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

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

Albumentations

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

  • The library works with images in HWC format.
  • 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 3.5-3.7
  • 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

Keypoints example_keypoints.ipynb

Custom targets example_multi_target.ipynb

Weather transforms example_weather_transforms.ipynb

Serialization serialization.ipynb

Replay/Deterministic mode replay.ipynb

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

parrot

inria

medical

vistas

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

And it also works in Kaggle GPU kernels (proof)

!pip install albumentations > /dev/null

Conda

To install albumentations using conda we need first to install imgaug via conda-forge collection

conda install -c conda-forge imgaug
conda install albumentations -c albumentations

Documentation

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

Pixel-level transforms

Pixel-level transforms will change just an input image and will leave any additional targets such as masks, bounding boxes, and keypoints unchanged. The list of pixel-level transforms:

Spatial-level transforms

Spatial-level transforms will simultaneously change both an input image as well as additional targets such as masks, bounding boxes, and keypoints. The following table shows which additional targets are supported by each transform.

Transform Image Masks BBoxes Keypoints
CenterCrop
Crop
CropNonEmptyMaskIfExists
ElasticTransform
Flip
GridDistortion
GridDropout
HorizontalFlip
IAAAffine
IAACropAndPad
IAAFliplr
IAAFlipud
IAAPerspective
IAAPiecewiseAffine
Lambda
LongestMaxSize
MaskDropout
NoOp
OpticalDistortion
PadIfNeeded
RandomCrop
RandomCropNearBBox
RandomGridShuffle
RandomResizedCrop
RandomRotate90
RandomScale
RandomSizedBBoxSafeCrop
RandomSizedCrop
Resize
Rotate
ShiftScaleRotate
SmallestMaxSize
Transpose
VerticalFlip

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 Xeon Platinum 8168 CPU.
All outputs are converted to a contiguous NumPy array with the np.uint8 data type.
The table shows how many images per second can be processed on a single core, higher is better.

albumentations
0.4.2
imgaug
0.3.0
torchvision (Pillow-SIMD backend)
0.4.1
keras
2.3.1
augmentor
0.2.6
solt
0.1.8
HorizontalFlip 2183 1403 1757 1068 1779 1031
VerticalFlip 4217 2334 1538 4196 1541 3820
Rotate 456 368 163 32 60 116
ShiftScaleRotate 800 549 146 34 - -
Brightness 2209 1288 405 211 403 2070
Contrast 2215 1387 338 - 337 2073
BrightnessContrast 2208 740 193 - 193 1060
ShiftRGB 2214 1303 - 407 - -
ShiftHSV 468 443 61 - - 144
Gamma 2281 - 730 - - 925
Grayscale 5019 436 788 - 1451 4191
RandomCrop64 173877 3340 43792 - 36869 36178
PadToSize512 2906 - 553 - - 2711
Resize512 663 506 968 - 954 673
RandomSizedCrop_64_512 2565 933 1395 - 1353 2360
Equalize 759 457 - - 684 -

Python and library versions: Python 3.7.5 (default, Oct 19 2019, 00:03:48) [GCC 8.3.0], numpy 1.17.3, pillow-simd 6.0.0.post0, opencv-python 4.1.1.26, scikit-image 0.16.2, scipy 1.3.1.

Contributing

To create a pull request to the repository follow the documentation at docs/contributing.rst

Adding new transforms

If you are contributing a new transformation, make sure to update "Pixel-level transforms" or/and "Spatial-level transforms" sections of this file (README.md). To do this, simply run (with python3 only):

python3 tools/make_transforms_docs.py make

and copy/paste the results into the corresponding sections. To validate your modifications, you
can run:

python3 tools/make_transforms_docs.py check README.md

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

GitHub

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