133 lines
3.9 KiB
Markdown
133 lines
3.9 KiB
Markdown
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# Documentation
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- Class name: ImageTransformCropCorners
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- Category: image/transform
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- Output node: False
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ImageTransformCropCorners 节点用于对图像的角落进行裁剪操作，可以实现指定半径的圆角效果。它支持选择性地对每个角落进行圆角处理，并使用超采样抗锯齿（SSAA）技术来获得更高质量的结果。
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# Input types
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## Required
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- images
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- 需要进行角落裁剪处理的输入图像。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- radius
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- 需要圆化的角落的半径。
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- Comfy dtype: INT
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- Python dtype: int
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- top_left_corner
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- 指示是否对左上角进行圆角处理的标志。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: str
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- top_right_corner
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- 指示是否对右上角进行圆角处理的标志。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: str
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- bottom_right_corner
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- 指示是否对右下角进行圆角处理的标志。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: str
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- bottom_left_corner
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- 指示是否对左下角进行圆角处理的标志。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: str
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- SSAA
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- 超采样抗锯齿因子，用于提高圆角处理的质量。
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- Comfy dtype: INT
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- Python dtype: int
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- method
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- 在SSAA过程中用于调整图像大小的方法。
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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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- image
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- 经过指定角落裁剪和圆角处理后的图像。
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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 ImageTransformCropCorners:
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def __init__(self):
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pass
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@classmethod
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def INPUT_TYPES(cls):
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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": 180,
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"max": 360,
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"step": 1
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}),
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"top_left_corner": (["true", "false"],),
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"top_right_corner": (["true", "false"],),
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"bottom_right_corner": (["true", "false"],),
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"bottom_left_corner": (["true", "false"],),
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"SSAA": ("INT", {
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"default": 4,
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"min": 1,
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"max": 16,
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"step": 1
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}),
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"method": (["lanczos", "bicubic", "hamming", "bilinear", "box", "nearest"],),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "node"
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CATEGORY = "image/transform"
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# noinspection PyUnresolvedReferences, PyArgumentList
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def node(
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self,
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images,
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radius,
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top_left_corner,
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top_right_corner,
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bottom_right_corner,
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bottom_left_corner,
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SSAA,
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method
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):
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sampler = get_sampler_by_name(method)
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height, width = images[0, :, :, 0].shape
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canvas = Image.new("RGBA", (width * SSAA, height * SSAA), (0, 0, 0, 0))
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draw = ImageDraw.Draw(canvas)
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draw.rounded_rectangle(
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((0, 0), (width * SSAA, height * SSAA)),
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radius * SSAA, (255, 255, 255, 255),
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corners=(
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True if top_left_corner == "true" else False,
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True if top_right_corner == "true" else False,
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True if bottom_right_corner == "true" else False,
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True if bottom_left_corner == "true" else False
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)
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)
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canvas = canvas.resize((width, height), sampler)
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mask = 1.0 - canvas.image_to_tensor()[:, :, 3]
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def crop_tensor(tensor):
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return torch.stack([
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(tensor[:, :, i] - mask).clamp(0, 1) for i in range(tensor.shape[2])
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], dim=2)
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return (torch.stack([
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crop_tensor(images[i]) for i in range(len(images))
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]),)
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```
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