Peter M. Groen / openbr

Commit da980eca4c85876b4b00a83306a0ec3269657000

Authored by Scott Klum
2 parents a641da3a 6e0e624d

Merge branch 'boost'

Inline Side-by-side
Showing 1 changed file with 83 additions and 227 deletions
openbr/core/boost.cpp
  View file @da980ec
... ... @@ -21,7 +21,6 @@ logRatio( double val )
21 21  
22 22 #define CV_CMP_NUM_IDX(i,j) (aux[i] < aux[j])
23 23 static CV_IMPLEMENT_QSORT_EX( icvSortIntAux, int, CV_CMP_NUM_IDX, const float* )
24   -static CV_IMPLEMENT_QSORT_EX( icvSortUShAux, unsigned short, CV_CMP_NUM_IDX, const float* )
25 24  
26 25 #define CV_THRESHOLD_EPS (0.00001F)
27 26  
... ... @@ -73,7 +72,7 @@ static CvMat* cvPreprocessIndexArray( const CvMat* idx_arr, int data_arr_size, b
73 72  
74 73 if( idx_selected == 0 )
75 74 CV_ERROR( CV_StsOutOfRange, "No components/input_variables is selected!" );
76   - } else if (type == CV_32SC(channels)) {
  75 + } else if (type == CV_32SC1) {
77 76 // idx_arr is array of integer indices of selected components
78 77 if( idx_total > data_arr_size )
79 78 CV_ERROR( CV_StsOutOfRange,
... ... @@ -95,10 +94,10 @@ static CvMat* cvPreprocessIndexArray( const CvMat* idx_arr, int data_arr_size, b
95 94 "(it should be 8uC1, 8sC1 or 32sC1)" );
96 95 }
97 96  
98   - CV_CALL( idx = cvCreateMat( 1, idx_selected, CV_32SC(channels) ));
  97 + CV_CALL( idx = cvCreateMat( 1, idx_selected, CV_32SC1 ));
99 98 dsti = idx->data.i;
100 99  
101   - if( type < CV_32SC(channels) )
  100 + if( type < CV_32SC1 )
102 101 {
103 102 for( i = 0; i < idx_total; i++ )
104 103 if( srcb[i*step] )
... ... @@ -181,7 +180,12 @@ struct CascadeBoostTrainData : CvDTreeTrainData
181 180  
182 181 virtual CvDTreeNode* subsample_data(const CvMat* _subsample_idx);
183 182  
184   - virtual const int* getBufferValues(CvDTreeNode* n, int* labelsBuf, uint64_t vi);
  183 + inline uint64_t getLength() const
  184 + {
  185 + return ((uint64_t)work_var_count + 1) * sample_count;
  186 + }
  187 +
  188 + virtual const int* getBufferValues(CvDTreeNode* n, uint64_t vi);
185 189 virtual const int* get_class_labels(CvDTreeNode* n, int* labelsBuf);
186 190 virtual const int* get_cv_labels(CvDTreeNode* n, int* labelsBuf);
187 191 virtual const int* get_sample_indices(CvDTreeNode* n, int* indicesBuf);
... ... @@ -254,12 +258,12 @@ CvDTreeNode* CascadeBoostTrainData::subsample_data( const CvMat* _subsample_idx
254 258 int* sidx = isubsample_idx->data.i;
255 259 // co - array of count/offset pairs (to handle duplicated values in _subsample_idx)
256 260 int* co, cur_ofs = 0;
257   - int workVarCount = get_work_var_count();
  261 + uint64_t workVarCount = get_work_var_count();
258 262 int count = isubsample_idx->rows + isubsample_idx->cols - 1;
259 263  
260 264 root = new_node( 0, count, 1, 0 );
261 265  
262   - CV_Assert( (subsample_co = cvCreateMat( 1, sample_count*2, CV_32SC(channels) )) != 0);
  266 + CV_Assert( (subsample_co = cvCreateMat( 1, sample_count*2, CV_32SC1 )) != 0);
263 267 cvZero( subsample_co );
264 268 co = subsample_co->data.i;
265 269 for( int i = 0; i < count; i++ )
... ... @@ -293,67 +297,29 @@ CvDTreeNode* CascadeBoostTrainData::subsample_data( const CvMat* _subsample_idx
293 297 int num_valid = data_root->get_num_valid(vi);
294 298 CV_Assert( num_valid == sample_count );
295 299  
296   - if (is_buf_16u)
