- 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
from albumentations import (
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=0.5):
return Compose([
RandomRotate90(),
Flip(),
Transpose(),
OneOf([
IAAAdditiveGaussianNoise(),
GaussNoise(),
], p=0.2),
OneOf([
MotionBlur(p=.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(),
RandomContrast(),
RandomBrightness(),
], 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"]See example.ipynb
You can use pip to install albumentations:
pip install albumentations
If you want to get the latest version of the code before it is released on PyPI you can install the library from GitHub:
pip install -U git+https://github.com/albu/albumentations
The full documentation is available at albumentations.readthedocs.io.
You can use this Google Colaboratory notebook to adjust image augmentation parameters and see the resulting images.
example_16_bit_tiff.ipynb shows how albumentations can be used to work with non-8-bit images (such as 16-bit and 32-bit TIFF images).
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 | - |
- Clone the repository:
git clone [email protected]:albu/albumentations.git
cd albumentations
- Install the library in development mode:
pip install -e .[tests]
- Run tests:
pytest
- 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
In some systems, in the multiple GPU regime PyTorch may deadlock the DataLoader if OpenCV was compiled with OpenCL optimizations. Adding the following two lines before the library import may help. For more details pytorch/pytorch#1355
cv2.setNumThreads(0)
cv2.ocl.setUseOpenCL(False)Special thanks to @creafz for refactoring, documentation, tests, CI and benchmarks. Awesome work!