59 lines
1.5 KiB
Python
59 lines
1.5 KiB
Python
import torch
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from PIL import Image
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from .imagefunc import log, tensor2pil, image2mask, expand_mask
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class MaskGrow:
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def __init__(self):
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self.NODE_NAME = 'MaskGrow'
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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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"mask": ("MASK", ), #
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"invert_mask": ("BOOLEAN", {"default": True}), # 反转mask
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"grow": ("INT", {"default": 4, "min": -999, "max": 999, "step": 1}),
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"blur": ("INT", {"default": 4, "min": 0, "max": 999, "step": 1}),
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},
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"optional": {
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}
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}
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RETURN_TYPES = ("MASK",)
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RETURN_NAMES = ("mask",)
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FUNCTION = 'mask_grow'
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CATEGORY = '😺dzNodes/LayerMask'
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def mask_grow(self, mask, invert_mask, grow, blur,):
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l_masks = []
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ret_masks = []
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if mask.dim() == 2:
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mask = torch.unsqueeze(mask, 0)
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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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for i in range(len(l_masks)):
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_mask = l_masks[i]
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ret_masks.append(expand_mask(image2mask(_mask), grow, blur) )
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log(f"{self.NODE_NAME} Processed {len(ret_masks)} mask(s).", message_type='finish')
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return (torch.cat(ret_masks, dim=0),)
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NODE_CLASS_MAPPINGS = {
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"LayerMask: MaskGrow": MaskGrow
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"LayerMask: MaskGrow": "LayerMask: MaskGrow"
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} |