68 lines
2.2 KiB
Markdown
68 lines
2.2 KiB
Markdown
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# Documentation
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- Class name: CLAHE
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- Category: Bmad/CV/Thresholding
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- Output node: False
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CLAHE节点应用对比度受限自适应直方图均衡算法来增强图像对比度。它通过局部调整图像强度来增强对比度，同时考虑了诸如裁剪限制和网格大小等特定参数，以实现对处理过程更精细的控制。
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# Input types
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## Required
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- src
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- 待处理的源图像。它作为输入，CLAHE算法将在其上应用以增强局部对比度。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- clip_limit
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- 定义对比度限制的阈值。该参数有助于控制对比度增强程度，防止在图像相对均匀的区域过度放大噪声。
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- Comfy dtype: INT
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- Python dtype: int
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- tile_grid_x
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- 指定图像在水平方向上的分块数量。它决定了对比度增强过程的细粒度。
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- Comfy dtype: INT
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- Python dtype: int
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- tile_grid_y
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- 指定图像在垂直方向上的分块数量。与tile_grid_x类似，它影响对比度增强的细粒度。
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- Comfy dtype: INT
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- Python dtype: int
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# Output types
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- image
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- 应用CLAHE后的输出图像。它是输入图像的增强版本，具有改善的局部对比度。
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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 CLAHE:
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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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"src": ("IMAGE",),
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"clip_limit": ("INT", {"default": 2, "step": 1}),
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# 40 is the default in documentation, but prob. a bit high no?
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"tile_grid_x": ("INT", {"default": 8, "min": 2, "step": 1}),
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"tile_grid_y": ("INT", {"default": 8, "min": 2, "step": 1}),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "eq"
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CATEGORY = "Bmad/CV/Thresholding"
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def eq(self, src, clip_limit, tile_grid_x, tile_grid_y):
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src = tensor2opencv(src, 1)
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clahe = cv.createCLAHE(clipLimit=clip_limit, tileGridSize=(tile_grid_x, tile_grid_y))
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eq = clahe.apply(src)
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eq = cv.cvtColor(eq, cv.COLOR_GRAY2RGB)
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eq = opencv2tensor(eq)
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return (eq,)
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```
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