#!/usr/bin/env python3 """人脸检测和识别工具 - 检测图像中的人脸并保存裁剪的人脸图像。""" import os import sys import argparse import cv2 import numpy as np from datetime import datetime try: import face_recognition except ImportError: print("❌ 缺少依赖库: face_recognition。请运行: pip install face_recognition") sys.exit(1) class FaceDetector: """人脸检测和识别工具""" def __init__(self): self.faces_dir = os.path.expanduser("~/.aibox/faces") self.known_faces_dir = os.path.expanduser("~/.aibox/known_faces") self._ensure_dirs() self.known_face_encodings = [] self.known_face_names = [] self._load_known_faces() def _ensure_dirs(self): os.makedirs(self.faces_dir, exist_ok=True) os.makedirs(self.known_faces_dir, exist_ok=True) def _load_known_faces(self): """加载已知人脸编码""" self.known_face_encodings = [] self.known_face_names = [] if os.path.exists(self.known_faces_dir): for filename in os.listdir(self.known_faces_dir): if filename.endswith(('.jpg', '.jpeg', '.png')): filepath = os.path.join(self.known_faces_dir, filename) try: image = face_recognition.load_image_file(filepath) encodings = face_recognition.face_encodings(image) if encodings: name = os.path.splitext(filename)[0] self.known_face_encodings.append(encodings[0]) self.known_face_names.append(name) except Exception as e: print(f"加载已知人脸失败 {filename}: {e}", file=sys.stderr) def detect_faces(self, image_path: str, save_cropped: bool = True, recognize: bool = True, tolerance: float = 0.6) -> str: """检测图像中的人脸并保存裁剪的人脸""" try: if not os.path.exists(image_path): return f"❌ 错误：文件不存在 - {image_path}" image = face_recognition.load_image_file(image_path) face_locations = face_recognition.face_locations(image) if not face_locations: return "📭 未检测到人脸" face_encodings = face_recognition.face_encodings(image, face_locations) results = [] saved_files = [] for i, (top, right, bottom, left) in enumerate(face_locations): face_data = { "index": i + 1, "position": {"top": top, "right": right, "bottom": bottom, "left": left}, "size": {"width": right - left, "height": bottom - top} } if recognize and self.known_face_encodings and i < len(face_encodings): matches = face_recognition.compare_faces( self.known_face_encodings, face_encodings[i], tolerance=tolerance ) if True in matches: match_index = matches.index(True) name = self.known_face_names[match_index] face_data["name"] = name face_data["match"] = True distances = face_recognition.face_distance( self.known_face_encodings, face_encodings[i] ) face_data["similarity"] = f"{1 - distances[match_index]:.3f}" else: face_data["name"] = "Unknown" face_data["match"] = False else: face_data["name"] = f"Face_{i+1}" face_data["match"] = False if save_cropped: image_array = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) margin = 20 top_crop = max(0, top - margin) bottom_crop = min(image_array.shape[0], bottom + margin) left_crop = max(0, left - margin) right_crop = min(image_array.shape[1], right + margin) face_crop = image_array[top_crop:bottom_crop, left_crop:right_crop] timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") safe_name = face_data['name'].replace(' ', '_') filename = f"{timestamp}_face_{i+1}_{safe_name}.jpg" filepath = os.path.join(self.faces_dir, filename) cv2.imwrite(filepath, face_crop) face_data["saved_file"] = filename saved_files.append(filename) results.append(face_data) output = [] output.append(f"✅ 检测到 {len(face_locations)} 个人脸") output.append(f"📁 人脸图像保存位置: {self.faces_dir}") output.append("") for face in results: output.append(f"人脸 #{face['index']}:") output.append(f" 位置: 左={face['position']['left']}, 上={face['position']['top']}, " f"右={face['position']['right']}, 下={face['position']['bottom']}") output.append(f" 大小: {face['size']['width']}x{face['size']['height']} 像素") output.append(f" 识别结果: {face['name']}") if 'similarity' in face: output.append(f" 相似度: {face['similarity']}") if 'saved_file' in face: output.append(f" 保存文件: {face['saved_file']}") output.append("") if saved_files: output.append("📋 保存的文件列表:") for f in saved_files: output.append(f" • {f}") return "\n".join(output) except Exception as e: return f"❌ 人脸检测失败: {str(e)}" def list_saved_faces(self, limit: int = 20) -> str: """列出已保存的人脸图像""" try: files = [] if os.path.exists(self.faces_dir): for filename in sorted(os.listdir(self.faces_dir), reverse=True): if filename.endswith(('.jpg', '.jpeg', '.png')): files.append(filename) if len(files) >= limit: break if files: output = [f"📁 已保存的人脸图像 (最近 {len(files)} 张):"] for f in files: output.append(f" • {f}") output.append(f"\n📂 保存位置: {self.faces_dir}") return "\n".join(output) else: return "📭 尚未保存任何人脸图像" except Exception as e: return f"❌ 列出失败: {str(e)}" def list_known_faces(self) -> str: """列出已知人脸库中的人脸""" try: faces = [] if os.path.exists(self.known_faces_dir): for filename in os.listdir(self.known_faces_dir): if filename.endswith(('.jpg', '.jpeg', '.png')): name = os.path.splitext(filename)[0] faces.append(name) if faces: output = [f"👥 已知人脸库 (共 {len(faces)} 人):"] for name in faces: output.append(f" • {name}") output.append(f"\n📂 数据库位置: {self.known_faces_dir}") return "\n".join(output) else: return "📭 