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opencv/apps/traincascade/boost.cpp

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#include "opencv2/core.hpp"
#include "boost.h"
#include "cascadeclassifier.h"
#include <queue>
#include "cxmisc.h"
#include "cvconfig.h"
#ifdef HAVE_TBB
# include "tbb/tbb_stddef.h"
# if TBB_VERSION_MAJOR*100 + TBB_VERSION_MINOR >= 202
# include "tbb/tbb.h"
# include "tbb/task.h"
# undef min
# undef max
# else
# undef HAVE_TBB
# endif
#endif
#ifdef HAVE_TBB
typedef tbb::blocked_range<int> BlockedRange;
template<typename Body> static inline
void parallel_for( const BlockedRange& range, const Body& body )
{
tbb::parallel_for(range, body);
}
#else
class BlockedRange
{
public:
BlockedRange() : _begin(0), _end(0), _grainsize(0) {}
BlockedRange(int b, int e, int g=1) : _begin(b), _end(e), _grainsize(g) {}
int begin() const { return _begin; }
int end() const { return _end; }
int grainsize() const { return _grainsize; }
protected:
int _begin, _end, _grainsize;
};
template<typename Body> static inline
void parallel_for( const BlockedRange& range, const Body& body )
{
body(range);
}
#endif
using namespace std;
static inline double
logRatio( double val )
{
const double eps = 1e-5;
val = max( val, eps );
val = min( val, 1. - eps );
return log( val/(1. - val) );
}
template<typename T, typename Idx>
class LessThanIdx
{
public:
LessThanIdx( const T* _arr ) : arr(_arr) {}
bool operator()(Idx a, Idx b) const { return arr[a] < arr[b]; }
const T* arr;
};
static inline int cvAlign( int size, int align )
{
CV_DbgAssert( (align & (align-1)) == 0 && size < INT_MAX );
return (size + align - 1) & -align;
}
#define CV_THRESHOLD_EPS (0.00001F)
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" );
__CV_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" );
}
}
__CV_END__;
if( cvGetErrStatus() < 0 )
cvReleaseMat( &idx );
return idx;
}
//----------------------------- CascadeBoostParams -------------------------------------------------
CvCascadeBoostParams::CvCascadeBoostParams() : minHitRate( 0.995F), maxFalseAlarm( 0.5F )
{
boost_type = CvBoost::GENTLE;
use_surrogates = use_1se_rule = truncate_pruned_tree = false;
}
CvCascadeBoostParams::CvCascadeBoostParams( int _boostType,
float _minHitRate, float _maxFalseAlarm,
double _weightTrimRate, int _maxDepth, int _maxWeakCount ) :
CvBoostParams( _boostType, _maxWeakCount, _weightTrimRate, _maxDepth, false, 0 )
{
boost_type = CvBoost::GENTLE;
minHitRate = _minHitRate;
maxFalseAlarm = _maxFalseAlarm;
use_surrogates = use_1se_rule = truncate_pruned_tree = false;
}
void CvCascadeBoostParams::write( FileStorage &fs ) const
{
string boostTypeStr = boost_type == CvBoost::DISCRETE ? CC_DISCRETE_BOOST :
boost_type == CvBoost::REAL ? CC_REAL_BOOST :
boost_type == CvBoost::LOGIT ? CC_LOGIT_BOOST :
boost_type == CvBoost::GENTLE ? CC_GENTLE_BOOST : string();
CV_Assert( !boostTypeStr.empty() );
fs << CC_BOOST_TYPE << boostTypeStr;
fs << CC_MINHITRATE << minHitRate;
fs << CC_MAXFALSEALARM << maxFalseAlarm;
fs << CC_TRIM_RATE << weight_trim_rate;
fs << CC_MAX_DEPTH << max_depth;
fs << CC_WEAK_COUNT << weak_count;
}
bool CvCascadeBoostParams::read( const FileNode &node )
{
string boostTypeStr;
FileNode rnode = node[CC_BOOST_TYPE];
rnode >> boostTypeStr;
boost_type = !boostTypeStr.compare( CC_DISCRETE_BOOST ) ? CvBoost::DISCRETE :
!boostTypeStr.compare( CC_REAL_BOOST ) ? CvBoost::REAL :
!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" );
node[CC_MINHITRATE] >> minHitRate;
node[CC_MAXFALSEALARM] >> maxFalseAlarm;
node[CC_TRIM_RATE] >> weight_trim_rate ;
node[CC_MAX_DEPTH] >> max_depth ;
node[CC_WEAK_COUNT] >> weak_count ;
if ( minHitRate <= 0 || minHitRate > 1 ||
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");
return true;
}
void CvCascadeBoostParams::printDefaults() const
{
cout << "--boostParams--" << endl;
cout << " [-bt <{" << CC_DISCRETE_BOOST << ", "
<< CC_REAL_BOOST << ", "
<< CC_LOGIT_BOOST ", "
<< CC_GENTLE_BOOST << "(default)}>]" << endl;
cout << " [-minHitRate <min_hit_rate> = " << minHitRate << ">]" << endl;
cout << " [-maxFalseAlarmRate <max_false_alarm_rate = " << maxFalseAlarm << ">]" << endl;
cout << " [-weightTrimRate <weight_trim_rate = " << weight_trim_rate << ">]" << endl;
cout << " [-maxDepth <max_depth_of_weak_tree = " << max_depth << ">]" << endl;
cout << " [-maxWeakCount <max_weak_tree_count = " << weak_count << ">]" << endl;
}
void CvCascadeBoostParams::printAttrs() const
{
string boostTypeStr = boost_type == CvBoost::DISCRETE ? CC_DISCRETE_BOOST :
boost_type == CvBoost::REAL ? CC_REAL_BOOST :
boost_type == CvBoost::LOGIT ? CC_LOGIT_BOOST :
boost_type == CvBoost::GENTLE ? CC_GENTLE_BOOST : string();
CV_Assert( !boostTypeStr.empty() );
cout << "boostType: " << boostTypeStr << endl;
cout << "minHitRate: " << minHitRate << endl;
cout << "maxFalseAlarmRate: " << maxFalseAlarm << endl;
cout << "weightTrimRate: " << weight_trim_rate << endl;
