OneOf

class deeptrack.features.OneOf(collection: Iterable[Feature], key: int | None = None, **kwargs: dict[str, Any])

Bases: Feature

Resolves one feature from a given collection.

This feature selects and applies one of multiple features from a given collection. The default behavior selects a feature randomly, but this behavior can be controlled by specifying a key, which determines the index of the feature to apply.

The collection should be an iterable (e.g., list, tuple, or set), and it will be converted to a tuple internally to ensure consistent indexing.

Parameters

collection: Iterable[Feature]

A collection of features to choose from.

key: int | None, optional

The index of the feature to resolve from the collection. If not provided, a feature is selected randomly at each execution.

**kwargs: dict of str to Any

Additional keyword arguments passed to the parent Feature class.

Attributes

__distributed__: bool

Indicates whether this feature distributes computation across inputs.

Methods

_process_properties(propertydict: dict) -> dict

Processes the properties to determine the selected feature index.

get(image: Any, key: int, _ID: tuple[int, …], **kwargs: dict[str, Any]) -> Any

Applies the selected feature to the input image.

Examples

>>> import deeptrack as dt
>>> import numpy as np

Define multiple features: >>> feature_1 = dt.Add(value=10) >>> feature_2 = dt.Multiply(value=2)

Create a OneOf feature that randomly selects a transformation: >>> one_of_feature = dt.OneOf([feature_1, feature_2])

Apply it to an input image: >>> input_image = np.array([1, 2, 3]) >>> output_image = one_of_feature(input_image) >>> print(output_image) # The output depends on the randomly selected feature.

Use a key to apply a specific feature: >>> controlled_feature = dt.OneOf([feature_1, feature_2], key=0) >>> output_image = controlled_feature(input_image) >>> print(output_image) # Adds 10 to each element.

Methods Summary

get(image, key[, _ID])

Apply the selected feature to the input image.

Methods Documentation

get(image: Any, key: int, _ID: tuple[int, ...] = (), **kwargs: dict[str, Any]) → Any

Apply the selected feature to the input image.

Parameters

image: Any

The input image or data to process.

key: int

The index of the feature to apply from the collection.

_ID: tuple[int, …], optional

A unique identifier for caching and parallel processing.

**kwargs: dict of str to Any

Additional parameters passed to the selected feature.

Returns

Any

The output of the selected feature applied to the input image.