Note

This page is a reference documentation. It only explains the class signature, and not how to use it. Please refer to the user guide for the big picture.

nidl.volume.transforms.preprocessing.CropOrPad

class nidl.volume.transforms.preprocessing.CropOrPad(target_shape, padding_mode='constant', constant_values=0.0, **kwargs)

Bases: VolumeTransform

Crop and/or pad a 3d volume to match the target shape.

It handles numpy.ndarray or torch.Tensor as input and returns a consistent output (same type).

Parameters:

target_shape: int or tuple[int, int, int]

Expected output shape. If int, apply the same size across all dimensions.

padding_mode: str in {‘edge’, ‘maximum’, ‘constant’, ‘mean’, ‘median’, ‘minimum’, ‘reflect’, ‘symmetric’}

Possible modes for padding. See more infos in the Numpy documentation.

constant_values: float or tuple[float, float, float]

The values to set the padded values for each axis if the padding mode is ‘constant’.

kwargs: dict

Keyword arguments given to nidl.transforms.Transform.

__init__(target_shape, padding_mode='constant', constant_values=0.0, **kwargs)

apply_transform(data)

Crop and/or pad the input data to match target shape.

Parameters:

data: np.ndarray or torch.Tensor

The input data with shape or . Transformation is applied across all channels.

Returns:

data: np.ndarray or torch.Tensor

Cropped or padded data with same type as input and shape target_shape.