Update processing_phi3_v.py

This model was not able to process list of numpy arrays (and probably with pytorch tensors)
you can try yourself in the example code on this model card. Just use 'np.array(Image.open(requests.get(url, stream=True).raw)) '


I solved this issue implementing a function to convert any type of valid image to PIL Image object.

processing_phi3_v.py

@@ -56,7 +56,7 @@ logger = logging.get_logger(__name__)
 if is_vision_available():
     from PIL import Image
 
-import torch
+import torch  # why is this imported twice?
 import torchvision
 
 def padding_336(b):
@@ -205,6 +205,22 @@ class Phi3VImageProcessor(BaseImageProcessor):
         num_img_tokens = int((new_height // 336 * new_width // 336 + 1) * 144 + 1 + (new_height // 336 + 1) * 12)  
         return num_img_tokens
 
+    def convert_PIL(self, image):
+        """
+        Convert an image to a PIL Image object if it is not already one.
+    
+        Args:
+            image (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`): The image to be converted. Can be a numpy array or a torch tensor or PIL object.
+    
+        Returns:
+            A PIL Image object.
+        """
+            if not isinstance(image, Image.Image):
+                return torchvision.transforms.functional.to_pil_image(image)
+            else:
+                return image
+        
+            
     def preprocess(
         self,
         images: ImageInput,
@@ -245,8 +261,8 @@ class Phi3VImageProcessor(BaseImageProcessor):
                 "torch.Tensor, tf.Tensor or jax.ndarray."
             )
 
-        if do_convert_rgb:
-            images = [convert_to_rgb(image) for image in images]
+        # if do_convert_rgb:
+            # images = [convert_to_rgb(image) for image in images]
 
         image_sizes = []
         img_processor = torchvision.transforms.Compose([
@@ -256,7 +272,9 @@ class Phi3VImageProcessor(BaseImageProcessor):
 
         # PIL images
         # HD_transform pad images to size of multiiply of 336, 336
-        # convert to RGB first
+        # check and convert if the images are in PIL format
+        images = [convert_PIL(image) for image in images]
+        # convert to RGB first (I think the argument "do_convert_rgb is useless, since it is forced here")
         images = [image.convert('RGB') for image in images]
         elems = [HD_transform(im, hd_num = self.num_crops) for im in images] 
         # tensor transform and normalize