torchio.data.subject.Subject.is_2d() - Code Metrics - Inspection of "Fix CropOrPad for 2D images (#435)" - fepegar/torchio - Measure and Improve Code Quality continuously with Scrutinizer

Completed

Push — master ( 17d4df...a2657b )

by Fernando

created 2021-01-28 17:13 UTC

torchio.data.subject.Subject.is_2d() A

↳ Parent: torchio.data.subject

Complexity

Conditions

Size

Total Lines	2
Code Lines	2

Duplication

Lines	0
Ratio	0 %

Importance

Changes

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

import copy
import pprint
from typing import Any, Dict, List, Tuple, Optional, Sequence, TYPE_CHECKING

import numpy as np

from ..constants import TYPE, INTENSITY
from .image import Image
from ..utils import get_subclasses

if TYPE_CHECKING:
    from ..transforms import Transform, Compose


class Subject(dict):
    """Class to store information about the images corresponding to a subject.

    Args:
        *args: If provided, a dictionary of items.
        **kwargs: Items that will be added to the subject sample.

    Example:

        >>> import torchio as tio
        >>> # One way:
        >>> subject = tio.Subject(
        ...     one_image=tio.ScalarImage('path_to_image.nii.gz'),
        ...     a_segmentation=tio.LabelMap('path_to_seg.nii.gz'),
        ...     age=45,
        ...     name='John Doe',
        ...     hospital='Hospital Juan Negrín',
        ... )
        >>> # If you want to create the mapping before, or have spaces in the keys:
        >>> subject_dict = {
        ...     'one image': tio.ScalarImage('path_to_image.nii.gz'),
        ...     'a segmentation': tio.LabelMap('path_to_seg.nii.gz'),
        ...     'age': 45,
        ...     'name': 'John Doe',
        ...     'hospital': 'Hospital Juan Negrín',
        ... }
        >>> subject = tio.Subject(subject_dict)

    """

    def __init__(self, *args, **kwargs: Dict[str, Any]):
        if args:
            if len(args) == 1 and isinstance(args[0], dict):
                kwargs.update(args[0])
            else:
                message = (
                    'Only one dictionary as positional argument is allowed')
                raise ValueError(message)
        super().__init__(**kwargs)
        self._parse_images(self.get_images(intensity_only=False))
        self.update_attributes()  # this allows me to do e.g. subject.t1
        self.applied_transforms = []

    def __repr__(self):
        num_images = len(self.get_images(intensity_only=False))
        string = (
            f'{self.__class__.__name__}'
            f'(Keys: {tuple(self.keys())}; images: {num_images})'
        )
        return string

    def __copy__(self):
        result_dict = {}
        for key, value in self.items():
            if isinstance(value, Image):
                value = copy.copy(value)
            else:
                value = copy.deepcopy(value)
            result_dict[key] = value
        new = Subject(result_dict)
        new.applied_transforms = self.applied_transforms[:]
        return new

    def __len__(self):
        return len(self.get_images(intensity_only=False))

    @staticmethod
    def _parse_images(images: List[Tuple[str, Image]]) -> None:
        # Check that it's not empty
        if not images:
            raise ValueError('A subject without images cannot be created')

    @property
    def shape(self):
        """Return shape of first image in subject.

        Consistency of shapes across images in the subject is checked first.
        """
        self.check_consistent_attribute('shape')
        return self.get_first_image().shape

    @property
    def spatial_shape(self):
        """Return spatial shape of first image in subject.

        Consistency of spatial shapes across images in the subject is checked
        first.
        """
        self.check_consistent_spatial_shape()
        return self.get_first_image().spatial_shape

    @property
    def spacing(self):
        """Return spacing of first image in subject.

