| import math | |
| import torch | |
| from typing import List | |
| from torch import Tensor | |
| from .filter import gaussian_blur | |
| def perlin(shape, smoothing, magnitude=1.0, device=None): | |
| """ | |
| Generates a perlin noise image. | |
| Parameters | |
| ---------- | |
| shape : List[int] | |
| The desired shape of the output tensor. Can be 2D or 3D. | |
| smoothing : float or List[float] | |
| The spatial smoothing sigma in voxel coordinates. If a | |
| single value is provided, it will be used for all dimensions. | |
| magnitude : float or List[float] | |
| The standard deviation of the noise across dimensions. If a single value is | |
| provided, it will be used for all dimensions. | |
| device : torch.device or None, optional | |
| The device on which the output tensor is allocated. If None, defaults to CPU. | |
| Returns | |
| ------- | |
| Tensor | |
| A Perlin noise image of shape `shape`. | |
| """ | |
| noise = torch.normal(0, 1, size=shape, device=device).unsqueeze(0) | |
| noise = gaussian_blur(noise, smoothing).squeeze(0) | |
| noise *= magnitude / noise.std() | |
| return noise | |