torchio.utils.get_torchio_cache_dir() - Code Metrics - Inspection of "Add support for 4D images" - fepegar/torchio - Measure and Improve Code Quality continuously with Scrutinizer

Passed

Pull Request — master (#246)

by Fernando

created 2020-08-02 16:03 UTC

torchio.utils.get_torchio_cache_dir() A

↳ Parent: torchio.utils

Complexity

Conditions

Size

Total Lines	2
Code Lines	2

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	1
eloc	2
nop	0
dl	0
loc	2
rs	10
c	0
b	0
f	0

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,
    REPO_URL,
)


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:
    """Create a SimpleITK image from a tensor and a 4x4 affine matrix.

    Args:
        data: PyTorch tensor or NumPy array
        affine: # TODO
    """
    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)
    array = array.transpose()  # (W, H, D, C) or (W, H, D)
    image = sitk.GetImageFromArray(array)
    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())
    if data.ndim == 4:
        assert image.GetNumberOfComponentsPerPixel() == data.shape[0]
    assert image.GetSize() == data.shape[-3:]
    return image


def sitk_to_nib(image: sitk.Image) -> Tuple[np.ndarray, np.ndarray]:
    data = sitk.GetArrayFromImage(image).transpose()
    num_components = image.GetNumberOfComponentsPerPixel()
    if num_components == 1:
        data = data[np.newaxis]  # add channels dimension
    input_spatial_dims = image.GetDimension()
    data = ensure_4d(data, False, num_spatial_dims=input_spatial_dims)
    assert data.shape[0] == num_components
    assert data.shape[-input_spatial_dims:] == image.GetSize()
    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 ensure_4d(
        tensor: TypeData,
        channels_last: bool,
        num_spatial_dims=None,
        ) -> TypeData:
    """[summary]

    Args:
        tensor: [description].
        channels_last: If ``True``, last dimension of the input represents
            channels.
        num_spatial_dims: [description].

    Raises:
        ValueError: [description]
    """
    # I wish named tensors were properly supported in PyTorch
    num_dimensions = tensor.ndim
    if num_dimensions == 5:  # hope (X, X, X, 1, X)
        if tensor.shape[-1] == 1:
            tensor = tensor[..., 0, :]
    if num_dimensions == 4:  # assume 3D multichannel
        if channels_last:  # (D, H, W, C)
            tensor = tensor.permute(3, 0, 1, 2)  # (C, D, H, W)
    elif num_dimensions == 2:  # assume 2D monochannel (H, W)
        tensor = tensor[np.newaxis, np.newaxis]  # (1, 1, H, W)
    elif num_dimensions == 3:  # 2D multichannel or 3D monochannel?
        if num_spatial_dims == 2:
            if channels_last:  # (H, W, C)
                tensor = tensor.permute(2, 0, 1)  # (C, H, W)
            tensor = tensor[:, np.newaxis]  # (C, 1, H, W)
        elif num_spatial_dims == 3:  # (D, H, W)
            tensor = tensor[np.newaxis]  # (1, D, H, W)
        else:  # try to guess
            shape = tensor.shape
            maybe_rgb = 3 in shape
            if maybe_rgb:
                if shape[-1] == 3:  # (H, W, 3)
                    tensor = tensor.permute(2, 0, 1)  # (3, H, W)
                tensor = tensor[:, np.newaxis]  # (3, 1, H, W)
            else:  # (D, H, W)
                tensor = tensor[np.newaxis]  # (1, D, H, W)
    else:
        message = (
            f'{num_dimensions}D images not supported yet. Please create an'
            f' issue in {REPO_URL} if you would like support for them'
        )
        raise ValueError(message)
    assert tensor.ndim == 4
    return tensor


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)


