from asyncio import CancelledError

import cv2

from cv2 import erode

from cv2 import dilate

import numpy as np

# 滤除的最小矩形的范围

min_w = 90

min_h = 90

# 定义检测线的高度，与视频大小有关，视频左上角为（ 0 ， 0 ）

line_high = 550

# 线的偏移量

offset = 7

# 统计车的数量

carno = 0

# 存放有效车辆的数组

cars = []

# 求车的中心点

def center(x,y,w,h):

     x1 = int(w/2)

     y1 = int(h/2)

     cx = x + x1

     cy = y + y1

     return cx,cy

cap = cv2.VideoCapture('E:\\video.mp4') # 加载视频

bgsubmog = cv2.createBackgroundSubtractorMOG2()

# 形态学 kernel

kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5,5))

while True:

     ret, frame = cap.read()

     if (ret == True) :

         # 灰度化处理

         cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

         # print(frame.shape)   # 获取视频大小的信息

         # exit()   # 该命令之后的所有程序都不执行

         # 去噪（高斯）

         blur = cv2.GaussianBlur(frame, (7,7),5)

         # 去背景

         mask = bgsubmog.apply(blur)

         # 腐蚀

         erode = cv2.erode(mask, kernel)

         # 膨胀

         dilate = cv2.dilate(erode, kernel, iterations= 2)

         # 形态学闭运算，去除图像里面的噪点

         close = cv2.morphologyEx(dilate, cv2.MORPH_CLOSE, kernel)

         # 查找轮廓

         cnts, h = cv2.findContours(close, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

         # 绘制一条检测线

         cv2.line(frame, (10,line_high),(1200, line_high),(255,255,0),3)

         # 对所有轮廓进行遍历

         for (i, c) in enumerate(cnts):

             (x,y,w,h) = cv2.boundingRect(c)

             # 对车辆的宽高进行判断，以验证是否为有效的车辆

             isValid = (w >= min_w) and ( h >= min_h)

             if( not isValid):

                 continue

             # 到这里都是有效的车，绘制车的矩形

             cv2.rectangle(frame, (x,y),(x+w,y+h),(0,0,255),2)

             # 存储车的中心点

             cpoint = center(x,y,w,h)    # 计算车的中心点

             cars.append(cpoint)    #跟单网gendan5.com  将中心点数据存储到 cars 这个数组中

             for (x,y) in cars :   # 遍历数组，如果车的中心点落在检测线的有效区域内，则计数 +1 ，然后去除该数组

                 if ((y > line_high - offset) and (y < line_high + offset)):

                     carno += 1

                     cars.remove((x,y))  

                     print(carno)

         # 显示统计信息

         cv2.putText(frame, "Cars Count:" + str(carno),(500,60), cv2.FONT_HERSHEY_SIMPLEX, 2, (255, 0, 0), 5)   

         cv2.imshow('video', frame)

         # cv2.imshow('close', close)   

     key = cv2.waitKey(1)

     if(key == 27) : # esc 键

         break

cap.release()

cv2.destroyAllWindows()