opencv学习之路（37）、运动物体检测（二）

2022-02-13 22:21:05

一、运动物体轮廓椭圆拟合及中心

 #include "opencv2/opencv.hpp"

 #include<iostream>

 using namespace std;

 using namespace cv;

 Mat MoveDetect(Mat frame1, Mat frame2)

 {

     Mat result = frame2.clone();

     Mat gray1, gray2;

     cvtColor(frame1, gray1, CV_BGR2GRAY);

     cvtColor(frame2, gray2, CV_BGR2GRAY);

     Mat diff;

     absdiff(gray1, gray2, diff);

     imshow("absdiss", diff);

     threshold(diff, diff, , , CV_THRESH_BINARY);

     imshow("threshold", diff);

     Mat element = getStructuringElement(MORPH_RECT, Size(, ));

     Mat element2 = getStructuringElement(MORPH_RECT, Size(, ));

     erode(diff, diff, element);

     imshow("erode", diff);

     dilate(diff, diff, element2);

     imshow("dilate", diff);

     vector<vector<Point>> contours;

     vector<Vec4i> hierarcy;

     //画椭圆及中心

     findContours(diff, contours, hierarcy, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_NONE);

     cout<<"num="<<contours.size()<<endl;

     vector<RotatedRect> box(contours.size());

     for(int i=; i<contours.size(); i++)

     {

         box[i] = fitEllipse(Mat(contours[i]));

         ellipse(result, box[i], Scalar(, , ), , );

         circle(result, box[i].center, , Scalar(, , ), -, );

     }

     return result;

 }

 void main()

 {

     VideoCapture cap("E://man.avi");

     if(!cap.isOpened()) //检查打开是否成功

          return;

     Mat frame;

     Mat result;

     Mat background;

     int count=;

     while()

     {

         cap>>frame;

         if(frame.empty())

             break;

         else{

             count++;

             if(count==)

                  background = frame.clone(); //提取第一帧为背景帧

             imshow("video", frame);

             result = MoveDetect(background, frame);

             imshow("result", result);

             if(waitKey()==)

                break;

         }

     }

     cap.release();

 }

和上一篇文章代码的不同点在30-38行，天台行人视频适合用背景减法处理，自行车视频适合帧差法处理

opencv学习之路（37）、运动物体检测（二）

二、滤波方法去除噪声

上篇文章中使用腐蚀膨胀消除噪声，这次使用滤波方法去除噪声

中值滤波

　　//二值化后使用中值滤波+膨胀
　　Mat element = getStructuringElement(MORPH_RECT, Size(, ));

    medianBlur(diff, diff, );//中值滤波

    imshow("medianBlur", diff);

    dilate(diff, diff, element);

    imshow("dilate", diff);

opencv学习之路（37）、运动物体检测（二）

均值滤波

#include "opencv2/opencv.hpp"

#include<iostream>

using namespace std;

using namespace cv;

//int to string helper function

string intToString(int number)

{

    stringstream ss;

    ss << number;

    return ss.str();

}

Mat MoveDetect(Mat background, Mat img)

{

    Mat result = img.clone();

    Mat gray1, gray2;

    cvtColor(background, gray1, CV_BGR2GRAY);

    cvtColor(img, gray2, CV_BGR2GRAY);

    Mat diff;

    absdiff(gray1, gray2, diff);

    threshold(diff, diff, , , CV_THRESH_BINARY);

    imshow("threshold", diff);

    blur(diff, diff, Size(, ));//均值滤波

    imshow("blur", diff);

    vector<vector<Point>> contours;

    vector<Vec4i> hierarcy;

    findContours(diff, contours, hierarcy, CV_RETR_EXTERNAL, CHAIN_APPROX_NONE); //查找轮廓

    vector<Rect> boundRect(contours.size()); //定义外接矩形集合

    //drawContours(img2, contours, -1, Scalar(0, 0, 255), 1, 8);  //绘制轮廓

    int x0=, y0=, w0=, h0=;

    for(int i=; i<contours.size(); i++)

    {

        boundRect[i] = boundingRect((Mat)contours[i]); //查找每个轮廓的外接矩形

        x0 = boundRect[i].x;  //获得第i个外接矩形的左上角的x坐标

        y0 = boundRect[i].y; //获得第i个外接矩形的左上角的y坐标

        w0 = boundRect[i].width; //获得第i个外接矩形的宽度

        h0 = boundRect[i].height; //获得第i个外接矩形的高度

        //rectangle(result, Point(x0, y0), Point(x0+w0, y0+h0), Scalar(0, 255, 0), 2, 8); //绘制第i个外接矩形

        circle(result, Point(x0+w0/, y0+h0/), , Scalar(, , ), , );

        line(result, Point(x0+w0/-, y0+h0/), Point(x0+w0/+, y0+h0/), Scalar(, , ), , );

        line(result, Point(x0+w0/, y0+h0/-), Point(x0+w0/, y0+h0/+), Scalar(, , ), , );

        putText(result,"(" + intToString(x0+w0/)+","+intToString(y0+h0/)+")",Point(x0+w0/+, y0+h0/), , ,Scalar(,,),);

