#include "precomp.hpp"#include <stdio.h>#define CV_ADJUST_FEATURES 1#define CV_ADJUST_WEIGHTS 0typedef int sumtype;typedef double sqsumtype;00066 typedef struct CvHidHaarFeature{ struct { sumtype *p0, *p1, *p2, *p3; float weight; } rect[CV_HAAR_FEATURE_MAX];}CvHidHaarFeature;00078 typedef struct CvHidHaarTreeNode{ CvHidHaarFeature feature; float threshold; int left; int right;}CvHidHaarTreeNode;00088 typedef struct CvHidHaarClassifier{ int count; CvHidHaarTreeNode* node; float* alpha;}CvHidHaarClassifier;00098 typedef struct CvHidHaarStageClassifier{ int count; float threshold; CvHidHaarClassifier* classifier; int two_rects; struct CvHidHaarStageClassifier* next; struct CvHidHaarStageClassifier* child; struct CvHidHaarStageClassifier* parent;}CvHidHaarStageClassifier;00112 struct CvHidHaarClassifierCascade{ int count; int isStumpBased; int has_tilted_features; int is_tree; double inv_window_area; CvMat sum, sqsum, tilted; CvHidHaarStageClassifier* stage_classifier; sqsumtype *pq0, *pq1, *pq2, *pq3; sumtype *p0, *p1, *p2, *p3; void** ipp_stages;};const int icv_object_win_border = 1;const float icv_stage_threshold_bias = 0.0001f;static CvHaarClassifierCascade*icvCreateHaarClassifierCascade( int stage_count ){ CvHaarClassifierCascade* cascade = 0; int block_size = sizeof(*cascade) + stage_count*sizeof(*cascade->stage_classifier); if( stage_count <= 0 ) CV_Error( CV_StsOutOfRange, "Number of stages should be positive" ); cascade = (CvHaarClassifierCascade*)cvAlloc( block_size ); memset( cascade, 0, block_size ); cascade->stage_classifier = (CvHaarStageClassifier*)(cascade + 1); cascade->flags = CV_HAAR_MAGIC_VAL; cascade->count = stage_count; return cascade;}static voidicvReleaseHidHaarClassifierCascade( CvHidHaarClassifierCascade** _cascade ){ if( _cascade && *_cascade ) {#ifdef HAVE_IPP CvHidHaarClassifierCascade* cascade = *_cascade; if( cascade->ipp_stages ) { int i; for( i = 0; i < cascade->count; i++ ) { if( cascade->ipp_stages[i] ) ippiHaarClassifierFree_32f( (IppiHaarClassifier_32f*)cascade->ipp_stages[i] ); } } cvFree( &cascade->ipp_stages );#endif cvFree( _cascade ); }}static CvHidHaarClassifierCascade*icvCreateHidHaarClassifierCascade( CvHaarClassifierCascade* cascade ){ CvRect* ipp_features = 0; float *ipp_weights = 0, *ipp_thresholds = 0, *ipp_val1 = 0, *ipp_val2 = 0; int* ipp_counts = 0; CvHidHaarClassifierCascade* out = 0; int i, j, k, l; int datasize; int total_classifiers = 0; int total_nodes = 0; char errorstr[100]; CvHidHaarClassifier* haar_classifier_ptr; CvHidHaarTreeNode* haar_node_ptr; CvSize orig_window_size; int has_tilted_features = 0; int max_count = 0; if( !CV_IS_HAAR_CLASSIFIER(cascade) ) CV_Error( !cascade ? CV_StsNullPtr : CV_StsBadArg, "Invalid classifier pointer" ); if( cascade->hid_cascade ) CV_Error( CV_StsError, "hid_cascade has been already created" ); if( !cascade->stage_classifier ) CV_Error( CV_StsNullPtr, "" ); if( cascade->count <= 0 ) CV_Error( CV_StsOutOfRange, "Negative number of cascade stages" ); orig_window_size = cascade->orig_window_size; for( i = 0; i < cascade->count; i++ ) { CvHaarStageClassifier* stage_classifier = cascade->stage_classifier + i; if( !stage_classifier->classifier || stage_classifier->count <= 0 ) { sprintf( errorstr, "header of the stage classifier #%d is invalid " "(has null pointers or non-positive classfier count)", i ); CV_Error( CV_StsError, errorstr ); } max_count = MAX( max_count, stage_classifier->count ); total_classifiers += stage_classifier->count; for( j = 0; j < stage_classifier->count; j++ ) { CvHaarClassifier* classifier = stage_classifier->classifier + j; total_nodes += classifier->count; for( l = 0; l < classifier->count; l++ ) { for( k = 0; k < CV_HAAR_FEATURE_MAX; k++ ) { if( classifier->haar_feature[l].rect[k].r.width ) { CvRect r = classifier->haar_feature[l].rect[k].r; int tilted = classifier->haar_feature[l].tilted; has_tilted_features |= tilted != 0; if( r.width < 0 || r.height < 0 || r.y < 0 || r.x + r.width > orig_window_size.width || (!tilted && (r.x < 0 || r.y + r.height > orig_window_size.height)) || (tilted && (r.x - r.height < 0 || r.y + r.width + r.height > orig_window_size.height))) { sprintf( errorstr, "rectangle #%d of the classifier #%d of " "the stage classifier #%d is not inside " "the reference (original) cascade window", k, j, i ); CV_Error( CV_StsNullPtr, errorstr ); } } } } } } datasize = sizeof(CvHidHaarClassifierCascade) + sizeof(CvHidHaarStageClassifier)*cascade->count + sizeof(CvHidHaarClassifier) * total_classifiers + sizeof(CvHidHaarTreeNode) * total_nodes + sizeof(void*)*(total_nodes + total_classifiers); out = (CvHidHaarClassifierCascade*)cvAlloc( datasize ); memset( out, 0, sizeof(*out) ); out->count = cascade->count; out->stage_classifier = (CvHidHaarStageClassifier*)(out + 1); haar_classifier_ptr = (CvHidHaarClassifier*)(out->stage_classifier + cascade->count); haar_node_ptr = (CvHidHaarTreeNode*)(haar_classifier_ptr + total_classifiers); out->isStumpBased = 1; out->has_tilted_features = has_tilted_features; out->is_tree = 0; for( i = 0; i < cascade->count; i++ ) { CvHaarStageClassifier* stage_classifier = cascade->stage_classifier + i; CvHidHaarStageClassifier* hid_stage_classifier = out->stage_classifier + i; hid_stage_classifier->count = stage_classifier->count; hid_stage_classifier->threshold = stage_classifier->threshold - icv_stage_threshold_bias; hid_stage_classifier->classifier = haar_classifier_ptr; hid_stage_classifier->two_rects = 1; haar_classifier_ptr += stage_classifier->count; hid_stage_classifier->parent = (stage_classifier->parent == -1) ? NULL : out->stage_classifier + stage_classifier->parent; hid_stage_classifier->next = (stage_classifier->next == -1) ? NULL : out->stage_classifier + stage_classifier->next; hid_stage_classifier->child = (stage_classifier->child == -1) ? NULL : out->stage_classifier + stage_classifier->child; out->is_tree |= hid_stage_classifier->next != NULL; for( j = 0; j < stage_classifier->count; j++ ) { CvHaarClassifier* classifier = stage_classifier->classifier + j; CvHidHaarClassifier* hid_classifier = hid_stage_classifier->classifier + j; int node_count = classifier->count; float* alpha_ptr = (float*)(haar_node_ptr + node_count); hid_classifier->count = node_count; hid_classifier->node = haar_node_ptr; hid_classifier->alpha = alpha_ptr; for( l = 0; l < node_count; l++ ) { CvHidHaarTreeNode* node = hid_classifier->node + l; CvHaarFeature* feature = classifier->haar_feature + l; memset( node, -1, sizeof(*node) ); node->threshold = classifier->threshold[l]; node->left = classifier->left[l]; node->right = classifier->right[l]; if( fabs(feature->rect[2].weight) < DBL_EPSILON || feature->rect[2].r.width == 0 || feature->rect[2].r.height == 0 ) memset( &(node->feature.rect[2]), 0, sizeof(node->feature.rect[2]) ); else hid_stage_classifier->two_rects = 0; } memcpy( alpha_ptr, classifier->alpha, (node_count+1)*sizeof(alpha_ptr[0])); haar_node_ptr = (CvHidHaarTreeNode*)cvAlignPtr(alpha_ptr+node_count+1, sizeof(void*)); out->isStumpBased &= node_count == 1; } }#ifdef