3694 lines
175 KiB
Python
3694 lines
175 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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设备管理模块
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负责摄像头、IMU传感器和压力传感器的连接和数据采集
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以及视频推流功能
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"""
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import cv2
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import numpy as np
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import time
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import threading
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import json
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import queue
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import base64
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import gc
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import os
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import psutil
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import configparser
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from datetime import datetime
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from pathlib import Path
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from typing import Dict, List, Optional, Any, Tuple
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from concurrent.futures import ThreadPoolExecutor
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import logging
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# 添加串口通信支持
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import serial
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# SMiTSense DLL支持
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import ctypes
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from ctypes import Structure, c_int, c_float, c_char_p, c_void_p, c_uint32, c_uint16, POINTER, byref
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# matplotlib相关导入（用于深度图渲染）
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try:
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from matplotlib.colors import LinearSegmentedColormap
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import matplotlib.pyplot as plt
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MATPLOTLIB_AVAILABLE = True
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except ImportError:
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MATPLOTLIB_AVAILABLE = False
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print("警告: matplotlib库未安装，将使用默认深度图渲染")
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# 数据库管理
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# from backend.app import get_detection_sessions
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from database import DatabaseManager
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# FemtoBolt深度相机支持
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try:
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import pykinect_azure as pykinect
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# 重新启用FemtoBolt功能，使用正确的Orbbec SDK K4A Wrapper路径
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FEMTOBOLT_AVAILABLE = True
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print("信息: pykinect_azure库已安装，FemtoBolt深度相机功能已启用")
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print("使用Orbbec SDK K4A Wrapper以确保与FemtoBolt设备的兼容性")
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except ImportError:
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FEMTOBOLT_AVAILABLE = False
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print("警告: pykinect_azure库未安装，FemtoBolt深度相机功能将不可用")
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print("请使用以下命令安装: pip install pykinect_azure")
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logger = logging.getLogger(__name__)
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class DeviceManager:
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"""设备管理器"""
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def __init__(self, db_manager: DatabaseManager = None):
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self.camera = None
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self.femtobolt_camera = None
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self.imu_device = None
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self.pressure_device = None
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self.device_status = {
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'camera': False,
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'femtobolt': False,
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'imu': False,
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'pressure': False
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}
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self.calibration_data = {}
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self.data_lock = threading.Lock()
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self.camera_lock = threading.Lock() # 摄像头访问锁
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self.latest_data = {}
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# 数据库连接
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self.db_manager = db_manager
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# 推流状态和线程
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self.camera_streaming = False
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self.femtobolt_streaming = False
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self.imu_streaming = False
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self.pressure_streaming = False
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self.camera_streaming_thread = None
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self.femtobolt_streaming_thread = None
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self.imu_thread = None
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self.pressure_thread = None
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self.streaming_stop_event = threading.Event()
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# 全局帧缓存机制
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self.frame_cache = {}
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self.frame_cache_lock = threading.RLock() # 可重入锁
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self.max_cache_size = 10 # 最大缓存帧数
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self.cache_timeout = 5.0 # 缓存超时时间（秒）
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# 同步录制状态
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self.sync_recording = False
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self.current_session_id = None
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self.current_patient_id = None
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self.recording_start_time = None
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# 三路视频录制器
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self.feet_video_writer = None
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self.body_video_writer = None
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self.screen_video_writer = None
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# 录制线程和控制
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self.feet_recording_thread = None
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self.body_recording_thread = None
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self.screen_recording_thread = None
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self.recording_stop_event = threading.Event()
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# 屏幕录制队列
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self.screen_frame_queue = queue.Queue(maxsize=100)
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# 兼容旧版录制状态
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self.recording = False
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self.video_writer = None
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# FemtoBolt相机相关
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self.femtobolt_config = None
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self.femtobolt_recording = False
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self.femtobolt_color_writer = None
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self.femtobolt_depth_writer = None
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# WebSocket连接（用于推流）
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self.socketio = None
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# 延迟设备初始化，避免启动时阻塞
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# self._init_devices() # 注释掉自动初始化，改为按需初始化
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def _init_devices(self):
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"""初始化所有设备"""
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# 分别初始化各个设备，单个设备失败不影响其他设备
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# try:
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# self._init_camera()
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# except Exception as e:
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# logger.error(f'摄像头初始化失败: {e}')
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try:
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if FEMTOBOLT_AVAILABLE:
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self._init_femtobolt_camera()
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except Exception as e:
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logger.error(f'FemtoBolt深度相机初始化失败: {e}')
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try:
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logger.error('IMU传感器初始化!!!!!!!!!!!!!!!!')
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self._init_imu()
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except Exception as e:
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logger.error(f'IMU传感器初始化失败: {e}')
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try:
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self._init_pressure_sensor()
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except Exception as e:
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logger.error(f'压力传感器初始化失败: {e}')
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logger.info('设备初始化完成')
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def _init_camera(self):
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"""初始化足部监视摄像头"""
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try:
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# 从数据库读取摄像头设备索引配置
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device_index = 0 # 默认值
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if self.db_manager:
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try:
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monitor_config = self.db_manager.get_system_setting('monitor_device_index')
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if monitor_config:
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device_index = int(monitor_config)
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logger.info(f'从数据库读取摄像头设备索引: {device_index}')
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else:
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logger.info('数据库中未找到monitor_device_index配置，使用默认值0')
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except Exception as e:
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logger.warning(f'读取摄像头设备索引配置失败，使用默认值0: {e}')
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else:
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logger.warning('数据库管理器未初始化，使用默认摄像头索引0')
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self.device_status['camera'] = True
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except Exception as e:
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logger.error(f'摄像头初始化异常: {e}')
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self.camera = None
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def _init_femtobolt_camera(self):
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"""初始化FemtoBolt深度相机"""
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if not FEMTOBOLT_AVAILABLE:
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logger.warning('FemtoBolt深度相机库未安装，跳过初始化')
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self.femtobolt_camera = None
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self.device_status['femtobolt'] = False
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return
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try:
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# 初始化pykinect_azure库（优先使用指定SDK路径）
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# 首先尝试手动指定路径（优先级最高）
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sdk_paths = self._get_femtobolt_sdk_paths()
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for sdk_path in sdk_paths:
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if os.path.exists(sdk_path):
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try:
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pykinect.initialize_libraries(track_body=False, module_k4a_path=sdk_path)
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logger.info(f'✓ 成功使用FemtoBolt SDK: {sdk_path}')
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break
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except Exception as e:
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logger.warning(f'✗ FemtoBolt SDK路径失败: {sdk_path} - {e}')
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continue
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# 配置FemtoBolt设备参数
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self.femtobolt_config = pykinect.default_configuration
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# logger.info('FemtoBolt配置参数。。。。。。。。。。。。。。。。。')
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# logger.warning(pykinect.default_configuration)
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# 从config.ini读取配置
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import configparser
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config = configparser.ConfigParser()
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config.read(os.path.join(os.path.dirname(__file__), 'config.ini'))
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# color_res_str = config.get('DEFAULT', 'femtobolt_color_resolution', fallback='1080P')
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# depth_range_min = config.getint('DEFAULT', 'femtobolt_depth_range_min', fallback=500)
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# depth_range_max = config.getint('DEFAULT', 'femtobolt_depth_range_max', fallback=4500)
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# # 解析分辨率配置，分为宽度和高度
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# resolution_map = {
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# '1024x1024': (1024, 1024),
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# '1920x1080': (1920, 1080),
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# '1280x720': (1280, 720),
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# '720x720': (720, 720)
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# }
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# width, height = resolution_map.get(color_res_str, (1920, 1080))
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# 假设SDK支持设置宽高参数，示例代码如下（需根据实际SDK调整）
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# if hasattr(self.femtobolt_config, 'color_resolution_width') and hasattr(self.femtobolt_config, 'color_resolution_height'):
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# self.femtobolt_config.color_resolution_width = width
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# self.femtobolt_config.color_resolution_height = height
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# else:
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# logger.info('FemtoBolt存在分辨率参数。。。。。。。。。。。。。。。。。')
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# # 兼容原有枚举设置
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# if color_res_str == '720P':
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# self.femtobolt_config.color_resolution = pykinect.K4A_COLOR_RESOLUTION_720P
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# elif color_res_str == '1080P':
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# self.femtobolt_config.color_resolution = pykinect.K4A_COLOR_RESOLUTION_1080P
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# else:
