Core API (volumentations.core)

Composition

class volumentations.core.composition.Compose(transforms, additional_targets=None, p=1.0)

Compose transforms and handle all transformations regrading bounding boxes.

Parameters

• transforms (list) – list of transformations to compose.

• additional_targets (dict) – Dict with keys - new target name, values - old target name. ex: {‘image2’: ‘image’}

• p (float) – probability of applying all list of transforms. Default: 1.0.

class volumentations.core.composition.OneOf(transforms, p=0.5)

Select one of transforms to apply.

Parameters

• transforms (list) – list of transformations to compose.

• p (float) – probability of applying selected transform. Default: 0.5.

class volumentations.core.composition.ReplayCompose(transforms, additional_targets=None, p=1.0, save_key='replay')

Transforms interface

volumentations.core.transforms_interface.to_tuple(param, low=None, bias=None)

Convert input argument to min-max tuple

Parameters

• param (scalar, tuple or list of 2+ elements) – Input value. If value is scalar, return value would be (offset - value, offset + value). If value is tuple, return value would be value + offset (broadcasted).

• low – Second element of tuple can be passed as optional argument

• bias – An offset factor added to each element

class volumentations.core.transforms_interface.PointCloudsTransform(always_apply=False, p=0.5)

Transform for point clouds.

class volumentations.core.transforms_interface.NoOp(always_apply=False, p=0.5)

Does nothing

Serialization

volumentations.core.serialization.to_dict(transform, on_not_implemented_error='raise')

Take a transform pipeline and convert it to a serializable representation that uses only standard python data types: dictionaries, lists, strings, integers, and floats.

Parameters

transform (object) – A transform that should be serialized. If the transform doesn’t implement the to_dict method and on_not_implemented_error equals to ‘raise’ then NotImplementedError is raised. If on_not_implemented_error equals to ‘warn’ then NotImplementedError will be ignored but no transform parameters will be serialized.

volumentations.core.serialization.from_dict(transform_dict, lambda_transforms=None)

Parameters

• transform (dict) – A dictionary with serialized transform pipeline.

• lambda_transforms (dict) – A dictionary that contains lambda transforms, that is instances of the Lambda class. This dictionary is required when you are restoring a pipeline that contains lambda transforms. Keys in that dictionary should be named same as name arguments in respective lambda transforms from a serialized pipeline.

volumentations.core.serialization.save(transform, filepath, data_format='json', on_not_implemented_error='raise')

Take a transform pipeline, serialize it and save a serialized version to a file using either json or yaml format.

Parameters

• transform (obj) – Transform to serialize.

• filepath (str) – Filepath to write to.

• data_format (str) – Serialization format. Should be either json or ‘yaml’.

• on_not_implemented_error (str) – Parameter that describes what to do if a transform doesn’t implement the to_dict method. If ‘raise’ then NotImplementedError is raised, if warn then the exception will be ignored and no transform arguments will be saved.

volumentations.core.serialization.load(filepath, data_format='json', lambda_transforms=None)

Load a serialized pipeline from a json or yaml file and construct a transform pipeline.

Parameters

• transform (obj) – Transform to serialize.

• filepath (str) – Filepath to read from.

• data_format (str) – Serialization format. Should be either json or ‘yaml’.

• lambda_transforms (dict) – A dictionary that contains lambda transforms, that is instances of the Lambda class. This dictionary is required when you are restoring a pipeline that contains lambda transforms. Keys in that dictionary should be named same as name arguments in respective lambda transforms from a serialized pipeline.