    }

    return result;

}

void main()

{

    VideoCapture cap("E://ball.avi");

    if(!cap.isOpened()) //检查打开是否成功

         return;

    Mat frame;

    Mat result;

    Mat background;

    int count=;

    while()

    {

        cap>>frame;

        if(frame.empty())

            break;

        else{

            count++;

            if(count==)

                 background = frame.clone(); //提取第一帧为背景帧

            imshow("video", frame);

            result = MoveDetect(background, frame);

            imshow("result", result);

            if(waitKey()==)

               break;

        }

    }

    cap.release();

}

opencv学习之路（37）、运动物体检测（二）

三、轮廓筛选去除噪声(效果挺好的)

    //其余代码相同

    int x0=, y0=, w0=, h0=;

    for(int i=; i<contours.size(); i++)

    {

        boundRect[i] = boundingRect((Mat)contours[i]); //查找每个轮廓的外接矩形

        x0 = boundRect[i].x;  //获得第i个外接矩形的左上角的x坐标

        y0 = boundRect[i].y; //获得第i个外接矩形的左上角的y坐标

        w0 = boundRect[i].width; //获得第i个外接矩形的宽度

        h0 = boundRect[i].height; //获得第i个外接矩形的高度

        //筛选

        if(w0> && h0>)

            rectangle(result, Point(x0, y0), Point(x0+w0, y0+h0), Scalar(, , ), , ); //绘制第i个外接矩形

    }

opencv学习之路（37）、运动物体检测（二）

四、运动轨迹绘制

#include "opencv2/opencv.hpp"

#include<iostream>

using namespace std;

using namespace cv;

Point center;

Point fre_center;//存储前一帧中心坐标

int num=;

vector<Point> points;

Mat MoveDetect(Mat background, Mat img)

{

    Mat result = img.clone();

    Mat gray1, gray2;

    cvtColor(background, gray1, CV_BGR2GRAY);

    cvtColor(img, gray2, CV_BGR2GRAY);

    Mat diff;

    absdiff(gray1, gray2, diff);

    imshow("absdiss", diff);

    threshold(diff, diff, , , CV_THRESH_BINARY);

    imshow("threshold", diff);

    Mat element = getStructuringElement(MORPH_RECT, Size(, ));

    Mat element2 = getStructuringElement(MORPH_RECT, Size(, ));

    erode(diff, diff, element);

    imshow("erode", diff);

    dilate(diff, diff, element2);

    imshow("dilate", diff);

    vector<vector<Point>> contours;

    vector<Vec4i> hierarcy;

    findContours(diff, contours, hierarcy, CV_RETR_EXTERNAL, CHAIN_APPROX_NONE); //查找轮廓

    vector<Rect> boundRect(contours.size()); //定义外接矩形集合

    //drawContours(img2, contours, -1, Scalar(0, 0, 255), 1, 8);  //绘制轮廓

    vector<RotatedRect> box(contours.size());

    int x0=, y0=, w0=, h0=;

    for(int i=; i<contours.size(); i++)

    {

        boundRect[i] = boundingRect((Mat)contours[i]); //查找每个轮廓的外接矩形

        x0 = boundRect[i].x;  //获得第i个外接矩形的左上角的x坐标

        y0 = boundRect[i].y; //获得第i个外接矩形的左上角的y坐标

        w0 = boundRect[i].width; //获得第i个外接矩形的宽度

        h0 = boundRect[i].height; //获得第i个外接矩形的高度

        if(w0> && h0>)//筛选长宽大于30的轮廓

        {

            num++;

            //rectangle(result, Point(x0, y0), Point(x0+w0, y0+h0), Scalar(0, 255, 0), 2, 8); //绘制第i个外接矩形

            box[i] = fitEllipse(Mat(contours[i]));

            ellipse(result, box[i], Scalar(, , ), , );            //椭圆轮廓

            circle(result, box[i].center, , Scalar(, , ), -, ); //画中心

            center = box[i].center;//当前帧的中心坐标

            points.push_back(center);//中心塞进points向量集

            if(num !=)

