OpenCV背景去除的几种方法

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 allocation
    pImageHSV = 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 image
    cvCvtColor(pImage,pImageHSV,CV_BGR2HSV);

    //Then split HSV to three single channel images
    cvCvtPixToPlane(pImageHSV,pImageH,pImageS,NULL,NULL);
    //The skin scalar range in H and S, Do they AND algorithm
    cvInRangeS(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 dilate
    cvErode(pImageProcessed,pImageProcessed,0,2);
    cvDilate(pImageProcessed,pImageProcessed,0,1);

    cvCopy(pImageProcessed,process,0);
    //do clean
    cvReleaseImage(&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);//Cb
    for (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 test
            params.weight_init = 0.05;
            params.variance_init = 30;
            params.minArea = 15.f;
            params.n_gauss = 5; //= K =Number of gaussian in mixture
            params.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 frame
            if(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 trackbar
    cvCreateTrackbar("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 frame
    pImgFrame = 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 dilate
        cvErode(pImgProcessed, pImgProcessed, 0, 1);
        cvDilate(pImgProcessed, pImgProcessed, 0, 1);

        //background update
        cvRunningAvg(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;
}