| /*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 |
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| // 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, |
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| // (including, but not limited to, procurement of substitute goods or services; |
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| // 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 <stdio.h> |
| |
| /* these settings affect the quality of detection: change with care */ |
| #define CV_ADJUST_FEATURES 1 |
| #define CV_ADJUST_WEIGHTS 1 |
| |
| typedef int sumtype; |
| typedef double sqsumtype; |
| |
| typedef struct MyCvHidHaarFeature |
| { |
| struct |
| { |
| sumtype *p0, *p1, *p2, *p3; |
| int weight; |
| } |
| rect[CV_HAAR_FEATURE_MAX]; |
| } |
| MyCvHidHaarFeature; |
| |
| |
| typedef struct MyCvHidHaarTreeNode |
| { |
| MyCvHidHaarFeature feature; |
| int threshold; |
| int left; |
| int right; |
| } |
| MyCvHidHaarTreeNode; |
| |
| |
| typedef struct MyCvHidHaarClassifier |
| { |
| int count; |
| //CvHaarFeature* orig_feature; |
| MyCvHidHaarTreeNode* node; |
| float* alpha; |
| } |
| MyCvHidHaarClassifier; |
| |
| |
| typedef struct MyCvHidHaarStageClassifier |
| { |
| int count; |
| float threshold; |
| MyCvHidHaarClassifier* classifier; |
| int two_rects; |
| |
| struct MyCvHidHaarStageClassifier* next; |
| struct MyCvHidHaarStageClassifier* child; |
| struct MyCvHidHaarStageClassifier* parent; |
| } |
| MyCvHidHaarStageClassifier; |
| |
| |
| struct MyCvHidHaarClassifierCascade |
| { |
| int count; |
| int is_stump_based; |
| int has_tilted_features; |
| int is_tree; |
| double inv_window_area; |
| CvMat sum, sqsum, tilted; |
| MyCvHidHaarStageClassifier* 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 int myis_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; |
| } |
| |
| static void |
| myicvReleaseHidHaarClassifierCascade( MyCvHidHaarClassifierCascade** _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( &cascade->ipp_stages );*/ |
| cvFree( _cascade ); |
| } |
| } |
| |
| /* create more efficient internal representation of haar classifier cascade */ |
| static MyCvHidHaarClassifierCascade* |
| myicvCreateHidHaarClassifierCascade( CvHaarClassifierCascade* cascade ) |
| { |
| CvRect* ipp_features = 0; |
| float *ipp_weights = 0, *ipp_thresholds = 0, *ipp_val1 = 0, *ipp_val2 = 0; |
| int* ipp_counts = 0; |
| |
| MyCvHidHaarClassifierCascade* out = 0; |
| |
| CV_FUNCNAME( "icvCreateHidHaarClassifierCascade" ); |
| |
| __BEGIN__; |
| |
| int i, j, k, l; |
| int datasize; |
| int total_classifiers = 0; |
| int total_nodes = 0; |
| char errorstr[100]; |
| MyCvHidHaarClassifier* haar_classifier_ptr; |
| MyCvHidHaarTreeNode* 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; |
| |
| /* 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(MyCvHidHaarClassifierCascade) + |
| sizeof(MyCvHidHaarStageClassifier)*cascade->count + |
| sizeof(MyCvHidHaarClassifier) * total_classifiers + |
| sizeof(MyCvHidHaarTreeNode) * total_nodes + |
| sizeof(void*)*(total_nodes + total_classifiers); |
| |
| CV_CALL( out = (MyCvHidHaarClassifierCascade*)cvAlloc( datasize )); |
| memset( out, 0, sizeof(*out) ); |
| |
| /* init header */ |
| out->count = cascade->count; |
| out->stage_classifier = (MyCvHidHaarStageClassifier*)(out + 1); |
| haar_classifier_ptr = (MyCvHidHaarClassifier*)(out->stage_classifier + cascade->count); |
