!/usr/bin/python3

from flask import Response from flask import Flask from flask import render_template

from imutils.video import VideoStream

import cv2

import numpy
import threading
import argparse
import datetime
import imutils

import time

Initialize a Flask object (by convention, this is how we do it)

app = Flask(__name__)

Create an object to initialize the camera and manipulate it (we let some time to warm-up)
vs = VideoStream(usePiCamera=1).start()
vs = cv2.VideoCapture(0)
time.sleep(2.0)

Create a function that will generate frames and return them as jpeg

def gen_Frames():

   #Create an object to initialize the camera and manipulate it (we let some time to warm-up)
   vs = VideoStream(usePiCamera=1).start()
   #vs = cv2.VideoCapture(0)
   time.sleep(2.0)
   
   #Create an infinite loop
   while True:
       # read the frames from our camera
       error, frame = vs.read()
       if error:
           break
       else:            
           if frame is None:
               input('empty frame')
           else:
               (ret, buffer) = cv2.imencode(".jpg", frame)
               #if not ret:                
               #    continue
               frame = buffer.tobytes()
               #frame = bytearray(buffer)
               yield(b'--frame\r\n' b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n')
           
           # We add the date
           #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)

We define the URL pattern (let's not reinvent the wheel and stay at the root level)
We also define the index that will create the index.html
This is the route of the default page of our Flask web app

@app.route('/') def index():

   return render_template('index.html')

We define the video feed route

@app.route('/video_feed') def video_feed():

   return Response(gen_Frames(), mimetype='multipart/x-mixed-replace; boundary=frame')

Main function, starting the Flask server

if __name__ == "__main__":

   #arg = argparse.ArgumentParser()
   #arg.add_argument("-i", "--ip", type=str, required=True, help="ip address of our server")
   #arg.add_argument("-p", "--port", type=int, required=True, help="port number of our server (> 1024)")
   #args = vars(arg.parse_args())
   
   #app.run(host=args["ip"], port=args["port"], debug=True)
   
   app.run(debug=True)

!/usr/bin/python3
import the necessary packages

from singlemotiondetector 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)

@app.route("/") def index():

   # return the rendered template
   return render_template("index.html")

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("/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()

!/usr/bin/python3
import the necessary packages

import numpy as np import imutils import cv2 class SingleMotionDetector:

   def __init__(self, accumWeight=0.5):
       # store the accumulated weight factor
       self.accumWeight = accumWeight
       # initialize the background model
       self.bg = None
       
   def update(self, image):
       # if the background model is None, initialize it
       if self.bg is None:
           self.bg = image.copy().astype("float")
           return

       # update the background model by accumulating the weighted
       # average
       cv2.accumulateWeighted(image, self.bg, self.accumWeight)
       
       
   def detect(self, image, tVal=25):
       # compute the absolute difference between the background model
       # and the image passed in, then threshold the delta image
       delta = cv2.absdiff(self.bg.astype("uint8"), image)
       thresh = cv2.threshold(delta, tVal, 255, cv2.THRESH_BINARY)[1]
       # perform a series of erosions and dilations to remove small
       # blobs
       thresh = cv2.erode(thresh, None, iterations=2)
       thresh = cv2.dilate(thresh, None, iterations=2)
       
       # find contours in the thresholded image and initialize the
       # minimum and maximum bounding box regions for motion
       cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
           cv2.CHAIN_APPROX_SIMPLE)
       cnts = imutils.grab_contours(cnts)
       (minX, minY) = (np.inf, np.inf)
       (maxX, maxY) = (-np.inf, -np.inf)
       
       # if no contours were found, return None
       if len(cnts) == 0:
           return None
       # otherwise, loop over the contours
       for c in cnts:
           # compute the bounding box of the contour and use it to
           # update the minimum and maximum bounding box regions
           (x, y, w, h) = cv2.boundingRect(c)
           (minX, minY) = (min(minX, x), min(minY, y))
           (maxX, maxY) = (max(maxX, x + w), max(maxY, y + h))
       # otherwise, return a tuple of the thresholded image along
       # with bounding box
       return (thresh, (minX, minY, maxX, maxY))

RPI4 - Camera V3