#加载视频
import cv2
import numpy as np
cap = cv2.VideoCapture('car.mp4')
#去除背景
mog= cv2.bgsegm.createBackgroundSubtractorMOG()
kernel = cv2.getStructuringElement(cv2.MORPH_RECT,(7,7))
min_w=90
min_h=90
line_hig=400
#偏移量
offset=2
cars=[]
carno=0
#计算外矩形的中心点
def center(x,y,w,h):x1=int(w/2)y1=int(h/2)cx=int(x)+x1cy=int(y)+y1return cx,cy
#循环读取视频帧
while True:ret,frame = cap.read()if ret == True:# 把原始帧进行灰度化gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)#采用高斯去噪blur = cv2.GaussianBlur(gray,(7,7),8)fgmask = mog.apply(blur)#腐蚀erode = cv2.erode(fgmask,kernel)#膨胀，把图像还原回来dialte=cv2.dilate(erode,kernel,iterations=2)#消除内部的小块# 执行开运算opening = cv2.morphologyEx(dialte, cv2.MORPH_OPEN, kernel)#闭运算close=cv2.morphologyEx(opening,cv2.MORPH_CLOSE,kernel)#查找轮廓result,contours,h=cv2.findContours(close,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)# 如何画线，画在什么位置cv2.line(frame,(10,line_hig),(1600,line_hig),(255,255,0),3)#画出所有检测的轮廓for contour in contours:#最大外接矩形(x,y,w,h)=cv2.boundingRect(contour)#通过外接矩形的宽高大小来过滤掉小的矩形is_valid = (w>=min_w)&(h>=min_h)if not is_valid:continue#要求坐标点数都是整数cv2.rectangle(frame,(int(x),int(y)),(int(x+w),int(y+h)),(0,0,255),2)#把车抽象为一点，即外接矩形的中心点#要通过外接矩形计算矩形的中心点cpoint=center(x,y,w,h)cars.append(cpoint)cv2.circle(frame,(cpoint),5,(0,0,255),-1)#判断汽车是否过线# 如何画线，画在什么位置#如何去计数for(x,y) in cars:if y>(line_hig-offset) and y<(line_hig+offset):#落入有郊期间carno+=1cars.remove((x,y))print(carno)#cv2.imshow('video',erode)#cv2.imshow('opening', opening)cv2.putText(frame,'Vehicle Count:'+str(carno),(500,60),cv2.FONT_HERSHEY_SIMPLEX,2,(0,0,255),5)cv2.imshow('frame', frame)key= cv2.waitKey(2)#用户按ESC退出if key ==27:break
#释放资源
cap.release()
cv2.destroyAllWindows()