1380 lines
46 KiB
C++
1380 lines
46 KiB
C++
#include "boost.h"
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#include "cascadeclassifier.h"
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#include <queue>
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#include "cxmisc.h"
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using namespace std;
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static inline double
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logRatio( double val )
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{
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const double eps = 1e-5;
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val = max( val, eps );
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val = min( val, 1. - eps );
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return log( val/(1. - val) );
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}
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#define CV_CMP_FLT(i,j) (i < j)
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static CV_IMPLEMENT_QSORT_EX( icvSortFlt, float, CV_CMP_FLT, const float* )
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#define CV_CMP_NUM_IDX(i,j) (aux[i] < aux[j])
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static CV_IMPLEMENT_QSORT_EX( icvSortIntAux, int, CV_CMP_NUM_IDX, const float* )
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static CV_IMPLEMENT_QSORT_EX( icvSortUShAux, unsigned short, CV_CMP_NUM_IDX, const float* )
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#define CV_THRESHOLD_EPS (0.00001F)
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static const int MinBlockSize = 1 << 16;
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static const int BlockSizeDelta = 1 << 10;
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//----------------------------- CascadeBoostParams -------------------------------------------------
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CvCascadeBoostParams::CvCascadeBoostParams() : minHitRate( 0.995F), maxFalseAlarm( 0.5F )
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{
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boost_type = CvBoost::GENTLE;
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use_surrogates = use_1se_rule = truncate_pruned_tree = false;
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}
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CvCascadeBoostParams::CvCascadeBoostParams( int _boostType,
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float _minHitRate, float _maxFalseAlarm,
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double _weightTrimRate, int _maxDepth, int _maxWeakCount ) :
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CvBoostParams( _boostType, _maxWeakCount, _weightTrimRate, _maxDepth, false, 0 )
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{
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boost_type = CvBoost::GENTLE;
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minHitRate = _minHitRate;
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maxFalseAlarm = _maxFalseAlarm;
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use_surrogates = use_1se_rule = truncate_pruned_tree = false;
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}
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void CvCascadeBoostParams::write( FileStorage &fs ) const
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{
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String boostTypeStr = boost_type == CvBoost::DISCRETE ? CC_DISCRETE_BOOST :
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boost_type == CvBoost::REAL ? CC_REAL_BOOST :
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boost_type == CvBoost::LOGIT ? CC_LOGIT_BOOST :
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boost_type == CvBoost::GENTLE ? CC_GENTLE_BOOST : String();
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CV_Assert( !boostTypeStr.empty() );
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fs << CC_BOOST_TYPE << boostTypeStr;
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fs << CC_MINHITRATE << minHitRate;
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fs << CC_MAXFALSEALARM << maxFalseAlarm;
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fs << CC_TRIM_RATE << weight_trim_rate;
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fs << CC_MAX_DEPTH << max_depth;
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fs << CC_WEAK_COUNT << weak_count;
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}
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bool CvCascadeBoostParams::read( const FileNode &node )
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{
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String boostTypeStr;
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FileNode rnode = node[CC_BOOST_TYPE];
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rnode >> boostTypeStr;
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boost_type = !boostTypeStr.compare( CC_DISCRETE_BOOST ) ? CvBoost::DISCRETE :
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!boostTypeStr.compare( CC_REAL_BOOST ) ? CvBoost::REAL :
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!boostTypeStr.compare( CC_LOGIT_BOOST ) ? CvBoost::LOGIT :
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!boostTypeStr.compare( CC_GENTLE_BOOST ) ? CvBoost::GENTLE : -1;
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if (boost_type == -1)
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CV_Error( CV_StsBadArg, "unsupported Boost type" );
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node[CC_MINHITRATE] >> minHitRate;
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node[CC_MAXFALSEALARM] >> maxFalseAlarm;
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node[CC_TRIM_RATE] >> weight_trim_rate ;
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node[CC_MAX_DEPTH] >> max_depth ;
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node[CC_WEAK_COUNT] >> weak_count ;
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if ( minHitRate <= 0 || minHitRate > 1 ||
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maxFalseAlarm <= 0 || maxFalseAlarm > 1 ||
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weight_trim_rate <= 0 || weight_trim_rate > 1 ||
