Siwat Sirichai
22370e2040
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709 lines
36 KiB
Python
709 lines
36 KiB
Python
from typing import Any, Dict
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import os
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import json
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import time
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import queue
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import torch
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import cv2
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import numpy as np
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import base64
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import logging
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import threading
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import requests
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import asyncio
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import psutil
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import zipfile
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from urllib.parse import urlparse
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from fastapi import FastAPI, WebSocket, HTTPException
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from fastapi.websockets import WebSocketDisconnect
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from fastapi.responses import Response
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from websockets.exceptions import ConnectionClosedError
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from ultralytics import YOLO
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# Import shared pipeline functions
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from siwatsystem.pympta import load_pipeline_from_zip, run_pipeline
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app = FastAPI()
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# Global dictionaries to keep track of models and streams
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# "models" now holds a nested dict: { camera_id: { modelId: model_tree } }
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models: Dict[str, Dict[str, Any]] = {}
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streams: Dict[str, Dict[str, Any]] = {}
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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.INFO, # Set to INFO level for less verbose output
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format="%(asctime)s [%(levelname)s] %(name)s: %(message)s",
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handlers=[
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logging.FileHandler("detector_worker.log"), # Write logs to a file
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logging.StreamHandler() # Also output to console
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]
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)
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# Create a logger specifically for this application
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logger = logging.getLogger("detector_worker")
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logger.setLevel(logging.DEBUG) # Set app-specific logger to DEBUG level
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# Ensure all other libraries (including root) use at least INFO level
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logging.getLogger().setLevel(logging.INFO)
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logger.info("Starting detector worker application")
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logger.info(f"Configuration: Target FPS: {TARGET_FPS}, Max streams: {max_streams}, Max retries: {max_retries}")
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# Ensure the models directory exists
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os.makedirs("models", exist_ok=True)
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logger.info("Ensured models directory exists")
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# Constants for heartbeat and timeouts
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HEARTBEAT_INTERVAL = 2 # seconds
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WORKER_TIMEOUT_MS = 10000
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logger.debug(f"Heartbeat interval set to {HEARTBEAT_INTERVAL} seconds")
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# Locks for thread-safe operations
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streams_lock = threading.Lock()
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models_lock = threading.Lock()
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logger.debug("Initialized thread locks")
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# Add helper to download mpta ZIP file from a remote URL
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def download_mpta(url: str, dest_path: str) -> str:
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try:
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logger.info(f"Starting download of model from {url} to {dest_path}")
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os.makedirs(os.path.dirname(dest_path), exist_ok=True)
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response = requests.get(url, stream=True)
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if response.status_code == 200:
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file_size = int(response.headers.get('content-length', 0))
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logger.info(f"Model file size: {file_size/1024/1024:.2f} MB")
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downloaded = 0
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with open(dest_path, "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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downloaded += len(chunk)
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if file_size > 0 and downloaded % (file_size // 10) < 8192: # Log approximately every 10%
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logger.debug(f"Download progress: {downloaded/file_size*100:.1f}%")
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logger.info(f"Successfully downloaded mpta file from {url} to {dest_path}")
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return dest_path
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else:
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logger.error(f"Failed to download mpta file (status code {response.status_code}): {response.text}")
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return None
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except Exception as e:
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logger.error(f"Exception downloading mpta file from {url}: {str(e)}", exc_info=True)
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return None
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# Add helper to fetch snapshot image from HTTP/HTTPS URL
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def fetch_snapshot(url: str):
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try:
