BodyBalanceEvaluation/backend/devices/camera_manager.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
普通相机管理器
负责普通USB摄像头的连接、配置和数据采集
"""

import cv2
import threading
import time
import base64
import numpy as np
from typing import Optional, Dict, Any
import logging
import queue
import gc

try:
    from .base_device import BaseDevice
    from .utils.config_manager import ConfigManager
except ImportError:
    from base_device import BaseDevice
    from utils.config_manager import ConfigManager


class CameraManager(BaseDevice):
    """普通相机管理器"""
    
    def __init__(self, socketio, config_manager: Optional[ConfigManager] = None):
        """
        初始化相机管理器
        
        Args:
            socketio: SocketIO实例
            config_manager: 配置管理器实例
        """
        # 配置管理
        self.config_manager = config_manager or ConfigManager()
        config = self.config_manager.get_device_config('camera')
        
        super().__init__("camera", config)
        
        # 保存socketio实例
        self._socketio = socketio
        
        # 相机相关属性
        self.cap = None
        self.device_index = config.get('device_index', 0)
        self.width = config.get('width', 1280)
        self.height = config.get('height', 720)
        self.fps = config.get('fps', 30)
        self.buffer_size = config.get('buffer_size', 1)
        self.fourcc = config.get('fourcc', 'MJPG')
        
        # OpenCV后端配置 (DirectShow性能最佳)
        backend_name = config.get('backend', 'directshow').lower()
        self.backend_map = {
            'directshow': cv2.CAP_DSHOW,
            'dshow': cv2.CAP_DSHOW,
            'msmf': cv2.CAP_MSMF,
            'any': cv2.CAP_ANY
        }
        self.preferred_backend = self.backend_map.get(backend_name, cv2.CAP_DSHOW)
        self.backend_name = backend_name

        # 额外可调的降采样宽度（不改变外部配置语义，仅内部优化传输）
        self._tx_max_width = int(config.get('tx_max_width', 640))
        
        # 流控制
        self.streaming_thread = None
        # 减小缓存长度，保留最近2帧即可，避免累计占用
        self.frame_cache = queue.Queue(maxsize=int(config.get('frame_cache_len', 2)))
        self.last_frame = None
        self.frame_count = 0
        self.dropped_frames = 0
        
        # 性能监控
        self.fps_counter = 0
        self.fps_start_time = time.time()
        self.actual_fps = 0
        
        # 重连与断连检测机制（-1 表示无限重连）
        self.max_reconnect_attempts = int(config.get('max_reconnect_attempts', -1))
        self.reconnect_delay = float(config.get('reconnect_delay', 2.0))
        self.read_fail_threshold = int(config.get('read_fail_threshold', 30))
        self._last_connected_state = None
        
        # 设备标识和性能统计
        self.device_id = f"camera_{self.device_index}"
        self.performance_stats = {
            'frames_processed': 0,
            'actual_fps': 0,
            'dropped_frames': 0
        }
        
        # 全局帧队列（用于录制）
        self.frame_queue = queue.Queue(maxsize=10)  # 最大长度10，自动丢弃旧帧
        
        # 属性缓存机制 - 避免重复设置相同属性值
        self._property_cache = {}
        self._cache_enabled = True

        # OpenCV优化开关和性能设置
        try:
            cv2.setUseOptimized(True)
            # 设置OpenCV线程数以提高性能
            cv2.setNumThreads(4)  # 使用4个线程
        except Exception:
            pass
        
        self.logger.info(f"相机管理器初始化完成 - 设备索引: {self.device_index}")
        
    def _set_property_optimized(self, prop, value):
        """
        优化的属性设置方法，使用缓存避免重复设置
        
        Args:
            prop: OpenCV属性常量
            value: 属性值
            
        Returns:
            bool: 是否实际执行了设置操作
        """
        if not self.cap:
            return False
            
        # 检查缓存，避免重复设置相同值
        if self._cache_enabled and prop in self._property_cache:
            if self._property_cache[prop] == value:
                return False  # 值未改变，跳过设置
                
        # 执行属性设置
        result = self.cap.set(prop, value)
        
        # 更新缓存
        if self._cache_enabled:
            self._property_cache[prop] = value
            
        return True
        
    def initialize(self) -> bool:
        """
        初始化相机设备
        
        Returns:
            bool: 初始化是否成功
        """
        start_time = time.time()
        try:
            self.logger.info(f"正在初始化相机设备...")
            
