363 lines
No EOL
16 KiB
Python
363 lines
No EOL
16 KiB
Python
from typing import List
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from fastapi import FastAPI, WebSocket
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from fastapi.websockets import WebSocketDisconnect
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from websockets.exceptions import ConnectionClosedError
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from ultralytics import YOLO
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import torch
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import cv2
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import base64
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import numpy as np
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import json
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import logging
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import threading
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import queue
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import os
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import requests
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from urllib.parse import urlparse
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import asyncio
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import psutil
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app = FastAPI()
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models = {}
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with open("config.json", "r") as f:
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config = json.load(f)
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poll_interval = config.get("poll_interval_ms", 100)
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reconnect_interval = config.get("reconnect_interval_sec", 5)
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TARGET_FPS = config.get("target_fps", 10)
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poll_interval = 1000 / TARGET_FPS
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logging.info(f"Poll interval: {poll_interval}ms")
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max_streams = config.get("max_streams", 5)
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max_retries = config.get("max_retries", 3)
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# Configure logging
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logging.basicConfig(
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level=logging.DEBUG,
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format="%(asctime)s [%(levelname)s] %(message)s",
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handlers=[
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logging.FileHandler("app.log"),
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logging.StreamHandler()
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]
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)
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# Ensure the models directory exists
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os.makedirs("models", exist_ok=True)
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# Add constants for heartbeat
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HEARTBEAT_INTERVAL = 2 # seconds
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WORKER_TIMEOUT_MS = 10000
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# Add a lock for thread-safe operations on shared resources
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streams_lock = threading.Lock()
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models_lock = threading.Lock()
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@app.websocket("/")
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async def detect(websocket: WebSocket):
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import asyncio
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import time
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logging.info("WebSocket connection accepted")
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streams = {}
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# This function is user-modifiable
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# Save data you want to persist across frames in the persistent_data dictionary
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async def handle_detection(camera_id, stream, frame, websocket, model: YOLO, persistent_data):
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try:
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boxes = []
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for r in model.track(frame, stream=False, persist=True):
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for box in r.boxes:
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track_id = None
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if hasattr(box, "id") and box.id is not None:
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track_id = box.id.item()
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box_cpu = box.cpu()
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boxes.append({
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"class": model.names[int(box_cpu.cls[0])],
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"confidence": float(box_cpu.conf[0]),
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"id": track_id,
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})
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# Broadcast to all subscribers of this URL
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detection_data = {
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"type": "imageDetection",
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"cameraIdentifier": camera_id,
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"timestamp": time.time(),
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"data": {
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"detections": boxes,
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"modelId": stream['modelId'],
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"modelName": stream['modelName']
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}
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}
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logging.debug(f"Sending detection data for camera {camera_id}: {detection_data}")
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await websocket.send_json(detection_data)
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return persistent_data
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except Exception as e:
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logging.error(f"Error in handle_detection for camera {camera_id}: {e}")
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return persistent_data
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def frame_reader(camera_id, cap, buffer, stop_event):
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import time
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retries = 0
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try:
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while not stop_event.is_set():
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try:
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ret, frame = cap.read()
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if not ret:
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logging.warning(f"Connection lost for camera: {camera_id}, retry {retries+1}/{max_retries}")
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cap.release()
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time.sleep(reconnect_interval)
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retries += 1
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if retries > max_retries:
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logging.error(f"Max retries reached for camera: {camera_id}")
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break
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# Re-open the VideoCapture
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cap = cv2.VideoCapture(streams[camera_id]['rtsp_url'])
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if not cap.isOpened():
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logging.error(f"Failed to reopen RTSP stream for camera: {camera_id}")
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continue
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continue
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retries = 0 # Reset on success
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if not buffer.empty():
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try:
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buffer.get_nowait() # Discard the old frame
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except queue.Empty:
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pass
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buffer.put(frame)
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except cv2.error as e:
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logging.error(f"OpenCV error for camera {camera_id}: {e}")
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cap.release()
