python-opencv 视频车辆检测方法（单车道）

落日映苍穹つ 2024-03-27 14:34 31阅读 0赞

import numpy as np
    import cv2 as cv
    from PIL import Image
    
    //门口摄像头
    
    //背景去除的三种方法：
    //1）BackgroundSubtractorMOG   ---去除效果最好
    //2）BackgroundSubtractorMOG2
    //3）BackgroundSubtractorGMG
    
    //本地录像文件视频
    cap = cv.VideoCapture("d:/Download/cars.mp4") 
    //摄像头RTSP流
    cap = cv.VideoCapture("rtsp://admin:admin%40sbdz@192.168.1.40:554/h264/sub/av_stream",cv.CAP_ANY)  
    min_w=30
    min_h=30
    carno=0
    
    // 门口
    leftpoint=144
    rightpoint=208
    
    // 存放有效车辆数组
    cars = []
    // 检测线的高度
    // 门口
    line_heigh = 180
    
    // 线的偏移量
    offset = 6 
    
    def center(x,y,w,h):
       x1=int(w/2)
       y1=int(h/2)
       cx=x+x1
       cy=y+y1
       return cx,cy
    
    
    
    def img_paste(image, roi_t):
        
        im = np.zeros(image.shape[:3], dtype="uint8")
             
        # #把所有的点画出来
        cv.polylines(im, roi_t, True, (255, 0, 255))  # 画任意多边形
        # 把所有点连接起来，形成封闭区域
        cv.fillPoly(im, roi_t, (255, 255, 255))
        mask = im
    
        #将连接起来的区域对应的数组和原图对应位置按位相与
        masked = cv.bitwise_and(image, mask)
        
        #cv2中的图片是按照bgr顺序生成的，我们需要按照rgb格式生成
        b, g, r = cv.split(masked)
        masked = cv.merge([r, g, b])
        return masked  
    
    bgsubmog = cv.bgsegm.createBackgroundSubtractorMOG()
    #形态学kernel
    kernel=cv.getStructuringElement(cv.MORPH_RECT,(5,5))
    frameno=0
    historyFrameNo=0
    while(1):
        ret, frame = cap.read()
        if(ret==True):
           frameno+=1
           frame=cv.resize(frame,(702,576))
           roi_as = []
           
           #门口:根据矩形框抠出视频
           roi_as.append(np.array([[215,144], [256, 144], [100, 271], [12, 267]],dtype=np.int32)) #702X576
           img=img_paste(frame, roi_as)
           cropped = img [120:280,0:262]
           sp = cropped.shape 
           leftpoint=0
           rightpoint=sp[1]
           line_heigh=int(sp[0]/2)
           #灰度
           gray=cv.cvtColor(cropped,cv.COLOR_BGR2GRAY)
           #去噪（高期）
           blur=cv.GaussianBlur(gray,(3,3),5)
           #去背景
           mask=bgsubmog.apply(blur)
           #腐蚀
           erode=cv.erode(mask,kernel)
           #膨胀
           dilate=cv.dilate(erode,kernel,iterations=9)
           #闭操作：两次闭操作
           close=cv.morphologyEx(dilate,cv.MORPH_CLOSE,kernel)
           close=cv.morphologyEx(close,cv.MORPH_CLOSE,kernel)
           #查找轮廓
           cnts, h = cv.findContours(close,cv.RETR_EXTERNAL,cv.CHAIN_APPROX_SIMPLE)#cv.RETR_EXTERNAL只检测外轮廓
           # 画一条检测线
           cv.line(cropped,(leftpoint,line_heigh),(rightpoint,line_heigh),(255,255,0),2)
           for(i,c) in enumerate(cnts):
              (x,y,w,h) = cv.boundingRect(c)
              #对车辆的宽高进行判断
              #以验证是否是有效的车辆
              isValid=(w>=min_w) and (h>=min_h)
              if(not isValid):
                 continue
              #到这里都是有效的车
              cv.rectangle(cropped,(x,y),(x+w,y+h),(0,0,255),2)
              cpoint=center(x,y,w,h)
              cars.append(cpoint)
              for(x,y) in cars:
                 if((y>line_heigh-offset) and (y<line_heigh+offset)):
                     if(historyFrameNo==0):
                       carno+=1
                       historyFrameNo=frameno
                     if(carno>=1):
                        if(frameno-historyFrameNo>30):
                           carno+=1
                           historyFrameNo=frameno
                     cars.clear()
           cv.putText(frame,"Car Count:" + str(carno),(300,60),cv.FONT_HERSHEY_SIMPLEX,1,(255,0,0),3)
           cv.imshow('frame',frame)
           cv.imshow("cropped",cropped)
           k = cv.waitKey(1)
           if k == 27:
              break
    cap.release()
    cv.destroyAllWindows(
    )