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from itertools import islice |
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from typing import Union, Sequence, Tuple |
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import torch |
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import numpy as np |
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from ...utils import to_tuple |
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9
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10
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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: Union[int, Sequence[int]]): |
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patch_size = np.array(to_tuple(patch_size, length=3)) |
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if np.any(patch_size < 1): |
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message = ( |
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'Patch dimensions must be positive integers,' |
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f' not {patch_size}' |
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) |
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raise ValueError(message) |
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self.patch_size = patch_size.astype(np.uint16) |
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def __call__(self): |
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raise NotImplementedError |
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def get_probability_map(self): |
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raise NotImplementedError |
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def extract_patch(self): |
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raise NotImplementedError |
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def crop( |
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image: Union[np.ndarray, torch.Tensor], |
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index_ini: np.ndarray, |
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index_fin: np.ndarray, |
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) -> Union[np.ndarray, torch.Tensor]: |
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i_ini, j_ini, k_ini = index_ini |
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i_fin, j_fin, k_fin = index_fin |
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return image[..., i_ini:i_fin, j_ini:j_fin, k_ini:k_fin] |
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def get_random_indices_from_shape( |
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shape: Tuple[int, int, int], |
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patch_size: Tuple[int, int, int], |
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) -> Tuple[np.ndarray, np.ndarray]: |
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shape_array = np.array(shape) |
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patch_size_array = np.array(patch_size) |
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max_index_ini = shape_array - patch_size_array |
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if (max_index_ini < 0).any(): |
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message = ( |
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f'Patch size {patch_size} must not be' |
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f' larger than image size {shape}' |
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) |
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raise ValueError(message) |
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max_index_ini = max_index_ini.astype(np.uint16) |
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coordinates = [] |
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for max_coordinate in max_index_ini.tolist(): |
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if max_coordinate == 0: |
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coordinate = 0 |
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else: |
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coordinate = torch.randint(max_coordinate, size=(1,)).item() |
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coordinates.append(coordinate) |
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index_ini = np.array(coordinates, np.uint16) |
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index_fin = index_ini + patch_size_array |
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return index_ini, index_fin |
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fepegar /
torchio
