【转载】浅谈OpenCV人脸检测以及两个函数cvHaarDetectObjects和cvRunHaarClassifierCascade

本文转自：http://2000liuzhenxing.blog.163.com/blog/static/51677475200981952828662/和http://2000liuzhenxing.blog.163.com/blog/static/5167747520098195217494/。

第一部分  cvHaarDetectObjects

OpenCV的人脸检测主要是调用训练好的cascade（Haar分类器）来进行模式匹配。

cvHaarDetectObjects，先将图像灰度化，根据传入参数判断是否进行canny边缘处理(默认不使用)，再进行匹配。匹配后收集找出的匹配块,过滤噪声，计算相邻个数如果超过了规定值（传入的min_neighbors）就当成输出结果，否则删去。

匹配循环：将匹配分类器放大scale（传入值）倍，同时原图缩小scale倍，进行匹配，直到匹配分类器的大小大于原图，则返回匹配结果。匹配的时候调用cvRunHaarClassifierCascade来进行匹配，将所有结果存入CvSeq* Seq （可动态增长元素序列），将结果传给cvHaarDetectObjects。

cvRunHaarClassifierCascade函数整体是根据传入的图像和cascade来进行匹配。并且可以根据传入的cascade类型不同（树型、stump（不完整的树）或其他的），进行不同的匹配方式。

函数 cvRunHaarClassifierCascade 用于对单幅图片的检测。在函数调用前首先利用 cvSetImagesForHaarClassifierCascade设定积分图和合适的比例系数 (=> 窗口尺寸)。当分析的矩形框全部通过级联分类器每一层的时返回正值(这是一个候选目标)，否则返回0或负值。

为了了解OpenCV人脸检测中寻找匹配图像的详细过程，就把cvHaarDetectObjects和cvRunHaarClassifierCascade的源文件详细看了一遍，并打上了注释。方便大家阅读。

附cvHaarDetectObjects代码：

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多通道矩阵  *img=_img指针代换传入图
    CvMat *temp = 0, *sum = 0, *tilted = 0, *sqsum = 0, *norm_img = 0, *sumcanny = 0, *img_small = 0;
    CvSeq* seq = 0;
    CvSeq* seq2 = 0;                                                                            //CvSeq可动态增长元素序列
    CvSeq* idx_seq = 0;
    CvSeq* result_seq = 0;
    CvMemStorage* temp_storage = 0;
    CvAvgComp* comps = 0;
    int i;
   
#ifdef _OPENMP
    CvSeq* seq_thread[CV_MAX_THREADS] = {0};
    int max_threads = 0;
#endif
   
    CV_FUNCNAME( “cvHaarDetectObjects” );
 
    __BEGIN__;
 
    double factor;
    int npass = 2, coi;                                                                                                                 //npass=2
    int do_canny_pruning = flags & CV_HAAR_DO_CANNY_PRUNING;                 //true做canny边缘处理
 
    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” );                                    //一些出错代码
 
    if( CV_MAT_DEPTH(img->type) != CV_8U )
        CV_ERROR( CV_StsUnsupportedFormat, “Only 8-bit images are supported” );
 
    CV_CALL( temp = cvCreateMat( img->rows, img->cols, CV_8UC1 ));
    CV_CALL( sum = cvCreateMat( img->rows + 1, img->cols + 1, CV_32SC1 ));
    CV_CALL( sqsum = cvCreateMat( img->rows + 1, img->cols + 1, CV_64FC1 ));
    CV_CALL( temp_storage = cvCreateChildMemStorage( storage ));
 
#ifdef _OPENMP
    max_threads = cvGetNumThreads();
    for( i = 0; i < max_threads; i++ )
    {
        CvMemStorage* temp_storage_thread;
        CV_CALL( temp_storage_thread = cvCreateMemStorage(0));                 //CV_CALL就是运行，假如出错就报错。
        CV_CALL( seq_thread[i] = cvCreateSeq( 0, sizeof(CvSeq),                //CvSeq可动态增长元素序列
 
