76 lines
2.4 KiB
Python
76 lines
2.4 KiB
Python
import torch
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from PIL import Image
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from .imagefunc import log, tensor2pil, pil2tensor, image2mask, mask2image, expand_mask, pixel_spread
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class PixelSpread:
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def __init__(self):
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self.NODE_NAME = 'PixelSpread'
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@classmethod
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def INPUT_TYPES(self):
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return {
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"required": {
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"image": ("IMAGE", ), #
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"invert_mask": ("BOOLEAN", {"default": False}), # 反转mask
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"mask_grow": ("INT", {"default": 0, "min": -999, "max": 999, "step": 1}),
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},
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"optional": {
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"mask": ("MASK",), #
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}
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}
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RETURN_TYPES = ("IMAGE", )
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RETURN_NAMES = ("image", )
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FUNCTION = 'pixel_spread'
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CATEGORY = '😺dzNodes/LayerMask'
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def pixel_spread(self, image, invert_mask, mask_grow, mask=None):
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l_images = []
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l_masks = []
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ret_images = []
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for l in image:
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i = tensor2pil(torch.unsqueeze(l, 0))
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l_images.append(i)
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if i.mode == 'RGBA':
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l_masks.append(i.split()[-1])
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else:
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l_masks.append(Image.new('L', i.size, 'white'))
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if mask is not None:
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if mask.dim() == 2:
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mask = torch.unsqueeze(mask, 0)
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l_masks = []
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for m in mask:
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if invert_mask:
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m = 1 - m
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l_masks.append(tensor2pil(torch.unsqueeze(m, 0)).convert('L'))
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max_batch = max(len(l_images), len(l_masks))
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for i in range(max_batch):
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_image = l_images[i] if i < len(l_images) else l_images[-1]
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_mask = l_masks[i] if i < len(l_masks) else l_masks[-1]
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if mask_grow != 0:
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_mask = expand_mask(image2mask(_mask), mask_grow, 0) # 扩张，模糊
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_mask = mask2image(_mask)
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if _image.size != _mask.size:
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log(f"Error: {self.NODE_NAME} skipped, because the mask is not match image.", message_type='error')
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return (image,)
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ret_image = pixel_spread(_image.convert('RGB'), _mask.convert('RGB'))
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ret_images.append(pil2tensor(ret_image))
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log(f"{self.NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
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return (torch.cat(ret_images, dim=0),)
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NODE_CLASS_MAPPINGS = {
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"LayerMask: PixelSpread": PixelSpread
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"LayerMask: PixelSpread": "LayerMask: PixelSpread"
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} |