cout << "maxDepth: " << max_depth << endl;
cout << "maxWeakCount: " << weak_count << endl;
}
bool CvCascadeBoostParams::scanAttr( const string prmName, const string val)
{
bool res = true;
if( !prmName.compare( "-bt" ) )
{
boost_type = !val.compare( CC_DISCRETE_BOOST ) ? CvBoost::DISCRETE :
!val.compare( CC_REAL_BOOST ) ? CvBoost::REAL :
!val.compare( CC_LOGIT_BOOST ) ? CvBoost::LOGIT :
!val.compare( CC_GENTLE_BOOST ) ? CvBoost::GENTLE : -1;
if (boost_type == -1)
res = false;
}
else if( !prmName.compare( "-minHitRate" ) )
{
minHitRate = (float) atof( val.c_str() );
}
else if( !prmName.compare( "-maxFalseAlarmRate" ) )
{
maxFalseAlarm = (float) atof( val.c_str() );
}
else if( !prmName.compare( "-weightTrimRate" ) )
{
weight_trim_rate = (float) atof( val.c_str() );
}
else if( !prmName.compare( "-maxDepth" ) )
{
max_depth = atoi( val.c_str() );
}
else if( !prmName.compare( "-maxWeakCount" ) )
{
weak_count = atoi( val.c_str() );
}
else
res = false;
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 )) != 0 );
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 )) != 0);
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*get_length_subbuf() +
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*get_length_subbuf() +
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*get_length_subbuf() +
(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*get_length_subbuf() +
(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*get_length_subbuf() +
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*get_length_subbuf() +
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,
const CvDTreeParams& _params )
{
is_classifier = true;
var_all = var_count = (int)_featureEvaluator->getNumFeatures();
featureEvaluator = _featureEvaluator;
shared = true;
set_params( _params );
max_c_count = MAX( 2, featureEvaluator->getMaxCatCount() );
var_type = cvCreateMat( 1, var_count + 2, CV_32SC1 );
if ( featureEvaluator->getMaxCatCount() > 0 )
{
numPrecalcIdx = 0;
cat_var_count = var_count;
ord_var_count = 0;
for( int vi = 0; vi < var_count; vi++ )
{
var_type->data.i[vi] = vi;
}
}
else
{
cat_var_count = 0;
ord_var_count = var_count;
for( int vi = 1; vi <= var_count; vi++ )
{
var_type->data.i[vi-1] = -vi;
}
}
var_type->data.i[var_count] = cat_var_count;
var_type->data.i[var_count+1] = cat_var_count+1;
int maxSplitSize = cvAlign(sizeof(CvDTreeSplit) + (MAX(0,max_c_count - 33)/32)*sizeof(int),sizeof(void*));
int treeBlockSize = MAX((int)sizeof(CvDTreeNode)*8, maxSplitSize);
treeBlockSize = MAX(treeBlockSize + BlockSizeDelta, MinBlockSize);
tree_storage = cvCreateMemStorage( treeBlockSize );
node_heap = cvCreateSet( 0, sizeof(node_heap[0]), sizeof(CvDTreeNode), tree_storage );
split_heap = cvCreateSet( 0, sizeof(split_heap[0]), maxSplitSize, tree_storage );
}
CvCascadeBoostTrainData::CvCascadeBoostTrainData( const CvFeatureEvaluator* _featureEvaluator,
int _numSamples,
int _precalcValBufSize, int _precalcIdxBufSize,
const CvDTreeParams& _params )
{
setData( _featureEvaluator, _numSamples, _precalcValBufSize, _precalcIdxBufSize, _params );
}
void CvCascadeBoostTrainData::setData( const CvFeatureEvaluator* _featureEvaluator,
int _numSamples,
int _precalcValBufSize, int _precalcIdxBufSize,
const CvDTreeParams& _params )
{
int* idst = 0;
unsigned short* udst = 0;
uint64 effective_buf_size = 0;
int effective_buf_height = 0, effective_buf_width = 0;
clear();
shared = true;
have_labels = true;
have_priors = false;
is_classifier = true;
rng = &cv::theRNG();
set_params( _params );
CV_Assert( _featureEvaluator );
featureEvaluator = _featureEvaluator;
max_c_count = MAX( 2, featureEvaluator->getMaxCatCount() );
_resp = featureEvaluator->getCls();
responses = &_resp;
// 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" );
var_count = var_all = featureEvaluator->getNumFeatures() * featureEvaluator->getFeatureSize();
sample_count = _numSamples;
is_buf_16u = false;
if (sample_count < 65536)
is_buf_16u = true;
numPrecalcVal = min( cvRound((double)_precalcValBufSize*1048576. / (sizeof(float)*sample_count)), var_count );
numPrecalcIdx = min( cvRound((double)_precalcIdxBufSize*1048576. /
((is_buf_16u ? sizeof(unsigned short) : sizeof (int))*sample_count)), var_count );
assert( numPrecalcIdx >= 0 && numPrecalcVal >= 0 );
valCache.create( numPrecalcVal, sample_count, CV_32FC1 );
var_type = cvCreateMat( 1, var_count + 2, CV_32SC1 );
if ( featureEvaluator->getMaxCatCount() > 0 )
{
numPrecalcIdx = 0;
cat_var_count = var_count;
ord_var_count = 0;
for( int vi = 0; vi < var_count; vi++ )
{
var_type->data.i[vi] = vi;
}
}
else
{
cat_var_count = 0;
ord_var_count = var_count;
for( int vi = 1; vi <= var_count; vi++ )
{
var_type->data.i[vi-1] = -vi;
}
}
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/*cv_lables*/;
buf_count = 2;
buf_size = -1; // the member buf_size is obsolete
