# USAGE
# python motion_detector.py
# python motion_detector.py --video videos/example_01.mp4

# import the necessary packages
from imutils.video import VideoStream
import argparse
import datetime
import imutils
import time
import cv2




def increase_brightness(img, value=30):
    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
    h, s, v = cv2.split(hsv)

    lim = 255 - value
    v[v > lim] = 255
    v[v <= lim] += value

    final_hsv = cv2.merge((h, s, v))
    img = cv2.cvtColor(final_hsv, cv2.COLOR_HSV2BGR)

    return img

# 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("-amin", "--min-area", type=int, default=3000, help="minimum area size")
ap.add_argument("-amax", "--max-area", type=int, default=10000, help="minimum area size")
args = vars(ap.parse_args())

time.sleep(5)

# if the video argument is None, then we are reading from webcam
args["video"] = "http://192.168.178.53:8000/stream.mjpg"
#args["video"] = "./videos/example_02.mp4"
vs = cv2.VideoCapture(args["video"])
counter = 0
threashold = 50
delay = 2
framerate = 30

# 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
	frame = vs.read()
	frame = frame if args.get("video", None) is None else frame[1]
	text = "Unoccupied"

	# if the frame could not be grabbed, then we have reached the end
	# of the video
	if frame is None:
		break

	# resize the frame, convert it to grayscale, and blur it
	frame = imutils.resize(frame, width=500)
	#frame = increase_brightness(frame, value=50)
	gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
	gray = cv2.GaussianBlur(gray, (31, 31), 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(gray, firstFrame)

	thresh = cv2.threshold(frameDelta, threashold, 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)
	cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
	cnts = imutils.grab_contours(cnts)

	# loop over the contours
	for c in cnts:
		# if the contour is too small, ignore it
		if cv2.contourArea(c) < args["min_area"]:
			continue
		if cv2.contourArea(c) > args["max_area"]:
			continue
		# compute the bounding box for the contour, draw it on the frame,
		# and update the text

		print(cv2.contourArea(c))

		(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", thresh)
	cv2.imshow("Frame Delta", frameDelta)

	img_yuv = cv2.cvtColor(frame, cv2.COLOR_BGR2YUV)
	# equalize the histogram of the Y channel
	img_yuv[:,:,0] = cv2.equalizeHist(img_yuv[:,:,0])
	# convert the YUV image back to RGB format
	img_output = cv2.cvtColor(img_yuv, cv2.COLOR_YUV2BGR)

	cv2.imshow("equalized", img_output)

	key = cv2.waitKey(1) & 0xFF

	# if the `q` key is pressed, break from the lop
	if key == ord("q"):
		break
	counter+=1
	#if counter % (framerate * delay) == 0:
	#	firstFrame = gray

# cleanup the camera and close any open windows
vs.stop() if args.get("video", None) is None else vs.release()
cv2.destroyAllWindows()