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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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>>> import torchio |
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>>> from torchio import Image, Subject |
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>>> # One way: |
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>>> subject = Subject( |
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... one_image=Image('path_to_image.nii.gz', type=torchio.INTENSITY), |
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... a_segmentation=Image('path_to_seg.nii.gz', type=torchio.LABEL), |
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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': Image('path_to_image.nii.gz', type=torchio.INTENSITY), |
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... 'a segmentation': Image('path_to_seg.nii.gz', type=torchio.LABEL), |
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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.images = [ |
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(k, v) for (k, v) in self.items() |
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if isinstance(v, Image) |
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] |
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self._parse_images(self.images) |
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self.__dict__.update(self) # this allows me to do e.g. subject.t1 |
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self.history = [] |
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def __repr__(self): |
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string = ( |
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f'{self.__class__.__name__}' |
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f'(Keys: {tuple(self.keys())}; images: {len(self.images)})' |
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) |
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return string |
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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 sample. |
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Consistency of shapes across images in the sample is checked first. |
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""" |
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self.check_consistent_shape() |
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image = self.get_images(intensity_only=False)[0] |
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return 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 sample. |
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Consistency of shapes across images in the sample is checked first. |
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""" |
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return self.shape[1:] |
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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 check_consistent_shape(self) -> None: |
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shapes_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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shapes_dict[image_name] = image.shape |
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num_unique_shapes = len(set(shapes_dict.values())) |
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if num_unique_shapes > 1: |
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message = ( |
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'Images in sample have inconsistent shapes:' |
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f'\n{pprint.pformat(shapes_dict)}' |
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) |
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raise ValueError(message) |
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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.history.append((transform.name, parameters_dict)) |
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def load(self): |
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for image in self.get_images(intensity_only=False): |
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image.load() |
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def crop(self, index_ini, index_fin): |
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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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# patch.clone() is much faster than copy.deepcopy(patch) |
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value = value.crop(index_ini, index_fin) |
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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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return Subject(result_dict) |
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fepegar /
torchio
