deeptrack.math Module

Mathematical operations and structures.

This module provides classes and utilities to perform common mathematical operations and transformations on images, including clipping, normalization, blurring, and pooling. These are implemented as subclasses of Feature for seamless integration with the feature-based design of the library.

Classes

Clip

Clip the input values within a specified minimum and maximum range.

NormalizeMinMax

Perform min-max normalization on images.

Classes

Average([features, axis])	Average of input images
AverageBlur([ksize])	Blur an image by computing simple means over neighbourhoods.
AveragePooling([ksize])	Apply average pooling to images.
BilateralBlur([d, sigma_color, sigma_space])	Blur an image using a bilateral filter.
Blur(filter_function[, mode])
BlurCV2(*args, **kwargs)
Clip([min, max])	Clip the input within a minimum and a maximum value.
Feature([_input])	Base feature class.
GaussianBlur([sigma])	Augmenter to blur images using gaussian kernels.
Image(value[, copy])	Wrapper for array-like values with property tracking.
MaxPooling([ksize])	Apply max pooling to images.
MedianBlur([ksize])	Blur an image by computing median values over neighbourhoods.
MedianPooling([ksize])	Apply median pooling to images.
MinPooling([ksize])	Apply min pooling to images.
NormalizeMinMax([min, max, featurewise])	Image normalization.
NormalizeQuantile([quantiles, featurewise])	Image normalization.
NormalizeStandard([featurewise])	Image normalization.
Pool(pooling_function[, ksize])	Downsamples the image by applying a function to local regions of the image.
Resize([dsize])	Resize an image.