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RandomRotate90

Targets:
image
mask
bboxes
keypoints
volume
mask3d
Image Types:uint8, float32

Randomly rotate the input by 90 degrees zero or more times.

Even with p=1.0, the transform has a 1/4 probability of being identity:

  • With probability p * 1/4: no rotation (0 degrees)
  • With probability p * 1/4: rotate 90 degrees
  • With probability p * 1/4: rotate 180 degrees
  • With probability p * 1/4: rotate 270 degrees

For example:

  • With p=1.0: Each rotation angle (including 0°) has 0.25 probability
  • With p=0.8: Each rotation angle has 0.2 probability, and no transform has 0.2 probability
  • With p=0.5: Each rotation angle has 0.125 probability, and no transform has 0.5 probability

Common applications:

  • Aerial/satellite imagery: Objects can appear in any orientation
  • Medical imaging: Scans/slides may not have a consistent orientation
  • Document analysis: Pages or symbols might be rotated
  • Microscopy: Cell orientation is often arbitrary
  • Game development: Sprites/textures that should work in multiple orientations

Not recommended for:

  • Natural scene images where gravity matters (e.g., landscape photography)
  • Face detection/recognition tasks
  • Text recognition (unless text can appear rotated)
  • Tasks where object orientation is important for classification

Note: If your domain has both 90-degree rotation AND flip symmetries (e.g., satellite imagery, microscopy), consider using D4 transform instead. D4 is more efficient and mathematically correct as it: - Samples uniformly from all 8 possible combinations of rotations and flips - Properly represents the dihedral group D4 symmetries - Avoids potential correlation between separate rotation and flip augmentations

Arguments
p
float
1

probability of applying the transform. Default: 1.0. Note that even with p=1.0, there's still a 0.25 probability of getting a 0-degree rotation (identity transform).

Examples
>>> import numpy as np
>>> import albumentations as A
>>> # Create example data
>>> image = np.random.randint(0, 256, (100, 100, 3), dtype=np.uint8)
>>> mask = np.random.randint(0, 2, (100, 100), dtype=np.uint8)
>>> bboxes = np.array([[10, 10, 50, 50], [40, 40, 80, 80]], dtype=np.float32)
>>> bbox_labels = [1, 2]  # Class labels for bounding boxes
>>> keypoints = np.array([[20, 30], [60, 70]], dtype=np.float32)
>>> keypoint_labels = [0, 1]  # Labels for keypoints
>>> # Define the transform
>>> transform = A.Compose([
...     A.RandomRotate90(p=1.0),
... ], bbox_params=A.BboxParams(coord_format='pascal_voc', label_fields=['bbox_labels']),
...    keypoint_params=A.KeypointParams(coord_format='xy', label_fields=['keypoint_labels']))
>>> # Apply the transform to all targets
>>> transformed = transform(
...     image=image,
...     mask=mask,
...     bboxes=bboxes,
...     bbox_labels=bbox_labels,
...     keypoints=keypoints,
...     keypoint_labels=keypoint_labels
... )
>>> rotated_image = transformed["image"]
>>> rotated_mask = transformed["mask"]
>>> rotated_bboxes = transformed["bboxes"]
>>> rotated_bbox_labels = transformed["bbox_labels"]
>>> rotated_keypoints = transformed["keypoints"]
>>> rotated_keypoint_labels = transformed["keypoint_labels"]
Notes

If your domain has both 90-degree rotation AND flip symmetries (e.g., satellite imagery, microscopy), consider using D4 transform instead. D4 is more efficient and mathematically correct as it:

  • Samples uniformly from all 8 possible combinations of rotations and flips
  • Properly represents the dihedral group D4 symmetries
  • Avoids potential correlation between separate rotation and flip augmentations