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response = requests.get(url, timeout=10)
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if response.status_code == 200:
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# Convert response content to numpy array
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nparr = np.frombuffer(response.content, np.uint8)
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# Decode image
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frame = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
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if frame is not None:
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logger.debug(f"Successfully fetched snapshot from {url}, shape: {frame.shape}")
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return frame
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else:
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logger.error(f"Failed to decode image from snapshot URL: {url}")
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return None
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else:
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logger.error(f"Failed to fetch snapshot (status code {response.status_code}): {url}")
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return None
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except Exception as e:
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logger.error(f"Exception fetching snapshot from {url}: {str(e)}")
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return None
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####################################################
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# REST API endpoint for image retrieval
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####################################################
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@app.get("/camera/{camera_id}/image")
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async def get_camera_image(camera_id: str):
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"""
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Get the current frame from a camera as JPEG image
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"""
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try:
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with streams_lock:
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if camera_id not in streams:
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raise HTTPException(status_code=404, detail=f"Camera {camera_id} not found or not active")
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stream = streams[camera_id]
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buffer = stream["buffer"]
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if buffer.empty():
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raise HTTPException(status_code=404, detail=f"No frame available for camera {camera_id}")
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# Get the latest frame (non-blocking)
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try:
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frame = buffer.queue[-1] # Get the most recent frame without removing it
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except IndexError:
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raise HTTPException(status_code=404, detail=f"No frame available for camera {camera_id}")
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# Encode frame as JPEG
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success, buffer_img = cv2.imencode('.jpg', frame, [cv2.IMWRITE_JPEG_QUALITY, 85])
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if not success:
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raise HTTPException(status_code=500, detail="Failed to encode image as JPEG")
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# Return image as binary response
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return Response(content=buffer_img.tobytes(), media_type="image/jpeg")
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except HTTPException:
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raise
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except Exception as e:
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logger.error(f"Error retrieving image for camera {camera_id}: {str(e)}", exc_info=True)
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raise HTTPException(status_code=500, detail=f"Internal server error: {str(e)}")
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####################################################
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# Detection and frame processing functions
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####################################################
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@app.websocket("/")
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async def detect(websocket: WebSocket):
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logger.info("WebSocket connection accepted")
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persistent_data_dict = {}
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async def handle_detection(camera_id, stream, frame, websocket, model_tree, persistent_data):
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try:
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logger.debug(f"Processing frame for camera {camera_id} with model {stream['modelId']}")
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start_time = time.time()
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detection_result = run_pipeline(frame, model_tree)
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process_time = (time.time() - start_time) * 1000
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logger.debug(f"Detection for camera {camera_id} completed in {process_time:.2f}ms")
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# Log the raw detection result for debugging
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logger.debug(f"Raw detection result for camera {camera_id}:\n{json.dumps(detection_result, indent=2, default=str)}")
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# Direct class result (no detections/classifications structure)
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if detection_result and isinstance(detection_result, dict) and "class" in detection_result and "confidence" in detection_result:
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highest_confidence_detection = {
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"class": detection_result.get("class", "none"),
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"confidence": detection_result.get("confidence", 1.0),
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"box": [0, 0, 0, 0] # Empty bounding box for classifications
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}
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# Handle case when no detections found or result is empty
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elif not detection_result or not detection_result.get("detections"):