  300 + int* idst_idx = buf->data.i + root->buf_idx*getLength() + (uint64_t)vi*sample_count + root->offset;
  301 + for( int i = 0; i < num_valid; i++ )
297 302 {
298   - unsigned short* udst_idx = (unsigned short*)(buf->data.s + root->buf_idx*get_length_subbuf() +
299   - vi*sample_count + data_root->offset);
300   - for( int i = 0; i < num_valid; i++ )
301   - {
302   - idx = src_idx[i];
303   - count_i = co[idx*2];
304   - if( count_i )
305   - for( cur_ofs = co[idx*2+1]; count_i > 0; count_i--, j++, cur_ofs++ )
306   - udst_idx[j] = (unsigned short)cur_ofs;
307   - }
308   - }
309   - else
310   - {
311   - int* idst_idx = buf->data.i + root->buf_idx*get_length_subbuf() +
312   - vi*sample_count + root->offset;
313   - for( int i = 0; i < num_valid; i++ )
314   - {
315   - idx = src_idx[i];
316   - count_i = co[idx*2];
317   - if( count_i )
318   - for( cur_ofs = co[idx*2+1]; count_i > 0; count_i--, j++, cur_ofs++ )
319   - idst_idx[j] = cur_ofs;
320   - }
  303 + idx = src_idx[i];
  304 + count_i = co[idx*2];
  305 + if( count_i )
  306 + for( cur_ofs = co[idx*2+1]; count_i > 0; count_i--, j++, cur_ofs++ )
  307 + idst_idx[j] = cur_ofs;
321 308 }
322 309 }
323 310  
324 311 // subsample cv_lables
325 312 const int* src_lbls = get_cv_labels(data_root, (int*)(uchar*)inn_buf);
326   - if (is_buf_16u)
327   - {
328   - unsigned short* udst = (unsigned short*)(buf->data.s + root->buf_idx*get_length_subbuf() +
329   - (workVarCount-1)*sample_count + root->offset);
330   - for( int i = 0; i < count; i++ )
331   - udst[i] = (unsigned short)src_lbls[sidx[i]];
332   - }
333   - else
334   - {
335   - int* idst = buf->data.i + root->buf_idx*get_length_subbuf() +
336   - (workVarCount-1)*sample_count + root->offset;
337   - for( int i = 0; i < count; i++ )
338   - idst[i] = src_lbls[sidx[i]];
339   - }
  313 + int* idst = buf->data.i + root->buf_idx*getLength() +
  314 + (workVarCount-1)*sample_count + root->offset;
  315 + for( int i = 0; i < count; i++ )
  316 + idst[i] = src_lbls[sidx[i]];
340 317  
341 318 // subsample sample_indices
342 319 const int* sample_idx_src = get_sample_indices(data_root, (int*)(uchar*)inn_buf);
343   - if (is_buf_16u)
344   - {
345   - unsigned short* sample_idx_dst = (unsigned short*)(buf->data.s + root->buf_idx*get_length_subbuf() +
346   - workVarCount*sample_count + root->offset);
347   - for( int i = 0; i < count; i++ )
348   - sample_idx_dst[i] = (unsigned short)sample_idx_src[sidx[i]];
349   - }
350   - else
351   - {
352   - int* sample_idx_dst = buf->data.i + root->buf_idx*get_length_subbuf() +
353   - workVarCount*sample_count + root->offset;
354   - for( int i = 0; i < count; i++ )
355   - sample_idx_dst[i] = sample_idx_src[sidx[i]];
356   - }
  320 + int* sample_idx_dst = buf->data.i + root->buf_idx*getLength() + workVarCount*sample_count + root->offset;
  321 + for( int i = 0; i < count; i++ )
  322 + sample_idx_dst[i] = sample_idx_src[sidx[i]];
357 323  
358 324 for( int vi = 0; vi < var_count; vi++ )
359 325 root->set_num_valid(vi, count);
... ... @@ -390,7 +356,7 @@ CascadeBoostTrainData::CascadeBoostTrainData(const FeatureEvaluator* _featureEva
390 356  
391 357 void CascadeBoostTrainData::initVarType()
392 358 {
393   - var_type = cvCreateMat( 1, var_count + 2, CV_32SC(channels) );
  359 + var_type = cvCreateMat( 1, var_count + 2, CV_32SC1 );