| haar_node_ptr = (MyCvHidHaarTreeNode*)(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; |
| MyCvHidHaarStageClassifier* 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; |
| MyCvHidHaarClassifier* 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++ ) |
| { |
| MyCvHidHaarTreeNode* node = hid_classifier->node + l; |
| CvHaarFeature* feature = classifier->haar_feature + l; |
| memset( node, -1, sizeof(*node) ); |
| node->threshold = (int)((classifier->threshold[l]) * 65536.0); |
| 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 = |
| (MyCvHidHaarTreeNode*)cvAlignPtr(alpha_ptr+node_count+1, sizeof(void*)); |
| |
| out->is_stump_based &= node_count == 1; |
| } |
| } |
| |
| /*{ |
| int can_use_ipp = icvHaarClassifierInitAlloc_32f_p != 0 && |
| icvHaarClassifierFree_32f_p != 0 && |
| icvApplyHaarClassifier_32f_C1R_p != 0 && |
| icvRectStdDev_32f_C1R_p != 0 && |
| !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( &out->ipp_stages ); |
| } |
| } |
| }*/ |
| |
| cascade->hid_cascade = (CvHidHaarClassifierCascade*)out; |
| assert( (char*)haar_node_ptr - (char*)out <= datasize ); |
| |
| __END__; |
| |
| if( cvGetErrStatus() < 0 ) |
| myicvReleaseHidHaarClassifierCascade( &out ); |
| |
| cvFree( &ipp_features ); |
| cvFree( &ipp_weights ); |
| cvFree( &ipp_thresholds ); |
| cvFree( &ipp_val1 ); |
| cvFree( &ipp_val2 ); |
| cvFree( &ipp_counts ); |
| |
| return out; |
| } |
| |
| #define calc_sum(rect,offset) \ |
| ((rect).p0[offset] - (rect).p1[offset] - (rect).p2[offset] + (rect).p3[offset]) |
| |
| |
| CV_INLINE |
| double myicvEvalHidHaarClassifier( MyCvHidHaarClassifier* classifier, |
| double variance_norm_factor, |
| size_t p_offset ) |
| { |
| int idx = 0; |
| do |
| { |
| MyCvHidHaarTreeNode* 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]; |
| } |
| |
| /*********************** Special integer sqrt **************************/ |
| |
| int |
| isqrt(int x) |
| { |
| /* |
| * Logically, these are unsigned. We need the sign bit to test |
| * whether (op - res - one) underflowed. |
| */ |
| |
| register int op, res, one; |
| |
| op = x; |
| res = 0; |
| |
| /* "one" starts at the highest power of four <= than the argument. */ |
| |
| one = 1 << 30; /* second-to-top bit set */ |
| while (one > op) one >>= 2; |
| |
| while (one != 0) { |
| if (op >= res + one) { |
| op = op - (res + one); |
| res = res + 2 * one; |
| } |
| res /= 2; |
| one /= 4; |
| } |
| return(res); |
| } |
| |
| #define NEXT(n, i) (((n) + (i)/(n)) >> 1) |
| |
| unsigned int isqrt1(int number) { |
| unsigned int n = 1; |
| unsigned int n1 = NEXT(n, (unsigned int)number); |
| |
| while(abs((int)(n1 - n)) > 1) { |
| n = n1; |
| n1 = NEXT(n, number); |
| } |
| while((n1*n1) > number) { |
| n1 -= 1; |
| } |
| return n1; |
| } |
| /***********************************************************************/ |
| |
| CV_IMPL int |
| mycvRunHaarClassifierCascade( CvHaarClassifierCascade* _cascade, |
| CvPoint pt, int start_stage ) |
| { |
| int result = -1; |
| CV_FUNCNAME("mycvRunHaarClassifierCascade"); |
| |
| __BEGIN__; |
| |
| int p_offset, pq_offset; |
| int pq0, pq1, pq2, pq3; |
| int i, j; |
| double mean; |
| int variance_norm_factor; |
| MyCvHidHaarClassifierCascade* cascade; |
| |
| if( !CV_IS_HAAR_CLASSIFIER(_cascade) ) |
| CV_ERROR( !_cascade ? CV_StsNullPtr : CV_StsBadArg, "Invalid cascade pointer" ); |
| |