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# self.femtobolt_config.color_resolution = pykinect.K4A_COLOR_RESOLUTION_1080P
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self.femtobolt_config.depth_mode = pykinect.K4A_DEPTH_MODE_NFOV_UNBINNED
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# self.femtobolt_config.depth_mode = pykinect.K4A_DEPTH_MODE_NFOV_UNBINNED
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self.femtobolt_config.camera_fps = pykinect.K4A_FRAMES_PER_SECOND_15
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self.femtobolt_config.synchronized_images_only = False
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self.femtobolt_config.color_resolution = 0
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# 视效范围参数示例，假设SDK支持depth_range_min和depth_range_max
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# 直接尝试启动设备（pykinect_azure库没有设备数量检测API）
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# logger.info('准备启动FemtoBolt设备...')
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# 启动FemtoBolt设备
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logger.info(f'尝试启动FemtoBolt设备...,参数详情是{self.femtobolt_config}')
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||
self.femtobolt_camera = pykinect.start_device(config=self.femtobolt_config)
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if self.femtobolt_camera:
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self.device_status['femtobolt'] = True
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logger.info('✓ FemtoBolt深度相机初始化成功!')
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||
else:
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raise Exception('设备启动返回None')
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||
|
||
except Exception as e:
|
||
logger.warning(f'FemtoBolt深度相机初始化失败: {e}')
|
||
logger.warning('FemtoBolt深度相机功能将不可用，但不影响其他功能')
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||
logger.warning('可能的解决方案:')
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||
logger.warning('1. 检查FemtoBolt设备是否正确连接并被识别')
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logger.warning('2. 安装Orbbec官方的K4A兼容驱动程序')
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logger.warning('3. 确保没有其他应用程序占用设备')
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logger.warning('4. 尝试重新插拔设备或重启计算机')
|
||
logger.warning('5. 考虑使用Orbbec原生SDK而非Azure Kinect SDK')
|
||
self.femtobolt_camera = None
|
||
self.device_status['femtobolt'] = False
|
||
# 不再抛出异常，让系统继续运行其他功能
|
||
|
||
def _get_femtobolt_sdk_paths(self) -> List[str]:
|
||
"""获取FemtoBolt SDK可能的路径列表"""
|
||
import platform
|
||
sdk_paths = []
|
||
if platform.system() == "Windows":
|
||
# 优先使用Orbbec SDK K4A Wrapper（与azure_kinect_image_example.py一致）
|
||
base_dir = os.path.dirname(os.path.abspath(__file__))
|
||
dll_path = os.path.join(base_dir, "dll","femtobolt","bin", "k4a.dll")
|
||
sdk_paths.append(dll_path)
|
||
return sdk_paths
|
||
|
||
def _init_imu(self):
|
||
"""初始化IMU传感器"""
|
||
logger.info('开始初始化IMU传感器...')
|
||
try:
|
||
# 从config.ini读取串口配置
|
||
config = configparser.ConfigParser()
|
||
# 优先读取根目录config.ini，否则读取backend/config.ini
|
||
root_config_path = os.path.join(os.path.dirname(__file__), 'config.ini')
|
||
app_root_config_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'config.ini')
|
||
logger.debug(f'尝试读取配置文件: {root_config_path}, {app_root_config_path}')
|
||
|
||
read_files = config.read([app_root_config_path, root_config_path], encoding='utf-8')
|
||
logger.debug(f'成功读取的配置文件: {read_files}')
|
||
|
||
if not read_files:
|
||
logger.warning('未能读取到config.ini，将使用默认串口配置COM7@9600')
|
||
|
||
imu_port = config.get('DEVICES', 'imu_port', fallback='COM7')
|
||
imu_baudrate = config.getint('DEVICES', 'imu_baudrate', fallback=9600)
|
||
logger.info(f'从配置文件读取IMU串口配置: {imu_port}@{imu_baudrate}')
|
||
|
||
# 初始化真实IMU设备
|
||
logger.debug('创建RealIMUDevice实例...')
|
||
self.imu_device = RealIMUDevice(port=imu_port, baudrate=imu_baudrate)
|
||
|
||
# 测试读取数据
|
||
logger.debug('测试IMU设备数据读取...')
|
||
test_data = self.imu_device.read_data()
|
||
logger.debug(f'IMU设备测试数据: {test_data}')
|
||
|
||
self.device_status['imu'] = True
|
||
logger.info(f'IMU传感器初始化成功（真实设备）: {imu_port}@{imu_baudrate}')
|
||
except Exception as e:
|
||
logger.error(f'IMU传感器初始化失败: {e}', exc_info=True)
|
||
self.imu_device = None
|
||
self.device_status['imu'] = False
|
||
|
||
def _init_pressure_sensor(self):
|
||
"""初始化压力传感器"""
|
||
try:
|
||
# 优先尝试连接真实设备
|
||
try:
|
||
self.pressure_device = RealPressureDevice()
|
||
self.device_status['pressure'] = True
|
||
logger.info('压力传感器初始化成功（真实设备）')
|
||
return
|
||
except Exception as real_e:
|
||
logger.warning(f'真实压力传感器初始化失败，使用模拟设备。原因: {real_e}')
|
||
|
||
# 回退到模拟设备
|
||
self.pressure_device = MockPressureDevice()
|
||
self.device_status['pressure'] = True
|
||
logger.info('压力传感器初始化成功（模拟）')
|
||
except Exception as e:
|
||
logger.error(f'压力传感器初始化失败: {e}')
|
||
self.pressure_device = None
|
||
|
||
def get_device_status(self) -> Dict[str, bool]:
|
||
"""获取设备状态"""
|
||
return self.device_status.copy()
|
||
|
||
def get_connected_devices(self) -> List[str]:
|
||
"""获取已连接的设备列表"""
|
||
return [device for device, status in self.device_status.items() if status]
|
||
|
||
def refresh_devices(self):
|
||
"""刷新设备连接"""
|
||
logger.info('刷新设备连接...')
|
||
|
||
# 使用锁保护摄像头重新初始化
|
||
with self.camera_lock:
|
||
if self.camera:
|
||
self.camera.release()
|
||
self.camera = None
|
||
|
||
self._init_devices()
|
||
|
||
def calibrate_devices(self) -> Dict[str, Any]:
|
||
"""校准设备"""
|
||
calibration_result = {}
|
||
|
||
try:
|
||
# 摄像头校准
|
||
# if self.device_status['camera']:
|
||
# camera_calibration = self._calibrate_camera()
|
||
# calibration_result['camera'] = camera_calibration
|
||
|
||
# IMU校准
|
||
if self.device_status['imu']:
|
||
imu_calibration = self._calibrate_imu()
|
||
calibration_result['imu'] = imu_calibration
|
||
|
||
# 压力传感器校准
|
||
if self.device_status['pressure']:
|
||
pressure_calibration = self._calibrate_pressure()
|
||
calibration_result['pressure'] = pressure_calibration
|
||
|
||
self.calibration_data = calibration_result
|
||
logger.info('设备校准完成')
|
||
|
||
except Exception as e:
|
||
logger.error(f'设备校准失败: {e}')
|
||
raise
|
||
|
||
return calibration_result
|
||
|
||
def _calibrate_camera(self) -> Dict[str, Any]:
|
||
"""校准摄像头"""
|
||
if not self.camera or not self.camera.isOpened():
|
||
return {'status': 'failed', 'error': '摄像头未连接'}
|
||
|
||
try:
|
||
# 获取几帧图像进行校准
|
||
frames = []
|
||
for _ in range(10):
|
||
ret, frame = self.camera.read()
|
||
if ret:
|
||
frames.append(frame)
|
||
time.sleep(0.1)
|
||
|
||
if not frames:
|
||
return {'status': 'failed', 'error': '无法获取图像'}
|
||
|
||
# 计算平均亮度和对比度
|
||
avg_brightness = np.mean([np.mean(cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)) for frame in frames])
|
||
calibration = {
|
||
'status': 'success',
|
||
'brightness': float(avg_brightness),
|
||
'resolution': (int(self.camera.get(cv2.CAP_PROP_FRAME_WIDTH)),
|
||
int(self.camera.get(cv2.CAP_PROP_FRAME_HEIGHT))),
|
||
'fps': float(self.camera.get(cv2.CAP_PROP_FPS)),
|
||
'timestamp': datetime.now().isoformat()
|
||
}
|
||
|
||
return calibration
|
||
|
||
except Exception as e:
|
||
return {'status': 'failed', 'error': str(e)}
|
||
|
||
def _calibrate_imu(self) -> Dict[str, Any]:
|
||
"""标准校准：采样较多帧，计算稳定零点偏移"""
|
||
if not self.imu_device:
|
||
return {'status': 'failed', 'error': 'IMU设备未连接'}
|
||
try:
|
||
samples = []
|
||
for _ in range(100):
|
||
data = self.imu_device.read_data(apply_calibration=False)
|
||
if data and 'head_pose' in data:
|
||
samples.append(data['head_pose'])
|
||
time.sleep(0.01)
|
||
if not samples:
|
||
return {'status': 'failed', 'error': '无法获取IMU数据进行校准'}
|
||
head_pose_offset = {
|
||
'rotation': float(np.mean([s['rotation'] for s in samples])),
|
||
'tilt': float(np.mean([s['tilt'] for s in samples])),
|
||
'pitch': float(np.mean([s['pitch'] for s in samples]))
|
||
}
|
||
calibration = {
|
||
'status': 'success',
|
||
'head_pose_offset': head_pose_offset,
|
||
'timestamp': datetime.now().isoformat()
|
||
}
|
||
if hasattr(self.imu_device, 'set_calibration'):
|
||
self.imu_device.set_calibration(calibration)
|
||
return calibration
|
||
except Exception as e:
|
||
return {'status': 'failed', 'error': str(e)}
|
||
|
||
def _quick_calibrate_imu(self) -> Dict[str, Any]:
|
||
"""快速校准：采样少量帧，以当前姿态为零点（用于每次推流启动）"""
|
||
if not self.imu_device:
|
||
return {'status': 'failed', 'error': 'IMU设备未连接'}
|
||
try:
|
||
samples = []
|
||
for _ in range(10): # 少量采样，加快启动
|
||
data = self.imu_device.read_data(apply_calibration=False)
|
||
if data and 'head_pose' in data:
|
||
samples.append(data['head_pose'])
|
||
time.sleep(0.01)
|
||
if not samples:
|
||
return {'status': 'failed', 'error': '无法获取IMU数据进行快速校准'}
|
||
head_pose_offset = {
|
||
'rotation': float(np.median([s['rotation'] for s in samples])),
|
||
'tilt': float(np.median([s['tilt'] for s in samples])),
|
||
'pitch': float(np.median([s['pitch'] for s in samples]))
|
||
}
|
||
calibration = {
|
||
'status': 'success',
|
||
'head_pose_offset': head_pose_offset,
|
||
'timestamp': datetime.now().isoformat()
|
||
}
|
||
if hasattr(self.imu_device, 'set_calibration'):
|
||
self.imu_device.set_calibration(calibration)
|
||
return calibration
|
||
except Exception as e:
|
||
return {'status': 'failed', 'error': str(e)}
|
||
|
||
def _calibrate_pressure(self) -> Dict[str, Any]:
|
||
"""校准压力传感器"""
|
||
if not self.pressure_device:
|
||
return {'status': 'failed', 'error': '压力传感器未连接'}
|
||
|
||
try:
|
||
# 收集零压力数据
|
||
samples = []
|
||
for _ in range(50):
|
||
data = self.pressure_device.read_data()
|
||
samples.append(data)
|
||
time.sleep(0.02)
|
||
|
||
# 计算零点偏移
|
||
zero_offset = {
|
||
'left_foot': np.mean([s['left_foot'] for s in samples]),
|
||
'right_foot': np.mean([s['right_foot'] for s in samples])
|
||
}
|
||
|
||
calibration = {
|
||
'status': 'success',
|
||
'zero_offset': zero_offset,
|
||
'timestamp': datetime.now().isoformat()
|
||
}
|
||
|
||
return calibration
|
||
|
||
except Exception as e:
|
||
return {'status': 'failed', 'error': str(e)}
|
||
|
||
def collect_data(self, session_id: str, patient_id: str, screen_image_base64: str = None) -> Dict[str, Any]:
|
||
# 实例化VideoStreamManager（VideoStreamManager类在同一文件中定义）
|
||
video_stream_manager = VideoStreamManager(device_manager=self)
|
||
"""采集所有设备数据并保存到指定目录结构
|
||
|
||
Args:
|
||
session_id: 检测会话ID
|
||
patient_id: 患者ID
|
||
screen_image_base64: 前端界面截图的base64数据
|
||
|
||
Returns:
|
||
Dict: 包含所有采集数据的字典，符合detection_data表结构
|
||
"""
|
||
# 生成采集时间戳
|
||
timestamp = datetime.now().strftime('%Y%m%d_%H%M%S_%f')[:-3] # 精确到毫秒
|
||
|
||
# 创建数据存储目录
|
||
data_dir = Path(f'data/patients/{patient_id}/{session_id}/{timestamp}')
|
||
data_dir.mkdir(parents=True, exist_ok=True)
|
||
|
||
# 设置目录权限为777（完全权限）
|
||
try:
|
||
import os
|
||
import stat
|
||
os.chmod(str(data_dir), stat.S_IRWXU | stat.S_IRWXG | stat.S_IRWXO) # 777权限
|
||
logger.debug(f"已设置目录权限为777: {data_dir}")
|
||
except Exception as perm_error:
|
||
logger.warning(f"设置目录权限失败: {perm_error}，但目录创建成功")
|
||
|
||
# 初始化数据字典
|
||
data = {
|
||
'session_id': session_id,
|
||
'head_pose': None,
|
||
'body_pose': None,
|
||
'body_image': None,
|
||
'foot_data': None,
|
||
'foot_image': None,
|
||
'foot_data_image': None,
|
||
'screen_image': None,
|
||
'timestamp': timestamp
|
||
}
|
||
|
||
try:
|
||
# # 1. 采集头部姿态数据（从IMU设备获取）
|
||
# if self.device_status['imu']:
|
||
# head_pose_data = self._collect_head_pose_data()
|
||
# if head_pose_data:
|
||
# data['head_pose'] = json.dumps(head_pose_data)
|
||
# logger.debug(f'头部姿态数据采集成功: {session_id}')
|
||
|
||
# # 2. 采集身体姿态数据（从FemtoBolt深度相机获取）
|
||
# if self.device_status['femtobolt']:
|
||
# body_pose_data = self._collect_body_pose_data()
|
||
# if body_pose_data:
|
||
# data['body_pose'] = json.dumps(body_pose_data)
|
||
# logger.debug(f'身体姿态数据采集成功: {session_id}')
|
||
|
||
# 3. 采集身体视频截图（从FemtoBolt深度相机获取）
|
||
if self.device_status['femtobolt']:
|
||
try:
|
||
body_image_path = video_stream_manager._capture_body_image(data_dir, self)
|
||
if body_image_path:
|
||
data['body_image'] = str(body_image_path)
|
||
logger.debug(f'身体截图保存成功: {body_image_path}')
|
||
except Exception as e:
|
||
logger.error(f'调用_video_stream_manager._capture_body_image异常: {e}')
|
||
|
||
# # 4. 采集足部压力数据（从压力传感器获取）
|
||
# if self.device_status['pressure']:
|
||
# foot_data = self._collect_foot_pressure_data()
|
||
# if foot_data:
|
||
# data['foot_data'] = json.dumps(foot_data)
|
||
# logger.debug(f'足部压力数据采集成功: {session_id}')
|
||
|
||
# 5. 采集足部监测视频截图（从摄像头获取）
|
||
if self.device_status['camera']:
|
||
foot_image_path = video_stream_manager._capture_foot_image(data_dir,self)
|
||
if foot_image_path:
|
||
data['foot_image'] = str(foot_image_path)
|
||
logger.debug(f'足部截图保存成功: {foot_image_path}')
|
||
|
||
# # 6. 生成足底压力数据图（从压力传感器数据生成）
|
||
# if self.device_status['pressure']:
|
||
# foot_data_image_path = self._generate_foot_pressure_image(data_dir)
|
||
# if foot_data_image_path:
|
||
# data['foot_data_image'] = str(foot_data_image_path)
|
||
# logger.debug(f'足底压力数据图生成成功: {foot_data_image_path}')
|
||
|
||
# 7. 保存屏幕录制截图（从前端传入的base64数据）
|
||
if screen_image_base64:
|
||
try:
|
||
# logger.debug(f'屏幕截图保存.................{screen_image_base64}')
|
||
# 保存屏幕截图的base64数据为图片文件
|
||
screen_image_path = None
|
||
if screen_image_base64:
|
||
try:
|
||
if screen_image_base64.startswith('data:image/'):
|
||
base64_data = screen_image_base64.split(',')[1]
|
||
else:
|
||
base64_data = screen_image_base64
|
||
image_data = base64.b64decode(base64_data)
|
||
image_path = data_dir / 'screen_image.png'
|
||
with open(image_path, 'wb') as f:
|
||
f.write(image_data)
|
||
abs_image_path = image_path.resolve()
|
||
abs_cwd = Path.cwd().resolve()
|
||
screen_image_path = str(abs_image_path.relative_to(abs_cwd))
|
||
logger.debug(f'屏幕截图保存成功: {screen_image_path}')
|
||
except Exception as e:
|
||
logger.error(f'屏幕截图保存失败: {e}')
|
||
import traceback
|
||
logger.error(traceback.format_exc())
|
||
|
||
if screen_image_path:
|
||
data['screen_image'] = str(screen_image_path)
|
||
logger.debug(f'屏幕截图保存成功: {screen_image_path}')
|
||
except Exception as e:
|
||
logger.error(f'屏幕截图保存失败: {e}')
|
||
import traceback
|
||
logger.error(traceback.format_exc())
|
||
|
||
# 更新最新数据
|
||
with self.data_lock:
|
||
self.latest_data = data.copy()
|
||
|
||
logger.debug(f'数据采集完成: {session_id}, 时间戳: {timestamp}')
|
||
|
||
except Exception as e:
|
||
logger.error(f'数据采集失败: {e}')
|
||
|
||
return data
|
||
|
||
|
||
def start_femtobolt_stream(self):
|
||
"""开始FemtoBolt深度相机推流"""
|
||
if not FEMTOBOLT_AVAILABLE or self.femtobolt_camera is None:
|
||
logger.error('FemtoBolt深度相机未初始化')
|
||
return False
|
||
|
||
try:
|
||
# 检查是否已经在推流
|
||
if self.femtobolt_streaming:
|
||
logger.warning('FemtoBolt深度相机推流已在运行')
|
||
return True
|
||
|
||
# 重置停止事件
|
||
self.streaming_stop_event.clear()
|
||
|
||
# 设置推流标志
|
||
self.femtobolt_streaming = True
|
||
|
||
# 启动推流线程
|
||
self.femtobolt_streaming_thread = threading.Thread(
|
||
target=self._femtobolt_streaming_thread,
|
||
daemon=True,
|
||
name='FemtoBoltStreamingThread'
|
||
)
|
||
self.femtobolt_streaming_thread.start()
|
||
|
||
# logger.info('FemtoBolt深度相机推流已开始')
|
||
return True
|
||
except Exception as e:
|
||
logger.error(f'FemtoBolt深度相机推流启动失败: {e}')
|
||
self.femtobolt_streaming = False
|
||
return False
|
||
|
||
def stop_femtobolt_stream(self):
|
||
"""停止FemtoBolt深度相机推流"""
|
||
self.femtobolt_streaming = False
|
||
logger.debug('FemtoBolt深度相机推流已停止')
|
||
def set_socketio(self, socketio):
|
||
"""设置WebSocket连接"""
|
||
self.socketio = socketio
|
||
|
||
def start_imu_streaming(self):
|
||
"""启动IMU头部姿态数据推流"""
|
||
try:
|
||
if self.imu_streaming:
|
||
logger.warning('IMU数据推流已在运行')
|
||
return True
|
||
|
||
if not self.imu_device:
|
||
logger.error('IMU设备未初始化')
|
||
return False
|
||
|
||
# 在启动推流前进行快速零点校准（自动以当前姿态为基准）
|
||
logger.info('正在进行IMU零点校准...')
|
||
calibration_result = self._quick_calibrate_imu()
|
||
if calibration_result.get('status') == 'success':
|
||
logger.info(f'IMU零点校准完成: {calibration_result["head_pose_offset"]}')
|
||
else:
|
||
logger.warning(f'IMU零点校准失败，将使用默认零偏移: {calibration_result.get("error", "未知错误")}')
|
||
|
||
self.imu_streaming = True
|
||
self.imu_thread = threading.Thread(target=self._imu_streaming_thread, daemon=True)
|
||
self.imu_thread.start()
|
||
logger.info('IMU头部姿态数据推流已启动')
|
||
return True
|
||
|
||
except Exception as e:
|
||
logger.error(f'启动IMU数据推流失败: {e}')
|
||
self.imu_streaming = False
|
||
return False
|
||
|
||
def stop_imu_streaming(self):
|
||
"""停止IMU头部姿态数据推流"""
|
||
try:
|
||
if not self.imu_streaming:
|
||
logger.warning('IMU数据推流未运行')
|
||
return True
|
||
|
||
self.imu_streaming = False
|
||
if self.imu_thread and self.imu_thread.is_alive():
|
||
self.imu_thread.join(timeout=2)
|
||
|
||
logger.info('IMU头部姿态数据推流已停止')
