Skip to main content

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

Project description

Albumentations

Build Status Documentation Status

  • 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

Vladimir_Iglovikov

Authors

Alexander Buslaev

Alex Parinov

Vladimir Iglovikov

Example usage

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=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(),
            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

Installation

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

Documentation

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

Demo

You can use this Google Colaboratory notebook to adjust image augmentation parameters and see the resulting images.

Working with non-8-bit 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).

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 git@github.com:albu/albumentations.git
cd albumentations
  1. Install the library in development mode:
pip install -e .[tests]
  1. Run tests:
pytest

Building the documentation

  1. Go to docs/ directory
cd docs
  1. Install required libraries
pip install -r requirements.txt
  1. Build html files
make html
  1. 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

Comments

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 https://github.com/pytorch/pytorch/issues/1355

cv2.setNumThreads(0)	
cv2.ocl.setUseOpenCL(False)

Thanks:

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

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

albumentations-0.0.10.tar.gz (34.0 kB view hashes)

Uploaded Source

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page