            # 使用构造函数中已加载的配置，避免并发读取配置文件
            config_time = time.time()
            self.logger.info(f"使用已加载配置: device_index={self.device_index}, resolution={self.width}x{self.height}, fps={self.fps} (耗时: {(config_time - start_time)*1000:.1f}ms)")
            
            # 使用配置的后端，如果失败则尝试其他后端
            if self.preferred_backend == cv2.CAP_DSHOW:
                backends = [cv2.CAP_DSHOW, cv2.CAP_MSMF, cv2.CAP_ANY]
            elif self.preferred_backend == cv2.CAP_MSMF:
                backends = [cv2.CAP_MSMF, cv2.CAP_DSHOW, cv2.CAP_ANY]
            else:
                backends = [self.preferred_backend, cv2.CAP_DSHOW, cv2.CAP_MSMF, cv2.CAP_ANY]
            camera_open_time = time.time()
            
            for backend in backends:
                backend_start = time.time()
                try:
                    # 快速打开相机，减少超时等待
                    self.cap = cv2.VideoCapture(self.device_index, backend)
                    
                    # 设置较短的超时时间以加快检测
                    if hasattr(cv2, 'CAP_PROP_OPEN_TIMEOUT_MSEC'):
                        self.cap.set(cv2.CAP_PROP_OPEN_TIMEOUT_MSEC, 3000)  # 3秒超时
                    
                    if self.cap.isOpened():
                        backend_time = (time.time() - backend_start) * 1000
                        self.logger.info(f"使用后端 {backend} 成功打开相机 (耗时: {backend_time:.1f}ms)")
                        break
                except Exception as e:
                    backend_time = (time.time() - backend_start) * 1000
                    self.logger.warning(f"后端 {backend} 打开相机失败: {e} (耗时: {backend_time:.1f}ms)")
                    continue
            else:
                total_open_time = (time.time() - camera_open_time) * 1000
                self.logger.warning(f"所有后端都无法打开相机，相机设备不可用 (总耗时: {total_open_time:.1f}ms)")
                return False
                
            total_open_time = (time.time() - camera_open_time) * 1000
            self.logger.info(f"相机打开完成 (总耗时: {total_open_time:.1f}ms)")
                
            # 设置相机属性
            config_start = time.time()
            self._configure_camera()
            config_time = (time.time() - config_start) * 1000
            self.logger.info(f"相机配置完成 (耗时: {config_time:.1f}ms)")
            
            # 验证相机是否正常工作（优化测试过程）
            test_start = time.time()
            if not self._test_camera():
                test_time = (time.time() - test_start) * 1000
                self.logger.warning(f"相机测试失败，相机设备不可用 (耗时: {test_time:.1f}ms)")
                return False
            test_time = (time.time() - test_start) * 1000
            self.logger.info(f"相机测试完成 (耗时: {test_time:.1f}ms)")
                
            # 使用set_connected方法来正确启动连接监控线程
            self.set_connected(True)
            self._last_connected_state = True
            self._device_info.update({
                'device_index': self.device_index,
                'resolution': f"{self.width}x{self.height}",
                'fps': self.fps,
                'backend': self.cap.getBackendName() if hasattr(self.cap, 'getBackendName') else 'Unknown'
            })
            
            total_time = (time.time() - start_time) * 1000
            self.logger.info(f"相机初始化成功 (总耗时: {total_time:.1f}ms)")
            return True
            
        except Exception as e:
            error_msg = f"相机初始化失败: {e}"
            
            # 提供更详细的错误信息和解决建议
            if "所有后端都无法打开相机" in str(e):
                error_msg += f"\n可能原因：\n1. 相机设备索引 {self.device_index} 不存在或被其他程序占用\n2. 相机驱动程序未正确安装\n3. 相机硬件连接问题\n建议：检查相机连接状态，尝试更换设备索引或重启相机设备"
            elif "Camera index out of range" in str(e):
                error_msg += f"\n相机设备索引 {self.device_index} 超出范围，请检查可用的相机设备"
            elif "backend" in str(e).lower():
                error_msg += "\n相机后端初始化失败，可能是驱动程序问题"
                
            self.logger.error(error_msg)
            self.is_connected = False
            if self.cap:
                try:
                    self.cap.release()
                except Exception:
                    pass
                self.cap = None
            return False
            
    def _configure_camera(self):
        """
        配置相机参数
        """
        if not self.cap:
            return
            
        try:
            # 批量设置相机属性以提高效率
            config_start = time.time()
            
            # 设置缓冲区大小（优先设置，减少延迟）
            buffer_start = time.time()
            try:
                self.cap.set(cv2.CAP_PROP_BUFFERSIZE, min(self.buffer_size, 1))  # 使用最小缓冲区减少延迟
            except Exception:
                pass
            buffer_time = (time.time() - buffer_start) * 1000
            self.logger.debug(f"缓冲区设置耗时: {buffer_time:.1f}ms")
            