|
||
return True
|
||
|
||
except Exception as e:
|
||
logger.error(f'停止IMU数据推流失败: {e}')
|
||
return False
|
||
|
||
def start_pressure_streaming(self):
|
||
"""启动压力传感器足部压力数据推流"""
|
||
try:
|
||
if self.pressure_streaming:
|
||
logger.warning('压力传感器数据推流已在运行')
|
||
return True
|
||
|
||
# 确保设备已初始化（懒加载+自动重连）
|
||
if not self.pressure_device:
|
||
try:
|
||
self._init_pressure_sensor()
|
||
except Exception as init_e:
|
||
logger.error(f'压力传感器设备初始化失败: {init_e}')
|
||
return False
|
||
else:
|
||
# 如果是真实设备且未连接，尝试重连
|
||
try:
|
||
if hasattr(self.pressure_device, 'is_connected') and not getattr(self.pressure_device, 'is_connected', True):
|
||
logger.info('检测到压力设备未连接，尝试重新初始化...')
|
||
self._init_pressure_sensor()
|
||
except Exception as reinit_e:
|
||
logger.warning(f'压力设备重连失败: {reinit_e}')
|
||
|
||
# 再次确认
|
||
if not self.pressure_device:
|
||
logger.error('压力传感器设备未初始化')
|
||
return False
|
||
|
||
self.pressure_streaming = True
|
||
self.pressure_thread = threading.Thread(target=self._pressure_streaming_thread, daemon=True)
|
||
self.pressure_thread.start()
|
||
logger.info('压力传感器足部压力数据推流已启动')
|
||
return True
|
||
|
||
except Exception as e:
|
||
logger.error(f'启动压力传感器数据推流失败: {e}')
|
||
self.pressure_streaming = False
|
||
return False
|
||
|
||
def stop_pressure_streaming(self):
|
||
"""停止压力传感器足部压力数据推流"""
|
||
try:
|
||
if not self.pressure_streaming:
|
||
logger.warning('压力传感器数据推流未运行')
|
||
return True
|
||
|
||
self.pressure_streaming = False
|
||
if self.pressure_thread and self.pressure_thread.is_alive():
|
||
self.pressure_thread.join(timeout=2)
|
||
|
||
# 关闭压力设备连接
|
||
if self.pressure_device and hasattr(self.pressure_device, 'close'):
|
||
try:
|
||
self.pressure_device.close()
|
||
except Exception as close_e:
|
||
logger.warning(f'关闭压力设备连接失败: {close_e}')
|
||
|
||
logger.info('压力传感器足部压力数据推流已停止')
|
||
return True
|
||
|
||
except Exception as e:
|
||
logger.error(f'停止压力传感器数据推流失败: {e}')
|
||
return False
|
||
|
||
# def _femtobolt_streaming_thread(self):
|
||
# import matplotlib
|
||
# matplotlib.use("Agg") # 无GUI后端
|
||
# import matplotlib.pyplot as plt
|
||
# from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
|
||
# from matplotlib.colors import LinearSegmentedColormap
|
||
|
||
# frame_count = 0
|
||
|
||
# try:
|
||
# # 读取一次配置，避免每帧IO
|
||
# config = configparser.ConfigParser()
|
||
# config.read('config.ini', encoding='utf-8')
|
||
# try:
|
||
# depth_range_min = int(config.get('DEFAULT', 'femtobolt_depth_range_min', fallback='1400'))
|
||
# depth_range_max = int(config.get('DEFAULT', 'femtobolt_depth_range_max', fallback='1900'))
|
||
# except Exception:
|
||
# depth_range_min = None
|
||
# depth_range_max = None
|
||
|
||
# # 如果可以用matplotlib，提前初始化绘图对象
|
||
# if MATPLOTLIB_AVAILABLE and depth_range_min is not None and depth_range_max is not None:
|
||
# colors = ['fuchsia', 'red', 'yellow', 'lime', 'cyan', 'blue'] * 4
|
||
# mcmap = LinearSegmentedColormap.from_list("custom_cmap", colors)
|
||
|
||
# # 创建独立figure和axes
|
||
# fig, ax = plt.subplots(figsize=(7, 7))
|
||
# canvas = FigureCanvas(fig)
|
||
|
||
# # 灰色背景（假设分辨率不会超过）
|
||
# max_h, max_w = 1080, 1920
|
||
# background = np.ones((max_h, max_w)) * 0.5
|
||
# ax.imshow(background, origin='lower', cmap='gray', alpha=0.3)
|
||
# ax.grid(True, which='both', axis='both',
|
||
# color='white', linestyle='-', linewidth=1, zorder=0)
|
||
# ax.set_axis_off()
|
||
# plt.tight_layout(pad=0)
|
||
|
||
# contour = None # 用于保存等高线对象
|
||
|
||
# while self.femtobolt_streaming and not self.streaming_stop_event.is_set():
|
||
# if self.femtobolt_camera and self.socketio:
|
||
# try:
|
||
# capture = self.femtobolt_camera.update()
|
||
# if capture is not None:
|
||
# ret, depth_image = capture.get_depth_image()
|
||
# if ret and depth_image is not None:
|
||
# if MATPLOTLIB_AVAILABLE and depth_range_min is not None and depth_range_max is not None:
|
||
# # 数据过滤
|
||
# depth_image = depth_image.copy()
|
||
# depth_image[(depth_image > depth_range_max) |
|
||
# (depth_image < depth_range_min)] = 0
|
||
# depth_masked = np.ma.masked_equal(depth_image, 0)
|
||
|
||
# # 删除旧等高线
|
||
# if contour:
|
||
# for coll in contour.collections:
|
||
# coll.remove()
|
||
|
||
# # 绘制新等高线
|
||
# contour = ax.contourf(
|
||
# depth_masked,
|
||
# levels=200,
|
||
# cmap=mcmap,
|
||
# vmin=depth_range_min,
|
||
# vmax=depth_range_max,
|
||
# origin='upper',
|
||
# zorder=2
|
||
# )
|
||
|
||
# # 渲染到numpy
|
||
# canvas.draw()
|
||
# img = np.frombuffer(canvas.tostring_rgb(), dtype=np.uint8)
|
||
# img = img.reshape(fig.canvas.get_width_height()[::-1] + (3,))
|
||
# depth_colored = img
|
||
# else:
|
||
# # OpenCV伪彩模式
|
||
# depth_normalized = np.clip(depth_image, depth_range_min, depth_range_max)
|
||
# depth_normalized = ((depth_normalized - depth_range_min) /
|
||
# (depth_range_max - depth_range_min) * 255).astype(np.uint8)
|
||
# depth_colored = cv2.applyColorMap(depth_normalized, cv2.COLORMAP_JET)
|
||
# mask_outside = (depth_image < depth_range_min) | (depth_image > depth_range_max)
|
||
# depth_colored[mask_outside] = [0, 0, 0]
|
||
|
||
# # 裁剪
|
||
# height, width = depth_colored.shape[:2]
|
||
# target_width = height // 2
|
||
# if width > target_width:
|
||
# left = (width - target_width) // 2
|
||
# right = left + target_width
|
||
# depth_colored = depth_colored[:, left:right]
|
||
|
||
# # 缓存帧
|
||
# self._save_frame_to_cache(depth_colored.copy(), 'femtobolt')
|
||
|
||
# # 发送
|
||
# success, buffer = cv2.imencode('.jpg', depth_colored, [int(cv2.IMWRITE_JPEG_QUALITY), 80])
|
||
# if success and self.socketio:
|
||
# jpg_as_text = base64.b64encode(buffer).decode('utf-8')
|
||
# self.socketio.emit('depth_camera_frame', {
|
||
# 'image': jpg_as_text,
|
||
# 'frame_id': frame_count,
|
||
# 'timestamp': time.time()
|
||
# })
|
||
# frame_count += 1
|
||
# else:
|
||
# time.sleep(0.01)
|
||
# except Exception as e:
|
||
# logger.debug(f'FemtoBolt帧推送失败: {e}')
|
||
# time.sleep(0.1)
|
||
|
||
# time.sleep(1 / 30) # 30 FPS
|
||
# except Exception as e:
|
||
# logger.debug(f'FemtoBolt推流线程异常: {e}')
|
||
# finally:
|
||
# self.femtobolt_streaming = False
|
||
|
||
# def _femtobolt_streaming_thread(self):
|
||
# """FemtoBolt深度相机推流线程（优化版本）"""
|
||
# frame_count = 0
|
||
# import matplotlib
|
||
# matplotlib.use("Agg") # 使用无GUI的Agg后端，加速渲染
|
||
# import matplotlib.pyplot as plt
|
||
# from matplotlib.colors import LinearSegmentedColormap
|
||
# from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
|
||
# try:
|
||
# # 读取深度范围配置（只读一次）
|
||
# config = configparser.ConfigParser()
|
||
# config.read('config.ini')
|
||
# try:
|
||
# depth_range_min = int(config.get('DEFAULT', 'femtobolt_depth_range_min', fallback='1400'))
|
||
# depth_range_max = int(config.get('DEFAULT', 'femtobolt_depth_range_max', fallback='1900'))
|
||
# except Exception:
|
||
# depth_range_min = None
|
||
# depth_range_max = None
|
||
|
||
# # 如果启用matplotlib模式，提前准备绘图对象
|
||
# if MATPLOTLIB_AVAILABLE and depth_range_min is not None and depth_range_max is not None:
|
||
# colors = ['fuchsia', 'red', 'yellow', 'lime', 'cyan', 'blue'] * 4
|
||
# mcmap = LinearSegmentedColormap.from_list("custom_cmap", colors)
|
||
|
||
# fig, ax = plt.subplots(figsize=(7, 7))
|
||
# canvas = FigureCanvas(fig)
|
||
|
||
# background = np.ones((720, 1280)) * 0.5 # 假设最大分辨率，后面裁剪
|
||
# bg_img = ax.imshow(background, origin='lower', cmap='gray', alpha=0.3)
|
||
# ax.grid(True, which='both', axis='both', color='white', linestyle='-', linewidth=1, zorder=0)
|
||
|
||
# contour = None # 等高线对象占位
|
||
# ax.set_axis_off()
|
||
# plt.tight_layout(pad=0)
|
||
|
||
# while self.femtobolt_streaming and not self.streaming_stop_event.is_set():
|
||
# if self.femtobolt_camera and self.socketio:
|
||
# try:
|
||
# capture = self.femtobolt_camera.update()
|
||
# if capture is not None:
|
||
# ret, depth_image = capture.get_depth_image()
|
||
# if ret and depth_image is not None:
|
||
# if MATPLOTLIB_AVAILABLE and depth_range_min is not None and depth_range_max is not None:
|
||
# # 过滤范围外值
|
||
# depth_image = depth_image.copy()
|
||
# depth_image[(depth_image > depth_range_max) | (depth_image < depth_range_min)] = 0
|
||
# depth_masked = np.ma.masked_equal(depth_image, 0)
|
||
|
||
# # 清理旧的等高线
|
||
# if contour:
|
||
# for coll in contour.collections:
|
||
# coll.remove()
|
||
|
||
# # 绘制新的等高线
|
||
# contour = ax.contourf(
|
||
# depth_masked, levels=200, cmap=mcmap,
|
||
# vmin=depth_range_min, vmax=depth_range_max,
|
||
# origin='upper', zorder=2
|
||
# )
|
||
|
||
# # 渲染到 numpy
|
||
# canvas.draw()
|
||
# img = np.frombuffer(canvas.tostring_rgb(), dtype=np.uint8)
|
||
# img = img.reshape(fig.canvas.get_width_height()[::-1] + (3,))
|
||
# depth_colored = img
|
||
# else:
|
||
# # OpenCV 伪彩
|
||
# depth_normalized = np.clip(depth_image, depth_range_min, depth_range_max)
|
||
# depth_normalized = ((depth_normalized - depth_range_min) /
|
||
# (depth_range_max - depth_range_min) * 255).astype(np.uint8)
|
||
# depth_colored = cv2.applyColorMap(depth_normalized, cv2.COLORMAP_JET)
|
||
# mask_outside = (depth_image < depth_range_min) | (depth_image > depth_range_max)
|
||
# depth_colored[mask_outside] = [0, 0, 0]
|
||
|
||
# # 裁剪
|
||
# height, width = depth_colored.shape[:2]
|
||
# target_width = height // 2
|
||
# if width > target_width:
|
||
# left = (width - target_width) // 2
|
||
# right = left + target_width
|
||
# depth_colored = depth_colored[:, left:right]
|
||
|
||
# # 保存到缓存
|
||
# self._save_frame_to_cache(depth_colored.copy(), 'femtobolt')
|
||
|
||
# # 编码并推送
|
||
# success, buffer = cv2.imencode('.jpg', depth_colored, [int(cv2.IMWRITE_JPEG_QUALITY), 80])
|
||
# if success and self.socketio:
|
||
# jpg_as_text = base64.b64encode(buffer).decode('utf-8')
|
||
# self.socketio.emit('depth_camera_frame', {
|
||
# 'image': jpg_as_text,
|
||
# 'frame_id': frame_count,
|
||
# 'timestamp': time.time()
|
||
# })
|
||
# frame_count += 1
|
||
# else:
|
||
# time.sleep(0.01)
|
||
# except Exception as e:
|
||
# logger.debug(f'FemtoBolt帧推送失败: {e}')
|
||
# time.sleep(0.1)
|
||
|
||
# time.sleep(1 / 30) # 控制帧率
|
||
|
||
# except Exception as e:
|
||
# logger.debug(f'FemtoBolt推流线程异常: {e}')
|
||
# finally:
|
||
# self.femtobolt_streaming = False
|
||
|
||
# def _femtobolt_streaming_thread(self):
|
||
"""FemtoBolt深度相机推流线程"""
|
||
frame_count = 0
|
||
try:
|
||
while self.femtobolt_streaming and not self.streaming_stop_event.is_set():
|
||
if self.femtobolt_camera and self.socketio:
|
||
try:
|
||
# 获取FemtoBolt帧
|
||
capture = self.femtobolt_camera.update()
|
||
# 检查capture是否有效并获取彩色深度图像
|
||
if capture is not None:
|
||
ret, depth_image = capture.get_depth_image()
|
||
height2, width2 = depth_image.shape[:2]
|
||
# logger.debug(f'FemtoBolt原始帧宽: {width2}')
|
||
# logger.debug(f'FemtoBolt原始帧高: {height2}')
|
||
|
||
if ret and depth_image is not None:
|
||
# 读取config.ini中的深度范围配置
|
||
import configparser
|
||
config = configparser.ConfigParser()
|
||
config.read('config.ini')
|
||
try:
|
||
depth_range_min = int(config.get('DEFAULT', 'femtobolt_depth_range_min', fallback='1400'))
|
||
depth_range_max = int(config.get('DEFAULT', 'femtobolt_depth_range_max', fallback='1900'))
|
||
except Exception:
|
||
depth_range_min = None
|
||
depth_range_max = None
|
||
# 使用matplotlib渲染深度图，参考display_x.py
|
||
if MATPLOTLIB_AVAILABLE and depth_range_min is not None and depth_range_max is not None:
|
||
import numpy as np
|
||
import cv2
|
||
# 假设 depth_image 已经是 np.uint16 格式
|
||
depth_image = depth_image.copy()
|
||
depth_image[depth_image > depth_range_max] = 0
|
||
depth_image[depth_image < depth_range_min] = 0
|
||
# 归一化到 0-255
|
||
depth_normalized = np.clip(depth_image, depth_range_min, depth_range_max)
|
||
depth_normalized = ((depth_normalized - depth_range_min) /(depth_range_max - depth_range_min) * 255).astype(np.uint8)
|
||
# 用 OpenCV 生成彩色映射（用 COLORMAP_JET 或自定义 LUT 代替 LinearSegmentedColormap）
|
||
# === 对比度增强 ===
|
||
alpha = 1.5 # 对比度增益 (>1 增强对比，1.5 比较明显)
|
||
beta = 0 # 亮度偏移
|
||
depth_normalized = cv2.convertScaleAbs(depth_normalized, alpha=alpha, beta=beta)
|
||
# 可选：伽马校正，增强中间层次感
|
||
gamma = 0.8 # <1 提亮暗部, >1 压暗暗部
|
||
look_up_table = np.array([((i / 255.0) ** gamma) * 255 for i in range(256)]).astype("uint8")
|
||
depth_normalized = cv2.LUT(depth_normalized, look_up_table)
|
||
depth_colored = cv2.applyColorMap(depth_normalized, cv2.COLORMAP_JET)
|
||
# 创建灰色背景
|
||
rows, cols = depth_colored.shape[:2]
|
||
background = np.ones_like(depth_colored, dtype=np.uint8) * 128 # 灰色
|
||
# 画网格（和 matplotlib 网格类似）
|
||
rows, cols = depth_colored.shape[:2]
|
||
grid_color = (255, 255, 255) # 白色
|
||
line_thickness = 1
|
||
grid_bg = np.zeros_like(depth_colored)
|
||
cell_size = 50 # 可以根据原 contourf 分辨率调
|
||
for x in range(0, cols, cell_size):
|
||
cv2.line(grid_bg, (x, 0), (x, rows), grid_color, line_thickness)
|
||
for y in range(0, rows, cell_size):
|
||
cv2.line(grid_bg, (0, y), (cols, y), grid_color, line_thickness)
|
||
bg_with_grid = background.copy()
|
||
mask_grid = (grid_bg.sum(axis=2) > 0)
|
||
depth_colored[mask_grid] = grid_bg[mask_grid]
|
||
else:
|
||
# 如果没有matplotlib则使用原有OpenCV伪彩色映射
|
||
depth_normalized = np.clip(depth_image, depth_range_min, depth_range_max)
|
||
depth_normalized = ((depth_normalized - depth_range_min) / (depth_range_max - depth_range_min) * 255).astype(np.uint8)
|
||
depth_colored = cv2.applyColorMap(depth_normalized, cv2.COLORMAP_JET)
|
||
mask_outside = (depth_image < depth_range_min) | (depth_image > depth_range_max)
|
||
depth_colored[mask_outside] = [0, 0, 0]
|
||
|
||
height, width = depth_colored.shape[:2]
|
||
# logger.debug(f'FemtoBolt帧宽: {width}')
|
||
# logger.debug(f'FemtoBolt帧高: {height}')
|
||
target_width = height // 2
|
||
if width > target_width:
|
||
left = (width - target_width) // 2
|
||
right = left + target_width
|
||
depth_colored = depth_colored[:, left:right]
|
||
height1, width1 = depth_colored.shape[:2]
|
||
# logger.debug(f'FemtoBolt帧裁剪完以后得宽: {width1}')
|
||
# logger.debug(f'FemtoBolt帧裁剪完以后得宽: {height1}')
|
||
# 保存处理好的身体帧到全局缓存
|
||
self._save_frame_to_cache(depth_colored.copy(), 'femtobolt')
|
||
|
||
success, buffer = cv2.imencode('.jpg', depth_colored, [int(cv2.IMWRITE_JPEG_QUALITY), 80])
|
||
if success and self.socketio:
|
||
jpg_as_text = base64.b64encode(buffer).decode('utf-8')
|
||
self.socketio.emit('depth_camera_frame', {
|
||
'image': jpg_as_text,
|
||
'frame_id': frame_count,
|
||
'timestamp': time.time()
|
||
})
|
||
frame_count += 1
|
||
|
||
else:
|
||
# 如果没有获取到有效帧，短暂等待后继续
|
||
time.sleep(0.01)
|
||
|
||
except Exception as e:
|
||
logger.debug(f'FemtoBolt帧推送失败: {e}')
|
||
# 发生错误时短暂等待，避免快速循环
|
||
time.sleep(0.1)
|
||
|
||
# 控制帧率
|
||
time.sleep(1/30) # 30 FPS
|
||
|
||
except Exception as e:
|
||
logger.debug(f'FemtoBolt推流线程异常: {e}')
|
||
finally:
|
||
self.femtobolt_streaming = False
|
||
def _femtobolt_streaming_thread(self):
|
||
"""FemtoBolt深度相机推流线程"""
|
||
frame_count = 0
|
||
try:
|
||
while self.femtobolt_streaming and not self.streaming_stop_event.is_set():
|
||
if self.femtobolt_camera and self.socketio:
|
||
try:
|
||
capture = self.femtobolt_camera.update()
|
||
if capture is not None:
|
||
ret, depth_image = capture.get_depth_image()
|
||
if ret and depth_image is not None:
|
||
import configparser
|
||
config = configparser.ConfigParser()
|
||
config.read('config.ini')
|
||
try:
|
||
depth_range_min = int(config.get('DEFAULT', 'femtobolt_depth_range_min', fallback='1400'))
|
||
depth_range_max = int(config.get('DEFAULT', 'femtobolt_depth_range_max', fallback='1900'))
|
||
except Exception:
|
||
depth_range_min = None
|
||
depth_range_max = None
|
||
|
||
if MATPLOTLIB_AVAILABLE and depth_range_min is not None and depth_range_max is not None:
|
||
import numpy as np
|
||
import cv2
|
||
depth_image = depth_image.copy()
|
||
|
||
# === 生成灰色背景 + 白色网格 ===
|
||
rows, cols = depth_image.shape[:2]
|
||
background = np.ones((rows, cols, 3), dtype=np.uint8) * 128
|
||
cell_size = 50
|
||
grid_color = (255, 255, 255)
|
||
grid_bg = np.zeros_like(background)
|
||
for x in range(0, cols, cell_size):
|
||
cv2.line(grid_bg, (x, 0), (x, rows), grid_color, 1)
|
||
for y in range(0, rows, cell_size):
|
||
cv2.line(grid_bg, (0, y), (cols, y), grid_color, 1)
|
||
mask_grid = (grid_bg.sum(axis=2) > 0)
|
||
background[mask_grid] = grid_bg[mask_grid]
|
||
|
||
# === 处理深度图满足区间的部分 ===
|
||
depth_clipped = depth_image.copy()
|
||
depth_clipped[depth_clipped < depth_range_min] = 0
|
||
depth_clipped[depth_clipped > depth_range_max] = 0
|
||
depth_normalized = np.clip(depth_clipped, depth_range_min, depth_range_max)
|
||
depth_normalized = ((depth_normalized - depth_range_min) / (depth_range_max - depth_range_min) * 255).astype(np.uint8)
|
||
|
||
# 对比度和伽马校正
|
||
alpha, beta, gamma = 1.5, 0, 0.8
|
||
depth_normalized = cv2.convertScaleAbs(depth_normalized, alpha=alpha, beta=beta)
|
||
lut = np.array([((i / 255.0) ** gamma) * 255 for i in range(256)]).astype("uint8")