        Consistency of spacings across images in the subject is checked first.
        """
        self.check_consistent_attribute('spacing')
        return self.get_first_image().spacing

    @property
    def history(self):
        # Kept for backwards compatibility
        return self.get_applied_transforms()

    def is_2d(self):
        return all(i.is_2d() for i in self.get_images(intensity_only=False))

    def get_applied_transforms(
            self,
            ignore_intensity: bool = False,
            image_interpolation: Optional[str] = None,
            ) -> List['Transform']:
        from ..transforms.transform import Transform
        from ..transforms.intensity_transform import IntensityTransform
        name_to_transform = {
            cls.__name__: cls
            for cls in get_subclasses(Transform)
        }
        transforms_list = []
        for transform_name, arguments in self.applied_transforms:
            transform = name_to_transform[transform_name](**arguments)
            if ignore_intensity and isinstance(transform, IntensityTransform):
                continue
            resamples = hasattr(transform, 'image_interpolation')
            if resamples and image_interpolation is not None:
                parsed = transform.parse_interpolation(image_interpolation)
                transform.image_interpolation = parsed
            transforms_list.append(transform)
        return transforms_list

    def get_composed_history(
            self,
            ignore_intensity: bool = False,
            image_interpolation: Optional[str] = None,
            ) -> 'Compose':
        from ..transforms.augmentation.composition import Compose
        transforms = self.get_applied_transforms(
            ignore_intensity=ignore_intensity,
            image_interpolation=image_interpolation,
        )
        return Compose(transforms)

    def get_inverse_transform(
            self,
            warn: bool = True,
            ignore_intensity: bool = True,
            image_interpolation: Optional[str] = None,
            ) ->  'Compose':
        """Get a reversed list of the inverses of the applied transforms.

        Args:
            warn: Issue a warning if some transforms are not invertible.
            ignore_intensity: If ``True``, all instances of
                :class:`~torchio.transforms.intensity_transform.IntensityTransform`
                will be ignored.
            image_interpolation: Modify interpolation for scalar images inside
                transforms that perform resampling.
        """
        history_transform = self.get_composed_history(
            ignore_intensity=ignore_intensity,
            image_interpolation=image_interpolation,
        )
        inverse_transform = history_transform.inverse(warn=warn)
        return inverse_transform

    def apply_inverse_transform(self, **kwargs) -> 'Subject':
        """Try to apply the inverse of all applied transforms, in reverse order.

        Args:
            **kwargs: Keyword arguments passed on to
                :meth:`~torchio.data.subject.Subject.get_inverse_transform`.
        """
        inverse_transform = self.get_inverse_transform(**kwargs)
        transformed = inverse_transform(self)
        transformed.clear_history()
        return transformed

    def clear_history(self) -> None:
        self.applied_transforms = []

    def check_consistent_attribute(self, attribute: str) -> None:
        values_dict = {}
        iterable = self.get_images_dict(intensity_only=False).items()
        for image_name, image in iterable:
            values_dict[image_name] = getattr(image, attribute)
        num_unique_values = len(set(values_dict.values()))
        if num_unique_values > 1:
            message = (
                f'More than one {attribute} found in subject images:'
                f'\n{pprint.pformat(values_dict)}'
            )
            raise RuntimeError(message)

    def check_consistent_spatial_shape(self) -> None:
        self.check_consistent_attribute('spatial_shape')

    def check_consistent_orientation(self) -> None:
        self.check_consistent_attribute('orientation')

    def check_consistent_affine(self):
        # https://github.com/fepegar/torchio/issues/354
        affine = None
        first_image = None
        iterable = self.get_images_dict(intensity_only=False).items()
        for image_name, image in iterable:
            if affine is None:
                affine = image.affine
                first_image = image_name
            elif not np.allclose(affine, image.affine, rtol=1e-6, atol=1e-6):
                message = (
                    f'Images "{first_image}" and "{image_name}" do not occupy'
                    ' the same physical space.'
                    f'\nAffine of "{first_image}":'
                    f'\n{pprint.pformat(affine)}'
                    f'\nAffine of "{image_name}":'
                    f'\n{pprint.pformat(image.affine)}'
                )
                raise RuntimeError(message)

    def check_consistent_space(self):
        self.check_consistent_spatial_shape()
        self.check_consistent_affine()

