RandomCropPerturber

class nrtk.impls.perturb_image.generic.random_crop_perturber.RandomCropPerturber(crop_size: tuple[int, int] | None = None, seed: int | Generator | None = 1)

RandomCropPerturber randomly crops an image and adjusts bounding boxes accordingly.

Attributes:

crop_size (tuple[int, int]): Target crop dimensions for the input image. seed (int | numpy.random.Generator): Random seed or number generator for deterministic behavior.

Methods:

perturb:

Applies a random crop to an input image and adjusts bounding boxes.

__call__:

Calls the perturb method with the given input image.

get_config:

Returns the current configuration of the RandomCropPerturber 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 RandomCropPerturber 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	Check whether this class is available for use.
perturb	Randomly crops an image and adjusts bounding boxes.

__init__(crop_size: tuple[int, int] | None = None, seed: int | Generator | None = 1) → None

RandomCropPerturber applies a random cropping perturbation to an input image.

It ensures that bounding boxes are adjusted correctly to reflect the new cropped region.

Args:

crop_size:

Target crop size as (crop_height, crop_width). If crop_size is None, it defaults to the size of the input image.

seed:

Seed for rng. Defaults to 1.

get_config() → dict[str, Any]

Returns the current configuration of the RandomCropPerturber instance.

Returns:

return dict[str, Any]:

Configuration dictionary with current settings.

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

Randomly crops an image and adjusts bounding boxes.

Args:

image:

Input image as a numpy array of shape (H, W, C).

boxes:

List of bounding boxes in AxisAlignedBoundingBox format and their corresponding classes.

additional_params:

Unused

Returns:

return tuple[np.ndarray, Iterable[tuple[AxisAlignedBoundingBox, dict[Hashable, float]]] | None]:

Cropped image with the modified bounding boxes.