Posterize

Targets:

image

Image Types:uint8, float32

Reduce bits per color channel (e.g. 8→4). num_bits_range controls strength; lower gives stronger posterization. Simulates low-bit-depth or compression.

This transform applies color posterization, a technique that reduces the number of distinct colors used in an image. It works by lowering the number of bits used to represent each color channel, effectively creating a "poster-like" effect with fewer color gradations.

Arguments

num_bits

tuple[int, int] | list[tuple[int, int]]

[4,4]

Defines the number of bits to keep for each color channel. Can be specified as:

tuple of two ints: (min_bits, max_bits) to randomly choose from. Range for each: [1, 7].
list of per-channel tuples: Ranges per channel [(r_min, r_max), (g_min, g_max), ...]. Default: (4, 4)

p

float

0.5

Probability of applying the transform. Default: 0.5.

Examples

>>> import numpy as np
>>> import albumentations as A
>>> image = np.random.randint(0, 256, [100, 100, 3], dtype=np.uint8)

# Posterize all channels to 3 bits
>>> transform = A.Posterize(num_bits=(3, 3), p=1.0)
>>> posterized_image = transform(image=image)["image"]

# Randomly posterize between 2 and 5 bits
>>> transform = A.Posterize(num_bits=(2, 5), p=1.0)
>>> posterized_image = transform(image=image)["image"]

# Range of bits for each channel
>>> transform = A.Posterize(num_bits=[(1, 3), (3, 5), (2, 4)], p=1.0)
>>> posterized_image = transform(image=image)["image"]

Notes

The effect becomes more pronounced as the number of bits is reduced.
This transform can create interesting artistic effects or be used for image compression simulation.
Posterization is particularly useful for:
- Creating stylized or retro-looking images
- Reducing the color palette for specific artistic effects
- Simulating the look of older or lower-quality digital images
- Data augmentation in scenarios where color depth might vary

References

Color Quantizationhttps://en.wikipedia.org/wiki/Color_quantization
Posterizationhttps://en.wikipedia.org/wiki/Posterization

>>> import numpy as np >>> import albumentations as A >>> image = np.random.randint(0, 256, [100, 100, 3], dtype=np.uint8) # Posterize all channels to 3 bits >>> transform = A.Posterize(num_bits=(3, 3), p=1.0) >>> posterized_image = transform(image=image)["image"] # Randomly posterize between 2 and 5 bits >>> transform = A.Posterize(num_bits=(2, 5), p=1.0) >>> posterized_image = transform(image=image)["image"] # Range of bits for each channel >>> transform = A.Posterize(num_bits=[(1, 3), (3, 5), (2, 4)], p=1.0) >>> posterized_image = transform(image=image)["image"]