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RandomGridShuffle

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

Randomly shuffles the grid's cells on an image, mask, or keypoints, effectively rearranging patches within the image. This transformation divides the image into a grid and then permutes these grid cells based on a random mapping.

Arguments
grid
tuple[int, int]
[3,3]

Size of the grid for splitting the image into cells. Each cell is shuffled randomly. For example, (3, 3) will divide the image into a 3x3 grid, resulting in 9 cells to be shuffled. Default: (3, 3)

p
float
0.5

Probability that the transform will be applied. Should be in the range [0, 1]. Default: 0.5

Examples
>>> import numpy as np
>>> import albumentations as A
>>> # Prepare sample 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]
>>> keypoints = np.array([[20, 30], [60, 70]], dtype=np.float32)
>>> keypoint_labels = [0, 1]
>>>
>>> # Define transform with grid as a tuple
>>> transform = A.Compose([
...     A.RandomGridShuffle(grid=(3, 3), 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
>>> transformed = transform(
...     image=image,
...     mask=mask,
...     bboxes=bboxes,
...     bbox_labels=bbox_labels,
...     keypoints=keypoints,
...     keypoint_labels=keypoint_labels
... )
>>>
>>> # Get the transformed data
>>> transformed_image = transformed['image']     # Grid-shuffled image
>>> transformed_mask = transformed['mask']       # Grid-shuffled mask
>>> transformed_bboxes = transformed['bboxes']   # Grid-shuffled bounding boxes
>>> transformed_keypoints = transformed['keypoints']  # Grid-shuffled keypoints
>>>
>>> # Visualization example with a simpler grid
>>> simple_image = np.array([
...     [1, 1, 1, 2, 2, 2],
...     [1, 1, 1, 2, 2, 2],
...     [1, 1, 1, 2, 2, 2],
...     [3, 3, 3, 4, 4, 4],
...     [3, 3, 3, 4, 4, 4],
...     [3, 3, 3, 4, 4, 4]
... ])
>>> simple_transform = A.RandomGridShuffle(grid=(2, 2), p=1.0)
>>> simple_result = simple_transform(image=simple_image)
>>> simple_transformed = simple_result['image']
>>> # The result could look like:
>>> # array([[4, 4, 4, 2, 2, 2],
>>> #        [4, 4, 4, 2, 2, 2],
>>> #        [4, 4, 4, 2, 2, 2],
>>> #        [3, 3, 3, 1, 1, 1],
>>> #        [3, 3, 3, 1, 1, 1],
>>> #        [3, 3, 3, 1, 1, 1]])
Notes
  • This transform maintains consistency across all targets. If applied to an image and its corresponding mask or keypoints, the same shuffling will be applied to all.
  • The number of cells in the grid should be at least 2 (i.e., grid should be at least (1, 2), (2, 1), or (2, 2)) for the transform to have any effect.
  • Keypoints are moved along with their corresponding grid cell.
  • This transform could be useful when only micro features are important for the model, and memorizing the global structure could be harmful. For example:
    • Identifying the type of cell phone used to take a picture based on micro artifacts generated by phone post-processing algorithms, rather than the semantic features of the photo. See more at https://ieeexplore.ieee.org/abstract/document/8622031
    • Identifying stress, glucose, hydration levels based on skin images.