Opencv实战(一) 视频人数统计(C++ & Opencv)前后背景分离方法

来源：互联网发布：怎么弄个java服务器编辑：程序博客网时间：2024/06/05 07:31

在博客《视频人数统计（opencv）》中，作者使用的Absdiff帧差法降低背景影响，进而通过二值化，边缘化，滤波器，形态学变化，查找轮廓，轮廓面积控制，绘制轮廓等一系列方法完成了对样例图片的处理，并实现了人数统计的功能。同样的，笔者最初也是和这位作者采用了同样的方法来做，但是在笔者的样例视频中统计的效果并不是很理性。之后笔者使用了前后背景分离的方法来代替Absdiff帧差法，最后得到了较为理想的效果。闲话不多说，直接上代码了。

using namespace std;using namespace cv;int main(int argc, const char** argv){    VideoCapture cap;    bool update_bg_model = true;    //cap.open(0);    cap.open("People.mp4");    if( !cap.isOpened() )    {        printf("can not open camera or video file\n");        return -1;    }    namedWindow("image", WINDOW_AUTOSIZE);    namedWindow("foreground mask", WINDOW_AUTOSIZE);    namedWindow("foreground image", WINDOW_AUTOSIZE);    namedWindow("mean background image", WINDOW_AUTOSIZE);    BackgroundSubtractorMOG2 bg_model;//(100, 3, 0.3, 5);建立背景模型    Mat img, fgmask, fgimg;    int i = 0;    for(;;)    {        i++;        cap >> img;        if( img.empty() )            break;        img = img(Rect(40, 0, 300, img.rows));        if( fgimg.empty() )            fgimg.create(img.size(), img.type());        //更新模型        bg_model(img, fgmask, update_bg_model ? -1 : 0);        medianBlur(fgmask, fgmask, 13);        threshold(fgmask, fgmask, 150, 255, THRESH_BINARY);        Mat element = getStructuringElement(MORPH_RECT, Size(3, 3));        /*erode(fgmask, fgmask, element, Point(0, 0), 3);        dilate(fgmask, fgmask, element, Point(0, 0), 3);*/        Mat srcGrayImage = fgmask.clone();        vector<vector<Point>> vContours;        vector<Vec4i> vHierarchy;        findContours(srcGrayImage, vContours, vHierarchy, RETR_CCOMP, CHAIN_APPROX_SIMPLE, Point(0, 0));        int count = 0;        RNG rng(12345);        for (int i = 0; i < vContours.size(); i++)        {            double area = contourArea(vContours[i], false);            RotatedRect smallRect = minAreaRect(vContours[i]);            Point2f smallRect_center = smallRect.center;            float smallRect_width = smallRect.size.width;            float smallRect_height = smallRect.size.height;            float smallRect_angle = 0;            smallRect = RotatedRect(smallRect_center, Size2f(smallRect_height, smallRect_width), smallRect_angle);            Point2f P[4];            smallRect.points(P);            if (area>1000 && area < 4200)            {                count++;                for (int j = 0; j <= 3; j++)                {                    line(img, P[j], P[(j + 1) % 4], Scalar(255, 0, 0), 2);                }            }            if (area>4200 && area < 6000)            {                count+=2;                for (int j = 0; j <= 3; j++)                {                    line(img, P[j], P[(j + 1) % 4], Scalar(255, 0, 0), 2);                }            }        }        Scalar color = Scalar(rng.uniform(0, 255), rng.uniform(0, 255), rng.uniform(0, 255));//任意值        putText(img, int2str(count), Point(220, 40), FONT_HERSHEY_TRIPLEX, 1, color, 2);        fgimg = Scalar::all(0);        img.copyTo(fgimg, fgmask);        Mat bgimg;        bg_model.getBackgroundImage(bgimg);        imshow("image", img);        /*string windows_name = "Video/image_" + int2str(i);        string windows_name_ext = windows_name + ".jpg";        imwrite(windows_name_ext, img);*/        imshow("foreground mask", fgmask);        imshow("foreground image", fgimg);        if(!bgimg.empty())            imshow("mean background image", bgimg );        char k = (char)waitKey(1);        if( k == 27 ) break;        if( k == ' ' )        {            update_bg_model = !update_bg_model;            if(update_bg_model)                printf("\t>背景更新(Background update)已打开\n");            else                printf("\t>背景更新(Background update)已关闭\n");        }    }    return 0;}