本文实例为大家分享了OpenCV实现检测和追踪车辆的具体代码，供大家参考，具体内容如下
完整源码GitHub
- 使用高斯混合模型（BackgroundSubtractorMOG2）对背景建模，提取出前景
- 使用中值滤波去掉椒盐噪声，再闭运算和开运算填充空洞
- 使用cvBlob库追踪车辆，我稍微修改了cvBlob源码来通过编译
由于要对背景建模，这个方法要求背景是静止的
另外不同车辆白色区域不能连通，否则会认为是同一物体
void processVideo(char* videoFilename) {
Mat frame;
// current frame Mat fgMaskMOG2;
// fg mask fg mask generated by MOG2 method Mat bgImg;
// background Ptr<BackgroundSubtractorMOG2> pMOG2 = createBackgroundSubtractorMOG2(200, 36.0, false);
// MOG2 Background subtractor while (true) {
VideoCapture capture(videoFilename);
if (!capture.isOpened()) {
cerr << "Unable to open video file: " << videoFilename << endl;
return;
}
int width = (int)capture.get(CV_CAP_PROP_FRAME_WIDTH);
int height = (int)capture.get(CV_CAP_PROP_FRAME_HEIGHT);
unique_ptr<IplImage, void(*)(IplImage*)> labelImg(cvCreateImage(cvSize(width, height), IPL_DEPTH_LABEL, 1), [](IplImage* p){
cvReleaseImage(&p);
}
);
CvBlobs blobs;
CvTracks tracks;
while (true) {
// read input data. ESC or 'q' for quitting int key = waitKey(1);
if (key == 'q' || key == 27) return;
if (!capture.read(frame)) break;
// update background pMOG2->apply(frame, fgMaskMOG2);
pMOG2->getBackgroundImage(bgImg);
imshow("BG", bgImg);
imshow("Original mask", fgMaskMOG2);
// post process medianBlur(fgMaskMOG2, fgMaskMOG2, 5);
imshow("medianBlur", fgMaskMOG2);
morphologyEx(fgMaskMOG2, fgMaskMOG2, MORPH_CLOSE, getStructuringElement(MORPH_RECT, Size(5, 5)));
// fill black holes morphologyEx(fgMaskMOG2, fgMaskMOG2, MORPH_OPEN, getStructuringElement(MORPH_RECT, Size(5, 5)));
// fill white holes imshow("morphologyEx", fgMaskMOG2);
// track cvLabel(&IplImage(fgMaskMOG2), labelImg.get(), blobs);
cvFilterByArea(blobs, 64, 10000);
cvUpdateTracks(blobs, tracks, 10, 90, 30);
cvRenderTracks(tracks, &IplImage(frame), &IplImage(frame));
// show imshow("Frame", frame);
key = waitKey(30);
}
}
}
以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持脚本之家。
您可能感兴趣的文章:
- Opencv基于CamShift算法实现目标跟踪
- opencv+arduino实现物体点追踪效果
- Opencv光流运动物体追踪详解
