85 lines
3.8 KiB
Markdown
85 lines
3.8 KiB
Markdown
# Documentation
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- Class name: CombineMasks
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- Category: ♾️Mixlab/Mask
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- Output node: False
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- Repo Ref: https://github.com/shadowcz007/comfyui-CLIPSeg
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CombineMasks是一个旨在将多个掩码张量整合为一个综合掩码的节点，增强了底层数据的表示。该节点不仅合并掩码，还生成如热图和二值叠加等视觉表示，提供了对掩码内容更详细的分析。
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# Input types
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## Required
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- input_image
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- 输入图像参数至关重要，因为它作为掩码组合过程的基础层。它提供了节点正确叠加和可视化掩码所需的空间上下文。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- mask_1
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- 第一个掩码是组合过程中不可或缺的组成部分，代表了将与后续掩码一起增强的初始分割信息层。
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- Comfy dtype: MASK
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- Python dtype: torch.Tensor
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- mask_2
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- 第二个掩码增加了额外的分割层，有助于提高组合掩码的复杂性和细节。
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- Comfy dtype: MASK
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- Python dtype: torch.Tensor
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## Optional
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- mask_3
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- 可选的第三个掩码通过引入更细粒度的细节来进一步细化分割，增强了整体掩码的表示。
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- Comfy dtype: MASK
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- Python dtype: torch.Tensor
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# Output types
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- Combined Mask
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- 组合掩码是节点的主要输出，代表了所有输入掩码融合为单一、统一表示的结果。
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- Comfy dtype: MASK
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- Python dtype: torch.Tensor
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- Heatmap Mask
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- 热图掩码可视化了组合分割，提供了彩色表示，增强了分割结果的可解释性和分析。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- BW Mask
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- 二值掩码提供了一个简化的黑白视图，强调核心元素，并提供了一个清晰、直接的分析工具。
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- Comfy dtype: IMAGE
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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 CombineMasks:
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def __init__(self):
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pass
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@classmethod
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def INPUT_TYPES(s):
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return {'required': {'input_image': ('IMAGE',), 'mask_1': ('MASK',), 'mask_2': ('MASK',)}, 'optional': {'mask_3': ('MASK',)}}
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CATEGORY = '♾️Mixlab/Mask'
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RETURN_TYPES = ('MASK', 'IMAGE', 'IMAGE')
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RETURN_NAMES = ('Combined Mask', 'Heatmap Mask', 'BW Mask')
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FUNCTION = 'combine_masks'
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def combine_masks(self, input_image: torch.Tensor, mask_1: torch.Tensor, mask_2: torch.Tensor, mask_3: Optional[torch.Tensor]=None) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
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"""A method that combines two or three masks into one mask. Takes in tensors and returns the mask as a tensor, as well as the heatmap and binary mask as tensors."""
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if mask_1 is not None:
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mask_1 = mask_1.squeeze()
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if mask_2 is not None:
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mask_2 = mask_2.squeeze()
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if mask_3 is not None:
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mask_3 = mask_3.squeeze()
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print(mask_1.shape, mask_2.shape, mask_3.shape)
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combined_mask = mask_1 + mask_2 + mask_3 if mask_3 is not None else mask_1 + mask_2
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image_np = tensor_to_numpy(input_image)
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heatmap = apply_colormap(combined_mask, cm.viridis)
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binary_mask = apply_colormap(combined_mask, cm.Greys_r)
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dimensions = (image_np.shape[1], image_np.shape[0])
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if dimensions is None or dimensions[0] == 0 or dimensions[1] == 0:
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raise ValueError('Invalid dimensions')
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heatmap_resized = resize_image(heatmap, dimensions)
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binary_mask_resized = resize_image(binary_mask, dimensions)
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(alpha_heatmap, alpha_binary) = (0.5, 1)
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overlay_heatmap = overlay_image(image_np, heatmap_resized, alpha_heatmap)
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overlay_binary = overlay_image(image_np, binary_mask_resized, alpha_binary)
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image_out_heatmap = numpy_to_tensor(overlay_heatmap)
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image_out_binary = numpy_to_tensor(overlay_binary)
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return (combined_mask, image_out_heatmap, image_out_binary)
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``` |