            # 性能优化设置：禁用可能导致延迟的自动功能
            optimization_start = time.time()
            try:
                # 禁用自动曝光以减少处理时间
                self.cap.set(cv2.CAP_PROP_AUTO_EXPOSURE, 0.25)  # 手动曝光模式
                # 禁用自动白平衡
                self.cap.set(cv2.CAP_PROP_AUTO_WB, 0)
                # 设置较低的曝光值以减少延迟
                self.cap.set(cv2.CAP_PROP_EXPOSURE, -6)
            except Exception as e:
                self.logger.debug(f"设置性能优化参数时出现警告: {e}")
            optimization_time = (time.time() - optimization_start) * 1000
            self.logger.debug(f"性能优化设置耗时: {optimization_time:.1f}ms")
            
            # 激进优化：跳过非关键属性设置，只设置必要属性
            resolution_start = time.time()
            
            # 优先级属性设置：只设置最关键的属性
            critical_properties = [
                (cv2.CAP_PROP_FRAME_WIDTH, self.width),
                (cv2.CAP_PROP_FRAME_HEIGHT, self.height)
            ]
            
            # 可选属性（在某些情况下可以跳过）
            optional_properties = [
                (cv2.CAP_PROP_FPS, self.fps)
            ]
            
            batch_start = time.time()
            actual_sets = 0
            skipped_sets = 0
            
            # 设置关键属性
            for prop, value in critical_properties:
                try:
                    if self._set_property_optimized(prop, value):
                        actual_sets += 1
                    else:
                        skipped_sets += 1
                except Exception as e:
                    self.logger.debug(f"设置属性 {prop} 失败: {e}")
                    skipped_sets += 1
            
            # 条件设置可选属性（如果时间允许）
            current_time = (time.time() - batch_start) * 1000
            if current_time < 1000:  # 如果已用时间少于1秒，才设置FPS
                for prop, value in optional_properties:
                    try:
                        if self._set_property_optimized(prop, value):
                            actual_sets += 1
                        else:
                            skipped_sets += 1
                    except Exception as e:
                        self.logger.debug(f"设置可选属性 {prop} 失败: {e}")
                        skipped_sets += 1
            else:
                self.logger.debug(f"跳过FPS设置以节省时间 (已耗时: {current_time:.1f}ms)")
                skipped_sets += len(optional_properties)
                    
            batch_time = (time.time() - batch_start) * 1000
            
            resolution_time = (time.time() - resolution_start) * 1000
            self.logger.debug(f"优化属性设置耗时: {batch_time:.1f}ms, 实际设置: {actual_sets}, 跳过: {skipped_sets}, 总计: {resolution_time:.1f}ms")
            
            # 将原来的单独计时变量设为批量时间的一部分用于兼容性
            width_time = batch_time * 0.5  # 估算宽度设置占比
            height_time = batch_time * 0.4  # 估算高度设置占比
            fps_time = batch_time * 0.1     # 估算帧率设置占比（可能被跳过）
            
            # 设置FOURCC编码（如果需要）
            if self.fourcc:
                fourcc_start = time.time()
                fourcc_code = cv2.VideoWriter_fourcc(*self.fourcc)
                self.cap.set(cv2.CAP_PROP_FOURCC, fourcc_code)
                fourcc_time = (time.time() - fourcc_start) * 1000
                self.logger.debug(f"FOURCC设置耗时: {fourcc_time:.1f}ms")
            
            # 激进优化：延迟验证机制 - 只在必要时验证
            verification_start = time.time()
            
            # 检查是否需要验证（基于配置或调试需求）
            need_verification = self.logger.isEnabledFor(logging.DEBUG) or getattr(self, '_force_verification', False)
            
            if need_verification:
                # 只读取关键属性进行验证
                batch_read_start = time.time()
                try:
                    actual_width = int(self.cap.get(cv2.CAP_PROP_FRAME_WIDTH))
                    actual_height = int(self.cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
                    # FPS验证可选，因为某些相机不支持精确FPS设置
                    actual_fps = self.cap.get(cv2.CAP_PROP_FPS) if actual_sets > 2 else self.fps
                    batch_read_time = (time.time() - batch_read_start) * 1000
                    self.logger.debug(f"验证读取耗时: {batch_read_time:.1f}ms")
                except Exception as e:
                    # 验证失败不影响主流程
                    actual_width, actual_height, actual_fps = self.width, self.height, self.fps
                    batch_read_time = (time.time() - batch_read_start) * 1000
                    self.logger.debug(f"属性验证失败，使用默认值: {e}")
            else:
                # 跳过验证，使用设置值
                actual_width, actual_height, actual_fps = self.width, self.height, self.fps
                batch_read_time = 0.0
                self.logger.debug("跳过属性验证以节省时间")
            
