import os import numpy as np import cv2 import gc import time import threading from PIL import Image from insightface.app import FaceAnalysis # 导入获取人脸信息的服务 from service.face_service import get_all_face_name_with_eigenvalue # 用于检查GPU状态 try: import pynvml pynvml.nvmlInit() _nvml_available = True except ImportError: print("警告: pynvml库未安装、无法检测GPU状态、将默认使用0号GPU") _nvml_available = False # 全局变量 _face_app = None _known_faces_embeddings = {} # 存储姓名到特征值的映射 _known_faces_names = [] # 存储所有已知姓名 _using_gpu = False # 标记是否使用GPU _used_gpu_id = -1 # 记录当前使用的GPU ID # 资源管理变量 _ref_count = 0 _last_used_time = 0 _lock = threading.Lock() _release_timeout = 8 # 5秒无使用则释放 _is_releasing = False # 标记是否正在释放 # 调试用计数器 _debug_counter = { "created": 0, "released": 0, "detected": 0 } def check_gpu_availability(gpu_id, threshold=0.7): """检查指定GPU是否可用（内存使用率低于阈值）""" if not _nvml_available: return True # 无法检测时默认认为可用 try: handle = pynvml.nvmlDeviceGetHandleByIndex(gpu_id) mem_info = pynvml.nvmlDeviceGetMemoryInfo(handle) usage = mem_info.used / mem_info.total # 内存使用率低于阈值则认为可用 return usage < threshold except Exception as e: print(f"检查GPU {gpu_id} 状态时出错: {e}") return False def select_best_gpu(preferred_gpus=[0, 1]): """选择最佳可用GPU、严格按照首选列表顺序检查、优先使用0号GPU""" # 首先检查首选GPU列表 for gpu_id in preferred_gpus: try: # 检查GPU是否存在 if _nvml_available: pynvml.nvmlDeviceGetHandleByIndex(gpu_id) # 检查GPU是否可用 if check_gpu_availability(gpu_id): print(f"GPU {gpu_id} 可用、将使用该GPU") return gpu_id else: if gpu_id == 0: print(f"GPU 0 内存使用率过高（繁忙）、尝试切换到其他GPU") except Exception as e: print(f"GPU {gpu_id} 不存在或无法访问: {e}") continue # 如果所有首选GPU都不可用、返回-1表示使用CPU print("所有指定的GPU都不可用、将使用CPU进行计算") return -1 def _release_engine(): """释放人脸识别引擎资源""" global _face_app, _is_releasing, _known_faces_embeddings, _known_faces_names if not _face_app or _is_releasing: return try: _is_releasing = True # 释放InsightFace资源 if hasattr(_face_app, 'model'): # 清除模型资源 _face_app.model = None _face_app = None # 清空人脸数据 _known_faces_embeddings.clear() _known_faces_names.clear() _debug_counter["released"] += 1 print(f"Face recognition engine released. Stats: {_debug_counter}") # 清理GPU缓存 gc.collect() try: import torch if torch.cuda.is_available(): torch.cuda.empty_cache() torch.cuda.ipc_collect() except ImportError: pass try: import tensorflow as tf tf.keras.backend.clear_session() except ImportError: pass finally: _is_releasing = False def _monitor_thread(): """监控线程、检查并释放超时未使用的资源""" global _ref_count, _last_used_time, _face_app while True: time.sleep(5) # 每5秒检查一次 with _lock: # 只有当引擎存在、没有引用且超时、才释放 if _face_app and _ref_count == 0 and not _is_releasing: elapsed = time.time() - _last_used_time if elapsed > _release_timeout: print(f"Idle timeout ({elapsed:.1f}s > {_release_timeout}s), releasing face engine") _release_engine() def load_model(prefer_gpu=True, preferred_gpus=[0, 1]): """加载人脸识别模型及已知人脸特征库、默认优先使用0号GPU""" global _face_app, _known_faces_embeddings, _known_faces_names, _using_gpu, _used_gpu_id # 确保监控线程只启动一次 if not any(t.name == "FaceMonitor" for t in threading.enumerate()): threading.Thread(target=_monitor_thread, daemon=True, name="FaceMonitor").start() print("Face monitor thread started") # 如果正在释放中、等待释放完成 while _is_releasing: time.sleep(0.1) # 如果已经初始化、直接返回 if _face_app: return True # 初始化InsightFace模型 try: # 初始化InsightFace print("正在初始化InsightFace人脸识别引擎...") _face_app = FaceAnalysis(name='buffalo_l', root='~/.insightface') # 