feat: added register face embeddings func and face match for dlib fac…

…e-rec

client/models/face_recognition/recognition.py

@@ -1,8 +1,9 @@
 from importlib import resources
 
 import numpy as np
-from data.routines import FACES_FOLDER
+from data.routines import FACES_FOLDER, register_face_embeddings, iter_face_embeddings
 from deepface import DeepFace
+import face_recognition
 
 MODEL_NAME = "GhostFaceNet"
 
@@ -21,11 +22,43 @@ def get_face_match(login_face: np.ndarray) -> int:
     Returns:
         Matching user id, or -1 if no users matched
     """
-    with resources.as_file(FACES_FOLDER) as faces_folder:
-        try:
-            dfs = DeepFace.find(login_face, str(faces_folder), model_name=MODEL_NAME)
-        except ValueError:
+    # with resources.as_file(FACES_FOLDER) as faces_folder:
+    #     try:
+    #         dfs = DeepFace.find(login_face, str(faces_folder), model_name=MODEL_NAME)
+    #     except ValueError:
+    #         return -1
+    #     df = dfs[0]
+    #     user_id = int(_path_to_user_id(df.iloc[0]["identity"]))
+    #     return user_id
+
+    for user_id, user_embeddings in iter_face_embeddings():
+        login_embeddings = face_recognition.face_encodings(login_face, model="small")
+
+        # Should only detect one face
+        if len(login_embeddings) != 1:
+            return -1
+
+        login_embedding = login_embeddings[0]
+        matches = face_recognition.compare_faces(user_embeddings, login_embedding)
+
+        if any(matches):
+            return user_id
+
+    return -1
+
+
+def register_faces(user_id: int, faces: list[np.ndarray]) -> int:
+    face_embeddings = []
+    for face in faces:
+        all_faces_embed = face_recognition.face_encodings(face, model="small")
+
+        # Should only detect one face
+        if len(all_faces_embed) != 1:
             return -1
-        df = dfs[0]
-        user_id = int(_path_to_user_id(df.iloc[0]["identity"]))
-        return user_id
+
+        face_embedding = all_faces_embed[0]
+        face_embeddings.append(face_embedding)
+
+    register_face_embeddings(user_id, face_embeddings)
+
+    return 0