OpenCV的人脸检测:cvRunHaarClassifierCascade函数解析
2013-07-22 14:51
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转自:http://www.cnblogs.com/freecloudinsky/archive/2013/05/20/3088890.html
OpenCV的人脸检测:cvRunHaarClassifierCascade函数解析
2013-05-20 16:55 by freecloudinsky, 24 阅读, 0 评论, 收藏, 编辑cvRunHaarClassifierCascade的:转载:http://2000liuzhenxing.blog.163.com/blog/static/51677475200981952828662/
最近学习OpenCV的人脸检测,有cvHaarDetectObjects,此函数中又有两个函数很重要,一个cvRunHaarClassifierCascade,另一个cvSetImagesForHaarClassifierCascade。这两个函数很重要,上网看到了,就把它转过来了,希望对大家有帮助。
//此函数是一个匹配函数,根据不同的分类器(tree、stump)进行不同的匹配,返回整形值
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 ) //超边退出
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] - //左上+右下-右上-左下
cascade->pq2[pq_offset] + cascade->pq3[pq_offset];
variance_norm_factor = variance_norm_factor*cascade->inv_window_area – mean*mean; //求方差(varance) =Ex2-(Ex)2
if( variance_norm_factor >= 0. )
variance_norm_factor = sqrt(variance_norm_factor);
else
variance_norm_factor = 1.;
if( cascade->is_tree ) //是树形的分类器,就按照层来匹配.
{
CvHidHaarStageClassifier* ptr;
assert( start_stage == 0 ); //start_stage==0继续
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, //层判断
variance_norm_factor, p_offset );
}
if( stage_sum >= ptr->threshold )
{
ptr = ptr->child; //层判断通过,到下一层.
}
else
{
while( ptr && ptr->next == NULL ) ptr = ptr->parent; //未通过,且当前子分类器没有同层分类器,没有返回上层
if( ptr == NULL ) //如果刚才已经是最顶层了.
{
result = 0; //返回0,退出.
EXIT;
}
ptr = ptr->next; //指向下一个分类器.
}
}
}
else if( cascade->is_stump_based ) //如果是stump类的分类器
{
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 )
{ //没通过.则返回负的没通过的分类器数.
result = -i;
EXIT;
}
}
}
else //如果不是那两种强分类器
{
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; //返回结果
}
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