            verification_time = (time.time() - verification_start) * 1000
            
            # 为兼容性保留单独计时变量
            width_read_time = batch_read_time * 0.4
            height_read_time = batch_read_time * 0.4
            fps_read_time = batch_read_time * 0.2
            
            self.logger.debug(f"延迟验证耗时: {batch_read_time:.1f}ms, 总计: {verification_time:.1f}ms")
            
            total_config_time = (time.time() - config_start) * 1000
            
            self.logger.info(f"相机配置完成 - 分辨率: {actual_width}x{actual_height}, FPS: {actual_fps}")
            self.logger.info(f"配置耗时统计 - 缓冲区: {buffer_time:.1f}ms, 优化设置: {optimization_time:.1f}ms, 分辨率: {resolution_time:.1f}ms, 帧率: {fps_time:.1f}ms, 验证: {verification_time:.1f}ms, 总计: {total_config_time:.1f}ms")
            self.logger.debug(f"配置详情 - 分辨率设置: {resolution_time:.1f}ms, FPS设置: {fps_time:.1f}ms, 验证: {verification_time:.1f}ms, 总计: {total_config_time:.1f}ms")
            
        except Exception as e:
            self.logger.warning(f"配置相机参数失败: {e}")
            
    def _test_camera(self) -> bool:
        """
        测试相机是否正常工作
        
        Returns:
            bool: 测试是否成功
        """
        try:
            # 快速测试：只读取一帧进行验证
            read_start = time.time()
            ret, frame = self.cap.read()
            read_time = (time.time() - read_start) * 1000
            
            if ret and frame is not None:
                # 基本帧验证
                if len(frame.shape) >= 2 and frame.shape[0] > 0 and frame.shape[1] > 0:
                    self.logger.info(f"相机测试成功 - 帧大小: {frame.shape}, 读取耗时: {read_time:.1f}ms")
                    
                    # 清理测试帧内存
                    del frame
                    return True
                else:
                    self.logger.error(f"相机测试失败 - 帧数据无效: {frame.shape if frame is not None else 'None'}")
                    return False
            else:
                self.logger.error(f"相机测试失败 - 无法读取帧, 读取耗时: {read_time:.1f}ms")
                return False
        except Exception as e:
            self.logger.error(f"相机测试异常: {e}")
            return False
            
    def calibrate(self) -> bool:
        """
        校准相机（对于普通相机，主要是验证连接和设置）
        
        Returns:
            bool: 校准是否成功
        """
        calibrate_start = time.time()
        try:
            self.logger.info("开始相机校准...")
            
            if not self.is_connected:
                init_start = time.time()
                if not self.initialize():
                    return False
                init_time = (time.time() - init_start) * 1000
                self.logger.info(f"校准中初始化完成 (耗时: {init_time:.1f}ms)")
                    
            # 优化：减少稳定帧数量，只读取2帧来稳定相机
            stabilize_start = time.time()
            stable_frames = 2  # 减少从5帧到2帧
            
            for i in range(stable_frames):
                frame_start = time.time()
                ret, frame = self.cap.read()
                frame_time = (time.time() - frame_start) * 1000
                
                if not ret:
                    self.logger.warning(f"校准时读取第{i+1}帧失败 (耗时: {frame_time:.1f}ms)")
                else:
                    # 立即释放帧内存
                    if frame is not None:
                        del frame
                    self.logger.debug(f"校准帧{i+1}读取成功 (耗时: {frame_time:.1f}ms)")
            
            stabilize_time = (time.time() - stabilize_start) * 1000
            total_time = (time.time() - calibrate_start) * 1000
            
            self.logger.info(f"相机校准完成 - 稳定化耗时: {stabilize_time:.1f}ms, 总耗时: {total_time:.1f}ms")
            return True
            
        except Exception as e:
            total_time = (time.time() - calibrate_start) * 1000
            self.logger.error(f"相机校准失败: {e} (耗时: {total_time:.1f}ms)")
            return False
            
    def start_streaming(self) -> bool:
        """
        开始数据流推送
        
        Returns:
            bool: 启动是否成功
        """
        if self.is_streaming:
            self.logger.warning("相机流已在运行")
            return True
            
        if not self.is_connected:
            if not self.initialize():
                return False
                
