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import copy |
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import pprint |
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from typing import Any, Dict, List, Tuple |
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from ..torchio import TYPE, INTENSITY |
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from .image import Image |
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class Subject(dict): |
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"""Class to store information about the images corresponding to a subject. |
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Args: |
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*args: If provided, a dictionary of items. |
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**kwargs: Items that will be added to the subject sample. |
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Example: |
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>>> from torchio import ScalarImage, LabelMap, Subject |
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>>> # One way: |
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>>> subject = Subject( |
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... one_image=ScalarImage('path_to_image.nii.gz'), |
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... a_segmentation=LabelMap('path_to_seg.nii.gz'), |
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... age=45, |
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... name='John Doe', |
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... hospital='Hospital Juan Negrín', |
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... ) |
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>>> # If you want to create the mapping before, or have spaces in the keys: |
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>>> subject_dict = { |
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... 'one image': ScalarImage('path_to_image.nii.gz'), |
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... 'a segmentation': LabelMap('path_to_seg.nii.gz'), |
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... 'age': 45, |
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... 'name': 'John Doe', |
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... 'hospital': 'Hospital Juan Negrín', |
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... } |
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>>> Subject(subject_dict) |
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""" |
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def __init__(self, *args, **kwargs: Dict[str, Any]): |
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if args: |
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if len(args) == 1 and isinstance(args[0], dict): |
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kwargs.update(args[0]) |
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else: |
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message = ( |
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'Only one dictionary as positional argument is allowed') |
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raise ValueError(message) |
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super().__init__(**kwargs) |
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self._parse_images(self.get_images(intensity_only=False)) |
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self.update_attributes() # this allows me to do e.g. subject.t1 |
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self.applied_transforms = [] |
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def __repr__(self): |
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num_images = len(self.get_images(intensity_only=False)) |
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string = ( |
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f'{self.__class__.__name__}' |
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f'(Keys: {tuple(self.keys())}; images: {num_images})' |
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) |
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return string |
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def __copy__(self): |
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result_dict = {} |
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for key, value in self.items(): |
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if isinstance(value, Image): |
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value = copy.copy(value) |
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else: |
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value = copy.deepcopy(value) |
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result_dict[key] = value |
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new = Subject(result_dict) |
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new.applied_transforms = self.applied_transforms[:] |
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return new |
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def __len__(self): |
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return len(self.get_images(intensity_only=False)) |
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@staticmethod |
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def _parse_images(images: List[Tuple[str, Image]]) -> None: |
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# Check that it's not empty |
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if not images: |
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raise ValueError('A subject without images cannot be created') |
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@property |
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def shape(self): |
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"""Return shape of first image in subject. |
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Consistency of shapes across images in the subject is checked first. |
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""" |
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self.check_consistent_attribute('shape') |
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return self.get_first_image().shape |
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@property |
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def spatial_shape(self): |
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"""Return spatial shape of first image in subject. |
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Consistency of spatial shapes across images in the subject is checked |
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first. |
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""" |
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self.check_consistent_spatial_shape() |
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return self.get_first_image().spatial_shape |
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@property |
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def spacing(self): |
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"""Return spacing of first image in subject. |
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Consistency of spacings across images in the subject is checked first. |
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""" |
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self.check_consistent_attribute('spacing') |
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return self.get_first_image().spacing |
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@property |
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def history(self): |
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from .. import transforms |
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transforms_list = [] |
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for transform_name, arguments in self.applied_transforms: |
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transform = getattr(transforms, transform_name)(**arguments) |
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transforms_list.append(transform) |
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return transforms_list |
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def get_composed_history(self): |
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from ..transforms.augmentation.composition import Compose |
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return Compose(self.history) |
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def check_consistent_attribute(self, attribute: str) -> None: |
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values_dict = {} |
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iterable = self.get_images_dict(intensity_only=False).items() |
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for image_name, image in iterable: |
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values_dict[image_name] = getattr(image, attribute) |
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num_unique_values = len(set(values_dict.values())) |
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if num_unique_values > 1: |
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message = ( |
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f'More than one {attribute} found in subject images:' |
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f'\n{pprint.pformat(values_dict)}' |
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) |
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raise RuntimeError(message) |
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def check_consistent_spatial_shape(self) -> None: |
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self.check_consistent_attribute('spatial_shape') |
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def check_consistent_orientation(self) -> None: |
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self.check_consistent_attribute('orientation') |
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def get_images_dict(self, intensity_only=True): |
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images = {} |
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for image_name, image in self.items(): |
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if not isinstance(image, Image): |
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continue |
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if intensity_only and not image[TYPE] == INTENSITY: |
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continue |
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images[image_name] = image |
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return images |
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def get_images(self, intensity_only=True): |
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images_dict = self.get_images_dict(intensity_only=intensity_only) |
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return list(images_dict.values()) |
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def get_first_image(self): |
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return self.get_images(intensity_only=False)[0] |
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# flake8: noqa: F821 |
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def add_transform( |
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self, |
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transform: 'Transform', |
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parameters_dict: dict, |
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) -> None: |
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self.applied_transforms.append((transform.name, parameters_dict)) |
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def load(self): |
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"""Load images in subject.""" |
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for image in self.get_images(intensity_only=False): |
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image.load() |
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def update_attributes(self): |
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# This allows to get images using attribute notation, e.g. subject.t1 |
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self.__dict__.update(self) |
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def add_image(self, image: Image, image_name: str) -> None: |
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"""Add an image.""" |
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self[image_name] = image |
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self.update_attributes() |
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def remove_image(self, image_name: str) -> None: |
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"""Remove an image.""" |
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del self[image_name] |
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def plot(self, **kwargs) -> None: |
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"""Plot images.""" |
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from ..visualization import plot_subject # avoid circular import |
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plot_subject(self, **kwargs) |
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fepegar /
torchio