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time.sleep(reconnect_interval)
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retries += 1
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if retries > max_retries and max_retries != -1:
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logging.error(f"Max retries reached after OpenCV error for camera: {camera_id}")
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break
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# Re-open the VideoCapture
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cap = cv2.VideoCapture(streams[camera_id]['rtsp_url'])
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if not cap.isOpened():
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logging.error(f"Failed to reopen RTSP stream for camera {camera_id} after OpenCV error")
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continue
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except Exception as e:
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logging.error(f"Unexpected error for camera {camera_id}: {e}")
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cap.release()
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break
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except Exception as e:
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logging.error(f"Error in frame_reader thread for camera {camera_id}: {e}")
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async def process_streams():
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global models
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logging.info("Started processing streams")
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persistent_data_dict = {}
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try:
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while True:
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start_time = time.time()
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# Round-robin processing
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with streams_lock:
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current_streams = list(streams.items())
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for camera_id, stream in current_streams:
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buffer = stream['buffer']
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if not buffer.empty():
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frame = buffer.get()
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with models_lock:
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model = models.get(camera_id, {}).get(stream['modelId'])
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key = (camera_id, stream['modelId'])
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persistent_data = persistent_data_dict.get(key, {})
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updated_persistent_data = await handle_detection(camera_id, stream, frame, websocket, model, persistent_data)
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persistent_data_dict[key] = updated_persistent_data
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elapsed_time = (time.time() - start_time) * 1000 # in ms
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sleep_time = max(poll_interval - elapsed_time, 0)
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logging.debug(f"Elapsed time: {elapsed_time}ms, sleeping for: {sleep_time}ms")
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await asyncio.sleep(sleep_time / 1000.0)
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except asyncio.CancelledError:
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logging.info("Stream processing task cancelled")
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except Exception as e:
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logging.error(f"Error in process_streams: {e}")
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async def send_heartbeat():
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while True:
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try:
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cpu_usage = psutil.cpu_percent()
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memory_usage = psutil.virtual_memory().percent
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if torch.cuda.is_available():
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gpu_usage = torch.cuda.memory_allocated() / (1024 ** 2) # Convert to MB
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gpu_memory_usage = torch.cuda.memory_reserved() / (1024 ** 2) # Convert to MB
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else:
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gpu_usage = None
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gpu_memory_usage = None
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camera_connections = [
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{
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"cameraIdentifier": camera_id,
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"modelId": stream['modelId'],
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"modelName": stream['modelName'],
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"online": True
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}
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for camera_id, stream in streams.items()
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]
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state_report = {
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"type": "stateReport",
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"cpuUsage": cpu_usage,
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"memoryUsage": memory_usage,
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"gpuUsage": gpu_usage,
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"gpuMemoryUsage": gpu_memory_usage,
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"cameraConnections": camera_connections
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}
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await websocket.send_text(json.dumps(state_report))
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logging.debug("Sent stateReport as heartbeat")
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await asyncio.sleep(HEARTBEAT_INTERVAL)
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except Exception as e:
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logging.error(f"Error sending stateReport heartbeat: {e}")
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break
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async def on_message():
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global models
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while True:
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try:
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msg = await websocket.receive_text()
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logging.debug(f"Received message: {msg}")
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print(f"Received message: {msg}")
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data = json.loads(msg)
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msg_type = data.get("type")
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if msg_type == "subscribe":
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payload = data.get("payload", {})
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camera_id = payload.get("cameraIdentifier")
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rtsp_url = payload.get("rtspUrl")
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model_url = payload.get("modelUrl")
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modelId = payload.get("modelId")
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modelName = payload.get("modelName")
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if model_url:
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with models_lock:
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if camera_id not in models:
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models[camera_id] = {}
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if modelId not in models[camera_id]:
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print(f"Downloading model from {model_url}")
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parsed_url = urlparse(model_url)
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filename = os.path.basename(parsed_url.path)
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model_filename = os.path.join("models", filename)
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# Download the model
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response = requests.get(model_url, stream=True)
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if response.status_code == 200:
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with open(model_filename, 'wb') as f:
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for chunk in response.iter_content(chunk_size=8192):
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f.write(chunk)
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logging.info(f"Downloaded model from {model_url} to {model_filename}")
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model = YOLO(model_filename)