394 360 if ( featureEvaluator->getMaxCatCount() > 0 )
395 361 {
396 362 numPrecalcIdx = 0;
... ... @@ -459,17 +425,15 @@ void CascadeBoostTrainData::setData( const FeatureEvaluator* _featureEvaluator,
459 425 sample_count = _numSamples;
460 426  
461 427 is_buf_16u = false;
462   - if (sample_count < 65536)
463   - is_buf_16u = true;
464 428  
465 429 // 1048576 is the number of bytes in a megabyte
466 430 numPrecalcVal = min( cvRound((double)_precalcValBufSize*1048576. / (sizeof(float)*sample_count)), var_count );
467   - numPrecalcIdx = min( cvRound((double)_precalcIdxBufSize*1048576. / ((is_buf_16u ? sizeof(unsigned short) : sizeof (int))*sample_count)), var_count );
  431 + numPrecalcIdx = min( cvRound((double)_precalcIdxBufSize*1048576. / (sizeof (int)*sample_count)), var_count );
468 432  
469 433 assert( numPrecalcIdx >= 0 && numPrecalcVal >= 0 );
470 434  
471 435 valCache.create( numPrecalcVal, sample_count, CV_32FC1 );
472   - var_type = cvCreateMat( 1, var_count + 2, CV_32SC(channels) );
  436 + var_type = cvCreateMat( 1, var_count + 2, CV_32SC1 );
473 437  
474 438 if ( featureEvaluator->getMaxCatCount() > 0 )
475 439 {
... ... @@ -500,7 +464,7 @@ void CascadeBoostTrainData::setData( const FeatureEvaluator* _featureEvaluator,
500 464  
501 465 buf_size = -1; // the member buf_size is obsolete
502 466  
503   - effective_buf_size = (work_var_count + 1)*sample_count * buf_count; // this is the total size of "CvMat buf" to be allocated
  467 + effective_buf_size = getLength()*buf_count;
504 468  
505 469 effective_buf_width = sample_count;
506 470 effective_buf_height = work_var_count+1;
... ... @@ -510,15 +474,11 @@ void CascadeBoostTrainData::setData( const FeatureEvaluator* _featureEvaluator,
510 474 effective_buf_width *= buf_count;
511 475  
512 476 if (effective_buf_width * effective_buf_height != effective_buf_size)
513   - {
514 477 CV_Error(CV_StsBadArg, "The memory buffer cannot be allocated since its size exceeds integer fields limit");
515   - }
516   - if ( is_buf_16u )
517   - buf = cvCreateMat( effective_buf_height, effective_buf_width, CV_16UC(channels) );
518   - else
519   - buf = cvCreateMat( effective_buf_height, effective_buf_width, CV_32SC(channels) );
520 478  
521   - cat_count = cvCreateMat( 1, cat_var_count + 1, CV_32SC(channels) );
  479 + buf = cvCreateMat( effective_buf_height, effective_buf_width, CV_32SC1 );
  480 +
  481 + cat_count = cvCreateMat( 1, cat_var_count + 1, CV_32SC1 );
522 482  
523 483 // precalculate valCache and set indices in buf
524 484 precalculate();
... ... @@ -542,10 +502,7 @@ void CascadeBoostTrainData::setData( const FeatureEvaluator* _featureEvaluator,
542 502 data_root = new_node( 0, sample_count, 0, 0 );
543 503  
544 504 // set sample labels
545   - if (is_buf_16u)
546   - udst = (unsigned short*)(buf->data.s + (uint64_t)work_var_count*sample_count);
547   - else
548   - idst = buf->data.i + (uint64_t)work_var_count*sample_count;
  505 + idst = buf->data.i + (uint64_t)work_var_count*sample_count;
549 506  
550 507 for (int si = 0; si < sample_count; si++)
551 508 {
... ... @@ -568,9 +525,9 @@ void CascadeBoostTrainData::setData( const FeatureEvaluator* _featureEvaluator,
568 525 priors = cvCreateMat( 1, get_num_classes(), CV_64F );
569 526 cvSet(priors, cvScalar(1));
570 527 priors_mult = cvCloneMat( priors );