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max_depth <= 0 || weak_count <= 0 )
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CV_Error( CV_StsBadArg, "bad parameters range");
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return true;
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}
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void CvCascadeBoostParams::printDefaults() const
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{
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cout << "--boostParams--" << endl;
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cout << " [-bt <{" << CC_DISCRETE_BOOST << ", "
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<< CC_REAL_BOOST << ", "
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<< CC_LOGIT_BOOST ", "
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<< CC_GENTLE_BOOST << "(default)}>]" << endl;
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cout << " [-minHitRate <min_hit_rate> = " << minHitRate << ">]" << endl;
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cout << " [-maxFalseAlarmRate <max_false_alarm_rate = " << maxFalseAlarm << ">]" << endl;
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cout << " [-weightTrimRate <weight_trim_rate = " << weight_trim_rate << ">]" << endl;
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cout << " [-maxDepth <max_depth_of_weak_tree = " << max_depth << ">]" << endl;
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cout << " [-maxWeakCount <max_weak_tree_count = " << weak_count << ">]" << endl;
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}
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void CvCascadeBoostParams::printAttrs() const
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{
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String boostTypeStr = boost_type == CvBoost::DISCRETE ? CC_DISCRETE_BOOST :
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boost_type == CvBoost::REAL ? CC_REAL_BOOST :
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boost_type == CvBoost::LOGIT ? CC_LOGIT_BOOST :
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boost_type == CvBoost::GENTLE ? CC_GENTLE_BOOST : String();
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CV_Assert( !boostTypeStr.empty() );
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cout << "boostType: " << boostTypeStr << endl;
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cout << "minHitRate: " << minHitRate << endl;
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cout << "maxFalseAlarmRate: " << maxFalseAlarm << endl;
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cout << "weightTrimRate: " << weight_trim_rate << endl;
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cout << "maxDepth: " << max_depth << endl;
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cout << "maxWeakCount: " << weak_count << endl;
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}
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bool CvCascadeBoostParams::scanAttr( const String prmName, const String val)
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{
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bool res = true;
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if( !prmName.compare( "-bt" ) )
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{
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boost_type = !val.compare( CC_DISCRETE_BOOST ) ? CvBoost::DISCRETE :
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!val.compare( CC_REAL_BOOST ) ? CvBoost::REAL :
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!val.compare( CC_LOGIT_BOOST ) ? CvBoost::LOGIT :
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!val.compare( CC_GENTLE_BOOST ) ? CvBoost::GENTLE : -1;
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if (boost_type == -1)
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res = false;
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}
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else if( !prmName.compare( "-minHitRate" ) )
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{
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minHitRate = (float) atof( val.c_str() );
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}
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else if( !prmName.compare( "-maxFalseAlarmRate" ) )
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{
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maxFalseAlarm = (float) atof( val.c_str() );
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}
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else if( !prmName.compare( "-weightTrimRate" ) )
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{
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weight_trim_rate = (float) atof( val.c_str() );
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}
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else if( !prmName.compare( "-maxDepth" ) )
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{
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max_depth = atoi( val.c_str() );
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}
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else if( !prmName.compare( "-maxWeakCount" ) )
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{
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weak_count = atoi( val.c_str() );
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}
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else
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res = false;
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return res;
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}
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//---------------------------- CascadeBoostTrainData -----------------------------
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CvCascadeBoostTrainData::CvCascadeBoostTrainData( const CvFeatureEvaluator* _featureEvaluator,
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const CvDTreeParams& _params )
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{
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is_classifier = true;
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var_all = var_count = (int)_featureEvaluator->getNumFeatures();
