Source code for torchio.transforms.preprocessing.spatial.crop

import numpy as np
import nibabel as nib

from import Subject
from .bounds_transform import BoundsTransform, TypeBounds

[docs]class Crop(BoundsTransform): r"""Crop an image. Args: cropping: Tuple :math:`(w_{ini}, w_{fin}, h_{ini}, h_{fin}, d_{ini}, d_{fin})` defining the number of values cropped from the edges of each axis. If the initial shape of the image is :math:`W \times H \times D`, the final shape will be :math:`(- w_{ini} + W - w_{fin}) \times (- h_{ini} + H - h_{fin}) \times (- d_{ini} + D - d_{fin})`. If only three values :math:`(w, h, d)` are provided, then :math:`w_{ini} = w_{fin} = w`, :math:`h_{ini} = h_{fin} = h` and :math:`d_{ini} = d_{fin} = d`. If only one value :math:`n` is provided, then :math:`w_{ini} = w_{fin} = h_{ini} = h_{fin} = d_{ini} = d_{fin} = n`. **kwargs: See :class:`~torchio.transforms.Transform` for additional keyword arguments. """ def __init__( self, cropping: TypeBounds, **kwargs ): super().__init__(cropping, **kwargs) self.cropping = cropping self.args_names = ('cropping',) def apply_transform(self, sample) -> Subject: low = self.bounds_parameters[::2] high = self.bounds_parameters[1::2] index_ini = low index_fin = np.array(sample.spatial_shape) - high for image in self.get_images(sample): new_origin = nib.affines.apply_affine(image.affine, index_ini) new_affine = image.affine.copy() new_affine[:3, 3] = new_origin i0, j0, k0 = index_ini i1, j1, k1 = index_fin image.set_data([:, i0:i1, j0:j1, k0:k1].clone()) image.affine = new_affine return sample def inverse(self): from .pad import Pad return Pad(self.cropping)