HAVE_IPP int can_use_ipp = !out->has_tilted_features && !out->is_tree && out->isStumpBased; if( can_use_ipp ) { int ipp_datasize = cascade->count*sizeof(out->ipp_stages[0]); float ipp_weight_scale=(float)(1./((orig_window_size.width-icv_object_win_border*2)* (orig_window_size.height-icv_object_win_border*2))); out->ipp_stages = (void**)cvAlloc( ipp_datasize ); memset( out->ipp_stages, 0, ipp_datasize ); ipp_features = (CvRect*)cvAlloc( max_count*3*sizeof(ipp_features[0]) ); ipp_weights = (float*)cvAlloc( max_count*3*sizeof(ipp_weights[0]) ); ipp_thresholds = (float*)cvAlloc( max_count*sizeof(ipp_thresholds[0]) ); ipp_val1 = (float*)cvAlloc( max_count*sizeof(ipp_val1[0]) ); ipp_val2 = (float*)cvAlloc( max_count*sizeof(ipp_val2[0]) ); ipp_counts = (int*)cvAlloc( max_count*sizeof(ipp_counts[0]) ); for( i = 0; i < cascade->count; i++ ) { CvHaarStageClassifier* stage_classifier = cascade->stage_classifier + i; for( j = 0, k = 0; j < stage_classifier->count; j++ ) { CvHaarClassifier* classifier = stage_classifier->classifier + j; int rect_count = 2 + (classifier->haar_feature->rect[2].r.width != 0); ipp_thresholds[j] = classifier->threshold[0]; ipp_val1[j] = classifier->alpha[0]; ipp_val2[j] = classifier->alpha[1]; ipp_counts[j] = rect_count; for( l = 0; l < rect_count; l++, k++ ) { ipp_features[k] = classifier->haar_feature->rect[l].r; ipp_weights[k] = classifier->haar_feature->rect[l].weight*ipp_weight_scale; } } if( ippiHaarClassifierInitAlloc_32f( (IppiHaarClassifier_32f**)&out->ipp_stages[i], (const IppiRect*)ipp_features, ipp_weights, ipp_thresholds, ipp_val1, ipp_val2, ipp_counts, stage_classifier->count ) < 0 ) break; } if( i < cascade->count ) { for( j = 0; j < i; j++ ) if( out->ipp_stages[i] ) ippiHaarClassifierFree_32f( (IppiHaarClassifier_32f*)out->ipp_stages[i] ); cvFree( &out->ipp_stages ); } }#endif cascade->hid_cascade = out; assert( (char*)haar_node_ptr - (char*)out <= datasize ); cvFree( &ipp_features ); cvFree( &ipp_weights ); cvFree( &ipp_thresholds ); cvFree( &ipp_val1 ); cvFree( &ipp_val2 ); cvFree( &ipp_counts ); return out;}#define sum_elem_ptr(sum,row,col) \ ((sumtype*)CV_MAT_ELEM_PTR_FAST((sum),(row),(col),sizeof(sumtype)))#define sqsum_elem_ptr(sqsum,row,col) \ ((sqsumtype*)CV_MAT_ELEM_PTR_FAST((sqsum),(row),(col),sizeof(sqsumtype)))#define calc_sum(rect,offset) \ ((rect).p0[offset] - (rect).p1[offset] - (rect).p2[offset] + (rect).p3[offset])CV_IMPL voidcvSetImagesForHaarClassifierCascade( CvHaarClassifierCascade* _cascade, const CvArr* _sum, const CvArr* _sqsum, const CvArr* _tilted_sum, double scale ){ CvMat sum_stub, *sum = (CvMat*)_sum; CvMat sqsum_stub, *sqsum = (CvMat*)_sqsum; CvMat tilted_stub, *tilted = (CvMat*)_tilted_sum; CvHidHaarClassifierCascade* cascade; int coi0 = 0, coi1 = 0; int i; CvRect equRect; double weight_scale; if( !CV_IS_HAAR_CLASSIFIER(_cascade) ) CV_Error( !_cascade ? CV_StsNullPtr : CV_StsBadArg, "Invalid classifier pointer" ); if( scale <= 0 ) CV_Error( CV_StsOutOfRange, "Scale must be positive" ); sum = cvGetMat( sum, &sum_stub, &coi0 ); sqsum = cvGetMat( sqsum, &sqsum_stub, &coi1 ); if( coi0 || coi1 ) CV_Error( CV_BadCOI, "COI is not supported" ); if( !CV_ARE_SIZES_EQ( sum, sqsum )) CV_Error( CV_StsUnmatchedSizes, "All integral images must have the same size" ); if( CV_MAT_TYPE(sqsum->type) != CV_64FC1 || CV_MAT_TYPE(sum->type) != CV_32SC1 ) CV_Error( CV_StsUnsupportedFormat, "Only (32s, 64f, 32s) combination of (sum,sqsum,tilted_sum) formats is allowed" ); if( !_cascade->hid_cascade ) icvCreateHidHaarClassifierCascade(_cascade); cascade = _cascade->hid_cascade; if( cascade->has_tilted_features ) { tilted = cvGetMat( tilted, &tilted_stub, &coi1 ); if( CV_MAT_TYPE(tilted->type) != CV_32SC1 ) CV_Error( CV_StsUnsupportedFormat, "Only (32s, 64f, 32s) combination of (sum,sqsum,tilted_sum) formats is allowed" ); if( sum->step != tilted->step ) CV_Error( CV_StsUnmatchedSizes, "Sum and tilted_sum must have the same stride (step, widthStep)" ); if( !CV_ARE_SIZES_EQ( sum, tilted )) CV_Error( CV_StsUnmatchedSizes, "All integral images must have the same size" ); cascade->tilted = *tilted; } _cascade->scale = scale; _cascade->real_window_size.width = cvRound( _cascade->orig_window_size.width * scale ); _cascade->real_window_size.height = cvRound( _cascade->orig_window_size.height * scale ); cascade->sum = *sum; cascade->sqsum = *sqsum; equRect.x = equRect.y = cvRound(scale); equRect.width = cvRound((_cascade->orig_window_size.width-2)*scale); equRect.height = cvRound((_cascade->orig_window_size.height-2)*scale); weight_scale = 1./(equRect.width*equRect.height); cascade->inv_window_area = weight_scale; cascade->p0 = sum_elem_ptr(*sum, equRect.y, equRect.x); cascade->p1 = sum_elem_ptr(*sum, equRect.y, equRect.x + equRect.width ); cascade->p2 = sum_elem_ptr(*sum, equRect.y + equRect.height, equRect.x ); cascade->p3 = sum_elem_ptr(*sum, equRect.y + equRect.height, equRect.x + equRect.width ); cascade->pq0 = sqsum_elem_ptr(*sqsum, equRect.y, equRect.x); cascade->pq1 = sqsum_elem_ptr(*sqsum, equRect.y, equRect.x + equRect.width ); cascade->pq2 = sqsum_elem_ptr(*sqsum, equRect.y + equRect.height, equRect.x ); cascade->pq3 = sqsum_elem_ptr(*sqsum, equRect.y + equRect.height, equRect.x + equRect.width ); for( i = 0; i < _cascade->count; i++ ) { int j, k, l; for( j = 0; j < cascade->stage_classifier[i].count; j++ ) { for( l = 0; l < cascade->stage_classifier[i].classifier[j].count; l++ ) { CvHaarFeature* feature = &_cascade->stage_classifier[i].classifier[j].haar_feature[l]; CvHidHaarFeature* hidfeature = &cascade->stage_classifier[i].classifier[j].node[l].feature; double sum0 = 0, area0 = 0; CvRect r[3]; int base_w = -1, base_h = -1; int new_base_w = 0, new_base_h = 0; int kx, ky; int flagx = 0, flagy = 0; int x0 = 0, y0 = 0; int nr; for( k = 0; k < CV_HAAR_FEATURE_MAX; k++ ) { if( !hidfeature->rect[k].p0 ) break; r[k] = feature->rect[k].r; base_w = (int)CV_IMIN( (unsigned)base_w, (unsigned)(r[k].width-1) ); base_w = (int)CV_IMIN( (unsigned)base_w, (unsigned)(r[k].x - r[0].x-1) ); base_h = (int)CV_IMIN( (unsigned)base_h, (unsigned)(r[k].height-1) ); base_h = (int)CV_IMIN( (unsigned)base_h, (unsigned)(r[k].y - r[0].y-1) ); } nr = k; base_w += 1; base_h += 1; kx = r[0].width / base_w; ky = r[0].height / base_h; if( kx <= 0 ) { flagx = 1; new_base_w = cvRound( r[0].width * scale ) / kx; x0 = cvRound( r[0].x * scale ); } if( ky <= 0 ) { flagy = 1; new_base_h = cvRound( r[0].height * scale ) / ky; y0 = cvRound( r[0].y * scale ); } for( k = 0; k < nr; k++ ) { CvRect tr; double correction_ratio; if( flagx ) { tr.x = (r[k].x - r[0].x) * new_base_w / base_w + x0; tr.width = r[k].width * new_base_w / base_w; } else { tr.x = cvRound( r[k].x * scale ); tr.width = cvRound( r[k].width * scale ); } if( flagy ) { tr.y = (r[k].y - r[0].y) * new_base_h / base_h + y0; tr.height = r[k].height * new_base_h / base_h; } else { tr.y = cvRound( r[k].y * scale ); tr.height = cvRound( r[k].height * scale ); }#if CV_ADJUST_WEIGHTS { const float orig_feature_size = (float)(feature->rect[k].r.width)*feature->rect[k].r.height; const float orig_norm_size = (float)(_cascade->orig_window_size.width)*(_cascade->orig_window_size.height); const float feature_size = float(tr.width*tr.height); float target_ratio = orig_feature_size / orig_norm_size; correction_ratio = target_ratio / feature_size; }#else correction_ratio = weight_scale * (!feature->tilted ? 