1			import ast
2			import shutil
3			import tempfile
4			from pathlib import Path
5			from typing import Union, Iterable, Tuple, Any, Optional, List
6
7			import torch
8			import numpy as np
9			import nibabel as nib
10			import SimpleITK as sitk
11			from tqdm import trange
12			from .torchio import (
13			INTENSITY,
14			LABEL,
15			TypeData,
16			TypeNumber,
17			TypePath,
18			REPO_URL,
19			)
20
21
22			FLIP_XY = np.diag((-1, -1, 1)) # used to switch between LPS and RAS
23
24
25			def to_tuple(
26			value: Union[TypeNumber, Iterable[TypeNumber]],
27			length: int = 1,
28			) -> Tuple[TypeNumber, ...]:
29			"""
30			to_tuple(1, length=1) -> (1,)
31			to_tuple(1, length=3) -> (1, 1, 1)
32
33			If value is an iterable, n is ignored and tuple(value) is returned
34			to_tuple((1,), length=1) -> (1,)
35			to_tuple((1, 2), length=1) -> (1, 2)
36			to_tuple([1, 2], length=3) -> (1, 2)
37			"""
38			try:
39			iter(value)
40			value = tuple(value)
41			except TypeError:
42			value = length * (value,)
43			return value
44
45
46			def get_stem(path: TypePath) -> str:
47			"""
48			'/home/user/image.nii.gz' -> 'image'
49			"""
50			path = Path(path)
51			return path.name.split('.')[0]
52
53
54			def create_dummy_dataset(
55			num_images: int,
56			size_range: Tuple[int, int],
57			directory: Optional[TypePath] = None,
58			suffix: str = '.nii.gz',
59			force: bool = False,
60			verbose: bool = False,
61			):
62			from .data import Image, Subject
63			output_dir = tempfile.gettempdir() if directory is None else directory
64			output_dir = Path(output_dir)
65			images_dir = output_dir / 'dummy_images'
66			labels_dir = output_dir / 'dummy_labels'
67
68			if force:
69			shutil.rmtree(images_dir)
70			shutil.rmtree(labels_dir)
71
72			subjects: List[Subject] = []
73			if images_dir.is_dir():
74			for i in trange(num_images):
75			image_path = images_dir / f'image_{i}{suffix}'
76			label_path = labels_dir / f'label_{i}{suffix}'
77			subject = Subject(
78			one_modality=Image(image_path, INTENSITY),
79			segmentation=Image(label_path, LABEL),
80			)
81			subjects.append(subject)
82			else:
83			images_dir.mkdir(exist_ok=True, parents=True)
84			labels_dir.mkdir(exist_ok=True, parents=True)
85			if verbose:
86			print('Creating dummy dataset...')
87			iterable = trange(num_images)
88			else:
89			iterable = range(num_images)
90			for i in iterable:
91			shape = np.random.randint(*size_range, size=3)
92			affine = np.eye(4)
93			image = np.random.rand(*shape)
94			label = np.ones_like(image)
95			label[image < 0.33] = 0
96			label[image > 0.66] = 2
97			image *= 255
98
99			image_path = images_dir / f'image_{i}{suffix}'
100			nii = nib.Nifti1Image(image.astype(np.uint8), affine)
101			nii.to_filename(str(image_path))
102
103			label_path = labels_dir / f'label_{i}{suffix}'
104			nii = nib.Nifti1Image(label.astype(np.uint8), affine)
105			nii.to_filename(str(label_path))
106
107			subject = Subject(
108			one_modality=Image(image_path, INTENSITY),
109			segmentation=Image(label_path, LABEL),
110			)
111			subjects.append(subject)
112			return subjects
113
114
115			def apply_transform_to_file(
116			input_path: TypePath,
117			transform, # : Transform seems to create a circular import (TODO)
118			output_path: TypePath,
119			type: str = INTENSITY,
120			verbose: bool = False,
121			):
122			from . import Image, ImagesDataset, Subject
123			subject = Subject(image=Image(input_path, type))
124			transformed = transform(subject)
125			transformed.image.save(output_path)
126			if verbose and transformed.history:
127			print(transformed.history[0])
128
129			def guess_type(string: str) -> Any:
130			# Adapted from
131			# https://www.reddit.com/r/learnpython/comments/4599hl/module_to_guess_type_from_a_string/czw3f5s
132			string = string.replace(' ', '')
133			try:
134			value = ast.literal_eval(string)
135			except ValueError:
136			result_type = str
137			else:
138			result_type = type(value)
139			if result_type in (list, tuple):
140			string = string[1:-1] # remove brackets
141			split = string.split(',')
142			list_result = [guess_type(n) for n in split]
143			value = tuple(list_result) if result_type is tuple else list_result
144			return value
145			try:
146			value = result_type(string)
147			except TypeError:
148			value = None
149			return value
150
151
152			def get_rotation_and_spacing_from_affine(
153			affine: np.ndarray,
154			) -> Tuple[np.ndarray, np.ndarray]:
155			# From https://github.com/nipy/nibabel/blob/master/nibabel/orientations.py
156			rotation_zoom = affine[:3, :3]
157			spacing = np.sqrt(np.sum(rotation_zoom * rotation_zoom, axis=0))
158			rotation = rotation_zoom / spacing
159			return rotation, spacing
160
161
162			def nib_to_sitk(data: TypeData, affine: TypeData) -> sitk.Image:
