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_data=cv2.threshold(delta_frame,30,255,cv2.THRESH_BINARY)[1]
    thresh_data=cv2.dilate(thresh_data,None,iterations=2)
    (cnts,_)= cv2.findContours(thresh_data.copy(),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)
        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("Gray Frame",gray)
    cv2.imshow("Delta Frame",delta_frame)
    cv2.imshow("Threshold Frame",thresh_data)
    cv2.imshow("Color Frame",frame)



    key=cv2.waitKey(1)
    print(times)
    #print(gray)
    #print(delta_frame)
    for i in range(0,len(times),2):
        df=df.append({"Start":Times[i],"End":times[i+1]},ignore_index=True)
        
    df.to_csv("Times.csv")
    if key==ord('q'):
        if status==1:
            times.append(datetime.now())
        break
    print(status_list)
    
    
video.release(0)
cv2.destroyAllWindows