Flask app working really slow with opencv

One of the issue which even I had was regrading the encoding and decoding. Like the encoder of Opencv is too slow so try to use encoder from simplejpeg. Use pip3 install simplejpeg and with respect to using cv2.imencode() use simplejpeg.encode_jpeg()

• One option is using VideoStream

• The reason VideoCapture is so slow because the VideoCapture pipeline spends the most time on the reading and decoding the next frame. While the next frame is being read, decode, and returned the OpenCV application is completely blocked.

• VideoStream solves the problem by using a queue structure, concurrently read, decode, and return the current frame.

• VideoStream supports both PiCamera and webcam.

All you need to is:

• 1. Install imutils:

• For virtual environment: pip install imutils

  ---

• For anaconda environment: conda install -c conda-forge imutils

• 2. Initialize VideoStream on main.py

• import timefrom imutils.video import VideoStreamvs = VideoStream(usePiCamera=True).start()  # For PiCamera# vs = VideoStream(usePiCamera=False).start() # For Webcamcamera = Camera(vs, framesNormalQue, framesDetectionQue)

• 3. In your Camera.py

• In run(self) method:

* ```python  def run(self):      while True:          frame = self.__cam.read()          frame = cv2.resize(frame, None, fx=0.5, fy=0.5, interpolation=cv2.INTER_AREA)            _, jpeg = cv2.imencode('.jpg', frame)            self.__normalQue.put(jpeg.tobytes())            self.__detectedQue.put(deepcopy(jpeg.tobytes()))        if self.__shouldStop:            break  ```

I'm not really surprised about your problem, in general "detection" using a lot of your computation time, becauseperforming a cascaded classification algorithm is a demanding computational task.I found a source which compares cascaded classification algos for there performance link

An easy solution, would be to reduce the frame rate, whenprocessing your detection.An easy implementation to reduce performance demand could be something like a skip counter e.g.

frameSkipFactor = 3 # use every third frameframeCounter = 0if (frameCounter%frameSkipFactor==0):         #processelse:         print("ignore frame", frameCounter)frameCounter+=1

Nevertheless you will have a lag, because the detection calculationwill produce always a time offset.I you planning to construct a "real time" classification camera system, please look for another class of classification algos,which are more designed for this use-case.I followed an discussion here: real time class algos

Another solution could be using a bigger "hardware hammer" than the rpi e.g. an GPU implementation of the algo via Cuda etc.