add reference code

2025-06-23 20:42:49 +08:00
parent 1c44578d9a
commit ca2c13e0ef
131 changed files with 18966 additions and 0 deletions
@@ -0,0 +1,90 @@
+import torch
+from PIL import Image
+from .imagefunc import log, tensor2pil, pil2tensor, image2mask
+
+
+class ExtendCanvas:
+
+    def __init__(self):
+        self.NODE_NAME = 'ExtendCanvas'
+
+    @classmethod
+    def INPUT_TYPES(self):
+
+        return {
+            "required": {
+                "image": ("IMAGE", ),
+                "invert_mask": ("BOOLEAN", {"default": True}),  # 反转mask
+                "top": ("INT", {"default": 0, "min": 0, "max": 99999, "step": 1}),
+                "bottom": ("INT", {"default": 0, "min": 0, "max": 99999, "step": 1}),
+                "left": ("INT", {"default": 0, "min": 0, "max": 99999, "step": 1}),
+                "right": ("INT", {"default": 0, "min": 0, "max": 99999, "step": 1}),
+                "color": ("COLOR", {"default": "#000000"},),
+            },
+            "optional": {
+                "mask": ("MASK",),  #
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE", "MASK",)
+    RETURN_NAMES = ("image", "mask")
+    FUNCTION = 'extend_canvas'
+    CATEGORY = '😺dzNodes/LayerUtility'
+
+    def extend_canvas(self, image, invert_mask,
+                  top, bottom, left, right, color,
+                  mask=None,
+                  ):
+
+        l_images = []
+        l_masks = []
+        ret_images = []
+        ret_masks = []
+
+        for l in image:
+            l_images.append(torch.unsqueeze(l, 0))
+            m = tensor2pil(l)
+            if m.mode == 'RGBA':
+                l_masks.append(m.split()[-1])
+
+        if mask is not None:
+            if mask.dim() == 2:
+                mask = torch.unsqueeze(mask, 0)
+            l_masks = []
+            for m in mask:
+                if invert_mask:
+                    m = 1 - m
+                l_masks.append(tensor2pil(torch.unsqueeze(m, 0)).convert('L'))
+        else:
+            if len(l_masks) == 0:
+                l_masks.append(Image.new('L', size=tensor2pil(l_images[0]).size, color='white'))
+
+        max_batch = max(len(l_images), len(l_masks))
+        for i in range(max_batch):
+
+            _image = l_images[i] if i < len(l_images) else l_images[-1]
+            _image = tensor2pil(_image).convert('RGB')
+            _mask = l_masks[i] if i < len(l_masks) else l_masks[-1]
+
+            width = _image.width + left + right
+            height = _image.height + top + bottom
+            _canvas = Image.new('RGB', (width, height), color)
+            _mask_canvas = Image.new('L', (width, height), "black")
+
+            _canvas.paste(_image, box=(left,top))
+            _mask_canvas.paste(_mask.convert('L'), box=(left, top))
+
+            ret_images.append(pil2tensor(_canvas))
+            ret_masks.append(image2mask(_mask_canvas))
+
+        log(f"{self.NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
+        return (torch.cat(ret_images, dim=0), torch.cat(ret_masks, dim=0),)
+
+
+NODE_CLASS_MAPPINGS = {
+    "LayerUtility: ExtendCanvas": ExtendCanvas
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "LayerUtility: ExtendCanvas": "LayerUtility: ExtendCanvas"
+}