TypeError: cannot pickle ‘_asyncio.Future’ object

[Abdul_Rafey]

I am making a face recognition attendance system using python, whenever a request comes to WebSocket it opens a new camera in a new process using multiprocessing because in real world multiple classes took place parallelly at a school, so I want to open multiple cameras one by one according to class time which may open parallelly to record students' attendance. Everything works fine but when it comes to send message from process created with multiprocessing, I get an error (TypeError: cannot pickle ‘_asyncio.Future’ object) (COMPLETE ERROR IS GIVEN BELOW), The error “Can’t pickle local object” occurs because the Process class from the multiprocessing module requires the arguments passed to its target function to be picklable. Pickling is the process of converting a Python object into a byte stream so that it can be stored on disk or transmitted over a network. In my code, I’m passing the websocket object as an argument to the process_camera function, which is intended to be executed in a separate process. However, the websocket object is an instance of a class that is not picklable, meaning it cannot be serialized into a byte stream. This is because the websocket object contains references to underlying socket connections and other resources that cannot be serialized.

Any idea how I can send messages from multiple processes to the other side.



Extra Information: I’m using electron (a framework which is used to make cross-platform desktop applications with only HTML, CSS and JS.) with python and I am using websockets as a medium of communication between them. Each time the user wants to open the camera from the front-end, a request using websockets goes to python to open the camera in a separate process and start face recognition, I want to get responses from those camera processes but I’m getting errors.

I’m new to websockets and asyncio and don’t know to much about them.
The whole point is,

I want my code to run fast efficiency and use full PC resources (CPU) if available (I may add use of GPU later when I use Tenserflow for deep learning).
I want a proper bi-directional communication between python and electron. For now, I only want to send message when a camera is turned off (or a process finished) and want to return error if any exception happened while doing face recognition. But may be in future I wish to send more data of specific type as my application grows.

Code link (code not editable): https://github.com/khawajaRafey/Face-Rec..._camera.py
Code link (code editable): https://replit.com/join/fhkxkqbpre-abdulrafey7
ERROR IS IN THE END OF THE CODE AS A COMMENT

I appreciate if you show me solution practically by modifying my code.

import asyncio
import sqlite3
import pickle
import face_recognition
import cv2
import time
import numpy as np
import websockets
import json
from multiprocessing import Process

def process_camera(id, camera_name, websocket):
    try:
        # Connect to the SQLite database
        conn = sqlite3.connect('data.db')
        cursor = conn.cursor()

        # Execute the SQL query
        query = """
            SELECT students.name, students.image_encodings 
            FROM students 
            LEFT JOIN subjects ON students.class_id = subjects.class_id 
            WHERE subjects.id = ? AND students.image_encodings IS NOT NULL;
        """

        cursor.execute(query, (id,))

        # Fetch the results
        results = cursor.fetchall()

        # Close the database connection
        conn.close()

        known_face_encodings = []
        known_face_names = []

        for row in results:
            name, image_encodings = row
            for encoding in pickle.loads(image_encodings):
                known_face_names.append(name)
                known_face_encodings.append(encoding)  

        # Get a reference to webcam #0 (the default one)
        video_capture = cv2.VideoCapture(camera_name)
        
        # Initialize some variables
        Thershold = 0.5
        face_locations = []
        face_encodings = []
        face_names = []
        process_this_frame = True

        comparison_interval = 1  # Change this to your desired interval
        last_comparison_time = time.time()

        while True:
            # Grab a single frame of video
            ret, frame = video_capture.read()
            if not ret:
                raise Exception("Error capturing frame")

            current_time = time.time()
            if current_time - last_comparison_time >= comparison_interval:
                process_this_frame = True
                last_comparison_time = current_time
            else:
                process_this_frame = False

            # Only process every other frame of video to save time
            if process_this_frame:
                # Resize frame of video to 1/4 size for faster face recognition processing
                small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
                # small_frame = frame

                # Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
                rgb_small_frame = np.ascontiguousarray(small_frame[:, :, ::-1])
                
                # Find all the faces and face encodings in the current frame of video
                face_locations = face_recognition.face_locations(rgb_small_frame, 2)
                face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations)

                face_names = []
                for face_encoding in face_encodings:
                    # See if the face is a match for the known face(s)
                    matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
                    name = "Unknown"

                    # If a match was found in known_face_encodings, just use the first one.
                    # if True in matches:
                    #     first_match_index = matches.index(True)
                    #     name = known_face_names[first_match_index]

                    # Or instead, use the known face with the smallest distance to the new face
                    if True in matches:
                        face_distances = face_recognition.face_distance(known_face_encodings, face_encoding)
                        best_match_index = np.argmin(face_distances)
                        if face_distances[best_match_index] < Thershold:
                            name = known_face_names[best_match_index]
                            
                    face_names.append(name)
        

            process_this_frame = not process_this_frame

            # Send the face names to the client via the queue
            websocket.send(json.dumps(face_names))

            # Display the results (optional)
            for (top, right, bottom, left), name in zip(face_locations, face_names):
                # Scale back up face locations since the frame we detected in was scaled to 1/4 size
                top *= 4
                right *= 4
                bottom *= 4
                left *= 4

