[OpenCV] Web Streaming from IP camera with Flask

webstreaming.py

# import the necessary packages
from pyimagesearch.motion_detection import SingleMotionDetector
from imutils.video import VideoStream
from flask import Response, Flask, render_template
import threading
import argparse 
import datetime, time
import imutils
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()

source = "rtsp://admin:[email protected]/profile2/media.smp"
cap = cv2.VideoCapture(source)
time.sleep(2.0)

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

def stream(frameCount):
    global outputFrame, lock
    
    while True:
        ret_val, frame = cap.read()
        frame = cv2.resize(frame, (640, 360))
        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        gray = cv2.GaussianBlur(gray, (7,7), 0)
        with lock:
            outputFrame = frame.copy()

def detect_motion(frameCount):
    # grab global references to the video stream, output frame, and lock variables
    # global vs, outputFrame, lock
    global outputFrame, lock
 
    # initialize the motion detector and the total number of frames
    # read thus far
    md = SingleMotionDetector.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()
        ret_val, frame = cap.read()
        # frame = imutils.resize(frame, width=400)
        frame = cv2.resize(frame, (640,360))
        # gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        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()
            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("/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()

    t = threading.Thread(target=stream, 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
cap.release()
cap.stop()