Feat/batch predict age and gender

deepface/models/Demography.py

@@ -1,4 +1,4 @@
-from typing import Union
+from typing import Union, List
 from abc import ABC, abstractmethod
 import numpy as np
 from deepface.commons import package_utils
@@ -18,5 +18,31 @@ class Demography(ABC):
     model_name: str
 
     @abstractmethod
-    def predict(self, img: np.ndarray) -> Union[np.ndarray, np.float64]:
+    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
+            image_batch = np.expand_dims(image_batch, axis=0)
+
+        return image_batch

deepface/models/demography/Age.py

@@ -1,3 +1,7 @@
+# stdlib dependencies
+
+from typing import List, Union
+
 # 3rd party dependencies
 import numpy as np
 
@@ -37,12 +41,28 @@ def __init__(self):
         self.model = load_model()
         self.model_name = "Age"
 
-    def predict(self, img: np.ndarray) -> np.float64:
-        # model.predict causes memory issue when it is called in a for loop
-        # age_predictions = self.model.predict(img, verbose=0)[0, :]
-        age_predictions = self.model(img, training=False).numpy()[0, :]
-        return find_apparent_age(age_predictions)
-
+    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:
+            np.ndarray (n,)
+        """
+        # Preprocessing input image or image list.
+        imgs = self._preprocess_batch_or_single_input(img)
+
+        # Batch prediction
+        age_predictions = self.model.predict_on_batch(imgs)
+
+        # Calculate apparent ages
+        apparent_ages = np.array(
+            [find_apparent_age(age_prediction) for age_prediction in age_predictions]
+        )
+
+        return apparent_ages
 
 def load_model(
     url=WEIGHTS_URL,
@@ -65,7 +85,7 @@ def load_model(
 
     # --------------------------
 
-    age_model = Model(inputs=model.input, outputs=base_model_output)
+    age_model = Model(inputs=model.inputs, outputs=base_model_output)
 
     # --------------------------
 

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 +46,42 @@ 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
+        """
+        # Preprocessing input image or image list.
+        imgs = self._preprocess_batch_or_single_input(img)
+
+        # 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

@@ -1,3 +1,7 @@
+# stdlib dependencies
+
+from typing import List, Union
+
 # 3rd party dependencies
 import numpy as np
 
@@ -37,11 +41,23 @@ def __init__(self):
         self.model = load_model()
         self.model_name = "Gender"
 
-    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 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:
+            np.ndarray (n, 2)
+        """
+        # Preprocessing input image or image list.
+        imgs = self._preprocess_batch_or_single_input(img)
+
+        # Batch prediction
+        predictions = self.model.predict_on_batch(imgs)
 
+        return predictions
 
 def load_model(
     url=WEIGHTS_URL,
@@ -64,7 +80,7 @@ def load_model(
 
     # --------------------------
 
-    gender_model = Model(inputs=model.input, outputs=base_model_output)
+    gender_model = Model(inputs=model.inputs, outputs=base_model_output)
 
     # --------------------------
 

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 +40,24 @@ 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
+        """
+        # Preprocessing input image or image list.
+        imgs = self._preprocess_batch_or_single_input(img)
+
+        # Batch prediction
+        predictions = self.model.predict_on_batch(imgs)
+
+        return predictions
 
 
 def load_model(
@@ -62,7 +79,7 @@ def load_model(
 
     # --------------------------
 
-    race_model = Model(inputs=model.input, outputs=base_model_output)
+    race_model = Model(inputs=model.inputs, outputs=base_model_output)
 
     # --------------------------