diff --git a/groundingdino/util/inference.py b/groundingdino/util/inference.py
index 84a962e..70ea789 100644
--- a/groundingdino/util/inference.py
+++ b/groundingdino/util/inference.py
@@ -37,6 +37,17 @@ def load_model(model_config_path: str, model_checkpoint_path: str, device: str =
 
 
 def load_image(image_path: str) -> Tuple[np.array, torch.Tensor]:
+    """
+    Load an image and apply transformations.
+    This function takes the path to an image file, loads the image, and applies a series of transformations to it.
+    The transformations include resizing the image, converting it to a tensor, and normalizing its pixel values.
+
+    Parameters:
+    image_path (str): The path to the image file.
+
+    Returns:
+    Tuple[np.array, torch.Tensor]: A tuple containing the original image as a NumPy array and the transformed image as a PyTorch tensor.
+    """
     transform = T.Compose(
         [
             T.RandomResize([800], max_size=1333),
@@ -49,6 +60,29 @@ def load_image(image_path: str) -> Tuple[np.array, torch.Tensor]:
     image_transformed, _ = transform(image_source, None)
     return image, image_transformed
 
+def transform_image(PIL_image: PIL.Image.Image) -> Tuple[np.array, torch.Tensor]:
+    """
+    Transform an RGB image and convert it to a tensor.
+    
+    This function takes a PIL Image, applies a series of transformations to it, and returns the original and transformed images.
+    The transformations include resizing the image, converting it to a tensor, and normalizing its pixel values.
+
+    Parameters:
+    PIL_image (PIL.Image.Image): The input image.
+
+    Returns:
+    Tuple[np.array, torch.Tensor]: A tuple containing the original image as a NumPy array and the transformed image as a PyTorch tensor.
+    """
+    transform = T.Compose(
+        [
+            T.RandomResize([800], max_size=1333),
+            T.ToTensor(),
+            T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
+        ]
+    )
+    image = np.asarray(PIL_image)
+    image_transformed, _ = transform(PIL_image, None)
+    return image, image_transformed
 
 def predict(
         model,