Engineering stuff, remove redundant code.

As mentioned:
https://github.com/serengil/deepface/pull/1396#discussion_r1900017766

deepface/models/Demography.py

@@ -20,3 +20,29 @@ class Demography(ABC):
     @abstractmethod
     def predict(self, img: Union[np.ndarray, List[np.ndarray]]) -> Union[np.ndarray, np.float64]:
         pass
+
+    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.
+        Args:
+            img: Single image as np.ndarray (224, 224, 3) or
+                List of images as List[np.ndarray] or
+                Batch of images as np.ndarray (n, 224, 224, 3)
+        Returns:
+            Four-dimensional numpy array (n, 224, 224, 3)
+        """
+        if isinstance(img, list): # Convert from list to image batch.
+            image_batch = np.array(img)
+        else:
+            image_batch = 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
+            imgs = np.expand_dims(image_batch, axis=0)
+
+        return image_batch

deepface/models/demography/Age.py

@@ -51,19 +51,8 @@ class ApparentAgeClient(Demography):
         Returns:
             np.ndarray (n,)
         """
-        # Convert to numpy array if input is list
+        # Preprocessing input image or image list.
-        if isinstance(img, list):
+        imgs = self._preprocess_batch_or_single_input(img)
-            imgs = np.array(img)
-        else:
-            imgs = img
-
-        # Remove batch dimension if exists
-        imgs = imgs.squeeze()
-
-        # Check input dimension
-        if len(imgs.shape) == 3:
-            # Single image - add batch dimension
-            imgs = np.expand_dims(imgs, axis=0)
 
         # Batch prediction
         age_predictions = self.model.predict_on_batch(imgs)

deepface/models/demography/Emotion.py

@@ -69,19 +69,8 @@ class EmotionClient(Demography):
             np.ndarray (n, n_emotions)
             where n_emotions is the number of emotion categories
         """
-        # Convert to numpy array if input is list
+        # Preprocessing input image or image list.
-        if isinstance(img, list):
+        imgs = self._preprocess_batch_or_single_input(img)
-            imgs = np.array(img)
-        else:
-            imgs = img
-
-        # Remove batch dimension if exists
-        imgs = imgs.squeeze()
-
-        # Check input dimension
-        if len(imgs.shape) == 3:
-            # Single image - add batch dimension
-            imgs = np.expand_dims(imgs, axis=0)
 
         # Preprocess each image
         processed_imgs = np.array([self._preprocess_image(img) for img in imgs])

deepface/models/demography/Gender.py

@@ -51,19 +51,8 @@ class GenderClient(Demography):
         Returns:
             np.ndarray (n, 2)
         """
-        # Convert to numpy array if input is list
+        # Preprocessing input image or image list.
-        if isinstance(img, list):
+        imgs = self._preprocess_batch_or_single_input(img)
-            imgs = np.array(img)
-        else:
-            imgs = img
-
-        # Remove batch dimension if exists
-        imgs = imgs.squeeze()
-
-        # Check input dimension
-        if len(imgs.shape) == 3:
-            # Single image - add batch dimension
-            imgs = np.expand_dims(imgs, axis=0)
 
         # Batch prediction
         predictions = self.model.predict_on_batch(imgs)

deepface/models/demography/Race.py

@@ -51,19 +51,8 @@ class RaceClient(Demography):
             np.ndarray (n, n_races)
             where n_races is the number of race categories
         """
-        # Convert to numpy array if input is list
+        # Preprocessing input image or image list.
-        if isinstance(img, list):
+        imgs = self._preprocess_batch_or_single_input(img)
-            imgs = np.array(img)
-        else:
-            imgs = img
-
-        # Remove batch dimension if exists
-        imgs = imgs.squeeze()
-
-        # Check input dimension
-        if len(imgs.shape) == 3:
-            # Single image - add batch dimension
-            imgs = np.expand_dims(imgs, axis=0)
 
         # Batch prediction
         predictions = self.model.predict_on_batch(imgs)