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# Check if we have classification results
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if detection_result and detection_result.get("classifications"):
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# Get the highest confidence classification
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classifications = detection_result.get("classifications", [])
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highest_confidence_class = max(classifications, key=lambda x: x.get("confidence", 0)) if classifications else None
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if highest_confidence_class:
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highest_confidence_detection = {
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"class": highest_confidence_class.get("class", "none"),
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"confidence": highest_confidence_class.get("confidence", 1.0),
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"box": [0, 0, 0, 0] # Empty bounding box for classifications
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}
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else:
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highest_confidence_detection = {
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"class": "none",
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"confidence": 1.0,
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"box": [0, 0, 0, 0]
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}
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else:
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highest_confidence_detection = {
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"class": "none",
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"confidence": 1.0,
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"box": [0, 0, 0, 0]
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}
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else:
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# Find detection with highest confidence
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detections = detection_result.get("detections", [])
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highest_confidence_detection = max(detections, key=lambda x: x.get("confidence", 0)) if detections else {
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"class": "none",
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"confidence": 1.0,
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"box": [0, 0, 0, 0]
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}
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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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"detection": highest_confidence_detection, # Send only the highest confidence detection
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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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if highest_confidence_detection["class"] != "none":
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logger.info(f"Camera {camera_id}: Detected {highest_confidence_detection['class']} with confidence {highest_confidence_detection['confidence']:.2f} using model {stream['modelName']}")
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await websocket.send_json(detection_data)
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logger.debug(f"Sent detection data to client for camera {camera_id}:\n{json.dumps(detection_data, indent=2)}")
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return persistent_data
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except Exception as e:
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logger.error(f"Error in handle_detection for camera {camera_id}: {str(e)}", exc_info=True)
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return persistent_data
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def frame_reader(camera_id, cap, buffer, stop_event):
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retries = 0
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logger.info(f"Starting frame reader thread for camera {camera_id}")
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frame_count = 0
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last_log_time = time.time()
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try:
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# Log initial camera status and properties
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if cap.isOpened():
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width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
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height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
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fps = cap.get(cv2.CAP_PROP_FPS)
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logger.info(f"Camera {camera_id} opened successfully with resolution {width}x{height}, FPS: {fps}")
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else:
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logger.error(f"Camera {camera_id} failed to open initially")
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while not stop_event.is_set():
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try:
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if not cap.isOpened():
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logger.error(f"Camera {camera_id} is not open before trying to read")
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# Attempt to reopen
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cap = cv2.VideoCapture(streams[camera_id]["rtsp_url"])
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time.sleep(reconnect_interval)
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continue
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logger.debug(f"Attempting to read frame from camera {camera_id}")
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ret, frame = cap.read()
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if not ret:
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logger.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 and max_retries != -1:
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logger.error(f"Max retries reached for camera: {camera_id}, stopping frame reader")
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break
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# Re-open
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logger.info(f"Attempting to reopen RTSP stream for camera: {camera_id}")
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cap = cv2.VideoCapture(streams[camera_id]["rtsp_url"])
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if not cap.isOpened():
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logger.error(f"Failed to reopen RTSP stream for camera: {camera_id}")
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continue
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logger.info(f"Successfully reopened RTSP stream for camera: {camera_id}")
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continue
