Refine type hints as per PR review

deepface/DeepFace.py

@@ -5,7 +5,9 @@
 import time
 import pickle
 import logging
-from typing import Any, Dict, List, Tuple
+from typing import Any, Dict, List, Tuple, Union
+from keras.engine.functional import Functional
+from deepface.basemodels import DlibResNet, SFace
 
 # 3rd party dependencies
 import numpy as np
@@ -46,7 +48,7 @@
 # -----------------------------------
 
 
-def build_model(model_name: str) -> Any:
+def build_model(model_name: str) -> Union[Functional, DlibResNet.DlibResNet, SFace.SFaceModel]:
     """
     This function builds a deepface model
     Parameters:
@@ -92,8 +94,8 @@ def build_model(model_name: str) -> Any:
 
 
 def verify(
-    img1_path: str,
-    img2_path: str,
+    img1_path: Union[str, np.ndarray],
+    img2_path: Union[str, np.ndarray],
     model_name: str = "VGG-Face",
     detector_backend: str = "opencv",
     distance_metric: str = "cosine",
@@ -232,7 +234,7 @@ def verify(
 
 
 def analyze(
-    img_path: str,
+    img_path: Union[str, np.ndarray],,
     actions: Tuple[str, ...] = ("emotion", "age", "gender", "race"),
     enforce_detection: bool = True,
     detector_backend: str = "opencv",
@@ -410,7 +412,7 @@ def analyze(
 
 
 def find(
-    img_path: str,
+    img_path: Union[str, np.ndarray],
     db_path : str,
     model_name : str ="VGG-Face",
     distance_metric : str ="cosine",
@@ -651,7 +653,7 @@ def find(
 
 
 def represent(
-    img_path: str,
+    img_path: Union[str, np.ndarray],
     model_name: str = "VGG-Face",
     enforce_detection: bool = True,
     detector_backend: str = "opencv",
@@ -817,7 +819,7 @@ def stream(
 
 
 def extract_faces(
-    img_path: str,
+    img_path: Union[str, np.ndarray],
     target_size: Tuple[int, int] = (224, 224),
     detector_backend: str = "opencv",
     enforce_detection: bool = True,