#!/usr/bin/env python3
#
# WebStreaming + Motion detector algorithm will detect motion by form of background subtraction
#
# Prerequisites:
# ---------------
# - pip install flask
# - pip install numpy
# - pip install opencv-contrib-python
# - pip install imutils
#
# Usage Examples:
# ---------------
# Live streaming from local device, i.e. camera which is physically connected to the host machine/server (both commands are equivalent):
# % python app.py --device_ip 0.0.0.0 --server_port 8000
# % env DEVICE_IP="0.0.0.0" SERVER_PORT=8000 ./app.py
#
# Live streaming from Spain:
# % env DEVICE_IP="0.0.0.0" SERVER_PORT=8000 REMOTE_IP="http://192.168.127.12:60001/cgi-bin/snapshot.cgi?chn=0&u=admin&p=&q=0&COUNTER" ./app.py
#
# Live streaming from Israel:
# % env DEVICE_IP="0.0.0.0" SERVER_PORT=8000 REMOTE_IP="http://172.16.17.32:8082/cgi-bin/faststream.jpg?stream=half&fps=15&rand=COUNTER" ./app.py
#
# Note:
# -----
# MULTI_THREAD_EN env can be applied for enabling/disabling multi-thread setup, i.e. whether or not video processing
# will be done in a separate thread or not (might become sensitive when running in a production server).
#
# References:
# -----------
# 1. https://www.pyimagesearch.com/2019/09/02/opencv-stream-video-to-web-browser-html-page
# 2. https://www.pyimagesearch.com/2019/04/15/live-video-streaming-over-network-with-opencv-and-imagezmq/
# 3. https://www.pyimagesearch.com/2017/02/06/faster-video-file-fps-with-cv2-videocapture-and-opencv
# 4. https://stackabuse.com/deploying-a-flask-application-to-heroku/
# 5. https://github.com/mrxmamun/camera-live-streaming
# 6. https://github.com/mrxmamun/live-stream-face-detection
# 6. https://www.insecam.org
#
# (C) All rights reserved to <NAME>, <EMAIL>, Nov-2019

# ---------------------------------------------------------------------------------------------------

from imutils.video import VideoStream
from flask import Response
from flask import Flask
from flask import render_template
import SingleMotionDetector
import threading
import argparse
import datetime
import imutils
import time
import cv2
import os

# ---------------------------------------------------------------------------------------------------
# Globals:
vs = None                 # stream reader (LOCAL mode)
cap = cv2.VideoCapture()  # stream reader (REMOTE mode)
outputFrame = None        # output frame initialization
lock = threading.Lock()   # ensures thread-safe exchanges of the output frames (multiple browsers/tabs view the stream)
frame_idx = 0             # Frame index

# Initialize the motion detector and the total number of frames read thus far
md = SingleMotionDetector(accumWeight=0.1)

bs_frame_count = int(os.environ.get('BS_FRAME_CNT', "32"))

multi_therad_en = bool(int(os.environ.get('MULTI_THREAD_EN', 1)))

remote_ip = os.environ.get('REMOTE_IP', "0.0.0.0")
local_mode = (remote_ip == "0.0.0.0")

# ---------------------------------------------------------------------------------------------------
# Initialize a flask object
app = Flask(__name__)

# ---------------------------------------------------------------------------------------------------
# Initialize the video stream and allow the camera sensor to warmup
if local_mode:
	# vs = VideoStream(usePiCamera=1).start()
	vs = VideoStream(src=0).start()
else:
	cap.open(remote_ip)

time.sleep(2.0)

# ---------------------------------------------------------------------------------------------------

@app.route("/")
def index():
	""" Return the rendered template """

	return render_template("index.html")

# ---------------------------------------------------------------------------------------------------

def detect_motion_core(frame, lock_en):
	""" Detect motion by form of background subtraction """

	# Grab global references to the output frame
	global outputFrame, md

	# PreProcessing: resize, convert to grayscale, and blur
	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)

	# Perform Motion-Detection, if the total number of frames has reached a sufficient number to construct a reasonable background model:
	if frame_idx > bs_frame_count:

		# Detect motion in the image
		motion = md.detect(gray)

		# Check 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)

	# Set the output frame
	if lock_en:
		with lock:
			outputFrame = frame.copy()
	else:
		outputFrame = frame.copy()

# ---------------------------------------------------------------------------------------------------

def detect_motion(frame):
	""" Detect motion by form of background subtraction """

