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from copy import deepcopy |
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import nibabel as nib |
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import numpy as np |
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from ....data.image import Image |
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from ....data.subject import Subject |
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from .bounds_transform import BoundsTransform |
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from .bounds_transform import TypeBounds |
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class Crop(BoundsTransform): |
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r"""Crop an image. |
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Args: |
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cropping: Tuple |
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:math:`(w_{ini}, w_{fin}, h_{ini}, h_{fin}, d_{ini}, d_{fin})` |
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defining the number of values cropped from the edges of each axis. |
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If the initial shape of the image is |
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:math:`W \times H \times D`, the final shape will be |
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:math:`(- w_{ini} + W - w_{fin}) \times (- h_{ini} + H - h_{fin}) |
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\times (- d_{ini} + D - d_{fin})`. |
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If only three values :math:`(w, h, d)` are provided, then |
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:math:`w_{ini} = w_{fin} = w`, |
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:math:`h_{ini} = h_{fin} = h` and |
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:math:`d_{ini} = d_{fin} = d`. |
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If only one value :math:`n` is provided, then |
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:math:`w_{ini} = w_{fin} = h_{ini} = h_{fin} |
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= d_{ini} = d_{fin} = n`. |
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copy: bool, optional |
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This transform overwrites the copy argument of the base transform and |
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copies only the cropped patch, instead of the whole image. |
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This can provide a significant speedup when cropping small patches from large images |
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If ``True``, each image will be cropped and the patch copied to a new subject. |
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If ``False``, each image will be cropped in place. Default: ``True``. |
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**kwargs: See :class:`~torchio.transforms.Transform` for additional |
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keyword arguments. |
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.. seealso:: If you want to pass the output shape instead, please use |
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:class:`~torchio.transforms.CropOrPad` instead. |
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""" |
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def __init__(self, cropping: TypeBounds, copy=True, **kwargs): |
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self.copy_patch = copy |
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# Transform base class deepcopies whole subject by default |
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# We want to copy only the cropped patch, so we overwrite the functionality |
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super().__init__(cropping, copy=False, **kwargs) |
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self.cropping = cropping |
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self.args_names = ['cropping'] |
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def apply_transform(self, subject: Subject) -> Subject: |
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assert self.bounds_parameters is not None |
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low = self.bounds_parameters[::2] |
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high = self.bounds_parameters[1::2] |
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index_ini = low |
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index_fin = np.array(subject.spatial_shape) - high |
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if self.copy_patch: |
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# Create a new subject with only the cropped patch |
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sample_attributes = {} |
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image_keys_to_crop = subject.get_images_dict( |
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intensity_only=False, include=self.include, exclude=self.exclude |
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).keys() |
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# Copy all non-image attributes |
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for key, value in subject.items(): |
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if key not in image_keys_to_crop: |
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sample_attributes[key] = deepcopy(value) |
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else: |
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sample_attributes[key] = self._crop_image( |
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value, index_ini, index_fin |
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) |
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cropped_sample = type(subject)(**sample_attributes) |
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# Copy applied transforms history |
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cropped_sample.applied_transforms = deepcopy(subject.applied_transforms) |
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cropped_sample.update_attributes() |
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return cropped_sample |
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else: |
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# Crop in place |
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for image in self.get_images(subject): |
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self._crop_image(image, index_ini, index_fin) |
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return subject |
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def _crop_image(self, image: Image, index_ini: tuple, index_fin: tuple) -> Image: |
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new_origin = nib.affines.apply_affine(image.affine, index_ini) |
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new_affine = image.affine.copy() |
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new_affine[:3, 3] = new_origin |
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i0, j0, k0 = index_ini |
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i1, j1, k1 = index_fin |
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# Crop the image data |
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if self.copy_patch: |
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# Create a new image with the cropped data |
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cropped_data = image.data[:, i0:i1, j0:j1, k0:k1].clone() |
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new_image = type(image)( |
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tensor=cropped_data, |
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affine=new_affine, |
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type=image.type, |
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path=image.path, |
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) |
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return new_image |
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else: |
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image.set_data(image.data[:, i0:i1, j0:j1, k0:k1].clone()) |
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image.affine = new_affine |
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return image |
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def inverse(self): |
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from .pad import Pad |
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return Pad(self.cropping) |
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TorchIO-project /
torchio