                        sizeof(CvRect), temp_storage_thread ));
    }
#endif
 
    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 );         //多通道矩阵 图像长宽+1 4通道
 
    seq = cvCreateSeq( 0, sizeof(CvSeq), sizeof(CvRect), temp_storage );        //创建序列seq  矩形
    seq2 = cvCreateSeq( 0, sizeof(CvSeq), sizeof(CvAvgComp), temp_storage );    //创建序列seq2  矩形和邻近
    result_seq = cvCreateSeq( 0, sizeof(CvSeq), sizeof(CvAvgComp), storage );   //创建序列result_seq  矩形和邻近
 
    if( min_neighbors == 0 )
        seq = result_seq;
 
    if( CV_MAT_CN(img->type) > 1 )
    {
        cvCvtColor( img, temp, CV_BGR2GRAY );                                  //img转为灰度
        img = temp;                                                                                                                                
    }
   
    if( flags & CV_HAAR_SCALE_IMAGE )                                                                                        //flag && 匹配图
    {
        CvSize win_size0 = cascade->orig_window_size;                         //CvSize win_size0为分类器的原始大小
        int use_ipp = cascade->hid_cascade->ipp_stages != 0 &&              
                    icvApplyHaarClassifier_32s32f_C1R_p != 0;                 //IPP相关函数
 
        if( use_ipp )
            CV_CALL( norm_img = cvCreateMat( img->rows, img->cols, CV_32FC1 ));           //图像的矩阵化 4通道.
        CV_CALL( img_small = cvCreateMat( img->rows + 1, img->cols + 1, CV_8UC1 ));       //小图矩阵化 单通道 长宽+1
 
        for( factor = 1; ; factor *= scale_factor )                                       //成scale_factor倍数匹配
        {
            int positive = 0;
            int x, y;
            CvSize win_size = { cvRound(win_size0.width*factor),
                                cvRound(win_size0.height*factor) };                       //winsize         分类器行列（扩大factor倍）        
            CvSize sz = { cvRound( img->cols/factor ), cvRound( img->rows/factor ) };     //sz 图像行列（缩小factor倍）               三个Cvsize
            CvSize sz1 = { sz.width – win_size0.width, sz.height – win_size0.height };    //sz1 图像 减 分类器行列
            CvRect rect1 = { icv_object_win_border, icv_object_win_border,
                win_size0.width – icv_object_win_border*2,                                //icv_object_win_border （int） 初始值=1
                win_size0.height – icv_object_win_border*2 };                                  //矩形框rect1
            CvMat img1, sum1, sqsum1, norm1, tilted1, mask1;                              //多通道矩阵
            CvMat* _tilted = 0;
 
            if( sz1.width <= 0 || sz1.height <= 0 )                                       //图片宽或高小于分类器–>跳出
                break;
            if( win_size.width < min_size.width || win_size.height < min_size.height )    //分类器高或宽小于给定的mini_size的高或宽–>继续
                continue;
//CV_8UC1见定义.
//#define CV_MAKETYPE(depth,cn) ((depth) + (((cn)-1) << CV_CN_SHIFT))   
//深度+（cn-1）左移3位   depth，depth+8，depth+16，depth+24.
            img1 = cvMat( sz.height, sz.width, CV_8UC1, img_small->data.ptr );            //小图的矩阵化 img1 单通道    
            sum1 = cvMat( sz.height+1, sz.width+1, CV_32SC1, sum->data.ptr );             //长宽+1 4通道8位            多通道矩阵
            sqsum1 = cvMat( sz.height+1, sz.width+1, CV_64FC1, sqsum->data.ptr );         //长宽+1 4通道16位
            if( tilted )
            {
                tilted1 = cvMat( sz.height+1, sz.width+1, CV_32SC1, tilted->data.ptr );   //长宽+1 4通道8位
                _tilted = &tilted1;                                                                  //长宽+1 4通道8位
            }
            norm1 = cvMat( sz1.height, sz1.width, CV_32FC1, norm_img ? norm_img->data.ptr : 0 );  //norm1 图像 减 分类器行列 4通道
            mask1 = cvMat( sz1.height, sz1.width, CV_8UC1, temp->data.ptr );                      //mask1 灰度图
 
            cvResize( img, &img1, CV_INTER_LINEAR );                                      //img双线性插值 输出到img1
            cvIntegral( &img1, &sum1, &sqsum1, _tilted );                                                                  //计算积分图像
 
            if( use_ipp && icvRectStdDev_32s32f_C1R_p( sum1.data.i, sum1.step,
                sqsum1.data.db, sqsum1.step, norm1.data.fl, norm1.step, sz1, rect1 ) < 0 )
                use_ipp = 0;
 
