Composition API (core.composition)

class albumentations.core.composition.Compose (transforms, bbox_params=None, keypoint_params=None, additional_targets=None, p=1.0, is_check_shapes=True) [view source on GitHub]

Compose transforms and handle all transformations regarding bounding boxes

Parameters:

Name	Type	Description
transforms	list	list of transformations to compose.
bbox_params	BboxParams	Parameters for bounding boxes transforms
keypoint_params	KeypointParams	Parameters for keypoints transforms
additional_targets	dict	Dict with keys - new target name, values - old target name. ex: {'image2': 'image'}
p	float	probability of applying all list of transforms. Default: 1.0.
is_check_shapes	bool	If True shapes consistency of images/mask/masks would be checked on each call. If you would like to disable this check - pass False (do it only if you are sure in your data consistency).

class albumentations.core.composition.OneOf (transforms, p=0.5) [view source on GitHub]

Select one of transforms to apply. Selected transform will be called with force_apply=True. Transforms probabilities will be normalized to one 1, so in this case transforms probabilities works as weights.

Parameters:

Name	Type	Description
transforms	list	list of transformations to compose.
p	float	probability of applying selected transform. Default: 0.5.

class albumentations.core.composition.OneOrOther (first=None, second=None, transforms=None, p=0.5) [view source on GitHub]

Select one or another transform to apply. Selected transform will be called with force_apply=True.

class albumentations.core.composition.PerChannel (transforms, channels=None, p=0.5) [view source on GitHub]

Apply transformations per-channel

Parameters:

Name	Type	Description
transforms	list	list of transformations to compose.
channels	sequence	channels to apply the transform to. Pass None to apply to all. Default: None (apply to all)
p	float	probability of applying the transform. Default: 0.5.

class albumentations.core.composition.Sequential (transforms, p=0.5) [view source on GitHub]

Sequentially applies all transforms to targets.

Note: This transform is not intended to be a replacement for Compose. Instead, it should be used inside Compose the same way OneOf or OneOrOther are used. For instance, you can combine OneOf with Sequential to create an augmentation pipeline that contains multiple sequences of augmentations and applies one randomly chose sequence to input data (see the Example section for an example definition of such pipeline).

Examples:

>>> import albumentations as A
>>> transform = A.Compose([
>>>    A.OneOf([
>>>        A.Sequential([
>>>            A.HorizontalFlip(p=0.5),
>>>            A.ShiftScaleRotate(p=0.5),
>>>        ]),
>>>        A.Sequential([
>>>            A.VerticalFlip(p=0.5),
>>>            A.RandomBrightnessContrast(p=0.5),
>>>        ]),
>>>    ], p=1)
>>> ])

class albumentations.core.composition.SomeOf (transforms, n, replace=True, p=1) [view source on GitHub]

Select N transforms to apply. Selected transforms will be called with force_apply=True. Transforms probabilities will be normalized to one 1, so in this case transforms probabilities works as weights.

Parameters:

Name	Type	Description
transforms	list	list of transformations to compose.
n	int	number of transforms to apply.
replace	bool	Whether the sampled transforms are with or without replacement. Default: True.
p	float	probability of applying selected transform. Default: 1.