snipplets.dev/projects/Python/OpenCV_to_HTML/webstreaming.py

#!/usr/bin/env python3

# import the necessary packages
from 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

# initialize the output frame and a lock used to ensure thread-safe
# exchanges of the output frames (useful when multiple browsers/tabs
# are viewing the stream)
outputFrame = None
lock = threading.Lock()
# initialize a flask object
app = Flask(__name__)
# initialize the video stream and allow the camera sensor to
# warmup
# vs = VideoStream(usePiCamera=1).start()
vs = VideoStream(src=0).start()
time.sleep(2.0)


def detect_motion(frameCount):
    # grab global references to the video stream, output frame, and lock variables
    global vs, outputFrame, lock
    # initialize the motion detector and the total number of frames read thus far
    md = SingleMotionDetector(accumWeight=0.1)
    total = 0

    # 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
        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)
            # 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)
        total += 1
        # acquire the lock, set the output frame, and release the lock
        with lock:
            outputFrame = frame.copy()


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'
        )


@app.route('/')
def index():
    # return the rendered template
    return render_template('index.html')


@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=(args['frame_count'],))
    t.daemon = True
    t.start()
    # start the flask app
    app.run(host=args['ip'], port=args['port'], debug=True, threaded=True, use_reloader=False)
# release the video stream pointer
vs.stop()