fast image augmentation library and easy to use wrapper around other libraries
Project description
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
- Clone the repository:
git clone git@github.com:albu/albumentations.git
cd albumentations
- Install the library in development mode:
pip install -e .[tests]
- Run tests:
pytest
Building the documentation
- Go to
docs/directory
cd docs
- Install required libraries
pip install -r requirements.txt
- Build html files
make html
- Open
_build/html/index.htmlin 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!
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