1 : 0.5);#endif if( !feature->tilted ) { hidfeature->rect[k].p0 = sum_elem_ptr(*sum, tr.y, tr.x); hidfeature->rect[k].p1 = sum_elem_ptr(*sum, tr.y, tr.x + tr.width); hidfeature->rect[k].p2 = sum_elem_ptr(*sum, tr.y + tr.height, tr.x); hidfeature->rect[k].p3 = sum_elem_ptr(*sum, tr.y + tr.height, tr.x + tr.width); } else { hidfeature->rect[k].p2 = sum_elem_ptr(*tilted, tr.y + tr.width, tr.x + tr.width); hidfeature->rect[k].p3 = sum_elem_ptr(*tilted, tr.y + tr.width + tr.height, tr.x + tr.width - tr.height); hidfeature->rect[k].p0 = sum_elem_ptr(*tilted, tr.y, tr.x); hidfeature->rect[k].p1 = sum_elem_ptr(*tilted, tr.y + tr.height, tr.x - tr.height); } hidfeature->rect[k].weight = (float)(feature->rect[k].weight * correction_ratio); if( k == 0 ) area0 = tr.width * tr.height; else sum0 += hidfeature->rect[k].weight * tr.width * tr.height; } hidfeature->rect[0].weight = (float)(-sum0/area0); } } }}CV_INLINEdouble icvEvalHidHaarClassifier( CvHidHaarClassifier* classifier, double variance_norm_factor, size_t p_offset ){ int idx = 0; do { CvHidHaarTreeNode* node = classifier->node + idx; double t = node->threshold * variance_norm_factor; double sum = calc_sum(node->feature.rect[0],p_offset) * node->feature.rect[0].weight; sum += calc_sum(node->feature.rect[1],p_offset) * node->feature.rect[1].weight; if( node->feature.rect[2].p0 ) sum += calc_sum(node->feature.rect[2],p_offset) * node->feature.rect[2].weight; idx = sum < t ? node->left : node->right; } while( idx > 0 ); return classifier->alpha[-idx];}CV_IMPL intcvRunHaarClassifierCascadeSum( const CvHaarClassifierCascade* _cascade, CvPoint pt, double& stage_sum, int start_stage ){ int result = -1; int p_offset, pq_offset; int i, j; double mean, variance_norm_factor; CvHidHaarClassifierCascade* cascade; if( !CV_IS_HAAR_CLASSIFIER(_cascade) ) CV_Error( !_cascade ? CV_StsNullPtr : CV_StsBadArg, "Invalid cascade pointer" ); cascade = _cascade->hid_cascade; if( !cascade ) CV_Error( CV_StsNullPtr, "Hidden cascade has not been created.\n" "Use cvSetImagesForHaarClassifierCascade" ); if( pt.x < 0 || pt.y < 0 || pt.x + _cascade->real_window_size.width >= cascade->sum.width || pt.y + _cascade->real_window_size.height >= cascade->sum.height ) return -1; p_offset = pt.y * (cascade->sum.step/sizeof(sumtype)) + pt.x; pq_offset = pt.y * (cascade->sqsum.step/sizeof(sqsumtype)) + pt.x; mean = calc_sum(*cascade,p_offset)*cascade->inv_window_area; variance_norm_factor = cascade->pq0[pq_offset] - cascade->pq1[pq_offset] - cascade->pq2[pq_offset] + cascade->pq3[pq_offset]; variance_norm_factor = variance_norm_factor*cascade->inv_window_area - mean*mean; if( variance_norm_factor >= 0. ) variance_norm_factor = sqrt(variance_norm_factor); else variance_norm_factor = 1.; if( cascade->is_tree ) { CvHidHaarStageClassifier* ptr; assert( start_stage == 0 ); result = 1; ptr = cascade->stage_classifier; while( ptr ) { stage_sum = 0.0; for( j = 0; j < ptr->count; j++ ) { stage_sum += icvEvalHidHaarClassifier( ptr->classifier + j, variance_norm_factor, p_offset ); } if( stage_sum >= ptr->threshold ) { ptr = ptr->child; } else { while( ptr && ptr->next == NULL ) ptr = ptr->parent; if( ptr == NULL ) return 0; ptr = ptr->next; } } } else if( cascade->isStumpBased ) { for( i = start_stage; i < cascade->count; i++ ) {#ifndef CV_HAAR_USE_SSE stage_sum = 0.0;#else __m128d stage_sum = _mm_setzero_pd();#endif if( cascade->stage_classifier[i].two_rects ) { for( j = 0; j < cascade->stage_classifier[i].count; j++ ) { CvHidHaarClassifier* classifier = cascade->stage_classifier[i].classifier + j; CvHidHaarTreeNode* node = classifier->node;#ifndef CV_HAAR_USE_SSE double t = node->threshold*variance_norm_factor; double sum = calc_sum(node->feature.rect[0],p_offset) * node->feature.rect[0].weight; sum += calc_sum(node->feature.rect[1],p_offset) * node->feature.rect[1].weight; stage_sum += classifier->alpha[sum >= t];#else __m128d t = _mm_set_sd(node->threshold*variance_norm_factor); __m128d a = _mm_set_sd(classifier->alpha[0]); __m128d b = _mm_set_sd(classifier->alpha[1]); __m128d sum = _mm_set_sd(calc_sum(node->feature.rect[0],p_offset) * node->feature.rect[0].weight + calc_sum(node->feature.rect[1],p_offset) * node->feature.rect[1].weight); t = _mm_cmpgt_sd(t, sum); stage_sum = _mm_add_sd(stage_sum, _mm_blendv_pd(b, a, t));#endif } } else { for( j = 0; j < cascade->stage_classifier[i].count; j++ ) { CvHidHaarClassifier* classifier = cascade->stage_classifier[i].classifier + j; CvHidHaarTreeNode* node = classifier->node;#ifndef CV_HAAR_USE_SSE double t = node->threshold*variance_norm_factor; double sum = calc_sum(node->feature.rect[0],p_offset) * node->feature.rect[0].weight; sum += calc_sum(node->feature.rect[1],p_offset) * node->feature.rect[1].weight; if( node->feature.rect[2].p0 ) sum += calc_sum(node->feature.rect[2],p_offset) * node->feature.rect[2].weight; stage_sum += classifier->alpha[sum >= t];#else __m128d t = _mm_set_sd(node->threshold*variance_norm_factor); __m128d a = _mm_set_sd(classifier->alpha[0]); __m128d b = _mm_set_sd(classifier->alpha[1]); double _sum = calc_sum(node->feature.rect[0],p_offset) * node->feature.rect[0].weight; _sum += calc_sum(node->feature.rect[1],p_offset) * node->feature.rect[1].weight; if( node->feature.rect[2].p0 ) _sum += calc_sum(node->feature.rect[2],p_offset) * node->feature.rect[2].weight; __m128d sum = _mm_set_sd(_sum); t = _mm_cmpgt_sd(t, sum); stage_sum = _mm_add_sd(stage_sum, _mm_blendv_pd(b, a, t));#endif } }#ifndef CV_HAAR_USE_SSE if( stage_sum < cascade->stage_classifier[i].threshold )#else __m128d i_threshold = _mm_set_sd(cascade->stage_classifier[i].threshold); if( _mm_comilt_sd(stage_sum, i_threshold) )#endif return -i; } } else { for( i = start_stage; i < cascade->count; i++ ) { stage_sum = 0.0; for( j = 0; j < cascade->stage_classifier[i].count; j++ ) { stage_sum += icvEvalHidHaarClassifier( cascade->stage_classifier[i].classifier + j, variance_norm_factor, p_offset ); } if( stage_sum < cascade->stage_classifier[i].threshold ) return -i; } } return 1;}CV_IMPL intcvRunHaarClassifierCascade( const CvHaarClassifierCascade* _cascade, CvPoint pt, int start_stage ){ double stage_sum; return cvRunHaarClassifierCascadeSum(_cascade, pt, stage_sum, start_stage);}namespace cv{00826 struct HaarDetectObjects_ScaleImage_Invoker{ HaarDetectObjects_ScaleImage_Invoker( const CvHaarClassifierCascade* _cascade, int _stripSize, double _factor, const Mat& _sum1, const Mat& _sqsum1, Mat* _norm1, Mat* _mask1, Rect _equRect, ConcurrentRectVector& _vec, std::vector<int>& _levels, std::vector<double>& _weights, bool _outputLevels ) { cascade = _cascade; stripSize = _stripSize; factor = _factor; sum1 = _sum1; sqsum1 = _sqsum1; norm1 = _norm1; mask1 = _mask1; equRect = _equRect; vec = &_vec; rejectLevels = _outputLevels ? &_levels : 0; levelWeights = _outputLevels ? &_weights : 0; } void operator()( const BlockedRange& range ) const { Size winSize0 = cascade->orig_window_size; Size winSize(cvRound(winSize0.width*factor), cvRound(winSize0.height*factor)); int y1 = range.begin()*stripSize, y2 = min(range.end()*stripSize, sum1.rows - 1 - winSize0.height); if (y2 <= y1 || sum1.cols <= 1 + winSize0.width) return; Size ssz(sum1.cols - 1 - winSize0.width, y2 - y1); int x, y, ystep = factor > 2 ? 