effective_buf_size = (uint64)(work_var_count + 1)*(uint64)sample_count * buf_count; // this is the total size of "CvMat buf" to be allocated
effective_buf_width = sample_count;
effective_buf_height = work_var_count+1;
if (effective_buf_width >= effective_buf_height)
effective_buf_height *= buf_count;
else
effective_buf_width *= buf_count;
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");
}
if ( is_buf_16u )
buf = cvCreateMat( effective_buf_height, effective_buf_width, CV_16UC1 );
else
buf = cvCreateMat( effective_buf_height, effective_buf_width, CV_32SC1 );
cat_count = cvCreateMat( 1, cat_var_count + 1, CV_32SC1 );
// precalculate valCache and set indices in buf
precalculate();
// now calculate the maximum size of split,
// create memory storage that will keep nodes and splits of the decision tree
// allocate root node and the buffer for the whole training data
int maxSplitSize = cvAlign(sizeof(CvDTreeSplit) +
(MAX(0,sample_count - 33)/32)*sizeof(int),sizeof(void*));
int treeBlockSize = MAX((int)sizeof(CvDTreeNode)*8, maxSplitSize);
treeBlockSize = MAX(treeBlockSize + BlockSizeDelta, MinBlockSize);
tree_storage = cvCreateMemStorage( treeBlockSize );
node_heap = cvCreateSet( 0, sizeof(*node_heap), sizeof(CvDTreeNode), tree_storage );
int nvSize = var_count*sizeof(int);
nvSize = cvAlign(MAX( nvSize, (int)sizeof(CvSetElem) ), sizeof(void*));
int tempBlockSize = nvSize;
tempBlockSize = MAX( tempBlockSize + BlockSizeDelta, MinBlockSize );
temp_storage = cvCreateMemStorage( tempBlockSize );
nv_heap = cvCreateSet( 0, sizeof(*nv_heap), nvSize, temp_storage );
data_root = new_node( 0, sample_count, 0, 0 );
// set sample labels
if (is_buf_16u)
udst = (unsigned short*)(buf->data.s + work_var_count*sample_count);
else
idst = buf->data.i + work_var_count*sample_count;
for (int si = 0; si < sample_count; si++)
{
if (udst)
udst[si] = (unsigned short)si;
else
idst[si] = si;
}
for( int vi = 0; vi < var_count; vi++ )
data_root->set_num_valid(vi, sample_count);
for( int vi = 0; vi < cat_var_count; vi++ )
cat_count->data.i[vi] = max_c_count;
cat_count->data.i[cat_var_count] = 2;
maxSplitSize = cvAlign(sizeof(CvDTreeSplit) +
(MAX(0,max_c_count - 33)/32)*sizeof(int),sizeof(void*));
split_heap = cvCreateSet( 0, sizeof(*split_heap), maxSplitSize, tree_storage );
priors = cvCreateMat( 1, get_num_classes(), CV_64F );
cvSet(priors, cvScalar(1));
priors_mult = cvCloneMat( priors );
counts = cvCreateMat( 1, get_num_classes(), CV_32SC1 );
direction = cvCreateMat( 1, sample_count, CV_8UC1 );
split_buf = cvCreateMat( 1, sample_count, CV_32SC1 );//TODO: make a pointer
}
void CvCascadeBoostTrainData::free_train_data()
{
CvDTreeTrainData::free_train_data();
valCache.release();
}
const int* CvCascadeBoostTrainData::get_class_labels( CvDTreeNode* n, int* labelsBuf)
{
int nodeSampleCount = n->sample_count;
int rStep = CV_IS_MAT_CONT( responses->type ) ? 1 : responses->step / CV_ELEM_SIZE( responses->type );
int* sampleIndicesBuf = labelsBuf; //
const int* sampleIndices = get_sample_indices(n, sampleIndicesBuf);
for( int si = 0; si < nodeSampleCount; si++ )
{
int sidx = sampleIndices[si];
labelsBuf[si] = (int)responses->data.fl[sidx*rStep];
}
return labelsBuf;
}
const int* CvCascadeBoostTrainData::get_sample_indices( CvDTreeNode* n, int* indicesBuf )
{
return CvDTreeTrainData::get_cat_var_data( n, get_work_var_count(), indicesBuf );
}
const int* CvCascadeBoostTrainData::get_cv_labels( CvDTreeNode* n, int* labels_buf )
{
return CvDTreeTrainData::get_cat_var_data( n, get_work_var_count() - 1, labels_buf );
}
void CvCascadeBoostTrainData::get_ord_var_data( CvDTreeNode* n, int vi, float* ordValuesBuf, int* sortedIndicesBuf,
const float** ordValues, const int** sortedIndices, int* sampleIndicesBuf )
{
int nodeSampleCount = n->sample_count;
const int* sampleIndices = get_sample_indices(n, sampleIndicesBuf);
if ( vi < numPrecalcIdx )
{
if( !is_buf_16u )
*sortedIndices = buf->data.i + n->buf_idx*get_length_subbuf() + vi*sample_count + n->offset;
else
{
const unsigned short* shortIndices = (const unsigned short*)(buf->data.s + n->buf_idx*get_length_subbuf() +
vi*sample_count + n->offset );
for( int i = 0; i < nodeSampleCount; i++ )
sortedIndicesBuf[i] = shortIndices[i];
*sortedIndices = sortedIndicesBuf;
}
if( vi < numPrecalcVal )
{
for( int i = 0; i < nodeSampleCount; i++ )
{
int idx = (*sortedIndices)[i];
idx = sampleIndices[idx];
ordValuesBuf[i] = valCache.at<float>( vi, idx);
}
}
else
{
for( int i = 0; i < nodeSampleCount; i++ )
{
int idx = (*sortedIndices)[i];
idx = sampleIndices[idx];
ordValuesBuf[i] = (*featureEvaluator)( vi, idx);
}
}
}
else // vi >= numPrecalcIdx
{
cv::AutoBuffer<float> abuf(nodeSampleCount);
float* sampleValues = &abuf[0];
if ( vi < numPrecalcVal )
{
for( int i = 0; i < nodeSampleCount; i++ )
{
sortedIndicesBuf[i] = i;
sampleValues[i] = valCache.at<float>( vi, sampleIndices[i] );
}
}
else
{
for( int i = 0; i < nodeSampleCount; i++ )
{
sortedIndicesBuf[i] = i;
