OpenCV背景去除的几种方法

1、肤色侦测法
   肤色提取是基于人机互动方面常见的方法。因为肤色是人体的一大特征，它可以迅速从复杂的背景下分离出自己的特征区域。一下介绍两种常见的肤色提取：

（1）HSV空间的肤色提取
     HSV色彩空间是一个圆锥形的模型，具体如右图所示：
 色相（H）是色彩的基本属性，就是平常说的颜色名称，例如红色、黄色等，
依照右图的标准色轮上的位置，取360度得数值。（也有0~100%的方法确定） 饱和度（S）是色彩的纯度，越高色彩越纯，低则变灰。取值为0~100%。明度（V）也叫亮度，取值0~100。
     根据肤色在HSV三个分量上的值，就可以简单的侦测出一张图像上肤色的部分。一下是肤色侦测函数的源代码：
void skinDetectionHSV(IplImage* pImage,int lower,int upper,IplImage* process){IplImage* pImageHSV = NULL;IplImage* pImageH = NULL;IplImage* pImageS = NULL;IplImage* pImageProcessed = NULL;IplImage* tmpH = NULL;IplImage* tmpS = NULL;static IplImage* pyrImage = NULL;CvSize imgSize;imgSize.height = pImage->height;imgSize.width = pImage->width ;//create you want to use image and give them memory allocationpImageHSV = cvCreateImage(imgSize,IPL_DEPTH_8U,3);pImageH = cvCreateImage(imgSize,IPL_DEPTH_8U,1);pImageS = cvCreateImage(imgSize,IPL_DEPTH_8U,1);tmpS = cvCreateImage(imgSize,IPL_DEPTH_8U,1);tmpH = cvCreateImage(imgSize,IPL_DEPTH_8U,1);pImageProcessed = cvCreateImage(imgSize,IPL_DEPTH_8U,1);pyrImage = cvCreateImage(cvSize(pImage->width/2,pImage->height/2),IPL_DEPTH_8U,1);//convert RGB image to HSV imagecvCvtColor(pImage,pImageHSV,CV_BGR2HSV);//Then split HSV to three single channel imagescvCvtPixToPlane(pImageHSV,pImageH,pImageS,NULL,NULL);//The skin scalar range in H and S,Do they AND algorithmcvInRangeS(pImageH,cvScalar(0.0,0.0,0,0),cvScalar(lower,0.0,0,0),tmpH);cvInRangeS(pImageS,cvScalar(26,0.0,0,0),cvScalar(upper,0.0,0,0),tmpS);cvAnd(tmpH,tmpS,pImageProcessed,0);////cvPyrDown(pImageProcessed,pyrImage,CV_GAUSSIAN_5x5);//cvPyrUp(pyrImage,pImageProcessed,CV_GAUSSIAN_5x5);//Erode and dilatecvErode(pImageProcessed,pImageProcessed,0,2);cvDilate(pImageProcessed,pImageProcessed,0,1);cvCopy(pImageProcessed,process,0);//do cleancvReleaseImage(&pyrImage);cvReleaseImage(&pImageHSV);cvReleaseImage(&pImageH);cvReleaseImage(&pImageS);cvReleaseImage(&pyrImage);cvReleaseImage(&tmpH);cvReleaseImage(&tmpS);cvReleaseImage(&pImageProcessed);}

 

 (2)YCrCb空间的肤色提取
   YCrCb也是一种颜色空间，也可以说是YUV的颜色空间。Y是亮度的分量，而肤色侦测是对亮度比较敏感的，由摄像头拍摄的RGB图像转化为YCrCb空间的话可以去除亮度对肤色侦测的影响。下面给出基于YCrCb肤色侦测函数的源代码：

void skinDetectionYCrCb(IplImage* imageRGB,int lower,int upper,IplImage* imgProcessed){ assert(imageRGB->nChannels==3);IplImage* imageYCrCb = NULL;IplImage* imageCb = NULL；imageYCrCb = cvCreateImage(cvGetSize(imageRGB),8,3);imageCb = cvCreateImage(cvGetSize(imageRGB),8,1);cvCvtColor(imageRGB,imageYCrCb,CV_BGR2YCrCb);cvSplit(imageYCrCb,0,0,imageCb,0);//Cbfor (int h=0;h<imageCb->height;h++) {for (int w=0;w<imageCb->width;w++) {unsigned char* p =(unsigned char*)(imageCb->imageData+h*imageCb->widthStep+w);if (*p<=upper&&*p>=lower) {*p=255;} else {*p=0;}}}cvCopy(imageCb,imgProcessed,NULL);}

