187 lines
8.3 KiB
Python
187 lines
8.3 KiB
Python
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import os
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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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class FaceRecognizer:
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"""
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封装InsightFace人脸识别功能，支持从文件夹加载已知人脸。
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"""
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def __init__(self, known_faces_dir: str):
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self.known_faces_dir = known_faces_dir
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self.app = self._initialize_insightface()
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self.known_faces_embeddings = {}
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self.known_faces_names = []
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self._load_known_faces()
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def _initialize_insightface(self):
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"""
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初始化InsightFace FaceAnalysis应用。
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默认使用CPU，如果检测到CUDA，会自动使用GPU。
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"""
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print("正在初始化InsightFace人脸识别引擎...")
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try:
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# 默认模型是 'buffalo_l'，包含检测、对齐、识别功能
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# 如果需要更小的模型，可以尝试 'buffalo_s' 或 'buffalo_m'
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# ctx_id=0 表示使用GPU，ctx_id=-1 表示使用CPU
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# InsightFace会自动检测CUDA并选择GPU，所以通常不需要手动设置ctx_id
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app = FaceAnalysis(name='buffalo_l', root='~/.insightface') # 模型下载到用户目录
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app.prepare(ctx_id=0, det_size=(640, 640)) # det_size影响检测性能和精度
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print("InsightFace人脸识别引擎初始化成功。")
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return app
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except Exception as e:
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print(f"InsightFace人脸识别引擎初始化失败: {e}")
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print("请确保已安装insightface和onnxruntime，并且模型文件已下载或可访问。")
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return None
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def _load_known_faces(self):
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"""
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扫描已知人脸目录，加载每个人的照片并计算人脸特征。
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"""
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if not os.path.exists(self.known_faces_dir):
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print(f"警告: 已知人脸目录 '{self.known_faces_dir}' 不存在。请创建并放入照片。")
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os.makedirs(self.known_faces_dir, exist_ok=True)
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return
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print(f"正在加载已知人脸特征从: '{self.known_faces_dir}'...")
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for person_name in os.listdir(self.known_faces_dir):
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person_dir = os.path.join(self.known_faces_dir, person_name)
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if os.path.isdir(person_dir):
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print(f" 加载人物: {person_name}")
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embeddings = []
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for filename in os.listdir(person_dir):
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if filename.lower().endswith(('.png', '.jpg', '.jpeg')):
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image_path = os.path.join(person_dir, filename)
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try:
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img = cv2.imread(image_path)
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if img is None:
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print(f" 警告: 无法读取图片 '{image_path}'，已跳过。")
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continue
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# 查找人脸并提取特征
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faces = self.app.get(img)
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if faces:
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# 通常一张照片只有一个人脸，取第一个
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embeddings.append(faces[0].embedding)
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print(f" 成功提取 '{filename}' 的人脸特征。")
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else:
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print(f" 警告: 在图片 '{filename}' 中未检测到人脸，已跳过。")
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except Exception as e:
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print(f" 处理图片 '{image_path}' 时发生错误: {e}")
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if embeddings:
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# 将多张照片的特征取平均，作为该人物的最终特征
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self.known_faces_embeddings[person_name] = np.array(embeddings).mean(axis=0)
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self.known_faces_names.append(person_name)
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print(f" 人物 '{person_name}' 加载完成，共 {len(embeddings)} 张照片。")
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else:
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print(f" 警告: 人物 '{person_name}' 没有有效的人脸特征，已跳过。")
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print(f"已知人脸加载完成。共 {len(self.known_faces_names)} 个人物。")
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def recognize(self, frame, threshold=0.4):
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"""
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在视频帧中识别人脸。
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Args:
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frame: 输入的图像帧 (NumPy数组, BGR格式)。
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threshold (float): 识别相似度阈值。0.0到1.0，越高越严格。
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Returns:
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tuple[bool, str|None, float|None]: 是否检测到已知人脸，检测到的人名，以及相似度。
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"""
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if not self.app or not self.known_faces_names:
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return False, None, None
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faces = self.app.get(frame) # 在帧中检测并提取所有人的脸
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if not faces:
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return False, None, None
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for face in faces:
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# 遍历已知人脸，进行比对
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for known_name in self.known_faces_names:
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known_embedding = self.known_faces_embeddings[known_name]
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# --- 关键修改：手动计算余弦相似度 ---
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# 确保embedding是float32类型，避免潜在的类型不匹配问题
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embedding1 = face.embedding.astype(np.float32)
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embedding2 = known_embedding.astype(np.float32)
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# 计算点积
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dot_product = np.dot(embedding1, embedding2)
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# 计算L2范数（向量长度）
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norm_embedding1 = np.linalg.norm(embedding1)
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norm_embedding2 = np.linalg.norm(embedding2)
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# 避免除以零
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if norm_embedding1 == 0 or norm_embedding2 == 0:
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similarity = 0.0
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else:
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similarity = dot_product / (norm_embedding1 * norm_embedding2)
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# -------------------------------------
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if similarity >= threshold:
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print(f"!!! 人脸识别检测到已知人物: '{known_name}' (相似度: {similarity:.4f}) !!!")
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return True, known_name, similarity # 只要检测到一个就立即返回
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return False, None, None # 没有检测到已知人脸
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# def test_single_image(self, image_path: str, threshold=0.4):
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# """
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# 测试单张图片的人脸识别效果
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#
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# Args:
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# image_path: 图片路径
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# threshold: 识别阈值
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#
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# Returns:
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# tuple[bool, str|None, float|None]: 是否检测到已知人脸，检测到的人名，以及相似度
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# """
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# if not os.path.exists(image_path):
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# print(f"错误: 图片 '{image_path}' 不存在")
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# return False, None, None
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#
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# # 读取图片
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# frame = cv2.imread(image_path)
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# if frame is None:
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# print(f"错误: 无法读取图片 '{image_path}'")
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# return False, None, None
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#
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# # 调用识别方法
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# result, name, similarity = self.recognize(frame, threshold)
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#
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# # 显示结果
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# if result:
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# print(f"测试结果: 在图片中识别到 {name}，相似度: {similarity:.4f}")
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#
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# # 绘制识别结果并显示图片
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# faces = self.app.get(frame)
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# for face in faces:
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# bbox = face.bbox.astype(int)
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# # 绘制 bounding box
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# cv2.rectangle(frame, (bbox[0], bbox[1]), (bbox[2], bbox[3]), (0, 255, 0), 2)
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# # 绘制姓名和相似度
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# text = f"{name}: {similarity:.2f}"
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# cv2.putText(frame, text, (bbox[0], bbox[1] - 10),
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# cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)
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#
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# # 显示图片
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# cv2.imshow('Recognition Result', frame)
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# print("按任意键关闭图片窗口...")
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# cv2.waitKey(0)
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# cv2.destroyAllWindows()
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# else:
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# print("测试结果: 未在图片中识别到已知人脸")
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#
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# return result, name, similarity
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# if __name__ == "__main__":
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# # 初始化人脸识别器，指定已知人脸目录
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# recognizer = FaceRecognizer(known_faces_dir="known_faces")
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#
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# # 测试单张图片
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# test_image_path = r"F:\OCR\RapidOCR-main\known_faces\B\14sino-qiu02-master1050.jpg" # 替换为你的测试图片路径
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# recognizer.test_single_image(test_image_path, threshold=0.4)
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