一、RGB color space
检测代码如下：
void SkinRGB(IplImage* src,IplImage* dst) { //RGB颜色空间 //均匀照明：R>95,G>40,B>20,R-B>15,R-G>15,R>B%R //侧向照明:R>200,G>210,B>170,R-B<=15,R>B,G>B int height = src->height, width = src->width, channel = src->nChannels, step = src->widthStep; int b = 0, g = 1, r = 2; cvZero(dst); unsigned char* p_src = (unsigned char*)src->imageData; unsigned char* p_dst = (unsigned char*)dst->imageData; for(int j = 0; j < height; j++){ for(int i = 0; i < width; i++){ if((p_src[j*step+i*channel+r] > 95 && p_src[j*step+i*channel+g] > 40 && p_src[j*step+i*channel+b] > 20 && (p_src[j*step+i*channel+r] - p_src[j*step+i*channel+b]) > 15 && (p_src[j*step+i*channel+r] - p_src[j*step+i*channel+g]) > 15) || (p_src[j*step+i*channel+r] > 200 && p_src[j*step+i*channel+g] > 210 && p_src[j*step+i*channel+b] > 170 && (p_src[j*step+i*channel+r] - p_src[j*step+i*channel+b]) <= 15 && p_src[j*step+i*channel+r] > p_src[j*step+i*channel+b] && p_src[j*step+i*channel+g] > p_src[j*step+i*channel+b])) p_dst[j*width+i]=255; } } }
二、二次多项式模式检测（RG color space）
void cvSkinRG(IplImage* rgb,IplImage* gray) { assert(rgb->nChannels==3&&gray->nChannels==1); const int R=2; const int G=1; const int B=0; double Aup=-1.8423; double Bup=1.5294; double Cup=0.0422; double Adown=-0.7279; double Bdown=0.6066; double Cdown=0.1766;
for (int h=0;h<rgb->height;h++) { unsigned char* pGray=(unsigned char*)gray->imageData+h*gray->widthStep; unsigned char* pRGB=(unsigned char* )rgb->imageData+h*rgb->widthStep; for (int w=0;w<rgb->width;w++){ int s=pRGB[R]+pRGB[G]+pRGB[B]; double r=(double)pRGB[R]/s; double g=(double)pRGB[G]/s; double Gup=Aup*r*r+Bup*r+Cup; double Gdown=Adown*r*r+Bdown*r+Cdown; double Wr=(r-0.33)*(r-0.33)+(g-0.33)*(g-0.33); if (g<Gup && g>Gdown && Wr>0.004){ *pGray=255; } else{ *pGray=0; } pGray++; pRGB+=3; } } }
三、Ycrcb之cr分量+otsu阈值化
原理： a.将RGB图像转换到YCrCb颜色空间，提取Cr分量图像
b.对Cr做自适应二值化处理（Ostu法）
void cvSkinOtsu(IplImage* src, IplImage* dst) { //Cr自适应阈值法 // IplImage* img_ycrcb=cvCreateImage(cvGetSize(src),IPL_DEPTH_8U,3); IplImage* img_cr=cvCreateImage(cvGetSize(src),IPL_DEPTH_8U,1); cvCvtColor(src,img_ycrcb,CV_BGR2YCrCb); cvSplit(img_ycrcb,0,img_cr,0,0); cvThresholdOtsu(img_cr,img_cr); cvCopy(img_cr,dst); cvReleaseImage(&img_ycrcb); cvReleaseImage(&img_cr); } void cvThresholdOtsu(IplImage* src, IplImage* dst) { int height=src->height,width=src->width,threshold=0; double histogram[256]={0}; double average=0.0,max_variance=0.0,w=0.0,u=0.0; IplImage* temp=cvCreateImage(cvGetSize(src),src->depth,1); if(src->nChannels!=1)cvCvtColor(src,temp,CV_BGR2GRAY); else cvCopy(src,temp); unsigned char* p_temp=(unsigned char*)temp->imageData; //计算灰度直方图 // for(int j=0;j<height;j++) { for(int i=0;i<width;i++) { histogram[p_temp[j*width+i]]++; } } for(int i=0;i<256;i++)histogram[i]=histogram[i]/(double)(height*width); //计算平局值 for(int i=0;i<256;i++)average+=i*histogram[i]; for(int i=0;i<256;i++) { w+=histogram[i]; u+=i*histogram[i]; double t=average*w-u; double variance=t*t/(w*(1-w)); if(variance>max_variance) { max_variance=variance; threshold=i; } } cvThreshold(temp,dst,threshold,255,CV_THRESH_BINARY); cvReleaseImage(&temp); }
