96 lines
3.1 KiB
Markdown
96 lines
3.1 KiB
Markdown
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# Documentation
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- Class name: MaskOrImageToWeight
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- Category: KJNodes
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- Output node: False
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- Repo Ref: https://github.com/comfyanonymous/ComfyUI
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此节点用于计算输入的掩码或图像的平均值，但不能同时处理两者。它支持将计算得到的平均值转换为不同的输出类型，包括列表、pandas系列或张量，具体取决于指定的输出类型。
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# Input types
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## Required
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- output_type
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- 指定输出格式，可以是列表、pandas系列或张量，决定了从输入掩码或图像计算得到的平均值的返回方式。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: str
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## Optional
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- images
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- 可选的图像列表，用于计算平均值。如果提供了图像，则不应使用掩码。
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- Comfy dtype: IMAGE
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- Python dtype: List[torch.Tensor]
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- masks
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- 可选的掩码列表，用于计算平均值。如果提供了掩码，则不应使用图像。
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- Comfy dtype: MASK
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- Python dtype: List[torch.Tensor]
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# Output types
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- float
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- 输入掩码或图像的计算平均值，以output_type参数指定的格式返回。
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- Comfy dtype: FLOAT
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- Python dtype: Union[List[float], pandas.Series, torch.Tensor]
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## Usage tips
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- Infra type: `CPU`
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- Common nodes: unknown
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## Source code
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```python
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class MaskOrImageToWeight:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"output_type": (
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[
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'list',
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'pandas series',
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'tensor',
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],
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{
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"default": 'list'
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}),
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},
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"optional": {
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"images": ("IMAGE",),
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"masks": ("MASK",),
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},
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}
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RETURN_TYPES = ("FLOAT",)
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FUNCTION = "execute"
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CATEGORY = "KJNodes"
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DESCRIPTION = """
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Gets the mean values from mask or image batch
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and returns that as the selected output type.
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"""
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def execute(self, output_type, images=None, masks=None):
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mean_values = []
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if masks is not None and images is None:
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for mask in masks:
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mean_values.append(mask.mean().item())
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elif masks is None and images is not None:
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for image in images:
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mean_values.append(image.mean().item())
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elif masks is not None and images is not None:
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raise Exception("MaskOrImageToWeight: Use either mask or image input only.")
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# Convert mean_values to the specified output_type
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if output_type == 'list':
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return mean_values,
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elif output_type == 'pandas series':
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try:
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import pandas as pd
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except:
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raise Exception("MaskOrImageToWeight: pandas is not installed. Please install pandas to use this output_type")
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return pd.Series(mean_values),
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elif output_type == 'tensor':
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return torch.tensor(mean_values, dtype=torch.float32),
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else:
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raise ValueError(f"Unsupported output_type: {output_type}")
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```
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