53 lines
1.7 KiB
Markdown
53 lines
1.7 KiB
Markdown
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# Documentation
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- Class name: MorphologicSkeletoning
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- Category: Bmad/CV/Morphology
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- Output node: False
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- Repo Ref: https://github.com/comfyanonymous/ComfyUI
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MorphologicSkeletoning 节点用于计算图像的形态学骨架。它将输入图像转换成一种最小化但完全具有代表性的骨架形式，该形式保留了原始形状的结构和连通性。这种转换在图像分析和处理中非常有用，可以提取出图像的本质特征。
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# Input types
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## Required
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- src
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- src 参数代表需要处理的源图像。它对于确定骨架变换中原始形状的结构和连通性至关重要。源图像的质量和特征直接影响最终骨架的精度和代表性。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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# Output types
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- image
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- 输出是输入图像的骨架表示。这种表示以最小化的形式保留了原始形状的结构和连通性。骨架图像通常呈现为细线条结构，突出显示了原始图像的主要特征和拓扑结构。
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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: `GPU`
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- Common nodes: unknown
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## Source code
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```python
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class MorphologicSkeletoning:
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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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"src": ("IMAGE",)
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "compute"
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CATEGORY = "Bmad/CV/Morphology"
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def compute(self, src):
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from skimage.morphology import skeletonize
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img = tensor2opencv(src, 1)
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_, img = cv.threshold(img, 127, 1, cv.THRESH_BINARY) # ensure it is binary and set max value to 1.
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skel = skeletonize(img) * 255
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img = opencv2tensor(skel)
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return (img,)
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```
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