            if( use_ipp )                                                                                 //如果ipp=true   (intel视频处理加速等的函数库)
            {
                positive = mask1.cols*mask1.rows;                                                                                  //mask1长乘宽–>positive
                cvSet( &mask1, cvScalarAll(255) );                                                                                  //mask1赋值为255
                for( i = 0; i < cascade->count; i++ )
                {
                    if( icvApplyHaarClassifier_32s32f_C1R_p(sum1.data.i, sum1.step,
                        norm1.data.fl, norm1.step, mask1.data.ptr, mask1.step,
                        sz1, &positive, cascade->hid_cascade->stage_classifier[i].threshold,
                        cascade->hid_cascade->ipp_stages[i]) < 0 )
                    {
                        use_ipp = 0;                                                                         //ipp=false;
                        break;
                    }
                    if( positive <= 0 )
                        break;
                }
            }
           
            if( !use_ipp )                                                                                         //如果ipp=false
            {
                cvSetImagesForHaarClassifierCascade( cascade, &sum1, &sqsum1, 0, 1. );
                for( y = 0, positive = 0; y < sz1.height; y++ )
                    for( x = 0; x < sz1.width; x++ )
                    {
                        mask1.data.ptr[mask1.step*y + x] =
                            cvRunHaarClassifierCascade( cascade, cvPoint(x,y), 0 ) > 0;   //匹配图像.
                        positive += mask1.data.ptr[mask1.step*y + x];
                    }
            }
 
            if( positive > 0 )
            {
                for( y = 0; y < sz1.height; y++ )
                    for( x = 0; x < sz1.width; x++ )
                        if( mask1.data.ptr[mask1.step*y + x] != 0 )
                        {
                            CvRect obj_rect = { cvRound(y*factor), cvRound(x*factor),    
                                                win_size.width, win_size.height };
                            cvSeqPush( seq, &obj_rect );                                        //将匹配块放到seq中
                        }
            }
        }
    }
    else                                                                                                        //!(flag && 匹配图)
    {
        cvIntegral( img, sum, sqsum, tilted );
   
        if( do_canny_pruning )
        {
            sumcanny = cvCreateMat( img->rows + 1, img->cols + 1, CV_32SC1 );                  //如果 做canny边缘检测
            cvCanny( img, temp, 0, 50, 3 );
            cvIntegral( temp, sumcanny );
        }
   
        if( (unsigned)split_stage >= (unsigned)cascade->count ||
            cascade->hid_cascade->is_tree )                                                                                                                
        {
            split_stage = cascade->count;
            npass = 1;
        }
 
        for( factor = 1; factor*cascade->orig_window_size.width < img->cols – 10 &&                                //匹配
                         factor*cascade->orig_window_size.height < img->rows – 10;
             factor *= scale_factor )
        {
            const double ystep = MAX( 2, factor );
            CvSize win_size = { cvRound( cascade->orig_window_size.width * factor ),
                                cvRound( cascade->orig_window_size.height * factor )};
            CvRect equ_rect = { 0, 0, 0, 0 };
            int *p0 = 0, *p1 = 0, *p2 = 0, *p3 = 0;
            int *pq0 = 0, *pq1 = 0, *pq2 = 0, *pq3 = 0;
            int pass, stage_offset = 0;
            int stop_height = cvRound((img->rows – win_size.height) / ystep);
 
            if( win_size.width < min_size.width || win_size.height < min_size.height )                        //超边跳出
                continue;
 
            cvSetImagesForHaarClassifierCascade( cascade, sum, sqsum, tilted, factor );                        //匹配
            cvZero( temp );                                                                                                //清空temp数组
 
            if( do_canny_pruning )                                                                                        //canny边缘检测
            {
                equ_rect.x = cvRound(win_size.width*0.15);
                equ_rect.y = cvRound(win_size.height*0.15);
                equ_rect.width = cvRound(win_size.width*0.7);
                equ_rect.height = cvRound(win_size.height*0.7);
 
                p0 = (int*)(sumcanny->data.ptr + equ_rect.y*sumcanny->step) + equ_rect.x;
                p1 = (int*)(sumcanny->data.ptr + equ_rect.y*sumcanny->step)
                            + equ_rect.x + equ_rect.width;
                p2 = (int*)(sumcanny->data.ptr + (equ_rect.y + equ_rect.height)*sumcanny->step) + equ_rect.x;
                p3 = (int*)(sumcanny->data.ptr + (equ_rect.y + equ_rect.height)*sumcanny->step)
                            + equ_rect.x + equ_rect.width;
 
