[update] make Race and Emotion batch prediction

deepface/models/demography/Age.py

@@ -2,7 +2,6 @@
 
 from typing import List, Union
 
-
 # 3rd party dependencies
 import numpy as np
 
@@ -42,16 +41,15 @@ def __init__(self):
         self.model = load_model()
         self.model_name = "Age"
 
-    def predict(self, img: Union[np.ndarray, List[np.ndarray]]) -> Union[np.float64, np.ndarray]:
+    def predict(self, img: Union[np.ndarray, List[np.ndarray]]) -> np.ndarray:
         """
         Predict apparent age(s) for single or multiple faces
         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:
-            Single age as np.float64 or
-            Multiple ages as np.ndarray (n,)
+            np.ndarray (n,)
         """
         # Convert to numpy array if input is list
         if isinstance(img, list):
@@ -66,9 +64,6 @@ def predict(self, img: Union[np.ndarray, List[np.ndarray]]) -> Union[np.float64,
         if len(imgs.shape) == 3:
             # Single image - add batch dimension
             imgs = np.expand_dims(imgs, axis=0)
-            is_single = True
-        else:
-            is_single = False
 
         # Batch prediction
         age_predictions = self.model.predict_on_batch(imgs)
@@ -78,11 +73,9 @@ def predict(self, img: Union[np.ndarray, List[np.ndarray]]) -> Union[np.float64,
             [find_apparent_age(age_prediction) for age_prediction in age_predictions]
         )
 
-        # Return single value for single image
-        if is_single:
-            return apparent_ages[0]
         return apparent_ages
 
+
     def predicts(self, imgs: List[np.ndarray]) -> np.ndarray:
         """
         Predict apparent ages of multiple faces
@@ -107,6 +100,7 @@ def predicts(self, imgs: List[np.ndarray]) -> np.ndarray:
         return apparent_ages
 
 
+
 def load_model(
     url=WEIGHTS_URL,
 ) -> Model:

deepface/models/demography/Emotion.py

@@ -1,3 +1,6 @@
+# stdlib dependencies
+from typing import List, Union
+
 # 3rd party dependencies
 import numpy as np
 import cv2
@@ -43,16 +46,53 @@ def __init__(self):
         self.model = load_model()
         self.model_name = "Emotion"
 
-    def predict(self, img: np.ndarray) -> np.ndarray:
-        img_gray = cv2.cvtColor(img[0], cv2.COLOR_BGR2GRAY)
+    def _preprocess_image(self, img: np.ndarray) -> np.ndarray:
+        """
+        Preprocess single image for emotion detection
+        Args:
+            img: Input image (224, 224, 3)
+        Returns:
+            Preprocessed grayscale image (48, 48)
+        """
+        img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
         img_gray = cv2.resize(img_gray, (48, 48))
-        img_gray = np.expand_dims(img_gray, axis=0)
-
-        # model.predict causes memory issue when it is called in a for loop
-        # emotion_predictions = self.model.predict(img_gray, verbose=0)[0, :]
-        emotion_predictions = self.model(img_gray, training=False).numpy()[0, :]
-
-        return emotion_predictions
+        return img_gray
+
+    def predict(self, img: Union[np.ndarray, List[np.ndarray]]) -> np.ndarray:
+        """
+        Predict emotion probabilities for single or multiple faces
+        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:
+            np.ndarray (n, n_emotions)
+            where n_emotions is the number of emotion categories
+        """
+        # Convert to numpy array if input is list
+        if isinstance(img, list):
+            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])
+
+        # Add channel dimension for grayscale images
+        processed_imgs = np.expand_dims(processed_imgs, axis=-1)
+
+        # Batch prediction
+        predictions = self.model.predict_on_batch(processed_imgs)
+
+        return predictions
 
 
 def load_model(

deepface/models/demography/Gender.py

@@ -2,7 +2,6 @@
 
 from typing import List, Union
 
-
 # 3rd party dependencies
 import numpy as np
 
@@ -42,16 +41,15 @@ def __init__(self):
         self.model = load_model()
         self.model_name = "Gender"
 
-    def predict(self, img: Union[np.ndarray, List[np.ndarray]]) -> Union[np.ndarray, np.ndarray]:
+    def predict(self, img: Union[np.ndarray, List[np.ndarray]]) -> np.ndarray:
         """
         Predict gender probabilities for single or multiple faces
         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:
-            Single prediction as np.ndarray (2,) [female_prob, male_prob] or
-            Multiple predictions as np.ndarray (n, 2)
+            np.ndarray (n, 2)
         """
         # Convert to numpy array if input is list
         if isinstance(img, list):
@@ -66,18 +64,13 @@ def predict(self, img: Union[np.ndarray, List[np.ndarray]]) -> Union[np.ndarray,
         if len(imgs.shape) == 3:
             # Single image - add batch dimension
             imgs = np.expand_dims(imgs, axis=0)
-            is_single = True
-        else:
-            is_single = False
 
         # Batch prediction
         predictions = self.model.predict_on_batch(imgs)
 
-        # Return single prediction for single image
-        if is_single:
-            return predictions[0]
         return predictions
 
+
     def predicts(self, imgs: List[np.ndarray]) -> np.ndarray:
         """
         Predict apparent ages of multiple faces
@@ -97,6 +90,7 @@ def predicts(self, imgs: List[np.ndarray]) -> np.ndarray:
         return self.model.predict_on_batch(imgs_)
 
 
+
 def load_model(
     url=WEIGHTS_URL,
 ) -> Model:

deepface/models/demography/Race.py

@@ -1,3 +1,6 @@
+# stdlib dependencies
+from typing import List, Union
+
 # 3rd party dependencies
 import numpy as np
 
@@ -37,10 +40,35 @@ def __init__(self):
         self.model = load_model()
         self.model_name = "Race"
 
-    def predict(self, img: np.ndarray) -> np.ndarray:
-        # model.predict causes memory issue when it is called in a for loop
-        # return self.model.predict(img, verbose=0)[0, :]
-        return self.model(img, training=False).numpy()[0, :]
+    def predict(self, img: Union[np.ndarray, List[np.ndarray]]) -> np.ndarray:
+        """
+        Predict race probabilities for single or multiple faces
+        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:
+            np.ndarray (n, n_races)
+            where n_races is the number of race categories
+        """
+        # Convert to numpy array if input is list
+        if isinstance(img, list):
+            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)
+
+        return predictions
 
 
 def load_model(
@@ -62,7 +90,7 @@ def load_model(
 
     # --------------------------
 
-    race_model = Model(inputs=model.input, outputs=base_model_output)
+    race_model = Model(inputs=model.inputs, outputs=base_model_output)
 
     # --------------------------