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RandomFlip

RandomFlip

Bases: RandomTransform, SpatialTransform

Reverse the order of elements in an image along the given axes.

Parameters:

Name Type Description Default
axes int | tuple[int, ...]

Index or tuple of indices of the spatial dimensions along which the image might be flipped. If they are integers, they must be in (0, 1, 2). Anatomical labels may also be used, such as 'Left', 'Right', 'Anterior', 'Posterior', 'Inferior', 'Superior', 'Height' and 'Width', 'AP' (antero-posterior), 'lr' (lateral), 'w' (width) or 'i' (inferior). Only the first letter of the string will be used. If the image is 2D, 'Height' and 'Width' may be used.

 0
flip_probability float

Probability that the image will be flipped. This is computed on a per-axis basis.

 0.5
**kwargs

See Transform for additional keyword arguments.

 {}

Examples:

>>> import torchio as tio
>>> fpg = tio.datasets.FPG()
>>> flip = tio.RandomFlip(axes=('LR',))  # flip along lateral axis only
Tip

It is handy to specify the axes as anatomical labels when the image orientation is not known.

__call__(data)

Transform data and return a result of the same type.

Parameters:

Name Type Description Default
data InputType

Instance of torchio.Subject, 4D torch.Tensor or numpy.ndarray with dimensions \((C, W, H, D)\), where \(C\) is the number of channels and \(W, H, D\) are the spatial dimensions. If the input is a tensor, the affine matrix will be set to identity. Other valid input types are a SimpleITK image, a torchio.Image, a NiBabel Nifti1 image or a dict. The output type is the same as the input type.

 required

get_base_args()

Provides easy access to the arguments used to instantiate the base class (Transform) of any transform.

This method is particularly useful when a new transform can be represented as a variant of an existing transform (e.g. all random transforms), allowing for seamless instantiation of the existing transform with the same arguments as the new transform during apply_transform.

Note

The p argument (probability of applying the transform) is excluded to avoid multiplying the probability of both existing and new transform.

add_base_args(arguments, overwrite_on_existing=False)

Add the init args to existing arguments

validate_keys_sequence(keys, name) staticmethod

Ensure that the input is not a string but a sequence of strings.

to_hydra_config()

Return a dictionary representation of the transform for Hydra instantiation.