Script I used to create the GIF on https://torchio.readthedocs.io/transforms/augmentation.html

torchio_augmentation.py

from pathlib import Path

import torch
import torchvision
import numpy as np
from PIL import Image
import torchio as tio
from tqdm import trange

output_dir = Path('/tmp/transformed')
output_dir.mkdir(exist_ok=True)

image = tio.datasets.FPG().t1
image = tio.ToCanonical()(image)
image = tio.Resample(0.5)(image)

rescale = tio.RescaleIntensity((0, 255), (1, 99))
composed_transform = tio.Compose((
    tio.OneOf({
        tio.RandomElasticDeformation(): 2,
        tio.RandomAffine(): 8,
    }),
    tio.RandomAnisotropy(p=0.2),
    tio.RandomBiasField(p=0.2),
    tio.RandomBlur(p=0.1),
    tio.RandomGamma(p=0.1),
    tio.RandomNoise(p=0.1),
    tio.OneOf((
        tio.RandomGhosting(),
        tio.RandomMotion(),
        tio.RandomSpike(),
        ),
        p=0.1,
    ),
    rescale,
))

spacing = image.spacing[0]
rows = 1
for i in trange(6):
    transform = rescale if i == 0 else composed_transform
    num_images = rows ** 2
    images = []
    for j in trange(num_images, leave=False):
        transformed = transform(image)
        si, sj, sk = transformed.spatial_shape
        slice = transformed.numpy()[0, si//2, :, :].astype(np.uint8)
        slice = np.rot90(slice)[np.newaxis]
        images.append(torch.from_numpy(slice.copy()))
    tensor = torch.stack(images)
    grid = torchvision.utils.make_grid(
        tensor,
        nrow=int(np.sqrt(num_images)),
        padding=0,
    )
    name = f'{i}.png'
    path = output_dir / name
    Image.fromarray(grid.permute(1, 2, 0).numpy()).save(path)
    old_spacing = image.spacing[0]
    spacing = 2 * spacing
    downsample = tio.Compose((
        tio.Blur(tio.Resample.get_sigma(2, old_spacing)),
        tio.Resample(spacing),
    ))
    image = downsample(image)
    rows *= 2