                pq0 = (int*)(sum->data.ptr + equ_rect.y*sum->step) + equ_rect.x;
                pq1 = (int*)(sum->data.ptr + equ_rect.y*sum->step)
                            + equ_rect.x + equ_rect.width;
                pq2 = (int*)(sum->data.ptr + (equ_rect.y + equ_rect.height)*sum->step) + equ_rect.x;
                pq3 = (int*)(sum->data.ptr + (equ_rect.y + equ_rect.height)*sum->step)
                            + equ_rect.x + equ_rect.width;
            }
 
            cascade->hid_cascade->count = split_stage;                                                //分裂级
 
            for( pass = 0; pass < npass; pass++ )
            {
#ifdef _OPENMP
    #pragma omp parallel for num_threads(max_threads), schedule(dynamic)
#endif
                for( int _iy = 0; _iy < stop_height; _iy++ )
                {
                    int iy = cvRound(_iy*ystep);
                    int _ix, _xstep = 1;
                    int stop_width = cvRound((img->cols – win_size.width) / ystep);
                    uchar* mask_row = temp->data.ptr + temp->step * iy;
 
                    for( _ix = 0; _ix < stop_width; _ix += _xstep )
                    {
                        int ix = cvRound(_ix*ystep); // it really should be ystep
                   
                        if( pass == 0 )                                                   //第一次循环 做
                        {
                            int result;
                            _xstep = 2;
 
                            if( do_canny_pruning )                                                        //canny边缘检测
                            {
                                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 );                //匹配结果存到result里
                            if( result > 0 )
                            {
                                if( pass < npass – 1 )
                                    mask_row[ix] = 1;
                                else
                                {
                                    CvRect rect = cvRect(ix,iy,win_size.width,win_size.height);
#ifndef _OPENMP                                                                                                        //如果用OpenMP
                                    cvSeqPush( seq, &rect );                                                        //result 放到seq中
#else                                                                                                                        //如果不用OpenMP
                                    cvSeqPush( seq_thread[omp_get_thread_num()], &rect );                        //result放到seq_thread里
#endif
                                }
                            }
                            if( result < 0 )
                                _xstep = 1;
                        }
                        else if( mask_row[ix] )                     //不是第一次
                        {
                            int result = cvRunHaarClassifierCascade( cascade, cvPoint(ix,iy),
                                                                     stage_offset );
                            if( result > 0 )
                            {
                                if( pass == npass – 1 )          //如果是最后一次
                                {
                                    CvRect rect = cvRect(ix,iy,win_size.width,win_size.height);
#ifndef _OPENMP
                                    cvSeqPush( seq, &rect );
#else
                                    cvSeqPush( seq_thread[omp_get_thread_num()], &rect );
#endif
                                }
                            }
                            else
                                mask_row[ix] = 0;
                        }
                    }
                }
                stage_offset = cascade->hid_cascade->count;
                cascade->hid_cascade->count = cascade->count;
            }
        }
    }
 
#ifdef _OPENMP
// gather the results                                               //收集结果
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 );                  //结果输出到seq
}
#endif
 
    if( min_neighbors != 0 )
    {
        // group retrieved rectangles in order to filter out noise         收集找出的匹配块,过滤噪声
        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                                   计算相邻个数
        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                                    计算重心
        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 );                                   //结果输入到seq2
            }
        }
 
        // filter out small face rectangles inside large face rectangles                在大的面块中找出小的面块
        for( i = 0; i < seq2->total; i++ )                                                                                //在seq2中寻找
        {
            CvAvgComp r1 = *(CvAvgComp*)cvGetSeqElem( seq2, i );                //r1指向结果
            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 );      //添加r1到返回结果.
                /* cvSeqPush( result_seq, &r1.rect ); */
            }
        }
    }
 
    __END__;

第二部分  cvRunHaarClassifierCascade

#ifdef _OPENMP
for( i = 0; i < max_threads; i++ )
{
if( seq_thread[i] )
            cvReleaseMemStorage( &seq_thread[i]->storage );                //如果使用了OpenMP就释放使用的seq_thread
}
#endif
 
    cvReleaseMemStorage( &temp_storage );
    cvReleaseMat( &sum );
    cvReleaseMat( &sqsum );
    cvReleaseMat( &tilted );                                                                //释放使用的空间
    cvReleaseMat( &temp );
    cvReleaseMat( &sumcanny );
    cvReleaseMat( &norm_img );
    cvReleaseMat( &img_small );
    cvFree( &comps );
 
    return result_seq;                                                                                //返回结果
}
 
下面是cvRunHaarClassifierCascade的：
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;
    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
}