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fixed traincascade for ordered features

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pull/13383/head
Maria Dimashova 13 years ago
parent b4f17ab79e
commit 48ea65e69c
4 changed files with 312 additions and 37 deletions
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  1. 2
      modules/ml/src/boost.cpp
  2. 24
      modules/ml/src/tree.cpp
  3. 319
      modules/traincascade/boost.cpp
  4. 4
      modules/traincascade/boost.h

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
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@ -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->params.cv_folds > 0 )
CV_CALL( prune_cv() );
if( !data->shared )
data->free_train_data();
if( !data->shared )
data->free_train_data();
result = true;
result = true;
}
__END__;

319
modules/traincascade/boost.cpp
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@ -27,6 +27,115 @@ static CV_IMPLEMENT_QSORT_EX( icvSortUShAux, unsigned short, CV_CMP_NUM_IDX, con
static const int MinBlockSize = 1 << 16;
static const int BlockSizeDelta = 1 << 10;
// TODO remove this code duplication with ml/precomp.hpp
static int CV_CDECL icvCmpIntegers( const void* a, const void* b )
{
return *(const int*)a - *(const int*)b;
}
static CvMat* cvPreprocessIndexArray( const CvMat* idx_arr, int data_arr_size, bool check_for_duplicates=false )
{
CvMat* idx = 0;
CV_FUNCNAME( "cvPreprocessIndexArray" );
__BEGIN__;
int i, idx_total, idx_selected = 0, step, type, prev = INT_MIN, is_sorted = 1;
uchar* srcb = 0;
int* srci = 0;
int* dsti;
if( !CV_IS_MAT(idx_arr) )
CV_ERROR( CV_StsBadArg, "Invalid index array" );
if( idx_arr->rows != 1 && idx_arr->cols != 1 )
CV_ERROR( CV_StsBadSize, "the index array must be 1-dimensional" );
idx_total = idx_arr->rows + idx_arr->cols - 1;
srcb = idx_arr->data.ptr;
srci = idx_arr->data.i;
type = CV_MAT_TYPE(idx_arr->type);
step = CV_IS_MAT_CONT(idx_arr->type) ? 1 : idx_arr->step/CV_ELEM_SIZE(type);
switch( type )
{
case CV_8UC1:
case CV_8SC1:
// 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,
"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!" );
break;
case CV_32SC1:
// idx_arr is array of integer indices of selected components
if( idx_total > data_arr_size )
CV_ERROR( CV_StsOutOfRange,
"index array may not contain more elements than the total number of input variables" );
idx_selected = idx_total;
// check if sorted already
for( i = 0; i < idx_total; i++ )
{
int val = srci[i*step];
if( val >= prev )
{
is_sorted = 0;
break;
}
prev = val;
}
break;
default:
CV_ERROR( CV_StsUnsupportedFormat, "Unsupported index array data type "
"(it should be 8uC1, 8sC1 or 32sC1)" );
}
CV_CALL( idx = cvCreateMat( 1, idx_selected, CV_32SC1 ));
dsti = idx->data.i;
if( type < CV_32SC1 )
{
for( i = 0; i < idx_total; i++ )
if( srcb[i*step] )
*dsti++ = i;
}
else
{
for( i = 0; i < idx_total; i++ )
dsti[i] = srci[i*step];
if( !is_sorted )
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" );
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" );
}
}
__END__;
if( cvGetErrStatus() < 0 )
cvReleaseMat( &idx );
return idx;
}
//----------------------------- CascadeBoostParams -------------------------------------------------
CvCascadeBoostParams::CvCascadeBoostParams() : minHitRate( 0.995F), maxFalseAlarm( 0.5F )
@ -153,6 +262,171 @@ bool CvCascadeBoostParams::scanAttr( const String prmName, const String val)
return res;
}
CvDTreeNode* CvCascadeBoostTrainData::subsample_data( const CvMat* _subsample_idx )
{
CvDTreeNode* root = 0;
CvMat* isubsample_idx = 0;
CvMat* subsample_co = 0;
bool isMakeRootCopy = true;
if( !data_root )
CV_Error( CV_StsError, "No training data has been set" );
if( _subsample_idx )
{
CV_Assert( isubsample_idx = cvPreprocessIndexArray( _subsample_idx, sample_count ) );
if( isubsample_idx->cols + isubsample_idx->rows - 1 == sample_count )
