www.pudn.com > OpenCV-Intel.zip > cvhaar.cpp
/*M/////////////////////////////////////////////////////////////////////////////////////// // // IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. // // By downloading, copying, installing or using the software you agree to this license. // If you do not agree to this license, do not download, install, // copy or use the software. // // // Intel License Agreement // For Open Source Computer Vision Library // // Copyright (C) 2000, Intel Corporation, all rights reserved. // Third party copyrights are property of their respective owners. // // Redistribution and use in source and binary forms, with or without modification, // are permitted provided that the following conditions are met: // // * Redistribution's of source code must retain the above copyright notice, // this list of conditions and the following disclaimer. // // * Redistribution's in binary form must reproduce the above copyright notice, // this list of conditions and the following disclaimer in the documentation // and/or other materials provided with the distribution. // // * The name of Intel Corporation may not be used to endorse or promote products // derived from this software without specific prior written permission. // // This software is provided by the copyright holders and contributors "as is" and // any express or implied warranties, including, but not limited to, the implied // warranties of merchantability and fitness for a particular purpose are disclaimed. // In no event shall the Intel Corporation or contributors be liable for any direct, // indirect, incidental, special, exemplary, or consequential damages // (including, but not limited to, procurement of substitute goods or services; // loss of use, data, or profits; or business interruption) however caused // and on any theory of liability, whether in contract, strict liability, // or tort (including negligence or otherwise) arising in any way out of // the use of this software, even if advised of the possibility of such damage. // //M*/ /* Haar features calculation */ #include "_cv.h" #include/* these settings affect the quality of detection: change with care */ #define CV_ADJUST_FEATURES 1 #define CV_ADJUST_WEIGHTS 0 typedef int sumtype; typedef double sqsumtype; typedef struct CvHidHaarFeature { struct { sumtype *p0, *p1, *p2, *p3; float weight; } rect[CV_HAAR_FEATURE_MAX]; } CvHidHaarFeature; typedef struct CvHidHaarTreeNode { CvHidHaarFeature feature; float threshold; int left; int right; } CvHidHaarTreeNode; typedef struct CvHidHaarClassifier { int count; //CvHaarFeature* orig_feature; CvHidHaarTreeNode* node; float* alpha; } CvHidHaarClassifier; typedef struct CvHidHaarStageClassifier { int count; float threshold; CvHidHaarClassifier* classifier; int two_rects; struct CvHidHaarStageClassifier* next; struct CvHidHaarStageClassifier* child; struct CvHidHaarStageClassifier* parent; } CvHidHaarStageClassifier; struct CvHidHaarClassifierCascade { int count; int is_stump_based; 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; }; /* IPP functions for object detection */ icvHaarClassifierInitAlloc_32f_t icvHaarClassifierInitAlloc_32f_p = 0; icvHaarClassifierFree_32f_t icvHaarClassifierFree_32f_p = 0; icvApplyHaarClassifier_32s32f_C1R_t icvApplyHaarClassifier_32s32f_C1R_p = 0; icvRectStdDev_32s32f_C1R_t icvRectStdDev_32s32f_C1R_p = 0; const int icv_object_win_border = 1; const float icv_stage_threshold_bias = 0.0001f; static CvHaarClassifierCascade* icvCreateHaarClassifierCascade( int stage_count ) { CvHaarClassifierCascade* cascade = 0; CV_FUNCNAME( "icvCreateHaarClassifierCascade" ); __BEGIN__; 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" ); CV_CALL( 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; __END__; return