140 lines
5.2 KiB
Python
140 lines
5.2 KiB
Python
from core.ocr import load_model as ocrLoadModel, detect as ocrDetect
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from core.face import load_model as faceLoadModel, detect as faceDetect
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from core.yolo import load_model as yoloLoadModel, detect as yoloDetect
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from concurrent.futures import ThreadPoolExecutor, Future
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import threading
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import cv2
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import numpy as np
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# -------------------------- 核心配置参数 --------------------------
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MAX_WORKERS = 6 # 线程池最大线程数
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DETECTION_ORDER = ["yolo", "face", "ocr"] # 检测执行顺序
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TIMEOUT = 30 # 检测超时时间(秒) 【确保此常量可被外部导入】
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# -------------------------- 全局状态管理 --------------------------
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_executor = None # 线程池实例
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_model_loaded = False # 模型加载状态标记
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_model_lock = threading.Lock() # 模型加载线程锁
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_executor_lock = threading.Lock() # 线程池初始化锁
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_task_counter = 0 # 任务计数器
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_task_counter_lock = threading.Lock() # 任务计数锁
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# -------------------------- 工具函数 --------------------------
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def _get_next_task_id():
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"""获取唯一任务ID、用于日志追踪"""
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global _task_counter
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with _task_counter_lock:
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_task_counter += 1
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return _task_counter
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# -------------------------- 模型加载 --------------------------
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def load_model():
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"""加载所有检测模型并初始化线程池（仅执行一次）"""
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global _model_loaded
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if not _model_loaded:
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with _model_lock:
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if not _model_loaded:
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print("=== 开始加载检测模型 ===")
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# 按顺序加载模型
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print("加载YOLO模型...")
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yoloLoadModel()
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print("加载人脸检测模型...")
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faceLoadModel()
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print("加载OCR模型...")
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ocrLoadModel()
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_model_loaded = True
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print("=== 所有模型加载完成 ===")
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# 初始化线程池
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_init_thread_pool()
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# -------------------------- 线程池管理 --------------------------
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def _init_thread_pool():
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"""初始化线程池（仅内部调用）"""
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global _executor
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with _executor_lock:
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if _executor is None:
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_executor = ThreadPoolExecutor(
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max_workers=MAX_WORKERS,
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thread_name_prefix="DetectionThread"
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)
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print(f"=== 线程池初始化完成、最大线程数: {MAX_WORKERS} ===")
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def shutdown():
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"""关闭线程池、释放资源"""
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global _executor
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with _executor_lock:
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if _executor is not None:
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_executor.shutdown(wait=True)
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_executor = None
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print("=== 线程池已安全关闭 ===")
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# -------------------------- 检测逻辑实现 --------------------------
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def _detect_in_thread(frame: np.ndarray, task_id: int) -> tuple:
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"""在子线程中执行检测逻辑（返回4元素tuple：检测是否成功、结果数据、检测器类型、任务ID）"""
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thread_name = threading.current_thread().name
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print(f"任务[{task_id}] 开始执行、线程: {thread_name}")
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try:
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# 按照配置顺序执行检测
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for detector in DETECTION_ORDER:
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if detector == "yolo":
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success, result = yoloDetect(frame)
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elif detector == "face":
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success, result = faceDetect(frame)
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elif detector == "ocr":
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success, result = ocrDetect(frame)
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else:
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success, result = False, None
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print(f"任务[{task_id}] {detector}检测状态: {'成功' if success else '未检测到内容'}")
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if success:
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print(f"任务[{task_id}] 完成检测、使用检测器: {detector}")
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return (success, result, detector, task_id) # 4元素tuple
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# 所有检测器均未检测到结果
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print(f"任务[{task_id}] 所有检测器均未检测到有效内容")
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return (False, "未检测到任何有效内容", "none", task_id) # 4元素tuple
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except Exception as e:
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print(f"任务[{task_id}] 检测过程发生错误: {str(e)}")
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return (False, f"检测错误: {str(e)}", "error", task_id) # 4元素tuple
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# -------------------------- 外部调用接口 --------------------------
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def detect(frame: np.ndarray) -> Future:
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"""
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提交检测任务到线程池（返回Future对象，需调用result()获取4元素结果）
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参数:
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frame: 待检测图像(ndarray格式、cv2.imdecode生成)
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返回:
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Future对象、result()返回tuple: (success, data, detector_type, task_id)
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success: 布尔值，表示是否检测到有效内容
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data: 检测结果数据（成功时为具体结果，失败时为错误信息）
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detector_type: 使用的检测器类型（"yolo"/"face"/"ocr"/"none"/"error"）
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task_id: 任务唯一标识
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"""
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# 确保模型已加载
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if not _model_loaded:
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print("警告: 模型尚未加载、将自动加载")
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load_model()
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# 生成任务ID
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task_id = _get_next_task_id()
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# 提交任务到线程池（返回Future）
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future = _executor.submit(_detect_in_thread, frame, task_id)
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print(f"任务[{task_id}]: 已提交到线程池")
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return future
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