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Rotate

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

Rotate the input by an angle selected randomly from the uniform distribution.

Arguments
limit
tuple[float, float] | float
[-90,90]

Range from which a random angle is picked. If limit is a single float, an angle is picked from (-limit, limit). Default: (-90, 90)

interpolation
0 | 1 | 2 | 3 | 4
1

Flag that is used to specify the interpolation algorithm. Should be one of: cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_LANCZOS4. Default: cv2.INTER_LINEAR.

border_mode
0 | 1 | 2 | 3 | 4
0

Flag that is used to specify the pixel extrapolation method. Should be one of: cv2.BORDER_CONSTANT, cv2.BORDER_REPLICATE, cv2.BORDER_REFLECT, cv2.BORDER_WRAP, cv2.BORDER_REFLECT_101. Default: cv2.BORDER_CONSTANT

fill
tuple[float, ...] | float
0

Padding value if border_mode is cv2.BORDER_CONSTANT.

fill_mask
tuple[float, ...] | float
0

Padding value if border_mode is cv2.BORDER_CONSTANT applied for masks.

rotate_method
largest_box | ellipse
largest_box

Method to rotate bounding boxes. Should be 'largest_box' or 'ellipse'. Default: 'largest_box'

crop_border
bool
false

Whether to crop border after rotation. If True, the output image size might differ from the input. Default: False

mask_interpolation
0 | 1 | 2 | 3 | 4
0

flag that is used to specify the interpolation algorithm for mask. Should be one of: cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_LANCZOS4. Default: cv2.INTER_NEAREST.

p
float
0.5

Probability of applying the transform. Default: 0.5.

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.Rotate(limit=45, 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
  • The rotation angle is randomly selected for each execution within the range specified by 'limit'.
  • When 'crop_border' is False, the output image will have the same size as the input, potentially introducing black triangles in the corners.
  • When 'crop_border' is True, the output image is cropped to remove black triangles, which may result in a smaller image.
  • Bounding boxes are rotated and may change size or shape.
  • Keypoints are rotated around the center of the image.