Notebooks

example.ipynb. Defining a simple augmentation pipeline for image augmentation.
example_16_bit_tiff.ipynb. Working with non-8-bit images.
example_bboxes.ipynb. Using Albumentations to augment bounding boxes for object detection tasks.
example_bboxes2.ipynb. How to use Albumentations for detection tasks if you need to keep all bounding boxes.
example_kaggle_salt.ipynb. Using Albumentations for a semantic segmentation task.
example_keypoints.ipynb. Using Albumentations to augment keypoints.
example_multi_target.ipynb. Applying the same augmentation with the same parameters to multiple images, masks, bounding boxes, or keypoints.
example_weather_transforms.ipynb. Weather augmentations in Albumentations.
migrating_from_torchvision_to_albumentations.ipynb. Migrating from torchvision to Albumentations.
replay.ipynb. Debugging an augmentation pipeline with ReplayCompose.
serialization.ipynb. How to save and load parameters of an augmentation pipeline.
showcase.ipynb. Showcase. Cool augmentation examples on diverse set of images from various real-world tasks.
tensorflow-example.ipynb. Using Albumentations with Tensorflow.
pytorch_classification.ipynb. PyTorch and Albumentations for image classification.
pytorch_semantic_segmentation.ipynb. PyTorch and Albumentations for semantic segmentation.

Usage examples

For detailed examples see notebooks.

from albumentations import (
    HorizontalFlip, IAAPerspective, ShiftScaleRotate, CLAHE, RandomRotate90,
    Transpose, ShiftScaleRotate, Blur, OpticalDistortion, GridDistortion, HueSaturationValue,
    IAAAdditiveGaussianNoise, GaussNoise, MotionBlur, MedianBlur, IAAPiecewiseAffine,
    IAASharpen, IAAEmboss, RandomBrightnessContrast, Flip, OneOf, Compose
)
import numpy as np

def strong_aug(p=0.5):
    return Compose([
        RandomRotate90(),
        Flip(),
        Transpose(),
        OneOf([
            IAAAdditiveGaussianNoise(),
            GaussNoise(),
        ], p=0.2),
        OneOf([
            MotionBlur(p=0.2),
            MedianBlur(blur_limit=3, p=0.1),
            Blur(blur_limit=3, p=0.1),
        ], p=0.2),
        ShiftScaleRotate(shift_limit=0.0625, scale_limit=0.2, rotate_limit=45, p=0.2),
        OneOf([
            OpticalDistortion(p=0.3),
            GridDistortion(p=0.1),
            IAAPiecewiseAffine(p=0.3),
        ], p=0.2),
        OneOf([
            CLAHE(clip_limit=2),
            IAASharpen(),
            IAAEmboss(),
            RandomBrightnessContrast(),
        ], p=0.3),
        HueSaturationValue(p=0.3),
    ], p=p)

image = np.ones((300, 300, 3), dtype=np.uint8)
mask = np.ones((300, 300), dtype=np.uint8)
whatever_data = "my name"
augmentation = strong_aug(p=0.9)
data = {"image": image, "mask": mask, "whatever_data": whatever_data, "additional": "hello"}
augmented = augmentation(**data)
image, mask, whatever_data, additional = augmented["image"], augmented["mask"], augmented["whatever_data"], augmented["additional"]

Augmentations examples.

MultiplicativeNoise

Original image
MultiplicativeNoise(multiplier=0.5, p=1)
MultiplicativeNoise(multiplier=1.5, p=1)
MultiplicativeNoise(multiplier=[0.5, 1.5], per_channel=True, p=1)
MultiplicativeNoise(multiplier=[0.5, 1.5], elementwise=True, p=1)
MultiplicativeNoise(multiplier=[0.5, 1.5], elementwise=True, per_channel=True, p=1)