106 lines
4.3 KiB
Markdown
106 lines
4.3 KiB
Markdown
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# Documentation
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- Class name: FadeMaskEdges
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- Category: Bmad/CV/Misc
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- Output node: False
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- Repo Ref: https://github.com/bmad4ever/ComfyUI-Bmad-Custom-Nodes
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FadeMaskEdges节点主要用于对图像主体的边缘进行预乘和Alpha混合，以防止外部像素渗入。这对于具有黑色轮廓的绘图等风格化主体特别有用，通过允许不同的边缘淡化效果来优化主体与背景的混合。
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# Input types
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## Required
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- binary_image
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- 需要处理的二值图像，主要针对其边缘进行预乘和Alpha混合。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- edge_size
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- 定义边缘淡化的大小，影响主体边缘与背景的混合程度。
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- Comfy dtype: FLOAT
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- Python dtype: float
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- edge_tightness
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- 控制边缘淡化的紧密度，影响从主体到背景的过渡平滑度。
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- Comfy dtype: FLOAT
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- Python dtype: float
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- edge_exponent
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- 决定淡化曲线，允许自定义边缘过渡效果。
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- Comfy dtype: FLOAT
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- Python dtype: float
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- smoothing_diameter
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- 指定边缘平滑的直径，用于在预乘和Alpha设置后优化混合效果。
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- Comfy dtype: INT
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- Python dtype: int
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- paste_original_blacks
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- 布尔标志，表示是否将原始黑色像素粘贴回图像，有助于保持主体的完整性。
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- Comfy dtype: BOOLEAN
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- Python dtype: bool
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# Output types
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- image
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- 经过处理的图像，其边缘已进行预乘和Alpha混合，可用于进一步处理或展示。
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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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- Common nodes: unknown
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## Source code
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```python
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class FadeMaskEdges:
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"""
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The original intent is to premultiply and alpha blend a subject's edges to avoid outer pixels creeping in.
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A very slight blur near the edges afterwards when using paste_original_blacks and low tightness may be required,
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but this should be done after premultiplying and setting the alpha.
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Stylized subject's, such as drawings with black outlines, may benefit from using different 2 edge fades:
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1. a fade with higher edge size for the premultiplication, fading the subject into blackness
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2. a tighter fade for the alpha
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"""
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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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"binary_image": ("IMAGE",),
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"edge_size": ("FLOAT", {"default": 5.0, "min": 1.0, "step": 1.0}),
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# how quick does it fade to black
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"edge_tightness": ("FLOAT", {"default": 1.1, "min": 1.0, "max": 10.0, "step": 0.05}),
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# how does it fade, may be used to weaken small lines; 1 = linear transition
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"edge_exponent": ("FLOAT", {"default": 1, "min": 0.1, "max": 10.0, "step": 0.1}),
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"smoothing_diameter": ("INT", {"default": 10, "min": 2, "max": 256, "step": 1}),
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"paste_original_blacks": ("BOOLEAN", {"default": True})
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}
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "apply"
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CATEGORY = "Bmad/CV/Misc"
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def apply(self, binary_image, edge_size, edge_tightness, edge_exponent, smoothing_diameter, paste_original_blacks):
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binary_image = tensor2opencv(binary_image, 1)
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# _, binary_image = cv.threshold(gray_image, 128, 255, cv.THRESH_BINARY) # suppose it's already binary
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# compute L2 (euclidean) distance -> normalize with respect to edge size -> smooth
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distance_transform = cv.distanceTransform(binary_image, cv.DIST_L2, cv.DIST_MASK_3)
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normalized_distance = distance_transform / edge_size
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smoothed_distance = cv.bilateralFilter(normalized_distance, smoothing_diameter, 75, 75)
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# darken the white pixels based on smoothed distance and "edge tightness"
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diff = 1 - smoothed_distance
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darkened_image = (abs(diff * edge_tightness) ** (1 / edge_exponent)) * np.sign(diff)
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darkened_image = np.clip(darkened_image, 0, 1)
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darkened_image = (darkened_image * 255).astype(np.uint8)
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if paste_original_blacks: # mask original black pixels
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black_mask = binary_image < 1
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darkened_image[black_mask] = 0
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output_image = binary_image - darkened_image # darken original image
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output_image = opencv2tensor(output_image)
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return (output_image,)
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```
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