OneOfDict

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

Bases: Feature

Resolve one feature from a dictionary and apply it to an input.

This feature selects a feature from a dictionary and applies it to an input. The selection is made randomly by default, but it can be controlled using the key argument.

If key is not specified, a random key from the dictionary is selected, and the corresponding feature is applied. Otherwise, the feature mapped to key is resolved.

Parameters

collection: dict[Any, Feature]

A dictionary where keys are identifiers and values are features.

key: Any | None, optional

The key of the feature to resolve from the dictionary. If None, a random key is selected.

**kwargs: dict of str to Any

Additional parameters passed to the parent Feature class.

Attributes

__distributed__: bool

Indicates whether this feature distributes computation across inputs.

Methods

_process_properties(propertydict: dict) -> dict

Determines which feature to use based on key.

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

Resolves the selected feature and applies it to the input image.

Examples

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

Define a dictionary of features: >>> features_dict = { … “add”: dt.Add(value=10), … “multiply”: dt.Multiply(value=2), … } >>> one_of_dict_feature = dt.OneOfDict(features_dict)

Apply a randomly selected feature: >>> input_image = np.array([1, 2, 3]) >>> output_image = one_of_dict_feature(input_image) >>> print(output_image)

Use a specific key to apply a predefined feature: >>> controlled_feature = dt.OneOfDict(features_dict, key=”add”) >>> output_image = controlled_feature(input_image) >>> print(output_image) # Adds 10 to each element.

Methods Summary

get(image, key[, _ID])

Resolve the selected feature and apply it to the input.

Methods Documentation

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

Resolve the selected feature and apply it to the input.

Parameters

image: Any

The input image or data to be processed.

key: Any

The key of the feature to apply from the dictionary.

_ID: tuple[int, …], optional

A unique identifier for caching and parallel execution.

**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.