ConditionalSetProperty#

class deeptrack.features.ConditionalSetProperty(feature: Feature, condition: PropertyLike[str | bool] | None = None, **kwargs: dict[str, Any])#

Bases: StructuralFeature

Conditionally override the properties of a child feature.

This feature modifies the properties of a child feature only when a specified condition is met. If the condition evaluates to True, the given properties are applied; otherwise, the child feature remains unchanged.

Note: It is advisable to use dt.Arguments instead when possible, since this feature overwrites properties, which may affect future calls to the feature.

Parameters#

feature: Feature

The child feature whose properties will be modified conditionally.

condition: PropertyLike[str] or PropertyLike[bool]

Either a boolean value (True/False) or the name of a boolean property in the feature’s property dictionary. If the condition evaluates to True, the specified properties are applied.

**kwargs: dict[str, Any]

The properties to be applied to the child feature if condition is True.

Attributes#

__distributed__: bool

Indicates whether this feature distributes computation across inputs.

Methods#

get(image: Any, condition: str | bool, **kwargs: dict[str, Any]) -> Any

Resolves the child feature, conditionally applying the specified properties.

Notes#

  • If condition is a string, the condition must be explicitly passed when resolving.

  • The properties applied do not persist unless explicitly stored.

Examples#

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

Define a Gaussian noise feature: >>> gaussian_noise = dt.Gaussian(sigma=0)

— Using a boolean condition — Apply sigma=5 only if condition=True: >>> conditional_feature = dt.ConditionalSetProperty( … gaussian_noise, sigma=5 … )

Define an image: >>> image = np.ones((512, 512))

Resolve with condition met: >>> noisy_image = conditional_feature.update(image, condition=True) >>> print(noisy_image.std()) # Should be ~5 4.987707046984823

Resolve without condition: >>> clean_image = conditional_feature.update(image, condition=False) >>> print(clean_image.std()) # Should be 0 0.0

— Using a string-based condition — Define condition as a string: >>> conditional_feature = dt.ConditionalSetProperty( … gaussian_noise, sigma=5, condition=”is_noisy” … )

Resolve with condition met: >>> noisy_image = conditional_feature.update(image, is_noisy=True) >>> print(noisy_image.std()) # Should be ~5 5.006310381139811

Resolve without condition: >>> clean_image = conditional_feature.update(image, is_noisy=False) >>> print(clean_image.std()) # Should be 0 0.0

Methods Summary

get(image, condition, **kwargs)

Resolve the child, conditionally applying specified properties.

Methods Documentation

get(image: Any, condition: str | bool, **kwargs: dict[str, Any]) → Any#

Resolve the child, conditionally applying specified properties.

Parameters#

image: Any

The input data or image to process.

condition: str or bool

A boolean value or the name of a boolean property in the feature’s property dictionary. If the condition evaluates to True, the specified properties are applied.

**kwargs:: dict of str to Any

Additional properties to apply to the child feature if the condition is True.

Returns#

Any

The resolved child feature, with properties conditionally modified.