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if torch.cuda.is_available():
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model.to('cuda')
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models[camera_id][modelId] = model
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logging.info(f"Loaded model {modelId} for camera {camera_id}")
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else:
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logging.error(f"Failed to download model from {model_url}")
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continue
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if camera_id and rtsp_url:
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with streams_lock:
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if camera_id not in streams and len(streams) < max_streams:
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cap = cv2.VideoCapture(rtsp_url)
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if not cap.isOpened():
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logging.error(f"Failed to open RTSP stream for camera {camera_id}")
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continue
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buffer = queue.Queue(maxsize=1)
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stop_event = threading.Event()
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thread = threading.Thread(target=frame_reader, args=(camera_id, cap, buffer, stop_event))
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thread.daemon = True
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thread.start()
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streams[camera_id] = {
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'cap': cap,
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'buffer': buffer,
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'thread': thread,
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'rtsp_url': rtsp_url,
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'stop_event': stop_event,
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'modelId': modelId,
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'modelName': modelName
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}
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logging.info(f"Subscribed to camera {camera_id} with modelId {modelId}, modelName {modelName} and URL {rtsp_url}")
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elif camera_id and camera_id in streams:
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stream = streams.pop(camera_id)
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stream['cap'].release()
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logging.info(f"Unsubscribed from camera {camera_id}")
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if camera_id in models and modelId in models[camera_id]:
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del models[camera_id][modelId]
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if not models[camera_id]:
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del models[camera_id]
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elif msg_type == "unsubscribe":
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payload = data.get("payload", {})
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camera_id = payload.get("cameraIdentifier")
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logging.debug(f"Unsubscribing from camera {camera_id}")
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with streams_lock:
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if camera_id and camera_id in streams:
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stream = streams.pop(camera_id)
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stream['stop_event'].set()
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stream['thread'].join()
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stream['cap'].release()
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logging.info(f"Unsubscribed from camera {camera_id}")
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if camera_id in models and modelId in models[camera_id]:
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del models[camera_id][modelId]
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if not models[camera_id]:
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del models[camera_id]
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elif msg_type == "requestState":
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# Handle state request
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cpu_usage = psutil.cpu_percent()
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memory_usage = psutil.virtual_memory().percent
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if torch.cuda.is_available():
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gpu_usage = torch.cuda.memory_allocated() / (1024 ** 2) # Convert to MB
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gpu_memory_usage = torch.cuda.memory_reserved() / (1024 ** 2) # Convert to MB
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else:
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gpu_usage = None
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gpu_memory_usage = None
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camera_connections = [
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{
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"cameraIdentifier": camera_id,
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"modelId": stream['modelId'],
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"modelName": stream['modelName'],
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"online": True
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}
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for camera_id, stream in streams.items()
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]
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state_report = {
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"type": "stateReport",
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"cpuUsage": cpu_usage,
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"memoryUsage": memory_usage,
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"gpuUsage": gpu_usage,
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"gpuMemoryUsage": gpu_memory_usage,
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"cameraConnections": camera_connections
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}
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await websocket.send_text(json.dumps(state_report))
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else:
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logging.error(f"Unknown message type: {msg_type}")
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except json.JSONDecodeError:
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logging.error("Received invalid JSON message")
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except (WebSocketDisconnect, ConnectionClosedError) as e:
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logging.warning(f"WebSocket disconnected: {e}")
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break
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except Exception as e:
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logging.error(f"Error handling message: {e}")
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break
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try:
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await websocket.accept()
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task = asyncio.create_task(process_streams())
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heartbeat_task = asyncio.create_task(send_heartbeat())
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message_task = asyncio.create_task(on_message())
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await asyncio.gather(heartbeat_task, message_task)
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except Exception as e:
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logging.error(f"Error in detect websocket: {e}")
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finally:
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task.cancel()
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await task
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with streams_lock:
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for camera_id, stream in streams.items():
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stream['stop_event'].set()
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stream['thread'].join()
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stream['cap'].release()
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stream['buffer'].queue.clear()
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logging.info(f"Released camera {camera_id} and cleaned up resources")
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streams.clear()
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with models_lock:
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models.clear()
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logging.info("WebSocket connection closed") |