cascade; } static void icvReleaseHidHaarClassifierCascade( CvHidHaarClassifierCascade** _cascade ) { if( _cascade && *_cascade ) { CvHidHaarClassifierCascade* cascade = *_cascade; if( cascade->ipp_stages && icvHaarClassifierFree_32f_p ) { int i; for( i = 0; i < cascade->count; i++ ) { if( cascade->ipp_stages[i] ) icvHaarClassifierFree_32f_p( cascade->ipp_stages[i] ); } } cvFree( (void**)&cascade->ipp_stages ); cvFree( (void**)_cascade ); } } /* create more efficient internal representation of haar classifier 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; CV_FUNCNAME( "icvCreateHidHaarClassifierCascade" ); __BEGIN__; 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 can_use_ipp = icvHaarClassifierInitAlloc_32f_p != 0 && icvHaarClassifierFree_32f_p != 0 && icvApplyHaarClassifier_32s32f_C1R_p != 0 && icvRectStdDev_32s32f_C1R_p != 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; /* check input structure correctness and calculate total memory size needed for internal representation of the classifier cascade */ 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 ); } } } } } } // this is an upper boundary for the whole hidden cascade size datasize = sizeof(CvHidHaarClassifierCascade) + sizeof(CvHidHaarStageClassifier)*cascade->count + sizeof(CvHidHaarClassifier) * total_classifiers + sizeof(CvHidHaarTreeNode) * total_nodes + sizeof(void*)*(total_nodes + total_classifiers); CV_CALL( out = (CvHidHaarClassifierCascade*)cvAlloc( datasize )); memset( out, 0, sizeof(*out) ); /* init header */ 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->is_stump_based = 1; out->has_tilted_features = has_tilted_features; out->is_tree = 0; /* initialize internal representation */ 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->is_stump_based &= node_count == 1; } } can_use_ipp &= !out->has_tilted_features && !out->is_tree && out->is_stump_based; 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))); CV_CALL( out->ipp_stages = (void**)cvAlloc( ipp_datasize )); memset( out->ipp_stages, 0, ipp_datasize ); CV_CALL( ipp_features = (CvRect*)cvAlloc( max_count*3*sizeof(ipp_features[0]) )); CV_CALL( ipp_weights = (float*)cvAlloc( max_count*3*sizeof(ipp_weights[0]) )); CV_CALL( ipp_thresholds = (float*)cvAlloc( max_count*sizeof(ipp_thresholds[0]) )); CV_CALL( ipp_val1 = (float*)cvAlloc( max_count*sizeof(ipp_val1[0]) )); CV_CALL( ipp_val2 = (float*)cvAlloc( max_count*sizeof(ipp_val2[0]) )); CV_CALL( 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_features[k].y = orig_window_size.height - ipp_features[k].y - ipp_features[k].height; ipp_weights[k] = classifier->haar_feature->rect[l].weight*ipp_weight_scale; } } if( icvHaarClassifierInitAlloc_32f_p( &out->ipp_stages[i], 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( icvHaarClassifierFree_32f_p && out->ipp_stages[i] ) icvHaarClassifierFree_32f_p( out->ipp_stages[i] ); cvFree( (void**)&out->ipp_stages ); } } cascade->hid_cascade = out; assert( (char*)haar_node_ptr - (char*)out <= datasize ); __END__; if( cvGetErrStatus() < 0 ) icvReleaseHidHaarClassifierCascade( &out ); cvFree( (void**)&ipp_features ); cvFree( (void**)&ipp_weights ); cvFree( (void**)&ipp_thresholds ); cvFree( (void**)&ipp_val1 ); cvFree( (void**)&ipp_val2 ); cvFree( (void**)&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 void cvSetImagesForHaarClassifierCascade( CvHaarClassifierCascade* _cascade, const CvArr* _sum, const CvArr* _sqsum, const CvArr* _tilted_sum, double scale ) { CV_FUNCNAME("cvSetImagesForHaarClassifierCascade"); __BEGIN__; 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, j, k, l; CvRect equ_rect; 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" ); CV_CALL( sum = cvGetMat( sum, &sum_stub, &coi0 )); CV_CALL( 