Merge branch 'feat/merge-predicts-functions' into feat/make-Race-and-Emotion-batch

deepface/models/demography/Age.py

@@ -1,4 +1,5 @@
 # stdlib dependencies
+
 from typing import List, Union
 
 # 3rd party dependencies
@@ -82,6 +83,31 @@ class ApparentAgeClient(Demography):
         return apparent_ages
 
 
+    def predicts(self, imgs: List[np.ndarray]) -> np.ndarray:
+        """
+        Predict apparent ages of multiple faces
+        Args:
+            imgs (List[np.ndarray]): (n, 224, 224, 3)
+        Returns:
+            apparent_ages (np.ndarray): (n,)
+        """
+        # Convert list to numpy array
+        imgs_:np.ndarray = np.array(imgs)
+        # Remove batch dimension if exists
+        imgs_ = imgs_.squeeze()
+        # Check if the input is a single image
+        if len(imgs_.shape) == 3:
+            # Add batch dimension if not exists
+            imgs_ = np.expand_dims(imgs_, axis=0)
+        # Batch prediction
+        age_predictions = self.model.predict_on_batch(imgs_)
+        apparent_ages = np.array(
+            [find_apparent_age(age_prediction) for age_prediction in age_predictions]
+        )
+        return apparent_ages
+
+
+
 def load_model(
     url=WEIGHTS_URL,
 ) -> Model:

deepface/models/demography/Gender.py

@@ -1,4 +1,5 @@
 # stdlib dependencies
+
 from typing import List, Union
 
 # 3rd party dependencies
@@ -77,6 +78,26 @@ class GenderClient(Demography):
         return predictions
 
 
+    def predicts(self, imgs: List[np.ndarray]) -> np.ndarray:
+        """
+        Predict apparent ages of multiple faces
+        Args:
+            imgs (List[np.ndarray]): (n, 224, 224, 3)
+        Returns:
+            apparent_ages (np.ndarray): (n,)
+        """
+        # Convert list to numpy array
+        imgs_:np.ndarray = np.array(imgs)
+        # Remove redundant dimensions
+        imgs_ = imgs_.squeeze()
+        # Check if the input is a single image
+        if len(imgs_.shape) == 3:
+            # Add batch dimension
+            imgs_ = np.expand_dims(imgs_, axis=0)
+        return self.model.predict_on_batch(imgs_)
+
+
+
 def load_model(
     url=WEIGHTS_URL,
 ) -> Model: