63 lines
2.0 KiB
Python
63 lines
2.0 KiB
Python
import torch
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import numpy as np
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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 {
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"required": {
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"image": ("IMAGE",),
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"amplitude": ("FLOAT", {
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"default": 10,
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"min": 0,
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"max": 150,
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"step": 5
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}),
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"frequency": ("FLOAT", {
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"default": 5,
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"min": 0,
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"max": 20,
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"step": 1
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}),
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"direction": (["horizontal", "vertical"],),
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},
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}
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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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NODE_CLASS_MAPPINGS = {
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"SineWave": SineWave,
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}
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