sampleValues[i] = (*featureEvaluator)( vi, sampleIndices[i]);
}
}
std::sort(sortedIndicesBuf, sortedIndicesBuf + nodeSampleCount, LessThanIdx<float, int>(&sampleValues[0]) );
for( int i = 0; i < nodeSampleCount; i++ )
ordValuesBuf[i] = (&sampleValues[0])[sortedIndicesBuf[i]];
*sortedIndices = sortedIndicesBuf;
}
*ordValues = ordValuesBuf;
}
const int* CvCascadeBoostTrainData::get_cat_var_data( CvDTreeNode* n, int vi, int* catValuesBuf )
{
int nodeSampleCount = n->sample_count;
int* sampleIndicesBuf = catValuesBuf; //
const int* sampleIndices = get_sample_indices(n, sampleIndicesBuf);
if ( vi < numPrecalcVal )
{
for( int i = 0; i < nodeSampleCount; i++ )
catValuesBuf[i] = (int) valCache.at<float>( vi, sampleIndices[i]);
}
else
{
if( vi >= numPrecalcVal && vi < var_count )
{
for( int i = 0; i < nodeSampleCount; i++ )
catValuesBuf[i] = (int)(*featureEvaluator)( vi, sampleIndices[i] );
}
else
{
get_cv_labels( n, catValuesBuf );
}
}
return catValuesBuf;
}
float CvCascadeBoostTrainData::getVarValue( int vi, int si )
{
if ( vi < numPrecalcVal && !valCache.empty() )
return valCache.at<float>( vi, si );
return (*featureEvaluator)( vi, si );
}
struct FeatureIdxOnlyPrecalc : ParallelLoopBody
{
FeatureIdxOnlyPrecalc( const CvFeatureEvaluator* _featureEvaluator, CvMat* _buf, int _sample_count, bool _is_buf_16u )
{
featureEvaluator = _featureEvaluator;
sample_count = _sample_count;
udst = (unsigned short*)_buf->data.s;
idst = _buf->data.i;
is_buf_16u = _is_buf_16u;
}
void operator()( const Range& range ) const
{
cv::AutoBuffer<float> valCache(sample_count);
float* valCachePtr = (float*)valCache;
for ( int fi = range.start; fi < range.end; fi++)
{
for( int si = 0; si < sample_count; si++ )
{
valCachePtr[si] = (*featureEvaluator)( fi, si );
if ( is_buf_16u )
*(udst + fi*sample_count + si) = (unsigned short)si;
else
*(idst + fi*sample_count + si) = si;
}
if ( is_buf_16u )
std::sort(udst + fi*sample_count, udst + (fi + 1)*sample_count, LessThanIdx<float, unsigned short>(valCachePtr) );
else
std::sort(idst + fi*sample_count, idst + (fi + 1)*sample_count, LessThanIdx<float, int>(valCachePtr) );
}
}
const CvFeatureEvaluator* featureEvaluator;
int sample_count;
int* idst;
unsigned short* udst;
bool is_buf_16u;
};
struct FeatureValAndIdxPrecalc : ParallelLoopBody
{
FeatureValAndIdxPrecalc( const CvFeatureEvaluator* _featureEvaluator, CvMat* _buf, Mat* _valCache, int _sample_count, bool _is_buf_16u )
{
featureEvaluator = _featureEvaluator;
valCache = _valCache;
sample_count = _sample_count;
udst = (unsigned short*)_buf->data.s;
idst = _buf->data.i;
is_buf_16u = _is_buf_16u;
}
void operator()( const Range& range ) const
{
for ( int fi = range.start; fi < range.end; fi++)
{
for( int si = 0; si < sample_count; si++ )
{
valCache->at<float>(fi,si) = (*featureEvaluator)( fi, si );
if ( is_buf_16u )
*(udst + fi*sample_count + si) = (unsigned short)si;
else
*(idst + fi*sample_count + si) = si;
}
if ( is_buf_16u )
std::sort(idst + fi*sample_count, idst + (fi + 1)*sample_count, LessThanIdx<float, unsigned short>(valCache->ptr<float>(fi)) );
else
std::sort(idst + fi*sample_count, idst + (fi + 1)*sample_count, LessThanIdx<float, int>(valCache->ptr<float>(fi)) );
}
}
const CvFeatureEvaluator* featureEvaluator;
Mat* valCache;
int sample_count;
int* idst;
unsigned short* udst;
bool is_buf_16u;
};
struct FeatureValOnlyPrecalc : ParallelLoopBody
{
FeatureValOnlyPrecalc( const CvFeatureEvaluator* _featureEvaluator, Mat* _valCache, int _sample_count )
{
featureEvaluator = _featureEvaluator;
valCache = _valCache;
sample_count = _sample_count;
}
void operator()( const Range& range ) const
{
for ( int fi = range.start; fi < range.end; fi++)
for( int si = 0; si < sample_count; si++ )
valCache->at<float>(fi,si) = (*featureEvaluator)( fi, si );
}
const CvFeatureEvaluator* featureEvaluator;
Mat* valCache;
int sample_count;
};
void CvCascadeBoostTrainData::precalculate()
{
int minNum = MIN( numPrecalcVal, numPrecalcIdx);
double proctime = -TIME( 0 );
parallel_for_( Range(numPrecalcVal, numPrecalcIdx),
FeatureIdxOnlyPrecalc(featureEvaluator, buf, sample_count, is_buf_16u!=0) );
parallel_for_( Range(0, minNum),
FeatureValAndIdxPrecalc(featureEvaluator, buf, &valCache, sample_count, is_buf_16u!=0) );
parallel_for_( Range(minNum, numPrecalcVal),
FeatureValOnlyPrecalc(featureEvaluator, &valCache, sample_count) );
cout << "Precalculation time: " << (proctime + TIME( 0 )) << endl;
}
//-------------------------------- CascadeBoostTree ----------------------------------------
CvDTreeNode* CvCascadeBoostTree::predict( int sampleIdx ) const
{
CvDTreeNode* node = root;
if( !node )
CV_Error( CV_StsError, "The tree has not been trained yet" );
if ( ((CvCascadeBoostTrainData*)data)->featureEvaluator->getMaxCatCount() == 0 ) // ordered
{
while( node->left )
{
CvDTreeSplit* split = node->split;
float val = ((CvCascadeBoostTrainData*)data)->getVarValue( split->var_idx, sampleIdx );