|
||
depth_normalized = cv2.LUT(depth_normalized, lut)
|
||
|
||
# 伪彩色
|
||
depth_colored = cv2.applyColorMap(depth_normalized, cv2.COLORMAP_JET)
|
||
|
||
# 将有效深度覆盖到灰色背景上
|
||
mask_valid = (depth_clipped > 0)
|
||
for c in range(3):
|
||
background[:, :, c][mask_valid] = depth_colored[:, :, c][mask_valid]
|
||
|
||
depth_colored_final = background
|
||
|
||
else:
|
||
# 没有matplotlib则使用原OpenCV伪彩色
|
||
depth_normalized = np.clip(depth_image, depth_range_min, depth_range_max)
|
||
depth_normalized = ((depth_normalized - depth_range_min) / (depth_range_max - depth_range_min) * 255).astype(np.uint8)
|
||
depth_colored_final = cv2.applyColorMap(depth_normalized, cv2.COLORMAP_JET)
|
||
mask_outside = (depth_image < depth_range_min) | (depth_image > depth_range_max)
|
||
depth_colored_final[mask_outside] = [128, 128, 128] # 灰色
|
||
|
||
# 裁剪宽度
|
||
height, width = depth_colored_final.shape[:2]
|
||
target_width = height // 2
|
||
if width > target_width:
|
||
left = (width - target_width) // 2
|
||
right = left + target_width
|
||
depth_colored_final = depth_colored_final[:, left:right]
|
||
|
||
# 保存到缓存
|
||
self._save_frame_to_cache(depth_colored_final.copy(), 'femtobolt')
|
||
|
||
# 推送SocketIO
|
||
success, buffer = cv2.imencode('.jpg', depth_colored_final, [int(cv2.IMWRITE_JPEG_QUALITY), 80])
|
||
if success and self.socketio:
|
||
import base64, time
|
||
jpg_as_text = base64.b64encode(buffer).decode('utf-8')
|
||
self.socketio.emit('depth_camera_frame', {
|
||
'image': jpg_as_text,
|
||
'frame_id': frame_count,
|
||
'timestamp': time.time()
|
||
})
|
||
frame_count += 1
|
||
else:
|
||
time.sleep(0.01)
|
||
else:
|
||
time.sleep(0.01)
|
||
|
||
except Exception as e:
|
||
logger.debug(f'FemtoBolt帧推送失败: {e}')
|
||
time.sleep(0.1)
|
||
|
||
time.sleep(1/30) # 30 FPS
|
||
|
||
except Exception as e:
|
||
logger.debug(f'FemtoBolt推流线程异常: {e}')
|
||
finally:
|
||
self.femtobolt_streaming = False
|
||
|
||
def _imu_streaming_thread(self):
|
||
"""IMU头部姿态数据推流线程"""
|
||
# logger.info('IMU头部姿态数据推流线程已启动')
|
||
|
||
try:
|
||
loop_count = 0
|
||
while self.imu_streaming and self.socketio:
|
||
try:
|
||
loop_count += 1
|
||
# 从IMU设备读取数据
|
||
imu_data = self.imu_device.read_data()
|
||
|
||
if imu_data and 'head_pose' in imu_data:
|
||
# 直接使用设备提供的头部姿态数据，减少数据包装
|
||
head_pose = imu_data['head_pose']
|
||
# logger.warning(f'推送数据{head_pose}')
|
||
# 优化：直接发送最精简的数据格式，避免重复时间戳
|
||
rotation = head_pose.get('rotation')
|
||
tilt = head_pose.get('tilt')
|
||
pitch = head_pose.get('pitch')
|
||
try:
|
||
rotation = round(float(rotation), 2) if rotation is not None else rotation
|
||
except Exception:
|
||
pass
|
||
try:
|
||
tilt = round(float(tilt), 2) if tilt is not None else tilt
|
||
except Exception:
|
||
pass
|
||
try:
|
||
pitch = round(float(pitch), 2) if pitch is not None else pitch
|
||
except Exception:
|
||
pass
|
||
self.socketio.emit('imu_data', {
|
||
'rotation': rotation, # 旋转角：左旋(-), 右旋(+)
|
||
'tilt': tilt, # 倾斜角：左倾(-), 右倾(+)
|
||
'pitch': pitch, # 俯仰角：俯角(-), 仰角(+)
|
||
})
|
||
|
||
# 优化：提高数据发送频率到30Hz，降低延时
|
||
time.sleep(0.033)
|
||
|
||
except Exception as e:
|
||
# 减少异常日志的详细程度
|
||
logger.warning(f'IMU数据推流异常: {e}')
|
||
time.sleep(0.033)
|
||
|
||
except Exception as e:
|
||
logger.error(f'IMU推流线程异常: {e}', exc_info=True)
|
||
finally:
|
||
logger.info('IMU头部姿态数据推流线程已结束')
|
||
|
||
def _pressure_streaming_thread(self):
|
||
"""压力传感器足部压力数据推流线程"""
|
||
logger.info('压力传感器足部压力数据推流线程已启动')
|
||
|
||
try:
|
||
while self.pressure_streaming and self.socketio:
|
||
try:
|
||
# 从压力传感器设备读取数据
|
||
pressure_data = self.pressure_device.read_data()
|
||
|
||
if pressure_data and 'foot_pressure' in pressure_data:
|
||
foot_pressure = pressure_data['foot_pressure']
|
||
# logger.error(f"压力传感器数据{foot_pressure}")
|
||
# 获取各区域压力值
|
||
left_front = foot_pressure['left_front']
|
||
left_rear = foot_pressure['left_rear']
|
||
right_front = foot_pressure['right_front']
|
||
right_rear = foot_pressure['right_rear']
|
||
left_total = foot_pressure['left_total']
|
||
right_total = foot_pressure['right_total']
|
||
|
||
# 计算总压力
|
||
total_pressure = left_total + right_total
|
||
|
||
# 计算平衡比例（左脚压力占总压力的比例）
|
||
balance_ratio = left_total / total_pressure if total_pressure > 0 else 0.5
|
||
|
||
# 计算压力中心偏移
|
||
pressure_center_offset = (balance_ratio - 0.5) * 100 # 转换为百分比
|
||
|
||
# 计算前后足压力分布
|
||
left_front_ratio = left_front / left_total if left_total > 0 else 0.5
|
||
right_front_ratio = right_front / right_total if right_total > 0 else 0.5
|
||
|
||
# 构建完整的足部压力数据
|
||
complete_pressure_data = {
|
||
# 分区压力值
|
||
'pressure_zones': {
|
||
'left_front': left_front,
|
||
'left_rear': left_rear,
|
||
'right_front': right_front,
|
||
'right_rear': right_rear,
|
||
'left_total': left_total,
|
||
'right_total': right_total,
|
||
'total_pressure': total_pressure
|
||
},
|
||
# 平衡分析
|
||
'balance_analysis': {
|
||
'balance_ratio': round(balance_ratio, 3),
|
||
'pressure_center_offset': round(pressure_center_offset, 2),
|
||
'balance_status': 'balanced' if abs(pressure_center_offset) < 10 else 'unbalanced',
|
||
'left_front_ratio': round(left_front_ratio, 3),
|
||
'right_front_ratio': round(right_front_ratio, 3)
|
||
},
|
||
# 压力图片
|
||
'pressure_image': pressure_data.get('pressure_image', ''),
|
||
'timestamp': pressure_data['timestamp']
|
||
}
|
||
|
||
# 通过WebSocket发送足部压力数据
|
||
self.socketio.emit('pressure_data', {
|
||
'foot_pressure': complete_pressure_data,
|
||
'timestamp': datetime.now().isoformat()
|
||
})
|
||
|
||
# 控制数据发送频率（20Hz）
|
||
time.sleep(0.05)
|
||
|
||
except Exception as e:
|
||
logger.error(f'压力传感器数据推流异常: {e}')
|
||
time.sleep(0.1)
|
||
|
||
except Exception as e:
|
||
logger.error(f'压力传感器推流线程异常: {e}')
|
||
finally:
|
||
logger.info('压力传感器足部压力数据推流线程已结束')
|
||
|
||
def start_recording(self, session_id: str, patient_id: str) -> Dict[str, Any]:
|
||
video_manager=VideoStreamManager()
|
||
"""启动同步录制
|
||
|
||
Args:
|
||
session_id: 检测会话ID
|
||
patient_id: 患者ID
|
||
|
||
Returns:
|
||
Dict: 录制启动状态和信息
|
||
{
|
||
'success': bool,
|
||
'session_id': str,
|
||
'patient_id': str,
|
||
'recording_start_time': str,
|
||
'video_paths': {
|
||
'feet_video': str,
|
||
'body_video': str,
|
||
'screen_video': str
|
||
},
|
||
'message': str
|
||
}
|
||
"""
|
||
result = {
|
||
'success': False,
|
||
'session_id': session_id,
|
||
'patient_id': patient_id,
|
||
'recording_start_time': None,
|
||
'video_paths': {
|
||
'feet_video': None,
|
||
'body_video': None,
|
||
'screen_video': None
|
||
},
|
||
'message': ''
|
||
}
|
||
|
||
try:
|
||
# 检查是否已在录制
|
||
if self.sync_recording:
|
||
result['message'] = f'已在录制中，当前会话ID: {self.current_session_id}'
|
||
return result
|
||
|
||
# 设置录制参数
|
||
self.current_session_id = session_id
|
||
self.current_patient_id = patient_id
|
||
self.recording_start_time = datetime.now()
|
||
|
||
# 创建存储目录
|
||
base_path = os.path.join('data', 'patients', patient_id, session_id)
|
||
try:
|
||
os.makedirs(base_path, exist_ok=True)
|
||
logger.info(f'录制目录创建成功: {base_path}')
|
||
|
||
# 设置目录权限为777（所有用户完全权限）
|
||
try:
|
||
import stat
|
||
import subprocess
|
||
import platform
|
||
# 在Windows系统上使用icacls命令设置更详细的权限
|
||
if platform.system() == 'Windows':
|
||
try:
|
||
# 为Users用户组授予完全控制权限
|
||
subprocess.run([
|
||
'icacls', base_path, '/grant', 'Users:(OI)(CI)F'
|
||
], check=True, capture_output=True, text=True)
|
||
|
||
# 为Everyone用户组授予完全控制权限
|
||
subprocess.run([
|
||
'icacls', base_path, '/grant', 'Everyone:(OI)(CI)F'
|
||
], check=True, capture_output=True, text=True)
|
||
|
||
logger.info(f"已设置Windows目录权限（Users和Everyone完全控制）: {base_path}")
|
||
except subprocess.CalledProcessError as icacls_error:
|
||
logger.warning(f"Windows权限设置失败: {icacls_error}")
|
||
else:
|
||
logger.info(f"已设置目录权限为777: {base_path}")
|
||
|
||
except Exception as perm_error:
|
||
logger.warning(f"设置目录权限失败: {perm_error}，但目录创建成功")
|
||
except Exception as dir_error:
|
||
logger.error(f'创建录制目录失败: {base_path}, 错误: {dir_error}')
|
||
result['success'] = False
|
||
result['message'] = f'创建录制目录失败: {dir_error}'
|
||
return result
|
||
|
||
# 定义视频文件路径
|
||
feet_video_path = os.path.join(base_path, 'feet.mp4')
|
||
body_video_path = os.path.join(base_path, 'body.mp4')
|
||
screen_video_path = os.path.join(base_path, 'screen.webm')
|
||
result['video_paths']['feet_video'] = feet_video_path
|
||
result['video_paths']['body_video'] = body_video_path
|
||
result['video_paths']['screen_video'] = screen_video_path
|
||
|
||
# 更新数据库中的视频路径
|
||
if self.db_manager:
|
||
try:
|
||
# 更新会话状态为录制中
|
||
if not self.db_manager.update_session_status(session_id, 'recording'):
|
||
logger.error(f'更新会话状态为录制中失败 - 会话ID: {session_id}')
|
||
|
||
# 更新视频文件路径
|
||
self.db_manager.update_session_normal_video_path(session_id, feet_video_path)
|
||
self.db_manager.update_session_femtobolt_video_path(session_id, body_video_path)
|
||
self.db_manager.update_session_screen_video_path(session_id, screen_video_path)
|
||
|
||
logger.debug(f'数据库视频路径更新成功 - 会话ID: {session_id}')
|
||
except Exception as db_error:
|
||
logger.error(f'更新数据库视频路径失败: {db_error}')
|
||
# 数据库更新失败不影响录制启动，继续执行
|
||
|
||
# 视频编码参数
|
||
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
|
||
fps = 30
|
||
|
||
# 初始化视频写入器
|
||
if self.device_status['camera']:
|
||
target_width,target_height = video_manager.MAX_FRAME_SIZE
|
||
self.feet_video_writer = cv2.VideoWriter(feet_video_path, fourcc, fps, (target_width, target_height))
|
||
|
||
# 检查视频写入器是否初始化成功
|
||
if self.feet_video_writer.isOpened():
|
||
logger.info(f'脚部视频写入器初始化成功: {feet_video_path}')
|
||
else:
|
||
logger.error(f'脚部视频写入器初始化失败: {feet_video_path}')
|
||
# # 获取摄像头分辨率
|
||
# if self.camera and self.camera.isOpened():
|
||
# target_width,target_height = video_manager.MAX_FRAME_SIZE
|
||
# self.feet_video_writer = cv2.VideoWriter(
|
||
# feet_video_path, fourcc, fps, (target_width, target_height)
|
||
# )
|
||
|
||
# # 检查视频写入器是否初始化成功
|
||
# if self.feet_video_writer.isOpened():
|
||
# logger.info(f'脚部视频写入器初始化成功: {feet_video_path}')
|
||
# else:
|
||
# logger.error(f'脚部视频写入器初始化失败: {feet_video_path}')
|
||
# else:
|
||
# logger.error('摄像头未打开，无法初始化脚部视频写入器')
|
||
else:
|
||
logger.warning('摄像头设备未启用，跳过脚部视频写入器初始化')
|
||
if self.device_status['femtobolt']:
|
||
frame1, frame_timestamp1 = self._get_latest_frame_from_cache('femtobolt')
|
||
if frame1 is not None:
|
||
actual_height,actual_width=frame1.shape[:2]
|
||
logger.info(f'初始化身体视频写入器 裁剪后分辨率: {actual_height}x{actual_width}')
|
||
|
||
# 确保图像数据类型正确
|
||
if frame1.dtype != np.uint8:
|
||
logger.warning(f'身体帧数据类型不是uint8: {frame1.dtype}，将进行转换')
|
||
|
||
self.body_video_writer = cv2.VideoWriter(
|
||
body_video_path, fourcc, fps, (actual_width, actual_height)
|
||
)
|
||
if self.body_video_writer.isOpened():
|
||
logger.info(f'身体视频写入器初始化成功: {body_video_path}, 分辨率: {actual_width}x{actual_height}')
|
||
else:
|
||
logger.error(f'身体视频写入器初始化失败: {body_video_path}, 分辨率: {actual_width}x{actual_height}')
|
||
# 尝试使用默认分辨率重新初始化
|
||
logger.info('尝试使用默认分辨率重新初始化身体视频写入器')
|
||
self.body_video_writer = cv2.VideoWriter(
|
||
body_video_path, fourcc, fps, (288, 576) # 默认分辨率
|
||
)
|
||
if self.body_video_writer.isOpened():
|
||
logger.info(f'身体视频写入器使用默认分辨率初始化成功: {body_video_path}')
|
||
else:
|
||
logger.error(f'身体视频写入器使用默认分辨率初始化仍然失败: {body_video_path}')
|
||
else:
|
||
logger.warning('无法从缓存获取FemtoBolt帧数据，使用默认分辨率初始化身体视频写入器')
|
||
self.body_video_writer = cv2.VideoWriter(
|
||
body_video_path, fourcc, fps, (288, 576) # 默认分辨率
|
||
)
|
||
if self.body_video_writer.isOpened():
|
||
logger.info(f'身体视频写入器使用默认分辨率初始化成功: {body_video_path}')
|
||
else:
|
||
logger.error(f'身体视频写入器使用默认分辨率初始化失败: {body_video_path}')
|
||
# FemtoBolt默认分辨率
|
||
# capture = self.femtobolt_camera.update()
|
||
# if capture is not None:
|
||
# ret, depth_image = capture.get_depth_image()
|
||
# femtoboltheight, femtoboltwidth = depth_image.shape[:2]
|
||
# # 计算裁剪后的实际分辨率（与推流处理保持一致）
|
||
# target_width = femtoboltheight // 2
|
||
# actual_height = femtoboltheight
|
||
# actual_width = target_width
|
||
|
||
# logger.info(f'初始化身体视频写入器，原始分辨率: {femtoboltheight}x{femtoboltwidth}, 裁剪后分辨率: {actual_height}x{actual_width}')
|
||
# self.body_video_writer = cv2.VideoWriter(
|
||
# body_video_path, fourcc, fps, (actual_width, actual_height)
|
||
# )
|
||
# if self.body_video_writer.isOpened():
|
||
# logger.info(f'身体视频写入器初始化成功: {body_video_path}, 分辨率: {actual_width}x{actual_height}')
|
||
# else:
|
||
# logger.error(f'身体视频写入器初始化失败: {body_video_path}, 分辨率: {actual_width}x{actual_height}')
|
||
|
||
else:
|
||
logger.warning('FemtoBolt设备未启用，跳过身体视频写入器初始化')
|
||
|
||
# # 屏幕录制写入器（默认分辨率，后续根据实际帧调整）
|
||
# self.screen_video_writer = cv2.VideoWriter(
|
||
# screen_video_path, fourcc, fps, (1920, 1080)
|
||
# )
|
||
|
||
# 重置停止事件
|
||
self.recording_stop_event.clear()
|
||
self.sync_recording = True
|
||
# 启动录制线程
|
||
if self.feet_video_writer:
|
||
self.feet_recording_thread = threading.Thread(
|
||
target=self._feet_recording_thread,
|
||
daemon=True,
|
||
name='FeetRecordingThread'
|
||
)
|
||
self.feet_recording_thread.start()
|
||
|
||
# if self.body_video_writer:
|
||
# self.body_recording_thread = threading.Thread(
|
||
# target=self._body_recording_thread,
|
||
# daemon=True,
|
||
# name='BodyRecordingThread'
|
||
# )
|
||
# self.body_recording_thread.start()
|
||
# #屏幕录制
|
||
# if self.screen_video_writer:
|
||
# self.screen_recording_thread = threading.Thread(
|
||
# target=self._screen_recording_thread,
|
||
# daemon=True,
|
||
# name='ScreenRecordingThread'
|
||
# )
|
||
# self.screen_recording_thread.start()
|
||
|
||
# 设置录制状态
|
||
|
||
result['success'] = True
|
||
result['recording_start_time'] = self.recording_start_time.isoformat()
|
||
result['message'] = '同步录制已启动'
|
||
|
||
logger.debug(f'同步录制已启动 - 会话ID: {session_id}, 患者ID: {patient_id}')
|
||
|
||
except Exception as e:
|
||
logger.error(f'启动同步录制失败: {e}')
|
||
result['message'] = f'启动录制失败: {str(e)}'
|
||
# 清理已创建的写入器
|
||
self._cleanup_video_writers()
|
||
|
||
return result
|
||
|
||
def stop_recording(self, session_id: str, video_data_base64) -> Dict[str, Any]:
|
||
"""停止同步录制
|
||
|
||
Args:
|
||
session_id: 检测会话ID
|
||
video_data_base64: 屏幕录制视频的base64编码数据，可选
|
||
|
||
Returns:
|
||
Dict: 录制停止状态和信息
|
||
"""
|
||
result = {
|
||
'success': False,
|
||
'session_id': session_id,
|
||
'recording_duration': 0,
|
||
'video_files': [],
|
||
'message': ''
|
||
}
|
||
|
||
try:
|
||
# 检查录制状态
|
||
if not self.sync_recording:
|
||
result['message'] = '当前没有进行录制'
|
||
return result
|
||
|
||
if self.current_session_id != session_id:
|
||
result['message'] = f'会话ID不匹配，当前录制会话: {self.current_session_id}'
|
||
return result
|
||
|
||
# 设置停止事件
|
||
self.recording_stop_event.set()
|
||
session_data = self.db_manager.get_session_data(session_id)
|
||
base_path = os.path.join('data', 'patients', session_data['patient_id'], session_id)
|
||
|
||
# 定义视频文件路径
|
||
feet_video_path = os.path.join(base_path, 'feet.mp4')
|
||
body_video_path = os.path.join(base_path, 'body.mp4')
|
||
screen_video_path = os.path.join(base_path, 'screen.webm')
|
||
|
||
# 等待录制线程结束
|
||
threads_to_join = [
|
||
(self.feet_recording_thread, 'feet'),
|
||
(self.body_recording_thread, 'body')
|
||
]
|
||
|
||
logger.info(f"正在停止录制线程 - 会话ID: {session_id}")
|
||
|
||
for thread, name in threads_to_join:
|
||
if thread and thread.is_alive():
|
||
logger.debug(f"等待{name}录制线程结束...")
|
||
thread.join(timeout=3)
|
||
if thread.is_alive():
|
||
logger.warning(f'{name}录制线程未能在3秒内正常结束，可能存在阻塞')
|
||
else:
|
||
logger.debug(f'{name}录制线程已正常结束')
|
||
else:
|
||
logger.debug(f'{name}录制线程未运行或已结束')
|
||
|
||
# 计算录制时长
|
||
if self.recording_start_time:
|
||
duration = (datetime.now() - self.recording_start_time).total_seconds()
|
||
result['recording_duration'] = duration
|
||
|
||
# 清理视频写入器并收集文件信息
|
||
# video_files = self._cleanup_video_writers()
|
||
# 保存传入的屏幕录制视频数据，替代原有屏幕录制视频保存逻辑
|
||
# video_bytes = base64.b64decode(video_data_base64)
|
||
with open(screen_video_path, 'wb') as f:
|
||
f.write(video_data_base64)
|
||
# video_files.append(screen_video_path)
|
||
logger.info(f'屏幕录制视频保存成功，路径: {screen_video_path}, 文件大小: {os.path.getsize(screen_video_path)} 字节')
|
||
|
||
result['video_files'] = screen_video_path
|
||
|
||
# 更新数据库中的会话信息
|
||
if self.db_manager and result['recording_duration'] > 0:
|
||
try:
|
||
duration_seconds = int(result['recording_duration'])
|
||
self.db_manager.update_session_duration(session_id, duration_seconds)
|
||
self.db_manager.update_session_normal_video_path(session_id, feet_video_path)
|
||
self.db_manager.update_session_femtobolt_video_path(session_id, body_video_path)
|
||
self.db_manager.update_session_screen_video_path(session_id, screen_video_path)
|
||
|
||
# 更新会话状态为已完成
|
||
if self.db_manager.update_session_status(session_id, 'completed'):
|
||
logger.debug(f'数据库会话信息更新成功 - 会话ID: {session_id}, 持续时间: {duration_seconds}秒')
|
||
else:
|
||
logger.error(f'更新会话状态为已完成失败 - 会话ID: {session_id}')
|
||
except Exception as db_error:
|
||
logger.error(f'更新数据库会话信息失败: {db_error}')
|
||
|
||
# 重置录制状态
|
||
self.sync_recording = False
|
||
self.current_session_id = None
|
||
self.current_patient_id = None
|
||
self.recording_start_time = None
|
||
|
||
result['success'] = True
|
||
result['message'] = '同步录制已停止'
|
||
|
||
logger.debug(f'同步录制已停止 - 会话ID: {session_id}, 录制时长: {result["recording_duration"]:.2f}秒')
|
||
|
||
except Exception as e:
|
||
logger.error(f'停止同步录制失败: {e}', exc_info=True)
|
||
result['message'] = f'停止录制失败: {str(e)}'
|
||
|
||
return result
|
||
|
||
def add_screen_frame(self, frame_data: str):
|
||
"""添加屏幕录制帧
|
||
|
||
Args:
|
||
frame_data: base64编码的屏幕截图数据
|
||
"""
|
||
if self.sync_recording and not self.screen_frame_queue.full():
|
||
try:
|
||
self.screen_frame_queue.put(frame_data, block=False)
|
||
except:
|
||
# 队列满时丢弃帧
|
||
pass
|
||
|
||
def _feet_recording_thread(self):
|
||
"""足部视频录制线程"""
|
||
consecutive_failures = 0
|
||
max_consecutive_failures = 10
|
||
|
||
# logger.info(f"足部录制线程已启动 - 会话ID: {self.current_session_id}")
|
||
logger.info(f"视频写入器状态: {self.feet_video_writer.isOpened() if self.feet_video_writer else 'None'}")
|
||
|
||
try:
|
||
while self.sync_recording and not self.recording_stop_event.is_set():
|
||
if self.feet_video_writer:
|
||
# 从全局缓存获取最新帧
|
||
frame, frame_timestamp = self._get_latest_frame_from_cache('camera')
|
||
# 详细记录帧获取情况
|
||
if frame is not None:
|
||
logger.debug(f"成功获取帧 - 尺寸: {frame.shape}, 数据类型: {frame.dtype}, 时间戳: {frame_timestamp}")
|
||
# 检查视频写入器状态
|
||
if not self.feet_video_writer.isOpened():
|
||
logger.error(f"脚部视频写入器已关闭，无法写入帧 - 会话ID: {self.current_session_id}")
|
||
break
|
||
try:
|
||
# 复制帧数据避免引用问题
|
||
image = frame.copy()
|
||
# 写入录制文件
|
||
write_success = self.feet_video_writer.write(image)
|
||
# 检查写入是否成功
|
||
if write_success is False:
|
||
logger.error(f"视频帧写入返回False - 可能写入失败")
|
||
consecutive_failures += 1
|
||
else:
|
||
consecutive_failures = 0 # 重置失败计数
|
||
|
||
# 记录录制统计
|
||
if hasattr(self, 'recording_frame_count'):
|
||
self.recording_frame_count += 1
|
||
else:
|
||
self.recording_frame_count = 1
|
||
except Exception as write_error:
|
||
logger.error(f"写入脚部视频帧异常: {write_error}")
|
||
consecutive_failures += 1
|
||
if consecutive_failures >= 10:
|
||
logger.error("连续写入失败次数过多，停止录制")
|
||
break
|
||
|
||
else:
|
||
logger.warning(f"从缓存获取的帧为None - 连续失败{consecutive_failures + 1}次")
|
||
consecutive_failures += 1
|
||
if consecutive_failures <= 3:
|
||
logger.warning(f"录制线程无法从缓存获取帧 (连续失败{consecutive_failures}次)")
|
||
elif consecutive_failures == max_consecutive_failures:
|
||
logger.error(f"录制线程连续失败{max_consecutive_failures}次，可能缓存无数据或推流已停止")
|
||
|
||
# 等待一段时间再重试
|
||
time.sleep(0.1)
|
||
else:
|
||
logger.error("足部视频写入器未初始化")
|
||
break
|
||
|
||
# 检查连续失败情况
|
||
if consecutive_failures >= max_consecutive_failures:
|
||
logger.error(f"连续失败次数达到上限({max_consecutive_failures})，停止录制")
|
||
break
|
||
|
||
time.sleep(1/30) # 30 FPS
|
||
|
||
except Exception as e:
|
||
logger.error(f'足部录制线程异常: {e}')
|
||
finally:
|
||
logger.info(f"足部录制线程已结束 - 会话ID: {self.current_session_id}, 总录制帧数: {getattr(self, 'recording_frame_count', 0)}")
|
||
# 确保视频写入器被正确关闭
|
||
if self.feet_video_writer:
|
||
self.feet_video_writer.release()
|
||
self.feet_video_writer = None
|
||
logger.debug("足部视频写入器已释放")
|
||
|
||
def _body_recording_thread(self):
|
||
"""身体视频录制线程"""
|
||
consecutive_failures = 0
|
||
max_consecutive_failures = 10
|
||
|
||
# logger.info(f"身体录制线程启动 - 会话ID: {self.current_session_id}")
|
||
|
||
try:
|
||
while self.sync_recording and not self.recording_stop_event.is_set():
|
||
if self.body_video_writer:
|
||
# 从全局缓存获取最新帧
|
||
frame, frame_timestamp = self._get_latest_frame_from_cache('femtobolt')
|
||
|
||
if frame is not None:
|
||
# 检查视频写入器状态
|
||
if not self.body_video_writer.isOpened():
|
||
logger.error(f"身体视频写入器已关闭，无法写入帧 - 会话ID: {self.current_session_id}")
|
||
break
|
||
|
||
# 添加帧信息日志
|
||
logger.debug(f"获取到身体帧 - 形状: {frame.shape}, 数据类型: {frame.dtype}, 时间戳: {frame_timestamp}")
|
||
|
||
try:
|
||
# 复制帧数据避免引用问题
|
||
image = frame.copy()
|
||
|
||
# 检查图像有效性
|
||
if image is None or image.size == 0:
|
||
logger.warning(f"身体帧数据无效 - 会话ID: {self.current_session_id}")
|
||
consecutive_failures += 1
|
||
continue
|
||
|
||
# 确保图像数据类型正确
|
||
if image.dtype != np.uint8:
|
||
logger.debug(f"转换身体帧数据类型从 {image.dtype} 到 uint8")
|
||
image = image.astype(np.uint8)
|
||
|
||
# 确保图像是3通道BGR格式
|
||
if len(image.shape) != 3 or image.shape[2] != 3:
|
||
logger.warning(f"身体帧格式异常: {image.shape}，期望3通道BGR格式")
|
||
consecutive_failures += 1
|
||
continue
|
||
|
||
# 检查并调整图像分辨率以匹配视频写入器
|
||
current_height, current_width = image.shape[:2]
|
||
expected_width, expected_height = 288, 576 # 默认期望分辨率
|
||
|
||
if current_width != expected_width or current_height != expected_height:
|
||
logger.debug(f"调整身体帧分辨率从 {current_width}x{current_height} 到 {expected_width}x{expected_height}")
|
||
image = cv2.resize(image, (expected_width, expected_height))
|
||
|
||
# 确保图像数据连续性（OpenCV要求）
|
||
if not image.flags['C_CONTIGUOUS']:
|
||
logger.debug("转换身体帧为连续内存布局")
|
||
image = np.ascontiguousarray(image)
|
||
|
||
# 写入录制文件
|
||
logger.debug(f"尝试写入身体视频帧 - 图像形状: {image.shape}, 数据类型: {image.dtype}, 连续性: {image.flags['C_CONTIGUOUS']}")
|
||
write_success = self.body_video_writer.write(image)
|
||
|
||
# 检查写入是否成功 - cv2.VideoWriter.write()可能返回None、False或True
|
||
if write_success is False:
|
||
consecutive_failures += 1
|
||
logger.warning(f"身体视频帧写入明确失败 - 会话ID: {self.current_session_id}, 连续失败次数: {consecutive_failures}, 图像形状: {image.shape}, 写入器状态: {self.body_video_writer.isOpened()}")
|
||
|
||
if consecutive_failures >= max_consecutive_failures:
|
||
logger.error(f"身体视频写入连续失败{max_consecutive_failures}次，停止录制")
|
||
break
|
||
elif write_success is None:
|
||
# 某些OpenCV版本可能返回None，这通常表示写入失败
|
||
consecutive_failures += 1
|
||
logger.warning(f"身体视频帧写入返回None - 会话ID: {self.current_session_id}, 连续失败次数: {consecutive_failures}, 可能是编解码器问题")
|
||
|
||
if consecutive_failures >= max_consecutive_failures:
|
||
logger.error(f"身体视频写入连续返回None {max_consecutive_failures}次，停止录制")
|
||
break
|
||
else:
|
||
consecutive_failures = 0
|
||
logger.debug(f"成功写入身体视频帧 - 会话ID: {self.current_session_id}")
|
||
|
||
# 释放图像内存
|
||
# del image
|
||
|
||
except Exception as e:
|
||
consecutive_failures += 1
|
||
logger.error(f'身体视频帧写入异常: {e}, 连续失败次数: {consecutive_failures}, 帧形状: {frame.shape if frame is not None else "None"}')
|
||
|
||
if consecutive_failures >= max_consecutive_failures:
|
||
logger.error(f"身体视频写入连续异常{max_consecutive_failures}次，停止录制")
|
||
break
|
||
else:
|
||
# 没有可用帧，短暂等待
|
||
logger.debug(f"未获取到身体帧，等待中... - 会话ID: {self.current_session_id}")
|
||
time.sleep(0.01)
|
||
continue
|
||
else:
|
||
logger.warning(f"身体视频写入器未初始化 - 会话ID: {self.current_session_id}")
|
||
time.sleep(0.1)
|
||
continue
|
||
|
||
# 控制录制帧率
|
||
time.sleep(1/30) # 30 FPS
|
||
|
||
except Exception as e:
|
||
logger.error(f'身体录制线程异常: {e}')
|
||
finally:
|
||
logger.info(f"身体录制线程结束 - 会话ID: {self.current_session_id}")
|
||
|
||
def _screen_recording_thread(self):
|
||
"""屏幕录制线程"""
|
||
try:
|
||
while self.sync_recording and not self.recording_stop_event.is_set():
|
||
try:
|
||
# 从队列获取屏幕帧
|
||
frame_data = self.screen_frame_queue.get(timeout=1)
|
||
|
||
# 解码base64图像
|
||
image_data = base64.b64decode(frame_data)
|
||
nparr = np.frombuffer(image_data, np.uint8)
|
||
frame = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
|
||
|
||
if frame is not None and self.screen_video_writer:
|
||
# 调整到录制分辨率
|
||
frame = cv2.resize(frame, (1920, 1080))
|
||
self.screen_video_writer.write(frame)
|
||
|
||
except queue.Empty:
|
||
continue
|
||
except Exception as e:
|
||
logger.error(f'屏幕录制帧处理失败: {e}')
|
||
|
||
except Exception as e:
|
||
logger.error(f'屏幕录制线程异常: {e}')
|
||
|
||
def _cleanup_video_writers(self) -> List[str]:
|
||
"""清理视频写入器并返回文件列表"""
|
||
video_files = []
|
||
|
||
try:
|
||
if self.feet_video_writer:
|
||
self.feet_video_writer.release()
|
||
self.feet_video_writer = None
|
||
if self.current_patient_id and self.current_session_id:
|
||
feet_path = os.path.join('data', 'patients', self.current_patient_id,
|
||
self.current_session_id, 'feet.mp4')
|
||
if os.path.exists(feet_path):
|
||
video_files.append(feet_path)
|
||
|
||
if self.body_video_writer:
|
||
self.body_video_writer.release()
|
||
self.body_video_writer = None
|
||
if self.current_patient_id and self.current_session_id:
|
||
body_path = os.path.join('data', 'patients', self.current_patient_id,
|
||
self.current_session_id, 'body.mp4')
|
||
if os.path.exists(body_path):
|
||
video_files.append(body_path)
|
||
|
||
if self.screen_video_writer:
|
||
self.screen_video_writer.release()
|
||
self.screen_video_writer = None
|
||
if self.current_patient_id and self.current_session_id:
|
||
screen_path = os.path.join('data', 'patients', self.current_patient_id,
|
||
self.current_session_id, 'screen.mp4')
|
||
if os.path.exists(screen_path):
|
||
video_files.append(screen_path)
|
||
|
||
except Exception as e:
|
||
logger.error(f'清理视频写入器失败: {e}')
|
||
|
||
return video_files
|
||
|
||
def _save_frame_to_cache(self, frame, frame_type='camera'):
|
||
"""保存帧到全局缓存"""
|
||
try:
|
||
import time
|
||
with self.frame_cache_lock:
|
||
current_time = time.time()
|
||
|
||
# 清理过期帧
|
||
self._cleanup_expired_frames()
|
||
|
||
# 如果缓存已满，移除最旧的帧
|
||
if frame_type in self.frame_cache and len(self.frame_cache[frame_type]) >= self.max_cache_size:
|
||
oldest_key = min(self.frame_cache[frame_type].keys())
|
||
del self.frame_cache[frame_type][oldest_key]
|
||
|
||
# 初始化帧类型缓存
|
||
if frame_type not in self.frame_cache:
|
||
self.frame_cache[frame_type] = {}
|
||
|
||
# 保存帧（深拷贝避免引用问题）
|
||
frame_data = {
|
||
'frame': frame.copy(),
|
||
'timestamp': current_time,
|
||
'frame_id': len(self.frame_cache[frame_type])
|
||
}
|
||
|
||
self.frame_cache[frame_type][current_time] = frame_data
|
||
# logger.debug(f'成功保存帧到缓存: {frame_type}, 缓存大小: {len(self.frame_cache[frame_type])}, 帧尺寸: {frame.shape}')
|
||
|
||
except Exception as e:
|
||
logger.error(f'保存帧到缓存失败: {e}')
|
||
|
||
def _get_latest_frame_from_cache(self, frame_type='camera'):
|
||
"""从缓存获取最新帧"""
|
||
try:
|
||
import time
|
||
with self.frame_cache_lock:
|
||
# logger.debug(f'尝试从缓存获取帧: {frame_type}')
|
||
|
||
if frame_type not in self.frame_cache:
|
||
logger.debug(f'缓存中不存在帧类型: {frame_type}, 可用类型: {list(self.frame_cache.keys())}')
|
||
return None, None
|
||
|
||
if not self.frame_cache[frame_type]:
|
||
logger.debug(f'帧类型 {frame_type} 的缓存为空')
|
||
return None, None
|
||
|
||
# 清理过期帧
|
||
self._cleanup_expired_frames()
|
||
|
||
if not self.frame_cache[frame_type]:
|
||
logger.debug(f'清理过期帧后，帧类型 {frame_type} 的缓存为空')
|
||
return None, None
|
||
|
||
# 获取最新帧
|
||
latest_timestamp = max(self.frame_cache[frame_type].keys())
|
||
frame_data = self.frame_cache[frame_type][latest_timestamp]
|
||
|
||
current_time = time.time()
|
||
frame_age = current_time - frame_data['timestamp']
|
||
# logger.debug(f'成功获取最新帧: {frame_type}, 帧龄: {frame_age:.2f}秒, 缓存大小: {len(self.frame_cache[frame_type])}')
|
||
|
||
return frame_data['frame'].copy(), frame_data['timestamp']
|
||
|
||
except Exception as e:
|
||
logger.error(f'从缓存获取帧失败: {e}')
|
||
return None, None
|
||
|
||
def _cleanup_expired_frames(self):
|
||
"""清理过期的缓存帧"""
|
||
try:
|
||
import time
|
||
current_time = time.time()
|
||
|
||
for frame_type in list(self.frame_cache.keys()):
|
||
expired_keys = []
|
||
for timestamp in self.frame_cache[frame_type].keys():
|
||
if current_time - timestamp > self.cache_timeout:
|
||
expired_keys.append(timestamp)
|
||
|
||
# 删除过期帧
|
||
for key in expired_keys:
|
||
del self.frame_cache[frame_type][key]
|
||
|
||
# if expired_keys:
|
||
# logger.debug(f'清理了 {len(expired_keys)} 个过期帧: {frame_type}')
|
||
|
||
except Exception as e:
|
||
logger.error(f'清理过期帧失败: {e}')
|
||
class RealIMUDevice:
|
||
"""真实IMU设备，通过串口读取姿态数据"""
|
||
def __init__(self, port, baudrate):
|
||
self.port = port
|
||
self.baudrate = baudrate
|
||
self.ser = None
|
||
self.buffer = bytearray()
|
||
self.calibration_data = None
|
||
self.head_pose_offset = {'rotation': 0, 'tilt': 0, 'pitch': 0}
|
||
self.last_data = {
|
||
'roll': 0.0,
|
||
'pitch': 0.0,
|
||
'yaw': 0.0,
|
||
'temperature': 25.0
|
||
}
|
||
logger.debug(f'RealIMUDevice 初始化: port={self.port}, baudrate={self.baudrate}')
|
||
self._connect()
|
||
|
||
def _connect(self):
|
||
try:
|
||
logger.debug(f'尝试打开串口: {self.port} @ {self.baudrate}')
|
||
self.ser = serial.Serial(self.port, self.baudrate, timeout=1)
|
||
if hasattr(self.ser, 'reset_input_buffer'):
|
||
try:
|
||
self.ser.reset_input_buffer()
|
||
logger.debug('已清空串口输入缓冲区')
|
||
except Exception as e:
|
||
logger.debug(f'重置串口输入缓冲区失败: {e}')
|
||
logger.info(f'IMU设备连接成功: {self.port} @ {self.baudrate}bps')
|
||
except Exception as e:
|
||
# logger.error(f'IMU设备连接失败: {e}', exc_info=True)
|
||
self.ser = None
|
||
|
||
def set_calibration(self, calibration: Dict[str, Any]):
|
||
self.calibration_data = calibration
|
||
if 'head_pose_offset' in calibration:
|
||
self.head_pose_offset = calibration['head_pose_offset']
|
||
logger.debug(f'应用IMU校准数据: {self.head_pose_offset}')
|
||
|
||
def apply_calibration(self, raw_data: Dict[str, Any]) -> Dict[str, Any]:
|
||
"""应用校准：将当前姿态减去初始偏移，得到相对于初始姿态的变化量"""
|
||
if not raw_data or 'head_pose' not in raw_data:
|
||
return raw_data
|
||
|
||
# 应用校准偏移
|
||
calibrated_data = raw_data.copy()
|
||
head_pose = raw_data['head_pose'].copy()
|
||
|
||
# 减去基准值（零点偏移）
|
||
head_pose['rotation'] = head_pose['rotation'] - self.head_pose_offset['rotation']
|
||
head_pose['tilt'] = head_pose['tilt'] - self.head_pose_offset['tilt']
|
||
head_pose['pitch'] = head_pose['pitch'] - self.head_pose_offset['pitch']
|
||
|
||
calibrated_data['head_pose'] = head_pose
|
||
return calibrated_data
|
||
|
||
@staticmethod
|
||
def _checksum(data: bytes) -> int:
|
||
return sum(data[:-1]) & 0xFF
|
||
|
||
def _parse_packet(self, data: bytes) -> Optional[Dict[str, float]]:
|
||
if len(data) != 11:
|
||
logger.debug(f'无效数据包长度: {len(data)}')
|
||
return None
|
||
if data[0] != 0x55:
|
||
logger.debug(f'错误的包头: 0x{data[0]:02X}')
|
||
return None
|
||
if self._checksum(data) != data[-1]:
|
||
logger.debug(f'校验和错误: 期望{self._checksum(data):02X}, 实际{data[-1]:02X}')
|
||
return None
|
||
packet_type = data[1]
|
||
vals = [int.from_bytes(data[i:i+2], 'little', signed=True) for i in range(2, 10, 2)]
|
||
if packet_type == 0x53: # 姿态角，单位0.01°
|
||
pitchl, rxl, yawl, temp = vals # 注意这里 vals 已经是有符号整数
|
||
# 使用第一段代码里的比例系数
|
||
k_angle = 180.0
|
||
roll = -round(rxl / 32768.0 * k_angle,2)
|
||
pitch = -round(pitchl / 32768.0 * k_angle,2)
|
||
yaw = -round(yawl / 32768.0 * k_angle,2)
|
||
temp = temp / 100.0
|
||
self.last_data = {
|
||
'roll': roll,
|
||
'pitch': pitch,
|
||
'yaw': yaw,
|
||
'temperature': temp
|
||
}
|
||
# logger.debug(f'解析姿态角包: roll={roll}, pitch={pitch}, yaw={yaw}, temp={temp}')
|
||
return self.last_data
|
||
else:
|
||
# logger.debug(f'忽略的数据包类型: 0x{packet_type:02X}')
|
||
return None
|
||
|
||
def read_data(self, apply_calibration: bool = True) -> Dict[str, Any]:
|
||
if not self.ser or not getattr(self.ser, 'is_open', False):
|
||
logger.warning('IMU串口未连接，尝试重新连接...')
|
||
self._connect()
|
||
return {
|
||
'head_pose': {
|
||
'rotation': self.last_data['yaw'],
|
||
'tilt': self.last_data['roll'],
|
||
'pitch': self.last_data['pitch']
|
||
},
|
||
'temperature': self.last_data['temperature'],
|
||
'timestamp': datetime.now().isoformat()
|
||
}
|
||
try:
|
||
bytes_waiting = self.ser.in_waiting
|
||
if bytes_waiting:
|
||
# logger.debug(f'串口缓冲区待读字节: {bytes_waiting}')
|
||
chunk = self.ser.read(bytes_waiting)
|
||
# logger.debug(f'读取到字节: {len(chunk)}')
|
||
self.buffer.extend(chunk)
|
||
while len(self.buffer) >= 11:
|
||
if self.buffer[0] != 0x55:
|
||
dropped = self.buffer.pop(0)
|
||
logger.debug(f'丢弃无效字节: 0x{dropped:02X}')
|
||
continue
|
||
packet = bytes(self.buffer[:11])
|
||
parsed = self._parse_packet(packet)
|
||
del self.buffer[:11]
|
||
if parsed is not None:
|
||
raw = {
|
||
'head_pose': {
|
||
'rotation': parsed['yaw'], # rotation = roll
|
||
'tilt': parsed['roll'], # tilt = yaw
|
||
'pitch': parsed['pitch'] # pitch = pitch
|
||
},
|
||
'temperature': parsed['temperature'],
|
||
'timestamp': datetime.now().isoformat()
|
||
}
|
||
# logger.debug(f'映射后的头部姿态: {raw}')
|
||
return self.apply_calibration(raw) if apply_calibration else raw
|
||
raw = {
|
||
'head_pose': {
|
||
'rotation': self.last_data['yaw'],
|
||
'tilt': self.last_data['roll'],
|
||
'pitch': self.last_data['pitch']
|
||
},
|
||
'temperature': self.last_data['temperature'],
|
||
'timestamp': datetime.now().isoformat()
|
||
}
|
||
return self.apply_calibration(raw) if apply_calibration else raw
|
||
except Exception as e:
|
||
logger.error(f'IMU数据读取异常: {e}', exc_info=True)
|
||
raw = {
|
||