                # Draw a box around the face
                cv2.rectangle(frame, (left, top), (right, bottom), (255, 255, 255), 2)

                # Draw a label with a name below the face
                cv2.rectangle(frame, (left, bottom), (right, bottom + 35), (255, 255, 255), cv2.FILLED)
                font = cv2.FONT_HERSHEY_DUPLEX
                cv2.putText(frame, name, (left + 6, bottom + 27), font, 1.0, (0, 0, 0), 1)

            # Display the resulting image
            cv2.imshow('Face Detection', frame)

            keyCode = cv2.waitKey(1) # wait for input to break the loop
            if cv2.getWindowProperty('Face Detection', cv2.WND_PROP_VISIBLE) <1:
                break

    except Exception as e:
        print(str(e))
        websocket.send({'error': str(e)})

    finally:
        # Release handle to the webcam
        video_capture.release()
        cv2.destroyAllWindows()

async def start_server(websocket, path):
    try:
        # Get the JSON data from the WebSocket connection
        async for message in websocket:
            data = json.loads(message)
            if data:
                # Extract the subject ID from the JSON data
                subject_id = data.get('subject_id')
                camera_name = data.get('ip')
                                       
                # Start camera capture in a separate process
                p = Process(target=process_camera, args=(subject_id, camera_name, websocket))
                p.start()



    except websockets.exceptions.ConnectionClosedOK:
        print("Client connection closed")


async def main():
    async with websockets.serve(start_server, "localhost", 8765):
        await asyncio.Future()  # Keep the server running indefinitely


if __name__ == "__main__":
    asyncio.run(main())

Error:
Error:

connection handler failed
Traceback (most recent call last):
  File "C:\Users\Abdul Rafey\AppData\Local\Programs\Python\Python38\lib\site-packages\websockets\legacy\server.py", line 236, in handler     
    await self.ws_handler(self)
  File "C:\Users\Abdul Rafey\AppData\Local\Programs\Python\Python38\lib\site-packages\websockets\legacy\server.py", line 1175, in _ws_handler
    return await cast(
  File "c:/Users/Abdul Rafey/OneDrive/Desktop/electron app/electron-quick-start/python/camera.py", line 31, in start_server
    p.start()
  File "C:\Users\Abdul Rafey\AppData\Local\Programs\Python\Python38\lib\multiprocessing\process.py", line 121, in start
    self._popen = self._Popen(self)
  File "C:\Users\Abdul Rafey\AppData\Local\Programs\Python\Python38\lib\multiprocessing\context.py", line 224, in _Popen
    return _default_context.get_context().Process._Popen(process_obj)
  File "C:\Users\Abdul Rafey\AppData\Local\Programs\Python\Python38\lib\multiprocessing\context.py", line 327, in _Popen
    return Popen(process_obj)
  File "C:\Users\Abdul Rafey\AppData\Local\Programs\Python\Python38\lib\multiprocessing\popen_spawn_win32.py", line 93, in __init__
    reduction.dump(process_obj, to_child)
  File "C:\Users\Abdul Rafey\AppData\Local\Programs\Python\Python38\lib\multiprocessing\reduction.py", line 60, in dump
    ForkingPickler(file, protocol).dump(obj)
TypeError: cannot pickle '_asyncio.Future' object
Traceback (most recent call last):
  File "<string>", line 1, in <module>
  File "C:\Users\Abdul Rafey\AppData\Local\Programs\Python\Python38\lib\multiprocessing\spawn.py", line 107, in spawn_main
    new_handle = reduction.duplicate(pipe_handle,
  File "C:\Users\Abdul Rafey\AppData\Local\Programs\Python\Python38\lib\multiprocessing\reduction.py", line 79, in duplicate
    return _winapi.DuplicateHandle(
OSError: [WinError 6] The handle is invalid

buran write Mar-07-2024, 04:07 PM:
post code here, using BBCode tags. I did that for you this time. No need for repo link for small code like this

[deanhystad]

Is "don't use windows" an acceptable answer? Your application uses pickle because multiprocessing in windows spawns processes. New processes don't share any resources with the parent process. If you did this on Linux (or maybe on mac?) the new processes would be forked. The new process starts knowing everything the parent process did up to that point. You would have no problem with passing a websocket to the new process because the websocket object would exist in both processes with no serialization required.

If you can't do that, how about making your main process a server application? The main process creates a thread to listen for connect requests.
The spawned processes open a socket connection to the server and pass facial recognition information (some sort of image?) to the server. I don't know a lot about web anything, but this is how I would create an application like this using normal sockets.