2、基于混合高斯模型去除背景法

   高斯模型去除背景法也是背景去除的一种常用的方法，经常会用到视频图像侦测中。这种方法对于动态的视频图像特征侦测比较适合，因为模型中是前景和背景分离开来的。分离前景和背景的基准是判断像素点变化率，会把变化慢的学习为背景，变化快的视为前景。
//#include "stdafx.h"#include "cv.h"#include "highgui.h"#include "cxtypes.h"#include "cvaux.h"# include <iostream>using namespace std;int _tmain(int argc, _TCHAR* argv[]){ //IplImage* pFirstFrame = NULL;IplImage* pFrame = NULL;IplImage* pFrImg = NULL;IplImage* pBkImg = NULL;IplImage* FirstImg = NULL;static IplImage* pyrImg =NULL;CvCapture* pCapture = NULL;int nFrmNum = 0;int first = 0,next = 0;int thresh = 0;cvNamedWindow("video",0);//cvNamedWindow("background",0);cvNamedWindow("foreground",0);cvResizeWindow("video",400,400);cvResizeWindow("foreground",400,400);//cvCreateTrackbar("thresh","foreground",&thresh,255,NULL);//cvMoveWindow("background",360,0);//cvMoveWindow("foregtound",0,0);if(!(pCapture = cvCaptureFromCAM(1))){printf("Could not initialize camera , please check it !");return -1;}CvGaussBGModel* bg_model = NULL;while(pFrame = cvQueryFrame(pCapture)){nFrmNum++;if(nFrmNum == 1){pBkImg = cvCreateImage(cvGetSize(pFrame),IPL_DEPTH_8U,3);pFrImg = cvCreateImage(cvGetSize(pFrame),IPL_DEPTH_8U,1);FirstImg = cvCreateImage(cvGetSize(pFrame),IPL_DEPTH_8U,1);pyrImg = cvCreateImage(cvSize(pFrame->width/2,pFrame->height/2),IPL_DEPTH_8U,1);CvGaussBGStatModelParams params;params.win_size = 2000; //Learning rate = 1/win_size;params.bg_threshold = 0.7; //Threshold sum of weights for background testparams.weight_init = 0.05;params.variance_init = 30;params.minArea = 15.f;params.n_gauss = 5;//= K =Number of gaussian in mixtureparams.std_threshold = 2.5;//cvCopy(pFrame,pFirstFrame,0);bg_model = (CvGaussBGModel*)cvCreateGaussianBGModel(pFrame,¶ms);}else{int regioncount = 0;int totalNum = pFrImg->width *pFrImg->height ;cvSmooth(pFrame,pFrame,CV_GAUSSIAN,3,0,0,0);cvUpdateBGStatModel(pFrame,(CvBGStatModel*)bg_model,-0.00001);cvCopy(bg_model->foreground ,pFrImg,0);cvCopy(bg_model->background ,pBkImg,0);//cvShowImage("background",pBkImg);//cvSmooth(pFrImg,pFrImg,CV_GAUSSIAN,3,0,0,0);//cvPyrDown(pFrImg,pyrImg,CV_GAUSSIAN_5x5);//cvPyrUp(pyrImg,pFrImg,CV_GAUSSIAN_5x5);//cvSmooth(pFrImg,pFrImg,CV_GAUSSIAN,3,0,0,0);cvErode(pFrImg,pFrImg,0,1);cvDilate(pFrImg,pFrImg,0,3);//pBkImg->origin = 1;//pFrImg->origin = 1;cvShowImage("video",pFrame);cvShowImage("foreground",pFrImg);//cvReleaseBGStatModel((CvBGStatModel**)&bg_model);//bg_model = (CvGaussBGModel*)cvCreateGaussianBGModel(pFrame,0);/*//catch target frameif(nFrmNum>10 &&(double)cvSumImage(pFrImg)>0.3 * totalNum){first = cvSumImage(FirstImg);next = cvSumImage(pFrImg);printf("Next number is :%d /n",next);cvCopy(pFrImg,FirstImg,0);}cvShowImage("foreground",pFrImg);cvCopy(pFrImg,FirstImg,0);*/if(cvWaitKey(2)== 27){break;}}}cvReleaseBGStatModel((CvBGStatModel**)&bg_model);cvDestroyAllWindows();cvReleaseImage(&pFrImg);cvReleaseImage(&FirstImg);cvReleaseImage(&pFrame);cvReleaseImage(&pBkImg);cvReleaseCapture(&pCapture);return 0;}