	# Grab global references to the output frame
	global outputFrame, frame_idx

	detect_motion_core(frame, False)
	frame_idx += 1

# ---------------------------------------------------------------------------------------------------

def detect_motion_thread():
	""" Detect motion by form of background subtraction """

	# Grab global references to the video stream, output frame, and lock variables
	global vs, cap, outputFrame, frame_idx, lock

	# Loop over frames from the video stream
	while True:

		# Read the next frame from the video stream, resize it, convert the frame to grayscale, and blur it
		ret = True
		if local_mode:
			frame = vs.read()
		else:
			ret, frame = cap.read()

		if ret:
			detect_motion_core(frame, True)
			frame_idx += 1

# ---------------------------------------------------------------------------------------------------

def generate():

	# Grab global references to the output frame and lock variables
	global outputFrame, cap, lock

	# Loop over frames from the output stream
	while True:

		# Wait until the lock is acquired
		with lock:

			# TODO - need to debug why detect_motion thread doesn't work on server...:
			if not multi_therad_en:
				ret = True
				if local_mode:
					frame = vs.read()
				else:
					ret, frame = cap.read()

				if ret:
					detect_motion(frame)

			# 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')

# ---------------------------------------------------------------------------------------------------

@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", "--device_ip",
					type=str,
					default=os.environ.get('DEVICE_IP', "0.0.0.0"),
					help="ip address of the local device (0.0.0.0 means listen on all public IPs)")

	ap.add_argument("-o", "--server_port",
					type=int,
					default=int(os.environ.get('SERVER_PORT', "8000")),
					help="ephemeral port number of the server (1024 to 65535)")
	args = vars(ap.parse_args())

	# -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- --
	# Start a thread that will perform motion detection
	if multi_therad_en:
		t = threading.Thread(target=detect_motion_thread)
		t.daemon = True
		t.start()

	# -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- --
	# Start the flask app
	app.run(host=args["device_ip"],
			port=args["server_port"],
			debug=True,
			threaded=True,
			use_reloader=False)

	# -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- --
	# Release the video stream pointer
	if local_mode:
		vs.stop()
	else:
		cap.release()

#!/usr/bin/env python3
#
# WebStreaming + Motion detector algorithm will detect motion by form of background subtraction
#
# Prerequisites:
# ---------------
# - pip install flask
# - pip install numpy
# - pip install opencv-contrib-python
# - pip install imutils
#
# Usage Examples:
# ---------------
# Live streaming from local device, i.e. camera which is physically connected to the host machine/server (both commands are equivalent):
# % python app.py --device_ip 0.0.0.0 --server_port 8000
# % env DEVICE_IP="0.0.0.0" SERVER_PORT=8000 ./app.py
#
# Live streaming from Spain:
# % env DEVICE_IP="0.0.0.0" SERVER_PORT=8000 REMOTE_IP="http://192.168.127.12:60001/cgi-bin/snapshot.cgi?chn=0&u=admin&p=&q=0&COUNTER" ./app.py
#
# Live streaming from Israel:
# % env DEVICE_IP="0.0.0.0" SERVER_PORT=8000 REMOTE_IP="http://172.16.17.32:8082/cgi-bin/faststream.jpg?stream=half&fps=15&rand=COUNTER" ./app.py
#
# Note:
# -----
# MULTI_THREAD_EN env can be applied for enabling/disabling multi-thread setup, i.e. whether or not video processing
# will be done in a separate thread or not (might become sensitive when running in a production server).
#
# References:
# -----------
# 1. https://www.pyimagesearch.com/2019/09/02/opencv-stream-video-to-web-browser-html-page
# 2. https://www.pyimagesearch.com/2019/04/15/live-video-streaming-over-network-with-opencv-and-imagezmq/
# 3. https://www.pyimagesearch.com/2017/02/06/faster-video-file-fps-with-cv2-videocapture-and-opencv
# 4. https://stackabuse.com/deploying-a-flask-application-to-heroku/
# 5. https://github.com/mrxmamun/camera-live-streaming
# 6. https://github.com/mrxmamun/live-stream-face-detection
# 6. https://www.insecam.org
#
# (C) All rights reserved to <NAME>, <EMAIL>, Nov-2019

