图像肤色初步检測实现
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肤色检測输出结果中有很多瑕疵，待于进一步处理（如：滤波操作.....）。
在此贴出几种图像肤色检測相关代码，供大家參考。
第一种：RGB color space
// skin region location using rgb limitation
void ImageSkin::ImageSkinRGB(IplImage* rgb,IplImage* _dst)
{
assert(rgb->nChannels==3&& _dst->nChannels==3);
static const int R=2;
static const int G=1;
static const int B=0;
IplImage* dst=cvCreateImage(cvGetSize(_dst),8,3);
cvZero(dst);
for (int h=0;h<rgb->height;h++) {
unsigned char* prgb=(unsigned char*)rgb->imageData+h*rgb->widthStep;
unsigned char* pdst=(unsigned char*)dst->imageData+h*dst->widthStep;
for (int w=0;w<rgb->width;w++) {
if ((prgb[R]>95 && prgb[G]>40 && prgb[B]>20 &&
prgb[R]-prgb[B]>15 && prgb[R]-prgb[G]>15)||//uniform illumination
(prgb[R]>200 && prgb[G]>210 && prgb[B]>170 &&
abs(prgb[R]-prgb[B])<=15 && prgb[R]>prgb[B]&& prgb[G]>prgb[B])
) {
memcpy(pdst,prgb,3);
}
prgb+=3;
pdst+=3;
}
}
cvCopyImage(dst,_dst);
cvReleaseImage(&dst);
}
另外一种：RG color space
// skin detection in rg space
void ImageSkin::ImageSkinRG(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;
}
}
}
第三种：otsu阈值化
// reference: Rafael C. Gonzalez. Digital Image Processing Using MATLAB
void ImageSkin::ImageThresholdOtsu(IplImage* src, IplImage* dst)
{
int height=src->height;
int width=src->width;
//histogram
float histogram[256]={0};
for(int i=0;i<height;i++) {
unsigned char* p=(unsigned char*)src->imageData+src->widthStep*i;
for(int j=0;j<width;j++) {
histogram[*p++]++;
}
}
//normalize histogram
int size=height*width;
for(int i=0;i<256;i++) {
histogram[i]=histogram[i]/size;
}
//average pixel value
float avgValue=0;
for(int i=0;i<256;i++) {
avgValue+=i*histogram[i];
}
int threshold;
float maxVariance=0;
float w=0,u=0;
for(int i=0;i<256;i++) {
w+=histogram[i];
u+=i*histogram[i];
float t=avgValue*w-u;
float variance=t*t/(w*(1-w));
if(variance>maxVariance) {
maxVariance=variance;
threshold=i;
}
}
cvThreshold(src,dst,threshold,255,CV_THRESH_BINARY);
} 第四种：Ycrcb之cr分量+otsu阈值化
void ImageSkin::ImageSkinOtsu(IplImage* src, IplImage* dst)
{
assert(dst->nChannels==1&& src->nChannels==3);
IplImage* ycrcb=cvCreateImage(cvGetSize(src),8,3);
IplImage* cr=cvCreateImage(cvGetSize(src),8,1);
cvCvtColor(src,ycrcb,CV_BGR2YCrCb);
cvSplit(ycrcb,0,cr,0,0);
ImageThresholdOtsu(cr,cr);
cvCopyImage(cr,dst);
cvReleaseImage(&cr);
cvReleaseImage(&ycrcb);
}
第五种：YCrCb中133<=Cr<=173 77<=Cb<=127
void ImageSkin::ImageSkinYUV(IplImage* src,IplImage* dst)
{
IplImage* ycrcb=cvCreateImage(cvGetSize(src),8,3);
//IplImage* cr=cvCreateImage(cvGetSize(src),8,1);
//IplImage* cb=cvCreateImage(cvGetSize(src),8,1);
cvCvtColor(src,ycrcb,CV_BGR2YCrCb);
//cvSplit(ycrcb,0,cr,cb,0);
static const int Cb=2;
static const int Cr=1;
static const int Y=0;
//IplImage* dst=cvCreateImage(cvGetSize(_dst),8,3);
cvZero(dst);
for (int h=0;h<src->height;h++) {
unsigned char* pycrcb=(unsigned char*)ycrcb->imageData+h*ycrcb->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 (pycrcb[Cr]>=133&&pycrcb[Cr]<=173&&pycrcb[Cb]>=77&&pycrcb[Cb]<=127)
{
memcpy(pdst,psrc,3);
}
pycrcb+=3;
psrc+=3;
pdst+=3;
}
}
//cvCopyImage(dst,_dst);
//cvReleaseImage(&dst);
}
主程序測试
IplImage* img= cvLoadImage("test.jpg");
IplImage* dstRGB=cvCreateImage(cvGetSize(img),8,3);
IplImage* dstRG=cvCreateImage(cvGetSize(img),8,1);
IplImage* dst_crotsu=cvCreateImage(cvGetSize(img),8,1);
IplImage* dst_YUV=cvCreateImage(cvGetSize(img),8,3);
cvNamedWindow("Original WIN", CV_WINDOW_AUTOSIZE);
cvShowImage("Original WIN", img);
cvWaitKey(0);
ImageSkin ImgS;
ImgS.ImageSkinRGB(img,dstRGB);
cvNamedWindow("ImageSkin WIN", CV_WINDOW_AUTOSIZE);
cvShowImage("ImageSkin WIN", dstRGB);
cvWaitKey(0);
ImgS.ImageSkinRG(img,dstRG);
cvNamedWindow("ImageSkinRG WIN", CV_WINDOW_AUTOSIZE);
cvShowImage("ImageSkinRG WIN", dstRG);
cvWaitKey(0);
ImgS.ImageSkinOtsu(img,dst_crotsu);
cvNamedWindow("ImageSkinOtsu WIN", CV_WINDOW_AUTOSIZE);
cvShowImage("ImageSkinOtsu WIN", dst_crotsu);
cvWaitKey(0);
ImgS.ImageSkinYUV(img,dst_YUV);
cvNamedWindow("ImageSkinYUV WIN", CV_WINDOW_AUTOSIZE);
cvShowImage("ImageSkinYUV WIN", dst_YUV); 关于Image Engineering & Computer Vision很多其它讨论与交流，敬请关注本博客和新浪微博songzi_tea.
