# import the necessary packages
from pyimagesearch.tempimage import TempImage
import argparse
import warnings
import datetime
import dropbox
import imutils
import json
import time
import cv2
# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-c", "--conf", required=True,
                help="path to the JSON configuration file")
args = vars(ap.parse_args())
# filter warnings, load the configuration and initialize the Dropbox
# client
warnings.filterwarnings("ignore")
conf = json.load(open(args["conf"]))
client = None

# setup dropbox client
if conf["use_dropbox"]:
    # connect to dropbox and start the session authorization process
    client = dropbox.Dropbox(conf["dropbox_access_token"])
    print("[SUCCESS] dropbox account linked")

# initialize the camera and grab a reference to the raw camera capture
camera = VideoStream(src=0).start()
camera.resolution = tuple(conf["resolution"])
camera.framerate = conf["fps"]
rawCapture = cv2.VideoCapture(args["video"])
# allow the camera to warmup, then initialize the average frame, last
# uploaded timestamp, and frame motion counter
print("[INFO] warming up...")
time.sleep(conf["camera_warmup_time"])
avg = None
lastUploaded = datetime.datetime.now()
motionCounter = 0

# capture frames from the camera
for f in camera.capture_continuous(rawCapture, format="bgr", use_video_port=True):
    # grab the raw NumPy array representing the image and initialize
    # the timestamp and occupied/unoccupied text
    frame = f.array
    timestamp = datetime.datetime.now()
    text = "Unoccupied"
    # resize the frame, convert it to grayscale, and blur it
    frame = imutils.resize(frame, width=500)
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    gray = cv2.GaussianBlur(gray, (21, 21), 0)
    # if the average frame is None, initialize it
    if avg is None:
        print("[INFO] starting background model...")
        avg = gray.copy().astype("float")
        rawCapture.truncate(0)
        continue
    # accumulate the weighted average between the current frame and
    # previous frames, then compute the difference between the current
    # frame and running average
    cv2.accumulateWeighted(gray, avg, 0.5)
    frameDelta = cv2.absdiff(gray, cv2.convertScaleAbs(avg))

    # threshold the delta image, dilate the thresholded image to fill
    # in holes, then find contours on thresholded image
    thresh = cv2.threshold(frameDelta, conf["delta_thresh"], 255,
                           cv2.THRESH_BINARY)[1]
    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) < conf["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
    ts = timestamp.strftime("%A %d %B %Y %I:%M:%S%p")
    cv2.putText(frame, "Room Status: {}".format(text), (10, 20),
                cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
    cv2.putText(frame, ts, (10, frame.shape[0] - 10), cv2.FONT_HERSHEY_SIMPLEX,
                0.35, (0, 0, 255), 1)

    # uploading to dropbox
    # check to see if the room is occupied
    if text == "Occupied":
        # check to see if enough time has passed between uploads
        if (timestamp - lastUploaded).seconds >= conf["min_upload_seconds"]:
            # increment the motion counter
            motionCounter += 1
            # check to see if the number of frames with consistent motion is
            # high enough
            if motionCounter >= conf["min_motion_frames"]:
                # check to see if dropbox sohuld be used
                if conf["use_dropbox"]:
                    # write the image to temporary file
                    t = TempImage()
                    cv2.imwrite(t.path, frame)
                    # upload the image to Dropbox and cleanup the tempory image
                    print("[UPLOAD] {}".format(ts))
                    path = "/{base_path}/{timestamp}.jpg".format(
                        base_path=conf["dropbox_base_path"], timestamp=ts)
                    client.files_upload(open(t.path, "rb").read(), path)
                    t.cleanup()
                # update the last uploaded timestamp and reset the motion
                # counter
                lastUploaded = timestamp
                motionCounter = 0
    # otherwise, the room is not occupied
    else:
        motionCounter = 0

    # check to see if the frames should be displayed to screen
    if conf["show_video"]:
        # display the security feed
        cv2.imshow("Security Feed", frame)
        key = cv2.waitKey(1) & 0xFF
        # if the `q` key is pressed, break from the lop
        if key == ord("q"):
            break
    # clear the stream in preparation for the next frame
    rawCapture.truncate(0)












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

# # 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:
#     vs = VideoStream(src=0).start()
#     time.sleep(2.0)

# # otherwise, we are reading from a video file
# else:
#     vs = 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
#     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)
#     gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
#     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)
#     cots = 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

#         # 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", thresh)
#     cv2.imshow("Frame Delta", frameDelta)
#     key = cv2.waitKey(1) & 0xFF

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

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