Peter M. Groen / openbr

Commit 74cfaa03328e572247d385c437a44e9ddb94efb4

Authored by Scott Klum
1 parent 08b73def

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Showing 1 changed file with 47 additions and 20 deletions
openbr/core/boost.cpp
  View file @74cfaa0
1 1 #include <opencv2/imgproc/imgproc.hpp>
  2 +#include <QDebug>
2 3  
3 4 #include "boost.h"
4 5 #include "cxmisc.h"
... ... @@ -457,6 +458,7 @@ void CascadeBoostTrainData::setData( const FeatureEvaluator* _featureEvaluator,
457 458  
458 459 valCache.create( numPrecalcVal, sample_count, CV_32FC1 );
459 460 var_type = cvCreateMat( 1, var_count + 2, CV_32SC(channels) );
  461 +
460 462 if ( featureEvaluator->getMaxCatCount() > 0 )
461 463 {
462 464 numPrecalcIdx = 0;
... ... @@ -484,9 +486,9 @@ void CascadeBoostTrainData::setData( const FeatureEvaluator* _featureEvaluator,
484 486 buf_size = -1; // the member buf_size is obsolete
485 487  
486 488 effective_buf_size = (uint64)(work_var_count + 1)*(uint64)sample_count * buf_count; // this is the total size of "CvMat buf" to be allocated
  489 +
487 490 effective_buf_width = sample_count;
488 491 effective_buf_height = work_var_count+1;
489   -
490 492 if (effective_buf_width >= effective_buf_height)
491 493 effective_buf_height *= buf_count;
492 494 else
... ... @@ -527,9 +529,9 @@ void CascadeBoostTrainData::setData( const FeatureEvaluator* _featureEvaluator,
527 529  
528 530 // set sample labels
529 531 if (is_buf_16u)
530   - udst = (unsigned short*)(buf->data.s + work_var_count*sample_count);
  532 + udst = (unsigned short*)(buf->data.s + (uint64)work_var_count*sample_count);
531 533 else
532   - idst = buf->data.i + work_var_count*sample_count;
  534 + idst = buf->data.i + (uint64)work_var_count*sample_count;
533 535  
534 536 for (int si = 0; si < sample_count; si++)
535 537 {
... ... @@ -568,7 +570,7 @@ const int* CascadeBoostTrainData::get_class_labels( CvDTreeNode* n, int* labelsB
568 570 int nodeSampleCount = n->sample_count;
569 571 int rStep = CV_IS_MAT_CONT( responses->type ) ? 1 : responses->step / CV_ELEM_SIZE( responses->type );
570 572  
571   - int* sampleIndicesBuf = labelsBuf; //
  573 + int* sampleIndicesBuf = labelsBuf;
572 574 const int* sampleIndices = get_sample_indices(n, sampleIndicesBuf);
573 575 for( int si = 0; si < nodeSampleCount; si++ )
574 576 {
... ... @@ -580,12 +582,32 @@ const int* CascadeBoostTrainData::get_class_labels( CvDTreeNode* n, int* labelsB
580 582  
581 583 const int* CascadeBoostTrainData::get_sample_indices( CvDTreeNode* n, int* indicesBuf )
582 584 {
583   - return CvDTreeTrainData::get_cat_var_data( n, get_work_var_count(), indicesBuf );
  585 + const uint64 vi = get_work_var_count();
  586 + const int* cat_values = 0;
  587 + if (!is_buf_16u)
  588 + cat_values = buf->data.i + n->buf_idx*get_length_subbuf() + vi*sample_count + n->offset;
  589 + else {
  590 + const unsigned short* short_values = (const unsigned short*)(buf->data.s + n->buf_idx*get_length_subbuf() + vi*sample_count + n->offset);
  591 + for (int i = 0; i < n->sample_count; i++)
