104 lines
3.9 KiB
Python
104 lines
3.9 KiB
Python
from django.conf import settings
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import uuid
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import face_recognition
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import os
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from apps.hrm.models import Employee
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from apps.hrm.tasks import update_all_user_facedata_cache
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from apps.system.models import User
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from django.core.cache import cache
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class HRMService:
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@classmethod
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def face_compare_from_path(cls, path, tolerance=0.42):
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filepath = settings.BASE_DIR +path
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try:
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unknown_picture = face_recognition.load_image_file(filepath)
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unknown_face_encoding = face_recognition.face_encodings(unknown_picture, num_jitters=2)[0]
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# os.remove(filepath)
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except:
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# os.remove(filepath)
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return None, '识别失败,请调整位置'
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# 匹配人脸库
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face_datas = cache.get('face_datas')
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if face_datas is None:
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update_all_user_facedata_cache()
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face_datas = cache.get('face_datas')
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face_users = cache.get('face_users')
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results = face_recognition.compare_faces(face_datas,
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unknown_face_encoding, tolerance=tolerance)
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user_index = cls.get_user_index(results)
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user_index_len = len(user_index)
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if user_index_len == 1:
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user = User.objects.get(id=face_users[user_index[0]])
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return user, ''
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elif user_index_len == 0:
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return None, '人脸未匹配,请调整位置'
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else:
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user_ids = []
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for i in user_index:
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user_ids.append(face_users[i])
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user_name_str = ','.join(list(User.objects.filter(id__in=user_ids).values_list('name', flat=True)))
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return None, '匹配多张人脸:' + user_name_str
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@classmethod
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def face_compare_from_base64(cls, base64_data, tolerance=0.42):
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filename = str(uuid.uuid4())
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filepath = settings.BASE_DIR +'/temp/' + filename +'.png'
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with open(filepath, 'wb') as f:
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f.write(base64_data)
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try:
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unknown_picture = face_recognition.load_image_file(filepath)
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unknown_face_encoding = face_recognition.face_encodings(unknown_picture, num_jitters=2)[0]
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os.remove(filepath)
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except:
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os.remove(filepath)
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return None, '识别失败,请调整位置'
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# 匹配人脸库
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face_datas = cache.get('face_datas')
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if face_datas is None:
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update_all_user_facedata_cache()
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face_datas = cache.get('face_datas')
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face_users = cache.get('face_users')
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results = face_recognition.compare_faces(face_datas,
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unknown_face_encoding, tolerance=tolerance)
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user_index = cls.get_user_index(results)
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user_index_len = len(user_index)
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if user_index_len == 1:
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user = User.objects.get(id=face_users[user_index[0]])
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return user, ''
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elif user_index_len == 0:
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return None, '人脸未匹配,请调整位置'
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else:
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user_ids = []
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for i in user_index:
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user_ids.append(face_users[i])
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user_name_str = ','.join(list(User.objects.filter(id__in=user_ids).values_list('name', flat=True)))
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return None, '匹配多张人脸:' + user_name_str
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@classmethod
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def get_user_index(cls, results):
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"""
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返回user_index列表
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"""
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true_num = 0
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user_index = []
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for index, value in enumerate(results):
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if value:
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true_num = true_num + 1
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user_index.append(index)
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return user_index
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@classmethod
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def get_facedata_from_img(cls, img_path):
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try:
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photo_path = settings.BASE_DIR + img_path
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picture_of_me = face_recognition.load_image_file(photo_path)
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my_face_encoding = face_recognition.face_encodings(picture_of_me, num_jitters=2)[0]
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face_data_list = my_face_encoding.tolist()
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return face_data_list, ''
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except:
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return None, '人脸数据获取失败请重新上传图片' |