v1.1.6

load_image_from_path.py

@@ -0,0 +1,66 @@
+import torch
+import numpy as np
+from PIL import Image, ImageOps, ImageSequence
+import node_helpers
+
+class LoadImageWithTransparencyFromPath:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image_path": ("STRING", {"default": "", "multiline": False}),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE", "MASK", "STRING")
+    RETURN_NAMES = ("image", "mask", "image_path")
+    FUNCTION = "load_image_alpha"
+    CATEGORY = "Bjornulf"
+
+    def load_image_alpha(self, image_path):
+        # Validate that image_path is not None or empty
+        if not image_path:
+            raise ValueError("image_path cannot be None or empty")
+
+        # Load the image using the provided path
+        img = node_helpers.pillow(Image.open, image_path)
+
+        output_images = []
+        output_masks = []
+        w, h = None, None
+        excluded_formats = ['MPO']
+
+        # Process each frame in the image sequence
+        for i in ImageSequence.Iterator(img):
+            i = node_helpers.pillow(ImageOps.exif_transpose, i)
+
+            if i.mode == 'I':
+                i = i.point(lambda i: i * (1 / 255))
+            image = i.convert("RGBA")
+
+            if len(output_images) == 0:
+                w = image.size[0]
+                h = image.size[1]
+
+            if image.size[0] != w or image.size[1] != h:
+                continue
+
+            image = np.array(image).astype(np.float32) / 255.0
+            image = torch.from_numpy(image)[None,]
+            if 'A' in i.getbands():
+                mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0
+                mask = 1. - torch.from_numpy(mask)  # Invert mask as per ComfyUI convention
+            else:
+                mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu")
+            output_images.append(image)
+            output_masks.append(mask.unsqueeze(0))
+
+        # Handle multi-frame images
+        if len(output_images) > 1 and img.format not in excluded_formats:
+            output_image = torch.cat(output_images, dim=0)
+            output_mask = torch.cat(output_masks, dim=0)
+        else:
+            output_image = output_images[0]
+            output_mask = output_masks[0]
+
+        return (output_image, output_mask, image_path)