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from typing import Optional
import torch
from ....data.subject import Subject
from .normalization_transform import NormalizationTransform
class ClampIntensity(NormalizationTransform):
"""Clamp intensity values to a certain range.
Args:
out_min: See :func:`~torch.clamp`.
out_max: See :func:`~torch.clamp`.
Example:
>>> import torchio as tio
>>> ct = tio.ScalarImage('ct_scan.nii.gz')
>>> ct_air, ct_bone = -1000, 1000
>>> clamp = tio.ClampIntensity(out_min=ct_air, out_max=ct_bone)
>>> ct_clamped = clamp(ct)
"""
def __init__(
self,
out_min: Optional[float] = None,
out_max: Optional[float] = None,
**kwargs
):
super().__init__(**kwargs)
self.out_min, self.out_max = out_min, out_max
def apply_normalization(
subject: Subject,
image_name: str,
mask: torch.Tensor,
) -> None:
image = subject[image_name]
image.set_data(self.clamp(image.data, image_name))
def clamp(
tensor: torch.Tensor,
) -> torch.Tensor:
return tensor.clamp(self.out_min, self.out_max)
fepegar /
torchio
