Core Module

Composition Module

class bio_volumentations.core.composition.Compose(transforms, p=1.0, targets=(['image'], ['mask'], ['float_mask']), conversion=None)

Bases: object

Compose a list of transformations into a callable transformation pipeline.

It is strongly recommended to use Compose to define and use the transformation pipeline.

In addition, basic input image checks and conversions are performed. Optionally, datatype conversion (e.g. from numpy.ndarray to torch.Tensor) is performed.

Parameters:

• transforms (List[Transform]) – List of transforms (objects of type Transform).

• p (float, optional) –

  Chance of applying the whole pipeline.

  Defaults to 1.

• targets (Tuple[List[str]] | List[List[str]], optional) –

  List of targets.

  Defaults to (['image'], ['mask'], ['float_mask']).

• conversion (Transform | None, optional) –

  Image datatype conversion transform, applied after the transformations.

  Defaults to None.

get_always_apply_transforms()

Transforms Interface Module

class bio_volumentations.core.transforms_interface.DualTransform(always_apply=False, p=0.5)

Bases: Transform

The base class of transformations applied to all target types.

Targets:

image, mask

apply_to_float_mask(float_mask, **params)

apply_to_mask(mask, **params)

class bio_volumentations.core.transforms_interface.ImageOnlyTransform(always_apply=False, p=0.5)

Bases: Transform

The base class of transformations applied to the image target only.

Targets:

image

property targets

class bio_volumentations.core.transforms_interface.Transform(always_apply=False, p=0.5)

Bases: object

The base class for transformations.

Parameters:

• always_apply (bool, optional) –

  Always apply this transformation.

  Defaults to False.

• p (float, optional) –

  Chance of applying this transformation.

  Defaults to 0.5.

apply(volume, **params)

get_params(**data)