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utils.py
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utils.py
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import ast
import shutil
import tempfile
from pathlib import Path
from typing import Union, Iterable, Tuple, Any, Optional, List
import torch
import numpy as np
import nibabel as nib
import SimpleITK as sitk
from tqdm import trange
from .torchio import (
INTENSITY,
LABEL,
TypeData,
TypeNumber,
TypePath,
)
FLIP_XY = np.diag((-1, -1, 1)) # used to switch between LPS and RAS
def to_tuple(
value: Union[TypeNumber, Iterable[TypeNumber]],
length: int = 1,
) -> Tuple[TypeNumber, ...]:
"""
to_tuple(1, length=1) -> (1,)
to_tuple(1, length=3) -> (1, 1, 1)
If value is an iterable, n is ignored and tuple(value) is returned
to_tuple((1,), length=1) -> (1,)
to_tuple((1, 2), length=1) -> (1, 2)
to_tuple([1, 2], length=3) -> (1, 2)
"""
try:
iter(value)
value = tuple(value)
except TypeError:
value = length * (value,)
return value
def get_stem(path: TypePath) -> str:
"""
'/home/user/image.nii.gz' -> 'image'
"""
path = Path(path)
return path.name.split('.')[0]
def create_dummy_dataset(
num_images: int,
size_range: Tuple[int, int],
directory: Optional[TypePath] = None,
suffix: str = '.nii.gz',
force: bool = False,
verbose: bool = False,
):
from .data import Image, Subject
output_dir = tempfile.gettempdir() if directory is None else directory
output_dir = Path(output_dir)
images_dir = output_dir / 'dummy_images'
labels_dir = output_dir / 'dummy_labels'
if force:
shutil.rmtree(images_dir)
shutil.rmtree(labels_dir)
subjects: List[Subject] = []
if images_dir.is_dir():
for i in trange(num_images):
image_path = images_dir / f'image_{i}{suffix}'
label_path = labels_dir / f'label_{i}{suffix}'
subject = Subject(
one_modality=Image(image_path, INTENSITY),
segmentation=Image(label_path, LABEL),
)
subjects.append(subject)
else:
images_dir.mkdir(exist_ok=True, parents=True)
labels_dir.mkdir(exist_ok=True, parents=True)
if verbose:
print('Creating dummy dataset...')
iterable = trange(num_images)
else:
iterable = range(num_images)
for i in iterable:
shape = np.random.randint(*size_range, size=3)
affine = np.eye(4)
image = np.random.rand(*shape)
label = np.ones_like(image)
label[image < 0.33] = 0
label[image > 0.66] = 2
image *= 255
image_path = images_dir / f'image_{i}{suffix}'
nii = nib.Nifti1Image(image.astype(np.uint8), affine)
nii.to_filename(str(image_path))
label_path = labels_dir / f'label_{i}{suffix}'
nii = nib.Nifti1Image(label.astype(np.uint8), affine)
nii.to_filename(str(label_path))
subject = Subject(
one_modality=Image(image_path, INTENSITY),
segmentation=Image(label_path, LABEL),
)
subjects.append(subject)
return subjects
def apply_transform_to_file(
input_path: TypePath,
transform, # : Transform seems to create a circular import (TODO)
output_path: TypePath,
type: str = INTENSITY,
verbose: bool = False,
):
from . import Image, ImagesDataset, Subject
subject = Subject(image=Image(input_path, type))
transformed = transform(subject)
transformed.image.save(output_path)
if verbose and transformed.history:
print(transformed.history[0])
def guess_type(string: str) -> Any:
# Adapted from
# https://www.reddit.com/r/learnpython/comments/4599hl/module_to_guess_type_from_a_string/czw3f5s
string = string.replace(' ', '')
try:
value = ast.literal_eval(string)
except ValueError:
result_type = str
else:
result_type = type(value)
if result_type in (list, tuple):
string = string[1:-1] # remove brackets
split = string.split(',')
list_result = [guess_type(n) for n in split]
value = tuple(list_result) if result_type is tuple else list_result
return value
try:
value = result_type(string)
except TypeError:
value = None
return value
def get_rotation_and_spacing_from_affine(
affine: np.ndarray,
) -> Tuple[np.ndarray, np.ndarray]:
# From https://github.com/nipy/nibabel/blob/master/nibabel/orientations.py
rotation_zoom = affine[:3, :3]
spacing = np.sqrt(np.sum(rotation_zoom * rotation_zoom, axis=0))
rotation = rotation_zoom / spacing
return rotation, spacing
def nib_to_sitk(data: TypeData, affine: TypeData) -> sitk.Image:
array = data.numpy() if isinstance(data, torch.Tensor) else data
affine = affine.numpy() if isinstance(affine, torch.Tensor) else affine
origin = np.dot(FLIP_XY, affine[:3, 3]).astype(np.float64)
rotation, spacing = get_rotation_and_spacing_from_affine(affine)
direction = np.dot(FLIP_XY, rotation)
image = sitk.GetImageFromArray(array.transpose())
if array.ndim == 2: # ignore first dimension if 2D (1, 1, H, W)
direction = direction[1:3, 1:3]
image.SetOrigin(origin)
image.SetSpacing(spacing)
image.SetDirection(direction.flatten())
return image
def sitk_to_nib(image: sitk.Image) -> Tuple[np.ndarray, np.ndarray]:
data = sitk.GetArrayFromImage(image).transpose()
spacing = np.array(image.GetSpacing())
direction = np.array(image.GetDirection())
origin = image.GetOrigin()
if len(direction) == 9:
rotation = direction.reshape(3, 3)
elif len(direction) == 4: # ignore first dimension if 2D (1, 1, H, W)
rotation_2d = direction.reshape(2, 2)
rotation = np.eye(3)
rotation[1:3, 1:3] = rotation_2d
spacing = 1, *spacing
origin = 0, *origin
rotation = np.dot(FLIP_XY, rotation)
rotation_zoom = rotation * spacing
translation = np.dot(FLIP_XY, origin)
affine = np.eye(4)
affine[:3, :3] = rotation_zoom
affine[:3, 3] = translation
return data, affine
def get_torchio_cache_dir():
return Path('~/.cache/torchio').expanduser()
def round_up(value: float) -> float:
"""Round half towards infinity.
Args:
value: The value to round.
Example:
>>> round(2.5)
2
>>> round(3.5)
4
>>> round_up(2.5)
3
>>> round_up(3.5)
4
"""
return np.floor(value + 0.5)