| cascade = (MyCvHidHaarClassifierCascade*)_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; |
| pq0 = cascade->pq0[pq_offset]; |
| pq1 = cascade->pq1[pq_offset]; |
| pq2 = cascade->pq2[pq_offset]; |
| pq3 = cascade->pq3[pq_offset]; |
| variance_norm_factor = pq0 - pq1 - pq2 + pq3; |
| 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 ) |
| // { |
| // MyCvHidHaarStageClassifier* 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 += myicvEvalHidHaarClassifier( 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++ ) |
| { |
| MyCvHidHaarClassifier* classifier = cascade->stage_classifier[i].classifier + j; |
| MyCvHidHaarTreeNode* node = classifier->node; |
| int t = node->threshold * variance_norm_factor; |
| int 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 |
| { |
| for( j = 0; j < cascade->stage_classifier[i].count; j++ ) |
| { |
| MyCvHidHaarClassifier* classifier = cascade->stage_classifier[i].classifier + j; |
| MyCvHidHaarTreeNode* node = classifier->node; |
| int t = node->threshold * variance_norm_factor; |
| int 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]; |
| } |
| } |
| |
| 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 += myicvEvalHidHaarClassifier( |
| // 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; |
| } |
| |
| #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))) |
| |
| |
| CV_IMPL void |
| mycvSetImagesForHaarClassifierCascade( 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; |
| MyCvHidHaarClassifierCascade* cascade; |
| int coi0 = 0, coi1 = 0; |
| int i; |
| 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( myicvCreateHidHaarClassifierCascade(_cascade) ); |
| |
| cascade = (MyCvHidHaarClassifierCascade*)_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 */ |
| { |
| #ifdef _OPENMP |
| int max_threads = cvGetNumThreads(); |
| #pragma omp parallel for num_threads(max_threads) schedule(dynamic) |
| #endif // _OPENMP |
| 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]; |
| /* CvHidHaarClassifier* classifier = |
| cascade->stage_classifier[i].classifier + j; */ |
| MyCvHidHaarFeature* 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 |
| |
| float tmpweight[3] = {0}; |
| |
| 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); |
| tmpweight[k] = (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; |
| sum0 += tmpweight[k] * tr.width * tr.height; |
| } |
| |
| tmpweight[0] = (float)(-sum0/area0); |
| |
| for(int ii = 0; ii < nr; hidfeature->rect[ii].weight = (int)(tmpweight[ii] * 65536.0), ii++); |
| } /* l */ |
| } /* j */ |
| } |
| } |
| |
| __END__; |
| } |
| |
| CvMat *temp = 0, *sum = 0, *sqsum = 0; |
| double tickFreqTimes1000 = ((double)cvGetTickFrequency()*1000.); |
| |
| CV_IMPL CvSeq* |
| mycvHaarDetectObjects( 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 *tilted = 0, *norm_img = 0, *sumcanny = 0, *img_small = 0; |
| CvSeq* result_seq = 0; |
| CvMemStorage* temp_storage = 0; |
| CvAvgComp* comps = 0; |
| CvSeq* seq_thread[CV_MAX_THREADS] = {0}; |
| int i, max_threads = 0; |
| double t1; |
| |
| CV_FUNCNAME( "cvHaarDetectObjects" ); |
| |
| __BEGIN__; |
| |
| double t = (double)cvGetTickCount(); |
| |
| CvSeq *seq = 0, *seq2 = 0, *idx_seq = 0, *big_seq = 0; |
| CvAvgComp result_comp = {{0,0,0,0},0}; |
| double factor; |
| int npass = 2, coi; |
| bool do_canny_pruning = (flags & CV_HAAR_DO_CANNY_PRUNING) != 0; |