  592 + indicesBuf[i] = short_values[i];
  593 + cat_values = indicesBuf;
  594 + }
  595 + return cat_values;
584 596 }
585 597  
586   -const int* CascadeBoostTrainData::get_cv_labels( CvDTreeNode* n, int* labels_buf )
  598 +const int* CascadeBoostTrainData::get_cv_labels( CvDTreeNode* n, int* indicesBuf )
587 599 {
588   - return CvDTreeTrainData::get_cat_var_data( n, get_work_var_count() - 1, labels_buf );
  600 + const uint64 vi = get_work_var_count()-1;
  601 + const int* cat_values = 0;
  602 + if (!is_buf_16u)
  603 + cat_values = buf->data.i + n->buf_idx*get_length_subbuf() + vi*sample_count + n->offset;
  604 + else {
  605 + const unsigned short* short_values = (const unsigned short*)(buf->data.s + n->buf_idx*get_length_subbuf() + vi*sample_count + n->offset);
  606 + for (int i = 0; i < n->sample_count; i++)
  607 + indicesBuf[i] = short_values[i];
  608 + cat_values = indicesBuf;
  609 + }
  610 + return cat_values;
589 611 }
590 612  
591 613 void CascadeBoostTrainData::get_ord_var_data( CvDTreeNode* n, int vi, float* ordValuesBuf, int* sortedIndicesBuf,
... ... @@ -597,7 +619,7 @@ void CascadeBoostTrainData::get_ord_var_data( CvDTreeNode* n, int vi, float* ord
597 619 if ( vi < numPrecalcIdx )
598 620 {
599 621 if( !is_buf_16u )
600   - *sortedIndices = buf->data.i + n->buf_idx*get_length_subbuf() + vi*sample_count + n->offset;
  622 + *sortedIndices = buf->data.i + n->buf_idx*get_length_subbuf() + (uint64)vi*sample_count + n->offset;
601 623 else
602 624 {
603 625 const unsigned short* shortIndices = (const unsigned short*)(buf->data.s + n->buf_idx*get_length_subbuf() +
... ... @@ -744,17 +766,17 @@ struct FeatureValAndIdxPrecalc : ParallelLoopBody
744 766 for ( int fi = range.start; fi < range.end; fi++)
745 767 {
746 768 for( int si = 0; si < sample_count; si++ )
747   - {
  769 + {
748 770 valCache->at<float>(fi,si) = (*featureEvaluator)( fi, si );
749   - if ( is_buf_16u )
750   - *(udst + fi*sample_count + si) = (unsigned short)si;
  771 + if ( is_buf_16u )
  772 + *(udst + (uint64)fi*sample_count + si) = (unsigned short)si;
751 773 else
752   - *(idst + fi*sample_count + si) = si;
  774 + *(idst + (uint64)fi*sample_count + si) = si;
753 775 }
754 776 if ( is_buf_16u )
755   - icvSortUShAux( udst + fi*sample_count, sample_count, valCache->ptr<float>(fi) );
  777 + icvSortUShAux( udst + (uint64)fi*sample_count, sample_count, valCache->ptr<float>(fi) );
756 778 else
757   - icvSortIntAux( idst + fi*sample_count, sample_count, valCache->ptr<float>(fi) );
  779 + icvSortIntAux( idst + (uint64)fi*sample_count, sample_count, valCache->ptr<float>(fi) );
758 780 }
759 781 }
760 782 const FeatureEvaluator* featureEvaluator;
... ... @@ -1078,6 +1100,7 @@ void CascadeBoost::train(const FeatureEvaluator* _featureEvaluator,
1078 1100 {
1079 1101 CascadeBoostTree* tree = new CascadeBoostTree;
1080 1102 if (!tree->train( data, subsample_mask, this)) {
  1103 + qDebug() << "Deleting tree!";
1081 1104 delete tree;
1082 1105 return;
1083 1106 }
... ... @@ -1085,12 +1108,14 @@ void CascadeBoost::train(const FeatureEvaluator* _featureEvaluator,
