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from .weighted import WeightedSampler
class LabelSampler(WeightedSampler):
r"""Extract random patches containing labeled voxels.
This iterable dataset yields patches whose center value is greater than 0
in the :py:attr:`label_name`.
Args:
patch_size: Tuple of integers :math:`(d, h, w)` to generate patches
of size :math:`d \times h \times w`.
If a single number :math:`n` is provided,
:math:`d = h = w = n`.
label_name: Name of the label image in the sample that will be used to
generate the sampling probability map.
"""
def __init__(self, patch_size, label_name):
super().__init__(patch_size, probability_map=label_name)
def get_probability_map(self, sample):
"""Return binarized image for sampling."""
if self.probability_map_name in sample:
data = sample[self.probability_map_name].data > 0.5
else:
message = (
f'Image "{self.probability_map_name}"'
f' not found in subject sample: {sample}'
)
raise KeyError(message)
return data
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