qing
9064750f33
- gitclient: delete 限工作区内 .git 仓库（S2）；分支名白名单防选项注入（S3） - camera/screenshot: --filename 拒绝分隔符/绝对路径/父目录引用（S9/S10） - facerec: person_name 白名单（兼容中文）接入 add/remove/get-info（S11） - vision: 图片魔数白名单校验，data URL MIME 按实测类型（S12） - sftp-transfer: listdir 条目名双向过滤防恶意服务端目录逃逸（S6） - test-auto-appimage: exec_name 白名单防 sed 替换注入（S13） 测试 493 passed / 4 failed（既有平台环境项）/ 1 skipped，零回归
416 lines
17 KiB
Python
Executable File
416 lines
17 KiB
Python
Executable File
#!/usr/bin/env python3
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"""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。"""
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import os
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import re
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import sys
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import argparse
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import cv2
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import numpy as np
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from datetime import datetime
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try:
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import face_recognition
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except ImportError:
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print("❌ 缺少依赖库: face_recognition。请运行: pip install face_recognition")
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sys.exit(1)
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# S11（审计 2026-08-20）：person_name 白名单——仅允许文字/数字/._-，
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# 拒绝路径分隔符与纯点名（"."/".."），防止经 f"{name}.jpg" 拼接逃逸 known_faces 目录。
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# \w 在 py3 默认 UNICODE，兼容中文人名（如 "张三"）。
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_PERSON_NAME_RE = re.compile(r"^(?!\.+$)[\w.-]{1,64}$")
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def _valid_person_name(name):
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return bool(name) and bool(_PERSON_NAME_RE.match(name))
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class FaceDetector:
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"""人脸检测和识别工具"""
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def __init__(self):
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self.faces_dir = os.path.expanduser("~/.aibox/faces")
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self.known_faces_dir = os.path.expanduser("~/.aibox/known_faces")
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self._ensure_dirs()
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self.known_face_encodings = []
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self.known_face_names = []
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self._load_known_faces()
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def _ensure_dirs(self):
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os.makedirs(self.faces_dir, exist_ok=True)
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os.makedirs(self.known_faces_dir, exist_ok=True)
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def _load_known_faces(self):
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"""加载已知人脸编码"""
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self.known_face_encodings = []
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self.known_face_names = []
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if os.path.exists(self.known_faces_dir):
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for filename in os.listdir(self.known_faces_dir):
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if filename.endswith(('.jpg', '.jpeg', '.png')):
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filepath = os.path.join(self.known_faces_dir, filename)
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try:
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image = face_recognition.load_image_file(filepath)
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encodings = face_recognition.face_encodings(image)
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if encodings:
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name = os.path.splitext(filename)[0]
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self.known_face_encodings.append(encodings[0])
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self.known_face_names.append(name)
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except Exception as e:
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print(f"加载已知人脸失败 {filename}: {e}", file=sys.stderr)
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def detect_faces(self, image_path: str, save_cropped: bool = True,
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recognize: bool = True, tolerance: float = 0.6) -> str:
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"""检测图像中的人脸并保存裁剪的人脸"""
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try:
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if not os.path.exists(image_path):
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return f"❌ 错误：文件不存在 - {image_path}"
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image = face_recognition.load_image_file(image_path)
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face_locations = face_recognition.face_locations(image)
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if not face_locations:
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return "📭 未检测到人脸"
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face_encodings = face_recognition.face_encodings(image, face_locations)
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results = []
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saved_files = []
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for i, (top, right, bottom, left) in enumerate(face_locations):
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face_data = {
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"index": i + 1,
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"position": {"top": top, "right": right, "bottom": bottom, "left": left},
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"size": {"width": right - left, "height": bottom - top}
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}
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if recognize and self.known_face_encodings and i < len(face_encodings):
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matches = face_recognition.compare_faces(
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self.known_face_encodings,
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face_encodings[i],
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tolerance=tolerance
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)
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if True in matches:
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match_index = matches.index(True)
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name = self.known_face_names[match_index]
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face_data["name"] = name
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face_data["match"] = True
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distances = face_recognition.face_distance(
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self.known_face_encodings,
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face_encodings[i]
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)
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face_data["similarity"] = f"{1 - distances[match_index]:.3f}"
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else:
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face_data["name"] = "Unknown"
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face_data["match"] = False
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else:
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face_data["name"] = f"Face_{i+1}"
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face_data["match"] = False
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if save_cropped:
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image_array = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
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margin = 20
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top_crop = max(0, top - margin)
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bottom_crop = min(image_array.shape[0], bottom + margin)
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left_crop = max(0, left - margin)
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right_crop = min(image_array.shape[1], right + margin)
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face_crop = image_array[top_crop:bottom_crop, left_crop:right_crop]
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timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
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safe_name = face_data['name'].replace(' ', '_')
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filename = f"{timestamp}_face_{i+1}_{safe_name}.jpg"
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filepath = os.path.join(self.faces_dir, filename)
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cv2.imwrite(filepath, face_crop)
