+++ /dev/null
-/*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 <stdio.h>
-
-/* 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_32f_C1R_t icvApplyHaarClassifier_32f_C1R_p = 0;
-icvRectStdDev_32f_C1R_t icvRectStdDev_32f_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( &cascade->ipp_stages );
- cvFree( _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 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;
- }
- }
-
- {
- 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 = out;
- assert( (char*)haar_node_ptr - (char*)out <= datasize );
-
- __END__;
-
- if( cvGetErrStatus() < 0 )
- icvReleaseHidHaarClassifierCascade( &out );
-
- cvFree( &ipp_features );
- cvFree( &ipp_weights );
- cvFree( &ipp_thresholds );
- cvFree( &ipp_val1 );
- cvFree( &ipp_val2 );
- cvFree( &ipp_counts );
-
- return out;
-}
-
-
-#define sum_elem_ptr(sum,row,col) \
- ((sumtype*)CV_MAT_ELEM_PTR_FAST((sum),(row),(col),sizeof(sumtype)))
-
-#define sqsum_elem_ptr(sqsum,row,col) \
- ((sqsumtype*)CV_MAT_ELEM_PTR_FAST((sqsum),(row),(col),sizeof(sqsumtype)))
-
-#define calc_sum(rect,offset) \
- ((rect).p0[offset] - (rect).p1[offset] - (rect).p2[offset] + (rect).p3[offset])
-
-
-CV_IMPL 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;
- 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 */
- {
-#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; */
- 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;
-}
-
-
-#define VERY_ROUGH_SEARCH 0
-
-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* result_seq = 0;
- CvMemStorage* temp_storage = 0;
- CvAvgComp* comps = 0;
- CvSeq* seq_thread[CV_MAX_THREADS] = {0};
- int i, max_threads = 0;
-
- CV_FUNCNAME( "cvHaarDetectObjects" );
-
- __BEGIN__;
-
- 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( find_biggest_object )
- flags &= ~CV_HAAR_SCALE_IMAGE;
-
- 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 );
-
- 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, j;
- 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( 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] =
- cvRunHaarClassifierCascade( 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
- {
- 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 ||
- 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;
- }
-
- 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;
- }
-
- 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);
- }
-
- 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 = cvRunHaarClassifierCascade( 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 = cvRunHaarClassifierCascade( 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 = cascade->hid_cascade->count;
- 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, is_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
- }
- }
- }
- }
-
- 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, 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 );
- }
- }
-
- 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;
- }
- }
- }
- }
-
- 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;
-}
-
-
-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( &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( &cascade->stage_classifier[i].classifier[j].haar_feature );
- cvFree( &cascade->stage_classifier[i].classifier );
- }
- icvReleaseHidHaarClassifierCascade( &cascade->hid_cascade );
- cvFree( _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;
-}
-
-
-CvType haar_type( CV_TYPE_NAME_HAAR, icvIsHaarClassifier,
- (CvReleaseFunc)cvReleaseHaarClassifierCascade,
- icvReadHaarClassifier, icvWriteHaarClassifier,
- icvCloneHaarClassifier );
-
-/* End of file. */
projects / opencv / blobdiff