PlasmaBrightnessContrast

Targets:

image

volume

Image Types:uint8, float32

Apply plasma fractal pattern to modify image brightness and contrast.

Uses Diamond-Square algorithm to generate organic-looking fractal patterns that create spatially-varying brightness and contrast adjustments.

Arguments

brightness_range

tuple[float, float]

[-0.3,0.3]

Range for brightness adjustment strength. Values between -1 and 1:

Positive values increase brightness
Negative values decrease brightness
0 means no brightness change Default: (-0.3, 0.3)

contrast_range

tuple[float, float]

[-0.3,0.3]

Range for contrast adjustment strength. Values between -1 and 1:

Positive values increase contrast
Negative values decrease contrast
0 means no contrast change Default: (-0.3, 0.3)

plasma_size

int

256

Size of the initial plasma pattern grid. Larger values create more detailed patterns but are slower to compute. The pattern will be resized to match the input image dimensions. Default: 256

roughness

float

Controls how quickly the noise amplitude increases at each iteration. Must be greater than 0:

Low values (< 1.0): Smoother, more gradual pattern
Medium values (~2.0): Natural-looking pattern
High values (> 3.0): Very rough, noisy pattern Default: 3.0

p

float

0.5

Probability of applying the transform. Default: 0.5.

Examples

>>> import albumentations as A
>>> import numpy as np

# Default parameters
>>> transform = A.PlasmaBrightnessContrast(p=1.0)

# Custom adjustments
>>> transform = A.PlasmaBrightnessContrast(
...     brightness_range=(-0.5, 0.5),
...     contrast_range=(-0.3, 0.3),
...     plasma_size=512,    # More detailed pattern
...     roughness=0.7,      # Smoother transitions
...     p=1.0
... )

Notes

Works with any number of channels (grayscale, RGB, multispectral)
The same plasma pattern is applied to all channels
Operations are performed in float32 precision
Final values are clipped to valid range [0, max_value]