OpenCV编程-肤色检测

本文主要是介绍OpenCV编程-肤色检测，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

最近搞色彩认知计算，对于肤色检测也是兴趣使然。

我们在处理图像时，首先考虑的就是去噪，但是去噪不仅是去掉噪声，也需要考虑光照对图像造成的噪声，所以如果硬件跟得上的话，可以引入颜色恒常性计算来去噪，之后开始正题吧。

肤色检测说起来很简单，只要是色彩的空间分离，在这个过程中难免有误检，如果对目标能进行有监督的框定，再进行肤色检测，效果应该好很多。

接下来贴代码：

  
void SkinDetect(IplImage* src,IplImage* dst)   
{   // 创建图像头IplImage* hsv = cvCreateImage(cvGetSize(src), IPL_DEPTH_8U, 3);//用于存图像的个中间变量，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D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);   // 高斯模糊 cvSmooth(src,src_tmp1,CV_GAUSSIAN,3,3); //高斯模糊   // hue色度saturation饱和度value纯度   cvCvtColor(src_tmp1, hsv, CV_BGR2HSV );//颜色转换 cvSplit(hsv,H,S,V,0);//分为3个通道   /*********************肤色检测部分·**************/  cvInRangeS(H,cvScalar(26.0,0.0,0,0),cvScalar(34.0,0.0,0,0),tmpH1);   cvInRangeS(S,cvScalar(43.0,0.0,0,0),cvScalar(255.0,0.0,0,0),tmpS1);   cvAnd(tmpH1,tmpS1,tmpH1,0);   // Red Hue with Low Saturation   // Hue 0 to 26 degree and Sat 20 to 90   cvInRangeS(H,cvScalar(11.0,0.0,0,0),cvScalar(25.0,0.0,0,0),tmpH2);    cvInRangeS(S,cvScalar(43.0,0.0,0,0),cvScalar(255.0,0.0,0,0),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);   int avg,white=0,black=0;CvScalar s;for(int i=0;i<dst->height;i++){for (int j = 0;j<dst->width;j++){s = cvGet2D(dst,i,j);if(s.val[0]==255)white++;elseblack++;}}printf("white = %dn",white);printf("black = %dn",black);cvReleaseImage(&hsv);   cvReleaseImage(&tmpH1);   cvReleaseImage(&tmpS1);   cvReleaseImage(&tmpH2);   cvReleaseImage(&tmpS2);   cvReleaseImage(&tmpH3);   cvReleaseImage(&tmpS3);   cvReleaseImage(&H);   cvReleaseImage(&S);   cvReleaseImage(&V);   cvReleaseImage(&src_tmp1);   
}

测试图像：