    def get_images_dict(
            self,
            intensity_only=True,
            include: Optional[Sequence[str]] = None,
            exclude: Optional[Sequence[str]] = None,
            ) -> Dict[str, Image]:
        images = {}
        for image_name, image in self.items():
            if not isinstance(image, Image):
                continue
            if intensity_only and not image[TYPE] == INTENSITY:
                continue
            if include is not None and image_name not in include:
                continue
            if exclude is not None and image_name in exclude:
                continue
            images[image_name] = image
        return images

    def get_images(
            self,
            intensity_only=True,
            include: Optional[Sequence[str]] = None,
            exclude: Optional[Sequence[str]] = None,
            ) -> List[Image]:
        images_dict = self.get_images_dict(
            intensity_only=intensity_only,
            include=include,
            exclude=exclude,
        )
        return list(images_dict.values())

    def get_first_image(self) -> Image:
        return self.get_images(intensity_only=False)[0]

    # flake8: noqa: F821
    def add_transform(
            self,
            transform: 'Transform',
            parameters_dict: dict,
            ) -> None:
        self.applied_transforms.append((transform.name, parameters_dict))

    def load(self) -> None:
        """Load images in subject on RAM."""
        for image in self.get_images(intensity_only=False):
            image.load()

    def update_attributes(self) -> None:
        # This allows to get images using attribute notation, e.g. subject.t1
        self.__dict__.update(self)

    def add_image(self, image: Image, image_name: str) -> None:
        """Add an image."""
        self[image_name] = image
        self.update_attributes()

    def remove_image(self, image_name: str) -> None:
        """Remove an image."""
        del self[image_name]
        delattr(self, image_name)

    def plot(self, **kwargs) -> None:
        """Plot images using matplotlib.

        Args:
            **kwargs: Keyword arguments that will be passed on to
                :class:`~torchio.data.image.Image`.
        """
        from ..visualization import plot_subject  # avoid circular import
        plot_subject(self, **kwargs)


