123 lines
3.8 KiB
Markdown
123 lines
3.8 KiB
Markdown
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# Documentation
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- Class name: AlphaClean
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- Category: image/filters
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- Output node: False
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- Repo Ref: https://github.com/comfyanonymous/ComfyUI
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AlphaClean节点专门用于图像预处理，特别是针对图像alpha通道的增强和清理。它采用了双边滤波、高斯模糊和动态阈值等技术来优化图像的透明度和边缘，这在需要精确alpha通道操作的图形和图像处理应用中尤其有用。
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# Input types
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## Required
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- images
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- 需要处理的输入图像，预期其alpha通道将被清理和增强。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- radius
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- 指定高斯模糊的半径，影响alpha通道边缘的平滑度。
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- Comfy dtype: INT
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- Python dtype: int
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- fill_holes
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- 决定是否以及在多大程度上填充alpha通道中的空洞，以提高图像的完整性。
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- Comfy dtype: INT
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- Python dtype: int
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- white_threshold
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- 设置将像素视为白色的阈值，有助于将前景与背景分离。
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- Comfy dtype: FLOAT
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- Python dtype: float
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- extra_clip
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- 对图像应用额外的裁剪因子，调整清理效果的强度。
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- Comfy dtype: FLOAT
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- Python dtype: float
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# Output types
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- image
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- 处理后的图像，其alpha通道已被清理和增强，可供进一步使用或显示。
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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: `GPU`
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- Common nodes: unknown
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## Source code
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```python
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class AlphaClean:
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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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"images": ("IMAGE",),
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"radius": ("INT", {
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"default": 8,
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"min": 1,
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"max": 64,
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"step": 1
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}),
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"fill_holes": ("INT", {
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"default": 1,
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"min": 0,
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"max": 16,
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"step": 1
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}),
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"white_threshold": ("FLOAT", {
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"default": 0.9,
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"min": 0.01,
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"max": 1.0,
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"step": 0.01
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}),
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"extra_clip": ("FLOAT", {
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"default": 0.98,
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"min": 0.01,
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"max": 1.0,
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"step": 0.01
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}),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "alpha_clean"
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CATEGORY = "image/filters"
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def alpha_clean(self, images: torch.Tensor, radius: int, fill_holes: int, white_threshold: float, extra_clip: float):
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d = radius * 2 + 1
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i_dup = copy.deepcopy(images.cpu().numpy())
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for index, image in enumerate(i_dup):
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cleaned = cv2.bilateralFilter(image, 9, 0.05, 8)
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alpha = np.clip((image - white_threshold) / (1 - white_threshold), 0, 1)
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rgb = image * alpha
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alpha = cv2.GaussianBlur(alpha, (d,d), 0) * 0.99 + np.average(alpha) * 0.01
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rgb = cv2.GaussianBlur(rgb, (d,d), 0) * 0.99 + np.average(rgb) * 0.01
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rgb = rgb / np.clip(alpha, 0.00001, 1)
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rgb = rgb * extra_clip
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cleaned = np.clip(cleaned / rgb, 0, 1)
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if fill_holes > 0:
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fD = fill_holes * 2 + 1
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gamma = cleaned * cleaned
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kD = np.ones((fD, fD), np.uint8)
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kE = np.ones((fD + 2, fD + 2), np.uint8)
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gamma = cv2.dilate(gamma, kD, iterations=1)
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gamma = cv2.erode(gamma, kE, iterations=1)
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gamma = cv2.GaussianBlur(gamma, (fD, fD), 0)
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cleaned = np.maximum(cleaned, gamma)
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i_dup[index] = cleaned
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return (torch.from_numpy(i_dup),)
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```
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