| bool find_biggest_object = (flags & CV_HAAR_FIND_BIGGEST_OBJECT) != 0; |
| bool rough_search = (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" ); |
| |
| 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" ); |
| |
| if( scale_factor <= 1 ) |
| CV_ERROR( CV_StsOutOfRange, "scale factor must be > 1" ); |
| |
| if( find_biggest_object ) |
| flags &= ~CV_HAAR_SCALE_IMAGE; |
| |
| if(!temp) { |
| CV_CALL( temp = cvCreateMat( img->rows, img->cols, CV_8UC1 )); |
| } |
| if(!sum) { |
| CV_CALL( sum = cvCreateMat( img->rows + 1, img->cols + 1, CV_32SC1 )); |
| } |
| if(!sqsum) { |
| CV_CALL( sqsum = cvCreateMat( img->rows + 1, img->cols + 1, CV_64FC1 )); |
| } |
| CV_CALL( temp_storage = cvCreateChildMemStorage( storage )); |
| |
| if( !cascade->hid_cascade ) |
| CV_CALL( myicvCreateHidHaarClassifierCascade(cascade) ); |
| |
| if( ((MyCvHidHaarClassifierCascade*)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 ); |
| |
| max_threads = cvGetNumThreads(); |
| if( max_threads > 1 ) |
| for( i = 0; i < max_threads; i++ ) |
| { |
| CvMemStorage* temp_storage_thread; |
| CV_CALL( temp_storage_thread = cvCreateMemStorage(0)); |
| CV_CALL( seq_thread[i] = cvCreateSeq( 0, sizeof(CvSeq), |
| sizeof(CvRect), temp_storage_thread )); |
| } |
| else |
| seq_thread[0] = seq; |
| |
| if( CV_MAT_CN(img->type) > 1 ) |
| { |
| cvCvtColor( img, temp, CV_BGR2GRAY ); |
| img = temp; |
| } |
| |
| if( flags & CV_HAAR_FIND_BIGGEST_OBJECT ) |
| flags &= ~(CV_HAAR_SCALE_IMAGE|CV_HAAR_DO_CANNY_PRUNING); |
| |
| // if( flags & CV_HAAR_SCALE_IMAGE ) |
| // { |
| // CvSize win_size0 = cascade->orig_window_size; |
| // /*int use_ipp = cascade->hid_cascade->ipp_stages != 0 && |
| // icvApplyHaarClassifier_32f_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 strip_count, strip_size; |
| // int ystep = factor > 2. ? 1 : 2; |
| // 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 equ_rect = { 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( max_threads > 1 ) |
| // { |
| // strip_count = MAX(MIN(sz1.height/ystep, max_threads*3), 1); |
| // strip_size = (sz1.height + strip_count - 1)/strip_count; |
| // strip_size = (strip_size / ystep)*ystep; |
| // } |
| // else |
| // { |
| // strip_count = 1; |
| // strip_size = sz1.height; |
| // } |
| // |
| // //if( !use_ipp ) |
| // cvSetImagesForHaarClassifierCascade( cascade, &sum1, &sqsum1, 0, 1. ); |
| // /*else |
| // { |
| // for( i = 0; i <= sz.height; i++ ) |
| // { |
| // const int* isum = (int*)(sum1.data.ptr + sum1.step*i); |
| // float* fsum = (float*)isum; |
| // const int FLT_DELTA = -(1 << 24); |
| // int j; |
| // for( j = 0; j <= sz.width; j++ ) |
| // fsum[j] = (float)(isum[j] + FLT_DELTA); |
| // } |
| // }*/ |
| // |
| //#ifdef _OPENMP |
| //#pragma omp parallel for num_threads(max_threads) schedule(dynamic) |
| //#endif |
| // for( i = 0; i < strip_count; i++ ) |
| // { |
| // int thread_id = cvGetThreadNum(); |
| // int positive = 0; |
| // int y1 = i*strip_size, y2 = (i+1)*strip_size/* - ystep + 1*/; |
| // CvSize ssz; |
| // int x, y; |
| // if( i == strip_count - 1 || y2 > sz1.height ) |
| // y2 = sz1.height; |
| // ssz = cvSize(sz1.width, y2 - y1); |
| // |
| // /*if( use_ipp ) |
| // { |
| // icvRectStdDev_32f_C1R_p( |
| // (float*)(sum1.data.ptr + y1*sum1.step), sum1.step, |
| // (double*)(sqsum1.data.ptr + y1*sqsum1.step), sqsum1.step, |
| // (float*)(norm1.data.ptr + y1*norm1.step), norm1.step, ssz, equ_rect ); |
