优化
This commit is contained in:
ZZX9599
139
ws/ws.py
139
ws/ws.py
@ -11,6 +11,8 @@ from schema.device_action_schema import DeviceActionCreate
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import cv2
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import numpy as np
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from fastapi import WebSocket, APIRouter, WebSocketDisconnect, FastAPI
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from queue import Queue
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from threading import Lock
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from ocr.model_violation_detector import MultiModelViolationDetector
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@ -20,14 +22,8 @@ FORBIDDEN_WORDS_PATH = r"D:\Git\bin\video\ocr\forbidden_words.txt"
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OCR_CONFIG_PATH = r"D:\Git\bin\video\ocr\config\1.yaml"
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KNOWN_FACES_DIR = r"D:\Git\bin\video\ocr\known_faces"
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# 创建检测器实例
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detector = MultiModelViolationDetector(
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forbidden_words_path=FORBIDDEN_WORDS_PATH,
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ocr_config_path=OCR_CONFIG_PATH,
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yolo_model_path=YOLO_MODEL_PATH,
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known_faces_dir=KNOWN_FACES_DIR,
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ocr_confidence_threshold=0.5
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)
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# 模型池配置（根据GPU显存调整，每个模型约占1G显存）
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MODEL_POOL_SIZE = 3 # 最大并发客户端数
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# 配置常量
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HEARTBEAT_INTERVAL = 30 # 心跳检查间隔（秒）
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@ -36,7 +32,39 @@ WS_ENDPOINT = "/ws" # WebSocket端点路径
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FRAME_QUEUE_SIZE = 1 # 帧队列大小限制
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# 工具函数：获取格式化时间字符串（统一时间戳格式）
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# 模型池实现 - 提前初始化固定数量的模型实例
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class ModelPool:
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def __init__(self, pool_size: int = MODEL_POOL_SIZE):
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self.pool = Queue(maxsize=pool_size)
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self.lock = Lock()
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# 提前初始化模型实例（显存会在此阶段预分配）
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for i in range(pool_size):
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detector = MultiModelViolationDetector(
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forbidden_words_path=FORBIDDEN_WORDS_PATH,
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ocr_config_path=OCR_CONFIG_PATH,
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yolo_model_path=YOLO_MODEL_PATH,
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known_faces_dir=KNOWN_FACES_DIR,
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ocr_confidence_threshold=0.5
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)
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self.pool.put(detector)
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print(f"[{get_current_time_str()}] 模型池初始化：第{i + 1}/{pool_size}个模型加载完成")
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def get_model(self) -> MultiModelViolationDetector:
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"""从池子里获取模型（阻塞直到有可用实例）"""
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with self.lock:
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return self.pool.get()
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def return_model(self, detector: MultiModelViolationDetector):
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"""将模型归还给池子"""
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with self.lock:
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self.pool.put(detector)
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# 初始化模型池（程序启动时加载所有模型，显存会一次性占用 MODEL_POOL_SIZE * 单模型显存）
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model_pool = ModelPool(pool_size=MODEL_POOL_SIZE)
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# 工具函数：获取格式化时间字符串
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def get_current_time_str() -> str:
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return datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
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@ -54,12 +82,16 @@ class ClientConnection:
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self.frame_queue = asyncio.Queue(maxsize=FRAME_QUEUE_SIZE)
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self.consumer_task: Optional[asyncio.Task] = None
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# 从模型池获取专属模型（每个客户端独立占用一个模型实例）
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self.detector = model_pool.get_model()
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print(f"[{get_current_time_str()}] 客户端{self.client_ip}：已获取模型池中的模型实例（显存独立）")
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def update_heartbeat(self):
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"""更新心跳时间（客户端发送心跳时调用）"""
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"""更新心跳时间"""
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self.last_heartbeat = datetime.datetime.now()
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def is_alive(self) -> bool:
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"""判断客户端是否存活（心跳超时检查）"""
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"""判断客户端是否存活"""
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timeout = (datetime.datetime.now() - self.last_heartbeat).total_seconds()
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return timeout < HEARTBEAT_TIMEOUT
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@ -68,11 +100,13 @@ class ClientConnection:
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self.consumer_task = asyncio.create_task(self.consume_frames())
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return self.consumer_task
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def release_model(self):
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"""客户端断开时归还模型到池"""
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model_pool.return_model(self.detector)
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print(f"[{get_current_time_str()}] 客户端{self.client_ip}：模型已归还至模型池（显存可复用）")
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async def send_frame_permit(self):
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"""
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发送「帧发送许可信号」
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通知客户端可发送下一帧图像
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"""
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"""发送帧发送许可信号"""
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try:
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frame_permit_msg = {
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"type": "frame",
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@ -80,26 +114,24 @@ class ClientConnection:
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"client_ip": self.client_ip
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}
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await self.websocket.send_json(frame_permit_msg)
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print(f"[{get_current_time_str()}] 客户端{self.client_ip}：已发送帧发送许可信号（取帧后立即通知）")
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print(f"[{get_current_time_str()}] 客户端{self.client_ip}：已发送帧发送许可信号")
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except Exception as e:
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print(f"[{get_current_time_str()}] 客户端{self.client_ip}：帧许可信号发送失败 - {str(e)}")
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async def consume_frames(self) -> None:
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"""消费队列中的帧并处理（核心调整：取帧后立即发许可，再处理帧）"""
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"""消费队列中的帧并处理（并行执行核心）"""
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try:
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while True:
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# 1. 从队列取出帧（阻塞直到有帧可用）
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# 1. 从队列取出帧
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frame_data = await self.frame_queue.get()
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# -------------------------- 核心修改：取出帧后立即发送下一帧许可 --------------------------
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await self.send_frame_permit() # 取帧即通知客户端发下一帧，无需等处理完成
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# -----------------------------------------------------------------------------------------
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# 2. 立即发送下一帧许可
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await self.send_frame_permit()
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try:
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# 2. 处理取出的帧（即使处理慢，客户端也已收到许可，可提前准备下一帧）
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# 3. 并行处理帧：用线程池执行AI检测（真正并发）
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await self.process_frame(frame_data)
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finally:
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# 3. 标记帧任务完成（无论处理成功/失败，都需清理队列）
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self.frame_queue.task_done()
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except asyncio.CancelledError:
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@ -108,7 +140,7 @@ class ClientConnection:
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print(f"[{get_current_time_str()}] 客户端{self.client_ip}：帧消费逻辑错误 - {str(e)}")
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async def process_frame(self, frame_data: bytes) -> None:
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"""处理单帧图像数据（检测违规后发送危险通知 + 调用违规次数加一方法）"""
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"""处理单帧图像数据（使用客户端专属模型）"""
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# 二进制数据转OpenCV图像
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nparr = np.frombuffer(frame_data, np.uint8)
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img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
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@ -119,14 +151,18 @@ class ClientConnection:
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# 确保图像保存目录存在
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os.makedirs('images', exist_ok=True)
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# 保存图像（按IP+时间戳命名，避免冲突）
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# 保存图像
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filename = f"images/{self.client_ip.replace('.', '_')}_{get_current_time_file_str()}.jpg"
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try:
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cv2.imwrite(filename, img)
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print(f"[{get_current_time_str()}] 图像已保存至：{filename}")
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# 执行违规检测
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has_violation, violation_type, details = detector.detect_violations(img)
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# 关键修改：使用客户端专属模型 + 线程池并行执行AI检测
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has_violation, violation_type, details = await asyncio.to_thread(
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self.detector.detect_violations, # 客户端独立模型
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img # 输入图像
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)
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if has_violation:
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print(
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f"[{get_current_time_str()}] 客户端{self.client_ip}：检测到违规 - 类型: {violation_type}, 详情: {details}")
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@ -138,7 +174,7 @@ class ClientConnection:
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except Exception as e:
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print(f"[{get_current_time_str()}] 客户端{self.client_ip}：违规次数更新失败 - {str(e)}")
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# 发送「危险通知」
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# 发送危险通知
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danger_msg = {
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"type": "danger",
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"timestamp": get_current_time_str(),
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@ -153,24 +189,34 @@ class ClientConnection:
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# 全局状态管理
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connected_clients: Dict[str, ClientConnection] = {}
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client_lock = asyncio.Lock() # 保护connected_clients的锁
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heartbeat_task: Optional[asyncio.Task] = None
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# 心跳检查（定时清理超时客户端 + 调用离线状态更新方法）
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# 心跳检查
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async def heartbeat_checker():
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while True:
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current_time = get_current_time_str()
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# 加锁保护字典遍历
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async with client_lock:
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timeout_ips = [ip for ip, conn in connected_clients.items() if not conn.is_alive()]
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if timeout_ips:
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print(f"[{current_time}] 心跳检查：{len(timeout_ips)}个客户端超时（IP：{timeout_ips}）")
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for ip in timeout_ips:
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try:
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conn = connected_clients[ip]
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async with client_lock:
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conn = connected_clients.get(ip)
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if not conn:
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continue
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if conn.consumer_task and not conn.consumer_task.done():
