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from typing import Union, Generator, Optional |
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import numpy as np |
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from ...utils import to_tuple |
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from ...constants import LOCATION |
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from ...data.subject import Subject |
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from ...typing import TypePatchSize |
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from ...typing import TypeTripletInt |
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from .sampler import PatchSampler |
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class GridSampler(PatchSampler): |
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r"""Extract patches across a whole volume. |
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Grid samplers are useful to perform inference using all patches from a |
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volume. It is often used with a :class:`~torchio.data.GridAggregator`. |
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Args: |
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subject: Instance of :class:`~torchio.data.Subject` |
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from which patches will be extracted. This argument should only be |
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used before instantiating a :class:`~torchio.data.GridAggregator`, |
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or to precompute the number of patches that would be generated from |
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a subject. |
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patch_size: Tuple of integers :math:`(w, h, d)` to generate patches |
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of size :math:`w \times h \times d`. |
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If a single number :math:`n` is provided, |
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:math:`w = h = d = n`. |
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This argument is mandatory (it is a keyword argument for backward |
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compatibility). |
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patch_overlap: Tuple of even integers :math:`(w_o, h_o, d_o)` |
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specifying the overlap between patches for dense inference. If a |
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single number :math:`n` is provided, :math:`w_o = h_o = d_o = n`. |
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padding_mode: Same as :attr:`padding_mode` in |
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:class:`~torchio.transforms.Pad`. If ``None``, the volume will not |
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be padded before sampling and patches at the border will not be |
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cropped by the aggregator. |
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Otherwise, the volume will be padded with |
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:math:`\left(\frac{w_o}{2}, \frac{h_o}{2}, \frac{d_o}{2} \right)` |
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on each side before sampling. If the sampler is passed to a |
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:class:`~torchio.data.GridAggregator`, it will crop the output |
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to its original size. |
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Example:: |
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>>> import torchio as tio |
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>>> sampler = tio.GridSampler(patch_size=88) |
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>>> colin = tio.datasets.Colin27() |
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>>> for i, patch in enumerate(sampler(colin)): |
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... patch.t1.save(f'patch_{i}.nii.gz') |
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... |
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>>> # To figure out the number of patches beforehand: |
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>>> sampler = tio.GridSampler(subject=colin, patch_size=88) |
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>>> len(sampler) |
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8 |
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.. note:: Adapted from NiftyNet. See `this NiftyNet tutorial |
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<https://niftynet.readthedocs.io/en/dev/window_sizes.html>`_ for more |
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information about patch based sampling. Note that |
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:attr:`patch_overlap` is twice :attr:`border` in NiftyNet |
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tutorial. |
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""" |
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def __init__( |
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self, |
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subject: Optional[Subject] = None, |
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patch_size: TypePatchSize = None, |
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patch_overlap: TypePatchSize = (0, 0, 0), |
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padding_mode: Union[str, float, None] = None, |
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): |
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if patch_size is None: |
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raise ValueError('A value for patch_size must be given') |
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super().__init__(patch_size) |
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self.patch_overlap = np.array(to_tuple(patch_overlap, length=3)) |
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self.padding_mode = padding_mode |
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if subject is not None and not isinstance(subject, Subject): |
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raise ValueError('The subject argument must be None or Subject') |
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self.subject = self._pad(subject) |
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self.locations = self._compute_locations(self.subject) |
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def __len__(self): |
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return len(self.locations) |
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def __getitem__(self, index): |
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# Assume 3D |
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location = self.locations[index] |
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index_ini = location[:3] |
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cropped_subject = self.crop(self.subject, index_ini, self.patch_size) |
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cropped_subject[LOCATION] = location |
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return cropped_subject |
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def _pad(self, subject: Subject) -> Subject: |
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if self.padding_mode is not None: |
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from ...transforms import Pad |
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border = self.patch_overlap // 2 |
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padding = border.repeat(2) |
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pad = Pad(padding, padding_mode=self.padding_mode) |
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subject = pad(subject) |
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return subject |
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def _compute_locations(self, subject: Subject): |
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if subject is None: |
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return None |
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sizes = subject.spatial_shape, self.patch_size, self.patch_overlap |
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self._parse_sizes(*sizes) |
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return self._get_patches_locations(*sizes) |
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def _generate_patches( |
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self, |
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subject: Subject, |
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) -> Generator[Subject, None, None]: |
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subject = self._pad(subject) |
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sizes = subject.spatial_shape, self.patch_size, self.patch_overlap |
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self._parse_sizes(*sizes) |
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locations = self._get_patches_locations(*sizes) |
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for location in locations: |
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index_ini = location[:3] |
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yield self.extract_patch(subject, index_ini) |
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@staticmethod |
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def _parse_sizes( |
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image_size: TypeTripletInt, |
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patch_size: TypeTripletInt, |
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patch_overlap: TypeTripletInt, |
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) -> None: |
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image_size = np.array(image_size) |
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patch_size = np.array(patch_size) |
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patch_overlap = np.array(patch_overlap) |
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if np.any(patch_size > image_size): |
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message = ( |
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f'Patch size {tuple(patch_size)} cannot be' |
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f' larger than image size {tuple(image_size)}' |
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) |
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raise ValueError(message) |
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if np.any(patch_overlap >= patch_size): |
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message = ( |
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f'Patch overlap {tuple(patch_overlap)} must be smaller' |
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f' than patch size {tuple(patch_size)}' |
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) |
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raise ValueError(message) |
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if np.any(patch_overlap % 2): |
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message = ( |
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'Patch overlap must be a tuple of even integers,' |
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f' not {tuple(patch_overlap)}' |
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) |
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raise ValueError(message) |
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@staticmethod |
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def _get_patches_locations( |
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image_size: TypeTripletInt, |
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patch_size: TypeTripletInt, |
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patch_overlap: TypeTripletInt, |
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) -> np.ndarray: |
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# Example with image_size 10, patch_size 5, overlap 2: |
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# [0 1 2 3 4 5 6 7 8 9] |
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# [0 0 0 0 0] |
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# [1 1 1 1 1] |
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# [2 2 2 2 2] |
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# Locations: |
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# [[0, 5], |
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# [3, 8], |
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# [5, 10]] |
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indices = [] |
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zipped = zip(image_size, patch_size, patch_overlap) |
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for im_size_dim, patch_size_dim, patch_overlap_dim in zipped: |
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end = im_size_dim + 1 - patch_size_dim |
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step = patch_size_dim - patch_overlap_dim |
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indices_dim = list(range(0, end, step)) |
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if indices_dim[-1] != im_size_dim - patch_size_dim: |
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indices_dim.append(im_size_dim - patch_size_dim) |
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indices.append(indices_dim) |
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indices_ini = np.array(np.meshgrid(*indices)).reshape(3, -1).T |
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indices_ini = np.unique(indices_ini, axis=0) |
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indices_fin = indices_ini + np.array(patch_size) |
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locations = np.hstack((indices_ini, indices_fin)) |
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return np.array(sorted(locations.tolist())) |
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fepegar /
torchio
