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/* 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. */