78 lines
2.1 KiB
Markdown
78 lines
2.1 KiB
Markdown
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# Documentation
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- Class name: RemapRange
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- Category: image/filters
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- Output node: False
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RemapRange节点旨在通过重新映射图像的黑点和白点来调整图像的亮度范围。这一过程可以增强图像的视觉对比度，或调整图像整体的明暗水平。
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# Input types
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## Required
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- image
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- 待处理的输入图像。该图像的亮度级别将根据指定的黑点和白点进行调整。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- blackpoint
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- 重新映射为黑色的亮度范围下限。低于此点的值将被调整为黑色，从而影响图像的对比度和亮度。
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- Comfy dtype: FLOAT
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- Python dtype: float
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- whitepoint
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- 重新映射为白色的亮度范围上限。高于此点的值将被调整为白色，从而影响图像的对比度和亮度水平。
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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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- 经过黑点和白点重新映射后的输出图像，其亮度级别已被调整。
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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 RemapRange:
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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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"image": ("IMAGE",),
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"blackpoint": ("FLOAT", {
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"default": 0.0,
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"min": 0.0,
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"max": 1.0,
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"step": 0.01
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}),
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"whitepoint": ("FLOAT", {
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"default": 1.0,
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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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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "remap"
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CATEGORY = "image/filters"
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def remap(self, image: torch.Tensor, blackpoint: float, whitepoint: float):
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bp = min(blackpoint, whitepoint - 0.001)
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scale = 1 / (whitepoint - bp)
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i_dup = copy.deepcopy(image.cpu().numpy())
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i_dup = np.clip((i_dup - bp) * scale, 0.0, 1.0)
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return (torch.from_numpy(i_dup),)
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```
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