        try:
            self.is_streaming = True
            self.streaming_thread = threading.Thread(
                target=self._streaming_worker,
                name=f"Camera-{self.device_index}-Stream",
                daemon=True
            )
            self.streaming_thread.start()
            
            self.logger.info("相机流启动成功")
            return True
            
        except Exception as e:
            self.logger.error(f"启动相机流失败: {e}")
            self.is_streaming = False
            return False
            
    def stop_streaming(self) -> bool:
        """
        停止数据流推送
        
        Returns:
            bool: 停止是否成功
        """
        try:
            self.is_streaming = False
            
            if self.streaming_thread and self.streaming_thread.is_alive():
                # 等待线程退出
                self.streaming_thread.join(timeout=3.0)
            self.streaming_thread = None
                
            self.logger.info("相机流已停止")
            return True
            
        except Exception as e:
            self.logger.error(f"停止相机流失败: {e}")
            return False
            
    def _streaming_worker(self):
        """
        流处理工作线程
        """
        self.logger.info("相机流工作线程启动")
        
        reconnect_attempts = 0
        consecutive_read_failures = 0

        # 基于目标FPS的简单节拍器，防止无上限地读取/编码/发送导致对象堆积
        frame_interval = 1.0 / max(self.fps, 1)
        next_tick = time.time()

        while self.is_streaming:
            loop_start = time.time()
            try:
                # 如果设备未打开，进入重连流程
                if not self.cap or not self.cap.isOpened():
                    # 仅在状态变化时广播一次断连状态
                    if self._last_connected_state is not False:
                        try:
                            self._socketio.emit('camera_status', {
                                'status': 'disconnected',
                                'device_id': self.device_id,
                                'timestamp': time.time()
                            }, namespace='/devices')
                        except Exception:
                            pass
                        self._last_connected_state = False

                    # 无限重连：max_reconnect_attempts == -1；否则按次数重试
                    if self.max_reconnect_attempts == -1 or reconnect_attempts < self.max_reconnect_attempts:
                        self.logger.warning(f"相机连接丢失，尝试重连 ({'∞' if self.max_reconnect_attempts == -1 else reconnect_attempts + 1}/{self.max_reconnect_attempts if self.max_reconnect_attempts != -1 else '∞'})")
                        if not self.is_streaming:
                            break
                        if self._reconnect():
                            reconnect_attempts = 0
                            consecutive_read_failures = 0
                            # 广播恢复
                            try:
                                self._socketio.emit('camera_status', {
                                    'status': 'connected',
                                    'device_id': self.device_id,
                                    'timestamp': time.time()
                                }, namespace='/devices')
                            except Exception:
                                pass
                            self._last_connected_state = True
                            continue
                        else:
                            reconnect_attempts += 1
                            time.sleep(self.reconnect_delay)
                            continue
                    else:
                        # 超过次数也不退出线程，降频重试，防止永久停机
                        self.logger.error("相机重连失败次数过多，进入降频重试模式")
                        time.sleep(max(self.reconnect_delay, 5.0))
                        # 重置计数以便继续尝试
                        reconnect_attempts = 0
                        continue
                        
                ret, frame = self.cap.read()
                
                if not ret or frame is None:
                    consecutive_read_failures += 1
                    self.dropped_frames += 1
                    if consecutive_read_failures >= getattr(self, 'read_fail_threshold', 30):
                        self.logger.warning(f"连续读帧失败 {consecutive_read_failures} 次，执行相机软复位并进入重连")
                        try:
                            if self.cap:
                                try:
                                    self.cap.release()
                                except Exception:
                                    pass
                                self.cap = None
                            self.is_connected = False
                        except Exception:
                            pass
                        # 进入下一轮循环会走到未打开分支
                        consecutive_read_failures = 0
                        time.sleep(self.reconnect_delay)
                        continue

                    if self.dropped_frames > 10:
                        self.logger.warning(f"连续丢帧过多: {self.dropped_frames}")
                        # 仅在异常情况下触发一次GC，避免高频强制GC
                        try:
                            gc.collect()
                        except Exception:
                            pass
                        self.dropped_frames = 0
                    # 防止空转占满CPU
                    time.sleep(0.02)
                    continue
                    
                # 读帧成功，重置失败计数
                consecutive_read_failures = 0
                self.dropped_frames = 0
                
                # 更新心跳时间，防止连接监控线程判定为超时
                self.update_heartbeat()
                
                # 保存原始帧到队列（用于录制）
                try:
                    self.frame_queue.put_nowait({
                        'frame': frame.copy(),
                        'timestamp': time.time()
                    })
                except queue.Full:
                    # 队列满时丢弃最旧的帧，添加新帧
                    try:
                        self.frame_queue.get_nowait()  # 移除最旧的帧
                        self.frame_queue.put_nowait({
                            'frame': frame.copy(),
                            'timestamp': time.time()
                        })
                    except queue.Empty:
                        pass  # 队列为空，忽略
                
