57 lines
2.3 KiB
Markdown
57 lines
2.3 KiB
Markdown
# Documentation
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- Class name: WAS_Mask_Ceiling_Region
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- Category: WAS Suite/Image/Masking
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- Output node: False
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- Repo Ref: https://github.com/WASasquatch/was-node-suite-comfyui
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WAS_Mask_Ceiling_Region节点的ceiling_region方法旨在处理输入掩码并识别其中的最上区域。它的工作原理是将掩码转换为PIL图像，应用天花板效果以隔离顶部区域，然后将结果转换回适合进一步图像处理任务的张量格式。
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# Input types
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## Required
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- masks
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- 输入掩码参数对于ceiling_region方法至关重要，因为它提供了节点将处理的原始掩码数据。此参数直接影响节点的输出，它决定了图像中的哪些区域被识别为最上区域。
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- Comfy dtype: torch.Tensor
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- Python dtype: torch.Tensor
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# Output types
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- MASKS
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- MASKS输出参数代表包含从输入掩码中处理过的区域的张量。这个输出很重要，因为它是节点处理的成果，用于图像分析流程中的后续任务。
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- Comfy dtype: torch.Tensor
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- Python dtype: torch.Tensor
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# Usage tips
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- Infra type: CPU
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# Source code
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```
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class WAS_Mask_Ceiling_Region:
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def __init__(self):
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self.WT = WAS_Tools_Class()
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@classmethod
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def INPUT_TYPES(cls):
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return {'required': {'masks': ('MASK',)}}
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CATEGORY = 'WAS Suite/Image/Masking'
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RETURN_TYPES = ('MASK',)
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RETURN_NAMES = ('MASKS',)
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FUNCTION = 'ceiling_region'
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def ceiling_region(self, masks):
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if masks.ndim > 3:
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regions = []
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for mask in masks:
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mask_np = np.clip(255.0 * mask.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)
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pil_image = Image.fromarray(mask_np, mode='L')
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region_mask = self.WT.Masking.ceiling_region(pil_image)
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region_tensor = pil2mask(region_mask).unsqueeze(0).unsqueeze(1)
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regions.append(region_tensor)
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regions_tensor = torch.cat(regions, dim=0)
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return (regions_tensor,)
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else:
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mask_np = np.clip(255.0 * masks.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)
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pil_image = Image.fromarray(mask_np, mode='L')
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region_mask = self.WT.Masking.ceiling_region(pil_image)
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region_tensor = pil2mask(region_mask).unsqueeze(0).unsqueeze(1)
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return (region_tensor,)
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``` |