内容安全审核
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util/__pycache__/face_util.cpython-310.pyc
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util/__pycache__/face_util.cpython-310.pyc
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util/face_util.py
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util/face_util.py
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import cv2
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import numpy as np
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import insightface
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from insightface.app import FaceAnalysis
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from io import BytesIO
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from PIL import Image
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import logging
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from mysql.connector import Error as MySQLError
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from ds.db import db
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# 配置日志（便于排查）
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logging.basicConfig(level=logging.INFO, format='%(asctime)s - [FaceUtil] - %(levelname)s - %(message)s')
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logger = logging.getLogger(__name__)
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# 全局变量存储InsightFace引擎和特征列表
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_insightface_app = None
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_feature_list = []
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def init_insightface():
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"""初始化InsightFace引擎（确保成功后再使用）"""
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global _insightface_app
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try:
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if _insightface_app is not None:
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logger.info("InsightFace引擎已初始化、无需重复执行")
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return _insightface_app
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logger.info("正在初始化InsightFace引擎（模型：buffalo_l）...")
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# 手动指定模型下载路径（避免权限问题、可选）
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app = FaceAnalysis(
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name='buffalo_l',
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root='~/.insightface', # 模型默认下载路径
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providers=['CPUExecutionProvider'] # 强制用CPU（若有GPU可加'CUDAExecutionProvider'）
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)
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app.prepare(ctx_id=0, det_size=(640, 640)) # det_size越大、小人脸检测越准
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logger.info("InsightFace引擎初始化完成")
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_insightface_app = app
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return app
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except Exception as e:
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logger.error(f"InsightFace初始化失败：{str(e)}", exc_info=True) # 打印详细堆栈
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_insightface_app = None
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return None
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def add_binary_data(binary_data):
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"""
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接收单张图片的二进制数据、提取特征并保存
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返回：(True, 特征值numpy数组) 或 (False, 错误信息字符串)
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"""
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global _insightface_app, _feature_list
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# 1. 先检查引擎是否初始化成功
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if not _insightface_app:
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init_result = init_insightface() # 尝试重新初始化
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if not init_result:
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error_msg = "InsightFace引擎未初始化、无法检测人脸"
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logger.error(error_msg)
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return False, error_msg
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try:
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# 2. 验证二进制数据有效性
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if len(binary_data) < 1024: # 过滤过小的无效图片（小于1KB）
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error_msg = f"图片过小（{len(binary_data)}字节）、可能不是有效图片"
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logger.warning(error_msg)
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return False, error_msg
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# 3. 二进制数据转CV2格式（关键步骤、避免通道错误）
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try:
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img = Image.open(BytesIO(binary_data)).convert("RGB") # 强制转RGB
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frame = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR) # InsightFace需要BGR格式
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except Exception as e:
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error_msg = f"图片格式转换失败：{str(e)}"
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logger.error(error_msg, exc_info=True)
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return False, error_msg
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# 4. 检查图片尺寸（避免极端尺寸导致检测失败）
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height, width = frame.shape[:2]
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if height < 64 or width < 64: # 人脸检测最小建议尺寸
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error_msg = f"图片尺寸过小（{width}x{height}）、需至少64x64像素"
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logger.warning(error_msg)
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return False, error_msg
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# 5. 调用InsightFace检测人脸
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logger.info(f"开始检测人脸（图片尺寸：{width}x{height}、格式：BGR）")
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faces = _insightface_app.get(frame)
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if not faces:
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error_msg = "未检测到人脸（请确保图片包含清晰正面人脸、无遮挡、不模糊）"
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logger.warning(error_msg)
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return False, error_msg
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# 6. 提取特征并保存
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current_feature = faces[0].embedding
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_feature_list.append(current_feature)
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logger.info(f"人脸检测成功、提取特征值（维度：{current_feature.shape[0]}）、累计特征数：{len(_feature_list)}")
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return True, current_feature
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except Exception as e:
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error_msg = f"处理图片时发生异常：{str(e)}"
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logger.error(error_msg, exc_info=True)
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return False, error_msg
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# ------------------------------
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# 获取数据库最新的人脸及其特征
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# ------------------------------
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def get_all_face_name_with_eigenvalue() -> dict:
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conn = None
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cursor = None
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try:
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conn = db.get_connection()
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cursor = conn.cursor(dictionary=True)
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query = "SELECT name, eigenvalue FROM face WHERE name IS NOT NULL"
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cursor.execute(query)
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faces = cursor.fetchall()
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name_to_eigenvalues = {}
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for face in faces:
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name = face["name"]
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eigenvalue = face["eigenvalue"]
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if name in name_to_eigenvalues:
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name_to_eigenvalues[name].append(eigenvalue)
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else:
