86 lines
2.8 KiB
Markdown
86 lines
2.8 KiB
Markdown
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# Documentation
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- Class name: SaltMaskAdaptiveThresholdingRegion
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- Category: SALT/Masking/Filter
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- Output node: False
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该节点对一组遮罩图像应用自适应阈值处理，根据局部像素值的变化将其转换为二值图像。它旨在通过动态调整整个图像的阈值，增强不同照明区域中特征的可见性。
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# Input types
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## Required
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- masks
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- 需要进行自适应阈值处理的输入遮罩。这些遮罩将使用局部自适应阈值转换为二值图像。
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- Comfy dtype: MASK
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- Python dtype: List[torch.Tensor]
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## Optional
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- block_size
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- 用于计算每个像素阈值的局部区域大小。它决定了阈值处理过程对局部照明变化的适应性。
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- Comfy dtype: INT
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- Python dtype: List[int] or int
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- constant
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- 从block_size计算的平均值或加权平均值中减去的常数。这用于微调阈值处理过程。
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- Comfy dtype: INT
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- Python dtype: List[int] or int
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# Output types
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- MASKS
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- 包含应用自适应阈值处理后的区域的输出张量，增强了遮罩中的局部特征。
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- Comfy dtype: MASK
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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 SaltMaskAdaptiveThresholdingRegion:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"masks": ("MASK",),
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},
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"optional": {
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"block_size": ("INT", {"default": 11, "min": 3, "max": 255, "step": 2}),
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"constant": ("INT", {"default": 2, "min": 0, "max": 10, "step": 1}),
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}
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}
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CATEGORY = f"{NAME}/Masking/Filter"
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RETURN_TYPES = ("MASK",)
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RETURN_NAMES = ("MASKS",)
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FUNCTION = "adaptive_thresholding"
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def adaptive_thresholding(self, masks, block_size=11, constant=2):
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if not isinstance(block_size, list):
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block_size = [block_size]
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if not isinstance(constant, list):
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constant = [constant]
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block_size = [closest_odd(val) for val in block_size]
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regions = []
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for i, mask in enumerate(masks):
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pil_image = mask2pil(mask.unsqueeze(0))
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image_array = np.array(pil_image.convert('L'))
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current_block_size = block_size[i if i < len(block_size) else -1]
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current_C = constant[i if i < len(constant) else -1]
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adaptive_thresh = cv2.adaptiveThreshold(image_array, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
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cv2.THRESH_BINARY, current_block_size, current_C)
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adaptive_pil = ImageOps.invert(Image.fromarray(adaptive_thresh))
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region_tensor = pil2mask(adaptive_pil)
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regions.append(region_tensor)
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regions_tensor = torch.cat(regions, dim=0)
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return (regions_tensor,)
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```
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