opencv车辆检测系统
import cv2import numpy as np
cap = cv2.VideoCapture('video.mp4')
bg = cv2.bgsegm.createBackgroundSubtractorMOG()
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5,5))
min_w = 90
min_h = 90
#检测线的高度
line_high = 550
#线的偏移量
offset = 7
#统计车的数量
carno = 0
#存放有效车辆数组
cars = []
#定义中心点
def center (x, y, w, h):
x1 = int(w/2)
y1 = int(h/2)
cx = x + x1
cy = y + y1
return cx, cy
#循环去读取视频中的每一帧 视频是由一幅幅图画组成的
while True:
#ret判断读取视频的帧是否成功了，成功了还返回另一个值frame
ret, frame = cap.read()
if (ret == True):
#灰度化
cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
#高斯去噪
blur = cv2.GaussianBlur(frame, (3, 3), 5)
#去背景
mask = bg.apply(blur)
#腐蚀:去除小斑块
erode = cv2.erode(mask, kernel)
#膨胀:还原
dilate = cv2.dilate(erode, kernel, iterations = 3)
#闭操作，去掉物体内部的小块
close = cv2.morphologyEx(dilate, cv2.MORPH_CLOSE, kernel)
close = cv2.morphologyEx(close, cv2.MORPH_CLOSE, kernel)
cnts, h = cv2.findContours(close, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
#绘制检测线
cv2.line(frame, (10, line_high), (1200, line_high), (255,255,0), 3)
for (i, c) in enumerate(cnts):
(x,y,w,h) = cv2.boundingRect(c)
#对车辆的宽高进行判断 以验证是否是有效车辆
isValid = (w >= min_w) and (h >= min_h)
if (not isValid):
continue
#到这里都是有效的车
#找车轮廓的中心线，过了线计数，未过线
# 存放车轮廓数组
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 0, 255), 2)
cpoint = center(x, y, w, h)
cars.append(cpoint)
for (x, y) in cars:
# 要有一条线 有范围（6个像素） 从数组中间去
if ((y < line_high + offset) and (y > line_high - offset)):
carno +=1
cars.remove((x, y))
print(carno)
cv2.putText(frame, "Cars Counts:" + str(carno), (500,60),
cv2.FONT_HERSHEY_SIMPLEX, 2, (255,0,0), 5)
cv2.imshow("video", frame)
else:
break
key = cv2.waitKey(1)
if(key == 27):
break
cap.release()
cv2.destroyAllWindows()
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