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 ) CV_CALL( icvCreateHidHaarClassifierCascade(_cascade) ); cascade = _cascade->hid_cascade; if( cascade->has_tilted_features ) { CV_CALL( 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; equ_rect.x = equ_rect.y = cvRound(scale); equ_rect.width = cvRound((_cascade->orig_window_size.width-2)*scale); equ_rect.height = cvRound((_cascade->orig_window_size.height-2)*scale); weight_scale = 1./(equ_rect.width*equ_rect.height); cascade->inv_window_area = weight_scale; cascade->p0 = sum_elem_ptr(*sum, equ_rect.y, equ_rect.x); cascade->p1 = sum_elem_ptr(*sum, equ_rect.y, equ_rect.x + equ_rect.width ); cascade->p2 = sum_elem_ptr(*sum, equ_rect.y + equ_rect.height, equ_rect.x ); cascade->p3 = sum_elem_ptr(*sum, equ_rect.y + equ_rect.height, equ_rect.x + equ_rect.width ); cascade->pq0 = sqsum_elem_ptr(*sqsum, equ_rect.y, equ_rect.x); cascade->pq1 = sqsum_elem_ptr(*sqsum, equ_rect.y, equ_rect.x + equ_rect.width ); cascade->pq2 = sqsum_elem_ptr(*sqsum, equ_rect.y + equ_rect.height, equ_rect.x ); cascade->pq3 = sqsum_elem_ptr(*sqsum, equ_rect.y + equ_rect.height, equ_rect.x + equ_rect.width ); /* init pointers in haar features according to real window size and given image pointers */ for( i = 0; i < _cascade->count; i++ ) { 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]; /* CvHidHaarClassifier* classifier = cascade->stage_classifier[i].classifier + j; */ CvHidHaarFeature* hidfeature = &cascade->stage_classifier[i].classifier[j].node[l].feature; double sum0 = 0, area0 = 0; CvRect r[3]; #if CV_ADJUST_FEATURES 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; #endif int nr; /* align blocks */ for( k = 0; k < CV_HAAR_FEATURE_MAX; k++ ) { if( !hidfeature->rect[k].p0 ) break; #if CV_ADJUST_FEATURES 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) ); #endif } nr = k; #if CV_ADJUST_FEATURES 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 ); } #endif for( k = 0; k < nr; k++ ) { CvRect tr; double correction_ratio; #if CV_ADJUST_FEATURES 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 #endif { tr.x = cvRound( r[k].x * scale ); tr.width = cvRound( r[k].width * scale ); } #if CV_ADJUST_FEATURES 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 #endif { tr.y = cvRound( r[k].y * scale ); tr.height = cvRound( r[k].height * scale ); } #if CV_ADJUST_WEIGHTS { // RAINER START 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); //const float normSize = float(equ_rect.width*equ_rect.height); float target_ratio = orig_feature_size / orig_norm_size; //float isRatio = featureSize / normSize; //correctionRatio = targetRatio / isRatio / normSize; correction_ratio = target_ratio / feature_size; // RAINER END } #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); } /* l */ } /* j */ } __END__; } CV_INLINE double 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 int cvRunHaarClassifierCascade( CvHaarClassifierCascade* _cascade, CvPoint pt, int start_stage ) { int result = -1; CV_FUNCNAME("cvRunHaarClassifierCascade"); __BEGIN__; 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-2 || pt.y + _cascade->real_window_size.height >= cascade->sum.height-2 ) EXIT; 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 ) { double stage_sum = 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 ) { result = 0; EXIT; } ptr = ptr->next; } } } else if( cascade->is_stump_based ) { for( i = start_stage; i < cascade->count; i++ ) { double stage_sum = 0; 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; double sum, t = node->threshold*variance_norm_factor, a, b; 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; a = classifier->alpha[0]; b = classifier->alpha[1]; stage_sum += sum < t ? a : b; } } else { for( j = 0; j < cascade->stage_classifier[i].count; j++ ) { CvHidHaarClassifier* classifier = cascade->stage_classifier[i].classifier + j; CvHidHaarTreeNode* node = classifier->node; double sum, t = node->threshold*variance_norm_factor, a, b; 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; a = classifier->alpha[0]; b = classifier->alpha[1]; stage_sum += sum < t ? a : b; } } if( stage_sum < cascade->stage_classifier[i].threshold ) { result = -i; EXIT; } } } else { for( i = start_stage; i < cascade->count; i++ ) { double stage_sum = 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 ) { result = -i; EXIT; } } } result = 1; __END__; return result; } static int is_equal( const void* _r1, const void* _r2, void* ) { const CvRect* r1 = (const CvRect*)_r1; const CvRect* r2 = (const CvRect*)_r2; int distance = cvRound(r1->width*0.2); return r2->x <= r1->x + distance && r2->x >= r1->x - distance && r2->y <= r1->y + distance && r2->y >= r1->y - distance && r2->width <= cvRound( r1->width * 1.2 ) && cvRound( r2->width * 1.2 ) >= r1->width; } CV_IMPL CvSeq* cvHaarDetectObjects( const CvArr* _img, CvHaarClassifierCascade* cascade, CvMemStorage* storage, double scale_factor, int min_neighbors, int flags, CvSize min_size ) { int split_stage = 2; CvMat stub, *img = (CvMat*)_img; CvMat *temp = 0, *sum = 0, *tilted = 0, *sqsum = 0, *norm_img = 0, *sumcanny = 0, *img_small = 0; CvSeq* seq = 0; CvSeq* seq2 = 0; CvSeq* idx_seq = 0; CvSeq* result_seq = 0; CvMemStorage* temp_storage = 0; CvAvgComp* comps = 0; CV_FUNCNAME( "cvHaarDetectObjects" ); __BEGIN__; double factor; int i, npass = 2, coi; int do_canny_pruning = flags & CV_HAAR_DO_CANNY_PRUNING; 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" ); CV_CALL( 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" ); CV_CALL( temp = cvCreateMat( img->rows, img->cols, CV_8UC1 )); CV_CALL( sum = cvCreateMat( img->rows + 1, img->cols + 1, CV_32SC1 )); CV_CALL( sqsum = cvCreateMat( img->rows + 1, img->cols + 1, CV_64FC1 )); CV_CALL( temp_storage = cvCreateChildMemStorage( storage )); if( !cascade->hid_cascade ) CV_CALL( icvCreateHidHaarClassifierCascade(cascade) ); if( cascade->hid_cascade->has_tilted_features ) tilted = cvCreateMat( img->rows + 1, img->cols + 1, CV_32SC1 ); seq = cvCreateSeq( 0, sizeof(CvSeq), sizeof(CvRect), temp_storage ); seq2 = cvCreateSeq( 0, sizeof(CvSeq), sizeof(CvAvgComp), temp_storage ); result_seq = cvCreateSeq( 0, sizeof(CvSeq), sizeof(CvAvgComp), storage ); if( min_neighbors == 0 ) seq = result_seq; if( CV_MAT_CN(img->type) > 1 ) { cvCvtColor( img, temp, CV_BGR2GRAY ); img = temp; } if( flags & CV_HAAR_SCALE_IMAGE ) { CvSize win_size0 = cascade->orig_window_size; int use_ipp = cascade->hid_cascade->ipp_stages != 0 && icvApplyHaarClassifier_32s32f_C1R_p != 0; if( use_ipp ) CV_CALL( norm_img = cvCreateMat( img->rows, img->cols, CV_32FC1 )); CV_CALL( img_small = cvCreateMat( img->rows + 1, img->cols + 1, CV_8UC1 )); for( factor = 1; ; factor *= scale_factor ) { int positive = 0; int x, y; CvSize win_size = { cvRound(win_size0.width*factor), cvRound(win_size0.height*factor) }; CvSize sz = { cvRound( img->cols/factor ), cvRound( img->rows/factor ) }; CvSize sz1 = { sz.width - win_size0.width, sz.height - win_size0.height }; CvRect rect1 = { icv_object_win_border, icv_object_win_border, win_size0.width - icv_object_win_border*2, win_size0.