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featureEvaluator = _featureEvaluator;
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shared = true;
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set_params( _params );
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max_c_count = MAX( 2, featureEvaluator->getMaxCatCount() );
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var_type = cvCreateMat( 1, var_count + 2, CV_32SC1 );
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if ( featureEvaluator->getMaxCatCount() > 0 )
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{
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numPrecalcIdx = 0;
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cat_var_count = var_count;
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ord_var_count = 0;
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for( int vi = 0; vi < var_count; vi++ )
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{
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var_type->data.i[vi] = vi;
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}
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}
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else
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{
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cat_var_count = 0;
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ord_var_count = var_count;
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for( int vi = 1; vi <= var_count; vi++ )
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{
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var_type->data.i[vi-1] = -vi;
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}
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}
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var_type->data.i[var_count] = cat_var_count;
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var_type->data.i[var_count+1] = cat_var_count+1;
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int maxSplitSize = cvAlign(sizeof(CvDTreeSplit) + (MAX(0,max_c_count - 33)/32)*sizeof(int),sizeof(void*));
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int treeBlockSize = MAX((int)sizeof(CvDTreeNode)*8, maxSplitSize);
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treeBlockSize = MAX(treeBlockSize + BlockSizeDelta, MinBlockSize);
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tree_storage = cvCreateMemStorage( treeBlockSize );
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node_heap = cvCreateSet( 0, sizeof(node_heap[0]), sizeof(CvDTreeNode), tree_storage );
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split_heap = cvCreateSet( 0, sizeof(split_heap[0]), maxSplitSize, tree_storage );
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}
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CvCascadeBoostTrainData::CvCascadeBoostTrainData( const CvFeatureEvaluator* _featureEvaluator,
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int _numSamples,
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int _precalcValBufSize, int _precalcIdxBufSize,
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const CvDTreeParams& _params )
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{
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setData( _featureEvaluator, _numSamples, _precalcValBufSize, _precalcIdxBufSize, _params );
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}
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void CvCascadeBoostTrainData::setData( const CvFeatureEvaluator* _featureEvaluator,
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int _numSamples,
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int _precalcValBufSize, int _precalcIdxBufSize,
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const CvDTreeParams& _params )
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{
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int* idst = 0;
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unsigned short* udst = 0;
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clear();
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shared = true;
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have_labels = true;
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have_priors = false;
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is_classifier = true;
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rng = &cv::theRNG();
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set_params( _params );
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CV_Assert( _featureEvaluator );
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featureEvaluator = _featureEvaluator;
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max_c_count = MAX( 2, featureEvaluator->getMaxCatCount() );
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_resp = featureEvaluator->getCls();
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responses = &_resp;
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// TODO: check responses: elements must be 0 or 1
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if( _precalcValBufSize < 0 || _precalcIdxBufSize < 0)
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CV_Error( CV_StsOutOfRange, "_numPrecalcVal and _numPrecalcIdx must be positive or 0" );
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var_count = var_all = featureEvaluator->getNumFeatures() * featureEvaluator->getFeatureSize();
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sample_count = _numSamples;
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is_buf_16u = false;
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if (sample_count < 65536)
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is_buf_16u = true;
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numPrecalcVal = min( cvRound((double)_precalcValBufSize*1048576. / (sizeof(float)*sample_count)), var_count );
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numPrecalcIdx = min( cvRound((double)_precalcIdxBufSize*1048576. /
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((is_buf_16u ? sizeof(unsigned short) : sizeof (int))*sample_count)), var_count );
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assert( numPrecalcIdx >= 0 && numPrecalcVal >= 0 );
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valCache.create( numPrecalcVal, sample_count, CV_32FC1 );