node = val <= split->ord.c ? node->left : node->right;
}
}
else // categorical
{
while( node->left )
{
CvDTreeSplit* split = node->split;
int c = (int)((CvCascadeBoostTrainData*)data)->getVarValue( split->var_idx, sampleIdx );
node = CV_DTREE_CAT_DIR(c, split->subset) < 0 ? node->left : node->right;
}
}
return node;
}
void CvCascadeBoostTree::write( FileStorage &fs, const Mat& featureMap )
{
int maxCatCount = ((CvCascadeBoostTrainData*)data)->featureEvaluator->getMaxCatCount();
int subsetN = (maxCatCount + 31)/32;
queue<CvDTreeNode*> internalNodesQueue;
int size = (int)pow( 2.f, (float)ensemble->get_params().max_depth);
Ptr<float> leafVals = new float[size];
int leafValIdx = 0;
int internalNodeIdx = 1;
CvDTreeNode* tempNode;
CV_DbgAssert( root );
internalNodesQueue.push( root );
fs << "{";
fs << CC_INTERNAL_NODES << "[:";
while (!internalNodesQueue.empty())
{
tempNode = internalNodesQueue.front();
CV_Assert( tempNode->left );
if ( !tempNode->left->left && !tempNode->left->right) // left node is leaf
{
leafVals[-leafValIdx] = (float)tempNode->left->value;
fs << leafValIdx-- ;
}
else
{
internalNodesQueue.push( tempNode->left );
fs << internalNodeIdx++;
}
CV_Assert( tempNode->right );
if ( !tempNode->right->left && !tempNode->right->right) // right node is leaf
{
leafVals[-leafValIdx] = (float)tempNode->right->value;
fs << leafValIdx--;
}
else
{
internalNodesQueue.push( tempNode->right );
fs << internalNodeIdx++;
}
int fidx = tempNode->split->var_idx;
fidx = featureMap.empty() ? fidx : featureMap.at<int>(0, fidx);
fs << fidx;
if ( !maxCatCount )
fs << tempNode->split->ord.c;
else
for( int i = 0; i < subsetN; i++ )
fs << tempNode->split->subset[i];
internalNodesQueue.pop();
}
fs << "]"; // CC_INTERNAL_NODES
fs << CC_LEAF_VALUES << "[:";
for (int ni = 0; ni < -leafValIdx; ni++)
fs << leafVals[ni];
fs << "]"; // CC_LEAF_VALUES
fs << "}";
}
void CvCascadeBoostTree::read( const FileNode &node, CvBoost* _ensemble,
CvDTreeTrainData* _data )
{
int maxCatCount = ((CvCascadeBoostTrainData*)_data)->featureEvaluator->getMaxCatCount();
int subsetN = (maxCatCount + 31)/32;
int step = 3 + ( maxCatCount>0 ? subsetN : 1 );
queue<CvDTreeNode*> internalNodesQueue;
FileNodeIterator internalNodesIt, leafValsuesIt;
CvDTreeNode* prntNode, *cldNode;
clear();
data = _data;
ensemble = _ensemble;
pruned_tree_idx = 0;
// read tree nodes
FileNode rnode = node[CC_INTERNAL_NODES];
internalNodesIt = rnode.end();
leafValsuesIt = node[CC_LEAF_VALUES].end();
internalNodesIt--; leafValsuesIt--;
for( size_t i = 0; i < rnode.size()/step; i++ )
{
prntNode = data->new_node( 0, 0, 0, 0 );
if ( maxCatCount > 0 )
{
prntNode->split = data->new_split_cat( 0, 0 );
for( int j = subsetN-1; j>=0; j--)
{
*internalNodesIt >> prntNode->split->subset[j]; internalNodesIt--;
}
}
else
{
float split_value;
*internalNodesIt >> split_value; internalNodesIt--;
prntNode->split = data->new_split_ord( 0, split_value, 0, 0, 0);
}
*internalNodesIt >> prntNode->split->var_idx; internalNodesIt--;
int ridx, lidx;
*internalNodesIt >> ridx; internalNodesIt--;
*internalNodesIt >> lidx;internalNodesIt--;
if ( ridx <= 0)
{
prntNode->right = cldNode = data->new_node( 0, 0, 0, 0 );
*leafValsuesIt >> cldNode->value; leafValsuesIt--;
cldNode->parent = prntNode;
}
else
{
prntNode->right = internalNodesQueue.front();
prntNode->right->parent = prntNode;
internalNodesQueue.pop();
}
if ( lidx <= 0)
{
prntNode->left = cldNode = data->new_node( 0, 0, 0, 0 );
*leafValsuesIt >> cldNode->value; leafValsuesIt--;
cldNode->parent = prntNode;
}
else
{
prntNode->left = internalNodesQueue.front();
prntNode->left->parent = prntNode;
internalNodesQueue.pop();
}
internalNodesQueue.push( prntNode );
}
root = internalNodesQueue.front();
internalNodesQueue.pop();
}
void CvCascadeBoostTree::split_node_data( CvDTreeNode* node )
{
int n = node->sample_count, nl, nr, scount = data->sample_count;
char* dir = (char*)data->direction->data.ptr;
CvDTreeNode *left = 0, *right = 0;
int* newIdx = data->split_buf->data.i;
int newBufIdx = data->get_child_buf_idx( node );
int workVarCount = data->get_work_var_count();
CvMat* buf = data->buf;
size_t length_buf_row = data->get_length_subbuf();
cv::AutoBuffer<uchar> inn_buf(n*(3*sizeof(int)+sizeof(float)));
int* tempBuf = (int*)(uchar*)inn_buf;
bool splitInputData;
complete_node_dir(node);
for( int i = nl = nr = 0; i < n; i++ )
{
int d = dir[i];
// initialize new indices for splitting ordered variables
newIdx[i] = (nl & (d-1)) | (nr & -d); // d ? ri : li
nr += d;
nl += d^1;
}
node->left = left = data->new_node( node, nl, newBufIdx, node->offset );
node->right = right = data->new_node( node, nr, newBufIdx, node->offset + nl );
splitInputData = node->depth + 1 < data->params.max_depth &&
(node->left->sample_count > data->params.min_sample_count ||
node->right->sample_count > data->params.min_sample_count);
// split ordered variables, keep both halves sorted.
