基于OpenCV的车辆监测与计数

#include "opencv2/opencv.hpp" #include<iostream> using namespace std; using namespace cv; int CarNum = 0;//初始化车的数量为0 Mat MoveDetect(Mat frame1, Mat frame2){//移动监测 Mat result = frame2.clone();//克隆frame2 Mat gray1, gray2; cvtColor(frame1, gray1, CV_BGR2GRAY);//颜色空间转换,将frame1图像转换为灰度图 cvtColor(frame2, gray2, CV_BGR2GRAY);//颜色空间转换,将frame2图像转换为灰度图 Mat diff; absdiff(gray1, gray2, diff);//帧差法，计算两个数组差的绝对值的函数。 imshow("帧差法显示", diff); //threshold(diff, diff, 45, 255, CV_THRESH_BINARY); //简单二值化 adaptiveThreshold(diff, diff, 255, ADAPTIVE_THRESH_GAUSSIAN_C, THRESH_BINARY_INV, 15, 3);//自适应阈值化 imshow("二值化显示", diff); //图像的二值化就是将图像上的像素点的灰度值设置为0或255， //这样将使整个图像呈现出明显的黑白效果。在数字图像处理中， //二值图像占有非常重要的地位，图像的二值化使图像中数据量大为减少， //从而能凸显出目标的轮廓。OpenCV中提供了函数cv::threshold(); Mat element = getStructuringElement(MORPH_RECT, Size(3, 3));//返回矩形的结构元素，核大小为3*3 Mat element2 = getStructuringElement(MORPH_RECT, Size(31, 31));//返回矩形的结构元素，核大小为31*31 //进行形态学操作 morphologyEx(diff, diff, MORPH_OPEN, element);//输入为diff，输出为diff，开运算的形态学操作 medianBlur(diff, diff, 9);//中值滤波 dilate(diff, diff, element2);//膨胀操作 imshow("膨胀窗口", diff);//图像膨胀 vector<vector<Point>> contours; vector<Vec4i> hierarcy; findContours(diff, contours, hierarcy, CV_RETR_EXTERNAL, CHAIN_APPROX_NONE); //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=0, y0=0, w0=0, h0=0; char Num[10]; for(int i=0; i<contours.size(); i++){ approxPolyDP(Mat(contours[i]), contours_poly[i], 3, true); //多边形逼近 boundRect[i] = boundingRect((Mat)contours_poly[i]); //查找每个轮廓的外接矩形 if(boundRect[i].width > 55 && boundRect[i].width <180 && boundRect[i].height > 55 && boundRect[i].height < 180){ 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 } if((y0 + h0/2 + 1) >= 138 && (y0 + h0/2 - 1) <= 142){ CarNum++; } line(result, Point(0,140), Point(568,140), Scalar(0,0,255), 1, 8); Point org(0,35); sprintf(Num, "CarNum=%d", CarNum); putText(result, Num, org, CV_FONT_HERSHEY_SIMPLEX, 0.8f, CV_RGB(0,255,0), 2); } return result; } void main(){ VideoCapture cap("car.avi");//打开一个视频文件 if(!cap.isOpened()) //检查打开是否成功 return; Mat frame; Mat result; Mat temp; int count=0; while(1){ cap>>frame; if(!frame.empty()){//frame不为空 count++; if(count==1) result = MoveDetect(frame, frame); else result = MoveDetect(temp, frame); imshow("原图", frame);//原图 imshow("结果", result);//结果图 temp = frame.clone(); if(waitKey(5)==27)//判断用户的操作，若用户在5ms时间段中按下ESC则跳出循环 break; } else break; } cap.release(); waitKey(0); }