Repeat

class deeptrack.features.Repeat(feature: Feature, N: int, **kwargs: dict[str, Any])

Bases: Feature

Applies a feature multiple times in sequence.

The Repeat feature iteratively applies another feature, passing the output of each iteration as the input to the next. This enables chained transformations, where each iteration builds upon the previous one. The number of repetitions is defined by N.

Each iteration operates with its own set of properties, and the index of the current iteration is accessible via _ID or replicate_index. _ID is extended to include the current iteration index, ensuring deterministic behavior when needed.

Parameters

feature: Feature

The feature to be repeated.

N: int

The number of times to apply the feature in sequence.

**kwargs: dict of str to Any

Attributes

__distributed__: bool

Always False for Repeat, since it processes sequentially rather than distributing computation across inputs.

Methods

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

Applies the feature N times in sequence, passing the output of each iteration as the input to the next.

Examples

>>> import deeptrack as dt

Define an Add feature that adds 10 to its input: >>> add_ten = dt.Add(value=10)

Apply this feature 3 times using Repeat: >>> pipeline = dt.Repeat(add_ten, N=3)

Process an input list: >>> print(pipeline.resolve([1, 2, 3])) [31, 32, 33]

Step-by-step breakdown: - Iteration 1: [1, 2, 3] + 10 → [11, 12, 13] - Iteration 2: [11, 12, 13] + 10 → [21, 22, 23] - Iteration 3: [21, 22, 23] + 10 → [31, 32, 33]

Alternative shorthand using ^ operator: >>> pipeline = dt.Add(value=10) ^ 3 >>> print(pipeline.resolve([1, 2, 3])) [31, 32, 33]

Methods Summary

get(image, N[, _ID])

Sequentially apply the feature N times.

Methods Documentation

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

Sequentially apply the feature N times.

This method applies the feature N times, passing the output of each iteration as the input to the next. The _ID tuple is updated at each iteration, ensuring dynamic property updates and reproducibility.

Parameters

image: Any

The input data to be transformed by the repeated feature.

N: int

The number of times to sequentially apply the feature, where each iteration builds on the previous output.

_ID: tuple[int, …], optional

A unique identifier for tracking the iteration index, ensuring reproducibility, caching, and dynamic property updates.

**kwargs: dict of str to Any

Additional keyword arguments passed to the feature.

Returns

Any

The output of the final iteration after N sequential applications of the feature.