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conn.consumer_task.cancel()
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await conn.websocket.close(code=1008, reason="心跳超时")
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# 归还模型
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conn.release_model()
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# 超时设为离线并记录
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try:
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await asyncio.to_thread(update_online_status_by_ip, ip, 0)
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@ -180,8 +226,10 @@ async def heartbeat_checker():
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except Exception as e:
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print(f"[{current_time}] 客户端{ip}：离线状态更新失败 - {str(e)}")
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finally:
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async with client_lock:
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connected_clients.pop(ip, None)
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else:
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async with client_lock:
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print(f"[{current_time}] 心跳检查：{len(connected_clients)}个客户端在线")
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await asyncio.sleep(HEARTBEAT_INTERVAL)
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@ -215,6 +263,7 @@ async def send_heartbeat_ack(conn: ClientConnection):
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print(f"[{get_current_time_str()}] 客户端{conn.client_ip}：已发送心跳确认")
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return True
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except Exception as e:
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async with client_lock:
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connected_clients.pop(conn.client_ip, None)
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print(f"[{get_current_time_str()}] 客户端{conn.client_ip}：心跳确认发送失败 - {str(e)}")
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return False
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@ -252,22 +301,26 @@ async def websocket_endpoint(websocket: WebSocket):
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print(f"[{current_time}] 客户端{client_ip}：WebSocket连接已建立")
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is_online_updated = False
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new_conn = None
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try:
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# 处理重复连接
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async with client_lock:
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if client_ip in connected_clients:
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old_conn = connected_clients[client_ip]
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if old_conn.consumer_task and not old_conn.consumer_task.done():
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old_conn.consumer_task.cancel()
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await old_conn.websocket.close(code=1008, reason="同一IP新连接建立")
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old_conn.release_model() # 归还旧连接的模型
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connected_clients.pop(client_ip)
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print(f"[{current_time}] 客户端{client_ip}：已关闭旧连接")
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print(f"[{current_time}] 客户端{client_ip}：已关闭旧连接并回收模型")
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# 注册新连接
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new_conn = ClientConnection(websocket, client_ip)
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async with client_lock:
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connected_clients[client_ip] = new_conn
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new_conn.start_consumer()
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# 初始许可：连接建立后立即发一次，让客户端知道可发第一帧（后续靠取帧后自动发）
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# 初始许可：连接建立后立即发一次
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await new_conn.send_frame_permit()
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# 标记上线并记录
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@ -280,6 +333,7 @@ async def websocket_endpoint(websocket: WebSocket):
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except Exception as e:
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print(f"[{current_time}] 客户端{client_ip}：上线状态更新失败 - {str(e)}")
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async with client_lock:
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print(f"[{current_time}] 客户端{client_ip}：新连接注册成功，在线数：{len(connected_clients)}")
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# 消息循环
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@ -296,11 +350,16 @@ async def websocket_endpoint(websocket: WebSocket):
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print(f"[{get_current_time_str()}] 客户端{client_ip}：连接异常 - {str(e)[:50]}")
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finally:
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# 清理资源并标记离线
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if client_ip in connected_clients:
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conn = connected_clients[client_ip]
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if new_conn and client_ip in connected_clients:
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async with client_lock:
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conn = connected_clients.get(client_ip)
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if conn:
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if conn.consumer_task and not conn.consumer_task.done():
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conn.consumer_task.cancel()
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# 归还模型到模型池
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conn.release_model()
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# 主动/异常断开时标记离线
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if is_online_updated:
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try:
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@ -312,4 +371,16 @@ async def websocket_endpoint(websocket: WebSocket):
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print(f"[{get_current_time_str()}] 客户端{client_ip}：断开后离线更新失败 - {str(e)}")
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connected_clients.pop(client_ip, None)
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async with client_lock:
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print(f"[{get_current_time_str()}] 客户端{client_ip}：资源已清理，在线数：{len(connected_clients)}")
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# 创建FastAPI应用
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app = FastAPI(lifespan=lifespan)
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app.include_router(ws_router)
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if __name__ == "__main__":
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import uvicorn
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uvicorn.run(app, host="0.0.0.0", port=8000)
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Block a user