四、OpenCV自带肤色检测类——CvAdaptiveSkinDetector
通过颜色阈值分割肤色部分，皮肤检测算法是在HSV空间进行。流程如下：
//构造函数 CvAdaptiveSkinDetector(int samplingDivider = 1, int morphingMethod = MORPHING_METHOD_NONE); /* 参数1：样本采样的间隔，默认情况下为1，即表示不进行降采样 参数2：图形学操作方式，即对用皮肤检测后的图像进行图形学操作。其取值有3种可能——MORPHING_METHOD_ERODE（只进行一次腐蚀操作）；MORPHING_METHOD_ERODE_ERODE（连续进行2次腐蚀操作）；MORPHING_METHOD_ERODE_DILATE（先进行一次腐蚀操作，后进行一次膨胀操作） */
virtual void process(IplImage *inputBGRImage, IplImage *outputHueMask); /* 参数1：需要进行皮肤检测的输入图像 参数2：输出皮肤的掩膜图像——值为1代表该像素为皮肤，值为0代表非皮肤。 */
PS：这个函数只有opencv的c版本的，因为CvAdaptiveSkinDetector这个类放在opencv源码里的contrib目录里，即表示比较新的但不成熟的算法。
#include <opencvcv.h> #include <opencvhighgui.h> #include <contribcontrib.hpp> #include <corecore.hpp> #include <imgprocimgproc.hpp> #include<time.h> int main() {
CvCapture* capture=cvCreateCameraCapture(0); cvNamedWindow("Input Video",1); cvNamedWindow("Output Video",1); IplImage* img_src=NULL; IplImage* input_img=NULL; IplImage* output_mask=NULL; IplImage* output_img=NULL; clock_t start,finish; double duration; CvAdaptiveSkinDetector skin_detector(1,CvAdaptiveSkinDetector::MORPHING_METHOD_ERODE_DILATE); //定义肤色检测算子 while(1)
{ img_src=cvQueryFrame(capture); if(!img_src) break; cvShowImage("Input Video",img_src); if(input_img==NULL){ input_img=cvCreateImage(cvGetSize(img_src),img_src->depth,img_src->nChannels); } cvCopy(img_src,input_img); output_img=cvCreateImage(cvGetSize(img_src),img_src->depth,img_src->nChannels); cvZero(output_img); if(output_mask==NULL){ output_mask=cvCreateImage(cvGetSize(img_src),img_src->depth,1); } //肤色检测 // start=clock(); skin_detector.process(input_img,output_mask); finish=clock(); duration=(double)(finish-start)/CLOCKS_PER_SEC; printf("elapsed time :%.0f 毫秒 ",duration*1000); cvCopy(img_src,output_img,output_mask); cvShowImage("Output Video",output_img); char c=cvWaitKey(33); if(c==27)break; } cvReleaseCapture(&capture); cvDestroyWindow("Video"); }
五、HSV检测
void cvSkinHSV(IplImage* src,IplImage* dst) { IplImage* hsv=cvCreateImage(cvGetSize(src),8,3); //IplImage* cr=cvCreateImage(cvGetSize(src),8,1); //IplImage* cb=cvCreateImage(cvGetSize(src),8,1); cvCvtColor(src,hsv,CV_BGR2HSV); //cvSplit(ycrcb,0,cr,cb,0); static const int V=2; static const int S=1; static const int H=0; //IplImage* dst=cvCreateImage(cvGetSize(_dst),8,3); cvZero(dst); for (int h=0;h<src->height;h++) { unsigned char* phsv=(unsigned char*)hsv->imageData+h*hsv->widthStep; unsigned char* psrc=(unsigned char*)src->imageData+h*src->widthStep; unsigned char* pdst=(unsigned char*)dst->imageData+h*dst->widthStep; for (int w=0;w<src->width;w++) { if (phsv[H]>=7&&phsv[H]<=29) { memcpy(pdst,psrc,3); } phsv+=3; psrc+=3; pdst+=3; } } //cvCopyImage(dst,_dst); //cvReleaseImage(&dst); }