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 +1,76 @@
+import torch
+from PIL import Image
+from .imagefunc import log, tensor2pil, pil2tensor, image2mask, mask2image, expand_mask, pixel_spread
+
+
+
+class PixelSpread:
+
+    def __init__(self):
+        self.NODE_NAME = 'PixelSpread'
+
+    @classmethod
+    def INPUT_TYPES(self):
+
+        return {
+            "required": {
+                "image": ("IMAGE", ),  #
+                "invert_mask": ("BOOLEAN", {"default": False}),  # 反转mask
+                "mask_grow": ("INT", {"default": 0, "min": -999, "max": 999, "step": 1}),
+            },
+            "optional": {
+                "mask": ("MASK",),  #
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE", )
+    RETURN_NAMES = ("image", )
+    FUNCTION = 'pixel_spread'
+    CATEGORY = '😺dzNodes/LayerMask'
+
+    def pixel_spread(self, image, invert_mask, mask_grow, mask=None):
+
+        l_images = []
+        l_masks = []
+        ret_images = []
+
+        for l in image:
+            i = tensor2pil(torch.unsqueeze(l, 0))
+            l_images.append(i)
+            if i.mode == 'RGBA':
+                l_masks.append(i.split()[-1])
+            else:
+                l_masks.append(Image.new('L', i.size, 'white'))
+        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'))
+        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]
+            _mask = l_masks[i] if i < len(l_masks) else l_masks[-1]
+            if mask_grow != 0:
+                _mask = expand_mask(image2mask(_mask), mask_grow, 0)  # 扩张，模糊
+                _mask = mask2image(_mask)
+
+            if _image.size != _mask.size:
+                log(f"Error: {self.NODE_NAME} skipped, because the mask is not match image.", message_type='error')
+                return (image,)
+            ret_image = pixel_spread(_image.convert('RGB'), _mask.convert('RGB'))
+            ret_images.append(pil2tensor(ret_image))
+
+        log(f"{self.NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
+        return (torch.cat(ret_images, dim=0),)
+
+NODE_CLASS_MAPPINGS = {
+    "LayerMask: PixelSpread": PixelSpread
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "LayerMask: PixelSpread": "LayerMask: PixelSpread"
+}