opencv车辆计数代码

opencv车辆计数代码:

import cv2
import numpy as np

min_w = 90
min_h = 90

#检测线的高度
line_high = 550

#线的偏移
offset = 7

#统计车的数量
carno =0

#存放有效车辆的数组
cars = []

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('video.mp4')

bgsubmog = cv2.bgsegm.createBackgroundSubtractorMOG()

#形态学kernel
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5,5))

while True:
    ret, frame = cap.read()
    if(ret == True):     

        #灰度
        cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        #去噪(高斯)
        blur = cv2.GaussianBlur(frame, (3,3), 5)
        #去背影
        mask = bgsubmog.apply(blur)

        #腐蚀, 去掉图中小斑块
        erode = cv2.erode(mask, kernel) 

        #膨胀, 还原放大
        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)
    
        #画一条检测线
        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
            #以验证是否是有效的车辆
            isValid = ( w >= min_w ) and ( h >= min_h) 
            if( not isValid):
                continue

            #到这里都是有效的车 
            cv2.rectangle(frame, (x, y), (x+w, y+h), (0,0,255), 2)
            cpoint = center(x, y, w, h)
            cars.append(cpoint) 
            cv2.circle(frame, (cpoint), 5, (0,0,255), -1)

            for (x, y) in cars:
                if( (y > line_high - offset) and (y < line_high + offset ) ):
                    carno +=1
                    cars.remove((x , y ))
                    print(carno)
        
        cv2.putText(frame, "Cars Count:" + str(carno), (500, 60), cv2.FONT_HERSHEY_SIMPLEX, 2, (255,0,0), 5)
        cv2.imshow('video', frame)
        #cv2.imshow('erode', close)
    
    key = cv2.waitKey(1)
    if(key == 27):
        break

cap.release()
cv2.destroyAllWindows()

视频链接:https://live.youkuaiyun.com/v/428224

在使用Python和OpenCV进行车辆计数时,可以采用以下步骤: 1. 导入所需的库: ```python import cv2 import numpy as np ``` 2. 加载视频并初始化参数: ```python video = cv2.VideoCapture('path/to/video') # 替换为你的视频路径 width = int(video.get(cv2.CAP_PROP_FRAME_WIDTH)) height = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT)) fps = video.get(cv2.CAP_PROP_FPS) total_frames = int(video.get(cv2.CAP_PROP_FRAME_COUNT)) ``` 3. 定义车辆检测器(例如使用Haar级联分类器或深度学习模型): ```python car_cascade = cv2.CascadeClassifier('path/to/car_cascade.xml') # 替换为你的分类器路径 ``` 4. 创建计数器变量: ```python car_count = 0 prev_car_count = 0 ``` 5. 逐帧处理视频: ```python while video.isOpened(): ret, frame = video.read() if not ret: break gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # 使用车辆检测器检测车辆 cars = car_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=3, minSize=(30, 30)) # 绘制检测到的车辆框并更新车辆计数 for (x, y, w, h) in cars: cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2) car_count += 1 # 显示帧和当前的车辆计数 cv2.putText(frame, "Car Count: " + str(car_count), (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2) cv2.imshow('Video', frame) # 按 'q' 键退出程序 if cv2.waitKey(1) & 0xFF == ord('q'): break video.release() cv2.destroyAllWindows() ``` 请注意,以上代码仅为示例,你需要根据你的实际情况进行适当的调整和更改。另外,你也可以使用其他的车辆检测方法和技术来实现车辆计数
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