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Composition API (core.composition)

class albumentations.core.composition.BboxParams (format, label_fields=None, min_area=0.0, min_visibility=0.0, check_each_transform=True) [view source on GitHub]

Parameters of bounding boxes

Parameters:

Name Type Description
format str

format of bounding boxes. Should be 'coco', 'pascal_voc', 'albumentations' or 'yolo'.

The coco format [x_min, y_min, width, height], e.g. [97, 12, 150, 200]. The pascal_voc format [x_min, y_min, x_max, y_max], e.g. [97, 12, 247, 212]. The albumentations format is like pascal_voc, but normalized, in other words: [x_min, y_min, x_max, y_max], e.g. [0.2, 0.3, 0.4, 0.5]. Theyoloformat[x, y, width, height], e.g. [0.1, 0.2, 0.3, 0.4];x,y- normalized bbox center;width,height` - normalized bbox width and height.
label_fields list

list of fields that are joined with boxes, e.g labels. Should be same type as boxes.
min_area float

minimum area of a bounding box. All bounding boxes whose visible area in pixels is less than this value will be removed. Default: 0.0.
min_visibility float

minimum fraction of area for a bounding box to remain this box in list. Default: 0.0.
check_each_transform bool

if True, then bboxes will be checked after each dual transform. Default: True

class albumentations.core.composition.Compose (transforms, bbox_params=None, keypoint_params=None, additional_targets=None, p=1.0) [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.

class albumentations.core.composition.KeypointParams (format, label_fields=None, remove_invisible=True, angle_in_degrees=True, check_each_transform=True) [view source on GitHub]

Parameters of keypoints

Parameters:

Name Type Description
format str

format of keypoints. Should be 'xy', 'yx', 'xya', 'xys', 'xyas', 'xysa'.

x - X coordinate,

y - Y coordinate

s - Keypoint scale

a - Keypoint orientation in radians or degrees (depending on KeypointParams.angle_in_degrees)
label_fields list

list of fields that are joined with keypoints, e.g labels. Should be same type as keypoints.
remove_invisible bool

to remove invisible points after transform or not
angle_in_degrees bool

angle in degrees or radians in 'xya', 'xyas', 'xysa' keypoints
check_each_transform bool

if True, then keypoints will be checked after each dual transform. Default: True

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 list

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:

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>>> 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)
>>> ])