59 lines
1.8 KiB
Python
59 lines
1.8 KiB
Python
import insightface
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from insightface.app import FaceAnalysis
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import cv2
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import numpy as np
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from numpy.linalg import norm
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# 初始化模型
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app = FaceAnalysis(name='buffalo_l')
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app.prepare(ctx_id=0, det_size=(640, 640))
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# 创建一个数据库来存储已知人脸的特征和姓名
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face_database = {}
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# --- 注册阶段：将已知人脸加入数据库 ---
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def register_face(image_path, person_name):
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img = cv2.imread(image_path)
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faces = app.get(img)
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if len(faces) != 1:
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print(f"警告：在 {image_path} 中未检测到或检测到多张人脸，跳过。")
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return
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embedding = faces[0].embedding
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face_database[person_name] = embedding
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print(f"成功注册：{person_name}")
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# 注册多个人
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img1 = cv2.imread('./resources/face/01.png')
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register_face('./resources/face/01.png', '01')
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register_face('./resources/face/02.png', '02')
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# --- 识别阶段：识别未知图片中的人 ---
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def recognize_face(image_path):
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img = cv2.imread(image_path)
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faces = app.get(img)
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if len(faces) == 0:
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print("未检测到人脸。")
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return
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for i, face in enumerate(faces):
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unknown_embedding = face.embedding
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max_sim = -1
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identity = "未知"
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# 与数据库中的每个人脸进行比对
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for name, known_embedding in face_database.items():
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cos_sim = unknown_embedding @ known_embedding.T / (norm(unknown_embedding) * norm(known_embedding))
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if cos_sim > max_sim:
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max_sim = cos_sim
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identity = name
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# 根据阈值决定最终身份
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threshold = 0.6
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if max_sim < threshold:
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identity = "未知"
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print(f"人脸 {i+1}: 识别为 【{identity}】, 相似度：{max_sim:.4f}")
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# 识别一张新图片
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recognize_face('./resources/face/16.jpg') |