137 lines
4.8 KiB
Markdown
137 lines
4.8 KiB
Markdown
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# Documentation
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- Class name: AlphaMatte
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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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AlphaMatte节点是一个专门用于图像处理的高级工具，主要功能是提取和优化图像的alpha遮罩（透明度通道）。它利用深度学习技术，通过分析图像及其alpha trimap（三值图），能够准确地将前景与背景分离，即使在复杂场景中也能表现出色。该节点具备预模糊处理、调整黑白点以及进行迭代优化的能力，以实现高质量的遮罩提取。
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# Input types
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## Required
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- images
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- 需要提取alpha遮罩的输入图像。这个参数至关重要，因为它直接影响遮罩提取过程的质量和准确性。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- alpha_trimap
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- 一个三值图，为前景、背景和未知区域提供初步猜测，指导遮罩提取过程。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- preblur
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- 指定应用于trimap的预模糊量，用于平滑硬边缘并改善遮罩提取效果。
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- Comfy dtype: INT
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- Python dtype: int
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- blackpoint
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- 用于调整trimap对比度的黑点值，有助于更清晰地分离前景和背景。
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- Comfy dtype: FLOAT
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- Python dtype: float
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- whitepoint
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- 用于调整trimap亮度的白点值，增强前景和背景区域之间的区分度。
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- Comfy dtype: FLOAT
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- Python dtype: float
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- max_iterations
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- 允许优化alpha遮罩的最大迭代次数，确保质量和计算效率之间的平衡。
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- Comfy dtype: INT
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- Python dtype: int
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- estimate_fg
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- 一个标志，指示是否除alpha遮罩外还要估算前景，提供更完整的图像元素分离。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: str
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# Output types
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- alpha
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- 提取出的alpha遮罩，表示图像中前景元素的透明度级别。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- fg
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- 当estimate_fg标志为真时获得的估算图像前景。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- bg
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- 估算的图像背景，适用于合成或进一步处理。
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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 AlphaMatte:
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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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"alpha_trimap": ("IMAGE",),
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"preblur": ("INT", {
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"default": 8,
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"min": 0,
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"max": 256,
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"step": 1
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}),
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"blackpoint": ("FLOAT", {
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"default": 0.01,
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"min": 0.0,
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"max": 0.99,
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"step": 0.01
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}),
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"whitepoint": ("FLOAT", {
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"default": 0.99,
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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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"max_iterations": ("INT", {
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"default": 1000,
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"min": 100,
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"max": 10000,
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"step": 100
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}),
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"estimate_fg": (["true", "false"],),
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},
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}
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RETURN_TYPES = ("IMAGE", "IMAGE", "IMAGE",)
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RETURN_NAMES = ("alpha", "fg", "bg",)
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FUNCTION = "alpha_matte"
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CATEGORY = "image/filters"
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def alpha_matte(self, images, alpha_trimap, preblur, blackpoint, whitepoint, max_iterations, estimate_fg):
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d = preblur * 2 + 1
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i_dup = copy.deepcopy(images.cpu().numpy().astype(np.float64))
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a_dup = copy.deepcopy(alpha_trimap.cpu().numpy().astype(np.float64))
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fg = copy.deepcopy(images.cpu().numpy().astype(np.float64))
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bg = copy.deepcopy(images.cpu().numpy().astype(np.float64))
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for index, image in enumerate(i_dup):
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trimap = a_dup[index][:,:,0] # convert to single channel
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if preblur > 0:
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trimap = cv2.GaussianBlur(trimap, (d, d), 0)
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trimap = fix_trimap(trimap, blackpoint, whitepoint)
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alpha = estimate_alpha_cf(image, trimap, laplacian_kwargs={"epsilon": 1e-6}, cg_kwargs={"maxiter":max_iterations})
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if estimate_fg == "true":
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fg[index], bg[index] = estimate_foreground_ml(image, alpha, return_background=True)
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a_dup[index] = np.stack([alpha, alpha, alpha], axis = -1) # convert back to rgb
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return (
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torch.from_numpy(a_dup.astype(np.float32)), # alpha
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torch.from_numpy(fg.astype(np.float32)), # fg
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torch.from_numpy(bg.astype(np.float32)), # bg
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)
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```
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