1			import copy
2			import pprint
3			from typing import Any, Dict, List, Tuple, Optional, Sequence, TYPE_CHECKING
4
5			import numpy as np
6
7			from ..constants import TYPE, INTENSITY
8			from .image import Image
9			from ..utils import get_subclasses
10
11			if TYPE_CHECKING:
12			from ..transforms import Transform, Compose
13
14
15			class Subject(dict):
16			"""Class to store information about the images corresponding to a subject.
17
18			Args:
19			*args: If provided, a dictionary of items.
20			**kwargs: Items that will be added to the subject sample.
21
22			Example:
23
24			>>> import torchio as tio
25			>>> # One way:
26			>>> subject = tio.Subject(
27			... one_image=tio.ScalarImage('path_to_image.nii.gz'),
28			... a_segmentation=tio.LabelMap('path_to_seg.nii.gz'),
29			... age=45,
30			... name='John Doe',
31			... hospital='Hospital Juan Negrín',
32			... )
33			>>> # If you want to create the mapping before, or have spaces in the keys:
34			>>> subject_dict = {
35			... 'one image': tio.ScalarImage('path_to_image.nii.gz'),
36			... 'a segmentation': tio.LabelMap('path_to_seg.nii.gz'),
37			... 'age': 45,
38			... 'name': 'John Doe',
39			... 'hospital': 'Hospital Juan Negrín',
40			... }
41			>>> subject = tio.Subject(subject_dict)
42
43			"""
44
45			def __init__(self, args, *kwargs: Dict[str, Any]):
46			if args:
47			if len(args) == 1 and isinstance(args[0], dict):
48			kwargs.update(args[0])
49			else:
50			message = (
51			'Only one dictionary as positional argument is allowed')
52			raise ValueError(message)
53			super().__init__(**kwargs)
54			self._parse_images(self.get_images(intensity_only=False))
55			self.update_attributes() # this allows me to do e.g. subject.t1
56			self.applied_transforms = []
57
58			def __repr__(self):
59			num_images = len(self.get_images(intensity_only=False))
60			string = (
61			f'{self.__class__.__name__}'
62			f'(Keys: {tuple(self.keys())}; images: {num_images})'
63			)
64			return string
65
66			def __copy__(self):
67			result_dict = {}
68			for key, value in self.items():
69			if isinstance(value, Image):
70			value = copy.copy(value)
71			else:
72			value = copy.deepcopy(value)
73			result_dict[key] = value
74			new = Subject(result_dict)
75			new.applied_transforms = self.applied_transforms[:]
76			return new
77
78			def __len__(self):
79			return len(self.get_images(intensity_only=False))
80
81			@staticmethod
82			def _parse_images(images: List[Tuple[str, Image]]) -> None:
83			# Check that it's not empty
84			if not images:
85			raise ValueError('A subject without images cannot be created')
86
87			@property
88			def shape(self):
89			"""Return shape of first image in subject.
90
91			Consistency of shapes across images in the subject is checked first.
92			"""
93			self.check_consistent_attribute('shape')
94			return self.get_first_image().shape
95
96			@property
97			def spatial_shape(self):
98			"""Return spatial shape of first image in subject.
99
100			Consistency of spatial shapes across images in the subject is checked
101			first.
102			"""
103			self.check_consistent_spatial_shape()
104			return self.get_first_image().spatial_shape
105
106			@property
107			def spacing(self):
108			"""Return spacing of first image in subject.
109
110			Consistency of spacings across images in the subject is checked first.
111			"""
112			self.check_consistent_attribute('spacing')
113			return self.get_first_image().spacing
114
115			@property
116			def history(self):
117			# Kept for backwards compatibility
118			return self.get_applied_transforms()
119
120			def is_2d(self):
121			return all(i.is_2d() for i in self.get_images(intensity_only=False))
122
123			def get_applied_transforms(
124			self,
125			ignore_intensity: bool = False,
126			image_interpolation: Optional[str] = None,
127			) -> List['Transform']:
128			from ..transforms.transform import Transform
129			from ..transforms.intensity_transform import IntensityTransform
130			name_to_transform = {
131			cls.__name__: cls
132			for cls in get_subclasses(Transform)
133			}
134			transforms_list = []
135			for transform_name, arguments in self.applied_transforms:
136			transform = name_to_transform[transform_name](**arguments)
137			if ignore_intensity and isinstance(transform, IntensityTransform):
138			continue
139			resamples = hasattr(transform, 'image_interpolation')
140			if resamples and image_interpolation is not None:
141			parsed = transform.parse_interpolation(image_interpolation)
142			transform.image_interpolation = parsed
143			transforms_list.append(transform)
144			return transforms_list
145
146			def get_composed_history(
147			self,
148			ignore_intensity: bool = False,
149			image_interpolation: Optional[str] = None,
150			) -> 'Compose':
151			from ..transforms.augmentation.composition import Compose
152			transforms = self.get_applied_transforms(
153			ignore_intensity=ignore_intensity,
154			image_interpolation=image_interpolation,
155			)
156			return Compose(transforms)
157
158			def get_inverse_transform(
159			self,
160			warn: bool = True,
161			ignore_intensity: bool = True,
162			image_interpolation: Optional[str] = None,
163			) -> 'Compose':
