motion_detector.py

import cv2,time,pandas
from datetime import datetime

first_frame=None
status_list=[None,None]
times=[]
df=pandas.DataFrame(columns=["START","END"])

video=cv2.VideoCapture(0)

while True:
    check,frame=video.read()
    status=0

    gray=cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)
    gray=cv2.GaussianBlur(gray,(21,21),0)
    if first_frame is None:
        first_frame=gray
        continue
    
    delta_frame=cv2.absdiff(first_frame,gray)
    
    thresh_frame=cv2.threshold(delta_frame,20,255,cv2.THRESH_BINARY)[1]
    
    thresh_frame=cv2.dilate(thresh_frame,None,iterations=2)

    (cnts,_)=cv2.findContours(thresh_frame,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)

    for contour in cnts:
        if cv2.contourArea(contour)<10000:
            continue
        status=1
        (x,y,w,h)=cv2.boundingRect(contour)
        frame=cv2.rectangle(frame,(x,y),(x+w,y+h),(0,255,0),3)
    
    status_list.append(status)
    status_list=status_list[-2:] 

    if status_list[-1]==1 and status_list[-2]==0:
        times.append(datetime.now())
    if status_list[-1]==0 and status_list[-2]==1:
        times.append(datetime.now())

    cv2.imshow("Motion detector",frame)
    
    #cv2.imshow("Threshold Frame",thresh_frame)

    key=cv2.waitKey(1)
    
    if key==ord('q'):
        if status==1:
            times.append(datetime.now())
        break
for t in range(0,len(times),2):
    df=df.append({"START":times[t],"END":times[t+1]},ignore_index=True)

df.to_csv("Motion_Times.csv")
video.release()

cv2.destroyAllWindows()