| // |
| // positive = (ssz.width/ystep)*((ssz.height + ystep-1)/ystep); |
| // memset( mask1.data.ptr + y1*mask1.step, ystep == 1, mask1.height*mask1.step); |
| // |
| // if( ystep > 1 ) |
| // { |
| // for( y = y1, positive = 0; y < y2; y += ystep ) |
| // for( x = 0; x < ssz.width; x += ystep ) |
| // mask1.data.ptr[mask1.step*y + x] = (uchar)1; |
| // } |
| // |
| // for( int j = 0; j < cascade->count; j++ ) |
| // { |
| // if( icvApplyHaarClassifier_32f_C1R_p( |
| // (float*)(sum1.data.ptr + y1*sum1.step), sum1.step, |
| // (float*)(norm1.data.ptr + y1*norm1.step), norm1.step, |
| // mask1.data.ptr + y1*mask1.step, mask1.step, ssz, &positive, |
| // cascade->hid_cascade->stage_classifier[j].threshold, |
| // cascade->hid_cascade->ipp_stages[j]) < 0 ) |
| // { |
| // positive = 0; |
| // break; |
| // } |
| // if( positive <= 0 ) |
| // break; |
| // } |
| // } |
| // else*/ |
| // { |
| // for( y = y1, positive = 0; y < y2; y += ystep ) |
| // for( x = 0; x < ssz.width; x += ystep ) |
| // { |
| // mask1.data.ptr[mask1.step*y + x] = |
| // mycvRunHaarClassifierCascade( cascade, cvPoint(x,y), 0 ) > 0; |
| // positive += mask1.data.ptr[mask1.step*y + x]; |
| // } |
| // } |
| // |
| // if( positive > 0 ) |
| // { |
| // for( y = y1; y < y2; y += ystep ) |
| // for( x = 0; x < ssz.width; x += ystep ) |
| // if( mask1.data.ptr[mask1.step*y + x] != 0 ) |
| // { |
| // CvRect obj_rect = { cvRound(x*factor), cvRound(y*factor), |
| // win_size.width, win_size.height }; |
| // cvSeqPush( seq_thread[thread_id], &obj_rect ); |
| // } |
| // } |
| // } |
| // |
| // // gather the results |
| // if( max_threads > 1 ) |
| // for( i = 0; i < max_threads; i++ ) |
| // { |
| // CvSeq* s = seq_thread[i]; |
| // int j, total = s->total; |
| // CvSeqBlock* b = s->first; |
| // for( j = 0; j < total; j += b->count, b = b->next ) |
| // cvSeqPushMulti( seq, b->data, b->count ); |
| // } |
| // } |
| // } |
| // else |
| t1 = (double)cvGetTickCount(); |
| // printf( "init time = %gms\n", (t1 - t)/tickFreqTimes1000); |
| t = t1; |
| |
| { |
| int n_factors = 0; |
| CvRect scan_roi_rect = {0,0,0,0}; |
| bool is_found = false, scan_roi = false; |
| |
| 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 || |
| ((MyCvHidHaarClassifierCascade*)cascade->hid_cascade)->is_tree ) |
| { |
| split_stage = cascade->count; |
| npass = 1; |
| } |
| |
| 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 *= scale_factor ) |
| ; |
| |
| if( find_biggest_object ) |
| { |
| scale_factor = 1./scale_factor; |
| factor *= scale_factor; |
| big_seq = cvCreateSeq( 0, sizeof(CvSeq), sizeof(CvRect), temp_storage ); |
| } |
| else |
| factor = 1; |
| |
| for( ; n_factors-- > 0 && !is_found; 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 start_x = 0, start_y = 0; |
| int end_x = cvRound((img->cols - win_size.width) / ystep); |
| int end_y = cvRound((img->rows - win_size.height) / ystep); |
| |
| if( win_size.width < min_size.width || win_size.height < min_size.height ) |
| { |
| if( find_biggest_object ) |
| break; |
| continue; |
| } |
| |
| mycvSetImagesForHaarClassifierCascade( 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; |
| // } |
| |
| if( scan_roi ) |
| { |
| //adjust start_height and stop_height |
| start_y = cvRound(scan_roi_rect.y / ystep); |
| end_y = cvRound((scan_roi_rect.y + scan_roi_rect.height - win_size.height) / ystep); |
| |
