86 lines
3.2 KiB
Markdown
86 lines
3.2 KiB
Markdown
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# Documentation
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- Class name: AdaptiveThresholding
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- Category: Bmad/CV/Thresholding
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- Output node: False
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AdaptiveThresholding节点对图像应用自适应阈值处理技术，根据局部图像特征将灰度图像转换为二值图像。这一过程能够增强不同光照条件下的特征可见性，适用于计算机视觉任务中的图像预处理。
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# Input types
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## Required
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- src
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- 需要进行阈值处理的源图像。它是定义将要应用自适应阈值处理的输入图像的关键参数。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- max_value
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- 阈值处理后像素可以达到的最大强度值。它决定了输出二值图像中白色区域的亮度。
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- Comfy dtype: INT
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- Python dtype: int
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- adaptive_method
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- 指定根据像素邻域值计算像素阈值的方法。它影响阈值处理过程的自适应性。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: str
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- threshold_type
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- 根据与阈值的比较，决定像素值是设为最大值还是零。它影响阈值处理的二值化结果。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: str
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- block_size
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- 用于计算每个像素阈值的邻域区域大小。它影响阈值处理的粒度。
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- Comfy dtype: INT
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- Python dtype: int
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- c
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- 从计算得到的均值或加权均值中减去的常数。它通过调整阈值来微调阈值处理过程。
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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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- 自适应阈值处理过程产生的二值图像。它通过将图像转换为二值格式来突出显示图像中感兴趣的特征。
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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 AdaptiveThresholding:
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adaptive_modes_map = {
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"ADAPTIVE_THRESH_MEAN_C": cv.ADAPTIVE_THRESH_MEAN_C,
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"ADAPTIVE_THRESH_GAUSSIAN_C": cv.ADAPTIVE_THRESH_GAUSSIAN_C,
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}
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adaptive_modes = list(adaptive_modes_map.keys())
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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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"max_value": ("INT", {"default": 255, "min": 0, "max": 255, "step": 1}),
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# maybe should just allow for 255? may just confuse some people that don't read documentation
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"adaptive_method": (s.adaptive_modes, {"default": s.adaptive_modes[1]}),
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"threshold_type": (thresh_types, {"default": thresh_types[0]}),
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"block_size": ("INT", {"default": 4, "min": 2, "step": 2}),
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"c": ("INT", {"default": 2, "min": -999, "step": 1}),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "thresh"
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CATEGORY = "Bmad/CV/Thresholding"
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def thresh(self, src, max_value, adaptive_method, threshold_type, block_size, c):
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# maybe allow to use from a specific channel 1st? nah, just create a node to fetch the channel
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# might be useful for other nodes
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src = tensor2opencv(src, 1)
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src = cv.adaptiveThreshold(src, max_value, self.adaptive_modes_map[adaptive_method], \
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thresh_types_map[threshold_type], block_size + 1, c)
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src = cv.cvtColor(src, cv.COLOR_GRAY2RGB)
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src = opencv2tensor(src)
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return (src,)
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```
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