'head_pose': {
|
||
'rotation': self.last_data['yaw'],
|
||
'tilt': self.last_data['roll'],
|
||
'pitch': self.last_data['pitch']
|
||
},
|
||
'temperature': self.last_data['temperature'],
|
||
'timestamp': datetime.now().isoformat()
|
||
}
|
||
return self.apply_calibration(raw) if apply_calibration else raw
|
||
|
||
def __del__(self):
|
||
try:
|
||
if self.ser and getattr(self.ser, 'is_open', False):
|
||
self.ser.close()
|
||
logger.info('IMU设备串口已关闭')
|
||
except Exception:
|
||
pass
|
||
|
||
class MockIMUDevice:
|
||
"""模拟IMU设备"""
|
||
|
||
def __init__(self):
|
||
self.noise_level = 0.1
|
||
self.calibration_data = None # 校准数据
|
||
self.head_pose_offset = {'rotation': 0, 'tilt': 0, 'pitch': 0} # 头部姿态零点偏移
|
||
|
||
def set_calibration(self, calibration: Dict[str, Any]):
|
||
"""设置校准数据"""
|
||
self.calibration_data = calibration
|
||
if 'head_pose_offset' in calibration:
|
||
self.head_pose_offset = calibration['head_pose_offset']
|
||
|
||
def apply_calibration(self, raw_data: Dict[str, Any]) -> Dict[str, Any]:
|
||
"""应用校准：将当前姿态减去初始偏移，得到相对姿态"""
|
||
if not raw_data or 'head_pose' not in raw_data:
|
||
return raw_data
|
||
|
||
calibrated_data = raw_data.copy()
|
||
head_pose = raw_data['head_pose'].copy()
|
||
head_pose['rotation'] = head_pose['rotation'] - self.head_pose_offset['rotation']
|
||
head_pose['tilt'] = head_pose['tilt'] - self.head_pose_offset['tilt']
|
||
head_pose['pitch'] = head_pose['pitch'] - self.head_pose_offset['pitch']
|
||
calibrated_data['head_pose'] = head_pose
|
||
return calibrated_data
|
||
|
||
def read_data(self, apply_calibration: bool = True) -> Dict[str, Any]:
|
||
"""读取IMU数据"""
|
||
# 生成头部姿态角度数据，角度范围(-90°, +90°)
|
||
# 使用正弦波模拟自然的头部运动，添加随机噪声
|
||
import time
|
||
current_time = time.time()
|
||
|
||
# 旋转角（左旋为负，右旋为正）
|
||
rotation_angle = 30 * np.sin(current_time * 0.5) + np.random.normal(0, self.noise_level * 5)
|
||
rotation_angle = np.clip(rotation_angle, -90, 90)
|
||
|
||
# 倾斜角（左倾为负，右倾为正）
|
||
tilt_angle = 20 * np.sin(current_time * 0.3 + np.pi/4) + np.random.normal(0, self.noise_level * 5)
|
||
tilt_angle = np.clip(tilt_angle, -90, 90)
|
||
|
||
# 俯仰角（俯角为负，仰角为正）
|
||
pitch_angle = 15 * np.sin(current_time * 0.7 + np.pi/2) + np.random.normal(0, self.noise_level * 5)
|
||
pitch_angle = np.clip(pitch_angle, -90, 90)
|
||
|
||
# 生成原始数据
|
||
raw_data = {
|
||
'head_pose': {
|
||
'rotation': rotation_angle, # 旋转角：左旋(-), 右旋(+)
|
||
'tilt': tilt_angle, # 倾斜角：左倾(-), 右倾(+)
|
||
'pitch': pitch_angle # 俯仰角：俯角(-), 仰角(+)
|
||
},
|
||
'timestamp': datetime.now().isoformat()
|
||
}
|
||
|
||
# 应用校准并返回
|
||
return self.apply_calibration(raw_data) if apply_calibration else raw_data
|
||
|
||
|
||
class RealPressureDevice:
|
||
"""真实SMiTSense压力传感器设备"""
|
||
|
||
def __init__(self, dll_path=None):
|
||
"""初始化SMiTSense压力传感器
|
||
|
||
Args:
|
||
dll_path: DLL文件路径，如果为None则使用默认路径
|
||
"""
|
||
self.dll = None
|
||
self.device_handle = None
|
||
self.is_connected = False
|
||
self.rows = 0
|
||
self.cols = 0
|
||
self.frame_size = 0
|
||
self.buf = None
|
||
|
||
# 设置DLL路径 - 使用正确的DLL文件名
|
||
if dll_path is None:
|
||
# 尝试多个可能的DLL文件名
|
||
dll_candidates = [
|
||
os.path.join(os.path.dirname(__file__), 'dll', 'smitsense', 'SMiTSenseUsbWrapper.dll'),
|
||
os.path.join(os.path.dirname(__file__), 'dll', 'smitsense', 'SMiTSenseUsb-F3.0.dll')
|
||
]
|
||
dll_path = None
|
||
for candidate in dll_candidates:
|
||
if os.path.exists(candidate):
|
||
dll_path = candidate
|
||
break
|
||
|
||
if dll_path is None:
|
||
raise FileNotFoundError(f"未找到SMiTSense DLL文件，检查路径: {dll_candidates}")
|
||
|
||
self.dll_path = dll_path
|
||
logger.info(f'初始化真实压力传感器设备，DLL路径: {dll_path}')
|
||
|
||
try:
|
||
self._load_dll()
|
||
self._initialize_device()
|
||
except Exception as e:
|
||
logger.error(f'压力传感器初始化失败: {e}')
|
||
# 如果真实设备初始化失败，可以选择降级为模拟设备
|
||
raise
|
||
|
||
def _load_dll(self):
|
||
"""加载SMiTSense DLL并设置函数签名"""
|
||
try:
|
||
if not os.path.exists(self.dll_path):
|
||
raise FileNotFoundError(f"DLL文件未找到: {self.dll_path}")
|
||
|
||
# 加载DLL
|
||
self.dll = ctypes.WinDLL(self.dll_path)
|
||
logger.info(f"成功加载DLL: {self.dll_path}")
|
||
|
||
# 设置函数签名（基于testsmit.py的工作代码）
|
||
self.dll.SMiTSenseUsb_Init.argtypes = [ctypes.c_int]
|
||
self.dll.SMiTSenseUsb_Init.restype = ctypes.c_int
|
||
|
||
self.dll.SMiTSenseUsb_ScanDevices.argtypes = [ctypes.POINTER(ctypes.c_int)]
|
||
self.dll.SMiTSenseUsb_ScanDevices.restype = ctypes.c_int
|
||
|
||
self.dll.SMiTSenseUsb_OpenAndStart.argtypes = [
|
||
ctypes.c_int,
|
||
ctypes.POINTER(ctypes.c_uint16),
|
||
ctypes.POINTER(ctypes.c_uint16)
|
||
]
|
||
self.dll.SMiTSenseUsb_OpenAndStart.restype = ctypes.c_int
|
||
|
||
self.dll.SMiTSenseUsb_GetLatestFrame.argtypes = [
|
||
ctypes.POINTER(ctypes.c_uint16),
|
||
ctypes.c_int
|
||
]
|
||
self.dll.SMiTSenseUsb_GetLatestFrame.restype = ctypes.c_int
|
||
|
||
self.dll.SMiTSenseUsb_StopAndClose.argtypes = []
|
||
self.dll.SMiTSenseUsb_StopAndClose.restype = ctypes.c_int
|
||
|
||
logger.info("DLL函数签名设置完成")
|
||
|
||
except Exception as e:
|
||
logger.error(f"加载DLL失败: {e}")
|
||
raise
|
||
|
||
def _initialize_device(self):
|
||
"""初始化设备连接"""
|
||
try:
|
||
# 初始化USB连接
|
||
ret = self.dll.SMiTSenseUsb_Init(0)
|
||
if ret != 0:
|
||
raise RuntimeError(f"USB初始化失败: {ret}")
|
||
|
||
# 扫描设备
|
||
count = ctypes.c_int()
|
||
ret = self.dll.SMiTSenseUsb_ScanDevices(ctypes.byref(count))
|
||
if ret != 0 or count.value == 0:
|
||
raise RuntimeError(f"设备扫描失败或未找到设备: {ret}, count: {count.value}")
|
||
|
||
logger.info(f"发现 {count.value} 个SMiTSense设备")
|
||
|
||
# 打开并启动第一个设备
|
||
rows = ctypes.c_uint16()
|
||
cols = ctypes.c_uint16()
|
||
ret = self.dll.SMiTSenseUsb_OpenAndStart(0, ctypes.byref(rows), ctypes.byref(cols))
|
||
if ret != 0:
|
||
raise RuntimeError(f"设备启动失败: {ret}")
|
||
|
||
self.rows = rows.value
|
||
self.cols = cols.value
|
||
self.frame_size = self.rows * self.cols
|
||
self.buf_type = ctypes.c_uint16 * self.frame_size
|
||
self.buf = self.buf_type()
|
||
self.is_connected = True
|
||
|
||
logger.info(f"SMiTSense压力传感器初始化成功: {self.rows}行 x {self.cols}列")
|
||
|
||
except Exception as e:
|
||
logger.error(f"设备初始化失败: {e}")
|
||
raise
|
||
|
||
def read_data(self) -> Dict[str, Any]:
|
||
"""读取压力数据并转换为与MockPressureDevice兼容的格式"""
|
||
try:
|
||
if not self.is_connected or not self.dll:
|
||
logger.error("设备未连接")
|
||
return self._get_empty_data()
|
||
|
||
# 读取原始压力数据
|
||
ret = self.dll.SMiTSenseUsb_GetLatestFrame(self.buf, self.frame_size)
|
||
if ret != 0:
|
||
logger.warning(f"读取数据帧失败: {ret}")
|
||
return self._get_empty_data()
|
||
|
||
# 转换为numpy数组
|
||
raw_data = np.frombuffer(self.buf, dtype=np.uint16).reshape((self.rows, self.cols))
|
||
|
||
# 计算足部区域压力 (基于传感器的实际布局)
|
||
foot_zones = self._calculate_foot_pressure_zones(raw_data)
|
||
|
||
# 生成压力图像
|
||
pressure_image_base64 = self._generate_pressure_image(
|
||
foot_zones['left_front'],
|
||
foot_zones['left_rear'],
|
||
foot_zones['right_front'],
|
||
foot_zones['right_rear'],
|
||
raw_data
|
||
)
|
||
|
||
return {
|
||
'foot_pressure': {
|
||
'left_front': round(foot_zones['left_front'], 2),
|
||
'left_rear': round(foot_zones['left_rear'], 2),
|
||
'right_front': round(foot_zones['right_front'], 2),
|
||
'right_rear': round(foot_zones['right_rear'], 2),
|
||
'left_total': round(foot_zones['left_total'], 2),
|
||
'right_total': round(foot_zones['right_total'], 2)
|
||
},
|
||
'pressure_image': pressure_image_base64,
|
||
'timestamp': datetime.now().isoformat()
|
||
}
|
||
|
||
except Exception as e:
|
||
logger.error(f"读取压力数据异常: {e}")
|
||
return self._get_empty_data()
|
||
|
||
def _calculate_foot_pressure_zones(self, raw_data):
|
||
"""计算足部区域压力，返回百分比：
|
||
- 左足、右足：相对于双足总压的百分比
|
||
- 左前、左后：相对于左足总压的百分比
|
||
- 右前、右后：相对于右足总压的百分比
|
||
基于原始矩阵按行列各等分为四象限（上半部为前、下半部为后，左半部为左、右半部为右）。
|
||
"""
|
||
try:
|
||
# 防护：空数据
|
||
if raw_data is None:
|
||
raise ValueError("raw_data is None")
|
||
|
||
# 转为浮点以避免 uint16 溢出
|
||
rd = np.asarray(raw_data, dtype=np.float64)
|
||
rows, cols = rd.shape if rd.ndim == 2 else (0, 0)
|
||
if rows == 0 or cols == 0:
|
||
raise ValueError("raw_data has invalid shape")
|
||
|
||
# 行列对半分（上=前，下=后；左=左，右=右）
|
||
mid_r = rows // 2
|
||
mid_c = cols // 2
|
||
|
||
# 四象限求和
|
||
left_front = float(np.sum(rd[:mid_r, :mid_c], dtype=np.float64))
|
||
left_rear = float(np.sum(rd[mid_r:, :mid_c], dtype=np.float64))
|
||
right_front = float(np.sum(rd[:mid_r, mid_c:], dtype=np.float64))
|
||
right_rear = float(np.sum(rd[mid_r:, mid_c:], dtype=np.float64))
|
||
|
||
# 绝对总压
|
||
left_total_abs = left_front + left_rear
|
||
right_total_abs = right_front + right_rear
|
||
total_abs = left_total_abs + right_total_abs
|
||
|
||
# 左右足占比（相对于双足总压）
|
||
left_total_pct = float((left_total_abs / total_abs * 100) if total_abs > 0 else 0)
|
||
right_total_pct = float((right_total_abs / total_abs * 100) if total_abs > 0 else 0)
|
||
|
||
# 前后占比（相对于各自单足总压）
|
||
left_front_pct = float((left_front / left_total_abs * 100) if left_total_abs > 0 else 0)
|
||
left_rear_pct = float((left_rear / left_total_abs * 100) if left_total_abs > 0 else 0)
|
||
right_front_pct = float((right_front / right_total_abs * 100) if right_total_abs > 0 else 0)
|
||
right_rear_pct = float((right_rear / right_total_abs * 100) if right_total_abs > 0 else 0)
|
||
|
||
return {
|
||
'left_front': int(left_front_pct),
|
||
'left_rear': int(left_rear_pct),
|
||
'right_front': int(right_front_pct),
|
||
'right_rear': int(right_rear_pct),
|
||
'left_total': int(left_total_pct),
|
||
'right_total': int(right_total_pct),
|
||
'total_pressure': int(total_abs)
|
||
}
|
||
except Exception as e:
|
||
logger.error(f"计算足部区域压力异常: {e}")
|
||
return {
|
||
'left_front': 0, 'left_rear': 0, 'right_front': 0, 'right_rear': 0,
|
||
'left_total': 0, 'right_total': 0, 'total_pressure': 0
|
||
}
|
||
|
||
def _generate_pressure_image(self, left_front, left_rear, right_front, right_rear, raw_data=None) -> str:
|
||
"""生成足部压力图片的base64数据"""
|
||
try:
|
||
if MATPLOTLIB_AVAILABLE and raw_data is not None:
|
||
# 使用原始数据生成更详细的热力图
|
||
return self._generate_heatmap_image(raw_data)
|
||
else:
|
||
# 降级到简单的区域显示图
|
||
return self._generate_simple_pressure_image(left_front, left_rear, right_front, right_rear)
|
||
|
||
except Exception as e:
|
||
logger.warning(f"生成压力图片失败: {e}")
|
||
return "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mNkYPhfDwAChwGA60e6kgAAAABJRU5ErkJggg=="
|
||
def _generate_heatmap_image(self, raw_data) -> str:
|
||
"""生成基于原始数据的热力图（OpenCV实现，固定范围映射，效果与matplotlib一致）"""
|
||
try:
|
||
import cv2
|
||
import numpy as np
|
||
import base64
|
||
from io import BytesIO
|
||
from PIL import Image
|
||
|
||
# 固定映射范围（与 matplotlib vmin/vmax 一致）
|
||
vmin, vmax = 0, 1000
|
||
norm_data = np.clip((raw_data - vmin) / (vmax - vmin) * 255, 0, 255).astype(np.uint8)
|
||
|
||
# 应用 jet 颜色映射
|
||
heatmap = cv2.applyColorMap(norm_data, cv2.COLORMAP_JET)
|
||
|
||
# OpenCV 生成的是 BGR，转成 RGB
|
||
heatmap_rgb = cv2.cvtColor(heatmap, cv2.COLOR_BGR2RGB)
|
||
|
||
# 转成 Pillow Image
|
||
img = Image.fromarray(heatmap_rgb)
|
||
|
||
# 输出为 Base64 PNG
|
||
buffer = BytesIO()
|
||
img.save(buffer, format="PNG")
|
||
buffer.seek(0)
|
||
image_base64 = base64.b64encode(buffer.getvalue()).decode("utf-8")
|
||
|
||
return f"data:image/png;base64,{image_base64}"
|
||
|
||
except Exception as e:
|
||
logger.warning(f"生成热力图失败: {e}")
|
||
return self._generate_simple_pressure_image(0, 0, 0, 0)
|
||
# def _generate_heatmap_image(self, raw_data) -> str:
|
||
# """生成基于原始数据的热力图"""
|
||
# try:
|
||
# import matplotlib
|
||
# matplotlib.use('Agg')
|
||
# import matplotlib.pyplot as plt
|
||
# from io import BytesIO
|
||
|
||
# # 参考 tests/testsmit.py 的渲染方式：使用 jet 色图、nearest 插值、固定范围并关闭坐标轴
|
||
# fig, ax = plt.subplots()
|
||
# im = ax.imshow(raw_data, cmap='jet', interpolation='nearest', vmin=0, vmax=1000)
|
||
# ax.axis('off')
|
||
|
||
# # 紧凑布局并导出为 base64
|
||
# from io import BytesIO
|
||
# buffer = BytesIO()
|
||
# plt.savefig(buffer, format='png', bbox_inches='tight', dpi=100, pad_inches=0, facecolor='black')
|
||
# buffer.seek(0)
|
||
# image_base64 = base64.b64encode(buffer.getvalue()).decode('utf-8')
|
||
# plt.close(fig)
|
||
|
||
# return f"data:image/png;base64,{image_base64}"
|
||
|
||
# except Exception as e:
|
||
# logger.warning(f"生成热力图失败: {e}")
|
||
# return self._generate_simple_pressure_image(0, 0, 0, 0)
|
||
|
||
def _generate_simple_pressure_image(self, left_front, left_rear, right_front, right_rear) -> str:
|
||
"""生成简单的足部压力区域图"""
|
||
try:
|
||
import matplotlib
|
||
matplotlib.use('Agg')
|
||
import matplotlib.pyplot as plt
|
||
import matplotlib.patches as patches
|
||
from io import BytesIO
|
||
|
||
# 创建图形
|
||
fig, ax = plt.subplots(1, 1, figsize=(6, 8))
|
||
ax.set_xlim(0, 10)
|
||
ax.set_ylim(0, 12)
|
||
ax.set_aspect('equal')
|
||
ax.axis('off')
|
||
|
||
# 定义颜色映射
|
||
max_pressure = max(left_front, left_rear, right_front, right_rear)
|
||
if max_pressure > 0:
|
||
left_front_color = plt.cm.Reds(left_front / max_pressure)
|
||
left_rear_color = plt.cm.Reds(left_rear / max_pressure)
|
||
right_front_color = plt.cm.Reds(right_front / max_pressure)
|
||
right_rear_color = plt.cm.Reds(right_rear / max_pressure)
|
||
else:
|
||
left_front_color = left_rear_color = right_front_color = right_rear_color = 'lightgray'
|
||
|
||
# 绘制足部区域
|
||
left_front_rect = patches.Rectangle((1, 6), 2, 4, linewidth=1, edgecolor='black', facecolor=left_front_color)
|
||
left_rear_rect = patches.Rectangle((1, 2), 2, 4, linewidth=1, edgecolor='black', facecolor=left_rear_color)
|
||
right_front_rect = patches.Rectangle((7, 6), 2, 4, linewidth=1, edgecolor='black', facecolor=right_front_color)
|
||
right_rear_rect = patches.Rectangle((7, 2), 2, 4, linewidth=1, edgecolor='black', facecolor=right_rear_color)
|
||
|
||
ax.add_patch(left_front_rect)
|
||
ax.add_patch(left_rear_rect)
|
||
ax.add_patch(right_front_rect)
|
||
ax.add_patch(right_rear_rect)
|
||
|
||
# 添加标签
|
||
ax.text(2, 8, f'{left_front:.1f}', ha='center', va='center', fontsize=10, weight='bold')
|
||
ax.text(2, 4, f'{left_rear:.1f}', ha='center', va='center', fontsize=10, weight='bold')
|
||
ax.text(8, 8, f'{right_front:.1f}', ha='center', va='center', fontsize=10, weight='bold')
|
||
ax.text(8, 4, f'{right_rear:.1f}', ha='center', va='center', fontsize=10, weight='bold')
|
||
|
||
ax.text(2, 0.5, '左足', ha='center', va='center', fontsize=12, weight='bold')
|
||
ax.text(8, 0.5, '右足', ha='center', va='center', fontsize=12, weight='bold')
|
||
|
||
# 保存为base64
|
||
buffer = BytesIO()
|
||
plt.savefig(buffer, format='png', bbox_inches='tight', dpi=100, facecolor='black')
|
||
buffer.seek(0)
|
||
image_base64 = base64.b64encode(buffer.getvalue()).decode('utf-8')
|
||
plt.close(fig)
|
||
|
||
return f"data:image/png;base64,{image_base64}"
|
||
|
||
except Exception as e:
|
||
logger.warning(f"生成简单压力图片失败: {e}")
|
||
return "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mNkYPhfDwAChwGA60e6kgAAAABJRU5ErkJggg=="
|
||
|
||
def _get_empty_data(self):
|
||
"""返回空的压力数据"""
|
||
return {
|
||
'foot_pressure': {
|
||
'left_front': 0.0,
|
||
'left_rear': 0.0,
|
||
'right_front': 0.0,
|
||
'right_rear': 0.0,
|
||
'left_total': 0.0,
|
||
'right_total': 0.0
|
||
},
|
||
'pressure_image': "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mNkYPhfDwAChwGA60e6kgAAAABJRU5ErkJggg==",
|
||
'timestamp': datetime.now().isoformat()
|
||
}
|
||
|
||
def close(self):
|
||
"""显式关闭压力传感器连接"""
|
||
try:
|
||
if self.is_connected and self.dll:
|
||
self.dll.SMiTSenseUsb_StopAndClose()
|
||
self.is_connected = False
|
||
logger.info('SMiTSense压力传感器连接已关闭')
|
||
except Exception as e:
|
||
logger.error(f'关闭压力传感器连接异常: {e}')
|
||
|
||
def __del__(self):
|
||
"""析构函数，确保资源清理"""
|
||
self.close()
|
||
|
||
|
||
class MockPressureDevice:
|
||
"""模拟压力传感器设备，模拟真实SMiTSense设备的行为"""
|
||
|
||
def __init__(self):
|
||
self.base_pressure = 500 # 基础压力值
|
||
self.noise_level = 10
|
||
self.rows = 4 # 模拟传感器矩阵行数
|
||
self.cols = 4 # 模拟传感器矩阵列数
|
||
self.time_offset = np.random.random() * 10 # 随机时间偏移，让每个实例的波形不同
|
||
|
||
def read_data(self) -> Dict[str, Any]:
|
||
"""读取压力数据，模拟基于矩阵数据的真实设备行为"""
|
||
try:
|
||
# 生成模拟的传感器矩阵数据
|
||
raw_data = self._generate_simulated_matrix_data()
|
||
|
||
# 使用与真实设备相同的计算逻辑
|
||
foot_zones = self._calculate_foot_pressure_zones(raw_data)
|
||
|
||
# 生成压力图像
|
||
pressure_image_base64 = self._generate_pressure_image(
|
||
foot_zones['left_front'],
|
||
foot_zones['left_rear'],
|
||
foot_zones['right_front'],
|
||
foot_zones['right_rear'],
|
||
raw_data
|
||
)
|
||
|
||
return {
|
||
'foot_pressure': {
|
||
'left_front': round(foot_zones['left_front'], 2),
|
||
'left_rear': round(foot_zones['left_rear'], 2),
|
||
'right_front': round(foot_zones['right_front'], 2),
|
||
'right_rear': round(foot_zones['right_rear'], 2),
|
||
'left_total': round(foot_zones['left_total'], 2),
|
||
'right_total': round(foot_zones['right_total'], 2)
|
||
},
|
||
'pressure_image': pressure_image_base64,
