121 lines
4.3 KiB
Markdown
121 lines
4.3 KiB
Markdown
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# Documentation
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- Class name: `ImageTransformRotate`
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- Category: `image/transform`
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- Output node: `False`
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ImageTransformRotate节点用于将一批图像旋转指定角度。它支持可选的图像扩展以完全容纳旋转后的图像，并采用超采样抗锯齿(SSAA)技术以获得更高质量的结果。该节点可以处理各种旋转方法，为图像变换任务提供了灵活性。
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# Input types
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## Required
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- **`images`**
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- 要旋转的图像批次。这个参数至关重要，因为它指定了将要进行旋转的输入图像。
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- Comfy dtype: IMAGE
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- Python dtype: List[torch.Tensor]
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- **`angle`**
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- 图像将被旋转的角度。这个参数决定了应用于批次中每个图像的旋转程度。
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- Comfy dtype: FLOAT
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- Python dtype: float
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- **`expand`**
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- 一个布尔标志，指示是否应扩展图像以适应旋转后的图像而不进行裁剪。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: bool
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- **`SSAA`**
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- 指示应用于图像的超采样抗锯齿级别，以提高旋转质量。更高的SSAA值会产生更平滑的边缘和减少锯齿，从而提高旋转图像的视觉质量。
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- Comfy dtype: INT
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- Python dtype: int
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- **`method`**
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- 指定用于旋转图像的方法，允许采用不同的图像旋转方法。
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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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- 旋转后的图像，以批次形式返回。这个输出反映了在应用指定旋转和任何额外处理步骤后的变换图像。
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- Comfy dtype: IMAGE
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- Python dtype: List[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 ImageTransformRotate:
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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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"angle": ("FLOAT", {
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"default": 35.0,
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"max": 360.0,
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"step": 0.1
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}),
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"expand": (["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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def node(self, images, angle, expand, SSAA, method):
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height, width = images[0, :, :, 0].shape
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def rotate_tensor(tensor):
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if method == "lanczos":
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resize_sampler = Image.LANCZOS
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rotate_sampler = Image.BICUBIC
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elif method == "bicubic":
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resize_sampler = Image.BICUBIC
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rotate_sampler = Image.BICUBIC
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elif method == "hamming":
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resize_sampler = Image.HAMMING
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rotate_sampler = Image.BILINEAR
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elif method == "bilinear":
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resize_sampler = Image.BILINEAR
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rotate_sampler = Image.BILINEAR
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elif method == "box":
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resize_sampler = Image.BOX
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rotate_sampler = Image.NEAREST
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elif method == "nearest":
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resize_sampler = Image.NEAREST
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rotate_sampler = Image.NEAREST
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else:
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raise ValueError()
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if SSAA > 1:
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img = tensor.tensor_to_image()
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img_us_scaled = img.resize((width * SSAA, height * SSAA), resize_sampler)
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img_rotated = img_us_scaled.rotate(angle, rotate_sampler, expand == "true", fillcolor=(0, 0, 0, 0))
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img_down_scaled = img_rotated.resize((img_rotated.width // SSAA, img_rotated.height // SSAA), resize_sampler)
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result = img_down_scaled.image_to_tensor()
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else:
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img = tensor.tensor_to_image()
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img_rotated = img.rotate(angle, rotate_sampler, expand == "true", fillcolor=(0, 0, 0, 0))
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result = img_rotated.image_to_tensor()
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return result
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if angle == 0.0 or angle == 360.0:
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return (images,)
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else:
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return (torch.stack([
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rotate_tensor(images[i]) for i in range(len(images))
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]),)
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```
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