1085 1108 classifiers.append(tree);
1086 1109 update_weights(tree);
1087 1110 trim_weights();
1088   - if (cvCountNonZero(subsample_mask) == 0)
  1111 + if (cvCountNonZero(subsample_mask) == 0) {
  1112 + qDebug() << "Subsample mask is empty!";
1089 1113 return;
  1114 + }
1090 1115 }
1091 1116 while (!isErrDesired() && (classifiers.size() < params.weak_count));
1092 1117  
1093   - clear();
  1118 + //clear();
1094 1119 }
1095 1120  
1096 1121 float CascadeBoost::predict(int sampleIdx, bool returnSum) const
... ... @@ -1101,6 +1126,7 @@ float CascadeBoost::predict(int sampleIdx, bool returnSum) const
1101 1126  
1102 1127 if (!returnSum)
1103 1128 sum = sum < threshold - CV_THRESHOLD_EPS ? 0.0 : 1.0;
  1129 +
1104 1130 return (float)sum;
1105 1131 }
1106 1132  
... ... @@ -1125,6 +1151,7 @@ void CascadeBoost::update_weights(CvBoostTree* tree)
1125 1151 ( !tree ? n*sizeof(int) : 0 );
1126 1152 cv::AutoBuffer<uchar> inn_buf(inn_buf_size);
1127 1153 uchar* cur_inn_buf_pos = (uchar*)inn_buf;
  1154 +
1128 1155 if ( (params.boost_type == LOGIT) || (params.boost_type == GENTLE) )
1129 1156 {
1130 1157 step = CV_IS_MAT_CONT(data->responses_copy->type) ?
... ... @@ -1133,6 +1160,7 @@ void CascadeBoost::update_weights(CvBoostTree* tree)
1133 1160 sampleIdxBuf = (int*)cur_inn_buf_pos; cur_inn_buf_pos = (uchar*)(sampleIdxBuf + n);
1134 1161 sampleIdx = data->get_sample_indices( data->data_root, sampleIdxBuf );
1135 1162 }
  1163 +
1136 1164 CvMat* buf = data->buf;
1137 1165 size_t length_buf_row = data->get_length_subbuf();
1138 1166 if( !tree ) // before training the first tree, initialize weights and other parameters
... ... @@ -1158,8 +1186,7 @@ void CascadeBoost::update_weights(CvBoostTree* tree)
1158 1186  
1159 1187 if (data->is_buf_16u)
1160 1188 {
1161   - unsigned short* labels = (unsigned short*)(buf->data.s + data->data_root->buf_idx*length_buf_row +
1162   - data->data_root->offset + (data->work_var_count-1)*data->sample_count);
  1189 + unsigned short* labels = (unsigned short*)(buf->data.s + data->data_root->buf_idx*length_buf_row + data->data_root->offset + (uint64)(data->work_var_count-1)*data->sample_count);
1163 1190 for( int i = 0; i < n; i++ )
1164 1191 {
1165 1192 // save original categorical responses {0,1}, convert them to {-1,1}
... ... @@ -1176,8 +1203,7 @@ void CascadeBoost::update_weights(CvBoostTree* tree)
1176 1203 }
1177 1204 else
1178 1205 {
1179   - int* labels = buf->data.i + data->data_root->buf_idx*length_buf_row +
1180   - data->data_root->offset + (data->work_var_count-1)*data->sample_count;
  1206 + int* labels = buf->data.i + data->data_root->buf_idx*length_buf_row + data->data_root->offset + (uint64)(data->work_var_count-1)*data->sample_count;
1181 1207  
1182 1208 for( int i = 0; i < n; i++ )
1183 1209 {
... ... @@ -1388,5 +1414,6 @@ bool CascadeBoost::isErrDesired()
1388 1414 cout << "|" << endl;
1389 1415 cout << "+----+---------+---------+" << endl;
1390 1416  
  1417 + qDebug() << falseAlarm << maxFalseAlarm;
1391 1418 return falseAlarm <= maxFalseAlarm;
1392 1419 }
... ...