Source code for torchio.transforms.augmentation.intensity.random_blur

from collections import defaultdict
from typing import Dict
from typing import Tuple
from typing import Union

import numpy as np
import scipy.ndimage as ndi
import torch

from .. import RandomTransform
from ... import IntensityTransform
from ....data.subject import Subject
from ....typing import TypeData
from ....typing import TypeSextetFloat
from ....typing import TypeTripletFloat


[docs]class RandomBlur(RandomTransform, IntensityTransform): r"""Blur an image using a random-sized Gaussian filter. Args: std: Tuple :math:`(a_1, b_1, a_2, b_2, a_3, b_3)` representing the ranges (in mm) of the standard deviations :math:`(\sigma_1, \sigma_2, \sigma_3)` of the Gaussian kernels used to blur the image along each axis, where :math:`\sigma_i \sim \mathcal{U}(a_i, b_i)`. If two values :math:`(a, b)` are provided, then :math:`\sigma_i \sim \mathcal{U}(a, b)`. If only one value :math:`x` is provided, then :math:`\sigma_i \sim \mathcal{U}(0, x)`. If three values :math:`(x_1, x_2, x_3)` are provided, then :math:`\sigma_i \sim \mathcal{U}(0, x_i)`. **kwargs: See :class:`~torchio.transforms.Transform` for additional keyword arguments. """ def __init__( self, std: Union[float, Tuple[float, float]] = (0, 2), **kwargs ): super().__init__(**kwargs) self.std_ranges = self.parse_params(std, None, 'std', min_constraint=0) def apply_transform(self, subject: Subject) -> Subject: arguments: Dict[str, dict] = defaultdict(dict) for name in self.get_images_dict(subject): std = self.get_params(self.std_ranges) arguments['std'][name] = std transform = Blur(**self.add_include_exclude(arguments)) transformed = transform(subject) assert isinstance(transformed, Subject) return transformed def get_params(self, std_ranges: TypeSextetFloat) -> TypeTripletFloat: std = self.sample_uniform_sextet(std_ranges) return std
class Blur(IntensityTransform): r"""Blur an image using a Gaussian filter. Args: std: Tuple :math:`(\sigma_1, \sigma_2, \sigma_3)` representing the the standard deviations (in mm) of the Gaussian kernels used to blur the image along each axis. **kwargs: See :class:`~torchio.transforms.Transform` for additional keyword arguments. """ def __init__( self, std: Union[TypeTripletFloat, Dict[str, TypeTripletFloat]], **kwargs ): super().__init__(**kwargs) self.std = std self.args_names = ['std'] def apply_transform(self, subject: Subject) -> Subject: stds = self.std for name, image in self.get_images_dict(subject).items(): if self.arguments_are_dict(): assert isinstance(self.std, dict) stds = self.std[name] repets = image.num_channels, 1 stds_channels: np.ndarray stds_channels = np.tile(stds, repets) # type: ignore[arg-type] transformed_tensors = [] for std, channel in zip(stds_channels, image.data): transformed_tensor = blur( channel, image.spacing, std, ) transformed_tensors.append(transformed_tensor) image.set_data(torch.stack(transformed_tensors)) return subject def blur( data: TypeData, spacing: TypeTripletFloat, std_physical: TypeTripletFloat, ) -> torch.Tensor: assert data.ndim == 3 # For example, if the standard deviation of the kernel is 2 mm and the # image spacing is 0.5 mm/voxel, the kernel should be # (2 mm / 0.5 mm/voxel) = 4 voxels wide std_voxel = np.array(std_physical) / np.array(spacing) blurred = ndi.gaussian_filter(data, std_voxel) tensor = torch.as_tensor(blurred) return tensor