albumentations.augmentations.dropout.mask_dropout

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Implementation of mask-based dropout augmentation. This module provides the MaskDropout transform, which identifies objects in a segmentation mask and drops out random objects completely. This augmentation is particularly useful for instance segmentation and object detection tasks, as it simulates occlusions or missing objects in a semantically meaningful way, rather than dropping out random pixels or regions.

Members

classMaskDropout

MaskDropoutclass

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Apply dropout to random objects in a mask, zeroing out the corresponding regions in both the image and mask. This transform identifies objects in the mask (where each unique non-zero value represents a distinct object), randomly selects a number of these objects, and sets their corresponding regions to zero in both the image and mask. It can also handle bounding boxes and keypoints, removing or adjusting them based on the dropout regions.

Parameters

Name	Type	Default	Description
max_objects	One of: tuple[int, int] int	(1, 1)	Maximum number of objects to dropout. If a single int is provided, it's treated as the upper bound. If a tuple of two ints is provided, it's treated as a range [min, max].
fill	One of: float Literal['inpaint_telea', 'inpaint_ns']	0	Value to fill dropped out regions in the image. Can be one of: - float: Constant value to fill the regions (e.g., 0 for black, 255 for white) - "inpaint_telea": Use Telea inpainting algorithm (for 3-channel images only) - "inpaint_ns": Use Navier-Stokes inpainting algorithm (for 3-channel images only)
fill_mask	float	0	Value to fill the dropped out regions in the mask.
p	float	0.5	Probability of applying the transform. 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.zeros((100, 100), dtype=np.uint8)
>>> mask[20:40, 20:40] = 1  # Object 1
>>> mask[60:80, 60:80] = 2  # Object 2
>>> bboxes = np.array([[20, 20, 40, 40], [60, 60, 80, 80]], dtype=np.float32)
>>> bbox_labels = [1, 2]
>>> keypoints = np.array([[30, 30], [70, 70]], dtype=np.float32)
>>> keypoint_labels = [0, 1]
>>>
>>> # Define the transform with tuple for max_objects
>>> transform = A.Compose(
...     transforms=[
...         A.MaskDropout(
...             max_objects=(1, 2),  # Using tuple to specify min and max objects to drop
...             fill=0,  # Fill value for dropped regions in image
...             fill_mask=0,  # Fill value for dropped regions in mask
...             p=1.0
...         ),
...     ],
...     bbox_params=A.BboxParams(
...         format='pascal_voc',
...         label_fields=['bbox_labels'],
...         min_area=1,
...         min_visibility=0.1
...     ),
...     keypoint_params=A.KeypointParams(
...         format='xy',
...         label_fields=['keypoint_labels'],
...         remove_invisible=True
...     )
... )
>>>
>>> # 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']  # Image with dropped out regions
>>> transformed_mask = transformed['mask']    # Mask with dropped out regions
>>> transformed_bboxes = transformed['bboxes']  # Remaining bboxes after dropout
>>> transformed_bbox_labels = transformed['bbox_labels']  # Labels for remaining bboxes
>>> transformed_keypoints = transformed['keypoints']  # Remaining keypoints after dropout
>>> transformed_keypoint_labels = transformed['keypoint_labels']  # Labels for remaining keypoints

Notes