提取肤色信息原理及操作——opencv

网上也有很多的资料，讲述怎么提取肤色的，大致有5种方法。这几种方法转载/article/1943575.html

第一种：RGB color space

第二种：RG color space

第三种：Ycrcb之cr分量+otsu阈值化

第四种：YCrCb中133<=Cr<=173 77<=Cb<=127

第五种：HSV中 7<H<29

我来讲述一下提取肤色的原理。

这几种方法都不外乎一种操作，首先将图像的各个通道分离出来，如RGB RG Ycrcb以及HSV几种单通道，然后对在各种通道上的数据分析，数据在一定范围内的图像提取出来，其余的数据都赋值0，这其实就是所谓的阈值处理，然而这个阈值会对光照，背景甚至摄像头的性能的影响，需要自己调整参数。

示例1：使用了opencv的cvInRangeS函数处理各个通道的数据，因为摄像头不好所以使用了高斯模糊以平滑图像，最后各处理后的通道图像按位与，合成一个图像。

void CAIGesture::SkinDetect(IplImage* src,IplImage* dst)
{
IplImage* hsv = cvCreateImage(cvGetSize(src), IPL_DEPTH_8U, 3);//用于存图像的一个中间变量，是用来分通道用的，分成hsv通道
IplImage* tmpH1 = cvCreateImage( cvGetSize(src), IPL_DEPTH_8U, 1);//通道的中间变量，用于肤色检测的中间变量
IplImage* tmpS1 = cvCreateImage(cvGetSize(src), IPL_DEPTH_8U, 1);
IplImage* tmpH2 = cvCreateImage(cvGetSize(src), IPL_DEPTH_8U, 1);
IplImage* tmpS3 = cvCreateImage(cvGetSize(src), IPL_DEPTH_8U, 1);
IplImage* tmpH3 = cvCreateImage(cvGetSize(src), IPL_DEPTH_8U, 1);
IplImage* tmpS2 = cvCreateImage(cvGetSize(src), IPL_DEPTH_8U, 1);
IplImage* H = cvCreateImage( cvGetSize(src), IPL_DEPTH_8U, 1);
IplImage* S = cvCreateImage( cvGetSize(src), IPL_DEPTH_8U, 1);
IplImage* V = cvCreateImage( cvGetSize(src), IPL_DEPTH_8U, 1);
IplImage* src_tmp1=cvCreateImage(cvGetSize(src),8,3);
cvSmooth(src,src_tmp1,CV_GAUSSIAN,3,3); //高斯模糊
cvCvtColor(src_tmp1, hsv, CV_BGR2HSV );//颜色转换
cvCvtPixToPlane(hsv,H,S,V,0);//分为3个通道
/*********************肤色检测部分**************/
cvInRangeS(H,cvScalar(0.0,0.0,0,0),cvScalar(20.0,0.0,0,0),tmpH1);
cvInRangeS(S,cvScalar(75.0,0.0,0,0),cvScalar(200.0,0.0,0,0),tmpS1);
cvAnd(tmpH1,tmpS1,tmpH1,0);
// Red Hue with Low Saturation
// Hue 0 to 26 degree and Sat 20 to 90
cvInRangeS(H,cvScalar(0.0,0.0,0,0),cvScalar(13.0,0.0,0,0),tmpH2);
cvInRangeS(S,cvScalar(20.0,0.0,0,0),cvScalar(90.0,0.0,0,0),tmpS2);
cvAnd(tmpH2,tmpS2,tmpH2,0);

// Red Hue to Pink with Low Saturation
// Hue 340 to 360 degree and Sat 15 to 90
cvInRangeS(H,cvScalar(170.0,0.0,0,0),cvScalar(180.0,0.0,0,0),tmpH3);
cvInRangeS(S,cvScalar(15.0,0.0,0,0),cvScalar(90.,0.0,0,0),tmpS3);
cvAnd(tmpH3,tmpS3,tmpH3,0);

// Combine the Hue and Sat detections
cvOr(tmpH3,tmpH2,tmpH2,0);
cvOr(tmpH1,tmpH2,tmpH1,0);

cvCopy(tmpH1,dst);

}

示例2：

主要原理就是通过在Cb Cr空间上找到一个可以拟合常规肤色分布的椭圆形，然后把在椭圆形区域内的像素点标记为肤色。

其实代码很简单，就是把Y Cb Cr三个通道分开，然后用指针分别对这三个通道的每一个像素进行处理。

需要作修改的就是if(y<100) (*pMask)=(value<700) ? 255:0; else (*pMask)=(value<850)? 255:0; 这条做阈值判断的命令

void cvSkinSegment(IplImage* img, IplImage* mask){

CvSize imageSize = cvSize(img->width, img->height);

IplImage *imgY = cvCreateImage(imageSize, IPL_DEPTH_8U, 1);

IplImage *imgCr = cvCreateImage(imageSize, IPL_DEPTH_8U, 1);

IplImage *imgCb = cvCreateImage(imageSize, IPL_DEPTH_8U, 1);

IplImage *imgYCrCb = cvCreateImage(imageSize, img->depth, img->nChannels);

cvCvtColor(img,imgYCrCb,CV_BGR2YCrCb);

cvSplit(imgYCrCb, imgY, imgCr, imgCb, 0);

int y, cr, cb, l, x1, y1, value;

unsigned char *pY, *pCr, *pCb, *pMask;

pY = (unsigned char *)imgY->imageData;

pCr = (unsigned char *)imgCr->imageData;

pCb = (unsigned char *)imgCb->imageData;

pMask = (unsigned char *)mask->imageData;

cvSetZero(mask);

l = img->height * img->width;

for (int i = 0; i < l; i++){

y = *pY;

cr = *pCr;

cb = *pCb;

cb -= 109;

cr -= 152

;

x1 = (819*cr-614*cb)/32 + 51;

y1 = (819*cr+614*cb)/32 + 77;

x1 = x1*41/1024;

y1 = y1*73/1024;

value = x1*x1+y1*y1;

if(y<100) (*pMask)=(value<700) ? 255:0;

else (*pMask)=(value<850)? 255:0;

pY++;

pCr++;

pCb++;

pMask++;

}

cvReleaseImage(&imgY);

cvReleaseImage(&imgCr);

cvReleaseImage(&imgCb);

cvReleaseImage(&imgYCrCb);

}