|
||
'timestamp': datetime.now().isoformat()
|
||
}
|
||
|
||
except Exception as e:
|
||
logger.error(f"模拟压力设备读取数据异常: {e}")
|
||
return self._get_empty_data()
|
||
|
||
def _generate_simulated_matrix_data(self):
|
||
"""生成模拟的传感器矩阵数据，模拟真实的足部压力分布"""
|
||
import time
|
||
current_time = time.time() + self.time_offset
|
||
|
||
# 创建4x4的传感器矩阵
|
||
matrix_data = np.zeros((self.rows, self.cols))
|
||
|
||
# 模拟动态的压力分布，使用正弦波叠加噪声
|
||
for i in range(self.rows):
|
||
for j in range(self.cols):
|
||
# 基础压力值，根据传感器位置不同
|
||
base_value = self.base_pressure * (0.3 + 0.7 * np.random.random())
|
||
|
||
# 添加时间变化（模拟人体重心变化）
|
||
time_variation = np.sin(current_time * 0.5 + i * 0.5 + j * 0.3) * 0.3
|
||
|
||
# 添加噪声
|
||
noise = np.random.normal(0, self.noise_level)
|
||
|
||
# 确保压力值非负
|
||
matrix_data[i, j] = max(0, base_value * (1 + time_variation) + noise)
|
||
|
||
return matrix_data
|
||
|
||
def _calculate_foot_pressure_zones(self, raw_data):
|
||
"""计算足部区域压力，返回百分比：
|
||
- 左足、右足：相对于双足总压的百分比
|
||
- 左前、左后：相对于左足总压的百分比
|
||
- 右前、右后：相对于右足总压的百分比
|
||
基于原始矩阵按行列各等分为四象限（上半部为前、下半部为后，左半部为左、右半部为右）。
|
||
"""
|
||
try:
|
||
# 防护：空数据
|
||
if raw_data is None:
|
||
raise ValueError("raw_data is None")
|
||
|
||
# 转为浮点以避免 uint16 溢出
|
||
rd = np.asarray(raw_data, dtype=np.float64)
|
||
rows, cols = rd.shape if rd.ndim == 2 else (0, 0)
|
||
if rows == 0 or cols == 0:
|
||
raise ValueError("raw_data has invalid shape")
|
||
|
||
# 行列对半分（上=前，下=后；左=左，右=右）
|
||
mid_r = rows // 2
|
||
mid_c = cols // 2
|
||
|
||
# 四象限求和
|
||
left_front = float(np.sum(rd[:mid_r, :mid_c], dtype=np.float64))
|
||
left_rear = float(np.sum(rd[mid_r:, :mid_c], dtype=np.float64))
|
||
right_front = float(np.sum(rd[:mid_r, mid_c:], dtype=np.float64))
|
||
right_rear = float(np.sum(rd[mid_r:, mid_c:], dtype=np.float64))
|
||
|
||
# 绝对总压
|
||
left_total_abs = left_front + left_rear
|
||
right_total_abs = right_front + right_rear
|
||
total_abs = left_total_abs + right_total_abs
|
||
|
||
# 左右足占比（相对于双足总压）
|
||
left_total_pct = float((left_total_abs / total_abs * 100) if total_abs > 0 else 0)
|
||
right_total_pct = float((right_total_abs / total_abs * 100) if total_abs > 0 else 0)
|
||
|
||
# 前后占比（相对于各自单足总压）
|
||
left_front_pct = float((left_front / left_total_abs * 100) if left_total_abs > 0 else 0)
|
||
left_rear_pct = float((left_rear / left_total_abs * 100) if left_total_abs > 0 else 0)
|
||
right_front_pct = float((right_front / right_total_abs * 100) if right_total_abs > 0 else 0)
|
||
right_rear_pct = float((right_rear / right_total_abs * 100) if right_total_abs > 0 else 0)
|
||
|
||
return {
|
||
'left_front': left_front_pct,
|
||
'left_rear': left_rear_pct,
|
||
'right_front': right_front_pct,
|
||
'right_rear': right_rear_pct,
|
||
'left_total': left_total_pct,
|
||
'right_total': right_total_pct,
|
||
'total_pressure': float(total_abs)
|
||
}
|
||
except Exception as e:
|
||
logger.error(f"计算足部区域压力异常: {e}")
|
||
return {
|
||
'left_front': 0, 'left_rear': 0, 'right_front': 0, 'right_rear': 0,
|
||
'left_total': 0, 'right_total': 0, 'total_pressure': 0
|
||
}
|
||
|
||
def _generate_pressure_image(self, left_front, left_rear, right_front, right_rear, raw_data=None) -> str:
|
||
"""生成足部压力图片的base64数据"""
|
||
try:
|
||
if MATPLOTLIB_AVAILABLE and raw_data is not None:
|
||
# 使用原始数据生成更详细的热力图
|
||
return self._generate_heatmap_image(raw_data)
|
||
else:
|
||
# 降级到简单的区域显示图
|
||
return self._generate_simple_pressure_image(left_front, left_rear, right_front, right_rear)
|
||
|
||
except Exception as e:
|
||
logger.warning(f"生成模拟压力图片失败: {e}")
|
||
return "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mNkYPhfDwAChwGA60e6kgAAAABJRU5ErkJggg=="
|
||
|
||
def _generate_heatmap_image(self, raw_data) -> str:
|
||
"""生成基于原始数据的热力图"""
|
||
try:
|
||
import matplotlib
|
||
matplotlib.use('Agg')
|
||
import matplotlib.pyplot as plt
|
||
from io import BytesIO
|
||
|
||
# 参考 tests/testsmit.py 的渲染方式：使用 jet 色图、nearest 插值、固定范围并关闭坐标轴
|
||
fig, ax = plt.subplots()
|
||
im = ax.imshow(raw_data, cmap='jet', interpolation='nearest', vmin=0, vmax=1000)
|
||
ax.axis('off')
|
||
|
||
# 紧凑布局并导出为 base64
|
||
from io import BytesIO
|
||
buffer = BytesIO()
|
||
plt.savefig(buffer, format='png', bbox_inches='tight', dpi=100, pad_inches=0)
|
||
buffer.seek(0)
|
||
image_base64 = base64.b64encode(buffer.getvalue()).decode('utf-8')
|
||
plt.close(fig)
|
||
|
||
return f"data:image/png;base64,{image_base64}"
|
||
|
||
except Exception as e:
|
||
logger.warning(f"生成热力图失败: {e}")
|
||
return self._generate_simple_pressure_image(0, 0, 0, 0)
|
||
|
||
def _generate_simple_pressure_image(self, left_front, left_rear, right_front, right_rear) -> str:
|
||
"""生成简单的足部压力区域图"""
|
||
try:
|
||
import matplotlib
|
||
matplotlib.use('Agg') # 设置非交互式后端，避免Tkinter错误
|
||
import matplotlib.pyplot as plt
|
||
import matplotlib.patches as patches
|
||
from io import BytesIO
|
||
|
||
# 临时禁用PIL的调试日志
|
||
pil_logger = logging.getLogger('PIL')
|
||
original_level = pil_logger.level
|
||
pil_logger.setLevel(logging.WARNING)
|
||
|
||
# 创建图形
|
||
fig, ax = plt.subplots(1, 1, figsize=(6, 8))
|
||
ax.set_xlim(0, 10)
|
||
ax.set_ylim(0, 12)
|
||
ax.set_aspect('equal')
|
||
ax.axis('off')
|
||
|
||
# 定义颜色映射（根据压力值）
|
||
max_pressure = max(left_front, left_rear, right_front, right_rear)
|
||
if max_pressure > 0:
|
||
left_front_color = plt.cm.Reds(left_front / max_pressure)
|
||
left_rear_color = plt.cm.Reds(left_rear / max_pressure)
|
||
right_front_color = plt.cm.Reds(right_front / max_pressure)
|
||
right_rear_color = plt.cm.Reds(right_rear / max_pressure)
|
||
else:
|
||
left_front_color = left_rear_color = right_front_color = right_rear_color = 'lightgray'
|
||
|
||
# 绘制左脚
|
||
left_front_rect = patches.Rectangle((1, 6), 2, 4, linewidth=1, edgecolor='black', facecolor=left_front_color)
|
||
left_rear_rect = patches.Rectangle((1, 2), 2, 4, linewidth=1, edgecolor='black', facecolor=left_rear_color)
|
||
|
||
# 绘制右脚
|
||
right_front_rect = patches.Rectangle((7, 6), 2, 4, linewidth=1, edgecolor='black', facecolor=right_front_color)
|
||
right_rear_rect = patches.Rectangle((7, 2), 2, 4, linewidth=1, edgecolor='black', facecolor=right_rear_color)
|
||
|
||
# 添加到图形
|
||
ax.add_patch(left_front_rect)
|
||
ax.add_patch(left_rear_rect)
|
||
ax.add_patch(right_front_rect)
|
||
ax.add_patch(right_rear_rect)
|
||
|
||
# 添加标签
|
||
ax.text(2, 8, f'{left_front:.1f}', ha='center', va='center', fontsize=10, weight='bold')
|
||
ax.text(2, 4, f'{left_rear:.1f}', ha='center', va='center', fontsize=10, weight='bold')
|
||
ax.text(8, 8, f'{right_front:.1f}', ha='center', va='center', fontsize=10, weight='bold')
|
||
ax.text(8, 4, f'{right_rear:.1f}', ha='center', va='center', fontsize=10, weight='bold')
|
||
|
||
ax.text(2, 0.5, '左足', ha='center', va='center', fontsize=12, weight='bold')
|
||
ax.text(8, 0.5, '右足', ha='center', va='center', fontsize=12, weight='bold')
|
||
|
||
# 保存为base64
|
||
buffer = BytesIO()
|
||
plt.savefig(buffer, format='png', bbox_inches='tight', dpi=100, facecolor='white')
|
||
buffer.seek(0)
|
||
image_base64 = base64.b64encode(buffer.getvalue()).decode('utf-8')
|
||
plt.close(fig)
|
||
|
||
# 恢复PIL的日志级别
|
||
pil_logger.setLevel(original_level)
|
||
|
||
return f"data:image/png;base64,{image_base64}"
|
||
|
||
except Exception as e:
|
||
# 确保在异常情况下也恢复PIL的日志级别
|
||
try:
|
||
pil_logger.setLevel(original_level)
|
||
except:
|
||
pass
|
||
logger.warning(f"生成压力图片失败: {e}")
|
||
# 返回一个简单的占位符base64图片
|
||
return "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mNkYPhfDwAChwGA60e6kgAAAABJRU5ErkJggg=="
|
||
|
||
def _get_empty_data(self):
|
||
"""返回空的压力数据"""
|
||
return {
|
||
'foot_pressure': {
|
||
'left_front': 0.0,
|
||
'left_rear': 0.0,
|
||
'right_front': 0.0,
|
||
'right_rear': 0.0,
|
||
'left_total': 0.0,
|
||
'right_total': 0.0
|
||
},
|
||
'pressure_image': "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mNkYPhfDwAChwGA60e6kgAAAABJRU5ErkJggg==",
|
||
'timestamp': datetime.now().isoformat()
|
||
}
|
||
|
||
|
||
class VideoStreamManager:
|
||
"""视频推流管理器"""
|
||
|
||
def __init__(self, socketio=None, device_manager=None):
|
||
self.socketio = socketio
|
||
self.device_manager = device_manager
|
||
self.device_index = None
|
||
self.video_thread = None
|
||
self.video_running = False
|
||
|
||
# # 用于异步编码的线程池和队列
|
||
self.encoding_executor = ThreadPoolExecutor(max_workers=2)
|
||
self.frame_queue = queue.Queue(maxsize=1) # 只保留最新的一帧
|
||
|
||
# 内存优化配置
|
||
self.frame_skip_counter = 0
|
||
self.FRAME_SKIP_RATIO = 1 # 每3帧发送1帧，减少网络和内存压力
|
||
self.MAX_FRAME_SIZE = (640, 480) # 进一步减小帧尺寸以节省内存
|
||
self.MAX_MEMORY_USAGE = 200 * 1024 * 1024 # 200MB内存限制
|
||
self.memory_check_counter = 0
|
||
# 移除了MEMORY_CHECK_INTERVAL，改为每30帧检查一次内存
|
||
|
||
# 读取RTSP配置
|
||
self._load_rtsp_config()
|
||
|
||
def _load_rtsp_config(self):
|
||
"""加载RTSP配置"""
|
||
start_time = time.time()
|
||
logger.info(f'[TIMING] 开始加载RTSP配置 - {datetime.now().strftime("%H:%M:%S.%f")[:-3]}')
|
||
|
||
try:
|
||
config = configparser.ConfigParser()
|
||
config_path = os.path.join(os.path.dirname(__file__), 'config.ini')
|
||
config.read(config_path, encoding='utf-8')
|
||
device_index_str = config.get('DEVICES', 'camera_index', fallback='0')
|
||
self.device_index = int(device_index_str) if device_index_str else 0
|
||
|
||
end_time = time.time()
|
||
logger.info(f'[TIMING] RTSP配置加载完成，设备号: {self.device_index} - 耗时: {(end_time - start_time) * 1000:.2f}ms')
|
||
except Exception as e:
|
||
end_time = time.time()
|
||
logger.error(f'[TIMING] 视频监控设备配置失败: {e} - 耗时: {(end_time - start_time) * 1000:.2f}ms')
|
||
self.device_index = None
|
||
|
||
def get_memory_usage(self):
|
||
"""获取当前进程内存使用量（字节）"""
|
||
try:
|
||
process = psutil.Process(os.getpid())
|
||
return process.memory_info().rss
|
||
except:
|
||
return 0
|
||
|
||
def async_encode_frame(self, frame, frame_count):
|
||
"""异步编码帧 - 内存优化版本"""
|
||
try:
|
||
# 内存检查
|
||
self.memory_check_counter += 1
|
||
if self.memory_check_counter >= self.MEMORY_CHECK_INTERVAL:
|
||
self.memory_check_counter = 0
|
||
current_memory = self.get_memory_usage()
|
||
if current_memory > self.MAX_MEMORY_USAGE:
|
||
logger.warning(f"内存使用过高: {current_memory / 1024 / 1024:.2f}MB，强制清理")
|
||
gc.collect()
|
||
# 如果内存仍然过高，跳过此帧
|
||
if self.get_memory_usage() > self.MAX_MEMORY_USAGE:
|
||
del frame
|
||
return
|
||
|
||
# 更激进的图像尺寸压缩以节省内存
|
||
height, width = frame.shape[:2]
|
||
target_width, target_height = self.MAX_FRAME_SIZE
|
||
|
||
if width > target_width or height > target_height:
|
||
# 计算缩放比例，保持宽高比
|
||
scale_w = target_width / width
|
||
scale_h = target_height / height
|
||
scale = min(scale_w, scale_h)
|
||
|
||
new_width = int(width * scale)
|
||
new_height = int(height * scale)
|
||
|
||
# 使用更快的插值方法减少CPU使用
|
||
frame = cv2.resize(frame, (new_width, new_height), interpolation=cv2.INTER_AREA)
|
||
self.device_manager._save_frame_to_cache(frame, 'camera')
|
||
# 优化JPEG编码参数：优先考虑速度和内存
|
||
encode_param = [
|
||
int(cv2.IMWRITE_JPEG_QUALITY), 50, # 进一步降低质量以减少内存使用
|
||
int(cv2.IMWRITE_JPEG_OPTIMIZE), 1, # 启用优化
|
||
int(cv2.IMWRITE_JPEG_PROGRESSIVE), 0 # 禁用渐进式以减少内存
|
||
]
|
||
|
||
success, buffer = cv2.imencode('.jpg', frame, encode_param)
|
||
if not success:
|
||
logger.error('图像编码失败')
|
||
return
|
||
|
||
# 立即释放frame内存
|
||
del frame
|
||
|
||
jpg_as_text = base64.b64encode(buffer).decode('utf-8')
|
||
|
||
# 立即释放buffer内存
|
||
del buffer
|
||
# 发送数据
|
||
if self.socketio:
|
||
self.socketio.emit('video_frame', {
|
||
'image': jpg_as_text,
|
||
'frame_id': frame_count,
|
||
'timestamp': time.time()
|
||
})
|
||
|
||
# 立即释放base64字符串
|
||
del jpg_as_text
|
||
|
||
except Exception as e:
|
||
logger.error(f'异步编码帧失败: {e}')
|
||
finally:
|
||
# 定期强制垃圾回收
|
||
if self.memory_check_counter % 10 == 0:
|
||
gc.collect()
|
||
|
||
def frame_encoding_worker(self):
|
||
"""帧编码工作线程"""
|
||
while self.video_running:
|
||
try:
|
||
# 从队列获取帧
|
||
frame, frame_count = self.frame_queue.get(timeout=1)
|
||
|
||
# 提交到线程池进行异步编码
|
||
self.encoding_executor.submit(self.async_encode_frame, frame, frame_count)
|
||
except queue.Empty:
|
||
continue
|
||
except Exception as e:
|
||
logger.error(f'帧编码工作线程异常: {e}')
|
||
|
||
def generate_test_frame(self, frame_count):
|
||
"""生成测试帧"""
|
||
width, height = self.MAX_FRAME_SIZE
|
||
|
||
# 创建黑色背景
|
||
frame = np.zeros((height, width, 3), dtype=np.uint8)
|
||
|
||
# 添加动态元素
|
||
timestamp = datetime.now().strftime('%Y-%m-%d %H:%M:%S.%f')[:-3]
|
||
|
||
# 添加时间戳
|
||
cv2.putText(frame, timestamp, (10, 90), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 255), 2)
|
||
|
||
# 添加帧计数
|
||
cv2.putText(frame, f'TEST Frame: {frame_count}', (10, 120), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 255), 2)
|
||
|
||
# 添加移动的圆形
|
||
center_x = int(320 + 200 * np.sin(frame_count * 0.1))
|
||
center_y = int(240 + 100 * np.cos(frame_count * 0.1))
|
||
cv2.circle(frame, (center_x, center_y), 30, (255, 0, 0), -1)
|
||
|
||
# 添加变化的矩形
|
||
rect_size = int(50 + 30 * np.sin(frame_count * 0.05))
|
||
cv2.rectangle(frame, (500, 200), (500 + rect_size, 200 + rect_size), (0, 0, 255), -1)
|
||
|
||
return frame
|
||
|
||
def generate_video_frames(self):
|
||
"""生成视频监控帧"""
|
||
t0 = time.time()
|
||
frame_count = 0
|
||
error_count = 0
|
||
use_test_mode = False
|
||
first_frame_sent = False
|
||
last_frame_time = time.time()
|
||
width,height=self.MAX_FRAME_SIZE
|
||
# logger.info(f'[TIMING] 进入generate_video_frames - {datetime.now().strftime("%H:%M:%S.%f")[:-3]}')
|
||
|
||
try:
|
||
t_open_start = time.time()
|
||
# logger.info(f'[TIMING] 开始打开VideoCapture({self.device_index})')
|
||
|
||
# 依次尝试不同后端，选择最快可用的（Windows推荐优先MSMF，然后DSHOW）
|
||
backends = [
|
||
(cv2.CAP_MSMF, 'MSMF'),
|
||
(cv2.CAP_DSHOW, 'DSHOW'),
|
||
(cv2.CAP_ANY, 'ANY')
|
||
]
|
||
cap = None
|
||
selected_backend = None
|
||
for api, name in backends:
|
||
try:
|
||
t_try = time.time()
|
||
logger.info(f'[TIMING] 尝试后端: {name}')
|
||
tmp = cv2.VideoCapture(self.device_index, api)
|
||
create_ms = (time.time() - t_try) * 1000
|
||
# logger.info(f'[TIMING] 后端{name} 创建VideoCapture耗时: {create_ms:.2f}ms')
|
||
if tmp.isOpened():
|
||
cap = tmp
|
||
selected_backend = name
|
||
# logger.info(f'[TIMING] 选择后端{name} 打开成功')
|
||
break
|
||
else:
|
||
tmp.release()
|
||
logger.info(f'[TIMING] 后端{name} 打开失败')
|
||
except Exception as e:
|
||
logger.warning(f'[TIMING] 后端{name} 异常: {e}')
|
||
|
||
# logger.info(f'[TIMING] VideoCapture对象创建耗时: {(time.time()-t_open_start)*1000:.2f}ms（选用后端: {selected_backend}）')
|
||
|
||
t_open_check = time.time()
|
||
if cap is None or not cap.isOpened():
|
||
logger.warning(f'[TIMING] 无法打开视频监控流: {self.device_index}，切换到测试模式（isOpened检查耗时: {(time.time()-t_open_check)*1000:.2f}ms）')
|
||
use_test_mode = True
|
||
if self.socketio:
|
||
self.socketio.emit('video_status', {'status': 'started', 'message': '使用测试视频源'})
|
||
else:
|
||
# 设置相机属性（逐项记录耗时与是否成功）
|
||
total_set_start = time.time()
|
||
|
||
# 先设置编码
|
||
t_prop = time.time()
|
||
ok_fourcc = cap.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter_fourcc('M','J','P','G'))
|
||
# logger.info(f'[TIMING] 设置FOURCC=MJPG 返回: {ok_fourcc} 耗时: {(time.time()-t_prop)*1000:.2f}ms')
|
||
|
||
# 再设置分辨率
|
||
t_prop = time.time()
|
||
ok_w = cap.set(cv2.CAP_PROP_FRAME_WIDTH, width)
|
||
# logger.info(f'[TIMING] 设置宽度={width} 返回: {ok_w} 耗时: {(time.time()-t_prop)*1000:.2f}ms')
|
||
|
||
t_prop = time.time()
|
||
ok_h = cap.set(cv2.CAP_PROP_FRAME_HEIGHT, height)
|
||
# logger.info(f'[TIMING] 设置高度={height} 返回: {ok_h} 耗时: {(time.time()-t_prop)*1000:.2f}ms')
|
||
|
||
# 最后设置帧率和缓冲
|
||
t_prop = time.time()
|
||
ok_fps = cap.set(cv2.CAP_PROP_FPS, 30) # 先用30fps更兼容
|
||
# logger.info(f'[TIMING] 设置FPS=30 返回: {ok_fps} 耗时: {(time.time()-t_prop)*1000:.2f}ms')
|
||
|
||
t_prop = time.time()
|
||
ok_buf = cap.set(cv2.CAP_PROP_BUFFERSIZE, 1) # 使用极小缓冲区（不支持的后端会忽略）
|
||
# logger.info(f'[TIMING] 设置BUFFERSIZE=1 返回: {ok_buf} 耗时: {(time.time()-t_prop)*1000:.2f}ms')
|
||
|
||
# logger.info(f'[TIMING] 设置相机属性耗时: {(time.time()-total_set_start)*1000:.2f}ms')
|
||
|
||
# 拉一帧，触发真实初始化
|
||
t_first_read = time.time()
|
||
warmup_ok, _ = cap.read()
|
||
# logger.info(f'[TIMING] 首帧读取耗时: {(time.time()-t_first_read)*1000:.2f}ms, 成功: {warmup_ok}')
|
||
if self.socketio:
|
||
self.socketio.emit('video_status', {'status': 'started', 'message': f'使用视频监控视频源（{selected_backend or "unknown"}）'})
|
||
|
||
self.video_running = True
|
||
# logger.info(f'[TIMING] generate_video_frames初始化总耗时: {(time.time()-t0)*1000:.2f}ms')
|
||
|
||
# # 启动帧编码工作线程
|
||
# encoding_thread = threading.Thread(target=self.frame_encoding_worker)
|
||
# encoding_thread.daemon = True
|
||
# encoding_thread.start()
|
||
|
||
while self.video_running:
|
||
if use_test_mode:
|
||
# 使用测试模式生成帧
|
||
frame = self.generate_test_frame(frame_count)
|
||
ret = True
|
||
else:
|
||
# 使用视频监控流，添加帧跳过机制减少延迟
|
||
ret, frame = cap.read()
|
||
if not ret:
|
||
error_count += 1
|
||
logger.debug(f'视频监控读取帧失败（第{error_count}次），尝试重连...')