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var_type = cvCreateMat( 1, var_count + 2, CV_32SC1 );
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if ( featureEvaluator->getMaxCatCount() > 0 )
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{
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numPrecalcIdx = 0;
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cat_var_count = var_count;
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ord_var_count = 0;
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for( int vi = 0; vi < var_count; vi++ )
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{
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var_type->data.i[vi] = vi;
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}
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}
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else
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{
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cat_var_count = 0;
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ord_var_count = var_count;
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for( int vi = 1; vi <= var_count; vi++ )
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{
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var_type->data.i[vi-1] = -vi;
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}
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}
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var_type->data.i[var_count] = cat_var_count;
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var_type->data.i[var_count+1] = cat_var_count+1;
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work_var_count = ( cat_var_count ? 0 : numPrecalcIdx ) + 1;
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buf_size = (work_var_count + 1) * sample_count;
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buf_count = 2;
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if ( is_buf_16u )
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buf = cvCreateMat( buf_count, buf_size, CV_16UC1 );
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else
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buf = cvCreateMat( buf_count, buf_size, CV_32SC1 );
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cat_count = cvCreateMat( 1, cat_var_count + 1, CV_32SC1 );
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// precalculate valCache and set indices in buf
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precalculate();
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// now calculate the maximum size of split,
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// create memory storage that will keep nodes and splits of the decision tree
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// allocate root node and the buffer for the whole training data
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int maxSplitSize = cvAlign(sizeof(CvDTreeSplit) +
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(MAX(0,sample_count - 33)/32)*sizeof(int),sizeof(void*));
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int treeBlockSize = MAX((int)sizeof(CvDTreeNode)*8, maxSplitSize);
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treeBlockSize = MAX(treeBlockSize + BlockSizeDelta, MinBlockSize);
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tree_storage = cvCreateMemStorage( treeBlockSize );
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node_heap = cvCreateSet( 0, sizeof(*node_heap), sizeof(CvDTreeNode), tree_storage );
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int nvSize = var_count*sizeof(int);
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nvSize = cvAlign(MAX( nvSize, (int)sizeof(CvSetElem) ), sizeof(void*));
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int tempBlockSize = nvSize;
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tempBlockSize = MAX( tempBlockSize + BlockSizeDelta, MinBlockSize );
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temp_storage = cvCreateMemStorage( tempBlockSize );
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nv_heap = cvCreateSet( 0, sizeof(*nv_heap), nvSize, temp_storage );
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data_root = new_node( 0, sample_count, 0, 0 );
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// set sample labels
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if (is_buf_16u)
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udst = (unsigned short*)(buf->data.s + work_var_count*sample_count);
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else
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idst = buf->data.i + work_var_count*sample_count;
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for (int si = 0; si < sample_count; si++)
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{
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if (udst)
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udst[si] = (unsigned short)si;
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else
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idst[si] = si;
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}
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for( int vi = 0; vi < var_count; vi++ )
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data_root->set_num_valid(vi, sample_count);
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for( int vi = 0; vi < cat_var_count; vi++ )
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cat_count->data.i[vi] = max_c_count;
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cat_count->data.i[cat_var_count] = 2;
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maxSplitSize = cvAlign(sizeof(CvDTreeSplit) +
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(MAX(0,max_c_count - 33)/32)*sizeof(int),sizeof(void*));
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split_heap = cvCreateSet( 0, sizeof(*split_heap), maxSplitSize, tree_storage );
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priors = cvCreateMat( 1, get_num_classes(), CV_64F );
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cvSet(priors, cvScalar(1));
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priors_mult = cvCloneMat( priors );
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counts = cvCreateMat( 1, get_num_classes(), CV_32SC1 );
