albumentations 0.1.8

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

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 - custom_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

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.

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 0.1.2	imgaug 0.2.6	torchvision (Pillow backend) 0.2.1	torchvision (Pillow-SIMD backend) 0.2.1	Keras 2.2.4
RandomCrop64	746838	-	98793	100889	-
PadToSize512	8270	-	759	823	-
HorizontalFlip	1319	938	6307	6495	1025
VerticalFlip	11362	5005	8545	8651	11108
Rotate	1084	786	123	210	37
ShiftScaleRotate	1993	1228	107	188	40
Brightness	896	841	426	567	199
ShiftHSV	219	144	57	73	-
ShiftRGB	725	900	-	-	663
Gamma	1354	-	1724	1713	-
Grayscale	2603	330	1145	1544	-

Python and library versions: Python 3.6.6 | Anaconda, numpy 1.15.2, pillow 5.3.0, pillow-simd 5.2.0.post0, opencv-python 3.4.3.18, scikit-image 0.14.1, scipy 1.1.0.

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
   

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