|
||
if 'cap' in locals():
|
||
cap.release()
|
||
|
||
if error_count > 5:
|
||
logger.debug('视频监控连接失败次数过多，切换到测试模式')
|
||
use_test_mode = True
|
||
if self.socketio:
|
||
self.socketio.emit('video_status', {'status': 'switched', 'message': '已切换到测试视频源'})
|
||
continue
|
||
|
||
# 立即重连，不等待
|
||
cap = cv2.VideoCapture(self.device_index)
|
||
if cap.isOpened():
|
||
# 重连时应用相同的激进实时设置
|
||
cap.set(cv2.CAP_PROP_BUFFERSIZE, 0)
|
||
cap.set(cv2.CAP_PROP_FPS, 60)
|
||
cap.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter_fourcc('M', 'J', 'P', 'G'))
|
||
cap.set(cv2.CAP_PROP_FRAME_WIDTH, width)
|
||
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, height)
|
||
continue
|
||
|
||
error_count = 0 # 重置错误计数
|
||
|
||
# 内存优化的帧跳过策略
|
||
# 减少跳帧数量，避免过度内存使用
|
||
skip_count = 0
|
||
while skip_count < 3: # 减少到最多跳过3帧
|
||
temp_ret, temp_frame = cap.read()
|
||
if temp_ret:
|
||
# 立即释放之前的帧
|
||
if 'frame' in locals():
|
||
del frame
|
||
frame = temp_frame
|
||
skip_count += 1
|
||
else:
|
||
break
|
||
|
||
# 降低帧率以减少内存压力
|
||
current_time = time.time()
|
||
if current_time - last_frame_time < 1/20: # 降低到20fps最大频率
|
||
continue
|
||
last_frame_time = current_time
|
||
|
||
frame_count += 1
|
||
|
||
# 实现帧跳过以减少内存和网络压力
|
||
self.frame_skip_counter += 1
|
||
|
||
if self.frame_skip_counter % (self.FRAME_SKIP_RATIO + 1) != 0:
|
||
# 跳过此帧，立即释放内存
|
||
del frame
|
||
continue
|
||
|
||
try:
|
||
# 直接在主循环中执行帧处理逻辑（替代异步工作线程）
|
||
|
||
# 内存检查
|
||
self.memory_check_counter += 1
|
||
if self.memory_check_counter % 30 == 0:
|
||
memory_usage = psutil.virtual_memory().percent
|
||
if memory_usage > 85:
|
||
logger.warning(f'内存使用率过高: {memory_usage}%，跳过当前帧')
|
||
del frame
|
||
continue
|
||
|
||
# 按照MAX_FRAME_SIZE裁剪帧
|
||
cropped_frame = frame.copy()
|
||
width, height = self.MAX_FRAME_SIZE
|
||
if cropped_frame.shape[1] > width or cropped_frame.shape[0] > height:
|
||
# 计算裁剪区域（居中裁剪）
|
||
start_x = max(0, (cropped_frame.shape[1] - width) // 2)
|
||
start_y = max(0, (cropped_frame.shape[0] - height) // 2)
|
||
end_x = min(cropped_frame.shape[1], start_x + width)
|
||
end_y = min(cropped_frame.shape[0], start_y + height)
|
||
cropped_frame = cropped_frame[start_y:end_y, start_x:end_x]
|
||
|
||
# 保存帧到全局缓存
|
||
if self.device_manager:
|
||
self.device_manager._save_frame_to_cache(cropped_frame, 'camera')
|
||
# 每1000帧记录一次缓存保存状态
|
||
# if frame_count % 1000 == 0:
|
||
# logger.debug(f"视频推流已保存第 {frame_count} 帧到全局缓存")
|
||
else:
|
||
logger.warning("VideoStreamManager未关联DeviceManager，无法保存帧到缓存")
|
||
|
||
# JPEG编码和socketio发送
|
||
try:
|
||
# 使用较低的JPEG质量以节省内存
|
||
encode_param = [int(cv2.IMWRITE_JPEG_QUALITY), 70]
|
||
result, buffer = cv2.imencode('.jpg', cropped_frame, encode_param)
|
||
|
||
if result:
|
||
# 转换为base64字符串
|
||
jpg_as_text = base64.b64encode(buffer).decode('utf-8')
|
||
|
||
# 立即释放buffer内存
|
||
del buffer
|
||
|
||
# 发送数据
|
||
if self.socketio:
|
||
self.socketio.emit('video_frame', {
|
||
'image': jpg_as_text,
|
||
'frame_id': frame_count,
|
||
'timestamp': time.time()
|
||
})
|
||
if not first_frame_sent:
|
||
first_frame_sent = True
|
||
# logger.info(f'[TIMING] 第一帧已发送 - 总耗时: {(time.time()-t0)*1000:.2f}ms')
|
||
|
||
# 立即释放base64字符串
|
||
del jpg_as_text
|
||
|
||
except Exception as e:
|
||
logger.error(f'帧编码失败: {e}')
|
||
|
||
# 立即释放帧内存
|
||
del frame
|
||
del cropped_frame
|
||
|
||
if frame_count % 60 == 0: # 每60帧记录一次
|
||
|
||
# 定期强制垃圾回收
|
||
gc.collect()
|
||
|
||
except Exception as e:
|
||
logger.error(f'帧队列处理失败: {e}')
|
||
|
||
except Exception as e:
|
||
# logger.error(f'监控视频推流异常: {e}')
|
||
if self.socketio:
|
||
self.socketio.emit('video_status', {'status': 'error', 'message': f'推流异常: {str(e)}'})
|
||
finally:
|
||
if 'cap' in locals():
|
||
cap.release()
|
||
self.video_running = False
|
||
|
||
|
||
def start_video_stream(self):
|
||
"""启动视频监控推流"""
|
||
try:
|
||
if self.video_thread and self.video_thread.is_alive():
|
||
logger.warning('视频监控线程已在运行')
|
||
return {'status': 'already_running', 'message': '视频监控已在运行'}
|
||
|
||
t_start = time.time()
|
||
logger.info(f'[TIMING] 准备启动视频监控线程，设备号: {self.device_index} - {datetime.now().strftime("%H:%M:%S.%f")[:-3]}')
|
||
self.video_thread = threading.Thread(target=self.generate_video_frames, name='VideoStreamThread')
|
||
self.video_thread.daemon = True
|
||
self.video_thread.start()
|
||
self.video_running = True
|
||
# logger.info(f'[TIMING] 视频监控线程创建完成，耗时: {(time.time()-t_start)*1000:.2f}ms')
|
||
|
||
return {'status': 'started', 'message': '视频监控线程已启动'}
|
||
|
||
except Exception as e:
|
||
logger.error(f'[TIMING] 视频监控线程启动失败: {e}')
|
||
return {'status': 'error', 'message': f'视频监控线程启动失败: {str(e)}'}
|
||
|
||
def stop_video_stream(self):
|
||
"""停止视频监控推流"""
|
||
try:
|
||
self.video_running = False
|
||
logger.info('视频监控推流已停止')
|
||
return {'status': 'stopped', 'message': '视频监控推流已停止'}
|
||
|
||
except Exception as e:
|
||
logger.error(f'停止视频监控推流失败: {e}')
|
||
return {'status': 'error', 'message': f'停止失败: {str(e)}'}
|
||
|
||
def is_streaming(self):
|
||
"""检查是否正在推流"""
|
||
return self.video_running
|
||
|
||
def get_stream_status(self):
|
||
"""获取推流状态"""
|
||
return {
|
||
'running': self.video_running,
|
||
'device_index': self.device_index,
|
||
'thread_alive': self.video_thread.is_alive() if self.video_thread else False
|
||
}
|
||
|
||
|
||
def _collect_head_pose_data(self) -> Dict[str, Any]:
|
||
"""采集头部姿态数据（从IMU设备获取）"""
|
||
try:
|
||
# 模拟IMU头部姿态数据
|
||
head_pose = {
|
||
'roll': np.random.uniform(-30, 30),
|
||
'pitch': np.random.uniform(-30, 30),
|
||
'yaw': np.random.uniform(-180, 180),
|
||
'acceleration': {
|
||
'x': np.random.uniform(-2, 2),
|
||
'y': np.random.uniform(-2, 2),
|
||
'z': np.random.uniform(8, 12)
|
||
},
|
||
'gyroscope': {
|
||
'x': np.random.uniform(-5, 5),
|
||
'y': np.random.uniform(-5, 5),
|
||
'z': np.random.uniform(-5, 5)
|
||
},
|
||
'timestamp': datetime.now().isoformat()
|
||
}
|
||
return head_pose
|
||
except Exception as e:
|
||
logger.error(f'头部姿态数据采集失败: {e}')
|
||
return None
|
||
|
||
def _collect_body_pose_data(self) -> Dict[str, Any]:
|
||
"""采集身体姿态数据（从FemtoBolt深度相机获取）"""
|
||
try:
|
||
# 模拟身体姿态关键点数据
|
||
body_pose = {
|
||
'keypoints': {
|
||
'head': {'x': 320, 'y': 100, 'confidence': 0.95},
|
||
'neck': {'x': 320, 'y': 150, 'confidence': 0.92},
|
||
'left_shoulder': {'x': 280, 'y': 180, 'confidence': 0.88},
|
||
'right_shoulder': {'x': 360, 'y': 180, 'confidence': 0.90},
|
||
'left_elbow': {'x': 250, 'y': 220, 'confidence': 0.85},
|
||
'right_elbow': {'x': 390, 'y': 220, 'confidence': 0.87},
|
||
'left_wrist': {'x': 220, 'y': 260, 'confidence': 0.82},
|
||
'right_wrist': {'x': 420, 'y': 260, 'confidence': 0.84},
|
||
'spine': {'x': 320, 'y': 250, 'confidence': 0.93},
|
||
'left_hip': {'x': 300, 'y': 350, 'confidence': 0.89},
|
||
'right_hip': {'x': 340, 'y': 350, 'confidence': 0.91},
|
||
'left_knee': {'x': 290, 'y': 450, 'confidence': 0.86},
|
||
'right_knee': {'x': 350, 'y': 450, 'confidence': 0.88},
|
||
'left_ankle': {'x': 285, 'y': 550, 'confidence': 0.83},
|
||
'right_ankle': {'x': 355, 'y': 550, 'confidence': 0.85}
|
||
},
|
||
'balance_score': np.random.uniform(0.6, 1.0),
|
||
'center_of_mass': {'x': 320, 'y': 350},
|
||
'timestamp': datetime.now().isoformat()
|
||
}
|
||
return body_pose
|
||
except Exception as e:
|
||
logger.error(f'身体姿态数据采集失败: {e}')
|
||
return None
|
||
|
||
def _capture_body_image(self, data_dir: Path, device_manager) -> Optional[str]:
|
||
"""采集身体视频截图（从FemtoBolt深度相机获取）"""
|
||
try:
|
||
image = None
|
||
|
||
# 检查是否有device_manager实例且FemtoBolt深度相机可用
|
||
if (device_manager is not None and
|
||
FEMTOBOLT_AVAILABLE and
|
||
hasattr(device_manager, 'femtobolt_camera') and
|
||
device_manager.femtobolt_camera is not None):
|
||
|
||
# 从FemtoBolt深度相机获取真实图像
|
||
logger.info('正在从FemtoBolt深度相机获取身体图像...')
|
||
capture = device_manager.femtobolt_camera.update()
|
||
|
||
if capture is not None:
|
||
# 获取深度图像
|
||
ret, depth_image = capture.get_depth_image()
|
||
if ret and depth_image is not None:
|
||
# 读取config.ini中的深度范围配置
|
||
import configparser
|
||
config = configparser.ConfigParser()
|
||
config.read('config.ini')
|
||
try:
|
||
depth_range_min = int(config.get('DEFAULT', 'femtobolt_depth_range_min', fallback='1400'))
|
||
depth_range_max = int(config.get('DEFAULT', 'femtobolt_depth_range_max', fallback='1900'))
|
||
except Exception:
|
||
depth_range_min = None
|
||
depth_range_max = None
|
||
|
||
# 优化深度图彩色映射，范围外用黑色，区间内用Jet模型从蓝色到黄色到红色渐变
|
||
if depth_range_min is not None and depth_range_max is not None:
|
||
# 归一化深度值到0-255范围
|
||
depth_normalized = np.clip(depth_image, depth_range_min, depth_range_max)
|
||
depth_normalized = ((depth_normalized - depth_range_min) / (depth_range_max - depth_range_min) * 255).astype(np.uint8)
|
||
|
||
# 应用OpenCV的COLORMAP_JET进行伪彩色映射
|
||
depth_colored = cv2.applyColorMap(depth_normalized, cv2.COLORMAP_JET)
|
||
|
||
# 范围外用黑色
|
||
mask_outside = (depth_image < depth_range_min) | (depth_image > depth_range_max)
|
||
depth_colored[mask_outside] = [0, 0, 0] # BGR黑色
|
||
else:
|
||
# 如果没有配置，使用默认伪彩色映射
|
||
depth_colored = cv2.convertScaleAbs(depth_image, alpha=0.03)
|
||
depth_colored = cv2.applyColorMap(depth_colored, cv2.COLORMAP_JET)
|
||
|
||
# 转换颜色格式（如果需要）
|
||
if len(depth_colored.shape) == 3 and depth_colored.shape[2] == 4:
|
||
depth_colored = cv2.cvtColor(depth_colored, cv2.COLOR_BGRA2BGR)
|
||
elif len(depth_colored.shape) == 3 and depth_colored.shape[2] == 3:
|
||
pass
|
||
|
||
# 预处理：裁剪成宽460，高819，保持高度不裁剪，宽度从中间裁剪
|
||
height, width = depth_colored.shape[:2]
|
||
target_width = 460
|
||
target_height = 819
|
||
|
||
# 计算宽度裁剪起点
|
||
if width > target_width:
|
||
left = (width - target_width) // 2
|
||
right = left + target_width
|
||
cropped_image = depth_colored[:, left:right]
|
||
else:
|
||
cropped_image = depth_colored
|
||
|
||
# 如果高度不足target_height，进行上下填充黑边
|
||
cropped_height = cropped_image.shape[0]
|
||
if cropped_height < target_height:
|
||
pad_top = (target_height - cropped_height) // 2
|
||
pad_bottom = target_height - cropped_height - pad_top
|
||
cropped_image = cv2.copyMakeBorder(cropped_image, pad_top, pad_bottom, 0, 0, cv2.BORDER_CONSTANT, value=[0,0,0])
|
||
elif cropped_height > target_height:
|
||
# 如果高度超过target_height，裁剪高度中间部分
|
||
top = (cropped_height - target_height) // 2
|
||
cropped_image = cropped_image[top:top+target_height, :]
|
||
|
||
# 最终调整大小，保持宽460，高819
|
||
image = cv2.resize(cropped_image, (target_width, target_height))
|
||
|
||
logger.info(f'成功获取FemtoBolt深度图像，尺寸: {image.shape}')
|
||
else:
|
||
logger.warning('无法从FemtoBolt获取深度图像，使用模拟图像')
|
||
# 使用模拟图像作为备用
|
||
image = np.zeros((819, 460, 3), dtype=np.uint8)
|
||
cv2.rectangle(image, (50, 50), (410, 769), (0, 255, 0), 2)
|
||
cv2.putText(image, 'FemtoBolt Unavailable', (75, 400), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)
|
||
else:
|
||
logger.warning('FemtoBolt capture为None，使用模拟图像')
|
||
# 使用模拟图像作为备用
|
||
image = np.zeros((819, 460, 3), dtype=np.uint8)
|
||
cv2.rectangle(image, (50, 50), (410, 769), (0, 255, 0), 2)
|
||
cv2.putText(image, 'Capture Failed', (120, 400), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)
|
||
else:
|
||
logger.warning('FemtoBolt深度相机不可用，使用模拟图像')
|
||
# 使用模拟图像作为备用
|
||
image = np.zeros((819, 460, 3), dtype=np.uint8)
|
||
cv2.rectangle(image, (50, 50), (410, 769), (0, 255, 0), 2)
|
||
cv2.putText(image, 'Camera Not Available', (60, 400), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)
|
||
|
||
# 保存图片
|
||
image_path = data_dir / 'body_image.jpg'
|
||
cv2.imwrite(str(image_path), image)
|
||
logger.info(f'身体图像已保存到: {image_path}')
|
||
|
||
return image_path
|
||
except Exception as e:
|
||
logger.error(f'身体截图保存失败: {e}')
|
||
return None
|
||
|
||
def _collect_foot_pressure_data(self) -> Dict[str, Any]:
|
||
"""采集足部压力数据（从压力传感器获取）"""
|
||
try:
|
||
# 模拟压力传感器数据
|
||
pressure_data = {
|
||
'left_foot': {
|
||
'heel': np.random.uniform(0, 100),
|
||
'arch': np.random.uniform(0, 50),
|
||
'ball': np.random.uniform(0, 80),
|
||
'toes': np.random.uniform(0, 60),
|
||
'total_pressure': 0
|
||
},
|
||
'right_foot': {
|
||
'heel': np.random.uniform(0, 100),
|
||
'arch': np.random.uniform(0, 50),
|
||
'ball': np.random.uniform(0, 80),
|
||
'toes': np.random.uniform(0, 60),
|
||
'total_pressure': 0
|
||
},
|
||
'balance_ratio': 0,
|
||
'timestamp': datetime.now().isoformat()
|
||
}
|
||
|
||
# 计算总压力和平衡比例
|
||
left_total = sum(pressure_data['left_foot'][key] for key in ['heel', 'arch', 'ball', 'toes'])
|
||
right_total = sum(pressure_data['right_foot'][key] for key in ['heel', 'arch', 'ball', 'toes'])
|
||
|
||
pressure_data['left_foot']['total_pressure'] = left_total
|
||
pressure_data['right_foot']['total_pressure'] = right_total
|
||
|
||
if left_total + right_total > 0:
|
||
pressure_data['balance_ratio'] = left_total / (left_total + right_total)
|
||
|
||
return pressure_data
|
||
except Exception as e:
|
||
logger.error(f'足部压力数据采集失败: {e}')
|
||
return None
|
||
|
||
def _capture_foot_image(self, data_dir: Path, device_manager=None) -> Optional[str]:
|
||
"""采集足部监测视频截图（从全局缓存获取）"""
|
||
try:
|
||
image = None
|
||
|
||
# 直接使用self获取缓存帧
|
||
logger.info('正在从全局缓存获取最新图像...')
|
||
|
||
# 从全局缓存获取最新帧
|
||
frame, frame_timestamp = device_manager._get_latest_frame_from_cache('camera')
|
||
#frame, frame_count = self.frame_queue.get(timeout=1)
|
||
if frame is not None:
|
||
# 使用缓存中的图像
|
||
image = frame.copy() # 复制帧数据避免引用问题
|
||
current_time = time.time()
|
||
frame_age = current_time - frame_timestamp if frame_timestamp else 0
|
||
logger.info(f'成功获取缓存图像，尺寸: {image.shape}，帧龄: {frame_age:.2f}秒')
|
||
else:
|
||
logger.warning('缓存中无可用图像，使用模拟图像')
|
||
image = np.zeros((480, 640, 3), dtype=np.uint8)
|
||
cv2.rectangle(image, (50, 50), (590, 430), (0, 255, 0), 2)
|
||
cv2.putText(image, 'No Cached Frame', (120, 250), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)
|
||
|
||
# 保存图片
|
||
image_path = data_dir / 'foot_image.jpg'
|
||
cv2.imwrite(str(image_path), image)
|
||
logger.info(f'足部图像已保存到: {image_path}')
|
||
|
||
return image_path
|
||
except Exception as e:
|
||
logger.error(f'足部截图保存失败: {e}')
|
||
# 即使出错也要保存一个模拟图像
|
||
try:
|
||
image = np.zeros((480, 640, 3), dtype=np.uint8)
|
||
cv2.rectangle(image, (50, 50), (590, 430), (255, 0, 0), 2)
|
||
cv2.putText(image, 'Error Occurred', (180, 250), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)
|
||
image_path = data_dir / 'foot_image.jpg'
|
||
cv2.imwrite(str(image_path), image)
|
||
return image_path
|
||
except Exception:
|
||
return None
|
||
|
||
def _generate_foot_pressure_image(self, data_dir: Path) -> Optional[str]:
|
||
"""生成足底压力数据图（从压力传感器数据生成）"""
|
||
try:
|
||
# 创建压力分布热力图
|
||
fig_width, fig_height = 400, 600
|
||
pressure_map = np.zeros((fig_height, fig_width, 3), dtype=np.uint8)
|
||
|
||
# 模拟左脚压力分布
|
||
left_foot_x = fig_width // 4
|
||
left_foot_y = fig_height // 2
|
||
|
||
# 模拟右脚压力分布
|
||
right_foot_x = 3 * fig_width // 4
|
||
right_foot_y = fig_height // 2
|
||
|
||
# 绘制压力点（用不同颜色表示压力大小）
|
||
for i in range(20):
|
||
x = np.random.randint(left_foot_x - 50, left_foot_x + 50)
|
||
y = np.random.randint(left_foot_y - 100, left_foot_y + 100)
|
||
pressure = np.random.randint(0, 255)
|
||
cv2.circle(pressure_map, (x, y), 5, (0, pressure, 255 - pressure), -1)
|
||
|
||
x = np.random.randint(right_foot_x - 50, right_foot_x + 50)
|
||
y = np.random.randint(right_foot_y - 100, right_foot_y + 100)
|
||
pressure = np.random.randint(0, 255)
|
||
cv2.circle(pressure_map, (x, y), 5, (0, pressure, 255 - pressure), -1)
|
||
|
||
# 保存图片
|
||
image_path = data_dir / 'foot_data_image.jpg'
|
||
cv2.imwrite(str(image_path), pressure_map)
|
||
|
||
return str(image_path.relative_to(Path.cwd()))
|
||
except Exception as e:
|
||
logger.error(f'足底压力数据图生成失败: {e}')
|
||
return None
|
||
|
||
def _save_screen_image(self, data_dir: Path, screen_image_base64: str) -> Optional[str]:
|
||
"""保存屏幕录制截图（从前端传入的base64数据）"""
|
||
try:
|
||
# 解码base64数据
|
||
if screen_image_base64.startswith('data:image/'):
|
||
# 移除data:image/jpeg;base64,前缀
|
||
base64_data = screen_image_base64.split(',')[1]
|
||
else:
|
||
base64_data = screen_image_base64
|
||
|
||
# 解码并保存图片
|
||
image_data = base64.b64decode(base64_data)
|
||
image_path = data_dir / 'screen_image.jpg'
|
||
|
||
with open(image_path, 'wb') as f:
|
||
f.write(image_data)
|
||
|
||
return str(image_path.relative_to(Path.cwd()))
|
||
except Exception as e:
|
||
logger.error(f'屏幕截图保存失败: {e}')
|
||
return None |