OpenCV背景去除的几种方法

 

本文转自 http://www.cnblogs.com/mfryf/archive/2012/03/29/2424024.html

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。

 

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;
}