Update add_border_to_image.py

FIX: TENSOR

add_border_to_image.py

@@ -1,7 +1,13 @@
 import numpy as np
 from PIL import Image
+import torch
+# Tensor to PIL
+def tensor2pil(image):
+    return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
 
-
+# PIL to Tensor
+def pil2tensor(image):
+    return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
 class ChangeImageBorder:
     def __init__(self):
         pass
@@ -21,28 +27,90 @@ class ChangeImageBorder:
     FUNCTION = "change_border_to"
     CATEGORY = "Tools"
 
-    def change_border_to(image_path, value, border_size=2):
-        # Open the image file
-        with Image.open(image_path) as img:
-            # Convert to RGBA if it's not already in this mode
-            img = img.convert("RGBA")
+    def change_border_to(self, image, value, border_size):
+        tensors = []
+        if len(image) > 1:
+            print('aaaaa')
+            for img in image:
 
+                pil_image = None
+                # PIL Image
+                pil_image = tensor2pil(img)
+                
+                pil_image = pil_image.convert("RGBA")
+                # Get the image data
+                data = np.array(pil_image)
+        
+                # Change the top and bottom border pixels
+                data[:border_size, :] = [value, value, value, 255]  # top
+                data[-border_size:, :] = [value, value, value, 255]  # bottom
+        
+                # Change the left and right border pixels
+                data[:, :border_size] = [value, value, value, 255]  # left
+                data[:, -border_size:] = [value, value, value, 255]  # right
+        
+                # Create a new image from the modified data
+                new_img = Image.fromarray(data, mode='RGBA')
+                # Output image
+                out_image = (pil2tensor(new_img) if pil_image else img)
+
+                tensors.append(out_image)
+
+            tensors = torch.cat(tensors, dim=0)
+
+        else:
+            print('bbbb')
+            pil_image = None
+            img = image
+            # PIL Image
+            pil_image = tensor2pil(img)
+            pil_image = pil_image.convert("RGBA")
             # Get the image data
-            data = np.array(img)
-
+            data = np.array(pil_image)
+        
             # Change the top and bottom border pixels
             data[:border_size, :] = [value, value, value, 255]  # top
             data[-border_size:, :] = [value, value, value, 255]  # bottom
-
+        
             # Change the left and right border pixels
             data[:, :border_size] = [value, value, value, 255]  # left
             data[:, -border_size:] = [value, value, value, 255]  # right
-
+        
             # Create a new image from the modified data
             new_img = Image.fromarray(data, mode='RGBA')
+            # Output image
+            out_image = (pil2tensor(new_img) if pil_image else img)
 
-            # Return the new image
-            return new_img
+            tensors = out_image
+
+        return (tensors, )
+        print(type(image))
+        # image = pil2tensor(image)
+        # return (image, )
+        # Convert torch.Tensor to PIL Image
+        # img = Image.fromarray(tensor_image.mul(255).byte().permute(1, 2, 0).numpy())
+        # # Convert to RGBA if it's not already in this mode
+        # image = image.convert("RGBA")
+
+        # Get the image data
+        data = np.array(image)
+
+        # Change the top and bottom border pixels to black
+        # data[:border_size, :] = [0, 0, 0, 255]  # top
+        # data[-border_size:, :] = [0, 0, 0, 255]  # bottom
+
+        # # Change the left and right border pixels to black
+        # data[:, :border_size] = [0, 0, 0, 255]  # left
+        # data[:, -border_size:] = [0, 0, 0]  # right
+
+        # Create a new image from the modified data
+        new_img = Image.fromarray(data, mode='RGBA')
+
+        
+        # Convert to PyTorch tensor and add batch dimension
+        tensor_image = torch.from_numpy(data).permute(2, 0, 1).unsqueeze(0)
+        return tensor_image
+        return (pil2tensor(new_img), )
 
 # Set the web directory, any .js file in that directory will be loaded by the frontend as a frontend extension
 # WEB_DIRECTORY = "./somejs"