christopherdcunha / albumentations

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

Albumentations

• The library is faster than other libraries on most of the transformations.
• Based on numpy, OpenCV, imgaug picking the best from each of them.
• Simple, flexible API that allows the library to be used in any computer vision pipeline.
• Large, diverse set of transformations.
• Easy to extend the library to wrap around other libraries.
• Easy to extend to other tasks.
• Supports transformations on images, masks, key points and bounding boxes.
• Supports python 2.7-3.7
• Easy integration with PyTorch.
• Easy transfer from torchvision.
• Was used to get top results in many DL competitions at Kaggle, topcoder, CVPR, MICCAI.
• Written by Kaggle Masters.

How to use

All in one showcase notebook - showcase.ipynb

Classification - example.ipynb

Object detection - example_bboxes.ipynb

Non-8-bit images - example_16_bit_tiff.ipynb

Image segmentation example_kaggle_salt.ipynb

Custom tasks such as autoencoders, more then three channel images - refer to Compose class documentation to use additional_targets.

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

Authors

Alexander Buslaev

Alex Parinov

Vladimir I. Iglovikov

Evegene Khvedchenya

Installation

PyPI

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

Conda

To install albumentations using conda we need first to install imgaug with pip

pip install imgaug
conda install albumentations -c albumentations

Documentation

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

Migrating from torchvision to albumentations

Migrating from torchvision to albumentations is simple - you just need to change a few lines of code. Albumentations has equivalents for common torchvision transforms as well as plenty of transforms that are not presented in torchvision. migrating_from_torchvision_to_albumentations.ipynb shows how one can migrate code from torchvision to albumentations.

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. The table shows how many images per second can be processed on a single core, higher is better.

	albumentations 0.1.11	imgaug 0.2.6	torchvision (Pillow backend) 0.2.1	torchvision (Pillow-SIMD backend) 0.2.1	Keras 2.2.4
RandomCrop64	740915	-	93057	97160	-
PadToSize512	7484	-	789	782	-
HorizontalFlip	1093	982	6519	5923	1092
VerticalFlip	11097	5093	7908	8105	10834
Rotate	1076	802	124	206	37
ShiftScaleRotate	2188	1282	107	184	40
Brightness	775	888	426	562	199
Contrast	898	761	303	401	-
BrightnessContrast	692	412	173	229	-
ShiftHSV	219	148	57	74	-
ShiftRGB	736	889	-	-	668
Gamma	1215	-	1685	1671	-
Grayscale	2760	475	1182	1493	-

Python and library versions: Python 3.6.8 | Anaconda, numpy 1.15.4, pillow 5.4.1, pillow-simd 5.3.0.post0, opencv-python 4.0.0.21, scikit-image 0.14.2, scipy 1.2.0.

Contributing

1. Clone the repository:

   git clone [email protected]:albu/albumentations.git
   cd albumentations
   

2. Install the library in development mode:

   pip install -e .[tests]
   

3. Run tests:

   pytest
   

4. Run flake8 to perform PEP8 and PEP257 style checks and to check code for lint errors.

   flake8
   

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

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 pytorch/pytorch#1355

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

Citing

If you find this library useful for your research, please consider citing:

@article{2018arXiv180906839B,
    author = {A. Buslaev, A. Parinov, E. Khvedchenya, V.~I. Iglovikov and A.~A. Kalinin},
     title = "{Albumentations: fast and flexible image augmentations}",
   journal = {ArXiv e-prints},
    eprint = {1809.06839},
      year = 2018      
}