{
const int* sidx = isubsample_idx->data.i;
for( int i = 0; i < sample_count; i++ )
{
if( sidx[i] != i )
{
isMakeRootCopy = false;
break;
}
}
}
else
isMakeRootCopy = false;
}
if( isMakeRootCopy )
{
// make a copy of the root node
CvDTreeNode temp;
int i;
root = new_node( 0, 1, 0, 0 );
temp = *root;
*root = *data_root;
root->num_valid = temp.num_valid;
if( root->num_valid )
{
for( i = 0; i < var_count; i++ )
root->num_valid[i] = data_root->num_valid[i];
}
root->cv_Tn = temp.cv_Tn;
root->cv_node_risk = temp.cv_node_risk;
root->cv_node_error = temp.cv_node_error;
}
else
{
int* sidx = isubsample_idx->data.i;
// co - array of count/offset pairs (to handle duplicated values in _subsample_idx)
int* co, cur_ofs = 0;
int workVarCount = get_work_var_count();
int count = isubsample_idx->rows + isubsample_idx->cols - 1;
root = new_node( 0, count, 1, 0 );
CV_Assert( subsample_co = cvCreateMat( 1, sample_count*2, CV_32SC1 ));
cvZero( subsample_co );
co = subsample_co->data.i;
for( int i = 0; i < count; i++ )
co[sidx[i]*2]++;
for( int i = 0; i < sample_count; i++ )
{
if( co[i*2] )
{
co[i*2+1] = cur_ofs;
cur_ofs += co[i*2];
}
else
co[i*2+1] = -1;
}
cv::AutoBuffer<uchar> inn_buf(sample_count*(2*sizeof(int) + sizeof(float)));
// subsample ordered variables
for( int vi = 0; vi < numPrecalcIdx; vi++ )
{
int ci = get_var_type(vi);
CV_Assert( ci < 0 );
int *src_idx_buf = (int*)(uchar*)inn_buf;
float *src_val_buf = (float*)(src_idx_buf + sample_count);
int* sample_indices_buf = (int*)(src_val_buf + sample_count);
const int* src_idx = 0;
const float* src_val = 0;
get_ord_var_data( data_root, vi, src_val_buf, src_idx_buf, &src_val, &src_idx, sample_indices_buf );
int j = 0, idx, count_i;
int num_valid = data_root->get_num_valid(vi);
CV_Assert( num_valid == sample_count );
if (is_buf_16u)
{
unsigned short* udst_idx = (unsigned short*)(buf->data.s + root->buf_idx*buf->cols +
vi*sample_count + data_root->offset);
for( int i = 0; i < num_valid; i++ )
{
idx = src_idx[i];
count_i = co[idx*2];
if( count_i )
for( cur_ofs = co[idx*2+1]; count_i > 0; count_i--, j++, cur_ofs++ )
udst_idx[j] = (unsigned short)cur_ofs;
}
}
else
{
int* idst_idx = buf->data.i + root->buf_idx*buf->cols +
vi*sample_count + root->offset;
for( int i = 0; i < num_valid; i++ )
{
idx = src_idx[i];
count_i = co[idx*2];
if( count_i )
for( cur_ofs = co[idx*2+1]; count_i > 0; count_i--, j++, cur_ofs++ )
idst_idx[j] = cur_ofs;
}
}
}
// subsample cv_lables
const int* src_lbls = get_cv_labels(data_root, (int*)(uchar*)inn_buf);
if (is_buf_16u)
{
unsigned short* udst = (unsigned short*)(buf->data.s + root->buf_idx*buf->cols +
(workVarCount-1)*sample_count + root->offset);
for( int i = 0; i < count; i++ )
udst[i] = (unsigned short)src_lbls[sidx[i]];
}
else
{
int* idst = buf->data.i + root->buf_idx*buf->cols +
(workVarCount-1)*sample_count + root->offset;
for( int i = 0; i < count; i++ )
idst[i] = src_lbls[sidx[i]];
}
// subsample sample_indices
const int* sample_idx_src = get_sample_indices(data_root, (int*)(uchar*)inn_buf);
if (is_buf_16u)
{
unsigned short* sample_idx_dst = (unsigned short*)(buf->data.s + root->buf_idx*buf->cols +
workVarCount*sample_count + root->offset);
for( int 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 +
workVarCount*sample_count + root->offset;
for( int i = 0; i < count; i++ )
sample_idx_dst[i] = sample_idx_src[sidx[i]];
}
for( int vi = 0; vi < var_count; vi++ )
root->set_num_valid(vi, count);
}
cvReleaseMat( &isubsample_idx );
cvReleaseMat( &subsample_co );
return root;
}
//---------------------------- CascadeBoostTrainData -----------------------------
CvCascadeBoostTrainData::CvCascadeBoostTrainData( const CvFeatureEvaluator* _featureEvaluator,
@ -270,8 +544,8 @@ void CvCascadeBoostTrainData::setData( const CvFeatureEvaluator* _featureEvaluat
}
var_type->data.i[var_count] = cat_var_count;