| start_x = cvRound(scan_roi_rect.x / ystep); |
| end_x = cvRound((scan_roi_rect.x + scan_roi_rect.width - win_size.width) / ystep); |
| } |
| |
| ((MyCvHidHaarClassifierCascade*)cascade->hid_cascade)->count = split_stage; |
| |
| for( pass = 0; pass < npass; pass++ ) |
| { |
| #ifdef _OPENMP |
| #pragma omp parallel for num_threads(max_threads) schedule(dynamic) |
| #endif |
| for( int _iy = start_y; _iy < end_y; _iy++ ) |
| { |
| int thread_id = cvGetThreadNum(); |
| int iy = cvRound(_iy*ystep); |
| int _ix, _xstep = 1; |
| uchar* mask_row = temp->data.ptr + temp->step * iy; |
| |
| for( _ix = start_x; _ix < end_x; _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 = mycvRunHaarClassifierCascade( cascade, cvPoint(ix,iy), 0 ); |
| 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_thread[thread_id], &rect ); |
| } |
| } |
| if( result < 0 ) |
| _xstep = 1; |
| } |
| else if( mask_row[ix] ) |
| { |
| int result = mycvRunHaarClassifierCascade( cascade, cvPoint(ix,iy), |
| stage_offset ); |
| if( result > 0 ) |
| { |
| if( pass == npass - 1 ) |
| { |
| CvRect rect = cvRect(ix,iy,win_size.width,win_size.height); |
| cvSeqPush( seq_thread[thread_id], &rect ); |
| } |
| } |
| else |
| mask_row[ix] = 0; |
| } |
| } |
| } |
| stage_offset = ((MyCvHidHaarClassifierCascade*)cascade->hid_cascade)->count; |
| ((MyCvHidHaarClassifierCascade*)cascade->hid_cascade)->count = cascade->count; |
| } |
| |
| // gather the results |
| if( max_threads > 1 ) |
| for( i = 0; i < max_threads; i++ ) |
| { |
| CvSeq* s = seq_thread[i]; |
| int j, total = s->total; |
| CvSeqBlock* b = s->first; |
| for( j = 0; j < total; j += b->count, b = b->next ) |
| cvSeqPushMulti( seq, b->data, b->count ); |
| } |
| |
| if( find_biggest_object ) |
| { |
| CvSeq* bseq = min_neighbors > 0 ? big_seq : seq; |
| |
| if( min_neighbors > 0 && !scan_roi ) |
| { |
| // group retrieved rectangles in order to filter out noise |
| int ncomp = cvSeqPartition( seq, 0, &idx_seq, myis_equal, 0 ); |
| CV_CALL( comps = (CvAvgComp*)cvAlloc( (ncomp+1)*sizeof(comps[0]))); |
| memset( comps, 0, (ncomp+1)*sizeof(comps[0])); |
| |
| #if VERY_ROUGH_SEARCH |
| if( rough_search ) |
| { |
| 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 = n; |
| cvSeqPush( bseq, &comp ); |
| } |
| } |
| } |
| else |
| #endif |
| { |
| for( i = 0 ; i <= ncomp; i++ ) |
| comps[i].rect.x = comps[i].rect.y = INT_MAX; |
| |
| // 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++; |
| |
| // rect.width and rect.height will store coordinate of right-bottom corner |
| comps[idx].rect.x = MIN(comps[idx].rect.x, r1.x); |
| comps[idx].rect.y = MIN(comps[idx].rect.y, r1.y); |
| comps[idx].rect.width = MAX(comps[idx].rect.width, r1.x+r1.width-1); |
| comps[idx].rect.height = MAX(comps[idx].rect.height, r1.y+r1.height-1); |
| } |
| |
| // calculate enclosing box |
| for( i = 0; i < ncomp; i++ ) |
| { |
| int n = comps[i].neighbors; |
| if( n >= min_neighbors ) |
| { |
| CvAvgComp comp; |
| int t; |
| double min_scale = rough_search ? 0.6 : 0.4; |
| comp.rect.x = comps[i].rect.x; |
| comp.rect.y = comps[i].rect.y; |
| comp.rect.width = comps[i].rect.width - comps[i].rect.x + 1; |
| comp.rect.height = comps[i].rect.height - comps[i].rect.y + 1; |
| |
| // update min_size |
| t = cvRound( comp.rect.width*min_scale ); |
| min_size.width = MAX( min_size.width, t ); |
| |
| t = cvRound( comp.rect.height*min_scale ); |