1 : 2; #ifdef HAVE_IPP if( cascade->hid_cascade->ipp_stages ) { IppiRect iequRect = {equRect.x, equRect.y, equRect.width, equRect.height}; ippiRectStdDev_32f_C1R(sum1.ptr<float>(y1), sum1.step, sqsum1.ptr<double>(y1), sqsum1.step, norm1->ptr<float>(y1), norm1->step, ippiSize(ssz.width, ssz.height), iequRect ); int positive = (ssz.width/ystep)*((ssz.height + ystep-1)/ystep); if( ystep == 1 ) (*mask1) = Scalar::all(1); else for( y = y1; y < y2; y++ ) { uchar* mask1row = mask1->ptr(y); memset( mask1row, 0, ssz.width ); if( y % ystep == 0 ) for( x = 0; x < ssz.width; x += ystep ) mask1row[x] = (uchar)1; } for( int j = 0; j < cascade->count; j++ ) { if( ippiApplyHaarClassifier_32f_C1R( sum1.ptr<float>(y1), sum1.step, norm1->ptr<float>(y1), norm1->step, mask1->ptr<uchar>(y1), mask1->step, ippiSize(ssz.width, ssz.height), &positive, cascade->hid_cascade->stage_classifier[j].threshold, (IppiHaarClassifier_32f*)cascade->hid_cascade->ipp_stages[j]) < 0 ) positive = 0; if( positive <= 0 ) break; } if( positive > 0 ) for( y = y1; y < y2; y += ystep ) { uchar* mask1row = mask1->ptr(y); for( x = 0; x < ssz.width; x += ystep ) if( mask1row[x] != 0 ) { vec->push_back(Rect(cvRound(x*factor), cvRound(y*factor), winSize.width, winSize.height)); if( --positive == 0 ) break; } if( positive == 0 ) break; } } else#endif for( y = y1; y < y2; y += ystep ) for( x = 0; x < ssz.width; x += ystep ) { double gypWeight; int result = cvRunHaarClassifierCascadeSum( cascade, cvPoint(x,y), gypWeight, 0 ); if( rejectLevels ) { if( result == 1 ) result = -1*cascade->count; if( cascade->count + result < 4 ) { vec->push_back(Rect(cvRound(x*factor), cvRound(y*factor), winSize.width, winSize.height)); rejectLevels->push_back(-result); levelWeights->push_back(gypWeight); } } else { if( result > 0 ) vec->push_back(Rect(cvRound(x*factor), cvRound(y*factor), winSize.width, winSize.height)); } } } const CvHaarClassifierCascade* cascade; int stripSize; double factor; Mat sum1, sqsum1, *norm1, *mask1; Rect equRect; ConcurrentRectVector* vec; std::vector<int>* rejectLevels; std::vector<double>* levelWeights;}; 00953 struct HaarDetectObjects_ScaleCascade_Invoker{ HaarDetectObjects_ScaleCascade_Invoker( const CvHaarClassifierCascade* _cascade, Size _winsize, const Range& _xrange, double _ystep, size_t _sumstep, const int** _p, const int** _pq, ConcurrentRectVector& _vec ) { cascade = _cascade; winsize = _winsize; xrange = _xrange; ystep = _ystep; sumstep = _sumstep; p = _p; pq = _pq; vec = &_vec; } void operator()( const BlockedRange& range ) const { int iy, startY = range.begin(), endY = range.end(); const int *p0 = p[0], *p1 = p[1], *p2 = p[2], *p3 = p[3]; const int *pq0 = pq[0], *pq1 = pq[1], *pq2 = pq[2], *pq3 = pq[3]; bool doCannyPruning = p0 != 0; int sstep = (int)(sumstep/sizeof(p0[0])); for( iy = startY; iy < endY; iy++ ) { int ix, y = cvRound(iy*ystep), ixstep = 1; for( ix = xrange.start; ix < xrange.end; ix += ixstep ) { int x = cvRound(ix*ystep); if( doCannyPruning ) { int offset = y*sstep + x; int s = p0[offset] - p1[offset] - p2[offset] + p3[offset]; int sq = pq0[offset] - pq1[offset] - pq2[offset] + pq3[offset]; if( s < 100 || sq < 20 ) { ixstep = 2; continue; } } int result = cvRunHaarClassifierCascade( cascade, cvPoint(x, y), 0 ); if( result > 0 ) vec->push_back(Rect(x, y, winsize.width, winsize.height)); ixstep = result != 0 ? 1 : 2; } } } const CvHaarClassifierCascade* cascade; double ystep; size_t sumstep; Size winsize; Range xrange; const int** p; const int** pq; ConcurrentRectVector* vec;}; } CvSeq*cvHaarDetectObjectsForROC( const CvArr* _img, CvHaarClassifierCascade* cascade, CvMemStorage* storage, std::vector<int>& rejectLevels, std::vector<double>& levelWeights, double scaleFactor, int minNeighbors, int flags, CvSize minSize, CvSize maxSize, bool outputRejectLevels ){ const double GROUP_EPS = 0.2; CvMat stub, *img = (CvMat*)_img; cv::Ptr<CvMat> temp, sum, tilted, sqsum, normImg, sumcanny, imgSmall; CvSeq* result_seq = 0; cv::Ptr<CvMemStorage> temp_storage; cv::ConcurrentRectVector allCandidates; std::vector<cv::Rect> rectList; std::vector<int> rweights; double factor; int coi; bool doCannyPruning = (flags & CV_HAAR_DO_CANNY_PRUNING) != 0; bool findBiggestObject = (flags & CV_HAAR_FIND_BIGGEST_OBJECT) != 0; bool roughSearch = (flags & CV_HAAR_DO_ROUGH_SEARCH) != 0; if( !CV_IS_HAAR_CLASSIFIER(cascade) ) CV_Error( !cascade ? CV_StsNullPtr : CV_StsBadArg, "Invalid classifier cascade" ); if( !storage ) CV_Error( CV_StsNullPtr, "Null storage pointer" ); img = cvGetMat( img, &stub, &coi ); if( coi ) CV_Error( CV_BadCOI, "COI is not supported" ); if( CV_MAT_DEPTH(img->type) != CV_8U ) CV_Error( CV_StsUnsupportedFormat, "Only 8-bit images are supported" ); if( scaleFactor <= 1 ) CV_Error( CV_StsOutOfRange, "scale factor must be > 1" ); if( findBiggestObject ) flags &= ~CV_HAAR_SCALE_IMAGE; if( maxSize.height == 0 || maxSize.width == 0 ) { maxSize.height = img->rows; maxSize.width = img->cols; } temp = cvCreateMat( img->rows, img->cols, CV_8UC1 ); sum = cvCreateMat( img->rows + 1, img->cols + 1, CV_32SC1 ); sqsum = cvCreateMat( img->rows + 1, img->cols + 1, CV_64FC1 ); if( !cascade->hid_cascade ) icvCreateHidHaarClassifierCascade(cascade); if( cascade->hid_cascade->has_tilted_features ) tilted = cvCreateMat( img->rows + 1, img->cols + 1, CV_32SC1 ); result_seq = cvCreateSeq( 0, sizeof(CvSeq), sizeof(CvAvgComp), storage ); if( CV_MAT_CN(img->type) > 1 ) { cvCvtColor( img, temp, CV_BGR2GRAY ); img = temp; } if( findBiggestObject ) flags &= ~(CV_HAAR_SCALE_IMAGE|CV_HAAR_DO_CANNY_PRUNING); if( flags & CV_HAAR_SCALE_IMAGE ) { CvSize winSize0 = cascade->orig_window_size;#ifdef HAVE_IPP int use_ipp = cascade->hid_cascade->ipp_stages != 0; if( use_ipp ) normImg = cvCreateMat( img->rows, img->cols, CV_32FC1 );#endif imgSmall = cvCreateMat( img->rows + 1, img->cols + 1, CV_8UC1 ); for( factor = 1; ; factor *= scaleFactor ) { CvSize winSize = { cvRound(winSize0.width*factor), cvRound(winSize0.height*factor) }; CvSize sz = { cvRound( img->cols/factor ), cvRound( img->rows/factor ) }; CvSize sz1 = { sz.width - winSize0.width + 1, sz.height - winSize0.height + 1 }; CvRect equRect = { icv_object_win_border, icv_object_win_border, winSize0.width - icv_object_win_border*2, winSize0.height - icv_object_win_border*2 }; CvMat img1, sum1, sqsum1, norm1, tilted1, mask1; CvMat* _tilted = 0; if( sz1.width <= 0 || sz1.height <= 0 ) break; if( winSize.width > maxSize.width || winSize.height > maxSize.height ) break; if( winSize.width < minSize.width || winSize.height < minSize.height ) continue; img1 = cvMat( sz.height, sz.width, CV_8UC1, imgSmall->data.ptr ); sum1 = cvMat( sz.height+1, sz.width+1, CV_32SC1, sum->data.ptr ); sqsum1 = cvMat( sz.height+1, sz.width+1, CV_64FC1, sqsum->data.ptr ); if( tilted ) { tilted1 = cvMat( sz.height+1, sz.width+1, CV_32SC1, tilted->data.ptr ); _tilted = &tilted1; } norm1 = cvMat( sz1.height, sz1.width, CV_32FC1, normImg ? normImg->data.ptr : 0 ); mask1 = cvMat( sz1.height, sz1.width, CV_8UC1, temp->data.ptr ); cvResize( img, &img1, CV_INTER_LINEAR ); cvIntegral( &img1, &sum1, &sqsum1, _tilted ); int ystep = factor > 2 ? 