data->var_count )
CV_ERROR( CV_StsOutOfRange, "Split variable index is out of range" );
CV_ERROR( cv::Error::StsOutOfRange, "Split variable index is out of range" );
ci = data->get_var_type(vi);
if( ci >= 0 ) // split on categorical var
@@ -3957,14 +3957,14 @@ CvDTreeSplit* CvDTree::read_split( const cv::FileNode& fnode )
}
if( inseq.empty() ||
(!inseq.isSeq() && !inseq.isInt()))
CV_ERROR( CV_StsParseError,
CV_ERROR( cv::Error::StsParseError,
"Either 'in' or 'not_in' tags should be inside a categorical split data" );
if( inseq.isInt() )
{
val = (int) inseq;
if( (unsigned)val >= (unsigned)n )
CV_ERROR( CV_StsOutOfRange, "some of in/not_in elements are out of range" );
CV_ERROR( cv::Error::StsOutOfRange, "some of in/not_in elements are out of range" );
split->subset[val >> 5] |= 1 << (val & 31);
}
@@ -3977,7 +3977,7 @@ CvDTreeSplit* CvDTree::read_split( const cv::FileNode& fnode )
cv::FileNode inode = *reader;
val = (int) inode;
if( !inode.isInt() || (unsigned)val >= (unsigned)n )
CV_ERROR( CV_StsOutOfRange, "some of in/not_in elements are out of range" );
CV_ERROR( cv::Error::StsOutOfRange, "some of in/not_in elements are out of range" );
split->subset[val >> 5] |= 1 << (val & 31);
reader++;
@@ -4025,12 +4025,12 @@ CvDTreeNode* CvDTree::read_node( const cv::FileNode& fnode, CvDTreeNode* parent
int i, depth;
if( fnode.empty() || !fnode.isMap() )
CV_ERROR( CV_StsParseError, "some of the tree elements are not stored properly" );
CV_ERROR( cv::Error::StsParseError, "some of the tree elements are not stored properly" );
CV_CALL( node = data->new_node( parent, 0, 0, 0 ));
depth = fnode[ "depth" ].empty() ? -1 : (int) fnode[ "depth" ];
if( depth != node->depth )
CV_ERROR( CV_StsParseError, "incorrect node depth" );
CV_ERROR( cv::Error::StsParseError, "incorrect node depth" );
node->sample_count = (int) fnode[ "sample_count" ];
node->value = (double) fnode[ "value" ];
@@ -4051,7 +4051,7 @@ CvDTreeNode* CvDTree::read_node( const cv::FileNode& fnode, CvDTreeNode* parent
CvDTreeSplit* last_split = 0;
if( !splits.isSeq() )
CV_ERROR( CV_StsParseError, "splits tag must stored as a sequence" );
CV_ERROR( cv::Error::StsParseError, "splits tag must stored as a sequence" );
reader = splits.begin();
for( i = 0; i < (int) (*reader).size(); i++ )
@@ -4137,7 +4137,7 @@ void CvDTree::read( const cv::FileNode& node, CvDTreeTrainData* _data )
tree_nodes = node[ "nodes" ];
if( tree_nodes.empty() || !tree_nodes.isSeq() )
CV_ERROR( CV_StsParseError, "nodes tag is missing" );
CV_ERROR( cv::Error::StsParseError, "nodes tag is missing" );
pruned_tree_idx = node[ "best_tree_idx" ].empty() ? -1 : node[ "best_tree_idx" ];
read_tree_nodes( tree_nodes );