RandomBrightnessContrast

Targets:

image

Image Types:uint8, float32

Randomly adjust brightness and contrast with separate ranges. Simple and fast; good baseline color augmentation for classification and detection.

This transform adjusts the brightness and contrast of an image simultaneously, allowing for a wide range of lighting and contrast variations. It's particularly useful for data augmentation in computer vision tasks, helping models become more robust to different lighting conditions.

Arguments

brightness_range

tuple[float, float]

[-0.2,0.2]

Factor range for changing brightness, sampled per image. Values should typically be in the range [-1.0, 1.0], where 0 means no change, 1.0 means maximum brightness, and -1.0 means minimum brightness. Default: (-0.2, 0.2).

contrast_range

tuple[float, float]

[-0.2,0.2]

Factor range for changing contrast, sampled per image. Values should typically be in the range [-1.0, 1.0], where 0 means no change, 1.0 means maximum increase in contrast, and -1.0 means maximum decrease in contrast. Default: (-0.2, 0.2).

brightness_by_max

bool

true

If True, adjusts brightness by scaling pixel values up to the maximum value of the image's dtype. If False, uses the mean pixel value for adjustment. Default: True.

ensure_safe_output

bool

false

If True, adjusts alpha and beta to prevent overflow/underflow. This keeps output values inside the valid range for the image dtype without clipping. Default: False.

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)

# Default usage
>>> transform = A.RandomBrightnessContrast(p=1.0)
>>> augmented_image = transform(image=image)["image"]

# Custom brightness and contrast limits
>>> transform = A.RandomBrightnessContrast(
...     brightness_range=(-0.3, 0.3),
...     contrast_range=(-0.3, 0.3),
...     p=1.0,
... )
>>> augmented_image = transform(image=image)["image"]

# Adjust brightness based on mean value
>>> transform = A.RandomBrightnessContrast(
...     brightness_range=(-0.2, 0.2),
...     contrast_range=(-0.2, 0.2),
...     brightness_by_max=False,
...     p=1.0,
... )
>>> augmented_image = transform(image=image)["image"]

Notes

The order of operation is: contrast adjustment, then brightness adjustment.
For uint8 images, the output is clipped to [0, 255] range.
For float32 images, the output is clipped to [0, 1] range.
The brightness_by_max parameter affects how brightness is adjusted:
- If True, brightness adjustment is more pronounced and can lead to more saturated results.
- If False, brightness adjustment is more subtle and preserves the overall lighting better.
This transform is useful for:
- Simulating different lighting conditions
- Enhancing low-light or overexposed images
- Data augmentation to improve model robustness

References

Brightnesshttps://en.wikipedia.org/wiki/Brightness
Contrasthttps://en.wikipedia.org/wiki/Contrast_(vision)

>>> import numpy as np >>> import albumentations as A >>> image = np.random.randint(0, 256, [100, 100, 3], dtype=np.uint8) # Default usage >>> transform = A.RandomBrightnessContrast(p=1.0) >>> augmented_image = transform(image=image)["image"] # Custom brightness and contrast limits >>> transform = A.RandomBrightnessContrast( ... brightness_range=(-0.3, 0.3), ... contrast_range=(-0.3, 0.3), ... p=1.0, ... ) >>> augmented_image = transform(image=image)["image"] # Adjust brightness based on mean value >>> transform = A.RandomBrightnessContrast( ... brightness_range=(-0.2, 0.2), ... contrast_range=(-0.2, 0.2), ... brightness_by_max=False, ... p=1.0, ... ) >>> augmented_image = transform(image=image)["image"]