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# Successfully read a frame
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frame_count += 1
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current_time = time.time()
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# Log frame stats every 5 seconds
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if current_time - last_log_time > 5:
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logger.info(f"Camera {camera_id}: Read {frame_count} frames in the last {current_time - last_log_time:.1f} seconds")
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frame_count = 0
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last_log_time = current_time
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logger.debug(f"Successfully read frame from camera {camera_id}, shape: {frame.shape}")
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retries = 0
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# Overwrite old frame if buffer is full
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if not buffer.empty():
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try:
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buffer.get_nowait()
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logger.debug(f"Removed old frame from buffer for camera {camera_id}")
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except queue.Empty:
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pass
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buffer.put(frame)
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logger.debug(f"Added new frame to buffer for camera {camera_id}")
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# Short sleep to avoid CPU overuse
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time.sleep(0.01)
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except cv2.error as e:
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logger.error(f"OpenCV error for camera {camera_id}: {e}", exc_info=True)
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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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logger.error(f"Max retries reached after OpenCV error for camera {camera_id}")
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break
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logger.info(f"Attempting to reopen RTSP stream after OpenCV error for camera: {camera_id}")
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cap = cv2.VideoCapture(streams[camera_id]["rtsp_url"])
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if not cap.isOpened():
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logger.error(f"Failed to reopen RTSP stream for camera {camera_id} after OpenCV error")
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continue
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logger.info(f"Successfully reopened RTSP stream after OpenCV error for camera: {camera_id}")
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except Exception as e:
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logger.error(f"Unexpected error for camera {camera_id}: {str(e)}", exc_info=True)
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cap.release()
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break
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except Exception as e:
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logger.error(f"Error in frame_reader thread for camera {camera_id}: {str(e)}", exc_info=True)
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finally:
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logger.info(f"Frame reader thread for camera {camera_id} is exiting")
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if cap and cap.isOpened():
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cap.release()
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def snapshot_reader(camera_id, snapshot_url, snapshot_interval, buffer, stop_event):
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"""Frame reader that fetches snapshots from HTTP/HTTPS URL at specified intervals"""
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retries = 0
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logger.info(f"Starting snapshot reader thread for camera {camera_id} from {snapshot_url}")
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frame_count = 0
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last_log_time = time.time()
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try:
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interval_seconds = snapshot_interval / 1000.0 # Convert milliseconds to seconds
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logger.info(f"Snapshot interval for camera {camera_id}: {interval_seconds}s")
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while not stop_event.is_set():
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try:
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start_time = time.time()
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frame = fetch_snapshot(snapshot_url)
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if frame is None:
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logger.warning(f"Failed to fetch snapshot for camera: {camera_id}, retry {retries+1}/{max_retries}")
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retries += 1
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if retries > max_retries and max_retries != -1:
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logger.error(f"Max retries reached for snapshot camera: {camera_id}, stopping reader")
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break
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time.sleep(min(interval_seconds, reconnect_interval))
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continue
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# Successfully fetched a frame
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frame_count += 1
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current_time = time.time()
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# Log frame stats every 5 seconds
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if current_time - last_log_time > 5:
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logger.info(f"Camera {camera_id}: Fetched {frame_count} snapshots in the last {current_time - last_log_time:.1f} seconds")
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frame_count = 0
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last_log_time = current_time
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logger.debug(f"Successfully fetched snapshot from camera {camera_id}, shape: {frame.shape}")
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retries = 0
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# Overwrite old frame if buffer is full
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if not buffer.empty():
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try:
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buffer.get_nowait()
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logger.debug(f"Removed old snapshot from buffer for camera {camera_id}")
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except queue.Empty:
