74 lines
3.6 KiB
Markdown
74 lines
3.6 KiB
Markdown
# Documentation
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- Class name: MaskComposite
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- Category: mask
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- Output node: False
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- Repo Ref: https://github.com/comfyanonymous/ComfyUI
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MaskComposite节点旨在对掩码图像执行各种操作，允许根据指定的操作组合源掩码和目标掩码。它在需要掩码操作的图像处理工作流程中扮演着关键角色，例如在计算机视觉或图形设计应用中。
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# Input types
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## Required
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- destination
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- 目标参数是将通过操作修改的基础掩码。它至关重要，因为它决定了在应用任何修改之前掩码的初始状态。
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- Comfy dtype: MASK
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- Python dtype: torch.Tensor
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- source
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- 源参数表示要与目标掩码组合的掩码。它的作用至关重要，因为它对掩码操作的最终结果有贡献。
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- Comfy dtype: MASK
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- Python dtype: torch.Tensor
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- x
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- x参数指定了源掩码相对于目标掩码的水平位置。它很重要，因为它决定了源在目标掩码中的位置。
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- Comfy dtype: INT
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- Python dtype: int
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- y
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- y参数定义了源掩码相对于目标掩码的垂直位置。它的重要性在于确定源在目标掩码中的垂直位置。
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- Comfy dtype: INT
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- Python dtype: int
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- operation
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- 操作参数指示源掩码和目标掩码之间要执行的组合类型。它至关重要，因为它定义了掩码合并的逻辑。
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- Comfy dtype: COMBO['multiply', 'add', 'subtract', 'and', 'or', 'xor']
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- Python dtype: str
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# Output types
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- output
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- 输出参数是掩码组合操作的结果。它包含了应用所有转换后掩码的最终状态。
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- Comfy dtype: MASK
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- Python dtype: torch.Tensor
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# Usage tips
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- Infra type: GPU
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# Source code
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```
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class MaskComposite:
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@classmethod
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def INPUT_TYPES(cls):
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return {'required': {'destination': ('MASK',), 'source': ('MASK',), 'x': ('INT', {'default': 0, 'min': 0, 'max': MAX_RESOLUTION, 'step': 1}), 'y': ('INT', {'default': 0, 'min': 0, 'max': MAX_RESOLUTION, 'step': 1}), 'operation': (['multiply', 'add', 'subtract', 'and', 'or', 'xor'],)}}
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CATEGORY = 'mask'
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RETURN_TYPES = ('MASK',)
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FUNCTION = 'combine'
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def combine(self, destination, source, x, y, operation):
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output = destination.reshape((-1, destination.shape[-2], destination.shape[-1])).clone()
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source = source.reshape((-1, source.shape[-2], source.shape[-1]))
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(left, top) = (x, y)
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(right, bottom) = (min(left + source.shape[-1], destination.shape[-1]), min(top + source.shape[-2], destination.shape[-2]))
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(visible_width, visible_height) = (right - left, bottom - top)
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source_portion = source[:, :visible_height, :visible_width]
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destination_portion = destination[:, top:bottom, left:right]
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if operation == 'multiply':
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output[:, top:bottom, left:right] = destination_portion * source_portion
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elif operation == 'add':
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output[:, top:bottom, left:right] = destination_portion + source_portion
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elif operation == 'subtract':
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output[:, top:bottom, left:right] = destination_portion - source_portion
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elif operation == 'and':
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output[:, top:bottom, left:right] = torch.bitwise_and(destination_portion.round().bool(), source_portion.round().bool()).float()
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elif operation == 'or':
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output[:, top:bottom, left:right] = torch.bitwise_or(destination_portion.round().bool(), source_portion.round().bool()).float()
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elif operation == 'xor':
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output[:, top:bottom, left:right] = torch.bitwise_xor(destination_portion.round().bool(), source_portion.round().bool()).float()
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output = torch.clamp(output, 0.0, 1.0)
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return (output,)
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``` |