62 lines
1.9 KiB
Python
62 lines
1.9 KiB
Python
from .imagefunc import *
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NODE_NAME = 'MaskStroke'
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class MaskStroke:
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def __init__(self):
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pass
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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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"stroke_grow": ("INT", {"default": 0, "min": -999, "max": 999, "step": 1}), # 收缩值
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"stroke_width": ("INT", {"default": 20, "min": 0, "max": 999, "step": 1}), # 扩张值
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"blur": ("INT", {"default": 6, "min": 0, "max": 100, "step": 1}), # 模糊
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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_stroke'
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CATEGORY = '😺dzNodes/LayerMask'
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def mask_stroke(self, mask, invert_mask, stroke_grow, stroke_width, 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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grow_offset = int(stroke_width / 2)
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inner_stroke = stroke_grow - grow_offset
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outer_stroke = inner_stroke + stroke_width
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inner_mask = expand_mask(image2mask(_mask), inner_stroke, blur)
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outer_mask = expand_mask(image2mask(_mask), outer_stroke, blur)
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stroke_mask = subtract_mask(outer_mask, inner_mask)
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ret_masks.append(stroke_mask)
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log(f"{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: MaskStroke": MaskStroke
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"LayerMask: MaskStroke": "LayerMask: MaskStroke"
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} |