164			"""Get a reversed list of the inverses of the applied transforms.
165
166			Args:
167			warn: Issue a warning if some transforms are not invertible.
168			ignore_intensity: If ``True``, all instances of
169			:class:`~torchio.transforms.intensity_transform.IntensityTransform`
170			will be ignored.
171			image_interpolation: Modify interpolation for scalar images inside
172			transforms that perform resampling.
173			"""
174			history_transform = self.get_composed_history(
175			ignore_intensity=ignore_intensity,
176			image_interpolation=image_interpolation,
177			)
178			inverse_transform = history_transform.inverse(warn=warn)
179			return inverse_transform
180
181			def apply_inverse_transform(self, **kwargs) -> 'Subject':
182			"""Try to apply the inverse of all applied transforms, in reverse order.
183
184			Args:
185			**kwargs: Keyword arguments passed on to
186			:meth:`~torchio.data.subject.Subject.get_inverse_transform`.
187			"""
188			inverse_transform = self.get_inverse_transform(**kwargs)
189			transformed = inverse_transform(self)
190			transformed.clear_history()
191			return transformed
192
193			def clear_history(self) -> None:
194			self.applied_transforms = []
195
196			def check_consistent_attribute(self, attribute: str) -> None:
197			values_dict = {}
198			iterable = self.get_images_dict(intensity_only=False).items()
199			for image_name, image in iterable:
200			values_dict[image_name] = getattr(image, attribute)
201			num_unique_values = len(set(values_dict.values()))
202			if num_unique_values > 1:
203			message = (
204			f'More than one {attribute} found in subject images:'
205			f'\n{pprint.pformat(values_dict)}'
206			)
207			raise RuntimeError(message)
208
209			def check_consistent_spatial_shape(self) -> None:
210			self.check_consistent_attribute('spatial_shape')
211
212			def check_consistent_orientation(self) -> None:
213			self.check_consistent_attribute('orientation')
214
215			def check_consistent_affine(self):
216			# https://github.com/fepegar/torchio/issues/354
217			affine = None
218			first_image = None
219			iterable = self.get_images_dict(intensity_only=False).items()
220			for image_name, image in iterable:
221			if affine is None:
222			affine = image.affine
223			first_image = image_name
224			elif not np.allclose(affine, image.affine, rtol=1e-6, atol=1e-6):
225			message = (
226			f'Images "{first_image}" and "{image_name}" do not occupy'
227			' the same physical space.'
228			f'\nAffine of "{first_image}":'
229			f'\n{pprint.pformat(affine)}'
230			f'\nAffine of "{image_name}":'
231			f'\n{pprint.pformat(image.affine)}'
232			)
233			raise RuntimeError(message)
234
235			def check_consistent_space(self):
236			self.check_consistent_spatial_shape()
237			self.check_consistent_affine()
238
239			def get_images_dict(
240			self,
241			intensity_only=True,
242			include: Optional[Sequence[str]] = None,
243			exclude: Optional[Sequence[str]] = None,
244			) -> Dict[str, Image]:
245			images = {}
246			for image_name, image in self.items():
247			if not isinstance(image, Image):
248			continue
249			if intensity_only and not image[TYPE] == INTENSITY:
250			continue
251			if include is not None and image_name not in include:
252			continue
253			if exclude is not None and image_name in exclude:
254			continue
255			images[image_name] = image
256			return images
257
258			def get_images(
259			self,
260			intensity_only=True,
261			include: Optional[Sequence[str]] = None,
262			exclude: Optional[Sequence[str]] = None,
263			) -> List[Image]:
264			images_dict = self.get_images_dict(
265			intensity_only=intensity_only,
266			include=include,
267			exclude=exclude,
268			)
269			return list(images_dict.values())
270
271			def get_first_image(self) -> Image:
272			return self.get_images(intensity_only=False)[0]
273
274			# flake8: noqa: F821
275			def add_transform(
276			self,
277			transform: 'Transform',
278			parameters_dict: dict,
279			) -> None:
280			self.applied_transforms.append((transform.name, parameters_dict))
281
282			def load(self) -> None:
283			"""Load images in subject on RAM."""
284			for image in self.get_images(intensity_only=False):
285			image.load()
286
287			def update_attributes(self) -> None:
288			# This allows to get images using attribute notation, e.g. subject.t1
289			self.__dict__.update(self)
290
291			def add_image(self, image: Image, image_name: str) -> None:
292			"""Add an image."""
293			self[image_name] = image
294			self.update_attributes()
295
296			def remove_image(self, image_name: str) -> None:
297			"""Remove an image."""
298			del self[image_name]
299			delattr(self, image_name)
300
301			def plot(self, **kwargs) -> None:
302			"""Plot images using matplotlib.
303
304			Args:
305			**kwargs: Keyword arguments that will be passed on to
306			:class:`~torchio.data.image.Image`.
307			"""
308			from ..visualization import plot_subject # avoid circular import
309			plot_subject(self, **kwargs)
310

fepegar / torchio

Push — master ( 17d4df...a2657b )

torchio.data.subject.Subject.is_2d() A

Complexity

Size

Duplication

Importance

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