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face_data["saved_file"] = filename
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saved_files.append(filename)
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results.append(face_data)
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output = []
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output.append(f"✅ 检测到 {len(face_locations)} 个人脸")
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output.append(f"📁 人脸图像保存位置: {self.faces_dir}")
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output.append("")
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for face in results:
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output.append(f"人脸 #{face['index']}:")
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output.append(f" 位置: 左={face['position']['left']}, 上={face['position']['top']}, "
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f"右={face['position']['right']}, 下={face['position']['bottom']}")
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output.append(f" 大小: {face['size']['width']}x{face['size']['height']} 像素")
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output.append(f" 识别结果: {face['name']}")
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if 'similarity' in face:
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output.append(f" 相似度: {face['similarity']}")
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if 'saved_file' in face:
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output.append(f" 保存文件: {face['saved_file']}")
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output.append("")
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if saved_files:
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output.append("📋 保存的文件列表:")
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for f in saved_files:
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output.append(f" • {f}")
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return "\n".join(output)
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except Exception as e:
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return f"❌ 人脸检测失败: {str(e)}"
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def list_saved_faces(self, limit: int = 20) -> str:
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"""列出已保存的人脸图像"""
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try:
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files = []
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if os.path.exists(self.faces_dir):
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for filename in sorted(os.listdir(self.faces_dir), reverse=True):
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if filename.endswith(('.jpg', '.jpeg', '.png')):
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files.append(filename)
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if len(files) >= limit:
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break
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if files:
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output = [f"📁 已保存的人脸图像 (最近 {len(files)} 张):"]
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for f in files:
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output.append(f" • {f}")
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output.append(f"\n📂 保存位置: {self.faces_dir}")
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return "\n".join(output)
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else:
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return "📭 尚未保存任何人脸图像"
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except Exception as e:
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return f"❌ 列出失败: {str(e)}"
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def list_known_faces(self) -> str:
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"""列出已知人脸库中的人脸"""
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try:
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faces = []
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if os.path.exists(self.known_faces_dir):
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for filename in os.listdir(self.known_faces_dir):
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if filename.endswith(('.jpg', '.jpeg', '.png')):
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name = os.path.splitext(filename)[0]
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faces.append(name)
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if faces:
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output = [f"👥 已知人脸库 (共 {len(faces)} 人):"]
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for name in faces:
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output.append(f" • {name}")
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output.append(f"\n📂 数据库位置: {self.known_faces_dir}")
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return "\n".join(output)
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else:
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return "📭 已知人脸库为空"
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except Exception as e:
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return f"❌ 列出失败: {str(e)}"
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def add_known_face(self, image_path: str, person_name: str) -> str:
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"""添加已知人脸到数据库"""
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try:
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if not _valid_person_name(person_name):
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return f"❌ 非法人名标识: {person_name}（仅允许文字/数字/._-，≤64字符）"
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if not os.path.exists(image_path):
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return f"❌ 文件不存在: {image_path}"
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image = face_recognition.load_image_file(image_path)
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face_locations = face_recognition.face_locations(image)
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if not face_locations:
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return "❌ 未检测到人脸，无法添加"
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if len(face_locations) > 1:
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return f"⚠️ 检测到 {len(face_locations)} 个人脸，请使用单人脸图片"
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filepath = os.path.join(self.known_faces_dir, f"{person_name}.jpg")
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if os.path.exists(filepath):
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os.remove(filepath)
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image_array = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
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top, right, bottom, left = face_locations[0]
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margin = 20
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top_crop = max(0, top - margin)
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bottom_crop = min(image_array.shape[0], bottom + margin)
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left_crop = max(0, left - margin)
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right_crop = min(image_array.shape[1], right + margin)
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face_crop = image_array[top_crop:bottom_crop, left_crop:right_crop]
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cv2.imwrite(filepath, face_crop)
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self._load_known_faces()
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return f"✅ 已添加已知人脸: {person_name}\n📁 保存位置: {filepath}"
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except Exception as e:
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return f"❌ 添加失败: {str(e)}"
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def remove_known_face(self, person_name: str) -> str:
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"""从已知人脸库中删除人脸"""
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try:
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if not _valid_person_name(person_name):
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return f"❌ 非法人名标识: {person_name}（仅允许文字/数字/._-，≤64字符）"
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filepath = os.path.join(self.known_faces_dir, f"{person_name}.jpg")
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if not os.path.exists(filepath):
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for filename in os.listdir(self.known_faces_dir):
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if filename.endswith(('.jpg', '.jpeg', '.png')):
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name = os.path.splitext(filename)[0]
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if name.lower() == person_name.lower():
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filepath = os.path.join(self.known_faces_dir, filename)
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break
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else:
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return f"❌ 未找到已知人脸: {person_name}"
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os.remove(filepath)
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self._load_known_faces()