571   - counts = cvCreateMat( 1, get_num_classes(), CV_32SC(channels) );
572   - direction = cvCreateMat( 1, sample_count, CV_8UC(channels) );
573   - split_buf = cvCreateMat( 1, sample_count, CV_32SC(channels) );//TODO: make a pointer
  528 + counts = cvCreateMat( 1, get_num_classes(), CV_32SC1 );
  529 + direction = cvCreateMat( 1, sample_count, CV_8UC1 );
  530 + split_buf = cvCreateMat( 1, sample_count, CV_32SC1 );//TODO: make a pointer
574 531 }
575 532  
576 533 void CascadeBoostTrainData::free_train_data()
... ... @@ -594,29 +551,21 @@ const int* CascadeBoostTrainData::get_class_labels( CvDTreeNode* n, int* labelsB
594 551 return labelsBuf;
595 552 }
596 553  
597   -const int* CascadeBoostTrainData::getBufferValues(CvDTreeNode* n, int* indicesBuf, uint64_t vi)
  554 +const int* CascadeBoostTrainData::getBufferValues(CvDTreeNode* n, uint64_t vi)
598 555 {
599   - const int* cat_values = 0;
600   - if (!is_buf_16u)
601   - cat_values = buf->data.i + n->buf_idx*get_length_subbuf() + vi*sample_count + n->offset;
602   - else {
603   - const unsigned short* short_values = (const unsigned short*)(buf->data.s + n->buf_idx*get_length_subbuf() + vi*sample_count + n->offset);
604   - for (int i = 0; i < n->sample_count; i++)
605   - indicesBuf[i] = short_values[i];
606   - cat_values = indicesBuf;
607   - }
608   -
609   - return cat_values;
  556 + return buf->data.i + n->buf_idx*getLength() + vi*sample_count + n->offset;
610 557 }
611 558  
612   -const int* CascadeBoostTrainData::get_sample_indices( CvDTreeNode* n, int* indicesBuf )
  559 +const int* CascadeBoostTrainData::get_sample_indices(CvDTreeNode* n, int* indicesBuf)
613 560 {
614   - return getBufferValues(n,indicesBuf,get_work_var_count());
  561 + Q_UNUSED(indicesBuf)
  562 + return getBufferValues(n,get_work_var_count());
615 563 }
616 564  
617   -const int* CascadeBoostTrainData::get_cv_labels( CvDTreeNode* n, int* indicesBuf )
  565 +const int* CascadeBoostTrainData::get_cv_labels(CvDTreeNode* n, int* indicesBuf)
618 566 {
619   - return getBufferValues(n,indicesBuf,get_work_var_count()-1);
  567 + Q_UNUSED(indicesBuf)
  568 + return getBufferValues(n,get_work_var_count()-1);
620 569 }
621 570  
622 571 void CascadeBoostTrainData::get_ord_var_data( CvDTreeNode* n, int vi, float* ordValuesBuf, int* sortedIndicesBuf, const float** ordValues, const int** sortedIndices, int* sampleIndicesBuf )
... ... @@ -629,14 +578,7 @@ void CascadeBoostTrainData::get_ord_var_data( CvDTreeNode* n, int vi, float* ord
629 578 // For this feature (this code refers to features as values, hence vi == value index),
630 579 // have we precalculated (presorted) the training samples by their feature response?
631 580 if (vi < numPrecalcIdx) {
632   - if (!is_buf_16u)
633   - *sortedIndices = buf->data.i + n->buf_idx*get_length_subbuf() + (uint64_t)vi*sample_count + n->offset;
634   - else {
635   - const unsigned short* shortIndices = (const unsigned short*)(buf->data.s + n->buf_idx*get_length_subbuf() + (uint64_t)vi*sample_count + n->offset );
636   - for (int i = 0; i < nodeSampleCount; i++)
637   - sortedIndicesBuf[i] = shortIndices[i];
638   - *sortedIndices = sortedIndicesBuf;
639   - }
  581 + *sortedIndices = getBufferValues(n, vi);
640 582  
641 583 // For this feature, have we precalculated all of the feature responses?