var_type->data.i[var_count+1] = cat_var_count+1;
work_var_count = ( cat_var_count ? 0 : numPrecalcIdx ) + 1;
buf_size = (work_var_count + 1) * sample_count;
work_var_count = ( cat_var_count ? 0 : numPrecalcIdx ) + 1/*cv_lables*/;
buf_size = (work_var_count + 1) * sample_count/*sample_indices*/;
buf_count = 2;
if ( is_buf_16u )
@ -814,10 +1088,7 @@ void CvCascadeBoostTree::split_node_data( CvDTreeNode* node )
ldst++;
}
}
assert( n1 == n);
left->set_num_valid(vi, (int)(ldst - ldst0));
right->set_num_valid(vi, (int)(rdst - rdst0));
CV_Assert( n1 == n );
}
else
{
@ -844,10 +1115,7 @@ void CvCascadeBoostTree::split_node_data( CvDTreeNode* node )
ldst++;
}
}
left->set_num_valid(vi, (int)(ldst - ldst0));
right->set_num_valid(vi, (int)(rdst - rdst0));
CV_Assert( n1 == n);
CV_Assert( n1 == n );
}
}
@ -860,11 +1128,11 @@ void CvCascadeBoostTree::split_node_data( CvDTreeNode* node )
if (data->is_buf_16u)
{
unsigned short *ldst = (unsigned short *)(buf->data.s + left->buf_idx*buf->cols +
unsigned short *ldst = (unsigned short *)(buf->data.s + left->buf_idx*buf->cols +
(workVarCount-1)*scount + left->offset);
unsigned short *rdst = (unsigned short *)(buf->data.s + right->buf_idx*buf->cols +
(workVarCount-1)*scount + right->offset);
unsigned short *rdst = (unsigned short *)(buf->data.s + right->buf_idx*buf->cols +
(workVarCount-1)*scount + right->offset);
for( int i = 0; i < n; i++ )
{
int idx = tempBuf[i];
@ -883,11 +1151,11 @@ void CvCascadeBoostTree::split_node_data( CvDTreeNode* node )
}
else
{
int *ldst = buf->data.i + left->buf_idx*buf->cols +
int *ldst = buf->data.i + left->buf_idx*buf->cols +
(workVarCount-1)*scount + left->offset;
int *rdst = buf->data.i + right->buf_idx*buf->cols +
int *rdst = buf->data.i + right->buf_idx*buf->cols +
(workVarCount-1)*scount + right->offset;
for( int i = 0; i < n; i++ )
{
int idx = tempBuf[i];
@ -902,13 +1170,8 @@ void CvCascadeBoostTree::split_node_data( CvDTreeNode* node )
ldst++;
}
}
}
for( int vi = 0; vi < data->var_count; vi++ )
{
left->set_num_valid(vi, (int)(nl));
right->set_num_valid(vi, (int)(nr));
}
// split sample indices
int *sampleIdx_src_buf = tempBuf + n;
const int* sampleIdx_src = data->get_sample_indices(node, sampleIdx_src_buf);
@ -959,6 +1222,12 @@ void CvCascadeBoostTree::split_node_data( CvDTreeNode* node )
}
}
for( int vi = 0; vi < data->var_count; vi++ )
{
left->set_num_valid(vi, (int)(nl));
right->set_num_valid(vi, (int)(nr));
}
// deallocate the parent node data that is not needed anymore
data->free_node_data(node);
}
@ -1008,10 +1277,8 @@ bool CvCascadeBoost::train( const CvFeatureEvaluator* _featureEvaluator,
CvCascadeBoostTree* tree = new CvCascadeBoostTree;
if( !tree->train( data, subsample_mask, this ) )
{
// TODO: may be should finish the loop (!!!)
assert(0);
delete tree;
continue;
break;
}
cvSeqPush( weak, &tree );
update_weights( tree );

4
modules/traincascade/boost.h
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@ -32,6 +32,8 @@ struct CvCascadeBoostTrainData : CvDTreeTrainData
const CvDTreeParams& _params=CvDTreeParams() );
void precalculate();
virtual CvDTreeNode* subsample_data( const CvMat* _subsample_idx );
virtual const int* get_class_labels( CvDTreeNode* n, int* labelsBuf );
virtual const int* get_cv_labels( CvDTreeNode* n, int* labelsBuf);
virtual const int* get_sample_indices( CvDTreeNode* n, int* indicesBuf );
@ -67,7 +69,7 @@ public:
const CvCascadeBoostParams& _params=CvCascadeBoostParams() );
virtual float predict( int sampleIdx, bool returnSum = false ) const;
float getThreshold() const { return threshold; };
float getThreshold() const { return threshold; }
void write( FileStorage &fs, const Mat& featureMap ) const;
bool read( const FileNode &node, const CvFeatureEvaluator* _featureEvaluator,
const CvCascadeBoostParams& _params );

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