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( win_size.width < min_size.width || win_size.height < min_size.height ) continue; img1 = cvMat( sz.height, sz.width, CV_8UC1, img_small->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, norm_img ? norm_img->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 ); if( use_ipp && icvRectStdDev_32s32f_C1R_p( sum1.data.i, sum1.step, sqsum1.data.db, sqsum1.step, norm1.data.fl, norm1.step, sz1, rect1 ) < 0 ) use_ipp = 0; if( use_ipp ) { positive = mask1.cols*mask1.rows; cvSet( &mask1, cvScalarAll(255) ); for( i = 0; i < cascade->count; i++ ) { if( icvApplyHaarClassifier_32s32f_C1R_p(sum1.data.i, sum1.step, norm1.data.fl, norm1.step, mask1.data.ptr, mask1.step, sz1, &positive, cascade->hid_cascade->stage_classifier[i].threshold, cascade->hid_cascade->ipp_stages[i]) < 0 ) { use_ipp = 0; break; } if( positive <= 0 ) break; } } if( !use_ipp ) { cvSetImagesForHaarClassifierCascade( cascade, &sum1, &sqsum1, 0, 1. ); for( y = 0, positive = 0; y < sz1.height; y++ ) for( x = 0; x < sz1.width; x++ ) { mask1.data.ptr[mask1.step*y + x] = cvRunHaarClassifierCascade( cascade, cvPoint(x,y), 0 ) > 0; positive += mask1.data.ptr[mask1.step*y + x]; } } if( positive > 0 ) { for( y = 0; y < sz1.height; y++ ) for( x = 0; x < sz1.width; x++ ) if( mask1.data.ptr[mask1.step*y + x] != 0 ) { CvRect obj_rect = { cvRound(y*factor), cvRound(x*factor), win_size.width, win_size.height }; cvSeqPush( seq, &obj_rect ); } } } } else { cvIntegral( img, sum, sqsum, tilted ); if( do_canny_pruning ) { sumcanny = cvCreateMat( img->rows + 1, img->cols + 1, CV_32SC1 ); cvCanny( img, temp, 0, 50, 3 ); cvIntegral( temp, sumcanny ); } if( (unsigned)split_stage >= (unsigned)cascade->count || cascade->hid_cascade->is_tree ) { split_stage = cascade->count; npass = 1; } for( factor = 1; factor*cascade->orig_window_size.width < img->cols - 10 && factor*cascade->orig_window_size.height < img->rows - 10; factor *= scale_factor ) { const double ystep = MAX( 2, factor ); CvSize win_size = { cvRound( cascade->orig_window_size.width * factor ), cvRound( cascade->orig_window_size.height * factor )}; CvRect equ_rect = { 0, 0, 0, 0 }; int *p0 = 0, *p1 = 0, *p2 = 0, *p3 = 0; int *pq0 = 0, *pq1 = 0, *pq2 = 0, *pq3 = 0; int pass, stage_offset = 0; int stop_height = cvRound((img->rows - win_size.height) / ystep); if( win_size.width < min_size.width || win_size.height < min_size.height ) continue; cvSetImagesForHaarClassifierCascade( cascade, sum, sqsum, tilted, factor ); cvZero( temp ); if( do_canny_pruning ) { equ_rect.x = cvRound(win_size.width*0.15); equ_rect.y = cvRound(win_size.height*0.15); equ_rect.width = cvRound(win_size.width*0.7); equ_rect.height = cvRound(win_size.height*0.7); p0 = (int*)(sumcanny->data.ptr + equ_rect.y*sumcanny->step) + equ_rect.x; p1 = (int*)(sumcanny->data.ptr + equ_rect.y*sumcanny->step) + equ_rect.x + equ_rect.width; p2 = (int*)(sumcanny->data.ptr + (equ_rect.y + equ_rect.height)*sumcanny->step) + equ_rect.x; p3 = (int*)(sumcanny->data.ptr + (equ_rect.y + equ_rect.height)*sumcanny->step) + equ_rect.x + equ_rect.width; pq0 = (int*)(sum->data.ptr + equ_rect.y*sum->step) + equ_rect.x; pq1 = (int*)(sum->data.ptr + equ_rect.y*sum->step) + equ_rect.x + equ_rect.width; pq2 = (int*)(sum->data.ptr + (equ_rect.y + equ_rect.height)*sum->step) + equ_rect.x; pq3 = (int*)(sum->data.ptr + (equ_rect.y + equ_rect.height)*sum->step) + equ_rect.x + equ_rect.width; } cascade->hid_cascade->count = split_stage; for( pass = 0; pass < npass; pass++ ) { #ifdef _OPENMP #pragma omp parallel for shared(cascade, stop_height, seq, ystep, temp, \ win_size, pass, npass, sum, p0, p1, p2, p3, pq0, pq1, pq2, pq3, stage_offset) #endif // _OPENMP for( int _iy = 0; _iy < stop_height; _iy++ ) { int iy = cvRound(_iy*ystep); int _ix, _xstep = 1; int stop_width = cvRound((img->cols - win_size.width) / ystep); uchar* mask_row = temp->data.ptr + temp->step * iy; for( _ix = 0; _ix < stop_width; _ix += _xstep ) { int ix = cvRound(_ix*ystep); // it really should be ystep if( pass == 0 ) { int result; _xstep = 2; if( do_canny_pruning ) { int offset; int s, sq; offset = iy*(sum->step/sizeof(p0[0])) + ix; s = p0[offset] - p1[offset] - p2[offset] + p3[offset]; sq = pq0[offset] - pq1[offset] - pq2[offset] + pq3[offset]; if( s < 100 || sq < 20 ) continue; } result = cvRunHaarClassifierCascade( cascade, cvPoint(ix,iy), 0 ); #ifdef _OPENMP #pragma omp critical #endif if( result > 0 ) { if( pass < npass - 1 ) mask_row[ix] = 1; else { CvRect rect = cvRect(ix,iy,win_size.width,win_size.height); cvSeqPush( seq, &rect ); } } if( result < 0 ) _xstep = 1; } else if( mask_row[ix] ) { int result = cvRunHaarClassifierCascade( cascade, cvPoint(ix,iy), stage_offset ); #ifdef _OPENMP #pragma omp critical #endif if( result > 0 ) { if( pass == npass - 1 ) { CvRect rect = cvRect(ix,iy,win_size.width,win_size.height); cvSeqPush( seq, &rect ); } } else mask_row[ix] = 0; } } } stage_offset = cascade->hid_cascade->count; cascade->hid_cascade->count = cascade->count; } } } if( min_neighbors != 0 ) { // group retrieved rectangles in order to filter out noise int ncomp = cvSeqPartition( seq, 0, &idx_seq, is_equal, 0 ); CV_CALL( comps = (CvAvgComp*)cvAlloc( (ncomp+1)*sizeof(comps[0]))); memset( comps, 0, (ncomp+1)*sizeof(comps[0])); // count number of neighbors for( i = 0; i < seq->total; i++ ) { CvRect r1 = *(CvRect*)cvGetSeqElem( seq, i ); int idx = *(int*)cvGetSeqElem( idx_seq, i ); assert( (unsigned)idx < (unsigned)ncomp ); comps[idx].neighbors++; comps[idx].rect.x += r1.x; comps[idx].rect.y += r1.y; comps[idx].rect.width += r1.width; comps[idx].rect.height += r1.height; } // calculate average bounding box for( i = 0; i < ncomp; i++ ) { int n = comps[i].neighbors; if( n >= min_neighbors ) { CvAvgComp comp; comp.rect.x = (comps[i].rect.x*2 + n)/(2*n); comp.rect.y = (comps[i].rect.y*2 + n)/(2*n); comp.rect.width = (comps[i].rect.width*2 + n)/(2*n); comp.rect.height = (comps[i].rect.height*2 + n)/(2*n); comp.neighbors = comps[i].neighbors; cvSeqPush( seq2, &comp ); } } // filter out small face rectangles inside large face rectangles for( i = 0; i < seq2->total; i++ ) { CvAvgComp r1 = *(CvAvgComp*)cvGetSeqElem( seq2, i ); int j, flag = 1; for( j = 0; j < seq2->total; j++ ) { CvAvgComp r2 = *(CvAvgComp*)cvGetSeqElem( seq2, j ); int distance = cvRound( r2.rect.width * 0.2 ); if( i != j && r1.rect.x >= r2.rect.x - distance && r1.rect.y >= r2.rect.y - distance && r1.rect.x + r1.rect.width <= r2.rect.x + r2.rect.width + distance && r1.rect.y + r1.rect.height <= r2.rect.y + r2.rect.height + distance && (r2.neighbors > MAX( 3, r1.neighbors ) || r1.neighbors < 3) ) { flag = 0; break; } } if( flag ) { cvSeqPush( result_seq, &r1 ); /* cvSeqPush( result_seq, &r1.rect ); */ } } } __END__; cvReleaseMemStorage( &temp_storage ); cvReleaseMat( &sum ); cvReleaseMat( &sqsum ); cvReleaseMat( &tilted ); cvReleaseMat( &temp ); cvReleaseMat( &sumcanny ); cvReleaseMat( &norm_img ); cvReleaseMat( &img_small ); cvFree( (void**)&comps ); return result_seq; } 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; /* tree links */ 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; /* load tree links */ 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 #endif CV_IMPL CvHaarClassifierCascade* cvLoadHaarClassifierCascade( const char* directory, CvSize orig_window_size ) { const char** input_cascade = 0; CvHaarClassifierCascade *cascade = 0; CV_FUNCNAME( "cvLoadHaarClassifierCascade" ); __BEGIN__; 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] == '/' ? "" : "/"; /* try to read the classifier from directory */ 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] ) { CV_CALL( cascade = (CvHaarClassifierCascade*)cvLoad( directory )); EXIT; } else if( n == 0 ) CV_ERROR( CV_StsBadArg, "Invalid path" ); size += (n+1)*sizeof(char*); CV_CALL( 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 ); __END__; if( input_cascade ) cvFree( (void**)&input_cascade ); if( cvGetErrStatus() < 