            {

                //line(result, fre_center, center, Scalar(255, 0, 0), 2, 8);

                for(int j=; j<points.size()-; j++)

                    line(result, points[j], points[j+], Scalar(, , ), , );

            }

            //fre_center = center;

        }

    }

    return result;

}

void main()

{

    VideoCapture cap("E://man.avi");

    if(!cap.isOpened()) //检查打开是否成功

         return;

    Mat frame;

    Mat background;

    Mat result;

    int count=;

    while()

    {

        cap>>frame;

        if(!frame.empty())

        {

            count++;

            if(count==)

                background = frame.clone(); //提取第一帧为背景帧

            imshow("video", frame);

            result = MoveDetect(background, frame);

            imshow("result", result);

            if(waitKey()==)

               break;

        }

        else

            break;

    }

    cap.release();

}

opencv学习之路（37）、运动物体检测（二）

五、车辆数量检测

1.帧差法检测运动目标

2.预处理：a.转灰度图，绝对值做差　　b.二值化，腐蚀，中值滤波，膨胀　　c.查找轮廓，筛选轮廓，绘制外接矩形，计数，输出

 #include "opencv2/opencv.hpp"

 #include<iostream>

 using namespace std;

 using namespace cv;

 int CarNum = ;

 //int to string helper function

 string intToString(int number)

 {

     //this function has a number input and string output

     stringstream ss;

     ss << number;

     return ss.str();

 }

 Mat MoveDetect(Mat frame1, Mat frame2) {

     Mat result = frame2.clone();

     Mat gray1, gray2;

     cvtColor(frame1, gray1, CV_BGR2GRAY);

     cvtColor(frame2, gray2, CV_BGR2GRAY);

     Mat diff;

     absdiff(gray1, gray2, diff);

     //imshow("absdiss", diff);

     threshold(diff, diff, , , CV_THRESH_BINARY);

     imshow("threshold", diff);

     Mat element = getStructuringElement(MORPH_RECT, Size(, ));

     Mat element2 = getStructuringElement(MORPH_RECT, Size(, ));

     erode(diff, diff, element);

     //imshow("erode", dst);

     medianBlur(diff, diff, );

     imshow("medianBlur", diff);

     dilate(diff, diff, element2);

     imshow("dilate", diff);

     vector<vector<Point>> contours;

     vector<Vec4i> hierarcy;

     findContours(diff, contours, hierarcy, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE, Point(, ));//查找轮廓

     vector<vector<Point>>contours_poly(contours.size());

     vector<Rect> boundRect(contours.size()); //定义外接矩形集合

                                              //drawContours(img2, contours, -1, Scalar(0, 0, 255), 1, 8);  //绘制轮廓

     int x0 = , y0 = , w0 = , h0 = ;

     for (int i = ; i<contours.size(); i++)

     {

         approxPolyDP(Mat(contours[i]), contours_poly[i], , true);//对图像轮廓点进行多边形拟合：轮廓点组成的点集，输出的多边形点集，精度（即两个轮廓点之间的距离），输出多边形是否封闭

         boundRect[i] = boundingRect(Mat(contours_poly[i]));

         if (boundRect[i].width> && boundRect[i].width< && boundRect[i].height> && boundRect[i].height<) {//轮廓筛选

             x0 = boundRect[i].x;

             y0 = boundRect[i].y;

             w0 = boundRect[i].width;

             h0 = boundRect[i].height;

             rectangle(result, Point(x0, y0), Point(x0 + w0, y0 + h0), Scalar(, , ), , , );

             if ((y0 + h0 /  + ) >=  && (y0 + h0 /  - ) <= ) {//经过这条线（区间），车辆数量+1

                 CarNum++;

             }

         }

         line(result, Point(, ), Point(, ), Scalar(, , ), , );//画红线

         Point org(, );

         putText(result, "CarNum=" + intToString(CarNum), org, CV_FONT_HERSHEY_SIMPLEX, 0.8f, Scalar(, , ), );

     }

     return result;

 }

 void main()

 {

     VideoCapture cap("E://2.avi");

     if (!cap.isOpened()) //检查打开是否成功

         return;

     Mat frame;

     Mat tmp;

     Mat result;

     int count = ;

     while ()

     {

         cap >> frame;

         if(frame.empty())//检查视频是否结束

             break;

         else{

             count++;

             if (count == )

                 result = MoveDetect(frame, frame);

             else    result = MoveDetect(tmp, frame);

             imshow("video", frame);

             imshow("result", result);

             tmp = frame.clone();

             if (waitKey() == )

                 break;

         }

     }

     cap.release();

 }

opencv学习之路（37）、运动物体检测（二）

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