163			"""Create a SimpleITK image from a tensor and a 4x4 affine matrix.
164
165			Args:
166			data: PyTorch tensor or NumPy array
167			affine: # TODO
168			"""
169			array = data.numpy() if isinstance(data, torch.Tensor) else data
170			affine = affine.numpy() if isinstance(affine, torch.Tensor) else affine
171			origin = np.dot(FLIP_XY, affine[:3, 3]).astype(np.float64)
172			rotation, spacing = get_rotation_and_spacing_from_affine(affine)
173			direction = np.dot(FLIP_XY, rotation)
174			array = array.transpose() # (W, H, D, C) or (W, H, D)
175			image = sitk.GetImageFromArray(array)
176			if array.ndim == 2: # ignore first dimension if 2D (1, 1, H, W)
177			direction = direction[1:3, 1:3]
178			image.SetOrigin(origin)
179			image.SetSpacing(spacing)
180			image.SetDirection(direction.flatten())
181			if data.ndim == 4:
182			assert image.GetNumberOfComponentsPerPixel() == data.shape[0]
183			assert image.GetSize() == data.shape[-3:]
184			return image
185
186
187			def sitk_to_nib(image: sitk.Image) -> Tuple[np.ndarray, np.ndarray]:
188			data = sitk.GetArrayFromImage(image).transpose()
189			num_components = image.GetNumberOfComponentsPerPixel()
190			if num_components == 1:
191			data = data[np.newaxis] # add channels dimension
192			input_spatial_dims = image.GetDimension()
193			data = ensure_4d(data, False, num_spatial_dims=input_spatial_dims)
194			assert data.shape[0] == num_components
195			assert data.shape[-input_spatial_dims:] == image.GetSize()
196			spacing = np.array(image.GetSpacing())
197			direction = np.array(image.GetDirection())
198			origin = image.GetOrigin()
199			if len(direction) == 9:
200			rotation = direction.reshape(3, 3)
201			elif len(direction) == 4: # ignore first dimension if 2D (1, 1, H, W)
202			rotation_2d = direction.reshape(2, 2)
203			rotation = np.eye(3)
204			rotation[1:3, 1:3] = rotation_2d
205			spacing = 1, *spacing
206			origin = 0, *origin
207			rotation = np.dot(FLIP_XY, rotation)
			0 ignored issues – show introduced 2020-05-26 09:22 UTC by Report Bug Copy Issue Report The variable `rotation` does not seem to be defined for all execution paths. Loading history...
208			rotation_zoom = rotation * spacing
209			translation = np.dot(FLIP_XY, origin)
210			affine = np.eye(4)
211			affine[:3, :3] = rotation_zoom
212			affine[:3, 3] = translation
213			return data, affine
214
215
216			def ensure_4d(
217			tensor: TypeData,
218			channels_last: bool,
219			num_spatial_dims=None,
220			) -> TypeData:
221			"""[summary]
222
223			Args:
224			tensor: [description].
225			channels_last: If ``True``, last dimension of the input represents
226			channels.
227			num_spatial_dims: [description].
228
229			Raises:
230			ValueError: [description]
231			"""
232			# I wish named tensors were properly supported in PyTorch
233			num_dimensions = tensor.ndim
234			if num_dimensions == 5: # hope (X, X, X, 1, X)
235			if tensor.shape[-1] == 1:
236			tensor = tensor[..., 0, :]
237			if num_dimensions == 4: # assume 3D multichannel
238			if channels_last: # (D, H, W, C)
239			tensor = tensor.permute(3, 0, 1, 2) # (C, D, H, W)
240			elif num_dimensions == 2: # assume 2D monochannel (H, W)
241			tensor = tensor[np.newaxis, np.newaxis] # (1, 1, H, W)
242			elif num_dimensions == 3: # 2D multichannel or 3D monochannel?
243			if num_spatial_dims == 2:
244			if channels_last: # (H, W, C)
245			tensor = tensor.permute(2, 0, 1) # (C, H, W)
246			tensor = tensor[:, np.newaxis] # (C, 1, H, W)
247			elif num_spatial_dims == 3: # (D, H, W)
248			tensor = tensor[np.newaxis] # (1, D, H, W)
249			else: # try to guess
250			shape = tensor.shape
251			maybe_rgb = 3 in shape
252			if maybe_rgb:
253			if shape[-1] == 3: # (H, W, 3)
254			tensor = tensor.permute(2, 0, 1) # (3, H, W)
255			tensor = tensor[:, np.newaxis] # (3, 1, H, W)
256			else: # (D, H, W)
257			tensor = tensor[np.newaxis] # (1, D, H, W)
258			else:
259			message = (
260			f'{num_dimensions}D images not supported yet. Please create an'
261			f' issue in {REPO_URL} if you would like support for them'
262			)
263			raise ValueError(message)
264			assert tensor.ndim == 4
265			return tensor
266
267
268			def get_torchio_cache_dir():
269			return Path('~/.cache/torchio').expanduser()
270
271
272			def round_up(value: float) -> float:
273			"""Round half towards infinity.
274
275			Args:
276			value: The value to round.
277
278			Example:
279
280			>>> round(2.5)
281			2
282			>>> round(3.5)
283			4
284			>>> round_up(2.5)
285			3
286			>>> round_up(3.5)
287			4
288
289			"""
290			return np.floor(value + 0.5)
291

fepegar / torchio

Pull Request — master (#246)

torchio.utils.get_torchio_cache_dir() A

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