for( int vi = 0; vi < ((CvCascadeBoostTrainData*)data)->numPrecalcIdx; vi++ )
{
int ci = data->get_var_type(vi);
if( ci >= 0 || !splitInputData )
continue;
int n1 = node->get_num_valid(vi);
float *src_val_buf = (float*)(tempBuf + n);
int *src_sorted_idx_buf = (int*)(src_val_buf + n);
int *src_sample_idx_buf = src_sorted_idx_buf + n;
const int* src_sorted_idx = 0;
const float* src_val = 0;
data->get_ord_var_data(node, vi, src_val_buf, src_sorted_idx_buf, &src_val, &src_sorted_idx, src_sample_idx_buf);
for(int i = 0; i < n; i++)
tempBuf[i] = src_sorted_idx[i];
if (data->is_buf_16u)
{
ushort *ldst, *rdst;
ldst = (ushort*)(buf->data.s + left->buf_idx*length_buf_row +
vi*scount + left->offset);
rdst = (ushort*)(ldst + nl);
// split sorted
for( int i = 0; i < n1; i++ )
{
int idx = tempBuf[i];
int d = dir[idx];
idx = newIdx[idx];
if (d)
{
*rdst = (ushort)idx;
rdst++;
}
else
{
*ldst = (ushort)idx;
ldst++;
}
}
CV_Assert( n1 == n );
}
else
{
int *ldst, *rdst;
ldst = buf->data.i + left->buf_idx*length_buf_row +
vi*scount + left->offset;
rdst = buf->data.i + right->buf_idx*length_buf_row +
vi*scount + right->offset;
// split sorted
for( int i = 0; i < n1; i++ )
{
int idx = tempBuf[i];
int d = dir[idx];
idx = newIdx[idx];
if (d)
{
*rdst = idx;
rdst++;
}
else
{
*ldst = idx;
ldst++;
}
}
CV_Assert( n1 == n );
}
}
// split cv_labels using newIdx relocation table
int *src_lbls_buf = tempBuf + n;
const int* src_lbls = data->get_cv_labels(node, src_lbls_buf);
for(int i = 0; i < n; i++)
tempBuf[i] = src_lbls[i];
if (data->is_buf_16u)
{
unsigned short *ldst = (unsigned short *)(buf->data.s + left->buf_idx*length_buf_row +
(workVarCount-1)*scount + left->offset);
unsigned short *rdst = (unsigned short *)(buf->data.s + right->buf_idx*length_buf_row +
(workVarCount-1)*scount + right->offset);
for( int i = 0; i < n; i++ )
{
int idx = tempBuf[i];
if (dir[i])
{
*rdst = (unsigned short)idx;
rdst++;
}
else
{
*ldst = (unsigned short)idx;
ldst++;
}
}
}
else
{
int *ldst = buf->data.i + left->buf_idx*length_buf_row +
(workVarCount-1)*scount + left->offset;
int *rdst = buf->data.i + right->buf_idx*length_buf_row +
(workVarCount-1)*scount + right->offset;
for( int i = 0; i < n; i++ )
{
int idx = tempBuf[i];
if (dir[i])
{
*rdst = idx;
rdst++;
}
else
{
*ldst = idx;
ldst++;
}
}
}
// split sample indices
int *sampleIdx_src_buf = tempBuf + n;
const int* sampleIdx_src = data->get_sample_indices(node, sampleIdx_src_buf);
for(int i = 0; i < n; i++)
tempBuf[i] = sampleIdx_src[i];
if (data->is_buf_16u)
{
unsigned short* ldst = (unsigned short*)(buf->data.s + left->buf_idx*length_buf_row +
workVarCount*scount + left->offset);
unsigned short* rdst = (unsigned short*)(buf->data.s + right->buf_idx*length_buf_row +
workVarCount*scount + right->offset);
for (int i = 0; i < n; i++)
{
unsigned short idx = (unsigned short)tempBuf[i];
if (dir[i])
{
*rdst = idx;
rdst++;
}
else
{
*ldst = idx;
ldst++;
}
}
}
else
{
int* ldst = buf->data.i + left->buf_idx*length_buf_row +
workVarCount*scount + left->offset;
int* rdst = buf->data.i + right->buf_idx*length_buf_row +
workVarCount*scount + right->offset;
for (int i = 0; i < n; i++)
{
int idx = tempBuf[i];
if (dir[i])
{
*rdst = idx;
rdst++;
}
else
{
*ldst = idx;
ldst++;
}
}
}
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);
}
static void auxMarkFeaturesInMap( const CvDTreeNode* node, Mat& featureMap)
{
if ( node && node->split )
{
featureMap.ptr<int>(0)[node->split->var_idx] = 1;
auxMarkFeaturesInMap( node->left, featureMap );
auxMarkFeaturesInMap( node->right, featureMap );
}
}
void CvCascadeBoostTree::markFeaturesInMap( Mat& featureMap )
{
auxMarkFeaturesInMap( root, featureMap );
}
//----------------------------------- CascadeBoost --------------------------------------
bool CvCascadeBoost::train( const CvFeatureEvaluator* _featureEvaluator,
int _numSamples,
int _precalcValBufSize, int _precalcIdxBufSize,
const CvCascadeBoostParams& _params )
{
bool isTrained = false;
CV_Assert( !data );
clear();
data = new CvCascadeBoostTrainData( _featureEvaluator, _numSamples,
_precalcValBufSize, _precalcIdxBufSize, _params );
CvMemStorage *storage = cvCreateMemStorage();
weak = cvCreateSeq( 0, sizeof(CvSeq), sizeof(CvBoostTree*), storage );
storage = 0;
set_params( _params );
if ( (_params.boost_type == LOGIT) || (_params.boost_type == GENTLE) )
data->do_responses_copy();
update_weights( 0 );
cout << "+----+---------+---------+" << endl;
cout << "| N | HR | FA |" << endl;
cout << "+----+---------+---------+" << endl;
do
{
CvCascadeBoostTree* tree = new CvCascadeBoostTree;
if( !tree->train( data, subsample_mask, this ) )
{
delete tree;
break;
}
cvSeqPush( weak, &tree );
update_weights( tree );
trim_weights();
if( cvCountNonZero(subsample_mask) == 0 )
break;
}
while( !isErrDesired() && (weak->total < params.weak_count) );
if(weak->total > 0)
{
data->is_classifier = true;
data->free_train_data();
isTrained = true;
}
else
clear();
return isTrained;
}
float CvCascadeBoost::predict( int sampleIdx, bool returnSum ) const
{
CV_Assert( weak );
double sum = 0;
CvSeqReader reader;
cvStartReadSeq( weak, &reader );
cvSetSeqReaderPos( &reader, 0 );
for( int i = 0; i < weak->total; i++ )
{
CvBoostTree* wtree;
CV_READ_SEQ_ELEM( wtree, reader );
sum += ((CvCascadeBoostTree*)wtree)->predict(sampleIdx)->value;
}
if( !returnSum )
sum = sum < threshold - CV_THRESHOLD_EPS ? 0.0 : 1.0;
return (float)sum;
}
bool CvCascadeBoost::set_params( const CvBoostParams& _params )
{
minHitRate = ((CvCascadeBoostParams&)_params).minHitRate;
maxFalseAlarm = ((CvCascadeBoostParams&)_params).maxFalseAlarm;
return ( ( minHitRate > 0 ) && ( minHitRate < 1) &&
( maxFalseAlarm > 0 ) && ( maxFalseAlarm < 1) &&
CvBoost::set_params( _params ));
}
void CvCascadeBoost::update_weights( CvBoostTree* tree )
{
int n = data->sample_count;
double sumW = 0.;
int step = 0;
float* fdata = 0;
int *sampleIdxBuf;
const int* sampleIdx = 0;
int inn_buf_size = ((params.boost_type == LOGIT) || (params.boost_type == GENTLE) ? n*sizeof(int) : 0) +
( !tree ? n*sizeof(int) : 0 );
cv::AutoBuffer<uchar> inn_buf(inn_buf_size);
uchar* cur_inn_buf_pos = (uchar*)inn_buf;
if ( (params.boost_type == LOGIT) || (params.boost_type == GENTLE) )
{
step = CV_IS_MAT_CONT(data->responses_copy->type) ?
