244 lines
7.9 KiB
Python
244 lines
7.9 KiB
Python
import piexif
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from PIL import Image
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import face_recognition
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from datetime import datetime
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from models import db, User, Photo
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from werkzeug.utils import secure_filename
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import os
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import numpy as np
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def process_face_encoding(photo_path):
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"""提取人脸特征编码并裁剪出最大的面部区域"""
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# 加载图片
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image = face_recognition.load_image_file(photo_path)
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# 获取所有人脸的位置
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face_locations = face_recognition.face_locations(image)
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if not face_locations:
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return None # 没有检测到人脸
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# 找到最大的人脸
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largest_face = max(face_locations, key=lambda x: (x[2] - x[0]) * (x[3] - x[1])) # 按照面积选取最大的人脸
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# 获取该人脸的特征编码
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face_encodings = face_recognition.face_encodings(image, [largest_face])
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if face_encodings:
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# 裁剪出最大的人脸部分
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top, right, bottom, left = largest_face
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image_pil = Image.open(photo_path)
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cropped_face = image_pil.crop((left, top, right, bottom))
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# 保存裁剪后的图像（可选）
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cropped_face_path = photo_path.replace(".jpg", "_cropped.jpg")
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cropped_face.save(cropped_face_path)
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return face_encodings[0], cropped_face_path # 返回面部编码和裁剪后的人脸图片路径
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return None # 如果没有提取到人脸特征编码
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def add_user(username, userphoto):
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"""添加用户并存储人脸特征"""
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if userphoto:
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filename = secure_filename(userphoto.filename)
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user_dir = os.path.join("static/uploads/users",username)
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user_dir =user_dir.replace("\\","/")
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os.makedirs(user_dir, exist_ok=True)
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photo_path = os.path.join(user_dir,"photo.jpg")
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photo_path = photo_path.replace("\\","/")
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# 保存头像
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userphoto.save(photo_path)
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# 处理人脸特征
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face_encoding = process_face_encoding(photo_path)
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if face_encoding is None:
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print("未检测到人脸，无法添加用户")
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return False
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# 存储到数据库
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new_user = User(username=username, encoding=face_encoding, photo=photo_path)
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db.session.add(new_user)
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db.session.commit()
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return True
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return False
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# 删除用户
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def delete_user(user_id):
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try:
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user = User.query.get(user_id)
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if user:
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db.session.delete(user)
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db.session.commit()
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return True
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return False
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except Exception as e:
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print(e)
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return False
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# 编辑用户
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def edit_user(user_id, username, userphoto):
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try:
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user = User.query.get(user_id)
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if user:
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user.username = username
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if userphoto:
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photo_path = os.path.join('static', 'uploads', 'users', str(user.id))
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os.makedirs(photo_path, exist_ok=True)
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photo_file = os.path.join(photo_path, userphoto.filename)
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userphoto.save(photo_file)
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# 更新照片路径
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photo = Photo.query.filter_by(user_id=user.id).first()
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photo.photo_path = photo_file
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db.session.commit()
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return True
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return False
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except Exception as e:
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print(e)
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return False
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# 获取用户
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def get_user(user_id):
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return User.query.get(user_id)
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def save_temp_photo(photo):
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"""保存临时照片"""
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temp_dir = 'static/uploads/temp'
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os.makedirs(temp_dir, exist_ok=True)
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photo_path = os.path.join(temp_dir, photo.filename).replace("\\", "/")
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photo.save(photo_path)
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return photo_path
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def get_largest_face(image_path):
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"""检测并裁剪最大的人脸"""
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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 None # 没有检测到人脸
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# 找到最大的人脸
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largest_face = max(face_locations, key=lambda loc: (loc[2] - loc[0]) * (loc[3] - loc[1]))
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top, right, bottom, left = largest_face
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# 裁剪最大的人脸
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with Image.open(image_path) as img:
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face_img = img.crop((left, top, right, bottom))
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face_crop_path = image_path.replace(".jpg", "_face.jpg") # 生成裁剪后的人脸图片路径
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face_img.save(face_crop_path)
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return face_crop_path
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def match_face(face_path):
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"""匹配人脸并返回匹配的用户"""
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face_image = face_recognition.load_image_file(face_path)
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face_encodings = face_recognition.face_encodings(face_image)
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if not face_encodings:
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return None # 未检测到人脸
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face_encoding = face_encodings[0] # 取第一张脸
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users = User.query.all()
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for user in users:
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if user.encoding:
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user_encoding = np.array(user.encoding) # 从数据库中获取存储的编码
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match = face_recognition.compare_faces([user_encoding], face_encoding, tolerance=0.35)
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if match[0]:
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return user # 匹配成功
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return None # 未找到匹配
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def get_exif_time(photo_path):
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"""提取 EXIF 时间"""
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try:
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with Image.open(photo_path) as img:
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exif_data = img._getexif()
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if exif_data:
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timestamp = exif_data.get(36867, None) # 36867 为 EXIF 拍摄时间
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return timestamp if timestamp else datetime.now().strftime("%Y-%m-%d %H:%M:%S")
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except Exception as e:
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print(f"无法提取 EXIF 时间: {e}")
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return datetime.now().strftime("%Y-%m-%d %H:%M:%S")
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def save_classified_photo(photo, user, timestamp):
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"""保存分类后的照片"""
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user_dir = os.path.join('static/uploads/users', str(user.id), timestamp)
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os.makedirs(user_dir, exist_ok=True)
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new_photo_path = os.path.join(user_dir, photo.filename)
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try:
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with Image.open(photo) as img:
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img.save(new_photo_path, quality=95)
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except Exception as e:
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print(f"保存图片时出错: {e}")
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# 保存到数据库
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photo_record = Photo(user_id=user.id, created_at=timestamp, classification=True,
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classification_image_path=new_photo_path)
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db.session.add(photo_record)
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db.session.commit()
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print(f"照片 {photo.filename} 已保存至 {new_photo_path}")
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def classify_photos(photos):
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"""处理上传的照片"""
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for photo in photos:
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temp_photo_path = save_temp_photo(photo)
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cropped_face_path = get_largest_face(temp_photo_path)
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if not cropped_face_path:
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print(f"没有检测到人脸：{photo.filename}")
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continue
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matched_user = match_face(cropped_face_path)
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timestamp = get_exif_time(temp_photo_path)
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if matched_user:
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save_classified_photo(photo, matched_user, timestamp)
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else:
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print(f"没有找到匹配的人脸：{photo.filename}")
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# 根据姓名和时间查找照片
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def search_photos(name, date):
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user = User.query.filter_by(username=name).first()
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if not user:
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return []
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photos = Photo.query.filter_by(user_id=user.id).all()
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filtered_photos = []
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for photo in photos:
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# 过滤日期
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exif_data = piexif.load(photo.photo_path)
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date_taken = exif_data.get(piexif.ExifIFD.DateTimeOriginal, b"").decode("utf-8")
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if date_taken and date in date_taken:
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filtered_photos.append(photo)
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return filtered_photos
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def find_match(image_path, known_face_encodings):
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# 加载图片
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unknown_image = face_recognition.load_image_file(image_path)
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# 获取图片中的人脸特征编码
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unknown_face_encoding = face_recognition.face_encodings(unknown_image)
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if unknown_face_encoding:
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unknown_face_encoding = unknown_face_encoding[0]
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# 设置一个匹配的阈值，例如0.6，值越低匹配越宽松
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matches = face_recognition.compare_faces(known_face_encodings, unknown_face_encoding, tolerance=0.6)
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if True in matches:
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match_index = matches.index(True)
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return match_index
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return None
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