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from typing import Tuple, Optional, Generator |
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import numpy as np |
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from ... import TypePatchSize |
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from ...data.subject import Subject |
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from ...utils import to_tuple |
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class PatchSampler: |
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r"""Base class for TorchIO samplers. |
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Args: |
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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, :math:`d = h = w = n`. |
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""" |
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def __init__(self, patch_size: TypePatchSize): |
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patch_size_array = np.array(to_tuple(patch_size, length=3)) |
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if np.any(patch_size_array < 1): |
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message = ( |
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'Patch dimensions must be positive integers,' |
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f' not {patch_size_array}' |
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) |
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raise ValueError(message) |
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self.patch_size = patch_size_array.astype(np.uint16) |
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def __call__( |
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self, |
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sample: Subject, |
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num_patches: Optional[int] = None, |
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) -> Generator[Subject, None, None]: |
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raise NotImplementedError |
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def get_probability_map(self, sample: Subject): |
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raise NotImplementedError |
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def extract_patch(self): |
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raise NotImplementedError |
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@staticmethod |
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def get_crop_transform( |
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sample, |
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index_ini, |
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patch_size: TypePatchSize, |
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): |
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from ...transforms.preprocessing.spatial.crop import Crop |
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shape = np.array(sample.spatial_shape, dtype=np.uint16) |
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index_ini = np.array(index_ini, dtype=np.uint16) |
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patch_size = np.array(patch_size, dtype=np.uint16) |
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index_fin = index_ini + patch_size |
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crop_ini = index_ini.tolist() |
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crop_fin = (shape - index_fin).tolist() |
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TypeBounds = Tuple[int, int, int, int, int, int] |
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start = () |
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cropping: TypeBounds = sum(zip(crop_ini, crop_fin), start) |
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return Crop(cropping) |
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fepegar /
torchio
