85 lines
2.8 KiB
Markdown
85 lines
2.8 KiB
Markdown
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# Documentation
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- Class name: ColorMatchImage
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- Category: image/filters
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- Output node: False
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ColorMatchImage节点旨在调整图像的颜色，使其与目标色彩方案或另一张图像相匹配或协调。这一过程在图像编辑和合成工作流程中至关重要，特别是在需要确保不同元素之间颜色一致性的场景下。
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# Input types
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## Required
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- images
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- 'images'输入代表需要进行颜色调整的源图像。它是定义颜色匹配过程起点的关键。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- reference
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- 'reference'输入指定了源图像应该匹配的目标图像或色彩方案。它对于引导颜色匹配过程朝向预期结果至关重要。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- blur
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- 'blur'输入控制应用于参考图像的模糊程度，影响颜色匹配的柔和度。
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- Comfy dtype: INT
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- Python dtype: float
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- factor
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- 'factor'输入决定了颜色匹配效果的强度，允许对最终外观进行微调。
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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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- 'image'输出提供了颜色调整后的结果图像，展示了颜色匹配过程的效果。
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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 ColorMatchImage:
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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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"reference": ("IMAGE", ),
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"blur": ("INT", {"default": 0, "min": 0, "max": 1023}),
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"factor": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01, "round": 0.01}),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "batch_normalize"
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CATEGORY = "image/filters"
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def batch_normalize(self, images, reference, blur, factor):
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t = images.detach().clone()
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ref = reference.detach().clone()
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if ref.shape[0] < t.shape[0]:
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ref = ref[0].unsqueeze(0).repeat(t.shape[0], 1, 1, 1)
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if blur == 0:
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mean = torch.mean(t, (1,2), keepdim=True)
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mean_ref = torch.mean(ref, (1,2), keepdim=True)
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for i in range(t.shape[0]):
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for c in range(3):
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t[i,:,:,c] /= mean[i,0,0,c]
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t[i,:,:,c] *= mean_ref[i,0,0,c]
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else:
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d = blur * 2 + 1
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blurred = cv_blur_tensor(torch.clamp(t, 0.001, 1), d, d)
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blurred_ref = cv_blur_tensor(torch.clamp(ref, 0.001, 1), d, d)
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for i in range(t.shape[0]):
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for c in range(3):
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t[i,:,:,c] /= blurred[i,:,:,c]
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t[i,:,:,c] *= blurred_ref[i,:,:,c]
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t = torch.lerp(images, t, factor)
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return (t,)
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```
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