torchio.data.subject.Subject.add_transform() - Code Metrics - Inspection of "Add option to ignore intensity transforms when inv..." - fepegar/torchio - Measure and Improve Code Quality continuously with Scrutinizer

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Pull Request — master (#417)

by Fernando

created 2021-01-11 15:27 UTC

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

↳ Parent: torchio.data.subject

Complexity

Conditions

Size

Total Lines	6
Code Lines	5

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	1
eloc	5
nop	3
dl	0
loc	6
rs	10
c	0
b	0
f	0

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

import numpy as np

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


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 get_applied_transforms(self, ignore_intensity: bool = False):
        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
            transforms_list.append(transform)
        return transforms_list

    def get_composed_history(
            self,
            ignore_intensity: bool = False,
            ) -> 'Transform':
        from ..transforms.augmentation.composition import Compose
        transforms = self.get_applied_transforms(
            ignore_intensity=ignore_intensity)
        return Compose(transforms)

    def get_inverse_transform(
            self,
            warn: bool = True,
            ignore_intensity: bool = True,
            ) ->  'Transform':
        history_transform = self.get_composed_history(
            ignore_intensity=ignore_intensity)
        inverse_transform = history_transform.inverse(warn=warn)
        return inverse_transform

    def apply_inverse_transform(
            self,
            warn: bool = True,
            ignore_intensity: bool = True,
            ) -> 'Subject':
        inverse_transform = self.get_inverse_transform(
            warn=warn,
            ignore_intensity=ignore_intensity,
        )
        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."""
        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]

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

fepegar / torchio

Pull Request — master (#417)

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

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