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name_to_eigenvalues[name] = [eigenvalue]
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face_dict = {}
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for name, eigenvalues in name_to_eigenvalues.items():
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if len(eigenvalues) > 1:
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face_dict[name] = get_average_feature(eigenvalues)
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else:
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face_dict[name] = eigenvalues[0]
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return face_dict
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except MySQLError as e:
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raise Exception(f"获取人脸特征失败: {str(e)}") from e
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finally:
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db.close_connection(conn, cursor)
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# 以下函数保持不变
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def get_average_feature(features=None):
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global _feature_list
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try:
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if features is None:
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features = _feature_list
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if not isinstance(features, list) or len(features) == 0:
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logger.warning("输入必须是包含至少一个特征值的列表")
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return None
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processed_features = []
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for i, embedding in enumerate(features):
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try:
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if isinstance(embedding, str):
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embedding_str = embedding.replace('[', '').replace(']', '').replace(',', ' ').strip()
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embedding_list = [float(num) for num in embedding_str.split() if num.strip()]
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embedding_np = np.array(embedding_list, dtype=np.float32)
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else:
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embedding_np = np.array(embedding, dtype=np.float32)
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if len(embedding_np.shape) == 1:
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processed_features.append(embedding_np)
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logger.info(f"已添加第 {i + 1} 个特征值用于计算平均值")
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else:
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logger.warning(f"跳过第 {i + 1} 个特征值：不是一维数组")
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except Exception as e:
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logger.error(f"处理第 {i + 1} 个特征值时出错：{str(e)}")
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if not processed_features:
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logger.warning("没有有效的特征值用于计算平均值")
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return None
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dims = {feat.shape[0] for feat in processed_features}
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if len(dims) > 1:
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logger.error(f"特征值维度不一致：{dims}、无法计算平均值")
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return None
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avg_feature = np.mean(processed_features, axis=0)
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logger.info(f"计算成功：{len(processed_features)} 个特征值的平均向量（维度：{avg_feature.shape[0]}）")
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return avg_feature
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except Exception as e:
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logger.error(f"计算平均特征值出错：{str(e)}", exc_info=True)
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return None
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def clear_features():
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global _feature_list
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_feature_list = []
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logger.info("已清空所有特征数据")
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def get_feature_list():
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global _feature_list
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logger.info(f"当前特征列表长度：{len(_feature_list)}")
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return _feature_list.copy()
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61
util/model_util.py
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61
util/model_util.py
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import os
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import numpy as np
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import traceback
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from ultralytics import YOLO
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from typing import Optional
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def load_yolo_model(model_path: str) -> Optional[YOLO]:
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"""
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加载YOLO模型（支持v5/v8）、并校验模型有效性
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:param model_path: 模型文件的绝对路径
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:return: 加载成功返回YOLO模型实例、失败返回None
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"""
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try:
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# 加载前的基础信息检查
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print(f"\n[模型工具] 开始加载模型：{model_path}")
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print(f"[模型工具] 文件是否存在：{os.path.exists(model_path)}")
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if os.path.exists(model_path):
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print(f"[模型工具] 文件大小：{os.path.getsize(model_path) / 1024 / 1024:.2f} MB")
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# 强制重新加载模型、避免缓存问题
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model = YOLO(model_path)
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# 兼容性校验：使用numpy空数组测试模型
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dummy_image = np.zeros((640, 640, 3), dtype=np.uint8)
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try:
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# 优先使用新版本参数
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model.predict(
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source=dummy_image,
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imgsz=640,
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conf=0.25,
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verbose=False,
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stream=False
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)
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except Exception as pred_e:
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print(f"[模型工具] 预测校验兼容处理：{str(pred_e)}")
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# 兼容旧版本YOLO参数
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model.predict(
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img=dummy_image,
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imgsz=640,
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conf=0.25,
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verbose=False
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)
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# 验证模型基本属性
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if not hasattr(model, 'names'):
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print("[模型工具] 警告：模型缺少类别名称属性")
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else:
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print(f"[模型工具] 模型包含类别：{list(model.names.values())[:5]}...") # 显示前5个类别
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print(f"[模型工具] 模型加载成功！")
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return model
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except Exception as e:
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# 详细错误信息输出
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print(f"\n[模型工具] 加载模型失败！路径：{model_path}")
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print(f"[模型工具] 异常类型：{type(e).__name__}")
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print(f"[模型工具] 异常详情：{str(e)}")
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print(f"[模型工具] 堆栈跟踪：\n{traceback.format_exc()}")
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return None
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