1 : 2; #ifdef HAVE_TBB const int LOCS_PER_THREAD = 1000; int stripCount = ((sz1.width/ystep)*(sz1.height + ystep-1)/ystep + LOCS_PER_THREAD/2)/LOCS_PER_THREAD; stripCount = std::min(std::max(stripCount, 1), 100); #else const int stripCount = 1; #endif #ifdef HAVE_IPP if( use_ipp ) { cv::Mat fsum(sum1.rows, sum1.cols, CV_32F, sum1.data.ptr, sum1.step); cv::Mat(&sum1).convertTo(fsum, CV_32F, 1, -(1<<24)); } else#endif cvSetImagesForHaarClassifierCascade( cascade, &sum1, &sqsum1, _tilted, 1. ); cv::Mat _norm1(&norm1), _mask1(&mask1); cv::parallel_for(cv::BlockedRange(0, stripCount), cv::HaarDetectObjects_ScaleImage_Invoker(cascade, (((sz1.height + stripCount - 1)/stripCount + ystep-1)/ystep)*ystep, factor, cv::Mat(&sum1), cv::Mat(&sqsum1), &_norm1, &_mask1, cv::Rect(equRect), allCandidates, rejectLevels, levelWeights, outputRejectLevels)); } } else { int n_factors = 0; cv::Rect scanROI; cvIntegral( img, sum, sqsum, tilted ); if( doCannyPruning ) { sumcanny = cvCreateMat( img->rows + 1, img->cols + 1, CV_32SC1 ); cvCanny( img, temp, 0, 50, 3 ); cvIntegral( temp, sumcanny ); } for( n_factors = 0, factor = 1; factor*cascade->orig_window_size.width < img->cols - 10 && factor*cascade->orig_window_size.height < img->rows - 10; n_factors++, factor *= scaleFactor ) ; if( findBiggestObject ) { scaleFactor = 1./scaleFactor; factor *= scaleFactor; } else factor = 1; for( ; n_factors-- > 0; factor *= scaleFactor ) { const double ystep = std::max( 2., factor ); CvSize winSize = { cvRound( cascade->orig_window_size.width * factor ), cvRound( cascade->orig_window_size.height * factor )}; CvRect equRect = { 0, 0, 0, 0 }; int *p[4] = {0,0,0,0}; int *pq[4] = {0,0,0,0}; int startX = 0, startY = 0; int endX = cvRound((img->cols - winSize.width) / ystep); int endY = cvRound((img->rows - winSize.height) / ystep); if( winSize.width < minSize.width || winSize.height < minSize.height ) { if( findBiggestObject ) break; continue; } cvSetImagesForHaarClassifierCascade( cascade, sum, sqsum, tilted, factor ); cvZero( temp ); if( doCannyPruning ) { equRect.x = cvRound(winSize.width*0.15); equRect.y = cvRound(winSize.height*0.15); equRect.width = cvRound(winSize.width*0.7); equRect.height = cvRound(winSize.height*0.7); p[0] = (int*)(sumcanny->data.ptr + equRect.y*sumcanny->step) + equRect.x; p[1] = (int*)(sumcanny->data.ptr + equRect.y*sumcanny->step) + equRect.x + equRect.width; p[2] = (int*)(sumcanny->data.ptr + (equRect.y + equRect.height)*sumcanny->step) + equRect.x; p[3] = (int*)(sumcanny->data.ptr + (equRect.y + equRect.height)*sumcanny->step) + equRect.x + equRect.width; pq[0] = (int*)(sum->data.ptr + equRect.y*sum->step) + equRect.x; pq[1] = (int*)(sum->data.ptr + equRect.y*sum->step) + equRect.x + equRect.width; pq[2] = (int*)(sum->data.ptr + (equRect.y + equRect.height)*sum->step) + equRect.x; pq[3] = (int*)(sum->data.ptr + (equRect.y + equRect.height)*sum->step) + equRect.x + equRect.width; } if( scanROI.area() > 0 ) { startY = cvRound(scanROI.y / ystep); endY = cvRound((scanROI.y + scanROI.height - winSize.height) / ystep); startX = cvRound(scanROI.x / ystep); endX = cvRound((scanROI.x + scanROI.width - winSize.width) / ystep); } cv::parallel_for(cv::BlockedRange(startY, endY), cv::HaarDetectObjects_ScaleCascade_Invoker(cascade, winSize, cv::Range(startX, endX), ystep, sum->step, (const int**)p, (const int**)pq, allCandidates )); if( findBiggestObject && !allCandidates.empty() && scanROI.area() == 0 ) { rectList.resize(allCandidates.size()); std::copy(allCandidates.begin(), allCandidates.end(), rectList.begin()); groupRectangles(rectList, std::max(minNeighbors, 1), GROUP_EPS); if( !rectList.empty() ) { size_t i, sz = rectList.size(); cv::Rect maxRect; for( i = 0; i < sz; i++ ) { if( rectList[i].area() > maxRect.area() ) maxRect = rectList[i]; } allCandidates.push_back(maxRect); scanROI = maxRect; int dx = cvRound(maxRect.width*GROUP_EPS); int dy = cvRound(maxRect.height*GROUP_EPS); scanROI.x = std::max(scanROI.x - dx, 0); scanROI.y = std::max(scanROI.y - dy, 0); scanROI.width = std::min(scanROI.width + dx*2, img->cols-1-scanROI.x); scanROI.height = std::min(scanROI.height + dy*2, img->rows-1-scanROI.y); double minScale = roughSearch ? 0.6 : 0.4; minSize.width = cvRound(maxRect.width*minScale); minSize.height = cvRound(maxRect.height*minScale); } } } } rectList.resize(allCandidates.size()); if(!allCandidates.empty()) std::copy(allCandidates.begin(), allCandidates.end(), rectList.begin()); if( minNeighbors != 0 || findBiggestObject ) { if( outputRejectLevels ) { groupRectangles(rectList, rejectLevels, levelWeights, minNeighbors, GROUP_EPS ); } else { groupRectangles(rectList, rweights, std::max(minNeighbors, 1), GROUP_EPS); } } else rweights.resize(rectList.size(),0); if( findBiggestObject && rectList.size() ) { CvAvgComp result_comp = {{0,0,0,0},0}; for( size_t i = 0; i < rectList.size(); i++ ) { cv::Rect r = rectList[i]; if( r.area() > cv::Rect(result_comp.rect).area() ) { result_comp.rect = r; result_comp.neighbors = rweights[i]; } } cvSeqPush( result_seq, &result_comp ); } else { for( size_t i = 0; i < rectList.size(); i++ ) { CvAvgComp c; c.rect = rectList[i]; c.neighbors = !rweights.empty() ? rweights[i] : 0; cvSeqPush( result_seq, &c ); } } return result_seq;}CV_IMPL CvSeq*cvHaarDetectObjects( const CvArr* _img, CvHaarClassifierCascade* cascade, CvMemStorage* storage, double scaleFactor, int minNeighbors, int flags, CvSize minSize, CvSize maxSize ){ std::vector<int> fakeLevels; std::vector<double> fakeWeights; return cvHaarDetectObjectsForROC( _img, cascade, storage, fakeLevels, fakeWeights, scaleFactor, minNeighbors, flags, minSize, maxSize, false );}static CvHaarClassifierCascade*icvLoadCascadeCART( const char** input_cascade, int n, CvSize orig_window_size ){ int i; CvHaarClassifierCascade* cascade = icvCreateHaarClassifierCascade(n); cascade->orig_window_size = orig_window_size; for( i = 0; i < n; i++ ) { int j, count, l; float threshold = 0; const char* stage = input_cascade[i]; int dl = 0; int parent = -1; int next = -1; sscanf( stage, "%d%n", &count, &dl ); stage += dl; assert( count > 0 ); cascade->stage_classifier[i].count = count; cascade->stage_classifier[i].classifier = (CvHaarClassifier*)cvAlloc( count*sizeof(cascade->stage_classifier[i].classifier[0])); for( j = 0; j < count; j++ ) { CvHaarClassifier* classifier = cascade->stage_classifier[i].classifier + j; int k, rects = 0; char str[100]; sscanf( stage, "%d%n", &classifier->count, &dl ); stage += dl; classifier->haar_feature = (CvHaarFeature*) cvAlloc( classifier->count * ( sizeof( *classifier->haar_feature ) + sizeof( *classifier->threshold ) + sizeof( *classifier->left ) + sizeof( *classifier->right ) ) + (classifier->count + 1) * sizeof( *classifier->alpha ) ); classifier->threshold = (float*) (classifier->haar_feature+classifier->count); classifier->left = (int*) (classifier->threshold + classifier->count); classifier->right = (int*) (classifier->left + classifier->count); classifier->alpha = (float*) (classifier->right + classifier->count); for( l = 0; l < classifier->count; l++ ) { sscanf( stage, "%d%n", &rects, &dl ); stage += dl; assert( rects >= 2 && rects <= CV_HAAR_FEATURE_MAX ); for( k = 0; k < rects; k++ ) { CvRect r; int band = 0; sscanf( stage, "%d%d%d%d%d%f%n", &r.x, &r.y, &r.width, &r.height, &band, &(classifier->haar_feature[l].rect[k].weight), &dl ); stage += dl; classifier->haar_feature[l].rect[k].r = r; } sscanf( stage, "%s%n", str, &dl ); stage += dl; classifier->haar_feature[l].tilted = strncmp( str, "tilted", 6 ) == 0; for( k = rects; k < CV_HAAR_FEATURE_MAX; k++ ) { memset( classifier->haar_feature[l].rect + k, 0, sizeof(classifier->haar_feature[l].rect[k]) ); } sscanf( stage, "%f%d%d%n", &(classifier->threshold[l]), &(classifier->left[l]), &(classifier->right[l]), &dl ); stage += dl; } for( l = 0; l <= classifier->count; l++ ) { sscanf( stage, "%f%n", &(classifier->alpha[l]), &dl ); stage += dl; } } sscanf( stage, "%f%n", &threshold, &dl ); stage += dl; cascade->stage_classifier[i].threshold = threshold; if( sscanf( stage, "%d%d%n", &parent, &next, &dl ) != 2 ) { parent = i - 1; next = -1; } stage += dl; cascade->stage_classifier[i].parent = parent; cascade->stage_classifier[i].next = next; cascade->stage_classifier[i].child = -1; if( parent != -1 && cascade->stage_classifier[parent].child == -1 ) { cascade->stage_classifier[parent].child = i; } } return cascade;}#ifndef _MAX_PATH#define _MAX_PATH 1024#endifCV_IMPL CvHaarClassifierCascade*cvLoadHaarClassifierCascade( const char* directory, CvSize orig_window_size ){ const char** input_cascade = 0; CvHaarClassifierCascade *cascade = 0; int i, n; const char* slash; char name[_MAX_PATH]; int size = 0; char* ptr = 0; if( !directory ) CV_Error( CV_StsNullPtr, "Null path is passed" ); n = (int)strlen(directory)-1; slash = directory[n] == '\\' || directory[n] == '/' ? "" : "/"; for( n = 0; ; n++ ) { sprintf( name, "%s%s%d/AdaBoostCARTHaarClassifier.txt", directory, slash, n ); FILE* f = fopen( name, "rb" ); if( !f ) break; fseek( f, 0, SEEK_END ); size += ftell( f ) + 1; fclose(f); } if( n == 0 && slash[0] ) return (CvHaarClassifierCascade*)cvLoad( directory ); if( n == 0 ) CV_Error( CV_StsBadArg, "Invalid path" ); size += (n+1)*sizeof(char*); input_cascade = (const char**)cvAlloc( size ); ptr = (char*)(input_cascade + n + 1); for( i = 0; i < n; i++ ) { sprintf( name, "%s/%d/AdaBoostCARTHaarClassifier.txt", directory, i ); FILE* f = fopen( name, "rb" ); if( !f ) CV_Error( CV_StsError, "" ); fseek( f, 0, SEEK_END ); size = ftell( f ); fseek( f, 0, SEEK_SET ); fread( ptr, 1, size, f ); fclose(f); input_cascade[i] = ptr; ptr += size; *ptr++ = '\0'; } input_cascade[n] = 0; cascade = icvLoadCascadeCART( input_cascade, n, orig_window_size ); if( input_cascade ) cvFree( &input_cascade ); return cascade;}CV_IMPL voidcvReleaseHaarClassifierCascade( CvHaarClassifierCascade** _cascade ){ if( _cascade && *_cascade ) { int i, j; CvHaarClassifierCascade* cascade = *_cascade; for( i = 0; i < cascade->count; i++ ) { for( j = 0; j < cascade->stage_classifier[i].count; j++ ) cvFree( &cascade->stage_classifier[i].classifier[j].haar_feature ); cvFree( &cascade->stage_classifier[i].classifier ); } icvReleaseHidHaarClassifierCascade( &cascade->hid_cascade ); cvFree( _cascade ); }}#define ICV_HAAR_SIZE_NAME "size"#define ICV_HAAR_STAGES_NAME "stages"#define ICV_HAAR_TREES_NAME "trees"#define ICV_HAAR_FEATURE_NAME "feature"#define ICV_HAAR_RECTS_NAME "rects"#define ICV_HAAR_TILTED_NAME "tilted"#define ICV_HAAR_THRESHOLD_NAME "threshold"#define ICV_HAAR_LEFT_NODE_NAME "left_node"#define ICV_HAAR_LEFT_VAL_NAME "left_val"#define ICV_HAAR_RIGHT_NODE_NAME "right_node"#define ICV_HAAR_RIGHT_VAL_NAME "right_val"#define ICV_HAAR_STAGE_THRESHOLD_NAME "stage_threshold"#define ICV_HAAR_PARENT_NAME "parent"#define ICV_HAAR_NEXT_NAME "next"static inticvIsHaarClassifier( const void* struct_ptr ){ return CV_IS_HAAR_CLASSIFIER( struct_ptr );}static void*icvReadHaarClassifier( CvFileStorage* fs, CvFileNode* node ){ CvHaarClassifierCascade* cascade = NULL; char buf[256]; CvFileNode* seq_fn = NULL; CvFileNode* fn = NULL; CvFileNode* stages_fn = NULL; CvSeqReader stages_reader; int n; int i, j, k, l; int parent, next; stages_fn = cvGetFileNodeByName( fs, node, ICV_HAAR_STAGES_NAME ); if( !stages_fn || !CV_NODE_IS_SEQ( stages_fn->tag) ) CV_Error( CV_StsError, "Invalid stages node" ); n = stages_fn->data.seq->total; cascade = icvCreateHaarClassifierCascade(n); seq_fn = cvGetFileNodeByName( fs, node, ICV_HAAR_SIZE_NAME ); if( !seq_fn || !CV_NODE_IS_SEQ( seq_fn->tag ) || seq_fn->data.seq->total != 2 ) CV_Error( CV_StsError, "size node is not a valid sequence." ); fn = (CvFileNode*) cvGetSeqElem( seq_fn->data.seq, 0 ); if( !CV_NODE_IS_INT( fn->tag ) || fn->data.i <= 0 ) CV_Error( CV_StsError, "Invalid size node: width must be positive integer" ); cascade->orig_window_size.width = fn->data.i; fn = (CvFileNode*) cvGetSeqElem( seq_fn->data.seq, 1 ); if( !CV_NODE_IS_INT( fn->tag ) || fn->data.i <= 0 ) CV_Error( CV_StsError, "Invalid size node: height must be positive integer" ); cascade->orig_window_size.height = fn->data.i; cvStartReadSeq( stages_fn->data.seq, &stages_reader ); for( i = 0; i < n; ++i ) { CvFileNode* stage_fn; CvFileNode* trees_fn; CvSeqReader trees_reader; stage_fn = (CvFileNode*) stages_reader.ptr; if( !CV_NODE_IS_MAP( stage_fn->tag ) ) { sprintf( buf, "Invalid stage %d", i ); CV_Error( CV_StsError, buf ); } trees_fn = cvGetFileNodeByName( fs, stage_fn, ICV_HAAR_TREES_NAME ); if( !trees_fn || !CV_NODE_IS_SEQ( trees_fn->tag ) || trees_fn->data.seq->total <= 0 ) { sprintf( buf, "Trees node is not a valid sequence. (stage %d)", i ); CV_Error( CV_StsError, buf ); } cascade->stage_classifier[i].classifier = (CvHaarClassifier*) cvAlloc( trees_fn->data.seq->total * sizeof( cascade->stage_classifier[i].classifier[0] ) ); for( j = 0; j < trees_fn->data.seq->total; ++j ) { cascade->stage_classifier[i].classifier[j].haar_feature = NULL; } cascade->stage_classifier[i].count = trees_fn->data.seq->total; cvStartReadSeq( trees_fn->data.seq, &trees_reader ); for( j = 0; j < trees_fn->data.seq->total; ++j ) { CvFileNode* tree_fn; CvSeqReader tree_reader; CvHaarClassifier* classifier; int last_idx; classifier = &cascade->stage_classifier[i].classifier[j]; tree_fn = (CvFileNode*) trees_reader.ptr; if( !CV_NODE_IS_SEQ( tree_fn->tag ) || tree_fn->data.seq->total <= 0 ) { sprintf( buf, "Tree node is not a valid sequence." " (stage %d, tree %d)", i, j ); CV_Error( CV_StsError, buf ); } classifier->count = tree_fn->data.seq->total; classifier->haar_feature = (CvHaarFeature*) cvAlloc( classifier->count * ( sizeof( *classifier->haar_feature ) + sizeof( *classifier->threshold ) + sizeof( *classifier->left ) + sizeof( *classifier->right ) ) + (classifier->count + 1) * sizeof( *classifier->alpha ) ); classifier->threshold = (float*) (classifier->haar_feature+classifier->count); classifier->left = (int*) (classifier->threshold + classifier->count); classifier->right = (int*) (classifier->left + classifier->count); classifier->alpha = (float*) (classifier->right + classifier->count); cvStartReadSeq( tree_fn->data.seq, &tree_reader ); for( k = 0, last_idx = 0; k < tree_fn->data.seq->total; ++k ) { CvFileNode* node_fn; CvFileNode* feature_fn; CvFileNode* rects_fn; CvSeqReader rects_reader; node_fn = (CvFileNode*) tree_reader.ptr; if( !CV_NODE_IS_MAP( node_fn->tag ) ) { sprintf( buf, "Tree node %d is not a valid map. (stage %d, tree %d)", k, i, j ); CV_Error( CV_StsError, buf ); } feature_fn = cvGetFileNodeByName( fs, node_fn, ICV_HAAR_FEATURE_NAME ); if( !feature_fn || !CV_NODE_IS_MAP( feature_fn->tag ) ) { sprintf( buf, "Feature node is not a valid map. " "(stage %d, tree %d, node %d)", i, j, k ); CV_Error( CV_StsError, buf ); } rects_fn = cvGetFileNodeByName( fs, feature_fn, ICV_HAAR_RECTS_NAME ); if( !rects_fn || !CV_NODE_IS_SEQ( rects_fn->tag ) || rects_fn->data.seq->total < 1 || rects_fn->data.seq->total > CV_HAAR_FEATURE_MAX ) { sprintf( buf, "Rects node is not a valid sequence. " "(stage %d, tree %d, node %d)", i, j, k ); CV_Error( CV_StsError, buf ); } cvStartReadSeq( rects_fn->data.seq, &rects_reader ); for( l = 0; l < rects_fn->data.seq->total; ++l ) { CvFileNode* rect_fn; CvRect r; rect_fn = (CvFileNode*) rects_reader.ptr; if( !CV_NODE_IS_SEQ( rect_fn->tag ) || rect_fn->data.seq->total != 5 ) { sprintf( buf, "Rect %d is not a valid sequence. " "(stage %d, tree %d, node %d)", l, i, j, k ); CV_Error( CV_StsError, buf ); } fn = CV_SEQ_ELEM( rect_fn->data.seq, CvFileNode, 0 ); if( !CV_NODE_IS_INT( fn->tag ) || fn->data.i < 0 ) { sprintf( buf, "x coordinate must be non-negative integer. " "(stage %d, tree %d, node %d, rect %d)", i, j, k, l ); CV_Error( CV_StsError, buf ); } r.x = fn->data.i; fn = CV_SEQ_ELEM( rect_fn->data.seq, CvFileNode, 1 ); if( !CV_NODE_IS_INT( fn->tag ) || fn->data.i < 0 ) { sprintf( buf, "y coordinate must be non-negative integer. " "(stage %d, tree %d, node %d, rect %d)", i, j, k, l ); CV_Error( CV_StsError, buf ); } r.y = fn->data.i; fn = CV_SEQ_ELEM( rect_fn->data.seq, CvFileNode, 2 ); if( !CV_NODE_IS_INT( fn->tag ) || fn->data.i <= 0 || r.x + fn->data.i > cascade->orig_window_size.width ) { sprintf( buf, "width must be positive integer and " "(x + width) must not exceed window width. " "(stage %d, tree %d, node %d, rect %d)", i, j, k, l ); CV_Error( CV_StsError, buf ); } r.width = fn->data.i; fn = CV_SEQ_ELEM( rect_fn->data.seq, CvFileNode, 3 ); if( !CV_NODE_IS_INT( fn->tag ) || fn->data.i <= 0 || r.y + fn->data.i > cascade->orig_window_size.height ) { sprintf( buf, "height must be positive integer and " "(y + height) must not exceed window height. " "(stage %d, tree %d, node %d, rect %d)", i, j, k, l ); CV_Error( CV_StsError, buf ); } r.height = fn->data.i; fn = CV_SEQ_ELEM( rect_fn->data.seq, CvFileNode, 4 ); if( !CV_NODE_IS_REAL( fn->tag ) ) { sprintf( buf, "weight must be real number. " "(stage %d, tree %d, node %d, rect %d)", i, j, k, l ); CV_Error( CV_StsError, buf ); } classifier->haar_feature[k].rect[l].weight = (float) fn->data.f; classifier->haar_feature[k].rect[l].r = r; CV_NEXT_SEQ_ELEM( sizeof( *rect_fn ), rects_reader ); } for( l = rects_fn->data.seq->total; l < CV_HAAR_FEATURE_MAX; ++l ) { classifier->haar_feature[k].rect[l].weight = 0; classifier->haar_feature[k].rect[l].r = cvRect( 0, 0, 0, 0 ); } fn = cvGetFileNodeByName( fs, feature_fn, ICV_HAAR_TILTED_NAME); if( !fn || !CV_NODE_IS_INT( fn->tag ) ) { sprintf( buf, "tilted must be 0 or 1. " "(stage %d, tree %d, node %d)", i, j, k ); CV_Error( CV_StsError, buf ); } classifier->haar_feature[k].tilted = ( fn->data.i != 0 ); fn = cvGetFileNodeByName( fs, node_fn, ICV_HAAR_THRESHOLD_NAME); if( !fn || !CV_NODE_IS_REAL( fn->tag ) ) { sprintf( buf, "threshold must be real number. " "(stage %d, tree %d, node %d)", i, j, k ); CV_Error( CV_StsError, buf ); } classifier->threshold[k] = (float) fn->data.f; fn = cvGetFileNodeByName( fs, node_fn, ICV_HAAR_LEFT_NODE_NAME); if( fn ) { if( !CV_NODE_IS_INT( fn->tag ) || fn->data.i <= k || fn->data.i >= tree_fn->data.seq->total ) { sprintf( buf, "left node must be valid node number. " "(stage %d, tree %d, node %d)", i, j, k ); CV_Error( CV_StsError, buf ); } classifier->left[k] = fn->data.i; } else { fn = cvGetFileNodeByName( fs, node_fn, ICV_HAAR_LEFT_VAL_NAME ); if( !fn ) { sprintf( buf, "left node or left value must be specified. " "(stage %d, tree %d, node %d)", i, j, k ); CV_Error( CV_StsError, buf ); } if( !CV_NODE_IS_REAL( fn->tag ) ) { sprintf( buf, "left value must be real number. " "(stage %d, tree %d, node %d)", i, j, k ); CV_Error( CV_StsError, buf ); } if( last_idx >= classifier->count + 1 ) { sprintf( buf, "Tree structure is broken: too many values. " "(stage %d, tree %d, node %d)", i, j, k ); CV_Error( CV_StsError, buf ); } classifier->left[k] = -last_idx; classifier->alpha[last_idx++] = (float) fn->data.f; } fn = cvGetFileNodeByName( fs, node_fn,ICV_HAAR_RIGHT_NODE_NAME); if( fn ) { if( !CV_NODE_IS_INT( fn->tag ) || fn->data.i <= k || fn->data.i >= tree_fn->data.seq->total ) { sprintf( buf, "right node must be valid node number. " "(stage %d, tree %d, node %d)", i, j, k ); CV_Error( CV_StsError, buf ); } classifier->right[k] = fn->data.i; } else { fn = cvGetFileNodeByName( fs, node_fn, ICV_HAAR_RIGHT_VAL_NAME ); if( !fn ) { sprintf( buf, "right node or right value must be specified. " "(stage %d, tree %d, node %d)", i, j, k ); CV_Error( CV_StsError, buf ); } if( !CV_NODE_IS_REAL( fn->tag ) ) { sprintf( buf, "right value must be real number. " "(stage %d, tree %d, node %d)", i, j, k ); CV_Error( CV_StsError, buf ); } if( last_idx >= classifier->count + 1 ) { sprintf( buf, "Tree structure is broken: too many values. " "(stage %d, tree %d, node %d)", i, j, k ); CV_Error( CV_StsError, buf ); } classifier->right[k] = -last_idx; classifier->alpha[last_idx++] = (float) fn->data.f; } CV_NEXT_SEQ_ELEM( sizeof( *node_fn ), tree_reader ); } if( last_idx != classifier->count + 1 ) { sprintf( buf, "Tree structure is broken: too few values. " "(stage %d, tree %d)", i, j ); CV_Error( CV_StsError, buf ); } CV_NEXT_SEQ_ELEM( sizeof( *tree_fn ), trees_reader ); } fn = cvGetFileNodeByName( fs, stage_fn, ICV_HAAR_STAGE_THRESHOLD_NAME); if( !fn || !CV_NODE_IS_REAL( fn->tag ) ) { sprintf( buf, "stage threshold must be real number. (stage %d)", i ); CV_Error( CV_StsError, buf ); } cascade->stage_classifier[i].threshold = (float) fn->data.f; parent = i - 1; next = -1; fn = cvGetFileNodeByName( fs, stage_fn, ICV_HAAR_PARENT_NAME ); if( !fn || !CV_NODE_IS_INT( fn->tag ) || fn->data.i < -1 || fn->data.i >= cascade->count ) { sprintf( buf, "parent must be integer number. (stage %d)", i ); CV_Error( CV_StsError, buf ); } parent = fn->data.i; fn = cvGetFileNodeByName( fs, stage_fn, ICV_HAAR_NEXT_NAME ); if( !fn || !CV_NODE_IS_INT( fn->tag ) || fn->data.i < -1 || fn->data.i >= cascade->count ) { sprintf( buf, "next must be integer number. (stage %d)", i ); CV_Error( CV_StsError, buf ); } next = fn->data.i; cascade->stage_classifier[i].parent = parent; cascade->stage_classifier[i].next = next; cascade->stage_classifier[i].child = -1; if( parent != -1 && cascade->stage_classifier[parent].child == -1 ) { cascade->stage_classifier[parent].child = i; } CV_NEXT_SEQ_ELEM( sizeof( *stage_fn ), stages_reader ); } return cascade;}static voidicvWriteHaarClassifier( CvFileStorage* fs, const char* name, const void* struct_ptr, CvAttrList attributes ){ int i, j, k, l; char buf[256]; const CvHaarClassifierCascade* cascade = (const CvHaarClassifierCascade*) struct_ptr; cvStartWriteStruct( fs, name, CV_NODE_MAP, CV_TYPE_NAME_HAAR, attributes ); cvStartWriteStruct( fs, ICV_HAAR_SIZE_NAME, CV_NODE_SEQ | CV_NODE_FLOW ); cvWriteInt( fs, NULL, cascade->orig_window_size.width ); cvWriteInt( fs, NULL, cascade->orig_window_size.height ); cvEndWriteStruct( fs ); cvStartWriteStruct( fs, ICV_HAAR_STAGES_NAME, CV_NODE_SEQ ); for( i = 0; i < cascade->count; ++i ) { cvStartWriteStruct( fs, NULL, CV_NODE_MAP ); sprintf( buf, "stage %d", i ); cvWriteComment( fs, buf, 1 ); cvStartWriteStruct( fs, ICV_HAAR_TREES_NAME, CV_NODE_SEQ ); for( j = 0; j < cascade->stage_classifier[i].count; ++j ) { CvHaarClassifier* tree = &cascade->stage_classifier[i].classifier[j]; cvStartWriteStruct( fs, NULL, CV_NODE_SEQ ); sprintf( buf, "tree %d", j ); cvWriteComment( fs, buf, 1 ); for( k = 0; k < tree->count; ++k ) { CvHaarFeature* feature = &tree->haar_feature[k]; cvStartWriteStruct( fs, NULL, CV_NODE_MAP ); if( k ) { sprintf( buf, "node %d", k ); } else { sprintf( buf, "root node" ); } cvWriteComment( fs, buf, 1 ); cvStartWriteStruct( fs, ICV_HAAR_FEATURE_NAME, CV_NODE_MAP ); cvStartWriteStruct( fs, ICV_HAAR_RECTS_NAME, CV_NODE_SEQ ); for( l = 0; l < CV_HAAR_FEATURE_MAX && feature->rect[l].r.width != 0; ++l ) { cvStartWriteStruct( fs, NULL, CV_NODE_SEQ | CV_NODE_FLOW ); cvWriteInt( fs, NULL, feature->rect[l].r.x ); cvWriteInt( fs, NULL, feature->rect[l].r.y ); cvWriteInt( fs, NULL, feature->rect[l].r.width ); cvWriteInt( fs, NULL, feature->rect[l].r.height ); cvWriteReal( fs, NULL, feature->rect[l].weight ); cvEndWriteStruct( fs ); } cvEndWriteStruct( fs ); cvWriteInt( fs, ICV_HAAR_TILTED_NAME, feature->tilted ); cvEndWriteStruct( fs ); cvWriteReal( fs, ICV_HAAR_THRESHOLD_NAME, tree->threshold[k]); if( tree->left[k] > 0 ) { cvWriteInt( fs, ICV_HAAR_LEFT_NODE_NAME, tree->left[k] ); } else { cvWriteReal( fs, ICV_HAAR_LEFT_VAL_NAME, tree->alpha[-tree->left[k]] ); } if( tree->right[k] > 0 ) { cvWriteInt( fs, ICV_HAAR_RIGHT_NODE_NAME, tree->right[k] ); } else { cvWriteReal( fs, ICV_HAAR_RIGHT_VAL_NAME, tree->alpha[-tree->right[k]] ); } cvEndWriteStruct( fs ); } cvEndWriteStruct( fs ); } cvEndWriteStruct( fs ); cvWriteReal( fs, ICV_HAAR_STAGE_THRESHOLD_NAME, cascade->stage_classifier[i].threshold); cvWriteInt( fs, ICV_HAAR_PARENT_NAME, cascade->stage_classifier[i].parent ); cvWriteInt( fs, ICV_HAAR_NEXT_NAME, cascade->stage_classifier[i].next ); cvEndWriteStruct( fs ); } cvEndWriteStruct( fs ); cvEndWriteStruct( fs ); }static void*icvCloneHaarClassifier( const void* struct_ptr ){ CvHaarClassifierCascade* cascade = NULL; int i, j, k, n; const CvHaarClassifierCascade* cascade_src = (const CvHaarClassifierCascade*) struct_ptr; n = cascade_src->count; cascade = icvCreateHaarClassifierCascade(n); cascade->orig_window_size = cascade_src->orig_window_size; for( i = 0; i < n; ++i ) { cascade->stage_classifier[i].parent = cascade_src->stage_classifier[i].parent; cascade->stage_classifier[i].next = cascade_src->stage_classifier[i].next; cascade->stage_classifier[i].child = cascade_src->stage_classifier[i].child; cascade->stage_classifier[i].threshold = cascade_src->stage_classifier[i].threshold; cascade->stage_classifier[i].count = 0; cascade->stage_classifier[i].classifier = (CvHaarClassifier*) cvAlloc( cascade_src->stage_classifier[i].count * sizeof( cascade->stage_classifier[i].classifier[0] ) ); cascade->stage_classifier[i].count = cascade_src->stage_classifier[i].count; for( j = 0; j < cascade->stage_classifier[i].count; ++j ) cascade->stage_classifier[i].classifier[j].haar_feature = NULL; for( j = 0; j < cascade->stage_classifier[i].count; ++j ) { const CvHaarClassifier* classifier_src = &cascade_src->stage_classifier[i].classifier[j]; CvHaarClassifier* classifier = &cascade->stage_classifier[i].classifier[j]; classifier->count = classifier_src->count; classifier->haar_feature = (CvHaarFeature*) cvAlloc( classifier->count * ( sizeof( *classifier->haar_feature ) + sizeof( *classifier->threshold ) + sizeof( *classifier->left ) + sizeof( *classifier->right ) ) + (classifier->count + 1) * sizeof( *classifier->alpha ) ); classifier->threshold = (float*) (classifier->haar_feature+classifier->count); classifier->left = (int*) (classifier->threshold + classifier->count); classifier->right = (int*) (classifier->left + classifier->count); classifier->alpha = (float*) (classifier->right + classifier->count); for( k = 0; k < classifier->count; ++k ) { classifier->haar_feature[k] = classifier_src->haar_feature[k]; classifier->threshold[k] = classifier_src->threshold[k]; classifier->left[k] = classifier_src->left[k]; classifier->right[k] = classifier_src->right[k]; classifier->alpha[k] = classifier_src->alpha[k]; } classifier->alpha[classifier->count] = classifier_src->alpha[classifier->count]; } } return cascade;}CvType haar_type( CV_TYPE_NAME_HAAR, icvIsHaarClassifier, (CvReleaseFunc)cvReleaseHaarClassifierCascade, icvReadHaarClassifier, icvWriteHaarClassifier, icvCloneHaarClassifier );#if 0namespace cv{HaarClassifierCascade::HaarClassifierCascade() {}HaarClassifierCascade::HaarClassifierCascade(const String& filename){ load(filename); } bool HaarClassifierCascade::load(const String& filename){ cascade = Ptr<CvHaarClassifierCascade>((CvHaarClassifierCascade*)cvLoad(filename.c_str(), 0, 0, 0)); return (CvHaarClassifierCascade*)cascade != 0;}void HaarClassifierCascade::detectMultiScale( const Mat& image, Vector<Rect>& objects, double scaleFactor, int minNeighbors, int flags, Size minSize ){ MemStorage storage(cvCreateMemStorage(0)); CvMat _image = image; CvSeq* _objects = cvHaarDetectObjects( &_image, cascade, storage, scaleFactor, minNeighbors, flags, minSize ); Seq<Rect>(_objects).copyTo(objects);}int HaarClassifierCascade::runAt(Point pt, int startStage, int) const{ return cvRunHaarClassifierCascade(cascade, pt, startStage);}void HaarClassifierCascade::setImages( const Mat& sum, const Mat& sqsum, const Mat& tilted, double scale ){ CvMat _sum = sum, _sqsum = sqsum, _tilted = tilted; cvSetImagesForHaarClassifierCascade( cascade, &_sum, &_sqsum, &_tilted, scale );}}#endif