选择合适的GPU、默认优先使用0号 ctx_id = 0 if prefer_gpu: ctx_id = select_best_gpu(preferred_gpus) _using_gpu = ctx_id != -1 _used_gpu_id = ctx_id if _using_gpu else -1 if _using_gpu: print(f"成功初始化、使用GPU {ctx_id} 进行计算") else: print("成功初始化、使用CPU进行计算") # 准备模型 _face_app.prepare(ctx_id=ctx_id, det_size=(640, 640)) print("InsightFace人脸识别引擎初始化成功。") _debug_counter["created"] += 1 print(f"Face engine initialized. Stats: {_debug_counter}") except Exception as e: print(f"初始化失败: {e}") return False # 从服务获取所有人脸姓名和特征值 try: face_data = get_all_face_name_with_eigenvalue() # 处理获取到的人脸数据 for person_name, eigenvalue_data in face_data.items(): # 处理特征值数据 - 兼容数组和字符串两种格式 if isinstance(eigenvalue_data, np.ndarray): # 如果已经是numpy数组、直接使用 eigenvalue = eigenvalue_data.astype(np.float32) elif isinstance(eigenvalue_data, str): # 清理字符串: 移除方括号、换行符和多余空格 cleaned = eigenvalue_data.replace('[', '').replace(']', '').replace('\n', '').strip() # 按空格或逗号分割（处理可能的不同分隔符） values = [v for v in cleaned.split() if v] # 转换为数组 eigenvalue = np.array(list(map(float, values)), dtype=np.float32) else: # 不支持的类型 print(f"Unsupported eigenvalue type for {person_name}") continue # 归一化处理 norm = np.linalg.norm(eigenvalue) if norm != 0: eigenvalue = eigenvalue / norm _known_faces_embeddings[person_name] = eigenvalue _known_faces_names.append(person_name) except Exception as e: print(f"Error loading face data from service: {e}") return True if _face_app else False def detect(frame, threshold=0.4): """检测并识别人脸、返回结果元组(是否匹配到已知人脸, 结果字符串)""" global _face_app, _known_faces_embeddings, _known_faces_names, _using_gpu, _used_gpu_id global _ref_count, _last_used_time # 验证前置条件 if frame is None or frame.size == 0: return (False, "无效帧数据") # 增加引用计数并获取引擎实例 engine = None with _lock: _ref_count += 1 _last_used_time = time.time() _debug_counter["detected"] += 1 # 初始化引擎（如果未初始化且不在释放中） if not _face_app and not _is_releasing: if not load_model(prefer_gpu=True): _ref_count -= 1 # 恢复引用计数 return (False, "引擎初始化失败") # 获取当前引擎引用 engine = _face_app # 检查引擎是否可用 if not engine or not _known_faces_names: with _lock: _ref_count = max(0, _ref_count - 1) return (False, "人脸识别引擎不可用或未初始化") try: # 如果使用GPU、确保输入帧在处理前是连续的数组 if _using_gpu and not frame.flags.contiguous: frame = np.ascontiguousarray(frame) faces = _face_app.get(frame) except Exception as e: print(f"Face detect error: {e}") # 检测到错误时尝试重新选择GPU并重新初始化 print("尝试重新选择GPU并重新初始化...") with _lock: _ref_count = max(0, _ref_count - 1) load_model(prefer_gpu=True) # 重新初始化时保持默认GPU优先级 return (False, f"检测错误: {str(e)}") result_parts = [] has_matched = False # 标记是否有匹配到的已知人脸 for face in faces: # 特征归一化 embedding = face.embedding.astype(np.float32) norm = np.linalg.norm(embedding) if norm == 0: continue embedding = embedding / norm # 对比已知人脸 max_sim, best_name = -1.0, "Unknown" for name in _known_faces_names: known_emb = _known_faces_embeddings[name] sim = np.dot(embedding, known_emb) if sim > max_sim: max_sim = sim best_name = name # 判断匹配结果 is_match = max_sim >= threshold if is_match: has_matched = True # 只要有一个匹配成功、就标记为True bbox = face.bbox result_parts.append( f"{'匹配' if is_match else '不匹配'}: {best_name} (相似度: {max_sim:.2f}, 边界框: {bbox})" ) # 构建结果字符串 if not result_parts: result_str = "未检测到人脸" else: result_str = "; ".join(result_parts) # 减少引用计数、确保线程安全 with _lock: _ref_count = max(0, _ref_count - 1) # 持续使用时更新最后使用时间 if _ref_count > 0: _last_used_time = time.time() # 第一个返回值为: 是否匹配到已知人脸 return (has_matched, result_str)