94 lines
3.6 KiB
Markdown
94 lines
3.6 KiB
Markdown
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# Documentation
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- Class name: SaltScheduledBinaryComparison
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- Category: SALT/Scheduling/Image
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- Output node: False
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- Repo Ref: Unknown/Unavailable
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SaltScheduledBinaryComparison节点对一批图像执行定时二元比较，应用可选考虑误差范围（epsilon）的阈值操作。它根据提供的调度动态调整批次中每个图像的比较阈值，实现灵活的图像处理操作，可以随时间或跨不同图像而变化。
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# Input types
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## Required
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- images
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- 要处理的图像批次。此参数对于定义将应用二元阈值操作的图像集至关重要，直接影响节点的执行和结果。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- comparison_schedule
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- 用作二元比较阈值的比较值调度表，适用于整个图像批次。这个调度表直接影响阈值操作，实现每个图像的动态调整。
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- Comfy dtype: LIST
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- Python dtype: List[float]
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## Optional
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- epsilon_schedule
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- 可选的epsilon值调度表，用于在比较中允许误差范围，使接近阈值的值也被视为匹配。此参数通过适应微小变化，为阈值操作增加了灵活性。
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- Comfy dtype: LIST
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- Python dtype: List[float]
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- use_epsilon
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- 决定是否在比较中使用epsilon误差范围的标志。这影响是否将接近阈值的值视为匹配，为二元比较增加了一层灵活性。
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- Comfy dtype: BOOLEAN
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- Python dtype: bool
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# Output types
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- images
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- 二元阈值操作的结果，其中图像批次中的每个像素根据比较结果被设置为1或0。
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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 SaltScheduledBinaryComparison:
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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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"images": ("IMAGE",),
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"comparison_schedule": ("LIST",),
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},
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"optional": {
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"epsilon_schedule": ("LIST", {}),
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"use_epsilon": ("BOOLEAN", {"default": True})
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}
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}
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RETURN_TYPES = ("IMAGE",)
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RETURN_NAMES = ("images",)
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FUNCTION = "binary_threshold"
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CATEGORY = "SALT/Scheduling/Image"
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def binary_threshold(self, images, comparison_schedule, epsilon_schedule=[0.1], use_epsilon=True):
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batch_size = images.shape[0]
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if len(comparison_schedule) < batch_size:
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last_val = comparison_schedule[-1]
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comparison_schedule.extend([last_val] * (batch_size - len(comparison_schedule)))
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comparison_schedule = comparison_schedule[:batch_size]
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thresholds_tensor = torch.tensor(comparison_schedule, dtype=images.dtype).view(batch_size, 1, 1, 1)
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if use_epsilon:
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if epsilon_schedule is None:
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epsilon_schedule = [0.1] * batch_size
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if len(epsilon_schedule) < batch_size:
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last_eps = epsilon_schedule[-1]
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epsilon_schedule.extend([last_eps] * (batch_size - len(epsilon_schedule)))
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epsilon_schedule = epsilon_schedule[:batch_size]
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epsilon_tensor = torch.tensor(epsilon_schedule, dtype=images.dtype).view(batch_size, 1, 1, 1)
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condition_met = ((images == thresholds_tensor) |
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(torch.abs(images - thresholds_tensor) <= epsilon_tensor))
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else:
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condition_met = images >= thresholds_tensor
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thresholded_images = torch.where(condition_met, torch.tensor(1.0, dtype=images.dtype), torch.tensor(0.0, dtype=images.dtype))
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return (thresholded_images, )
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```
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