AlbumentationsPerturber

class nrtk.impls.perturb_image.generic.albumentations_perturber.AlbumentationsPerturber(perturber: str, parameters: dict[str, Any] | None = None, box_alignment_mode: str = 'extent', seed: int | None = None)

AlbumentationsPerturber applies a BasicTransform from Albumentations Methods: perturb: Applies the specified to an input image. __call__: Calls the perturb method with the given input image. get_config: Returns the current configuration of the AlbumentationsPerturber instance.

Methods

`from_config`	Instantiate a new instance of this class given the configuration JSON-compliant dictionary encapsulating initialization arguments.
`get_config`	Returns the current configuration of the AlbumentationsPerturber instance.
`get_default_config`	Generate and return a default configuration dictionary for this class.
`get_impls`	Discover and return a set of classes that implement the calling class.
`get_type_string`	Returns the fully qualified type string of the PerturbImage class or its subclass.
`is_usable`	Checks if the required albumentations module is available.
`perturb`	Apply a BasicTransform from Albumentations to an image

__init__(perturber: str, parameters: dict[str, Any] | None = None, box_alignment_mode: str = 'extent', seed: int | None = None) → None

AlbumentationsPerturber applies a BasicTransform from Albumentations

Attributes:: perturber (string): The name of the BasicTransform perturber to apply parameters (dict): Keyword arguments that should be passed to the given perturber seed (int): An optional seed for reproducible results

get_config() → dict[str, Any]

Returns the current configuration of the AlbumentationsPerturber instance.

Returns:: dict[str, Any]: Configuration dictionary with current settings.

classmethod is_usable() → bool

Checks if the required albumentations module is available.

Returns:: bool: True if albumentations is installed; False otherwise.

perturb(image: np.ndarray, boxes: Iterable[tuple[AxisAlignedBoundingBox, dict[Hashable, float]]] | None = None, additional_params: dict[str, Any] | None = None) → tuple[np.ndarray, Iterable[tuple[AxisAlignedBoundingBox, dict[Hashable, float]]] | None]

Apply a BasicTransform from Albumentations to an image

Parameters:

image – Input image as a numpy array of shape (H, W, C).
boxes – List of bounding boxes in AxisAlignedBoundingBox format and their corresponding classes.

Returns:

Tuple containing: Image with transform applied as numpy array Bounding boxes with their coordinates adjusted by the transform