                # 处理帧（降采样以优化传输负载）
                processed_frame = self._process_frame(frame)

                # 发送帧数据
                self._send_frame_data(processed_frame)
                
                # 更新统计
                self._update_statistics()

                # 主动释放局部引用，帮助GC更快识别可回收对象
                del frame

                # 限速：保证不超过目标FPS，减小发送端积压
                now = time.time()
                # 下一个tick基于固定间隔前移，避免误差累积
                next_tick += frame_interval
                sleep_time = next_tick - now
                if sleep_time > 0:
                    time.sleep(sleep_time)
                else:
                    # 如果处理耗时超过间隔，纠正节拍器，避免持续为负
                    next_tick = now

            except Exception as e:
                self.logger.error(f"相机流处理异常: {e}")
                # 小退避，避免异常情况下空转
                time.sleep(0.05)
                
        self.logger.info("相机流工作线程结束")
        
    def _process_frame(self, frame: np.ndarray) -> np.ndarray:
        """
        处理视频帧
        
        Args:
            frame: 原始帧
            
        Returns:
            np.ndarray: 处理后的帧
        """
        try:
            # 调整大小以优化传输（使用 INTER_AREA 质量好且更省内存/CPU）
            h, w = frame.shape[:2]
            if w > self._tx_max_width:
                scale = self._tx_max_width / float(w)
                new_w = self._tx_max_width
                new_h = int(h * scale)
                frame = cv2.resize(frame, (new_w, new_h), interpolation=cv2.INTER_AREA)
            return frame
        except Exception as e:
            self.logger.error(f"处理帧失败: {e}")
            return frame
            
    def _send_frame_data(self, frame: np.ndarray):
        """
        发送帧数据
        
        Args:
            frame: 视频帧
        """
        # 将临时对象局部化，并在 finally 中删除引用，加速回收
        buffer = None
        frame_bytes = None
        frame_data = None
        try:
            # 编码为JPEG
            encode_param = [int(cv2.IMWRITE_JPEG_QUALITY), 80]
            ok, buffer = cv2.imencode('.jpg', frame, encode_param)
            if not ok or buffer is None:
                self.logger.warning("帧JPEG编码失败")
                return
            
            # 转换为bytes再做base64，减少中间numpy对象的长生命周期
            frame_bytes = buffer.tobytes()
            frame_data = base64.b64encode(frame_bytes).decode('utf-8')
            
            # 发送数据
            data = {
                'timestamp': time.time(),
                'frame_count': self.frame_count,
                'image': frame_data,
                'fps': self.actual_fps,
                'device_id': self.device_id
            }
            
            self._socketio.emit('camera_frame', data, namespace='/devices')
            
        except Exception as e:
            self.logger.error(f"发送帧数据失败: {e}")
        finally:
            # 显式删除临时大对象的引用，避免在高吞吐下堆积
            del buffer
            del frame_bytes
            del frame_data
            
    def _update_statistics(self):
        """
        更新性能统计
        """
        self.frame_count += 1
        self.fps_counter += 1
        
        # 每秒计算一次实际FPS
        current_time = time.time()
        if current_time - self.fps_start_time >= 1.0:
            self.actual_fps = self.fps_counter / (current_time - self.fps_start_time)
            self.fps_counter = 0
            self.fps_start_time = current_time
            
            # 更新性能统计
            self.performance_stats.update({
                'frames_processed': self.frame_count,
                'actual_fps': round(self.actual_fps, 2),
                'dropped_frames': self.dropped_frames
            })
            
    def _reconnect(self) -> bool:
        """
        重新连接相机
        
        Returns:
            bool: 重连是否成功
        """
        try:
            if self.cap:
                try:
                    self.cap.release()
                except Exception:
                    pass
                self.cap = None
                
            time.sleep(1.0)  # 等待设备释放
            
            return self.initialize()
            
        except Exception as e:
            self.logger.error(f"相机重连失败: {e}")
            return False
            
    def get_status(self) -> Dict[str, Any]:
        """
        获取设备状态
        
        Returns:
            Dict[str, Any]: 设备状态信息
        """
        status = super().get_status()
        status.update({
            'device_index': self.device_index,
            'resolution': f"{self.width}x{self.height}",
            'target_fps': self.fps,
            'actual_fps': self.actual_fps,
            'frame_count': self.frame_count,
            'dropped_frames': self.dropped_frames,
            'has_frame': self.last_frame is not None
        })
        return status
        
    def capture_image(self, save_path: Optional[str] = None) -> Optional[np.ndarray]:
        """
        捕获单张图像
        
