albumentations 0.0.4

fast image augmentation library and easy to use wrapper around other libraries

Albumentations

• Great fast augmentations based on highly-optimized OpenCV library
• Super simple yet powerful interface for different tasks like (segmentation, detection, etc.)
• Easy to customize
• Easy to add other frameworks

Example usage:

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

def strong_aug(p=.5):
    return Compose([
        RandomRotate90(),
        Flip(),
        Transpose(),
        OneOf([
            IAAAdditiveGaussianNoise(),
            GaussNoise(),
        ], p=0.2),
        OneOf([
            MotionBlur(p=.2),
            MedianBlur(blur_limit=3, p=.1),
            Blur(blur_limit=3, p=.1),
        ], p=0.2),
        ShiftScaleRotate(shift_limit=0.0625, scale_limit=0.2, rotate_limit=45, p=.2),
        OneOf([
            OpticalDistortion(p=0.3),
            GridDistortion(p=.1),
            IAAPiecewiseAffine(p=0.3),
        ], p=0.2),
        OneOf([
            CLAHE(clip_limit=2),
            IAASharpen(),
            IAAEmboss(),
            RandomContrast(),
            RandomBrightness(),
        ], p=0.3),
        HueSaturationValue(p=0.3),
    ], p=p)

image = np.ones((300, 300))
mask = np.ones((300, 300))
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"]

See example.ipynb

Installation

You can use pip to install the latest version from GitHub:

pip install -U git+https://github.com/albu/albumentations

Documentation

The full documentation is available at albumentations.readthedocs.io.

Benchmarking results

To run the benchmark yourself follow the instructions in benchmark/README.md

Results for running the benchmark on first 2000 images from the ImageNet validation set using an Intel Core i7-7800X CPU. All times are in seconds, lower is better.

	albumentations	imgaug	torchvision (Pillow backend)	torchvision (Pillow-SIMD backend)	Keras
RandomCrop64	0.0017	-	0.0182	0.0182	-
PadToSize512	0.2413	-	2.493	2.3682	-
HorizontalFlip	0.7765	2.2299	0.3031	0.3054	2.0508
VerticalFlip	0.178	0.3899	0.2326	0.2308	0.1799
Rotate	3.8538	4.0581	16.16	9.5011	50.8632
ShiftScaleRotate	2.0605	2.4478	18.5401	10.6062	47.0568
Brightness	2.1018	2.3607	4.6854	3.4814	9.9237
ShiftHSV	10.3925	14.2255	34.7778	27.0215	-
ShiftRGB	2.6159	2.1989	-	-	3.0598
Gamma	1.4832	-	1.1397	1.1447	-
Grayscale	1.2048	5.3895	1.6826	1.2721	-

Contributing

1. Clone the repository:

git clone [e-mail]:albu/albumentations.git
cd albumentations

2. Install the library in development mode:

pip install -e .[tests]

3. Run tests:

pytest

Building the documentation

1. Go to docs/ directory

cd docs

2. Install required libraries

pip install -r requirements.txt

3. Build html files

make html

4. Open _build/html/index.html in browser.

Alternatively, you can start a web server that rebuilds the documentation automatically when a change is detected by running make livehtml

Thanks:

Special thanks to @creafz for refactoring, documentation, tests, CI and benchmarks. Awesome work!