0 ) cvReleaseHaarClassifierCascade( &cascade ); return cascade; } CV_IMPL void cvReleaseHaarClassifierCascade( 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( (void**) &(cascade->stage_classifier[i].classifier[j].haar_feature) ); cvFree( (void**) &(cascade->stage_classifier[i].classifier) ); } icvReleaseHidHaarClassifierCascade( &cascade->hid_cascade ); cvFree( (void**)_cascade ); } } /****************************************************************************************\ * Persistence functions * \****************************************************************************************/ /* field names */ #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 int icvIsHaarClassifier( const void* struct_ptr ) { return CV_IS_HAAR_CLASSIFIER( struct_ptr ); } static void* icvReadHaarClassifier( CvFileStorage* fs, CvFileNode* node ) { CvHaarClassifierCascade* cascade = NULL; CV_FUNCNAME( "cvReadHaarClassifier" ); __BEGIN__; char buf[256]; CvFileNode* seq_fn = NULL; /* sequence */ CvFileNode* fn = NULL; CvFileNode* stages_fn = NULL; CvSeqReader stages_reader; int n; int i, j, k, l; int parent, next; CV_CALL( 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; CV_CALL( cascade = icvCreateHaarClassifierCascade(n) ); /* read size */ CV_CALL( 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." ); CV_CALL( 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; CV_CALL( 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; CV_CALL( 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 ); } CV_CALL( 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 ); } CV_CALL( 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; CV_CALL( 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; CV_CALL( 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); CV_CALL( 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 ); } CV_CALL( 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 ); } CV_CALL( 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 ); } CV_CALL( 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 each rect */ 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 ); } CV_CALL( 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 ); CV_CALL( 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; CV_CALL( 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 ); } /* left node */ classifier->left[k] = fn->data.i; } else { CV_CALL( 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 ); } /* left value */ 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; } CV_CALL( 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 ); } /* right node */ classifier->right[k] = fn->data.i; } else { CV_CALL( 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 ); } /* right value */ 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 ); } /* for each node */ 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 ); } /* for each tree */ CV_CALL( 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; CV_CALL( 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; CV_CALL( 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 ); } /* for each stage */ __END__; if( cvGetErrStatus() < 0 ) { cvReleaseHaarClassifierCascade( &cascade ); cascade = NULL; } return cascade; } static void icvWriteHaarClassifier( CvFileStorage* fs, const char* name, const void* struct_ptr, CvAttrList attributes ) { CV_FUNCNAME( "cvWriteHaarClassifier" ); __BEGIN__; int i, j, k, l; char buf[256]; const CvHaarClassifierCascade* cascade = (const CvHaarClassifierCascade*) struct_ptr; /* TODO: parameters check */ CV_CALL( cvStartWriteStruct( fs, name, CV_NODE_MAP, CV_TYPE_NAME_HAAR, attributes ) ); CV_CALL( cvStartWriteStruct( fs, ICV_HAAR_SIZE_NAME, CV_NODE_SEQ | CV_NODE_FLOW ) ); CV_CALL( cvWriteInt( fs, NULL, cascade->orig_window_size.width ) ); CV_CALL( cvWriteInt( fs, NULL, cascade->orig_window_size.height ) ); CV_CALL( cvEndWriteStruct( fs ) ); /* size */ CV_CALL( cvStartWriteStruct( fs, ICV_HAAR_STAGES_NAME, CV_NODE_SEQ ) ); for( i = 0; i < cascade->count; ++i ) { CV_CALL( cvStartWriteStruct( fs, NULL, CV_NODE_MAP ) ); sprintf( buf, "stage %d", i ); CV_CALL( cvWriteComment( fs, buf, 1 ) ); CV_CALL( 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]; CV_CALL( cvStartWriteStruct( fs, NULL, CV_NODE_SEQ ) ); sprintf( buf, "tree %d", j ); CV_CALL( cvWriteComment( fs, buf, 1 ) ); for( k = 0; k < tree->count; ++k ) { CvHaarFeature* feature = &tree->haar_feature[k]; CV_CALL( cvStartWriteStruct( fs, NULL, CV_NODE_MAP ) ); if( k ) { sprintf( buf, "node %d", k ); } else { sprintf( buf, "root node" ); } CV_CALL( cvWriteComment( fs, buf, 1 ) ); CV_CALL( cvStartWriteStruct( fs, ICV_HAAR_FEATURE_NAME, CV_NODE_MAP ) ); CV_CALL( cvStartWriteStruct( fs, ICV_HAAR_RECTS_NAME, CV_NODE_SEQ ) ); for( l = 0; l < CV_HAAR_FEATURE_MAX && feature->rect[l].r.width != 0; ++l ) { CV_CALL( cvStartWriteStruct( fs, NULL, CV_NODE_SEQ | CV_NODE_FLOW ) ); CV_CALL( cvWriteInt( fs, NULL, feature->rect[l].r.x ) ); CV_CALL( cvWriteInt( fs, NULL, feature->rect[l].r.y ) ); CV_CALL( cvWriteInt( fs, NULL, feature->rect[l].r.width ) ); CV_CALL( cvWriteInt( fs, NULL, feature->rect[l].r.height ) ); CV_CALL( cvWriteReal( fs, NULL, feature->rect[l].weight ) ); CV_CALL( cvEndWriteStruct( fs ) ); /* rect */ } CV_CALL( cvEndWriteStruct( fs ) ); /* rects */ CV_CALL( cvWriteInt( fs, ICV_HAAR_TILTED_NAME, feature->tilted ) ); CV_CALL( cvEndWriteStruct( fs ) ); /* feature */ CV_CALL( cvWriteReal( fs, ICV_HAAR_THRESHOLD_NAME, tree->threshold[k]) ); if( tree->left[k] > 0 ) { CV_CALL( cvWriteInt( fs, ICV_HAAR_LEFT_NODE_NAME, tree->left[k] ) ); } else { CV_CALL( cvWriteReal( fs, ICV_HAAR_LEFT_VAL_NAME, tree->alpha[-tree->left[k]] ) ); } if( tree->right[k] > 0 ) { CV_CALL( cvWriteInt( fs, ICV_HAAR_RIGHT_NODE_NAME, tree->right[k] ) ); } else { CV_CALL( cvWriteReal( fs, ICV_HAAR_RIGHT_VAL_NAME, tree->alpha[-tree->right[k]] ) ); } CV_CALL( cvEndWriteStruct( fs ) ); /* split */ } CV_CALL( cvEndWriteStruct( fs ) ); /* tree */ } CV_CALL( cvEndWriteStruct( fs ) ); /* trees */ CV_CALL( cvWriteReal( fs, ICV_HAAR_STAGE_THRESHOLD_NAME, cascade->stage_classifier[i].threshold) ); CV_CALL( cvWriteInt( fs, ICV_HAAR_PARENT_NAME, cascade->stage_classifier[i].parent ) ); CV_CALL( cvWriteInt( fs, ICV_HAAR_NEXT_NAME, cascade->stage_classifier[i].next ) ); CV_CALL( cvEndWriteStruct( fs ) ); /* stage */ } /* for each stage */ CV_CALL( cvEndWriteStruct( fs ) ); /* stages */ CV_CALL( cvEndWriteStruct( fs ) ); /* root */ __END__; } static void* icvCloneHaarClassifier( const void* struct_ptr ) { CvHaarClassifierCascade* cascade = NULL; CV_FUNCNAME( "cvCloneHaarClassifier" ); __BEGIN__; int i, j, k, n; const CvHaarClassifierCascade* cascade_src = (const CvHaarClassifierCascade*) struct_ptr; n = cascade_src->count; CV_CALL( 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; CV_CALL( 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; CV_CALL( 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]; } } __END__; return cascade; } static int icvRegisterHaarClassifierType() { CV_FUNCNAME( "icvRegisterHaarClassifierType" ); __BEGIN__; CvTypeInfo info; info.header_size = sizeof( info ); info.is_instance = icvIsHaarClassifier; info.release = (CvReleaseFunc) cvReleaseHaarClassifierCascade; info.read = icvReadHaarClassifier; info.write = icvWriteHaarClassifier; info.clone = icvCloneHaarClassifier; info.type_name = CV_TYPE_NAME_HAAR; CV_CALL( cvRegisterType( &info ) ); __END__; return 1; } static int icv_register_haar_classifier_type = icvRegisterHaarClassifierType(); /* End of file. */