83 lines
2.1 KiB
Markdown
83 lines
2.1 KiB
Markdown
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# Documentation
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- Class name: BlurMaskFast
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- Category: mask/filters
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- Output node: False
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BlurMaskFast节点用于对遮罩应用高斯模糊，可以软化边缘并创建更平滑的遮罩外观。这种操作在需要减少遮罩边界锐利度的图像处理任务中特别有用。
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# Input types
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## Required
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- masks
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- 指定要进行模糊处理的遮罩。这个输入对于定义图像中将应用模糊效果的区域至关重要。
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- Comfy dtype: MASK
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- Python dtype: torch.Tensor
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- radius_x
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- 确定高斯模糊的水平半径。较大的半径会在水平方向产生更明显的模糊效果。
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- Comfy dtype: INT
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- Python dtype: int
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- radius_y
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- 确定高斯模糊的垂直半径。较大的半径会在垂直方向产生更明显的模糊效果。
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- Comfy dtype: INT
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- Python dtype: int
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# Output types
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- mask
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- 输出是经过高斯模糊处理的遮罩，与原始遮罩相比，边缘更加柔和。
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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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- Common nodes: unknown
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## Source code
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```python
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class BlurMaskFast:
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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 {
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"required": {
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"masks": ("MASK",),
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"radius_x": ("INT", {
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"default": 1,
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"min": 0,
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"max": 1023,
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"step": 1
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}),
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"radius_y": ("INT", {
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"default": 1,
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"min": 0,
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"max": 1023,
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"step": 1
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}),
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},
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}
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RETURN_TYPES = ("MASK",)
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FUNCTION = "blur_mask"
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CATEGORY = "mask/filters"
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def blur_mask(self, masks, radius_x, radius_y):
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if radius_x + radius_y == 0:
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return (masks,)
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dx = radius_x * 2 + 1
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dy = radius_y * 2 + 1
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dup = copy.deepcopy(masks.cpu().numpy())
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for index, mask in enumerate(dup):
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dup[index] = cv2.GaussianBlur(mask, (dx, dy), 0)
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return (torch.from_numpy(dup),)
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```
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