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pass
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buffer.put(frame)
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logger.debug(f"Added new snapshot to buffer for camera {camera_id}")
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# Wait for the specified interval
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elapsed = time.time() - start_time
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sleep_time = max(interval_seconds - elapsed, 0)
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if sleep_time > 0:
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time.sleep(sleep_time)
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except Exception as e:
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logger.error(f"Unexpected error fetching snapshot for camera {camera_id}: {str(e)}", exc_info=True)
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retries += 1
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if retries > max_retries and max_retries != -1:
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logger.error(f"Max retries reached after error for snapshot camera {camera_id}")
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break
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time.sleep(min(interval_seconds, reconnect_interval))
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except Exception as e:
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logger.error(f"Error in snapshot_reader thread for camera {camera_id}: {str(e)}", exc_info=True)
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finally:
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logger.info(f"Snapshot reader thread for camera {camera_id} is exiting")
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async def process_streams():
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logger.info("Started processing streams")
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try:
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while True:
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start_time = time.time()
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with streams_lock:
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current_streams = list(streams.items())
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if current_streams:
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logger.debug(f"Processing {len(current_streams)} active streams")
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else:
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logger.debug("No active streams to process")
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for camera_id, stream in current_streams:
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buffer = stream["buffer"]
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if buffer.empty():
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logger.debug(f"Frame buffer is empty for camera {camera_id}")
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continue
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logger.debug(f"Got frame from buffer for camera {camera_id}")
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frame = buffer.get()
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with models_lock:
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model_tree = models.get(camera_id, {}).get(stream["modelId"])
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if not model_tree:
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logger.warning(f"Model not found for camera {camera_id}, modelId {stream['modelId']}")
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continue
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logger.debug(f"Found model tree for camera {camera_id}, modelId {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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logger.debug(f"Starting detection for camera {camera_id} with modelId {stream['modelId']}")
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updated_persistent_data = await handle_detection(
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camera_id, stream, frame, websocket, model_tree, persistent_data
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)
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persistent_data_dict[key] = updated_persistent_data
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elapsed_time = (time.time() - start_time) * 1000 # ms
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sleep_time = max(poll_interval - elapsed_time, 0)
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logger.debug(f"Frame processing cycle: {elapsed_time:.2f}ms, sleeping for: {sleep_time:.2f}ms")
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await asyncio.sleep(sleep_time / 1000.0)
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except asyncio.CancelledError:
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logger.info("Stream processing task cancelled")
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except Exception as e:
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logger.error(f"Error in process_streams: {str(e)}", exc_info=True)
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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) # MB
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gpu_memory_usage = torch.cuda.memory_reserved() / (1024 ** 2) # MB
|
|
else:
|
|
gpu_usage = None
|
|
gpu_memory_usage = None
|
|
|
|
camera_connections = [
|
|
{
|
|
"cameraIdentifier": camera_id,
|
|
"modelId": stream["modelId"],
|
|
"modelName": stream["modelName"],
|
|
"online": True
|
|
}
|
|
for camera_id, stream in streams.items()
|
|
]
|
|
|
|
state_report = {
|
|
"type": "stateReport",
|
|
"cpuUsage": cpu_usage,
|
|
"memoryUsage": memory_usage,
|
|
"gpuUsage": gpu_usage,
|
|
"gpuMemoryUsage": gpu_memory_usage,
|
|
"cameraConnections": camera_connections
|
|
}
|
|
await websocket.send_text(json.dumps(state_report))
|
|
logger.debug(f"Sent stateReport as heartbeat: CPU {cpu_usage:.1f}%, Memory {memory_usage:.1f}%, {len(camera_connections)} active cameras")
|
|
await asyncio.sleep(HEARTBEAT_INTERVAL)
|
|
except Exception as e:
|
|
logger.error(f"Error sending stateReport heartbeat: {e}")
|
|
break
|
|
|
|
async def on_message():
|
|
while True:
|
|
try:
|
|
msg = await websocket.receive_text()
|
|
logger.debug(f"Received message: {msg}")
|
|
data = json.loads(msg)
|
|
msg_type = data.get("type")
|
|
|
|
if msg_type == "subscribe":
|
|
payload = data.get("payload", {})
|
|
camera_id = payload.get("cameraIdentifier")
|
|
rtsp_url = payload.get("rtspUrl")
|
|
snapshot_url = payload.get("snapshotUrl")
|
|
snapshot_interval = payload.get("snapshotInterval") # in milliseconds
|
|
model_url = payload.get("modelUrl") # may be remote or local
|
|
modelId = payload.get("modelId")
|
|
modelName = payload.get("modelName")
|
|
|
|
if model_url:
|
|
with models_lock:
|
|
if (camera_id not in models) or (modelId not in models[camera_id]):
|
|
logger.info(f"Loading model from {model_url} for camera {camera_id}, modelId {modelId}")
|
|
extraction_dir = os.path.join("models", camera_id, str(modelId))
|
|
os.makedirs(extraction_dir, exist_ok=True)
