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fixed traincascade for ordered features

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This commit is contained in:
Maria Dimashova
2011-12-22 11:19:27 +00:00
parent b4f17ab79e
commit 48ea65e69c
4 changed files with 312 additions and 37 deletions
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2
modules/ml/src/boost.cpp
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@@ -1066,7 +1066,7 @@ CvBoost::train( const CvMat* _train_data, int _tflag,
if( !tree->train( data, subsample_mask, this ) )
{
delete tree;
continue;
break;
}
//cvCheckArr( get_weak_response());
cvSeqPush( weak, &tree );

24
modules/ml/src/tree.cpp
View File

@@ -718,7 +718,7 @@ CvDTreeNode* CvDTreeTrainData::subsample_data( const CvMat* _subsample_idx )
// co - array of count/offset pairs (to handle duplicated values in _subsample_idx)
int* co, cur_ofs = 0;
int vi, i;
int work_var_count = get_work_var_count();
int workVarCount = get_work_var_count();
int count = isubsample_idx->rows + isubsample_idx->cols - 1;
root = new_node( 0, count, 1, 0 );
@@ -740,7 +740,7 @@ CvDTreeNode* CvDTreeTrainData::subsample_data( const CvMat* _subsample_idx )
}
cv::AutoBuffer<uchar> inn_buf(sample_count*(2*sizeof(int) + sizeof(float)));
for( vi = 0; vi < work_var_count; vi++ )
for( vi = 0; vi < workVarCount; vi++ )
{
int ci = get_var_type(vi);
@@ -841,14 +841,14 @@ CvDTreeNode* CvDTreeTrainData::subsample_data( const CvMat* _subsample_idx )
if (is_buf_16u)
{
unsigned short* sample_idx_dst = (unsigned short*)(buf->data.s + root->buf_idx*buf->cols +
get_work_var_count()*sample_count + root->offset);
workVarCount*sample_count + root->offset);
for (i = 0; i < count; i++)
sample_idx_dst[i] = (unsigned short)sample_idx_src[sidx[i]];
}
else
{
int* sample_idx_dst = buf->data.i + root->buf_idx*buf->cols +
get_work_var_count()*sample_count + root->offset;
workVarCount*sample_count + root->offset;
for (i = 0; i < count; i++)
sample_idx_dst[i] = sample_idx_src[sidx[i]];
}
@@ -1622,13 +1622,19 @@ bool CvDTree::do_train( const CvMat* _subsample_idx )
CV_CALL( try_split_node(root));
if( data->params.cv_folds > 0 )
CV_CALL( prune_cv() );
if( root->split )
{
CV_Assert( root->left );
CV_Assert( root->right );
if( !data->shared )
data->free_train_data();
if( data->params.cv_folds > 0 )
CV_CALL( prune_cv() );
result = true;
if( !data->shared )
data->free_train_data();
result = true;
}
__END__;