# ---------------------------------------------------------------------------------------------------

from imutils.video import VideoStream
from flask import Response
from flask import Flask
from flask import render_template
import SingleMotionDetector
import threading
import argparse
import datetime
import imutils
import time
import cv2
import os

# ---------------------------------------------------------------------------------------------------
# Globals:
vs = None                 # stream reader (LOCAL mode)
cap = cv2.VideoCapture()  # stream reader (REMOTE mode)
outputFrame = None        # output frame initialization
lock = threading.Lock()   # ensures thread-safe exchanges of the output frames (multiple browsers/tabs view the stream)
frame_idx = 0             # Frame index

# Initialize the motion detector and the total number of frames read thus far
md = SingleMotionDetector(accumWeight=0.1)

bs_frame_count = int(os.environ.get('BS_FRAME_CNT', "32"))

multi_therad_en = bool(int(os.environ.get('MULTI_THREAD_EN', 1)))

remote_ip = os.environ.get('REMOTE_IP', "0.0.0.0")
local_mode = (remote_ip == "0.0.0.0")

# ---------------------------------------------------------------------------------------------------
# Initialize a flask object
app = Flask(__name__)

# ---------------------------------------------------------------------------------------------------
# Initialize the video stream and allow the camera sensor to warmup
if local_mode:
	# vs = VideoStream(usePiCamera=1).start()
	vs = VideoStream(src=0).start()
else:
	cap.open(remote_ip)

time.sleep(2.0)

# ---------------------------------------------------------------------------------------------------

@app.route("/")
def index():
	""" Return the rendered template """

	return render_template("index.html")

# ---------------------------------------------------------------------------------------------------

def detect_motion_core(frame, lock_en):
	""" Detect motion by form of background subtraction """

	# Grab global references to the output frame
	global outputFrame, md

	# PreProcessing: resize, convert to grayscale, and blur
	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)

	# Perform Motion-Detection, if the total number of frames has reached a sufficient number to construct a reasonable background model:
	if frame_idx > bs_frame_count:

		# Detect motion in the image
		motion = md.detect(gray)

		# Check 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)

	# Set the output frame
	if lock_en:
		with lock:
			outputFrame = frame.copy()
	else:
		outputFrame = frame.copy()

# ---------------------------------------------------------------------------------------------------

def detect_motion(frame):
	""" Detect motion by form of background subtraction """

	# Grab global references to the output frame
	global outputFrame, frame_idx

	detect_motion_core(frame, False)
	frame_idx += 1

# ---------------------------------------------------------------------------------------------------

def detect_motion_thread():
	""" Detect motion by form of background subtraction """

	# Grab global references to the video stream, output frame, and lock variables
	global vs, cap, outputFrame, frame_idx, lock

	# Loop over frames from the video stream
	while True:

		# Read the next frame from the video stream, resize it, convert the frame to grayscale, and blur it
		ret = True
		if local_mode:
			frame = vs.read()
		else:
			ret, frame = cap.read()

		if ret:
			detect_motion_core(frame, True)
			frame_idx += 1

# ---------------------------------------------------------------------------------------------------

def generate():

	# Grab global references to the output frame and lock variables
	global outputFrame, cap, lock

	# Loop over frames from the output stream
	while True:

		# Wait until the lock is acquired
		with lock:

			# TODO - need to debug why detect_motion thread doesn't work on server...:
			if not multi_therad_en:
				ret = True
				if local_mode:
					frame = vs.read()
				else:
					ret, frame = cap.read()

				if ret:
					detect_motion(frame)

			# 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')

# ---------------------------------------------------------------------------------------------------

@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", "--device_ip",
					type=str,
					default=os.environ.get('DEVICE_IP', "0.0.0.0"),
					help="ip address of the local device (0.0.0.0 means listen on all public IPs)")

	ap.add_argument("-o", "--server_port",
					type=int,
					default=int(os.environ.get('SERVER_PORT', "8000")),
					help="ephemeral port number of the server (1024 to 65535)")
	args = vars(ap.parse_args())

	# -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- --
	# Start a thread that will perform motion detection
	if multi_therad_en:
		t = threading.Thread(target=detect_motion_thread)
		t.daemon = True
		t.start()

	# -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- --
	# Start the flask app
	app.run(host=args["device_ip"],
			port=args["server_port"],
			debug=True,
			threaded=True,
			use_reloader=False)

