motion_detector.py

import cv2, time, pandas
from datetime import datetime

first_frame = None

video = cv2.VideoCapture(0, cv2.CAP_DSHOW)
status_list = [None, None]
times = []
df = pandas.DataFrame(columns=["Start", "End"])
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
    # continue will make the loop run again and it wont continue with the next
    delta_frame = cv2.absdiff(first_frame, gray)
    thresh_frame = cv2.threshold(delta_frame, 30, 255, cv2.THRESH_BINARY)[1]
    thresh_frame = cv2.dilate(thresh_frame, None, iterations=2)

    (cnts, _) = cv2.findContours(thresh_frame.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    # contour area of 10000 is like 100 pixel by 100 pixel. Dependig on the kind of objects you want to detect..
    # if you want to detect smaller objects,you should use smaller Contour area
    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)

    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("Vid", gray)
    cv2.imshow("Lite", delta_frame)
    cv2.imshow("THreshold frame", thresh_frame)
    cv2.imshow("color frame", frame)
    key = cv2.waitKey(1)

    if key == ord("q"):
        break
    print(status)

# print(status_list)
print(times)
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")
video.release()
cv2.destroyAllWindows()