642 584 if (vi < numPrecalcVal) {
... ... @@ -731,26 +673,20 @@ struct IndexPrecalc : Precalc
731 673 {
732 674 int* idst;
733 675 unsigned short* udst;
734   - bool isBufShort;
735 676  
736   - IndexPrecalc(const FeatureEvaluator* featureEvaluator, CvMat* buf, int sampleCount, bool isBufShort) :
  677 + IndexPrecalc(const FeatureEvaluator* featureEvaluator, CvMat* buf, int sampleCount) :
737 678 Precalc(featureEvaluator, sampleCount),
738   - isBufShort(isBufShort)
739   - {
740   - udst = (unsigned short*)buf->data.s;
741   - idst = buf->data.i;
742   - }
  679 + idst(buf->data.i)
  680 + {}
743 681  
744 682 void setBuffer(uint64_t fi, uint64_t si) const
745 683 {
746   - if (isBufShort) *(udst + fi*sampleCount + si) = (unsigned short)si;
747   - else *(idst + fi*sampleCount + si) = si;
  684 + *(idst + fi*sampleCount + si) = si;
748 685 }
749 686  
750 687 void sortBuffer(uint64_t fi, float *valCachePtr) const
751 688 {
752   - if (isBufShort) icvSortUShAux(udst + fi*sampleCount, sampleCount, valCachePtr);
753   - else icvSortIntAux(idst + fi*sampleCount, sampleCount, valCachePtr);
  689 + icvSortIntAux(idst + fi*sampleCount, sampleCount, valCachePtr);
754 690 }
755 691  
756 692 virtual void operator()(const Range& range) const
... ... @@ -771,8 +707,8 @@ struct FeatureAndIndexPrecalc : IndexPrecalc
771 707 {
772 708 Mat *valCache;
773 709  
774   - FeatureAndIndexPrecalc(const FeatureEvaluator* featureEvaluator, CvMat* buf, Mat* valCache, int sampleCount, bool isBufShort) :
775   - IndexPrecalc(featureEvaluator, buf, sampleCount, isBufShort),
  710 + FeatureAndIndexPrecalc(const FeatureEvaluator* featureEvaluator, CvMat* buf, Mat* valCache, int sampleCount) :
  711 + IndexPrecalc(featureEvaluator, buf, sampleCount),
776 712 valCache(valCache)
777 713 {}
778 714  
... ... @@ -816,11 +752,11 @@ void CascadeBoostTrainData::precalculate()
816 752  
817 753 // Compute features and sort training samples for feature indices we are not going to cache
818 754 parallel_for_(Range(numPrecalcVal, numPrecalcIdx),
819   - IndexPrecalc(featureEvaluator, buf, sample_count, is_buf_16u!=0));
  755 + IndexPrecalc(featureEvaluator, buf, sample_count));
820 756  
821 757 // Compute features and sort training samples for features indices we are going to cache
822 758 parallel_for_(Range(0, minPrecalc),
823   - FeatureAndIndexPrecalc(featureEvaluator, buf, &valCache, sample_count, is_buf_16u!=0));
  759 + FeatureAndIndexPrecalc(featureEvaluator, buf, &valCache, sample_count));
824 760  
825 761 // Compute feature values for feature indices for which we are not going to sort training samples
826 762 parallel_for_(Range(minPrecalc, numPrecalcVal),
... ... @@ -874,14 +810,13 @@ void CascadeBoostTree::split_node_data( CvDTreeNode* node )
874 810 int* newIdx = data->split_buf->data.i;
875 811 int newBufIdx = data->get_child_buf_idx( node );
876 812 CvMat* buf = data->buf;
877   - size_t length_buf_row = data->get_length_subbuf();
  813 + size_t length_buf_row = ((CascadeBoostTrainData*)data)->getLength();
878 814 cv::AutoBuffer<uchar> inn_buf(nodeSampleCount*(3*sizeof(int)+sizeof(float)));
879 815 int* tempBuf = (int*)(uchar*)inn_buf;
880 816  
881 817 complete_node_dir(node);
882 818  
883   - for (uint64_t i = nLeft = nRight = 0; i < nodeSampleCount; i++)
884   - {
  819 + for (uint64_t i = nLeft = nRight = 0; i < nodeSampleCount; i++) {
885 820 int d = dir[i];
886 821 // initialize new indices for splitting ordered variables
