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RandomShadow

Targets:
image
volume
Image Types:uint8, float32

Simulates shadows for the image by reducing the brightness of the image in shadow regions.

This transform adds realistic shadow effects to images, which can be useful for augmenting datasets for outdoor scene analysis, autonomous driving, or any computer vision task where shadows may be present.

Arguments
shadow_roi
tuple[float, float, float, float]
[0,0.5,1,1]

Region of the image where shadows will appear (x_min, y_min, x_max, y_max). All values should be in range [0, 1]. Default: (0, 0.5, 1, 1).

num_shadows_limit
tuple[int, int]
[1,2]

Lower and upper limits for the possible number of shadows. Default: (1, 2).

shadow_dimension
int
5

Number of edges in the shadow polygons. Default: 5.

shadow_intensity_range
tuple[float, float]
[0.5,0.5]

Range for the shadow intensity. Larger value means darker shadow. Should be two float values between 0 and 1. Default: (0.5, 0.5).

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.RandomShadow(p=1.0)
>>> shadowed_image = transform(image=image)["image"]

# Custom shadow parameters
>>> transform = A.RandomShadow(
...     shadow_roi=(0.2, 0.2, 0.8, 0.8),
...     num_shadows_limit=(2, 4),
...     shadow_dimension=8,
...     shadow_intensity_range=(0.3, 0.7),
...     p=1.0
... )
>>> shadowed_image = transform(image=image)["image"]

# Combining with other transforms
>>> transform = A.Compose([
...     A.RandomShadow(p=0.5),
...     A.RandomBrightnessContrast(p=0.5),
... ])
>>> augmented_image = transform(image=image)["image"]
Notes
  • Shadows are created by generating random polygons within the specified ROI and reducing the brightness of the image in these areas.
  • The number of shadows, their shapes, and intensities can be randomized for variety.
  • This transform is particularly useful for:
    • Augmenting datasets for outdoor scene understanding
    • Improving robustness of object detection models to shadowed conditions
    • Simulating different lighting conditions in synthetic datasets
References