linting

deepface/DeepFace.py

@@ -387,7 +387,8 @@ def represent(
     Represent facial images as multi-dimensional vector embeddings.
 
     Args:
-        img_path (str, np.ndarray, IO[bytes], or Sequence[Union[str, np.ndarray, IO[bytes]]]): The exact path to the image, a numpy array
+        img_path (str, np.ndarray, IO[bytes], or Sequence[Union[str, np.ndarray, IO[bytes]]]): 
+            The exact path to the image, a numpy array
             in BGR format, a file object that supports at least `.read` and is opened in binary
             mode, or a base64 encoded image. If the source image contains multiple faces,
             the result will include information for each detected face. If a sequence is provided,

deepface/models/FacialRecognition.py

@@ -31,5 +31,4 @@ class FacialRecognition(ABC):
         embeddings = self.model(img, training=False).numpy()
         if embeddings.shape[0] == 1:
             return embeddings[0].tolist()
-        else:
-            return embeddings.tolist()
+        return embeddings.tolist()

deepface/models/facial_recognition/Dlib.py

@@ -53,8 +53,7 @@ class DlibClient(FacialRecognition):
         embeddings = [np.array(embedding).tolist() for embedding in embeddings]
         if len(embeddings) == 1:
             return embeddings[0]
-        else:
-            return embeddings
+        return embeddings
 
 
 class DlibResNet:

deepface/models/facial_recognition/SFace.py

@@ -47,8 +47,7 @@ class SFaceClient(FacialRecognition):
 
         if embeddings.shape[0] == 1:
             return embeddings[0].tolist()
-        else:
-            return embeddings.tolist()
+        return embeddings.tolist()
 
 
 def load_model(

deepface/models/facial_recognition/VGGFace.py

@@ -71,7 +71,7 @@ class VggFaceClient(FacialRecognition):
         # instead we are now calculating it with traditional way not with keras backend
         embedding = super().forward(img)
         if (
-            isinstance(embedding, list) and 
+            isinstance(embedding, list) and
             isinstance(embedding[0], list)
         ):
             embedding = verification.l2_normalize(embedding, axis=1)

deepface/modules/representation.py

@@ -25,8 +25,10 @@ def represent(
     Represent facial images as multi-dimensional vector embeddings.
 
     Args:
-        img_path (str, np.ndarray, or Sequence[Union[str, np.ndarray]]): The exact path to the image, a numpy array in BGR format,
-            a base64 encoded image, or a sequence of these. If the source image contains multiple faces,
+        img_path (str, np.ndarray, or Sequence[Union[str, np.ndarray]]): 
+            The exact path to the image, a numpy array in BGR format,
+            a base64 encoded image, or a sequence of these.
+            If the source image contains multiple faces,
             the result will include information for each detected face.
 
         model_name (str): Model for face recognition. Options: VGG-Face, Facenet, Facenet512,
@@ -84,7 +86,8 @@ def represent(
 
     for single_img_path in images:
         # ---------------------------------
-        # we have run pre-process in verification. so, this can be skipped if it is coming from verify.
+        # we have run pre-process in verification.
+        # so, this can be skipped if it is coming from verify.
         target_size = model.input_shape
         if detector_backend != "skip":
             img_objs = detection.extract_faces(