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direction = cvCreateMat( 1, sample_count, CV_8UC1 );
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split_buf = cvCreateMat( 1, sample_count, CV_32SC1 );
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}
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void CvCascadeBoostTrainData::free_train_data()
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{
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CvDTreeTrainData::free_train_data();
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valCache.release();
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}
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const int* CvCascadeBoostTrainData::get_class_labels( CvDTreeNode* n, int* labelsBuf)
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{
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int nodeSampleCount = n->sample_count;
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int rStep = CV_IS_MAT_CONT( responses->type ) ? 1 : responses->step / CV_ELEM_SIZE( responses->type );
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int* sampleIndicesBuf = labelsBuf; //
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const int* sampleIndices = get_sample_indices(n, sampleIndicesBuf);
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for( int si = 0; si < nodeSampleCount; si++ )
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{
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int sidx = sampleIndices[si];
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labelsBuf[si] = (int)responses->data.fl[sidx*rStep];
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}
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return labelsBuf;
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}
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const int* CvCascadeBoostTrainData::get_sample_indices( CvDTreeNode* n, int* indicesBuf )
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{
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return CvDTreeTrainData::get_cat_var_data( n, get_work_var_count(), indicesBuf );
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}
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const int* CvCascadeBoostTrainData::get_cv_labels( CvDTreeNode* n, int* labels_buf )
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{
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return CvDTreeTrainData::get_cat_var_data( n, get_work_var_count() - 1, labels_buf );
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}
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void CvCascadeBoostTrainData::get_ord_var_data( CvDTreeNode* n, int vi, float* ordValuesBuf, int* sortedIndicesBuf,
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const float** ordValues, const int** sortedIndices, int* sampleIndicesBuf )
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{
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int nodeSampleCount = n->sample_count;
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const int* sampleIndices = get_sample_indices(n, sampleIndicesBuf);
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if ( vi < numPrecalcIdx )
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{
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if( !is_buf_16u )
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*sortedIndices = buf->data.i + n->buf_idx*buf->cols + vi*sample_count + n->offset;
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else
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{
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const unsigned short* shortIndices = (const unsigned short*)(buf->data.s + n->buf_idx*buf->cols +
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vi*sample_count + n->offset );
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for( int i = 0; i < nodeSampleCount; i++ )
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sortedIndicesBuf[i] = shortIndices[i];
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*sortedIndices = sortedIndicesBuf;
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}
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if( vi < numPrecalcVal )
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{
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for( int i = 0; i < nodeSampleCount; i++ )
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{
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int idx = (*sortedIndices)[i];
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idx = sampleIndices[idx];
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ordValuesBuf[i] = valCache.at<float>( vi, idx);
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}
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}
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else
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{
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for( int i = 0; i < nodeSampleCount; i++ )
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{
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int idx = (*sortedIndices)[i];
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idx = sampleIndices[idx];
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ordValuesBuf[i] = (*featureEvaluator)( vi, idx);
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}
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}
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}
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else // vi >= numPrecalcIdx
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{
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cv::AutoBuffer<float> abuf(nodeSampleCount);
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float* sampleValues = &abuf[0];
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if ( vi < numPrecalcVal )
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{
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for( int i = 0; i < nodeSampleCount; i++ )
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{
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sortedIndicesBuf[i] = i;
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sampleValues[i] = valCache.at<float>( vi, sampleIndices[i] );
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}
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}
