OneHot

class deeptrack.features.OneHot(num_classes: int, **kwargs: dict[str, Any])

Bases: Feature

Converts the input to a one-hot encoded array.

This feature takes an input array of integer class labels and converts it into a one-hot encoded array. The last dimension of the input is replaced by the one-hot encoding.

Parameters

num_classes: int

The total number of classes for the one-hot encoding.

**kwargs:: dict of str to Any

Additional keyword arguments passed to the parent Feature class.

Methods

get(image: np.ndarray, num_classes: int, **kwargs: dict[str, Any]) -> np.ndarray

Convert the input array of class labels into a one-hot encoded array.

Examples

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

Create an input array of class labels: >>> input_data = np.array([0, 1, 2])

Apply a OneHot feature: >>> one_hot_feature = dt.OneHot(num_classes=3) >>> one_hot_feature = dt.OneHot(num_classes=3) >>> one_hot_encoded = one_hot_feature.get(input_data, num_classes=3) >>> print(one_hot_encoded) [[1. 0. 0.]

[0. 1. 0.] [0. 0. 1.]]

Methods Summary

get(image, num_classes, **kwargs)

Convert the input array of labels into a one-hot encoded array.

Methods Documentation

get(image: ndarray, num_classes: int, **kwargs: dict[str, Any]) → ndarray

Convert the input array of labels into a one-hot encoded array.

Parameters

image: np.ndarray

The input array of class labels. The last dimension should contain integers representing class indices.

num_classes: int

The total number of classes for the one-hot encoding.

**kwargs: Any

Additional keyword arguments (unused here).

Returns

np.ndarray

The one-hot encoded array. The last dimension is replaced with one-hot vectors of length num_classes.