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import copy |
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import torch |
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import numpy as np |
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from torch.utils.data import Dataset |
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from ..sampler.sampler import PatchSampler |
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from ...utils import to_tuple |
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from ...torchio import LOCATION, TypeTuple, DATA, TypeTripletInt |
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from ..subject import Subject |
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class GridSampler(PatchSampler, Dataset): |
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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 :py:class:`~torchio.data.GridAggregator`. |
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Args: |
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sample: Instance of :py:class:`~torchio.data.subject.Subject` |
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from which patches will be extracted. |
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patch_size: Tuple of integers :math:`(d, h, w)` to generate patches |
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of size :math:`d \times h \times w`. |
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If a single number :math:`n` is provided, |
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:math:`d = h = w = n`. |
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patch_overlap: Tuple of integers :math:`(d_o, h_o, w_o)` specifying the |
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overlap between patches for dense inference. If a single number |
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:math:`n` is provided, :math:`d_o = h_o = w_o = n`. |
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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. |
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""" |
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def __init__( |
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self, |
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sample: Subject, |
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patch_size: TypeTuple, |
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patch_overlap: TypeTuple, |
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): |
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self.sample = sample |
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PatchSampler.__init__(self, patch_size) |
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patch_size = to_tuple(patch_size, length=3) |
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patch_overlap = to_tuple(patch_overlap, length=3) |
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sizes = self.sample.spatial_shape, patch_size, patch_overlap |
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self.parse_sizes(*sizes) |
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self.locations = self.get_patches_locations(*sizes) |
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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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index_fin = location[3:] |
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cropped_sample = self.extract_patch(self.sample, index_ini, index_fin) |
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cropped_sample[LOCATION] = location |
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return cropped_sample |
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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' larger than patch size {tuple(image_size)}' |
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) |
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raise ValueError(message) |
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def extract_patch( |
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self, |
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sample: Subject, |
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index_ini: TypeTripletInt, |
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index_fin: TypeTripletInt, |
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) -> Subject: |
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crop = self.get_crop_transform( |
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sample.spatial_shape, |
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index_ini, |
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index_fin - index_ini, |
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) |
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cropped_sample = crop(sample) |
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return cropped_sample |
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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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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 im_size_dim % step: |
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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