|
|
# If model_url is remote, download it first.
|
|
parsed = urlparse(model_url)
|
|
if parsed.scheme in ("http", "https"):
|
|
logger.info(f"Downloading remote model from {model_url}")
|
|
local_mpta = os.path.join(extraction_dir, os.path.basename(parsed.path))
|
|
logger.debug(f"Download destination: {local_mpta}")
|
|
local_path = download_mpta(model_url, local_mpta)
|
|
if not local_path:
|
|
logger.error(f"Failed to download the remote mpta file from {model_url}")
|
|
error_response = {
|
|
"type": "error",
|
|
"cameraIdentifier": camera_id,
|
|
"error": f"Failed to download model from {model_url}"
|
|
}
|
|
await websocket.send_json(error_response)
|
|
continue
|
|
model_tree = load_pipeline_from_zip(local_path, extraction_dir)
|
|
else:
|
|
logger.info(f"Loading local model from {model_url}")
|
|
# Check if file exists before attempting to load
|
|
if not os.path.exists(model_url):
|
|
logger.error(f"Local model file not found: {model_url}")
|
|
logger.debug(f"Current working directory: {os.getcwd()}")
|
|
error_response = {
|
|
"type": "error",
|
|
"cameraIdentifier": camera_id,
|
|
"error": f"Model file not found: {model_url}"
|
|
}
|
|
await websocket.send_json(error_response)
|
|
continue
|
|
model_tree = load_pipeline_from_zip(model_url, extraction_dir)
|
|
if model_tree is None:
|
|
logger.error(f"Failed to load model {modelId} from mpta file for camera {camera_id}")
|
|
error_response = {
|
|
"type": "error",
|
|
"cameraIdentifier": camera_id,
|
|
"error": f"Failed to load model {modelId}"
|
|
}
|
|
await websocket.send_json(error_response)
|
|
continue
|
|
if camera_id not in models:
|
|
models[camera_id] = {}
|
|
models[camera_id][modelId] = model_tree
|
|
logger.info(f"Successfully loaded model {modelId} for camera {camera_id}")
|
|
success_response = {
|
|
"type": "modelLoaded",
|
|
"cameraIdentifier": camera_id,
|
|
"modelId": modelId
|
|
}
|
|
await websocket.send_json(success_response)
|
|
if camera_id and (rtsp_url or snapshot_url):
|
|
with streams_lock:
|
|
if camera_id not in streams and len(streams) < max_streams:
|
|
buffer = queue.Queue(maxsize=1)
|
|
stop_event = threading.Event()
|
|
|
|
# Choose between snapshot and RTSP based on availability
|
|
if snapshot_url and snapshot_interval:
|
|
logger.info(f"Using snapshot mode for camera {camera_id}: {snapshot_url}")
|
|
thread = threading.Thread(target=snapshot_reader, args=(camera_id, snapshot_url, snapshot_interval, buffer, stop_event))
|
|
thread.daemon = True
|
|
thread.start()
|
|
streams[camera_id] = {
|
|
"buffer": buffer,
|
|
"thread": thread,
|
|
"snapshot_url": snapshot_url,
|
|
"snapshot_interval": snapshot_interval,
|
|
"stop_event": stop_event,
|
|
"modelId": modelId,
|
|
"modelName": modelName,
|
|
"mode": "snapshot"
|
|
}
|
|
logger.info(f"Subscribed to camera {camera_id} (snapshot mode) with modelId {modelId}, modelName {modelName}, URL {snapshot_url}, interval {snapshot_interval}ms")
|
|
elif rtsp_url:
|
|
logger.info(f"Using RTSP mode for camera {camera_id}: {rtsp_url}")
|
|
cap = cv2.VideoCapture(rtsp_url)
|
|
if not cap.isOpened():
|
|
logger.error(f"Failed to open RTSP stream for camera {camera_id}")
|
|
continue
|
|
thread = threading.Thread(target=frame_reader, args=(camera_id, cap, buffer, stop_event))
|
|
thread.daemon = True
|
|
thread.start()
|
|
streams[camera_id] = {
|
|
"cap": cap,
|
|
"buffer": buffer,
|
|
"thread": thread,
|
|
"rtsp_url": rtsp_url,
|
|
"stop_event": stop_event,
|
|
"modelId": modelId,
|
|
"modelName": modelName,
|
|
"mode": "rtsp"
|
|
}
|
|
