# USAGE
# python webstreaming.py --ip 0.0.0.0 --port 8000

# import the necessary packages
from pyimagesearch.motion_detection import SingleMotionDetector
from imutils.video import VideoStream
from flask import Response
from flask import Flask
from flask import render_template
import threading
import argparse
import datetime
import imutils
import time
import cv2
import sqlite3
import math
import os.path
import json
import requests
#import urllib
import numpy as np
from flask_cors import CORS


# initialize the output frame and a lock used to ensure thread-safe
# exchanges of the output frames (useful for multiple browsers/tabs
# are viewing tthe stream)
outputFrame = None
lock = threading.Lock()

# initialize a flask object
app = Flask(__name__)
CORS(app)


# initialize the video stream and allow the camera sensor to
# warmup
#vs = VideoStream(usePiCamera=1).start()
#vs = VideoStream(src=0).start()
vs = cv2.VideoCapture("rtsp://192.168.0.23:8554/unicast")
if not vs:
    print("!!! Failed VideoCapture: invalid parameter!")
#vs=urllib.urlopen(cap)

#time.sleep(2.0)







def generate():
    # grab global references to the output frame and lock variables
    global outputFrame, lock
   
    # loop over frames from the output stream
    while True:
        # wait until the lock is acquired
        with lock:
            # check if the output frame is available, otherwise skip
            # the iteration of the loop
            if outputFrame is None:
                continue

            # encode the frame in JPEG format
            (flag, encodedImage) = cv2.imencode(".jpg", outputFrame)

            # ensure the frame was successfully encoded
            if not flag:
                continue

        # yield the output frame in the byte format
        yield(b'--frame\r\n' b'Content-Type: image/jpeg\r\n\r\n' + 
            bytearray(encodedImage) + b'\r\n')
        



def detect_motion(frameCount):
    # grab global references to the video stream, output frame, and
    # lock variables
    global vs, outputFrame, lock
    total = 0
   

    # initialize the motion detector and the total number of frames
    # read thus far
    md = SingleMotionDetector(accumWeight=0.1)
    # loop over frames from the video stream
    while True:
        
        #print('OKKK')
        # read the next frame from the video stream, resize it,
        # convert the frame to grayscale, and blur it
        ret, frame = vs.read()
        frame = imutils.resize(frame, width=400)
        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        gray = cv2.GaussianBlur(gray, (7, 7), 0)

        # grab the current timestamp and draw it on the frame
        timestamp = datetime.datetime.now()
        
        
        
        #cv2.putText(frame, timestamp.strftime("%A %d %B %Y %I:%M:%S%p"), (10, frame.shape[0] - 10),cv2.FONT_HERSHEY_SIMPLEX, 0.35, (0, 0, 255), 1)

        # if the total number of frames has reached a sufficient
        # number to construct a reasonable background model, then
        # continue to process the frame
        
        if total > frameCount:
            # detect motion in the image
            motion = md.detect(gray)
            
            # cehck to see if motion was found in the frame
            if motion is not None:
                # unpack the tuple and draw the box surrounding the
                # "motion area" on the output frame
               
                (thresh, (minX, minY, maxX, maxY)) = motion
                cv2.rectangle(frame, (minX, minY), (maxX, maxY),(0, 0, 255), 2)
                  
               
        
        # update the background model and increment the total number
        # of frames read thus far
        md.update(gray)
        total += 1

        # acquire the lock, set the output frame, and release the
        # lock
        with lock:
            outputFrame = frame.copy()
        
        

        

@app.route("/video_feed")
def video_feed():
    # return the response generated along with the specific media
    # type (mime type)
    return Response(generate(),
        mimetype = "multipart/x-mixed-replace; boundary=frame")

# check to see if this is the main thread of execution
if __name__ == '__main__':
    # construct the argument parser and parse command line arguments
    #ap = argparse.ArgumentParser()
    #ap.add_argument("-i", "--ip", type=str, required=True,help="ip address of the device")
    #ap.add_argument("-o", "--port", type=int, required=True,help="ephemeral port number of the server (1024 to 65535)")
    #ap.add_argument("-f", "--frame-count", type=int, default=32,help="# of frames used to construct the background model")
    #args = vars(ap.parse_args())
    
    # start a thread that will perform motion detection
    t = threading.Thread(target=detect_motion, args=(32,))
    t.daemon = True
    t.start()

    # start the flask app
    app.run(host="0.0.0.0", port=8001, debug=True,threaded=True, use_reloader=False)

# release the video stream pointer
vs.stop()