| min_size.height = MAX( min_size.height, t ); |
| |
| //expand the box by 20% because we could miss some neighbours |
| //see 'is_equal' function |
| #if 1 |
| int offset = cvRound(comp.rect.width * 0.2); |
| int right = MIN( img->cols-1, comp.rect.x+comp.rect.width-1 + offset ); |
| int bottom = MIN( img->rows-1, comp.rect.y+comp.rect.height-1 + offset); |
| comp.rect.x = MAX( comp.rect.x - offset, 0 ); |
| comp.rect.y = MAX( comp.rect.y - offset, 0 ); |
| comp.rect.width = right - comp.rect.x + 1; |
| comp.rect.height = bottom - comp.rect.y + 1; |
| #endif |
| |
| comp.neighbors = n; |
| cvSeqPush( bseq, &comp ); |
| } |
| } |
| } |
| |
| cvFree( &comps ); |
| } |
| |
| // extract the biggest rect |
| if( bseq->total > 0 ) |
| { |
| int max_area = 0; |
| for( i = 0; i < bseq->total; i++ ) |
| { |
| CvAvgComp* comp = (CvAvgComp*)cvGetSeqElem( bseq, i ); |
| int area = comp->rect.width * comp->rect.height; |
| if( max_area < area ) |
| { |
| max_area = area; |
| result_comp.rect = comp->rect; |
| result_comp.neighbors = bseq == seq ? 1 : comp->neighbors; |
| } |
| } |
| |
| //Prepare information for further scanning inside the biggest rectangle |
| |
| #if VERY_ROUGH_SEARCH |
| // change scan ranges to roi in case of required |
| if( !rough_search && !scan_roi ) |
| { |
| scan_roi = true; |
| scan_roi_rect = result_comp.rect; |
| cvClearSeq(bseq); |
| } |
| else if( rough_search ) |
| is_found = true; |
| #else |
| if( !scan_roi ) |
| { |
| scan_roi = true; |
| scan_roi_rect = result_comp.rect; |
| cvClearSeq(bseq); |
| } |
| #endif |
| } |
| } |
| } |
| } |
| |
| // t1 = (double)cvGetTickCount(); |
| // printf( "factors time = %gms\n", (t1 - t)/tickFreqTimes1000); |
| // t = t1; |
| |
| if( min_neighbors == 0 && !find_biggest_object ) |
| { |
| for( i = 0; i < seq->total; i++ ) |
| { |
| CvRect* rect = (CvRect*)cvGetSeqElem( seq, i ); |
| CvAvgComp comp; |
| comp.rect = *rect; |
| comp.neighbors = 1; |
| cvSeqPush( result_seq, &comp ); |
| } |
| } |
| |
| if( min_neighbors != 0 |
| #if VERY_ROUGH_SEARCH |
| && (!find_biggest_object || !rough_search) |
| #endif |
| ) |
| { |
| // group retrieved rectangles in order to filter out noise |
| int ncomp = cvSeqPartition( seq, 0, &idx_seq, myis_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 ); |
| } |
| } |
| |
| if( !find_biggest_object ) |
| { |
| // 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 ); |
| } |
| } |
| else |
| { |
| int max_area = 0; |
| for( i = 0; i < seq2->total; i++ ) |
| { |
| CvAvgComp* comp = (CvAvgComp*)cvGetSeqElem( seq2, i ); |
| int area = comp->rect.width * comp->rect.height; |
| if( max_area < area ) |
| { |
| max_area = area; |
| result_comp = *comp; |
| } |
| } |
| } |
| } |
| |
| t1 = (double)cvGetTickCount(); |
| // printf( "results eval time = %gms\n", (t1 - t)/tickFreqTimes1000); |
| t = t1; |
| |
| if( find_biggest_object && result_comp.rect.width > 0 ) |
| cvSeqPush( result_seq, &result_comp ); |
| |
| __END__; |
| |
| if( max_threads > 1 ) |
| for( i = 0; i < max_threads; i++ ) |
| { |
| if( seq_thread[i] ) |
| cvReleaseMemStorage( &seq_thread[i]->storage ); |
| } |
| |
| cvReleaseMemStorage( &temp_storage ); |
| cvReleaseMat( &sum ); |
| cvReleaseMat( &sqsum ); |
| cvReleaseMat( &tilted ); |
| cvReleaseMat( &temp ); |
| cvReleaseMat( &sumcanny ); |
| cvReleaseMat( &norm_img ); |
| cvReleaseMat( &img_small ); |
| cvFree( &comps ); |
| |
| return result_seq; |
| } |