887 822 newIdx[i] = (nLeft & (d-1)) | (nRight & -d); // d ? ri : li
... ... @@ -900,59 +835,27 @@ void CascadeBoostTree::split_node_data( CvDTreeNode* node )
900 835 if( ci >= 0 || !splitInputData )
901 836 continue;
902 837  
903   - int n1 = node->get_num_valid(vi);
904   - float *src_val_buf = (float*)(tempBuf + nodeSampleCount);
905   - int *src_sorted_idx_buf = (int*)(src_val_buf + nodeSampleCount);
906   - int *src_sample_idx_buf = src_sorted_idx_buf + nodeSampleCount;
907   - const int* src_sorted_idx = 0;
908   - const float* src_val = 0;
909   -
910   - data->get_ord_var_data(node, vi, src_val_buf, src_sorted_idx_buf, &src_val, &src_sorted_idx, src_sample_idx_buf);
911   -
  838 + const int* src_sorted_idx = ((CascadeBoostTrainData*)data)->getBufferValues(node, vi);
912 839 for(uint64_t i = 0; i < nodeSampleCount; i++)
913 840 tempBuf[i] = src_sorted_idx[i];
914 841  
915   - if (data->is_buf_16u) {
916   - ushort *ldst, *rdst;
917   - ldst = (ushort*)(buf->data.s + left->buf_idx*length_buf_row + vi*sampleCount + left->offset);
918   - rdst = (ushort*)(ldst + nLeft);
919   -
920   - // split sorted
921   - for (int i = 0; i < n1; i++) {
922   - int idx = tempBuf[i];
923   - int d = dir[idx];
924   - idx = newIdx[idx];
925   - if (d) {
926   - *rdst = (ushort)idx;
927   - rdst++;
928   - } else {
929   - *ldst = (ushort)idx;
930   - ldst++;
931   - }
932   - }
933   - }
934   - else
935   - {
936   - int *ldst, *rdst;
937   - ldst = buf->data.i + left->buf_idx*length_buf_row + vi*sampleCount + left->offset;
938   - rdst = buf->data.i + right->buf_idx*length_buf_row + vi*sampleCount + right->offset;
  842 + int *ldst, *rdst;
  843 + ldst = buf->data.i + left->buf_idx*length_buf_row + vi*sampleCount + left->offset;
  844 + rdst = buf->data.i + right->buf_idx*length_buf_row + vi*sampleCount + right->offset;
939 845  
940   - // split sorted
941   - for( int i = 0; i < n1; i++ )
942   - {
943   - int idx = tempBuf[i];
944   - int d = dir[idx];
945   - idx = newIdx[idx];
946   - if (d)
947   - {
948   - *rdst = idx;
949   - rdst++;
950   - }
951   - else
952   - {
953   - *ldst = idx;
954   - ldst++;
955   - }
  846 + int n1 = node->get_num_valid(vi);
  847 +
  848 + // split sorted
  849 + for (int i = 0; i < n1; i++) {
  850 + int idx = tempBuf[i];
  851 + int d = dir[idx];
  852 + idx = newIdx[idx];
  853 + if (d) {
  854 + *rdst = idx;
  855 + rdst++;
  856 + } else {
  857 + *ldst = idx;
  858 + ldst++;
956 859 }
957 860 }
958 861 }
... ... @@ -964,37 +867,16 @@ void CascadeBoostTree::split_node_data( CvDTreeNode* node )
964 867 for(uint64_t i = 0; i < nodeSampleCount; i++)
965 868 tempBuf[i] = src_lbls[i];
966 869  
967   - if (data->is_buf_16u) {
968   - unsigned short *ldst = (unsigned short *)(buf->data.s + left->buf_idx*length_buf_row + (workVarCount-1)*sampleCount + left->offset);
969   - unsigned short *rdst = (unsigned short *)(buf->data.s + right->buf_idx*length_buf_row + (workVarCount-1)*sampleCount + right->offset);
970   -
971   - for( uint64_t i = 0; i < nodeSampleCount; i++ ) {
972   - int idx = tempBuf[i];
973   - if (dir[i]) {
974   - *rdst = (unsigned short)idx;
975   - rdst++;
976   - } else {
977   - *ldst = (unsigned short)idx;
978   - ldst++;
979   - }
980   - }
981   -
982   - }
983   - else
984 870 {
985 871 int *ldst = buf->data.i + left->buf_idx*length_buf_row + (workVarCount-1)*sampleCount + left->offset;