logger.info(f"Subscribed to camera {camera_id} (RTSP mode) with modelId {modelId}, modelName {modelName}, URL {rtsp_url}")
|
|
else:
|
|
logger.error(f"No valid URL provided for camera {camera_id}")
|
|
continue
|
|
elif camera_id and camera_id in streams:
|
|
# If already subscribed, unsubscribe first
|
|
stream = streams.pop(camera_id)
|
|
stream["stop_event"].set()
|
|
stream["thread"].join()
|
|
if "cap" in stream:
|
|
stream["cap"].release()
|
|
logger.info(f"Unsubscribed from camera {camera_id} for resubscription")
|
|
with models_lock:
|
|
if camera_id in models and modelId in models[camera_id]:
|
|
del models[camera_id][modelId]
|
|
if not models[camera_id]:
|
|
del models[camera_id]
|
|
elif msg_type == "unsubscribe":
|
|
payload = data.get("payload", {})
|
|
camera_id = payload.get("cameraIdentifier")
|
|
logger.debug(f"Unsubscribing from camera {camera_id}")
|
|
with streams_lock:
|
|
if camera_id and camera_id in streams:
|
|
stream = streams.pop(camera_id)
|
|
stream["stop_event"].set()
|
|
stream["thread"].join()
|
|
# Only release cap if it exists (RTSP mode)
|
|
if "cap" in stream:
|
|
stream["cap"].release()
|
|
logger.info(f"Released RTSP capture for camera {camera_id}")
|
|
else:
|
|
logger.info(f"Released snapshot reader for camera {camera_id}")
|
|
logger.info(f"Unsubscribed from camera {camera_id}")
|
|
with models_lock:
|
|
if camera_id in models:
|
|
del models[camera_id]
|
|
elif msg_type == "requestState":
|
|
cpu_usage = psutil.cpu_percent()
|
|
memory_usage = psutil.virtual_memory().percent
|
|
if torch.cuda.is_available():
|
|
gpu_usage = torch.cuda.memory_allocated() / (1024 ** 2)
|
|
gpu_memory_usage = torch.cuda.memory_reserved() / (1024 ** 2)
|
|
else:
|
|
gpu_usage = None
|
|
gpu_memory_usage = None
|
|
|
|
camera_connections = [
|
|
{
|
|
"cameraIdentifier": camera_id,
|
|
"modelId": stream["modelId"],
|
|
"modelName": stream["modelName"],
|
|
"online": True
|
|
}
|
|
for camera_id, stream in streams.items()
|
|
]
|
|
|
|
state_report = {
|
|
"type": "stateReport",
|
|
"cpuUsage": cpu_usage,
|
|
"memoryUsage": memory_usage,
|
|
"gpuUsage": gpu_usage,
|
|
"gpuMemoryUsage": gpu_memory_usage,
|
|
"cameraConnections": camera_connections
|
|
}
|
|
await websocket.send_text(json.dumps(state_report))
|
|
else:
|
|
logger.error(f"Unknown message type: {msg_type}")
|
|
except json.JSONDecodeError:
|
|
logger.error("Received invalid JSON message")
|
|
except (WebSocketDisconnect, ConnectionClosedError) as e:
|
|
logger.warning(f"WebSocket disconnected: {e}")
|
|
break
|
|
except Exception as e:
|
|
logger.error(f"Error handling message: {e}")
|
|
break
|
|
|
|
try:
|
|
await websocket.accept()
|
|
stream_task = asyncio.create_task(process_streams())
|
|
heartbeat_task = asyncio.create_task(send_heartbeat())
|
|
message_task = asyncio.create_task(on_message())
|
|
await asyncio.gather(heartbeat_task, message_task)
|
|
except Exception as e:
|
|
logger.error(f"Error in detect websocket: {e}")
|
|
finally:
|
|
stream_task.cancel()
|
|
await stream_task
|
|
with streams_lock:
|
|
for camera_id, stream in streams.items():
|
|
stream["stop_event"].set()
|
|
stream["thread"].join()
|
|
# Only release cap if it exists (RTSP mode)
|
|
if "cap" in stream:
|
|
stream["cap"].release()
|
|
while not stream["buffer"].empty():
|
|
try:
|
|
stream["buffer"].get_nowait()
|
|
except queue.Empty:
|
|
pass
|
|
logger.info(f"Released camera {camera_id} and cleaned up resources")
|
|
streams.clear()
|
|
with models_lock:
|
|
models.clear()
|
|
logger.info("WebSocket connection closed")
|