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import ast |
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import gzip |
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import shutil |
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import tempfile |
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from pathlib import Path |
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from typing import Union, Iterable, Tuple, Any, Optional, List, Sequence |
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from torch.utils.data._utils.collate import default_collate |
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import numpy as np |
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import nibabel as nib |
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import SimpleITK as sitk |
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from tqdm import trange |
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from .constants import INTENSITY |
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from .typing import TypeNumber, TypePath |
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def to_tuple( |
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value: Union[TypeNumber, Iterable[TypeNumber]], |
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length: int = 1, |
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) -> Tuple[TypeNumber, ...]: |
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""" |
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to_tuple(1, length=1) -> (1,) |
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to_tuple(1, length=3) -> (1, 1, 1) |
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If value is an iterable, n is ignored and tuple(value) is returned |
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to_tuple((1,), length=1) -> (1,) |
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to_tuple((1, 2), length=1) -> (1, 2) |
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to_tuple([1, 2], length=3) -> (1, 2) |
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""" |
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try: |
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iter(value) |
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value = tuple(value) |
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except TypeError: |
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value = length * (value,) |
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return value |
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def get_stem( |
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path: Union[TypePath, List[TypePath]] |
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) -> Union[str, List[str]]: |
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""" |
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'/home/user/image.nii.gz' -> 'image' |
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""" |
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def _get_stem(path_string): |
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return Path(path_string).name.split('.')[0] |
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if isinstance(path, (str, Path)): |
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return _get_stem(path) |
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return [_get_stem(p) for p in path] |
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def create_dummy_dataset( |
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num_images: int, |
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size_range: Tuple[int, int], |
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directory: Optional[TypePath] = None, |
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suffix: str = '.nii.gz', |
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force: bool = False, |
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verbose: bool = False, |
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): |
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from .data import ScalarImage, LabelMap, Subject |
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output_dir = tempfile.gettempdir() if directory is None else directory |
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output_dir = Path(output_dir) |
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images_dir = output_dir / 'dummy_images' |
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labels_dir = output_dir / 'dummy_labels' |
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if force: |
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shutil.rmtree(images_dir) |
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shutil.rmtree(labels_dir) |
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subjects: List[Subject] = [] |
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if images_dir.is_dir(): |
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for i in trange(num_images): |
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image_path = images_dir / f'image_{i}{suffix}' |
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label_path = labels_dir / f'label_{i}{suffix}' |
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subject = Subject( |
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one_modality=ScalarImage(image_path), |
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segmentation=LabelMap(label_path), |
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) |
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subjects.append(subject) |
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else: |
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images_dir.mkdir(exist_ok=True, parents=True) |
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labels_dir.mkdir(exist_ok=True, parents=True) |
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if verbose: |
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print('Creating dummy dataset...') # noqa: T001 |
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iterable = trange(num_images) |
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else: |
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iterable = range(num_images) |
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for i in iterable: |
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shape = np.random.randint(*size_range, size=3) |
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affine = np.eye(4) |
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image = np.random.rand(*shape) |
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label = np.ones_like(image) |
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label[image < 0.33] = 0 |
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label[image > 0.66] = 2 |
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image *= 255 |
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image_path = images_dir / f'image_{i}{suffix}' |
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nii = nib.Nifti1Image(image.astype(np.uint8), affine) |
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nii.to_filename(str(image_path)) |
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label_path = labels_dir / f'label_{i}{suffix}' |
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nii = nib.Nifti1Image(label.astype(np.uint8), affine) |
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nii.to_filename(str(label_path)) |
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subject = Subject( |
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one_modality=ScalarImage(image_path), |
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segmentation=LabelMap(label_path), |
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) |
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subjects.append(subject) |
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return subjects |
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def apply_transform_to_file( |
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input_path: TypePath, |
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transform, # : Transform seems to create a circular import |
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output_path: TypePath, |
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type: str = INTENSITY, # noqa: A002 |
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verbose: bool = False, |
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): |
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from . import Image, Subject |
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subject = Subject(image=Image(input_path, type=type)) |
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transformed = transform(subject) |
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transformed.image.save(output_path) |
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if verbose and transformed.history: |
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print('Applied transform:', transformed.history[0]) # noqa: T001 |
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def guess_type(string: str) -> Any: |
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# Adapted from |
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# https://www.reddit.com/r/learnpython/comments/4599hl/module_to_guess_type_from_a_string/czw3f5s |
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string = string.replace(' ', '') |
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try: |
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value = ast.literal_eval(string) |
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except ValueError: |
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result_type = str |
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else: |
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result_type = type(value) |
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if result_type in (list, tuple): |
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string = string[1:-1] # remove brackets |
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split = string.split(',') |
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list_result = [guess_type(n) for n in split] |
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value = tuple(list_result) if result_type is tuple else list_result |
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return value |
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try: |
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value = result_type(string) |
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except TypeError: |
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value = None |
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return value |
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def get_torchio_cache_dir(): |
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return Path('~/.cache/torchio').expanduser() |
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def round_up(value: float) -> int: |
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"""Round half towards infinity. |
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Args: |
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value: The value to round. |
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Example: |
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>>> round(2.5) |
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2 |
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>>> round(3.5) |
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4 |
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>>> round_up(2.5) |
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3 |
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>>> round_up(3.5) |
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4 |
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""" |
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return int(np.floor(value + 0.5)) |
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def compress(input_path, output_path): |
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with open(input_path, 'rb') as f_in: |
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with gzip.open(output_path, 'wb') as f_out: |
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shutil.copyfileobj(f_in, f_out) |
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def check_sequence(sequence: Sequence, name: str): |
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try: |
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iter(sequence) |
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except TypeError: |
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message = f'"{name}" must be a sequence, not {type(name)}' |
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raise TypeError(message) |
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def get_major_sitk_version() -> int: |
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# This attribute was added in version 2 |
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# https://github.com/SimpleITK/SimpleITK/pull/1171 |
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version = getattr(sitk, '__version__', None) |
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major_version = 1 if version is None else 2 |
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return major_version |
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def history_collate(batch: Sequence, collate_transforms=True): |
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attr = 'history' if collate_transforms else 'applied_transforms' |
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# Adapted from |
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# https://github.com/romainVala/torchQC/blob/master/segmentation/collate_functions.py |
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from .data import Subject |
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first_element = batch[0] |
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if isinstance(first_element, Subject): |
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dictionary = { |
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key: default_collate([d[key] for d in batch]) |
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for key in first_element |
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} |
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if hasattr(first_element, attr): |
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dictionary.update({attr: [getattr(d, attr) for d in batch]}) |
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return dictionary |
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def get_subclasses(target_class: type) -> List[type]: |
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subclasses = target_class.__subclasses__() |
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subclasses += sum([get_subclasses(cls) for cls in subclasses], []) |
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return subclasses |
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fepegar /
torchio
