OpenCV背景去除的几种方法(转)

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;
}