 

3、背景相减背景去除方法

   所谓的背景相减，是指把摄像头捕捉的图像第一帧作为背景，以后的每一帧都减去背景帧，这样减去之后剩下的就是多出来的特征物体（要侦测的物体）的部分。但是相减的部分也会对特征物体的灰阶值产生影响，一般是设定相关阈值要进行判断。以下是代码部分：

 int _tmain(int argc, _TCHAR* argv[]){int thresh_low = 30;IplImage* pImgFrame = NULL; IplImage* pImgProcessed = NULL;IplImage* pImgBackground = NULL;IplImage* pyrImage = NULL;CvMat* pMatFrame = NULL;CvMat* pMatProcessed = NULL;CvMat* pMatBackground = NULL;CvCapture* pCapture = NULL;cvNamedWindow("video", 0);cvNamedWindow("background",0);cvNamedWindow("processed",0);//Create trackbarcvCreateTrackbar("Low","processed",&thresh_low,255,NULL);cvResizeWindow("video",400,400);cvResizeWindow("background",400,400);cvResizeWindow("processed",400,400);cvMoveWindow("video", 0, 0);cvMoveWindow("background", 400, 0);cvMoveWindow("processed", 800, 0);if( !(pCapture = cvCaptureFromCAM(1))){fprintf(stderr, "Can not open camera./n");return -2;}//first framepImgFrame = cvQueryFrame( pCapture );pImgBackground = cvCreateImage(cvSize(pImgFrame->width, pImgFrame->height), IPL_DEPTH_8U,1);pImgProcessed = cvCreateImage(cvSize(pImgFrame->width, pImgFrame->height), IPL_DEPTH_8U,1);pyrImage = cvCreateImage(cvSize(pImgFrame->width/2, pImgFrame->height/2), IPL_DEPTH_8U,1);pMatBackground = cvCreateMat(pImgFrame->height, pImgFrame->width, CV_32FC1);pMatProcessed = cvCreateMat(pImgFrame->height, pImgFrame->width, CV_32FC1);pMatFrame = cvCreateMat(pImgFrame->height, pImgFrame->width, CV_32FC1);cvSmooth(pImgFrame, pImgFrame, CV_GAUSSIAN, 3, 0, 0);cvCvtColor(pImgFrame, pImgBackground, CV_BGR2GRAY);cvCvtColor(pImgFrame, pImgProcessed, CV_BGR2GRAY);cvConvert(pImgProcessed, pMatFrame);cvConvert(pImgProcessed, pMatProcessed);cvConvert(pImgProcessed, pMatBackground);cvSmooth(pMatBackground, pMatBackground, CV_GAUSSIAN, 3, 0, 0);while(pImgFrame = cvQueryFrame( pCapture )){cvShowImage("video", pImgFrame);cvSmooth(pImgFrame, pImgFrame, CV_GAUSSIAN, 3, 0, 0);cvCvtColor(pImgFrame, pImgProcessed, CV_BGR2GRAY);cvConvert(pImgProcessed, pMatFrame);cvSmooth(pMatFrame, pMatFrame, CV_GAUSSIAN, 3, 0, 0);cvAbsDiff(pMatFrame, pMatBackground, pMatProcessed);//cvConvert(pMatProcessed,pImgProcessed);//cvThresholdBidirection(pImgProcessed,thresh_low);cvThreshold(pMatProcessed, pImgProcessed, 30, 255.0, CV_THRESH_BINARY);cvPyrDown(pImgProcessed,pyrImage,CV_GAUSSIAN_5x5);cvPyrUp(pyrImage,pImgProcessed,CV_GAUSSIAN_5x5);//Erode and dilatecvErode(pImgProcessed, pImgProcessed, 0, 1);cvDilate(pImgProcessed, pImgProcessed, 0, 1);//background updatecvRunningAvg(pMatFrame, pMatBackground, 0.0003, 0);cvConvert(pMatBackground, pImgBackground);cvShowImage("background", pImgBackground);cvShowImage("processed", pImgProcessed);//cvZero(pImgProcessed);if( cvWaitKey(10) == 27 ){break;}}cvDestroyWindow("video");cvDestroyWindow("background");cvDestroyWindow("processed");cvReleaseImage(&pImgProcessed);cvReleaseImage(&pImgBackground);cvReleaseMat(&pMatFrame);cvReleaseMat(&pMatProcessed);cvReleaseMat(&pMatBackground);cvReleaseCapture(&pCapture);return 0;}