70 lines
3.1 KiB
Markdown
70 lines
3.1 KiB
Markdown
# Documentation
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- Class name: HSVThresholdMask
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- Category: postprocessing/Masks
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- Output node: False
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- Repo Ref: https://github.com/EllangoK/ComfyUI-post-processing-nodes
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该节点类封装了HSV颜色空间内的阈值操作功能，能够基于特定的色调、饱和度或亮度范围创建二值掩码。这对于图像中分割和隔离感兴趣区域至关重要，促进了对象识别和降噪等多种图像处理任务的进行。
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# Input types
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## Required
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- image
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- 图像参数对于HSV阈值处理过程至关重要，因为它提供了进行阈值分割的源数据。它通过决定图像的哪些区域将受到掩码标准的处理，从而影响整个操作。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- low_threshold
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- 低阈值参数设置了HSV通道值的下限，对于定义将被考虑用于掩码的像素值范围起着至关重要的作用。
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- Comfy dtype: FLOAT
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- Python dtype: float
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- high_threshold
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- 高阈值参数建立了HSV通道值的上限，与低阈值一起决定了最终的掩码。
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- Comfy dtype: FLOAT
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- Python dtype: float
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- hsv_channel
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- hsv_channel参数指示在HSV颜色空间中将使用哪个通道进行阈值处理，这对生成的掩码及其在手头图像处理任务中的适用性有着重大影响。
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- Comfy dtype: COMBO['hue', 'saturation', 'value']
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- Python dtype: str
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# Output types
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- result
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- 结果参数代表通过应用HSV阈值标准生成的二值掩码，是进一步图像分析和操作的重要工具。
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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: CPU
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# Source code
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```
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class HSVThresholdMask:
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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 {'required': {'image': ('IMAGE',), 'low_threshold': ('FLOAT', {'default': 0.2, 'min': 0, 'max': 1, 'step': 0.1}), 'high_threshold': ('FLOAT', {'default': 0.7, 'min': 0, 'max': 1, 'step': 0.1}), 'hsv_channel': (['hue', 'saturation', 'value'],)}}
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RETURN_TYPES = ('IMAGE',)
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FUNCTION = 'hsv_threshold'
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CATEGORY = 'postprocessing/Masks'
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def hsv_threshold(self, image: torch.Tensor, low_threshold: float, high_threshold: float, hsv_channel: str):
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(batch_size, height, width, _) = image.shape
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result = torch.zeros(batch_size, height, width)
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if hsv_channel == 'hue':
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channel = 0
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(low_threshold, high_threshold) = (int(low_threshold * 180), int(high_threshold * 180))
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elif hsv_channel == 'saturation':
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channel = 1
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(low_threshold, high_threshold) = (int(low_threshold * 255), int(high_threshold * 255))
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elif hsv_channel == 'value':
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channel = 2
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(low_threshold, high_threshold) = (int(low_threshold * 255), int(high_threshold * 255))
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for b in range(batch_size):
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tensor_image = (image[b].numpy().copy() * 255).astype(np.uint8)
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hsv_image = cv2.cvtColor(tensor_image, cv2.COLOR_RGB2HSV)
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mask = cv2.inRange(hsv_image[:, :, channel], low_threshold, high_threshold)
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tensor = torch.from_numpy(mask).float() / 255.0
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result[b] = tensor
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return (result,)
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``` |