67 lines
2.0 KiB
Markdown
67 lines
2.0 KiB
Markdown
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# Documentation
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- Class name: `JitterImage`
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- Category: `image/filters/jitter`
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- Output node: `False`
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JitterImage节点通过对图像应用一系列仿射变换来模拟相机或物体运动，从而在图像上产生抖动效果。这种效果可以增强模型对输入数据变化的鲁棒性，或用于数据增强。
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# Input types
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## Required
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- images
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- 需要应用抖动效果的输入图像。这个参数对于定义将要进行变换的视觉内容至关重要。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- jitter_scale
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- 定义抖动效果的规模，调整应用于图像的变换强度。这个参数允许微调抖动效果的程度。
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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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- 应用抖动效果后的输出图像，展示变换后的视觉内容。
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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 JitterImage:
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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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"jitter_scale": ("FLOAT", {"default": 1.0, "min": 0.1, "step": 0.1}),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "jitter"
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CATEGORY = "image/filters/jitter"
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def jitter(self, images, jitter_scale):
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t = images.detach().clone().movedim(-1,1) # [B x C x H x W]
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theta = jitter_matrix.detach().clone().to(t.device)
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theta[:,0,2] *= jitter_scale * 2 / t.shape[3]
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theta[:,1,2] *= jitter_scale * 2 / t.shape[2]
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affine = torch.nn.functional.affine_grid(theta, torch.Size([9, t.shape[1], t.shape[2], t.shape[3]]))
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batch = []
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for i in range(t.shape[0]):
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jb = t[i].repeat(9,1,1,1)
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jb = torch.nn.functional.grid_sample(jb, affine, mode='bilinear', padding_mode='border', align_corners=None)
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batch.append(jb)
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t = torch.cat(batch, dim=0).movedim(1,-1) # [B x H x W x C]
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return (t,)
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```
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