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else
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{
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for( int i = 0; i < nodeSampleCount; i++ )
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{
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sortedIndicesBuf[i] = i;
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sampleValues[i] = (*featureEvaluator)( vi, sampleIndices[i]);
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}
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}
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icvSortIntAux( sortedIndicesBuf, nodeSampleCount, &sampleValues[0] );
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for( int i = 0; i < nodeSampleCount; i++ )
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ordValuesBuf[i] = (&sampleValues[0])[sortedIndicesBuf[i]];
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*sortedIndices = sortedIndicesBuf;
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}
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*ordValues = ordValuesBuf;
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}
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const int* CvCascadeBoostTrainData::get_cat_var_data( CvDTreeNode* n, int vi, int* catValuesBuf )
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{
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int nodeSampleCount = n->sample_count;
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int* sampleIndicesBuf = catValuesBuf; //
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const int* sampleIndices = get_sample_indices(n, sampleIndicesBuf);
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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
|
|
{
|
|
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 BlockedRange& range ) const
|
|
{
|
|
cv::AutoBuffer<float> valCache(sample_count);
|
|
float* valCachePtr = (float*)valCache;
|
|
for ( int fi = range.begin(); 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 )
|
|
icvSortUShAux( udst + fi*sample_count, sample_count, valCachePtr );
|
|
else
|
|
icvSortIntAux( idst + fi*sample_count, sample_count, valCachePtr );
|
|
}
|
|
}
|
|
const CvFeatureEvaluator* featureEvaluator;
|
|
int sample_count;
|
|
int* idst;
|
|
unsigned short* udst;
|
|
bool is_buf_16u;
|
|
};
|
|
|
|
struct FeatureValAndIdxPrecalc
|
|
{
|
|
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 BlockedRange& range ) const
|
|
{
|
|
for ( int fi = range.begin(); 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 )
|
|
icvSortUShAux( udst + fi*sample_count, sample_count, valCache->ptr<float>(fi) );
|
|
else
|
|
icvSortIntAux( idst + fi*sample_count, sample_count, valCache->ptr<float>(fi) );
|
|
}
|
|
}
|
|
const CvFeatureEvaluator* featureEvaluator;
|
|
Mat* valCache;
|
|
int sample_count;
|
|
int* idst;
|
|
unsigned short* udst;
|
|
bool is_buf_16u;
|
|
};
|
|
|
|
struct FeatureValOnlyPrecalc
|
|
{
|
|
FeatureValOnlyPrecalc( const CvFeatureEvaluator* _featureEvaluator, Mat* _valCache, int _sample_count )
|
|
{
|
|
featureEvaluator = _featureEvaluator;
|
|
valCache = _valCache;
|
|
sample_count = _sample_count;
|
|
}
|
|
void operator()( const BlockedRange& range ) const
|
|
{
|
|
for ( int fi = range.begin(); 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( BlockedRange(numPrecalcVal, numPrecalcIdx),
|
|
FeatureIdxOnlyPrecalc(featureEvaluator, buf, sample_count, is_buf_16u!=0) );
|
|
parallel_for( BlockedRange(0, minNum),
|
|
FeatureValAndIdxPrecalc(featureEvaluator, buf, &valCache, sample_count, is_buf_16u!=0) );
|
|
parallel_for( BlockedRange(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;
|
|
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)
|
|
{
|
|
unsigned short *ldst, *rdst, *ldst0, *rdst0;
|
|
ldst0 = ldst = (unsigned short*)(buf->data.s + left->buf_idx*buf->cols +
|
|
vi*scount + left->offset);
|
|
rdst0 = rdst = (unsigned short*)(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 = (unsigned short)idx;
|
|
rdst++;
|
|
}
|
|
else
|
|
{
|
|
*ldst = (unsigned short)idx;
|
|
ldst++;
|
|
}
|
|
}
|
|
assert( n1 == n);
|
|
|
|
left->set_num_valid(vi, (int)(ldst - ldst0));
|
|
right->set_num_valid(vi, (int)(rdst - rdst0));
|
|
}
|
|
else
|
|
{
|
|
int *ldst0, *ldst, *rdst0, *rdst;
|
|
ldst0 = ldst = buf->data.i + left->buf_idx*buf->cols +
|
|
vi*scount + left->offset;
|
|
rdst0 = rdst = buf->data.i + right->buf_idx*buf->cols +
|
|
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++;
|
|
}
|
|
}
|
|
|
|
left->set_num_valid(vi, (int)(ldst - ldst0));
|
|
right->set_num_valid(vi, (int)(rdst - rdst0));
|
|
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*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);
|
|
|
|
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*buf->cols +
|
|
(workVarCount-1)*scount + left->offset;
|
|
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];
|
|
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));
|
|
}
|
|
|
|
// 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*buf->cols +
|
|
workVarCount*scount + left->offset);
|
|
unsigned short* rdst = (unsigned short*)(buf->data.s + right->buf_idx*buf->cols +
|
|
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*buf->cols +
|
|
workVarCount*scount + left->offset;
|
|
int* rdst = buf->data.i + right->buf_idx*buf->cols +
|
|
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++;
|
|
}
|
|
}
|
|
}
|
|
|
|
// deallocate the parent node data that is not needed anymore
|
|
data->free_node_data(node);
|
|
}
|
|
|
|
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 )
|
|
{
|
|
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 ) )
|
|
{
|
|
// TODO: may be should finish the loop (!!!)
|
|
assert(0);
|
|
delete tree;
|
|
continue;
|
|
}
|
|
cvSeqPush( weak, &tree );
|
|
update_weights( tree );
|
|
trim_weights();
|
|
}
|
|
while( !isErrDesired() && (weak->total < params.weak_count) );
|
|
|
|
data->is_classifier = true;
|
|
data->free_train_data();
|
|
return true;
|
|
}
|
|
|
|
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;
|
|
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*buf->cols +
|
|
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*buf->cols +
|
|
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 );
|
|
icvSortFlt( &eval[0], numPos, 0 );
|
|
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 );
|
|
}
|
|
}
|