已知人脸库为空" except Exception as e: return f"❌ 列出失败: {str(e)}" def add_known_face(self, image_path: str, person_name: str) -> str: """添加已知人脸到数据库""" try: if not os.path.exists(image_path): return f"❌ 文件不存在: {image_path}" image = face_recognition.load_image_file(image_path) face_locations = face_recognition.face_locations(image) if not face_locations: return "❌ 未检测到人脸，无法添加" if len(face_locations) > 1: return f"⚠️ 检测到 {len(face_locations)} 个人脸，请使用单人脸图片" filepath = os.path.join(self.known_faces_dir, f"{person_name}.jpg") if os.path.exists(filepath): os.remove(filepath) image_array = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) top, right, bottom, left = face_locations[0] margin = 20 top_crop = max(0, top - margin) bottom_crop = min(image_array.shape[0], bottom + margin) left_crop = max(0, left - margin) right_crop = min(image_array.shape[1], right + margin) face_crop = image_array[top_crop:bottom_crop, left_crop:right_crop] cv2.imwrite(filepath, face_crop) self._load_known_faces() return f"✅ 已添加已知人脸: {person_name}\n📁 保存位置: {filepath}" except Exception as e: return f"❌ 添加失败: {str(e)}" def remove_known_face(self, person_name: str) -> str: """从已知人脸库中删除人脸""" try: filepath = os.path.join(self.known_faces_dir, f"{person_name}.jpg") if not os.path.exists(filepath): for filename in os.listdir(self.known_faces_dir): if filename.endswith(('.jpg', '.jpeg', '.png')): name = os.path.splitext(filename)[0] if name.lower() == person_name.lower(): filepath = os.path.join(self.known_faces_dir, filename) break else: return f"❌ 未找到已知人脸: {person_name}" os.remove(filepath) self._load_known_faces() return f"✅ 已删除已知人脸: {person_name}" except Exception as e: return f"❌ 删除失败: {str(e)}" def get_face_info(self, person_name: str) -> str: """获取已知人脸信息""" try: filepath = os.path.join(self.known_faces_dir, f"{person_name}.jpg") if not os.path.exists(filepath): for filename in os.listdir(self.known_faces_dir): if filename.endswith(('.jpg', '.jpeg', '.png')): name = os.path.splitext(filename)[0] if name.lower() == person_name.lower(): filepath = os.path.join(self.known_faces_dir, filename) person_name = name break else: return f"❌ 未找到人脸: {person_name}" file_size = os.path.getsize(filepath) modified_time = datetime.fromtimestamp(os.path.getmtime(filepath)) output = [] output.append(f"👤 人脸信息: {person_name}") output.append(f"📁 文件路径: {filepath}") output.append(f"📊 文件大小: {file_size} 字节 ({file_size/1024:.1f}KB)") output.append(f"🕐 最后修改: {modified_time.strftime('%Y-%m-%d %H:%M:%S')}") return "\n".join(output) except Exception as e: return f"❌ 获取人脸信息失败: {str(e)}" def main(): parser = argparse.ArgumentParser( description="人脸检测和识别工具 - 检测图像中的人脸并保存裁剪的人脸图像。", epilog=""" 使用示例: # 检测人脸 face-detector --action detect --image-path photo.jpg # 检测人脸（不保存裁剪） face-detector --action detect --image-path photo.jpg --no-save # 检测人脸（不进行识别） face-detector --action detect --image-path photo.jpg --no-recognize # 列出已保存的人脸图像 face-detector --action list-faces --limit 10 # 列出已知人脸库 face-detector --action list-known # 添加已知人脸 face-detector --action add-known --image-path face.jpg --person-name "张三" # 删除已知人脸 face-detector --action remove-known --person-name "张三" # 查看人脸信息 face-detector --action get-info --person-name "张三" 目录结构: ~/.aibox/faces/ - 人脸图像保存位置 ~/.aibox/known_faces/ - 已知人脸数据库 需要安装: pip install face_recognition opencv-python """ ) parser.add_argument("--action", "-a", required=True, choices=["detect", "list-faces", "list-known", "add-known", "remove-known", "get-info"], help="操作类型") # 检测参数 parser.add_argument("--image-path", "-i", help="图片文件路径") parser.add_argument("--no-save", action="store_true", help="不保存裁剪的人脸图像") parser.add_argument("--no-recognize", action="store_true", help="不进行人脸识别") parser.add_argument("--tolerance", "-t", type=float, default=0.6, help="识别容差（默认: 0.6）") # 列表参数 parser.add_argument("--limit", "-l", type=int, default=20, help="列出文件数量限制（默认: 20）") # 已知人脸参数 parser.add_argument("--person-name", "-n", help="人名标识") args = parser.parse_args() detector = FaceDetector() try: if args.action == "detect": if not args.image_path: print("❌ 需要提供 --image-path 参数") sys.exit(1) result = detector.detect_faces( image_path=args.image_path, save_cropped=not args.no_save, recognize=not args.no_recognize, tolerance=args.tolerance ) print(result) elif args.action == "list-faces": print(detector.list_saved_faces(args.limit)) elif args.action == "list-known": print(detector.list_known_faces()) elif args.action == "add-known": if not args.image_path or not args.person_name: print("❌ 需要提供 --image-path 和 --person-name 参数") sys.exit(1) result = detector.add_known_face(args.image_path, args.person_name) print(result) elif args.action == "remove-known": if not args.person_name: print("❌ 需要提供 --person-name 参数") sys.exit(1) result = detector.remove_known_face(args.person_name) print(result) elif args.action == "get-info": if not args.person_name: print("❌ 需要提供 --person-name 参数") sys.exit(1) result = detector.get_face_info(args.person_name) print(result) except Exception as e: print(f"❌ 执行错误: {str(e)}") sys.exit(1) if __name__ == "__main__": main()