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return f"✅ 已删除已知人脸: {person_name}"
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except Exception as e:
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return f"❌ 删除失败: {str(e)}"
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def get_face_info(self, person_name: str) -> str:
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"""获取已知人脸信息"""
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try:
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if not _valid_person_name(person_name):
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return f"❌ 非法人名标识: {person_name}（仅允许文字/数字/._-，≤64字符）"
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filepath = os.path.join(self.known_faces_dir, f"{person_name}.jpg")
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if not os.path.exists(filepath):
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for filename in os.listdir(self.known_faces_dir):
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if filename.endswith(('.jpg', '.jpeg', '.png')):
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name = os.path.splitext(filename)[0]
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if name.lower() == person_name.lower():
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filepath = os.path.join(self.known_faces_dir, filename)
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person_name = name
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break
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else:
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return f"❌ 未找到人脸: {person_name}"
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file_size = os.path.getsize(filepath)
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modified_time = datetime.fromtimestamp(os.path.getmtime(filepath))
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output = []
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output.append(f"👤 人脸信息: {person_name}")
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output.append(f"📁 文件路径: {filepath}")
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output.append(f"📊 文件大小: {file_size} 字节 ({file_size/1024:.1f}KB)")
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output.append(f"🕐 最后修改: {modified_time.strftime('%Y-%m-%d %H:%M:%S')}")
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return "\n".join(output)
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except Exception as e:
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return f"❌ 获取人脸信息失败: {str(e)}"
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def main():
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parser = argparse.ArgumentParser(
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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。",
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epilog="""
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使用示例:
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# 检测人脸
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face-detector --action detect --image-path photo.jpg
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# 检测人脸（不保存裁剪）
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face-detector --action detect --image-path photo.jpg --no-save
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# 检测人脸（不进行识别）
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face-detector --action detect --image-path photo.jpg --no-recognize
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# 列出已保存的人脸图像
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face-detector --action list-faces --limit 10
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# 列出已知人脸库
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face-detector --action list-known
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# 添加已知人脸
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face-detector --action add-known --image-path face.jpg --person-name "张三"
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# 删除已知人脸
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face-detector --action remove-known --person-name "张三"
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# 查看人脸信息
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face-detector --action get-info --person-name "张三"
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目录结构:
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~/.aibox/faces/ - 人脸图像保存位置
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~/.aibox/known_faces/ - 已知人脸数据库
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需要安装:
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pip install face_recognition opencv-python
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"""
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)
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parser.add_argument("--action", "-a", required=True,
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choices=["detect", "list-faces", "list-known", "add-known", "remove-known", "get-info"],
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help="操作类型")
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# 检测参数
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parser.add_argument("--image-path", "-i", help="图片文件路径")
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parser.add_argument("--no-save", action="store_true", help="不保存裁剪的人脸图像")
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parser.add_argument("--no-recognize", action="store_true", help="不进行人脸识别")
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parser.add_argument("--tolerance", "-t", type=float, default=0.6, help="识别容差（默认: 0.6）")
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# 列表参数
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parser.add_argument("--limit", "-l", type=int, default=20, help="列出文件数量限制（默认: 20）")
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# 已知人脸参数
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parser.add_argument("--person-name", "-n", help="人名标识")
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args = parser.parse_args()
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detector = FaceDetector()
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try:
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if args.action == "detect":
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if not args.image_path:
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print("❌ 需要提供 --image-path 参数")
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sys.exit(1)
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result = detector.detect_faces(
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image_path=args.image_path,
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save_cropped=not args.no_save,
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recognize=not args.no_recognize,
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tolerance=args.tolerance
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)
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print(result)
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elif args.action == "list-faces":
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print(detector.list_saved_faces(args.limit))
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elif args.action == "list-known":
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print(detector.list_known_faces())
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elif args.action == "add-known":
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if not args.image_path or not args.person_name:
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print("❌ 需要提供 --image-path 和 --person-name 参数")
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sys.exit(1)
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result = detector.add_known_face(args.image_path, args.person_name)
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print(result)
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elif args.action == "remove-known":
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if not args.person_name:
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print("❌ 需要提供 --person-name 参数")
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sys.exit(1)
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result = detector.remove_known_face(args.person_name)
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print(result)
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elif args.action == "get-info":
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if not args.person_name:
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print("❌ 需要提供 --person-name 参数")
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sys.exit(1)
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result = detector.get_face_info(args.person_name)
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print(result)
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except Exception as e:
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print(f"❌ 执行错误: {str(e)}")
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sys.exit(1)
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if __name__ == "__main__":
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main() |