	# -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- --
	# Release the video stream pointer
	if local_mode:
		vs.stop()
	else:
		cap.release()

#!/usr/bin/env python3 # # WebStreaming + Motion detector algorithm will detect motion by form of background subtraction # # Prerequisites: # --------------- # - pip install flask # - pip install numpy # - pip install opencv-contrib-python # - pip install imutils # # Usage Examples: # --------------- # Live streaming from local device, i.e. camera which is physically connected to the host machine/server (both commands are equivalent): # % python app.py --device_ip 0.0.0.0 --server_port 8000 # % env DEVICE_IP="0.0.0.0" SERVER_PORT=8000 ./app.py # # Live streaming from Spain: # % env DEVICE_IP="0.0.0.0" SERVER_PORT=8000 REMOTE_IP="http://192.168.127.12:60001/cgi-bin/snapshot.cgi?chn=0&u=admin&p=&q=0&COUNTER" ./app.py # # Live streaming from Israel: # % env DEVICE_IP="0.0.0.0" SERVER_PORT=8000 REMOTE_IP="http://172.16.17.32:8082/cgi-bin/faststream.jpg?stream=half&fps=15&rand=COUNTER" ./app.py # # Note: # ----- # MULTI_THREAD_EN env can be applied for enabling/disabling multi-thread setup, i.e. whether or not video processing # will be done in a separate thread or not (might become sensitive when running in a production server). # # References: # ----------- # 1. https://www.pyimagesearch.com/2019/09/02/opencv-stream-video-to-web-browser-html-page # 2. https://www.pyimagesearch.com/2019/04/15/live-video-streaming-over-network-with-opencv-and-imagezmq/ # 3. https://www.pyimagesearch.com/2017/02/06/faster-video-file-fps-with-cv2-videocapture-and-opencv # 4. https://stackabuse.com/deploying-a-flask-application-to-heroku/ # 5. https://github.com/mrxmamun/camera-live-streaming # 6. https://github.com/mrxmamun/live-stream-face-detection # 6. https://www.insecam.org # # (C) All rights reserved to <NAME>, <EMAIL>, Nov-2019 # --------------------------------------------------------------------------------------------------- # --------------------------------------------------------------------------------------------------- # Globals: # stream reader (LOCAL mode) # stream reader (REMOTE mode) # output frame initialization # ensures thread-safe exchanges of the output frames (multiple browsers/tabs view the stream) # Frame index # Initialize the motion detector and the total number of frames read thus far # --------------------------------------------------------------------------------------------------- # Initialize a flask object # --------------------------------------------------------------------------------------------------- # Initialize the video stream and allow the camera sensor to warmup # vs = VideoStream(usePiCamera=1).start() # --------------------------------------------------------------------------------------------------- Return the rendered template # --------------------------------------------------------------------------------------------------- Detect motion by form of background subtraction # Grab global references to the output frame # PreProcessing: resize, convert to grayscale, and blur # Grab the current timestamp and draw it on the frame # Perform Motion-Detection, if the total number of frames has reached a sufficient number to construct a reasonable background model: # Detect motion in the image # Check to see if motion was found in the frame # Unpack the tuple and draw the box surrounding the "motion area" on the output frame # Update the background model and increment the total number of frames read thus far # Set the output frame # --------------------------------------------------------------------------------------------------- Detect motion by form of background subtraction # Grab global references to the output frame # --------------------------------------------------------------------------------------------------- Detect motion by form of background subtraction # Grab global references to the video stream, output frame, and lock variables # Loop over frames from the video stream # Read the next frame from the video stream, resize it, convert the frame to grayscale, and blur it # --------------------------------------------------------------------------------------------------- # Grab global references to the output frame and lock variables # Loop over frames from the output stream # Wait until the lock is acquired # TODO - need to debug why detect_motion thread doesn't work on server...: # Check if the output frame is available, otherwise skip the iteration of the loop # Encode the frame in JPEG format # Ensure the frame was successfully encoded # Yield the output frame in the byte format # --------------------------------------------------------------------------------------------------- Return the response generated along with the specific media type (mime type) # =================================================================================================== # Check to see if this is the main thread of execution # -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- # Construct the argument parser and parse command line arguments # -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- # Start a thread that will perform motion detection # -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- # Start the flask app # -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- # Release the video stream pointer