        Args:
            save_path: 保存路径（可选）
            
        Returns:
            Optional[np.ndarray]: 捕获的图像，失败返回None
        """
        try:
            if not self.is_connected or not self.cap:
                self.logger.error("相机未连接")
                return None
                
            ret, frame = self.cap.read()
            if not ret or frame is None:
                self.logger.error("捕获图像失败")
                return None
                
            if save_path:
                cv2.imwrite(save_path, frame)
                self.logger.info(f"图像已保存到: {save_path}")
                
            return frame
            
        except Exception as e:
            self.logger.error(f"捕获图像异常: {e}")
            return None
            
  
        
    def disconnect(self):
        """
        断开相机连接
        """
        try:
            self.stop_streaming()
            
            if self.cap:
                try:
                    self.cap.release()
                except Exception:
                    pass
                self.cap = None
                
            self.is_connected = False
            self.logger.info("相机已断开连接")
            
        except Exception as e:
            self.logger.error(f"断开相机连接失败: {e}")
            
    def reload_config(self) -> bool:
        """
        重新加载设备配置
        
        Returns:
            bool: 重新加载是否成功
        """
        try:
            self.logger.info("正在重新加载相机配置...")
            

            
            # 获取最新配置
            config = self.config_manager.get_device_config('camera')
            
            # 更新配置属性
            self.device_index = config.get('device_index', 0)
            self.width = config.get('width', 1280)
            self.height = config.get('height', 720)
            self.fps = config.get('fps', 30)
            self.buffer_size = config.get('buffer_size', 1)
            self.fourcc = config.get('fourcc', 'MJPG')
            self._tx_max_width = int(config.get('tx_max_width', 640))
            # 新增：动态更新重连/阈值配置
            self.max_reconnect_attempts = int(config.get('max_reconnect_attempts', self.max_reconnect_attempts))
            self.reconnect_delay = float(config.get('reconnect_delay', self.reconnect_delay))
            self.read_fail_threshold = int(config.get('read_fail_threshold', self.read_fail_threshold))
            
            # 更新帧缓存队列大小
            frame_cache_len = int(config.get('frame_cache_len', 2))
            if frame_cache_len != self.frame_cache.maxsize:
                # 清空旧队列
                while not self.frame_cache.empty():
                    try:
                        self.frame_cache.get_nowait()
                    except queue.Empty:
                        break
                # 创建新队列
                self.frame_cache = queue.Queue(maxsize=frame_cache_len)
            
            # 更新设备信息
            self.device_id = f"camera_{self.device_index}"
            
            self.logger.info(f"相机配置重新加载成功 - 设备索引: {self.device_index}, 分辨率: {self.width}x{self.height}, FPS: {self.fps}")
            return True
            
        except Exception as e:
            self.logger.error(f"重新加载相机配置失败: {e}")
            return False
    
    def check_hardware_connection(self) -> bool:
        """
        检查相机硬件连接状态
        
        Returns:
            bool: 相机是否物理连接
        """
        try:
            if not self.cap:
                # 如果相机实例不存在，尝试重新创建
                self.logger.info("相机实例不存在，尝试重新创建-----------------")
                return self._attempt_device_reconnection()
            
            if not self.cap.isOpened():
                # 相机未打开，尝试重连
                self.logger.info("相机未打开，尝试重新连接-----------------")
                return self._attempt_device_reconnection()
            
            # 多层次验证相机连接状态
            try:
                # 第一步：使用grab()方法快速清除所有缓存帧
                self.logger.debug("快速清除相机缓存帧...")
                try:
                    # grab()方法只获取帧但不解码，速度更快
                    # 连续grab多次以清空内部缓冲区
                    for _ in range(15):  # 增加清除次数，确保缓存完全清空
                        if not self.cap.grab():
                            break  # 如果grab失败，说明没有更多缓存帧
                except Exception as e:
                    self.logger.debug(f"清除缓存帧时出现异常: {e}")
                
                # 第二步：严格的连续帧检测
                failed_count = 0
                total_frames = 15  # 增加检测帧数
                consecutive_failures = 0  # 连续失败计数
                
                for i in range(total_frames):
                    try:
                        ret, frame = self.cap.read()
                        if ret and frame is not None:
                            # 验证帧数据的有效性
                            if self._validate_frame_data(frame):
                                del frame
                                consecutive_failures = 0  # 重置连续失败计数
                            else:
                                failed_count += 1
                                consecutive_failures += 1
                                del frame
                        else:
                            failed_count += 1
                            consecutive_failures += 1
                        
