feat: 人脸数据保存在数据库中

apps/hrm/apps.py

@@ -1,4 +1,5 @@
 from django.apps import AppConfig
+from django.core.cache import cache
 
 
 class HrmConfig(AppConfig):
@@ -6,4 +7,8 @@ class HrmConfig(AppConfig):
     verbose_name = '人力资源管理'
 
     def ready(self):
-        import apps.hrm.signals
+        if cache.get('update_global_face_pd_task', True):
+            from apps.hrm.tasks import update_global_face_pd
+            update_global_face_pd.delay()
+            cache.set('update_global_face_pd_task', False, timeout=30)
+        return super().ready()

apps/hrm/migrations/0012_employee_facenet512_data.py

@@ -0,0 +1,18 @@
+# Generated by Django 3.2.12 on 2023-09-11 07:21
+
+from django.db import migrations, models
+
+
+class Migration(migrations.Migration):
+
+    dependencies = [
+        ('hrm', '0011_alter_certificate_number'),
+    ]
+
+    operations = [
+        migrations.AddField(
+            model_name='employee',
+            name='facenet512_data',
+            field=models.JSONField(blank=True, null=True, verbose_name='人脸数据'),
+        ),
+    ]

apps/hrm/models.py

@@ -44,7 +44,7 @@ class Employee(CommonBModel):
     not_work_remark = models.CharField('当前未打卡说明', null=True, blank=True, max_length=200)
     third_info = models.JSONField('三方信息', default=dict, null=False, blank=True)  # 主要是定位卡信息
     post = models.ForeignKey(Post, verbose_name='所属岗位', on_delete=models.SET_NULL, null=True, blank=True)
-
+    facenet512_data = models.JSONField('人脸数据', null=True, blank=True)
     class Meta:
         verbose_name = '员工补充信息'
         verbose_name_plural = verbose_name

apps/hrm/serializers.py

@@ -13,6 +13,8 @@ from apps.system.serializers import DeptSimpleSerializer, UserSimpleSerializer
 from django.db import transaction
 from django.core.cache import cache
 from apps.utils.tools import check_id_number_e, get_info_from_id
+from rest_framework.exceptions import ParseError
+from django.conf import settings
 
 
 class EmployeeSimpleSerializer(CustomModelSerializer):
@@ -69,8 +71,14 @@ class EmployeeCreateUpdateSerializer(CustomModelSerializer):
         old_name = instance.name
         instance = super().update(instance, validated_data)
         if old_photo != instance.photo:  # 如果照片有变动,需要更新人脸库
-            from apps.hrm.tasks import delete_face_pkl
+            face_data, msg = HrmService.get_facedata_from_img(settings.BASE_DIR + instance.photo)
-            delete_face_pkl.delay(instance.id)
+            if face_data:
+                instance.facenet512_data = face_data
+                instance.save()
+            else:
+                raise ParseError(msg)
+            from apps.hrm.tasks import update_global_face_pd
+            update_global_face_pd.delay()
         if instance.user and instance != old_name:
             instance.user.name = instance.name
             instance.user.save()

apps/hrm/services.py

@@ -15,9 +15,11 @@ from apps.third.dahua import dhClient
 from apps.third.models import TDevice
 from apps.third.tapis import dhapis
 from apps.utils.tools import rannum, ranstr
+from apps.utils.face import face_find
 
 myLogger = logging.getLogger('log')
 
+global_face_df = None  #全局人脸库dataframe
 
 class HrmService:
 
@@ -404,33 +406,25 @@ class HrmService:
 
     @classmethod
     def face_find_from_base64(cls, base64_data):
-        from deepface import DeepFace
         img_name = str(uuid.uuid4())
         img_path = settings.BASE_DIR +'/temp/face_' + img_name +'.jpg'
         with open(img_path, 'wb') as f:
             f.write(base64_data)
-        db_path = os.path.join(settings.BASE_DIR, 'media/face')
+        # db_path = os.path.join(settings.BASE_DIR, 'media/face')
-        dfs = DeepFace.find(img_path=img_path, db_path=db_path, model_name='Facenet512')
+        # cache_face_db = cache
+        dfs = face_find(img_path=img_path, global_df=global_face_df, model_name='Facenet512')
         df = dfs[0]
         if not df.empty:
             matched = df.iloc[0].identity
-            epId = matched.split('/')[-1].split('.')[0]
+            return Employee.objects.get(id=matched), ''
-            return Employee.objects.get(id=epId), ''
         else:
             return None, '人脸未匹配,请调整位置'
 
