57 lines
2.2 KiB
Markdown
57 lines
2.2 KiB
Markdown
# Documentation
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- Class name: CannyEdgeMask
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- Category: postprocessing/Masks
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- Output node: False
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- Repo Ref: https://github.com/EllangoK/ComfyUI-post-processing-nodes
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此类节点封装了对输入图像应用Canny边缘检测算法的功能，根据指定的阈值生成突出显示图像中边缘的二值边缘掩码。
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# Input types
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## Required
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- image
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- 要应用Canny边缘检测的输入图像。它是节点操作的关键，因为它是算法处理以产生边缘掩码的主要数据。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- lower_threshold
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- 下阈值参数对于控制边缘检测的灵敏度至关重要。它与上阈值一起工作，以确定哪些边缘足够强以包含在最终的边缘掩码中。
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- Comfy dtype: INT
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- Python dtype: int
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- upper_threshold
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- 上阈值参数与下阈值一起，在定义边缘检测的标准中起着重要作用。它通过只包括达到或超过此阈值的边缘来帮助细化边缘。
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- Comfy dtype: INT
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- Python dtype: int
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# Output types
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- edge_mask
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- 输出是一个二值边缘掩码，代表Canny算法检测到的边缘。这个掩码非常重要，因为它用于进一步的图像处理或分析。
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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 CannyEdgeMask:
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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': {'image': ('IMAGE',), 'lower_threshold': ('INT', {'default': 100, 'min': 0, 'max': 500, 'step': 10}), 'upper_threshold': ('INT', {'default': 200, 'min': 0, 'max': 500, 'step': 10})}}
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RETURN_TYPES = ('IMAGE',)
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FUNCTION = 'canny'
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CATEGORY = 'postprocessing/Masks'
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def canny(self, image: torch.Tensor, lower_threshold: int, upper_threshold: int):
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(batch_size, height, width, _) = image.shape
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result = torch.zeros(batch_size, height, width)
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for b in range(batch_size):
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tensor_image = image[b].numpy().copy()
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gray_image = (cv2.cvtColor(tensor_image, cv2.COLOR_RGB2GRAY) * 255).astype(np.uint8)
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canny = cv2.Canny(gray_image, lower_threshold, upper_threshold)
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tensor = torch.from_numpy(canny)
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result[b] = tensor
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return (result,)
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``` |