motion_detection.py

motion_detection.py

# Python program to implement  
# Webcam Motion Detector
import os

# importing OpenCV, time and Pandas library
import cv2, time, pandas
import numpy as np
import ffmpegcv

# importing datetime class from datetime library
from datetime import datetime, timedelta
import argparse
from multiprocessing import Process
import threading


def execute():
    frame1 = None

    minimum = 1000  # Define Min Contour area
    method = 'KNN'
    # Constructing a parser
    ap = argparse.ArgumentParser()
    try:
        os.mkdir("motion")
    except:
        print("motiuon already exist")
    # Adding arguments
    ap.add_argument("-v", "--video", help="Video Stream")
    ap.add_argument("-a", "--area", help="Contour area")
    args = vars(ap.parse_args())

    mog = cv2.createBackgroundSubtractorMOG2(detectShadows=False)
    knn = cv2.createBackgroundSubtractorKNN(detectShadows=False)

    # Capturing video
    video = cv2.VideoCapture(args["video"])
    m_time = os.path.getmtime(args["video"])
    # convert timestamp into DateTime object
    dt_m = datetime.fromtimestamp(m_time)
    print('Modified on:', dt_m)

    fps = video.get(cv2.CAP_PROP_FPS)
    print("FPS-" + str(fps))
    frame_no = 0
    motion = 0
    original_date_time = dt_m

    # Infinite while loop to treat stack of image as video
    while True:
        # Reading frame(image) from video
        exists, frame = video.read()
        if exists:
            date_time = original_date_time + timedelta(milliseconds=int(video.get(cv2.CAP_PROP_POS_MSEC)))
        else:
            if motion == 1:
                end_time = date_time.strftime("%m-%d-%Y_%H:%M:%S")
                try:
                    open("motion/" + end_time + "_end", "x").close()
                except:
                    print("motion end")
                print("motion end time : " + end_time)
                motion = 0
            break

        frame_no += 1

        vid = cv2.flip(frame, 1)

        try:
            # Converting color image to gray_scale image
            if method == 'MOG2':
                vid = cv2.cvtColor(vid, cv2.COLOR_BGR2GRAY)
                bgs = mog.apply(vid)
            elif method == 'KNN':
                vid = cv2.cvtColor(vid, cv2.COLOR_BGR2GRAY)
                bgs = knn.apply(vid)
            elif method == 'ABS':
                frame = cv2.GaussianBlur(vid, (7, 7), 0)
                frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
                # In first iteration we assign the value
                # of static_back to our first frame
                if frame1 is None:
                    frame1 = frame
                    continue
                    # Difference between static background
                    # and current frame(which is GaussianBlur)
                framedelta = cv2.absdiff(frame1, frame)
                # If change in between static background and
                # current frame is greater than 30 it will show white color(255)
                retval, bgs = cv2.threshold(framedelta.copy(), 30, 255, cv2.THRESH_BINARY)
        except:
            break

        mask = np.zeros_like(frame)

        # Finding contour of moving object
        contours, _ = cv2.findContours(bgs.copy(),
                                       cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
        contours = sorted(contours, key=cv2.contourArea, reverse=True)

        for cnt in contours:
            if cv2.contourArea(cnt) < minimum:
                if motion == 1:
                    end_time = date_time.strftime("%m-%d-%Y_%H:%M:%S")
                    try:
                        open("motion/" + end_time + "_end", "x").close()
                    except:
                        print("motion end")
                    print("motion end time : " + end_time)
                    motion = 0
                continue

            if motion == 0:
                motion = 1
                start_time = date_time.strftime("%m-%d-%Y_%H:%M:%S")
                try:
                    open("motion/" + start_time + "_start", "x").close()
                    print("motion start time : " + start_time)
                except:
                    open("motion/" + start_time + "__start", "x").close()
                    print("motion start time : " + start_time)

            (x, y, w, h) = cv2.boundingRect(cnt)
            cv2.rectangle(vid, (x, y), (x + w, y + h), (0, 255, 10), 1)
            cv2.putText(vid, f'{method}', (20, 20), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 255, 0, 2))
            cv2.putText(vid, 'Motion Detected', (20, 40), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 255, 0, 2))
            cv2.putText(vid, 'date_time ' + date_time.strftime("%m-%d-%Y_%H:%M:%S"), (20, 60), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 255, 0, 2))

            cv2.drawContours(mask, cnt, -1, 255, 3)
            break

       # cv2.imshow('Original Frame', vid)
       # cv2.imshow(method, bgs)

        key = cv2.waitKey(1)
        if key == ord('q') or key == ord('Q'):
            # if something is movingthen it append the end time of movement

            break
        elif key == ord('M') or key == ord('m'):
            method = 'MOG2'
        elif key == ord('K') or key == ord('k'):
            method = 'KNN'
        elif key == ord('A') or key == ord('a'):
            method = 'ABS'

    video.release()

    # Destroying all the windows
    cv2.destroyAllWindows()


if __name__ == '__main__':
    t1 = threading.Thread(target= execute)
    t1.start()
    t1.join()

# if __name__ == '__main__':
#     process_list = []
#
#     for num in range(0, 10):
#         process = Process(target=execute)
#         process_list.append(process)
#
#     for process in process_list:
#         process.start()
#
#     for process in process_list:
#         process.join()