# import the necessary packages
import argparse
import datetime
import imutils
import time
import cv2
import numpy as np

# construct the argument parser and parse the arguments

ap = argparse.ArgumentParser()
ap.add_argument("-v", "--video", help="path to the video file")
ap.add_argument("-a", "--min-area", type=int, default=500, help="minimum area size")
args = vars(ap.parse_args())

# if the video argument is None, then we are reading from webcam
if args.get("video", None) is None:
    camera = cv2.VideoCapture(0)
    time.sleep(0.25)

# otherwise, we are reading from a video file
else:
    camera = cv2.VideoCapture(args["video"])

# initialize the first frame in the video stream
firstFrame = None

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

    # if the frame could not be grabbed, then we have reached the end
    # of the video
    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_BGR2HSV)
    # gray = cv2.GaussianBlur(gray, (21, 21), 0)

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

    # compute the absolute difference between the current frame and
    # first frame
    frameDelta = cv2.absdiff(firstFrame, gray)
    thresh = cv2.threshold(frameDelta, 25, 255, cv2.THRESH_BINARY)[1]

    # dilate the thresholded image to fill in holes, then find contours
    # on thresholded image
    # thresh = cv2.dilate(thresh, None, iterations=2)

    # lower mask (0-10)
    lower_red = np.array([0, 50, 50])
    upper_red = np.array([180, 255, 255])
    mask0 = cv2.inRange(thresh.copy(), lower_red, upper_red)

    # upper mask (170-180)
    lower_red = np.array([170, 50, 50])
    upper_red = np.array([180, 255, 255])
    mask1 = cv2.inRange(thresh.copy(), lower_red, upper_red)

    mask = mask0

    output_hsv = thresh.copy()
    output_hsv[np.where(mask == 0)] = 0

    # _, cnts, _= cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
    #                              cv2.CHAIN_APPROX_SIMPLE)
    #
    # # loop over the contours
    # for c in cnts:
    #     # if the contour is too small, ignore it
    #     if cv2.contourArea(c) < args["min_area"]:
    #         continue
    #
    #     # compute the bounding box for the contour, draw it on the frame,
    #     # and update the text
    #     (x, y, w, h) = cv2.boundingRect(c)
    #     cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
    #     text = "Occupied"

    # 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("Security Feed", frame)
    cv2.imshow("Thresh", output_hsv)
    cv2.imshow("Frame Delta", frameDelta)
    key = cv2.waitKey(1) & 0xFF

    # if the `q` key is pressed, break from the lop
    if key == ord("q"):
        break

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