torchio.transforms.transform

Complexity

Total Complexity

12

Size/Duplication

Total Lines	69
Duplicated Lines	0 %

Importance

Changes

0

7 Methods

Rating	Name	Duplication	Size	Complexity
A	Transform.sitk_to_nib()	0	3	1
A	Transform.parse_probability()	0	10	3
A	Transform.__init__()	0	2	1
A	Transform.__call__()	0	8	2
A	Transform.nib_to_sitk()	0	3	1
A	Transform.parse_sample()	0	8	3
A	Transform.apply_transform()	0	3	1

import numbers
import warnings

The import warnings seems to be unused.

from copy import deepcopy
from abc import ABC, abstractmethod

import torch

Unable to import 'torch'

import SimpleITK as sitk

Unable to import 'SimpleITK'

from .. import TypeData, TYPE

The import TYPE seems to be unused.

from ..data.images import Subject
from ..utils import is_image_dict, nib_to_sitk, sitk_to_nib

Unused is_image_dict imported from utils

class Transform(ABC):
    """Abstract class for all TorchIO transforms.

    All classes used to transform a sample from an
    :py:class:`~torchio.ImagesDataset` should subclass it.
    All subclasses should overwrite
    :py:meth:`torchio.tranforms.Transform.apply_transform`,
    which takes a sample, applies some transformation and returns the result.

    Args:
        p: Probability that this transform will be applied.
    """
    def __init__(self, p: float = 1):
        self.probability = self.parse_probability(p)

    def __call__(self, sample: Subject):
        """Transform a sample and return the result."""
        self.parse_sample(sample)
        if torch.rand(1).item() > self.probability:
            return sample
        sample = deepcopy(sample)
        sample = self.apply_transform(sample)
        return sample

    @abstractmethod
    def apply_transform(self, sample: Subject):
        raise NotImplementedError

    @staticmethod
    def parse_probability(probability: float) -> float:
        is_number = isinstance(probability, numbers.Number)
        if not (is_number and 0 <= probability <= 1):
            message = (
                'Probability must be a number in [0, 1],'
                f' not {probability}'
            )
            raise ValueError(message)
        return probability

    @staticmethod
    def parse_sample(sample: Subject) -> None:
        if not isinstance(sample, Subject) or not sample.is_sample:
            message = (
                'Inputs to transforms must be instances of torchio.Subject'
                f' generated by a torchio.ImagesDataset, not "{type(sample)}"'
            )
            raise RuntimeError(message)

    @staticmethod
    def nib_to_sitk(data: TypeData, affine: TypeData):
        return nib_to_sitk(data, affine)

    @staticmethod
    def sitk_to_nib(image: sitk.Image):
        return sitk_to_nib(image)