1 : data->responses_copy->step / CV_ELEM_SIZE(data->responses_copy->type);
fdata = data->responses_copy->data.fl;
sampleIdxBuf = (int*)cur_inn_buf_pos; cur_inn_buf_pos = (uchar*)(sampleIdxBuf + n);
sampleIdx = data->get_sample_indices( data->data_root, sampleIdxBuf );
}
CvMat* buf = data->buf;
size_t length_buf_row = data->get_length_subbuf();
if( !tree ) // before training the first tree, initialize weights and other parameters
{
int* classLabelsBuf = (int*)cur_inn_buf_pos; cur_inn_buf_pos = (uchar*)(classLabelsBuf + n);
const int* classLabels = data->get_class_labels(data->data_root, classLabelsBuf);
// in case of logitboost and gentle adaboost each weak tree is a regression tree,
// so we need to convert class labels to floating-point values
double w0 = 1./n;
double p[2] = { 1, 1 };
cvReleaseMat( &orig_response );
cvReleaseMat( &sum_response );
cvReleaseMat( &weak_eval );
cvReleaseMat( &subsample_mask );
cvReleaseMat( &weights );
orig_response = cvCreateMat( 1, n, CV_32S );
weak_eval = cvCreateMat( 1, n, CV_64F );
subsample_mask = cvCreateMat( 1, n, CV_8U );
weights = cvCreateMat( 1, n, CV_64F );
subtree_weights = cvCreateMat( 1, n + 2, CV_64F );
if (data->is_buf_16u)
{
unsigned short* labels = (unsigned short*)(buf->data.s + data->data_root->buf_idx*length_buf_row +
data->data_root->offset + (data->work_var_count-1)*data->sample_count);
for( int i = 0; i < n; i++ )
{
// save original categorical responses {0,1}, convert them to {-1,1}
orig_response->data.i[i] = classLabels[i]*2 - 1;
// make all the samples active at start.
// later, in trim_weights() deactivate/reactive again some, if need
subsample_mask->data.ptr[i] = (uchar)1;
// make all the initial weights the same.
weights->data.db[i] = w0*p[classLabels[i]];
// set the labels to find (from within weak tree learning proc)
// the particular sample weight, and where to store the response.
labels[i] = (unsigned short)i;
}
}
else
{
int* labels = buf->data.i + data->data_root->buf_idx*length_buf_row +
data->data_root->offset + (data->work_var_count-1)*data->sample_count;
for( int i = 0; i < n; i++ )
{
// save original categorical responses {0,1}, convert them to {-1,1}
orig_response->data.i[i] = classLabels[i]*2 - 1;
subsample_mask->data.ptr[i] = (uchar)1;
weights->data.db[i] = w0*p[classLabels[i]];
labels[i] = i;
}
}
if( params.boost_type == LOGIT )
{
sum_response = cvCreateMat( 1, n, CV_64F );
for( int i = 0; i < n; i++ )
{
sum_response->data.db[i] = 0;
fdata[sampleIdx[i]*step] = orig_response->data.i[i] > 0 ? 2.f : -2.f;
}
// in case of logitboost each weak tree is a regression tree.
// the target function values are recalculated for each of the trees
data->is_classifier = false;
}
else if( params.boost_type == GENTLE )
{
for( int i = 0; i < n; i++ )
fdata[sampleIdx[i]*step] = (float)orig_response->data.i[i];
data->is_classifier = false;
}
}
else
{
// at this moment, for all the samples that participated in the training of the most
// recent weak classifier we know the responses. For other samples we need to compute them
if( have_subsample )
{
// invert the subsample mask
cvXorS( subsample_mask, cvScalar(1.), subsample_mask );
// run tree through all the non-processed samples
for( int i = 0; i < n; i++ )
if( subsample_mask->data.ptr[i] )
{
weak_eval->data.db[i] = ((CvCascadeBoostTree*)tree)->predict( i )->value;
}
}
// now update weights and other parameters for each type of boosting
if( params.boost_type == DISCRETE )
{
// Discrete AdaBoost:
// weak_eval[i] (=f(x_i)) is in {-1,1}
// err = sum(w_i*(f(x_i) != y_i))/sum(w_i)
// C = log((1-err)/err)
// w_i *= exp(C*(f(x_i) != y_i))
double C, err = 0.;
double scale[] = { 1., 0. };
for( int i = 0; i < n; i++ )
{
double w = weights->data.db[i];
sumW += w;
err += w*(weak_eval->data.db[i] != orig_response->data.i[i]);
}
if( sumW != 0 )
err /= sumW;
C = err = -logRatio( err );
scale[1] = exp(err);
sumW = 0;
for( int i = 0; i < n; i++ )
{
double w = weights->data.db[i]*
scale[weak_eval->data.db[i] != orig_response->data.i[i]];
sumW += w;
weights->data.db[i] = w;
}
tree->scale( C );
}
else if( params.boost_type == REAL )
{
// Real AdaBoost:
// weak_eval[i] = f(x_i) = 0.5*log(p(x_i)/(1-p(x_i))), p(x_i)=P(y=1|x_i)
// w_i *= exp(-y_i*f(x_i))
for( int i = 0; i < n; i++ )
weak_eval->data.db[i] *= -orig_response->data.i[i];
cvExp( weak_eval, weak_eval );
for( int i = 0; i < n; i++ )
{
double w = weights->data.db[i]*weak_eval->data.db[i];
sumW += w;
weights->data.db[i] = w;
}
}
else if( params.boost_type == LOGIT )
{
// LogitBoost:
// weak_eval[i] = f(x_i) in [-z_max,z_max]
// sum_response = F(x_i).
