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import torch
from ...data.subject import Subject
from ...torchio import TypePatchSize
from .sampler import RandomSampler
from typing import Optional, Tuple, Generator
import numpy as np
class UniformSampler(RandomSampler):
"""Randomly extract patches from a volume with uniform probability.
Args:
patch_size: See :py:class:`~torchio.data.PatchSampler`.
"""
def __init__(self, patch_size: TypePatchSize):
super().__init__(patch_size)
def get_probability_map(self, sample: Subject) -> torch.Tensor:
return torch.ones(1, *sample.spatial_shape)
def __call__(self, sample: Subject) -> Generator[Subject, None, None]:
sample.check_consistent_spatial_shape()
if np.any(self.patch_size > sample.spatial_shape):
message = (
f'Patch size {tuple(self.patch_size)} cannot be'
f' larger than image size {tuple(sample.spatial_shape)}'
)
raise RuntimeError(message)
valid_range = sample.spatial_shape - self.patch_size
corners = np.random.randint(valid_range + 1)
yield self.extract_patch(sample, corners)
fepegar /
torchio