-
+    @classmethod
-
+    def get_facedata_from_img(cls, img_path):
-# def VGGFaceloadModel(weight_path):
+        try:
-#     from deepface.basemodels import VGGFace
+            from deepface import DeepFace
-#     from keras.src.engine.training import Model
+            embedding_objs = DeepFace.represent(img_path=img_path, model_name='Facenet512')
-#     model = VGGFace.baseModel()
+            return embedding_objs[0]["embedding"], ''
-#     model.load_weights(weight_path)
+        except Exception as e:
-#     vgg_face_descriptor = Model(inputs=model.layers[0].input, outputs=model.layers[-2].output)
+            return None, '人脸数据获取失败请重新上传图片'
-#     return vgg_face_descriptor
-
-# def Facenet512loadModel(weight_path):
-#     from deepface.basemodels import Facenet
-#     model = Facenet.InceptionResNetV2(dimension=512)
-#     model.load_weights(weight_path)
-#     return model

apps/hrm/tasks.py

@@ -8,7 +8,7 @@ from dateutil import tz
 from django.core.cache import cache
 
 from apps.hrm.models import Employee
-from apps.hrm.services import HrmService
+from apps.hrm.services import HrmService, global_face_df
 from apps.third.dahua import dhClient
 from apps.third.tapis import dhapis
 from apps.utils.tasks import CustomTask
@@ -124,4 +124,12 @@ def delete_face_pkl(epId):
             os.remove(file_path)
         except Exception as e:
             delete_face_pkl.apply_async(countdown=5)
-    
+
+
+@shared_task(base=CustomTask)
+def update_global_face_pd():
+    import pandas as pd
+    facedata = Employee.objects.filter(facenet512_data__isnull=False, 
+                    user__is_active=True).values_list('id', 'facenet512_data', flat=True)
+    cache.set('global_face_data', facedata, timeout=None)
+    global_face_df = pd.DataFrame(list(facedata), columns=["identity", "Facenet512_representation"])

apps/utils/face.py

@@ -0,0 +1,90 @@
+from deepface import DeepFace
+from deepface.commons import functions, distance as dst
+import pandas as pd
+import time
+
+def face_find(
+    img_path,
+    global_df,
+    model_name="Facenet512",
+    distance_metric="cosine",
+    enforce_detection=True,
+    detector_backend="opencv",
+    align=True,
+    normalization="base",
+    silent=False,
+):
+    tic = time.time()
+    target_size = functions.find_target_size(model_name=model_name)
+    # now, we got representations for facial database
+    df = global_df
+
+    # img path might have more than once face
+    target_objs = functions.extract_faces(
+        img=img_path,
+        target_size=target_size,
+        detector_backend=detector_backend,
+        grayscale=False,
+        enforce_detection=enforce_detection,
+        align=align,
+    )
+
+    resp_obj = []
+
+    for target_img, target_region, _ in target_objs:
+        target_embedding_obj = DeepFace.represent(
+            img_path=target_img,
+            model_name=model_name,
+            enforce_detection=enforce_detection,
+            detector_backend="skip",
+            align=align,
+            normalization=normalization,
+        )
+
+        target_representation = target_embedding_obj[0]["embedding"]
+
+        result_df = df.copy()  # df will be filtered in each img
+        result_df["source_x"] = target_region["x"]
+        result_df["source_y"] = target_region["y"]
+        result_df["source_w"] = target_region["w"]
+        result_df["source_h"] = target_region["h"]
+
+        distances = []
+        for index, instance in df.iterrows():
+            source_representation = instance[f"{model_name}_representation"]
+
+            if distance_metric == "cosine":
+                distance = dst.findCosineDistance(source_representation, target_representation)
+            elif distance_metric == "euclidean":
+                distance = dst.findEuclideanDistance(source_representation, target_representation)
+            elif distance_metric == "euclidean_l2":
+                distance = dst.findEuclideanDistance(
+                    dst.l2_normalize(source_representation),
+                    dst.l2_normalize(target_representation),
+                )
+            else:
+                raise ValueError(f"invalid distance metric passes - {distance_metric}")
+
+            distances.append(distance)
+
+            # ---------------------------
+
+        result_df[f"{model_name}_{distance_metric}"] = distances
+
+        threshold = dst.findThreshold(model_name, distance_metric)
+        result_df = result_df.drop(columns=[f"{model_name}_representation"])
+        result_df = result_df[result_df[f"{model_name}_{distance_metric}"] <= threshold]
+        result_df = result_df.sort_values(
+            by=[f"{model_name}_{distance_metric}"], ascending=True
+        ).reset_index(drop=True)
+
+        resp_obj.append(result_df)
+
+    # -----------------------------------
+
+    toc = time.time()
+
+    if not silent:
+        print("find function lasts ", toc - tic, " seconds")
+
+    return resp_obj

server/settings.py

@@ -202,7 +202,7 @@ MEDIA_URL = '/media/'
 MEDIA_ROOT = os.path.join(BASE_DIR, 'media')
 
 # 人脸库配置
-# 如果地址不存在，则自动创建
+# 如果地址不存在，则自动创建/现在直接存库可不用
 FACE_PATH= os.path.join(BASE_DIR, 'media/face')
 if not os.path.exists(FACE_PATH):
     os.makedirs(FACE_PATH)