63 lines
2.0 KiB
Python
63 lines
2.0 KiB
Python
import torch
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from PIL import Image
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from .imagefunc import log, tensor2pil, pil2tensor, image2mask, expand_mask, chop_image_v2
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class MaskGrain:
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def __init__(self):
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self.NODE_NAME = 'MaskGrain'
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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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"grain": ("INT", {"default": 6, "min": 0, "max": 127, "step": 1}),
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"invert_mask": ("BOOLEAN", {"default": False}), # 反转mask
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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_grain'
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CATEGORY = '😺dzNodes/LayerMask'
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def mask_grain(self, mask, grain, invert_mask):
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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 mask in l_masks:
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if grain:
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white_mask = Image.new('L', mask.size, color="white")
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inner_mask = tensor2pil(expand_mask(image2mask(mask), 0 - grain, int(grain))).convert('L')
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outter_mask = tensor2pil(expand_mask(image2mask(mask), grain, int(grain * 2))).convert('L')
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ret_mask = Image.new('L', mask.size, color="black")
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ret_mask = chop_image_v2(ret_mask, outter_mask, blend_mode="dissolve", opacity=50).convert('L')
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ret_mask.paste(white_mask, mask=inner_mask)
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ret_masks.append(image2mask(ret_mask))
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else:
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ret_masks.append(image2mask(mask))
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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: MaskGrain": MaskGrain
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"LayerMask: MaskGrain": "LayerMask: Mask Grain"
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} |