From 688fbe6b902f5a368142284fad03cc6054bb0d9b Mon Sep 17 00:00:00 2001
From: NatLee <natlee.work@gmail.com>
Date: Mon, 13 Jan 2025 22:27:11 +0800
Subject: [PATCH] [fix] lint

---
 deepface/models/Demography.py  | 38 +++++++++++++++++++---------------
 deepface/modules/demography.py |  8 +++----
 2 files changed, 25 insertions(+), 21 deletions(-)

diff --git a/deepface/models/Demography.py b/deepface/models/Demography.py
index fb9d106..329a156 100644
--- a/deepface/models/Demography.py
+++ b/deepface/models/Demography.py
@@ -28,24 +28,32 @@ class Demography(ABC):
         And switch to batch prediction if receives batched images.
 
         Args:
-            img_batch: Batch of images as np.ndarray (n, x, y, c), with n >= 1, x = image width, y = image height, c = channel
-                       Or Single image as np.ndarray (1, x, y, c), with x = image width, y = image height and c = channel
-                       The channel dimension may be omitted if the image is grayscale. (For emotion model)
+            img_batch:
+                Batch of images as np.ndarray (n, x, y, c)
+                    with n >= 1, x = image width, y = image height, c = channel
+                Or Single image as np.ndarray (1, x, y, c)
+                    with x = image width, y = image height and c = channel
+                The channel dimension may be omitted if the image is grayscale. (For emotion model)
         """
         if not self.model_name: # Check if called from derived class
             raise NotImplementedError("no model selected")
-        
         assert img_batch.ndim == 4, "expected 4-dimensional tensor input"
+        # Single image
+        if img_batch.shape[0] == 1:
+            # Check if grayscale by checking last dimension, if not 3, it is grayscale.
+            if img_batch.shape[-1] != 3:
+                # Remove batch dimension
+                img_batch = img_batch.squeeze(0)
+            # Predict with legacy method.
+            return self.model(img_batch, training=False).numpy()[0, :]
+        # Batch of images
+        # Predict with batch prediction
+        return self.model.predict_on_batch(img_batch)
 
-        if img_batch.shape[0] == 1: # Single image
-            if img_batch.shape[-1] != 3: # Check if grayscale by checking last dimension, if not 3, it is grayscale.
-                img_batch = img_batch.squeeze(0) # Remove batch dimension
-            predict_result = self.model(img_batch, training=False).numpy()[0, :] # Predict with legacy method.
-            return predict_result
-        else: # Batch of images
-            return self.model.predict_on_batch(img_batch) # Predict with batch prediction
-
-    def _preprocess_batch_or_single_input(self, img: Union[np.ndarray, List[np.ndarray]]) -> np.ndarray:
+    def _preprocess_batch_or_single_input(
+        self,
+        img: Union[np.ndarray, List[np.ndarray]]
+    ) -> np.ndarray:
 
         """
         Preprocess single or batch of images, return as 4-D numpy array.
@@ -56,15 +64,11 @@ class Demography(ABC):
         Returns:
             Four-dimensional numpy array (n, 224, 224, 3)
         """
-
         image_batch = np.array(img)
-
         # Remove batch dimension in advance if exists
         image_batch = image_batch.squeeze()
-
         # Check input dimension
         if len(image_batch.shape) == 3:
             # Single image - add batch dimension
             image_batch = np.expand_dims(image_batch, axis=0)
-
         return image_batch
diff --git a/deepface/modules/demography.py b/deepface/modules/demography.py
index 9789007..c78f73b 100644
--- a/deepface/modules/demography.py
+++ b/deepface/modules/demography.py
@@ -168,9 +168,9 @@ def analyze(
         model = modeling.build_model(task="facial_attribute", model_name=action.capitalize())
         predictions = model.predict(faces_array)
 
-        # If the model returns a single prediction, reshape it to match the number of faces
-        # Use number of faces and number of predictions shape to determine the correct shape of predictions
-        # For example, if there are 1 face to predict with Emotion model, reshape predictions to (1, 7)
+        # If the model returns a single prediction, reshape it to match the number of faces.
+        # Determine the correct shape of predictions by using number of faces and predictions shape.
+        # Example: For 1 face with Emotion model, predictions will be reshaped to (1, 7).
         if faces_array.shape[0] == 1 and len(predictions.shape) == 1:
             # For models like `Emotion`, which return a single prediction for a single face
             predictions = predictions.reshape(1, -1)
@@ -229,4 +229,4 @@ def analyze(
             for result, race_result in zip(results, race_results):
                 result.update(race_result)
 
-    return results
\ No newline at end of file
+    return results