我正在做一个基于运动检测程序的项目。

它将背景中的变化检测为“运动”，因此我想要一种每隔几分钟重新捕获新的第一帧以替换当前帧以解决此问题的方法。

我使用的是 Raspberry Pi 2B 和罗技网络摄像头。

我使用的代码基于Pyimagesearch。

这是我的代码版本：

import sys
sys.path.append('/usr/local/lib/python3.4/site-packages')
import numpy as np
import cv2
import imutils
from imutils import contours
from skimage import measure
import datetime
import time

#capture feed from webcam    
cap = cv2.VideoCapture(0)    
#intialize first frame of video stream    
firstframe = None    
avg = None

#loop frames of video/stream    
while (cap.isOpened()):    
    #grab the current frame and initialize the occupied and unoccupied    
    (grabbed, frame) = cap.read()    
    text = "Unoccupied"

    #if the frame could not be grabbed, then we have reached the end of the vid    
    if not grabbed:    
        break

    #resize the frame, convert it to grayscale, and blur it    
    frame = imutils.resize(frame, width=500)    
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)    
    gray = cv2.GaussianBlur(gray, (21,21), 0)    
    gray = cv2.medianBlur(gray, 5)   

    #if first frame is None, initialize it    
    if firstframe is None:    
        firstframe = gray    
        continue

    if avg is None:    
        print ("[INFO] starting background model...")
        avg = gray.copy().astype("float")    
        cap    
        continue

    #compute the absolute difference between the current frame and the firstframe    
    cv2.accumulateWeighted(gray, avg, 0.5)
    frameDelta = cv2.absdiff(firstframe, gray)    
    thresh = cv2.threshold(frameDelta, 70, 255, cv2.THRESH_BINARY)[1]   

    #perform a series of erosions and dilations to remove any small    
    # blobs of noise from the thresholded image    
    # then from the threshold image find contours    
    thresh = cv2.erode(thresh, None, iterations=2)    
    thresh = cv2.dilate(thresh, None, iterations=4)    
    (_,cnts,hierarchy) = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)        

    #loop over the contours    
    for (i,c) in enumerate(cnts):    
        #if the contour is too small, ignore it    
        if cv2.contourArea(c) < 500:    
            continue  

        #compute the bounding box for the contour, draw it on frame, and update the text    
        #And print the time of object detection    
        (x, y, w, h) = cv2.boundingRect(c)    
        cv2.rectangle(frame, (x,y), (x+w,y+h), (0,255,0), 2)    
        cv2.putText(frame, "#{}".format(i + 1), (x, y - 15),    
        cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 0, 255), 2)    
        text = "Occupied"    
        print("Object detected at " + time.strftime("%I:%M:%S"))

    #draw the text and timestamp on the frame    
    cv2.putText(frame, "Room Status: {}". format(text), (10,20),    
                cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)   

    cv2.putText(frame, datetime.datetime.now().strftime("%A %d %B %Y %I:%M:%S%p"),    
                (10, frame.shape[0] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.35,(0,0,255), 1)    
    #show the frame and record if the user presses a key    
    cv2.imshow('frame',frame)    
    cv2.imshow('Thresh',thresh)    
    cv2.imshow('FrameDelta',frameDelta)    

    #if 'q' is pressed break from loop    
    if cv2.waitKey(1) & 0xFF == ord('q'):    
        break    

#cleanup the capture and close any open windows    
cap.release()    
cv2.destroyAllWindows()