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cvRunHaarClassifierCasca
cvRunHaarClassifierCasca
函数 cvRunHaarClassifierCasca
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cvRunHaarClassifierCasca
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
//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）
//winsize
CvSize sz = { cvRound( img->cols/factor ), cvRound( img->rows/factor ) }; //sz 图像行列（缩小factor倍）
三个Cvsize
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
（int） 初始值=1
win_size0.height – icv_object_win_border*2 };
//矩形框rect1
//矩形框rect1
CvMat img1, sum1, sqsum1, norm1, tilted1, mask1;
//多通道矩阵
//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
//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
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
//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
//mask1 灰度图
cvResize( img, &img1, CV_INTER_LINEAR );
//img双线性插值 输出到img1
//img双线性插值 输出到img1
cvIntegral( &img1, &sum1, &sqsum1, _tilted );
//计算积分图像
//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)
//如果ipp=true
{
positive = mask1.cols*mask1.rows;
//mask1长乘宽–>positive
//mask1长乘宽–>positive
cvSet( &mask1, cvScalarAll(255) );
//mask1赋值为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;
//ipp=false;
break;
}
if( positive <= 0 )
break;
}
}
if( !use_ipp )
//如果ipp=false
//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.
0;
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 };
win_size.width, win_size.height };
cvSeqPush( seq, &obj_rect );
//将匹配块放到seq中
//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
//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 )};
* factor )};