首先,我这篇博客是基于别人博客的研究,创建自己的project来练习
1、肤色侦测法(每种方法提供完整源码下载链接)
下载链接:点击打开链接http://download.csdn.net/detail/chenxun2009/7731489
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
完整project下载:http://download.csdn.net/detail/chenxun2009/7731641
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);
- for (int h=0;hheight;h++)
- {
- for (int w=0;wwidth;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
-
- using namespace std;
-
-
- int _tmain(int argc, _TCHAR* argv[])
- {
-
- 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("foreground",0);
- cvResizeWindow("video",400,400);
- cvResizeWindow("foreground",400,400);
-
-
-
-
- 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;
- params.bg_threshold = 0.7;
- params.weight_init = 0.05;
- params.variance_init = 30;
- params.minArea = 15.f;
- params.n_gauss = 5;
- params.std_threshold = 2.5;
-
-
-
- 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);
-
-
-
-
-
-
- cvErode(pFrImg,pFrImg,0,1);
- cvDilate(pFrImg,pFrImg,0,3);
-
-
-
-
- cvShowImage("video",pFrame);
- cvShowImage("foreground",pFrImg);
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- 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);
-
- 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;
- }
-
-
- 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);
-
-
- cvThreshold(pMatProcessed, pImgProcessed, 30, 255.0, CV_THRESH_BINARY);
-
- cvPyrDown(pImgProcessed,pyrImage,CV_GAUSSIAN_5x5);
- cvPyrUp(pyrImage,pImgProcessed,CV_GAUSSIAN_5x5);
-
- cvErode(pImgProcessed, pImgProcessed, 0, 1);
- cvDilate(pImgProcessed, pImgProcessed, 0, 1);
-
-
- cvRunningAvg(pMatFrame, pMatBackground, 0.0003, 0);
- cvConvert(pMatBackground, pImgBackground);
-
-
- cvShowImage("background", pImgBackground);
- cvShowImage("processed", 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;
- }