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__init__.py

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+from .canvas_node import CanvasView
+
+NODE_CLASS_MAPPINGS = {
+    "CanvasView": CanvasView
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "CanvasView": "Canvas View"
+}
+
+WEB_DIRECTORY = "./js"
+
+__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS", "WEB_DIRECTORY"] 

canvas_node.py

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+from PIL import Image, ImageOps
+import hashlib
+import torch
+import numpy as np
+import folder_paths
+from server import PromptServer
+
+class CanvasView:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "canvas_image": ("STRING", {"default": "canvas_image.png"}),
+                "trigger": ("INT", {"default": 0, "min": 0, "max": 99999999, "step": 1})
+            },
+            "hidden": {
+                "unique_id": "UNIQUE_ID"
+            }
+        }
+    
+    RETURN_TYPES = ("IMAGE", "MASK")
+    RETURN_NAMES = ("image", "mask")
+    FUNCTION = "process_canvas_image"
+    CATEGORY = "ycnode"
+
+    def process_canvas_image(self, canvas_image, trigger, unique_id):
+        try:
+            # 读取保存的画布图像和遮罩
+            path_image = folder_paths.get_annotated_filepath(canvas_image)
+            path_mask = folder_paths.get_annotated_filepath(canvas_image.replace('.png', '_mask.png'))
+            
+            # 处理主图像
+            i = Image.open(path_image)
+            i = ImageOps.exif_transpose(i)
+            if i.mode not in ['RGB', 'RGBA']:
+                i = i.convert('RGB')
+            image = np.array(i).astype(np.float32) / 255.0
+            if i.mode == 'RGBA':
+                rgb = image[..., :3]
+                alpha = image[..., 3:]
+                image = rgb * alpha + (1 - alpha) * 0.5
+            
+            # 处理遮罩图像
+            try:
+                mask = Image.open(path_mask).convert('L')
+                mask = np.array(mask).astype(np.float32) / 255.0
+                mask = torch.from_numpy(mask)[None,]
+            except:
+                # 如果没有遮罩文件，创建空白遮罩
+                mask = torch.zeros((1, image.shape[0], image.shape[1]), dtype=torch.float32)
+            
+            # 转换为tensor
+            image = torch.from_numpy(image)[None,]
+            
+            return (image, mask)
+        except Exception as e:
+            print(f"Error processing canvas image: {str(e)}")
+            # 返回白色图像和空白遮罩
+            blank = np.ones((512, 512, 3), dtype=np.float32)
+            blank_mask = np.zeros((512, 512), dtype=np.float32)
+            return (torch.from_numpy(blank)[None,], torch.from_numpy(blank_mask)[None,])