                        # 如果连续3帧失败，立即判定为断开
                        if consecutive_failures >= 3:
                            self.logger.warning(f"相机连接检测失败：连续{consecutive_failures}帧失败")
                            return False
                            
                        # 如果总失败帧数超过30%，判定为断开
                        if failed_count > total_frames * 0.3:
                            self.logger.warning(f"相机连接检测失败：{failed_count}/{i+1}帧读取失败，超过30%阈值")
                            return False
                            
                    except Exception as e:
                        failed_count += 1
                        consecutive_failures += 1
                        self.logger.debug(f"读取第{i+1}帧时异常: {e}")
                        
                        # 连续异常也判定为断开
                        if consecutive_failures >= 3:
                            self.logger.warning(f"相机连接检测异常：连续{consecutive_failures}帧异常")
                            return False
                    
                    # 短暂延时，避免过快读取
                    time.sleep(0.005)  # 减少延时提高检测速度
                
                # 第三步：最终判断
                success_rate = (total_frames - failed_count) / total_frames
                if success_rate >= 0.7:  # 成功率需要达到70%
                    self.logger.info(f"相机连接检测成功：{total_frames-failed_count}/{total_frames}帧读取成功，成功率{success_rate:.1%}")
                    return True
                else:
                    self.logger.warning(f"相机连接检测失败：成功率{success_rate:.1%}低于70%阈值")
                    return False
                    
            except Exception as e:
                self.logger.warning(f"相机连接检测过程中发生异常: {e}")
                return False                
        except Exception as e:
            self.logger.debug(f"检查相机硬件连接时发生异常: {e}")
            return False
    
    def _validate_frame_data(self, frame) -> bool:
        """
        验证帧数据的有效性
        
        Args:
            frame: 要验证的帧数据
            
        Returns:
            bool: 帧数据是否有效
        """
        try:
            if frame is None:
                return False
                
            # 检查帧尺寸
            if frame.shape[0] < 10 or frame.shape[1] < 10:
                return False
                
            # 检查帧数据是否全为零（可能是无效帧）
            if np.all(frame == 0):
                return False
                
            # 检查帧数据的方差（全黑或全白帧可能是无效的）
            if np.var(frame) < 1.0:
                return False
                
            return True
            
        except Exception:
            return False
    
    def _attempt_device_reconnection(self) -> bool:
        """
        尝试重新连接相机设备
        
        Returns:
            bool: 重连是否成功
        """
        try:
            self.logger.info("检测到相机设备断开，尝试重新连接...")
            
            # 清理旧的相机实例
            if self.cap:
                try:
                    self.cap.release()
                except Exception as e:
                    self.logger.debug(f"清理旧相机实例时出错: {e}")
            
            self.cap = None
            
            # 等待设备释放
            time.sleep(0.5)
            
            # 重新初始化相机
            if self.initialize():
                self._notify_status_change(True)
                # 重连成功后，确保数据流正在运行
                if not self.is_streaming:
                    self.logger.info("重连成功，启动相机数据流")
                    self.start_streaming()
                
                # 更新设备信息
                self._device_info.update({
                    'device_index': self.device_index,
                    'resolution': f"{self.width}x{self.height}",
                    'fps': self.fps,
                    'backend': self.cap.getBackendName() if hasattr(self.cap, 'getBackendName') else 'Unknown'
                })
                
                return True
            else:
                self.logger.warning("相机设备重连失败")
                return False
                
        except Exception as e:
            self.logger.error(f"相机设备重连过程中出错: {e}")
            self.cap = None
            return False
    
    def cleanup(self):
        """
        清理资源
        """
        try:
            self.logger.info("开始清理相机资源")
            
            # 清理监控线程
            self._cleanup_monitoring()
            
            # 停止流
            if self.is_streaming:
                self.stop_streaming()
            
            # 断开连接
            self.disconnect()
            
            # 清理帧缓存
            while not self.frame_cache.empty():
                try:
                    self.frame_cache.get_nowait()
                except queue.Empty:
                    break
            
            # 清理全局帧队列
            while not self.frame_queue.empty():
                try:
                    self.frame_queue.get_nowait()
                except queue.Empty:
                    break
            
            self.last_frame = None
            
            super().cleanup()
            self.logger.info("相机资源清理完成")
            
        except Exception as e:
            self.logger.error(f"清理相机资源时发生错误: {e}")