Source code for torchio.transforms.interpolation

# flake8: noqa: E501

import enum
import SimpleITK as sitk


[docs]class Interpolation(enum.Enum): """Interpolation techniques available in ITK. For a full quantitative comparison of interpolation methods, you can read `Meijering et al. 1999, Quantitative Comparison of Sinc-Approximating Kernels for Medical Image Interpolation <https://link.springer.com/chapter/10.1007/10704282_23>`_ Example: >>> import torchio as tio >>> transform = tio.RandomAffine(image_interpolation='bspline') """ #: Interpolates image intensity at a non-integer pixel position by copying the intensity for the nearest neighbor. NEAREST: str = 'sitkNearestNeighbor' #: Linearly interpolates image intensity at a non-integer pixel position. LINEAR: str = 'sitkLinear' #: B-Spline of order 3 (cubic) interpolation. BSPLINE: str = 'sitkBSpline' #: Same as ``nearest``. CUBIC: str = 'sitkBSpline' #: Gaussian interpolation. Sigma is set to 0.8 input pixels and alpha is 4 GAUSSIAN: str = 'sitkGaussian' #: Smoothly interpolate multi-label images. Sigma is set to 1 input pixel and alpha is 1 LABEL_GAUSSIAN: str = 'sitkLabelGaussian' #: Hamming windowed sinc kernel. HAMMING: str = 'sitkHammingWindowedSinc' #: Cosine windowed sinc kernel. COSINE: str = 'sitkCosineWindowedSinc' #: Welch windowed sinc kernel. WELCH: str = 'sitkWelchWindowedSinc' #: Lanczos windowed sinc kernel. LANCZOS: str = 'sitkLanczosWindowedSinc' #: Blackman windowed sinc kernel. BLACKMAN: str = 'sitkBlackmanWindowedSinc'
def get_sitk_interpolator(interpolation: str) -> int: if not isinstance(interpolation, str): message = ( f'Interpolation must be a string, not "{interpolation}"' f' of type {type(interpolation)}' ) raise ValueError(message) string = getattr(Interpolation, interpolation.upper()).value return getattr(sitk, string)