opencv图像处理5--车辆检测

void CTrafficsampleDlg::OnOK() //读入视频并检测车辆

{

// TODO: Add extra validation here

//此处添加图像处理的代码

//CDialog::OnOK();

IplImage *frame = NULL; //定义帧,每帧就是一张图

IplImage *B_part = NULL, *G_part = NULL, *R_part = NULL;

IplImage *pFrImg = NULL; //前景图片

IplImage *pBkImg = NULL; //背景图片

IplImage *dst = NULL;

CvMemStorage* storage = cvCreateMemStorage(0);

CvSeq* contour = 0;



//轮廓矩形

CvRect contourRect = cvRect(0,0,0,0);

//可以获取视频的各种属性

CvCapture * pCapture = cvCreateFileCapture("traffic.avi");

cvNamedWindow("Camera",CV_WINDOW_AUTOSIZE); //设置窗口

cvNamedWindow("temp", CV_WINDOW_AUTOSIZE);

cvNamedWindow("background",1);

cvNamedWindow("foreground",1);

cvNamedWindow("contours",1);



cvMoveWindow("background", 360, 0);

cvMoveWindow("foreground", 690, 0);

//初始化高斯混合模型参数

CvGaussBGModel *bg_model=NULL;

int count = 1;

while (frame = cvQueryFrame(pCapture))

{

if(count ==1)

{

B_part = cvCreateImage(cvGetSize(frame),frame->depth,1);

G_part = cvCreateImage(cvGetSize(frame),frame->depth,1);

R_part = cvCreateImage(cvGetSize(frame),frame->depth,1);

pBkImg = cvCreateImage(cvSize(frame->width, frame->height), IPL_DEPTH_8U,3);

pFrImg = cvCreateImage(cvSize(frame->width, frame->height), IPL_DEPTH_8U,1);

dst = cvCreateImage( cvGetSize(frame), 8, 3 );//找到轮廓并绘制轮廓

bg_model = (CvGaussBGModel*)cvCreateGaussianBGModel(frame, 0);

}

else

{

cvUpdateBGStatModel(frame, (CvBGStatModel *)bg_model );//更新高斯背景模型

//pFrImg为前景图像，只能为单通道

//pBkImg为背景图像，可以为单通道或与pFrame通道数相同

cvCopy(bg_model->foreground,pFrImg,0);

cvCopy(bg_model->background,pBkImg,0);

//把图像正过来

pBkImg->origin=1;

pFrImg->origin=1;

cvSplit(frame , B_part,G_part, R_part,0);

cvFlip(B_part,B_part,0);//沿X轴翻转一次

//下面进行阈值分割

//如何确定阈值？？是否存在编好的函数呢？

//固定阈值分割方式，会导致一张图片中如果亮暗不均，一张图的不同区域分割效果差距很大

//如果能够提前提取出图像的背景，然后想减的方式会使分割效果变好

cvThreshold(B_part,B_part, 195, 255,CV_THRESH_BINARY);

//阈值分割之后，进行膨胀操作和腐蚀操作

//默认的模板由于腐蚀和膨胀的幅度比较大，效果不是很好啊

//最好是自定义模板

//观察白线大多为竖直的，腐蚀时主要是横向腐蚀

//车体车头和车身由于在前玻璃的分割作用，数要是在竖向，膨胀时主要在竖向，但是需避免造成两车的太近而融为一体

IplConvKernel * kenel = cvCreateStructuringElementEx(3,1,1,0,CV_SHAPE_RECT,NULL);

IplConvKernel * kenel2 = cvCreateStructuringElementEx(1,3,0,1,CV_SHAPE_RECT,NULL);

cvErode(B_part, B_part,kenel,1 );

cvDilate(B_part,B_part,kenel,1);

cvErode(B_part, B_part,kenel2, 1);

vErode(B_part, B_part,kenel,1 );



cvDilate(B_part,B_part,kenel2,2);

cvDilate(B_part,B_part,kenel2,2);

cvDilate(B_part,B_part,NULL,1);

//获取并绘制轮廓

cvFindContours( B_part, storage, &contour, sizeof(CvContour), CV_RETR_CCOMP, CV_CHAIN_APPROX_SIMPLE );

cvZero( dst );

for( ; contour != 0; contour = contour->h_next )

{

contourRect = cvBoundingRect(contour,0);

if((contourRect.width > 10) &&(contourRect.height >12)){

CvScalar color = CV_RGB( rand()&255, rand()&255, rand()&255 );//每次都随机颜色显示

/* 用1替代 CV_FILLED 所指示的轮廓外形 */

cvDrawContours( dst, contour, color, color, -1, CV_FILLED, 8 );

//cvRectangle(pFrame,pt3,pt4,CV_RGB(255,0,0),1, 8, 0 );

cvRectangle(dst ,cvPoint(contourRect.x, contourRect.y),

cvPoint(contourRect.x + contourRect.width, contourRect.y + contourRect.height),

CV_RGB(255,0,0),

1,

8,

0);

}



}








cvShowImage("Camera", frame);

cvShowImage("temp" ,B_part);

cvShowImage("background", pBkImg);

cvShowImage("foreground", pFrImg);

cvShowImage( "contours", dst );

int key1;

key1 = cvWaitKey (300);

if (key1 == 'q' || key1 == 'Q')

break;

}

count++;

}

cvDestroyWindow("temp");

cvReleaseCapture(&pCapture); //销毁摄像头

cvDestroyWindow("Camera"); //销毁窗口

cvReleaseBGStatModel((CvBGStatModel**)&bg_model);

cvDestroyWindow("background");

cvDestroyWindow("foreground");

cvReleaseImage(&pFrImg);

cvReleaseImage(&pBkImg);

cvReleaseImage(&dst);

cvDestroyWindow("contours");

//CTrafficsampleDlg::OnOK();

}