986 872 int *rdst = buf->data.i + right->buf_idx*length_buf_row + (workVarCount-1)*sampleCount + right->offset;
987 873  
988   - for( uint64_t i = 0; i < nodeSampleCount; i++ )
989   - {
  874 + for (uint64_t i = 0; i < nodeSampleCount; i++) {
990 875 int idx = tempBuf[i];
991   - if (dir[i])
992   - {
  876 + if (dir[i]) {
993 877 *rdst = idx;
994 878 rdst++;
995   - }
996   - else
997   - {
  879 + } else {
998 880 *ldst = idx;
999 881 ldst++;
1000 882 }
... ... @@ -1008,35 +890,15 @@ void CascadeBoostTree::split_node_data( CvDTreeNode* node )
1008 890 for(uint64_t i = 0; i < nodeSampleCount; i++)
1009 891 tempBuf[i] = sampleIdx_src[i];
1010 892  
1011   - if (data->is_buf_16u) {
1012   - unsigned short* ldst = (unsigned short*)(buf->data.s + left->buf_idx*length_buf_row + workVarCount*sampleCount + left->offset);
1013   - unsigned short* rdst = (unsigned short*)(buf->data.s + right->buf_idx*length_buf_row + workVarCount*sampleCount + right->offset);
1014   - for (uint64_t i = 0; i < nodeSampleCount; i++) {
1015   - unsigned short idx = (unsigned short)tempBuf[i];
1016   - if (dir[i]) {
1017   - *rdst = idx;
1018   - rdst++;
1019   - } else {
1020   - *ldst = idx;
1021   - ldst++;
1022   - }
1023   - }
1024   - }
1025   - else
1026 893 {
1027 894 int* ldst = buf->data.i + left->buf_idx*length_buf_row + workVarCount*sampleCount + left->offset;
1028 895 int* rdst = buf->data.i + right->buf_idx*length_buf_row + workVarCount*sampleCount + right->offset;
1029   - for (uint64_t i = 0; i < nodeSampleCount; i++)
1030   - {
  896 + for (uint64_t i = 0; i < nodeSampleCount; i++) {
1031 897 int idx = tempBuf[i];
1032   - if (dir[i])
1033   - {
1034   -
  898 + if (dir[i]) {
1035 899 *rdst = idx;
1036 900 rdst++;
1037   - }
1038   - else
1039   - {
  901 + } else {
1040 902 *ldst = idx;
1041 903 ldst++;
1042 904 }
... ... @@ -1143,7 +1005,7 @@ void CascadeBoost::update_weights(CvBoostTree* tree)
1143 1005 }
1144 1006  
1145 1007 CvMat* buf = data->buf;
1146   - size_t length_buf_row = data->get_length_subbuf();
  1008 + uint64_t length_buf_row = ((CascadeBoostTrainData*)data)->getLength();
1147 1009 if( !tree ) // before training the first tree, initialize weights and other parameters
1148 1010 {
1149 1011 int* classLabelsBuf = (int*)cur_inn_buf_pos; cur_inn_buf_pos = (uchar*)(classLabelsBuf + n);
... ... @@ -1167,15 +1029,9 @@ void CascadeBoost::update_weights(CvBoostTree* tree)
1167 1029  
1168 1030 // set the labels to find (from within weak tree learning proc)
1169 1031 // the particular sample weight, and where to store the response.
1170   - if (data->is_buf_16u) {
1171   - unsigned short* labels = (unsigned short*)(buf->data.s + data->data_root->buf_idx*length_buf_row + data->data_root->offset + (uint64_t)(data->work_var_count-1)*data->sample_count);
1172   - for (int i = 0; i < n; i++)
1173   - labels[i] = (unsigned short)i;
1174   - } else {
1175   - int* labels = buf->data.i + data->data_root->buf_idx*length_buf_row + data->data_root->offset + (uint64_t)(data->work_var_count-1)*data->sample_count;
1176   - for( int i = 0; i < n; i++ )
1177   - labels[i] = i;
1178   - }
  1032 + int* labels = buf->data.i + data->data_root->buf_idx*length_buf_row + data->data_root->offset + (uint64_t)(data->work_var_count-1)*data->sample_count;
  1033 + for( int i = 0; i < n; i++ )
  1034 + labels[i] = i;
1179 1035  
1180 1036 for (int i = 0; i < n; i++) {
1181 1037 // save original categorical responses {0,1}, convert them to {-1,1}
... ...