// F(x_i) += 0.5*f(x_i)
// p(x_i) = exp(F(x_i))/(exp(F(x_i)) + exp(-F(x_i))=1/(1+exp(-2*F(x_i)))
// reuse weak_eval: weak_eval[i] <- p(x_i)
// w_i = p(x_i)*1(1 - p(x_i))
// z_i = ((y_i+1)/2 - p(x_i))/(p(x_i)*(1 - p(x_i)))
// store z_i to the data->data_root as the new target responses
const double lbWeightThresh = FLT_EPSILON;
const double lbZMax = 10.;
for( int i = 0; i < n; i++ )
{
double s = sum_response->data.db[i] + 0.5*weak_eval->data.db[i];
sum_response->data.db[i] = s;
weak_eval->data.db[i] = -2*s;
}
cvExp( weak_eval, weak_eval );
for( int i = 0; i < n; i++ )
{
double p = 1./(1. + weak_eval->data.db[i]);
double w = p*(1 - p), z;
w = MAX( w, lbWeightThresh );
weights->data.db[i] = w;
sumW += w;
if( orig_response->data.i[i] > 0 )
{
z = 1./p;
fdata[sampleIdx[i]*step] = (float)min(z, lbZMax);
}
else
{
z = 1./(1-p);
fdata[sampleIdx[i]*step] = (float)-min(z, lbZMax);
}
}
}
else
{
// Gentle AdaBoost:
// weak_eval[i] = f(x_i) in [-1,1]
// w_i *= exp(-y_i*f(x_i))
assert( params.boost_type == GENTLE );
for( int i = 0; i < n; i++ )
weak_eval->data.db[i] *= -orig_response->data.i[i];
cvExp( weak_eval, weak_eval );
for( int i = 0; i < n; i++ )
{
double w = weights->data.db[i] * weak_eval->data.db[i];
weights->data.db[i] = w;
sumW += w;
}
}
}
// renormalize weights
if( sumW > FLT_EPSILON )
{
sumW = 1./sumW;
for( int i = 0; i < n; ++i )
weights->data.db[i] *= sumW;
}
}
bool CvCascadeBoost::isErrDesired()
{
int sCount = data->sample_count,
numPos = 0, numNeg = 0, numFalse = 0, numPosTrue = 0;
vector<float> eval(sCount);
for( int i = 0; i < sCount; i++ )
if( ((CvCascadeBoostTrainData*)data)->featureEvaluator->getCls( i ) == 1.0F )
eval[numPos++] = predict( i, true );
std::sort(&eval[0], &eval[0] + numPos);
int thresholdIdx = (int)((1.0F - minHitRate) * numPos);
threshold = eval[ thresholdIdx ];
numPosTrue = numPos - thresholdIdx;
for( int i = thresholdIdx - 1; i >= 0; i--)
if ( abs( eval[i] - threshold) < FLT_EPSILON )
numPosTrue++;
float hitRate = ((float) numPosTrue) / ((float) numPos);
for( int i = 0; i < sCount; i++ )
{
if( ((CvCascadeBoostTrainData*)data)->featureEvaluator->getCls( i ) == 0.0F )
{
numNeg++;
if( predict( i ) )
numFalse++;
}
}
float falseAlarm = ((float) numFalse) / ((float) numNeg);
cout << "|"; cout.width(4); cout << right << weak->total;
cout << "|"; cout.width(9); cout << right << hitRate;
cout << "|"; cout.width(9); cout << right << falseAlarm;
cout << "|" << endl;
cout << "+----+---------+---------+" << endl;
return falseAlarm <= maxFalseAlarm;
}
void CvCascadeBoost::write( FileStorage &fs, const Mat& featureMap ) const
{
// char cmnt[30];
CvCascadeBoostTree* weakTree;
fs << CC_WEAK_COUNT << weak->total;
fs << CC_STAGE_THRESHOLD << threshold;
fs << CC_WEAK_CLASSIFIERS << "[";
for( int wi = 0; wi < weak->total; wi++)
{
/*sprintf( cmnt, "tree %i", wi );
cvWriteComment( fs, cmnt, 0 );*/
weakTree = *((CvCascadeBoostTree**) cvGetSeqElem( weak, wi ));
weakTree->write( fs, featureMap );
}
fs << "]";
}
bool CvCascadeBoost::read( const FileNode &node,
const CvFeatureEvaluator* _featureEvaluator,
const CvCascadeBoostParams& _params )
{
CvMemStorage* storage;
clear();
data = new CvCascadeBoostTrainData( _featureEvaluator, _params );
set_params( _params );
node[CC_STAGE_THRESHOLD] >> threshold;
FileNode rnode = node[CC_WEAK_CLASSIFIERS];
storage = cvCreateMemStorage();
weak = cvCreateSeq( 0, sizeof(CvSeq), sizeof(CvBoostTree*), storage );
for( FileNodeIterator it = rnode.begin(); it != rnode.end(); it++ )
{
CvCascadeBoostTree* tree = new CvCascadeBoostTree();
tree->read( *it, this, data );
cvSeqPush( weak, &tree );
}
return true;
}
void CvCascadeBoost::markUsedFeaturesInMap( Mat& featureMap )
{
for( int wi = 0; wi < weak->total; wi++ )
{
CvCascadeBoostTree* weakTree = *((CvCascadeBoostTree**) cvGetSeqElem( weak, wi ));
weakTree->markFeaturesInMap( featureMap );
}
}