114 lines
4.0 KiB
Python
114 lines
4.0 KiB
Python
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import os
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import numpy as np
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import cv2
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from PIL import Image # 确保正确导入Image类
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from insightface.app import FaceAnalysis
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# 导入获取人脸信息的服务
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from service.face_service import get_all_face_name_with_eigenvalue
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# 全局变量
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_face_app = None
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_known_faces_embeddings = {} # 存储姓名到特征值的映射
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_known_faces_names = [] # 存储所有已知姓名
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def load_model():
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"""加载人脸识别模型及已知人脸特征库"""
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global _face_app, _known_faces_embeddings, _known_faces_names
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# 初始化InsightFace模型
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try:
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_face_app = FaceAnalysis(name='buffalo_l', root=os.path.expanduser('~/.insightface'))
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_face_app.prepare(ctx_id=0, det_size=(640, 640))
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except Exception as e:
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print(f"Face model load failed: {e}")
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return False
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# 从服务获取所有人脸姓名和特征值
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try:
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face_data = get_all_face_name_with_eigenvalue()
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# 处理获取到的人脸数据
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for person_name, eigenvalue_data in face_data.items():
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# 处理特征值数据 - 兼容数组和字符串两种格式
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if isinstance(eigenvalue_data, np.ndarray):
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# 如果已经是numpy数组，直接使用
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eigenvalue = eigenvalue_data.astype(np.float32)
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elif isinstance(eigenvalue_data, str):
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# 清理字符串：移除方括号、换行符和多余空格
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cleaned = eigenvalue_data.replace('[', '').replace(']', '').replace('\n', '').strip()
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# 按空格或逗号分割（处理可能的不同分隔符）
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values = [v for v in cleaned.split() if v]
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# 转换为数组
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eigenvalue = np.array(list(map(float, values)), dtype=np.float32)
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else:
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# 不支持的类型
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print(f"Unsupported eigenvalue type for {person_name}")
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continue
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# 归一化处理
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norm = np.linalg.norm(eigenvalue)
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if norm != 0:
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eigenvalue = eigenvalue / norm
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_known_faces_embeddings[person_name] = eigenvalue
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_known_faces_names.append(person_name)
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except Exception as e:
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print(f"Error loading face data from service: {e}")
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return True if _face_app else False
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def detect(frame, threshold=0.4):
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"""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)"""
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global _face_app, _known_faces_embeddings, _known_faces_names
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if not _face_app or not _known_faces_names or frame is None:
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return (False, "未初始化或无效帧")
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try:
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faces = _face_app.get(frame)
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except Exception as e:
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print(f"Face detect error: {e}")
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return (False, f"检测错误: {str(e)}")
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result_parts = []
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has_matched = False # 新增标记：是否有匹配到的已知人脸
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for face in faces:
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# 特征归一化
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embedding = face.embedding.astype(np.float32)
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norm = np.linalg.norm(embedding)
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if norm == 0:
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continue
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embedding = embedding / norm
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# 对比已知人脸
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max_sim, best_name = -1.0, "Unknown"
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for name in _known_faces_names:
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known_emb = _known_faces_embeddings[name]
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sim = np.dot(embedding, known_emb)
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if sim > max_sim:
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max_sim = sim
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best_name = name
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# 判断匹配结果
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is_match = max_sim >= threshold
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if is_match:
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has_matched = True # 只要有一个匹配成功，就标记为True
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bbox = face.bbox
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result_parts.append(
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f"{'匹配' if is_match else '不匹配'}: {best_name} (相似度: {max_sim:.2f}, 边界框: {bbox})"
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)
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# 构建结果字符串
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if not result_parts:
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result_str = "未检测到人脸"
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else:
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result_str = "; ".join(result_parts)
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# 第一个返回值改为：是否匹配到已知人脸
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return (has_matched, result_str)
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