72 lines
3.1 KiB
Markdown
72 lines
3.1 KiB
Markdown
# Documentation
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- Class name: SineWave
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- Category: postprocessing/Effects
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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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SineWave类引入了一种方法，对图像应用正弦波扭曲效果，通过模拟波浪状图案增强其视觉外观。这种方法旨在以创造性的方式操纵图像数据，引入艺术性的改变，而不损害原始内容的完整性。
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# Input types
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## Required
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- image
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- 图像参数至关重要，因为它是节点操作的主要输入。它是应用正弦波效果的媒介，其属性直接影响扭曲结果。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- amplitude
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- 振幅决定了正弦波效果的强度，控制应用于图像的扭曲程度。它是塑造节点执行视觉结果的关键因素。
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- Comfy dtype: FLOAT
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- Python dtype: float
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- frequency
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- 频率决定了单位图像空间内的正弦波周期数，影响扭曲的粒度。它在定义图案的外观中起着重要作用。
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- Comfy dtype: FLOAT
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- Python dtype: float
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- direction
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- 方向指定了正弦波效果的方向，水平或垂直，这决定了扭曲如何应用于图像。
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- Comfy dtype: COMBO['horizontal', 'vertical']
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- Python dtype: str
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# Output types
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- output_image
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- 输出图像是应用于输入图像的正弦波效果的结果。它代表了转换，是节点的主要输出，包含了创造性的扭曲。
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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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# Source code
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```
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class SineWave:
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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',), 'amplitude': ('FLOAT', {'default': 10, 'min': 0, 'max': 150, 'step': 5}), 'frequency': ('FLOAT', {'default': 5, 'min': 0, 'max': 20, 'step': 1}), 'direction': (['horizontal', 'vertical'],)}}
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RETURN_TYPES = ('IMAGE',)
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FUNCTION = 'apply_sine_wave'
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CATEGORY = 'postprocessing/Effects'
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def apply_sine_wave(self, image: torch.Tensor, amplitude: float, frequency: float, direction: str):
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(batch_size, height, width, channels) = image.shape
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result = torch.zeros_like(image)
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for b in range(batch_size):
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tensor_image = image[b]
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result[b] = self.sine_wave_effect(tensor_image, amplitude, frequency, direction)
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return (result,)
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def sine_wave_effect(self, image: torch.Tensor, amplitude: float, frequency: float, direction: str):
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(height, width, _) = image.shape
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shifted_image = torch.zeros_like(image)
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for channel in range(3):
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if direction == 'horizontal':
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for i in range(height):
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offset = int(amplitude * np.sin(2 * torch.pi * i * frequency / height))
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shifted_image[i, :, channel] = torch.roll(image[i, :, channel], offset)
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elif direction == 'vertical':
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for j in range(width):
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offset = int(amplitude * np.sin(2 * torch.pi * j * frequency / width))
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shifted_image[:, j, channel] = torch.roll(image[:, j, channel], offset)
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return shifted_image
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``` |